Miracle-dz/UniFAR-FTUs huggingface dataset:
PWFT-RD: A High-Quality and Low-Complexity ISAR Imaging Approach for Maneuvering Target

For inverse synthetic aperture radar imaging, high-quality and real-time performances are two essential indicators in many real applications. Due to the complex motion of the maneuvering target, the Doppler frequency is generally time varying. Consequently, it is difficult for most imaging algorithms to satisfy these two indicators simultaneously. The approaches based on the traditional range-Doppler (RD) algorithm can generally achieve rapid imaging but often encounter image blurring problems while the range-instantaneous-Doppler (RID) algorithms can generate high-quality images but at the cost of tremendous computational complexity. Aiming at this issue, we propose a new approach, termed power-weighting Fourier transform-based range-Doppler (PWFT-RD), to achieve a better comprehensive performance of the image quality and computational cost simultaneously. Specifically, we first build up the generalized signal model to describe the target motion with high-order dynamics. Second, we propose the PWFT which has the property of modulating expanded the signal spectrum into a keen-edged envelope. After that, we implement high-accuracy motion compensation (MOCOMP) by formulating the high-order parameter estimation as the least-squares problem and finally develop the PWFT-RD approach. Analysis and comparison experiments are designed to validate the effectiveness of our approach on employing PWFT for improving MOCOMP accuracy and enhancing the azimuth focus quality. In particular, the PWFT-RD approach shows much better real-time performance than RID algorithms thanks to its linear and blind processing attributes with quite low computational complexity.
A Novel Data Collection Approach for Wireless Sensor Networks Powered with Ambient Energy

Wireless sensor networks have become the preferred solution in many applications. The main restriction in using wireless sensor nodes is that the power source of the sensor nodes is limited, as these sensors usually rely on batteries. Typically, a wireless sensor network (WSN) deploys a large number of sensors. When the sensors' batteries run out, they need to be replaced. The replacement process is a costly process. Using ambient resource such as solar, wind, etc. to power a wireless sensor network is a promising solution. In this paper, a Data Collection Energy-Efficient Routing Protocol with Harvested Energy is proposed. The node's reaming energy, energy harvesting rate, packet data rate and node capability are key parameters that are considered to build an efficient tree for collecting data. Extensive simulation experiments have been conducted using NS-3 to study the behavior of the proposed algorithm. The simulation results demonstrate the effectiveness of the proposed approach and its outstanding performance compared to AODV.
Exercise training suppresses scavenger receptor CD36 expression in kupffer cells of nonalcoholic steatohepatitis model mice

Although nonalcoholic steatohepatitis (NASH) is an important component of the metabolic syndrome, scavenger receptor CD36 also modulates NASH development. This study aimed to clarify whether exercise training suppresses CD36 expression in a mouse model of NASH. Male C57BL/6 mice were divided into four groups: normal diet (ND) sedentary, ND exercise, high‐fat diet and high‐fructose water (HFF) sedentary, and HFF exercise groups. The exercise groups were trained on a motorized treadmill at running speeds of 15–20 m/min for 60 min/day, 5 times/week for 16 weeks. CD36 cell surface expression of hepatic resident macrophages, peroxisome proliferator‐activated receptor (PPAR)‐γ protein, and mRNA levels in the liver were increased in HFF sedentary mice but were attenuated in HFF exercise mice. Hepatic resident macrophages were significantly lower in HFF exercise mice than in HFF sedentary mice. Our findings indicated that exercise training reduced macrophage quantity in the liver, and downregulated CD36 and PPAR‐γ expression in liver and macrophages.
Event-Based Under-Frequency Inertia Emulation Scheme for Severe Conditions

The existing UFLS schemes suggested for conventional power systems need to be reassessed in the presence of renewable energy sources and storage systems equipped with grid-supporting convertors. A new Event-Based Under-Frequency Inertia Emulation (EB-UFIE) plan is proposed in this paper as a substitute of traditional UFLS program to protect the system against the risk of generation trip and subsequent blackout. Trajectory sensitivities are used as a popular technique to predict the future behavior of the system through a mixed integer programming optimization problem. The proposed scheme has been tested on the modified New England 39-bus system including renewables and storages.
Effects of FSH on Sperm DNA Fragmentation: Review of Clinical Studies and Possible Mechanisms of Action

Sperm DNA fragmentation (sDF) is an important reproductive problem, associated to an increased time-to-pregnancy and a reduced success rate in natural and in vitro fertilization. sDF may virtually originate at any time of sperm's life: in the testis, in the epididymis, during transit in the ejaculatory ducts and even following ejaculation. Studies demonstrate that an apoptotic pathway, mainly occurring in the testis, and oxidative stress, likely acting in the male genital tract, are responsible for provoking the DNA strand breaks present in ejaculated spermatozoa. Although several pharmacological anti-oxidants tools have been used to reduce sDF, the efficacy of this type of therapies is questioned. Clearly, anti-apoptotic agents cannot be used because of the ubiquitous role of the apoptotic process in the body. A notable exception is represented by Follicle-stimulating hormone (FSH), which regulates testis development and function and has been demonstrated to exert anti-apoptotic actions on germ cells. Here, we review the existing clinical studies evaluating the effect of FSH administration on sDF and discuss the possible mechanisms through which the hormone may reduce sDF levels in infertile subjects. Although there is evidence for a beneficial effect of the hormone on sDF, further studies with clear and univocal patient inclusion criteria, including sDF cut-off levels and considering the use of a pharmacogenetic approach for patients selection are warranted to draw firm conclusions.
Evolutionary Algorithm Based Optimized Relay Vehicle Selection in Vehicular Communication

This paper addresses the broadcasting storm problem by proposing an optimized relay vehicle selection methodology in the vehicular ad-hoc networks (VANETs). A crucial effect observed in the broadcasting storm is the network congestion that is caused by multiple retransmissions generated by the relay vehicles to achieve the desired network coverage. In order to address this problem, we propose an optimized relay selection methodology based on multi-objective genetic algorithm (MOGA) consisting of a novel analytical fitness function. In this paper, we present a detailed study of previous research work and identify the gaps to achieve the optimum performance in terms of network coverage time. We introduce a component-based analytical model containing the proposed MOGA to reduce these performance gaps. A dedicated solver was designed using Python to implement the proposed model in both urban and highway environments. The numerical results obtained by the proposed MOGA are compared with the existing techniques. It is shown that the proposed method performs better in terms of number of retransmissions and network coverage time.
Software Requirements Prioritisation: A Systematic Literature Review on Significance, Stakeholders, Techniques and Challenges

As one of the gatekeepers of quality software systems, requirements’ prioritization (RP) is often used to select the most important requirements as perceived by system stakeholders. To date, many RP techniques that adopt various approaches have been proposed in the literature. To identify the strengths, opportunities, and limitations of these existing approaches, this paper studied and analyzed the RP field in terms of its significance in the software development process based on the standard review guidelines by Kitchenham. By a rigorous study selection strategy, 122 relevant studies were selected to address the defined research questions. Findings indicated that RP plays a vital role in ensuring the development of a quality system with defined constraints. The stakeholders involved in RP were reported, and new categories of the participating stakeholders were proposed. Additionally, 108 RP techniques were identified and analyzed with respect to their benefits, prioritization criteria, size of requirements, types in terms of automation level, and their limitations; 84 prioritization criteria were disclosed with their frequency usages in prioritizing the requirements. The study revealed that the existing techniques suffer from serious limitations in terms of scalability, the lack of quantification, and the prioritization of the participating stakeholders, time consumption, requirement interdependences, and the need for highly professional human intervention. These findings are useful for researchers and practitioners in improving the current state of the art and state of practices.
Electrical Characterization of Elongation Sensors Based on SBS-CTPU Filaments

The demand for wearable electronics is increasing. For measurements at human joints elongation sensors are necessary, which are skin compatible and use non toxic materials. We propose novel sensors based on SBS-CTPU filaments and investigated them under strain and temperature changes. The filaments are melt extruded by mixing SBS (Styrene-Butadiene-Styrene) and CTPU (Conductive Thermoplastic Urethane) pellets using a custom extruder machine in a ratio to achieve the percolation threshold for strain sensing. In the testing phase, a maximum elongation of 2.5% was applied to investigate the behavior of the sensor. Environmental influence in terms of temperature on the elongation sensors was also experimentally investigated in the temperature range from 30°C to 60°C. The filaments show a temperature sensitivity following a NTC behavior.
On Contract Design for Incentivizing Users in Cooperative Content Delivery With Adverse Selection

Cooperative content delivery using multiple air interfaces (CCDMI) is a powerful solution to mitigate congestion in cellular networks. In CCDMI, the operator distributes content to selected users that further distribute it locally among its nearby users. However, a user that is capable of contributing to CCDMI might act selfishly and refuse to participate. Although the operator can encourage user participation by offering incentives, it has incomplete information about the users’ willingness to participate. In order to overcome this problem of adverse selection in CCDMI, we propose two contract-based methods under information asymmetry. In both methods, the operator designs a performance-based contract set for the users that are capable of local content distribution. Using a mathematical analysis, we show that the optimal contract under information asymmetry achieves close to optimal utility for the users and the operator, compared with the information symmetry case. Moreover, the users with high willingness to participate get positive utility and the users with low willingness get zero utility. Hence, by assigning contracts, the operator can motivate user participation, despite the information asymmetry between them. Our results verify that the proposed methods improve the system performance in terms of the utility of the operator and the users.
Predicting Voltage Variations in Low Voltage Networks with Prosumers

Traditional feeder voltage estimation techniques in low voltage networks lack the capability to analyse 2-way power flow and are rooted in probabilistic techniques developed in the 1940's. With the increasing penetration of distributed generation, there is a greater expectation on the network operator to reduce over-voltage complaints and have knowledge of accurate voltages along feeders. Smart meters installed in the developed world record power usage, but often the voltages at only those time intervals where they fall outside the prescribed limits. This paper presents a modern approach for estimating feeder voltages, derived from a regression model based on power system first principles. The parameters of the model are determined using standard machine learning techniques, and account for demand diversity, phase unbalance along the feeder and the transformer tap position. The model is trained using half-hourly household consumption data from the utility and voltage results from the load flow simulation of a real-world network created for a given topology. The model is then shown to predict voltage variations in the network with new consumers added in. These predictions, when assessed against traditional voltage estimation practices or simulation results, provide a close match at each time instance. The proposed methodology hence provides a new way of computing accurate voltage drop and rise in a feeder with new prosumers. It allows the network operator to gain a better understanding of dynamic distribution network voltages, save on capital expenditure due to avoided over-investment, and reduce risk of voltage excursions when adding new prosumers.
Preceptorship during health personnel students’ clinical studies in nursing homes—An institutional perspective on an intervention

The recruitment and retention of preceptors to mentor health professional students and apprentices in their clinical studies are not easy. The aim of this study was to investigate factors that hindered the implementation of an intervention intended to improve the working conditions for preceptors in nursing homes. The preceptorship was related to clinical studies for health professional students and apprentices.
Sistema de reconhecimento de palavras manuscritas dependente do usuário

Este trabalho apresenta um sistema de reconhecimento de palavras manuscritas isoladas dependente do escritor. Este sistema caracteriza-se por utilizar uma etapa de pre-processamento, que visa corrigir imperfeicoes e normalizar variacoes na imagem da palavra manuscrita, uma etapa de segmentacao explicita, que visa dividir a palavra em caracteres ou segmentos de caracteres, uma etapa de extracao de caracteristicas, que tem por ﬁnalidade representar a imagem por tres vetores de caracteristicas (perceptivas, globais e direcionais) e um modulo de quantizacao vetorial, que tem o objetivo de realizar o mapeamento de um vetor de caracteristicas em um vetor de observacao (ou vetor de simbolos). Os simbolos correspondem aos indices (dos vetores-codigo) gerados na representacao (quantizacao vetorial) da sequencia de caracteristicas com o uso dos dicionarios. Finalizando, tem-se a etapa de classiﬁcacao realizada por Modelos Escondidos de Markov, na qual os caracteres sao reconhecidos individualmente e combinados para formar a palavra. Testes experimentais foram realizados com uma base de dados construida especiﬁcamente para este ﬁm, contendo amostras de manuscritos de4escritoresdistintos. Osistemadereconhecimentodepalavrasmanuscritasisoladas dependente do escritor obteve taxas de reconhecimento que variaram entre 83,31% a 92,96% dependendo do escritor analisado. Os resultados apresentados mostram que o sistema apresenta um otimo desempenho quando utilizado para reconhecer palavras atraves dos modelos de caracteres.
State Estimation Accuracy of Tuned Least Measurement Rejected Estimator

Weighted Least Square (WLS) and Weighted Least Absolute Value (WLAV) estimators are most commonly employed in electric power industry. WLS fails in the presence of bad data and WLAV estimator has large computational burden. Least Measurement Rejected (LMR) is a robust estimator which can handle bad data efficiently and has lower computational burden. LMR estimator has an important tolerance parameter which is used to reject bad data during estimation process. In this paper, an iterative tuning approach for the tolerance parameter of LMR has been proposed. The accuracy and computational efficiency of the proposed approach have been compared with most commonly used WLS and WLAV estimators. The accuracy has been computed in terms of Cumulative Estimation Error (CEE) indicator for IEEE-30 and IEEE-118 bus systems.
Dyadic Plasticity in Cardiomyocytes

Contraction of cardiomyocytes is dependent on sub-cellular structures called dyads, where invaginations of the surface membrane (t-tubules) form functional junctions with the sarcoplasmic reticulum (SR). Within each dyad, Ca2+ entry through t-tubular L-type Ca2+ channels (LTCCs) elicits Ca2+ release from closely apposed Ryanodine Receptors (RyRs) in the SR membrane. The efficiency of this process is dependent on the density and macroscale arrangement of dyads, but also on the nanoscale organization of LTCCs and RyRs within them. We presently review accumulating data demonstrating the remarkable plasticity of these structures. Dyads are known to form gradually during development, with progressive assembly of both t-tubules and junctional SR terminals, and precise trafficking of LTCCs and RyRs. While dyads can exhibit compensatory remodeling when required, dyadic degradation is believed to promote impaired contractility and arrythmogenesis in cardiac disease. Recent data indicate that this plasticity of dyadic structure/function is dependent on the regulatory proteins junctophilin-2, amphiphysin-2 (BIN1), and caveolin-3, which critically arrange dyadic membranes while stabilizing the position and activity of LTCCs and RyRs. Indeed, emerging evidence indicates that clustering of both channels enables “coupled gating”, implying that nanoscale localization and function are intimately linked, and may allow fine-tuning of LTCC-RyR crosstalk. We anticipate that improved understanding of dyadic plasticity will provide greater insight into the processes of cardiac compensation and decompensation, and new opportunities to target the basic mechanisms underlying heart disease.
Methodology for Analyzing of a Grid Weakness and Resiliency Factors - case of Niger National Grid

In general cases, particularly in developing countries, the weakness of the national electric grid is due to overloading, infrastructures aging, high temperatures and bad weather. In such systems, the interruptions of electricity supply can take several hours to days. To prevent the power deficit, consumers can use diesel generators that are very noisy and highly polluting. But, as alternative, renewable energy sources and storage devices can be used for load shifting. This can help to improve the grid resiliency and the supply continuity. But, to do this, it is necessary to have efficient methodology of weakness analysis and resiliency factors estimation. In this paper, experimental data are used to define the main parameters of the system performance based on the IEEE Std Std 1366TM-2012 indexes.
The lure of causal statements: Rampant mis-inference of causality in estimated connectivity

As neuroscientists we want to understand how causal interactions or mechanisms within the brain give rise to perception, cognition, and behavior. It is typical to estimate interaction effects from measured activity using statistical techniques such as functional connectivity, Granger Causality,or information flow, whose outcomes are often falsely treated as revealing mechanistic insight. Since these statistical techniques fit models to low-dimensional measurements from brains, they ignore the fact that brain activity is high-dimensional. Here we focus on the obvious confound of common inputs: the countless unobserved variables likely have more influence than the few observed ones. Any given observed correlation can be explained by an infinite set of causal models that take into account the unobserved variables. Therefore, correlations within massively undersampled measurements tell us little about mechanisms. We argue that these mis-inferences of causality from correlation are augmented by an implicit redefinition of words that suggest mechanisms, such as connectivity, causality, and flow.
Quantized Consensus of Unidirectional Pedestrian Flow based on Two-Time-Scale Hybrid Models

The convergence of pedestrian flow in a unidirectional corridor is studied based on the two-time-scale hybrid traffic models for pedestrian crowds. In the two-time-scale framework, the corridor is divided into a set of virtual lanes. The width of each lane is equal to the diameter of a pedestrian which is modeled as a disk. Being situated in a corridor, the pedestrians can either walk in a lane following each other or change to the adequate lanes. The in-lane movements of pedestrians are viewed as a one-line multi-agent dynamic system in continuous time. The lane changing behavior of pedestrians is modeled as a Markov decision process. Pedestrians will change lanes according to the transition probabilities which are functions depending on pedestrian velocities in the related lanes. Furthermore, the quantized consensus state of pedestrian flow is defined. We show that when pedestrians wish to change to faster lanes, the numbers of pedestrians in each lane converge to a balanced distribution (quantized consensus state) as shown typically in pedestrian flows. Numerical simulations and theoretical analysis are used to demonstrate the convergence properties of this method. Simulation results show that the model can capture the main features of unidirectional pedestrian flows.
Optimal Planning of Workplace Electric Vehicle Charging Infrastructure with Smart Charging Opportunities

We analyze the decision problem of a workplace manager determining the optimal number of electric vehicle (EV) charging stations to install in order to supply employees with adequate charging service and take advantage of EV smart charging opportunities, while also considering the investment costs of installing additional infrastructure. To optimize smart charging revenues, we introduce assignment methods that designate specific charging spots to arriving EVs. This includes two heuristic packing methods and an offline oracle. Under each assignment method, we perform a cost-benefit analysis that determines the optimal number of chargers to install on-site. Our results highlight the importance of considering revenues from demand charge reduction to incentivize investment in additional infrastructure needed to tap into the smart charging potential of EVs, at least under current energy market prices.
Incorporating digital anorectal examinations for anal cancer screening into routine HIV care for men who have sex with men living with HIV: a prospective cohort study

Men who have sex with men (MSM) living with HIV have a high risk of anal cancer, which is often detected at late stages, when morbidity and mortality are high. The objective of this study was to describe the feasibility and challenges to incorporating regular digital anorectal examination (DARE) into routine HIV care for MSM living with HIV, from the perspective of patients, physicians and the health service.