Volume 5, Issue 3

Increasing the telecommunications products that allow Vector Network Analyzer is becoming more common tools to measure the S-Parameter. It will be an absolute number from the S-Parameter measurements produced in real and imaginary, other words it is also known as the product of the calculation. The calculation findings do not include the systematic and random errors. It’s the reaction of the engineer to mitigate the likelihood of random and systemic errors. One of the common random error solutions is through the statistical analysis in the Vector Network Analyzer either repeated measurement or turn on high averaging measurement. The more data assessed, the greater the engineer’s confidence in evaluating random errors did not contribute significant errors. Systemic Error is consistent and reproducible when the measurement is made. One way of harmonizing these errors is to evaluate uncertainty measurements in the calculation for Vector Network Analyzer to perform measurements of reflection and transmission. Transmission measurements produce the three systematic errors that were directivity, source match and frequency response reflection tracking. This paper will concentrate from 300 kHz to 8.5 GHz directivity experimental to determine the accuracy of the Vector Network Analyzer. The experimental results will check balance with the Vector Network Analyzer specification. It is a validation process to ensure the Vector Network Analyzer meets the specification in order to perform an accurate measurement. The estimation of measurement uncertainty also refers to the Metrology 100 series Joint Committee for Guide to the Expression of Uncertainty in Measurement. The uncertainty expended should apply to Student Table’s confident level of 95%. It creates awareness to demonstrate the importance of measurement quality associated with the uncertainty, particularly for an ISO17025:2017 certified competence testing and calibration laboratory. Without the uncertainty associate to the measurement, it is not complying to the standard ISO17025:2017.

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To build a precise mathematical model describing a natural phenomenon, parameter estimation is needed to achieve the best parameter value. In this paper, we have calculated the best parameter value for a two-dimensional advection-dispersion differential equation of the biological oxygen demand degradation process in a facultative wastewater stabilization pond. This research was conducted with case study data collected from Sewon, Bantul facultative wastewater treatment facility located in Yogyakarta, Indonesia. The method employed in this research is based on the least square value by minimizing the difference between the observed data and the simulated data via quadratic programming using the interior point algorithm. This method gave the best value for the parameters observed in the model i.e. dispersion constant and the flow rate velocity. From the results, we have achieved that the best value for dispersion constant is 0.25, the velocity of the flow rate in the x-direction is 0.1, and the velocity of the flow rate in the y-direction is 0.15 whereas the relative error of this parameter estimation was 11.5% that is acceptable.

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It is difficult to accurately segment brain MRI in the complex structures of brain tumors, blurred borders, and external variables such as noise. Much research in developing as well as developed countries show that the number of individuals suffering tumor of the brain has died as a result of the inaccurate diagnosis. The proposed article, a novel hybrid method improves segmentation accuracy. The proposed research includes three basic steps. In the first step, the adaptive filter based on mean and local variance is utilized for noise removal in the input images. It helps in de-noising to a different orientation and scale, creates numerous responses for all components in the medical images while preserving the edges. In the second step, the development of a hybrid method takes place. It is the combination of extended K-mean clustering and fuzzy C-mean clustering. The purpose of the research is to develop a hybrid segmentation structure of single-channel T1 MR Images for multiform benign and malignant tumors. It removes the limitation of prefixed cluster size which helps in improving the segmentation accuracy by reducing the sensitivity of the clustering parameters. In the third step, the morphological non-linear operation performed for the removal of the non-tumor part. The proposed approach is evaluated against various statistical parameters such as mean, standard deviation, entropy, correlation, homogeneity, smoothness and variance. The parameters result predicts a greater balance between the automated tumor areas extracted by radiologists with the tumor areas extracted by the proposed method. The findings show that the proposed hybrid method achieves a 98% level of segmentation accuracy.

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This article considers the creation of a program / website as an example of a centralized system for all universities in order to more easily familiarize an applicant and / or student with the internal politics and capabilities of the university. Existing problems in the training system are examined, why distance learning is relevant, what problems exist in it and how they can be solved. Based on the proposed distance learning projects, an argument is built on its meaninglessness without further advancement, and how a website concept can help solve this problem. The concept of the website and its possible implementation using existing analogues in other areas that successfully complete the tasks are considered. Theoretically, using the proposed methods, the concept will simplify the choice of a further place of study for schoolchildren and applicants. The method consists in parsing the desired university website into the necessary categories: a number of specialties, disciplines, foreign programs, distance learning materials and so on. This will allow you to create a library of universities, their materials and open data (specialties, disciplines), without coming to excesses, such as creating a video hosting service for broadcasting recorded disciplines, less data to be stored, and more.

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Modern microcontrollers are capable to realize not only traditional PID-regulators but also adaptive ones. Object of control parameters estimation is the biggest part of adaptive control from the point of view of time consumption. The ways to reduce this time for digital control systems based on ARM-CORTEX 32-bit and 64-bit processors are shown in the article. These ways include source code refactoring, using vector registers and parallelism of code. As result of program improvement, a new algorithm for least squares method was suggested. Intrinsics for vector operations and OMP directives were added to the program to realize data and code parallelism. All options were tested for time consumption in order to find out the best decision. The program suggested may be useful while realizing adaptive controller based on single-board mini-computers and microcontrollers

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This study presents the design and construction of the Dual Axis Solar Tracking System to ensure maximum energy gain. The solar tracking system will automatically follow the sun’s position to maximize the intensity of the light emitted from the sun. When the light intensity decreases, the system automatically changes its direction to get the maximum light intensity. Light Dependent Resistor (LDR) is used to track the coordinates of the sun. The two servo motors that receive signals from the central processing unit will turn the solar panel to the appropriate location for optimum performance. The energy results obtained by the dual-axis solar system are compared with single and fixed solar systems. This research provides optimal solar energy usage.

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The development of electronic healthcare systems requires to adopt modern technologies and architectures. The use of Electronic Personal Health Record (E-PHR) should be supported by efficient storage such as cloud storage which enables more security, availability and accessibility of patients’ records. Actually, the increase of availability of E-PHR enhances parallel access, which improves the performance and the throughput of the system. Using distributed systems, users are able to communicate and to share resources to achieve specific goals. Such kind of access needs to have more coordination to maintain parallelism, which can be provided through leader election algorithms. In leader election algorithms, users elect one of them as a leader to coordinate the work and to prevent conflicts. This paper introduces an adoptive leader election algorithm (ALEA) that considers medical and healthcare specifications, since it uses leader election algorithm for E-PHR in the cloud environment. The use of ALEA improves performance by allowing more parallelism and reducing the number of coordinating messages within the system, as well as facilitating the medical specifications such as having a primary doctor or handling emergency situations. Moreover, the paper highlights the strengths and weaknesses of using Blockchain technology in the field of healthcare. In fact, the paper investigates the implementation challenges of ALEA concepts using Blockchain technology.

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Auditors now can no longer rely on the old-fashioned way of auditing manually. More and more jobs, increasingly complex work environment, the demands of the times, accuracy and speed of work require auditors inevitably must adopt technology. This research began with our success as academics in the audit family. Related to Indonesia, a large country and has several hundred public accounting firms and thousands of auditors, but computerized use of audits using software is still very little. The public accounting firm still uses a manual system, using normally typed paperwork. We want to find out what can boost the use of software among auditors. Our results are useful for auditors in Indonesia. From the results of statistical tests we found that auditors use compilation software by individual auditors themselves rather than organizations and individuals

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Over time, several routing protocols have been suggested for use in Mobile Ad Hoc Networks (MANETs). Because of availability of so many MANETs routing protocols, network engineers and administrators face difficulties in identifying an appropriate routing protocol for a particular scenario. This challenge results from the unavailability of adequate technical analytic studies designed to examine the effects of various algorithmic aspects of the available routing protocols. Availability of such studies are critical in routing identification and selection process, thus making the work of network engineers and administrators more manageable. Moreover, such studies can guide future development and implementation of MANETs routing protocols. Although there are studies meant to gauge comparative performance of various routing protocols, very little or no attempts have made to ascertain the effects of nodal topological position-information data on overall performance. This study used purposive sampling to select the routing protocols for study, literature review process to review and critique the available studies and simulation to determine the effects of nodal position-location information. Largely, MANETs routing protocols are either characterized as reactive, proactive, hybrid or location-aided. Through purposive sampling, we selected. protocol Zone Routing Protocol (ZRP) to represent hybrid routing whereas Ad hoc On-Demand Distance Vector routing (AODV) to represent reactive routing. Destination-Sequenced Distance Vector routing (DSDV) was selected purposively to represent proactive while Greedy Perimeter Stateless Routing (GPSR) was selected purposively to represent location-assisted routing. Initial elementary scalar variables of data throughput, packet drop rates, average delay and the number of packets received are simulated on NS2—to simulate ZRP and OMNET++–to simulate AODV, DSDV and GPSR. Simulation data from the two simulators was analyzed on RapidMiner. Simulation results indicate that GPSR outperforms other selected routing protocols. This result can possibly be attributed to presence of nodal topological position-location data in GPSR algorithm. However, this study held the number of nodes constant throughout the simulation process. Simulation results suggest that GPSR has better output in packet delivery ratio, delay and overall data packet throughput. The results suggest that inclusion of position-location algorithm in a routing protocol algorithm may enhance its performance. Clearly, the study findings suggest that it is prudent to select and implement a routing protocol that uses nodal position-location in its algorithm.

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In recent years, cyber security has been received interest from several research communities with respect to Intrusion Detection System (IDS). Cyber security is “a fast-growing field demanding a great deal of attention because of remarkable progresses in social networks, cloud and web technologies, online banking, mobile environment, smart grid, etc.” An IDS is a software that monitors a single or a network of computers from malicious activities (attacks). Detecting an intrusion or prevention (due to increase the usage of internet), is becoming a critical issue. In past, several techniques have been proposed to overcome or detect intrusion in a network. But most of the techniques (used now days in detecting IDS) are not able to overcome this problem (in efficient manner).Together this, Machine Learning (ML) also has been adopted in various applications (due to providing good accuracy results (in respective domain)). Hence, this work discusses “How machine learning anddata mining can be used to detect IDS in a network” in near future.ML use efficient methods like classification, regression, etc., with efficient results like high detection rates, lower false alarm rates and less communication costs. This work also provides a detail comparison with metrics in table 1-3 (with their performance/ algorithms/ dataset or metrics used).

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Location-based services have become increasingly prevalent with the advancement in the positioning capabilities of smart devices and their emergence in social networking. In order to acquire a service, users must submit their identity, query interest and location details to service providers. Such information shared by users are accumulated continuously, stored and analyzed in order to extract the knowledge base from it. Generally, this extracted information is used by service providers to provide users with personalized services. The accumulated data have enormous market value which is found to be used for many lucrative purposes. This work presents a detailed study on the evolution of existing privacy preservation models need to preserve privacy, and the opportunities to integrate fog computing services into privacy architectures. The study proposes a fog integrated privacy preservation model exploring the benefits and open research issues in traditional models and recent integrated fog models. Future directions of fog incorporated privacy preservation models are presented.

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The European drone market has been showing steady growth year after year. New EU drone rules will come into force as of July 1st, 2020, with the European Union setting itself the target to replace national rules with a common regulation with the ultimate goal of creating a harmonized European drone market. This study will clarify that the EU’s regulatory framework covers all types of existing and future drone operations, creating an international market for unmanned aircraft services. This move will facilitate the enforcement of citizen’s privacy rights and address security issues and environmental concerns to the benefit of the EU citizens. Moreover, this study will show that national legislators are now faced with the difficult task of replacing their national regulations with EU rules, however these were drafted so fast that they still leave a number of issues to be decided on by national legislators. The method of this study comprised of content analysis of existing legislation. The current doctrine were confronted with existing regulations, documents, and materials.

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Human behavior quantification is an essential part of psychological science. One of the cases is measuring human personality. Social media provide rich text, which can be beneficial as a data source to get valuable insight. Previous researches show that social media offered favorable circumstances for psychological researchers by tracking, analyzing, and predicting human character. In this research, we propose a personality measurement design to help to assess human character through linguistic usage from human digital traces. We construct our model by classifying social media text to the pre-determined personality facet from Big Five personality traits, mapping the knowledge to the ontology model, and implementing the model as a platform dictionary. Our model is based on the Indonesian language, which to the best of our knowledge is the first in the subject area. The platform is running effectively by using a well-established sorting algorithm, called the radix tree. Our objective is to support psychological science in adapting to a new technological era.

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The experimental findings of the main units of readout electronics of silicon photomultipliers (SiPMs) based on array chip (AC) MH2XA030: a charge-sensitive amplifier (CSA) with an adjustable conversion factor and a base line restorer (BLR) circuit and two types of voltage comparators are considered. The electrical circuits of the units, the measurement results of static and dynamic parameters are given.

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The main assets of universities are students. The performance of students plays a vital role in producing excellent graduate students who will be the future viable leader and manpower in charge of a country’s financial and societal progress. The purpose of this research is to develop a “University Students Result Analysis and Prediction System” that can help the students to predict their results and to identify their lacking so that they can put concentration to overcome these lacking and get better outcomes in the upcoming semesters. The prediction system can help not only the current students but also the upcoming students to find out exactly what they should do so that students can avoid poor achievement that will help to increase their academic results and other skills. To train the system, we collected data from the university student’s database and directly from students by survey using Google form containing information, such as gender, extracurricular activities, no of tuition, programming skills, class test mark, assignment mark, attendance, and previous semester Grade Point Average (GPA), where the main aim is to relate to student performances and Cumulative GPA (CGPA). We use Weka tools to train the system and to develop the decision tree. In decision tree, the acquired knowledge can be expressed in a readable form and produced classification rules that are easy to understand than other classification techniques. These rules used to develop a web-based system that can predict the grade points of students from their previous records. Moreover, the system notifies students’ lack and gives suggestions to improve their results. Finally, we compared the performance of three (J48, REPTree, and Hoeffding Tree) different decision tree algorithms, and comparative analysis shows that for our system, the J48 algorithm achieves the highest accuracy.

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Most buildings are equipped with various types of sensors to detect smoke in the event of a fire, though most are located internally. Practically, smoke has to reach the sensor in order for the sensor to react. The limitations of these sensors are their inability to respond in the early stages of a fire, and their questioned efficiency in accurately detecting the source of the smoke and locations in external environments. Image processing techniques are widely used in different critical applications in the domains of security, recognition, detection, etc. In this paper, we present an alternative image-based algorithm that can detect smoke in both indoor and outdoor environments. The algorithm operates over colored images to detect smoke at the early stages of a fire. The core of the algorithm relies on target extraction, color analysis and block subtraction components. Results shows that our proposed algorithm is capable of detecting smoke accurately at a rate of 95.10%, making it suitable for wide range of applications.

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Vegetable oils are often proved to be promising for industrial lubrication applications among which soybean oil found to be better due to its attractive and desirable thermo physical properties for machining compared to other vegetable oils. However, already it was established that influenced desirable thermophysical properties necessary for a vegetable oil through which better machining performance can be obtained. Among the various vegetable oils which are practically in use for machining applications as a cutting fluid soybean oil is found to be best and which has the scope for improvement of thermophysical properties nearer to the optimized values obtained. Therefore, the present work aims to improving the influencing thermophysical properties of soybean oil by reinforcing with suitable nanoparticles. Therefore, within the present work two categories of nanoparticles such as metallic-Aluminium oxide (Al2O3) and non-metallic reduced graphene oxide (RGO) particles were selected for dispersion in soybean oil with different concentrations for obtaining the required properties. The obtained results reveal that non-metallic nanoparticles i.e RGO with 0.5% concentration in soybean found to be better for imparting nearer to optimum properties required for obtaining better machining performance. Further sedimentation studies were carried out to ascertain the stability of particles. The studies revealed that at 0.5% concentration of RGO assisted with ultrasonication resulted for better stability of suspended nanoparticles for long term usage.

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The differences of stunting through socio-economic and productive indicators in high Andean community of Peru were evaluated (11°42’58.16” S, 75°37’31.13” W, altitude 3470 m). Cross-sectional study in 52 mothers with children under 5 years old was carried. A validated nutritional survey was applied. Z-scores height for age and nutritional status were determined using anthropometric methods and WHO criteria. The prevalence of stunting was evaluated by maternal educational level, food and health practices, economic level and family food production. Chi-square tests and Spearman correlations were performed in order to establish associations to P <0.05. Prevalence of stunting was 44.2%. The factors associated with stunting (P <0.05) were: Do not use gas for cooking (r=0.530), weekly economic income <50.00 dollars (r=0.503), weekly expenditure on family food <31.00 dollars (r=0.648), per capita / day expenditure on food <1.10 dollars (r=0.591), mother without studies (r=0.454), no own home ownership (r=0.413), consumption of food before 6 months old (r=0.410), low frequency of quinoa consumption (r=0.423), and fish (r=0.421), presence of childhood anemia (r=0.407); inadequate venting of smoke in the kitchen (r=0.491), not having soap for personal hygiene (r=0.413) and not having a bathroom (r=0.413). Stunting is associated with various socioeconomic, productive and access factors to food. These results demonstrate socio-economic and productive disparities for stunting in rural high Andean areas of central Peru, taking as a model the community of Tunanmarca in Jauja.

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Analytical researches on a potential performance of multipath multiple-input multiple-output (MIMO) systems inspire the development of new technologies that decompose a MIMO channel into independent sub-channels on the condition of constrained transmit power. Moreover, in current studies of inter-symbol interference (ISI) MIMO systems, there is an assumption that channel state information (CSI) at receivers and/or transmitters is perfect. In this paper, we propose a hybrid design of precoding and equalization schemes based on the unweighted minimum mean square error criterion that not only eliminates the ISI but also improves the system performance. Additionally, the impact of imperfect channel knowledge at receivers on the system performance of MIMO ISI system is investigated. The simulation result shown that the proposed hybrid design of precoding and equalization with shared redundancy outperforms the conventional method in all considered scenarios. Furthermore, the proposed and the conventional schemes are extremely sensitive to the CSI factor, the performance of these systems is quickly deteriorated when the accuracy of channel estimation decreases.

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Analytical researches on a potential performance of multipath multiple-input multiple-output (MIMO) systems inspire the development of new technologies that decompose a MIMO channel into independent sub-channels on the condition of constrained transmit power. Moreover, in current studies of inter-symbol interference (ISI) MIMO systems, there is an assumption that channel state information (CSI) at receivers and/or transmitters is perfect. In this paper, we propose a hybrid design of precoding and equalization schemes based on the unweighted minimum mean square error criterion that not only eliminates the ISI but also improves the system performance. Additionally, the impact of imperfect channel knowledge at receivers on the system performance of MIMO ISI system is investigated. The simulation result shown that the proposed hybrid design of precoding and equalization with shared redundancy outperforms the conventional method in all considered scenarios. Furthermore, the proposed and the conventional schemes are extremely sensitive to the CSI factor, the performance of these systems is quickly deteriorated when the accuracy of channel estimation decreases.

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The increase in the number of elderly people requires multiple efforts to maintain their well-being and health. A wide variety of products and services have been created to enable seniors to live at home for as long as possible. The market success of these solutions depends on acceptance by the different stakeholders. Older people are reluctant to change their environment, most notably their home. Solutions must provide a benefit and/or reduce risks related to their everyday life in order to be accepted. Design methods are mainly focused on needs analysis, while acceptability assessment models are based on the study of benefits. A need may also correspond to a risk that may be present in the initial situation and that has to be eliminated or reduced. The notion of risk is not sufficiently integrated in these models. This paper proposes a new approach to analyze the actual situation of elderly people at home based on risk analysis. The objective is to contribute to the design of solutions that will be more readily accepted by this population. A model for estimating the risk of falling has been proposed. The probability of two elderly people falling in their home is assessed using this model. The design and improvement of solutions are explored using the results obtained.

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This research makes the improvement of the errors surrounding edges and corners of thin electromagnetic regions by means of the magnetic field finite element formulation. Classical thin shell electromagnetic models are usually replaced by impedance-type interface conditions throughout surfaces that ignore errors in the calculation of the local fields (magnetic vector potentials, magnetic fields, eddy current densities and Joule power loss densities) in the vicinity of borders and corners. In this context, the inaccuracies of the local fields surrounding edges and curvatures related to the thin shell models are improved by a perturbation finite element technique, permitting to solve each subproblem on its own separately mesh and geometry, which makes reducing the computational time.

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The increasing cost of fishmeal and soybean meal forces us to look for unconventional sources of protein to feed guinea pigs, being able to use earthworm meal Eisenia foetida (EF) with 60-80% high quality raw protein; for this, the contribution of digestible nutrients and metabolizable energy must be known to formulate rations. The objective is to evaluate the nutritional quality of EF used in 10 and 20% in guinea pig diets. The research procedure considered the preparation of EF, proximal chemical analysis, digestibility tests “in vivo” by the direct method for the reference diet of barley meal (BM) and indirect tests for EF, using 5-month-old male guinea pigs and homogeneous weights (700-750 g), placed in individual metabolic cages and randomly distributed in 3 groups of 3 animals per group, (G1): Reference diet (BM), (G2): EF 10% + 90% BM and (G3): EF 20% + 80% BM. To determine the mean difference of digestibility, total digestible nutrients (TDN), digestible energy (DE) and metabolizable energy (ME) between G2 and G3. The average content of dry matter, crude protein, fat, nitrogen-free extract and organic matter of the EF was 77.16, 66.90, 10.0, 21.1 and 91.0%, and the average digestibility coefficients of these components were 68.01, 92.96, 72.40, 41.34 and 71.68%; the ME content was 3125.31 Kcal / kg. As the EF level increased from 10 to 20%, the digestibility coefficients of dry matter, protein, fat, nitrogen-free extract and organic matter increased by 7.75; 2.18; 5.45; 16.20 and 4.83%, the ME value increased by 7.25% (P < 0.05). Increasing the inclusion of EF from 10 to 20% in guinea pig diets improves digestibility nutrients and ME content. Reduction in the cost of animal protein production, added value for the cultivation of Eisenia foetida and contribution to environmental health.

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In this paper, we are proposing a device that generates vibration to possibly reduce the duration required for the orthodontic treatment by enhancing the tooth movement. This is achieved by harmonically exciting a bio-compatible material, namely polyvinylidene fluoride (PVDF) to generate vibration and consequently a cyclic force at a low frequency of 30 Hz. (PVDF) is a very common piezoelectric polymer due to the high elasticity it can provide, biocompatibility, and its low cost compared to other piezoelectric materials. Applying a cyclic loading (vibration) in general will reverse bone loss, stimulate bone mass, induce cranial growth, and consequently accelerate tooth movement. This has a major effect on the patient’s treatment experience by reducing the associated pain and discomfort throughout the treatment process and hence improves the patient’s compliance with the treatment with negligible side effects compared to therapeutic treatments. The proposed device is fitted to a positioner or tooth aligner. The proposed device can accommodate a voltage harmonic function generator, a casing to house the battery and the micro-processor. Modern methods require a bulky and extra-oral device that is quite cumbrous to the patient. In this paper, an analytical model based on the distributed parameter approach is employed. Finite Element Analysis (FEM) is also employed to study and analyze the Piezoelectric actuation behavior.

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Blockchain is a new generation technology that allows the central control mechanism or trusted authority to be removed, spreading the encrypted data across all participants in the network in a distributed database structure instead of central trust. The Smart Contract structure, which defines the rules and flow that allow the things we value to operate automatically as determined without the need for an external trigger mechanism, is the core element of this technology. Blockchain has gained popularity with its most famous application, Bitcoin. After Bitcoin became popular, it turned out that Blockchain might have new uses due to the advantage of technology such as security, brokerage, and transparency, and these areas are being investigated. Many big companies have started to invest in this technology in the face of the opportunities brought by Blockchain. HAVELSAN is a large-scale software company that studies and adapts new generation technologies. Blockchain technology has become of the new generation technologies that HAVELSAN is interested in due to its impressive advantages. HAVELSAN has a wide range of activities, so the company can develop various Blockchain-based applications depending on these areas. Combining this diversity with the importance of the Smart Contract concept, which can be considered as the basis for most Blockchain applications, it is decided to create a strong Smart Contract framework before starting to build different applications with Blockchain technology. The creation of HAVELSAN Blockchain Smart Contract Framework; which infrastructures are used during the development phase, the problems encountered during development and the structure of the most suitable applications to be created with the framework to be developed will be explained in this article.

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Knowledge is important now for the development of social society; it is necessary for knowledge management. Knowledge management (KM) aims to support the creation, transfer, and application of knowledge in social societies. This fact illustrates that in the management of social knowledge, the role of social communities is very important and is influenced by factors in the process. With this, this study will look for theories and factors that influence KM social interactions that occur in social societies. The method used is a systematic literature review. The results of this study found theories and factors that influence KM in social societies.

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This paper presents a solution that automates Abaca fiber grading which would help the time-consuming baling of Abaca fiber produce. The study introduces an objective instrument paired with a system to automate the grade classification of Abaca fiber using Convolutional Neural Network (CNN). In this study, 140 sample images of abaca fibers were used, which were divided into two sets: 70 images; 10 per grade, each for training and testing. The input images were then scaled to 112×112 pixels. Next, using a customized version of VGGNet-16 CNN architecture, the training set images were used for training. Finally, the performance of the classifier was evaluated by computing the overall accuracy of the system and its Cohen kappa value. Based on the result, the classifier achieved 83% accuracy in correctly classifying the Abaca fiber grade of a sample image and obtained a Cohen kappa value of 0.52 — Weak, Level of Agreement. The implementation of this study would greatly help Abaca producers and traders ensure that their Abaca fiber would be graded fairly and efficiently to maximize their profit.

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With the current amount of data nowadays, the need for processing power has vastly grown. By relying on CPU processing power, current processing power is depending on the frequency and parallelism of the current CPU device. This means this method will lead to increased power consumption. Current research has shown that by utilize the power of GPU processing power to help CPU to do data processing can compete with parallel CPU processing design but in a more energy-efficient way. The usage of GPU to help CPU on doing general-purpose processing has stimulated the appearance of GPU databases. GPU databases have gained its popularity due to its capabilities to process huge amount of data in seconds. In this paper we have explored the open issues on GPU database and introduce a machine learning model to enhance the GPU memory usage on the system by eliminating unnecessary data processing on GPU as on certain queries, CPU processing still outperforms the GPU processing speed. To achieve this, we develop and implement the proposed approach machine learning algorithm using python 3 languages and OmniSci 4.7 for the database system. The applications are running on Ubuntu Linux environment as the GPU environment and Docker as the CPU environment and the results we find that KNN algorithm performs well for this setup with 0.93 F1-Score value.

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Network Analyzer is equipment widely used for the execution of radio frequency application scattering parameters. Throughout absolute reading the scattering parameter measured. An absolute reading does not include error, drift, offset, linearity, resolution, coefficient of sensitivity and several other variables that will contribute to the measured measurement dispersion. Type A evaluations of measurement uncertainty clarified according to ISO / IEC Guide 98-1. Type A uncertainty assumption is always done best to characterize an input quantity given in repeated indication values. The assumption was calculated from arithmetic mean, variance of probability distribution and standard deviation respected to the frequency from 300 kHz to 8.5 GHz in a Network Analyzer measurements. Furthermore, ISO/IEC 17025 is the standard for accredited testing and calibration laboratory to calculate the uncertainty of measurement to be declared in the scope of accreditation. Type A uncertainty calculation is one of the mandatory requirements for an accredited testing and calibration laboratory. The Type A uncertainty will be combined with Type B uncertainty to calculate the expanded uncertainty in measurement.

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The emerging trend of modern industry automation requires intelligence to be embedded into mobile robot for ensuring optimal or near-optimal solutions to execute certain task. This yield to a lot of improvement and suggestions in many areas related to mobile robot such as path planning. The purpose of this paper is to review the mobile robots path planning problem, optimization criteria and various methodologies reported in the literature for global and local mobile robot path planning. In this paper, commonly use classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (AFP), and heuristics approaches such as genetic algorithm (GA), particle swarm optimization (PSO) approach and ant colony optimization (ACO) method are considered. It is observed that when it comes to dynamic environment where most of the information are unknown to the mobile robots before starting, heuristics approaches are more popular and widely used compared to classical approaches since it can handle uncertainty, interact with objects and making quick decision. Finally, few suggestions for future research work in this field are addressed at the end of this paper.

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The objective of the research is to determine the changes in wrinkling and thinning behavior in the stamping process of the top outer hatchback. The study was conducted using two types of materials, i.e., SCGA (Steel Cold rolled Galvanized Annealed) and SPCC (Steel Plate Cold rolled Coiled) with a thickness of 0.80 mm. During the stamping process, the coefficient of friction values varied of 0.0, 0.05, 0.10, and 0.15 for each material. The stamping process was carried out by using the simulation method to investigate the wrinkling and thinning behavior. The forming limit diagram (FLD) provides information on material changes and the amount of safe and unsafe areas on the blank. The results showed that in the fifth step, the parts that experienced high values of major-minor stress and strains, thinning, and wrinkling decreased in the last step. That caused by removing unused areas after the trimming process. Therefore, the safe zone of SCGA was increased from 9.52% to 18.19%. For the SPCC material, the safe area increased from 8.63% to 16.31%. The coefficient of friction affects the thinning and wrinkling defects. The greater of the friction coefficient will increase the value of thinning and wrinkling. Based on the Non-Linear FLD analysis, both materials SCGA and SPCC are still in a safe condition.

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Among the most popular tools in security field is the anomaly based Intrusion Detection System (IDS), it detects intrusions by learning to classify the normal activities of the network. Thus if any abnormal activity or behaviour is recognized it raises an alarm to inform the users of a given network. Nevertheless, IDS is generally susceptible to high false positive rate and low detection rate as a result of the huge useless information contained in the network traffic employed to build the IDS. To deal with this issue, many researchers tried to use a feature extraction methods as a pre-processing phase. Principal Component Analysis (PCA) is the excessively popular method used in detection intrusions area. Nonetheless, classical PCA is prone to outliers, very sensitive to noise and also restricted to linear principal components. In the current paper, to overcome that we propose a new variants of the Nonlinear Fuzzy Robust PCA (NFRPCA) utilizing the popular data sets KDDcup99 and NSL-KDD. The results of the conducted experiments demonstrated that the proposed approaches is more effective and gives a promising efficiency in comparison to NFRPCA and PCA.

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Visible light communication (VLC) that simultaneously gives illumination and information transmission abilities, is recognized as a hopeful competitor for prospective wireless net- works. Furthermore, a channel adaptive collaborative constellation (CASCC) with the capacity of adapting according to the channel condition to enhance the bit error rate (BER) while concurrently improving the versatility of the receiver mobility was regarded to be further effective than the contemporary CC. Nonetheless, the early CASCC barely presents modest performance enhancement in a strong correlation channel. Hence, by this study, we provide a design to form a channel-adaptation CC, namely the enhanced CC (ECC) for VLC systems. More specifically, from the basic constellation points, we form the optimization problem of efficient size that can be solved by any convex optimization solving technique. Also, the computational simulation outcomes confirm that the ECC is more beneficial than preceding constellations in term of BER for different channels. Moreover, we also provide the result comparison of the proposed scheme with other schemes in the imperfect channel condition. Overall, by effectively reducing the distance among the constellation points, a significant signal-to-noise ratio gain can be achieved.

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This paper investigates the trajectory tracking control (TTC) problem of a networked control system (NCS) against a replay-attack. The impact of data packet dropout and com- munication delay on the wireless network are taken into account. A new mathematical representation of the NCS under network issues (packet dropout, delay, and replay-attack) is proposed, the resulting closed-loop system is written in the form of an asynchronous dy- namical system. Linear matrix inequalities (LMIs) formulation and a cone complementary linearization (CCL) approach are used to calculate the controller gain F1 and the trajec- tory tracking gain F2. Finally, a DC motor simulation with MATLAB is carried out to demonstrate the effectiveness of our approach.

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In the last year, especially in Europe, the expression “digital sovereignty” has been used very frequently to describe, above all, the primacy of a State. Indeed, the “digital sovereignty” is a complex concept, which entails cross-reference with several sectors and contexts. We believe that the concept of “digital sovereignty” can be two sides of a coin. On the one hand, we can use the expression “digital sovereignty” to describe the supremacy and full control of a State on the digital area. On the other hand, we can use the same expression “digital sovereignty” to refer to the power on the digital domain – as we will explain in our contribution – that anyone is potentially able to use in the private or public sector. Our contribution aims to demonstrate that where someone, public or private, can have the control on the digital domain, there is “digital sovereignty”.

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Several drive mechanisms for different robots are at hand in current days. Bicycle steering, Ackerman steering, differential drive are some principal drive mechanisms that are being deployed in robots these days. The differential drive needs the wheel rotations to be updated very frequently. But it is most commonly deployed on the robots with two wheels and casters, as discussed in this work. It also can be used to have an independent control for each of the wheels with independent control signals. This work deals with the modeling of the differential drive mechanism for a robot with two main drive wheels and two casters, which takes the angular orientation of the steering wheel as input. For simplicity, this work considers that casters have no influence on any aspect of the differential drive. An adaptive model, whose output depends on the real-time input from the gamers steering wheel and produces required output has been formulated. This work differs from the other differential drives in the context that the steering wheel and the robot wheels have no physical connection. The proposed model has been implemented in python and integrated with the Robot Operating System (ROS). The steering wheel, which is used to generate commands using, is attached to the controller at the control station and the ROS_Node thus created is used to read the values from the steering and generate commands for each of the left and right wheels. These commands are transferred to the controller on the mobile platform, which in turn generates control signals for actuators. This work also deals with the deployment of the proposed model using the Universal Robot Description Format (URDF) of the robot in the Gazebo simulation and evaluating it using the Nitho Drive Pro steering wheel. To prove that the differential drive mechanism can be used to control the robot efficiently in any type of terrain, a ROS python node is used to control and maneuver the robot.

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This paper describes the design method of a nonlinear flight controller for a fixed-wing UAV with a thrust vectoring system (TVS) using the backstepping method. The flight dynamics of the UAV exhibits strong nonlinear coupling behavior between its translational and rotational motion. The backstepping algorithm has been successfully applied to controller design for such a nonlinear system. However, the main idea of the method is to use some of the state variables as virtual control inputs that need ungeneratable forces by the UAV. To overcome this problem, we use the TVS that can generate thrust in an arbitrary direction. Numerical simulation is performed to confirm the effectiveness of the proposed control method for a fixed-wing UAV with the TVS.

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This paper is an extension of work originally presented in 2019 Third World Conference on Smart Trends in Systems Security and Sustainability (WorldS4) [1]. Author describes two new methods: reactive protection method (without delay after detecting an attack), which consists in virtualizing the execution environment of supercomputers processes if the calculated state descriptor falls into the “risk” zone and based on monitoring requests for allocation of resources in accordance with the rules of the security policy in the form of temporal modal structures CTL logic and method for reconfiguring the runtime environment of the supercomputers taking into account the mobility requirements (built-in computations) based on the application of the trajectories of computing state security descriptors on Kripke structures. The methods develop a number of provisions of the theory of information security, based on the new concept of Information Security of stationary and onboard supercomputer computing systems as a calculated convolution of the states of the execution environment (hardware or virtual) and system software.

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The Gembong reservoir in Pati Regency, Java, Indonesia is a rain-fed reservoir, which experiences a depletion of it carrying capacity. The characteristic of local rainfall is one of the important factors in assessing the potential of hydrometeorology disasters in its area. Sedimentation in watersheds and reservoirs has covered water sources, so local rainfall determines the dynamics of water availability. This research is needed in the development of mitigation strategies. This article contains an analysis of the characteristics of local rainfall, and forecasting based on local daily rainfall data. This data was obtained from the rainfall gauge station in the Gembong reservoir area in 2007-2019. Variation coefficient, anomaly index, rainfall concentration index, and Mann-Kendall test were used to identify its characteristics. The time series model is used as modeling for forecasting. The results of empirical analysis show that rainfall volatility with irregular changes in high variability, meteorological drought in moderate category, rainfall trends follow fluctuating patterns and do not follow monotonic trend patterns but high concentrations of rainfall. Forecasting results with the Autoregressive Integrated Moving Average model for the wet months show an increasing in total rainfall by 17% in the next year. So, the potential for flooding is greater than the potential for drought. Based on the analysis of the local rainfall characteristics, then mitigation on flood is preferred.

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The paper proposes the design and simulation of a 6 Degree of Freedom (DOF) robotic arm, tailored for the coconut crop harvesting, assistive robots like wheelchairs and Home robots, Search and rescue robots for disastrous environments, Collaborative robots for research use. A kinematics-based solution has been developed for the robotic arm which makes it easier to operate and use. Keyboard, GUI, Joystick are the three control interfaces used in the paper. The robotic control interfaces proposed in the paper were developed using the Robot Operating System (ROS). The 6- DOF articulated robotic arm was designed and visualized in RVIZ. The kinematics helped for the easy manipulation of the robotic arm with the end effector. The methodologies proposed in the research work are easy to operate and inexpensive. The designed 6 DOF robotic arm, the first three DOFs are for positioning of the robot’s arm, while the residual three are used for manipulation of the gripper.

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Many advances in computer systems and IT infrastructures increases the risks associated with the use of these technologies. Specifically, intrusion into computer systems by unauthorized users is a growing problem and it is very challenging to detect. Intrusion detection technologies are therefore becoming extremely important to improve the overall security of computer systems. In the past decades, most of the intrusion detection systems designed suffer from the problem of high false negative and low efficiency rate. A powerful intrusion detection system (IDS) should be implemented to solve these issues and it is necessary to collect, reduce and analysis the data automatically. The integration of machine learning and artificial intelligence techniques serves this purpose in this paper. A use of particle swarm optimization (PSO) selects the optimal number of clusters and the integration of k-means based artificial neural network (ANN) achieves maximum efficiency when the number of clusters selected optimally. The proposed IDS are t bested with NSL-KD dataset and the experiment result shows the significance of the proposed IDS.

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Nowadays, process automation and smart systems have gained increasing importance in a wide variety of sectors, and robotics have a fundamental role in it. Therefore, it has attracted greater research interests; among them, Underactuated Mechanical Systems (UMS) have been the subject of many studies, due to their application capabilities in different disciplines. Nevertheless, control of UMS is remarkably more difficult compared to other mechanical systems, owing to their non-linearities caused by the presence of fewer independent control actuators with respect to the degrees of freedom of the mechanism (which characterizes the UMS). Among them, the Furuta Pendulum has been frequently listed as an ideal showcase for different controller models, controlled often through non-lineal controllers like Sliding-Mode and Model Reference Adaptive controllers (SMC and MRAC respectively). In the case of SMC the chattering is the price to be paid, meanwhile issues regarding the coupling between control and the adaptation loops are the main drawbacks for MRAC approaches; coupled with the obvious complexity of implementation of both controllers. Hence, recovering the best features of the MRAC, an Artificial Neural Network (ANN) is implemented in this work, in order to take advantage of their classification capabilities for non-linear systems, their low computational cost and therefore, their suitability for simple implementations. The proposal in this work, shows an improved behavior for the stabilization of the system in the upright position, compared to a typical MRAC-PID structure, managing to keep the pendulum in the desired position with reduced oscillations. This work, is oriented to the real implementation of the embedded controller system for the Furuta pendulum, through a Microcontroller Unit (MCU). Results in this work, shows an average 58.39% improvement regarding the error through time and the effort from the controller.

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The banking industry is very competitive. To utilize the information, they have in order to be a competitive advantage winner is reasonably very crucial for the company. At present, the company does not only focus on the company’s strategy that prioritizes products (e.g. product or service oriented), however also necessitates to focus on the company’s strategy in prioritizing customers. Customer segmentation, its attributes, and the appropriate analysis method are going to get accurate data segmentation results, so that it is able to be used as a reference by the company and as a basis for determining its products’ marketing strategies. This systematic literature review discusses the types of attributes operated, including customer balance attributes, whether or not they can be included in segmentation. In addition, it also discusses what popular analytical methods are widely used in the customer segmentation process. Literature searching in the digital library resulted in a total are 592,363, 1,361, and 21 papers respectively in the first, second, and third stage. 10 papers found finally in the final stage that were considered capable of answering research questions. Based on 10 papers selected, it can be concluded that customer balances can be functioned scientifically as one of attributes for segmentation use. The popular analytic methods operated for customer segmentation are recency, frequency, monetary (RFM) model (4 times appeared), K-Means algorithm (6 times occurred), and C-Means (2 times emerged).

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Competitive sport has one phenomenal or fundamental aspect of selecting players into playing squad for a game that can influence a Club or a team in almost all major aspects. Various Characteristics or behavioral aspects of players will be instrumental towards the selection of a specific player into a team depending on the nature, level, or type of completion the club or team participates in. Many parameters such as medical, physical, technical and, Psychological aspects of players make the task of mangers or coach a herculean to select 15 players out of 30 or 40 players available in his squad for a particular season. The role of managers or coaches is significantly challenging looking into the aspects most desirable towards the optimal contribution of players. Hence the parameters which are considered highly influential towards a Club or team cannot be analyzed manually due to various constraints such as time, the volume of players, or the limitation of human errors in decision making. The primary objective of this paper is towards assisting managers or coaches to see through this by applying Sports Parameter Estimation Gravitational Search Algorithm (SPEGSA) towards analytical ability in player selection considering minimal errors and time constraints using a stochastic approach. This paper gives an overview of how soft computing techniques help in optimization of selection procedures of team players for the matches to be played and competed in a soccer league for a given team at different levels of competition by measuring various influential parameters recorded at different point of juncture for every player in a team and estimating the parameter using the subset of evolutionary computation techniques and metaheuristic optimization algorithm.

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In this paper we focus on Sentence retrieval which is similar to Document retrieval but with a smaller unit of retrieval. Using data pre-processing in document retrieval is generally considered useful. When it comes to sentence retrieval the situation is not that clear. In this paper we use TF-ISF (term frequency – inverse sentence frequency) method for sentence retrieval. As pre-processing steps, we use stop word removal and language modeling techniques: stemming and lemmatization. We also experiment with different query lengths. The results show that data pre-processing with stemming and lemmatization is useful with sentences retrieval as it is with document retrieval. Lemmatization produces better results with longer queries, while stemming shows worse results with longer queries. For the experiment we used data of the Text Retrieval Conference (TREC) novelty tracks.

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This paper presents a design optimization of a dual-band branch-line coupler with stepped-impedance-stub lines. This coupler operates over 5G NR frequency bands n5 and n2, developed by 3GPP for the 5G (fifth generation) mobile network, and these two bands are centered at 0.85 GHz and 1.9 GHz respectively. To achieve the design specifications an adjusted Tuning Space Mapping method is used. This method of optimization moves the hardship of optimization from high-fidelity electromagnetic models to low-fidelity tuning models. The simulated and measured results of this enhanced coupler show good dual-band performance at the two bands.

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Recently, digital image forgery detection is an emergent and important area of image processing. Digital image plays a vital role in providing evidence for any unusual incident. However, the image forgery my hide evidence and prevents the detection of such criminal cases due to advancement in image processing and availability of sophisticated software tamper of an image can be easily performed. In this paper, we provide a comprehensive review of the work done on various image forgeries and forensic technology. Many techniques have been proposed to detect image forgery in the literature such as digital watermarking, digital signature, copy-move, image retouching, and splicing. The investigation done in this paper may help the researcher to understand the advantage and handles of the available image forensic technology to develop more efficient algorithms of image forgery detection. Moreover, the comparative study surveys the existing forgery detection mechanisms include deep learning and convolution neural networks concerning it is on benefits and demerits.

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Many organisations adopt and implement information technology (IT) but fail to operationalise it. As a result, the process of implementation is continually repeated without achieving the goals and objectives, which are often to gain competitive advantage and sustainability. This study employs structuration theory as lens to examine and understand the factors that influence operationalisation of IT strategy in an organisation. The case study approach was employed, and semi-structured interviews technique was used to collect data. The hermeneutics approach was used in the analysis, which was guided by the duality of structure from the perspective of structuration theory. From the analysis six factors were found to primarily influence the operationalisation of IT strategy in an organisation. Based on the factors, a model was developed, which is intended to guide both IT and business managers in the operationalisation of IT strategy.

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Our research investigates the use of causal modeling and its application towards mapping out cybersecurity threat patterns. We test the strength of various methods of data breaches over its impact on the breach’s discovery time as well as the number of records lost. Utilizing a Causal Modeling framework, we simulate the isolation of confounding variables while testing the robustness of varying estimators. The motivation is to shed a unique insight provided by the usage of Causal Modeling in cybersecurity.

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Face explicitly provides the direct and quick way to evaluate human soft biometric information such as race, age and gender. Race is a group of human beings who differ from human beings of other races with respect to physical or social attributes. Race identification plays a significant role in applications such as criminal judgment and forensic art, human computer interface, and psychology science based applications as it provides crucial information about the person. However, categorizing a person into respective race category is a challenging task because human faces comprise of complex and uncertain facial features. Several racial categorization methods are available in literature to identify race groups of humans. In this paper, we present a comprehensive and comparative review of these racial categorization methods. Our review covers survey of the important concepts, comparative analysis of single model as well as multi model racial categorization methods, applications, and challenges in racial categorization. Our review provides state-of-the-art technical information concerning racial categorization and hence, will be useful to the research community for development of efficient and robust racial categorization methods

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The field of humanitarian logistics has in recent times gained an increasing attention from both academics and practitioners communities alike. Although various research groups have addressed theoretical and technical developments in humanitarian logistics using conventional research tools, applied research appears to be often dependent on practitioners’ inputs. This paper is an attempt to fill the existing gaps between academic research and practitioners’ needs and proposes an integrated framework that consists of serious games and computer modelling. The serious games component aims to raise awareness on humanitarian logistics issues as well as provide a platform to facilitate the acquisition of inputs from humanitarian practitioners. Based on these inputs, a computer model will be developed. To test the framework, a real-life case study about the prepositioning of strategic stockpiles in Indonesia, one of the countries with the highest disaster risk exposure on a global scale, was used. Findings of this work highlight the role of serious games as risk-free environments for players to design strategies enhancing disaster preparedness in conjunction with broadly used research methodologies such as computer modelling.

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In today’s Moroccan business environment, companies need to implement organization agility by developing an agile workforce that is able to deal with the environment volatility. Thus, the agile workforce concept has been appeared as a necessary and sufficient condition to achieve agility. Focusing on agile enablers influencing workforce agility is an important area but currently there is limited literature available. Acknowledging its importance, we continued our literature exploration in order to identify the enablers of workforce agility. Then, we describe a list of four enablers with different criteria and attributes. This paper further proposed fuzzy logic approach to evaluate different measures of the workforce agility. The results suggest that engagement, knowledge sharing, acceptance of changes and self-motivation are the most important attributes of agile workforce. Apart from that, different agile workforce attributes need to be improved in order to achieve the extremely agile level of the workforce.

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In the smart power grid, designing an efficient and reliable communication architecture has an important role in improving efficiency, and maintaining the connectivity of different network components. The home area network (HAN) provides an energy management system in houses since it enables home energy control and monitoring. So, it is imperative to determine a HAN configuration that provides low cost and better performance. We propose in this paper a conceptual and experimental model for designing a wireless sensor network to monitor a HAN in a Smart Grid. The novelty of this study is that it considers real object and network configuration. The use of ZigBee standard for wireless communication requires a meticulous physical and logical design to overcome limitations related to nodes density, traffic load by PAN, network topology and motion pattern. The current study allows us to characterize the best configuration of the intelligent studied system and to provide a perspective on addressing the problem of a node failure.

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To reduce the risk of accidental system shutdowns, we propose to control system developers (supervisor, SCADA) a prediction tool to determine the occurrence date of an imminent failure event. The existing approaches report the rate of occurrence of a future failure event (stochastic method), but do not provide an estimation date of its occurrence. The date estimation allows to define the system repair date before a failure occurs. Thus, provide visibility into the future evolution of the system. The approach consists in modelling the operating modes of the system (nominal, degraded, failed); the goal is to follow the evolution of the system to detect its degradation (switching from nominal to degraded mode). When degradation is reported, a prognoser is generated to identify all possible sequences and more precisely those ending with a failure event. then it checks among the sequences (with failure event) which ones are prognosable. The last step of the approach is carried out in two parts: the first part consists in calculating the execution time of the so-called prognosable sequences (by optimizing the number of possible states and resolving an inequalities system). The second part makes it possible to find the minimum execution (the earliest occurrence of a failure event).

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We proposed two robust confidence interval estimators, namely, the median interquartile range confidence interval (MDIQR) and the trimean interquartile range confidence interval (TRIQR) for the population mean (µ) as an alternative to the classical confidence interval. The proposed methods are based on the asymptotic normal theorem (ANT) for the sample median (MD) and the sample trimean (TR). We compare the performance of the proposed interval estimators with the classical estimators by using a simulation study through the following criteria: (i) average width (AW) and (ii) empirical coverage probability (CP). It is evident from simulation study is that the proposed robust interval estimator performs well under both criterion and when the observations are sampled from contaminated normal distribution. However, when the observations are sampled from non-normal distributions, the classical confidence interval performs the best in the shorter width sense, but the coverage probability tends to be smaller than the two proposed robust confidence interval estimators for all sample sizes. For illustration purposes, two real life data sets are analyzed, which supported the findings of the simulation study to some extent.

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With a model predictive control approach and to set the motion trajectory for an autonomous vehicle in situations where emergency braking cannot be performed, in this paper, we propose a solution to apply the law on road traffic into a set of constraints and thereby build an objective function to create motion trajectory for autonomous vehicle. The newly created trajectory is created to improve performance and enhance the ability to avoid obstacle but ensure an optimal global trajectory. The performance of this solution is assessed through simulation with different scenarios, from which there are applied research orientations on the problem of autonomous vehicle in practice.

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Degradation measurements are often treated and analyzed for improvement the reliability of system. Our objective in this paper is to study a class of non linear systems, whose dynamics and observations are non linear functions of the state. Obviously, we develop an Extended Kalman Filtering (EKF) algorithm for detecting the additive failures in a two tank system. However, the EKF algorithm is used to estimate the state vector of pipeline system based on all collected measures history. Such as degradation process (clogging , partial blockage) is considered and can be described by a Wiener process. For reasons of improvement reliability and security, it is necessary to predict the Remaining Useful Life (RUL) of pipeline. It follows that a major preventive maintenance actions. Furthermore, we can evaluate the RUL based on Monte Carlo simulation and compare the results.

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This project aims to demonstrate the importance that digital media can have to the development of loyalty programs, namely in creating empathy and proximity relationships between brands and their target audience: young people. This study consisted of the creation of a digital platform for Toyota Portugal, named Spot Toyota, to communicate actions promoted by the car brand, especially during music festivals. With the support of advertising agency Caetsu, this mobile application was developed to bring the brand closer to a younger audience – festival fans – with potential interest in two Toyota fleet car models: Aygo and C-HR. Through strategies typical of loyalty programs, such as the awarding of vouchers or coupons, the accumulation of points or winning prizes, a system was produced with the main focus on attracting users to the platform in a continuity perspective. The working process of this investigation resulted in the design of a smartphone application, based not only on the analysis of other examples present in the market but also on the understanding of crucial subjects such as loyalty programs, UX and UI design, application of personas models, creation of wireframes and workflows, and development of usability tests.

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In this work, we completed a validation of the Varian Clinac IX equipped with the High Definition Multi-Leaf Collimator (HD 120 MLC) instead of the removable jaws, using GATE Monte Carlo Platform version 8.2. We validated the multileaf collimator (MLC) geometry by simulating two dosimetric functions (Percentage Depth Dose (PDD) and Dose Profile (DP)), for 6MV photon beam energy and different field sizes (3×3, 4×4, 6×6, 8×8, 10×10, 12×12, 15×15, and 20×20 cm²). We then compared the results with measurements realized with two detectors, namely the cylindrical ionization chamber and the micro-diode PTW silicon. By applying the Relative Dose Difference method (RDD), we noted a less than 2% and 1% agreement for the field sizes (10×10, 12×12, 15×15, 20×20 cm²) and (3×3, 4×4, 6×6, 8×8 cm²) respectively. Moreover, to evaluate the relevance of Monte Carlo method in a heterogeneous media, particularly in small field sizes (1×1, 2×2, 3×3 cm²), we have simulated three clinical studies based on the Physical Test Objects (PTOs) that are the equivalent slabs of lung and bone included in a water phantom. We noticed that the simulated PDDs exhibit two significant irregularities in the interface between water and lung. To eliminate these phenomena, we have used the “setMaxStepSizeInRegion” parameter implemented in GATE. We also noticed an important difference of 5% corresponding to the small field sizes, between homogeneous and heterogeneous simulated PDDs. We used the RDD method in this case as well. Moreover, we observed a difference between 1-4% between the simulated PDDs and the calculated ones by ECLIPSE Treatment Planning System (TPS). These results indicate that GATE (8.2) is useful in dosimetry with heterogeneous situations as well such as bone and lung.

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Oscillator phase noise (PN) has a strong impact on the spectral purity of the RF signal in wireless systems and is, therefore, a main challenge when designing a local oscillator. In this paper, we propose a new approach for designing a low PN oscillator based on the Time-Invariant Linear Model of phase noise. It leads on voltage-controlled oscillator (VCO) simulated good performances: a low phase noise (PN) near -123.2 dBc/Hz@1MHz offset from the carrier, an output power of 3.26 dBm, and an output signal frequency ranging from 27.98 GHz to 29.67 GHz. Low power-consumption (51mW) and small size (0.237 mm2) benefit from MMIC UMS foundry (United Monolithic Semiconductors) and 0.15 µm-pHEMT GaAs technology.

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The paper presents sharing several of experiences and practices on smart robotic application for overhead transmission line maintenance and inspection. First, the pilot-line pulling robot is an invention used to pull a pilot line which is an important step for additional high voltage conductor installation. The puller robot can traverse the overhead ground wire, OHGW, and pulls a lead line via a set of cradle blocks at intervals, carrying the line above the ground. The robotic puller passes over barriers below the power line, such as the road with traffic, power distribution lines, river, or vegetation making tasks achieved conveniently, safely, and rapidly without impact on nearby communities. The robot was further utilized to pull a lead line/conductor crossing over the electrical substation without interrupting energy and pull a lead line for the improvement of transmission line ground clearances. The developed pilot-line pulling robot has been accredited as the corporate best practice; the standards for innovation, operation, and maintenance are archived for works at all EGAT transmission line operation & maintenance units nationally. Moreover, EGAT was now jointly investigating with universities on a new robotic device for aerial transmission line inspection. The target of the research is to create a mobile robot prototype for inspection of overhead power lines. The inspection robot shall crawl along the ground wire and transpose autonomously across installed equipment on the ground wire, such as vibration dampers, suspension clamps, compression dead ends, etc. In addition, the inspection robot is able to take photos and videos during a transmission line inspection in both offline and online features. Using the robot, transmission line inspection’s labor cost can be reduced, and the new method helps improve patrol and inspection efficiency, comparing to the conventional manpower method. Trough utilization of the new maintenance and inspection robots, utilities can minimize transmission line operation & maintenance budget.

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The Center for Analysis and Information Services (Palinfo) at the Judicial Commission closely related to the management of information systems which are used to process organizational internal data and information systems on public services. Data processing and network management have an information system security risk. The Judicial Commission seeks to reduce risk and improve the quality of information security. This study aims to measure employee awareness of information security at the Center of Analysis and Information Services at the Judicial Commission, which also includes the Data/IT department. The study was conducted through an arranged interview with three experts and the dissemination of information security awareness questionnaires to all Palinfo employees, amounting to 25 persons. The results of the questionnaire were evaluated using The Human Aspects of Information Security Questionnaire (HAIS-Q) and the Analytic Hierarchy Process (AHP) method. The results showed that the level of information security awareness in Palinfo and the Data/IT section was at the “average” level. There is one focus area that shows a “good” level. While in the Data/IT department, several sections that show a “good” level. Based on these results, we recommend being used in maintaining information security, namely seven policies, ten information technology approaches, and socialization/training conducted in various ways.

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21st century learning focuses on the flow of information, media, and technology. In Malaysia, many university students face problems in English writing. Thus, students should be exposed to the technology training in innovative ways to produce students with a dynamic in this ever-changing world. Recently, the transformation and the evolution of mobile have created a huge impact on mobile users, as it is the current trend. Due to this matter, university students are now experiencing innovative learning development through mobile application and this can certainly improve their learning performance. The purpose of the study is to examine the application of mobile learning technologies through mind mapping applications for augmenting writing performance at Malaysian universities. The study was based on three different research theories -Flower and Hayes Writing Process Model, Radiant Thinking Theory, and Unified Theory of Acceptance and Use of Technology (UTAUT). The results of the study show that the students had positive responses towards English writing skills background, mobile technologies application background and mind mapping applications background. The proposed conceptual framework, Mobile-assisted Mind Mapping Technique Model (MMMTM) supports the need for Malaysian university students to augment their writing performance. It is hoped that this study will benefit the policymakers, tertiary educators and university students in teaching and learning specifically in writing courses.

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It was analyzed the reference information on Cybersecurity architectures, models, standards, evaluations, mechanisms, and procedures applied to IoT domains, and public and private health area. The problem is the lack of proposals for IoT Cybersecurity in public and private hospitals to minimize random failures, ensure the privacy of personal data of patients, avoid the paralysis of the IoT medical network and minimize attacks on information assets. The objective is to perform a survey and an IoT Cybersecurity recommendation for public and private hospitals in Ecuador. The exploratory research was used to review references and specific analytical reasoning to end in a known scoop with a trusted solution. A survey of cybersecurity vs. competitiveness of hospitals in Ecuador resulted, a Model conceptual prototype of IoT Cybersecurity for a public or private hospital, an Architecture prototype of IoT Cybersecurity for a public or private hospital, and an Algorithm prototype of cybersecurity for IoT architecture. It was concluded that the cybersecurity standards applied to the design of IoT for a public or private hospital generates trust on information assets, preserves the confidentiality, integrity and availability of the information at the operational, tactical and strategic levels; the architecture prototype is between 59.38% and 99.71% of acceptable workload. This proposal is scalable and applicable to a public or private hospital regardless of the dimensions of areas, devices, floors, workers or other characteristics; the architecture only considers the hospital’s own IoT devices and information; the devices of doctors or patients are not considered.

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Every institution nowadays relies on their online system and framework to do businesses. Such procedures need more attention due to the massive amount of attacks that occurs. These procedures have to go first through the management team of the institution, in order to prevent exploits of the attackers. Thus, the risk management can easily control and identify the risk that occurs. One of these risks is an intrusion, which is an action or an act that the attacker invades someone’s privacy to steal or damage their information. Various techniques have been proposed to prevent these actions in the literature. This research proposed an intrusion detection model to distinguish the most recent attacks using data mining techniques. Three machine learning classification models have been applied namely, J48, Random Forst and REPTree to improve the detection rate. Furthermore, a Feature Selection method has been applied in order to improve the effectiveness of the classifier and also overcome the high dimensionality which presents one of the main technical problems facing the intrusion detection systems and come up with the most important intrusion features affecting the system. These features can be very useful in protecting the systems from attackers. The results identify the top 11 effective features. The best results achieved by the J48 with a 76.271% accuracy rate.

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This study aims to design business processes or school management solutions that are right for the school. Widuri Indah School is private school that has not been integrated with the school management system. Qualitative method such as observation and interview are conducted to gain insights about main activities and support activities in Widuri Indah school business process. Furthermore, designing the use case diagram to construct the business process. The design will be able to produce business process that can integrating between users at school and school management systems in a cloud-based process It can be used as a guide in the procurement of school management system based on clout computing. The design is believed to bring improvement such as interoperability, flexibility, data management, and efficiency as a result.

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A high efficiency InAs/GaAs quantum wire solar cell is modelled embedding periodic array of InAs quantum wires (QW) in the intrinsic layer. The promising low dimensional heterostructure such as Quantum Wells, Quantum Wires, Quantum Dots or Dashed (elongated Dots) based intermediate-band-gap solar cells are recently being grasped the attention for ongoing third generation solar cell studies. In this particular work, we contrive, design and thereby simulate the solar cell incorporating QWs with a view to magnify the efficiency. After implementation of 15 layers of InAs QWs conjugated within the intrinsic layer and with the adaptation of AM1.5 solar irradiance, the proposed cell structure ensued a Voc of 1.26 V, Jsc of 32.83 mAcm-2 and a fill factor of 89.4%, which eventuates an overall cell efficiency of 37%. We achieved an efficiency of 26.98% with the same materials and dimensions without QWs in intrinsic layer. Therefore, it may be optimistically appealed that the insertion of QWs in the intrinsic layer has an affirmative impact on the efficiency of the cell.

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The human gut environment can contain hundreds to thousands bacterial species which are proven that they are associated with various diseases. Although Machine learning has been supporting and developing metagenomic researches to obtain great achievements in personalized medicine approaches to improve human health, we still face overfitting issues in Bioinformatics tasks related to metagenomic data classification where the performance in the training phase is rather high while we get low performance in testing. In this study, we present discretization methods on metagenomic data which include Microbial Compositions to obtain better results in disease prediction tasks. Data types used in the experiments consist of species abundance and read counts on various taxonomic ranks such as Genus, Family, Order, etc. The proposed data discretization approaches for metagenomic data in this work are unsupervised binning approaches including binning with equal width bins, considering the frequency of values and data distribution. The prediction results with the proposed methods on eight datasets with more than 2000 samples related to different diseases such as liver cirrhosis, colorectal cancer, Inflammatory bowel disease, obesity, type 2 diabetes and HIV reveal potential improvements on classification performances of classic machine learning as well as deep learning algorithms. These binning approaches are expected to be promising pre-processing techniques on various data domains to improve the performance of prediction tasks in metagenomics.

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4-DOF car motion simulator helps to simulate real-life experiences that drivers do not have the opportunity to access the real environment. The dynamics equation of 4-DOF car motion simulator is a very complex problem with many uncertain parameters, so it requires intelligent control algorithms. Sliding mode controller (SMC) can achieve good tracking performance and robustness to the disturbances, but SMC has worse stability and reliability than PID controller. As a most widely used controller, PID controller has many obvious advantages, but it has poorer tracking performance than SMC. In this paper, a control method of sliding mode type PID controllers is proposed to fully combine the advantages of the two controllers. In this study, based on the dynamics equation of 4-DOF car motion simulators the author develop two algorithms to control sliding mode control type PID (SMC-type-PID) and sliding mode controller type PID with GA optimization for 4-DOF car motion simulator. Firstly, the authors used Lyapunop theory to prove the stability of the system, next presenting the simulation results of two control algorithms with different uncertain components and comparing them to find and demonstrate the effectiveness of the new control method applied to the 4-DOF car motion simulator.

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In Peru, approximately since 2013, a necessary change has begun in the importance given to research, science, technology, and technological innovation. Likewise, in 2014, a new University Law was approved that among other aspects also promotes research production in universities. Against this context, the universities begin to improve with more emphasis activities related to research. The Universidad de Ciencias y Humanidades creates different research centers, one of them being the Image Processing Research Laboratory (INTI-Lab). Through INTI-Lab research projects are generated both with own resources and through external financing sources. INTI-Lab helps to increase the number of papers published and indexed in SCOPUS, increase the score in Researchgate platform, improve the position on the Webometrics ranking, and brings students closer to the research activity. In this context, a Knowledge Management strategy is essential. In the present work, an analysis of the different strategies and results will be presented. The analysis shows that the knowledge management strategies adopted by INTI-Lab contribute to increase the scientific production of the UCH.

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The paper presents a novel control strategy applying the particle swarm optimization (PSO) algorithm to optimize the scaling weights coefficients of the fuzzy-PID controller for the resistance furnace temperature control system, called PSO-based fuzzy-PID controller/ algorithm. The proposed PSO-based fuzzy-PID controller in this paper consist of the fuzzy-PID controller and the PSO algorithm. The proposed fuzzy-PID controller is combination of the advantage of PID control and fuzzy logic control. Firstly, the paper presents the mathematical model of the resistance furnace by identification method, based on the experimental data. Then, the design of the fuzzy-PID controller is given in this study. And then, the paper presents the design of the temperature control board using PIC16f with the installed PSO-based fuzzy-PID algorithm. Finally, the simulation and experimental results proved the stability of the proposed PSO-based fuzzy-PID controller with the disturbance, improved the furnace temperature control quality, through obtained major control criteria, such as overshoot, steady-state error, settling time, rising time.

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To have enough information on time can be helpful when companies try to reduce costs and operate more efficiently. An international company that supplies parts for the automotive industry is currently testing its new facilities in Mexico. The relocation of the raw materials and finished goods warehouses were tested using a P-Median model. The operating costs and risk factors were included in the model to provide a better solution and improve the operation of the warehouses and production lines. The research results compared different scenarios and indicated that the proposed better location isolates the forklift routes, mainly for finished products, and minimizes the cost of moving both raw materials and finished products to and from warehouses.

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With a model predictive control approach including boundary analysis and uncertain prediction of activities of different road participants, this paper proposes solutions that support motion control by steering control and appropriate acceleration to create safe motion trajectories for an autonomous vehicle. The motion control support element is determined by the principle of minimal intervention and can handle complex situations, while building control model to predict real-time operation with speed factors, ability to control driving and limit the long period. The performance of this solution is assessed through simulation, then there are applied research orientations on practical autonomous vehicle accounting.

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