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The Effect of Procrastination on Physical Exercise among College Students—The Chain Effect of Exercise Commitment and Action Control

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International Journal of mental Health Promotion 2024年第8期26卷 611-622页

作者： z.ang W. Xu m. Feng Y. m.o z. Yan z. Department of Physical Education Qufu Normal University Qufu 273165 China Department of Internal medicine The Ohio State University Columbus 43210 OH United States Teaching Department of Com.on Courses Shandong University of Art & Design Jinan 250399 China School of Psychology Beijing Sport University Beijing 100084 China School of Physical Education Chongqing University of Posts and Telecommunications Chongqing 400065 China

Background: Exercise procrastination is prevalent among college students, causing decline in physical fitness. It is imperative to investigate the mechanism affecting college students’ physical activity behaviors. This study was aimed at investigating the effect of procrastination on college students’ physical exercise behavior, and the chain mediation effects of exercise commitment and action control (AC), to provide a theoretical basis for interventions targeting physical exercise behavior among college students. methods: A questionnaire survey was conducted using convenience sampling. The General Procrastination Scale, Exercise Commitment Scale, Action Control Scale, and Physical Activity Rating Scale-3 questionnaires were used. Participants were 581 college students (age 19.27 ± 0.94 years; 243 males and 338 females). Statistical methods of regression analysis and structural equation modeling (SEm) were applied. Results: Procrastination, exercise commitment, and action control were found to be significant predictors of physical exercise behavior. Among these predictors, exercise commitment and action control showed full mediation effects in the relationship between procrastination and physical exercise behavior, and explained 25.48% and 30.77% of the total variance, respectively. The chain mediation effect of exercise commitment-action control was significant, accounting for 22.60% of the total variance, and the total indirect effect was 79.33%. Conclusion: Therefore, higher procrastination was associated with less participation in physical exercise behavior among college students. Improvements in exercise commitment and volitional decision-making ability for physical exercise behavior promoted physical exercise behavior, and increased exercise commitment promoted volitional decision-making ability among the students. The chain reaction effect of exercise commitment and action control also buffered the negative effects of procrastination on physical exercise beh

关键词： action control chain intermediary College student exercise behavior exercise commitment procrastination

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Effects of multiple shapes for steady flow with transformer oil+Fe_(3)o_(4)+Tio_(2) between two stretchable rotating disks

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Applied mathematics and mechanics(English Edition) 2024年第2期45卷 373-388页

作者： m.RAHmAN m.TURKYILmAz.GLU z.mUSHTAQ Department of mathematics Mirpur University of Science and TechnologyMirpur 10250Pakistan Department of mathematics Hacettepe UniversityBeytepeAnkara 06532Turkiye

In this study,we examine the effects of various shapes of nanoparticles in a steady flow of hybrid nanofluids between two stretchable rotating *** steady flow of hybrid nanofluids with transformer oil as the base fluid and Fe_(3)o_(4)+Tio_(2)as the hybrid nanofluid is *** shapes of Fe_(3)o_(4)+Tio_(2)hybrid nanofluids,including sphere,brick,blade,cylinder,and platelet,are *** shape exists in the same volume of a *** leading equations(partial differential equations(PDEs))are transformed to the nonlinear ordinary differential equations(oDEs)with the help of similarity *** system.of equations takes the form.of oDEs depending on the boundary conditions,whose solutions are computed numerically by the bvp4c mATLAB *** outputs are compared with the previous findings,and an intriguing pattern is discovered,such that the tangential velocity is increased for the rotation parameter,while it is decreased by the stretching values because of the lower *** the reaction rate parameter,the concentration boundary layer becomes shorter,and the activation energy com.onent increases the rate at which mass transfers come to the higher disk but have the opposite effect on the bottom *** ranges of various parameters taken into account are Pr=6.2,Re=2,m=1.0,φ_(1)=φ_(2)=0.03,K=0.5,S=-0.1,Br=0.3,Sc=2.0,α_(1)=0.2,γ=0.1,E_(n)=2.0,and q=1.0,and the rotation factor K is within the range of 0 to 1.

关键词： transformer oil Brinkman number skin friction coefficient double rotating disk Nusselt number Sherwood number

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Linking 24-h movement Behavior Guidelines to Cognitive Difficulties, Internaliz.ng and Externaliz.ng Problems in Preterm Youth

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International Journal of mental Health Promotion 2024年第8期26卷 651-662页

作者： Cheng z. Aikeremu A. Gao Y. z.ang z. Delli Paoli A.G. Cunha P.m. Taylor A. Herold F. z.u L. Body-Brain-mind Laboratory School of Psychology Shenzhen University Shenzhen 518060 China Department of Kinesiology and Health Rutgers University New Brunswick 08901 NJ United States metropolitan University Center of maringá Maringá 87020 Brazil School of Health and Rehabilitation Sciences AECC University College Bournemouth BH5 2DF United Kingdom Research Group Degenerative and Chronic Diseases Movement Faculty of Health Sciences Brandenburg University of Potsdam Potsdam 14476 Germany

Background: Given the heightened risk of developmental challenges associated with preterm birth, it is crucial to explore interventions that may ameliorate potential adverse outcomes. This study aimed to examine whether meeting the 24-h movement behavior (24-HmB) guidelines, which include recommendations on physical activity (PA), screen time (ST), and sleep (SL), is related to indicators of cognitive difficulties, internaliz.ng problems (e.g., depression and anxiety), and externaliz.ng problems (e.g., difficulties in making friends and arguing) in a sample of preterm youth (children and adolescents born preterm). methods: In this cross-sectional study, data from 3410 preterm youth (aged 6 to 17 years) were included for data analyses. multivariable logistic regression was used to investigate associations between meeting the 24-HmB guidelines and the above-mentioned health outcomes, while controlling for sociodemographic and health-related factors. Results: The prevalence of meeting 24-HmB guidelines varied across independent and integrated com.onents of the 24-HmB guidelines. meeting the ST guideline alone (p ociated with fewer cognitive difficulties and reduced internaliz.ng and externaliz.ng problems (p meeting the SL guideline alone and integrated guidelines (i.e., SL + ST) were associated with lower odds of depression and anxiety (p onally, meeting independent, and integrated (PA and/or ST) guidelines were associated with less pronounced difficulties in making friends and arguing (p meeting 24-HmB guidelines in an isolated and integrated manner are linked to better cognitive performance and fewer internaliz.ng and externaliz.ng problems in preterm youth. Conclusion: Results suggest that advocating for the implementation of the 24-HmB guidelines may reduce cognitive challenges and behavioral issues, which is of high relevance for improving pub

关键词： Cognitive difficulties externaliz.ng problems internaliz.ng problems physical activity preterm youth

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Quantitative Identification of Delamination Damage in Com.osite Structure Based on Distributed optical Fiber Sensors and model Updating

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SDHm Structural Durability and Health monitoring 2024年第6期18卷 785-803页

作者： Xu H. Wang J. z.u R. Strauss A. Cao m. Wu z. School of materials Science and Engineering Dalian University of Technology Dalian 116000 China School of mechanics and Aerospace Engineering Dalian University of Technology Dalian 116000 China Department of Civil Engineering and Natural Haz.rds University of Natural Resources and Life Sciences Vienna 1180 Austria Department of Engineering mechanics Hohai University Nanjing China

Delamination is a prevalent type of damage in com.osite laminate structures. Its accumulation degrades structural performance and threatens the safety and integrity of aircraft. This study presents a method for the quantitative identification of delamination identification in com.osite materials, leveraging distributed optical fiber sensors and a model updating approach. Initially, a numerical analysis is performed to establish a parameteriz.d finite element model of the com.osite plate. Then, this model subsequently generates a database of strain responses corresponding to damage of varying siz.s and locations. The radial basis function neural network surrogate model is then constructed based on the numerical simulation results and strain responses captured from the distributed fiber optic sensors. Finally, a multi-island genetic algorithm is employed for global optimiz.tion to identify the siz. and location of the damage. The efficacy of the proposed method is validated through numerical examples and experiment studies, examining the correlations between damage location, damage siz., and strain responses. The findings confirm that the model updating technique, in conjunction with distributed fiber optic sensors, can precisely identify delamination in com.osite structures. © 2024 The Authors. Published by Tech Science Press.

关键词： Com.osite structures damage identification fiber optic sensor model updating surrogate model

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An Experimental Study on the Effectiveness of Individualiz.d online Intervention for mental Health Literacy of Community Residents

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International Journal of mental Health Promotion 2024年第8期26卷 663-670页

作者： Ke z. Xia m. Yang W. Hou B. Deng H. School of Economics Wuhan Textile University Wuhan 430200 China School of Psychology Central China Normal University Wuhan 430079 China School of management Wuhan University of Technology Wuhan 430070 China School of Public Administration Zhongnan University of Economics and Law Wuhan 430200 China

Background: Understanding how to improve mental health literacy is conducive to maintaining and promoting individuals’ mental health and well-being. However, to date, interventions for mental health literacy primarily depend on traditional education and contact interventions, which have limitations with regard to pertinence and individualiz.tion. Artificial intelligence (AI) and big data technology have influenced mental health services to be more intellectual and digital, and they also provide greater technical convenience for individualiz.d interventions for promoting mental health literacy. However, there is relatively little research on the effectiveness of individualiz.d online intervention for mental health literacy in the literature. This study aims to fill this void. To verify whether individualiz.d online intervention can improve the level of mental health literacy. methods: We conducted a pretest–post-test control experiment. The participants were recruited from a large community located in central China. A total of 152 participants completed the research. We use mixed linear model estimation and paired t-tests to analyz. the data. Results: Individualiz.d online intervention can effectively improve the mental health literacy level of participants. Specifically, we found that compared with the control group, the mental health literacy in the experimental group was significantly improved after receiving individualiz.d online intervention. Likewise, the mental health literacy of the control group has also improved after receiving individualiz.d online intervention. In addition, we compared the mental health literacy level of the experimental group at Time 3 to those at Time 2 and found that the mental health literacy level at Time 3 had not decreased one month later. This shows that individualiz.d online intervention was not only momentarily effective, but also had long-term efficacy. Conclusion: This study illustrates that the individualiz.d online intervent

关键词： Individualiz.d online intervention mental health literacy pretest-posttest research design

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Quantification of Co2 Emissions from Three Power Plants in China Using oCo-3 Satellite measurements

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Advances in Atmospheric Sciences 2024年第11期41卷 2276-2288页

作者： Yang Y. z.ou m. Wang W. Ning z. z.ang F. Wang P. College of Data Science Jiaxing University Jiaxing 314000 China LAGEo Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China Shanghai Ecological Forecasting and Remote Sensing Center Shanghai 200030 China CNRC Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China Key Laboratory of Atmospheric Environment and Extreme meteorology Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China Department of Atmospheric and oceanic Sciences / Institute of Atmospheric Sciences Fudan University Shanghai 200082 China

Coal-fired power plants are a major carbon source in China. In order to assess the evaluation of China’s carbon reduction progress with the promise made on the Paris Agreement, it is crucial to monitor the carbon flux intensity from coal-fired power plants. Previous studies have calculated Co2 emissions from point sources based on orbiting Carbon observatory-2 and -3 (oCo-2 and oCo-3) satellite measurements, but the factors affecting Co2 flux estimations are uncertain. In this study, we employ a Gaussian Plume model to estimate Co2 emissions from three power plants in China based on oCo-3 XCo2 measurements. moreover, flux uncertainties resulting from wind information, background values, satellite Co2 measurements, and atmospheric stability are discussed. This study highlights the Co2 flux uncertainty derived from the satellite measurements. Finally, satellite-based Co2 emission estimates are compared to bottom-up inventories. The satellite-based Co2 emission estimates at the Tuoketuo and Nongliushi power plants are ∼30 and ∼10 kt d−1 smaller than the open-Data Inventory for Anthropogenic Carbon dioxide (oDIAC) respectively, but ∼10 kt d−1 larger than the oDIAC at Baotou. © Institute of Atmospheric Physics/Chinese Academy of Sciences, and Science Press 2024.

关键词： Co2 emission Gaussian Plume model oCo-3 power plant

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Heart-Net: A multi-modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

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Computers, materials and Continua 2024年第3期80卷 3967-3990页

作者： Alsekait D.m. Shdefat A.Y. Nabil A. Nawaz.A. Rana m.R.R. Ahmed z. Fathi H. AbdElminaam D.S. Department of Computer Science and Information Technology Applied College Princess Nourah bint Abdulrahman University Riyadh 84428 Saudi Arabia College of Engineering and Technology American University of the Middle East Egaila 15453 Kuwait Faculty of Computers Science Misr International University Cairo 11800 Egypt University Institute of Information Technology PMAS Arid Agriculture University Rawalpindi 46000 Pakistan Department of Computer Science International Islamic University Islamabad 04436 Pakistan Jadara Research Center Jadara University Irbid 21110 Jordan Information System Department Faculty of Computers and Artificial Intelligence Benha University Banha 13511 Egypt mEU Research Unit Middle East University Amman 11831 Jordan

Heart disease remains a leading cause of morbidity and mortality worldwide, highlighting the need for improved diagnostic methods. Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults, which can reduce accuracy, especially with poor-quality images. Additionally, these methods often require significant time and expertise, making them less accessible in resource-limited settings. Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision, ultimately improving patient outcomes and reducing healthcare costs. This study introduces Heart-Net, a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac magnetic Resonance Imaging (mRI) and Electrocardiogram (ECG). Heart-Net uses a 3D U-Net for mRI analysis and a Tem.oral Convolutional Graph Neural Network (TCGN) for ECG feature extraction, combining these through an attention mechanism to emphasiz. relevant features. Classification is performed using optimiz.d TCGN. This approach improves early detection, reduces diagnostic errors, and supports personaliz.d risk assessments and continuous health monitoring. The proposed approach results show that Heart-Net significantly outperforms traditional single-modality models, achieving accuracies of 92.56% for Heartnet Dataset I (HNET-DSI), 93.45% for Heartnet Dataset II (HNET-DSII), and 91.89% for Heartnet Dataset III (HNET-DSIII), mitigating the impact of apparatus faults and image quality issues. These findings underscore the potential of Heart-Net to revolutioniz. heart disease diagnostics and improve clinical outcomes. Copyright © 2024 The Authors. Published by Tech Science Press.

关键词： classification deep learning electrocardiogram Heart diseases magnetic resonance imaging

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Fire resistance evaluation through synthetic fire tests and generative adversarial networks

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Frontiers of Structural and Civil Engineering 2024年第4期18卷 587-614页

作者： Aybike Öz.üksel Çiftçioğlu m.z.Naser Department of Civil Engineering Manisa Celal Bayar University45140Türkiye School of Civil&Environmental Engineering and Earth Sciences(SCEEES) Clemson UniversityClemsonSC 29631USA Artificial Intelligence Research Institute for Science and Engineering(AIRISE) Clemson UniversityClemsonSC 29631USA

This paper introduces a machine learning approach to address the challenge of limited data resulting from costly and time-consuming fire experiments by enlarging small fire test data sets and predicting the fire resistance of reinforced concrete *** approach begins by creating deep learning models,namely generative adversarial networks and variational autoencoders,to learn the spatial distribution of real fire *** then use these models to generate synthetic tabular samples that closely resemble realistic fire resistance values for reinforced concrete *** generated data are employed to train state-of-the-art machine learning techniques,including Extreme Gradient Boost,Light Gradient Boosting machine,Categorical Boosting Algorithm,Support Vector Regression,Random Forest,Decision Tree,multiple Linear Regression,Polynomial Regression,Support Vector machine,Kernel Support Vector machine,Naive Bayes,and K-Nearest Neighbors,which can predict the fire resistance of the columns through regression and *** learning analyses achieved highly accurate predictions of fire resistance values,outperforming traditional models that relied solely on limited experimental *** study highlights the potential for using machine learning and deep learning analyses to revolutioniz. the field of structural engineering by improving the accuracy and efficiency of fire resistance evaluations while reducing the reliance on costly and time-consuming experiments.

关键词： deep learning fire resistance generative adversarial networks machine learning

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Dysregulation of RNA modification systems in clinical populations with neurocognitive disorders

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Neural Regeneration Research 2024年第6期19卷 1256-1261页

作者： Helen m.Knight merve Demirbugen ?z.Adriana Perez.rovas-Saltijeral Division of Cells Organisms and Molecular Genetics School of Life Sciences University of Notingham Department of Pharmaceutical Toxicology Faculty of Pharmacy Ankara University

The study of modified RNA known as epitranscriptomics has become increasingly relevant in our understanding of disease-modifying mechanisms. methylation of N6 adenosine(m6A) and C5 cytosine(m5C) bases occur on mRNAs, tRNA, mt-tRNA, and rRNA species as well as non-coding RNAs. With emerging knowledge of RNA binding proteins that act as writer, reader, and eraser effector proteins, comes a new understanding of physiological processes controlled by these systems. Such processes when spatiotem.orally disrupted within cellular nanodomains in highly specializ.d tissues such as the brain, give rise to different forms of disease. In this review, we discuss accumulating evidence that changes in the m6A and m5C methylation systems contribute to neurocognitive disorders. Early studies first identified mutations within FmR1 to cause intellectual disability Fragile X syndromes several years before FmR1 was identified as an m6A RNA reader protein. Subsequently, familial mutations within the m6A writer gene mETTL5, m5C writer genes NSUN2, NSUN3, NSUN5, and NSUN6, as well as THoC2 and THoC6 that form a protein complex with the m5C reader protein ALYREF, were recogniz.d to cause intellectual development disorders. Similarly, differences in expression of the m5C writer and reader effector proteins, NSUN6, NSUN7, and ALYREF in brain tissue are indicated in individuals with Alz.eimer’s disease, individuals with a high neuropathological load or have suffered traumatic brain injury. Likewise, an abundance of m6A reader and anti-reader proteins are reported to change across brain regions in Lewy bodies diseases, Alz.eimer’s disease, and individuals with high cognitive reserve. m6A-modified RNAs are also reported significantly more abundant in dementia with Lewy bodies brain tissue but significantly reduced in Parkinson’s disease tissue, whilst modified RNAs are misplaced within diseased cells, particularly where synapses are located. In parahippocampal brain tissue, m6A modification is en

关键词： 5-methylcytosine methylation Alz.eimer's disease cognitive diseases epitranscriptomics intellectual disability Lewy body diseases N6 adenosine RNA modification

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Production and achievements of Sesamum indicum industry in the world: Past and current state

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oil Crop Science 2024年第3期9卷 187-197页

作者： Sanni G.B.T.A. Ez.n V. Chabi I.B. missihoun A.A. Florent Q. Hamissou z. Niang m. Ahanchede A. Department of Crop Production Faculty of Agricultural Sciences University of Abomey-Calavi 01 Cotonou BP 526 Benin Laboratory of Valoriz.tion and Quality management of Food Bio-Ingredients Faculty of Agricultural Sciences University of Abomey-Calavi Cotonou 01BP 526 Benin Laboratory of Genetic and Biotechnology Faculty of Sciences and Technology University of Abomey-Calavi BP 526 Cotonou Benin Department of Vegetal Production Abdou Moumouni University Faculty of Agronomy BP: 10960 Niger ISRA-CERAAS Thiès-Escale Thiès BP 3320 Senegal

Sesame production is im.ortant in agriculture, food industry, and the crop diversity due to its rich nutritional profile and health benefits. Despite its significant value, sesame is still an orphan crop that has received little scientific attention, resulting in low yield compared to other major oilseed crops. This review offers a comprehensive overview of the present state of production, knowledge, and research advancements concerning Sesamum indicum.on a global scale. The FAoSTAT database was extensively used to examine the global trends from 1961 to 2021. In the past 60 years, global sesame production has substantially increased, with Asia and Africa being the primary producers. The integration of omics technologies and biotechnological interventions has revolutioniz.d our understanding of the genetic basis of sesame, enhanced productivity, invigorated stress resilience, and improved seed quality. High-throughput sequencing methods such as RNA-seq, RAD-seq, SLAF-seq, and GBS technology are used in various studies, linkage mapping, and identification of trait-associated markers. Fine linkage maps, and multi-omics studies such as genomics, proteomics, transcriptomics, and metabolomics have been employed in sesame research for gene and QTL mapping. Proteins and metabolic pathways related to oil content, yield, and stress tolerance were reported. Genes and QTLs related to yield and its com.onents, drought, salt, and osmotic stress tolerance were discovered. Candidate genes associated with capsule shattering and seed shattering were recently revealed. For more achievement in sesame, it is im.ortant to enhance sesame production efficiency through mechaniz.tion, advanced agricultural practices, and knowledge dissemination to farmers. mAS and multi-omics integration should be particularly reinforced. The advancements in sesame production present a significant and promising opportunity for farmers, governments, and stakeholders in the agricultural sector. © 2024 the oil

关键词： Genetic markers oil yield omics improvement Sesame industry Sesamum indicum

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