Miracle-dz/UniFAR-FTUs huggingface dataset:
Concurrent and lagged effects of drought on grassland net primary productivity: a case study in Xinjiang, China

Xinjiang grasslands play a crucial role in regulating the regional carbon cycle and maintaining ecosystem stability, and grassland net primary productivity (NPP) is highly vulnerable to drought. Drought events are frequent in Xinjiang due to the impact of global warming. However, there is a lack of more systematic research results on how Xinjiang grassland NPP responds to drought and how its heterogeneity is characterized. In this study, the CASA (Carnegie Ames Stanford Application) model was used to simulate the 1982–2020 grassland NPP in Xinjiang, and the standardized Precipitation Evapotranspiration Index (SPEI) was calculated using meteorological station data to characterize drought. The spatial and temporal variability of NPP and drought in Xinjiang grasslands from 1982 to 2020 were analyzed by the Sen trend method and the Mann-Kendall test, and the response characteristics of NPP to drought in Xinjiang grasslands were investigated by the correlation analysis method. The results showed that (1) the overall trend of NPP in Xinjiang grassland was increasing, and its value was growing season > summer > spring > autumn. Mild drought occurred most frequently in the growing season and autumn, and moderate drought occurred most frequently in spring. (2) A total of 64.63% of grassland NPP had a mainly concurrent effect on drought, and these grasslands were primarily located in the northern region of Xinjiang. The concurrent effect of drought on NPP was strongest in plain grassland and weakest in alpine subalpine grassland. (3) The lagged effect is mainly in the southern grasslands, the NPP of alpine subalpine meadows, meadows, and alpine subalpine grasslands showed mainly a 1-month time lag effect to drought, and desert grassland NPP showed mainly a 3-month time lag effect to drought. This research can contribute to a reliable theoretical basis for regional sustainable development.
The increased functional connectivity between the locus coeruleus and supramarginal gyrus in insomnia disorder with acupuncture modulation

Background Insomnia disorder (ID) seriously affects the quality of people’s daily life, and acupuncture is an effective therapy for it. As an essential component of the upward activation system, the locus coeruleus (LC) plays a crucial role in sleep–wake regulation, its aberrant functional connectivity (FC) is found to be involved in ID. The purpose of this study was to explore the modulation effect of acupuncture on the resting state FC of LC in ID patients. Methods 60 ID patients were recruited and randomly assigned to real acupuncture (RA) or sham acupuncture (SA) treatment. Resting-state functional magnetic resonance imaging (fMRI) data were collected before and after the treatment. With LC as the region of interest, the FC method was adopted to examine acupuncture-related modulation of intrinsic connectivity in ID patients. The Pittsburgh Sleep Quality Index (PSQI), Hyperarousal Scale (HAS), and actigraphy were used to assess sleep quality and cortical hyperarousal states. Associations between clinical outcomes and FC features were calculated using Pearson’s correlation analysis. Results The improvement in sleep quality and hyperarousal in the RA group was greater than that in the SA group. After treatment, the FC between the LC and left inferior frontal gyrus (IFG) decreased in the RA group. The FC between the LC and left insula and supramarginal gyrus (SMG) was higher in the RA group. The change of LC FC values with the SMG was negatively associated with the change in PSQI scores. Conclusion Acupuncture can modulate FC between the LC and IFG, insular gyrus, and SMG. This may imply the potential mechanism of acupuncture treatment for insomnia.
Effects of acute slackline exercise on executive function in college students

Background Physical exercise as an intervention for improving cognitive function, especially executive function, is receiving increasing attention because it is easily accessible, cost-effective and promises many additional health-related benefits. While previous studies focused on aerobic exercise and resistance exercise, recent findings have suggested that exercise with high coordination demand elicits beneficial effects on executive function. We therefore examined the effects of an acute slackline exercise on the executive functions of young adults. Methods In a crossover experimental design, 47 healthy participants (21 females), ranging in age from 18 to 27 years (M = 19.17, SD = 1.94) were randomly assigned to different sequences of two conditions (slackline exercise and film-watching). Before and after the 50 min intervention, a modified Simon task was used to assess participants’ executive function (inhibitory control and cognitive flexibility). Results College students showed better inhibitory control performance as indicated by shorter reaction times following acute slackline exercise than those who participated in the film-watching session. As there was no difference in accuracy between the slackline exercise and film-watching sessions, the shortened reaction time after slackline exercise provides evidence against a simple speed-accuracy trade-off. Conclusion Compared with film-watching, acute slackline exercise provides favorable effects on executive function necessitating inhibition in young adults. These findings provide insight into exercise prescription and cognition, and further evidence for the beneficial effects of coordination exercise on executive functions.
The impact of water and technology on the stock market in China and Japan based on a new heuristic model

Introduction: Water, as an essential strategic resource, is diminishing; this has been framed as a financial risk. We aim to quantitively investigate the impact of water use and technology on the stock market and compare the differences in China and Japan, which represent emerging and mature markets, respectively. Method: We constructed three models using the difference generalized method of moments. The first and second models focused on how water use could influence stock market volatility and returns; the third model added technology as an interaction to explore its impact on the above mechanism. We used an ARIMA-EGARCH model to predict the trend of marginal stock market return with an increase in industrial water use in the next 5 years. Results and Discussion: The results show that 1) water use increases the stock market volatility in both countries, but Japan shows a greater increase than China; 2) water use has a negative impact on stock market returns in China and a positive impact in Japan; 3) technology plays a positive role in the second model, while the ARIMA-EGARCH results correspond to the first two conclusions, which verifies the reasonability of the models. We conclude that heterogeneity exists in the two different market types because of technology level.
Simultaneous improvement and genetic dissection of drought and submergence tolerances in rice (Oryza sativa L.) by selective introgression

Introduction Drought and submergence are contrasting abiotic stresses that often occur in the same rice crop season and cause complete crop failure in many rain-fed lowland areas of Asia. Methods To develop rice varieties with good tolerances to drought and submergence, 260 introgression lines (ILs) selected for drought tolerance (DT) from nine BC2 populations were screened for submergence tolerance (ST), resulting in 124 ILs with significantly improved ST. Results Genetic characterization of the 260 ILs with DNA markers identified 59 DT quantitative trait loci (QTLs) and 68 ST QTLs with an average 55% of the identified QTLs associated with both DT and ST. Approximately 50% of the DT QTLs showed ‘epigenetic’ segregation with very high donor introgression and/or loss of heterozygosity (LOH). Detailed comparison of the ST QTLs identified in ILs selected only for ST with ST QTLs detected in the DT-ST selected ILs of the same populations revealed three groups of QTLs underlying the relationship between DT and ST in rice: a) QTLs with pleiotropic effects on both DT and ST; b) QTLs with opposite effects on DT and ST; and c) QTLs with independent effects on DT and ST. Combined evidence identified most likely candidate genes for eight major QTLs affecting both DT and ST. Moreover, group b QTLs were involved in the Sub1regulated pathway that were negatively associated with most group aQTLs. Discussion These results were consistent with the current knowledge that DT and ST in rice are controlled by complex cross-talks between or among different phytohormone-mediated signaling pathways. Again, the results demonstrated that the strategy of selective introgression was powerful and efficient for simultaneous improvement and genetic dissection of multiple complex traits, including DT and ST.
Mechanism of Qihuang needle therapy in the management of tic disorders: a clinical trial protocol

Background Qihuang needle therapy is a newly developed acupuncture therapy to treat tic disorders in clinical practice. However, the mechanism to reduce tic severity remains unknown. Changes in intestinal flora and circulation metabolites are perhaps the potential pathogenesis of tic disorders. As a result, we present a protocol for a controlled clinical trial using multi-omics analysis to probe the mechanism of the Qihuang needle in managing tic disorders. Methods This is a matched-pairs design, controlled, clinical trial for patients with tic disorders. Participants will be allocated to either an experimental group or a healthy control group. The main acupoints are Baihui (GV20), Yintang (EX-HN3), and Jueyinshu (BL14). The experimental group will receive Qihuang needle therapy for a month, while the control group will receive no interventions. Expected outcomes The change in the severity of the tic disorder is set as the main outcome. Secondary outcomes include gastrointestinal severity index and recurrence rate, which will be calculated after a 12-week follow-up. Gut microbiota, measured by 16S rRNA gene sequencing; serum metabolomics, assessed via LC/MS; and serum zonulin, assessed by enzyme-linked immunosorbent assay (ELISA), will be used as biological specimen analysis outcomes. The present study will investigate the possible interactions between intestinal flora and serum metabolites and the improvement of clinical profiles, which may elucidate the mechanism of Qihuang needle therapy for tic disorders. Trial registration This trial is registered at the Chinese Clinical Trial Registry (http://www.chictr.org.cn/). Registration number: ChiCTR2200057723, Date: 2022-04-14.
Numerical integration of differential-algebraic equations with harmless critical points

Differential-algebraic equations (DAEs) are implicit singular ordinary differential equations, which describe dynamical processes that are restricted by some constraints. In contrast to explicit regular ordinary differential equations, for a DAE not any value can be imposed as an initial condition. The initial values need to be consistent with the DAE. Furthermore, DAEs involve not only integration problems but also differentiation problems. The differentiation index of a DAE indicates the number of differentiations required in order to solve a DAE. Since approximately 1980, DAEs form a research area of applied mathematics, which primarily focuses on the characterization and classification of regular problem classes and the construction and foundation of integration methods for simulation software. Among others, S.L. Campbell, L.R. Petzold, E. Griepentrog, R. März, W.R. Rheinboldt, P. Rabier, E. Hairer, Ch. Lubich, V. Mehrmann, P. Kunkel, und R. Riaza have made significant contributions for this purpose. The numerical treatment of DAEs requires knowledge about their structure. I. Higueras, R. März, and C. Tischendorf have shown in 2003 that one can reliably integrate a general linear DAE with a properly stated leading term, A(t)(D(t)x(t))′ +B(t)x(t) = q(t), t ∈ J , (1) which is regular with tractability index 2 – in contrast to linear DAEs in standard form. The first study of the classification of critical points of linear DAEs have been published by R. Riaza and R. März in 2004-2008. Based on the tractability index, critical points are classified according to failures of certain rank conditions of matrix functions. Essentially, a critical point is said to be harmless, if the flow described by the inherent differential equation is not affected. The subject of this work are linear DAEs of the form (1). Index-2 DAEs with harmless critical points are characterized. Under the application of quasi-admissible projector functions instead of the admissible ones, besides DAEs which have almost everywhere the same characteristic values, DAEs with index changes can now be discussed for the first time. The main part of the work is to provide a proof of feasibility, convergence, and only weak instability of numerical integration methods (BDF, IRK (DAE)) for general linear index-2 DAEs with harmless critical points, as well as the development and testing of error estimators and stepsize control.
Hybrid Gradient Boost based Heart Failure Prediction System

Heart diseases are prevalent in today's world due to many factors like lipid disorder (hypercholesterolemia), corpulence (obesity), increase in triglycerides levels (lipids obtained from esterification fatty acids to glycerol), hypertension, etc. It is estimated that nearly 18 million lives are affected yearly due to various heart diseases. Early detection of such diseases could help save several lives. In the proposed system, heart failure prediction is estimated using the combination of Gradient boost detection and decision trees. The parallel handling approach is used for feature processing to speed up the results and for optimal performance. The generation and discrimination approach are used to verify the outcomes concerning other algorithms and pseudo-codes. This paper uses the data file from the University of California, Irvine Intelligent Systems Repository to test the results. It is observed from several experiments that it provides optimal performance compared to the remaining predictors in the context of f1 score, recall, and accuracy. The ROC curve of Gradient Boost provides a higher deviation for low false positives. The Gradient Boost shows a 0.919 ROC value and 92 % of accuracy with an F1 score of 0.928 and a recall of 0.934.
Learning Aided Intelligent Mechanism for Channel Estimation in 5G Wireless Networks

The attenuation of signals due to propagation environment is the major challenge of wireless communication which results in frequent call drops, reduced signal strength and low transmission rates. The propagation channel often degrades the signal quality at the receiver due to channel effects including fading, shadowing, path loss and other overhead. The propagation channel and its successful estimation is very important for ensuring communication reliability in next generation wireless systems. This paper presents an intelligent mechanism based on deep learning to estimate the wireless channel such that the system spectral efficiency is enhanced. The impact of signal distortion due to hardware effects is also considered. Further, the proposed scheme is compared with the conventional LMMSE channel estimation scheme. Also, to extract the data using the estimated channel, the performance of proposed scheme is evaluated using three receivers namely, RZF, MMSE and modified MMSE. It is observed that the proposed scheme outperforms the LMMSE scheme in terms of normalised mean square error (NMSE) by 14.43% and by 27.16% in the absence of distortion. In the end, the performance comparison with the system with known perfect channel state information (CSI) is also performed.
Numerical multi-scale time averaging approach to quantum systems with pseudo random potentials

We consider the problem of numerically solving the Schrodinger equation with a quasi periodic in space time potential, approximating multiplicative noise term. We introduce a numerical scheme based on a newly developed multi-time scale averaging technique. We show that with this new method we can solve efficiently and with rigorous control of the error the above equation for large times. A comparison with the standard split-step method shows order of magnitude improvement in computational times, besides the controlled errors. We apply this method to a free particle perturbed by quasi-periodic potential with many frequencies. We observe the (anomalous) diffusion in momentum space, over large time intervals, as large as the system is away from the Chirikov resonance strips. In the limit of zero potential, the diffusion rate becomes standard, but with a coefficient that approaches zero
Classification of Rolling Bearing Fault Based on Long Short Term Memory Neural Network

Bearings are the most prevalent and easily damaged mechanical components in mechanical systems. Nowadays, Deep learning emerges as a very efficient artificial intelligence method that offers a new approach to feature extraction from raw data. Long short-term memory (LSTM) neural network is a promising type of deep learning, which is mainly used for the diagnosis of faults. In this work, a new intelligent fault diagnosis and classification method based on Long Short-Term Memory (LSTM) neural network is developed. The developed model correctly classifies the different types of faults at different running conditions in real operation conditions, and the results are compared with existing techniques. The performance of the model is evaluated with various performance metrics such as Precision, Recall, F1 score, Receiver Operating Characteristics, Area under Curve, and Accuracy. The best results are obtained from the developed LSTM model than from the existing work.
A Blockchain Application on Bootstrapping Mobile Nodes within VANET

Blockchains within Vehicular Ad-hoc Networks (VANETs) can solve issues relating to security, trust, and secure message dissemination. Platooning requires these traits to facilitate safe operation. However, traditional blockchains are not a good fit for this application. Modified blockchains are required to operate correctly within these environments. In this early work, a discussion on the packets required to operate a basic blockchain within a VANET platoon alongside a simulation are presented to demonstrate the burden of traditional blockchains in these environments and how future work can overcome these challenges.
WHY SHOULD ONE TEACH PROBABILITY THEORY TO MAKE DECISIONS WHEN TESTING STATISTICAL HYPOTHESES

Nowadays one shouldn’t ignore the use of computers in statistics, the main reason being that they are faster and more reliable. Almost all statistical software computes the p-values; therefore students and researchers can take their decisions only based on its “usual” value, 0.05. If the pvalue is lower than 0.05 then the null hypotheses for a statistical test is “simply” rejected by the computer. Do users ask about the meaning of this software output? If we are testing statistical hypotheses we have the null hypotheses tested against alternative hypotheses. Do users think about them? Since the decisions are based on sampling, the statistical tests decisions involve uncertainty and so two types of errors can be made. Do users think about them? A questionnaire was constructed and given to students and researchers in order to make a first approach about these subjects.
Predictive Comment Updating With Heuristics and AST-Path-Based Neural Learning: A Two-Phase Approach

Just-in-time comment update is a promising way to reduce the burden of developers during software maintenance and evolution. Existing approaches can be divided into two categories: the heuristic-based approach and the deep-learning-based approach. The heuristic-based approach is restricted to a specific type of comment updates (i.e., code-indicative updates), but performs well on such type. The effectiveness of deep-learning-based approach is limited but it can handle diverse comment updates. Considering the complementary advantages of existing approaches, an intuitive idea is to combine them for better performance. To investigate this idea, we first conduct a pre-study experiment which shows that to construct an effective comment updater by combining heuristic-based and deep-learning-based approaches, we need to tackle two main challenges: 1) the heuristic-based approach may bring side effects to cases which cannot be updated by it; and 2) the current deep-learning-based approach is with limited effectiveness. Then, we propose a novel two-phase approach named S_Coachto cope with these two challenges and effectively perform comment updates. In the first phase, S_Coachintegrates nine distinctive features identified through our large-scale empirical analysis into a predictive model, which can predict whether the contents of the comment updates can be found in the corresponding code changes, namely, the comment updates are code-indicative updates. If so, the updates are then generated by an off-the-shelf heuristic-based approach; otherwise, S_Coachleverages a deep learning model, which we specially designed for non-code-indicative updates, to infer the new comment based on the old comment and code change. Motivated by our manual observation on the limitation of existing approaches on non-code-indicative updates, our deep learning model adopts the Abstract Syntax Tree path technique, which can capture the program structure information for effectively embedding code changes. Our evaluation shows that our approach outperforms the state-of-the-art by around 20% with respect to the number of correct comments it generates. Via in-depth analysis, we illustrate the rationale of each design decision as well as point out potential directions.
Comparative Analysis of Open-Source Vulnerability Assessment Tools for Campus Area Network

Vulnerability depicts the weakness in the system and it leads to risk in the extended form. Network security professionals targets to work on vulnerabilities of the network for securing information from intruder attacks. The network administrators of any organization are required to continuously take action against the vulnerabilities in the network. There is a requirement of detecting these vulnerabilities and many tools can fulfil this task. In this research paper, we presented a detailed comparative analysis of open-source vulnerability tools available in the market. This experimental study also presents a case study of campus area network scanning using the open-source innovative tool Zenmap. The results prove that the remote devices present in network infrastructure can be scanned using Zenmap without any special privilege and can provide detailed insights into the network to administrators so that they can form policies in the campus area network for threat assessment.