Volume 6, Issue 2

Today, the costs of most Renewable Energy (RE) technologies especially hydrogen energy technologies such as fuel cells, are still beyond the means of poorer economies in developing countries. Hence, there is little public awareness and local expertise in RE in these countries and even lesser in hydrogen energy. To solve this problem, it is important to train local manpower in RE, starting with enabling local schoolchildren to learn about RE, especially hydrogen fuel cells. RE provides an alternative, sustainable and clean energy that improves the environment and human life, expands the choice of available energy sources that improves energy security, and reduces consumption of fossil energy in electricity generation and public transportation. Hence it is critical that teaching modules for exposure, acceptance and uptake of RE technologies are developed to suit local conditions. The purpose of this paper is to review recent progress and advances in RE education especially in hydrogen fuel cell. Important features of the modules, educational materials and reports are discussed critically. This paper assesses the literature on RE teaching in schools, especially in hydrogen fuel cells, and discusses the problems faced and the optimal period for cost-effectiveness. A curriculum that integrates literacy and social concepts with science, technology, engineering and mathematics (STEM) concepts could be developed in the future. The literature shows that teaching and learning of fuel cells could be achieved by using the five “Es”; Engagement, Exploration, Explanation, Elaboration and Evaluation, and also by promoting collaboration, team work, communication and design in project based learning activities. Most teaching materials include a project for students to build their own single-cell Proton Exchange Membrane (PEM) fuel cells and electrolyzers, and to produce hydrogen by using solar energy. Appropriate and economic criteria are developed for the design and development of modules for teaching and learning of hydrogen fuel cells, which could be implemented in physical classrooms or on free blended online learning platforms during the COVID-19 pandemic.

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Contracts may range from a simple agreement between a tenant and a landlord or a gym contract, or it could be as important as an employment or marital contract. No matter the level of importance, individuals are legally obligated to obey and carry out all clauses in the contract. In this paper, we have identified that the majority of people seldom read through the entire contracts for several reasons such as the size of the contracts i.e. bulky contracts or the inability to fully comprehend a contract. As a solution to the identified problem, this paper presented a software tool that automatically comprehends and summarises legal contracts. We designed context-free grammar (CFG) rules for the recognition of critical clauses found in contracts. These CFG rules were implemented in the software tool. An evaluation of this tool showed that it was able to identify critical clauses in contracts to an accuracy of 79.2%.

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For its importance nowadays in a wide range of applications such as the anechoic chamber, we introduce a microwave ultra-broadband polarization-independent metamaterial absorber (MA) in the Ultra High Frequency (UHF)/ Super High Frequency (SHF) frequency bands. Through this work, we improved the Relative Absorptive Bandwidth (RAB) of the conventional pyramidal absorber (CPA) by modifying its altitude to a curved shape. As a result, the RAB increased from 25.9 % to 71.82 % with an absorptive level greater than 90% paving the way to an optimized structure for a broader band of absorption. As a second target, we looked for widening the broadband absorption of the CPA in the low-frequency region. To achieve this aim, we introduced two new prototypes. The first with a total thickness of 12.7 cm, consisting of 35 curved resonant layers where numerical simulations show an enhanced design with an absorption band from 0.3 GHz to 30 GHz referring ta a RAB of 182%. The second prototype consists of a cell containing different pyramidal absorbers grouped in-plane in a unit cell; such structures operate in complementary bands. This prototype is dedicated to combining these bands of absorption. After that, an enhancement is presented of this latest to reach a well-combined band with a RAB of 128.69%. We used for simulation, testing, and collecting results the High-Frequency Structure Simulator (HFSS) tool.

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In underwater photographs are look like low-quality images, the main reason is behind that due to attenuation of the propagated light, absorption and scattering effect. The absorption significantly reduces the light energy, while the dispersion causes changes in the light propagation path. They result in foggy appearance and degradation of contrast, causing misty distant objects. So, for getting the most effective result from that type of image, there must be an enhancement technique that has to be applied. We propose an efficient technique to enhance the images captured underwater by applying a fusion-based technique using super-resolution. For enhancing images, we have followed two steps. The first one illumination adjustment and another one is color correction. Then fusion technique is applied to the resultant image from illumination adjustment and color correction as two inputs and combined them with their maximum coefficient value and received output from there. After that, on the fused output image, we used the Super-Resolution method. In the Super-resolution procedure, low resolution and high-resolution images are used then a bi-cubic interpolation algorithm and finally, VDSR (very-deep super-resolution) neural network has been used to get the most effective result from an obscure underwater image. For getting the most effective result from an obscure image, a new high-quality and efficient image enhancement method has been proposed in this paper.

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Robotic arms or industrial robots are a machinery that is widely used in the medical and military industries because it is a flexible, highly accurate and reliable. It is very necessary to work in complex tasks requiring more accuracy than humans can work. This paper presents an estimate of the standard uncertainty of 6 DOF robotic arm, KUKA KR5 ARC robot, and describes the experimental setup of a laser tracker to measure the position of the reflector mirror installed on a robot end-effector. This research describes the method of testing and experimenting to calculate the errors of each joint by using the inverse kinematic model, calculating the actual angle of their joint in comparing it with a nominal joint angle. The Jacobian matrix was applied to calculate the robotic position error. The calculation of uncertainties of each joint was conducted by using the Jacobian matrix to calculate the uncertainty in the robot and the four points testing were designed for estimating the error value and uncertainty value. The results showed that the error and uncertainty of each test point were within the range of the average error and the average uncertainty of the robot specification. The position errors and the position uncertainties of all test points within the robotic moving space were calculated and estimated by the proposed method and model. Therefore, the position error tolerance of each required moving target point must be smaller than the position errors and the position uncertainties that are estimated from this proposed model. These estimated robot linear position end effector uncertainties were used to compare and adjust the robotic path based on the required robotic position target and tolerance control.

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This study tests the feasibility of manufacturing concrete blocks made of recycled materials. The paper is an extension of work originally presented in ASET conference in Dubai. The paper, depicts and analyzes how the characteristics of the blocks (strength/durability) are affected by the presence of recycled concrete ingredients (recycled aggregate (RA)) and recycled water (RW). The recycled materials (RA and RW) were mixed in 16 different configurations; from each one 10 samples were prepared for testing. In each concrete configuration the RA and RW gradually replaced the fresh materials at 25%, 50%, 75%, and 100%. The RA moderately impacted the bearing capacity but significantly impacted the durability. The results show that using recycled aggregate decreases the bearing capacity by 22% (at the 100% replacement), and the recycled water slightly affected the bearing capacity (at the 100% replacement). To boost the durability, the ground granulated blast furnace slag (GGBS) was used, in the concrete mix, instead of the ordinary Portland cement (OPC). The GGBS was used at 3 magnitudes: 25%, 50%, and 75% of OPC. As a result the carbon foot-print footprint (1000 kg/m3) was significantly lowered. Besides, the strength and durability of the blocks are reasonably enhanced. Generally, producing blocks from recycled materials is economical and feasible. The use of GGBS helps to lower the carbon footprint and enhance the strength and durability.

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The leading place among diseases of the musculoskeletal system is occupied by various feet deformations. Clinical movement analysis and posturological examination are required to objectively assess the distribution for load caused by the weight of human body on the feet and its locomotion effect. In normal conditions, the foot is exposed to elastic deformations. When analyzing the foot loads, it`s necessary to consider shock loads as one of dynamic load types. The foot is the first to perceive the shock impulse by support reaction, and the further nature for interaction with the environment directly depends on its functional capabilities. However, the foot supporting properties haven`t been fully researched. The purpose for this research is to increase the accuracy of estimating the human foot biomechanical parameters, by assessing the dynamic impact, namely short-term shock loads by step cycle relevant phases. This goal is solved by developing a method of static-dynamic load analysis, which allows to estimate dynamic and shock loads on foot and is reduced to determining the capacity coefficients, dynamic and shock loads. In the course of studies, conducted in this research, it was found that the maximum contact per unit time has front section (repulsion phase), then – the rear section (landing phase) and the smallest – the foot middle section (rolling phase), the greater speed and length step – so the greater shock loads coefficient, and their peak falls on the front and rear sections. The practical significance of the obtained results is to improve the existing methods of researching biomechanical parameters by comprehensively assessing by standing and gait features, foot step cycle and support properties.

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The behavior of rivers’ hydrology and flow under changing climate has been an objective of interest for long time. In this study the impacts of climate change on streamflow of the Arkansas River will be investigated. The paper is an extension of work originally presented in ASET conference in Dubai. The Arkansas River is a crucial element in the economy of the Colorado state in the USA. It is a vital transportation channel and main source of water for irrigated agriculture. In order to understand the direction and magnitude of climate change, the changes in the monthly flow regimes of the Arkansas River were projected using two future climate scenarios. The projections extend over 100 years (2000 – 2100). The projections were carried out in the period from April to September because this is the period of the river’s significant runoff. For better presentation the monthly flows were aggregated and presented on decadal time scale. Project stream flow is simulated using a neural network that was developed to autonomously model the relationship between different flow levels and the resultant changes in temperature and precipitation. In general, the projections depict a rise in the magnitude of the flow in the river. In general the increases concurred with the patterns of temperature and precipitation projected for the region. Noticeably, the high temperatures cause the precipitation to melt earlier shifting the peak flow to April instead of June. Statistical analysis show that in the future the current levels of flow would be surpassed more frequently. The probability of exceedance fluctuates between from month to month – reaching its peak in April-July; before retreating to a very low level in August and becoming almost negligible in September. Overall, the results reveal profound implications for regional water resource planning and management.

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The literature shows a growing interest in taking into account human and organizational factors (HOFs) to achieve safe and successful human performance by reducing the risk of errors. In this sense, the concept of maturity models aims to help companies in the integration of these factors by assessing the current level of maturity and define future areas for improvement. The HOFs maturity model shown in this article is based on the five main factors that can impact human performance and safety positively. The measurement methodology consists in applying the Fuzzy Analytic Hierarchy Process (FAHP) method to calculate the weighting of the elements of the model since they do not have the same importance. Next, the Fuzzy Comprehensive Evaluation Method (FCEM) is used to determine the maturity level in terms of HOFs among the five proposed by performing an assessment of the sub-factors using a questionnaire. The purpose of using fuzzy logic is to deal with vagueness and uncertainty of the human reasoning . The proposed model and methodologies are implemented to bring out the current situation of a Moroccan mining organization and identify the elements that require more effort to reach the next level of maturity.

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This paper describes an experimental setup that employs ultrasound to scan an area. This method utilizes ultrasonic waves to scan the surface of a submerged object in a water-coupled medium. A pulse-echo mode is used, and quantitative data are collected at various positions using a two- dimensional automated table. A microcontroller controls the motion of the scanner, whereas a script developed in MATLAB controls the ultrasonic pulser receiver process. The MATLAB script ultimately controls and correlated between the scanner movement and ultrasonic pulser receiver process. The intensities of the reflected waves are captured and used to generate the A-scan image for the external surface. The surface profile of the scanned object can be clearly obtained using the time arrival of the reflected waves. The experimental results based on a one-pound coin indicate that the precision of the proposed process. This simple and efficient method can be used in different engineering applications with minimum errors.

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This study attempts to develop theoretical criteria for verifying the morality of the actions of artificial intelligent agents, using the Turing test as an archetype and inspiration. This study develops ethical criteria established based on Kohlberg’s moral development theory that might help determine the types of moral acts committed by artificial intelligent agents. Subsequently, it leverages these criteria in a test experiment with Korean children aged around ten years. The study concludes that the 10-year-old test participants’ stage of moral development falls between the first and second types of moral acts in moral Turing tests. We evaluate the moral behavior type experiment by applying it to Korean elementary school students aged about ten years old. Moreover, this study argues that if a similar degree of reaction is obtained by applying this experiment to future healthcare robots, this healthcare robot can be recognized as passing the moral Turing test.

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Sustainable urban development and utilization of Internet of Things (IoT) technology is driving cities globally to evolve into Smart Cities (SC). The power of IoT services and applications will enable public agencies to provide personalized services to the citizens and inevitably improves their much-needed quality of life. However, although the use of IoT technology proves to be advantageous to citizens, it is not without challenges, particularly concerning with the management of information security. As agencies prepare towards SCs with the utilization of IoT, their Information Systems (IS) security management is even more critical. Current IS security management approaches must be reviewed and potentially revise appropriately in tandem with the increasing commercial use of the IoT technology. Therefore, this paper aims to discuss challenges in the IS management specifically in protecting and assuring information accuracy and completeness. Document analysis on relevant literature has been carried out to identify and analyse the challenges. The result discusses that the IS security management for IoT-enabled SC is challenged in five aspects: governance, integrity, interoperability, personalization, and self-organizing. Considerations of these challenges will support SC development concerning the IS security management in IoT-enabled SC.

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This paper is an extension of previous research that has been done on factors affecting digital payment adoption in the UAE. This study focuses on analyzing which relevant demographic factors affect new e-payment technologies, mainly in the smart city Dubai, with more complexities and dynamics of variables that affect users’ behavior toward adopting new technologies. The current research included a wider range of demographic factors compared to previous studies. Quantitative methods were conducted using a survey of 270 individuals living and working in Dubai. This study revealed that e-payment adoption is very high, which could be aligned with the national digital transformation strategy of the UAE. The results of the chi-square test for independence indicate that using e-payment technologies is positively associated with the level of education and the level of income. This is confirmed by the fact that the UAE’s demographic shape is identified by its high-income groups, positively influencing the residents’ e-payment adoption. Surprisingly, the significant results for independence were not found between using e-payments and the gender, marital status, age group, and the current professional position in Dubai. This research’s contribution adds to both academia and industry in the digital transformation and technology adoption field. Based on the results, it is recommended for decision-makers to leverage education, digital literacy, and income to accelerate moving toward a cashless economy. However, not having statistically significant differences between the rest demographic variables and adoption will encourage businesses and e-payment service providers to deliver new innovative e-payment models and technologies in a smart city context.

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In the phosphate industry, sulfuric acid is a key compound in phosphoric acid and fertilizer production. Industrially, the sulfuric acid H2SO4 is made generally in a sequence of three main steps: burning liquid sulfur with air, catalytic oxidation of sulfur dioxide SO2 to sulfur trioxide SO3, and formation of H2SO4 by the reaction of H2O with the SO3. The catalytic conversion of the SO2 into the SO3 is considered as the crucial reaction that affects the gas emissions and the performance of the process. In this paper, an industrial SO2 conversion unit of four catalytic beds reactors with vanadium pentoxide as a catalyst, and three heat exchangers were modeled. The model was based on heat transfer, energy and mass balance equations, and the kinetic reaction of the SO2 catalytic conversion was proposed and calibrated using the experimental plant data. The simulation of the four catalytic beds was carried out in steady-state and dynamic mode using Unisim Design R451 simulator. The proposed model was tested and validated using the studied plant measurements, and the accuracy of the model has exceeded 97%. A graphical interface of the SO2 conversion unit was integrated to make it suitable for industrial use and operator training. Finally, a digital twin (DT) of the studied conversion unit was developed based on an architecture integrating the plant, the virtual system, and the communication part in a Distributed Control System (DCS) context. The developed DT in this work makes it possible to simulate in real-time the SO2 conversion unit, predict the process performance, and optimize the unit efficiency.

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The main purpose of this paper is to present a reusability approach that helps the designer to assess the best practice to restore a heritage building. Based on the literature review, the reusability process and attributes was used as a method to restore the heritage building. Considering these approaches helps the designer to achieve useful results in terms of the built environment and building performance; moreover, it helps the designer to identify the suitable new usage of the building. Also, the designer validated the building performance through using the TAS to assess the thermal comfort of the building after using passive techniques and design restorations. The obvious finding was the successful achievement through considering this approach and decreasing the interior temperature two degrees. This study can be assessed as one of the optimistic practices that considered the sustainability dimensions during the restoration process, as well as improving the thermal comfort of the building for the end user. The research paper provides a useful understanding of designers, restorers and researchers.

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Human have the god gifted ability to focus on the essential part of a visual scenery irrespective of its background. This important area is called the salient region of an image. Computationally achieving this natural human quality is an attractive goal of today’s scientific world. Saliency detection is the technique of finding the salient region of a digital image. The color contrast between the foreground and background present in an image is usually used to extract this region. Seam Map is computed from the cumulative sum of energy values of an image. The proposed method uses seam importance map along with the weighted average of various color channels of Lab color space namely boundary aware color map to extract the saliency map. These two maps are combined and further optimized to get the final saliency output using the optimization technique proposed in a previous study. Some intermediate combinations which are closer to the proposed optimized version but differ in the optimization technique are also presented in this paper. Several standard benchmark datasets including the famous MSRA 10k dataset are used to evaluate performance of the suggested procedure. Precision-recall curve and F-beta values found from the experiments on those datasets and comparison with other state of the art techniques prove the superiority of the proposed method.

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The design of microelectronic systems is often complex, therefore metaheuristics can be of a great interest, because in most cases these systems have conflicting objectives and constraints. In this paper, we demonstrate the application of multi-criteria design strategies to a CMOS current conveyor. This provides designers with the ability to develop solutions that can meet several objectives respecting the design constraints. Therefore, three evolutionary algorithms well-known for their best performance in the resolution of more difficult multi-objective problems are proposed. They are first applied to the well-known benchmark functions and then for the optimal design of the current conveyor transistors in the framework of the 0.18µm CMOS technology. The aim is to maximize the bandwidth and minimize the parasitic input resistance respecting the technological constraints of the circuit. The obtained results are integrated in Cadence tool to show their validities. Final performances obtained by the three methods are in agreement and are better compared to the state-of-art-results.

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Software maintenance contributes the majority of software system life cycle costs. However, existing approaches with automated code analysis are limited by accuracy and scope. Using human-assessed methods or implementing quality standards are more comprehensive alternatives, but they are much more costly for smaller organizations, especially in open- source software projects. Instead, bugs are generally used to assess software quality, such as using bug fixing time as an estimate of maintenance effort. Although associated bug reports contain useful information that describe software faults, the content of these bug reports are rarely used. In this paper, we incorporate quality standards with natural language processing techniques to provide insight into software maintainability using the content of bug reports and feature requests. These issues are classified with an automated approach into various maintainability concerns whose generalizability has been validated against over 6000 issue summaries extracted from nine open source projects in previous works. Using this approach, we perform a large empirical study of 229,329 issue summaries from 61 different projects. We evaluate the differences in expressed maintainability concerns between domains, ecosystems, and types of issues. We have found differences in relative proportions across ecosystem, domain and issue severity. Further, we evaluate the evolution of maintainability across several versions in a case study of Apache Tomcat, identifying some trends within different versions and over time. In summary, our contributions include a refinement of definitions from the original empirical study on maintainability related issues, an automated approach and associated rules for identifying maintainability related quality concerns, identification of trends in the characteristics of maintainability related issue summaries through a large-scale empirical study across two major open source ecosystems, and a case study on changes in maintainability over versions in Apache Tomcat.

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In view of the current short-term traffic flow prediction methods that fail to fully consider the spatial correlation of traffic flow, and fail to make full use of historical data features, resulting in low prediction accuracy and poor robustness. Therefore, in paper, combining Non-negative Matrix Factorization (NMF) and LSTM model Based on Attention Mechanism (AttLSTM), the NMF-AttLSTM traffic flow prediction algorithm is proposed. The NMF algorithm is used to extract the spatial characteristics of traffic flow and reduce the data dimension. The attention mechanism can extract more valuable features from a long sequence of historical data. First, select high-correlation upstream and downstream roads, use NMF algorithm to perform dimensionality reduction and to extract historical data features of these roads, then combine with the historical data of this road as input. Finally, use the AttLSTM model to predict. Experiments with the PeMS public data set and Wuhan core roads data show that the method has higher prediction accuracy than other prediction models and is an effective traffic flow prediction method.

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This paper examines the impacts of the COVID-19 pandemic and selected commodity variables on Booking.com share price using the Markov-switching approach. Daily data spans from January 2017 through July 2020 are utilized in this study. Empirical evidence showed that COVID-19, international crude oil price, and gold price affected the Booking.com share price significantly. A positive relationship was detected between international crude oil price and gold price towards stock price whereas COVID-19 showed an inverse impact on stock price. The empirical findings evidenced a 1% increase in COVID-19 cases adversely affecting the share price by -0.27%. Our findings also suggested that the potential of another wave of COVID-19 is relatively higher as the bounce back period was identified as 67 days. The filtered and smoothed probabilities signaled the Booking.com share price chronologically, and transition probabilities were identified. Six cycles were outlined, and the effectiveness of the Markov-switching approach in detecting vulnerable financial forecasting was demonstrated. The adequate dating evolution provided satisfactory input for policymakers, investors, and researchers to design and mitigate volatility in commodities and crises.

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The control of the air-fuel ratio (AFR) is critical for the efficiency of the combustion. This is for achieving the better performance of the plant and result in high output energy. Computation of the AFR is gone considering the composition of the fuel regardless of the inlet pressure, density ad temperature of both fuel and the air. This paper models AFR as a function of the inlet temperature, density, and pressure. Formulated models have been checked using recorded data from the Jabana2 Oil Power Plant. The results show that the AFR increases by 1.5 units as the pressures of the gas increase by 0.6 bars but when it reaches 2.9 bar, AFR starts to decrease, 0.9% of the increase of the density leads to the decrease of the AFR of 0.4 in average. 3.5oC rise of inlet temperature lift the AFR by 0.2; however, it starts to decrease when the temperature reaches 78oC.

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Artificial Intelligence (AI) is a promising trend in ship energy management systems (EMS).
The motivations of this work are designing and implementation of an intelligent energy management system for ship’s electric power system based on an adaptive Neuro-Fuzzy Inference System (ANFIS) and the ship power source is an environmental friend system consists of proton exchange membrane fuel-cell (FCPM) considered as the main power source and battery bank as an electric storage system using (ANFIS) to manage the fuel cell generation by solving the optimization problem to reduce the Hydrogen fuel consumption and ensure the system balance.
The benefit of using this technique is to penetrate a new field of using renewable and sustainable energy sources in marine to reduce greenhouse emission and increase the sailing period, system reliability by interfacing with the ship’s integrated power system.
The simulation of this system is carried out by MATLAB® software and (EMS) is implemented to test rig hardware with computer and interface card to emulate the ship’s electric power system. The results obtained from the simulation are compared with the experimental results for the evaluation of the EMS performance.

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The wide acceptance of the semantic web language RDF for ontologies creation in various application fields has led to the emergence of numerous RDF data processing solutions, the so-called triplestores, for the storage of RDF data and its querying using the RDF query language SPARQL. Such solutions are however developed under various perspectives and on the basis of various architectures. It is therefore a necessity for users to be able to distinguish between these systems to decide about the appropriate triplestore for an efficient processing of their RDF data depending on their objectives, the characteristics of their data and the technologies at hand. To this end, we give an extended categorization of RDF data stores according to their main characteristics. Furthermore, we review relevant existing triplestores within their respective established categories. The categorization is established according to the motivations behind the adoption of one or the other triplestore for handling the main tasks of data storage and SPARQL querying. Furthermore, the categorization considers various aspects that specifically deal with RDF data modeling, organization of RDF data, the processing of SPARQL queries, scalability, as well as aspects related to the diverse related data processing technologies.

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Forecasting the gold price movement’s volatility has essential applications in areas such as risk management, options pricing, and asset allocation. The multivariate model is expected to generate more accurate forecasts than univariate models in time series data like gold prices. Multivariate analysis is based on observation and analysis of more than one statistical variable at a time. This paper mainly builds a multivariate prediction model based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) model to analyze and forecast the price of the gold commodity. In addition, the prediction model is optimized with a Cross-Validated Grid Search to find the optimum hyperparameter. The empirical results show that the proposed Timeseries Prediction model has an excellent accuracy in prediction, that proven by the lowest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Overall, in more than three years data period, LSTM has high accuracy, but for under three years period, GRU does better. This research aims to find a promising methodology for gold price forecasting with high accuracy.

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Consideration of building energy performance in the early stage of the design process is very important to help minimizing energy consumed by the built environment. Therefore, help in minimizing energy crisis problem. Optimization of building form and orientation at the early stage of the design process can save a significant amount of energy consumed by the building. This paper proposes an annual thermal energy performance-based form making (EPBFM) method that generates numerous design configurations and tests their annual thermal energy performance till it reaches an optimal solution. The proposed workflow uses 3d parametric modeling program, energy simulation program, and genetic algorithm. A case study of an open plan office building is used to evaluate the proposed workflow in three different cities with different climates, Cairo, London, and Chicago. Building’s contexts were not considered in order to highlight the change of the building form and orientation caused due to the change in climate conditions. Then, Scatterplots were developed to test the impact of each dynamic parameter on thermal energy use intensity (EUI). Compared to the initial square shaped building, optimization results showed that thermal EUI decreased by 22.76%, 29.7%, and 19.2% in Cairo, London, and Chicago, respectively. Manipulation of building area along one axis and each floor area along the other axis proved to have the highest positive impact in decreasing thermal EUI.

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The present work focuses on the effect of adding gum Arabic on the setting time of cement pastes, workability, and compressive strength of concrete. For workability and compressive strength liquid gum Arabic was added to concrete mixes at ratios of 0.0% (control mix), 0.3%, 0.5%, 0.7% of the weight of cement. In addition, from 0.1% to 0.7%, ratios of GA were added to cement paste to investigate the effect of this admixture on the initial and final setting time of cement. Concrete cube specimens cast using metallic molds measuring 150 x 150 x 150 mm, and cured at 7, 28 and 91 days. From the results obtained it has been shown that the setting time of cement paste delays with increasing GA ratios. The amount of slump of fresh concrete increases largely with increasing GA ratios. GA develops considerably more compressive strength than normal concrete. The highest strength was observed at 7% of GA at all ages.

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Distribution is the main activity in integrated product supply chain management. In the pharmaceutical industry, the process of drug distribution is important because of the handling, storage, and distribution of medicinal products with good standards and quality. The problem that occurs in the pharmaceutical industry is the circulation of counterfeit drugs by related parties, for example, unofficial or unregistered distributors or data collection for unregistered medicines circulated by distributors. Permits misused from drug manufacturing processes until they are distributed or circulated do not comply with the Food and Drug Supervisory Agency standard provisions. These problems must be resolved quickly with technological support to facilitate the distribution process in recording data distribution, providing data security, and traceability of transactions between related parties. This study proposes a good drug distribution model by applying blockchain technology. The Model development uses a qualitative approach and a user center design. The result of this study is a validated drug distribution model with blockchain technology. The model has the characteristics of transparency, security, traceability, decentralization, automation, immutability, and reliability. This model can help the government ensure public health and safety by ensuring that the drugs received are of good quality, thereby increasing the community safety and health and trust in drugs in circulation.

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The aim of this study is, on the one hand learn about this type of wood, and on the other hand, is to study the processes the thermophysical characterization of araucaria wood from the city of el Jadida, Morocco, and on the other hand, will study the processes related to evaporation. Wood is defined industrially as an anisotropic and heterogeneous material formed over many years of a tree’s life. The anatomical study of wood generally involves the examination of three directions of reference, the axial, radial and tangential directions. And a study of the constituent elements of wood being oriented in several directions, it follows from experience that its thermal properties differ in the longitudinal, radial or tangential direction, where thermophysical tests are carried out at wood moisture contents between 10 and 11%. Concerning the thermal properties, study highlighted, the determination of thermal conductivity, specific heat, thermal diffusivity and then the thermal effusivity of araucaria wood at different temperatures: 25°C, 35°C, 50°C and 60°C. The results are obtained experimentally by a device called ”CT-Metre”. He illustrated that this kind of wood is more conductive of heat in the longitudinal direction than the radial and tangential directions. Also, have determined the evaporation rate under well determined conditions, chose an enclosure to keep the set temperature, and then add NaCl to keep humidity at 75%, which also gives important results.

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This paper aims to measure the acceptance level of Indonesian customers to JD.ID X retail and to reveal the driving factors of behavioral intention to shop at the retail. JD.ID X is new retail that implements a new shopping technology called Just walk-out technology (JWOT) also known as retail without a cashier. JWOT is one of the latest innovations in the retail business that draws the authors intention as with this technology the shopping experiences becomes more efficient, effective, and enjoyable. We, therefore, examine some constructs related to this characteristic which are then classified as utilitarian and hedonic motivation. The method used in collecting data for this research is by distributing questionnaires through the Google form. Data is analyzed using Smart PLS 3.2.9. The results of this study indicate that there is a significant direct effect between automation, security/privacy risk, hedonic motivations on behavioral intention to shop at JD.ID X Mart. On the other hand, trust and utilitarian motivations do not have a significant direct effect on the dependent variable. Another finding is that, whilst trust does not mediate automation to the dependent variable, hedonic motivation shows a significant intermediating role between automation and the behavioral intention to use.

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Rated a high-risk cyber-attack type, Advanced Persistent Threat (APT) has become a cause for concern to cyber security experts. Detecting the presence of APT in order to mitigate this attack has been a major challenge as successful attacks to large organizations still abound. Our approach combines static rule anomaly detection through pattern recognition and machine learning-based classification technique in mitigating the APT. (1) The rules-based on patterns are derived using statistical analysis majorly Kruskal Wallis test for association. A Packet Capture (PCAP) dataset with 1,047,908 packet header data is analyzed in an attempt, to identify malicious versus normal data traffic patterns. 90% of the attack traffic utilizes unassigned and dynamic/private ports and, also data sizes of between 0 and 58 bytes. (2) The machine learning approach narrows down the algorithm utilized by evaluating the accuracy levels of four algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree and Random Forest with the accuracies 99.74, 87.11, 99.84 and 99.90 percent respectively. A load balance approach and modified entropy formula was applied to Random Forest. The model combines the two techniques giving it a minimum accuracy of 99.95% with added capabilities of detecting false positives. The results for both methods are matched in order to make a final decision. This approach can be easily adopted, as the data required is packet header data, visible in every network and provides results with commendable levels of accuracy, and the challenges of false positives greatly reduced.

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Effective Fetal Electrocardiogram (FECG) Extraction provides medical workers with precise knowledge for monitoring fetal health condition during gestational age. However, Fetal ECG Extraction still remains a challenge as the signal is weak and contaminated with noises of different kinds, more significantly maternal ECG. In this work, a new Moth Flame optimization algorithm (MFO)-based adaptive filter is proposed for the extraction of FECG signal. A noninvasive two-point method is used to record thoracic and abdominal ECG signals from the mother’s body. The abdominal ECG (AECG) signal is made up of fetal heart signal, the distorted maternal heart signal and noise. The thoracic signal contains the undistorted maternal heart signal. The two signals are applied to an adaptive filter whose coefficients are optimally determined by the conventional least means square (LMS) algorithm and MFO. Simulation results using both synthetic signals and the real data from Physionet data base developed by MIT- BIH show the superiority of the new approach over conventional methods. The performance has been proven by observation of the quality of the extracted wave forms and quantitatively by computing the Signal to Noise ratio (SNR) which was 10.28 for proposed algorithm as compared to 0.1028 for the connectional LMS and mean square error (MSE) which was 0.0215 for the proposed algorithm as compared to 0.0275 for the convectional LMS. The results indicate that the new approach is suitable for Fetal Electrocardiogram extraction from AECG.

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This paper describes a qualitative research design adopted in this study guided by deployment of a Grounded Theory (GT) methodology which was deployed to synthesize literature on technology adoption and digital transformation with an objective of developing theory. The philosophical worldview adopted was interpretivism/constructivist of a qualitative grounded theory inductive (theory building) approach where secondary data was sourced from industry reports and related academic peer reviewed literature. The grounded theory method was used to synthesize data which resulted in emergent dimensions that underpin digital transformation and technology adoption in the postal sector in the context of Southern Africa. The careful and laborious method of theoretical sampling, constant comparison and theoretical coding which underprops grounded theory research ensued in thirteen dimensions which were further advanced until theoretical saturation was established, the theoretical saturation resulted with emergence of the ten themes reinforced by constructs/concepts with associated allocated codes. The ten themes that emerged from the grounded theory research are adoption, shared vision, digital competitiveness, digital ecosystem, digital capability, digital investment, diverging interests, customer insights, digital culture, and operational efficiency. These emergent themes are the basis of the next leg of the research which is to develop a dynamic model archetypical of the digital embracing dynamics in the postal service in Southern Africa employing the System Dynamics modelling approach.

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Integration of Distributed Generation (DG) on distribution networks has a positive impact which includes the following: low power losses, improved utility system reliability and voltage improvement at buses. A real distribution network is radial in which energy flow is unidirectional from generation to transmission and from distribution to the load. However, when a DG is connected to it, the power flow becomes bidirectional, and the protection setting of the network may be affected. Therefore, the aim of this research work is to investigate the impact of distributed generation DG on power system protection. The test distribution network is first subjected to load flow analysis to determine its healthiness with and without DG connection. The load flow results confirm that the integration of the DG into the distribution network reduces the active power load loss by 92.68% and improves voltage profiles at each bus of the network by 90.72%. Thereafter, the impact of DG on the protection setting of the existing test network was investigated. Integrating DGs to the network, from our result, shows an increase in the fault currents, which in turn caused false tripping, nuisance tripping, and blinding of protection relay compared with when DGs are not connected. The protection relays were reset at the point of common coupling (PCC) to prevent any abnormal tripping. This is the major contribution of the research work.

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Worldwide, the epidemiological situation is constantly changing and with the exponential increase in the number of confirmed coronavirus cases, the number of health care workers has decreased significantly and this is due to the direct and daily contact of patients. One of the major challenges of the last few months is to prevent the spread of the new Covid-19 virus. The most recommended solution to this problem is telemedicine, which brings benefits in terms of reducing healthcare expenditure, improving the quality and safety of care, in order to protect healthcare workers from the risks of contamination and reduce the constraints related to patient shifting. This document presents our thoughts on how to guarantee the safety of healthcare workers, by implementing an application based on wireless sensor networks with introducing the 5G technologies to satisfy high-speed transmission. This involves transmitting medical data from several sensors placed on the patient’s body, which measure physiological signs in real time, to the hospital server via the Internet, to produce medical information useful for diagnostic and monitoring purposes. Therefore, the doctor will be able to consult and analyze the patient’s medical file through a communication interface between the patient and the doctor developed in JEE, as it can receive an SMS in case of emergency.

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In recent years water treatment methods under pressurized systems have been considered as the optimum high-rate filtration techniques. Unpressurized-slow sand filtration can be the cheapest and most efficient method, among others. This research aims to test the performance of a reliable seawater filtration system, using three different iterations. The filters have been designed considering many types of filtration layers such as sand, gravel, palm chlorophyll and other layers. The results of routine tests showed that the seawater pH, and TSS, and conductivity in the Gulf of Oman are relatively high. The pH values were decreased from 9.4 to 8.4 (filter 1), 9.0 (filter 2), and 8.7 (filter 3). Filter three has a reduced value of conductivity from 13.06 to 12.81 Ms/cm while a slight increase in filters 1 and 2. The TSS values were significantly reduced from 12.42 mg/L to 1.682 mg/L (filter 1), 2.478 mg/L (filter 2), and 1.200 mg/L (filter 3). This reflects the efficiency for each filter for this parameter is 86.5% for filter 1, 80% for filter 2, while 90.3% for filter three. Water velocity through each layer was monitored using Darcy law where the water of filter three has the longest residence time and slowest flow per time. The fastest flow was in filter one with an average of 0.5 L/minutes, filter two has an average flow of 0.088 L/minutes, while filter 3 has a flow rate of 0.026 L/minutes. The third filter has provided the best performance according to the results. Statistical analysis was conducted to understand the correlation between different parameters. As per Pearson correlation, there is a significant correlation between pH and conductivity values for 19 samples (0.989), while the correlation with TSS is relatively weak (0.364).

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The purpose of this study is to determine the improvement of the competence of junior high school students through the application of blended learning models. This is a Classroom Action Research comprising of two cycles and two observations of student activities, with the descriptive statistics used for data analysis. From the results of the learning process during the two cycles, it was found that students who learned using blended learning models obtained a better competency improvement in the second cycle. The results of the calculation from the first cycle showed that the average competence of students tested in English learning was 70.12 with an activity level of 65.5%. Furthermore, in the second cycle, the competence became 78.00 with an activity level of 70.23%.

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The web documents are automatically interacting to discover the information by web mining, which is one of the applications of Cloud Computing (CC) technologies. These documents may be in the form of structured, semi-structured, or unstructured formats. In current web technologies, the Semantic Web is an extension for better enabling the people and computers to work together, where the information is well defined. Before storing the data to the cloud server, data owners should encrypt their data for privacy and security concerns. At the same time, the end-user, who is finding the data related to specific keywords, suggests the research on searchable encryption technique. In this research work, fine-grained authorization of search was achieved by developing the Attribute-Based Encryption (ABE) search technique, which is under the distribution of multiple attribute authorization. Finally, to validate this approach, an experimental study is conducted on Wikipedia as an ontology with existing techniques. This research applies the Attribute based encryption and search method for the effective search and improve security in the cloud. Access policy, cipher text and secret key is developed based on the Attribute selected from the data. The Lagrange interpolation method is applied for the search process and registration key is applied to access the data. The privacy preserving efficiency of the proposed model is 99.2 % and existing Hierarchical-ABE method has 96 % efficiency.

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This study has two objectives: to determine the level of readiness of first-year undergraduate students at the Universitas Indonesia (UI) and to investigate student’s perception of MPKT (Integrated Character Development course) lecturers’ readiness to manage online learning class. Proportional cluster sampling was applied, and 1466 freshmen from thirteen faculties participated. Data clustering and imputation of missing values were utilized to analysis the data. Clustering based on gender, faculty, previous e-learning experience ** were applied. The study shows that students perceived themselves as being ready to learn in an e-learning environment except Computer Science students who have been more exposed to e-learning and implemented online collaborative learning. Most students agree that MPKT lecturers are able to teach well except those of the Faculty of Computer Science, Faculty of Pharmacy, and Faculty of Social and Political Sciences who think that the teaching ability of lecturers need to be improved. Recommendations and future research topics are proposed based on the study results and in-depth interviews with some experienced online lecturers.

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Biodiesel was produced from lard using a CaO catalyst derived from eggshells. The effects of catalyst loading and transesterification reaction time were investigated. The results revealed that the increase in yield of biodiesel occurred at all catalyst loading when the reaction time was increased. The optimal reaction time was obtained at 60 minutes. The results also indicated that there was an increase in yield of biodiesel when the catalyst loading was increased from 0.5% to 1%. Furthermore, increases in catalyst loading decreased biodiesel yields. The most optimum biodiesel yield of 92.69% was achieved when the reaction time, catalyst loading, methanol:oil molar ratio, reaction temperature, and pressure were 60 minutes, 1%, 6:1, 65 °C, and 1 atm, respectively. The FAME content in biodiesel product was 95.28%. The biodiesel obtained reflected a cetane number and heating value of 46.2 and 37.86 MJ/kg, respectively. Eggshell-derived CaO catalysts exhibited excellent reusability.

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The security of website application has become important in the last decades. According to the Open Web Application Security Project (OWASP), the SQL Injection is classified as one of the major vulnerabilities found in web application security. This research is focused on improving website security in dealing with SQL Injection attacks by stopping, monitoring, and dividing types of SQL Injection attacks using the features provided by the proposed Web Application Firewall (WAF). The architecture is designed to detect and prevent some types of SQL Injection attacks, including Tautologies, Logically Incorrect Queries, Union Queries, Piggy Backed Queries, Stored Procedures. For the testing scenario, this experiment uses an application that has become an industry standard in identifying and validating security holes on a website. The result of this research is that the proposed system is able to increase the website security from SQL Injection.

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Quality of service is a significant part of formulating a composing of web services to satisfy the user’s request, especially when several services exist and have been implemented in the same field and functionality. The selection process of web services among many options can be taken based on the quality of service considerations, fitness parameters, multiple objectives, and constraints. Moreover, some non-functional factors such as quality of service indicators such as integrity, cost, availability, reliability, security, and response time. Several composite services are required to achieve the user’s requirements. This paper presents an integrated approach based on immigrant schemes and multi-objective genetic algorithm (ISMOGA) to specify and maintain the diversity of composite services more efficiently. Research experiments evaluate the performance of elitism-based ISMOGA based on different probabilities of immigrant mutation to produce an adaptive mechanism and to improve the performance in the dynamic optimization problem. The proposed method is compared to the standard technique of genetic algorithms and multi-objective algorithms. The experimental results show that ISMOGA outperforms other algorithms and improves the searching process. Moreover, the proposed algorithm can quickly adapt to the different resources and environmental changes as well as increases the high-quality solutions to satisfy QoS requirements in di?erent configuration networks and communications channels.

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Vehicle number plate detection and recognition is an integral part of the Intelligent Transport System (ITS) as every vehicle has a number plate as part of its identity. The quantity of vehicles on road is growing in the modern age, so numerous crimes are also increasing day by day. Almost every day the news of missing vehicles and accidents are perceived. Vehicles tracking is often required to investigate all these illegal activities. So, vehicle number plate identification, as well as recognition, is an active field of study. However, vehicle number plate identification has always been a challenging task for some reasons, for example, brightness changes, vehicle shadows, and non-uniform license plate character type, various styles, and environment color effects. In this review work, various state-of-the-art vehicle number plate detection, as well as recognition strategies, have been outlined on how researchers have experimented with these techniques, which methods have been developed or used, what datasets have been focused on, what kinds of characters have been recognized and how much progress have been achieved. Hopefully, for future research, this review would be very useful.

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On the 11th of March 2020, the World Health Organization (WHO) declared COVID-19 as a pandemic. Part of controlling measures of the pandemic is to understand the disease’s trajectories. There are several possible interventions that can prevent and control its spread. Determining an optimal strategy is critical for policymakers to understand the impact of different scenarios. Many modelers are using agent-based simulations to virtually examine the efficiency of these scenarios. This paper aims to review published papers that discussed Agent-based simulation (ABS) for modelling the COVID-19 pandemic. Major databases were searched for published articles in 2020 from top-ranking journals. Ten published papers were carefully chosen, and their findings were summarized and discussed. Among the methods used, three ABS models were shared as open source. Major findings included mask-wearing and working/studying from home as the optimal strategies, whereas airport screening is insufficient, and vertical isolation is similar to ‘doing nothing’ scenarios. Finally, one paper discussed the gaps in ABS and proposed a call of actions to the scientific community and guidelines to responsibly improve the ABS modelling’s quality. This paper can contribute to understanding the current landscape of the COVID-19 pandemic simulation models and their limitations. It is proposed to access selected open-sourced agent-based models to evaluate, utilize, customize or learn from, to help conduct more accurate simulations.

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To this extent, the delineation of techno-economic evaluations for transformers becomes more intricate through a lens of Distributed Solar Photovoltaic (DSPV) market in South Africa. Essentially, the transformer price and loss evaluation techniques should be tailored for calculating the Total Ownership Cost (TOC) of transformers facilitating decentralized energy systems. In South Africa, the traditional coal power generation and renewables operate concurrently under liberalized energy markets but have distinct operational requirements and therefore have distinct methods for evaluating their generating states, service loss costs and TOC. As a result, their techno-economic evaluations should be different. In this work, new formulae have been developed to contemplate on a comprehensive technique for calculating the transformer prices and losses necessitated to estimate the cost of service losses and TOC for DSPV transformers. These formulae are based on experimental studies undertaken on a fleet of DSPV transformers ranging from 1.25 to 250MVA. In order to substantiate these new formulae, 4 case studies have been presented. The calculated losses and associated cost results against the pragmatic values from the case studies yield an error of estimation of less than 1% and 2% respectively in all cases. Further, these results are used to calculate the cost of losses and TOC using a methodology that has been proposed in previous work exclusively for power producers who are proprietors of DSPV generation systems.

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Compared to other types of autonomous vehicles, the single-seat is the simplest when designing, since its compact design makes it an option that can simplify different mechanical aspects and enhance those of greater importance such as the steering and the braking system. Likewise, the electronic and electrical design may be a great improvement on the vehicle. It enhances the safety on road by interacting with the mechanical parts of the vehicle and increasing the driver’s perspective or reaction in a larger range of scenarios. For an electric vehicle is also important to clarify that, as an internal combustion engine vehicle, it needs to be regulated and have all the necessary equipment to circulate on the streets. Other interesting information is that an electric vehicle can be recharged with electricity and it can come from renewable energy, diminishing its already lower carbon footprint. Therefore, to achieve autonomy over the detection and evasion of objects, the application of intelligent algorithms is dispensable. To achieve the obtained result, a Q-Learning algorithm was applied on the complete 3D model of the vehicle in a simulation environment, which allows finding the best parameters of forward and turning speed. In this way, by reaching a design that meets the requirements and applying the results obtained in the aforementioned algorithm, it allows their interaction in a real environment to be satisfactory.

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Coronavirus pandemic and transition to distance learning have significantly accelerated the introduction of Education 3.0 – 4.0 technologies into traditional educational process. This paper discusses questions of training of IT- specialists in context of Education 4.0. Based on our experience, approaches to the organization of the educational process of IT- students are considered. It is discussed, what elements of mobile learning, self-directed learning and informal learning are used by students. Informal learning in traditional educational process of IT- students is considered in such aspects as the source of knowledge, personalization, teaching/learning methods. The paper discusses a stage-by-stage approach to formation of interdisciplinary educational content for IT-students. In conclusion, the strategies of teaching / learning of Education 4.0, useful for forming of competencies for Industry 4.0, are discussed.

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South Africa is aiming to achieve a generation capacity of about 11.4GW through wind energy systems, which will contribute nearly 15.1% of the country’s energy mix by 2030. Wind energy is one of the principal renewable energy determinations by the South African government, owing to affluent heavy winds in vast and remote coastal areas. In the design of newfangled Wind Turbine Generator Step-Up (WTGSU) transformers, all feasible measures are now being made to drive the optimal use of active components with the purpose to raise frugality and to lighten the weight of these transformers. This undertaking is allied with numerous challenges and one of them, which is particularly theoretical, is delineated by the Eddy currents. Many times the transformer manufacturer and also the buyer will be inclined to come to terms with some shortcomings triggered by Eddy currents. Still and all, it is critical to understand where Eddy currents emanate and the amount of losses and wherefore the temperature rise that may be produced in various active part components of WTGSU transformers. This is the most ideal choice to inhibit potential failure of WTGSU transformers arising from excessive heating especially under distorted harmonic load conditions. In the current work, an extension of the author’s previous work, new analytical formulae for the Eddy loss computation in WTGSU transformer winding conductors have been explicitly derived, with appropriate contemplation of the fundamental and harmonic load current. These formulae allow the distribution of the skin effect and computation of the winding Eddy losses as a result of individual harmonics in the winding conductors. These results can be utilized to enhance the design of WTGSU transformers and consequently minimize the generation of hotspots in metallic structures.

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This study is a review of the literature on open access (OA), seeking to identify trends in research on the subject. This review was conducted in the SCOPUS database and focused on the following as the main topics: 1. Financial aspects, 2. Repositories, 3. Education, 4. Academic community’s perception of OA resources, 5. Tools, 6. Policies, 7. Institutions, 8. Stakeholders, and 9. Impact. Out of these topics, the financial aspect, especially in OA’s publication costs, was identified as driving great interest among researchers in the field. On the other hand, the study of the impact of OA is a subject little examined. Although research on OA in the higher education sector analyzes different perspectives and describes advances, challenges, and concerns, it is fair to conclude that OA encourages the creation and dissemination of knowledge and academic communication.

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The article suggests four circuit topologies for the practical simulation of grounded and floating lossy inductors. All the suggested circuits use commercially available integrated circuit LT1228 chips as active elements, and only two passive elements, namely one resistor and one capacitor. The first two of the proposed circuits employ only a single LT1228 active element and can realize grounded lossy inductors without the need for element-matching conditions. The last two of the proposed circuits can realize synthetic floating lossy inductors with only two LT1228s. The values of simulated equivalent elements can be tuned electronically by simply adjusting the external DC bias current of the LT1228. Non-ideal transfer error effects of the LT1228 on the synthetic inductor performance are inspected. Sensitivity performance concerning transfer errors and active and passive elements is also demonstrated. PSPICE simulation results and experimental measurements of the commercially available integrated circuit, LT1228, are incorporated to corroborate all our theoretical analyses.

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Fresh water is already scarce in the world, especially in the Middle East (ME). Desalination industry is the main supplier of the potable water to the municipalities in the ME region. It is well known the high cost of a liter of water produced by the desalination process. Unfortunately, car-washing service consumes substantial amount of this desalinated water. This paper describes a filtration system designed and tested for treatment and reuse of the wastewater generated in car-washing stations. The filtration system assembled from two filters: (1) sand and gravel mix, and (2) activated carbon. The paper is an extension of work originally presented in ASET conference in Dubai. The quality of the effluent (treated wastewater) was investigated and determined in Dubai central laboratories. Wastewater samples were grabbed from different car service stations. Representative samples were prepared and the concentrations of the following parameters were measured in each sample of the effluent: (1) Biological oxygen demand (BOD), (2) Chemical oxygen demand (COD), (3) Total dissolved solids (TDS), (4) Total suspended solids (TSS), and (5) Oil and grease (OG). The results show that the filter system removes the BOD and COD at an efficiency as high as 97.5%, the TSS at 90%, and the TDS and OG at 85.5%. In general, the quality of the effluent was found to fall within the standards set by Dubai regulatory authorities. Further research is recommended to enhance the filtration system performance and make it commercially applicable.

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Automatic Speech Recognition (ASR) technology has the potential to improve the learning experience of students in the classroom. This article addresses some of the key theoretical areas identified in the pursuit of implementing a speech recognition system, capable of lesson summary generation in the educational setting. The article discusses: some of the applica- tions of ASR technology in education; prominent feature extraction and speech enhancement techniques typically applied to digital speech; and established neural network-based machine learning models capable of keyword spotting or continuous speech recognition. Following the theoretical investigation, a model is proposed for the implementation of an automatic speech recognition system in a noisy educational environment to facilitate automated, speech-driven lesson summary generation. A prototype system was developed and improved based on this model, ultimately proving itself capable of generating a lesson summary intended to bolster students’ secondary contact with lesson content. This topic-oriented lesson summary provides students with a lesson transcript, but also helps them to monitor educator-defined keyword terms, their prevalence and order as communicated in the lesson, and their associations with educator- defined sections of course content. The prototype was developed using the Python programming language with a modular approach so that its implemented Continuous Speech Recognition system and noise management technique could be chosen at run-time. The prototype contrasts the performance of CMUSphinx and Google Speech Recognition for ASR, both accessed via a cloud-based programming library, and compared the change in accuracy when applying noise injection, noise cancellation or noise reduction to the educator’s speech. Proof of concept was established using the Google Speech Recognition System, which prevailed over CMUSphinx and enabled the prototype to achieve 100,00% accuracy in keyword identification and association on noise-free speech, contrasted with a 96,93% accuracy in keyword identification and association on noise-polluted speech using a noise-cancellation technique.

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Industry 4.0 implementation stipulates effective actuator control. In the present work, a complex order PI^a+jb D^c+jd (COPID) controller was designed for a fractional order model of a direct current (DC) motor system. For comparisons, the DC motor system model was also controlled using the conventional proportional integral (PI), proportional integral derivative (PID), proportional resonant (PR) and fractional order PID controllers (FOPID). Time domain results indicated that the PR controller performed exceedingly well for output signal responses, but fared poorly in case of control signal specifications. The PI controller responses suffered from high time domain characteristics for both control and output signals. The PID controller performed moderately in terms of time domain and peak overshoot metrics. The FOPID controller attained the best time domain characteristics, but was unable to effectively limit the control and output signal peak overshoots. It was only the COPID controller, that successfully minimised/eliminated peak overshoots in control and output signals (0.1 % and 0.0 % respectively). Moreover, the COPID controller was also successful in limiting the rise, peak and settling times. In addition, Bode diagram, root locus plot were obtained and system gain parameters were varied to confirm the robustness of the proposed COPID controller. Thus, COPID controller promises to be an effective solution towards accurate and robust actuator control in modern manufacturing.

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In this article supervised classification methods for the analysis of local Panamanian rice crops using Near-Infrared (NIR) spectral signatures are assessed. Neural network ( Multilayer Perceptron-MLP) and Tree based (Decision Trees-DT and Random Forest-RF) algorithms are used as regression and supervised classification of the spectral signatures by rice varieties, against other crops and by plant phenology (days after planting). Also, satellite derived spectral signature is validated with a field collected spectral model. Results suggest that MLP networks, either for regression or classification, were more efficient (RMSE of 8.78 and 0.068, respectively) than either tree based methods to regress/classify the rice spectral signature (RMSE of 19.37, 19.09 and 0.979, respectively). The validation made using satellite derived spectral signatures resulted in MLP models with RMSE of 0.216 and 7.318, respectively, leaving room for further improvement of the models. This work aimed to present a practical example of the employment of recent supervised classification algorithms for the determination of regression and classification models from reflectance spectral signatures in local rice varieties.

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A significant problem of routing protocols in the Flying Ad Hoc Networks (FANET) is a significant overhead cost due to the high mobility of networking nodes. The problem is caused by a need to send information messages about locations of unmanned aerial vehicles (UAVs). In order to reduce the amount of service information, the following trajectory approximation algorithms have been investigated: an algorithm for conjugating courses and an algorithm based on continuous piecewise-linear functions (CPLF). Four modifications of the CPLF-based algorithm are considered, which differ in the type of piecewise linear function used: basic CPLF, generalized CPLF, generalized CPLF with a compact notation form, and adaptive CPLF. The disadvantages of each algorithm are analyzed. The CPLF approximation of a fragment of an aircraft trajectory consisting of two straight sections and a curved section with variable steepness between them is performed. It is established that adaptive CPLF with variable step reduces the error of trajectory approximation due to the location of most points on the curved sections of the aircraft maneuvering. The modified version of ADV routing protocol has shown a lower overhead value (the gain for small pause time values reaches 23 %). Thus, the effectiveness of the proposed approximation-based routing in FANET is shown.

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The Anopheles gambiae’s CYP6Z3 protein belongs to the Cytochrome P450 family and functions in oxidation-reduction processes, many studies including our previous work on elucidating insecticide resistance genes of the Anopheles also implicated her in pyrethroid insecticide resistance. Model prediction, functional analysis, and enrichment of the target gene with triplex binding sites may become a useful diagnostic biomarker for the disease subtype, but wrong classification of the model by various existing alignment algorithms is a daunting issue that complicates and misleads in decision making during pathway and functional analysis. The aim of this study is to predict five in-silico model of CYP6Z3 Anopheles protein by homology modeling, evaluate and classify them to elucidate the performance of the sequence alignment algorithm deployed, then characterize the top model that is correctly classified. Template selection from three alignment algorithms with sequence of the target-protein, (Anopheles-CYP6Z3) obtained from UNIPROT served as input, Clustal omega and Clustalw2 algorithms was used to generate alignment files for homologous template search to the target-protein. Best template was sought, and the 3D model built in an-automated-mode. PROCHECK was used to evaluate the best-of-the-five-obtained models. Estimating the quality of all models, the prime model emerged from ClustalW2 alignment algorithm, but was wrongly classified as a homo-tetramer-state. These provided a misleading-information which was revealed during model evaluation and interpretation, that resulted to an inappropriate pathway and functional-analysis, false positive model was then isolated, and the current best model emerged from clustalo alignment algorithm having 87.7% amino residues in the most favorable regions, 0.7% in the disallowed regions at monomer oligo state. Functional analysis of the best Anopheles CYP6Z3 secondary structure showed characteristics that explain the different degrees of genetic regulation translating to resistance mechanism in the malaria vector.

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In grid connected wind turbine (WT) systems, the maximum power point tracking (MPPT) approach has a crucial role in optimizing the wind energy efficiency. To search for the maximum power value of the wind turbine, this contribution proposes a new Maximum Power Point Tracking System (MPPT) for wind turbine related to a permanent magnet synchronous generator (PMSG)). The new proposed MPPT combines two techniques: Artificial Neural Network (ANN) and Fuzzy Logic (FL). The ANN is employed to estimate the maximum voltage of the WT, for various values of wind speed, while the control of DC–DC boost converter operation is executed by applying Fuzzy Logic technique. The comparison of our proposed algorithm to P&O technique has shown that it ensures more efficiency, and we used for that a simulation under Matlab/Simulink.

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The Public Administration is forced to transform itself by taking advantage of the contribution of ICT to in the process of reducing bureaucracy and increase transparency, promoting the dematerialization of processes, increasing the quality of online services, allowing greater ubiquity of access, reducing response times, in the search for improvement of the quality of life of its citizens. Decision-making should consider objectives not only technical but also financial, environmental, and social objectives, ideally aligning with Green IT. The objective of the paper is to present a framework, supported by international standards and frameworks, that allows measuring and guiding the alignment of ICT with Green IT for the optimization of practices instituted in organizations, namely in Local Government. This framework includes a qualitative component with several phases and quantitative component with a metric that allows the evaluation of alternative strategies for a given goal. The phases considered are Problem Identification; Problem assessment; Study and planning; Project; Telecommunications and Printing; Information Security; Innovation; Improvement of Citizen Service; Evaluation / opinion. The complete metric includes four valences: IT, financial, environmental, and social. The IT valence, its indicators and corresponding weights are illustrated in a practical example. The proposed framework is an innovative contribution to the area, clearly integrating the support of the perspective of Green IT and thus actively contributing to the implementation of sustainability policies and alignment with Green IT best practices in Local Government, as well as presenting with greater level of detail the components of the structure that emphasize Green IT concerns. The main expected results of the application of the framework are contributing to the implementation, in local government, of sustainable policies and good practices aligned with Green IT, whist targeting cost reduction and optimization, ubiquity of access, increased productivity and ensuring safety standards.

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With the rising number of web services created to build complex business processes, selecting the appropriate web service from a large number of web services respond to the same client request with the same functionality are developed independently but with different quality of service (QoS) attributes. From this point, there are many approaches to web service selection. Nevertheless, this is still deficient due to a considerable number of discovered web services. The prefiltering is a solution to reduce the number of web services candidates. In this paper, the K-means clustering is applied to determine similar services based on QoS information. The results of this prefiltering are considered at the selection task using the Branch and Bound Skyline (BBS) algorithm. The experimental evaluation performed on real Dataset proves that our approach presents efficient results for web service selection.

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Internet of Things provides the support for devices, people and things to collaborate in collecting, analyzing and sharing sensitive information from one device onto the other through the internet. The internet of things is thriving largely due to access, connectivity, artificial intelligence and machine learning approaches that it supports. The stability and enhanced speed of the internet is also attributable to the huge adoption rate that IoT continues to enjoy from Governments, industry and academia in recent times. The increased incidences of cyber-attacks on connected systems in recent times, has inspired the heightened efforts from Governments, industry practitioners and the research world towards improving existing approaches and the engineering of new innovative schemes of securing devices, the software or the platforms for the deployment of IoT. Security solution for Internet of things includes the use of secure ciphers and key exchange algorithms that ensures the provisioning of a security layer for the: devices or hardware, communication channels, cloud, and the life cycle management constituting the Internet of things. The use of key exchange algorithms in resilient cryptographic solution that have less computational requirements without compromising the security efficiency in the encryption of messages for IoT continues to be the preferred approach in securing messages in a node-node exchange of data. This paper aims at providing a cryptographic solution that uses a key exchange cryptographic primitive and a strong cipher in encrypting messages for exchange between nodes in an IoT. Towards achieving this goal, the Diffie-Hellman key exchange (DHE) protocol was used to provide a secure key exchange between the communicating nodes, whiles the Twofish block cipher was used in the encryption and decryption of messages, assuring the security, privacy and integrity of messages in a node-node IoT data exchange. The cryptographic solution has a high throughput.

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This article aims at comparing two controls to follow the maximum power point, making use of DC-DC converters for PV uses. All transformers operate continuously. To fulfil maximum power, we will exploit two MPPT controls: a traditional perturb – observe ‘P&O’ and a smart one – the fuzzy logic ‘FL’. The goal of this article is two-fold: to scrutinize the efficiency of DC-DC transformers (Boost, Buck, Cuk and SEPIC), and to assess the outcomes of the simulation. For the construction of models and simulations, the Matlab / Simulink environment is employed.

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Induction motors (IM) are used widely in high energy applications where high torque is required such as ships/ aircrafts manufacturing industry. Those motor drivers are populated in many advantages especially their wide control strategies. However, new technology is invented for enhancing efficiency and smoothing the torque curve by increasing number of phases in the motor drive. Speed control can be achieved by changing the pole numbers between any two different phases using pole-phase modulation (PPM). The problem is raised when a large number of phases are used to enhance the voltage profile. This problem is manifested in the complexity of control and complexity of hardware (more devices need to be involved) which increases the cost and degrades the performance. This paper argues using a multilevel inverter to produce an enhanced voltage profile and motor speed control without needing to increase the number of devices. The performance of the proposed model is compared with conventional models in terms of efficiency, power factor, speed, torque ripple, and ripple frequency. Results obtained using multilevel inverter based induction motors are found optimum.

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A Japanese method in teaching at classroom show good result by implementing Kamishibai. On the other hand, technology is inseparable from daily life. Computer-based learning media innovations are fast and diverse, ranging from 2D animation to 3D environments. The hologram is one example of 3D object visualization to deliver the learning material. Based on Kamishibai’s opportunity and hologram multimedia utilization to enhance the environmental education teaching activity, research about the adaptation of 3D pyramid hologram for teaching environmental education in elementary school is proposed. Firstly, the kamishibai model for teaching environmental education from Japan is explored and modified to fit Indonesia’s condition. The kamishibai and hologram multimedia utilization in teaching environmental education has been experimented with in class. The results of learning kamishibai and multimedia holograms show that students will improve their abilities in environmental problems. The students lead the literacy and caring attitudes were following their level of knowledge and skills as well as their attitudes about caring about disposing of garbage in its place, learning to clean sewerage at school, learning to farm in the yard of schools, and understand how plants exist in school gardens.

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This article aims to determine to what extent the self-perception of acquiring professional skills has been affect ed, by the context of the Covid-19 pandemic in University Students of Business Administration; For which, the results obtained in two satisfaction survey processes, carried out in academic semesters 2019-B and 2020-A, have been compared, marking a before and after in relation to the declaration of a state of emergency in Peru. Initially, it was determined that the indicators “To solve specialty cases” and “To assume self-education” are the most affected in this dimension, with a percentage of decrease in satisfaction of 3.42% and 3.82%, respectively. Then it was determined through the use of the statistical analysis of crossed tables, that the percentage of totally dissatisfied students, in the self-perception of having acquired the competences referred to the two indicators with the greatest negative impact, has remained invariant, almost constant, around of 44%. With this, it can be concluded that the self-perception of acquiring professional skills has been affected, by the context of the Covid-19 pandemic, decreasing by 3.1%, student satisfaction, as observed in the two indicators with the greatest impact negative “To assume self-education”, and “To solve specialty cases”. These results will allow the Public University of Peru to establish improvement plans, in order to advance towards the development of the teaching-learning model in a virtual way in higher education.

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The ecosystems and landscape patterns in Lake Victoria basin are increasingly being modified by changes in land use/land cover. Understanding dynamics of these changes is essential for appropriate planning. This study evaluated changes in landscape environment, of the lakeside counties of the Kenyan Lake Victoria basin, which have occurred over a forty-year period (1978-2018) and their potential impacts on the lake using remote sensing and GIS techniques. Landsat imageries of 1978, 1988, 1998, 2008 and 2018 were analyzed for each county to develop land use and land cover maps and to detect and quantify changes using the Maximum Likelihood algorithm. Supervised classification was utilized. The study showed that the six land use/land cover classes, identified in the counties, have undergone drastic modifications in a span of four decades. Over the years, built-up areas steadily increased in all the counties, forested and vegetated areas steadily declined in all the counties, areas under water bodies remained relatively constant in all counties, while the rest of the land use and land cover types experienced periodic rise and falls in areal coverage. Generally, the major gains in coverage by the various land use and land cover types occurred between 2008 and 2018 while major losses occurred between 1978 and 1988. The study suggests that future regional conservation measures should take cognisance of the general ecological and socio-political processes in the entire Lake Victoria basin for integrated watershed conservation.

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Protecting the rights and interests of shareholders is the important research topic. This study takes ” Tatung Operation Rights Competition” as an example to execute case study. Game tools are applied to analyze which corporate supervision strategies should be used by government. The research and analysis results show that both the corporate group and the market group are suitable for using mixed strategies to compete for management rights in the operation rights competition case. However, doing something is the best regulatory strategy for government regulators to protect shareholders. In addition, this study suggests that government should assist enterprise with long-term business failures in their industrial upgrading and transformation in peacetime.

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Forecasting food prices play an important role in livestock and agriculture to maximize profits and minimizing risks. An accurate food price prediction model can help the government which leads to optimization of resource allocation. This paper uses ridge regression as an approach for forecasting with many predictors that are related to the target variable. Ridge regression is an expansion of linear regression. It’s fundamentally a regularization of the linear regression model. Ridge regression uses the damping factor (?) as a scalar that should be learned, normally it will utilize a method called cross-validation to find the value. But in this research, we will calculate the damping factor/ridge regression in the ridge regression (RR) model firsthand to minimize the running time used when using cross-validation. The RR model will be used to forecast the food price time-series data. The proposed method shows that calculating the damping factor/regression estimator first results in a faster computation time compared to the regular RR model and also ANFIS.

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The article substantiates the relevance of conducting research on the structural characteristics of a company’s employee knowledge, which is the result of his mental activity and his practical experience. The conditions under which information becomes a source of employee knowledge are determined. An abstract analysis of the process of transforming knowledge into an intellectual product – the competence of company employees is presented. In the process of the comparative analysis of the concept of «competence» its dual content is substantiated. Taking into account the recommendations of the scientific and methodological approach to building the model «Effective Consultant», its practical implementation was carried out on the example of the activities of consultants of a consulting company, taking into account the levels of their development and their performance of professional tasks. The article clarifies the essence of the concepts «consultant-manager» and «consultant-expert», describes the main stages of implementation of the model «Effective consultant» of the company. Calculations were carried out to assess competencies, which were previously divided according to group characteristics (superficial general and special, behavioral, adaptive and personal). Based on the results of the assessment, a set of key competencies of consultants was formed in the form of a package «Intellectual resource». Based on the results of the study of the dependence of the knowledge structure of the company’s employees on their competencies, the practical rationality of the application of the scientific and methodological approach to the formation of the «Effective Consultant» model has been brought. The practical significance of the calculations performed using the method of expert assessments – hierarchy and priorities has been established. It has been substantiated that such an approach will allow companies to effectively use the intellectual knowledge of employees as a stable competitive advantage, and in the shortest possible time to achieve a high economic effect.

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Waste is currently a serious problem often found in rural areas, rural areas, and even industrial areas. Waste is a side effect of activities carried out by humans to meet social or industrial needs. Increasing human productivity will also increase the amount of waste produced. To overcome this, a sorting management system is needed. Good waste is seen from the processing method to the recycling process. The waste management problem still relies on the old system transporting and disposing of waste to the final disposal site (TPA). The TPA itself sometimes piles up in one place so that the waste process becomes uneven and the sorting process is not good, causing type waste. This accumulates and mixes with other hazardous waste. In today’s modern era, the management and sorting system is the same. Object detection and waste classification are carried out in the Sensor system to introduce the previously prepared model. The prototype article recognition model is prepared with waste images to produce a freeze forecast graph used for object discovery which is carried out via the camera associated with the Arduino Uno as the basic unit handling. Ultrasonic sensors are inserted into each garbage compartment to filter out the refill filling rate. The sorting system itself can use computer-based image processing methods. Image Processing is used to process data in real-time and fill the trash level. The sensor module that is implanted to detect waste management personnel, the results of this study prove that image management can accommodate waste particles and tested in the BlackBox method produces results following the required quantitative with the accuracy in both the camera, sensor, and image process used can detect an average of 70%.

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Music Emotion Recognition (MER) is a study to recognize emotion in a music or song. MER is still challenging in the music world since recognizing emotion in music is affected by several features; audio is one of them. This paper uses a deep learning approach for MER, specifically Convolutional Neural Network (CNN) and Convolutional Recurrent Neural Network (CRNN) with 361 Indonesian songs as the dataset. The music is classified into three main emotion groups: positive, neutral, and negative. This paper demonstrates that the best model for MER on Indonesian music is CRNN with the accuracy of 58.33%, outperforming that achieved by CNN.

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Neighbor Discovery Protocol (NDP) is used to discover the MAC address of the connected hosts in Internet Protocol Version 6 (IPv6) in a networked environment. Neighbor Cache Entry (NCE) table holds the association between a host’s IP address and MAC address. However, according to the protocol, the MAC address could be overwritten by sending a single fake packet to its victim. This is a serious security loophole as traffic can easily be sniffed by the attacker. In this paper, we present a scheme to address this problem. Our proposal suggests that when Neighbor Solicitation (NS) and Neighbor Advertisement (NA) process completes, a randomly generated key can be exchanged between them so that, in case of an attack, that key can be used to verify the request. We implemented the proposed scheme in NS3 and simulation results show that our proposed scheme can perform effectively while circumventing the attack that uses override flag of IPv6.

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This work is concerned with the detection and classification of birds that have applications like monitoring extinct and migrated birds. Recent computer vision algorithms can precise this kind of task but still there are some dominant issues like low light, very little differences between subspecies of birds, etc are to be studied. As Convolution Neural Network is a state- of-the-art method with respect to the accuracy of various computer vision related work like object detection, image classification, and segmentation, so CNN based architecture has been proposed to do the experiment for this work. Besides we applied Gaussian and Gabor filters for noise reduction and texture analysis respectively. Histogram of Oriented Gradient (HOG) has been utilized for feature extraction as it is a widely accepted method and it can extract features from all portions of the image. LeNet and ResNet are two good architectures of CNN. In our work, we used the HOG extracted features as input to implement LeNet and ResNet. A standard dataset is used for the experiment and we found that LeNet based CNN gives better results than other methods like ResNet based CNN, SVM, AdaBoost, Random Forest, (we used for the experiment) and other existing state-of-the-art proposed work as well. The experimental results using LeNet based CNN gives 99.6% accuracy with 99.2% F-score , and 96.01% accuracy with 94.14% F-score in detection and classification of birds respectively.

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Information and Communications Technologies are developing faster today than ever before, giving an important contribution to the global economy. Organizations in developed and developing economies explore new technologies to gain advantage and add value. That evolution also brings an increasing complexity to the organizations’ management. The alignment of organizational practices with international standards and best practices worldwide accepted in this domain is a relevant topic. To identify gaps in Information and Communications Technologies management area, a brief analysis of international standards will be considered in the state-of-the-art. Considering that Information Technology Infrastructure Library and Control Objectives for Information and related Technology are the most used in the literature review, this paper will propose an Information and Communications Technologies management framework based on those two standards. The approach pretends to solve some gaps found in process alignment, continuing improvement of Information and Communications Technologies services in the context of the organization, driving stakeholder satisfaction and cost optimization. Concluding, the final goal of this paper is to present the framework features analysed, to allow an integrative and multidisciplinary vision, leading to cost optimization, increasing communication, and stakeholder satisfaction.

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Brain-computer interface (BCI) has extensively been used for rehabilitation purposes. Being in the research phase, the brainwave based wheelchair controlled systems suffer from several limitations, e.g., lack of focus on mental activity, complexity in neural behavior in different conditions, and lower accuracy. Being sensitive to the color stimuli, the EEG signal changes promises a better detection. Utilizing the Electroencephalogram (EEG changes due to different color stimuli, a methodology of wheelchair controlled by brainwaves has been presented in this study. Red, Green, Blue (primary colors) and Yellow (secondary color) were chosen as the color stimuli and utilized in a 2 × 2 color window for four-direction command, namely left and right, forward and stop. Alpha, beta, delta and theta EEG rhythms were analyzed, time and frequency domain features were extracted to find the most influential rhythm and accurate classification model. Four classifiers, namely, K- Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest Classifier (RFC) and Artificial Neural Networks (ANN) were trained and tested for assessing the performance of each of the EEG rhythm, with a five-fold cross-validation. Four different performance measures, i.e. sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were utilized to examine the wholescale performance. The results suggested that Beta EEG rhythm performs the best apart from all the rhythms for the color stimuli based wheelchair control. While comparing the performance of the classifiers, ANN-based classifier shows the best accuracy of 82.5%, which is higher than the performance of the three other classifiers.

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In the Internet of Things era it is expected that low-end processor domination of the embedded market will be further reaffirmed. Then, a question will arise, on whether it is possible to enhance performance of such processors without the cost of high-end architectures. In this work we propose a better-than-worst-case (BTWC) methodology which enables the processor pipeline to operate at higher clock frequencies compared to the worst-case design approach. We employ a novel timing analysis technique, which calculates the timing requirements of individual processor instructions statically, while also considering the dynamic instruction flow in the processor pipeline. Therefore, using an appropriate circuit that we designed within this work, we are able to selectively increase clock frequency, according to the timing needs of the instructions currently occupying the processor pipeline. In this way, the error-free instruction execution is preserved without requiring any error-correction hardware. We evaluate the proposed methodology on two different RiscV Rocket core implementations. Results with the SPEC 2017 CPU benchmark suite demonstrate a 12% to 76% increase on the BTWC design performance compared to the baseline architectures, depending on the appearance rate of instructions with strict timing requirements. We also observe a 4% to 37% increase on power consumption due to the operation of the pipeline at higher clock frequencies. Nevertheless, the performance increase is up to nine times larger than the power consumption increase for each case.

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Coordinated MultiPoint (CoMP) has been introduced for LTE-Advanced system to overcome the inter-cell interference problems and enhance the signal quality of cell-edge UEs (User Equipments). With such concept, the overall system performance should be improved considerably to support the significantly increasing amount of demand on data transmission via mobile communication that happens nowadays. Dynamic Point Selection (DPS) is one of the major CoMP techniques offering benefit through its practicality and low complexity. This work proposes the actual traffic-based load-aware DPS for LTE-Advanced system. The key important cell selection criterion employed in this work is based on the actual traffic load of the calls along with the UEs received signal indicator. The adapted Vienna downlink system level simulator has been used for the system evaluation. The video streaming traffic model was employed with the data rate of 512 kbps for the realistic use cases and four simulation scenarios including the uniformly distributed UEs case and different patterns of hotspots distribution use cases were deployed. The system performance evaluation includes the system throughput performance, the number of UEs achieving expected data rate, and eNBs’ traffic load. The results show that our proposed method offers a substantial improvement over the traditional system as well as the system embedded with the existing DPS mechanisms when the traffic loads are imbalanced such as in certain hotspot cases.

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Within the results of scientific research, vibrations and their reduction have been described for the brushless direct current motor with permanent magnets (BLDC motor). In this paper, calculations (the finite element method using commercial Finite Element Software Ansys) and measurements were performed to identifying the sources of vibrations in BLDC motor. The article presents numerical and experimental research on the resonant frequencies of the stator and the rotor; transient vibrations of the stator due to Maxwell forces in the motor have been analysed. It was shown that the natural frequencies were the main source of vibrations. The vibration sources indentification made possible formulation of better principles of choice of constructional motor parameters with the aim of attaining minimalization of vibrations.

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Haze removal is an extremely challenging task, and object detection in the hazy environment has recently gained much attention due to the popularity of autonomous driving and traffic surveillance. In this work, the authors propose a multiple linear regression haze removal model based on a widely adopted dehazing algorithm named Dark Channel Prior. Training this model with a synthetic hazy dataset, the proposed model can reduce the unanticipated deviations generated from the rough estimations of transmission map and atmospheric light in Dark Channel Prior. To increase object detection accuracy in the hazy environment, the authors further present an algorithm to build a synthetic hazy COCO training dataset by generating the artificial haze to the MS COCO training dataset. The experimental results demonstrate that the proposed model obtains higher image quality and shares more similarity with ground truth images than most conventional pixel-based dehazing algorithms and neural network based haze-removal models. The authors also evaluate the mean average precision of Mask R-CNN when training the network with synthetic hazy COCO training dataset and preprocessing test hazy dataset by removing the haze with the proposed dehazing model. It turns out that both approaches can increase the object detection accuracy significantly and outperform most existing object detection models over hazy images.

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Depth prediction plays a key role in understanding a 3D scene. Several techniques have been developed throughout the years, among which Convolutional Neural Network has recently achieved state-of-the-art performance on estimating depth from a single image. However, traditional CNNs suffer from the lower resolution and information loss caused by the pooling layers. And oversized parameters generated from fully connected layers often lead to a exploded memory usage problem. In this paper, we present an advanced Dilated Fully Convolutional Neural Network to address the deficiencies. Taking advantages of the exponential expansion of the receptive field in dilated convolutions, our model can minimize the loss of resolution. It also reduces the amount of parameters significantly by replacing the fully connected layers with the fully convolutional layers. We show experimentally on NYU Depth V2 datasets that the depth prediction obtained from our model is considerably closer to ground truth than that from traditional CNNs techniques.

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In this paper, second order generalized integrator (SOGI) is discretized to design the control algorithm of unified power quality conditioner (UPQC). A UPQC is combination two voltage source converter (VSC) and VSC are connected with back to back DC link. The first one VSC is in series to maintain the desire voltage at point of common coupling (PCC) and second is connected with shunt VSC to share the reactive power demand of load. Moreover, it protects the AC mains from pollution of the load. A phase lock loop (PLL) based SOGI model is implemented to design an algorithm for UPQC under light polluted load. Whereas under highly polluted load the Laplace Transformation based PLL-SOGI model is fail to eliminate harmonics of current and voltage at PCC. Thus, a discretization of PLL-SOGI is needed to meet disadvantage. In this paper, reference current is generated under polluted load using discretization of PLL-SOGI model and compared it with actual current to pulse the gate of shunt VSC. Moreover, a feedback unit (m) is proposed to pulse the gate of series VSC under voltage sag/swell condition. A hardware setup is performed in the lab to verify the proposed algorithm under highly polluted load.

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The Additive Manufacturing (AM) technology is a disruptive and novel technique that changes the paradigm of manufacturing methodology. It is based on the principle of having 3D parts by adding simple 2D layers on and on. Before AM was implemented, the conventional subtractive and chip-away techniques such as milling and turning processes had been used widely in the aviation industry. The mentioned conventional methods are still in use. However, it is observed that AM replacing legacy methods, especially for the complex and relatively heavy parts. Thanks to mutual-usage of the Topology Optimization (TO) techniques and AM, many weight reduction studies have been done successfully. The weight reduction studies have an impact on the Direct Operational Cost (DOC) of the aircraft. With the benefit of weight reduction studies, many airliner companies have the opportunity for carrying more payloads with the same type of commercial-passenger aircraft. Also, the TO and weight reduction studies are beneficial for lowering the carbon footprint. Obviously, the weight reduction, the DOC, and the carbon emission are interrelated with each other. In this paper, a research was carried out for a generic engine mount that is specifically used by famous aircraft types. Eventually, it was found that, with the help of AM, TO and material optimization studies it is possible to save weight on the engine mount.

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Formal methods are one of the efficient tools to verify and validate designs for different kinds of systems. Smart systems are attracting researchers’ attention due to the rapid spread of new technologies all over the world. Modeling a smart system requires connecting heterogeneous subsystems together to build it. Our contribution to this work is in focusing on using formal methods to prove that a design model meets its specifications. We have chosen to design a smart school building system due to the lack of research in this particular area, and to prove that formal methods are appropriate for different systems applications. In this paper, we have used UML diagrams and the formal specification language TLA+ to design a smart school building system. We validate our design using the TLC model checker. The smart school system has many subsystems connected together including a secure access system, lighting control system, climate control system, and smoke detection system. Safety is a very important attribute in this system. Our goal is to have a smart system that satisfies its functional requirements as well as any non-functional requirements like safety. The system provides safety for employees and students in the smart school.

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Harassment and violence against women have become one of the social security problems in Bangladesh. In this paper, we aim to develop safety devices for women named BOHNNI and BADHON which resemble legitimate jewelry. We used a microcontroller for the hardware device to make it most decisive and less immoderate. BOHNNI, a locating device, is the imitator of a locket including a voice recognizer, Bluetooth, Arduino, GPS, and GSM module. BADHON which imitates a bracelet is a rescue device for the victim whenever she thinks of herself being in a very deliberate situation. Both devices are activated by the user’s voice commands and also by a manual switch. The devices are aesthetically designed which will make the users enthusiastic to wear them. The device will generate messages to the predefined relative’s numbers with the victim’s location and relevant surrounding information. The device can also be used as a self-defending weapon as it can produce a shock up to 10 mA with an interval of two seconds which can temporarily paralyze or freeze a person. After calculating, we have obtained the lowest response time of BOHNNI and BADHON which is 1.95s, and the highest accuracy level of 91.67% in different situations that ensure the superlative performance level of our devices. We found our device as an all-in-one device that combines all the features in it regarding safety.

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Fossil fuel can no longer supply the constantly spiking demands of energy around the world, hence the increasing research on renewable energies as an alternative. The Stirling Engine is an external combustion engine, giving us a wide range of heat sources: solar, nuclear. The Stirling engine makes best of use of solar sources in an environmentally friendly way. It has no emissions and live longer as compared to Photovoltaic cells. The Stirling engine can operate at Low Temperature difference, which makes it prominent. In order to study the efficiency of a conversion from thermal energy to work, we need to take into account the energy efficiency, which is a key parameter in Low Temperature Difference Stirling Engine, even if its efficiency is lower than those of high temperature Stirling engine. In this article, we are studying the efficiency of the Stirling engine as a first step using a parabolic mirror to focus the sun’s radiation onto the engine. In this article, we are studying the efficiency of the Stirling engine as a first step, by making isothermal and adiabatic analysis of the engine to detail the operation throughout its process, and be able to act on the various input parameters that impact the value of the final yield, and in a second step, using a parabolic mirror to focus the sun’s radiation onto the engine.

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The main contribution of this paper is the framework of Application Programming Interface (API) to be integrated on a smartphone app. The integration with algorithm that generates fingerprints from the method ST-PSD with several parameter configurations (Windows size, threshold, and sub-score linear combination coefficient). An approach capable of recognizing an audio piece of music with an accuracy equal to 90% was further tested based on this result. In addition the implementation is done by algorithm using Java’s programming language, executed through an application developed in the Android operating system. Also, capturing the audio from the smartphone, which is subsequently compared with fingerprints, those present in a database.

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It has become difficult to handle traditional networks because of extensive network developments and an increase in the number of network users, and also because of new technologies like cloud computing and big data. Traditional networks are experiencing an increase in VM load and in the time taken for processing tasks. Hence, it has become essential to modify the traditional network architecture. A notion called Load balancing techniques that increases the conformance of network management was presented recently to deal with this problem. The critical need for load balancing emerges due to network resources limitations and requirements fulfillment that facilitates traffic distribution through various resources to enhance the efficiency and reliability of network resources. This task has been carried out by several researchers before, who have presented various algorithms with their benefits and shortcomings. The focus of this research is on the notion of cloud computing load balancing and on the advantages and disadvantages of a chosen load balancing algorithm. Furthermore, it examines the metrics and issues of these algorithms.

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With a view to detecting whether economic activity deterioration for elderly people at age of sixty-five or over could be observed, anonymous data (AD) were used as analysis data, which were obtained from the National Survey of Family Income and Expenditure (NSFIE) conducted by the Ministry of Internal Affairs and Communications (MIC). We have developed a method to detect dissaving risk among elderly people. In our previous analysis, AD were divided into test data and training data. Three kinds of methods were performed on the basis of income and savings. Then two-step methods were processed to determine dissaving risk. Nevertheless, in utilizing AD as it is, the security of anonymity could be questionable. Therefore, in order to enhance the anonymity of the data, random data (RD) were generated based on AD in this paper. Then RD were compared with the case of analyzing mere AD as it is, for the purpose of performance evaluation. Further analysis results suggest that using both RD and AD would be as effective as using only AD in evaluating the performance of the proposed method.

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The use of Unmanned Aerial Vehicle (UAV) can contribute to find solutions and add value to several agricultural problems, favoring thus productivity, better quality control processes and flexible farm management. In addition, the strategies that allow the acquisition and analysis of data from agricultural environments can help optimize current practices such as crop counting. The present research proposes a methodology based on the exploitation of deep learning approach, especially Convolutional Neural Networks (CNN) on UAV data for fruit tree detection and counting. We build models for the automatic extraction of fruit trees. This approach is divided into main phases: dataset pre-treatment, implementing a fruit trees detection model by exploiting several CNN architectures, validating and comparing the performances of different models. The exploitation of RGB UAV images as input information will allow the learning models to find a statistical structure, which will result in rules capable of automating the detection task. They can be applied to new images for automatically identify and count fruit trees. The application of the methodology on collected data has made it possible to reach estimates of detection and counting until 96 %.

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Before the COVID-19 crisis, the Southern African Developing Countries (SADC) had a varied energy mix including renewable energy, fossil fuels, and military energy production. The use of fossil fuels in the energy mix is known to be the source of the growing levels of greenhouse gases in the atmosphere. However, there was a reduction in GHG emissions following the pandemic, which reduced travel and trade, and worldwide disruption in economic activities. The priority of priority B in the 2015-2020 Regional Indicative Strategic Development Plan, which is Energy, continues. As a result, the availability of affordable and renewable energy is still a priority for south of the equator countries and their growth agenda. This paper is aimed at exploring the sustainability of SADC countries’ electricity sectors by using three sustainability pillars: Social, Environmental and Economic (SEE). SEE offers the main concepts of renewable energy, in a way that is socially, environmentally appropriate and economically viable. Study shows a gap in access rate in SADC countries with only Mauritius and Seychelles reaching 100% access to modern energy services (electricity) for both rural and urban areas. Currently all the member countries have set their RE goals for the year 2030. However, the subsidies by SADC member countries indicate that they are practiced as a way to make electricity affordable, and also to make electricity available to lower income households. In the period 2014-2017, big national budget deficits happened in various Southern African countries because of subsidies. Thus, this paper is of crucial importance to the foundational advancement of sustainable electricity sector growth in the country. The findings of this paper play a crucial role in helping and guiding politicians to better understand the existing and challenges future in the energy market and alternatives to address these problems. Additional research is given on how to arrive at sustainable decisions for the electricity sector in the region.

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In this paper, a novel is proposed for real-time tracking human targets in cases of high influence from complexity environment with a normal camera. Firstly, based on Oriented FAST and Rotated BRIEF features, the Lucas-Kanade Optical Flow algorithm is used to track reliable keypoints. This method represents a valuable performance to decline the effect of the illumination or displacement of human targets. Secondly, the area of the human target in the frame is determined more precise by using the Camshift algorithm. Compared to the existing approaches, the proposed method has some merits to some extents including rapid calculations in implementation, high accuracy in case of similar objects detection, the ability to deploy easily on mobile devices. Finally, the effectiveness of the proposed tracking algorithm is demonstrated via experimental results.

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Programming industrial robots in a real-life environment is a significant task necessary to be dealt with in modern facilities. The “pick up and place” task is undeniably one of the regular robot programming problems which needs to be solved. At the beginning of the “pick and place” task, the position determination and exact detection of the objects for picking must be performed. In this paper, an advanced approach to the detection and positioning of various objects is introduced. The approach is based on two consecutive steps. Firstly, the captured scene, containing attentive objects, is transformed using a segmentation neural network. The output of the segmentation process is a schematic image in which the types and positions of objects are represented by gradient circles of various colors. Secondly, these particular circle positions are determined by finding the local maxima in the schematic image. The proposed approach is tested on a complex detection and positioning problem by evaluation of total accuracy.

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Static Application Security Testing (SAST) is a popular quality assurance technique in software engineering. However, integrating SAST tools into industry-level product development and security assessment poses various technical and managerial challenges. In this work, we reported a longitudinal case study of adopting SAST as a part of a human-driven security assessment for an open-source e-government project. We described how SASTs are selected, evaluated, and combined into a novel approach for software security assessment. The approach was preliminarily evaluated using semi-structured interviews. Our result shows that (1) while some SAST tools outperform others, it is possible to achieve better performance by combining more than one SAST tools and (2) SAST tools should be used towards a practical performance and in the combination with triangulated approaches for human-driven vulnerability assessment in real-world projects.

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Light Emitting Diode (LED) lamps are used as a replacement of “old-fashioned” or incandescent lighting sources, as they reduce the amount of energy consumed. As a side-effect of more efficient energy usage they produce electrical noise. This noise reduces the efficiency of information signal transfer when Power Line Communications (PLC) are used. This study focuses on the noise signatures of LED lamps which have a direct impact on the information transfer of the PLC channel. The contribution of this study is that two categories of noise characterisations are given. First is equations describing the maximum and minimum bounds of the lamp noise current. This is useful in calculating channel throughput where an equation for the noise is required. For example, the Shannon-Hartley theorem. Second is a methodology to determine individual frequencies in the spectrum of harmonics emanating from the lamp. Both these characterisations will aid in designing communication schemes for PLC. An unexpected result of this study was to find LED lamps which had inadequate or no Electromagnetic Interference (EMI) filters. These lamps produce noise in orders of magnitude higher than properly filtered LED lamps.

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This paper presents a new observer-based method which deals with the extraction of amplitude of characteristic frequencies for the fault diagnosis in permanent magnet synchronous motors (PMSM). First, a pilot survey is made to investigate the typical harmonics in the line currents of PMSM. Second, an appropriate structure of observer is formulated with the input of current signature in the time domain. By transforming into the Laplace domain, the convergence of the observer is proven. Using the proposed observer, a feature extraction method for fault detection can be introduced; in which the Park’s vector module (PVM) of the line currents is selected as the signature for the feature extraction of the amplitude at the second order harmonic. Simulation and experiment of the PMSM operating in speed control mode are carried out to provide the line current data for analysis. The results show that the amplitude of second order harmonic can be calculated and on-line monitored that demonstrates the effectiveness of the proposed method.

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Artificial intelligence (AI) systems have been applied not only to numerical simulations of the economical sequences but also to the bio-signal, for instance, the statokinesigrams (SKGs). According to the nonlinear analysis of the bio-signal, we have considered that the motion process of the body sway is more random than that of the other bio-signal. In this study, we proposed a method for the numerical analysis of biological data using AI. The AI numerical solutions can indicate graphs that are very similar to the SKGs in degree of the determinism. In addition, we succeeded in extracting partial figure patterns that the AI regarded as a feature of 3D sickness. Compering with the properties resulting from the mathematical analysis, interpretations can be given for the black box processing in the AI.

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Face Recognition (FR) is considered as a heavily studied topic in computer vision field. The capability to automatically identify and authenticate human’s faces using real-time images is an important aspect in surveillance, security, and other related domains. There are separate applications that help in identifying individuals at specific locations which help in detecting intruders. The real-time recognition is mandatory for surveillance purposes. A number of machine learning methods along with classifiers are used for the recognition of faces. Existing Machine Learning (ML) methods are failed to achieve optimal performance due to their inability to accurately extract the features from the face image, and enhancing system’s recognition accuracy system becomes very difficult task. Majority of designed FR systems has two major steps: extraction of feature and classifier. Accurate FR system is still a challenge, primarily due to the higher computational time and separate feature extraction. In general, for various applications using images, deep learning algorithms are mostly recommended for solving these problems because it performs combined feature extraction and classification task. Deep learning algorithm reduces the computation time and enhances the recognition accuracy because of automatic extraction of feature. The major novelty of the work is to design a new VGG-16 with Transfer Learning algorithm for face recognition by varying active layers with three levels (3, 4, and 7). It also designs the Convolutional Neural Network (CNN) for FR system. The proposed work introduced a new Real Time Face Recognition (RTFR) system. The process is broken into three major steps: (1) database collection, (2) FR to identify particular persons and (3) Performance evaluation. For the first step, the system collects 1056 faces in real time for 24 persons using a camera with resolution of 112*92. Second step, efficient RTFR algorithm is then used to recognize faces with a known database. Here two different deep learning algorithms such as CNN and VGG-16 with Transfer Learning are introduced for RTFR system. This proposed system is implemented using Keras. Thirdly the performance of these two classifiers is measured using of precision, recall, F1-score, accuracy and k-fold cross validation. From results it concludes that proposed algorithm produces higher accuracy results of 99.37%, whereas the other existing classifiers such as VGG3, VGG7, and CNN gives the accuracy results of 75.71%, 96.53%, and 69.09% values respectively.

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Early detection and proper management of adult degenerative scoliosis (ADS) are important for health promotion. This study aims to develop an ADS screening tool from markerless digital photography and verify its reliability. The study included 17 participants. Outer canthus–horizontal angle (OHA) and trapezius–horizontal angle (THA) were calculated from the image of the upper body of the subject in a coronal plane using ImageJ. The Cobb angle was measured to investigate the correlation between OHA and THA. The intraclass correlation coefficient was analyzed to verify the reliability using the values of skilled and unskilled physiotherapists. The study results demonstrated an excellent correlation between THA and Cobb angle. THA also had an almost perfect intra- and interrater reliability. Because scoliosis is characterized by shoulder imbalance and THA is an index that reflects shoulder imbalance, the correlation with Cobb angle was excellent. THA is a scoliosis screening tool that can be used not only in hospitals but also in various places because even unskilled physiotherapists can measure highly reliable values.

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In recent days, the fast growth of populations leading to an increase in medically complicated cases, especially fast spread viral cases around the world. These phenomena increased demand on auto-diagnose systems to speed up the diagnosis process and reduce human contacts, especially for the COVID-19 pandemic using deep learning (DL). DL methods can successfully carry out these complicated works. A Deep Convolutional Neural Network (Deep CNN) is the most appropriate model for the medical image diagnosis process among DL techniques. This study focuses on follow-up and diagnosis of COVID-19 pneumonia cases. Deep CNN model can learn the chest computed tomography (CT) features properly synchronizing with the training options that involve the optimizer, number of epochs, and learning rate to get optimal accuracy with the lowest error rate. The auto diagnosis process aims to follow-up and diagnosis COVID-19 pneumonia and illuminate it from Streptococcus pneumonia and normal chest. Executed the present study on were 840 CT images of 24 patients from the Radiopedia database. Computed tomography (CT) is the best modality to visualize lung diseases, which own enough positions to interpret everything inside lung anatomy. Deep CNN model owns of enough layers and enable the model to extract and learn pneumonia features from the training set images. This process applied on MATLAB software. The model’s result exhibits that the proposed deep CNN approach had an accuracy level of 99.37%.

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Blockchain (BC) as the widespread innovations in the 21st century has recognized itself to be immutable, tamper-resistant, decentralize and secure. This emerging technology is used as a functional technology for refining present technology and forming new applications for its robustness and disintermediation. Decentralized Digital Self-Sovereign Identity (DDSSI) is an identity mapped with individual identity information along with the user’s reputation in the transaction. User’s information will be preserved in the decentralized cloud server which will be controlled and maintained by the user. In this research work, we suggest a Blockchain-centered DDSSI wallet to modernizes the existing identity management system that will be used to identify as well as access control to provide validation and endorsement of entities in a digital system. BC technology in this innovation ensures credible and safe information in a transaction besides. Here, we use Bitcoin cryptocurrencies to generate secure and unique DDSSI public key addresses by integrating the private key with the random number for transferring and accepting information and a token-based system to identify customer reputation.

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A new approach for designing RF energy harvester with a single stage converter circuit is presented in this paper. The proposed converter configuration is integrated with an antenna that is based on the coplanar waveguide (CPW) transmission line with improved gain resonated at 2.45 frequency ISM band. The CPW patch antenna as a harvester antenna is designed in a rectangular shape that uses FR-4 substrate with a loss tangent and relative permittivity of 0.025 and 4.3 respectively. The output from the harvester antenna is connected to the converter circuit using only two Schottky diodes. The rectifier design achieves between 0.1% to 37% of RF-DC power conversion efficiency over the ambient RF input signal range from -20 dBm to 0 dBm and the antenna exhibits a directivity of 3.896 dBi as well as a return loss of -48.85 dB. For an input power of 0 dBm, the proposed circuit can rectify an AC signal up to 6.09 V. Moreover, the proposed CPW antenna that is integrated with a converter circuit agrees for the harvesting of ambient electromagnetic energy to power low power electronic devices.

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The Micro-facial expression is the most effective way to display human emotional state. But it needs an expert coder to be decoded. Recently, new computer vision technologies have emerged to automatically extract facial expressions from human faces. In this study, a video-based emotion analysis system is implemented to detect human faces and recognize their emotions from recorded videos. Relevant information is presented on graphs and can be viewed on video to help understanding expressed emotions responses. The system recognizes and analyzes emotions frame by frame. The image-based facial expressions model used deep learning methods. It was tested with two pre-trained models on two different databases. To validate the video-based emotion analysis system, the aim of this study is to challenge it by comparing the performance of the initial implemented model to the iMotions Affectiva AFFDEX emotions analysis software on labeled sequences. These sequences were recorded and performed by a Tunisian actor and validated by an expert psychologist. Emotions to be recognized correspond to the six primary emotions defined by Paul Ekman : anger, disgust, fear, joy, sadness, surprise, and then their possible combinations according to Robert Plutchik’s psycho-evolutionary theory of emotions. Results show a progressive increase of the system’s performance, achieving a high correlation with Affectiva. Joy, surprise and disgust expressions can reliably be detected with an underprediction of anger from the two systems. The implemented system has shown more efficient results on recognizing sadness, fear and secondary emotions. Contrary to iMotions Affectiva analysis results, VEMOS system has recognized correctly sadness and contempt. It has also successfully recognized surprsie and fear and detect the alarm secondary emotion. iMotions Affectiva has confused surprise and fear. Finally, compared to iMotions the system was also able to detect peak of morbidness and remorse secondary emotions.

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The suitability of incorporating waste transfer stations (WTS) in likely future Municipal Solid Waste Management (MSWM) systems for Harare city and neighbouring urban centres was assessed under this study. WTS will bring about location of landfills and other MSWM facilities further away from population centres, increasing recycling, reducing waste collection costs and burden on the overall MSWM budget, an increase in waste collection effectiveness and efficiency, reduction in waste collection derived greenhouse gases (GHG) emissions and other associated impacts. Life cycle Assessment (LCA) on contribution of waste collection to human health impact potential of 34 DALYs as well as acidification, global warming and eutrophication impact potentials of 0.012, 0.065 and 0.0002 species.year respectively under all the six MSWM options were observed. Highest impacts in the species extinction impact categories was realised in the global warming impact category resultant of GHG emissions from fuel combustion during waste collection. The unavailability of land and the above factors support the incorporation of WTS in future MSWM options for Harare City and surrounding urban centres under a separation at source waste collection system to derive maximum benefits. Citizens drop off centres (CDOPs) and buy-back centres (BBCs) could also compliment the WTS leading to increased recycling. Though there is a relative sound supportive legislative, regulatory and policy framework that supports the need for waste recycling consequently supporting WTS, CDOPS and BBCs due to their recycling promotion capabilities, there is need for specific legislation, regulation and policies that support the development and operation of such facilities that will bring interest amongst would be operators, effectiveness and efficiency resultantly reducing associated human health and environmental impact. Further studies that determine the breakeven distance and LCA studies that specifically assess the associated environmental loads of incorporating WTS within the likely future MSWM systems are recommended.

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Municipal Solid Waste (MSW) is composed of items that are discarded or disposed of daily including paper, plastics, glass, metals, used gadgets, paint and old furniture. The plastic waste stream has proven to be problematic to manage sustainably on a global scale. Various researchers are trying to come up with innovative ways of alleviating the detrimental effects of plastic on the environment. Examples include the production of liquid fuel and synthetic gas through pyrolysis and gasification of plastic waste, use of microbial strains that can break down polyethylene, manufacture of plastic-infused tar, use of plastic waste in cement and concrete as well as its use in the manufacture of bricks. Conducting public awareness and outreach programmes has also been found to be beneficial in reducing plastic littering. This paper reviews South Africa’s strengths, weaknesses, and opportunities in plastic waste management as well as lessons from other jurisdictions that can be adopted in South Africa making it a role model for Africa with regards to plastic waste management. There exists an untapped opportunity for improvement of post-consumer plastic recycling rates to levels comparable to other recyclables in the country through compulsory separation of waste at source. Hence an enabling environment should be created to encourage this practice. Since this will require a fully functional waste management infrastructure, collection services should expand to cover rural areas and informal settlements while industries can assist municipalities to upgrade infrastructure through the extended producer responsibility (EPR) scheme. In addition, there is potential for more jobs to be created in the waste sector through recycling as compared to landfilling, thus urgent attention is needed to divert 100% waste from the landfill. Finally, the integration of informal waste pickers into the waste management chain should be prioritised.

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Municipal Solid Waste Management (MSWM) decentralisation brings about reductions in the amount of municipal solid waste (MSW) earmarked for landfilling worse still under situations where MSW is being sent to dumpsites. It also reduces the distances MSW collection vehicles travel during MSW collection, maintenance and transport costs due to the establishment of local level decentralised MSWM and treatment facilities. Subsequently fuel use, greenhouse gas and other emissions together with MSWM associated environmental and human health risks are reduced. The Zimbabwe National Integrated Solid Waste Management Plan (ZNISWMP) provides for the need for decentralisation in MSWM. This study therefore assessed the framework along which MSWM decentralisation can be achieved in Harare. The study noted the presence of various opportunities for MSWM decentralisation in Harare namely household backyard composting, community level and industrial scale anaerobic digestion or composting of organic MSW fraction, anaerobic co-digestion of organic MSW fraction and dewatered sewage sludge, SW source separation for material recovery, establishment of waste transfer stations, citizens drop off centers, buy back centers and thermal treatment facilities associated with energy recovery. Though the NISWMP plan provides for concrete actions for MSWM decentralisation, it was observed that almost all of the proposed actions have not been implemented hence the need for urgent review and subsequent operationalization and implementation of the review findings. MSWM has also been hindered by the lack of legislative and institutional reforms with ULAs remain reluctant to devolve and delegate some of the MSWM responsibilities and functions to other players prompting the need for such reforms to be implemented as provided in goal 10 of plan. The Presidential national cleanup day proclamation needs to be complemented with other initiatives that will increase residents’ interest in participation, allow for different types of participation such as provision of resources and equipment and above all the development of sustainable MSW disposal facilities unlike dumpsites.

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This article extends novelty of modeling capacitive accelerometer with PID controller to provide PI controller for better tuning and statistical test to determine the linear validation characteristics of closed capacitive accelerometer. Capacitive accelerometer is a sensor which uses the dynamic law of physics model Position-Velocity-Accelerator (PVA) by the movement of an electrode coupled to mass proof sandwich between parallel plates to detect vehicle/object displacement. The modelling of closed loop system helps to mitigate steady state error accumulation of measurements in open loop model. The accelerometer gives linear time dependence on output displacement after an input step-like function of acceleration. The closed model can predict the desired output signal. The linearity of the model is tested statistically using simple regression of 120 dataset. This shows a p-value of 2e-16 indicating that at any time, the acceleration predicts the displacement/position of vehicle/object.

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Municipal general waste accumulation including the general waste category of end-of-life tyres has become a universal predicament especially in the majority of first world countries as well as in the third world countries. South Africa is recognized for its economic growth and improved living standards of people which has led to the increased accumulation rates of waste tyres. Consequently, the South African government declared its intentions to divert all categories of end-of-life tyres away from municipal dumping grounds as they present acute health and ecological threats. The government gazetted the Recycling and Economic Development Initiative of South Africa (REDISA) Integrated Industry Waste Tyre Management Plan (IIWTMP) in 2015 that seeks to manage and reprocess waste tyres bringing about environmental sustainability and economic prosperity through the simultaneous creation of jobs. This work, therefore, is a theoretical literature review study that highlights the achievements and failures of the Plan. Despite it being a comprehensively drafted and well-rationalized plan, REDISA drew negative public scrutiny from various stakeholders and institutions such as the Organization Undoing Tax Abuse (OUTA), Retail Motor Industry Organization (RMI) and television programs like Carte Blanche. The findings show that REDISA did manage to make significant contributions to the different sectors governing the Plan such as the creation of jobs and small, medium, and micro-sized enterprises (SMMEs), the establishment of depots and waste tyre processing facilities and, the investment into several institutions of higher learning to further research and development in the waste tyre sector. The plan ultimately ceased operation citing several unsound practices such as corporate administrative issues, deviating from the National Environmental Management (NEM) Amendment Law Bill, failing to carry out the duties outlined in the original Plan. Lastly, REDISA did not comply with operational and performance goals.

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More than one year after the outbreak of the COVID-19 pandemic the behavior of figures published by official sources of the countries are skeptical for the public. Many probability tests are used to detect the reliability of information among which Benford’s Law. This study focused on the Western Balkan countries, as one of the foremost regions of South East Europe, where the appearance of COVID-19 was delayed by almost two months compared to the rest of Europe. In our work, we have analyzed the reliability of new cases and deaths figures published daily by official sources. Two study periods are considered separating the two waves of infections in the region. We have used Benford Law as one of the probability laws which has shown effectiveness in detecting possible data incorrectness or lack of information. Statistical tests such as Chi-Square have been used to check the probability adequacy of real data with Benford distribution. The results show a significant fluctuation of the figures from the Benford Law, especially during the first observed period. The study may be used by the policy makers to detect incorrectness or delays in reported number of new cases and/or deaths that have occurred during the COVID-19 pandemic.

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This paper presents a design of parallel gripper finger for robotic dual-arm working with an automatic push-down bottle crown cork cap opener on ABB’s Yumi collaborative bartender robot. A safe gripper finger is made from ABS plastic by 3D printing for human-based interaction design for grasping and holding a glass bottle. Rack design and proposed automatic push-down bottle cap opener using pneumatics are presented to support a gripper finger. Experimental tests as bartender environment with 4-different types of carbonated soft drink with crown cork cap show that can be achieved effectively with average of 91.5% percentage of successful cap opener.

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To get hired, an aspiring candidate needs a good CV/r´esum´e. This is not an easy task as it must be readable, well structured, concise with no grammatical errors, and containing all relevant information needed by employers. In this paper, we present a newly designed context-free grammar for the dynamic synthesis of a CV. The grammar rules were implemented in a software tool called Flex-CV. Flex-CV makes use of user input and selected CV templates to produce many CV instances in LaTeX. Examples of CVs generated with Flex-CV can be found at https://tinyurl.com/cv-instances. We then evaluated this tool based on its perceived usefulness among job seekers. Evaluation results indicated that this tool will be useful to those aiming to improve their employment prospects through better CV presentation.

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A Decision-Making Centers (DMCs) Environment facilitates stakeholders’ decision-making processes using predictive models and diverse what-if scenarios. An essential element of this environment is the management of Decision Support Components (e.g., models or systems) that need to be created with mature methodologies and good delivery time. However, there has been a gap in the understanding of project management best practices in DMC environments and in the application of methodologies to ease project execution. In the following paper, we address that gap by analyzing six predictive analytics projects executed in a Mexican DMC using Process Mining techniques. We perform process discovery using a detailed activity event log, which has not been possible in previous studies. Additionally, we perform a compliance evaluation versus the de facto methodology to identify the current process alignment gaps, and finally, we analyze the social networks present in the process execution. The research reveals that (1) process mining models are helpful to address management issues of PA/DM projects (2) PA/DM projects require alignment to mature methodologies to improve process performance and avoid execution problems (3) PA/DM project execution should be revised at the activity level to identify issues and to propose specific strategies. This study’s findings can help project managers to perform process analyses and to make informed decisions in PA/DM projects. The following paper is an extension of the article “Applying Process Mining to Support Management of Predictive Analytics/Data Mining Projects in a Decision-Making Center¨ presented in the 2019 International Conference on Systems and Informatics (ICSAI 2019).

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In recent years, the applications in the field of Unmanned Aerial Vehicle (UAV) systems has procured research interests among various communities. One of the primary factors being, thinking beyond the box of what could UAV system bring to the table other than military applications? Evidence to any answer for this question is the current day scenarios. We could see numerous applications of UAV starting from commercial applications of delivering consumer goods to life saving medical applications such as delievery of medical products. Using UAVs in for data transportation in remote locations or locations with no internet is a trivial challenge. In-order to perform the tasks and satisfy the requirement, the UAVs should be equipped with sensors and transmitters. Addition of hardware devices increases the number of connections in hardware design, leading to exposure during flight operation. This research proposes an advanced UAV system enabling wireless data transfer ability and secure data transmission with reduced wiring in comparison to a traditional design of UAV. The applications of this research idea targets using edge computing devices to acquire data in areas where internet connectivity is poor and regions where secured data transmission can be used along with UAV system for secure data transport.

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