Miracle-dz/UniFAR-FTUs huggingface dataset:
A Voltage-Based Multiple Fault Diagnosis Approach for Cascaded H-Bridge Multilevel Converters

This article proposes a fast and robust open-circuit fault diagnosis method without additional sensors for cascaded H-bridge multilevel converters (CHBMCs). The method is based on the error between the actual input-side voltage and the estimated one, with one detection threshold and <inline-formula> <tex-math notation="LaTeX">$2n$ </tex-math></inline-formula> comparators in <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> cascaded H-bridges. Benefitted from the simple design of the fault location, spike elimination, and parameter estimation, with the lower implementation burden, the single and multiple faults in arbitrary positions are accurately identified within a fundamental cycle, and the diagnostic robustness is guaranteed under different transient changes. In view of the low complexity, the proposed scheme can be easily applied to the CHBMCs with arbitrary modules. The experimental results from a CHBMC prototype verify the effectiveness of the proposed method under different operating conditions and fault scenarios.
Robust Semisupervised Land-Use Classification Using Remote Sensing Data With Weak Labels

This work develops robust semisupervised classifiers to tackle the three most challenging problems in land-use classification using remote sensing data, namely, information imbalance, label noise, and image uncertainty. Limited by technology and cost, collecting clean labels for remote sensing images is difficult and often impractical. The change of environment and time also increases the uncertainty of remote sensing images. To overcome the obstacles incurred by the mixed pixels and weak labels, this work proposes dividing the pixels in remote sensing images into two groups, namely, pixels with accurate labels and those with weak labels, before processing the weakly labeled pixels using a nuclear norm-based cost function. To address the imbalanced data problem in pixels with accurate labels, an improved cross-entropy-based cost function is proposed to weigh the contributions from data of different classes based on their importance by exploiting the term frequency-inverse document frequency (TF-IDF) algorithm. Finally, an artificial class called “unknown” is proposed to cope with the interference caused by weakly labeled data with unrepresentative spatial features. Extensive experiments validate the effectiveness of the proposed semisupervised classifier.
A Motivation-Hygiene Model of Open Source Software Code Contribution and Growth

The success of open source software (OSS) projects depends on sustained contributions by developers who often display a wide variety of contribution patterns. Project leaders and stakeholders would strongly prefer that developers not only maintain but preferably increase their contributions over time as they gain experience. Corporations increasingly complement OSS developer motivations (such as fit in terms of shared values with the project community) by paying them to sustain contributions. However, practitioners argue whether payment helps or hurts projects because monetary compensation may dampen developer motivation in the long run, making it difficult for project leaders to understand what to expect from developers over time. Using Herzberg motivation-hygiene framework, we explore how developer perceptions of value fit with the project and being paid interact to determine the level of code contribution and its rate of change over time (i.e., growth). Using a survey of 564 developers across 431 projects on GitHub, we build a three-level growth model explaining the code contribution and its growth over a six-month period. We find that value fit with the project positively influences both the level and growth of code contribution. However, there are notable differences among paid and unpaid developers in the impact of value fit on their level and growth in code contributions over time. The implications of our work will be of interest to researchers, practitioners, and organizations investing in open source projects.
Importance-weighted Ranking Methods for Preference the Covid-19 Pandemic
Social Assistance

Issues importance-weighted value is a critical aspect of decision making. Differences in weight, even the slightest change in weight assignment, can drastically change the final decision. Moreover, in the case of distributing social assistance during the Covid-19 pandemic, objectivity and accuracy of weighting the criteria for potential recipients are very important applied for the welfare of the community. The proposes study 3 popular models of ranking methods for weighting criteria in the internet data package assistance cases. Weighting is given to 390 alternatives with 5 decision-making criteria based on online learning needs and economic cost capabilities. The decision analysis method uses the reference point and optimization from Moora. The study results were found accuracy, precision and error rate performance each method using a confusion matrix approach. The study results discussed raised several important points of findings, that the three ranking methods (RS, RR, ROD) have their respective characteristics in weighting importance, where the level of accuracy and precision of the rank-sum method is better than the RR and ROD methods (for the case: 5 criteria; 390 alternatives). Other things in giving weight value from important to most important are comparable, and the weight value of the non-benefit (cost) criteria in the ranking method have a significant effect on performance results. These three methods are simple in use and with the assessment of replacement weights that can be determined how important these variables are to the principal of these criteria.
A review of species of the genus Theodoxus (Gastropoda: Neritidae) of the Ponto-Caspian region, with considerations on available type materials

An illustrated overview of the type series of nominal taxa of the gastropod genus Theodoxus (Neritidae) inhabiting the Ponto-Caspian region is presented. It is made on the basis of a revision of large European malacological collections and includes descriptions of the type specimens of 16 taxa of different rank (species, variety, morph). Apart of these, the “type” specimens of four taxa with unavailable names have been traced in ZIN collection; we illustrate them to show the variability of Ponto-Caspian Theodoxus. In most cases, the photographs of the type specimens are published for the first time. We provide a taxonomic analysis of all studied type series, with comments on the nomenclature, systematic position and taxonomic rank of all nominal taxa of Theodoxus recorded from the Ponto-Caspian area.
Pathospermia in the patients with high body mass index

The retrospective analysis of pathospermia different types dependence on body mass index has been done. A trend of semen analysis deterioration with increase of body mass index has been revealed. Patients with a body mass index 26 – 29.9 kg/ m2 are at risk group for starting decline of semen analysis. This problem demands attention and further detailed study.
Effects of a Multi-Intervention Multimedia Infection Controlmoduleon Critical Care Nurses’ Knowledge and Practice of Prevention and Control of Healthcare-Associated Infections

Aim: This studypurpose is to assess the effectiveness of the MIMIC module in improving the levels of adult critical care nurses’ knowledge and practice towards prevention and control of Healthcare-Associated Infections. Background: The Healthcare-Associated Infections are crucial health problem affecting healthcare system worldwide. While many educational programs and other interventions were developed, the nursing awareness and practice of IPC precautions are still inadequate for handling Healthcare-Associated Infections. Method: A one-group pretest-posttest quasi-experimental design was used and the MIMIC module was implemented as three 2-hour theoretical sessions for each of the three groups of adult critical care nurses and four 30-minute practical sessions for each of the four selected ICUs. Self-administered questionnaire was distributed at two points of time, immediately before and three months after the MIMIC module, in order to assess the effectiveness of the module. Results: A total of 121 adult critical care nurses participated in the study. Following the MIMIC module, the participants’ level of knowledge towards prevention and control of Healthcare-Associated Infections indicated a statistically significant improvement, F (1, 120)= 632.679, p < . 001, ηp2 = . 844 with large effect size. And a statistically significant improvement in levels of practice after implementation of the MIMIC module, F (1, 120)= 113.089, p < . 001, ηp2 = . 492with large effect size. Conclusion: The MIMIC module is a promising module which can be implemented as Continuing Nursing Education to improve adult critical care nurses’ knowledge and practice to prevent and control of Healthcare-Associated Infections.
ESTADO DA ARTE DA PESQUISA SOBRE CONSUMO DE ALIMENTOS ORGÂNICOS: DETERMINANTES, DISPOSIÇÃO A PAGAR E FRAMING EFFECT

Este trabalho tem como objetivo investigar o estado da arte das pesquisas que avaliam os condicionantes do consumo de alimentos orgânicos, investigando ainda a disposição a pagar e o framing effect na decisão de escolha de tais produtos. Para atingir tal meta, foi aplicado o método de revisão bibliométrica, consultando a base de dados da Web of Science e Scopus no período entre 2000 e 2020. A partir do uso do R Studio - Package bibliometrix, as variáveis nível de escolaridade, conhecimento sobre os produtos orgânicos, preocupação individual e coletiva em relação ao meio ambiente e à saúde se mostraram como fatores determinantes do consumo e da disposição a pagar por alimentos orgânicos. As investigações ainda apontaram que o framing effect, um dos vieses comportamentais presentes nas escolhas dos consumidores, impactou positivamente o consumo e a disposição a pagar dos consumidores de tais produtos.
Assessment Viability of Frozen-Thawed Oocytes Using Non- Invasive Technique based on Buffalo Follicular Fluid

Back ground: Cryopreservation method, equilibrium rapid freezing (vitrification), used to preserve oocytes. Vitrification is a common method for cryopreservation of gametes and early embryos. The follicular fluid (FF) contains a complex mixture of steroids, metabolites, polysaccharides, proteins and small peptides, reactive oxygen species (ROS) and antioxidant enzymes (1) . Objectives: The present study was aimed to improve rate of buffalo oocyte viability using follicular fluid with Trypan blue, after thawing. Materials and Methods: Cryopreservation of oocytes using vitrification method using common solution, then thawing oocytes after 2 months and assessment oocyte viability. Collection of oocytes from buffalo ovaries buffalo after sacrifice, then assessment viability of oocytes using trypan blue stain for 30 seconds. Using vitrification solution (DMSO, EG), then the oocytes viable loaded on vitricare and plunged in liquid nitrogen (-196 ℃ ) for 2 months, after thawing, the oocytes viability was assessed, using two different concentrations of buffalo follicular fluid (low and high concentration) mixed with trypan blue, the SMART medium that used in the present research. Results: Results of the present study appeared significant increment (P<0.05) in the percentages of oocytes viability using follicular fluid with trypan blue as compared to the trypan blue only. Conclusion: From the results of the present study it was concluded that the follicular fluid enriched to the trypan blue improved percentage of oocytes viability post thawing.
A Symplectic Conservative Perturbation Series Expansion Method for Linear Hamiltonian Systems with Perturbations and Its Applications

In this paper, a novel symplectic conservative perturbation series expansion method is proposed to investigate the dynamic response of linear Hamiltonian systems accounting for perturbations, which mainly originate from parameters dispersions and measurement errors. Taking the perturbations into account, the perturbed system is regarded as a modification of the nominal system. By combining the perturbation series expansion method with the deterministic linear Hamiltonian system, the solution to the perturbed system is expressed in the form of asymptotic series by introducing a small parameter and a series of Hamiltonian canonical equations to predict the dynamic response are derived. Eventually, the response of the perturbed system can be obtained successfully by solving these Hamiltonian canonical equations using the symplectic difference schemes. The symplectic conservation of the proposed method is demonstrated mathematically indicating that the proposed method can preserve the characteristic property of the system. The performance of the proposed method is evaluated by three examples compared with the Runge-Kutta algorithm. Numerical examples illustrate the superiority of the proposed method in accuracy and stability, especially symplectic conservation for solving linear Hamiltonian systems with perturbations and the applicability in structural dynamic response estimation. AMS subject classifications: 74H15, 37N15, 65P10, 65M06
Distributed Resource Management Framework for IoS Against Malicious Jamming

The internet of satellites (IoS), containing multiple low earth orbit (LEO) satellite constellations, can support tremendous traffic, massive connectivity, and vast coverage. But it also puts forward higher demands for resource management due to the high dynamics of the IoS networks, especially in the malicious jamming environment, where the jammers launch jamming attacks to reduce the efficiency and reliability. Thus, this paper investigates the problem of resource management in malicious jamming environment for IoS, which is divided into three sub-problems: traffic prediction problem, anti-jamming decision problem and resource matching problem. To solve these problems, we proposed a distributed resource management framework (DRMF), which consists of three sub-algorithms. Firstly, the traffic prediction algorithm (TPA) is proposed to deeply mine and accurately predict the traffic rule. Meanwhile, the dynamic anti-jamming algorithm (DAA) is developed to make anti-jamming decision autonomously. Then, based on the outputs obtained by TPA and DAA, the distributed resource matching algorithm (DRMA) is proposed for IoS, and the satellites with insufficient resource can apply for assistance from neighboring satellites with excess resource, thereby improving the safety and efficiency of the entire IoS network. Finally, experiment results and algorithm analysis verify the proposed scheme has better performance than the existing algorithms.
A Stochastic Control Approach to Defined Contribution Plan Decumulation: “The Nastiest, Hardest Problem in Finance”

We pose the decumulation strategy for a defined contribution (DC) pension plan as a problem in optimal stochastic control. The controls are the withdrawal amounts and the asset allocation strategy. We impose maximum and minimum constraints on the withdrawal amounts, and impose no-shorting no-leverage constraints on the asset allocation strategy. Our objective function measures reward as the expected total withdrawals over the decumulation horizon, and risk is measured by expected shortfall (ES) at the end of the decumulation period. We solve the stochastic control problem numerically, based on a parametric model of market stochastic processes. We find that, compared to a fixed constant withdrawal strategy, with minimum withdrawal set to the constant withdrawal amount the optimal strategy has a significantly higher expected average withdrawal, at the cost of a very small increase in ES risk. Tests on bootstrapped resampled historical market data indicate that this strategy is robust to parametric model misspecification.
Stress, Anxiety and Depression in Nurses during COVID-19 Pandemic: Evaluation of Coping Strategies

Introduction: Nurses who face a public health threat globally are more likely to develop symptoms of anxiety, stress and depression trying to use specific coping strategies to be as effective as possible in providing health care. Aim: To evaluate stress, anxiety and depression levels of nurses during the Covid-19 pandemic in Greek hospitals and to analyze coping strategies used more or less frequently by them. Methods: 130 Greek nurses completed online questionnaires about positive and negative attitude, resilience level, optimistic thoughts and stress, anxiety and depression questions during their daily practice working with patients who obtained or not coronavirus disease. According to these scales‘ scores, there was a further correlation with specific coping strategies. Results: 43.8% of the sample was working in reception of confirmed or possible coronavirus cases. 36.2% of the population experienced mild to extremely severe stress symptoms and one-third of them reported quite the same levels of depression and anxiety respectively. Behavioral disengagement, substance use, seeking support and avoidance were significantly and positively associated with the Stress subscale, while higher optimism and higher resilience were significantly associated with less depression, anxiety and stress symptoms. Conclusions: Early recognition of symptoms of mental disorder and their proper treatment can be crucial for nurses‘ strengthening. Educational and supportive programs could be beneficial to provide the best care to these patients.
redBERT: A Topic Discovery and Deep SentimentClassification Model on COVID-19 OnlineDiscussions Using BERT NLP Model

A Natural Language Processing (NLP) method was used to uncover various issues and sentiments surrounding COVID-19 from social media and get a deeper understanding of fluctuating public opinion in situations of wide-scale panic to guide improved decision making with the help of a sentiment analyser created for the automated extraction of COVID-19 related discussions based on topic modelling. Moreover, the BERT model was used for the sentiment classification of COVID-19 Reddit comments. These findings shed light on the importance of studying trends and using computational techniques to assess human psyche in times of distress.
Increasing Rice Yield through Targeting Genetic Potentials by Rice Types

Bangladesh needs an average rough rice yield of 9.11 t ha-1 by 2050 which can not be achieved equally across all the geographic regions since the country has various ‘rice types’ with varying yield potentials. This paper focuses on strategic innovations for reaching the yield target by refining rice types. Based on rice ecosystems and the pressing needs, we divided rice areas of Bangladesh in 17 different types. We estimated year-wise land areas and allocated achievable yield targets for each of the rice types. Finally, we compared the target yields and the yields of top-yielding rice varieties in Bangladesh by 2020 across the rice types to understand the current status of our varietal improvement programmes. We sorted out how much improvement is needed in each rice types. Among the rice types, cold-tolerant (Northern and Western) was sorted out as the most potential area of rice yield improvement where rice varieties will be released having a yield advantage of 4.04 t ha-1 by 2050. The chronology of next priority areas for high yielding variety development and their target yield advantages in t ha-1 are saline Boro (4.03), Favourable Boro (long duration) (4), cold-tolerant (Haor) (3.83), tidal submergence (3.8), Healthier rice (Boro) (3.58), Favourable Boro (short duration) (3.33), Healthier rice (Aman) (3.3), Favourable Aman (3.23), Flash flood (3.09), upland rice (2.89), Saline Aman (2.8), Healthier rice (Aus) (2.53), Premium quality rice (2.53), drought (2.38), T. Aus (2.05) and deepwater. Combined genetic interventions like population improvement through cyclic breeding, genomic selection, marker-assisted selection, genome editing, genetic transformation, germplasm utilization through genome-wide association study and phenomics, and development of super hybrid rice are being used in the country to attain yield target for different rice types.
Cases on Active Blended Learning in Higher Education

This chapter highlights the contributions of the EPICA project in reducing the skills gap of graduate students in sub-Saharan Africa. It presents the solution designed and implemented to improve the quality of employability skills development and visibility to prospective employers. The first part of this chapter provides an overview of the skills gap between higher education institutions and the workplace in sub-Saharan Africa. It includes the description of the specific eAssessment pedagogical framework and methodology supported by the EPICA ePortfolio as a transition tool designed to address this gap
A Novel Open-Circuit Fault Detection and Location for Open-End Winding PMSM Based on Differential-Mode Components

Detecting and locating the open-circuit (OC) fault of an insulated-gate bipolar transistor (IGBT) rapidly and accurately is very important to improve the security and reliability of the drive system. However, due to the symmetry of the dual-inverter topology, it is difficult to achieve fault detection for open-end winding permanent magnet synchronous motor (PMSM). Thus, this article proposes a fault diagnosis technology for open-end winding PMSM fed by a dual inverter with a common dc bus. In this method, the faulty phase and the faulty case are identified by the average value of the output of the closed-loop controller, which is used to eliminate the differential-mode current in the system. Then, the faulty phase is isolated by removing the gate signals. Finally, the faulty IGBT is located by restructuring the faulty phase and analyzing the current of the faulty phase. In this method, the OC fault can be detected and the faulty IGBT can be located accurately. Experimental results are shown to validate the proposed detection method.
KEY DETERMINANTS OF CHANNEL SWITCHING INTENTION DURING THE COVID-19 PANDEMIC: EVIDENCE FROM AN EMERGING MARKET

The COVID-19 pandemic has become a global problem with its rapid spread to almost the whole world, impacting many areas, especially health. Therefore, the negative climate created by this pandemic is likely to affect consumers' purchasing behavior. Therefore, this study aims to examine the relationships between perceived offline shopping risk, trust in offline stores, and perceived threat in relation to how this affects consumers' channel switching intentions. For this purpose, data were obtained from 422 online survey participants and analyzed using structural equation modeling. The results show that trust in offline stores has a negative relationship with perceived offline shopping risk, and that perceived risk of offline shopping and perceived threat have a significant positive relationship with consumers' channel switching intentions. These findings contribute to the development of understanding in relation to channel switching intentions in the retail sector. In addition, these findings provide retail managers and practitioners with practical insight into perceived risk, trust, perceived threat, and channel switching intentions during the COVID-19 pandemic.