Research on the Management Mechanism of Teachers in Public Security Colleges and Universities Based on Psychological Contract

In today＇s management of the teaching staff of public security colleges and universities, the research on the Psychological Contract problem is not only beneficial to the management and estimation of the teachers, but also has an important role in the coordination and stability of the whole system of public security colleges and universities. Based on the theory of Psychological Contract, this paper expounds the basic understanding and research value of Psychological Contract, and puts forward the measures to cultivate teachers＇ good character and understanding, and as well as coordinate teachers＇ needs, effectively change teachers＇ attitude and actively promote the establishment of good interpersonal relationship to explain the research on the Management Mechanism of Teachers in Public Security Colleges and Universities from the Perspective of Psychological Contract.

Systematizing Teacher Development:A Review of Foreign Language Teacher Learning

Foreign language teaching practice is developing rapidly,but research on foreign language teacher learning is currently relatively fragmented and unstructured.The book Foreign Language Teacher Learning,written by Professor Kang Yan from Capital Normal University,published in September 2022,makes a systematic introduction to foreign language teacher learning,which to some extent makes up for this shortcoming.Her book presents the lineage of foreign language teacher learning research at home and abroad,analyzes both theoretical and practical aspects,reviews the cuttingedge research results,and foresees the future development trend,painting a complete research picture for researchers in the field of foreign language teaching and teacher education as well as front-line teachers interested in foreign language teacher learning.This is an important inspiration for conducting foreign language teacher learning research in the future.And this paper makes a review of the book from aspects such as its content,major characteristics,contributions and limitations.

Contract Mechanism of Water Environment Regulation for Small and Medium Sized Enterprises Based on Optimal Control Theory

The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.

Bipartite consensus problems of Lurie multi-agent systems over signed graphs: A contraction approach

This paper examines the bipartite consensus problems for the nonlinear multi-agent systems in Lurie dynamics form with cooperative and competitive communication between different agents. Based on the contraction theory, some new conditions for the nonlinear Lurie multi-agent systems reaching bipartite leaderless consensus and bipartite tracking consensus are presented. Compared with the traditional methods, this approach degrades the dimensions of the conditions, eliminates some restrictions of the system matrix, and extends the range of the nonlinear function. Finally, two numerical examples are provided to illustrate the efficiency of our results.

Smart Contract Vulnerability Detection Method Based on Feature Graph and Multiple Attention Mechanisms

The fast-paced development of blockchain technology is evident.Yet,the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain ecosystem.Conventional smart contract vulnerability detection primarily relies on static analysis tools,which are less efficient and accurate.Although deep learning methods have improved detection efficiency,they are unable to fully utilize the static relationships within contracts.Therefore,we have adopted the advantages of the above two methods,combining feature extraction mode of tools with deep learning techniques.Firstly,we have constructed corresponding feature extraction mode for different vulnerabilities,which are used to extract feature graphs from the source code of smart contracts.Then,the node features in feature graphs are fed into a graph convolutional neural network for training,and the edge features are processed using a method that combines attentionmechanismwith gated units.Ultimately,the revised node features and edge features are concatenated through amulti-head attentionmechanism.The result of the splicing is a global representation of the entire feature graph.Our method was tested on three types of data:Timestamp vulnerabilities,reentrancy vulnerabilities,and access control vulnerabilities,where the F1 score of our method reaches 84.63%,92.55%,and 61.36%.The results indicate that our method surpasses most others in detecting smart contract vulnerabilities.

Development and validation of a novel questionnaire regarding vision screening among preschool teachers in Malaysia

AIM:To develop and evaluate the validity and reliability of a knowledge,attitude,and practice questionnaire related to vision screening(KAP-VST)among preschool teachers in Malaysia.METHODS:The questionnaire was developed through a literature review and discussions with experts.Content and face validation were conducted by a panel of experts(n=10)and preschool teachers(n=10),respectively.A pilot study was conducted for construct validation(n=161)and test-retest reliability(n=60)of the newly developed questionnaire.RESULTS:Based on the content and face validation,71 items were generated,and 68 items were selected after exploratory factor analysis.The content validity index for items(I-CVI)score ranged from 0.8-1.0,and the content validity index for scale(S-CVI)/Ave was 0.99.Internal consistency was KR^(2)0=0.93 for knowledge,Cronbach’s alpha=0.758 for attitude,and Cronbach’s alpha=0.856 for practice.CONCLUSION:The KAP-VST is a valid and reliable instrument for assessing knowledge,attitude,and practice in relation to vision screening among preschool teachers in Malaysia.

Preserving Data Secrecy and Integrity for Cloud Storage Using Smart Contracts and Cryptographic Primitives

Cloud computing has emerged as a viable alternative to traditional computing infrastructures,offering various benefits.However,the adoption of cloud storage poses significant risks to data secrecy and integrity.This article presents an effective mechanism to preserve the secrecy and integrity of data stored on the public cloud by leveraging blockchain technology,smart contracts,and cryptographic primitives.The proposed approach utilizes a Solidity-based smart contract as an auditor for maintaining and verifying the integrity of outsourced data.To preserve data secrecy,symmetric encryption systems are employed to encrypt user data before outsourcing it.An extensive performance analysis is conducted to illustrate the efficiency of the proposed mechanism.Additionally,a rigorous assessment is conducted to ensure that the developed smart contract is free from vulnerabilities and to measure its associated running costs.The security analysis of the proposed system confirms that our approach can securely maintain the confidentiality and integrity of cloud storage,even in the presence of malicious entities.The proposed mechanism contributes to enhancing data security in cloud computing environments and can be used as a foundation for developing more secure cloud storage systems.

A Review of Deep Learning-Based Vulnerability Detection Tools for Ethernet Smart Contracts

In recent years,the number of smart contracts deployed on blockchain has exploded.However,the issue of vulnerability has caused incalculable losses.Due to the irreversible and immutability of smart contracts,vulnerability detection has become particularly important.With the popular use of neural network model,there has been a growing utilization of deep learning-based methods and tools for the identification of vulnerabilities within smart contracts.This paper commences by providing a succinct overview of prevalent categories of vulnerabilities found in smart contracts.Subsequently,it categorizes and presents an overview of contemporary deep learning-based tools developed for smart contract detection.These tools are categorized based on their open-source status,the data format and the type of feature extraction they employ.Then we conduct a comprehensive comparative analysis of these tools,selecting representative tools for experimental validation and comparing them with traditional tools in terms of detection coverage and accuracy.Finally,Based on the insights gained from the experimental results and the current state of research in the field of smart contract vulnerability detection tools,we suppose to provide a reference standard for developers of contract vulnerability detection tools.Meanwhile,forward-looking research directions are also proposed for deep learning-based smart contract vulnerability detection.

Prevalence and Factors Associated with Overweight and Obesity among Public Secondary School Teachers in Parakou, Benin, in 2021

Introduction: Overweight and obesity represent a public health problem in Africa due to the epidemiological transition. The objectives of this work were to determine the prevalence of overweight and obesity and to identify associated factors among public secondary school teachers in Parakou, Benin in 2021. Methods: We conducted a descriptive and analytical cross-sectional study. Teachers working in public secondary schools in Parakou during the 2020-2021 academic year, present at work and who gave their written informed consent, were included. A two-stage random sampling was carried out. Data were collected during an individual interview using a questionnaire followed by the measurement of anthropometric parameters and blood pressure. Overweight and obesity were defined by a body mass index ≥ 25 kg/m2. Multivariable logistic regression was performed to identify associated factors. Results: A sample of 325 teachers, including 88.6% of men, was recruited with an average age of 36.2 ± 6.8 years. The prevalence of overweight and obesity was 43.7% (95% CI [38.1%-44.8%]). It increased significantly with income (p Conclusion: The results show a high prevalence of overweight and obesity. Actions are necessary to prevent overweight and obesity among secondary school teachers in Parakou, in Benin.

A Systematic Review and Performance Evaluation of Open-Source Tools for Smart Contract Vulnerability Detection

With the rise of blockchain technology,the security issues of smart contracts have become increasingly critical.Despite the availability of numerous smart contract vulnerability detection tools,many face challenges such as slow updates,usability issues,and limited installation methods.These challenges hinder the adoption and practicality of these tools.This paper examines smart contract vulnerability detection tools from 2016 to 2023,sourced from the Web of Science(WOS)and Google Scholar.By systematically collecting,screening,and synthesizing relevant research,38 open-source tools that provide installation methods were selected for further investigation.From a developer’s perspective,this paper offers a comprehensive survey of these 38 open-source tools,discussing their operating principles,installation methods,environmental dependencies,update frequencies,and installation challenges.Based on this,we propose an Ethereum smart contract vulnerability detection framework.This framework enables developers to easily utilize various detection tools and accurately analyze contract security issues.To validate the framework’s stability,over 1700 h of testing were conducted.Additionally,a comprehensive performance test was performed on the mainstream detection tools integrated within the framework,assessing their hardware requirements and vulnerability detection coverage.Experimental results indicate that the Slither tool demonstrates satisfactory performance in terms of system resource consumption and vulnerability detection coverage.This study represents the first performance evaluation of testing tools in this domain,providing significant reference value.

Mitigating Blackhole and Greyhole Routing Attacks in Vehicular Ad Hoc Networks Using Blockchain Based Smart Contracts

The rapid increase in vehicle traffic volume in modern societies has raised the need to develop innovative solutions to reduce traffic congestion and enhance traffic management efficiency.Revolutionary advanced technology,such as Intelligent Transportation Systems(ITS),enables improved traffic management,helps eliminate congestion,and supports a safer environment.ITS provides real-time information on vehicle traffic and transportation systems that can improve decision-making for road users.However,ITS suffers from routing issues at the network layer when utilising Vehicular Ad Hoc Networks(VANETs).This is because each vehicle plays the role of a router in this network,which leads to a complex vehicle communication network,causing issues such as repeated link breakages between vehicles resulting from the mobility of the network and rapid topological variation.This may lead to loss or delay in packet transmissions;this weakness can be exploited in routing attacks,such as black-hole and gray-hole attacks,that threaten the availability of ITS services.In this paper,a Blockchain-based smart contracts model is proposed to offer convenient and comprehensive security mechanisms,enhancing the trustworthiness between vehicles.Self-Classification Blockchain-Based Contracts(SCBC)and Voting-Classification Blockchain-Based Contracts(VCBC)are utilised in the proposed protocol.The results show that VCBC succeeds in attaining better results in PDR and TP performance even in the presence of Blackhole and Grayhole attacks.

Strategic Contracting for Software Upgrade Outsourcing in Industry 4.0

The advent of Industry 4.0 has compelled businesses to adopt digital approaches that combine software toenhance production efficiency. In this rapidly evolving market, software development is an ongoing process thatmust be tailored to meet the dynamic needs of enterprises. However, internal research and development can beprohibitively expensive, driving many enterprises to outsource software development and upgrades to externalservice providers. This paper presents a software upgrade outsourcing model for enterprises and service providersthat accounts for the impact of market fluctuations on software adaptability. To mitigate the risk of adverseselection due to asymmetric information about the service provider’s cost and asymmetric information aboutthe enterprise’s revenues, we propose pay-per-time and revenue-sharing contracts in two distinct informationasymmetry scenarios. These two contracts specify the time and transfer payments for software upgrades. Througha comparative analysis of the optimal solutions under the two contracts and centralized decision-making withfull-information, we examine the characteristics of the solutions under two information asymmetry scenarios andanalyze the incentive effects of the two contracts on the various stakeholders. Overall, our study offers valuableinsights for firms seeking to optimize their outsourcing strategies and maximize their returns on investment insoftware upgrades.

Revisiting the Electron Radius in Light of Length Contraction

In previous work, the electron radius was identified as the “actual electron radius.” However, this is more accurately described as the electron radius at rest. This study reexamines the electron with an emphasis on the electron radius under motion, incorporating the effects of length contraction. The findings suggest that the radius is subject to Lorentz contraction, which has interesting implications for relativistic effects at the subatomic level.

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.

An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same time.In order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction.Through MIC,a suitable input sequence length is selected for the LSTM model.To investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths.The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://rp5.ru)to improve the model’s expression capability,and the student model learns sequence information from other time series.An attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention mode.Finally,the predicted displacement is obtained through a linear layer.The proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention models.It achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PCCS)of 0.85.Additionally,it exhibits enhanced prediction stability and interpretability,rendering it an indispensable tool for landslide disaster prevention and mitigation.

LDAS&ET-AD:Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation

Adversarial distillation(AD)has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial training.However,fixed sample-agnostic and student-egocentric attack strategies are unsuitable for distillation.Additionally,the reliability of guidance from static teachers diminishes as target models become more robust.This paper proposes an AD method called Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation(LDAS&ET-AD).Firstly,a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored for distillation.A strategy model is introduced to produce attack strategies that enable adversarial examples(AEs)to be created in areas where the target model significantly diverges from the teachers by competing with the target model in minimizing or maximizing the AD loss.Secondly,a teacher evolution strategy is introduced to enhance the reliability and effectiveness of knowledge in improving the generalization performance of the target model.By calculating the experimentally updated target model’s validation performance on both clean samples and AEs,the impact of distillation from each training sample and AE on the target model’s generalization and robustness abilities is assessed to serve as feedback to fine-tune standard and robust teachers accordingly.Experiments evaluate the performance of LDAS&ET-AD against different adversarial attacks on the CIFAR-10 and CIFAR-100 datasets.The experimental results demonstrate that the proposed method achieves a robust precision of 45.39%and 42.63%against AutoAttack(AA)on the CIFAR-10 dataset for ResNet-18 and MobileNet-V2,respectively,marking an improvement of 2.31%and 3.49%over the baseline method.In comparison to state-of-the-art adversarial defense techniques,our method surpasses Introspective Adversarial Distillation,the top-performing method in terms of robustness under AA attack for the CIFAR-10 dataset,with enhancements of 1.40%and 1.43%for ResNet-18 and MobileNet-V2,respectively.These findings demonstrate the effectiveness of our proposed method in enhancing the robustness of deep learning networks(DNNs)against prevalent adversarial attacks when compared to other competing methods.In conclusion,LDAS&ET-AD provides reliable and informative soft labels to one of the most promising defense methods,AT,alleviating the limitations of untrusted teachers and unsuitable AEs in existing AD techniques.We hope this paper promotes the development of DNNs in real-world trust-sensitive fields and helps ensure a more secure and dependable future for artificial intelligence systems.

Design and pricing of maintenance service contract based on Nash non-cooperative game approach

Nowadays manufacturers are facing fierce challenge.Apart from the products,providing customers with multiple maintenance options in the service contract becomes more popular,since it can help to improve customer satisfaction,and ultimately promote sales and maximize profit for the manufacturer.By considering the combinations of corrective maintenance and preventive maintenance,totally three types of maintenance service contracts are designed.Moreover,attractive incentive and penalty mechanisms are adopted in the contracts.On this basis,Nash non-cooperative game is applied to analyze the revenue for both the manufacturer and customers,and so as to optimize the pricing mechanism of maintenance service contract and achieve a win-win situation.Numerical experiments are conducted.The results show that by taking into account the incentive and penalty mechanisms,the revenue can be improved for both the customers and manufacturer.Moreover,with the increase of repair rate and improvement factor in the preventive maintenance,the revenue will increase gradually for both the parties.

A Lonely Journey:An Autoethnography of the Fifteen-Year Identity Construction of a Female ESP Teacher in China

This autoethnographic study explores from a sociocultural perspective the author’s identity construction journey as a female English-for-specific-purposes(ESP)teacher at a university in China for more than 15 years.It delves into the author’s evolving exercise between teacher agency and language policy within both institutional and national contexts.The author aims to voice for marginalized ESP teachers and create a space for them to better negotiate with English-for-general-purposes(EGP)colleagues,academic staff,and policy makers.This study highlights the significance of autoethnography in teacher identity research,particularly for niche ESP teachers who direct novel enterprises through bottom-up implementation.Furthermore,the author emphasizes humanism behind ESP instruction,which significantly expands the view that the ESP features instrumental value.She also advocates for comprehensive support to sustain ESP teaching innovation,ESP teachers’identity construction,and their professional development.These insights have implications for all accountable parties in the context of educational reform to enable constructive negotiation,for ESP teachers to develop their sense of empowerment,and for inclusive education to become more accessible.

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.

Teachers’Perception for Evaluation of the Primary School Students With Final Grade

The most difficult and delicate part of the learning process is assessment.Assessment is difficult because during his/her accomplishment the teacher has to consider not only the acquisition of knowledge from students,but also the overall degree of development of their competencies.It is delicate,because through assessment we can influence the emotional side of students and their“willingness”to learn.Despite these facts,teachers need to evaluate students during the learning process.The purpose of the research is to find out what is the perception of teachers about the level of evaluation of students with final grade.The overall results showed that 89%of teachers agree,9%of them have a neutral attitude,and 2%do not agree that the evaluation of students with a final grade is done taking into account many aspects and using many methods,the overall average of the results,M=4.36.Based on the empirical results,it was found that teachers who have completed assessment training have a more positive approach to student assessment,as the average score is higher than teachers who have not completed assessment training.From the qualitative results it was understood that the Teachers did not encounter any difficulties during the assessment of the students.