Special Issue on Multidisciplinary Sciences and Engineering 2021-22

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

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Stepping-stone intrusion has been widely used by professional hackers to launch their attacks. Unfortunately, this important and typical offensive skill has not been taught in most colleges and universities. In this paper, after surveying the most popular detection techniques in stepping-stone intrusion, we develop 10 hands-on labs to enhance student-learning experience in cybersecurity education. The goal is not only to teach students offensive skills and the techniques to detect and prevent stepping-stone intrusion, but also to train them to be successfully adaptive to the fast-changing dynamic cybersecurity world.

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In the automation of the fish processing industry, the measurement surface-area and volume of the fish requires a method that focuses on processing automation. The creation of a stereo-vision based on real-time measurement method is one of the most essential aspects of this work. To do this task, we completed two steps. The first, the acquisition of the image of the fish using a stereo camera and calibrating the image for size using sample of the image acquisition. Second, by applying image processing techniques and vision system, the fish surface area and fish volume is obtained in real-time. The experimental results of the proposed method have good results for fish surface area and fish volume. The measuring process using stereo-vision only takes a short time, making it suitable for the real-time method.

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A transition towards electrification of the public transport sector is ongoing in many cities around the world, as a response to global warming and pollution. However, the question is whether the current state of technology is already sufficient to replace the conventional buses with electric ones and if the existing charging facilities are appropriate to ensure the smooth operation of the buses. Therefore, this work aims to verify the technical feasibility of the electrification of an existing urban line. The purpose is achieved by evaluating a case study on a public transport bus line in the city of Savona, Italy. The average energy consumption of an electric bus operating in the considered line path is estimated in order to investigate the possible locations and sizes of the charging systems to install. The results show that the correct service operation of the electric buses can be achieved by installing one opportunity charger of at least 300 kW in one of the terminals or by installing three 43 kW charging ports in the depot.

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The deep learning-based URL classification approach using massive observations has been verified especially in the field of phishing attack detection. Various improvements have been achieved through the modeling of character and word sequence of URL based on convolutional and recurrent neural networks, and it has been proven that an ensemble approach of each model has the best performance. However, existing ensemble methods have limitations in effectively fusing the nonlinear correlation between heterogeneous features extracted from characters and the sequence of sub-domains. In this paper, we propose a convolutional network-based ensemble learning approach to systematically fuse syntactic and semantic features for phishing URL detection. By learning the weights that integrating the heterogeneous features extracted from the URL, an ensemble rule that guarantees the best performance was obtained. A total of 45,000 benign URLs and 15,000 phishing URLs were collected and 10-fold cross-validation was conducted for quantitative validation. The obtained classification accuracy of 0.9804 indicates that the proposed method outperforms the existing machine learning algorithms and provides plausible solution for phishing URL detection. We demonstrated the superiority of the proposed method by receiver-operating characteristic (ROC) curve analysis and the case analysis and confirmed that the accuracy improved by 1.93% compared to the latest deep model.

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Navigation of a moving object (drone, vehicle, robot, and so on) and related localization in unknown scenes is nowadays a challenging subject to be addressed. Typically, different source devices, such as image sensor, Inertial Measurement Unit (IMU), Time of Flight (TOF), or a combination of them can be used to reach this goal. Recently, due to increasing accuracy and decreasing cost, the usage of 2D laser range scanners has growth in this subject. Inside a complete navigation scheme, using a 2D laser range scanner, the proposed paper considers alternative ways to estimate the core localization step with the usage of deep learning. We propose a simple but accurate neural network, using less than one hundred thousand overall parameters and reaching good precision performance in terms of Mean Absolute Error (MAE): one centimeter in translation and one degree in rotation. Moreover, the inference time of the neural network is quite fast, processing eight thousand scan pairs per second on Titan X (Pascal) GPU produced by Nvidia. For these reasons, the system is suitable for real-time processing and it is an interesting complement and/or integration for traditional localization methods.

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The main goal of the work is to determine the basic conceptual solutions of a nuclear reactor operating on the surface of another planet. The problem was solved using the example of the Martian nuclear power plant. The article uses calculation and optimization research methods and corresponding program codes (well-known and author’s). The results of the study made it possible to formulate the basic requirements for the Martian nuclear power plant and select the type of reactor. This is a new type of reactor: pressurized liquid metal fast reactor. It is proposed to use an innovative cermets nuclear fuel based on mixed mononitride and uranium metal nanopowder, which was previously considered by the author for new generation BN and BREST ground-based reactors. It is proposed to use a eutectic (or near-eutectic) NaKCs alloy as a coolant. Optimization of the alloy composition has been carried out. The fuel and coolant of the reactor contains long-lived radioactive waste to be transmuted. NaKCs alloy is less reactive than pure alkali metals including Na, K and Cs. With an electric power of 600 MW, it is possible to ensure the internal self-protection of the reactor. All emergency modes of the ATWS type (anticipated transient without scram) are not hazardous. This means that with a decrease in power to values characteristic of the initial stages of the colonization of Mars, the safety of the reactor is easily ensured. The relatively low chemical activity of the coolant makes it possible to use a two-circuit energy conversion scheme. The second circuit can use water or carbon dioxide. Carbon dioxide is preferred because of its presence in the atmosphere of Mars (95% CO2). The significance of the research lies in the possibility of constructing a Martian nuclear power plant within the framework of existing technologies.

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Sustainable energy is gradually becoming the norm today due to greenhouse warming effects; as a result, the quests for different renewable energy sources such as photovoltaic cells as well as energy efficient electrical appliances are becoming popular. Therefore, this article explores the alternative energy case for thermoelectricity with focus on the steady-state mathematics, mixed modelings and simulations of multiple TEGs and TECs modules to study their performance dynamics and to establish their optimal operation points using Matlab and Simulink. The research substantiates that the output current from TEGs or input current to TECs, initially respectively increases the output power of TEGs and the cooling power of TECs, until the current reaches a certain maximum optimal point, after which any further increase in the current, decreases the TEGs’ and or TECs’ respective output and cooling powers as well as efficiencies, due to Ohmic heating and or entropy change caused by the increasing current. The research main contributions are elaborate easy to understand TEGs/TECs theoretical formulations as well as static and dynamic simulated models in Matlab/Simulink, that can be used initially to dynamically investigate an infinite quantity of TEG and TEC modules connections, be it in series and or in parallel. This is to assist system designers grasp TEGs and TECs theoretical operations better and their limits, when designing energy efficient waste heat recovery (using TEGs)/cooling (using TECs) systems for industrial, residential, commercial and vehicular applications.

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The transmission network has been considered among the globe’s prevalent complex systems, comprised of hundreds of electrical transmission lines and other equipment used to transmit electrical energy from one location to another. Over a decade, power engineers have worked tirelessly to ensure that the transmission network operates reliably, transmitting electrical energy from the power station to the consumers without interruption. With growing generation capacity and the recent introduction of renewable energy systems (RES) such as wind turbines and solar energy, the transmission lines are increasingly being forced to run near their design limitations and greater unpredictability on the network operational configuration. As a result, the transmission network faces greater challenges than previously. As a worst-case scenario, large-scale electrical network power outages caused by electrical faults can disrupt electricity availability for several hours, impacting millions of customers and inflicting massive economic damage. These electrical faults must be repaired before electricity is restored to consumers. This necessitates a thorough grasp of the challenge and potential remedies to assure improved power efficiency. In the present work, an expansion of preceding work, a novel algorithm for estimating faults on transmission lines is presented. Impedance-based techniques are susceptible to producing errors or incorrect predictions. The presence of faults induced from high impedance sources produces an extra impedance to the ground, which negates the impedance calculation and produces errors in the distance to the fault. This results in inaccuracies that can affect a distance-to-fault estimation by 1-15 % of the overall line length. In this work, a design of a fault detection-location element (FDLE) algorithm is proposed. This algorithm relies on the dynamics of current and voltage signals on the transmission line while deserting impedance. Comparison research is undertaken against the impedance-based techniques to validate the proposed algorithm. Finally, the proposed algorithm findings are compared to fault location estimations using an impedance-based technique. Extensive trials on a simulated transmission line prove that the proposed algorithm is responsive to faults with an error as low as 1%, reaching a precision of 98.9%.

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Using Proofs of Retrievability (PORs), a file owner is able to check that a cloud server correctly stores her files. Using Proofs of Retrievability and Reliability (PORRs), she can even verify at the same time that the cloud server correctly stores both her original files and their replicas. In 2020, a new PORR combined with Verifiable Delay Functions (VDFs) was presented by Gritti. VDFs are special functions whose evaluation is slow while verification is fast. Therefore, those functions help guarantee that the original files and their replicas are stored at rest. Moreover, an important feature of the 2020 PORR solution is that anyone can verify the cloud provider’s behaviour, not only the file owner. This paper extends Gritti’s version. In particular, a realistic cloud framework is defined in order to implement and evaluate accurately. Results show that this PORR solution is well suitable for services provided for cloud storage.

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The transmission network has been considered among the globe’s prevalent complex systems, comprised of hundreds of electrical transmission lines and other equipment used to transmit electrical energy from one location to another. Over a decade, power engineers have worked tirelessly to ensure that the transmission network operates reliably, transmitting electrical energy from the power station to the consumers without interruption. With growing generation capacity and the recent introduction of renewable energy systems (RES) such as wind turbines and solar energy, the transmission lines are increasingly being forced to run near their design limitations and greater unpredictability on the network operational configuration. As a result, the transmission network faces greater challenges than previously. As a worst-case scenario, large-scale electrical network power outages caused by electrical faults can disrupt electricity availability for several hours, impacting millions of customers and inflicting massive economic damage. These electrical faults must be repaired before electricity is restored to consumers. This necessitates a thorough grasp of the challenge and potential remedies to assure improved power efficiency. In the present work, an expansion of preceding work, a novel algorithm for estimating faults on transmission lines is presented. Impedance-based techniques are susceptible to producing errors or incorrect predictions. The presence of faults induced from high impedance sources produces an extra impedance to the ground, which negates the impedance calculation and produces errors in the distance to the fault. This results in inaccuracies that can affect a distance-to-fault estimation by 1-15 % of the overall line length. In this work, a design of a fault detection-location element (FDLE) algorithm is proposed. This algorithm relies on the dynamics of current and voltage signals on the transmission line while deserting impedance. Comparison research is undertaken against the impedance-based techniques to validate the proposed algorithm. Finally, the proposed algorithm findings are compared to fault location estimations using an impedance-based technique. Extensive trials on a simulated transmission line prove that the proposed algorithm is responsive to faults with an error as low as 1%, reaching a precision of 98.9%.

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DC Microgrids (DCMGs) aggregate and integrate various distribution generation (DG) units through the use of power electronic converters (PECs) that are present on both the source side and the load side of the DCMGs. Tightly regulated PECs at the load side behave as constant power loads (CPLs) and may promote instability in the entire DCMG. Previous research has mostly focused on devising stabilization techniques with ideals CPLs that may not be feasible to realize; few publications that emulate DCMG stability with practical CPLs are restricted in application because they add components that considerably increase the cost of the DCMGs. This study aims at stabilizing the DCMG in the presence of practical CPL in a way that is economically feasible, i.e., without the addition of complex compensators. This paper presents a Simulink model of the smallest DCMG, i.e., a cascaded DC-DC power converter network with a practical CPL assumed at the load side of the network. Using theoretical calculations and computer simulations, we have determined the suitable CPL power level and the bandwidth of the current controller at which the smallest DCMG is stable. We have performed the stability analysis of the source side buck converter and the CPL with the derived power level and bandwidth, and found that individual converter systems are stable, thereby proving that the entire DCMG is stable despite the presence of a CPL.

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Our world is characterised by uncertainty and complex, relational structures that carry temporal information, yielding large dynamic probabilistic relational models at the centre of many applications. We consider an example from logistics in which the transportation of cargoes using vessels (objects) driven by the amount of supply and the potential to generate revenue (relational) changes over time (temporal or dynamic). If a model includes only a few objects, the model is still considerably small, but once including more objects, i.e., with increasing domain size, the complexity of the model increases. However, with an increase in the domain size, the likelihood of keeping redundant information in the model also increases. In the research field of lifted probabilistic inference, redundant information is referred to as symmetries, which, informally speaking, are exploited in query answering by using one object from a group of symmetrical objects as a representative in order to reduce computational complexity. In existing research, lifted graphical models are assumed to already contain symmetries, which do not need to be constructed in the first place. To the best of our knowledge, we are the first to propose symmetry construction a priori through a symbolisation scheme to approximate temporal symmetries, i.e., objects that tend to behave the same over time. Even if groups of objects show symmetrical behaviour in the long term, temporal deviations in the behaviour of objects that are actually considered symmetrical can lead to splitting a symmetry group, which is called grounding. A split requires to treat objects individually from that point on, which affects the efficiency in answering queries. According to the open-world assumption, we use symmetry groups to prevent groundings whenever objects deviate in behaviour, either due to missing or contrary observations.

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The context for the study was a naturally occurring quasi-experiment in the core mathematics program in a large Australian university. Delivery of teaching was changed in a sequence of two initial core mathematics subjects taken by engineering and science students. The change replaced one of two face-to-face tutorial classes per week by an online tutorial. Tasks in the online tutorial were designed to lead the students through the week’s topics, using initially simpler tasks as scaffolding for more complex tasks. This was the only change: syllabus and written materials were the same, as was students’ access to help from staff and discussion with peers. The study compared learning outcomes among students in two adjacent years: Cohort 1, the last before the change, and Cohort 2, in the first implementation of the change to a blended learning environment. Learning outcomes were assessed by a method derived from the SOLO taxonomy, which used a common scale for scoring written answers to examination questions in the two cohorts. In the first mathematics subject students doing online tutorials had significantly higher scores than those studying before the change. In the second mathematics subject there were no significant differences. The conclusion was that the online tutorials gave an advantage to students beginning university study and gave adequate support to those in the subject taken a little later. It can be concluded that the use of an online teaching component in the delivery of university mathematics programs is not only justifiable but desirable, subject to careful design of the teaching material offered.

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On Renewable Energy (RE), this field covers the most significant share of the world energy demand and challenges on the expensive measurement and maintenance equipment to be used. In all studies and designs, global solar radiation (GSR) measurements require assessment, estimation, and models to be applied together with the environment and meteorological data on installing stations at the specific location. These meteorology stations provide measured data throughout the year/ annually or at specified periods, depending on the site of interest. This study includes assessment and estimations of the solar radiation at the Vhembe District using the geographical data measured daily, monthly, and throughout a year in the area. It provides variables such as the geographical maps of the solar availability at a minimum and maximum temperatures obtained during the annual analyses. Determining the solar radiation at a specific location for energy generation involves several procedures, estimations, and calculations using the climatological weather data measurements through MATLAB simulations. In addition, the Geographical Remote Sensing (RS) and Mappings, and Spreadsheet Graph Analytics, were applied to the measured data from the nine installed Weather Stations (WS) in the Vhembe District area was used. The analysis determines the minimum and maximum solar radiation equations associated with the local climate patterns in accommodating the theoretical bases and period changes. The paper contributes to the main project objectives on renewable energy assessment for potentials and generation at a micro/small scale in the district. These parameters are fundamental in estimating and determining the potential solar energy radiation using its extraterrestrial solar radiation per day/ weekly/ monthly. Annual periods towards methods to develop micro/small energy projects for rural and urban communities for domestic and commercial use. As a result, the meteorology analysis is being presented in this study.

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Instead of approaches of solid mechanics or a formal description of experimental data an interdisciplinary approach is proposed to consider failure and deformation as thermodynamic processes. Mathematical modeling of the processes is carried out using rheological models of the material. One fracture criterion is used, that formally corresponds to the achievement of a threshold concentration of micro-damage in any volume of the material. The prediction of the durability of materials under constant or variable temperature and force conditions is performed by time steps, including situations with changes in the material structure. Calculations of durability of structural components are based on the relationship of plastic flow and failure processes distributed over the volume of the material.

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With the development of Internet of Things technology and the widespread use of smart devices, artificial intelligence is now being applied as a decision-making tool in a variety of fields. To make machine learning models, including deep neural network models, more interpretable, various techniques have been proposed. In this paper, a method for explaining the outputs of machine learning models using inductive logic programming is described. For an evaluation of this method, diagnostic models of bovine mastitis were trained using a dataset of dairy cows, and interpretable rules were obtained to explain the trained models. As a result, the rules obtained indicate that the trained classifiers detected mastitis cases depending on certain variations in the electrical conductivity (EC) values, and in some of these cases, the EC and lactate dehydrogenase fluctuated in different ways. The interpretable rules help people understand the outputs of machine learning models and encourage a practical introduction of the models as decision-making tools.

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The use of Covid-19 contact tracing applications has become almost irrelevant now that several flavors of Covid-19 vaccine have been developed and are constantly being distributed to people during the pandemic to help alleviate the need for lockdowns. Also, the availability of at-home testing kits and testing sites means that people do not need to contact trace as much since individuals can get tested and follow the health guidelines in ensuring their health and the safety of others around them. Nevertheless, governments around the world are still faced with the Covid-19 pandemic challenge because the virus is not yet controlled due to the different variants and the rapid contamination rate that outpaced the logistic supply chain processes in the distribution of the vaccines and the time it takes in convincing individuals to take the vaccine swiftly to reach herd immunity. Therefore, the current pressing need is that of addressing the infection rate by finding ways and solutions to minimize or slow down contamination among people especially with the increased number of variants. This paper is an extension of the “COVIDFREE App: The User-Enabling Contact Prevention Application” work originally presented in 2020 IEEE International Symposium on Technology and Society (ISTAS) conference that provided a smartphone application architecture with the goal of proactively enabling users to avoid encountering infected Covid-19 patients. This paper elucidates and discusses additional concerns not thoroughly addressed previously regarding the Covid-19 variants, vaccines, booster, and infection rates, and demonstrates the feasibility of the proposed architecture with a web application prototype. This paper also discusses the benefits of funding and developing contact tracing and prevention applications, such as the COVIDFREE App, to provide the needed ingredient in reducing the infection rate and provide citizens the needed preparedness and relief in actively fighting the virus.

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Dynamic Searchable Symmetric Encryption (DSSE) methods address the problem of securely outsourcing updating private data into a semi-trusted cloud server. Furthermore, Forward Privacy (FP) notion was introduced to limit data leakage and thwart the related attacks on DSSE approaches. FP schemes ensure previous search queries cannot be linked to future updates and newly added files. Since FP schemes use ephemeral search tokens and one-time use index entries, many scholars conclude that privacy attacks on traditional SSE schemes do not apply to SSE approaches that support forward privacy. However, to obtain efficiency, all FP approaches accept a certain level of data leakage, including access pattern leakage. Here, we introduce two new attacks on forward-private schemes. We demonstrate that it is still plausible to accurately unveil the search pattern by reversing the access pattern. Afterward, the attackers can exploit this information to uncover the search queries and consequently the documents. We also show that the traditional privacy attacks on SSE schemes are still applicable to schemes that support forward privacy. We then construct a new DSSE approach that supports parallelism and obfuscates the search and access pattern to thwart the introduced attacks. Our scheme is cost-efficient and provides secure search and update. Our performance analysis and security proof demonstrate our approach’s practicality, efficiency, and security.

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Blockchain technology is seeing a trend of popularity and adoption in many different application areas. One such area is healthcare, as there is a need to develop a system that can reliably store and share electronic health records (EHRs) among hospital-based health facilities. In this paper, we present a cloud-based hierarchical consortium blockchain framework for storing and sharing EHRs in a scalable, secure, and reliable manner. The framework enables data sharing between local hospital blockchain networks (HBNs) through high-level blockchain networks, namely, city blockchain networks (CBNs) and a state blockchain network (SBN). To support the timely publication of EHRs in HBNs, we adopt a temporary and permanent block scheme in hospital blockchains. In addition, we develop role-based access control (RBAC) policies for data authorization and procedures for concurrent search and retrieval of EHRs across cities and states. The experimental results show that our proposed approach is feasible and supports timely publication and efficient retrieval of EHRs in cloud-based hierarchical blockchain networks.

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The aim of the research is to contribute to the emergent field of strategic design as an approach to creating transformational impacts in organizations. This is driven by international strategies to promote sustainable business environments which are innovative and adaptive to change. The literature identifies a gap in the knowledge in relation to how knowledge flows within an organization from knowledge producing activities and back. This flow between management and operational activity is identified as the ongoing innovation capability and ultimately the area of transformational impact within the organization. However, the structures which support this flow and how it is measured are ambiguous and ill defined. Strategic design is a holistic approach to developing business strategies which may provide methods which support the flow of knowledge and provide the transformational impacts. A ‘research through design’ approach was taken to collect data from small to medium enterprises engaging in a series of strategic design workshops. Analysis was carried out through visual mapping of how strategic design is applied in an organization at management and operational level. This identifies the nuanced differences in application and design tools used to develop management strategy versus operational strategy. It also identifies the points of knowledge creation and transfer which builds business intelligence to inform both business and operational strategies. This is presented as a contextual framework which provides the basis for understanding the complexity of the flow between the two. This contributes to informing organizations of where the links between these strategies may be built, resulting in a more dynamic organization which is nimble, innovative, and adaptive to change.

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The development of an aggregated system consisting of autonomously developed components is usually implemented as a self-contained unit. If such an aggregation is understood as a system of systems (SoS) that communicates via interfaces with its autonomous subsystems and components, the interfaces and communication exchange should play a central role in the architectural design. In fact, complete and exact interface specifications simplify loose coupling of independent systems into an aggregation. Since an SoS consists of variant and non-variant subsystems, the main challenge in SoS development is the identification of all true variants and its deviating attributes within an SoS. If the system variants are identified at an early stage of the development process, redundant work in the interface design can be substantially reduced. This paper presents an efficient method to identify SoS variants with regard to life cycle management and it shows how to configure a variant-oriented SoS with a standardized communication interface. For the development, the forward-looking model-based systems engineering approach is recommended to create executable specification parts and to detect errors early on through simulations.

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The main purpose in this work is to explore the fact that chaos, as a biological characteristic in the brain, should be used in an Artificial Neural Network (ANN) system. In fact, as long as chaos is present in brain functionalities, its properties need empirical investigations to show their potential to enhance accuracies in artificial neural network models. In this paper, we present brain-inspired neural network models applied as pattern recognition techniques first as an intelligent data processing module for an optoelectronic multi-wavelength biosensor, and second for breast cancer identification. To this purpose, the simultaneous use of three different neural network behaviors in the present work allows a performance differentiation between the pioneer classifier such as the multilayer perceptron employing the Resilient back Propagation (RProp) algorithm as a learning rule, a heteroassociative Bidirectional Associative Memory (BAM), and a Chaotic-BAM (CBAM). It is to be noted that this would be in two different multidimensional space problems. The later model is experimented on a set of different chaotic output maps before converging to the ANN model that remarkably leads to a perfect recognition for both real-life domains. Empirical exploration of chaotic properties on the memory-based models and their performances shows the ability of a specific modelisation of the whole system that totally satisfies the exigencies of a perfect pattern recognition performance. Accordingly, the experimental results revealed that, beyond chaos’ biological plausibility, the perfect accuracy obtained stems from the potential of chaos in the model: (1) the model offers the ability to learn categories by developing prototype representations from exposition to a limited set of exemplars because of its interesting capacity of generalization, and (2) it can generate perfect outputs from incomplete and noisy data since chaos makes the ANN system capable of being resilient to noise.

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Gamified Instructional Media has recently been widely used in the education sector to improve students’ abilities. Using Gamified Instructional Media at the elementary school level becomes more interesting because it is in accordance with the way children learn K1-K6. The research aims to identify the gamified instructional using Genially to improve students’ critical and creative thinking skills. A quasi-experimental method was applied using a nonequivalent control group research design. The research subject is 40 students of Public Primary School in Pekanbaru. The results show a significant effect of the gamified instructional learning using Genially toward students’ critical and creative thinking skills. Besides, there is a significant difference in students’ critical and creative thinking skills between the control and experimental group. This study implies that gamified instructional media with Genially can support teachers and teaching practices.

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Predicting the credit card default is important to the bank and other lenders. The credit default risk directly affects the interest charged to the borrower and the business decision of the lenders. However, very little research about this problem used the Generalized Linear Model (GLM). In this paper, we apply the GLM to predict the risk of the credit card default payment and compare it with a decision tree, a random forest algorithm. The AUC, advantages, and disadvantages of each of the three algorithms are discussed. We explain why the GLM is a better algorithm than the other two algorithms owing to its high accuracy, easy interpretability, and implementation.

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The Internet of Things (IoT) is the evolving paradigm of interconnectedness of objects with varied architectures and resources to provide ubiquitous and desired services. The popularization of IoT-connected devices facilitating evolution of IoT applications does come with security challenges. The IoT with the integration of wireless sensor networks possess a number of unique characteristics, so the implementation of security in such a restrictive environment is a challenging task. Due to the perception that security is expensive in terms of computation, power and user-interface components, and as sensor nodes or low-power IoT objects have limited resources, it is desired to design security mechanisms especially routing protocols that are light weighted. Bio-inspired mechanisms are shown to be adaptive to environmental variations, robust and scalable, and require less computational and energy resources for designing secure routing algorithms for distributed optimization. In IoT network, the malicious intruders can exploit the routing system of the standardized routing protocol, e.g., RPL (The Routing Protocol for Low-Power and Lossy Networks), that does not observe the node’s routing behavior prior to data forwarding, and can launch various forms of routing attacks. To secure IoT networks from routing attacks, a secure trust aware ACO-based WSN routing protocol for IoT is proposed here that establishes secure routing with trustworthy nodes. The trust evaluation system, is enhanced to evaluate the node trust value, identify sensor node misbehavior, and maximize energy conservation. The performance of the proposed routing algorithm is demonstrated through MATLAB. Based on the proposed system, to find the secure and optimal path while aiming at providing trust in IoT environment, the average energy consumption is minimized by nearly 50% even as the number of nodes has increased, as compared with the conventional ACO algorithm, a current ant-based routing algorithm for IoT-communication, and a present routing protocol RPL for IoT.

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Information security behaviour is an integral part of modern business and has become a central theme in many research studies. One of the essential tools available that can be used to influence information security behaviour is information security policies (ISPs). These types of policies, which is mandatory in most organisations, are formalised rules and regulations which guide the safeguarding of information assets. Despite a significant number of ISP and related studies, a growing number of studies report ISP non-compliance as one of the main factors contributing to undesirable information security behaviour. It is noteworthy that these studies generally do not focus on the opinion of users or employees about the contents of the ISPs that they have to adhere to. The traditional approach to obtain user or employee opinions is to conduct a survey and ask for their opinion. However, surveys present unique challenges in fake answers and response bias, often rendering results unreliable and useless. This paper proposes a deep learning affective computing approach to perform sentiment analysis based on facial expressions. The aim is to address the problem of response bias that may occur during an opinion survey and provide decision-makers with a tool and methodology to evaluate the quality of their ISPs. The proposed affective computing methodology produced positive results in an experimental case study. The deep learning model accurately classified positive, negative, and neutral opinions based on the sentiment conveyed through facial expressions.

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