Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid

The stability of the power grid is concernment due to the high demand and supply to smart cities, homes, factories, and so on. Different machine learning (ML) and deep learning (DL) models can be used to tackle the problem of stability prediction for the energy grid. This study elaborates on the necessity of IoT technology to make energy grid networks smart. Different prediction models, namely, logistic regression, naïve Bayes, decision tree, support vector machine, random forest, XGBoost, k-nearest neighbor, and optimized artificial neural network (ANN), have been applied on openly available smart energy grid datasets to predict their stability. The present article uses metrics such as accuracy, precision, recall, f1-score, and ROC curve to compare different predictive models. Data augmentation and feature scaling have been applied to the dataset to get better results. The augmented dataset provides better results as compared with the normal dataset. This study concludes that the deep learning predictive model ANN optimized with Adam optimizer provides better results than other predictive models. The ANN model provides 97.27% accuracy, 96.79% precision, 95.67% recall, and 96.22% F1 score.
mG-FAST, a single pre-hospital stroke screen for evaluating large vessel and non-large vessel strokes

Background Several stroke scales have been implemented to enhance early recognition of large vessel occlusion (LVO) in the field. These scales necessitate a tiered approach requiring emergency medical services (EMS) to utilize two scales, one for identifying stroke and another for differentiating LVO from non-LVO. Ideally, a single stroke scale should be utilized by EMS for triage. Methods This is a prospective analysis of 150 consecutive patients presenting with stroke symptoms from the field. The stroke scale modified Gaze-Face-Arm-Speech-Time (mG-FAST) was used to simultaneously identify stroke and detect LVO in the pre-hospital setting. Imaging was used to confirm the presence of a LVO and determine the sensitivity and specificity of mG-FAST. The receiver operating curve (ROC) was plotted to calculate the area under the curve (AUC). Youden's index was used to determine the optimal cutoff score. Inter-rater reliability was obtained by comparing the EMS and stroke provider mG-FAST scores. EMS dispatch-to-thrombectomy-capable stroke center (mothership, MS) arrival time and groin puncture time were compared before and after the implementation of mG-FAST. Results 33/150 patients had a confirmed LVO by imaging. 32/33 patients had an mG-FAST score ≥3. The AUC of mG-FAST was 0.899. An mG-FAST cut-off point of ≥3 yielded a sensitivity of 0.97 and specificity of 0.55 for LVO. The accuracy of this cut-off point was 64%. The EMS dispatch-to-MS time and groin puncture time decreased by 22 and 40 min after implementation of mG-FAST, respectively. With admission to the MS, the EMS dispatch-to-MS time decreased by 174.7 min compared to admission to a drip-and-ship (DS) hospital. Conclusions Utilizing a single stroke scale in the field improves EMS dispatch-to-MS time, EMS dispatch-to-groin puncture time, and EMS door-to-intervention time. Implementation of mG-FAST as a pre-hospital screening tool is an effective method of triaging patients to the MS or DS hospitals.
Distribution Network Repair Scheduling With Multiple Types of Cooperation Using Dynamic Programming after Disasters

Distribution of network repair scheduling after disasters (DNRSD) is important for developing a reasonable repair strategy and improving the efficiency of distribution of network repair after disasters. This study considers two cooperative relationships in the repair model, namely, simultaneous cooperation (SC) and ordered cooperation (OC), and proposes a new post-disaster distribution network repair model, the multiple cooperation model (MCM). Compared with conventional modeling, MCM can help to adjust repair strategies and prevent repair strategies from being delayed while in implementation. The original problem is divided into two stages, and collaboratively solved via dynamic programming. Compared with conventional solving methods, the proposed two-stage dynamic programming (TDP) optimization method can obtain global optimal solutions and make more reasonable repair strategies. Test cases based on the modified IEEE 123-bus and 8500-bus distribution systems verify the advantages of our proposed modeling and optimization method.
A 3D finite element analysis of large‐scale nonlinear dynamic electromagnetic problems by harmonic balancing and domain decomposition

The dual–primal finite element tearing and interconnecting (FETI‐DP) method is applied together with the harmonic balance method and the fixed‐point (FP) method to improve the efficiency of three‐dimensional finite element analysis of large‐scale nonlinear dynamic electromagnetic problems. The FETI‐DP method decomposes the original problem into smaller subdomains problems. Combined with parallel computing techniques, the total computation time can be reduced significantly. To account for nonlinear B‐H characteristics, the FP method is applied together with the polarization formulation. Because the FP method assumes a constant reluctivity, it decouples the systems of different harmonics. The FETI‐DP method can then be applied to speed up the simulation of each harmonic in each FP iteration. Two benchmark problems and a three‐phase inductor are simulated by the proposed method to validate the formulation and demonstrate its performance. Copyright © 2015 John Wiley & Sons, Ltd.
Study of the LAT PSF of the Gamma Ray Large Area Telescope

GLAST is the next generation telescope for the study of the Gamma Ray Universe.The GLAST mission is composed of two instruments: the LAT (Large Area Telescope) exploring the energy range between 20 MeV and 300 GeV and the GBM (Gamma ray Burst Monitor) studying the region from 10 KeV up to 30 MeV.GLAST represents an important step beyond EGRET providing a large improvement in instrument performance: large Field of View (FOV), large energy range extending to unexplored region of energies larger than 10 GeV, large effective area, a factor of 30 improvement in sensitivity, a much smaller dead time and a very good Point Spread Function (PSF). Since GLAST will operate in a continuous scanning mode, for most of the time during the mission, photons from a source will be detected at different angles in the LAT field of view requiring a good PSF in order to disentangle between sources.We will present results on PSF studies performed with various sets of data. The selection criteria and algorithm have been initially .
Controlling the Controllables in Sport Organizations during the Time of Pandemic: Leadership Actions for Sport Psychology Practitioners

Abstract The time of pandemic has been a challenging one for most people in sport organizations. These individuals include athletes, coaches, support staff members, and administrators, as well as sport psychology practitioners. A range of human and systemic factors have coalesced to create volatility, ambiguity, and stress for all these stakeholders, affecting how they work, relate to one another, and make decisions. Within this context, this paper centers attention on why and how practitioners of sport psychology in professional sport organizations can manifest leadership by taking actions in support of athletes and coaches, actions that are under practitioner control and influence. The leadership actions considered include promoting an interdisciplinary perspective on providing mental health support to athletes; communicating and collaborating with other professionals; emphasizing acceptance; embracing routines; manifesting humility; and engaging in the process of professional practice. This article concludes with reflections about how these leadership actions can transfer from professional sport organizations to collegiate settings and scholastic school environments.
Wormhole attack mitigation strategies and their impact on wireless sensor network performance: A literature survey

Wireless sensor networks (WSNs) consist of hundreds of small sensors that collect and report data. During data sharing, these WSNs become vulnerable to numerous security threats, including the deadly ones. Mitigating this risk is a real challenge, especially in a low‐resource environment such as a WSN. In this work, using a systematic literature review, we surveyed a large body of research (28 studies) that focused on mitigating wormhole security attacks. Through this study, we evaluate the impact of many proposed security schemes and their impact on the WSN's performance. We compare schemes for effectiveness and pinpoint the limitations. We also analyze the various parameters and metrics used in them and highlight the open research challenges in the field.
Reduced VDAC1, Maintained Mitochondrial Dynamics and Enhanced Mitochondrial Biogenesis in a Transgenic Tau Mouse Model of Alzheimer’s Disease

Alzheimer’s disease (AD) is one of the most common forms of neurodegeneration, defined by reduced cognitive function, which is caused by the gradual death of neurons in the brain. Recent studies have shown an age-dependent rise in the levels of voltage-dependent anion channel 1 (VDAC1) in AD. In addition, we discovered an aberrant interaction between VDAC1 and P-TAU in the brains of AD patients, which led to abnormalities in the structural and functional integrity of the mitochondria. The purpose of our study is to understand the protective effects of reduced VDAC1 against impaired mitochondrial dynamics and defective mitochondrial biogenesis in transgenic TAU mice. Recently, we crossed heterozygote VDAC1 knockout (VDAC1+/−) mice with transgenic TAU mice to obtain double-mutant VDAC1+/−/TAU mice. Our goal was to evaluate whether a partial decrease in VDAC1 lessens the amount of mitochondrial toxicity in transgenic Tau (P301L) mice. We found that mitochondrial fission proteins were significantly reduced, and mitochondrial fusion and biogenesis proteins were increased in double-mutant mice compared to TAU mice. On the basis of these discoveries, the current work may have significance for the development of reduced-VDAC1-based treatments for individuals suffering from AD as well as other tauopathies.
Bioinformatic Tools for the Identification of MicroRNAs Regulating the Transcription Factors in Patients with β-Thalassemia

β-thalassemia is a significant health issue worldwide, with approximately 7% of the world’s population having defective hemoglobin genes. MicroRNAs (miRNAs) are short noncoding RNAs regulating gene expression at the post-transcriptional level by targeting multiple gene transcripts. The levels of fetal hemoglobin (HbF) can be increased by regulating the expression of the γ-globin gene using the suppressive effects of miRNAs on several transcription factors such as MYB, BCL11A, GATA1, and KLF. An early step in discovering miRNA:mRNA target interactions is the computational prediction of miRNA targets that can be later validated with wet-lab investigations. This review highlights some commonly employed computational tools such as miRBase, Target scan, DIANA-microT-CDS, miRwalk, miRDB, and micro-TarBase that can be used to predict miRNA targets. Upon comparing the miRNA target prediction tools, 4 main aspects of the miRNA:mRNA target interaction are shown to include a few common features on which most target prediction is based: conservation sites, seed match, free energy, and site accessibility. Understanding these prediction tools’ usage will help users select the appropriate tool and interpret the results accurately. This review will, therefore, be helpful to peers to quickly choose a list of the best miRNAs associated with HbF induction. Researchers will obtain significant results using these bioinformatics tools to establish a new important concept in managing β-thalassemia and delivering therapeutic strategies for improving their quality of life.
Non-pharmaceutical interventions taken by China during the prevention and control of COVID-19

To study and analyze the effective non-pharmaceutical interventions taken by China during the avoidance and management of COVID-19, in order to provide suggestions for future response to major public health emergencies. Search for keywords such as 'COVID-19', 'SARS-CoV-2', 'non-pharmaceutical interventions', and 'control strategies' on PubMed, and establish domestic and foreign literature as well as dynamic epidemic reports and expert comments issued by official institutions and summary. During the epidemic, traditional infectious disease prevention measures were adopted, such as isolation, case testing, personal protection (wearing masks), and new measures, such as expanding social distance and restricting travel. Through the precise implementation of prevention and control measures, most Asian countries, mainly China, have effectively controlled the spread of the new coronavirus in the first half of 2020. Studies have shown that measures taken during the epidemic have reduced the spread of the new coronavirus and the incidence of other respiratory infectious illnesses. For the outbreak center of the epidemic, taking Wuhan as an example, targeted prevention and control measures such as closing schools, closing cities, and establishing sheltered hospitals have been adopted, and these measures have achieved remarkable results. Only by doing so can we minimize the virus spread and curb the spread of the epidemic.
The class III peroxidase gene family is involved in ascorbic acid induced delay of internal browning in pineapple

Excessive production of reactive oxygen species (ROS) leads to potential toxicity in an organism. Class III peroxidases (PRXs) play an important role in maintaining ROS homeostasis in plants. Internal browning (IB) limits industrial development of pineapple, which is the third most important fruit trade in the world. IB is mainly caused by ROS, and the mechanism underlying IB is still unknown from the perspective of ROS. Here, we soaked pineapples in ascorbic acid after harvest and before storage to decrease excessive ROS and polyphenol oxidase (PPO) activity, ultimately restraining the spread and deterioration of IB. Using phylogenetic analysis; we identified 78 pineapple PRX genes (AcPRXs) and divided them into five subgroups. Gene structure analysis indicated that the exon numbers ranged from 2 to 14, and conserved motif analysis verified that all of the AcPRXs identified here have standard peroxidase domains. Analysis of duplication events suggested that tandem and segmental duplication events may have played equal and important roles in expanding the AcPRX family. Comprehensive transcriptomic analysis uncovered that AcPRXs may play an important role in negatively regulating the occurrence of IB. In summary, we found that ROS scavenging delayed IB occurrence. The results of characterized AcPRX family revealed that AcPRXs family responded to growth and development, and negatively regulated to IB occurrence in storage stage. This research provides potential target genes for future in-depth analysis of the molecular mechanisms underlying IB and contributes to develop IB-resistant pineapple varieties.
Indian Sign Language Recognition Using Fine-tuned Deep Transfer Learning Model

: The World Health Organization (WHO) estimates that over 5% of the world population suffers from hearing impairment. There are over 18 million hearing-impaired people living in India. In some cases, deafness comes from birth, hampering the speech learning capabilities of a child. Therefore, for this type of population, it is difficult to use spoken languages as a medium of communication. Sign languages come to the rescue of such people, providing a medium of expression and communication. However, it is difficult to decode sign language for other people who do not understand it. Computer vision and machine learning may play an important role in understanding what is said using sign language. The Indian sign language (ISL) is very popular in India and in many neighboring countries. It has millions of users. The paper presents a deep convolutional neural network (DCNN) model to recognize various symbols in ISL, belonging to 35 classes. These classes contain cropped images of hand gestures. Unlike other feature selection-based methods, DCNN has the advantage of automatic feature extraction during training. It is called end-to-end learning. A light weight transfer learning architecture makes the model train very fast, giving an accuracy of 100%. Further, a web-based system has been developed that can easily decode these symbols. Experimental results show that the model can classify Indian sign language symbols with accuracy and speed, ideal for real-time applications.
Low CP Overhead Waveform Design for Multi-Path Channels with Timing Synchronization Error

In classical orthogonal frequency division multiplexing (OFDM) systems, inserting the cyclic prefix (CP) is necessary before each symbol to overcome the multi-path effect, which, however, occupies numerous time-frequency radio resources, resulting in hampered spectrum efficiency. To address this issue, in this paper, symbol repetition aware OFDM (SR-OFDM) is developed to lower the overhead of CP. In the proposed SR-OFDM, multiple symbols share the same CP with which we examine that the multi-path channels can also be overcome by a simple single-tap equalization without causing any interference. Moreover, after the discrete Fourier transform at the receiver, different symbols are proved to be separated in the time domain, which is beneficial for lowering the demodulation complexity. Furthermore, it is revealed that the above conclusions still hold even under timing synchronization errors, which makes the proposed SR-OFDM favorable in real systems. Extensive simulations validate the efficacy of our proposed SR-OFDM system under the multi-path channels with or without timing synchronization errors.
Supersymmetry - Roots That Didn't Grow

This article is about early roots of supersymmetry, as found in the literature from 1940s and early 1950s. There were models where the power of "partners" in alleviating divergences in quantum field theory was recognized. However, other currently known remarkable features of supersymmetry, such as its role in the extension of the Poincar\'{e} group, were not known. There were, of course, no supersymmetric non-abelian quantum field theories in those days.
Microstructure optical fibers for a new generation of fiber-optic sources and converters of light pulses

Breakthroughs in microstructure-fiber technologies are pushing the development of a new class of fiber-optic frequency converters, broadband light sources, and short-pulse lasers. The frequency profile of dispersion and the spatial profile of electromagnetic field distribution in waveguide modes of microstructure fibers can be tailored by modifying the core and cladding design on a micro- and nanoscale, suggesting ways of creating novel fiber-optic devices providing the highly efficient spectral and temporal transformation of laser pulses with pulse widths ranging from dozens of nanoseconds to a few optical cycles (several femtoseconds) within a broad range of peak powers from hundreds of watts to several gigawatts. In new fiber lasers, microstructure fibers provide a precise balance of dispersion within a broad spectral range, allowing the creation of compact all-fiber sources of high-power ultrashort light pulses.
Comparison of in‐person and synchronous remote musculoskeletal exam using augmented reality and haptics: A pilot study

Utilization of telemedicine for health care delivery increased rapidly during the coronavirus disease 2019 (COVID‐19) pandemic. However, physical examination during telehealth visits remains limited. A novel telerehabilitation system—The Augmented Reality‐based Telerehabilitation System with Haptics (ARTESH)—shows promise for performing synchronous, remote musculoskeletal examination.
OG-SLAM: A real-time and high-accurate monocular visual SLAM framework

The challenge of improving the accuracy of monocular Simultaneous Localization and Mapping (SLAM) is considered, which widely appears in computer vision, autonomous robotics, and remote sensing. A new framework (ORB-GMS-SLAM (or OG-SLAM)) is proposed, which introduces the region-based motion smoothness into a typical Visual SLAM (V-SLAM) system. The region-based motion smoothness is implemented by integrating the Oriented Fast and Rotated Brief (ORB) features and the Grid-based Motion Statistics (GMS) algorithm into the feature matching process. The OG-SLAM significantly reduces the absolute trajectory error (ATE) on the key-frame trajectory estimation without compromising the real-time performance. This study compares the proposed OG-SLAM to an advanced V-SLAM system (ORB-SLAM2). The results indicate the highest accuracy improvement of almost 75% on a typical RGB-D SLAM benchmark. Compared with other ORB-SLAM2 settings (1800 key points), the OG-SLAM improves the accuracy by around 20% without losing performance in real-time. The OG-SLAM framework has a significant advantage over the ORB-SLAM2 system in that it is more robust for rotation, loop-free, and long ground-truth length scenarios. Furthermore, as far as the authors are aware, this framework is the first attempt to integrate the GMS algorithm into the V-SLAM.
Extending Electrical Engineering Research To Undergraduate Students Through A Multi Media Technology Internship Program

A means by which electrical engineering research can be effectively extended to undergraduate students through the use of a university-wide multi-media technology internship program is described. A group of ten full time students participated in the program over a period of ten weeks during the summer. Undergraduates were introduced to research through the task of developing electronic portfolios describing research programs associated with faculty members. This conduit created a one-on-one faculty-to-student interaction, which enabled an environment in which the student could begin to understand the essential ideas behind doing research at a university. In addition to engineering, a variety of other disciplines were represented, including chemistry, economics, and literature. Each student/faculty partnership approached the experience from different directions. This provided a unique atmosphere for an electrical engineering undergraduate student to learn about university research in a broader sense. The approach taken by the electrical engineering faculty/student partnership involved exposing the undergraduate to a small scale research project designed to reflect typical activities experienced by graduate students. The student went through the entire cycle of design, simulation, fabrication, and test of a working device prototype. Through this approach, the student experienced a microcosm of graduate school while interacting with graduate students, experiencing the difference between laboratory and simulation work, and developing technical writing skills through the development of the electronic portfolio.
Injection Optimization for Uniform QAM-Based Non-Orthogonal Multiplexing Systems

Non-orthogonal multiplexing (NOM) is a promising technique for wireless broadcasting which provides versatile broadcasting services to mobile and fixed users simultaneously. For an NOM system, injection optimization among signals in multiple layers is an important problem. In this paper, considering a uniform quadrature amplitude modulation-based NOM system, we propose to solve the injection optimization problem which minimizes the sum bit-error-rate (BER). To solve the problem, we theoretically derive the BER expressions of users in different subgroups, e.g., the subgroup of mobile users and that of fixed users in ATSC 3.0 standard. With the obtained BER expressions, a near optimal solution utilizing the logarithmic barrier method is proposed. It is shown through simulation results that among different injection level values, our proposed injection scheme achieves the lowest sum BER.