Miracle-dz/UniFAR-FTUs huggingface dataset:
Depression and Anxiety Disorders in COVID-19 Survivors: Role of Inflammatory Predictors.

Introduction Infection-triggered perturbation of the immune system, which was observed after previous coronavirus outbreaks, could induce psychiatric sequelae. The spreading of the Coronavirus-19 (COVID-19) pandemic could be associated with psychiatric implications. In this study, we aimed to evaluate the association between inflammatory biomarkers and the levels of depression and anxiety in patients who recovered from COVID-19. Methods We screened 109 COVID-19 survivor adults for psychiatric symptoms on the 15th day of follow-up after discharge from the hospital. The patients were split into two groups, the ones with depression and anxiety, and the ones without depression or anxiety, after the psychiatric interview. Self-rating Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI) were applied to assess the levels in patients with depression and anxiety. We collected and recorded the sociodemographic information, clinical data, and baseline inflammatory markers. Results Higher baseline neutrophil/lymphocyte ratio (NLR) and systemic immune-inflammation index (SII) were found in patients with depression and anxiety. Higher levels of depression and anxiety were found in younger and female patients. Besides, a significant correlation was found between BAI and ferritin levels in patients with anxiety, while no association was found between BAI and other inflammatory biomarkers. Moreover, no significant relationship was found between BDI scores and inflammatory biomarkers in patients with depression. Conclusion COVID-19 primarily affects the respiratory and cardiovascular systems. Nonetheless, psychiatric involvement is not uncommon and can lead to severe problems if not detected and managed at an early stage. It is recommended that clinicians should be vigilant in terms of psychiatric involvement in COVID-19 patients presenting with high inflammatory parameters.
Elastoplastic Normal Impact Dis sipation Model of Two Particles

To rapidly obtain the moving state of particle impact and simulate the particle impact damping system,a piecewise dynamic model is proposed for collision of two particles in a single period.In this model,a single impact period is divided into three stages,on the basis of which,the moving state of some key points for particles and thus the analytical expressions of velocity after impact,coefficient of restitution and energy dissipation are obtained.Finite element method(FEM) is used to simulate elastoplastic impact process of two particles,and the FEM results show good agreement with the analytical prediction,which verifies the proposed piecewise dynamic model.With the piecewise dynamic model,the influences of particle velocity before impact,particle material parameters including yield strength,elastic modulus,mass density,and particle size on energy dissipation are quantitatively analyzed.The results reveal that materials with low yield strength and high elastic modulus have better attenuation effect.In addition,materials with larger mass density also have better dissipation performance,which can be taken as a reference in choosing the material of impact partners in impact damper design.The study can be applied to predicting the character of particle impact damping and simulate the vibroimpact damping system.
Simulation Analysis of Flashover Characteristics of 220 kV Composite Insulator

As the flashover characteristics are controversial while replacing the glass insulator with the composite insulator,based on the flashover characteristics and discharge mechanism of insulator,the insulator is loaded with positive-polarity direct voltage to simulate electric field by computer simulation software,so as to analyze the dynamic changes of flashover arc.The DC flashover process of insulator is deduced in combination with the gas discharge theory.By studying the electric field changes and the trend with time of different sections of composite insulators,each composite insulator flashover path is predicted.On this basis,the arc-air flashover circuit model is established to solve the minimum flashover voltage with differential computation,which is verified combined with tests.
Poor Air Quality during Wildfires Related to Support for Public Safety Power Shutoffs

Abstract Millions of Americans experienced impacts from the 2020 wildfire season, including unhealthy air quality from smoke. We examine how exposure to poor air quality during wildfires relates to public opinion toward Public Safety Power Shutoffs (PSPSs). PSPSs have been increasingly deployed in the Western U.S. during extreme wildfire conditions to reduce ignition risk from power equipment by de-energizing sections of the electrical grid, potentially leaving impacted areas without electricity for multiple days. We surveyed Oregon residents (n = 1,308), a state where few PSPSs have been deployed, and found that a majority of respondents supported PSPSs, and that poorer air quality during wildfires, recorded from monitoring stations proximal to respondents, was related to increased support for PSPSs. We also found that females and those with liberal political orientations were more supportive of PSPSs. This research has implications for utilities, emergency managers, and policymakers as they consider deployment of PSPSs to mitigate wildfire.
A COMPARISON OF PERFORMANCES OF ELECTRONIC AND ELECTROMECHANICAL ENERGY METERS

The Ferraris (electromechanical) energy meter has had predominance in the metering of energy consumption using the alternating current supply system. Electronic energy meters are gaining popularity because of the possibility of remote reading and controllable non uniform rate of billing. In this work, an electronic energy meter which does not use a multiplier circuit but uses a voltage to frequency converter with frequency to voltage converter in the feedback loop was designed and built. The performances of this electronic meter were compared with those of the Ferraris meter. It is shown that at light loads, the errors of the electromechanical meter are greater than those of the electronic meter.
An Inspired from Nature Design Methodology for the Fashion Industry

Nature is effectively a significantly large laboratory which offers opportunities for inspirations to all sciences. Some commercial implementations of nature-based mimicking, includes smart system controls, robotic sensors and artificial muscles. From the conceptual side, product design uses a different approach in nature-based methodology. It uses similarities based on the geometrical form or/and the functionality. The proposed Conceptual Product Design (CPD) framework supports and highlights the necessity of a nature-based design to all product industries. A good example is the fashion industry, which offers great opportunities for innovative and sustainable design based on nature.
Regulatory dynamics distinguishing desiccation tolerance strategies within resurrection grasses

Desiccation tolerance has evolved recurrently in grasses using two unique strategies to mitigate photooxidative damage under anhydrobiosis. The grass Oropetium thomaeum protects and retains chlorophyll, thylakoids, and the photosynthetic apparatus during desiccation (Homoiochlorophyly), while Eragrostis nindensis degrades and resynthesizes these components under desiccation and rehydration (Poikilochlorophyly). Here, we surveyed chromatin architecture and gene expression during desiccation in these two closely related species to identify regulatory dynamics underlying the distinct desiccation tolerance strategies in grasses. In both grasses, we observed a strong association between nearby chromatin accessibility and gene expression in desiccated tissues compared to well-watered, reflecting an unusual chromatin stability under anhydrobiosis. Integration of chromatin accessibility (ATACseq) and expression data (RNAseq) revealed a core desiccation response across these two grasses including many genes with binding sites for the core seed development transcription factor ABI5. O. thomaeum had a unique set of desiccation induced genes and regulatory elements associated with photoprotection, pigment biosynthesis, and response to high light, reflecting its adaptation of homoiochlorophyly. A tandem array of early light induced proteins (ELIPs) had massive shifts in gene expression and chromatin openness under desiccation in only O. thomaeum, and ELIPs acquired a novel desiccation related cis-regulatory motif, reflecting regulatory neofunctionalization during the evolution of desiccation tolerance. Together, our results highlight the complex regulatory and expression dynamics underlying desiccation tolerance in grasses.
Conductivity effects during the transition from collisionless to collisional regimes in cylindrical inductively coupled plasmas

A numerical model is developed to study the conductivity effects during the transition from collisionless to collisional regimes in cylindrical inductively coupled argon plasmas at pressures of 0.1–20 Pa. The model consists of electron kinetics module, electromagnetics module, and global model module. It allows for self-consistent description of non-local electron kinetics and collisionless electron heating in terms of the conductivity of homogeneous hot plasma. Simulation results for non-local conductivity case are compared with predictions for the assumption of local conductivity case. Electron densities and effective electron temperatures under non-local and local conductivities show obvious differences at relatively low pressures. As increasing pressure, the results under the two cases of conductivities tend to converge, which indicates the transition from collisionless to collisional regimes. At relatively low pressures the local negative power absorption is predicted by non-local conductivity case but not captured by local conductivity case. The two-dimensional (2D) profiles of electron current density and electric field are coincident for local conductivity case in the pressure range of interest, but it roughly holds true for non-local conductivity case at very high pressure. In addition, an effective conductivity with consideration of non-collisional stochastic heating effect is introduced. The effective conductivity almost reproduces the electron density and effective electron temperature for the non-local conductivity case, but does not capture the non-local relation between electron current and electric field as well as the local negative power absorption that is observed for non-local conductivity case at low pressures.
CRISPR-Detector: Fast and Accurate Detection, Visualization, and Annotation of Genome-Wide Mutations Induced by Gene Editing Events

The leading edge of genome editing, driven by CRISPR/Cas technology, is revolutionizing biotechnologies. In pace with the burst of novel genome editing methods, bioinformatic tools to monitor the on/off-target events need to be more evolved as well. Existing tools suffer from limitations in speed and scalability, and they especially struggle with whole genome sequencing (WGS) data analysis which has the potential to detect off-target mutations at a genome-wide level via an unbiased manner. Here, we introduce our CRISPR-detector tool, which is a web-hosted or locally deployable pipeline with five key innovations: 1) optimized scalability allowing for WGS data analysis beyond BED (Browser Extensible Data) file-defined regions; 2) improved accuracy benefiting from haplotype-based variant calling to handle sequencing errors; 3) treated and control sample co-analysis to remove background variants existing prior to genome editing; 4) integrated structural variation (SV) calling; and 5) functional and clinical annotation of editing-induced mutations.
A Novel Precise Personalized Learning Recommendation Model Regularized with Trust and Influence

Many precise personalized learning recommendations in massive open online courses (MOOCs) have emerged in the intelligence education field. Up to now, most researches simply put the dual learner-resources relations into consideration and are short of studies looking deep into its intrinsic social relation, thus rarely introducing the influential factors such as social trust, which means to apply the mutual trust relation between learners in the precise personalized learning recommendation. Therefore, we propose a personalized learning recommendation method based on learners’ trust and conduct a quantitative analysis on two aspects: social trust and influence, so as to realize a precise personalized learning recommendation service. First, we establish a new module on social trust scale which integrates the interactive information and preference degree to reveal the implicit trust relation between learners in social networks and construct social trust networks. Next, we adopt improved structural hole (ISH) algorithm by integrating the topological structure of social trust network with learners’ interactive information and identify the most influential learners cluster by the ISH algorithm. For the final stage, we predict the score of target learners based on explicit and implicit feedback information and realize the personalized learning recommendation for new learners. Since the score is predicted, we compare MAE and RMSE in two real-world datasets which are Canvas Network and Wanke website, respectively. The result of experiment validates the accuracy and effectiveness of our recommendation model.
Characterization And Cytotoxicity of Novel Synthesis Selenium Nanoparticles Stabilized By Vitamin E TPGS

The aim of this research was to investigate synergistic effect of combination two related antioxidant agents by novel synthesis of selenium nanoparticles functionalized with D-α- Tocopherol polyethylene glycol 1000 succinate (TPGS) as a capping agent during chemical reduction of sodium selenite by ascorbic acid. Particle size, morphology, crystallite size of synthesized Se-TPGS were characterized by dynamic light scattering (DLS), transmission electron microscopy (TEM), Field Emission Scanning Electron Microscopy (FESEM), Ultraviolet-Visible spectrophotometry (UV/Vis), X-Ray diffraction (XRD). The in vitro antioxidant activity, cell toxicity and blood compatibility assays were investigate respectively. The DLS results revealed that the Se-TPGS possess very low polydispersity index value (PDI = 0.03) and having hydrodynamic particle size of 83.33 ± 14.7 nm with negatively value for zeta potential equal to -19.5 mV. Both SEM and TEM show regular spherical shape of SeNPs with size measurement less than DLS measuring. XRD result confirming that the produced SeNPs possessed a standard crystalline hexagonal phase structure. DPPH assay of total antioxidant activity display high antioxidant activity of Se-TPGS NPs when comparing with ascorbic acid as a reference material. The cytotoxicity of new formula Se-TPGS investigated for both cancerous (MCF-7) and normal (HBL-100) breast cell lines with significantly different. This study provides a facile and green method for chemically synthesizing stable SeNPs which are suitable for further evaluation in therapeutic applications. The SeNPs could be a blueprint in the field of biomedical application as an alternative chemotherapy for fighting cancerous cells.
Bit-serial convolution with prediction threshold for convolutional neural networks

ABSTRACT To reduce the implementation complexity and power consumption of the convolution operation in a convolutional neural network (CNN), this work proposes a new convolution method using the serial input and prediction threshold. To confirm the benefits of the proposed method, we use the original parameters, i.e. kernel weights and biases, of the popular AlexNet to verify the proposed algorithm and then implement its digital circuit. According to implementation data and comparison with traditional convolution using bit-parallel input, the implementation gain in terms of the throughput/power/area of the serial convolution is 7.57 times that of parallel convolution.
Maintenance Strategies Optimization of Equipment Using Risk Assessment

Life cycle cost management(LCCM) and condition based maintenance(CBM) are two effective tools used in asset management.CBM includes equipment condition assessment,risk assessment and aid-decision making.Its emphasis is not how to maintain the equipment but how to make maintenance decision.Based on LCCM theory and experiences in other domains,operation and maintain would be about 45% of the life-cycle cost.Their optimization might reduce only 5% of life cycle costs for the most costs were determined during the design and plan.Moreover,additional environment loss and social loss would be vital too.So there should be a solution to balance all the costs.Obviously,these costs are connected with the equipment condition.To get a quantitative description of them,probability analysis technique should be introduced.Failure rate and the probability of equipment which would fail in a short period should be derived from equipment condition.Economic benefit might be only one of the decision foundations,so LCCM would not meet all needs of power system and society,especially in China.Though there are some methods to turn environment loss cost,safety loss cost and social loss cost into economic index,the conception of cost is not widely accepted.Multi-objectives optimization which is derived from risk theory would be more reasonable.To optimize asset management in power systems governed by State Grid of China,a new technology is thus proposed.Compared with previous work,this technology gives a comprehensive solution to three problems: failure rate calculation based on equipment condition,risk assessment for power system and decision-making based on multi-objectives optimization.It can be regarded as a balance between the value of equipment themselves and benefits that they can bring to power systems,a balance between LCCM and CBM,and finally,a balance between technologies applied and management of power system.
Investors Attitude towards Pension Scheme (Special Reference To Kanpur District, Uttar Pradesh State, India)

Investment in pension schemes s is effected by the attitude of the investors. The objectives of the study are to identify the investor's attitude on pension schemes and to analyze the factors affecting investor's attitude towards pension scheme. The study aims at finding out the attitude of the investors towards investment in pension schemes in Kanpur. By adopting random sampling for 100 respondents, Simple statistical tools are used for analyzing the data whatsoever collected in this study. The present investigation outlined that the investors have positive approach towards investing in pension schemes. The investors opt to pension scheme for old age safety and investors collect the information from expert advisors then only invest the money. Mainly investors select the different types of market linked schemes for their investment in pension schemes.
Modulation of sleep by trafficking of lipids through the Drosophila blood-brain barrier

Endocytosis through Drosophila glia is a significant determinant of sleep amount and occurs preferentially during sleep in glia of the blood brain barrier (BBB). To identify metabolites whose trafficking is mediated by sleep-dependent endocytosis, we conducted metabolomic analysis of flies that have increased sleep due to a block in glial endocytosis. We report that acylcarnitines, molecules that conjugate with long chain fatty acids to promote their transport, accumulate in heads of these animals. In parallel, to identify transporters and receptors whose loss contributes to the sleep phenotype caused by blocked endocytosis, we screened genes enriched in barrier glia for effects on sleep. We find that knockdown of lipid transporters LRP1&2 as well as carnitine transporters ORCT1&2 increases sleep. In support of the idea that the block in endocytosis affects trafficking through specific transporters, knockdown of LRP or ORCT transporters also increases acylcarnitines in heads. We propose that lipid species, such as acylcarnitines, are trafficked through the BBB via sleep-dependent endocytosis, and their accumulation in the brain increases the need for sleep.
Exploring Undergraduate Students’ Online Interaction in Academic Reading Classroom via WhatsApp Application

This research aimed to discuss the potential of the WhatsApp application as a tool of online learning at a higher educational level that could stimulate students’ interactions and discussion with the lecturer or their peers. With interactions, they can experience and develop an awareness of behaviors required to facilitate their participation in online learning. Therefore, this study identified the specific and general types of undergraduate students’ online interactions and investigated their patterns across time. The study was carried out by 6 undergraduate students of the 2019/2020 academic year in the English Education Department, Wiralodra University. Students’ posts in WhatsApp group discussion were analyzed in terms of online interaction types identified by MacKinnon (2000) and Jung et al. (2002). The result shows that the students mostly expressed their opinions and judgments in academic-related discussions rather than moved to a higher level of knowledge construction, such as providing their own example upon their ideas and elaborating more on certain topics in the discussion. Further investigation can be done to find the factors that affect students’ interaction, the relationship between topics, and online interactions in another online learning platform.
DISTANCE EDUCATION FOR TEACHER TRAINING PROGRAM IN RURAL AREAS: A NECESSARY STRATEGY FOR COVID-19 SITUATION

Like other countries around the world, distance education approach has been exceptionally beneficial for the continuation of teaching learning activities in Bangladesh during the covid-19 pandemic situation. Teacher training is a very crucial and integrated part of teaching learning activities as it helps to enhance the quality of teaching. Despite the continuation of teaching learning through online and distance education approach, the teacher training was not happening on a regular basis during this pandemic period. Therefore, this study is an attempt to explain the necessity of distance education for the persistence of teachers training program during and after the covid-19 pandemic situation in order to maintain a continuity in the professional development especially for teachers in the rural areas in Bangladesh. This study was conducted by following the concurrent mixed method research design. Data were collected with the help of a semi-structured questionnaire and interview schedule from the secondary school teachers from rural areas, trainers and experts in distance education and teacher training program. The results of this study indicated that distance education can play a significant alternative role in arranging teacher training program for teacher across the country during this covid-19 crisis moment. Moreover, the participants also agreed that the continuation of distance education approach should be paralleled with the conventional approach for teacher training program in the post covid-19 period as it brings more flexibility, accessibility, inclusiveness and responsiveness. However, the study also revealed few challenges which can impair the arrangement of distance education in teacher training programs. Article visualizations: