Miracle-dz/UniFAR-FTUs huggingface dataset:
Static and Dynamic Effects of the Incomplete Ionization in Superjunction Devices

The incomplete ionization of impurity atoms affects the free carrier concentration of several wide-bandgap (WBG) semiconductor materials even at room temperature, thus modifying the electrical properties of power devices. In this paper, the influence of the partial ionization of the dopants on the static and dynamic behaviors of WBG semiconductor-based superjunction (SJ) devices has been numerically investigated through extensive 2-D finite-element simulations. While this physical effect has only a minor impact on the static device characteristics, if a reverse bias pulse with a rise time comparable or smaller than the ionization time constant is applied to the structure, a “dynamic ionization” phenomenon can take place. The onset of this time-dependent ionization is the cause of charge unbalance effects in the device structure, due to the temporal dependence of the activated number of dopants. Electrothermal simulations, which have been carried out for a 4H-silicon carbide SJ diode, show a nonuniform temperature distribution during the transients which leads, in turn, to the self-heating of the device. Through an accurate redesign of the cross-sectional view of the device, the drawbacks of the incomplete ionization have been mitigated and the device performance enhanced.
Multimode Optimization of the Phase-Shifted LLC Series Resonant Converter

This paper presents a loss optimization method for phase-shifted LLC resonant converters operating with phase shift. The method features automatic detection of the preferred operational mode from the five possibilities for the topology. For a given power level and voltage transformation ratio it simultaneously determines the values of the resonant elements and transformer turns ratio. This work supplements and extends the available optimization tools for variable frequency operation and extends these to phase-shifted operation. The combined methods allow converters with much wider voltage regulation ranges to be designed. Numerical programming methods to determine the mode boundaries and to solve the steady-state solutions for the different modes of LLC converter are presented. A detailed loss model for a phase-shifted LLC is also discussed. A prototype of 30–403V, 295W is built to validate the optimization results. A comparison is made between an optimized and unoptimized converter. An extension to multipoint optimization, as required to optimize the European Weighted Efficiency is described.
Somatosensory Perceptual Training Enhances Motor Learning by Observing

Neuroimaging and neurophysiological studies in humans have demonstrated that action observation activates brain regions involved in sensory-motor control. A growing body of work has shown that action observation can also facilitate motor learning; observing a tutor undergoing motor learning results in functional plasticity within the motor system and gains in subsequent motor performance. However, the effects of observing motor learning extend beyond the motor domain. Converging evidence suggests that learning also results in somatosensory functional plasticity and somatosensory perceptual changes. This work has raised the possibility that the somatosensory system is also involved in motor learning that results from observation. Here we tested this hypothesis using a somatosensory perceptual training paradigm. If the somatosensory system is indeed involved in motor learning by observing, then improving subjects' somatosensory function before observation should enhance subsequent observation-related gains in motor performance. Subjects performed a proprioceptive discrimination task in which a robotic manipulandum moved the subject’s passive upper limb and he or she made judgments about the position of the hand. Subjects in a Trained Learning group received trial-by-trial feedback to improve their proprioceptive acuity. Subjects in an Untrained Learning group performed the same task without feedback. All subjects then observed a learning video showing a tutor adapting her reaches to a left force field (FF). We found that subjects in the Trained Learning group, who had superior proprioceptive acuity prior to observation, benefited more from observing learning compared to subjects in the Untrained Learning group. Improving somatosensory function can therefore enhance subsequent observation-related gains in motor learning. This study provides further evidence in favor of the involvement of the somatosensory system in motor learning by observing. Abbreviations FF :Force field PD :Maximum perpendicular deviation IQR :interquartile range The authors report no financial interests or conflicts of interests.
Under the IOT Platform a Novel Algorithm for Minimization of Current Pulsation in PFC Circuit

In order to achieve the IOT, in the traditional one-cycle controlled PFC circuit, the system’s switching losses considerably increase with the control switching frequency and the system’s power, severely affecting the system’s efficiency. In order to address this problem, an improved one-cycle hysteresis controlling circuit is proposed in this paper. An improved one-cycle controlled PFC circuit is also proposed to minimize the input current pulsation. The duty ratio of the output-control power switching tube is computed in real-time by computing the rate of rise and fall of the current in the input inductance. The next current sampling point is computed in real-time based on the duty ratio of each switching period. A simulation model and 152VA prototype are established via Matlab, Simulation, and test results of the prototype demonstrate the correctness and feasibility of the proposed one-cycle hysteretic control circuit and the current pulsation minimization algorithm.
Functional Characterization of Cryptococcal Genes: Then and Now

Site-directed mutagenesis enables researchers to switch a gene of interest off for functional characterization of the gene. In the pathogenic yeasts, Cryptococcus neoformans and sister species C. deneoformans, this is almost exclusively achieved by introducing DNA into cells through either biolistic transformation or electroporation. The targeted gene is then disrupted by homologous recombination (HR) between the gene and the transforming DNA. Both techniques have downsides; biolistic transformation equipment is very expensive, limiting the use thereof to well-resourced laboratories, and HR occurs at extremely low frequencies in electroporated cryptococcal cells, making this method unappealing for gene targeting when not making use of additional modifications or methods to enhance HR in these cells. One approach to increase the frequency of HR in electroporated cryptococcal cells have recently been described. In this approach, CRISPR-Cas9 technology is utilized to form a double strand break in the targeted gene where after the occurrence of HR seems to be higher. The less expensive electroporation technique can therefore be used to deliver the CRISPR-Cas9 components into cells to disrupt a gene of interest, but only if the CRISPR components can be maintained for long enough in cells to enable their expression. Maintenance of episomal DNA occurs readily in C. deneoformans, but only under certain conditions in C. neoformans. In addition, CRISPR-Cas9 allows for gene complementation in order to fulfill Falkow’s molecular Koch’s postulates and adds other novel methods for studying genes as well, such as the addition of a fluorophore to an inactive Cas9 enzyme to highlight the location of a gene in a chromosome. These developments add less expensive alternatives to current methods, which could lead to more research on this yeast in developing countries where cryptococcal infections are more prevalent and researchers have access to more clinical isolates.
A Hybrid Method of Superpixel Segmentation Algorithm and Deep Learning Method in Histopathological Image Segmentation

Manual analysis of cell morphology in high resolutional histopathological images is a tedious and time consuming task for pathologists. In recent years, computer assisted diagnostic systems have gained considerable importance in order to assist the pathologists for analyzing cellular structures. In this study, the simple linear iterative clustering (SLIC) superpixel segmentation method and convolutional neural network are combined to segment the cellular structures in histopathological images. The proposed study is mainly composed of two stages. First, SLIC superpixel method was used as a pre-segmentation algorithm to perform segmentation of cellular superpixels and non-cellular superpixels. Then convolutional neural networks (CNN) based deep learning algorithm is used to classify those superpixels in order to obtain the final segmentation of the whole image. The overall accuracy of the system at classifying the superpixels was observed to be 0.9876. The analysis and confusion matrix of the study was also presented in experimental studies section.
Is nonalcoholic fatty liver disease associated with the development of prostate cancer? A nationwide study with 10,516,985 Korean men

Background Growing evidence supports that prostate cancer (PCa) is a metabolic syndrome-related cancer, but the evidence is lacking regarding the association between nonalcoholic fatty liver disease (NAFLD) and PCa. We aimed to investigate whether PCa is related with NAFLD in Korean adults. Methods Data from the National Health Insurance Corporation between 2009 and 2012 were analyzed using multivariate logistic regression method. NALFD was defined based on the fatty liver index (FLI) and hepatic steatosis index (HSI). Newly diagnosed PCa was identified using the claims data. Results NAFLD based on FLI and HSI was identified in 2,002,375 (19%) and 2,629,858 (25%) of 10,516,985 subjects, respectively. Each FLI ≥ 60 and HSI ≥ 36 was independently associated with the development of PCa after adjusting for other confounders (hazard ratio (HR) 1.09, 95% CI: 1.06–1.12 and HR 1.19, 95% CI: 1.16–1.23). The association was more prominent among those who were older (FLI, ≥ 65 years old and HSI, ≥ 40 years old), were not currently smoking, were presently consuming alcohol (< 30g/day) and had null components of metabolic syndrome than each counterpart. Non-obese persons with NAFLD defined by HSI had a higher risk of developing PCa than those with body mass index > 25 Kg/m2. Conclusions NAFLD defined by FLI or HSI may help identify high-risk individuals for developing PCa particular in the elderly, even in the absence of obesity or metabolic syndrome. Future studies on this topic should necessarily be repeated based on ultrasonographic findings.
An E-Learning Support Middleware with Quiz Automatic Selection for MOOC

Nowadays, MOOC courses have been kept generating in universities and other instructional institutions. The teachers of the MOOC courses have to design online courses, take camera shoots, etc. One of the MOOC course work is to prepare a list of quizzes to each knowledge points of each instruction video. Also, this work is often omitted due to efforts and time limitation. In this paper, an E-Iearning Support Middleware with quiz automatic selection for MOOC is put forward. This middleware leverage Vector Space Model(VSM) and be able to automatically select proper quizzes from the quiz books according to the description of knowledge points. This study is able to ease the burden of teacher on quizzes preparation. The experiment results show that this E-learning Support middleware has positive effects in Information Retrieval aspect and also satisfy instructional process.
Cost Effective Frame Work for implementing Elementary Education for Visually Impaired

Under developed countries like Pakistan do not offer sufficient educational resources for special children. There is lesser number of schools for special children with only primary education utilizing traditional methods for Braille communication. People are deprived of getting into schools due to distant areas, minimum transport facilities and expensive fees. Traditional systems are difficult to use and time-consuming. In contrast, modern systems utilize electronic methods for user-friendly and easier communication but are expensive. This paper implements a cost-effective Braille framework using hardware prototype with specifically designed software application running on teacher’s server/PC. Student enters Braille characters, which are converted into English and displayed on software application. The software application maintains and updates an automatic database for each student. Data can be accessed by teacher on server/PC. Entered Braille Characters can be printed via printer/embosser. Data can also be heard on headphones, interfaced with each Braille prototype. The over-all cost of our system remains affordable and also gives young children an opportunity to cope with technical advancements. The system was tested in "National Special Education Complex" in Karachi, Pakistan on 30 blind students from Class 1 to 5. Their performance is evaluated on the basis of score, speed and occurrence of errors. The results show significant improvements using our system over traditional systems.
Verifying CPS for Self-Adaptability

CPS (Cyber-Physical Systems) are physical and engineered systems featuring a tight combination of computation and physical processes by communication networks. CPS are mainly applied in some critical domains, so it is very essential to ensure the correctness of CPS. As a very important method for verifying system correctness, formal verification has been successfully applied in the verification of CPS; however, the high theoretical level of formal modeling techniques in formal verification and the lack of generality of formal models make it difficult to integrate formal verification with enterprise standard system development process. In this paper, we model CPS by HybridUML, an extension of UML which is now the de facto standard modeling language in system development practice, and then transform HybridUML model into the input language of theorem prover KeYmaera-QHP(Quantified Hybrid Program), and finally verify the QHP code with KeYmaera. When transforming a HybridUML model, we define the metamodels of HybridUML and QHP, and then find the association between them based on semantic consisteency, and finally define transformation rules using ATL (ATLAS Transformation Language) and template based code generation method to achieve transformation.
Community Detection Using Attribute Homogenous Motif

Network community detection with higher-order features has become a hot research topic recently since higher-order features that are captured at the level of small network subgraphs can help to gain new insights into the higher-order organizations of the overall network. However, most of the existing higher-order community detection methods only leverage the structural information of networks, yet fail to consider the rich information of node attributes, which typically makes them incapable of discovering semantically meaningful communities. To address this problem, we propose a novel method for community detection based on a newly designed higher-order feature termed Attribute Homogenous Motif (AHMotif), which integrates both node attributes and higher-order structure of the network in a seamless way. Specifically, the statistically significant motifs are identified for the network in terms of the topological structure, and homogeneity values are computed for structural motifs according to the attributes of the nodes that are involved in the motif. Then, the given attributed network can be further represented via a newly designed AHMotif adjacency matrix. The AHMotif adjacency matrix encodes both structures and attribute information and can characterize the network from a higher-order perspective. Afterward, several proximity based methods are utilized to obtain the final community detection results. Extensive experiments on several real-world data sets have shown the superiority of our AHMotif-based method in community detection.
Analysis and Modeling of Current Mismatch in Laterally Nonuniform MOSFETs

We present an analytical modeling approach for drain current (<inline-formula> <tex-math notation="LaTeX">${I}_{D}$ </tex-math></inline-formula>) mismatch in lateral nonuniformly doped (NUD) MOSFETs. The model uses the carrier number fluctuation theory to include the effect of random dopant fluctuations (RDFs) on inversion charge fluctuation (ICF) in different operating regions. Our analysis shows that the conventional models for uniformly doped device underestimate the <inline-formula> <tex-math notation="LaTeX">${I}_{D}$ </tex-math></inline-formula> mismatch by one-to-two orders of magnitude at low gate voltages in NUD devices. We have also considered the effect of RDF on mobility fluctuation (MF), which shows its significant role in strong inversion (SI). To investigate the physical mechanism leading to such a behavior, extensive technology computer-aided design simulations are performed for various conditions of bias, doping profiles, and temperature. Using the impedance field method to calculate the relative contributions of RDF to ICF and MF, we show that our model can successfully capture the drain current mismatch across subthreshold to SI in the NUD device.
Biometric Verification Enhancement with Ensemble Learning Classifiers

This research study asserts the potential of ensemble learning classifiers over traditional classification approaches in the field of biometric recognition. Three biometric traits: fingerprint, online signature and offline signature are considered to demonstrate the improvement in the accuracies using ensemble classifiers. Data standardization techniques are applied to scale the data in a standard form. Histogram of Oriented Gradients is applied to extract the features from the data and then fed to different classification techniques such as Support Vector Machine (SVM), k-nearest neighbors algorithm (k-NN), Multilayer Perceptron (MLP) Neural Network, Extra Tree Classifier (ETC) and Random Forest Classifier (RFC). It is evident from the experimental results acquired during this research that the ensemble learning classifiers outperformed the conventional classification methods.
A Survey on Human T-cell Lymphotropic Virus Type 1 (HTLV-1) and Xenotropic Murine Leukemia Virus-Related Virus (XMRV) Coinfection in Tehran, Iran

Background: Xenotropic murine leukemia virus-related virus (XMRV) is a gamma retrovirus, which has been detected in patients with prostate cancer, chronic fatigue syndrome, and general population with a number of acquired infections such as infection with human T-cell lymphotropic virus (HTLV) and human immunodeficiency virus (HIV). The aim of this study was to determine the HTLV-1 and XMRV coinfection for the first time in Iranian patients who were admitted to the Tehran hospitals. Materials and Methods: Two hundred and ninety one patients suspected with HTLV-1 were referred to the hospitals affiliated to the Iran University of Medical Sciences, Tehran, Iran from April 2012 to October 2016. Genomic deoxyribonucleic acid (DNA)/ribonucleic acid (RNA) from peripheral blood mononuclear cells/cerebrospinal fluids was extracted by High Pure Viral Nucleic Acid Kit (Roche, Germany). After complementary DNA synthesis, conventional reverse transcriptase polymerase chain reaction was used for the detection of HTLV-1 or XMRV-infected patients. Statistical Package for Social Sciences (SPSS) software, version 16 (SPSS, Chicago, IL, USA) was used for statistical analyses. Results: Of the 291 patients suspected of HTLV infection, 123 (42.3%) were male with a mean age of 38±15 years. HTLV-1 RNA was found in 93 (31.9%) specimens comprising 40 men (41.3%) and 53 women (56.9%). Of the 93 patients who were HTLV-1 positive, one sample (1%) was positive for XMRV env gene. Conclusion: These findings suggest that the lack of significant detection of XMRV in patients who were HTLV-1 positive could not be associated with complications of HTLV-1. Although this is a preliminary report from Iranian patients with HTLV-1, further studies are needed to show the actual prevalence of XMRV infection by geographical distribution and various populations.
An FPGA Based Real Time Reservoir Computing System for Neuromorphic Processors

In this paper, a real-time Field Programmable Gate Array (FPGA) implementation of the Echo State Network (ESN) architecture of Recurrent Neural Network (RNN) training has been presented, which computes the output weights of the particular Reservoir Computing (RC) architecture in FPGA in real-time. The proposed implementation is in strict conformance with the RC theory. The four parts of the ESN architecture, which are the input block, reservoir block, output block, and weight training block, were all constructed in FPGA. The training of the ESN was completed in real-time and its performance verified through implementation in Altera FPGA. The error rate is 8% in sinusoidal pattern recognition task, which showed that the proposed real-time FPGA implementation of the ESN can realize short-time memory and recognize various periodicities of input signals after training. The proposed method shows the massive parallel processing capability of the RC.
Harmonics and Reactive Current Detection of a Grid-Interfaced PV Generation in a Distribution System

The extraction of harmonics and reactive current components in a grid-interfaced photovoltaic (PV) system is proposed in this paper. The proposed method is based on a triangular function, which is useful in signal processing and provides harmonics and reactive current detection to estimate reference currents to control a grid-connected PV system. The proposed method is modeled and implemented in order to meet the grid requirements such as power quality improvement, distortion-free signals etc. The proposed technique for a three-phase grid-connected PV system is used to perform multiple functions including power quality improvement, extraction of maximum power from a given PV array, and feeding extracted solar PV power into the distribution system. Test results have vetted desirable performance of the proposed system.
Characterization of Cutsets in Networks With Application to Transient Stability Analysis of Power Systems

A cutset is a concept of importance in both graph theory and many engineering problems. In this paper, cutset properties are studied and applied to transient stability problems in power systems. First, we reveal that cutsets are intrinsically linked to cycles, another important concept in the graph theory. The main theoretical results include that any pair of edges in a cutset are concyclic (i.e., contained in the same cycle), and any edge together with some of its concyclic edges can form a cutset. Then, with the cycle constraint for the bus angles in power networks, we give a theoretical explanation of a common phenomenon in transient stability that the loss of synchronism initiates from the angle separation across the critical cutset in the underlying power network. This phenomenon is frequently observed but no proof has been presented yet. Moreover, an improved cutset index (ICI) is proposed based on these cutset properties. This index can better identify the vulnerable cutset and help to estimate the cutset-relevant unstable equilibrium points and critical energy for stability region determination. Numerical studies on two IEEE test systems show that the formation of critical cutset coincides with the explanation, and the ICI has better performance than the conventional cutset index especially in heavy-load cases.