Miracle-dz/UniFAR-FTUs huggingface dataset:
Concept and Definition of Dynamic Symmetrical Components With Time-Varying Amplitude and Frequency

This paper investigates the dynamic symmetrical component method of internal voltage vector with time-varying amplitude and frequency. With the large-scale integration of power electronic devices into the power grid, when there is an asymmetrical fault in the AC network, the voltages and currents present the characteristics of amplitude and frequency time-varying modulation. Hence, the traditional asymmetrical fault analysis method and concept based on phasor models with constant amplitude and frequency are not adaptive enough. In order to supplement the traditional symmetrical component method and concept, the phasor models and vector models with time-varying amplitude and frequency are established. Then, the asymmetrical three-phase phasors with time-varying amplitude and frequency are decomposed into three groups of symmetrical phasors with time-varying amplitude and frequency. Based on the mathematical and physical characteristics of symmetrical components, the applicable concept and definition are reestablished. Lastly, time-domain simulations corroborate the theoretical analysis.
A Deep Learning-Based Soft Sensing Prediction Model for Tubular Furnace

Tubular furnaces are necessary in petrochemical industry, whose high-level automation has been hampered by the complicated inner thermal mechanism. To realize the high-accuracy prediction of key parameters of furnace thermal state, including thermal efficiency, which cannot be measured directly by sensors, in this paper, a soft sensing prediction model for tubular furnace is proposed. Based on the traditional CNN-GRU network, which is composed by the convolutional neural network (CNN) and the gated recurrent neural network (GRU), that the two designed feature extraction modules are embed, ultimately compose the proposed Conv-GRU network. Comparative experiments demonstrate that the proposed combinational network with two well-designed modules outperforms general convolution networks and shallow neural networks in terms of prediction accuracy. The results prove that the proposed GRU-Conv can accurately model the tubular furnace inner state with low computational cost, providing improvements room for the performance of combustion optimization control systems for tubular heating furnaces.
Suppression of Eddy Current Loss in Multilayer NiFe-Polypyrrole Magnetic Cores Fabricated Using a Continuous Electrodeposition Process

Metallic magnetic alloys are of interest as core materials in ultracompact or integrated inductors and transformers. However, when operated at high frequencies, such materials should comprise a multilayer stack of magnetic material laminations and electrically insulating interlayers to suppress eddy current loss. To achieve scalable and continuous fabrication of such a structure, sequential multilayer electrodeposition is an attractive approach. To achieve sequential electrodeposition, interlayer’s electrical conductivity should be sufficiently high to permit electrodeposition of subsequent layers, but sufficiently low to suppress eddy current loss. Polypyrrole, an electrodepositable polymer, was investigated as an interlayer material. Finite element modeling demonstrated a negligible difference in eddy current loss between NiFe/polypyrrole and NiFe/vacuum multilayers. Experimental verification of the efficacy was demonstrated as well. Compared with a single-layer NiFe inductor that has a comparable low-frequency (10 kHz) inductance value, a laminated ten-layer NiFe core showed higher inductance retention (88% of the low-frequency inductance for the laminated core versus 21% for the single-layer core) and lower ac resistance (1.68 versus $12.7~\Omega $ ) at 8 MHz, both of which are signs of suppressed eddy current. This scalable fabrication approach to high-frequency inductors will facilitate power converter miniaturizations.
Deception Detection and Remote Physiological Monitoring: A Dataset and Baseline Experimental Results

We present the Deception Detection and Physiological Monitoring (DDPM) dataset and initial baseline results of deception detection on this dataset. Our application context is an interview scenario in which the interviewee attempts to deceive the interviewer on selected responses. The interviewee is recorded in visible, near-infrared, and long-wave infrared light, along with cardiac pulse, blood oxygenation, and audio. After collection, data were annotated for interviewer/interviewee, curated, ground-truthed, and organized into train / test parts for a set of canonical deception detection experiments. For the collected data, we estimated subject heart rates (remotely from face videos with a mean absolute error lower than two beats per minute), pupil size, and eye gaze. Fusion experiments discovered that a combination of remote plethysmography, pupil size, and thermal data yields the best deception detection results, with an equal error rate of 0.357. The database contains almost 13 hours of recordings of 70 subjects, and over 8 million visible-light, near-infrared, and thermal video frames, along with appropriate meta, audio and pulse oximeter data. To our knowledge, this is the only collection offering recordings of five modalities in an interview scenario that can be used in both deception detection and remote photoplethysmography research.
Convolutional Neural Network for Automated Colorectal Polyp Semantic Segmentation on Colonoscopy Frames

Colorectal cancer is one of the deadliest cancer types worldwide. Therefore, an early detection is crucial to winning the fight against this disease. In this work, to help ease polyp detection, we present a fully convolutional network for colorectal polyp semantic segmentation (FCN-SEG4). This approach uses VGG16 as the backbone for feature extraction, followed by a series of transpose convolutions to get an accurate semantic segmentation. After training the model on the CVC-clinicDB dataset, an overall precision of 86.75% was reached. We trained FCN -SEG4 using other datasets to study the effect it may have on the results. This proposal proved good potential with room for improvement especially when it comes to performance speed.
Optimization of Low-Frequency Magnetic Metamaterials for Efficiency Improvement in Magnetically Coupled Resonant Wireless Power Transfer Systems

With the extensive applications of magnetically coupled resonant wireless power transfer (WPT), improving the power transfer efficiency (PTE) of WPT systems is critical. Magnetic metamaterials (MTMs) can effectively improve the PTE. However, currently the design of MTMs is mainly based on microwave or optical theory, and their applicability in low-frequency WPT systems needs to be further explored. In this paper, a fast and effective method for the design and optimization of low-frequency MTM is provided. Firstly, based on circuit theory and mutual inductance calculation, the PTE of WPT systems is concluded in an equation that concerns the position of a 2-D MTM slab and compensation capacitance of MTM unit coils. Two WPT systems with large differences in coil size of the transmitter (Tx) and receiver (Rx) and the same size are considered. In addition, a directly separating variables method is proposed to optimize the MTM slab position and the compensation capacitance of unit coils to maximize PTE when the MTM slab position changes axially between Tx and Rx. It is found that the optimal MTM slab positions for the two systems are remarkably different, and the PTE impressively presents a capacitance splitting phenomenon with the change of compensation capacitance. Finally, two MTM-enhanced WPT systems are established to verify the theoretical calculation. The experiments show that the maximum PTE is significantly increased by 144% and 31% respectively by adopting optimized compensation capacitance compared with that without MTM for the two systems with different or same transceiver coil size.
Rack-aware MSR codes with optimal access

We derive a lower bound on the amount of information accessed to repair a single failed node from any number of helper racks in the rack-aware storage model that allows collective information processing in the nodes that share the same rack. Furthermore, we construct a family of rack-aware MSR codes with the number of symbols accessed for repair attaining the bound with equality for all admissible parameters. Constructions of rack-aware optimal-access MSR codes were only known for limited parameters.
Compact Model for Reverse TLP I-V Characteristics of ESD diodes

A physics-based compact model for the reverse bias transmission line pulse (TLP) I-V characteristics of ESD diodes is presented. Model formulations capture the post-reverse breakdown I-V behavior accurately and account for the temperature dependence of these characteristics. Model accuracy is demonstrated by comparing it with reverse TLP I-V measurements.
Combination of electro / impedance / seismo / phono cardiography in medicine

Cardiovascular diseases are the leading cause of the death worldwide. They are followed by the third most common cause and that is respiratory disease. Therefore, new methodologies such as seismocardiography (SCG), impedance cardiography (ICG) and phonocardiography (PCG) are beginning to appear [1]. Main goal of this article is to present a concept of a device for simultaneous measurement of electro, impedance, seismic and phono (EISP) cardiography and its application in the diagnosis of heart and respiration activity.
Prediction of suicidal ideation in a new Arabic annotated dataset

One of the major health issues facing the world is suicide. Nearly up to 700000 individuals commit it yearly. Nowa-days, people are progressively accessing social media platforms to communicate their feelings, leaving behind them a track of emotional states. Collecting and processing these posts can be quite useful in recognizing suicidal thoughts once published to provide assistance in time. To achieve so, several researchers have already examined the matter with machine learning algorithms often applied. However, most of these works focus on English content. In this paper, the major goal is to detect suicidal ideation in Arabic-language content automatically. Furthermore, we apply popular machine learning algorithms such as: Logistic Regression (LR), Support Vector Classification (SVC), Multinomial Naive Bayes (MNB) and Random Forest (RF) on the proposed dataset, and the obtained results were satisfactory.
Scenario-based simulation environmental and Integration of a Lane Keeping System with Vehicle Dynamics Control

The vehicle dynamics caused by vehicular motion control system is an important issue related to the human factor in the advanced driver assist system (ADAS). A lane keeping system (LKS), one of the ADAS, is the lateral control application which uses the electric power steering (EPS) system to keep the vehicle driven in a lane while the vehicle drifts towards an unintentional lane departure and reaches the condition of the time to lane crossing. In this paper, using Simcenter Prescan, we can virtually validate ADAS and automated vehicle functional method, it can be used to achieve the LKS function in a simulation test vehicle will be clearly described. The lane detection system and electric power steering system with the active steering function are also integrated with LKS. The paper also defines the limiting operating behavior of the steering motion according to the vehicle dynamic parameters in order to meet the requirements of the vehicle dynamics in ISO11270. Finally, the LKS algorithm will be achieved in two different operating conditions, one is variable lateral velocity and the other is variable longitudinal velocity, and performed in a simulation environment virtual vehicle to verify the function of the steering operating limitations, the simulation results show that the proposed algorithm can keep the vehicle in the lane and the vehicular dynamics also meet the requirement of ISO11270 standard.
A Review of Modeling and Mitigation Techniques for Bearing Currents in Electrical Machines With Variable-Frequency Drives

The converter switching in variable-frequency drives can generate high frequency common mode voltage between the machine winding and the converter ground, leading to high frequency parasitic currents which can flow through the machine bearings unless precautions are taken in design and installation. These parasitic and unintended currents in the bearings cause deterioration of the lubrication film and surface damage to the rolling elements of the bearings. These problems will be exacerbated as wide-bandgap semiconductors with faster switching rise times start becoming more widespread in variable-frequency drives. This paper reviews the modelling and mitigation techniques of high frequency bearing currents in inverter fed AC drives. It aims to provide a solid base for the research community to further understanding the bearing currents phenomenon and helping to find novel improved technique for their mitigation and measurement.
Function of Traffic Prediction in Alleviating Traffic Congestion

Traffic prediction technique can be used to guide people's travel, and there are many researches have been conducted to achieve a higher prediction accuracy. If the traffic prediction result can be timely conveyed to all travelers, personal travel plan may also change accordingly, thus to influence the traffic state of the road network. This paper considers decision-making model, cyclic causal model, and game process, and analyzes the function of traffic prediction in alleviating traffic congestion. Simulation results prove that the sharing mechanism of traffic forecast results is very important, meanwhile, a limited effect can be also found when using traffic prediction to alleviate traffic congestion.
Regret Analysis for RL using Renewal Bandit Feedback

Learning in a Markov Decision Process (MDP) framework is a fundamental challenge for sequential decision making under uncertainty. In this paper, we present a new perspective on model-based learning in MDPs using ideas from renewal theory. In particular, we reformulate the problem of controlling a Markov chain to one of controlling a renewal reward process with bandit feedback. For this reformulated problem, we provide a regret decomposition that informs novel algorithm design for MDPs. We further provide a naive algorithm based¨ on this reformulation along with the regret analysis. A simple greedy variant of the proposed algorithm is shown to empirically outperform popular value-based methods for finite MDPs.
LSV-LP: Large-Scale Video-Based License Plate Detection and Recognition

In the past few decades, license plate detection and recognition (LPDR) systems have made great strides relying on Convolutional Neural Networks (CNN). However, these methods are evaluated on small and non-representative datasets that perform poorly in complex natural scenes. Besides, most of existing license plate datasets are based on a single image, while the information source in the actual application of license plates is frequently based on video. The mainstream algorithms also ignore the dynamic clue between consecutive frames in the video, which makes the LPDR system have a lot of room for improvement. In order to solve these problems, this paper constructs a large-scale video-based license plate dataset named LSV-LP, which consists of 1,402 videos, 401,347 frames and 364,607 annotated license plates. Compared with other data sets, LSV-LP has stronger diversity, and at the same time, it has multiple sources due to different collection methods. There may be multiple license plates in a frame, which is more in line with complex natural scenes. Based on the proposed dataset, we further design a new framework that explores the information between adjacent frames, called MFLPR-Net. In addition to these, we release the annotation tools for license plates or vehicles in videos. By evaluating the performance of MFLPR-Net and some mainstream methods, it is proved that the proposed model is superior to other LPDR systems.In order to be more intuitive, we put some samples on https://drive.google. com/file/d/1udqRddpJZMpTdHHQdwZRll6vaYALUiql/view? usp=sharingGoogle Drive. The whole dataset is available at https://github.com/Forest-art/LSV-LP.
YouTube and Education: A Scoping Review

YouTube has evolved to a global platform for formal and informal education. In contrast to traditional sources of learning multimedia, YouTube is a social platform with numerous characteristics that make its real value for education not obvious. We neither know how reliable the learning content on YouTube is, what best-practice strategies for using this platform in education are nor how watching YouTube affects students’ performance and behavior. To shed light on these questions, we conducted a scoping review of the literature on YouTube and education. A total of 647 publications were included and analyzed thematically. Four research themes could be identified: (1) Content creation and assessment (2) User attitudes and acceptance (3) Usage strategies and behaviors (4) Impact on student learning. The findings of the respective studies were analyzed and compiled theme by theme. The main results of this review are: (1) There is an increasing concern about content quality on YouTube. (2) Despite versatile production and usage strategies, no relationships were established between such strategies and learning. (3) Most studies on the impact of YouTube on student learning reported positive results in terms of enhanced skills, competencies, interest, motivation, engagement levels, or test performance. We conclude that YouTube is a rich, free, easy-to-use, and enjoyable source of learning content. However, the challenges and risks associated with this platform suggest that it is best suitable for guided learning where teachers make or select the content and include it in a well-defined, pedagogy-driven learning context.
INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY: APPLIED BUSINESS AND EDUCATION RESEARCH

This study aimed to describe the application of Group Investigation Model (GIM) to improve students’ comprehension of subject concepts and their critical thinking skills and to understand the challenges faced by lecturers while practicing GIM in the classroom. The research approach used was a qualitative method of description involving 21 students who took the course of the Foundation of Education in the Education Administration study program at Muhammadiyah University of Prof.Dr.Hamka (Uhamka), Jakarta , Indonesia, in the odd semester 2019/2020. Data were collected through tests, observations, interviews, questionnaires, and documents. The application of GIM in the Education Foundation class can improve students’ critical thinking skills. The study findings suggested that the average student’s grades improved on formative tests. In using GIM there are several challenges that include aspects of learning and outside learning. This study suggests: (1) prior to implementing this model, careful preparation is needed, especially in the learning process so as to encourage students to actively think and ask questions. 2) In implementing this GIM, it is necessary to have guidance and motivation from the lecturer. (3) The implementation of this GIM can be successful if the stages one and two have been agreed and determined.
Chronic Active Epstein-Barr Virus Infection With Central Nervous System Involvement in Children: A Clinical Study of 22 Cases

Objective: To analyze the clinical features, treatment, and prognosis of chronic active Epstein-Barr virus infection (CAEBV) with central nervous system (CNS) involvement in children. Methods: Patients with CAEBV admitted to Beijing Children’s Hospital, Capital Medical University, were enrolled in this study from January 2017 to December 2020. They were divided into a CNS group and a non-CNS group based on the presence of CNS involvement. Results: Twenty-two patients developed CNS disease, accounting for 23.9% (22/92) of CAEBV patients in the same period. Of these, only 2 of 22 patients presented initially with neurologic symptoms in the CNS group, and they all improved after treatment. Cerebrospinal fluid (CSF) examination demonstrated normal protein concentration and cell number in all patients with CNS involvement. Only 7 patients were positive for CSF EBV-DNA. Twenty-one patients had neuroimaging abnormalities, such as white matter signal abnormalities, encephalography or calcification. In the CNS group, 7 (31.8%) patients died, including 5 who died of active hemophagocytic lymphohistiocytosis, 1 died of unrelated causes, and 1 died of respiratory failure caused by pulmonary lymphoproliferative disease progression after transplantation. The 3-year overall survival was lower in the CNS group than in the non-CNS group (63.6% ± 11.9% versus 86.9% ± 4.1%, P = 0.027). Hemophagocytic lymphohistiocytosis (HLH) is an independent risk factor for CNS involvement in patients with CAEBV (OR = 2.946, 95% CI: 1.042–8.335, P = 0.042). Compared with the non-CNS group, blood EBV-DNA loads and CD4+/CD8+ ratio of T lymphocytes in the CNS group were higher (P < 0.001), while fibrinogen levels and natural killer (NK)-cell activity were lower (P = 0.047). Children with CAEBV were more likely to develop CNS diseases with low NK-cell activity (NK-cell activity < 14.00%, P = 0.023) or high alanine aminotransferase (ALT) levels (ALT levels > 40 U/L, P = 0.032). Conclusion: CAEBV with CNS involvement has nonspecific clinical manifestations, laboratory data, neuroimaging but has a worse prognosis. Blood fibrinogen levels and NK-cell activity in CAEBV children with CNS involvement are lower than in those without CNS involvement. In contrast, blood EBV-DNA loads and CD4+/CD8+ ratio of T lymphocytes are higher. Children with CAEBV who presented with HLH, NK-cell activity <14.00%, serum ALT >40 U/L and high-blood EBV-DNA loads are prone to develop CNS diseases.
Prevalence and Characterization of Menopausal Symptoms in Climacteric Women of the Coffee Region, Colombia, 2018-2020

activas y no usuarias de terapia hormonal de la menopausia. Se aplicaron como instrumentos el Índice de Kupperman y el Menopause-Specific Quality of Life questionnaire (MENQOL). Se realizó un muestreo no probabilístico por conveniencia. Resultados : Participaron 594 mujeres, con edades promedio de 49,28 ± 6,17 años (edad media de la menopausia: 48,71 ± 4,93 años). La prevalencia de los síntomas de la menopausia fue del 71,88%. La mayoría de las mujeres presentaron sofocos, seguidos de irritabilidad e insomnio. La puntuación del Índice de Kupperman en la población total fue 18,58 ± 7,61 puntos. Según el MENQOL, los mayores promedios pertenecen a las dimensiones vasomotoras (7,13 ± 2,54) y psicosocial (6,48 ± 2,17), con afectación de la calidad de vida del 68,85%. Conclusiones : Las mujeres climatéricas del Eje Cafetero presentaron una prevalencia de síntomas de la menopausia próxima a las tres cuartas partes de las participantes, caracterizados principalmente por sofocos e irritabilidad. La mala calidad de vida se atribuye a los síntomas vasomotores. Se hace necesario explorar el impacto del síndrome climatérico durante la atención médica, a fin de ofrecer oportunas soluciones.