Appl. Syst. Innov., Volume 4, Issue 4 (December 2021) – 32 articles

Indoor and outdoor mapping studies can be completed relatively quickly, depending on the developments in Mobile Mapping Systems. Especially in indoor environments where high accuracy GNSS positions cannot be used, mapping studies can be carried out with SLAM algorithms. Although there are many different SLAM algorithms in the literature, each can produce results with different accuracy according to the mapped environment. In this study, 3D maps were produced with LOAM, A-LOAM, and HDL Graph SLAM algorithms in different environments such as long corridors, staircases, and outdoor environments, and the accuracies of the maps produced with different algorithms were compared. For this purpose, a mobile mapping platform using Velodyne VLP-16 LIDAR sensor was developed, and the odometer drift, which causes loss of accuracy in the data collected, was minimized by loop closure and plane detection methods. As a result of the tests, it was determined that the results of the LOAM algorithm were not as accurate as those of the A-LOAM and HDL Graph SLAM algorithms. Both indoor and outdoor environments and the A-LOAM results’ accuracy were two times better than HDL Graph SLAM results. Full article

Anomaly detection in the smart application domain can significantly improve the quality of data processing, especially when the size of a dataset is too small. Internet of Things (IoT) enables the development of numerous applications where sensor-data-aware anomalies can affect the decision making of the underlying system. In this paper, we propose a scheme: IoTDixon, which works on the Dixon’s Q test to identify point anomalies from a simulated normally distributed dataset. The proposed technique involves Q statistics, Kolmogorov–Smirnov test, and partitioning of a given dataset into a specific data packet. The proposed techniques use Q-test to detect point anomalies. We find that value 76.37 is statistically significant where $P=0.012<\alpha =0.05$, thus rejecting the null hypothesis for a test data packet. In other data packets, no such significance is observed; thus, no outlier is statistically detected. The proposed approach of IoTDixon can help to improve small-scale point anomaly detection for a small-size dataset as shown in the conducted experiments. Full article

A significant increase in System-of-Systems (SoS) is currently observed in the social and technical domains. As a result of the increasing number of constituent system components, Systems of Systems are becoming larger and more complex. Recent research efforts have highlighted the importance of identifying innovative statistical and theoretical approaches for analyzing complex systems to better understand how they work. This paper portrays the use of an agnostic two-stage examination structure for complex systems aimed towards developing an information theory-based approach to analyze complex technical and socio-technical systems. Towards the goal of characterizing system complexity with information entropy, work was carried out in exploring the potential application of entropy to a simulated case study to illustrate its applicability and to establish the use of information theory within the broad horizon of complex systems. Although previous efforts have been made to use entropy for understanding complexity, this paper provides a basic foundation for identifying a framework to characterize complexity, in order to analyze and assess complex systems in different operational domains. Full article

A direct use approach incorporating a cost approach assumed that replacing oversized electrical appliances with those better fit to actual energy consumption can reduce energy consumption, optimizing capacities of the new appliances to the maximum while reducing electricity costs. This study aimed to verify the assumption that the size of appliances has impacts on energy consumption and cost effectiveness. A mixed-method approach included these instruments for data elicitations (i.e., a questionnaire, data records of 485 transformers, two assessments of condominium technical caretakers, and two in-depth interviews of electrical engineering experts). The findings revealed that most condominiums installed electric appliances that are too large for their actual energy usage, which lies between 5.4% and 7.1% of the capacity. This study therefore proposed a total cost reduction of 54% by downsizing these appliances (i.e., MV Switchgear 2 sets, dry type transformer 2 sets 80,000, LV Cable 10 m. (XLPE), main distribution board, Busduct (MDB-DB), generator (20% of Tr.), and generator installation). Even though this analysis is limited to Bangkok, Thailand, this case may contribute decision-making on electrical appliance selection at early stage of investment or to downsize the currently installed appliances for the more energy efficient and cost-effective management of condominiums around the world. Full article

The increasing implementation of digital technologies has various positive impacts on companies. However, many companies often rush into such an implementation of technological trends without sufficient preparation and pay insufficient attention to the human factors involved in digitization. This phenomenon can be exacerbated when these technologies become highly dependent, as during the COVID-19 pandemic. This study aims to better understand challenges and to propose solutions for a successful implementation of digitized technology. A literature review is combined with survey results and specific consulting strategies. Data from the first wave of the COVID-19 pandemic in Germany were collected by means of an online survey, with a representative sample of the German population. However, we did not reveal any correlation between home office and suffering, mental health, and physical health (indicators of digitization usage to cope with COVID-19 pandemic), but rather that younger workers are more prone to using digitized technology. Based on previous findings that older individuals tend to have negative attitudes toward digital transformation, appropriate countermeasures are needed to help them become more tech-savvy. Accordingly, a software tool is proposed. The tool can help the management team to manage digitization efficiently. Employee well-being can be increased as companies are made aware of necessary measures such as training for individuals and groups at an early stage. Full article

Underwater images are extremely sensitive to distortion occurring in an aquatic underwater environment, with absorption, scattering, polarization, diffraction and low natural light penetration representing common problems caused by sea water. Because of these degradation of quality, effectiveness of the acquired images for underwater applications may be limited. An effective method of restoring underwater images has been demonstrated, by considering the wavelengths of red, blue, and green lights, attenuation and backscattering coefficients. The results from the underwater restoration method have been applied to various underwater applications; particularly, edge detection, Speeded Up Robust Feature detection, and image classification that uses machine learning. It has been shown that more edges and more SURF points can be detected as a result of using the method. Applying the method to restore underwater images in image classification tasks on underwater image datasets gives accuracy of up to 89% using a simple machine-learning algorithm. These results are significant as it demonstrates that the restoration method can be implemented on underwater system for various purposes. Full article

Protection schemes are usually implemented in the planning of transmission line operations. These schemes are expected to protect not only the network of transmission lines but also the entire power systems network during fault conditions. However, it is often a challenge for these schemes to differentiate accurately between various fault locations. This study analyses the deficiencies identified in existing protection schemes and investigates a different method that proposes to overcome these shortcomings. The proposed scheme operates by performing a wavelet transform on the fault-generated signal, which reduces the signal into frequency components. These components are then used as the input data for a multilayer perceptron neural network with backpropagation that can classify between different fault locations in the system. The study uses the transient signal generated during fault conditions to identify faults. The scientific research paradigm was adopted for the study. It also adopted the deduction research approach as it requires data collection via simulation using the Simscape electrical sub-program of Simulink within Matrix laboratory (MATLAB). The outcome of the study shows that the simulation correctly classifies 70.59% of the faults when tested. This implies that the majority of the faults can be detected and accurately isolated using boundary protection of transmission lines with the help of wavelet transforms and a neural network. The outcome also shows that more accurate fault identification and classification are achievable by using neural network than by the conventional system currently in use. Full article

Recently, the lack of expansion joint gaps on highway bridges in Korea has been increasing. In particular, with the increase in the number of days during the summer heatwave, the narrowing of the expansion joint gap causes symptoms such as expansion joint damage and pavement blow-up, which threaten traffic safety and structural safety. Therefore, in this study, we developed a machine vision (M/V)-technique-based inspection system that can monitor the expansion joint gap through image analysis while driving at high speed (100 km/h), replacing the current manual method that uses an inspector to inspect the expansion joint gap. To fix the error factors of image analysis that happened during the trial application, a machine learning method was used to improve the accuracy of measuring the gap between the expansion joint device. As a result, the expansion gap identification accuracy was improved by 27.5%, from 67.5% to 95.0%, and the use of the system reduces the survey time by more than 95%, from an average of approximately 1 h/bridge (existing manual inspection method) to approximately 3 min/bridge. We assume, in the future, maintenance practitioners can contribute to preventive maintenance that prepares countermeasures before problems occur. Full article

This research investigated how children aged five to six performed in social interactions and participation by learning American English through music technology activities in an inclusive class. The purposes of this research were to analyze, through music technology activities, the social interactions and participation of children in the inclusive class. Therefore, the research question was as follows: can music technology activities significantly improve children’s social interactions and participation in an inclusive class? There were two themes for the research teaching, each of which included seven weeks of instruction. The teaching content involved three stages, which were pre-test, implementation, and post-test. The research teaching was given 40 min per session twice a week and continued for 14 weeks with 28 teaching times. The methodology primarily consisted of a qualitative assessment of participation, observations, and interviews. In addition to collecting and analyzing qualitative data, quantitative data were also employed in the study. Data sources were semi-structured observation forms, anecdote records, language test scales and interview records, and feedback forms. The results indicated that all children had positive performance in social interactions and participating motivation, as supported by statistical results of social validity. Furthermore, the children’s cooperation and communication effectively improved through music technology activities. Nevertheless, the limitation of the study is the insufficient number of participants involved in the evaluation. For future research, utilizing more than 30 samples would be more appropriate and would supplement the social network analysis to carry out more in-depth investigations and discussions. Full article

Within the EU CEMEC project framework, a novel approach for using holographic showcases in museums has been conceived and experimented upon in different venues in the context of an itinerant exhibition dealing with Early Medieval European collections. The purpose of this holographic showcase, the so-called “box of stories”, is to improve the link and interaction between real and virtual contents in the museum’s context, making the exhibited object “alive” in the visitors’ perception. An Avar sword and a Byzantine treasure have been used as the main case studies, and they have been experienced in the museums of several European regions by audiences with different cultural backgrounds. This has been a great opportunity to carry out user experience (UX) evaluations in order to collect feedback (from about 600 museum visitors) regarding the attractiveness of such a mixed reality (MR) system, its usability, the comprehension of the contents, the efficacy of the logistics and environmental conditions, as well as the educational impact. The results of such inquiries helped the CNR ISPC team to identify the most meaningful User eXperience Analytics (UXA) able to support the work of UX evaluators and UX designers to assess the efficacy of digital cultural products. Indeed, this manuscript presents UXA and tries to draft a concrete and effective evaluation model for future digital projects for museum contexts. Full article

Taipei Metro, since its inception in 1996, has become the most important public transport option for commuters and travelers in the metropolitan Taipei area, delivering over two million daily rides. Nevertheless, the interior environment of Taipei Metro has a reputation for being disorienting, especially to the infrequent passengers. By incorporating the methods of behavioral mapping and visibility analysis, this study argues that the occurrence of disorientation is highly dependent on visual properties of Taipei Metro’s interior layout. Specifically, the number of decision-making stops and the visibility conditions of stairs and escalators are found to be particularly influential. To enhance the passengers’ wayfinding experience, a mobile application comprised of two components is proposed. The Route Planner is to advise the passengers to avoid the areas that cause disorientation, while the Navigator, by providing the panoramic views of certain locations, can help the passengers reach their destinations more easily. Full article

Prior research has shown qualitatively that organizations can increase the value created in their production and assembly processes through the implementation of three-dimensional (3D) product models in those processes. This paper moves beyond qualitative value to develop and calibrate a quantitative model for the value of 3D product model use in production and assembly processes. The principal contributions of this research are development of the quantitative model and determination of the quantitative value of deploying the 3D product model in assembly and production processes; findings developed through interviews with industry experts in industrial and systems engineering to gather the model inputs, calculate the outputs, and then calibrate the model with those industry experts. These results are then compared against the qualitative value categories from prior research to determine the alignment in order and magnitude with the quantitative model results. This paper concludes with a recommendation of where both industry and academia focus future implementation efforts and research based upon the associated results demonstrated in both the qualitative and quantitative model on the value of 3D product model use in assembly and production processes. Full article

Seismic hazard analysis of the earthquake-prone Eastern Anatolian Region (Turkey) has become more important due to its growing strategic importance as a global energy corridor. Most of the cities in that region have experienced the loss of life and property due to significant earthquakes. Thus, in this study, we attempted to estimate the seismic hazard in that region. Seismic moment variations were obtained using different types of earthquake magnitudes such as Mw, Ms, and Mb. The earthquake parameters were also determined for all provincial centers using the earthquake ground motion levels with some probabilities of exceedance. The spectral acceleration coefficients were compared based on the current and previous seismic design codes of the country. Additionally, structural analyses were performed using different earthquake ground motion levels for the Bingöl province, which has the highest peak ground acceleration values for a sample reinforced concrete building. The highest seismic moment variations were found between the Van and Hakkari provinces. The findings also showed that the peak ground acceleration values varied between 0.2–0.7 g for earthquakes, with a repetition period of 475 years. A comparison of the probabilistic seismic hazard curves of the Bingöl province with the well-known attenuation relationships showed that the current seismic design code indicates a higher earthquake risk than most of the others. Full article

The fields of electronics and information technology have witnessed rapid development during the last decades, providing significant technical support to the field of biological sciences. Radio-Frequency Identification (RFID) technology has been used to automate the monitoring of animal location and behaviors in a wide range of vertebrate and invertebrate species, including social insects such as ants and honey bees (Apis mellifera L.). This technology relies on electromagnetic fields to identify and track transponders attached to objects automatically. Implementing new technologies to serve research purposes could be time consuming and require technical expertise from entomologists and researchers. Herein, we present a detailed description on how to harness RFID technology to serve honey bee research effectively. We describe how to build and operate a 32-antenna RFID system used to monitor various honey bee behaviors such as foraging, robbing, and queen and drone mating, which can be used in other social insects as well. Preliminary data related to queen nuptial flights were obtained using this unit and presented in this study. Virgin queens labeled with ≈5 mg transponders performed multiple (one to four) nuptial/orientation flights a day (9 a.m. to 5 p.m.) ranging from 8 to 145 s each. Contrary to virgin queens, no hive exit was recorded for mated queens. At full capacity, this unit can monitor up to 32 honey bee colonies concurrently and is self-sustained by a solar panel to work in remote areas. All materials, hardware, and software needed to build and operate this unit are detailed in this study, offering researchers and beekeepers a practical solution and a comprehensive source of information enabling the implementation of RFID technology in their research perspective. Full article

This study provides an exploratory bibliometric analysis of the emerging literature on Industry 5.0, which is a new visionary concept on the future of industry. Industry 5.0 has in recent years begun to attract the interest of both practitioners and academics, but this new field can still be considered embryonic and not well documented. Therefore, this study aims to map the field and provide a preliminary picture of the emergence and status of the scientific literature on Industry 5.0. Bibliometric data covering the period from 2015 to 2021 were extracted from the Scopus database. Bibliometric analyses of overall publication volume and growth trajectory, influential documents, authors, sources and countries are performed. The exploratory analysis provides a preliminary overview of the birth and emergence of this new research area. The results are discussed in relation to theories on the emergence and evolution of new management concepts. The article closes with some speculations about the future trajectory of Industry 5.0. Full article

Metal matrix composites (MMC) are the materials of tomorrow. This paper presents an in-depth analysis of the MMC research articles published in Web of Science (WoS) during 2001–2020. The study firstly included year on year publications, publication types, sources, research directions as well as the most productive researchers, organizations and nations. Secondly, a detailed analysis of collaborations among various MMC researchers, organizations and countries has been presented. Thirdly, citations based linkages among the published articles, sources, researchers, institutions and places have been discussed relative to their respective collaborative link strengths. A co-occurrence analysis of MMC keywords was also conducted to highlight the most important keywords trending in this area. Finally, burst detection analyses of keywords and references were carried out to unearth sudden citation spikes of keywords and documents. Primary results indicate that research articles formed 80.54% of all MMC publications in WoS. The journal ‘Materials Science and Engineering A: Structural Materials, Properties, Microstructure and Processing’ published maximum MMC articles. Collaboration analysis results indicate that Zhang D, the Chinese Academy of Science and People’s Republic of China, attained topmost collaboration based total link strengths (TLS). Citations based analysis showed that Zhang D, the Shanghai Jiao Tong University (China), People’s Republic of China and the journal ‘Materials Science and Engineering A: Structural Materials, Properties, Microstructure and Processing’ received highest citation TLS values. Keyword ‘Graphene’ scored the highest citation burst strength (2018–2020). The future of MMC research lies in processing and characterization of novel nanocomposites with reinforcements such as graphene and boron carbide for various applications. Full article

Due to the increasing growth of social media content on websites such as Twitter and Facebook, analyzing textual sentiment has become a challenging task. Therefore, many studies have focused on textual sentiment analysis. Recently, deep learning models, such as convolutional neural networks and long short-term memory, have achieved promising performance in sentiment analysis. These models have proven their ability to cope with the arbitrary length of sequences. However, when they are used in the feature extraction layer, the feature distance is highly dimensional, the text data are sparse, and they assign equal importance to various features. To address these issues, we propose a hybrid model that combines a deep neural network with a multi-head attention mechanism (DNN–MHAT). In the DNN–MHAT model, we first design an improved deep neural network to capture the text’s actual context and extract the local features of position invariants by combining recurrent bidirectional long short-term memory units (Bi-LSTM) with a convolutional neural network (CNN). Second, we present a multi-head attention mechanism to capture the words in the text that are significantly related to long space and encoding dependencies, which adds a different focus to the information outputted from the hidden layers of BiLSTM. Finally, a global average pooling is applied for transforming the vector into a high-level sentiment representation to avoid model overfitting, and a sigmoid classifier is applied to carry out the sentiment polarity classification of texts. The DNN–MHAT model is tested on four reviews and two Twitter datasets. The results of the experiments illustrate the effectiveness of the DNN–MHAT model, which achieved excellent performance compared to the state-of-the-art baseline methods based on short tweets and long reviews. Full article

Space frame structures satisfy the ever-increasing requirements of societies for providing a variety of structural forms and architectural spaces with special characteristics, such as aesthetic and free-form features, population-wise capacities, and structural performance, among others. Structural behavior of these systems largely depend on the type of joints and their components which are to be considered appropriately in design and analysis. Screws comprise one of the key components of joints in these structures and play a pivotal role in the total cost of the structure, as well as the maximum stress level created in joints. The present study aims to evaluate the effect of screw size on the maximum stress generated in three MERO double-layer ball joints with diameters of 98, 110, and 132 mm as a sample numerical analysis case to pinpoint this fact. Numerical simulations were conducted using ANSYS workbench software. Based on the results, in order to achieve the maximum factor of safety (FOS), the minimum stress, and keeping the total construction cost optimal, it is recommended to use M16, M20, and M24 screws for the ball joints of diameters 98, 110, and 132 mm, respectively. Full article

The management and operation of an electrical switchboard originally was processed by an inspector so only tangible malfunctions could be identified while other intangible ones that can cause severe damages to the switchboard were overlooked. To solve this serious deprivation, this investigation, therefore, implemented an intranet sensors system in the electrical switchboard to create a new channel of communication via smart devices to operate and access it remotely, which will eventually lead to increased safety and efficiency of managing electrical switchboards, as well as manufacturing reliability and stability. All these will also increase competitiveness in business. The findings of this research indicate that the application could solve the deprivation by signaling all security malfunctions, both tangible and intangible, remotely via smartphones and laptops in the real-time operating system, which helps reduce severe damages to the switchboard, on-site inspection, and loss of service time to fix malfunctions and human and related risks, as well as increase manufacturing reliability and stability of the operation. The implemented intranet sensors system was also compatible with the current existing security system. This increased security, therefore, verifies the efficiency and business competitiveness of the intranet sensors system. Full article

Malaria is one of the most infectious diseases in the world, particularly in developing continents such as Africa and Asia. Due to the high number of cases and lack of sufficient diagnostic facilities and experienced medical personnel, there is a need for advanced diagnostic procedures to complement existing methods. For this reason, this study proposes the use of machine-learning models to detect the malaria parasite in blood-smear images. Six different features—VGG16, VGG19, ResNet50, ResNet101, DenseNet121, and DenseNet201 models—were extracted. Then Decision Tree, Support Vector Machine, Naïve Bayes, and K-Nearest Neighbour classifiers were trained using these six features. Extensive performance analysis is presented in terms of precision, recall, f-1score, accuracy, and computational time. The results showed that automating the process can effectively detect the malaria parasite in blood samples with an accuracy of over 94% with less complexity than the previous approaches found in the literature. Full article

In this article, the parabolic dense fuzzy set is defined, and its basic arithmetic operations are studied with graphical illustration. The lock set concept is incorporated in a parabolic dense fuzzy set. Then, it is applied to the problems of fishery culture via the modeling of an economic order quantity model. Here, the fingerlings are fed to reach the ideal size to fulfill the customer’s demand. The growth rate of the fingerlings is assumed as a linear function. After the sales of all fish, the pond is cleaned properly for a new cycle. Here, the model is solved in a crisp sense first. Then, we fuzzify the model considering the demand rate as a parabolic dense lock fuzzy number and obtain the result in a fuzzy environment. The main aim of our study was to find the quantity of the ordering items such that the total inventory cost gets a minimum value. Lastly, sensitivity analysis and graphical illustrations were added for better justification of our model. Full article

A decision support system (DSS) was developed to help reduce food waste at traditional food retailers while selling fresh horticultural products, but also to promote food safety and quality. This computational tool includes two major functions: (1) the prediction of the remaining shelf life of fresh horticultural product, namely lettuce, onion, carrot, and cabbage based on its microbial growth status, governed by extrinsic and intrinsic parameters (temperature, water activity and pH, respectively). The remaining shelf life of the studied horticultural products is determined by using the online predictive food microbiology tool— the Combined Database for Predictive Microbiology (Combase). The time to reach the infectious doses of bacteria considered in the study for each of the four horticultural products are predicted; (2) the calculation of the dynamic price of the produce that should be set each day, depending on the predicted end of the marketing period to increase the demand and potential for sale to the final consumer. The proposed dynamic pricing model assumes a linear relation with the remaining shelf life of the analyzed vegetable to set the selling price. The shelf life determined by the DSS for optimal storage conditions is, in general, conservative, ensuring food safety. The automatic dynamic pricing gives new opportunities to small retailers to manage their business, fostering profit and simultaneously contributing to reduce food waste. Thus, this decision support system can contribute to the sustainable value of reducing food waste by providing information to small grocers and retailers on the safety of their perishable status depending on storage conditions and allowing them to suggest a fair price depending on that quality. Full article

The outbreak of COVID-19 is a public health emergency that caused disastrous results in many countries. The global aim is to stop transmission and prevent the spread of the disease. To achieve it, every country needs to scale up emergency response mechanisms, educate and actively communicate with the public, intensify infected case finding, contact tracing, monitoring, quarantine of contacts, and isolation of cases. Responding to an emergency requires efficient collaboration and a multi-skilled approach (medical, information, statistical, political, social, and other expertise), which makes it hard to define one interface for all. As actors from different perspectives and domain backgrounds need to address diverse functions, the possibility to exchange available information quickly would be desirable. In Bosnia and Herzegovina, a joint state-level public health institution has not been established, but is covered by entity competencies. In this sense, a geoportal has been developed as an epidemiological location-intelligence system (ELIS) that supports the exchange of such information between the entities and the cantons. For its development, open source software components in the cloud were used as a working platform with all the necessary functionalities. The geoportal provides an entry point for access to geospatial, epidemiological, environmental and statistical data used for analysis, geocoding of confirmed COVID-19 cases, identification of disease dynamics, identification of vulnerable groups, mapping of health capacities, and general modeling of infection spread with application support for communication and collaboration between all institutions and the public. The paper describes the challenges and ways to overcome them in the development and use of ELIS. Full article

The proliferation of electric vehicle (EV) technology is an important step towards a more sustainable future. In the current work, two-layer feed-forward artificial neural-network-based machine learning is applied to design soft sensors to estimate the state of charge (SOC), state of energy (SOE), and power loss (PL) of a formula student electric vehicle (FSEV) battery-pack system. The proposed soft sensors were designed to predict the SOC, SOE, and PL of the EV battery pack on the basis of the input current profile. The input current profile was derived on the basis of the designed vehicle parameters, and formula Bharat track features and guidelines. All developed soft sensors were tested for mean squared error (MSE) and R-squared metrics of the dataset partitions; equations relating the derived and predicted outputs; error histograms of the training, validation, and testing datasets; training state indicators such as gradient, mu, and validation fails; validation performance over successive epochs; and predicted versus derived plots over one lap time. Moreover, the prediction accuracy of the proposed soft sensors was compared against linear or nonlinear regression models and parametric structure models used for system identification such as autoregressive with exogenous variables (ARX), autoregressive moving average with exogenous variables (ARMAX), output error (OE) and Box Jenkins (BJ). The testing dataset accuracy of the proposed FSEV SOC, SOE, PL soft sensors was 99.96%, 99.96%, and 99.99%, respectively. The proposed soft sensors attained higher prediction accuracy than that of the modelling structures mentioned above. FSEV results also indicated that the SOC and SOE dropped from 97% to 93.5% and 93.8%, respectively, during the running time of 118 s (one lap time). Thus, two-layer feed-forward neural-network-based soft sensors can be applied for the effective monitoring and prediction of SOC, SOE, and PL during the operation of EVs. Full article

The COVID-19 outbreak resulted in preventative measures and restrictions for Bangladesh during the summer of 2020—these unstable and stressful times led to multiple social problems (e.g., domestic violence and divorce). Globally, researchers, policymakers, governments, and civil societies have been concerned about the increase in domestic violence against women and children during the ongoing COVID-19 pandemic. In Bangladesh, domestic violence against women and children has increased during the COVID-19 pandemic. In this article, we investigated family violence among 511 families during the COVID-19 outbreak. Participants were given questionnaires to answer, for a period of over ten days; we predicted family violence using a machine learning-based model. To predict domestic violence from our data set, we applied random forest, logistic regression, and Naive Bayes machine learning algorithms to our model. We employed an oversampling strategy named the Synthetic Minority Oversampling Technique (SMOTE) and the chi-squared statistical test to, respectively, solve the imbalance problem and discover the feature importance of our data set. The performances of the machine learning algorithms were evaluated based on accuracy, precision, recall, and F-score criteria. Finally, the receiver operating characteristic (ROC) and confusion matrices were developed and analyzed for three algorithms. On average, our model, with the random forest, logistic regression, and Naive Bayes algorithms, predicted family violence with 77%, 69%, and 62% accuracy for our data set. The findings of this study indicate that domestic violence has increased and is highly related to two features: family income level during the COVID-19 pandemic and education level of the family members. Full article

Assuring security and privacy of data is a key challenge for organizations when developing WBAN applications. The reasons for this challenge include (i) developers have limited knowledge of market-specific regulatory requirements and security standards, and (ii) there are a vast number of security controls with insufficient implementation detail. To address these challenges, we have developed a WBAN data security and privacy risk management framework. The goal of this paper is trifold. First, we present the methodology used to develop the framework. The framework was developed by considering recommendations from legislation and standards. Second, we present the findings from an initial validation of the framework’s usability and effectiveness of the security and privacy controls. Finally, we present an updated version of the framework and explain how it addresses the aforementioned challenges. Full article

Wind energy is considered one of the most important sources of renewable energy in the world, because it contributes to reducing the negative effects on the environment. The most important types of wind turbines are horizontal and vertical axis wind turbines. This work presents the full details of design for vertical axis wind turbine (VAWT) and how to find the optimal values of necessary factors. Additionally, the results shed light on the efficiency and performance of the VAWT under different working conditions. It was taken into consideration the variety of surrounding environmental conditions (such as density and viscosity of fluid, number of elements of the blade, etc.) to simulate the working of vertical wind turbines under different working conditions. Furthermore, the effect of the design factors was investigated such as the number and size of the blades on the behavior and performance of VAWT. It was assumed that the vertical wind blade works in different sites of Iraq. QBlade software (Version 8) was used to achieve the calculations and optimization processes to obtain the optimal design of vertical axis wind turbines that is suitable for the promising sites. The results proved that accurate results can be obtained by using QBlade software. Full article

The article considers the tasks of intellectual support for decision support in relation to a complex technological object. The relevance is determined by a high level of responsibility, together with a variety of possible situations at a complex technological facility. The authors consider case-based reasoning (CBR) as a method for decision support. For a complex technological object, the problem defined is the uniqueness of the situations, which is determined by a variety of elements and the possible environmental influence. This problem complicates the implementation of CBR, especially the stages of comparing situations and a further selection of the most similar situation from the database. As a solution to this problem, the authors consider the use of neural networks. The work examines two neural network architectures. The first part of the research presents a neural network model that builds upon the multilayer perceptron. The second part considers the “Comparator-Adder” architecture. Experiments have shown that the proposed neural network architecture “Comparator-Adder” showed higher accuracy than the multilayer perceptron for the considered tasks of comparing situations. The results have a high level of generalization and can be used for decision support in various subject areas and systems where complex technological objects arise. Full article

5G technology is spreading extremely quickly. Many services, including Voice Over Internet Protocol (VoIP), have utilized the features of 5G technology to improve their performance. VoIP service is gradually ruling the telecommunication sector due to its various advantages (e.g., free calls). However, VoIP service wastes a substantial share of the VoIP 5G network’s bandwidth due to its lengthy packet header. For instance, the share of the packet header from bandwidth and channel time reaches 85.7% of VoIP 5G networks when using the IPv6 protocol. VoIP designers are exerting considerable efforts to solve this issue. This paper contributes to these efforts by designing a new technique named Zeroize (zero sizes). The core of the Zeroize technique is based on utilizing the unnecessary fields of the IPv6 protocol header to keep the packet payload (voice data), thereby reducing or “zeroizing” the payload of the VoIP packet. The Zeroize technique substantially reduces the expanded bandwidth of VoIP 5G networks, which is reflected in the wasted channel time. The results show that the Zeroize technique reduces the wasted bandwidth by 20% with the G.723.1 codec. Therefore, this technique successfully reduces the bandwidth and channel time of VoIP 5G networks when using the IPv6 protocol. Full article