Mechanisms Influencing Learning Gains Under Information Security: Structural Equation Modeling with Mediating Effect

With the expanding enrollments in higher education,the quality of col-lege education and the learning gains of students have attracted much attention.It is important to study the influencing factors and mechanisms of individual stu-dents’acquisition of learning gains to improve the quality of talent cultivation in colleges.However,in the context of information security,the original data of learning situation surveys in various universities involve the security of educa-tional evaluation data and daily privacy of teachers and students.To protect the original data,data feature mining and correlation analyses were performed at the model level.This study selected 12,181 pieces of data from X University,which participated in the Chinese College Student Survey(CCSS)from 2018 to 2021.A confirmatory factor analysis was conducted and a structural equation modeling was conducted using AMOS 24.0.Through hypothesis testing,this study explored the mechanisms that influence learning gains from the per-spectives of student involvement,teacher involvement,and school support.The results indicated that the quality of student involvement has an important mediat-ing effect on learning gains and that a supportive campus environment has the greatest influence on learning gains.Establishing positive emotional communica-tions between teachers and students is a more direct and effective method than improving the teaching level to improve the quality of student involvement.This study discusses the implications of these results on the research and practice of connotative development in higher education.

Drivers of spatial structure in thinned forests

Background:As is widely known,an increasing number of forest areas were managed to preserve and enhance the health of forest ecosystems.However,previous research on forest management has often overlooked the importance of structure-based.Aims:Our objectives were to define the direction of structure-based forest management.Subsequently,we investigated the relationships between forest structure and the regeneration,growth,and mortality of trees under different thinning treatments.Ultimately,the drivers of forest structural change were explored.Methods:On the basis of 92 sites selected from northeastern China,with different recovery time (from 1 to 15years) and different thinning intensities (0–59.9%) since the last thinning.Principal component analysis (PCA)identified relationships among factors determining forest spatial structure.The structural equation model (SEM)was used to analyze the driving factors behind the changes in forest spatial structure after thinning.Results:Light thinning (0–20%trees removed) promoted forest regeneration,and heavy thinning (over 35% of trees removed) facilitated forest growth.However,only moderate thinning (20%–35%trees removed) created a reasonable spatial structure.While dead trees were clustered,and they were hardly affected by thinning intensity.Additionally,thinning intensity,recovery time,and altitude indirectly improve the spatial structure of the forest by influencing diameter at breast height (DBH) and canopy area.Conclusion:Creating larger DBH and canopy area through thinning will promote the formation of complex forest structures,which cultivates healthy and stable forests.

Measurement Research Based on Bayesian Structural Equation Cognitive Model

The Bayesian structural equation model integrates the principles of Bayesian statistics, providing a more flexible and comprehensive modeling framework. In exploring complex relationships between variables, handling uncertainty, and dealing with missing data, the Bayesian structural equation model demonstrates unique advantages. Therefore, Bayesian methods are used in this paper to establish a structural equation model of innovative talent cognition, with the measurement of college students’ cognition of innovative talent being studied. An in-depth analysis is conducted on the effects of innovative self-efficacy, social resources, innovative personality traits, and school education, aiming to explore the factors influencing college students’ innovative talent. The results indicate that innovative self-efficacy plays a key role in perception, social resources are significantly positively correlated with the perception of innovative talents, innovative personality tendencies and school education are positively correlated with the perception of innovative talents, but the impact is not significant.

Assessment of Relationship of Some Causal Factors Associated with Productivity of Longwall Mining Using Structured Equation Modeling

Enhancement of productivity optimization is steadily gaining the priority in mining companies especially in the underground coal mining industry which faces a daunting task to balance marginal profit generation with a comparatively high cost of production, volatile market price and rapid grade variation. This paper is aimed to analyze some of the causal factors both technical and site specific which are directly or indirectly impacting the productivity of the longwall coal mining system such as downtime of equipment system in longwall panel, overloading of conveyors and bin, preventive maintenance, gas management practices and injury severity rate. Structured Equation Modelling (SEM) was used to study the causal relationships between the above-mentioned factors and mine productivity. The equipment considered for analysis included shearer, armored face conveyor, crusher, bridge stage loader, chock supports, main gate drive, gate conveyor, hydraulic pumps and crusher. SEM was applied to relate the correlations existing among these causal variables in order to assess their direct or indirect impact on mining productivity. Based on the data extracted over a period of 10 months which included the extraction of 2 longwall panels, the study revealed a significant negative causal relationship between injury severity rate (p < 0.01) and equipment downtime (p = 0.001), with mine productivity. However, preventive maintenance delay time, gas management delay time and conveyor overloading delay time were found to have an insignificant direct influence on mine productivity but indirectly modify it through a significant mediator relationship with equipment downtime. This information would assist mine management to take proper preventive measures.

Investigation of the role of personal factors on work injury in underground mines using structural equation modeling

Work injuries in mines are complex and generally characterized by several factors starting from personal to technical and technical to social characteristics.In this paper,investigation was made through the application of structural equation modeling to study the nature of relationships between the influencing/associating personal factors and work injury and their sequential relationships leading towards work injury occurrences in underground coal mines.Six variables namely,rebelliousness,negative affectivity,job boredom,job dissatisfaction and work injury were considered in this study.Instruments were developed to quantify them through a questionnaire survey.Underground mine workers were randomly selected for the survey.Responses from 300 participants were used for the analysis.The structural model of LISREL was used to estimate the interrelationships amongst the variables.The case study results show that negative affectivity and job boredom induce more job dissatisfaction to the workers whereas risk taking attitude of the individual is positively influenced by job dissatisfaction as well as by rebelliousness characteristics of the individual.Finally,risk taking and job dissatisfaction are having positive significant direct relationship with work injury.The findings of this study clearly reveal that rebelliousness,negative affectivity and job boredom are the three key personal factors influencing work related injuries in mines that need to be addressed properly through effective safety programs.

Factors influencing the internet banking adoption decision in North Cyprus: an evidence from the partial least square approach of the structural equation modeling

Purpose:This paper aims to examine how the adoption decision of the internet banking in North Cyprus would be affected based on the following dimensions;the technology features,the personal characteristics,the social environment and the expected risk.Design/methodology/approach:A self-administered survey was conducted with 291 participants responded to it.The partial least square approach of the structural equation modeling(PLS-SEM)is employed to investigate the direct effects of the proposed factors on the adoption decision.Additionally,the mediation test is used to examine indirect effects.Findings:Results showed that even though the participants appreciated the benefits of the online banking as the perceived usefulness factor exerts the greatest direct effect,they would rather use clear and easy-to-use websites,adding to that their assessments of the usefulness of these services are significantly influenced by the surrounding people’s views and prior experience.This is demonstrated by the total effects of the perceived ease of use and the subjective norm factors,which are greater than the direct effect of the perceived usefulness factor since both of these factors have significant direct and indirect effects mediated by the perceived usefulness factor.The negative impact of the perceived risk factor is weak compared to the previous factors.While the personal innovativeness factor showed the weakest effect among the proposed factors.

Linking Structural Equation Modeling with Bayesian Network and Its Application to Coastal Phytoplankton Dynamics in the Bohai Bay

Bayesian networks （BN） have many advantages over other methods in ecological modeling, and have become an increasingly popular modeling tool. However, BN are flawed in regard to building models based on inadequate existing knowledge. To overcome this limitation, we propose a new method that links BN with structural equation modeling （SEM）. In this method, SEM is used to improve the model structure for BN. This method was used to simulate coastal phytoplankton dynamics in the Bohai Bay. We demonstrate that this hybrid approach minimizes the need for expert elicitation, generates more reasonable structures for BN models, and increases the BN model＇s accuracy and reliability. These results suggest that the inclusion of SEM for testing and verifying the theoretical structure during the initial construction stage improves the effectiveness of BN models, especially for complex eco-environment systems. The results also demonstrate that in the Bohai Bay, while phytoplankton biomass has the greatest influence on phytoplankton dynamics, the impact of nutrients on phytoplankton dynamics is larger than the influence of the physical environment in summer. Furthermore, although the Redfield ratio indicates that phosphorus should be the primary nutrient limiting factor, our results show that silicate plays the most important role in regulating phytoplankton dynamics in the Bohai Bay.

Generalized structural equation modeling:Symptom heterogeneity in attention-deficit/hyperactivity disorder leading to poor treatment efficacy

BACKGROUND Treatment efficacy for attention-deficit/hyperactivity disorder(ADHD)is reported to be poor,possibly due to heterogeneity of ADHD symptoms.Little is known about poor treatment efficacy owing to ADHD heterogeneity.AIM To use generalized structural equation modeling(GSEM)to show how the heterogeneous nature of hyperactivity/impulsivity(H/I)symptoms in ADHD,irritable oppositional defiant disorder(ODD),and the presentation of aggression in children interferes with treatment responses in ADHD.METHODS A total of 231 children and adolescents completed ADHD inattention and H/I tests.ODD scores from the Swanson,Nolan,and Pelham,version IV scale were obtained.The child behavior checklist(CBCL)and parent’s satisfaction questionnaire were completed.The relationships were analyzed by GSEM.RESULTS GSEM revealed that the chance of ADHD remission was lower in children with a combination of H/I symptoms of ADHD,ODD symptoms,and childhood aggressive behavior.ODD directly mediated ADHD symptom severity.The chance of reaching remission based on H/I symptoms of ADHD was reduced by 13.494%[=exp(2.602)]in children with comorbid ADHD and ODD[odds ratio(OR)=2.602,95%confidence interval(CI):1.832-3.373,P=0.000]after adjusting for the effects of other factors.Childhood aggression mediated ODD symptom severity.The chance of reaching remission based on ODD symptoms was lowered by 11.000%[=1-exp(-0.117)]in children with more severe baseline symptoms of aggression based on the CBCL score at study entry[OR=-0.117,95%CI:(-0.190)-(-0.044),P=0.002].CONCLUSION Mediation through ODD symptoms and aggression may influence treatment effects in ADHD after adjusting for the effects of baseline ADHD symptom severity.More attention could be directed to the early recognition of risks leading to ineffective ADHD treatment,e.g.,symptoms of ODD and the presentation of aggressive or delinquent behaviors and thought problems in children with ADHD.

Analysis of Satisfaction with Business Cost in Shanghai Using Structural Equation Modeling Approach

Tracing the origins of business cost, to define the satisfaction assessment factors on the business cost in Shanghai was investigated in this paper. Arguing that these effects: a single low cost is not only the criterion determining satisfaction. Other important factors, such as enterprise internal cognitive attitude,stability of the external environment,the quality of perceptions of government services can affect satisfaction. Within structural equation modeling( SEM) approach, six sets of hypotheses are drawn up and tested with a sample of 338 enterprises using questionnaires. Additionally,the analyses of findings introduce the crucial importance of these features in the assessment of satisfaction with business cost in Shanghai. Finally, the results show that internal cognitive has a positive impact on satisfaction. Stability of the external environment has a significant relationship with the effectiveness of government services. Stability of the external environmeat and the effectiveness of government servises have a positive effect on satisfaction.

Factors Affecting Quality of Equipment Maintenance Based on Structural Equation Modeling

Factors affecting equipment maintenance quality usually include personnel,equipment and facilities,raw materials and spare parts, craft, environment and quality management. These six factors affecting maintenance quality are not all the same degree. In order to find out which factor can influence equipment maintenance quality greatly and achieve sophisticated management,characteristic quantities of six factors are presented. A structural equation modeling( SEM) is designed using the six factors as latent variables and their characteristic quantities as the observed variable.According to the basic data obtained from the questionnaire survey,calculate the standardized regression weight by parameter estimation of the SEM. Then the key factors affecting the quality of the equipment maintenance are determined with the help of the standardized regression weight.

Meta-analytic structural equation modeling:a primer

Meta-analytic structural equation modeling is a new research field which can help to explain the relationship between a group of variables in several studies by testing structural equation modeling.However,this methodology is still not very popular in many countries and researchers due to lack of promotion.This report aim s to giv e a primer on the principle of m eta-analytic structural equation modeling and provid e a reference for subsequent researchers to conduct m eta-analytic structural equation modeling analysis.

How Users Perceive Infrastructure Development Affects Their Transport Mode Choice

Transport infrastructure development and perception vary across and within countries, influencing mode choice among road users. This study explores how road users perceive the development of infrastructure modes, service attributes, embedded safety levels, and commuting modes. Additionally, the research examines whether participants’ environmental backgrounds impact their mode choice patterns. The study gathered responses from 1169 participants residing in two regions of Amman, Jordan, each with distinct infrastructure development and population densities. Participants completed a standardized questionnaire, and several statistical techniques were employed for analysis. The findings revealed that facilities’ infrastructure attributes, development, and safety were assessed using three indices. Both participant groups perceived these indices differently on average. Residents of low population density areas with relatively developed infrastructure showed more consistent assessments, irrespective of their most frequently used mode of transportation, tending towards lower scores. Interestingly, subjective ratings of infrastructure development were higher (4.96) than attribute-based ratings (4.32). Despite their generally low-quality perception, public transportation services received the highest appraisal (4.9). Conversely, pedestrian infrastructure complementing public transport received the lowest assessment (4.57), only slightly higher than street environments (4.59). The study found weak associations between subjective service characteristics ratings. Traveler and trip characteristics influenced mode choice and trips more than infrastructure perception. In conclusion, the study suggests that policies should be developed to encourage green transportation, ensure social equality and safety. In addition, the study contributes to understanding perceptions about transport infrastructure, modes of transportation, and the factors that influence sustainable and equitable transportation systems.

Effects of landscape fragmentation of plantation forests on carbon storage in the Loess Plateau,China

Tree plantation and forest restoration are the major strategies for enhancing terrestrial carbon sequestration and mitigating climate change.The Grain for Green Project in China has positively impacted global carbon sequestration and the trend towards fragmentation of plantation forests.Limited studies have been conducted on changes in plantation biomass and stand structure caused by fragmentation,and the effect of fragmentation on the carbon storage of plantation forests remains unclear.This study evaluated the differences between carbon storage and stand structure in black locust forests in fragmented and continuous landscape in the Ansai District,China and discussed the effects of ecological significance of four landscape indices on carbon storage and tree density.We used structural equation modelling to explore the direct and indirect effects of fragmentation,edge,abiotic factors,and stand structure on above-ground carbon storage.Diameter at breast height(DBH)in fragmented forests was 53.3%thicker,tree density was 40.9%lower,and carbon storage was 49.8%higher than those in continuous forests;for all given DBH>10 cm,the trees in fragmented forests were shorter than those in continuous forests.The patch area had a negative impact on carbon storage,i.e.,the higher the degree of fragmentation,the lower the density of the tree;and fragmentation and distance to edge(DTE)directly increased canopy coverage.However,canopy coverage directly decreased carbon storage,and fragmentation directly increased carbon storage and tree density.In non-commercial forests,fragmentation reduces the carbon storage potential of plantation,and the influence of patch area,edge,and patchy connection on plantation should be considered when follow-up trees are planted and for the plantation management.Thus,expanding the area of plantation patches,repairing the edges of complex-shaped patches,enhancing the connectivity of similar patches,and applying nutrients to plantation forests at regular intervals are recommended in fragmented areas of the Loess Plateau.

How Does Urban Shrinkage Affect Land Use Efficiency?A Case Study of Shrinking Cities in Northeast China

The effect of urban shrinkage has gradually become a new topic.Theoretically,urban shrinkage may exert great influence on land use efficiency(LUE)through various urban subsystems,but there is currently limited research examining these pathways.Using the Super-SBM-Undesirable model and the Structural Equation Model(SEM),this study calculates the LUE of shrinking cities in Northeast China and simulates the process of urban shrinkage affecting LUE.To quantify the process of urban shrinkage affecting LUE,three mediation variables,namely the economy,public services,and innovation,are used as latent variables to apply SEM.The results show that urban shrinkage will affect LUE through a direct path and indirect paths.In the direct path,urban shrinkage leads to an improvement in LUE.In the indirect paths,the economy and innovation will transmit the negative effect of urban shrinkage on LUE,while public services will reverse this effect.An important contribution of this study is that it quantifies the paths of urban shrinkage affecting LUE,thereby expanding the understanding of urban shrinkage effect and laying a foundation for the sustainable development of shrinking cities.

Change in self-image pressure level before and after autologous fat breast augmentation and its effect on social adaptability

BACKGROUND There is an increasingly strong demand for appearance and physical beauty in social life,marriage,and other aspects with the development of society and the improvement of material living standards.An increasing number of people have improved their appearance and physical shape through aesthetic plastic surgery.The female breast plays a significant role in physical beauty,and droopy or atrophied breasts can frequently lead to psychological inferiority and lack of confidence in women.This,in turn,can affect their mental health and quality of life.AIM To analyze preoperative and postoperative self-image pressure-level changes of autologous fat breast augmentation patients and their impact on social adaptability.METHODS We selected 160 patients who underwent autologous fat breast augmentation at the First Affiliated Hospital of Xinxiang Medical University from January 2020 to December 2022 using random sampling method.The general information,selfimage pressure level,and social adaptability of the patients were investigated using a basic information survey,body image self-assessment scale,and social adaptability scale.The self-image pressure-level changes and their effects on the social adaptability of patients before and after autologous fat breast augmentation were analyzed.RESULTS We collected 142 valid questionnaires.The single-factor analysis results showed no statistically significant difference in the self-image pressure level and social adaptability score of patients with different ages,marital status,and monthly income.However,there were significant differences in social adaptability among patients with different education levels and employment statuses.The correlation analysis results revealed a significant correlation between the self-image pressure level and social adaptability score before and after surgery.Multiple factors analysis results showed that the degree of concern caused by appearance in selfimage pressure,the degree of possible behavioral intervention,the related distress caused by body image,and the influence of body image on social life influenced the social adaptability of autologous fat breast augmentation patients.CONCLUSION The self-image pressure on autologous fat breast augmentation patients is inversely proportional to their social adaptability.

Analysis of Commuting Modal Shift in Consideration of Social Interaction of Consciousness for Environment

It is the matter for achievement of the low carbon transport system that the excessive use of private vehicles can be controlled appropriately.Not only improvement of service level of modes except private vehicle,but also consciousness for environmental problem of individual trip maker is important for eco-commuting promotion.On the other hand,consciousness for environment would be changed by influence of other person.Accordingly,it is aimed in the study that the structure of decision-making process for modal shift to the eco-commuting mode in the local city is described considering environmental consciousness and social interaction.For the purpose,the consciousness for the environment problem and the travel behavior of the commuter at the suburban area in the local city are investigated by the questionnaire survey.The covariance structure about the eco-consciousness is analyzed with the database of the questionnaire survey by structural equation modeling.As the result,it can be confirmed with the structural equation model that the individual environmental consciousness is strongly related with the intention of self-sacrifice and is influenced with the local interaction of the individual connections.On the other hand,the intention of modal shift for the commuting mode is analyzed with the database of the questionnaire survey.It can be found out that the environmental consciousness is not statistically significant for commuting mode choice with the present poor level of service of public transport.However,the intention of self-sacrifice for the prevention of the global warming is statistically confirmed as the factor of modal shift with the operation of eco-commuting bus service with the RP/SP integrated estimation method.As the result,the multi-agent simulation system with social interaction model for eco consciousness is developed to measure the effect of the eco-commuting promotion.For the purpose,the carbon dioxide emission is estimated based on traffic demand and road network condition in the traffic environment model.On the other hand,the relation between agents is defined based on the small world network.The proposed multi-agent simulation is applied to measure the effect of the eco-commuting promotion such as improvement of level of service on the public transport or education of eco-consciousness.The effect of the promotion plan can be observed with the proposed multi-agent system.Finally,it can be concluded that the proposed multi-agent simulation with social interaction for eco-consciousness is useful for planning of eco-commuting promotion.

Quality Management Model for Phase I Clinical Drug Trials:A Structural Equation Model

This study aimed to construct a quality management model for phase I clinical drug trials.A cross-sectional survey was conducted and data were collected from 604 respondents at 69 institutions in China engaged in phase I clinical drug trials.Exploratory and confirmatory factor analyses were used to develop the survey tool.Structural equation modeling was used to construct a quality management model for phase I clinical drug trials.The results showed that the final survey tool had good reliability and validity(Cronbach’sα=0.938,root mean square error of approximation=0.074,comparative fit index=0.962,and Tucker—Lewis index=0.955).The model included five dimensions:government regulation,industry management,medical institution management,research team management,and contract research organization(CRO)management.In total,22 measurement items were obtained.The structural equation model indicated government regulation,industry management,medical institution management,and CRO management significantly affected the quality of phase I clinical drug trials(β=0.195,β=0.331,β=0.279,andβ=−0.267,respectively;P<0.05).Research team management had no effect on the quality of trials(β=0.041,P=0.610).In conclusion,the model is valuable for identifying factors influencing phase I clinical drug trials and guiding quality management practices.

Structure complexity is the primary driver of functional diversity in the temperate forests of northeastern China

Background:Assessing functional diversity to identify its spatial patterns and drivers is an important step towards understanding the adaptive capacity of ecosystems to environmental change. However, until now, these mechanisms were poorly understood in the temperate forests of northeastern China, which prevented the development of new management methods aimed at increasing functional trait diversity and thus ecological resilience.Methods:In this study, we mapped functional diversity distributions using a Kriging Interpolation Method. A specific random forest model approach was adopted to test the importance ranking of 18 variables in explaining the spatial variation of functional diversity. Three piecewise structural equation models (pSEMs) with forest types as random effects were constructed for testing the direct effects of climate, and the indirect effects of stand structure on functional diversity across the large study region. Specific causal relationships in each forest type were also examined using 15 linear structural equation models.Results:Although environmental filtering by climate is important, stand structure explains most of the functional variation of the forest ecosystems in northeastern China. Our study thus only partially supports the stressdominance hypothesis. Several abundant species determine most of the functional diversity, which supports the mass ratio hypothesis.Conclusions:Our results suggest that forest management aimed at increasing structural complexity can contribute to increased functional diversity, especially regarding the mixing of coniferous and broad-leaved tree species.

A Re-Parametrization-Based Bayesian Differential Analysis Algorithm for Gene Regulatory Networks Modeled with Structural Equation Models

Under different conditions,gene regulatory networks(GRNs)of the same gene set could be similar but different.The differential analysis of GRNs under different conditions is important for understanding condition-specific gene regulatory relationships.In a naive approach,existing GRN inference algorithms can be used to separately estimate two GRNs under different conditions and identify the differences between them.However,in this way,the similarities between the pairwise GRNs are not taken into account.Several joint differential analysis algorithms have been proposed recently,which were proved to outperform the naive approach apparently.In this paper,we model the GRNs under different conditions with structural equation models(SEMs)to integrate gene expression data and genetic perturbations,and re-parameterize the pairwise SEMs to form an integrated model that incorporates the differential structure.Then,a Bayesian inference method is used to make joint differential analysis by solving the integrated model.We evaluated the performance of the proposed re-parametrization-based Bayesian differential analysis(ReBDA)algorithm by running simulations on synthetic data with different settings.The performance of the ReBDA algorithm was demonstrated better than another state-of-the-art joint differential analysis algorithm for SEMs ReDNet obviously.In the end,the ReBDA algorithm was applied to make differential analysis on a real human lung gene data set to illustrate its applicability and practicability.

Structural Dimensions Analysis of Service Employees' Innovation Behavior Based on Structural Equation Model

In order to know about the essence of service employees' innovation behavior, the service employees' innovation behavior scale based on their unique characteristics is designed. Data were collected from high-star hospitalities. Through the issuance and the collection of questionnaires, the scale is verified to have good reliability and validity by SPSS software analysis. Meanwhile,the structural equation model( SEM) is suited for testing structural dimensions of service employees' innovation behavior. The results showed that service employees' innovation behavior could be decomposed into a four-dimensional structure,namely innovation orientation, ideas generation, innovation implementation and innovation practice. In addition,there are significant differences in service employees' innovation behavior about subjects' gender,position,education background and years of working.