Miracle-dz/UniFAR-FTUs huggingface dataset:
TiO2 insertion layer deposited before passivation to reduce etch damage on AlGaN/GaN HEMT

Γ-gate has been used to increase the breakdown voltage of radio frequency (RF) devices, and the damage caused by gate pin etching has been studied. In this study, a TiO2 layer, acting as a protective layer, was inserted between the AlGaN barrier layer and SiN passivation layer. The AlGaN/GaN high-electron-mobility transistor (HEMT) with TiO2 had no kink effect in its output characteristic, and at high gate voltages, it demonstrated a higher transconductance than the HEMT without TiO2. The HEMT without TiO2 exhibited a more prominent saturation tendency for drain current. Additionally, the C–V test data show that the trap state density of the AlGaN/GaN interface of an AlGaN/GaN HEMT with TiO2 decreased compared with a HEMT without TiO2. DC and C–V test results show that the TiO2 layer can effectively reduce the etching damage of the material under the gate.
Anorexia Nervosa, Body Image Perception and Virtual Reality Therapeutic Applications: State of the Art and Operational Proposal

Anorexia Nervosa (AN) patients exhibit distorted body representation. The purpose of this study was to explore studies that analyze virtual reality (VR) applications, related to body image issues, to propose a new tool in this field. We conducted a systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, EMBASE, Scopus, and Web of Science databases were explored; the review included 25 studies. Research has increased over the last five years. The selected studies, clinical observational studies (n = 16), mostly concerning patients’ population with AN (n = 14) or eating disorders (EDs) diagnosis, presented multiple designs, populations involved, and procedures. Some of these studies included healthy control groups (n = 7). Studies on community sample populations were also selected if oriented toward clinical applications (n = 9). The VR technologies in the examined period (about 20 years) have evolved significantly, going from very complex and bulky systems, requiring very powerful computers, to agile systems. The advent of low-cost VR devices has given a big boost to research works. Moreover, the operational proposal that emerges from this work supports the use of biofeedback techniques aimed at evaluating the results of therapeutic interventions in the treatment of adolescent patients diagnosed with AN.
Two types of C-terminal regions of RNA-binding proteins play distinct roles in stress tolerance of Synechocystis sp. PCC 6803

Abstract In the phylogenetic tree of RRM-type Rbps (RNA-binding proteins) in cyanobacteria, Rbp1 of Synechocystis 6803, with a single RRM (RNA recognition motif) region and a C-terminal glycine-rich region, and Rbp2, without the C-terminal region, both belong to the cluster I, whereas Rbp3 with a different type of C-terminal region is in the cluster II. Rbp1 is required for the cold adaptability of the cyanobacterium, and Rbp3 is for salt tolerance. Here, we report that the C-terminal region of Rbp1 is not required for the cold adaptability function but the C-terminal region of Rbp3 can direct the RRM of Rbp1 to the salt tolerance function. Bioinformatic and experimental analyses indicate that Rbps in cyanobacteria should be classified as two types. It is the first report for the distinct roles of C-terminal regions of Rbps in stress tolerance of cyanobacteria.
Design of Motion Detection Device in Sports Based on Deep Learning of Internet of Things

With the improvement of people's income levels in recent years, people have gradually begun to pay more attention to health, and the number of exercise and fitness people has increased year by year. People are gradually willing to pay for sports and fitness, increase sports consumption, and promote the development of the sports and fitness industry. This article aims to study the deep learning based on the Internet of things to make people aware of the importance of sports. Not loving sports is a major problem that contemporary people need to overcome. This article proposes how to design a motion detection device in sports based on deep learning of the Internet of things. Based on the calculation of the economic volume of the deep learning of the Internet of things and the questionnaire survey method, it can be seen that, in today's globalization, although everyone knows the importance of sports, they are unwilling to practice it and would rather spend more time on the Internet. The experimental results of this article show that more than 50% of college students are very interested in sports and fitness, but the actual use is less than 30%, which is not optimistic. In social surveys, this number will be even lower, with only 14% of people interested in sports. Big data is like a “double-edged sword. ” It not only displays the user's exercise data in front of everyone through the built-in sensors of the mobile phone, but also manages their physical condition through these. How to use the strengths of sports applications at the same time properly disposing of private information is a part of the next development of sports applications that must be faced.
The source of teacher work stress: a factor analysis approach

This research is motivated by work stress among teachers with various negative impacts on physical, mental, and performance health. The purpose of the study was to determine the level of teacher work stress and its sources in South Sulawesi. The method used is a survey method and a factor analysis approach. The research subjects were teachers in South Sulawesi. The data collection technique used a questionnaire. Then it was analyzed quantitatively descriptively. Quantitative data analysis is the validity and reliability which is declared statistically valid. The results showed that teacher work stress in South Sulawesi was moderate, with a data trend of 41.9%. The category of work stress is based on six sources of work stress, namely (1) welfare; (2) teaching; (3) students; (4) relationships and conflicts; (5) organizational climate; and (6) the time dimension with the dominant or general source of teacher work stress comes from welfare factors and teaching factors, while other teacher work stress factors are student factors and organizational climate. The factor analysis approach shows that the six indicators are combined in only one factor in the work stress source variable.
A Novel Method of Trajectory Optimization for Underwater Gliders Based on Dynamic Identification

For underwater gliders (UGs), high trajectory accuracy is an important factor in improving the observation of ocean phenomena. In this paper, a novel method of trajectory optimization is proposed to increase the trajectory accuracy of UGs, which is approximately based on the nonlinear dynamic model, rather than the linearization model. Firstly, a dynamic model of UGs is established to analyze the effect of the input parameters on the trajectory error, based on some approximate models that replaced the dynamic model due to its strong nonlinearity. Then, an identification strategy for the trajectory error is proposed, and the trajectory optimization strategy is analyzed while considering gliding range loss and observation distance loss. Finally, the identification strategy and trajectory optimization strategy proposed in this paper are verified by a sea trial of Petrel-L. The dynamic model, identification strategy, and optimization strategy are appropriate for other UGs.
Green Finance, Chemical Fertilizer Use and Carbon Emissions from Agricultural Production

This study aimed to understand green finance’s impact on fertilizer use and agricultural carbon emissions. We selected the macro panel data of 30 provinces (cities) in China from 2000 to 2019. The main research methods are standardized test framework (cross-sectional dependence, unit root and cointegration test), the latest causal test, impulse response, and variance decomposition analysis. Examined the long-term equilibrium relationship between green finance, fertilizer use, and agricultural carbon emissions. The results show: fertilizer consumption and agricultural carbon emissions have a positive correlation. However, green finance can significantly reduce agricultural carbon emissions. The causal test confirmed the bidirectional causal relationship between agricultural carbon emissions and fertilizer use. At the same time, verified one-way causality from green finance to both of them. Interpret the results of impulse response and variance decomposition analysis: among the changes in agricultural carbon emissions, chemical fertilizers contributed 2.45%, green finance contributed 4.34%. In addition, the contribution rate of green finance to chemical fertilizer changes reached 11.37%. Green finance will make a huge contribution to reducing fertilizer use and agricultural carbon emissions within a decade. The research conclusions provide an important scientific basis for China’s provinces (cities) to formulate carbon emission reduction policies. China has initially formed a policy system and market environment to support the development of green finance, in 2020, the “dual carbon” goal was formally proposed. In 2021, the national “14th Five-Year Plan” and the 2035 Vision Goals emphasized the importance of green finance. It plays an important supporting role in carbon emission reduction goals, and green finance has become an important pillar of national strategic goals.
From genotype to phenotype: Genetic redundancy and the maintenance of an adaptive polymorphism in the context of high gene flow

A central question in evolution is how several adaptive phenotypes are maintained within a species. Theory predicts that the genetic determination of a trait, and in particular the amounts of redundancy in the mapping of genotypes to phenotypes, mediates evolutionary outcomes of phenotypic selection. In Mediterranean wild thyme, numerous discrete chemical phenotypes (chemotypes) occur in close geographic proximity. Chemotypes are defined by the predominant monoterpene produced by individual plants in their essential oil. In this study, we analyze the ecological genetics of six chemotypes nested within two well‐established chemical families (hereafter ecotypes). Ecotypes, and chemotypes within ecotypes, are spatially segregated, and their distributions track local differences in the abiotic environment. By combining population genomic, phenotypic, and environmental data from 700 individuals, we show how the genetics of ecotype determination mediates this evolutionary response. Variation in three terpene‐synthase loci explains variation in ecotype identity, with one single locus accounting for as much as 78% of this variation. Phenotypic selection combined with low segregating genotypic redundancy of ecotypes leaves a clear footprint at the genomic level: alleles associated with ecotype identity track environmental variation despite extensive gene flow. Different chemotypes within each ecotype differentially track environmental variation. Their identity is determined by multiple loci and displays a wider range of genotypic redundancy that dilutes phenotypic selection on their characteristic alleles. Our study thus provides a novel illustration of how genetic redundancy of a phenotype modulates the ability of selection to maintain adaptive differentiation. Identifying the precise genetics of the chemical polymorphism in thyme is the next crucial step for our understanding of the origin and maintenance of a polymorphism that is present in many aromatic plants.
Trade heterogeneity and virtual water exports of China

China is facing serious water scarcity, and the effects of international trade on its water resources have been widely examined. Processing exports account for nearly half of China’s gross exports. Adopting China’s multi-regional input–output table that captures processing exports, we enrich the literature on virtual water exports by accounting for trade heterogeneity. The results show that China’s virtual water exports show a significant trade heterogeneity. Normal and processing exports are attributed to 86.7% and 13.3% of the Agriculture sector’s water use induced by exports respectively. Conversely, normal and processing exports are attributed to 31.8% and 68.3% of the Communications Equipment, Computers sector’s water use induced by exports respectively. In addition, a cross-regional compensation is needed to deal with the unequal regional distribution of water uses and economic benefits related to exports.
Modeling Information Diffusion With Sequential Interactive Hypergraphs

In online social networks, numerous users generate and distribute tremendous content simultaneously, and how information spreads in online society has attracted critical attention. Recent studies introduce deep learning to help mine the complex diffusion pattern and forecast the diffusion trend. However, there are two limitations of the state-of-art methods. On the one hand, existing models only focus on the internal influence within the diffusion flow but ignore the external influence from the dissemination of other contents. On the other hand, the dynamics of user interest are barely considered, while the changes of user preference for contents also have a considerable impact on future diffusion. To address these issues, we introduce the hypergraph structure and a sequential framework to model complex interactions in social networks. Then, we propose dual-channel hypergraph neural networks to tackle the diffusion prediction problem, denoted by HyperINF. Specifically, in the user channel, we build sequential user interactive hypergraphs and learn the dynamic user representation, and in the diffusion channel, we construct a diffusion interactive graph to capture the cross-diffusion relation. At last, we consider the social relation to help make the prediction. Experimental results on three datasets suggest the effectiveness and practicability of the proposed framework.
Risk measures constituting risk metrics for decision making in the chemical process industry

Risk Measures Constituting Risk Metrics for Decision Making in the Chemical Process Industry. (December 2010) Katherine Priya Prem, BS. , Texas A&M University Chair of Advisory Committee: Dr. M. Sam Mannan The occurrence of catastrophic incidents in the process industry leave a marked legacy of resulting in staggering economic and societal losses incurred by the company, the government and the society. The work described herein is a novel approach proposed to help predict and mitigate potential catastrophes from occurring and for understanding the stakes at risk for better risk informed decision making. The methodology includes societal impact as risk measures along with tangible asset damage monetization. Predicting incidents as leading metrics is pivotal to improving plant processes and, for individual and societal safety in the vicinity of the plant (portfolio). From this study it can be concluded that the comprehensive judgments of all the risks and losses should entail the analysis of the overall results of all possible incident scenarios. Value-at-Risk (VaR) is most suitable as an overall measure for many scenarios and for large number of portfolio assets. FN-curves and F$-curves can be correlated and this is very beneficial for understanding the trends of historical incidents in the U.S. chemical process industry.
Diagnostic and Prognostic Protein Biomarkers of β-Cell Function in Type 2 Diabetes and Their Modulation with Glucose Normalization

Development of type-2 diabetes(T2D) is preceded by β-cell dysfunction and loss. However, accurate measurement of β-cell function remains elusive. Biomarkers have been reported to predict β-cell functional decline but require validation. Therefore, we determined whether reported protein biomarkers could distinguish patients with T2D (onset < 10-years) from controls. A prospective, parallel study in T2D (n = 23) and controls (n = 23) was undertaken. In T2D subjects, insulin-induced blood glucose normalization from baseline 7.6 ± 0.4 mmol/L (136.8 ± 7.2 mg/dL) to 4.5 ± 0.07 mmol/L (81 ± 1.2 mg/dL) was maintained for 1-h. Controls were maintained at 4.9 ± 0.1 mmol/L (88.2 ± 1.8 mg/dL). Slow Off-rate Modified Aptamer (SOMA) -scan plasma protein measurement determined a 43-protein panel reported as diagnostic and/or prognostic for T2D. At baseline, 9 proteins were altered in T2D. Three of 13 prognostic/diagnostic proteins were lower in T2D: Adiponectin (p < 0.0001), Endocan (p < 0.05) and Mast/stem cell growth factor receptor-Kit (KIT) (p < 0.01). Two of 14 prognostic proteins [Cathepsin-D (p < 0.05) and Cadherin-E (p < 0.005)], and four of 16 diagnostic proteins [Kallikrein-4 (p = 0.001), Aminoacylase-1 (p = 0.001), Insulin-like growth factor-binding protein-4 (IGFBP4) (p < 0.05) and Reticulon-4 receptor (RTN4R) (p < 0.001)] were higher in T2D. Protein levels were unchanged following glucose normalization in T2D. Our results suggest that a focused biomarker panel may be useful for assessing β-cell dysfunction and may complement clinical decision-making on insulin therapy. Unchanged post-glucose normalization levels indicate these are not acute-phase proteins or affected by glucose variability.
Portable locomotion activity monitor (pLAM): A cost‐effective setup for robust activity tracking in small animals

Characterising the frequency and timing of biological processes such as locomotion, eclosion or foraging, is often needed to get a complete picture of a species' ecology. Automated trackers are an invaluable tool for high‐throughput collection of activity data and have become more accurate and efficient with advances in computer vision and deep learning. However, tracking activity of small and fast flying animals remains a hurdle, especially in a field setting with variable light conditions. Commercial activity monitors can be expensive, closed source and generally limited to laboratory settings. Here, we present a portable locomotion activity monitor (pLAM), a mobile activity detector to quantify small animal activity. Our setup uses inexpensive components, builds upon open‐source motion tracking software, and is easy to assemble and use in the field. It runs off‐grid, supports low‐light tracking with infrared lights and can implement arbitrary light cycle colours and brightnesses with programmable LEDs. We provide a user‐friendly guide to assembling pLAM hardware, accessing its pre‐configured software and guidelines for using it in other systems. We benchmarked pLAM for insects under various laboratory and field conditions, then compared results to a commercial activity detector. They offer broadly similar activity measures, but our setup captures flight and bouts of motion that are often missed by beam breaking activity detection. pLAM can automate laboratory and field monitoring of activity and timing in a wide range of biological processes, including circadian rhythm, eclosion and diapause timing, pollination and flower foraging, or pest feeding activity. This low cost and easy setup allows high‐throughput animal behaviour studies for basic and applied ecology and evolution research.
A Selective Inhibitor of Cardiac Troponin I Phosphorylation by Delta Protein Kinase C (δPKC) as a Treatment for Ischemia-Reperfusion Injury

Myocardial infarction is the leading cause of cardiovascular mortality, with myocardial injury occurring during ischemia and subsequent reperfusion (IR). We previously showed that the inhibition of protein kinase C delta (δPKC) with a pan-inhibitor (δV1-1) mitigates myocardial injury and improves mitochondrial function in animal models of IR, and in humans with acute myocardial infarction, when treated at the time of opening of the occluded blood vessel, at reperfusion. Cardiac troponin I (cTnI), a key sarcomeric protein in cardiomyocyte contraction, is phosphorylated by δPKC during reperfusion. Here, we describe a rationally-designed, selective, high-affinity, eight amino acid peptide that inhibits cTnI’s interaction with, and phosphorylation by, δPKC (ψTnI), and prevents tissue injury in a Langendorff model of myocardial infarction, ex vivo. Unexpectedly, we also found that this treatment attenuates IR-induced mitochondrial dysfunction. These data suggest that δPKC phosphorylation of cTnI is critical in IR injury, and that a cTnI/δPKC interaction inhibitor should be considered as a therapeutic target to reduce cardiac injury after myocardial infarction.
A simulation-data-driven subdomain adaptation adversarial transfer learning network for rolling element bearing fault diagnosis

In recent years, increasing numbers of deep learning methods for fault diagnosis of rolling element bearings (REBS) have been proposed. However, in industry, the scarcity of available data to monitor the health condition of REBS leads to low recognition accuracy of the trained intelligent diagnostic models. To solve this problem, we propose a simulation-data-driven subdomain adaptation adversarial transfer learning (TL) network (SAATLN). Firstly, a defect vibration model is introduced to simulate vibration signals of different types of REBS faults. And the real signal and simulated signal are used as the target domain and source domain of the TL fault diagnosis methods, respectively. Secondly, SAATLN uses the designed residual squeeze-and-excitation (Re-SE) blocks to extract transfer features between different domains. Meanwhile, it combines adversarial learning and subdomain adaptation to adapt the marginal distribution and conditional distribution discrepancies of high-level features. Also, the local maximum mean discrepancy is introduced as the subdomain adaptation metric criterion. Finally, different transfer tasks are performed on the artificially damaged and run-to-failure REBS data sets. The results demonstrate the effectiveness and superiority of the SAATLN in the simulation-data-driven REBS fault diagnosis.
Effect of Genistein on Heat Shock Protein 47 and Collagen Type IV in Diabetic Rat

Diabetes nephropathy (DN) is one of the most common complication in Diabetes Mellitus (DM). DN is an inflammatory process which involved immune cells and effect of genistein prevent this mechanism. However, the effects on HSP 47 and collagen type IV are not yet verified. The purpose of this study was to investigate whether the genistein can suppress HSP 47 and collagen type IV.This study is experimental design used 25 rats. Rats were divided into five groups; normal group, hyperglycemia group, hyperglycemia by administering genistein 0.5 mg/kgw, 1mg/kgw, and 2 mg/kg. Streptozotocin induced 65 mg/kg administered intraperitoneal. Treatment duration is 4 weeks. After 4 weeks of blood was collected via the orbital vein and examined the levels of HSP 47 then rats’ kidneys were taken to see the levels of collagen type IV.The average levels of HSP 47 in non diabetic control group was 1.7982 ng/ml, diabetic control 7.9424 ng/ml, STZ; G 0.5 mg/kgw 5.4192 ng/ml, STZ; G 1 mg/kgw 3.1152 ng/ml and STZ; G 2 mg/kgw 1.849 ng/ml, with p value 0.000 (p