Miracle-dz/UniFAR-FTUs huggingface dataset:
Task-Specific Feature Purifying in Radar-Based Human Pose Estimation

Recently, radar-based human pose estimation has attracted increasing attention, which aims to reconstruct the human skeleton from the radar signals with the advanced deep neural networks. However, one fundamental challenge is the interferences from task-irrelevant ingredients due to the propagation characteristics of radar signals during feature learning. In this article, we present a two-stage framework for task-specific feature purifying to distill the feature representation with high discriminability specific to the pose estimation task but less interpersonal discrepancy, which is essential to handle the difficulties arising from the task-irrelevant interferences in reconstructing the human skeleton. The logic behind the proposed two-stage feature purifying architecture is that the discrepancy among individual personal data is first eliminated with an adversarial auto-encoder module to distill the interpersonal-independent features, and then the pose-irrelevant components are removed through a feature disentanglement module to form task-specific features for human pose estimations. The two-stage architecture turns out to be considerably effective for feature purifying against the interferences due to the task-irrelevant ingredients involved in the feature learning process. The experimental study is presented to illustrate the effectiveness of the proposed approach.
Effect of BaFe12O19 Nanoparticles Addition on (Bi,Pb)-2223 Superconducting Phase

The aim of this work is to investigate the effect of BaFe12O19 nanoparticles on the microstructure, phase formation and mechanical properties of (Bi,Pb)-2223 superconducting phase. Co-precipitation and solid-state reaction techniques were used to synthesize BaFe12O19 nanoparticles and (BaFe12O19)x(Bi,Pb)-2223 superconducting samples with , respectively. BaFe12O19 nanoparticles and (BaFe12O19)x(Bi,Pb)-2223 structures were performed using X-ray diffraction. The morphology of BaFe12O19 nanoparticles and (BaFe12O19)x(Bi,Pb)-2223 were observed by means of transmission electron microscope (TEM) and scanning electron microscope (SEM), respectively. The experimental results reveal the composition of Bi-2223 phase and traces of Bi-2212 as a secondary phase when compared to the undadded sample. Lattice parameters are not altered with BaFe12O19 addition which indicate that nanoparticles do not enter the host crystal of (Bi,Pb)-2223. Vickers microhardness Hv is measured as function of indentation load and time. It was found that microhardness has a non-linear trend with applied load and time. The experimental results were analyzed using different models. The analysis revealed that the HK model was more suitable than the other approaches in estimating the load independent hardness of the samples.
Porous polydimethylsiloxane films with specific surface wettability but distinct regular physical structures fabricated by 3D printing

Porous polydimethylsiloxane (PDMS) films with special surface wettability have potential applications in the biomedical, environmental, and structural mechanical fields. However, preparing porous PDMS films with a regular surface pattern using conventional methods, such as chemical foaming or physical pore formation, is challenging. In this study, porous PDMS films with a regular surface pattern are designed and prepared using 3D printing to ensure the formation of controllable and regular physical structures. First, the effect of the surface wettability of glass substrates with different surface energies (commercial hydrophilic glass and hydrophobic glass (F-glass) obtained by treating regular glass with 1H,1H,2H,2H-perfluorooctyl-trichlorosilane) on the structural characteristics of the 3D printed PDMS filaments is investigated systematically. Additionally, the effect of the printing speed and the surface wettability of the glass substrate on the PDMS filament morphology is investigated synchronously. Next, using the F-glass substrate and an optimized printing speed, the effects of the number of printed layers on both the morphologies of the individual PDMS filaments and porous PDMS films, and the surface wettability of the films are studied. This study reveals that regularly patterned porous PDMS films with distinct structural designs but the same controllable surface wettability, such as anisotropic surface wettability and superhydrophobicity, can be easily fabricated through 3D printing. This study provides a new method for fabricating porous PDMS films with a specific surface wettability, which can potentially expand the application of porous PDMS films.
Feasibility and acceptability of a new shoulder-specific warm-up programme to prevent injuries in community youth rugby union as compared to the FIFA 11+

Injury prevention exercise programmes (IPEPs) are efficacious, though there is no IPEP specifically designed to reduce shoulder injuries in rugby. This study aimed to determine the feasibility and acceptability of the Rugby Active Shoulder Injury Prevention (RASIP) programme. Three community rugby union clubs participated in a 12-week pilot study (players, n = 51; coaches, n = 7). Two teams were randomly allocated to the RASIP programme and one to the FIFA 11+ (IPEP) intervention. Feasibility was examined by players’ adherence to the IPEP, and acceptability was described by players’ and coaches’ perceptions of the IPEP. On average, more players were exposed to 11+ training sessions (100%) than the RASIP programme (19 out of 36 players, 53%). Higher percentage of exercises was completed over the season in the 11+ (97%) than the RASIP programme (58%). Across both groups, there were different approaches followed in delivering the allocated intervention. The coach-led, prescriptively followed 11+ IPEP was better at achieving higher adherence with exercises. Coaches were concerned of not having enough time in their sessions to include the IPEP. This pilot study has shown that the intervention is feasible to deliver and is acceptable with improvements recommended by community stakeholders, coaches and players in a community youth rugby setting.
Distribution and behavior of cattle grazing riparian pastures

approved: . Larry L. Larson Douglas E. Johnson One second GPS collars were deployed on cattle in three different pastures at three separate times during the year. In each riparian pasture the vegetative communities and stream bank edge were digitally mapped using low elevation aerial photographs and checked in the field for accuracy. A 5 m buffer zone was established on the outside of both stream banks to analyze steam bank edge. The Animal Movement Classification Tool (Johnson et al. 2009) was used to split the one second data into 24 hour periods and movement was determined by pre determined settings. The herd day (5 cattle) one second point files were overlaid with this map and amount of time spent was determined for each community or zone. Analysis was done to determine the type of movement done in each community (moving vs. stationary and 1 st half vs 2 nd half of trial). One typical day for each pasture was analyzed to show the movement of a cow for that day. Other descriptive analyses were used to explain cattle crossings. In all three pastures the cattle did not move evenly throughout the pastures. Cattle always preferred to rest in areas that were dry and open. Cattle were stationary for more than 50% of the time in each pasture and had a consistent resting period from about dark until 4:00 a.m. Stationary locations (stationary > 10 minutes) were found to be relatively well distributed within these areas. Interaction with the stream was found to be 1-2% of total occupancy. Cattle were either neutral in preference or avoided these areas relative to their acreage and a majority of the time spent in these areas was spent moving not resting. Cattle did not prefer to be in the stream bank zone in any pasture relative to their acreage. The stream bank zone was used as a travel corridor to get to and from the stream to drink or cross.
Resilience Enhancement Aimed Adaptive Identification of Arc Fault in Distribution System

The gradually increased proportion of power electronic devices and the distributed energy resource (DER), in the distribution network, induced more harmonic and pulse signals to be injected into the grid. Arc faults, as the critical and common faults in the distribution system, are the potential reasons for the safety of the personnel and equipment. Furthermore, intensely chaotic characteristics of the arc current and the harmonic generated from the power electronic devices pollute the fault signature, which may cause misjudgment or malfunctions of arc detection devices. Therefore, to identify the arc fault rapidly and accurately, this article starts with the mechanism of the arc formation. It analyzes the fault signature with resistive, inductive, and capacitive loads to reduce the detection of blind spots. Then, the time-frequency is utilized to analyze the fault current of each load type to capture the common feature in the frequency domain. Comparing multiple tests shows that the proposed approach can accurately and robustly identify the arc fault in different operation conditions.
Insight the Impact of TiO2 and Al2O3 Nanoparticles in Mineral, Vegetable Oil Based on AC Dielectric Properties—Conformity With Weibull Distribution

This article presents an experimental study on the influence of titania and alumina nanoparticles on mineral and vegetable oils (VOs). For this purpose, three types of nanofluids are prepared in the laboratory. First, nanofluids are prepared with titanium oxide (TiO2) or Al2O3 nanoparticles dispersed in mineral or vegetable oil. Next, the mixture of these two types of nanoparticles (maintaining a certain ratio) is dispersed in mineral or vegetable oil. After that, the mineral and vegetable oils are mixed (in a certain ratio), in which TiO2 or Al2O3 nanoparticles are dispersed. Then, the prepared samples are subjected to ac breakdown voltage (ACBDV) measurement. Again for estimating the average ACBDVs with 1%, 10%, and 50% risk probabilities, Weibull distribution functions are chosen to apply. With the conformity test, it is found that the breakdown voltage (BDV) data for most of the prepared nanofluids follow Weibull distributions. From the experimental results, it is observed that the ACBDV can be enhanced up to 71% and 67% by dispersing hybrid nanoparticles in mineral and vegetable oil, respectively. Further, it may be observed that mixed oil with TiO2 dispersion can enhance breakdown voltage up to 27%. Moreover, the effect of nanoparticles on ac conductivity at different stages of frequencies is studied by the ladder phenomenon occurred in ac conductivity.
Stress mindset as a mediator between self-efficacy and coping styles

Abstract Stress mindset is a lens through which one views stress and its consequences as beneficial or harmful for them. It is a distinct variable that differs from frequency, amount, and intensity of stress. The literature review indicated that stress mindset could mediate the link between self-efficacy and coping style, which was previously not tested. Hence, the study aimed; 1) to examine the relationship between self-efficacy, stress mindset, and coping style; 2) to investigate the influence of stress mindset and self-efficacy on coping styles; 3) to find whether stress mindset mediates the association between self-efficacy and coping styles. The study employed a correlational research design, whereby through multi-phase sampling recruited 727 participants (male = 300, female = 427, mean age = 16.26) studying in 11th and 12th standard. The researchers administered validated stress mindset, self-efficacy, and coping style and performed a multiple correlational and regression analysis. They computed mediation analysis using Haye’s model 4 in Process Macro. The finding indicated that the association between self-efficacy and self-controlling coping style is mediated by stress mindset. Furthermore, it mediated the connection between some sub-domains of self-efficacy and coping styles. The data were evident to infer that individual with high self-efficacy can interpret social stressors as beneficial and improve their coping skills.
Whole-Genome Resequencing Reveals Selection Signal Related to Sheep Wool Fineness

Simple Summary Wool is a very important agricultural product and an ideal raw material for the production of high-quality textiles and clothing. In addition, the wool industry provides important support for economic development and the livelihoods of rural communities, creating employment opportunities and sources of income for farmers. Whole-genome sequencing technology has important applications in the study of genetic diversity, population structure, and selection pressure in the sheep genome. In this study, we analyzed the population structure and genomic differences of eight breeds. In addition, we identified a series of candidate genes that may be related to hair follicle development, wool traits, lipid metabolism, and androgen metabolism. This study provides valuable genomic resources and a theoretical basis for future wool improvement. Abstract Wool fineness affects the quality of wool, and some studies have identified about forty candidate genes that affect sheep wool fineness, but these genes often reveal only a certain proportion of the variation in wool thickness. We further explore additional genes associated with the fineness of sheep wool. Whole-genome resequencing of eight sheep breeds was performed to reveal selection signals associated with wool fineness, including four coarse wool and four fine/semi-fine wool sheep breeds. Multiple methods to reveal selection signals (Fst and θπ Ratio and XP-EHH) were applied for sheep wool fineness traits. In total, 269 and 319 genes were annotated in the fine wool (F vs. C) group and the coarse wool (C vs. F) group, such as LGR4, PIK3CA, and SEMA3C and NFIB, OPHN1, and THADA. In F vs. C, 269 genes were enriched in 15 significant GO Terms (p < 0.05) and 38 significant KEGG Pathways (p < 0.05), such as protein localization to plasma membrane (GO: 0072659) and Inositol phosphate metabolism (oas 00562). In C vs. F, 319 genes were enriched in 21 GO Terms (p < 0.05) and 16 KEGG Pathways (p < 0.05), such as negative regulation of focal adhesion assembly (GO: 0051895) and Axon guidance (oas 04360). Our study has uncovered genomic information pertaining to significant traits in sheep and has identified valuable candidate genes. This will pave the way for subsequent investigations into related traits.
Effects of Lactobacillus plantarum Postbiotics on Growth Performance, Immune Status, and Intestinal Microflora of Growing Minks

Simple Summary The wet diet that minks have, due to its high moisture content, is prone to breeding harmful microorganisms in hot weather, resulting in severe diarrhea for minks. It has been proposed that probiotics can inhibit the intestinal colonization of pathogens that cause diarrhea. Probiotics as live microorganisms are beneficial in helping protect from chronic inflammatory bowel disease, improving intestinal flora structure and host immune function. However, it is not very convenient to maintain high viability of bacteria during processing, storage, and transportation of feeds with probiotics. Many studies have shown that inactivated probiotics and their metabolites also provide benefits for host health. These inactivated probiotics and their metabolites, known as postbiotics, have been accepted for their high safety and stability, easy storage and production, and long shelf life. This study proved that Lactobacillus plantarum postbiotics have biological effects on growing minks. Abstract The present experiment was conducted to investigate the effects of Lactobacillus plantarum postbiotics on growth performance, immune status, and intestinal microflora of growing minks. A total of 80 minks (40 males and 40 females) were divided into four groups, each group contained 20 minks (10 males and 10 females). The minks in the four groups were fed a basal diet supplemented with 0, 0.15%, 0.3%, and 0.45% Lactobacillus plantarum postbiotics (PLP), respectively. After one week of adaptation, the experiment ran for eight weeks. The results showed that Lactobacillus plantarum postbiotics tended to have effects on average daily again (ADG) during the first 4 wk of the study (p < 0.1), and had effects on immune status (p < 0.05). Lactobacillus plantarum postbiotics also affected the abundance of intestinal bacteria at genus level (p < 0.05), but had no effects on α diversity of growing minks (p > 0.05). Compared to the minks in the control group, minks in 0.30% PLP group tended to have greater ADG, and IgA and IgM content in serum as well as SIgA content in jejunal mucosa (p < 0.05), and had less jejunal mucosal TNF-α and IL-8 levels, while minks in 0.45% PLP group had less IL-2 (p < 0.05). Compared to the control, Lactobacillus plantarum postbiotics decreased the relative abundances of Bacteroides_vulgatus and Luteimonas_sp. in male minks, and the relative abundances of Streptococcus_halotolerans in female minks (p < 0.05), respectively. Males grew faster and ate more associated with less F/G than females (p < 0.05). Males also had greater serum IgA and IgG content (p < 0.05), and males had less jejunal mucosal IL-1β, IL-8, IL-2, IL-6, IL-12, IL-10, TNF-α, and IFN-γ levels (p < 0.05). These results suggest that dietary supplementation of 0.3% postbiotics harvested from Lactobacillus plantarum could improve growth performance and immune status, and modulated the intestinal bacteria abundance of growing minks.
Development and application of 4D light-field and quantum imaging technologies

Behavior is a consequence of computation in a neural circuit composed of massive millisecond neural firings that undergo in 4D space. We recently found that the nematode Caenorhabditis elegans exhibits collective pattern formations. To examine the neural computation mechanism in this collective behavior, we are developing a new light-field microscopy. Light-field microscopy is a computational imaging technology that enables scan-less, single-shot acquisition of a 3D volumetric image by postprocessing 2D camera snapshots recorded via a microlens array. Here, we show C. elegans collective behavior and high-resolution light-field microscopy. We performed single-shot 4D brain-wide imaging of a freely behaving C. elegans with high temporal resolution and 3D volumetric imaging of mouse hippocampus amyloid plaques. Finally, we also combined high-resolution light-field microscopy with a quantum sensor, and demonstrated an ultrasensitive 4D physical parameter sensing in Hela cells and in vivo.
When Tweets Get Viral - A Deep Learning Approach for Stance Analysis of Covid-19 Vaccines Tweets by Brazilian Political Elites

Social media platforms are crucial for understanding public opinion about policy issues. In this regard, detecting stance in Twitter posts is a vital tool. In this study, we built a corpus of tweets from 2020 and 2021, annotated with stance towards COVID-19 vaccines and vaccination, and test BERTimbau as a way to automatically detect stance in such tweets. Our model reached 86% accuracy in 2020, 77% in 2021, and 79% in the combined 2020/2021 set. Our results also highlight the time-dependent nature of data distribution and, as a consequence, stance classification. Therefore, this research also contributes to the field by shedding some light on the existing methodological challenges in analyzing complex public policy debates over time.
Alternative measures to evaluate the accuracy and bias of genomic predictions with censored records

This study aimed to propose and compare metrics of accuracy and bias of genomic prediction of breeding values for traits with censored data. Genotypic and censored-phenotypic information were simulated for four traits with QTL heritability and polygenic heritability, respectively: C1: 0.07-0.07, C2: 0.07-0.00, C3: 0.27-0.27, and C4: 0.27-0.00. Genomic breeding values were predicted using the Mixed Cox and Truncated Normal models. The accuracy of the models was estimated based on the Pearson (PC), maximal (MC), and Pearson correlation for censored data (PCC) while the genomic bias was calculated via simple linear regression (SLR) and Tobit (TB). MC and PCC were statistically superior to PC for the trait C3 with 10 and 40% censored information, for 70% censorship, PCC yielded better results than MC and PC. For the other traits, the proposed measures were superior or statistically equal to the PC. The coefficients associated with the marginal effects (TB) presented estimates close to those obtained for the SLR method, while the coefficient related to the latent variable showed almost unchanged pattern with the increase in censorship in most cases. From a statistical point of view, the use of methodologies for censored data should be prioritized, even for low censoring percentages.
Effects of water stress on yield components and yield of soyabean genotypes.

Pot experiment was conducted at the Department of Agronomy Crop Pavilion of the University of Ilorin, Ilorin (8 29’ N, 4 35’ E) to evaluate the effects of water stress on yield components and yield of soybean genotypes. Six soybean genotypes (TGX 536-02D, TGX 18302DE, TGX 1019-2EN, TGX 1740-2F, TGX 1485-1D and TGX 1817-12E) were subjected to water stress at three growth stages (vegetative, flowering, post-flowering, and stages) with a well-watered control. The experiment was designed as a factorial trial and laid out in split-plot arrangements. Morphological growth characters such as number of leaves, leaf area, branch and dry matter production were measured during growth while yield components and grain yield were measured at harvest. Results show that yield components such as number of flowers, total number of pods, percentage aborted flowers, harvest index, biomass and grain yields were significantly reduced by water stress occurring at both vegetative and flowering stages, while post-flowering stress has no appreciable effects thereby suggesting that water stress occurring at post-flowering stage is not critical to grain yield in soybean. Amongst the investigated genotypes TGX 1817-12E was the most tolerant while 1485-1D was the least tolerant genotype.
Improving Cardiovascular Disease Prognosis Using Outlier Detection and Hyperparameter Optimization of Machine Learning Models

Cardiovascular diseases, globally recognized as prominent contributors to morbidity and mortality, have led to an imperative demand for precise, accessible, and efficient diagnostic methodologies. This study introduces a hybrid classification system integrating an ensemble model and a Fuzzy C Means-based neural network with the objective of augmenting predictive accuracy. A comparative analysis on scalar standards was undertaken to determine the optimal feature scaling technique, thereby enhancing predictive proficiency while optimizing time efficiency. The study further incorporates Random Forest, Support Vector Machines, k-Nearest Neighbor, and deep learning models into the diagnostic framework, while employing a confusion matrix as a performance evaluation tool. The GridsearchCV technique is utilized for hyperparameter optimization, its influence on the accuracy of machine learning (ML) models is critically examined. Special attention is given to the role of outliers and their manipulation using supervised ML algorithms, investigating the impact of outlier exclusion on model accuracy. The experimental data was sourced from a cardiovascular patients dataset in the UCI Machine Learning Repository. The findings of the study suggest that the proposed classifier ensemble model surpasses comparable advancements, achieving an exemplary classification accuracy of 98.78%. This paper thus contributes to the evolving landscape of ML application in cardiovascular disease prediction, emphasizing the significance of outlier detection and hyperparameter optimization.