Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Switched Capacitor Boost Inverter Configuration for Three-Phase Induction Motor Driven Home Appliances

To operate a 3-<inline-formula><tex-math notation="LaTeX">${\phi}$</tex-math></inline-formula> induction motor from a voltage source of low magnitude, 3-<inline-formula><tex-math notation="LaTeX">${\phi}$</tex-math></inline-formula> inverters are traditionally cascaded with a boost-type dc–dc converter. However, the use of inductors in the boost stage results in larger volume and weight of the overall motor drive system besides increased losses. To address this issue, an inductor-less boost inverter configuration for 3-<inline-formula><tex-math notation="LaTeX">${\phi}$</tex-math></inline-formula>, low power home appliance motor drives is proposed in this article. The proposed converter is formed by integrating a switched capacitor voltage boosting circuit with a 3-<inline-formula><tex-math notation="LaTeX">${\phi}$</tex-math></inline-formula> inverter. As, the proposed configuration results in an unusual space vector (SV), two novel pulsewidth modulation strategies are proposed: virtual vector space vector modulation (VSVM) and common mode voltage reducing (CMVR) space vector modulation (SVM). The use of VSVM improves the current total harmonic distortion (THD) and reduces the torque ripple while the CMVR SVM results in a 66.6% reduction of CMV without using any external hardware other than the boosting network. As compared to the conventional solutions, the proposed configuration incurs lower losses, improved current THD, and reduced semiconductor requirement. Simulation and experimental results are presented to validate the various claims. A thorough switching and conduction loss analysis is also carried out using the PLECS software.
The implication of SARS virus on consumption: evidence from a prediction model

This paper provides empirical evidence regarding the implication of a public health event — the SARS outbreak. After the SARS epidemic broke out in 2003, the impact of the epidemic on consumption has become a hot topic. Combining previous theoretical research and practical conclusions, this article takes residential households as the research object and obtains micro-data through household surveys to study the impact of the SARS epidemic on residents' consumption. Micro-level evidence points to the conclusion that residents' willingness to consume has declined. I also show some factors that potentially drive the change in the consumption pattern.
1.2-kV Vertical GaN Fin-JFETs: High-Temperature Characteristics and Avalanche Capability

This work describes the high-temperature performance and avalanche capability of normally-<sc>off</sc> 1.2-K V-CLASS vertical gallium nitride (GaN) fin-channel junction field-effect transistors (Fin-JFETs). The GaN Fin-JFETs were fabricated by NexGen Power Systems, Inc. on 100-mm GaN-on-GaN wafers. The threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {TH}}$ </tex-math></inline-formula>) is over 2 V with less than 0.15 V shift from 25 °C to 200 °C. The specific ON-resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{ \mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula>) increases from 0.82 at 25 °C to 1.8 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot $ </tex-math></inline-formula>cm<sup>2</sup> at 200 °C. The thermal stability of <inline-formula> <tex-math notation="LaTeX">${V}_{\text {TH}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${R}_{ \mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula> are superior to the values reported in SiC MOSFETs and JFETs. At 200 °C, the gate leakage and drain leakage currents remain below <inline-formula> <tex-math notation="LaTeX">$100~\mu \text{A}$ </tex-math></inline-formula> at −7-V gate bias and 1200-V drain bias, respectively. The gate leakage current mechanism is consistent with carrier hopping across the lateral p-n junction. The high-bias drain leakage current can be well described by the Poole–Frenkel (PF) emission model. An avalanche breakdown voltage (<inline-formula> <tex-math notation="LaTeX">$BV_{\! \! \text {AVA}}$ </tex-math></inline-formula>) with positive temperature coefficient is shown in both the quasi-static <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> sweep and the unclamped inductive switching (UIS) tests. The UIS tests also reveal a <inline-formula> <tex-math notation="LaTeX">$BV_{\! \! \text {AVA}}$ </tex-math></inline-formula> over 1700 V and a critical avalanche energy (<inline-formula> <tex-math notation="LaTeX">${E}_{\text {AVA}}$ </tex-math></inline-formula>) of 7.44 J/cm<sup>2</sup>, with the <inline-formula> <tex-math notation="LaTeX">${E}_{\text {AVA}}$ </tex-math></inline-formula> comparable to that of state-of-the-art SiC MOSFETs. These results show the great potentials of vertical GaN Fin-JFETs for medium-voltage power electronics applications.
The Influence Relationship among Extrinsic Incentive, Intrinsic Motivation and Employee Innovation Performance based on Holmstrom and Milgrom model

Holmstrom-Milgrom model is a simplified model proposed for the purpose of simplifying analysis based on the general mathematical model of principal agent. In this study, the influence of intrinsic motivation and extrinsic incentive is fully taken into account, and the parametric principal-agent model is formed by parameter extension of the model. This model is used to conduct empirical study and verification on the relationship between extrinsic incentive, intrinsic motivation and employee innovation performance. The empirical results show that, intrinsic motivation is positively correlated with employee innovation performance, and extrinsic incentive is positively correlated with intrinsic motivation. In addition to the coordination relationship, other extrinsic incentives exert no significant and direct effect on employee innovation performance. The indirect effect of extrinsic incentive relies on the intrinsic motivation, which serves as an intermediary role and between extrinsic incentive and employee innovation performance. This study enriches the existing theoretical research and provides reference for enterprises to effectively implement incentive mechanism.
MOIRE: Mixed-Order Poisson Regression towards Fine-grained Urban Anomaly Detection at Nationwide Scale

The analysis of crowd flow in urban regions (urban dynamics) from GPS traces has been actively explored over the last decade. However, the existing prediction models assume that the population density in the analysis area is almost uniform, making it difficult to analyze fine-grained urban dynamics on a nationwide scale, where urban and rural areas coexist. In this paper, we propose a predictive model, called mixed-order Poisson regression (MOIRE), to capture changes in active populations nationwide by combining lower-order patterns and higher-order interaction effects. The proposed method utilizes multiple pieces of contextual information that greatly affect crowd flows (e.g., time-of-day, day-of-the-week, weather situation, holiday calendar information). We evaluated MOIRE on two massive GPS datasets gathered in urban regions at different scales. The results show that it has better predictive performance than the state-of-the- art method. Moreover, we implemented an anomaly detection system in urban dynamics for the whole nation of Japan in accordance with MOIRE specifications. This application enabled us to confirm MOIRE’s performance intuitively.
A Relative Dynamics Formulation for Hardware- in-the-Loop Simulation of On-Orbit Robotic Missions

In this letter, we propose a novel method for the on-ground simulation of a space robotic mission, while exploiting a robotic hardware-in-the-loop facility. The simulated dynamics replicated by the robots are defined with respect to a nominal motion. This approach enables simulating the motion of large satellites. In particular, we exploit a Lagrangian matching relative to the nominal motion for a satellite (client) and a manipulator-equipped spacecraft (servicer), which are modeled as a rigid body and a multi-body system, respectively. Stability of the proposed method is also analyzed. The effectiveness of the method is demonstrated through experiments on the OOS-Sim facility for the capture of Envisat, a free-tumbling satellite and the largest space debris in Low-Earth-Orbit.
Dramatic Improvement in the Rectifying Properties of Pd Schottky Contacts on β-Ga₂O₃ During Their High-Temperature Operation

<inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> is a candidate for high-efficiency power electronics and ultraviolet C (UVC) detectors capable of operating in harsh environments. However, electrical contacts that maintain their performance at high temperatures are likely to be important due to the material’s low thermal conductivity. In this study, we report on a dramatic improvement in the rectifying performance of Pd Schottky contacts (SCs) on (<inline-formula> <tex-math notation="LaTeX">$\bar {2}01$ </tex-math></inline-formula>) and (010) <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> at high temperatures in air. This effect involves a large thermally activated increase in the image-force-corrected barrier height (<inline-formula> <tex-math notation="LaTeX">$\Phi _{B,IF}$ </tex-math></inline-formula>) from 1.40 eV at room temperature (RT) to 1.75–2.20 eV, with most of the transition occurring between 250 °C and 450 °C. This improvement is due to the oxidation of the Pd SC layer that creates higher barrier height PdO regions that dominate the high-temperature current transport. In the most oxidized Pd SCs, <inline-formula> <tex-math notation="LaTeX">$\Phi _{B,IF}$ </tex-math></inline-formula> increased to 2.20 eV at 500 °C with leakage currents of <inline-formula> <tex-math notation="LaTeX">$\sim 1\times 10^{-6}$ </tex-math></inline-formula> Acm<sup>−2</sup> (−3 V) and rectification ratios (±3 V) of <inline-formula> <tex-math notation="LaTeX">$\sim 10^{7}$ </tex-math></inline-formula>, at 500 °C, similar to the best results for diamond and SiC SCs. Interestingly, very good agreement between the temperature dependence of the built-in voltage of the thermally oxidized Pd SCs and the optical bandgap of <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> (approximately −1.3 meV/°C in both cases) was observed between RT and 500 °C.
Cosine similarity distance pruning algorithm Based on graph attention mechanism

in recent years, graph neural network has been widely used. Attention mechanism is introduced into the graph neural network to make it more applicable. Both GAT and AGNN prove that attention mechanism plays an important role in graph neural network. Attention mechanism algorithms such as gat and AGNN directly use a self-learning variable to do the point product after calculating the connection (or similarity calculation) of node and neighbor features (without further processing of the calculation results). Finally, we get an aggregation of neighbor information. A cosine similarity distance pruning algorithm based on graph attention mechanism (CDP-GA) is proposed to optimize the attention matrix of nodes and their adjacent nodes. By calculating the cosine similarity between node features and neighbor features (the feature here is obtained by linear transformation), the similarity of nodes is regarded as the distance between nodes (or the weight of edges). And we think that the aggregation degree of node information is inversely proportional to the distance between nodes (similar to the heat conduction formula). In the method, we prune the neighborhood of the node according to the cosine similarity to get the final attention coefficient matrix. In this way, the attention mechanism in the graph neural network is further refined, and the loss of aggregation neighbor information is reduced. In the experiments of three datasets, our model is compared with the experimental classification of GAT and AGNN and the experiment of correlation graph neural network algorithm. Finally, it is proved that the algorithm is better than three known datasets.
A 3-kW Two-Stage Transformerless PV Inverter With Resonant DC Link and ZVS-PWM Operation

Photovoltaic (PV) inverters play important roles in renewable energy integration. Reducing the switching loss is a main challenge in improving the efficiency and power density. This article presents the design, implementation, and field test of a 3-kW two-stage commercial-ready transformerless PV inverter with resonant dc link and zero-voltage-switching pulsewidth modulation (ZVS-PWM) operation. The ZVS-PWM operation periodically resonates the dc-link voltage of the inverter to zero for ZVS of the inverter switches, which enables higher switching frequency and significantly reduced magnetic component size. A 30-kW distributed PV system comprising ten ZVS-PWM PV inverters was built and tested for more than 100 days to evaluate the long-term performance of the PV inverter. The key figure-of-merits for grid-interface operation, such as efficiency, power density, total harmonic distortion, and leakage current, are measured, analyzed, and reported.
E-ComSupResNet: Enhanced Face Super-Resolution Through Compact Network

Practical systems such as in surveillance applications capture Low-Resolution (LR) face images due to the wider angle of imaging or longer stand-off distance to the camera. However, face recognition applications demand high resolution face images for effective feature extraction and comparison. To meet the requirement of face recognition system despite the low resolution face captured, a number of works have been proposed to super-resolve the LR face images. Here, we propose a compact and computationally efficient Convolutional Neural Network (CNN) to increase the spatial resolution of an LR face image to obtain a High-Resolution (HR) face image with an upscaling factor of up to <inline-formula> <tex-math notation="LaTeX">$\times 8$ </tex-math></inline-formula> by improving our previous approach. The newly proposed approach, which we refer to as <italic>E-ComSupResNet</italic>, is an enhanced architecture of our preliminary approach ComSupResNet with multiple enhancements. Contrary to other earlier works, the proposed architecture with a compact network focuses on extracting both low-frequency and high-frequency features along with a reconstruction module. With the introduction of Residual Block (ResBlock), a novel upscaling network is provided, to super-resolve the feature maps for the factor of <inline-formula> <tex-math notation="LaTeX">$\times 2$ </tex-math></inline-formula>, a Global Residual Learning (GRL) and two newly customized loss functions imposing similarity in the new network architecture we obtain superior SR images compared to our earlier network and competing state-of-art approaches. The newly proposed architecture further has <inline-formula> <tex-math notation="LaTeX">$approx$ </tex-math></inline-formula>. <inline-formula> <tex-math notation="LaTeX">$1.5~M$ </tex-math></inline-formula> number of parameters as compared to similar earlier work which has more than <inline-formula> <tex-math notation="LaTeX">$57 ~M$ </tex-math></inline-formula> parameters. We further attempt to make the approach generalizable by learning the network in a cross-database setting and training the network on <italic>CASIA WebFace</italic> dataset while evaluating it both on <italic>CelebA</italic> and <italic>LFW</italic> datasets. Through empirical evaluations, we demonstrate the gain in high fidelity reconstruction in terms of Structural Similarity Index Metric (SSIM) and Peak-Signal-to-Noise Ratio (PSNR) measures despite the compactness of the model. As another new contribution, we conduct a series of experiments to measure the applicability of obtained SR images using the proposed network through conducting face recognition experiments using a commercial and open source Face Recognition System (FRS) with a clear gain in verification performance over the other existing techniques.
Deep Regression Versus Detection for Counting in Robotic Phenotyping

Work in robotic phenotyping requires computer vision methods that estimate the number of fruit or grains in an image. To decide what to use, we compared three methods for counting fruit or grains, each method representative of a class of approaches from the literature. These are two methods based on density estimation and regression (single and multiple column), and one method based on object detection. We found that when the density of objects in an image is low, the approaches are comparable, but as the density increases, counting by regression becomes steadily more accurate than counting by detection. With more than a hundred objects per image, the error in the count predicted by detection-based methods is up to 5 times higher than when using regression-based ones.
Dynamic Characteristics of a Superconducting Magnetic Bearing Under μm Displacements

The dynamic properties of a ring-shaped superconducting magnetic bearing used in textile machines were studied for a micrometer displacement range. Therefore, a new measurement setup was designed that allows to excite the freely levitating magnet ring in a defined direction with small excitation amplitudes. Here, we focus on radial and axial excitations with amplitudes between 2 μm and 30 μm. The data were analyzed by a fast Fourier transformation approach. The resultant excitation response was fitted by the transfer function of a spring-mass-damper system under harmonic force excitation, which gives the stiffness and damping coefficient for the particular excitation direction. The analysis shows a minor stiffness increase with decreasing excitation amplitudes in radial as well as in axial direction. At the same time, no clear trend was found for the damping coefficient. In contrast to other bearing designs, the studied ring-shaped bearing exhibits a ratio of 3 between the axial and radial stiffness.
Reinforcement Learning Based EV Charging Management Systems–A Review

To mitigate global warming and energy shortage, integration of renewable energy generation sources, energy storage systems, and plug-in electric vehicles (PEVs) have been introduced in recent years. The application of electric vehicles (EV) in the smart grid has shown a significant option to reduce carbon emission. However, due to the limited battery capacity, managing the charging and discharging process of EV as a distributed power supply is a challenging task. Moreover, the unpredictable nature of renewable energy generation, uncertainties of plug-in electric vehicles associated parameters, energy prices, and the time-varying load create new challenges for the researchers and industries to maintain a stable operation of the power system. The EV battery charging management system plays a main role in coordinating the charging and discharging mechanism to efficiently realize a secure, efficient, and reliable power system. More recently, there has been an increasing interest in data-driven approaches in EV charging modeling. Consequently, researchers are looking to deploy model-free approaches for solving the EV charging management with uncertainties. Among many existing model-free approaches, Reinforcement Learning (RL) has been widely used for EV charging management. Unlike other machine learning approaches, the RL technique is based on maximizing the cumulative reward. This article reviews the existing literature related to the RL-based framework, objectives, and architecture for the charging coordination strategies of electric vehicles in the power systems. In addition, the review paper presents a detailed comparative analysis of the techniques used for achieving different charging coordination objectives while satisfying multiple constraints. This article also focuses on the application of RL in EV coordination for research and development of the cutting-edge optimized energy management system (EMS), which are applicable for EV charging.
Relationship Between Learning Environments, Principal’s Gender and Students’ Academic Achievement in Public Secondary Schools in Marakwet East Sub County, Kenya

In the opinions of Cohen (2015) learning environment is the quality and character of school life. It is based on patterns of school life experiences and reflects norms, goals, values, interpersonal relationships, teaching, learning and leadership practices, and organizational structures. Tshui and Cai (2016), on the other hand, described learning environment as an orderly environment in which the school family feels valued and able to pursue the schools mission free from concern about disruptions and safety. Rohana, (2017) who describes secondary school as a formal organization where teachers and students‟ activities are planned, organized and controlled both internally and externally concludes that this is an epitome of the learning environment.
Association between knowledge and depression at rising time of COVID-19 in Bangladesh

Novel coronavirus (nCoV) has created a new challenging situation all over the world. In Bangladesh, people are facing some difficulties to response the emergencies. There are so many people who are lacking of proper quarantine information and knowledge about prevention practices towards coronavirus disease 2019 (COVID-19). COVID-19 has created an experience of mental disorder like depression, anxiety, and stress. Although social media, newspaper, news, television has focused on this issue, still there is to be needed to identify the psychological effects like negative impact on our mind and behavioral changes during lockdown. An online survey of 248 respondents was conducted between April, 15 2020 and May, 15 2020. The aim of this study was to assess the relationship between higher knowledge of public regarding safety measures and depression among the adult population of Bangladesh during lockdown. This study focused on correlation between knowledge level and mental health condition like depression. About 50% respondents were felt high depression after the first announcement of lockdown in Bangladesh. Approximately 50% respondents stated that people of their locality were panicked, not panicked were 26.21% and the probability of getting panic were 23.79% due to COVID-19 during lockdown in Bangladesh. The respondents who had gathered a higher knowledge about precautions were associated with depression.
Long circulating tracer tailored for magnetic particle imaging

Superparamagnetic iron oxide nanoparticle (SPION) tracers possessing long blood circulation time and tailored for magnetic particle imaging (MPI) performance are crucial for the development of this emerging molecular imaging modality. Here, single-core SPION MPI tracers coated with covalently bonded polyethyelene glycol (PEG) brushes were obtained using a semi-batch thermal decomposition synthesis with controlled addition of molecular oxygen, followed by an optimized PEG-silane ligand exchange procedure. The physical and magnetic properties, MPI performance, and blood circulation time of these newly synthesized tracers were compared to those of two commercially available SPIONs that were not tailored for MPI but are used for MPI: ferucarbotran and PEG-coated Synomag®-D. The new tailored tracer has MPI sensitivity that is ~3-times better than the commercial tracer ferucarbotran and much longer circulation half-life than both commercial tracers (t1/2=6.99 h for the new tracer, vs t1/2=0.59 h for ferucarbotran, and t1/2=0.62 h for PEG-coated Synomag®-D).