Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Model-Free Space Vector Modulation Technique for Multilevel Inverters Based On Deep Reinforcement Learning

Multilevel inverters provide various advantages over conventional inverters. However, control and switching these types of inverters is a challenge for an increased number of levels that necessitate a higher number of semiconductor devices. Space Vector Modulation (SVM) switching method is a popular one that provides several advantages over conventional Sinusoidal Pulse Width Modulation techniques. Advantages such as higher output voltage, lower switching loss, lower dv/dt, etc. Despite its advantages, the SVM switching method requires complex time-interval equations. This complexity increases exponentially by increasing the number of levels. In this paper, a new SVM method based on Deep Reinforcement Learning (DRL) is proposed. The latter is based on a Reinforcement Learning Agent (RL-Agent) that tries all the "allowed" switching states of an inverter regarding the reference signal in each time step. Then based on the accuracy of the output, the agent receives a reward or punishment (negative reward). Over several episodes of trial and error, the agent learns how to perform the switching to receive the maximum long-term reward. Since the agent is also rewarded for keeping the voltage of the DC-link capacitors in a given range, this method is capable of voltage balancing. This method is implemented on a three-phase three-level Neutral Point Clamped (NPC) inverter for performance evaluation. The RL-agent is trained in MATLAB while the inverter model is simulated in the Simulink environment. The simulation results demonstrate the decent performance of this method.
The World of Defacers: Looking Through the Lens of Their Activities on Twitter

Many web-based attacks have been studied to understand how web hackers behave, but web site defacement attacks (malicious content manipulations of victim web sites) and defacers’ behaviors have received less attention from researchers. This paper fills this research gap via a computational data-driven analysis of a public database of defacers and defacement attacks and activities of 96 selected defacers who were active on Twitter. We conducted a comprehensive analysis of the data: an analysis of a friendship graph with 10,360 nodes, an analysis on how sentiments of defacers related to attack patterns, and a topical modelling based analysis to study what defacers discussed publicly on Twitter. Our analysis revealed a number of key findings: a modular and hierarchical clustering method can help discover interesting sub-communities of defacers; sentiment analysis can help categorize behaviors of defacers in terms of attack patterns; and topic modelling revealed some focus topics (politics, country-specific topics, and technical discussions) among defacers on Twitter and also geographic links of defacers sharing similar topics. We believe that these findings are useful for a better understanding of defacers’ behaviors, which could help design and development of better solutions for detecting defacers and even preventing impeding defacement attacks.
Fast Local Map Construction of Robot Using Semantic Priors

When carrying out search or rescue missions, robots often need to explore in an unfamiliar environment. Inspired by the fact that human beings can use their semantic prior knowledge to find objects, robots can also perform search tasks independently according to the semantic relevance of objects in the new environment. In this paper, we propose a scheme that combines SLAM (Simultaneous Localization and Mapping), map construction and object detection to perform the task of target search. By presetting the semantic association rules of common indoor things, the robot can quickly build a local map of the exploration environment on the search path, which is convenient for subsequent use.
Zika Virus Induces Tumor Necrosis Factor-Related Apoptosis Inducing Ligand (TRAIL)-Mediated Apoptosis in Human Neural Progenitor Cells

Zika virus (ZIKV) remains as a public health threat due to the congenital birth defects the virus causes following infection of pregnant women. Congenital microcephaly is among the neurodevelopmental disorders the virus can cause in newborns, and this defect has been associated with ZIKV-mediated cytopathic effects in human neural progenitor cells (hNPCs). In this study, we investigated the cellular changes that occur in hNPCs in response to ZIKV (African and Asian lineages)-induced cytopathic effects. Transmission electron microscopy showed the progress of cell death as well as the formation of numerous vacuoles in the cytoplasm of ZIKV-infected hNPCs. Infection with both African and Asian lineages of ZIKV induced apoptosis, as demonstrated by the increased activation of caspase 3/7, 8, and 9. Increased levels of proinflammatory cytokines and chemokines (IL-6, IL-8, IL-1β) were also detected in ZIKV-infected hNPCs, while z-VAD-fmk-induced inhibition of cell death suppressed ZIKV-mediated cytokine production in a dose-dependent manner. ZIKV-infected hNPCs also displayed significantly elevated gene expression levels of the pro-apoptotic Bcl2-mediated family, in particular, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL). Furthermore, TRAIL signaling led to augmented ZIKV-mediated cell death and the knockdown of TRAIL-mediated signaling adaptor, FADD, resulted in enhanced ZIKV replication. In conclusion, our findings provide cellular insights into the cytopathic effects induced by ZIKV infection of hNPCs.
A Specification-Based Detection for Attacks in the Multi-Area System

In the past decade, cyber-attack events on the power grid have proven to be sophisticated and advanced. These attacks led to severe consequences on the grid operation, such as equipment damage or power outages. Hence, it is more critical than ever to develop tools for security assessment and detection of anomalies in the cyber-physical grid. For an extensive power grid, it is complex to analyze the causes of frequency deviations. Besides, if the system is compromised, attackers can leverage on the frequency deviation to bypass existing protection measures of the grid. This paper aims to develop a novel specification-based method to detect False Data Injection Attacks (FDIAs) in the multi-area system. Firstly, we describe the implementation of a three-area system model. Next, we assess the risk and devise several intrusion scenarios. Specifically, we inject false data into the frequency measurement and Automatic Generation Control (AGC) signals. We then develop a rule-based method to detect anomalies at the system-level. Our simulation results proves that the proposed algorithm can detect FDIAs in the system.
Circular RNA CircMAP3K5 Acts as a MicroRNA-22-3p Sponge to Promote Resolution of Intimal Hyperplasia via TET2-Mediated SMC Differentiation.

Background: Aberrant expression of circular RNA (CircRNA) contributes to human diseases. CircRNAs regulate gene expression by sequestering speciﬁc microRNAs (miRNAs). In this study, we investigated whether CircMAP3K5 could act as a competing endogenous miR-22-3p sponge and regulate neointimal hyperplasia. Methods: CircRNA profiling from genome-wide RNA sequencing data was compared between human coronary artery smooth muscle cells (HCASMCs) treated with or without PDGF. Expression levels of circular MAP3K5 (CircMAP3K5) was assessed in human coronary arteries from autopsies on patients with dilated cardiomyopathy (DCM) or coronary heart disease (CHD). The role of CircMAP3K5 in intimal hyperplasia was further investigated in mice with AAV9-mediated CircMAP3K5 transfection. SMC-specific Tet2 knockout mice and global miR-22-3p knockout mice were used to delineate the mechanism by which CircMAP3K5 attenuated neointimal hyperplasia using the femoral arterial wire injury model. Results: RNA sequencing demonstrated that treatment with PDGF-BB significantly reduced expression of CircMAP3K5 in HCASMCs. Wire-injured mouse femoral arteries and diseased arteries from CHD patients (where PDGF-BB is increased) confirmed in vivo downregulation of CircMAP3K5 associated with injury and disease. Lentivirus-mediated overexpression of CircMAP3K5 inhibited the proliferation of HCASMCs. In vivo AAV9-mediated transfection of CircMap3k5 specifically inhibited SMC proliferation in the wire-injured mouse arteries, resulting in reduced neointima formation. Using a luciferase reporter assay and RNA pull-down, CircMAP3K5 was found to sequester miR-22-3p, which in turn inhibited the expression of TET2. Both in vitro and in vivo results demonstrate that the loss of miR-22-3p recapitulated the anti-proliferative effect of CircMap3k5 on VSMCs. In SMC-specific Tet2 knockout mice, loss of Tet2 abolished the CircMap3k5-mediated anti-proliferative effect on VSMCs. Conclusions: We identify CircMAP3K5 as a master regulator of TET2-mediated VSMC differentiation. Targeting the CircMAP3K5/miR-22-3p/TET2 axis may provide a potential therapeutic strategy for diseases associated with intimal hyperplasia including restenosis and atherosclerosis.
Home Biofeedback Versus Physical Therapy for Stress Urinary Incontinence: A Randomized Trial

Objective The aim of the study was to assess whether home biofeedback is noninferior to supervised pelvic floor physical therapy (PFPT) for the treatment of stress urinary incontinence (SUI) in women. Methods The study used a randomized controlled noninferiority trial to compare a home biofeedback device with PFPT. Women older than 18 years with SUI and no history of a prior incontinence surgery or PFPT were eligible. Forty-two participants were required to determine noninferiority for the primary outcome, improvement in quality of life as measured by the International Consultation on Incontinence Questionnaire Short Form. The noninferiority margin was 4 points. Secondary outcomes included sexual function, overactive bladder symptoms, and patient impression of improvement. Results From June 2018 to October 2019, 54 women with SUI were recruited (27 biofeedback, 27 PFPT) and 43 (21 biofeedback, 22 PFPT) completed follow-up. The groups had comparable baseline characteristics. For the primary outcome of change in mean International Consultation on Incontinence Questionnaire–Short Form scores (where lower scores indicate less incontinence), home biofeedback was found to be noninferior to PFPT with a mean decrease from baseline of −3.95 (95% confidence interval [CI] = −2.21 to −5.70) in the home biofeedback group versus −4.73 (95% CI = −3.21 to −6.25) and −3.95 (95% CI = −2.21 to −5.70) in the PFPT group (P = 0.009). The PFPT group showed more improvement in overactive bladder symptoms, but not in incontinence severity without difference in sexual function. Conclusions Home biofeedback was noninferior to PFPT for the primary treatment of SUI in women at 3 months. These results support the use of personal biofeedback devices for the treatment of SUI. Clinical Trial Registration: ClinicalTrials.gov, http://www.clinicaltrials. gov, NCT03443687.
EZH2 as a Potential Target for NAFLD Therapy

Non-alcoholic fatty liver disease (NAFLD) is a complex disease that is affected by genetic predisposition and epigenetic modification. Deregulation of epigenetic pathways is now recognized as a frequent event in NAFLD, and understanding the mechanistic roles of these epigenetic factors may lead to new strategies for NAFLD treatment. Enhancer of zeste homolog 2 (EZH2) catalyzes methylation on Lys 27 of histone H3, which leads to chromatin compaction and gene silencing. EZH2 regulates embryonic development and cell lineage determination and is related to many human diseases. Recent studies show that EZH2 has critical roles in liver development, homeostasis, and regeneration. Moreover, aberrant activation of EZH2 promotes NAFLD progression. Several EZH2 inhibitors have been developed and studied both in vitro and in clinical trials. In this review, we summarize our current understanding of the role of EZH2 in NAFLD and highlight its potential as a novel therapeutic target for NAFLD treatment.
Self-Standing FBAR Transformer based on Shear Mode Zig-zag ScAlN Multilayer for Rectenna Application

Battery-less sensors for IoT require energy harvesting from the ambient environment. RF-DC conversion with a rectifying antenna (so-called rectenna) is generally used to obtain energy from ambient electromagnetic waves. However, RF-DC conversion efficiency of the diode significantly decreases for ambient electromagnetic waves due to their weak RF power. We previously reported bulk acoustic wave (BAW) piezoelectric transformers based on c-axis zig-zag ScAlN film/substrate structures (HBAR). However, very narrow multiple peaks in HBAR transformers are not suitable for rectenna applications. In this study, we report relativity wideband piezoelectric transformer based on a self-standing film structure (FBAR). The theoretical voltage gain was simulated by Mason's equivalent circuit model. We also derived the simple formula describing the voltage gain of the FBAR transformers without using the circuit model. This transformer was demonstrated with the wet etching process. S parameters of the FBAR transformers are measured by a network analyzer. As a result, the voltage gain approaching + 10 dB in 500 MHz range was obtained. The relative bandwidth of FBAR transformer is 30 times wider than that of HBAR transformer. This polarization inverted FBAR type transformer is promising for a relativity wideband transformer in the rectenna.
Impedance Reshaping Control Method to Improve Weak Grid Stability of Grid-Connected Inverters

Under weak grid conditions, the grid impedance and phase-locked loop (PLL) affects the robust stability of grid-connected inverters, leading to harmonic resonance, or even instability in the system. In this paper, a impedance remodeling control method is proposed to enhance the stability of grid-connected inverters with high grid impedance. First, the output impedance of the PLL is reshaped by connecting a complex-coefficient filter (CCF) structure in series with the input of a typical SRF-PLL. The purpose is to broaden the range of feasible phase angles that can maintain grid-connected inverters stability. Then, the output impedance of the current control loop is reshaped, to ensure that the system has sufficient phase margin. The introduction of the impedance reshaping control method enables the grid-connected inverter to adapt to a wider grid impedance range, which more friendly to weak grid. Experimental measurements verify the proposed strategy.
High-Dimensional Nonconvex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation

In this paper, we consider supervised learning problems over training sets in which the number of training examples and the dimension of feature vectors are both large. We focus on the case where the loss function defining the quality of the parameter we wish to estimate may be non-convex, but also has a convex regularization. We propose a Doubly Stochastic Successive Convex approximation scheme (DSSC) able to handle non-convex regularized expected risk minimization. The method operates by decomposing the decision variable into blocks and operating on random subsets of blocks at each step (fusing the merits of stochastic approximation with block coordinate methods), and then implements successive convex approximation. In contrast to many stochastic convex methods whose almost sure behavior is not guaranteed in non-convex settings, DSSC attains almost sure convergence to a stationary solution of the problem. Moreover, we show that the proposed DSSC algorithm achieves stationarity at a rate of ${\mathcal O}((\log t)/{t^{1/4}})$. Numerical experiments on a non-convex variant of a lasso regression problem show that DSSC performs favorably in this setting. We then apply this method to the task of dictionary learning from high-dimensional visual data collected from a ground robot, and observe reliable convergence behavior for a difficult non-convex stochastic program.
Injury Severity Prediction From Two-Vehicle Crash Mechanisms With Machine Learning and Ensemble Models

Machine learning algorithms aim to improve the power of predictors over conventional regression models. This study aims to tap the predictive potential of crash mechanism-related variables using ensemble machine learning models. The results demonstrate selected models can predict severity at a high level of accuracy. The stacking model with a linear blender is preferred for the designed ensemble combination. Most bagging, boosting, and stacking algorithms perform well, indicating ensemble models are capable of improving upon individual models.
Zero-Crossing Distortion and Neutral- Point Voltage Imbalance Analysis of VIENNA Rectifier with Carrier-Based Discontinuous PWM Control

Three-phase VIENNA rectifier with carrier-based pulse-width modulation (CBPWM) is widely used in various fields for its simple structure and high reliability. However, continuous high switching frequency of very semiconductors leads to considerable switching losses. The carrier-based discontinuous modulation (CB-DPWM) method is a preferable method to avoid the switching losses and achieve high efficiency. However, it results in extra issues around the zero-crossing area, causing current distortion. Therefore, a compensation strategy is deployed to address this problem. Moreover, a retrofit zero sequence component injection method is proposed to enhance the neutral-point potential balance ability of the CB-DPWM method. The feasibility of the employed methods are verified by simulation.
A Novel Approach in Determining Neural Networks Architecture to Classify Data With Large Number of Attributes

One of the challenges in the successful implementation of deep neural networks (DNN) lies on the determination of its architecture, in terms of the number of hidden layers and neurons for each hidden layer. In this research, a new approach is proposed to determine the neural networks architecture especially in the form of Multi-layer Perceptron (MLP) which will later be used as a machine learning method to classify data with large number of attribute. The new approach is proposed since the previous approaches are no longer applicable as general guidelines to determine the architecture of neural networks. Thus, the proposed approach aims to determine the number of hidden layers by using principal component analysis (PCA), while the number of neurons for each hidden layer is determined by using K-Means clustering. The determined neural network architecture is utilized to classify data with large number of attribute, such as the Gas Sensor Array Drift dataset which has 128 input attributes and six output classes and the Parkinson’s Disease Classification dataset which has 754 output attributes and two output classes. The results indicate that the best-performing architecture for the first dataset is the one that uses one hidden layer, with a PCA cumulative variance of 69.7%, while for the second dataset is the one that uses three hidden layers, with a PCA cumulative variance of 38.9%. Increasing the number of hidden layers does not always improve the performance of neural networks. Therefore, it is essential to determine the number of hidden layers and neurons that are appropriate to achieve good performance in neural networks. The use of PCA and K-Means clustering is expected to provide guidelines in determining neural networks architectures with good performance.
Advanced Control Design of Interleaved Boost Converter for Fuel Cell Applications

The power converters can play an important role in fuel cell systems, and robust control design of the converters can improve both the system efficiency and stability. In this paper, an advanced control strategy of a two-phase interleaved boost converter for fuel cell applications is proposed and implemented, which consists of the outer voltage regulation loop and the inner current tracking loop. The current inner loop is designed based on the Super-Twisting sliding mode control to track its reference current tightly. The active disturbance rejection control (ADRC) is applied to the voltage outer loop to regulate the output voltage and generate the reference current for the inner loop. The stability of the control loops is proved through the Lyapunov stability theory and Routh-Hurwitz criteria. Finally, the effectiveness of the proposed control strategy is demonstrated by numerical simulation results, which indicates that strong robustness can be achieved when compared with traditional dual-loop PI controller.
A Parameter Design Method of CLLC Resonant Converter Based On Time-Domain Model

A Parameter Design Method of CLLC resonant converter is introduced. Firstly, this paper analyzes the impact of the ratio (magnetizing inductance to resonant inductance, h) on the circuit characteristics, including the gain of CLLC resonant converter, the conditions at switching instants, the realization of soft switching and power losses. According to the analysis, a design method based on parameter optimization is demonstrated to achieve wide output voltage range with narrow frequency range. The proposed method is validated via experimental test on a 2-kW prototype, the efficiency is up to 98.3% within a certain frequency range.
Optimized Coupled Inductor DC/DC Converter by Integrating Snubber Circuit with Voltage Lift Technique

This paper proposes a high-efficiency and high conversion gain dc-dc converter to integrate alternative energy sources into dc nanogrid. The proposed configuration takes advantage of the coupled inductor technique to attain high voltage gain and reduced switch voltage stress. The voltage stress on the switch is substantially minimized compared to the high output voltage. Therefore, a low voltage rating switch is employed to enhance conversion efficiency. Furthermore, the coupled inductor utilization eliminates the reverse recovery losses of diodes. The operation principle and steady-state analysis of the proposed converter are elaborated. A prototype is developed to substantiate the experimental performance of the proposed converter. The experimental results demonstrate coherence with the theoretical study and simulation results. The converter shows a 96.3% peak conversion efficiency.
Optimal Energy Efficiency Based Power Adaptation for Downlink Multi-Cell Massive MIMO Systems

In this paper, the power transmission and energy efficiency (EE) in downlink multi-cell massive multiple-input–multiple-output (MIMO) systems are investigated and optimized. Most of the existing works do not take into account different user’s quality of service (QoS) requirements. These models also depend on a fixed transmit power consumption, which cannot reflect the actual EE levels concerning QoS. Therefore, in this paper, a new base station (BS) transmit power adaptation is firstly introduced, termed the BSTPA method. The transmitted power is adapted to channel condition and user-level QoS including data rate requirement and maximum allowable outage probability to minimize the total BS radiated power. An analytical closed-form expression of the average BS transmit power adaptation is derived. Then, a corresponding iterative optimization algorithm is proposed to maximize the average EE per BS and obtain the optimal design parameters. The proposed optimization algorithm aims to globally achieve the optimal EE value with the optimal amount of data rate, the number of BS antennas, and users. Simulation results are demonstrated to verify our analytical findings. For a wide range of different design parameters, the results indicate that the proposed method obtains remarkably higher EE levels compared to the conventional scenario, particularly if per-antenna circuit power is very small. The optimization results show that the case with lower per-antenna circuit power can achieve about 4.5 times better EE gain than the case with higher per-antenna circuit power with 13.3% optimum data rate improvement.