Biophysical warming patterns of an open-top chamber and its short-term influence on a Phragmites wetland ecosystem in China

Passive-warming, open-top chambers(OTCs) are widely applied for studying the effects of future climate warming on coastal wetlands. In this study, a set of six OTCs were established at a Phragmites wetland located in the Yellow River Delta of Dongying City, China. With data collected through online transmission and in-situ sensors, the attributes and patterns of realized OTCs warming are demonstrated.The authors also quantified the preliminary influence of experimental chamber warming on plant traits.OTCs produced an elevated average air temperature of 0.8°C(relative to controls) during the growing season(June to October) of 2018, and soil temperatures actually decreased by 0.54°C at a depth of 5 cm and 0.46°C at a depth of 30 cm in the OTCs. Variations in diel patterns of warming depend greatly on the heat sources of incoming radiation in the daytime versus soil heat flux at night. Warming effects were often larger during instantaneous analyses and influenced OTCs air temperatures from-2.5°C to 8.3°C dependent on various meteorological conditions at any given time, ranging from cooling influences from vertical heat exchange and vegetation to radiation-associated warming. Night-time temperature depressions in the OTCs were due to the low turbulence inside OTCs and changes in surface soilatmosphere heat transfer. Plant shoot density, basal diameter, and biomass of Phragmites decreased by23.2%, 6.3%, and 34.0%, respectively, under experimental warming versus controls, and plant height increased by 4.3%, reflecting less carbon allocation to stem structures as plants in the OTCs experienced simultaneous wind buffering. While these passive-warming OTCs created the desired warming effects both to the atmosphere and soils, pest damages on the plant leaves and lodging within the OTCs were extensive and serious, creating the need to consider control options for these chambers and the replicated OTCs studies underway in other Chinese Phragmites marshes(Panjin and Yancheng).

Influence of Production Factors on the Physico-Chemical Characteristics of Fermented Cassava Dough and Sensory Evaluation of Attieke

Attieke is an Ivorian semolina which obtained by fermenting, pressing and steaming cassava dough. Attieke production remains a traditional activity carried out by less literate women. However, perceived differences in measurable factors and attieke qualities require an investigation of their influence on the characteristics of the pressed dough and attieke. The aim of this study is to improve the quality of the dough in relation to that of the attieke produced. The experiment was carried out on 4 production factors, namely the type of boiled or braised ferment, the incorporation rate of the ferment between 8 and 10%, the addition of oil from 0.1 to 1% and the fermentation time from 12 to 15 hours applied to the Improved African Cassava (IAC) variety. A complete experiment design of 16 samples of fermented dough and attieke was employed. These samples underwent physic-chemical analyses for the fermented dough and sensory evaluation for the attieke. It was found that, except for titratable acidity, reducing sugar content and ash content, the physico-chemical characteristics of the dough of IAC variety were significantly influenced by all production factors and their interaction. Fermentation time significantly influences 60% of the physico-chemical characteristics of the fermented dough. The type of ferment, the oil addition and the ferment rate have a significant influence at 40% of these characteristics. At the sensory level, color, acidity and grain binding with an explained variance of 34.60% were essential for the appreciation of the attieke samples. Thus, these production factors could be considered for the improvement of the fermented dough and attieke production process.

Ascertaining the influences of auxiliary qubits on the Einstein–Podolsky–Rosen steering and its directions

Einstein–Podolsky–Rosen(EPR) steering is an example of nontrivial quantum nonlocality and characteristic in the non-classical world.The directivity(or asymmetry) is a fascinating trait of EPR steering,and it is different from other quantum nonlocalities.Here,we consider the strategy in which two atoms compose a two-qubit X state,and the two atoms are owned by Alice and Bob,respectively.The atom of Alice suffers from a reservoir,and the atom of Bob couples with a bit flip channel.The influences of auxiliary qubits on EPR steering and its directions are revealed by means of the entropy uncertainty relation.The results indicate that EPR steering declines with growing time t when adding fewer auxiliary qubits.The EPR steering behaves as damped oscillation when introducing more auxiliary qubits in the strong coupling regime.In the weak coupling regime,the EPR steering monotonously decreases as t increases when coupling auxiliary qubits.The increases in auxiliary qubits are responsible for the fact that the steerability from Alice to Bob(or from Bob to Alice) can be more effectively revealed.Notably,the introductions of more auxiliary qubits can change the situation that steerability from Alice to Bob is certain to a situation in which steerability from Bob to Alice is certain.

A Problem Not to Be Ignored: The Influencing Factors of Mobile Phone Addiction and Its Influence on Sleep Quality

Background and Objective: With the popularity and widespread use of mobile phones, the effects of mobile phone dependence and addiction on individuals’ physical and mental health have attracted more and more attention. The present study aims to analyze the current state of mobile phone addiction and its impact on sleep quality within the population, while also exploring the influence of related factors on sleep quality. Ultimately, this research will provide a scientific foundation for targeted intervention measures and strategies. Methods: A total of 253 permanent residents in Nanjing were randomly selected as study subjects. The Mobile Phone Addiction Index (MPAI) and Pittsburgh Sleep Quality Index (PSQI) were used to evaluate the degree of smartphone addiction and sleep quality of the study subjects. Body mass index (BMI) was measured according to standardized procedures. Independent sample t-test, Chi-square test, rank sum test and multiple linear regression were used to analyze the correlation between mobile phone addiction and sleep quality, and P Results: 117 people (46.2%) were addicted to mobile phones. Chi-square test showed that the rate of mobile phone addiction in drinking group was significantly higher than that in non-drinking group (P P P P P P P P P P Conclusion: Mobile phone addiction may lead to shorter sleep duration and reduce sleep efficiency. The withdrawal of mobile phone addiction may have a negative impact on sleep quality. According to the characteristics of the population, appropriate comprehensive intervention measures should be taken to build an effective evaluation system, so as to reduce the impact of mobile phone addiction and withdrawal problems on sleep and improve sleep quality.

Influencer identification of dynamical networks based on an information entropy dimension reduction method

Identifying critical nodes or sets in large-scale networks is a fundamental scientific problem and one of the key research directions in the fields of data mining and network science when implementing network attacks, defense, repair and control.Traditional methods usually begin from the centrality, node location or the impact on the largest connected component after node destruction, mainly based on the network structure.However, these algorithms do not consider network state changes.We applied a model that combines a random connectivity matrix and minimal low-dimensional structures to represent network connectivity.By using mean field theory and information entropy to calculate node activity,we calculated the overlap between the random parts and fixed low-dimensional parts to quantify the influence of node impact on network state changes and ranked them by importance.We applied this algorithm and the proposed importance algorithm to the overall analysis and stratified analysis of the C.elegans neural network.We observed a change in the critical entropy of the network state and by utilizing the proposed method we can calculate the nodes that indirectly affect muscle cells through neural layers.

Influence of transcatheter arterial embolization on symptom distress and fatigue in liver cancer patients

BACKGROUND Hepatocellular carcinoma(HCC)is a prevalent malignancy,and transcatheter arterial embolization(TAE)has emerged as a pivotal therapeutic modality.How-ever,TAE may induce symptom distress and fatigue,adversely affecting the quality of life of patients.AIM To investigate symptom distress,fatigue,and associated factors in HCC patients undergoing TAE.METHODS We used a cross-sectional design and purposive sampling to enroll HCC patients who underwent TAE at our institution from January to December 2022.Question-naires were utilized to collect data on symptom distress and fatigue scores from the first to the third day after TAE.RESULTS Our study revealed a significant reduction in fatigue and symptom distress among patients after TAE.Pain,fatigue,insomnia,fever and abdominal dis-tension were the most common symptoms troubling patients during the first 3 d post-TAE.Marital status,presence of family support,physical functional status,age,and symptom distress were identified as predictors of fatigue in patients.CONCLUSION Healthcare professionals should educate HCC patients on symptom distress and INTRODUCTION Hepatocellular carcinoma(HCC)ranks as the fifth most prevalent cancer and the third leading cause of cancer-related mortality globally.Surgical intervention remains the cornerstone of HCC treatment;however,due to the challenges associated with early diagnosis and the lack of specific diagnostic markers,a considerable proportion of patients are diagnosed at advanced stages,rendering them ineligible for surgical interventions.Transcatheter arterial embolization(TAE)is an interventional therapeutic approach involving the insertion of a fine catheter via the femoral artery to reach the vasculature near the tumor site.TAE aims to obstruct the arterial supply to the tumor by deploying embolic agents,thereby inducing necrosis in cancer cells.This procedure is suitable for patients with good liver function and overall health,particularly those with large HCCs that have not invaded the portal vein[1,2].Nonetheless,following TAE,hepatocytes incur variable degrees of damage,leading to the development of a constel-lation of symptoms reminiscent of acute hepatitis.These symptoms include fatigue and systemic discomfort,such as nausea,vomiting,fever,abdominal pain,as well as transient elevations in aspartate aminotransferase(AST)and alanine aminotransferase(ALT),collectively referred to as postembolization syndrome[3,4].These symptoms may arise due to ischemia of the liver and gallbladder,temporary liver enlargement,and peritoneal irritation.While many studies have explored fatigue and symptom distress in cancer patients both nationally and internationally,with some focusing on symptom distress following transcatheter arterial chemoembolization,there has been limited in-depth investigation into the fatigue and symptom distress resulting from TAE treatment[5-8].Fatigue and pain are both subjective experiences,typically arising from the gradual depletion of energy reserves during the course of illness[9].Fatigue is particularly prevalent among cancer patients,with rates soaring as high as 90%.Despite extensive research exploring cancer-related fatigue and its influencing factors,a unanimous consensus remains elusive.Hence,the primary objective of this study was to investigate the symptom distress and fatigue experienced by liver cancer patients following TAE treatment and to analyze potential contributing factors.

The Influence of Air Pollution Concentrations on Solar Irradiance Forecasting Using CNN-LSTM-mRMR Feature Extraction

Maintaining a steady power supply requires accurate forecasting of solar irradiance,since clean energy resources do not provide steady power.The existing forecasting studies have examined the limited effects of weather conditions on solar radiation such as temperature and precipitation utilizing convolutional neural network(CNN),but no comprehensive study has been conducted on concentrations of air pollutants along with weather conditions.This paper proposes a hybrid approach based on deep learning,expanding the feature set by adding new air pollution concentrations,and ranking these features to select and reduce their size to improve efficiency.In order to improve the accuracy of feature selection,a maximum-dependency and minimum-redundancy(mRMR)criterion is applied to the constructed feature space to identify and rank the features.The combination of air pollution data with weather conditions data has enabled the prediction of solar irradiance with a higher accuracy.An evaluation of the proposed approach is conducted in Istanbul over 12 months for 43791 discrete times,with the main purpose of analyzing air data,including particular matter(PM10 and PM25),carbon monoxide(CO),nitric oxide(NOX),nitrogen dioxide(NO_(2)),ozone(O₃),sulfur dioxide(SO_(2))using a CNN,a long short-term memory network(LSTM),and MRMR feature extraction.Compared with the benchmark models with root mean square error(RMSE)results of 76.2,60.3,41.3,32.4,there is a significant improvement with the RMSE result of 5.536.This hybrid model presented here offers high prediction accuracy,a wider feature set,and a novel approach based on air concentrations combined with weather conditions for solar irradiance prediction.

A Study on the Influence of Social Media Use on Psychological Anxiety among Young Women

To explore the relationship between social influence,social comparison,clarity of self-concept,and psychological anxiety among young women during their usage of social networking sites,our study selected 338 young women aged 14-34 from the social site platforms of Little Red Book and Weibo for questionnaire surveys.The Passive Social Network Utilization Scale,Social Comparison Scale(SCS),Social Influence Questionnaire,Self-Concept Clarity Scale(SCCS),and Generalized Anxiety Disorder Scale(GAD-7)were employed to measure the subjects.Our results show that the frequency of passive social media use is positively related to the level of psychological anxiety.Social comparison,social influence,and unclear self-concepts under social media use are negatively predictive of psychological anxiety.The chain mediation effects indicate that social comparison and social influence under social media use negatively predict the clarity of self-concept,thus having a negative impact on the psychological health of young women.Therefore,young women should strengthen their self-concepts,control their frequency of social media usage,avoid addiction,and pay special attention to their frequency of passive use,in order to protect their psychological health.Our study provides some practical implications and insights regarding the relationship between young women’s social media use and psychological health.

Coupled multiphysical model for investigation of influence factors in the application of microbially induced calcite precipitation

The study presents a comprehensive coupled thermo-bio-chemo-hydraulic(T-BCH)modeling framework for stabilizing soils using microbially induced calcite precipitation(MICP).The numerical model considers relevant multiphysics involved in MICP,such as bacterial ureolytic activities,biochemical reactions,multiphase and multicomponent transport,and alteration of the porosity and permeability.The model incorporates multiphysical coupling effects through well-established constitutive relations that connect parameters and variables from different physical fields.It was implemented in the open-source finite element code OpenGeoSys(OGS),and a semi-staggered solution strategy was designed to solve the couplings,allowing for flexible model settings.Therefore,the developed model can be easily adapted to simulate MICP applications in different scenarios.The numerical model was employed to analyze the effect of various factors,including temperature,injection strategies,and application scales.Besides,a TBCH modeling study was conducted on the laboratory-scale domain to analyze the effects of temperature on urease activity and precipitated calcium carbonate.To understand the scale dependency of MICP treatment,a large-scale heterogeneous domain was subjected to variable biochemical injection strategies.The simulations conducted at the field-scale guided the selection of an injection strategy to achieve the desired type and amount of precipitation.Additionally,the study emphasized the potential of numerical models as reliable tools for optimizing future developments in field-scale MICP treatment.The present study demonstrates the potential of this numerical framework for designing and optimizing the MICP applications in laboratory-,prototype-,and field-scale scenarios.

A framework of force of information influence and application for C4KISR system

The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.

Influence of Continental Atmospheric Forcing on the Decadal Variability of the West African Monsoon

The West African Monsoon (WAM) is characterized by strong decadal and multi-decadal variability and the impacts can be catastrophic for the local populations. One of the factors put forward to explain this variability involves the role of atmospheric dynamics, linked in particular to the Saharan Heat Low (SHL). This article addresses this question by comparing the sets of preindustrial control and historical simulation data from climate models carried out in the framework of the CMIP5 project and observations data over the 20th century. Through multivariate statistical analyses, it was established that decadal modes of ocean variability and decadal variability of Saharan atmospheric dynamics significantly influence decadal variability of monsoon precipitation. These results also suggest the existence of external anthropogenic forcing, which is superimposed on the decadal natural variability inducing an intensification of the signal in the historical simulations compared to preindustrial control simulations. We have also shown that decadal rainfall variability in the Sahel, once the influence of oceanic modes has been eliminated, appears to be driven mainly by the activity of the Arabian Heat Low (AHL) in the central Sahel, and by the structure of the meridional temperature gradient over the inter-tropical Atlantic in the western Sahel.

Short-term inbound rail transit passenger flow prediction based on BILSTM model and influence factor analysis

Accurate and real-time passenger flow prediction of rail transit is an important part of intelligent transportation systems(ITS).According to previous studies,it is found that the prediction effect of a single model is not good for datasets with large changes in passenger flow characteristics and the deep learning model with added influencing factors has better prediction accuracy.In order to provide persuasive passenger flow forecast data for ITS,a deep learning model considering the influencing factors is proposed in this paper.In view of the lack of objective analysis on the selection of influencing factors by predecessors,this paper uses analytic hierarchy processes(AHP)and one-way ANOVA analysis to scientifically select the factor of time characteristics,which classifies and gives weight to the hourly passenger flow through Duncan test.Then,combining the time weight,BILSTM based model considering the hourly travel characteristics factors is proposed.The model performance is verified through the inbound passenger flow of Ningbo rail transit.The proposed model is compared with many current mainstream deep learning algorithms,the effectiveness of the BILSTM model considering influencing factors is validated.Through comparison and analysis with various evaluation indicators and other deep learning models,the results show that the R2 score of the BILSTM model considering influencing factors reaches 0.968,and the MAE value of the BILSTM model without adding influencing factors decreases by 45.61%.

Experimental research on influence mechanism of loading rates on rock pressure stimulated currents

The study of pressure stimulated current(PSC)changes of rocks is significant to monitor dynamic disasters in mines and rock masses.The existing studies focus on change laws and mechanism of currents generated under the loading of rocks.An electrical and mechanics test system was established in this paper to explore the impacts of loading rates on PSCs.The results indicated that PSC curves of different rocks had different change laws under low/high loading rates.When the loading rate was relatively low,PSC curves firstly changed gently and then increased exponentially.Under high loading rates,PSC curves experienced the rapid increase stage,gentle increase stage and sudden change stage.The compressive strength could greatly affect the peak PSC in case of rock failure.The loading rate was a key factor in average PSC.Under low loading rates,the variations of PSCs conformed to the damage charge model of fracture mechanics,while they did not at the fracture moment.Under high loading rates,the PSCs at low stress didn’t fit the model due to the stress impact effects.The experimental results could provide theoretical basis for the influence of loading rates on PSCs.

An Influence Maximization Algorithm Based on Improved K-Shell in Temporal Social Networks

Influence maximization of temporal social networks(IMT)is a problem that aims to find the most influential set of nodes in the temporal network so that their information can be the most widely spread.To solve the IMT problem,we propose an influence maximization algorithm based on an improved K-shell method,namely improved K-shell in temporal social networks(KT).The algorithm takes into account the global and local structures of temporal social networks.First,to obtain the kernel value Ks of each node,in the global scope,it layers the network according to the temporal characteristic of nodes by improving the K-shell method.Then,in the local scope,the calculation method of comprehensive degree is proposed to weigh the influence of nodes.Finally,the node with the highest comprehensive degree in each core layer is selected as the seed.However,the seed selection strategy of KT can easily lose some influential nodes.Thus,by optimizing the seed selection strategy,this paper proposes an efficient heuristic algorithm called improved K-shell in temporal social networks for influence maximization(KTIM).According to the hierarchical distribution of cores,the algorithm adds nodes near the central core to the candidate seed set.It then searches for seeds in the candidate seed set according to the comprehensive degree.Experiments showthatKTIMis close to the best performing improved method for influence maximization of temporal graph(IMIT)algorithm in terms of effectiveness,but runs at least an order of magnitude faster than it.Therefore,considering the effectiveness and efficiency simultaneously in temporal social networks,the KTIM algorithm works better than other baseline algorithms.

Inexplicable Multi-Annual Astral Action on the Precession of Allais Pendulum: An Influence of the Solar System (and Especially of Jupiter?)

Between 1954 and 1961, Allais conducted 6 one-month observations of the azimuth of the plane of oscillation of a pendulum installed in his laboratory. That of 1958 also implemented a second pendulum, identical to the first, located 6 km away in an underground quarry. Although, over these 6 years, the average azimuth of each observation, the amplitude of the 24 h 50 min and 24 h waves, as well as certain other quantities, have evolved considerably, in 1958 their values were very close to those of the second pendulum. The analysis shows that these evolutions could only result from an action external to the pendulum, that no classical phenomenon seems to be able to explain, and which appears, at least mainly, to be an astral action. The evolution of the average azimuth of the pendulum and of the amplitudes of the 24 h and 24 h 50 min components can be decomposed into a component associated with the annual revolution of the Earth around the Sun, and a multi-annual component, whose harmonic 1 has a period which was estimated to 5.74 years. An action of Jupiter is an excellent candidate to explain a large part of the multi-annual action: everything happens as if there were an important action of the modulus of its declination on the multi-annual component, and an important daily action of its hour angle on the azimuth of the pendulum. We cannot exclude an action of the solar cycle, whose period was then about 11 years. The main results were obtained by Allais himself, but this was only published in his book “The Anisotropy of Space”, and remained very little known. Starting from the raw data of Allais, the author of this article found them again, and completed them on certain points.

Influence of Reservoir on Groundwater Environment in the Ecologically Fragile Area, Northwest of China

As the research proposed reservoirs after impact on the surrounding ecological fragile areas of groundwater level and scope, through the proposed reservoir area and its surrounding data collecting, hydrogeology survey and related test, for Modflow system simulation platform, through to the boundary conditions, initial conditions and source sink term and related hydrogeological parameters, the model identification and verification, The model of hydrogeological parameters in the study area is constructed. The simulation results show that the groundwater depth near the reservoir area will be higher than the critical value (1.8 m) of secondary salinization of soil. At the same time, according to the investigation and experiment, if the reservoir does not do seepage treatment, the water infiltration in the reservoir will aggravate the environmental hydrogeological problems in the ecologically fragile area.

Multi-Parameter Influence Analysis of Interaction Between Internal Solitary Wave and Fixed Submerged Body

To obtain the interaction characteristics between Internal solitary waves(ISWs)and submerged bodies,a three-dimensional numerical model for simulating ISWs was established in the present study based on the RANS equation.The velocity entrance method was adopted to generate the ISWs.First,the reliability of this numerical model was validated by comparing it with theoretical and literature results.Then,the influence of environmental and navigation parameters on interactions between ISWs and a fixed SUBOFF-submerged body was studied.According to research,the hydrodynamic performance of the submerged body has been significantly impacted by the ISWs when the body is nearing the central region of the wave.Besides,the pitching moment(y')will predominate when the body encounters the ISWs at a certain angle between 0°and 180°,and the lateral force is larger than the horizontal force.Additionally,the magnitude of the force acting on the body is mostly affected by the wave amplitude.The variation of the vertical force is the main way that ISWs affect the hydrodynamic performance of the bodies.The investigations and findings discussed above can serve as a guide to forecast how ISWs will interact with submerged bodies.

A Positive Influence Maximization Algorithm in Signed Social Networks

The influence maximization(IM)problem aims to find a set of seed nodes that maximizes the spread of their influence in a social network.The positive influence maximization(PIM)problem is an extension of the IM problem,which consider the polar relation of nodes in signed social networks so that the positive influence of seeds can be the most widely spread.To solve the PIM problem,this paper proposes the polar and decay related independent cascade(IC-PD)model to simulate the influence propagation of nodes and the decay of information during the influence propagation in signed social networks.To overcome the low efficiency of the greedy based algorithm,this paper defines the polar reverse reachable(PRR)set and devises a signed reverse influence sampling(SRIS)algorithm.The algorithm utilizes the ICPD model as well as the PRR set to select seeds.There are two phases in SRIS.One is the sampling phase,which utilizes the IC-PD model to generate the PRR set and a binary search algorithm to calculate the number of needed PRR sets.The other is the node selection phase,which uses a greedy coverage algorithm to select optimal seeds.Finally,Experiments on three real-world polar social network datasets demonstrate that SRIS outperforms the baseline algorithms in effectiveness.Especially on the Slashdot dataset,SRIS achieves 24.7% higher performance than the best-performing compared algorithm under the weighted cascade model when the seed set size is 25.

Maximizing Influence in Temporal Social Networks:A Node Feature-Aware Voting Algorithm

Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes.However,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social networks.To bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation model.Firstly,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes selection.Specifically,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their features.During the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability accordingly.The process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based algorithms.Secondly,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node features.Finally,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.

Influence of physical education on anxiety, depression, and selfesteem among college students

BACKGROUND Physical education is pivotal in our country’s education reform.Urban schools have notably enhanced the intensity of physical education in recent years.However,the effects of physical education on students'anxiety,depression,and self-esteem levels,as well as their interrelations,remain unexplored.Compared with the domestic norms,SAS and SDS scores were higher,and SES scores were lower(P<0.05).Among the participants,210(43.93%)had PARS-3 scores below 20,94(19.67%)had scores of 20-42,and 174(36.40%)had scores above 42.After adjusting for daily sleep time,gender,being an only child,major,father’s educational background,mother’s educational background,and family residence,PARS-3 scores were independent influencing factors for anxiety,depression,and low self-esteem(P<0.05).The AUC of PARS-3 scores predicting anxiety,depression,and low self-esteem were 0.805(0.760-0.849),0.799(0.755-0.843),and 0.831(0.788-0.874),respectively.The sensitivities were 0.799,0.801,and 0.748,and the specificities were 0.743,0.716,and 0.814,respectively.PARS-3 was negatively correlated with SAS and SDS scores(r=-0.566,-0.621,both P<0.001)and positively correlated with SES scores(r=-0.621,P<0.001).SES scores were negatively correlated with SAS and SDS scores(r=-0.508,r=-0.518,both P<0.001).CONCLUSION The amount of physical activity is negatively correlated with anxiety and depression degree and positively correlated with self-esteem degree.