Special Issue on Computing, Engineering and Multidisciplinary Sciences 2022-23

The use of a battery to power an electrical or electronic system is accompanied by battery management, i.e. a set of measures intended to preserve it for preventative maintenance, thus the cost reduction. This management is generally based on two key parameters, the (remaining useful life) RUL and the (State-of-health) SOH, which relate respectively to the charge output and the aging of the Lithium-ion battery. The issue will be resolved and advances in production, battery utilization, and optimization will be made possible by accurate SOH determination and dependable RUL prediction. The CNN-BGRU-DNN hybrid strategy, which we suggest in this study, integrates Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BGRU), and Deep Neural Networks (DNN) to increase the precision of SOH and RUL estimates for Lithium-ion batteries. To that purpose, the performance of the prediction findings is assessed using the MAE, RMSE, AE, and RE as well as the NASA datasets of lithium-ion batteries for experimental validation. The verification tests’ findings show that, in comparison to existing approaches in the literature, the suggested method may greatly reduce prediction error and achieve high estimation accuracy of the battery’s state of health.

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This paper presents the results of a documentary research on the use of information technology in emergency remote teaching in 66 higher educational institutions in Brazil. The theoretical background of this study is based on the works of Feenberg, Bagglaey, Veloso & Mill, Castañeda & Selwyn and Hodges. The methodological approach consisted of analyzing reports published by YDUQS, an educational holding responsible for managing all the 66 institutions examined in this research. Such analysis aimed at identifying data concerning investments in information technology and its use throughout the Covid-19 pandemic. Results have revealed that investment in information systems as well as technological mediation of academic routines and pedagogical practices paved the way for a rapid response to the crisis triggered by the pandemic and the maintenance of student satisfaction. Nevertheless, the data available within the reports was not enough to draw conclusions on learning management neither on other pedagogical aspects of emergency remote teaching.

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Carbon nanotube field effect transistor (CNTFET) has a huge advantage over the Si- MOSFET. In MOSFET switching occurs by altering channel resistivity whereas in CNTFET switching occurs by modulation contact resistance. CNTFET generates three to four times of drive current than MOSFET. Transconductance of CNTFET is four times higher than the MOSFET. The average carrier velocity is also very high almost double in CNTFET than that is in MOSFET. Its power consumption is low. Electron mobility is high. Threshold voltage is also low. It has better control over channel formation. There is no direct tunneling and gate leakage current is also reduced. Herein, the main objective is to investigate the effect of gate-insulator thickness on CNTFET, and to optimize the thickness so that current carrying capacity may reach higher.A detailed simulations have been made and IV characterizationis done to investigate the effect of Gate-Insulator Thickness on Co-Axial Cylindrical Carbon Nanotube Field Effect Transistor.Report shows with the increasing gate-insulator thickness current is decreased significantly. Where as the variation of nano diameter shows that the increasing rate of current is increased when the carbon tube diameter is increased.

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Most anthropomorphic robotic hands use a lot of actuators to imitate the number of joints and the movement of the human hand. As a result, the forearm of the robot hand has a large size for the installation of all actuators. This robot hand is designed to reduce the number of actuators, but also retain the number of movable joints like a human hand by using the four-bar linkage mechanism and only flexion-extension movements. This stamen is added in the problem statement according to the reviewer’s comment. The special features of this robotic hand are the ability to adjust the link length and the range of rotation for each joint to suit various applications and can fabricate with 3D printing and standard parts with costing about $750. All hardware CAD files and equations are published on the GitHub website, which benefits for researchers to utilize as an open-source approach that their project might be further expanded in the future. The anthropomorphic robotic hand has five fingers, 16 joints, and 12 active Degrees of Freedom (DOFs) with 12 servo motors applied to finger motion and one for wrist motion. The structure of the hand is designed using the average of Asian human hands in combination with the golden ratio. All servo motors are installed in the forearm designed in a ventilated structure with 12V vent exhaust fan motor to stabilize the operating temperature of the robotic hand. Size and weight of the hand included with the forearm are 20×54×16.5 centimeters and 2.2 kilograms respectively. The hand has achieved human-like movement by using a four-bar linkage mechanism and tendon with PTFE tube to guide operation path of the tendon with the lowest friction force. This paper presents the design processes, the experimental set-up, and the evaluation of the finger movements. From the experiment of grasping objects, this hand was able to grasp 10 basic grasp types including 32 different objects, perform 9 common gestures, and lift the object to 450 grams. From this paper, the kinematic equation is proved that the designed finger structure can move exactly as the equation with maximum error of repeatability test around 1.6 degrees.

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Droughts are undesirable and highly unwanted form of disasters. It is essential to analyse the cause of such extreme events and act accordingly to pave the way for a sustainable future. The present research work conducts a seasonality and trend analysis of rainfall over the eight districts of Marathwada region. The study is carried out for the last 39 years ranging from 1980 to 2018. The rainfall data pertaining to pre-monsoon season, monsoon season (Kharif), and annual average have been analysed. The trend has been estimated using Sen’s slope estimation process along with Mann-Kendal test. It was determined that the all the eight districts of the region show a negative trend in the annual rainfall received. Nanded district showed the largest negative trend in the annual rainfall. Out of eight districts seven districts of the region show a decline in rainfall during the monsoon season. The district of Nanded showed largest decline in the rainfall received during monsoon season. The research work presents the discussion on possible causes of such trends estimated. The research creates a robust foundation of advanced computation techniques for prediction of droughts.

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The paper presents two robust and efficient control algorithms based on (i) Optimal Control Allocation (OCA) and (ii) Nonlinear Model Predictive Control (NMPC). The robotics system consists of two rovers with mecanum wheels and mounted two 7-DOF arms carrying a common load. The overall system is an underdetermined one with non-holonomic constraints. The developed control algorithms focus on providing an optimal solution to the wheel and joint torque saturation problem, which is typically encountered while manipulating a large and heavy payload. The first control algorithm based on OCA minimizes a quadratic cost function consisting of robot joint and rover wheel torques, contact forces, and moments using only the current state values and the system dynamics. It is computationally very efficient. The NMPC algorithm minimizes a quadratic cost function which not only includes the current states but also the future state estimates, and the control inputs over a specified prediction horizon. The system consisting of multi-rover with a dual arm is highly non-linear. The linear MPC technique on which most of the previous studies relied is not adequate. On the other hand, the computational difficulties of a generic NMPC algorithm is remarkably high. In this paper, an elegant, discretized technique with exact realization is implemented to take into account the full non-linear model and yet provide a simple real-time solution satisfying a minimum performance index subject to constraints. The results show that the developed control algorithms OCA and NMPC work efficiently, and the minimum the contact moments and forces, and the joint torques are realized while two arms carry a common load and successfully track a reference end-effector trajectory. The results also indicate that although NMPC algorithm is computationally more involved, it provides superior results in reducing joint and wheel torques as well as contact moments and forces.

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In the present contribution, a robust output H∞ control ensuring the stability, reliability and security for nonlinear systems when actuator attacks (data deception attacks) occur. A new design method based on the polytopic rewriting of the attacked system as an uncertain one subject to external disturbances will be detailed. Robust polytopic state feedback observer sta- bilizing controller based on the PDC (Parallel Distributed Compensation) polytopic framework with disturbance attenuation for the obtained uncertain system will also be considered. The obtained methodology is used to ensure the stability and security of a quadrotor/UAV subject to stealthy actuator attacks. State and attacks estimations signals are given in order to highlight the efficiency of the developed approach.

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The relevance of static synchronous compensator (STATCOM) controllers in controlling power network parameters is causing them to be included in contemporary networks. But for the intended objectives to be attained, the best device positioning and parameter settings are essential. This work compares the performance of the particle swarm optimization (PSO) and firefly algorithm (FA) in sizing and placing a STATCOM device for the dual objectives of loss reduction and voltage deviation abasement. The effective mitigation of network loss and voltage fluctuations in the network will be achieved by the deployment of the efficient method during device allocation. While PSO and FA were taken into consideration due to their computational efficiency among other metaheuristic algorithms, STATCOM was chosen from among the Flexible Alternating Current Transmission System (FACTS) controllers as a consequence of its reactive power compensation capability. The MATLAB software was used to implement the simulations on an IEEE 14-bus system. When STATCOM was optimized with PSO and FA, it resulted in active power loss reductions of 432 and 733 kW, respectively, and reactive power loss reductions of 1622 and 2100 kVAr, respectively. As a result, the reductions in voltage variation and power losses in this instance show some benefits of FA over PSO. Additionally, this work has shown that metaheuristic algorithms are beneficial for allocating FACTS devices.

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Currently one of the urgent goals of mathematical education is the organization of effective work with gifted students. Based on the study of various approaches to teaching mathematically gifted students, many years of experience of teachers, students’ work, and an analysis of curricula and materials for schools with in-depth study of mathematics, an author’s model for the training of gifted students was developed. The novelty of this model is that it ensures a rational combination of various forms of education for gifted children on the basis of differentiation, individualization of the process of teaching mathematics, advanced learning, openness, democracy, reflection, and adequate control. The pedagogical experiment was carried out for two years in the Abulfazl Balami gymnasium for gifted children in the city of Vahdat, Republic of Tajikistan. 41 students and 18 teachers took part in the experiment. The data obtained from the experimental and control groups were subjected to qualitative and quantitative analysis. Over the same time interval there were significant changes in the performance of students in the experimental groups, with 40% of the students moving to a higher level. In the control groups, the change was not significant.

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Recently, many problems have been caused by battery fires. The existing BMS(Battery Managment System) measured the voltage of each cell of the battery through the physical connection between the battery and the control module. However, if a battery with up to 1000 VDC becomes inoperable due to an external factor, the battery is damaged, and accordingly, a large current of the battery breaks the control unit of the BMS with 5 VDC to 24 VDC, putting the BMS inoperable. If the battery is operated when the bms is in trouble, it poses a risk of battery fire.Recently, as bms technology was announced with a wireless function, battery information could be easily transferred from the outside, so that convenience was maximized, but stability is still weak. This paper physically separated the battery and control module by measuring the battery voltage depending on the strength of the LED by connecting the battery and LED. and furthermore, the measurement error should be less than 1 mV even when the temperature changes. In addition, it was designed to operate at a low output level of 200 μW to 360 μW using the sub-threshold section of the LED.

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The present work shows the development of a data collection platform that allows the researcher to collect new video and voice data sets in Spanish. It also allows the application of a standardized personality test and stores this information to analyze the effectiveness of the automatic personality recognizers concerning the results of a standardized personality test of the same participant. Thus, it has elements to improve the evaluated models. These optimized models can then be integrated into intelligent learning environments to personalize and adapt the content presented to students based on their dominant personality traits. To evaluate the developed platform, an intervention was conducted to apply the standardized personality test and record videos of the participants. The data collected were also used to evaluate three machine learning models for automatic personality recognition.

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The Activity and Event Network (AEN) is a new dynamic knowledge graph that models different network entities and the relationships between them. The graph is generated by processing various network security logs, such as network packets, system logs, and intrusion detection alerts, which allows the graph to capture security-relevant activity and events in the network. In this paper, we show how the AEN graph model can be used for threat identification by introduc- ing an unsupervised ensemble detection mechanism composed of two detection schemes, one signature-based and one anomaly-based. The signature-based scheme employs an isomorphic subgraph matching algorithm to search for generic attack patterns, called attack fingerprints, in the AEN graph. As a proof of concept, we describe fingerprints for three main attack categories: scanning, denial of service, and password guessing. The anomaly-based scheme, in turn, works by extracting statistical features from the graph upon which anomaly scores, based on the bits of meta-rarity metric first proposed by Ferragut et al., are calculated. In total, 15 features are proposed. The performance of the proposed model was assessed using two intrusion detection datasets yielding very encouraging results.

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This study presents an “Omnidirectional multi-view image measurement system”, which can be used to provide multi-camera 3D reconstruction and multi-view image information. Its characteristic is that four cameras take images from multiple perspectives in the co-sphere framework. The C0 is the middle camera fixed as the geometric center point of measurement, and provides a front image. The other three cameras C1~C3 provide side images, and the co-circular spheres are separated by 120 degrees to extend the circle. The arc rod adjusts the multi-angle imaging. Place the multi-view camera in the arc track and move to the specified position in the sphere to position and capture images. By changing the angle between the cameras, the range of images captured by the cameras can be changed. If the multi-view images of four cameras C0, C1, C2 and C3 are captured at the same time, a stereo camera pair can be formed by any two cameras. The stereo camera pair C0-C1, C0-C2 and C0-C3 can be compiled by using the parallax principle of left and right images matching. Finally, through the demonstration and verification of camera calibration and 3D reconstruction, it can be used for all-round multi-view image measurement.

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This research presents a “Multi-camera for pilot’s cockpit measurement system”, which uses four multi-view images to eliminate the instrument and human body shielding and record the touched area. That could record the body reaction time (velocity and acceleration) and trajectory of the tested personnel. Real-time conversion of multi-view images corresponding to the 3D skeletal joint coordinate information of the human body, which measure the human-computer interaction human factors engineering integration of limb reaction time and trajectory measurement system. Finally, make prototypes, test and optimize, and achieve the research on the optimal cockpit touch area by conducting multi-view image simulation feasibility experiment framework and measurement process method. Using multiple depth-sensing cameras to perform low-cost, standardized automatic labeling of human skeleton joint dynamic capture.

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Power losses exist naturally and have to be cared for in the operation of electrical power systems. Many researchers have worked on various methods and approaches to reduce losses by incorporating distributed generators (DG), particularly from renewable sources. These studies are based on the maximum unit penetration of the DGs, which is rarely achieved, resulting in inaccurate calculations. This paper proposes an advanced solution for calculating power losses by incorporating an Extreme Learning Machine (ELM) method for forecasting the solar irradiation. The ELM algorithm was used to create a model for forecasting solar radiation in the Manokwari region and its surroundings. Daily solar radiation in the region has been predicted using the model. NASA’s 8016 data on temperature and solar irradiation were used to train the ELM model. With an MAE value of around 0.6392 and a training time of 4.4375 seconds, the test results demonstrate that the built model has good accuracy. The operation of a 1000 kWp solar power plant based on the ELM data forecasting can reduce the power loss of the existing distribution network around the location from 1.5095 kW/hour to 0.9068 kW/hour. Furthermore, the power plant operation can minimize the power loss by 39.9249 percent, from 36.2280 kW to 21.7640 kW.

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In this paper, an automatic method for detection of olive trees in RGB images acquired by an unmanned aerial vehicle (UAV) is developed. Presented approach is based on the implementation of RetinaNet model and DeepForest Phyton package. Due to fact that original (pretrained) model used in DeepForest package has been built on images of various types of trees but without images of olive trees, original model detection was unsatisfactory. Therefore, a new image dataset of olive trees was created using sets of images chosen from five olive groves. For neural network training, individual olive trees were manually labeled, and new models were generated. Each model has been trained on different set of images from selected olive groves. Pretrained model and new models were compared and evaluated for various test scenarios. Obtained results showed high precision and recall values of proposed approach and great improvement in performance compared to the pretrained model.

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Decision-makers must make a suitable sequence of decisions under uncertainty in a relatively long period for particular projects and situations. Conventional decision-making approaches under uncertainty are based on expected utility theory and do not sufficiently reflect the one-time nature of decisions. Similarly, the conventional approaches do not adequately incorporate the decision-maker’s intuitions in the decision-analysis process. Numerous studies have demonstrated that salience information (attention-grabbing) is crucial in human decision-making exercises. However, there is limited information on the decision-making approaches incorporating the salience information and the applications of such approaches in actual practice. This study applies an approach called the multistage one-shot decision-making approach (MOSDMA) to reevaluate a previous decision problem related to a department technology project from the sultanate of Oman. Unlike traditional lottery-based approaches, MOSDMA is scenario-based, introducing an essential alternative for multistage decision-making under uncertainty. The paper is the first contribution to using the passive focus point introduced in MOSDMA in actual applications. The aim is to verify the explicability and effectiveness of the suggested method for solving decision-making under uncertainty problems in actual practice. The paper exhibits positive findings and promising potential of the approach advocating further future studies in theory and application aspects.

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Traditional project planning in effort and duration estimation techniques remain low to medium accurate. This study seeks to develop a highly reliable and efficient hybrid Machine Learning model that can improve cost and duration prediction accuracy. This experiment compared the performance of five machine learning models across three different datasets and six performance indicators. Then the best model was verified with three other types of live project data. The results indicated that the MLR-DNN is a highly reliable, effective, consistent, and accurate machine learning model with a significant increase in accuracy over conventional predictive project management tools. The finding pointed out a potential gap in the relationship between dataset quality and the Machine Learning model’s performance.

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Determining the best candidates for a certain job rapidly has been one of the most interesting subjects for recruiters and companies due to high costs and times that takes the process. The accuracy of the models, particularly, is heavily influenced by the discriminant variables that are chosen for predicting the candidates scores. This study aims to develop an performance job prediction systems based on hybrid neural network and particle swarm optimisation which can improve recruitment screening by analyzing historical performances and conditions of em- ployees. The system is built in four stages: data collection, data preprocessing, model building and optimisation and finally model evaluation. Additionally, we highlight the significance of Particle Swarm Optimization (PSO) in enhancing the performance of the models created by presenting a training algorithm that uses PSO. We conduct a study to compare the performance of each hybrid model and summarize the results.

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This work investigated the coupling behavior of the scattered-bend loss in displacement sensor during the bending of the fiber by using a multimode polymer optical fiber (POF). To utilize the scattered-bend effect for displacement measurement, a side coupling technique can be used by twisting a pair of POF fibers and bent the structure into a loop. The working principle of the sensor is quite simple. The bent radius grows smaller as the fiber draughts which simulate a change of displacement. The scattered-bend loss increases as the illuminating fiber is bent in decreasing angle and the light being coupled to the receiving fiber. The fabricated sensor is tested based on static measurement analysis and the sensor is characterized by its sensitivity, resolution, linearity, and repeatability error. From the experiment, the fabricated sensor has a range of roughly 160 mm with a sensitivity of 0.817 nW/mm, a resolution of 1.228 mm, and a repeatability error of 1.856 %. The sensor exhibits high linearity from 0 mm to 80 mm. The sensor’s design structure and analysis are simple, comprehensive, and cost-effective, with potential benefits in industrial applications.

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Cyber security is based on different principles such as confidentiality and integrity of transmitted data. One of the main methods to send confidential messages is to use a shared secret to encrypt and decrypt them. Even if the amortized computational complexity of the hashing functions is Ο(1), there are several situations when it is not possible to use them due to the lack of computing power or the need to keep completely hidden the communication to other parties in the network. Covert Channels (CCs) are an excellent alternative in all these cases because they hide the private message in legitimate communication channels without the need to allocate additional resources to communicate. For this reason, they are difficult to identify because they are fully camouflaged in legitimate traffic. Unfortunately, CC technique is also used by hackers to exfiltrate network data and initiate cyber-attacks against devices in the system: Internet of Medical Things (IoMT) are one of the most vulnerable devices affected by this type of attack. It is therefore essential to create a system that can autonomously identify the presence of a malicious CCs to safeguard the health of patients. This paper describes an approach to create a Covert Timing Channel (CTC) based on TCP packets between client and server and how it is possible to detect the hidden communication using an innovative pipeline composed by several Machine Learning (ML) and Deep Learning (DL) models, such as Convolutional Neural Network (CNN), Siamese Neural Network (SNN) and K-Nearest Neighbors (K-NN). Considering 4 different message types exchanged in CTC, the proposed pipeline achieved 94% accuracy in identifying covert messages in the channel.

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This paper deals with the analysis of the dynamic linkage, co-movement between COVID-19 out- break situations and German stock market. Firstly, Markov Regime Switching Analysis(MRSA) is proposed and employed to investigate the situations in the pandemic, as to catch the dynamics of how the daily number of the newly-infected changes, and also to assess the impact of the pandemic situations on German Market. Secondly, we compute the log growth rates of the weekly new cases and the log-returns of weekly DAX index, then fit the GARCH models to both of them to acquire their volatilities. We then employ the MRSA model once more to expose the dynamic linkages and co-movement between these two volatilities series. Through our empirical analyses, we find that GARCH models can capture the dynamics of stock returns and the growth rates. On the other hand, the MRSA models work well to identify the dynamics between different regimes with different states in dealing with the volatilities obtained from the estimated GARCH models. Our proposed econometric methods are highly practical, it indicates the possibility of replicating the results obtained in this study to assess the impact of other epidemics and negative factors on economic activities. Knowing what may happen during a pandemic, more effective measures and actions can be taken to protect people while dealing with another pandemic in the future.

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Limited interactive communication modes between students and teachers in online environments may lead to teachers misinterpreting or overlooking student needs during online teaching. Students learning online may also hesitate to make their needs known even when latent desires in teaching flow, pacing, and review, may be beneficial to the quality of the learning experience. The objective of the study is to construct and test models to infer student needs based on the facial expressions of students while they are learning online. Several Random Forest models were constructed to infer the reported conditions and tested using facial expression data extracted from the videos as action units in Facial Action Coding System (FACS). Exploratory Factor Analysis (EFA) was adopted to extract and combine the highly-related facial action units for building the training and testing data. The testing of the inference model yielded a result of 0.028 on the mean average error (MAE). This result suggests these methods would contribute to the development of improved online learning systems that assist teachers in understanding in real-time how students are responding to a lecture or other classroom experience.

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In this paper we present a measurement system that is able to evaluate radar algorithms for vital signs sensing applications. For such medical applications, it is crucial to develop robust and reliable algorithms that are tested in a laboratory environment. The presented measurement system generates reproducible vital sign micro movement and dynamic clutter using loudspeakers to replicate realistic scenarios with two moving objects. It is described how realistic vital sign movement patterns are prepared using signal synthesis or recorded measurements, e.g. from a published dataset. The capability of the system to evaluate radar algorithms is demonstrated by investigating the impact of a beamforming algorithm on dynamic clutter. During the measurements presented in this paper, one loudspeaker replicates different vital sign movement patterns and the other loudspeaker creates dynamic clutter. It is shown that a digital beamforming improves the dynamic clutter rejection and leads to a better quality of the radar phase signal.

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Indoor positioning is a complex issue with many, heterogeneous application cases, each one presenting different requirements and environments. In such a complex ecosystem, an agile taxonomy is needed to be able to select a proper solution for a given scenario, as well as practical recommendations for the most used solutions. Besides providing these tools, we analyze a real-world scenario and its requirements, selecting a practical solution and evaluating it together with its implications and consequences, providing a reference guideline for practical applications of indoor positioning.

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People with visual impairment face daily struggle of navigating through unfamiliar places. This problem mainly caused by their lack of spatial awareness, i.e., the ability to estimate the distance between themselves and their surroundings. In order for visually impaired people to navigate independently, an effective navigation aid is required. The proposed navigation aid device utilizes depth camera to collect visual information of surrounding objects. Then, it represents the obtained visual data into stereophonic sound to notify the user directly through an audio device. The aid device is designed to be portable, comfortable, and easy to use. It can further be developed and upgraded to suit the needs of visually impaired users. Designed to be wearable, this proposed device was tested and received excellent score in portability, comfortability, and ease of use. The subjects were able to detect the position of obstacles in front of them with 92.47% accuracy, and could also estimate the distance of the object with Mean Absolute Error of 0.8. Examination on their navigation ability indicated that the subjects could stop before collision with an object and maneuvers through the gap between two parallel obstacles.

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This paper extends the idea of creating a Quantum Machine Learning classifier and applying it to real weather data from the weather station behind the Pa Sak Jonlasit Dam. A systematic study of classical features and optimizers with different iterations of parametrized circuits is presented. The study of the weather behind the dam is based on weather data from 2016 to 2022 as a training dataset. Classification is one problem that can be effectively solved with quantum gates. There are several types of classifiers in the quantum domain, such as Quantum Support Vector Machine (QSVM) with kernel approximation, Quantum Neural Networks (QNN), and Variational Quantum Classification (VQC). According to the experiments conducted using Qiskit, an open-source software development kit developed by IBM, Quantum Support Vector Machine (QSVM), Quantum Neural Network (QNN), and Variable Quantum Classification (VQC) achieved accuracy 85.3%, 52.1%, and 70.1% respectively. Testing their performance on a test dataset would be interesting, even in these small examples.

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In conjunction with psychological stress, physiological indicators such as heart rate (HR) and heart rate variability (HRV) are frequently employed. This study uses a substitute for heart rate variability (HRV) known as short-term pulse rate variability (PRV) to evaluate emotional changes. We examined sixteen college students using a low-cost photoplethysmograph and obtained a short-term PRV reading from one of their index fingers. Each PRV’s parameters during resting condition were established using a particular Trier Social Stress Test (TSST) and divided into four phases (R1: baseline rest, R2: anticipatory stress, R3: stressful event, and R4: recovery period). The Positive Affect and Negative Affect Schedule (PANAS) questionnaire was used to assess the participants’ moods during the experiment. The psychological assessment based on PANAS shows that negative affect tends to increase along the TSST procedure, especially from the baseline rest (R1) to the stressful event (R3), even though it is not statistically significant (p > 0.05). The physiological assessment using PRV revealed that between R1 and R3, the short-term SDRR, pNN50, RMSSD, LF, HF, total power, SD1, and elliptical area of PRV tended to rise considerably (p < 0.01). The properties of PRV show that the heart rate fluctuation also represents psychological changes. We concluded that applying the TSST procedure to induce stress modulates the features of PRV, particularly the time and frequency domain variability properties. Observed patterns following stressful events (interview sessions) indicated an increase in PRV values, mainly the RMSSD, HF, SD1, and elliptical area (p < 0.001). Caution should be applied while perceiving physiological alterations as an immediate sign of mental threats. Nonetheless, these alterations are likely caused by the association between stress and negative emotions.

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In conjunction with psychological stress, physiological indicators such as heart rate (HR) and heart rate variability (HRV) are frequently employed. This study uses a substitute for heart rate variability (HRV) known as short-term pulse rate variability (PRV) to evaluate emotional changes. We examined sixteen college students using a low-cost photoplethysmograph and obtained a short-term PRV reading from one of their index fingers. Each PRV’s parameters during resting condition were established using a particular Trier Social Stress Test (TSST) and divided into four phases (R1: baseline rest, R2: anticipatory stress, R3: stressful event, and R4: recovery period). The Positive Affect and Negative Affect Schedule (PANAS) questionnaire was used to assess the participants’ moods during the experiment. The psychological assessment based on PANAS shows that negative affect tends to increase along the TSST procedure, especially from the baseline rest (R1) to the stressful event (R3), even though it is not statistically significant (p > 0.05). The physiological assessment using PRV revealed that between R1 and R3, the short-term SDRR, pNN50, RMSSD, LF, HF, total power, SD1, and elliptical area of PRV tended to rise considerably (p < 0.01). The properties of PRV show that the heart rate fluctuation also represents psychological changes. We concluded that applying the TSST procedure to induce stress modulates the features of PRV, particularly the time and frequency domain variability properties. Observed patterns following stressful events (interview sessions) indicated an increase in PRV values, mainly the RMSSD, HF, SD1, and elliptical area (p < 0.001). Caution should be applied while perceiving physiological alterations as an immediate sign of mental threats. Nonetheless, these alterations are likely caused by the association between stress and negative emotions.

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Blockchain technology has been successfully applied in many fields for immutable and secure data storage. However, for applications with on-chain big data, blockchain scalability remains to be a main concern. In this paper, we propose a novel scalable storage scheme, called HistoChain, for a consortium blockchain network to manage blockchain data. We use a current blockchain and historical blockchains to store on-chain big data, where the current blockchain and the historical blockchains store data from recent years and earlier years, respectively. Both the current blockchain and the historical blockchains are maintained by super peers in the network; while regular peers manage only the current blockchain and can retrieve historical data by making queries to the super peers. We present procedures for generating historical blockchains, dynamically balancing the data retrieval workload of super peers, and concurrently retrieving historical blockchain data in response to queries. We further provide a case study of healthcare data storage using a consortium blockchain, and the simulation results show that our scalable HistoChain storage scheme supports efficient access and sharing of big data on the blockchain.

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The main contradiction identified in the study is that the existing scientific and methodological solves of economy development management processes does not create prerequisites for improving the efficiency of their work, the introduction of progressive technologies for material and moral stimulation of the work of performers and administrative and managerial personnel, advanced social mechanisms for country’s economy development and social security employees. Economic and mathematical modeling of the complex system of social financing of enterprises and the economy of the country, scientifically sound personnel policy and the system of motivation of performers and administrative and management personnel is an important and urgent problem. The purpose of the study is to develop and implement an economic and mathematical model of a comprehensive system of social financing of enterprises and the economy of the country, optimizing the wages of the workforce, consistent with revenue growth, deductions for the development of the enterprise (relevant for the employer and the entire workforce), taxation and social contributions (important for the state). The results of the studies conducted and presented in this article allow us to conclude that the proposed social financial technologies for the development of enterprises and the economy of Russia, make it possible, at quite achievable rates of growth of gross domestic product (revenue of enterprises) by 3% per year, to ensure an increase in the wages of working citizens for 5 years by 34 %, which will make it possible to practically end poverty, and to increase contributions to the development fund over 5 years by 16%. Starting from 2026, increase receipts from income tax, tax on profit rate and value added tax and bring this growth to 30% by 2041, which will allow the state to solve many social problems.

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This paper describes the design of a phase shifter with electrically adjustable parameters employing only one voltage differencing gain amplifier (VDGA) and one floating capacitor. This circuit requires no external resistors, resulting in a resistorless design and a low component count. The proposed circuit implements a first-order all-pass filter response with electronic control of its passband gain, pole frequency, and phase difference via bias current modification. Non-ideal effects of the VDGA on the phase shifter circuit are also examined. PSPICE simulation results using TSMC 0.25-m real process parameters and practical test results using readily available LM13700s are incorporated to validate the theoretical conclusions. The results indicate that the simulations and experiments yielded phase shift deviations of 2.22% and 3.11%, respectively. The pole-frequency errors for simulations and experiments were 0.31% and 0.63%, respectively. The applicability of the suggested phase shifter is illustrated by the design of the voltage-mode quadrature oscillator.

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In this research paper, a technical and economic-environmental study was developed to investigate the possibility of establishing various hybrid power systems with different operation modes. Grid-connected and standalone hybrid systems (solar-wind with storage batteries and diesel generators) have been realized in order to carry out a comparative analysis study of two configurations. These systems have been investigated through the Hybrid Multi-Energy Resource Optimization software (HOMER), which calculates pollutant gas emissions, simulates and optimizes energy consumption based on energy demand and resources. As part of the economic analysis, the internal rate of return, the net present value and the payback period were estimated. Both configurations have been developed to meet the power consumption of an audiovisual system. The results obtained show that the first system is the most cost-effective to establish, considering in particular the energy production potential and gas emissions. It can be stated that the proffered grid-connected hybrid system is the most suitable and cost-effective system as it offers several advantage. The total net present cost is $5.425million and the total energy cost is approximately $0.0686 per unit.

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In the field of micro-LED displays, there is strong demand for red phosphors with high photoluminescence intensity, high color purity, and small particle size. Here, we focus on Eu(III) complexes because they produce sharp photoluminescence spectra with high color purity and can be dissolved in polymer, enabling a reduction in particle size to the molecular level. We have previously established novel molecular design concepts for Eu(III) complexes by coordinating two different phosphine oxide structures to one Eu(III) ion in order to enhance photoluminescence intensity and increase solubility in polymers and solvents. Many Eu(III) complexes have been developed based on these concepts and their photoluminescence properties investigated. Eu(III) complexes with two different phosphine oxide structures are important candidates for red phosphors in micro-LEDs.

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Medical adherence is a major concern globally and is increasing with improved access to medication. Unfortunately, patients taking multiple medications often struggle with confusion about when and how to take each medication. To address this issue, an inexpensive domestic device has been proposed to improve medication adherence. This device uses Wi-Fi and cellular Internet of Things (IoT) integration to dispense medication at the prescribed times, making it suitable for use in both home and long-term care settings. The device also includes a web interface that allows users to control the device and make changes to dosage and other related information. Additionally, the device features an intricate system for sorting pills to ensure accurate and efficient medication delivery. Automating medication taking through this device can improve patient adherence and overall health outcomes, which could significantly impact public health and quality of life for patients struggling with medication adherence.

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The COVID-19 pandemic has caused a substantial death toll throughout the world. The pandemic has created a threat to public health, the economy, food systems, and the workplace. An increased reprioritization of health expenditure towards COVID-19 vaccines will impact on allocations to other medical facilities. In developing countries, hospitals shortage the infrastructure to facilitate patients. Therefore, traditional checkups and clinics are not practical. According to research done in this article, 95 percent would prefer telemedicine and telehealth rather than conventional inspections. Even though smart healthcare technology has been implemented, it does not show adequate effectiveness when commercialized. Therefore, in this paper, a microcontroller-based, low-cost, automated, real-time system has been proposed to give a convenient solution for measuring the vital signs of the body. In this project, Multiple sensors with a microcontroller were intended to measure heartbeats per minute, Oxygen Saturation, body temperature and electrocardiogram of a patient at home without going to a hospital. The developed system indicated very less percentage error in temperature measurement and was able to maintain high accuracy on Beats per Minute, Oxygen Saturation and Electrocardiogram. This approach provides a feasible solution for both patients and medical professionals.

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Among the development of technology, a large number of medical devices have been implemented in various forms for therapy and treatment. Remote controllability, real-time response, small size, and non-toxicity of the devices are the critically requirement to be operated in the blind, unstructured and fluidic environments of biomedical regions. Untethered soft swimming milli-robots have been developed to fulfill the remote operation in such that region under magnetic navigation. A motor-less mechanism of the soft robots utilizes a high degree of freedom provided by magnetic compliance of the deformable structure with a minimal control of the oscillating magnetic field. Theoretically, magnetic property of the soft robots is defined by magnetic moments consisting of orientation and strength. Orientation of magnetic moments can be defined by magnetizing technique, and strength of magnetic moments is obtained by their quantity in the magnetic structure. Herein, this work investigates how magnetic moments through the details of magnetic orientation and quantity affects swimming behavior and performance. The soft robots are fabricated with elastomer embedded with NdFeB microparticles to obtain three types of distinguish magnetic property in the deformable structure; the I robot has non-uniform magnetic orientation and uniform magnetic strength, the II robot has uniform magnetic orientation and non-uniform magnetic strength, and the III robot has non-uniform magnetic orientation and non-uniform magnetic strength. The results interestingly report that each type of robot’s property functions mechanism and benefits swimming performance differently under the same magnetically control parameters. The I robot does not have any exceptional potential, but the II robot can be operated at the higher control frequency even reaching the step-out point. The III robot shows the greatest performance in swimming and maneuverability. These results would be useful to design a swimming soft-robot capable of applying for various purposes, especially when the demand concerns non-harm, small-scale size, soft interface and remote controllability.

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Continuously Variable Series Reactor (CVSR) is a device that can vary the reactance in an ac circuit by controlling the magnetization of a ferromagnetic core, shared by ac and dc windings. The bias dc current can change the equivalent ac reactance(inductance) in order to, for example, control load flow, damp oscillations, or fault current limitation. Gyrator-Capacitor (G-C) approach in modeling electromagnetic devices provides a strong and practical way in simulating an integrated system composed of magnetic and electric/electronic circuits. The G-C model provides key advantages in analysis of electromagnetic devices, including CVSR. Understanding the performance and the operational characteristics of the CVSR is essential for its proper utilization in the power grid. This paper presents a detailed G-C approach that includes the ferromagnetic core nonlinearities, namely, hysteresis and saturation. The approach has been applied in modeling the electromagnetic coupling between the ac and dc circuits of a three-phase CVSR. Analysis of the effect of different control dc circuit types on the equivalent ac inductance is presented, during operating conditions at different ferromagnetic states. In addition, induced voltages across the windings and the power exchange with the control circuit are presented.

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Like many STEM research projects, the members of the National Science Foundation-funded STEM SEALS project dramatically shifted from in-person delivery of a summer institute to distance-learning with minimal time for preparation. However, the daunting challenge also offered the unique opportunity to apply Design-based Research within an exploratory study to inform and document the progression and supply counsel to other STEM providers contemplating a shift to a virtual platform. The goals of this exploratory study include (1) to make apparent the barriers to transitioning to virtual STEM enrichment programming in rural spaces during the COVID-19 pandemic, (2) detail important decisions made in the move online, along with the reasoning behind those decisions, and (3) share best practices that arose during the inaugural effort. Methods included the review of multiple data sources, including project meeting minutes, educator reviews of materials, and pre/post institute student and teacher surveys. to inform rapid-paced learning cycles. As a result, the team adopted a mindset that focused on high-quality STEM experiences. Strategies supported by the research include effective substitutes for in-person demonstrations, leveraging existing platforms, employing mechanisms for troubleshooting, and framing failure in ways that encouraged the development of a positive STEM identity

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Passive remote monitoring applications of underwater signal processing in a shallow water environment are an impactful area of research for environmental and marine-life monitoring. The majority of the sound source localization techniques require carefully placed synchronized hydrophone arrays, which can be complicated and hard to maintain. In this paper, we utilized the modal dispersions of a signal to derive a localization method for a noisy, shallow water environment. Our proposed algorithm employs modal selection to process the most noise- resistive dispersion curves, improving the accuracy and noise-resistivity of the existing methods. Moreover, we proposed a 2D localization method with multiple unsynchronized hydrophones and minimal hardware requirements and limitations. Furthermore, we analyzed the effects of underwater ambient noise on the accuracy of the proposed method, using simulated and real recorded explosion and whale sounds, and compared our algorithm’s localization performance with others. Simulation results show increased localization accuracy of 30m for the recorded explosion sound and 360m for the Whale sound.

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Due to the variations in weather conditions, photovoltaic systems adopt a technique based on maximum power point tracking to extract the maximal power of the solar module. In the literature, there are many different methods classical and intelligent of maximum power point tracking (MPPT). But, due to the semiconductor effect, the current-voltage characteristics of the solar module is nonlinear. This affects its efficiency and make its control not easy. In this contribution, we present a new fuzzy PV MPPT based on custom defuzzification. The obtained power using the proposed fuzzy PV MPPT based on custom defuzzification is significant compared to Pertub & observe and fuzzy PV MPPT in term of performances indices such as: Rise time and overshoot.

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Metal Oxide Semiconductor Field-Effect Transistors are commonly taught in courses for electrical engineers as they are the most common components within integrated circuits. However, despite numerous papers and books on MOSFETs, students still struggle with understanding their behaviour, particularly in the saturation region. This paper presents an expanded explanation of MOSFET behaviour, with a consistent and causal derivation of Level 1 MOSFET behaviour from a few equations, aimed at students without an extensive technological background. The paper provides illustrative explanations to help them understand MOSFET behaviour and addresses common students’ questions, such as why the current is limited by charge carriers in the semiconductor substrate and why characteristic curves do not follow a parabolic curve in saturation. In addition to providing a comprehensive introduction to MOSFET behaviour from a circuit designer’s perspective, this paper also offers valuable insights into interpreting AC parameters in modern MOSFET models. These parameters are often key to understanding and solving circuit problems related to small signal behaviour and frequency response, as demonstrated through various industrial application examples. These examples highlight how to bridge modern MOS models, such as the BSIM model, with MOS-Level 2 modelling, which is easily interpreted by users. By presenting these real-world examples, analysed by a symbolic analysis tool incorporating the BSIM to Level 2 AC model, this paper provides a practical and accessible approach to teaching MOSFETs and their applications in industry.

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The levelized cost of electricity production is highly dependent on the cost of fuel oil on the world market. In order to reduce the dependency on the fuel oil, many countries are adopting an energy transition towards distributed generation. Distributed generation can be described as various means of generating electricity at or near where it will be used. Such generating mode can be a solar PV system, wind turbine generator and other renewable energy sources. However, it entails lots of challenges as it uses power electronics devices as the power grid interface, which causes a reduction in the system inertia and at the same time affecting the frequency, thereby affecting the stability. To enhance this stability, appropriate control measures need to be adopted. This paper brings forward a novel approach for frequency control support of a wind turbine generator (WTG) in a diesel generation mix. The novelty of this research paper explained on the concurrent application of a Proportional derivative (PD) and a Proportional Integral Derivative (PID) for speed and frequency control in a WTG. The analysis of this experimental research was carried out through the modelling of the rate of change of frequency (RoCoF) using MATLAB / Simulink software. The results showed that the use of these controllers in presence of WTG provide frequency support to the system as the frequency varied within the acceptable limit of 0.5Hz. Additionally, this experimental research work also proved that the use of speed / governor control in form of the PID improved the RoCoF and provided an enhancement in the stability of the test system. Finally, this paper confirmed that the integration of WTG to the grid required the use of appropriate control algorithm for an efficient exploitation of this kind of renewable energy source.

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The attributes of the vehicle routing problem (VRP) are as many additional characteristics or constraints which aim to better take into account the specificities of real application cs. The variants of the VRP thus formed are the support of an extremely rich literature, comprising an immense variety of heuristics. This article constitutes an industrial application and an objective synthesis of successful and challenging heuristic concepts for time-windowed VRP problems. The purpose will be to minimize transport costs and determining the optimal number of trucks by applying a transport algorithm. The results show that the solution method should help to increase the competitiveness of transportation operations in this important economic sector.

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