Special Issue on Innovation in Computing, Engineering Science & Technology 2021

Tourism is relatively high profit, low-cost and high work opportunity industry. Medical tourism is special kind of business type in tourism industry. The characteristic of medical tourism is high threshold and high profit for hospital and government. But, fewer literatures research about competitiveness of medical tourism industry until now. Especially, medical tourism industry faces big challenge when COVID-19 occurs which stop patient to go abroad for treating in the world. This research arranges relative literatures and invite some expert’s opinion to design criteria for evaluating competitiveness of medical tourism industry in each area. After that, linguistic VIKOR and Entropy will be integrated to analyze and evaluate performance of medical tourism industry among China, Taiwan, Japan and South Korea. Finally, some reasonable suggestion will be provided for Taiwan government and hospital.

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Enrolment figures have been expanding in South African institutions of higher-learning, however, the expansion has not been accompanied by a proportional increase in the percent- age of enrolled learners completing their degrees. In a recent undergraduate-cohort-studies report, the DHET highlight the low percentage of students completing their degrees in the allotted time, having remained between 25.7% and 32.2% for the academic years 2000 to 2017, that is, every year since 2000, more than 67% of the learners enrolled did not complete their degrees in minimum time. In this paper, we set up two prediction tasks aimed at the early-identification of learners that may need academic assistance in order to complete their studies in the allocated time. In the first task we employed six classification models to deduce a learner’s end-of-year outcome from the first year of registration until qualifying in a three-year degree. The classification task was a success, with Random Forests attaining top predictive accuracy at 95.45% classifying the “final outcome” variable. In the second task we attempt to predict the time it is most likely to take a student to complete their degree based on enrolment observations. We complete this task by employing six classifiers again to deduce the distribution over four risk profiles set up to represent the length of time taken to graduate. This phase of the study provided three main contributions to the current body of work: (1) an interactive program that can calculate the posterior probability over a student’s risk profile, (2) a comparison of the classifiers accuracy in deducing a learner’s risk profile, and (3) a ranking of the employed features according to their contribution in correctly classifying the risk profile variable. Random Forests attained the top accuracy in this phase of experiments as well, with an accuracy of 83%.

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E-learning, among the most prominent modes of learning, offers learners the opportunity to attend online courses. To improve the quality of online learning, social learning through social networks promotes interaction and collaboration among learners. As part of the learning process management in these environments, the implementation of recommendation systems facilitates the provision of content adapted to the needs and requirements of learners and generates recommendations likely to arouse their interest. Many researchers have been involved in several recommendation techniques such as the development of Machine Learning algorithms and the incorporation of social interactions between learners. However, the behavior within a learning environment can diverge from one learner to another. This must therefore be taken into consideration when generating recommendations, i.e., it is initially important to form groups of homogeneous learners prior to proposing recommendations. In this respect, the recommendations generated will be more appropriate to the learners’ profiles and level of interaction. On this basis, we raise an important issue which is the importance of grouping learners into homogeneous groups in a recommendation system. In the recommendation system we advocate, we group learners based on the degree of interaction within the learning environment before generating the recommendation list based on a hybrid approach for each cluster. The overall system is, therefore, based on the identification of communities based on the k-means algorithm and the generation of recommendations list for each community separately. Finally, we compare the results of the system integrating the classification of learners as a preliminary step to the system excluding the k-means algorithm. The results reveal that the integration of the clustering algorithm leads to improvements in terms of performance and accuracy.

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Disruptive innovation has created fast changes in the business environment and competition among companies, especially on information technology companies. Knowledge creation and entrepreneurial orientation are two variables that can improve firm performance. There is still limited study on how knowledge creation and entrepreneurial orientation both affects firm performance. This study aims to discuss how to effectively apply knowledge creation and entrepreneurial orientation to develop firm performance. A questionnaire has been conducted to 55 medium-large IT companies in Jakarta, Indonesia, and analyzed using structural equation modeling (SEM). The result showed that knowledge creation did not directly affect firm performance but indirectly affected entrepreneurial orientation. Knowledge creation also had a positive and significant effect on entrepreneurial orientation, and so does entrepreneurial orientation towards firm performance. Therefore, IT companies should consider both variables to improve their performance. Future studies may consider using qualitative or mixed-method approaches, conducting research for small IT companies and in other countries.

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Fiber-Wireless (FiWi) network is an integration of fiber optic and wireless connections in the same network. It is one of the best solutions to overcome rapid increment of Internet users and bandwidth-hungry services. To facilitate fundamental knowledge and further understanding on FiWi for students and researchers at the university level, this article proposes the development of a fast integration and scalable FiWi router testbed using Raspberry Pis as the embedded system-based hardware for lab-scale experiments. The performance of the router testbed in terms of end-to-end delay and throughput for upstream and downstream are evaluated. The delay values comply with IEEE 802.15.4 routing scheme. The performance of the router testbed is compared with the industrial grade off-the-shelf router in terms of throughput for each network. A testbed stress test is conducted by sending two data traffics simultaneously, and the performance test is repeated for Wireless-Fiber-Wireless and Fiber-Wireless-Fiber network architecture. The results show the proposed router testbed is scalable, flexible, and capable of fast integration.

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Fiber-Wireless (FiWi) network is an integration of fiber optic and wireless connections in the same network. It is one of the best solutions to overcome rapid increment of Internet users and bandwidth-hungry services. To facilitate fundamental knowledge and further understanding on FiWi for students and researchers at the university level, this article proposes the development of a fast integration and scalable FiWi router testbed using Raspberry Pis as the embedded system-based hardware for lab-scale experiments. The performance of the router testbed in terms of end-to-end delay and throughput for upstream and downstream are evaluated. The delay values comply with IEEE 802.15.4 routing scheme. The performance of the router testbed is compared with the industrial grade off-the-shelf router in terms of throughput for each network. A testbed stress test is conducted by sending two data traffics simultaneously, and the performance test is repeated for Wireless-Fiber-Wireless and Fiber-Wireless-Fiber network architecture. The results show the proposed router testbed is scalable, flexible, and capable of fast integration.

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

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

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

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

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

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

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

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

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Cloud is an Internet-based computing technology in which on-demand shared resources such as software, platforms, repositories, and information are delivered to customers. In the emerging era of computing cloud environment provide the use of resources with the concept of virtualization. Workflow of the tasks has vital role for the improvement of computing performance which leads to improved quality of service. As per the demand of user’s number of tasks are scheduled in such a way so that better performance is computed using partial deadline of the workflow. In this paper we have introduced with the workflow concepts, further we aim to diminish makespan for the proposed workflow scheduling algorithm. Here makespan refers to overall time duration taken for the sequence of tasks, by the resources so as to complete the execution of each and every task.

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This paper is an extended version of the work presented at a conference held in Kyiv, Ukraine, in October 2020, which reported the result of the numeric simulation on the artificial antigravity. This paper further describes the derivation of the idea of the artificial antigravity, and adds the simulation of angular momentum that is needed to describe the antigravity. Also, because the angular momentum is the perpendicular movement to a three-dimensional curved surface in a four-dimensional space-time, this paper challenges the limit of applying the curvature tensor in quantum mechanics; while, current quantum mechanics has been established on the flat surface. The artificial rotation of a hypothetical object is simulated, in which the gravity is so strong that the time-space can be distorted. The spherical polar coordinate system is selected to describe the curvature of the space, and the curvature tensor is formulated. Then the tensor is multiplied by the Euler’s rotation matrix to make the inner product for the gravitational energy and the outer cross-product for the angular momentum of the rotation. To simulate the distorted time-space, two cases are selected: the linear distortion and the non-linear distortion upon the distance from the center of the strong gravity; also, the speed of the rotation is set in two options: the slower and the faster. Then the equation of motion is set by the curvature tensor to calculate the coefficient of the gravitational energy on the surface of the sphere in the spherical polar coordinates, and to calculate the coefficient of the angular momentum in the perpendicular direction to the sphere. The result shows that the antigravity can be produced by rotating the object, and the angular momentum can show the opposite directions by the selection of the rotation speed.

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Encouraged by the huge publicly available genomic databases, research in the field of steganography was recently extended to utilize DNA sequence data to conceal secret information. As an extension of the work presented earlier by the author, this paper proposes an approach for a secure data communication channel between two parties. At one side of the communication, the sender starts the hiding process by encrypting the secret message using a bio-inspired 8×8 play-fair ciphering algorithm. Next, the secret sequence is randomly spliced and merged into the cover sequence replacing its non-coding regions. Using the secret key shared in advance, the receiver, on the other side of communication, can extract and concatenate the segments of the encrypted message and reveals the original message after deciphering. The method was proven to be robust to brute-force attacks while providing a hiding capacity up to two bit-per-nucleotide. A comparison with some existing techniques showed that the proposed method outperforms most of them not only in terms of the hiding capacity but also for the feature of blind extraction.

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The article further extends the researched issue of the unmanned aircraft use in the pre-flight and post-flight visual check of aircraft. Procedures of pre-flight inspection are fulfilled by the aircraft maintenance certified staff or the crew member before flight. The process is similar for all categories of aircraft, but its implementation differs for individual specific types of aircraft. Therefore, the article will deal only with small training aircraft, which will be used to verify the use of UAV (Unmanned Aerial Vehicle) in normal operation. It identifies and defines the problem of using multiple UAV in swarms and their usage in standard activities in aircraft operation. The outcome should be a reduction of the number of possible failures cause by the human factor with impact on the safety in operations. Proportionately important fact is the desirable minimization in the time necessary to carry out a pre-flight inspection process, which will improve the final indicator of the efficiency in aeroplane operations.

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Data mined from social media can be used in a variety of methods. The goal of this paper is to explore some of the various methods of mining data from social media and the different areas of its applications. From the analysis of other studies, it was clear that methods such as text analysis, classification, clustering, mapping, testing/validity methods, regression, and research methods were the overarching themes of the previously done research. Pros, cons, and possible extensions were examined for the current research evaluated in the social media data mining area. At the conclusion of this survey, our research team found that text analysis, sentiment analysis, and support vector machine classifiers were among the most common themes of the research methods in this field. In most cases, multiple methods were attempted for each topic to be able to cross compare results.

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The paper presents the technique for the user-friendly numerical simulation of coupled oscillators described by the Kuramoto model. Oscillators couplings are defined as arbitrary 2?-periodic functions given by the Fourier series. Matlab procedure was developed to generate netlist for the equivalent electrical circuit diagram of the Kuramoto model. The input data of the procedure include the natural frequencies of oscillators and the amplitudes of the couplings harmonics. Kirchhoff equations of the equivalent circuit coincide with the equations of the Kuramoto model. The generated netlists provide obtaining the simulation results using standard circuit simulator. These results numerically coincide with the transients computed using the original Kuramoto model. The presented examples confirm the convenience and effectiveness of the proposed approach.

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Microcontrollers have revolutionized the field of Engineering Education. Their popularity and cost-effectiveness have opened a large door of activity for innovative projects at both the undergraduate and graduate levels. The purpose of this article is to review this activity in terms of where two of these microcontrollers have been used in Engineering Education so as to recommend further possible applications. Focus is limited to papers presented at three IEEE-based conferences over the past 10 years that mentioned the Arduino or Raspberry Pi. Documentary analysis is thus used where the abstracts of the conference papers were reviewed. Results indicate that EDUCON dominated the field of microcontroller education from 2013 to 2016, while the last three years have seen more papers dedicated to this topic being presented at the FIE series of conferences. A total of nine papers relating to microcontroller education has also been presented at TALE between 2012 and 2019. The main application of these microcontrollers has been in the field of Robotics, with general electronics and design-based learning following suit. At least 11 conference papers focusing on the use of these microcontrollers at school level were found. Overall, the Arduino outranks the Raspberry Pi by almost 4:1, with the most cited papers relating to Robotics education, to helping students at home to complete science and technology experiments and to programming. Further applications can extend to energy monitoring and academic development workshops.

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With the advent of internet technology and social media, patterns of social communication in daily lives have changed whereby people use different social networking platforms. Microblog is a new platform for sharing opinions by means of emblematic expressions, which has become a resource for research on emotion analysis. Recognition of emotion from microblogs (REM) is an emerging research area in machine learning as the graphical emotional icons, known as emoticons, are becoming widespread with texts in microblogs. Studies hitherto have ignored emoticons for REM, which led to the current study where emoticons are translated into relevant emotional words and a REM method is proposed preserving the semantic relationship between texts and emoticons. The recognition is implemented using a Long-Short-Term Memory (LSTM) for the classification of emotions. The proposed REM method is verified on Twitter data and the recognition performances are compared with existing methods. The higher recognition accuracy unveils the potential of the emoticon-based REM for Microblogs applications.

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FPGAs constitute a flexible and increasingly popular controlling solution for robotic applications. Their core advantages regarding high computational performance and software-like flexibility make them suitable controller platforms for robots. These robotic applications include localization / navigation, image processing, industrial or even more complex procedures such as operating on medical or human assistant tasks. This paper provides an overview of the publications regarding different robotic FPGA application fields as well as the most commonly-used robot types used for those applications for the 10-year period of 2010-2019. A short description of each paper reviewed is also included, providing a total view of FPGA technology trends in robotic applications over the last decade.

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The purpose of this study is to evaluate the factors affecting the decisions of factors affecting the decision of individual customers to choose a savings bank at commercial banks in Da Nang city. . Through the use of appropriate research methods, the authors have found that there are 5 factors with 5 groups of factors that greatly affect the decision of individual customers to choose a savings bank. Service quality, safety, stakeholder influence, financial benefit, convenience. Through this result, it will help opinions, orientations and solutions to improve individual customers’ decisions on choosing a savings bank at joint stock commercial banks in Da Nang city.

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Genome-wide association studies, or GWAS, have reported associations between SNPs and specific diseases/traits. GWAS results contain variants located in different genomic regions, including variants in the 3’UTR. MicroRNAs, or miRNAs, are small noncoding RNAs that bind to the 3’UTRs of genes to regulate gene expression. However, variant(s) that are located in the 3’UTR could impact miRNA binding, thus affecting expression of its targeted gene(s). To specifically elucidate miRNA targeting pairs and binding site variants associated with a specific trait, well-designed downstream analysis along with careful experimental design are necessary. Currently, there is no available state-of-the-art methodology for identifying miRNA targeting pairs and associated variants that could contribute to phenotypes using GWAS. Moreover, it is unrealistic to conduct experiments for elucidating all possible miRNA targeting pairs and binding site variants across the entire genome. In this project, we developed a bioinformatic pipeline to computationally identify genes and their targeting miRNA pairs that are enriched over the miRNA-gene tissue expression network for the studied genetic traits and examined the binding site variants’ impact on Body Mass Index (BMI).

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We propose a novel framework exploiting domain-aware aspect similarity for solving the multi- source cross-domain sentiment classification problem under the constraint of little labeled data. Existing works mainly focus on identifying the common sentiment features from all domains with weighting based on the coarse-grained domain similarity. We argue that it might not provide an accurate similarity measure due to the negative effect of domain-specific aspects. In addition, existing models usually involve training sub-models using a small portion of the labeled data which might not be appropriate under the constraint of little labeled data. To tickle the above limitations, we propose a domain-aware topic model to exploit the fine-grained domain-aware aspect similarity. We utilize the novel domain-aware linear layer to control the exposure of various domains to latent aspect topics. The model discovers latent aspect topics and also captures the proportion of latent aspect topics of the input. Next, we utilize the proposed topic- attention network for training aspect models capturing the transferable sentiment knowledge regarding particular aspect topics. The framework finally makes predictions according to the aspect proportion of the testing data for adjusting the contribution of various aspect models. Experimental results show that our proposed framework achieves the state-of-the-art performance under the constraint of little labeled data. The framework has 71% classification accuracy when there are only 40 labeled data. The performance increases to around 82% with 200 labeled data. This proves the effectiveness of the fine-grained domain-aware aspect similarity measure.

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A reconfigurable Stepped Frequency Continuous Wave (SFCW) radar prototype for supporting the italian financial police to contrast smuggling, is proposed in this work. In particular, the proposed radar can provide information related to the container contents and the presence of false bottoms speeding up the control operations at borders and ports. Moreover, it is able to reveal the presence of people hidden behind reinforced concrete hiding places. Radar resolution is improved by using suitable post-processing method such as the MUltiple Signal Classification (MUSIC) algorithm. Numerical as well as experimental results obtained considering realistic operative scenarios demonstrated the potentialities and capabilities of this system as an effective tool for smuggling contrast. The preliminary experimental results have been obtained using a compact radar prototype equipped with high gain and directivity antennas to cover all the di bands.

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Classical clustering algorithms often require an a-priori number of expected clusters and the presence of all documents beforehand. From practical point of view, the use of these algorithms especially in more dynamic environments dealing with growing or shrinking corpora therefore is not applicable. Within the last years, graph-based representations of knowledge such as co-occurrence graphs of document corpora have gained attention from the scientific community. Accordingly, novel unsupervised and graph-based algorithms have been recently developed in order to group similar topics, represented by documents or terms, in clusters. The conducted work compares classical and novel graph-based algorithms, showing that classical clustering algorithms in general perform faster than graph-based clustering algorithms. Thus, the authors’ focus is to show that the graph-based algorithms provide similar clustering results without requiring an hyperparamter k to be determined a-priori. It can be observed that the identified clusters exhibit an associative relationship reflecting the topical and sub-topical orientation. In addition, it is shown in a more in-depth investigation that the Seqclu (sequential clustering algorithm) can be optimized performance-wise without loss of clustering quality.

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The new generation of mobile communications and the recent advances in data management are going to enable a fast transformation in the health sector of many countries. 5G networks, with superior technical characteristics, would allow the development of a new set of application and services gathered under the concept of eHealth. In this article we propose a remote monitoring system based on 5G networks that would allow to provide a varied set of medical services from long distance. However, for achieving an optimal performance, the network must guarantee high bandwidths and low latencies, at the time that a massive number of devices and its corresponding generated data are handle efficiently. Consequently, an appropriated system architecture and data model structure are proposed, taking into consideration the high security requirements that any health-related application or service inherently implies.

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This paper is an extended version of work published in IPFA 2020. In the previous paper, advanced physical failure analysis (PFA) techniques for rescuing damaged samples with cracks, scratches, or unevenness in delayering are introduced. In the present work, the techniques will be further exploited and summarized for the potential applications in general devices. The three typical rescue cases will be fully discussed through comprehensive analysis on the failure mechanism and the rescuing process. Compared to the conventional PFA techniques that normally require back-up samples, the novel rescue techniques offer more alternative solutions for coping with sample damage problems in delayering without starting over with a new sample that would waste machine time and human resources. These new PFA techniques involve only basic failure analysis (FA) skills that could be easily manipulated and FA equipment that is commonly available in FA labs, and would extend the scope and capability of the tradition PFA to help the FA engineers deliver FA results with high quality and high success rate in the daily work, especially for handling “one of a kind” devices.

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This study aims to design a gamification affordances pedagogy. Affordances are the ways in which we perceive environments to support the needs of learners in the educational system. The main questions are how gamification elements can influence student engagement to improve their affordances. Affordance behavior is a human behavior that refers to a mindset; an attitude or opinion, especially a habitual one. Motivational activities can change a learner’s behavior. A skill-based mindset can be created through the use of affordance motivation. Affordance refers to the points, badges, and leaderboards in gamification elements. This research aims to improve the affordance mindset design of interactive systems with gamification. The affordance design will improve the pedagogy related to engagement. The research focuses on the mindset factors and the relationship between the factors that promote the desired learning outcomes. The findings may help in designing the gamification affordance design method for affordance pedagogy. The expected model could improve learners’ affordances and instructional activities.

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This study aimed to develop an airflow-vibrator motivated facility and assess exercise behaviors. The combination design involved computer-controlled airflow/ vibrators, a user interface program, and an adjustable structure presenting interaction options. The teacher and the participants can choose specific music with adjustable speed. The researcher did interviews during the initial test and field study. During the intervention, all participants succeeded in following the impinged flow with a positive emotional display. A wireless module and gas flow clue lifted the distance limitation of the vibration connection and enabled prompts in a larger area covered by radio waves. The flexible structure fit individuals ergonomic and the affordance consideration. After practicing, the students knew exactly how to pass and asked for the ball from the classmate. Wireless switch and signals give students more confidence in pitching — the participants successfully swap the body to follow the airflow.

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Transformers are habitually designed and manufactured for operation at a fundamental frequency of 50Hz and sinusoidal load current. Transformers are susceptible to non-linear loads. The inception of switching action characterises Non-linear loads and consequently nonsinusoidal load current which brings about higher transformer service losses, hotspot temperature rise, and degradation of cellulosic and liquid insulation, and consequently untimely failure of transformers during service. This phenomenon yield current with different components that are multiples of the fundamental frequency of the distributed photovoltaic power (DPVP) generation system. In order to obviate these challenges, the continuous power rating of the transformer, which is intended to facilitate non-linear loads must be minimised using procedure ascribed by the standards as de-rating. This work, an extension of previous work, proposes a novel procedure by means of Finite Element Method (FEM) for the de-rating of DPVP transformers serving non-linear loads during their service life. The proposed procedure considers parameters such as skin effect, proximity effect, and the magnetic flux leakage on the windings that were not included in the IEEE recommended de-rating procedure. The theoretical examination is substantiated on a 500kVA, three-phase, oil-filled transformer.

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Although the perception of Environmental Kuznets Curve (EKC) has been thoroughly investigated, but there is inconsistency in the results. The relationship between nitrous oxide (N2O) emissions, financial development, economic growth, foreign direct investment, and electric power consumption in Bahrain over the period 1980 – 2012 is examined in this paper. The autoregressive distributed lags (ARDL) technique is employed to test for the cointegration in the long run. The results reveal a reversed U-shape long run relationship between N2O emissions and economic growth for Bahrain. Moreover, electric power consumption affects N2O emissions positively in the short and long run. Whereas foreign direct investments and financial development affects the emissions of N2O negatively. Therefore, Bahrain should assist households in installing solar cells to generate clean energy and enhance its financial sector.

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In control systems, several optimization problems have been overcome using Multi-Agent Sys- tems (MAS). Interactions of agents and the complexity of the system can be understood by using MAS. As a result, functional models are generated, which are closer to reality. Nevertheless, the use of models with permanent availability of information between agents is assumed in these systems. In this sense, some strategies have been developed to deal with scenarios of information limitations. Game theory emerges as a convenient framework that employs concepts of strategy to understand interactions between agents and maximize their outcomes. This paper proposes a learning method of distributed control that uses concepts from game theory and reinforcement learning (RL) to regulate the behavior of agents in MAS. Specifically, Q-learning is used in the dynamics found to incorporate the exploration concept in the classic equation of Replicator Dynamics (RD). Afterward, through the use of the Boltzmann distribution and concepts of biological evolution from Evolutionary Game Theory (EGT), the Boltzmann-Based Distributed Replicator Dynamics are introduced as an instrument to control the behavior of agents. Numerous engineering applications can use this approach, especially those with limita- tions in communications between agents. The performance of the method developed is validated in cases of optimization problems, classic games, and with a smart grid application. Despite the information limitations in the system, results obtained evidence that tuning some parameters of the distributed method allows obtaining an analogous behavior to that of the conventional centralized schemes.

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This paper addresses experiments with Scratch-developed games in the robotics introduction course at the Federal University of Technology – Paraná. It aims at assisting learning of classical and initial robotics concepts. This proposal, similar to the classic 80s war tanks game on Atari 2600, was developed using an autonomous vehicle. In the first experiment, applied to the class 2019/2, the students (players) had to battle against another autonomous tank developed (in two different ways, in Person and Distance Learning), using keyboard inputs to control their tank. In this game, the students were asked to create states machine models while were being introduced to fundamental concepts such as pose, other basic notions concerning controlled and autonomous robots, and the hierarchy of actions. At the end of the games, a questionnaire answered by the students extracted valuable findings of the examined concepts. In 2021/1 class, the second and third experiments were applied. The former was an extension of the first experiment, using autonomous parking cars. The latter was inspired by the classic Pong game, with the addition of more degrees of freedom (DOF). In this case, the player attempts to reach and catch a ball through the operation of a robotic arm with two rotating joints, using keyboard inputs. Each block or scenario will become more complex, and the student has time to perform a task. In the case of the third experiment, the concepts including 2-D workspace, multiple solutions, inverse, and direct kinematics were explored. Delivery rates for the first and second experiments were 90% and 80%, respectively. Even though three individual experiments were investigated, the single objective was achieved: the implementation of modern didactic tools to deliver critical pedagogical concepts to students in the robotics class.

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Survivability of systems is a very important system property and consists major concern for organizations and companies. Survivable systems should maintain their critical services functional in a timely manner. There are several approaches, proposed in the literature, on how to develop survivable telecommunication systems, but the majority is based on node outages or path failures, missing the main scope of survivability which is service failure. The contribution of this paper is that it presents a SDLC (Software Development Life Cycle) for developing survivable mobile telecommunication systems. Additionally, the main characteristic of a mobile telecommunication system is that it consists of different types of nodes (ex. MME, SGSN, etc.) that are connected to systems (ex. 5G, 4G, 3G, 2G etc.) and thus form an intersystem that provides services to end users. This interconnection and interoperability of network nodes is of high complexity constituting a threat to system survivability. Thus, another contribution of the current research work is that it provides a systematic approach for handling this complexity.

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An automatic speech recognition (ASR) is a key technology for preventing an ongoing global coronavirus epidemic. Due to the limited corpus database and the morphological diversity of the Thai language, Thai speech recognition is still difficult. In this research, the automatic speech recognition model was built differently from the traditional Thai NLP systems by using an alternative approach based on the keyword spotting (KWS) method using the Mel-frequency cepstral coefficient (MFCC) and convolutional neural network (CNN). MFCC was used in the speech feature extraction process which could convert the voice input signals into the voice feature images. Keywords on these images could then be treated as ordinary objects in the object detection domain. The YOLOv3, which is the popular CNN object detector, was proposed to localize and classify Thai keywords. The keyword spotting method was applied to categorize the Thai spontaneous spoken sentence based on the detected keywords. In order to find out the proposed technique’s performance, real-world tests were carried out with three connected airport tasks. The Tiny-YOLOv3 showed the comparative results with the standard YOLOv3, thus our method could be implemented on the low-resource platform with low latency and a small memory footprint.

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There is no doubt that energy is one of the most important requirements of life, and its importance increases with the passage of time, and this is what make countries to harness the capabilities and scientists in developing energy systems of all kinds, one of the most important energy systems these days is what is known as vertical axis wind turbines. If we compare this type of system with horizontal axis wind turbines, it is characterized by a relatively lower manufacturing cost. But on the other hand, it suffers from less efficiency in addition to the problem of starting the self-movement. The idea of this research revolves around the use of an engineering design for the vertical axis wind rotor that is very rarely used in the field of wind energy. This design takes the geometric shape of two inverted trapezoids. Within the framework of this study, the term “slant straight-blade vertical axis wind turbine” (SS-VAWT) was assigned to the wind rotor. Amendments have been made to the mathematical model of Multi stream tube to make it suitable for application and work on (SS-VAWT), where, it is known that the multi-stream tube model uses primarily and only for the original Darrieus and the H-Darrieus rotors. In order to prove the efficacy of the software used, the results obtained from it were compared with the practical results of previous studies, as it proved its effectiveness in obtaining the satisfactory results that were intended for this analysis. The analyzes and investigations that were conducted on the improved SS design included changing the geometry by changing some of its dimensional parameters represented in rotor height, rotor diameter, number of rotor blades, rotor blade section length, rotor blade section type and rotor blades inclination angle on the horizontal plane. Within the scope of the case studies that were worked on in this research, the results showed that the best efficiency of the SS rotor was achieved in the range of height to radius ratio (0.66 to 1), cord line length to radius ratio about 0.12 The angle of inclination of the blades is between 45- and 65-degrees Degree. In these ranges, the value of Max power factors has reached its turn, and the energetic range of the rotor has increased as a function of the peripheral relative velocity, in addition to a relatively large solution to the problem of starting self-movement, which appears through the highest-power factor values to move away from the limits of negative values in the range Terminal forgetfulness from 1 to 3. In addition, the effect of changing Raynaud’s number on the turbine aerodynamic performance has been investigated. The results showed that the higher the Reynolds value, the higher the power factor value, the higher the energy range and the lessening the problem of starting the self-movement.

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This study evaluated the competencies related to digital information use through technological tools aiming to acquire applicable knowledge by searching and retrieving information. Methodologically, a quasi-experimental design without a control group was applied to a sample of primary education students from Chile (n=266). First, a diagnosis of the digital-informational skills is performed, and, later, the results of a course in a blended learning context -b-learning- (treatment) are shown. The results show significant differences between the participant groups, confirming the learning in information competencies and distinguishing an initial level from a posterior intermediate level.

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This paper addresses the stock management aspect. Through this work, we provide a dynamic model of dimensioning and allocation of stocks to storage location for the automotive industry field. This model takes into consideration all constraints of the supply chain (24 constraints) from the suppliers passing by production, storage up to customer delivery and transport. At the end of this paper we will be able to specify the stock replenishment policy, particularly the definition of stock alerts (minimum, nominal, maximum) in quantity and days of stock, in space occupied, and in financial value. These stock thresholds will be integrated in material resource planning, storage allocation procedure and financial budget follow-up. The tool developed is decision-making support for logisticians. The algorithm proposed has solved a real instance and ensure a balanced stock fill rate (99%) in 1200 seconds.

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This paper presents the designs and the simulations of proposed structures of electronically frequency reconfigurable planar bow-tie antenna. In the first part, a modified wide band self-complementary bow-tie antenna is designed and implemented. In the second part, varactor and PIN diodes are integrated in top side to adjust electronically the modified structure of bow-tie antenna over multi-band frequency. By adjusting PIN diode between the two states and by tuning the varactor diode inside these two states; the proposed antenna demonstrates two different operational frequencies. In ON state, the antenna covers a narrow frequency bands and in OFF state the antenna demonstrates a wide-band operational frequency.
Furthermore, a new structure of reconfigurable antenna implemented with PIN diode and two hexagonal parasitic elements is developed to realize a multi-band operational frequency band and to cover GPS and GMS bands. Simulated results show a return loss less than -10dB with a gain varied between 0.5 and 3.5dB.

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To effectively implement the social distancing or digital contact tracing in epidemic using an RSSI-based localization approach through Bluetooth beacon is one of the most widely used technologies, but simply using RSSI measurement is not more relevant because the RF signal is affected by several factors and the environment of usage. Traditional distance or positioning algorithms have large-ranging errors when applied for moving objects because they do not account for the device orientation and use fixed path loss models. Hence, the distance between the nodes cannot be obtained accurately by RSSI measurement in a dynamic environment. In this paper, we propose a solution to compensate for the RSSI loss in real-time by filtering out the noise and then accounting for the antenna orientation using a Beacon Packet. Antenna Orientation is determined using 9DoF (9 Degrees of freedom ) IMU (Inertial Measurement Unit). The nodes simultaneously advertise their presence and scan for the presence of other similar beacons in their range. These nodes also deploy Low Power techniques during periods of inactivity to conserve battery power. Advertising is performed on three Bluetooth channels and no connection or response packet is required between the devices during advertising and scanning activities (ADV_NONCONN_IND). The addition of the Motion Sensor could also be used to optimize the battery life of the device.

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Serious games (SGs) are interactive and entertaining digital games with a special educational purpose. Studies have shown that SGs are effective in enhancing educational skills. Cognitive skills training through serious games have been used in improving students learning outcomes. In this article, we introduce the ‘plants kingdom’ serious game for improving adolescents’ cognitive skills, mainly attention (Focus, selection, and sustained attention) and understanding skills. The game used the grade 8 Science book in designing the game content. The plant kingdom lesson was used for developing the game story and objects, its methods and tools were designed for the purpose of attention and understanding skills improvement. The game was evaluated on 43 students from public schools between the ages of 13-15 years, the study selected data from the students who had completed 5 playing sessions. The attention and understanding skills were assessed using the automatic recording and analysis of the game player’s data. The variables utilized from the players’ data included player ID, session number, gender, number of trials, level, drag and drop time, distance, reason for failure, position, speed, status, time, and playing tool. Results showed that the game improved the attention and understanding skills of students by 27% and 25 % respectively. The study showed the significant effect of serious games in enhancing students’ cognition; thus, integrating serious games into the education system can potentially improve learning objectives and outcomes.

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The intention of this research is to study and design an automated agriculture commodity price prediction system with novel machine learning techniques. Due to the increasing large amounts historical data of agricultural commodity prices and the need of performing accurate prediction of price fluctuations, the solution has largely shifted from statistical methods to machine learning area. However, the selection of proper machine learning techniques for automated agriculture commodity price prediction still has limited consideration. On the other hand, when implementing machine learning techniques, finding a suitable model with optimal parameters for global solution, nonlinearity and avoiding curse of dimensionality are still biggest challenges. In this research, we address these problems by conducting a machine learning strategy study and propose a web-based automated system to predict agriculture commodity price. In the two series experiments, five popular machine learning algorithms, ARIMA, SVR, Prophet, XGBoost and LSTM have been compared with large historical datasets in Malaysia. The results validate the efficiency of the proposed Long Short-Term Memory Model (LSTM) to serve as the prediction engine for the proposed system. Particularly in the long-term experiment testing, the average performance of LSTM with MSE has improved 45.5% while ARIMA has dropped 74.1% and the average MSE of LSTM is 0.304 which outperformed all other four algorithms.

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In devices using Windows operating system based on x86 system, the real-time performance is not guaranteed by Windows. It is because Windows is not a real-time operating system. Users who develop applications in such a Windows environment generally use commercial solutions such as the RTX or the INtime to provide real-time performance to the system. However, when using functions and API for simple real-time processing, an issue of high development cost occurs in terms of cost-effectiveness.
In this paper, the RTiK+ was implemented in the type of a device driver by controlling the MSR_FSB_FREQ register to generate a timer interrupt independent of Windows in the Windows 8 and providing a real-time functionality to the user mode by re-setting the local APIC count register. And the operating frequency of the CPU is changed to minimize power consumption for battery life in a mobile device such as a Tablet PC.
In particular, the weapon system uses highly reliable MIL-STD-1553B communication and performs BC and RT functions of MIL-STD-1553B to transmit and/or receive data in communication between component and component. It is significantly importance to guarantee integrity of data without loss data during communication. For this purpose, it is proposed to implant the Scheduling algorithm with the RTiK+ for MIL-STD-1553B communication for Windows 8 to support a real-time processing in the Windows operating system on the embedded system, and to use the periods of 2ms (max), 5ms and 10ms provided by the RTiK+ for real-time processing when performing the BC and RT functions of MIL-STD-1553B communication. In this paper, the method of the scheduling algorithm with RTiK+ for MIL-STD-1553B to provide real-time processing is proposed for the Windows based on x86 system.

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Technical safety and health are especially important for mining-extracting industry. Even though the respective lows and good engineering practices exist, technologies develop and could address even better security for humans and equipment. The research question is to survey microclimate sensors in underground mining and to find whether they are ready for automation. The article is inspired from the research in “Computer System for Microclimate Management in Closed Areas of the Post-Mining Galleries and Greenhouses”. The author offers a short review of state and trends in development of sensors and devices for monitoring and reporting of environmental parameters in underground mining. All environmental parameters: air temperature; temperature of surrounding building constructions, heated surfaces of technological machines and equipment; heat flow, heat irradiation; relative humidity; velocity of air flow; noise; illumination; gas concentration; dust level; ionizing radiations; radon concentration in air are represented with relevant measurement devices and measurement units. The next step is representing of fast-developing sensors using scientific references. Author performs quality assessment of their suitability for automated data transfer and management. The assessment criteria are: Analogue measurement devices, Digital measurement devices, Availability of microcontroller. Findings are proposed for discussion: recent used devices in underground mining are not suitable for automatization, because they miss a controller. The availability of controller also presumes availability of management services. On the base of articles about management of sensors and controllers future work is proposed: 1. Integration of existing elements sensor and controller and defining of its management; 2. Moving of new elements and synthesis of algorithms for its management. This will lead to more precise assessment of industrial risk and improvement of safety activities.

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Quantum logic gates represent certain quantum operations to perform quantum computations. Of those quantum gates, there is a category of classical behavior gates called quantum permutation gates. As a quantum algorithm, quantum permutation pad or QPP consists of multiple quantum permutation gates to be implemented both in a quantum computing system as a quantum circuit operating on n-qubits’ states for transformations and in a classical computing system represented by a pad of n-bit permutation matrices. Since first time proposed in 2020, QPP has been recently applied to create a quantum safe lightweight block cipher by replacing SubBytes and AddRoundKey with QPP in AES called AES-QPP. In AES-QPP, QPP consists of 16 selected 8-bit permutation matrices based on the shared classical key materials. For quantum safe, the key length can be any size from 256 bits to 4 KB. That means, this QPP holds up to 4 KB of Shannon information entropy. Its code size is less than 2 KB with 4 KB of RAM memory. In this paper, we propose to apply QPP for a streaming cipher and carry out its encryption performance and the randomness analysis of this streaming cipher. The proposed QPP streaming cipher demonstrates not only good randomness in its ciphertexts but also huge performance improvement: 13x faster than AES-256, with an overall runtime space (6.8 KB).

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The development of Machine Learning methods and approaches offers enormous growth opportunities in the Healthcare field. One of the most exciting challenges in this field is the automation of clinical treatment selection for patient state optimization. Using necessary medical data and the application of Machine Learning methods (like the Genetic Algorithm and the Analytic Hierarchy Process) provides a solution to such a challenge. Research presented in this paper gives the general approach to solve the clinical treatment selection task, which can be used for any type of disease. The distinguishing feature of this approach is that clinical treatment is tailored to the patient’s initial state, thus making treatment personalized. The article also presents a comparison of the different classification methods used to model patient indicators after treatment. Additionally, special attention was paid to the possibilities and potential of using the developed approach in real Healthcare challenges and tasks.

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In the past decade, multiple anthropomorphic prosthetic hands have been developed to replace the role of human hands. Prostheses should not only replace the functions of human hands in functionality but also replicate human hands in appearance and sense of body-belonging intuitively. Human fingers have very delicate and complex structures, and it is these complex structures that make our hands dexterity. This study proposes a design based on the anatomical characteristics of the human hand. The proposed design replicates human fingers from bones, ligaments, extensor hoods, and extensor mechanism of tendon, intended to develop a prosthesis that has the same flexibility and appearance as human hand. To evaluate the performance of the proposed prosthetic in daily life, we conducted grasping experiments on common objects. It is successfully proved that the proposed design helps to improve the grasping performance of the artificial hand and has a natural appearance. In this paper, our design succeeds to improve the grasping performance of the artificial hand and gain natural appearance.

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The study aims to assess the machine learning techniques in predicting students’ associated factors that affect their academic performance. The study sample consisted of 5084 middle and high school students between the ages of 10 and 17, attending public and UNRWA schools in the West Bank. The ‘Health Behaviors School Children’ questionnaire for the 2013-2014 academic year was used for data collection, and was then analyzed through machine learning techniques in order to evaluate their relationship with student academic outcomes. Six machine learning techniques (Random Forest, Neural Network, Support Vector Machine, Decision Tree, Naïve Bayes, and Logistic Regression) were used for prediction. The results indicated that the logistic regression and Naïve Bayes models had the highest accuracy levels (94.3%, 94%) respectively, followed by a decision tree, Neural Network, Random Forest, and Support Vector Machine (93.3%,91.9%,91.7%, and 80.2%) respectively. Thus, the Logistic Regression and Naïve Bayes had the best performance in classifying and predicting student academic performance with the associated factors. Furthermore, Decision Tree, Random Forest, and Neural Network had better predictive performance than Support Vector Machine. The results indicated that perception, Smoking, Depression, PTSD, Healthy Food Consumption, Age, gender, Grade Level, and Family income are the most important and significant factors that influence student academic performance. Overall, machine learning techniques prove efficient tools for identifying and predicting the features that influence student academic performance. The deployment of machine learning techniques within schools’ information systems will facilitate the development of health prevention and intervention programs that will enhance students’ academic performance.

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In this research we developed an BIM-based system to monitor and visualize the real-time building users information. Concentrating on building in-use stages, advantages in tracking facial recognition should be revealed through the availability of real-time information. In this way could explore the possibility of how BIM and IoT could improve data-oriented facility management. The integration system called SyncBIM. The five system elements of the construction platform are also proposed to allow more efficient management and data transmission of the building O&M system. This study integrates the three pieces of technology respectively known as BIM platform, Internet of Things, and computer vision to explore the architecture and technology required by the building O&M system, and establishes a “Decision-Making BIM-Based Cloud Platform” to allow the opportunities of information integration and collaborative operations for the application of BIM and the introduction of computer vision technology.

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With increases in technological usage, cyber incidents are also on the rise and have become a major concern in schools across the globe. What is of significant concern is that cyber incidents in South African schools are also on the rise. Existing evidence suggests that, in South Africa there are no clear procedures that are consistently followed by schools on how to report cyber incidents. The aim of this research is therefore to propose cyber incident handling procedures to enhance the effectiveness in handling cyber incidents as well as ensuring that each role player has an important contribution in the intervention process that is designed to reduce cyber incidents in South African schools. The study also assessed the perception of learners on cyber incidents in South Africa. Using the literature review approach and thematic analysis of the data collected from learners the study highlighted the procedures and roles of role players that can assist in cyber incident handling in South African schools. The study also came up with a detailed analysis of the views of learners on cyber incidents in South Africa. The results presented can help to provide a framework that will act as a guide on reporting cyber incidents and directing school management, and all within the school, towards appropriate reporting procedures and intervention processes. The study also found out that the rise in cyber incidents in South African schools, if left unaddressed, can have a devastating effect on learners. Therefore, the government of South Africa, through the Department of Basic Education, must prioritize the handling of cyber incidents in schools as cyber incidents are now a threat to the efficient and effective execution of the mandate of the department.

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Chatbot operates task-oriented customer services in special and open domains at different mobile devices. Its related products such as knowledge base Question-Answer System also benefit daily activities. Chatbot functions generally include automatic speech recognition (ASR), natural language understanding (NLU), dialogue management (DM), natural language generation (NLG) and speech synthesis (SS). In this paper, we proposed a Transfer-based English Language learning chatbot with three learning system levels for real-world application, which integrate recognition service from Google and GPT-2 Open AI with dialogue tasks in NLU and NLG at a WeChat mini-program. From operational perspective, three levels for learning languages systematically were devised: phonetics, semantic and “free-style conversation” simulation in English. First level is to correct pronunciation in voice recognition and learning sentence syntactic. Second is a converse special-domain and the highest third level is a language chatbot communication as free-style conversation agent. From implementation perspective, the Language Learning agent integrates into a WeChat mini-program to devise three user interface levels and to fine-tune transfer learning GPT-2 [1] as back-end language model to generate responses for users. With the combination of the two parts about operation and implementation, based on the Neural Network model of transfer learning technology, different users test the system with open-domain topic acquiring good communication experience and proved it ready to be the industrial application to be used. All of our source codes had uploaded to GitHub: https://github.com/p930203110/EnglishLanguageRobot

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In this work, the impact of implementing a large amount of decentralized renewable energy sources (RES) of different scales on an extra-large power grid is investigated. Three scenarios are created, substituting 10%, 20%, and 30% of the conventional energy production by RES. For this purpose, the initial dynamic model of Continental Europe in combination with the industrial power system application DIgSILENT PowerFactory was used. In order to compare the behavior of different applied scenarios, a performance index was developed to evaluate and rank the effects of network disturbances by means of time-domain simulations. The performance index was designed based on three different criteria that analyze the oscillatory content and thus, the severity of a given event. The initial power flow of the dynamic model was identified as a limiting factor for the integration of RES, therefore two additional power flows were developed following an innovative procedure. Through the methodologies mentioned above, it was found that Turkey is the most sensitive to such changes, which are amplified by increasing implementation of RES and often lead to inter-area oscillation.

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The daily treats model reduction finite frequency (FFMR) design for Takagi Sugeno (T S) systems. This work is to FFMR design in such a way whether augmented model is steady get a reduced H? index in FF areas with noise is established as a prerequisite. To highlight the importance of suggested process, a practical application has been made.

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Radiofrequency catheter ablation is routinely used for the therapy of cardiac arrhythmias. Compared with the traditional unipolar ablation, bipolar ablation may improve the controllability of treatment, and prevent side effects and complications caused by catheter ablation. In addition, the variations of myocardial fat’s thickness and myocardial impedance may have significant influence on the performance of bipolar ablation. In this study, computer simulation was performed to study the effects of myocardium fat’s thickness and myocardial impedance on unipolar and bipolar ablation. The simulation demonstrates similar results with experimental ones using a swine heart. We observed that when the myocardial fat’s thickness increases, bipolar ablation’s heating effect and controllability may decrease. However, the final heating effect of bipolar ablation is invariably better than that of unipolar ablation. The ablation effects of unipolar and bipolar ablation are both reduced when myocardial impedance increases, while the heating effects of bipolar ablation are more sensitive to the variation of myocardial impedance and fat layers’ thickness compared with unipolar ablation. The unipolar ablation is more stable in terms of fat, impedance and ablation time.

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Affective states, a dimension of attitude, have a critical role in the learning process. In the educational setting, affective states are commonly captured by self-report tools or based on sentiment analysis on asynchronous textual chats, discussions, or students’ journals. Drawbacks of such tools include: distracting the learning process, demanding time and commitment from students to provide answers, and lack of emotional self-awareness which reduces the reliability. Research suggests speech is one of the most reliable modalities to capture emotion and affective states in real-time since it captures sentiments directly. This research, which is an extension of the work originally presented in FIE conference’20 [1], analyses students’ emotions during teamwork and explores the correlation of emotional states with students’ overall performance. The novelty of this research is using speech as the source of emotion mining in a learning context. We record students’ conversations as they work in low-stake teams in an introductory programming course (CS1) taught in active learning format and apply natural language processing algorithms on the speech transcription to extract different emotions from conversations. The result of our data analysis shows a strong positive correlation between students’ positive emotions as they work in teams and their overall performance in the course. We conduct aspect-based sentiment analysis to explore the themes of the positive emotions and conclude that the student’s positive feelings were mostly centered around course-related topics. The result of this analysis contributes to future development of predictive models to identify low-performing students based on the emotions they express in teams at earlier stages of the semester in order to provide timely feedback or pedagogical interventions to improve their learning experience.

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The ocean surface temperatures or sea surface temperatures have a significant influence on local and global weather. The change in sea surface temperatures will lead to the change in rainfall patterns. In this paper, the long-term rainfall forecasting is developed for planning and decision making in water resource management. The similarity of sea surface temperature images pattern that was applied to analyze and develop the monthly rainfall forecasting model will be proposed. In this work, the convolutional neural network and autoencoder techniques are applied to retrieve the similar sea surface temperature images in database store. The accuracy values of the monthly rainfall forecasting model which is the long-term forecasting were evaluated as well. The average value of the model accuracies was around 82.514%.

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Real-world applications modelled by time-dependent dynamical systems with specific properties such as long-range dependence or self-similarity are usually described by fractional Brownian motion. The investigation of the qualitative behaviour of its realisations is an important topic. For this purpose, efficient mappings from realisations of the dynamical system, i.e., time series, to a set of scalar-valued representations that capture certain properties are considered. Permutation entropy is a well-known measure to quantify the complexity of univariate time series in a scalar-valued representation, for example, to derive estimates for self-similarity or as features or representations in learning tasks. However, since many real-world problems involve multivariate time series, permutation entropy needs to be extended to the multivariate case. This work summarises the behaviour of pooled permutation entropy (PPE), multivariate multi-scale permutation entropy (MMSPE), and multivariate weighted permutation entropy (MWPE) on multivariate fractional Brownian motion, and this work fills the gaps in existing research. In addition, we provide a new study of multivariate ordinal pattern permutation entropy (MOPPE) on multivariate fractional Brownian motion. We conclude with a detailed experimental evaluation and comparison between all multivariate extensions, for example, demonstrating identical behaviour of PPE and MMSPE or uncovering different aspects such as amplitude and cross-correlations by using MWPE and MOPPE, respectively.

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The Conv-TasNet and Demucs algorithms, can differentiate between two mixed signals, such as music and speech, the mixing operation proceed without any support information. The network of convolutional time-domain audio separations is used in Conv-TasNet algorithm, while there is a new waveform-to-waveform model in Demucs algorithm. The Demucs algorithm utilizes a procedure like the audio generation model and sizable decoder capacity. The algorithms are not pretrained; so, the process of separation is blindly without any function of three Natural Languages (NL) detection. This research study evaluated the quality and execution time of the separation output signals. It focused on studying the effectiveness of NL in Both algorithms based on four sound signal experiments: (music & male), (music &female), (music & conversation), and finally (music & child). In addition, this research studies three NL, which are English, Arabic and Chinese. The results are evaluated based on R square and mir_eval libraries, mean absolute Error (MAE) scores and root mean square error (RMSE). Conv-TasNet has the highest Signal-to-distortion-Ratio (SDR) score is 9.21 of music at (music & female) experiment, and the highest SDR value of child signal is 8.14. The SDR score of music at (music & female) experiment is 7.8 during the Demucs algorithm, whereas child output signal has the highest SDR score 8.15. However, the average execution time of English experiment of Conv-TasNet is seven times faster than Demucs. For accuracy measure, RMSE indicates absolute values, and MAE handles the errors between observations and prediction signals. Both algorithms show high accuracy and excellent results in the separation process.

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This paper reports the algorithm, the input data and the result of the numeric simulation on the flows of the waves emitted from a rotating object that forms the artificial anti-gravity. First, an object with a heavy mass is placed in the 4-dimensional time and space, which is described by a fundamental tensor. Then the first-order derivative of the tensor describes the gravity, and the second-order derivative describes the waves. If the gravity created by the heavy mass is strong enough, time and space become dependent on each other. The input data for the simulation are discrete numbers that surrogate the infinity of the 4-dimensional time-space. The object is assumed to rotate and the tensor equations are solved. Then the coefficients are calculated, which present physical properties of the waves. The result of the simulation shows that the rotating object emits the waves with positive and negative energy intensities, and they have the spin angular momentum that changes its spinning direction upon the selection of the rotation-speed of the object.

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The data distribution used in model training is assumed to be similar with that when the model is applied. However, in some applications, data distributions may change over time. This situation is called the concept drift, which might decrease the model performance because the model is trained and evaluated in different distributions. To solve this problem for scene audio classification, this study proposes the kernel density drift detection (KD3) algorithm to detect the concept drift and the combine–merge Gaussian mixture model (CMGMM) algorithm to adapt to the concept drift. The strength of the CMGMM algorithm is its ability to perform adaptation and continuously learn from stream data with a local replacement strategy that enables it to preserve previously learned knowledge and avoid catastrophic forgetting. KD3 plays an essential role in detecting the concept drift and supplying adaptation data to the CMGMM. Their performance is evaluated for four types of concept drift with three systematically generated scenarios. The CMGMM is evaluated with and without the concept drift detector. In summary, the combination of the CMGMM and KD3 outperforms two of four other combination methods and shows its best performance at a recurring concept drift.

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Gate driver has been applied in many ways, exemplified by that, by using the DC-isolated and AC-pass characteristics of gate driver’s primary and secondary sides, the problem of floating endpoint in semiconductor power switch can be solved. However, the conventional design of isolated gate driver provides circuit voltage blocking by optically coupled components. Due to the need for optoelectronic conversion, it requires III-VI semiconductor process and non-standard CMOS process, and the cost is always high. Therefore, in order to better solve the above mentioned problem, an electronic isolated gate driver is proposed. It employs an on-chip transformer to provide voltage isolation between the primary and secondary sides of the circuit, and converts the control signal in the circuit into a high-frequency modulated signal, which in the secondary side is then demodulated by the rectifier through the on-chip transformer to produce the original control signal. The miniaturized isolated gate driver proposed herein adopts TSMC T25HVG2 process and uses an on-chip transformer design in lieu of an optically coupled components. As the amplitude shift keying, on-chip transformer, full-wave quadruple rectifier and data buffer amplifier involved in this design are all integrated on the same chip, the integration can be improved. The size can be smaller than the generally separating electronic isolated gate driver, with the interference from external noise being reduced. In addition, the proposed gate driver generates large signals, in terms of chip layout, therefore, the circuit is put inside the on-chip transformer, which can further save area.

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The Internet of Things (IoT) is at a face paced growth in the advanced Industrial Revolution (IR) 4.0 in the modern digital world. Considering the current network security challenges and sophistication of attacks in the heavily computerized and interconnected systems, such as an IoT ecosystem, the need for an innovative, robust, intelligent and adaptive malware attacks and threats security solution is becoming predominant in the current cyberspace. An integrated and scalable IoT malware detection framework called iDRP framework with deep learning method was proposed as a solution to current IoT malware attacks that are largely obfuscated. The novel framework utilized systematic pre-processing and post-processing techniques and methods on the BoTNetIoT malware datasets that contains both benign and malicious IoT traffic data infected by modern day IoT attacks such as Mirai and Gafgyt etc. IoT malware variants in an IoT ecosystem. The raw IoT malware binaries were converted to image files (Gray-scaled) and computed statistically with synthesised sparsed and differential evolutionary hidden feature structures techniques, which were cyclically trained, tested, and cross-validated to establish empirical anomalies with precision in the detection, recognizing, and prediction of malware anomalies in a modern IoT ecosystem. Preliminary experiments were conducted with standardized image binary files such as the MNIST (2-D), and NORB (3-D) datasets as sound scientific exploratory experiments with profound results. The comparative results of the performance of our integrated techniques and methods on the BoTNetIoT IoT malware datasets achieved a 99.98% accuracy, 99.99% ROC/AUC, 99.95% precision, and 99.93 recall rate etc. utilizing the integrated iDRP framework mechanisms for effectively detecting IoT malware in an IoT ecosystem.

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Internet addiction (IA) has adverse effects on psychophysiological responses, interpersonal relationships, and academic and occupational performance. IA detection has received increasing attention. Although questionnaires enable long-term assessment (over 6 months) and physiological measurements to aid the short-term evaluation (over 2 min) of IA, the lack of algorithms results in an inability to detect IA in real time. A computer-aided system can address this problem. This study used the extended classifier system with continuous real-coded variables (XCSR) for rule-based machine learning to classify IA risk. Chen Internet Addiction Scale (CIAS) items were verified and instantaneous respiratory features of IA were extracted with “don’t care” attribute values. The result demonstrated that the XCSR model achieved more than 95% classification accuracy. Using the “don’t care” attribute values, the CIAS items were reduced from 26 to 19, and the instantaneous frequency (IF) of respiratory muscle contractions, respiratory wall movements, and body movements were extracted as IA-related features. These findings suggested that the XCSR model is a potentially useful system for detecting IA. The modified 19-item CIAS and IF of respiration can be adopted to assist in the real-time detection of IA and explore the psychophysiological developments of IA users. In future studies, more samples must be collected to validate these findings and instantaneous physiological responses investigated with different window sizes while participants with IA engage in active online gameplay.

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Energy Efficiency (EE) plays a key role in decreasing energy consumption at a European level, while it is considered as one of the most cost-efficient means to achieve carbon reduction and reinforce energy sufficiency and security. EE financing is imperative to implement measures that will lead to achieving the desired carbon neutrality and, thus, avert climate change. The majority of EE investments ideas are abandoned during the first stages of investment generation as there is not enough interest by the involved actors to support the maturing of the idea. The present paper aims to boost EE investments by developing a web-based Tool that evaluates project ideas, connecting them with real financing proposals. All the above are being realised through standardised procedures, establishing a concrete typology of five (5) EE sectors, a well-structured risk assessment methodology of five (5) risk categories and (9) risk factors, and a benchmarking procedure that takes into account four (4) broadly used economic criteria and eleven (11) verified sustainability indicators. All the parameters are calculated using the candidate project data and EU official statistics, formulated into four (4) main criteria that are fed into a MultiCriteria Decision Analysis that performs the project’s benchmarking. The presented methodology is being practically tested through the development of three (3) innovative Tools (Assess, Agree, Assign) and a stakeholder consultation process with around 200 participants. The Tools filter and benchmark candidate project ideas, based on the standardised benchmarking and the EU Taxonomy sustainability principles, while connecting the most promising project ideas with state-of-the-art financing methods, such as the Green Loans, the Green Bonds and the Energy Efficiency Auctions. By this token, the developed Tools provenly provide added value to the respective stakeholders, offering standardisation in EE project benchmarking and financing, building trust between investors and projects developers.

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The article identifies an alternative style of thinking as one of the important components of human intellectual development. It is shown that it can be effectively implemented in mathematics lessons at school. The purpose of this study is to develop and substantiate a strategy for the formation of an alternative style of thinking among students in mathematics lessons. The innovation in this research lies in the development of a new approach to the formation of an alternative style of thinking of students, involving the progression of schoolchildren up the “ladder of levels”, purposefully correlating task structures with their “alternative analogs”. There are essential research findings. The levels of formation of the alternative style of thinking of schoolchildren are defined and their multiple characteristics are given. It is shown that as the main means of actualizing an alternative style of thinking, it is advisable to set tasks that provide alternative options for analyzing the elements of their content area. The stage-by-stage work of students as they move up the “ladder of levels” is presented. Methodological recommendations for teachers of mathematics have been created and partially tested. These were presented at several seminars / training sessions and were successfully applied in practice throughout the year. Statistical processing using Pearson’s criterion ?2 was applied at the end of the year to the results of the performance of special tasks by 52 students of grades 3-4 of one of the schools in Penza. Some students applied the traditional method (27 students), and the remainder applied the methodology proposed by the authors (25 students). Analysis of the results revealed a higher level of mastery of the alternative style of thinking among students in the experimental group.

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In this work we discuss a special technique for powering of sensor systems, based on a low-power primary cell consisting of two electrodes, made from different metals, with water (lake, rain or tap water) used as an electrolyte. Once placed into an aqueous solution, the primary cell generates a small electric current, which may be utilized for powering of sensor systems. The generated electrical energy is fed into an energy storage (capacitor). After transformation of the voltage by a step-up converter, it is used for supplying the electrical sensor circuit. The expected output power of the developed circuit is 10-15 mA by 2 V output voltage during 0.2-0.5 second. The improved voltage converter topology with implemented maximal power point techniques allows significant reduction of the energy storage’s size in the second revision of the circuit and thus reduction of the resulting size of the board. The implemented sensor board with discussed powering technique, assembled in Paderborn University was already tested in different practical scenarios.

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Discover DaVinci is a novel augmented reality system that incorporates blockchain technology with experiential learning to engage participants in an interactive discovery of Leonardo da Vinci’s oeuvre. The software was created by Marko Suvajdzic, first author of this paper, and it was produced at the University of Florida Digital Worlds Institute. In the true spirit of this “Renaissance man”, Discover DaVinci explores new ideas and technologies “ahead of their time”, opening up questions about usage of blockchain system in the domain of art and technology. This paper discusses some of these questions, such as relation of art and technology, usefulness of blockchain system for digital art, and new materiality of art in digital and informational age. Proposed work of this manuscript is to present the field of digital learning through a general review and more specifically through a prism of Discover DaVinci project created by Digital worlds Insititute at the University of Florida.

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Central authorities have registered economic transactions based on trust since the dawn of time. With the advent of paper, methods for documenting transactions and records became more difficult and detailed. With the widespread use of computers in recent years, digital recording has made record keeping easier and since then every technical advancement has made data recording more convenient and faster. Transactions have accelerated as technology has advanced, but the maintenance of records has remained under the jurisdiction of the authorities. The use of distributed ledger systems allows data to be released from the control of central authorities. A distributed ledger is a peer-to-peer network in which non-centralized data is shared by participants. The Bitcoin payment system and cryptocurrency, which arose after the 2008 financial crisis, was the first distributed ledger application. Central authorities’ poor economic decisions aided in the rise of Bitcoin. The aim of Bitcoin is to make the current financial systems liberal from the influence of central authorities. With the rise in popularity of Bitcoin, the blockchain technology that underpins it has begun to garner interest. While blockchain technology was initially associated with the financial sector due to Bitcoin, research into its use in other sectors such as supply chain, records management, electoral systems, notary services, file systems, energy and artificial intelligence has begun. Several Blockchain infrastructures have been built to allow the use of blockchain technology in a variety of industries. However, it has been revealed in practical approaches that blockchain technology has limitations in terms of speed and scalability. As a result, new distributed ledger technologies with increased speed and scalability have been established. Hashgraph, Tangle, Tempo, Holochain are examples of newly developed distributed ledger technologies. Different influential features distinguish new generation distributed ledger technologies from the conventional Blockchain methods, which can yield into several and practical modern applications. In this study, Blockchain and new generation Distributed Ledger Technologies are compared and possible future applications are outlined.

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The persistent difficulties in reading and spelling acquisition are a risk factor for learning motivation. Play-like intervention tools have been developed to face these difficulties. “I read” is a software that seeks to develop and introduce systematic reading and spelling skills training in a playful and complementary way. This software is intended for children at the beginning of their school journey, as well as for those who reveal reading and/or spelling difficulties. This article intends to present the goals and the structure of this software, as well as the preliminary results of its implementation and game enjoyment with 244 children between 5 and 7 years old. Results indicate that 58% of the participants completed the activities dedicated to alphabetical decoding, and 42% were able to reach the last stage of the game, dedicated to orthographic decoding. Regarding the enjoyment with the software, 96% of the participants classify the games as fun games. In conclusion, training with this software revealed to be beneficial for reading and spelling skills promotion, as well as to increase the overall enjoyment and motivation for learning.

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As artificial intelligence (AI) develops, it is expected that humans and AI will become more closely related than now. At the same time, however, the more closely humans and AI are related to each other, the more clearly they will face a moral dilemma, i.e., artificial intelligence will face a moral dilemma. To solve the moral dilemma problem, AI should understand and take into account human values and ethics. From this point of view. we designed a consciousness model based on Jungian post-psychological notes. As a result, we found that in order to implement the model of consciousness on a computer, it is necessary to design it with a structure similar to that of human beings, referring to human structures in various fields.

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It is widely known that cities now house more than half of the world’s population. Within this framework, this study presents the possibilities for real-world application of renewable energy sources (RES) in urban areas, as well as their contribution to the urban deployment of the new energy paradigm. A comparison is made between hybrid power systems, operating on Bulgarian territory and conventional (traditional) power systems. Proposed hybrid system is designed and actually implemented to power a single-family house and consists of wind turbine (WG) photovoltaics (PV), lithium-ion batteries for energy storage and suitable converter. The HOMER Pro software was used to model and explore the long-term continuous implementation of a hybrid power system and greenhouse (GHG) gas emissions investigation. The article discusses the amount of carbon dioxide (CO2) and nitrogen oxides (NOX), that can be reduced by using a hybrid power system (solar and wind) in conjunction with a battery storage system (BSS – lithium-ion batteries) in single-family houses. Renewable energy sources combined with energy storage, according to this report, result in a 50% reduction in dangerous carbon dioxide and nitrogen oxide emissions. The proposed system is optimized based on the lower cost of energy (COE) and proper dispatch strategies (load following and cycle charging), resulting in greenhouse gas emissions distributed by regional Bulgarian cities.

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This paper describes what can be accomplished by understanding sound environments. Understanding sound environments is achieved by extracting the features of the sound and visualizing the features. The visualization is realized by converting the three features, namely, loudness, continuity, and pitch, into RGB values and expressing the sound with color, where the color is painted in the estimated direction of the sound. The three features can distinguish falling objects within a building and roughly estimate the direction of the generated sounds. The effectiveness of the proposed sound visualization was confirmed using the sounds of cans and stones falling in a building; hence, it is shown that the proposed visualization method will be useful for monitoring the collapse of buildings by sound.

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We propose and analyze new impedance matching schemes to enhance applied voltage to an optical modulator and light modulation for a subcarrier multiplexed light transmitter or a radio-on-fiber transmitter that carries radio-frequency signal through an optical fiber. Our proposal includes two methods using a quarter-wavelength impedance transformer and a tapered microstrip line for impedance matching between a driver circuit and an electro-absorption modulator. Simulation results for both two schemes show that large enhancement is observed for 20 GHz and lower frequency and that some resonant boosts are observed for higher frequency as well. Discussions are described to design a circuit with improved performances. It is shown that our schemes can drive the electro-absorption modulator at a voltage higher than 1 V that is sufficient to drive the modulator.

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Lean Six Sigma (LSS) is a renowned approach for boosting operational excellence and competitive advantage through integrated core objectives of value creation and variation reduction. Despite its proven benefits in many leading companies, LSS implementation in the food sector is still behind compared with other sectors. LSS implementation is costly, and most businesses have failed due to a lack of preparation and an unsupportive organizational culture. Therefore, there is a need to identify LSS readiness factors that suit the food sector to minimize the risk of implementation failure in the industry. The current study concentrates on the LSS pre-implementation phase to determine the competency criteria to adopt LSS customized for the food business. This study will explore the LSS readiness criteria during the pre-implementation stage and critical success factors (CSFs) during the implementation stage in the food sector through Lewin’s Change Theory. Twelve food sector employees who were associated with quality management activities were interviewed using a semi-structured approach. The interview was recorded, transcribed and the transcription was analyzed using content analysis. The results showed six readiness themes in the food manufacturing sector with twenty-nine LSS readiness attributes, while seventeen factors out of thirty-one CSFs for the LSS at the implementation stage. The identified readiness factors are management commitment and leadership (ten attributes), organizational culture (nine attributes), employee involvement (six attributes), process management (four attributes), project management (four attributes) and external factors (three attributes). Through Pareto analysis, the most prioritized CSFs are from top management and leadership and employee involvement themes, with the training program being identified as the most important LSS CSFs (85%). This study will serve as a foundation for a benchmarking tool for managers to improve the effectiveness of an LSS implementation in the food sector.

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In the last decade, South Africa has attracted and attained more investment by means of the Renewable Energy Independent Power Producer Procurement Programme (REI4P), which is, a structured invitation to Independent Power Producers (IPPs) to submit a bid to generate and supply power to the electrical grid. In spite of REI4P’s undeniable triumph, a much-discussed drawback has been the related service lifetime cost of equipment that facilitate the renewable energy technologies. The description of the service lifetime loss method (SLLM) gets more complex in the new dawn of decarbonized electricity market. The SLLM must be modified for determining the total ownership cost of transformers facilitating Sustainable Energy Systems (SES’s) in the decentralized energy market. The main focus of this work is to indicatively formulate a fundamental advancement upon the conventional service lifetime losses evaluation formula to contemplate the service lifetime loss evaluation method for transformers exclusively of service to solar power plants in South Africa. The distinct operational features of a solar plant have been embedded in the formulated service lifetime loss evaluation formula by way of the plants’ Generation (GM) and Non-Generation Mode (NGM). Further, a levelized cost of energy supplied per unit of time by the solar plant is employed to determine the energy cost of the no-load and load losses that will be consumed by the studied transformers during their service life. Ultimately, the premier findings of this study indicate that the annual solar potential has an effect on the transformer service lifetime loss factors and the conventional method is not suitable thereof for this application. This is a characteristic that should be precisely considered, as it may influence the tender adjudication process to purchase a transformer based on the total ownership cost offers of various transformer manufacturers.

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The ageing of oil-immersed power transformers triggers several defects and damages in the insulating materials, particularly in the cellulose insulation. The decomposition of the cellulose paper produces dissolved gases into the insulating oil, in which the Dissolved Gas Analysis (DGA) of the oil samples can provide insights to incipient faults sustained by the transformer as well as the status of the insulation. The condition of the cellulose insulation can be established by the measurement of the Degree of Polymerization (DP). In some instances, when the DP is intricate to measure, the estimation can be realized by collating information with respect to oil Furan analysis (2-furfuraldehyde (2FAL)). In this work, regression analysis based on 120 transformer oil samples is developed to establish new formulae by generating an analogy between the DP and 2FAL to indicate the status of the cellulose insulation and percentage of remnant life expectancy. In present study, five different transformers are tested using the proposed formulae to compute the DP based on the 2FAL concentrations. The results, are compared with measurement results and four other existing models. The results indicate an error of estimate of less than 2% and 1% against the measured DP.

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Service robots are applied in many kinds of business fields and its various kinds of data can be collected with service robot in business scenes. Therefore, it becomes more important to utilize such data for business improvement. Additionally, due to limitation of design such as body size and battery life, a service robot cannot prepare much peripheral equipment on its body. In this paper, we will show two research results as for service robot. One is data analysis technology for business improvement as a function of operation and management system for service robot. Another is service extension of service robot by association of external equipment with proper design of utilization condition of external equipment. Through evaluation experiment, we confirmed potential effect of business improvement is 8.1 % by modifying service scenario of robot.

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Serious games are at the forefront of discussions about the future of learning. Research shows that they can help improve student motivation and knowledge transfer by making learning experiences more pleasurable. Teaching practices are increasingly enhanced or complemented by serious games; skills and knowledge are developed by recreating realistic situations allowing learners to enhance their procedural knowledge, all while having fun. Literature is scarce when it comes to identifying factors that influence learners’ satisfaction when using serious games. In this paper, we present empirical results from an ongoing research project. We developed a questionnaire using factors identified in the literature and collected the responses of n = 110 business students from classes using serious games as a tool. Analyses were performed using partial least squares structural equation modeling (PLS-SEM). We used the following predictor variables for our model: performance and status feedback and tracking, ease of use, reliability, perceived control, instructor support, aesthetics, realism entertainment, goal clarity, immersion, and progressive challenge. Results show that sense of control, entertainment and effectiveness have a direct positive influence on learners’ satisfaction while other factors influence satisfaction by mediation or as components of theoretically justified higher-order constructs. The sample size and its composition limit the generalization of results. Further studies are needed.

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The Japanese writing system is unique due to the number of characters employed and the methods used to write words. It consists of three different ’alphabets’, which may result in the methods used to process Latin script not being sufficient to obtain satisfactory results when attempting to apply them to a recognition of the Japanese script. The authors present an algorithm based on minutiae, i.e., feature points, to recognise the hiragana and katakana characters. A method using image processing algorithms is compared with a method using a neural network for the purpose of automating this process. Based on the distribution and type of minutiae, vectors of features have been created to recognise 96 different characters. The authors conducted a study showing the effect of the chosen segmentation method on the accuracy of the character recognition. The proposed solution has achieved a maximum accuracy at the level of 65.2%.

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The SENSORIA Reference Modeling Language (SRML) aims at modelling composite services at a high level. Continuous time SRML extends SRML so that it can model services whose components can perform both discrete processes and continuous processes. In order to show how continuous time SRML is applied, in this paper, we systematically introduce our study on continuous time SRML in the following approach: First we introduce the theoretical foundation of continuous time SRML, the Service-Oriented Hybrid Doubly Labeled Transition Systems. This is the semantic domain over which continuous time SRML is defined and interpreted. Then we design a case study of a traffic control system. In the case study, we illustrate the scenario of the system, explain the continuous time SRML model of the system, and show how to transform the model to a kind of Deterministic Finite Automata that can be used for testing and verification. Finally, we show our idea of testing our model with the IBM WebSphere Process Server. With this approach, we come to a conclusion that continuous time SRML can be used to model certain systems in the real-world, and can be tested with proper tools.

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Considering the importance of addressing innovation issues that impact the development of economic sectors, this document presents the research work aimed at establishing a state of the art related to technology transfer and intermediation issues, which can be adopted in the innovation systems. In this case, the agro-industrial innovation system is taken as a reference from the perspective of a country, Colombia. Likewise, it is proposed to consider the synergy between actors such as the university and the industry, from a holistic vision of the systems. In this sense, and making use of the Scopus, Web of Science and Google Scholar databases, through the implementation of a methodology in which three main phases of search, selection and reading of scientific publications were generated, a set of documents was obtained, and through these it was possible to identify: concepts on innovation systems, aspects that intervene in transfer processes of technology especially related to the articulation of the actors that are part of the innovation systems, as well as the way in which these issues can be adopted, taking the agro-industrial sector as a reference. Thus, through this research, the existence of structural gaps in the networks of actors is highlighted as a key factor, which, when trying to be moderated through the intervention of government actors, also require the participation of intermediary innovation agents, that facilitate the articulation and flow of data, information and communication between those who develop technology and those who require these developments to mitigate a need in the productive sectors.

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The main objective and contribution of the paper is to describe the creation of a model to support recruitment using social media information and its deployment in practice. The model includes the design of an automated solution for downloading social media data and a proposal for the subsequent analysis and creation of a predictive model based on the evaluation of user behavior on a social network. A final assessment of the effectiveness of the proposed model was made through formal validation and a case study. For the case study validation, the proposed model was implemented through use of the recruitment application called Prace Na Miru (PM; Tailored Work) for Facebook data extraction. A Myers Briggs Type Indicator (MBTI) personality test was used to determine the predictors of user social network behavior. Using cluster analysis and machine learning (or decision trees), a stochastic predictive model was developed to determine the personality type of candidates—the accuracy of MBTI personality category prediction ranges between 68% and 84% for individual cases, with a confirmation rate ranging between 43% and 81%. The case study confirmed the model’s usefulness for supporting recruitment in a real-life deployment of the PM model.

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The focus of this work is first to establish the effect of the chip temperature and thermal feedback on the determination of the power loss in a three-phase propulsion inverter, then to demonstrate the possibility of achieving an improved heat spreading through the different layers inside a SiC power module by using graphene assembled films in the packaging of the power module. The power loss analysis has been carried out for two Silicon Carbide (SiC) modules in a vehicle inverter, incorporating the MOSFET’s reverse conduction as well as including the impact of blanking time on the inverter on-state losses. This data for calculating the losses is determined at an operating situation below the field weakening speed with a high torque for a permanent magnet synchronous machine (PMSM). The operating point is found to be the worst operating condition point when looking at the power loss point. First, it can be noted that not accounting for the thermal feedback, the power loss is considerably underrated, i.e.,11-15% on the on-state converter. Following, the analysis of utilizing the graphene layer in the SiC module reveals a reduction of 10°C per SiC chips in the junction temperature of the SiC MOSFET is achievable. The reduction is calculated based on an applied power loss per SiC chips in steady-state simulation. Furthermore, up to 15°C decrease in the transient computation over the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) per SiC chip is noticed. Moreover, a reduction up to 50% for the junction to case thermal resistance (Rth,JC) is observed by adding the graphene layer in the power module.

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The paper describes the control and monitoring systems installed in medium voltage networks by Polish distribution network operators. It also outlines the expected directions of development of these systems, specifies the functions of the individual system components and describes the requirements applicable to them. In particular, attention is paid to the implementation of functions to detect short-circuits and automatic voltage control on the low voltage side of the transformer using the on-load tap changer. It also describes the communication functions implemented by the control and monitoring systems and the methods of ensuring a guaranteed power supply for the above systems.

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To determine the risk factors associated with the development of extensively resistant pulmonary tuberculosis in the DIRIS Lima Sur, 2017. The type of research was observational, quantitative, analytical, retrospective case-control approach. The population consisted of 158 patients with MDR-TB treatment schedule between 2016 and 2017. The sample consisted of 24 cases diagnosed with extensively drug-resistant pulmonary tuberculosis (XDR-TB) and 48 controls with MDR-TB. The survey was used for both groups (cases – controls) as a data collection technique and a questionnaire as an instrument. 5 risk factors associated with XDR-TB were identified. Among the internal factors associated at the bivariate level were: drug use, previous history of MDR-TB/TB treatment, having taken MDR-TB treatment for less than one year, history of failure to primary and individualized scheme for MDR-TB/TB (p<0.05); and the multivariate analysis confirmed the influence of the factor “history of failure to primary and individualized scheme for MDR-TB/TB”. Among the external factors most associated at the multivariate level were: having a history of family contact deceased by XDR-TB/MDR-TB and extra domiciliary contact in the neighborhood deceased by TB (p<0.05) It was concluded that the factors associated with the development of extensively resistant tuberculosis are “failure to follow the primary and individualized MDR-TB regimen” and “history of deceased family contact with XDR-TB-MDR-TB”.

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C programming is taught in a lot of universities across the world as the first computer programming language. Then, for novice students, it is important to read many simple C source codes and understand their behaviors to be familiar to the programming paradigm. Unfortunately, effective tools to support independent code reading study at home have not been well designed. Heretofore, we have proposed the value trace problem (VTP) for Java programming. A VTP instance consists of one source code, several questions, and the correct answers to them. Each question asks the value of a critical variable or output message in the source code. The correctness of any student answer is checked instantly by string matching at the answer interface for self-study. In this paper, we present the value trace problem (VTP) for code reading self-study of C programming. 42 VTP instances are generated using simple C source codes on basic grammar concepts and fundamental data structures & algorithms in textbooks and websites. In addition, for hard instances on pointer and algorithms, the devices of hints, multiple choice questions, and references are provided to improve their solution performances. For evaluations, we requested 49 undergraduate students in Japan, China, and Myanmar to independently solve them at home. Their average correct answer rate reached 94.29%, where our devices for hard instances improved it by 33.26%. Thus, the effectiveness of our proposal is confirmed in motivating self-study of C programming to novice students.

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