The Influence of Preschool Teachers’Social Skills on Job Burnout:A Moderated Mediation Model

Background:Teacher burnout is a serious issue in the field of education,particularly in early childhood education,where teachers face high levels of work stress and emotional labor,leading to emotional exhaustion and job burnout.However,past research has not sufficiently explored the mechanisms of social skills,empathy,and mindfulness in mitigating teacher burnout.Therefore,this study aims to investigate the relationship between preschool teachers’social skills,empathy,and mindfulness with job burnout,in order to provide theoretical basis and practical guidance for reducing teacher burnout.Methods:This research utilized a convenience sampling approach to target preschool teachers for a questionnaire survey.A total of 1109 questionnaires were collected.To ensure the quality of the data,we excluded questionnaires that were not carefully filled out in terms of lie scale questions,those with abnormal demographic variables,and outliers identified based on response time.Ultimately,901 valid questionnaires were obtained,achieving a valid response rate of 81.2%.Participants’levels of social skills,empathy,mindfulness,and job burnout were assessed using the Social Skills Scale(SKS),Empathy Scale(Measure of Empathy,ME),Mindful Attention Awareness Scale(MAAS),and the Maslach Burnout Inventory-Educators Survey(MBI-ES),respectively.Data analysis was conducted using SPSS.Results:After controlling for gender,age,teaching experience,educational level,grade taught,and location of the kindergarten,the study found:(1)There is a negative correlation between preschool teachers’social skills and the level of job burnout(r=−0.238);(2)Empathy has a dual-track effect on job burnout,where cognitive empathy negatively affects job burnout(r=−0.245),while emotional empathy has a positive effect(r=0.045);(3)Cognitive empathy partially mediates the relationship between social skills and job burnout(β=−0.124);(4)Mindfulness significantly impacts social skills,cognitive empathy,and job burnout(r=0.278;r=0.286;r=−0.539),and plays a moderating role in the mediation model(β=0.003;β=−0.023).Conclusion:These findings provide theoretical support for the development of burnout prevention and intervention strategies targeted at preschool teachers.They also point out new directions for future research and potential intervention targets,suggesting that enhancing preschool teachers’social skills and cognitive empathy,as well as increasing their mindfulness level,can help them cope with work-related stress and emotional labor,thereby alleviating job burnout.

Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem

The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem.

The Emergence of Loving Pedagogy in the L2 Affective Domain:How Affective Pedagogy Accounts for Chinese EFL Teachers’Professional Success and Job Satisfaction?

The success of teachers in professional environments has a desirable influence on their mental condition.Simply said,teachers’professional success plays a crucial role in improving their mental health.Due to the invaluable role of professional success in teachers’mental health,personal and professional variables helping teachers succeed in their profession need to be uncovered.While the role of teachers’personal qualities has been well researched,the function of professional variables has remained unknown.To address the existing gap,the current investigation measured the role of two professional variables,namely job satisfaction and loving pedagogy,in Chinese EFL teachers’professional success.To do this,three validated scales were provided to 1591 Chinese EFL teachers.Participants’answers to the questionnaires were analyzed using the Spearman correlation test and structural equation modeling.The data analysis demonstrated a strong,positive link between the variables.Moreover,loving pedagogy was found to be the positive,strong predictor of Chinese EFL teachers’job satisfaction and professional success.The findings of the current inquiry may help educational administrators enhance their instructors’professional success,which in turn promotes their mental and psychological conditions at work.

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

Post-COVID-19 Challenges for Full-Time Employees in China: Job Insecurity, Workplace Anxiety and Work-Life Conflict

Background:Though the COVID-19 pandemic recedes,and our society gradually returns to normal,Chinese people’s work and lifestyles are still influenced by the“pandemic aftermath”.In the post-pandemic era,employees may feel uncertainty at work due to the changed organizational operations and management and perceive the external environment to be more dynamic.Both these perceptions may increase employees’negative emotions and contribute to conflicts between work and life.Drawing from the ego depletion theory,this study aimed to examine the impact of job insecurity during the post-pandemic era on employees’work-life conflicts,and the mediating effect of workplace anxiety in this relationship.Besides,this study also considered the uncertainty of the external macro environment as a boundary condition on the direct and indirect relationship between job insecurity and work-life conflicts.Methods:A two-wave questionnaire survey was conducted from October to December 2023 to collect data.MBA students and graduates from business school with full-time jobs are invited to report their perception of job insecurity,work anxiety,perceived environment uncertainty,and work-life conflict.This resulted in 253 valid responses.Data analysis was performed using the SPSS,Amos,and PROCESS.Results:The results showed that:(1)Employees’job insecurity would directly intensify the work-life conflict(B=0.275,p<0.001,95%CI[0.182,0.367]).(2)Employees’workplace anxiety mediates the relationship between job insecurity and work-life conflict(B=0.083,p<0.001,95%CI[0.047,0.130]).(3)The mediating effect of workplace anxiety between job insecurity and work-life conflict exists when perceived environmental uncertainty is high(B=0.049,95%CI[0.011,0.114]),while vanishes when perceived environmental uncertainty is low(B=0.024,95%CI[−0.005,0.068]).Conclusion:Job insecurity combined with perceived environmental uncertainty in the postpandemic era fuels employees’workplace anxiety and work-life conflicts.Post-pandemic trauma lingers,necessitating urgent attention and response.

An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.

Preliminary Study on the Construction of Evaluation Index System for Clinical Medicine Postgraduates Based on Job Competency

Objective:To evaluate the practicality of constructing multiple evaluation index systems for postgraduates in clinical medicine professional degree based on job competency.Methods:The theoretical framework of the evaluation index system was initially developed using expert consultation,literature review,and other methods.20 survey experts were selected and consulted using the Delphi method to screen and evaluate the weights of multiple indicators.Data was then entered into a table to evaluate the practicality of the constructed indicators.Results:The questionnaire recovery rate of the first round of expert consultation was 92.50%,and the second round was 100.00%.The comparison between the two groups showed P<0.05.The authority level of the first round was 0.817,and the second round was 0.811.In the first round of coordination,the chi-square value of the first-level indicators was 0.498,and the chi-square value of the second-level indicators was 0.628.In the second round of coordination,the chi-square value of the first-level indicators was 0.573,and the chi-square value of the second-level indicators was 0.634.The comparison between the two rounds showed P<0.05.The evaluation index system included 2 first-level indicators,5 second-level indicators,and 31 third-level indicators.Conclusion:Constructing an evaluation index system based on job competency is highly scientific,with a reasonable construction process and high practical value.

A Study of the Relationship between Emotion Regulation and Job Burnout among Junior High School Novice English Teachers

This study aimed to examine the relationship between junior high school novice English teachers’emotion regulation and job burnout.To achieve this purpose,a survey consisting of various scales was administered to 133 primary school teachers selected from Yunnan Province in China.Statistical analyses revealed gender differences in job burnout and emotion regulation among these teachers and highlighted the association between these two variables.The findings established that male novice English teachers in junior schools generally experience lower levels of job burnout and possess better emotion regulation skills compared to their female counterparts.Additionally,a strong negative correlation was identified between job burnout and emotional regulation skills,indicating that the stronger the emotional regulation skills,the less likely novice English teachers are to experience job burnout.The study further emphasized caution in the use of cognitive reappraisal as an emotion regulation strategy,as it may have an adverse effect on mitigating job burnout.This study concluded with recommendations for providing junior high school novice English teachers with opportunities to develop and enhance their emotion regulation skills to reduce job burnout effectively.

Deep Reinforcement Learning Solves Job-shop Scheduling Problems

To solve the sparse reward problem of job-shop scheduling by deep reinforcement learning,a deep reinforcement learning framework considering sparse reward problem is proposed.The job shop scheduling problem is transformed into Markov decision process,and six state features are designed to improve the state feature representation by using two-way scheduling method,including four state features that distinguish the optimal action and two state features that are related to the learning goal.An extended variant of graph isomorphic network GIN++is used to encode disjunction graphs to improve the performance and generalization ability of the model.Through iterative greedy algorithm,random strategy is generated as the initial strategy,and the action with the maximum information gain is selected to expand it to optimize the exploration ability of Actor-Critic algorithm.Through validation of the trained policy model on multiple public test data sets and comparison with other advanced DRL methods and scheduling rules,the proposed method reduces the minimum average gap by 3.49%,5.31%and 4.16%,respectively,compared with the priority rule-based method,and 5.34%compared with the learning-based method.11.97%and 5.02%,effectively improving the accuracy of DRL to solve the approximate solution of JSSP minimum completion time.

The Docking of Talent Training Strategies and Job Market in Higher Vocational Colleges Under the Background of Slow and Difficult Employment

Students in higher vocational colleges are faced with difficult problems such as slow employment,which highlights the dislocation between education and the market.This study surveyed thousands of students and hundreds of enterprises,and put forward docking strategies.The analysis shows that the mismatch between skills adaptation and literacy is the main cause of slow employment.To this end,research and design training programs,including curriculum restructuring,school-enterprise cooperation practice platforms,and employment-oriented quality improvement plans are imperative.At the same time,we will develop teaching modules for career planning to enhance competitiveness and ensure that the content is synchronized with industry standards.After the implementation,the employment rate of students increased by 15%within six months,the degree of interconnection increased by 20%,and the degree of enterprise recognition increased by 25%.The research effectively promotes the docking of higher vocational education with the market,and has far-reaching significance for easing slow employment.

考虑工序相关性的动态Job shop调度问题启发式算法

提出一类考虑工序相关性的、工件批量到达的动态Job shop调度问题,在对工序相关性进行了定义和数学描述的基础上,进一步建立了动态Job shop调度问题的优化模型。设计了一种组合式调度规则RAN(FCFS,ODD),并提出了基于规则的启发式算法以及该类动态Job shop调度问题的算例生成方法。为验证算法和比较评估调度规则的性能,对算例采用文献提出的7种调度规则和RAN(FCFS,ODD)进行了仿真调度,对调度结果的分析表明了算法的有效性和RAN(FCFS,ODD)调度规则求解所提出的动态Job Shop调度问题的优越性能。

基于ACPM和BFSM的动态Job-Shop调度算法

通过对不同时刻开始加工的产品加工树的分解 ,可将产品加工工序分为具有惟一紧前、紧后的相关工序和独立工序 在对这两类工序研究分批综合应用拟关键路径法 (ACPM )和最佳适应调度方法 (BFSM)调度时 ,考虑了关键设备的工序紧凑性 通过分析与实例验证 ,所提出的调度方法对解决动态的Job

免疫模拟退火算法及其在柔性动态Job Shop中的应用

针对车间作业调度问题,在深入分析免疫算法和模拟退火算法的基础上,将两种算法巧妙结合,提出免疫模拟退火算法。该算法引入了免疫记忆、抽取疫苗和接种疫苗等免疫机制,有助于优良个体和基因的保留和利用,提高了算法收敛性,而且其基于概率突跳特性的爬山性能可以避免早熟现象。针对西安航空发动机(集团)有限公司的柔性动态Job Shop,分别用模拟退火算法、免疫算法和免疫模拟退火算法进行了仿真和比较,研究结果表明,免疫模拟退火算法比单一算法性能更优,是求解柔性动态Job Shop问题的有效实用算法。

基于遗传算法求解Job Shop调度优化的新方法

针对Job Shop调度问题,提出了一种遗传算法编码新方法和矩阵解码方法。该方法根据问题的特点,采用一种按工序进行总体排序染色体编码方案,并采用矩阵解码,解码时体现了编码与调度方案一一对应,并且该编码方案有多种交叉操作算子可用,不需要专门设计算子。算例计算结果表明,基于该编码方案的遗传算法是有效的,能适用解决Job Shop调度问题,通过比较,用该编码方案的遗传算法优化Job Shop调度操作简单并且收敛速度快。

解决JOB SHOP问题的粒子群优化算法

设计了2种解决Job shop问题的粒子群算法,即实数编码的粒子群调度算法和工序编码的粒子群调度算法。工序编码的粒子群调度算法更符合Job shop问题的特点,优化性能相对高。但粒子群调度算法容易陷入局部最优。为了提高优化性能,将粒子群算法和模拟退火算法结合,得到了粒子群-模拟退火混合调度算法。仿真结果表明了算法的有效性。

遗传算法求解柔性job shop调度问题

在分析柔性jobshop调度问题特点的基础上,提出一种新的求解该问题的遗传算法,即利用编码方法表示各工序的优先调度顺序及工序的加工机器,由此产生可行的调度方案,使得问题的约束条件在染色体中得以体现.所设计的遗传算子不仅能避免非法调度解的出现,保证后代的多样性,而且可使算法具有记忆功能.仿真结果证明了该算法的有效性.

求解Job Shop调度问题的改进禁忌搜索算法

提出一种改进的禁忌搜索算法,解决传统禁忌搜索算法优化效果对运行次数和初始解依赖的不足,提高这类问题的求解质量。根据Job Shop调度问题解的特征,提出基于关键工序的邻域选择方法,采用此邻域选择方法构造禁忌搜索算法,当无邻域时,重新产生初始解进行禁忌搜索,将传统的禁忌搜索算法从单起始点搜索改进成多起始点搜索。采用改进的禁忌搜索算法对13个难的benchmarks问题进行10次求解,得到的平均值8个优于TSAB算法,得到的最优解6个优于TSAB算法、4个与TSAB算法相同。采用基于关键工序的邻域结构构造的改进TS算法具有较强的搜索能力。

美国JOBS法案、资本市场变革与小企业成长

2012年4月5日,美国总统奥巴马签署JOBS法案,使之正式成为法律。该法案旨在通过适当放松管制,完善美国小型公司与资本市场的对接,鼓励和支持小型公司发展。本文详细介绍了JOBS法案的基本内容,全面分析法案出台后可能造成的影响,并就该法案对发展我国资本市场的启示和借鉴作用进行了深入的分析和思考。

一种求解Job Shop调度问题的混合粒子群优化算法

目的解决单一粒子群算法求解Job Shop调度问题存在的不足,提高这类问题的求解质量.方法采用粒子群算法进行全局搜索,将禁忌搜索算法用于并行局部搜索,禁忌搜索在找到改进解的邻域时采用动态记忆的方式.结果在较短时间内,找到了LA21,LA24等典型benchmarks问题的最优解,十次求解的平均值的平均相对误差百分比比并行遗传算法和禁忌搜索算法分别小了2.94%和0.56%.结论提出一种混合粒子群算法,增强了粒子群算法的局部搜索能力,说明该混合粒子群优化算法是有效的.

基于免疫算法的多目标柔性job-shop调度研究

建立了多目标柔性job-shop调度模型;然后提出了带有保优机制免疫算法,利用免疫记忆、接种疫苗等机制,在算法中保留并充分利用每代最优抗体和局部最优基因,使算法加快收敛;针对这类调度的柔性,提出基于工序设备双层抗体编码方案和基于设备能力空间的解码方案;采用多目标分级评价方法同时对时间、设备和成本等多目标进行评价和优化.最后,用Benchm ark标准问题的仿真和西安航空发动机(集团)有限公司的调度实例验证了算法、策略和调度模型的有效性和优越性.