Use of cognitive-behavioral career coaching to reduce work anxiety and depression in public employees

BACKGROUND Public employees worldwide are increasingly concerned about work anxiety and depression.Cognitive-behavioral career coaching has emerged as a promising strategy for addressing these mental health disorders,which can negatively impact on a person's overall well-being and performance.AIM To examine whether cognitive-behavioral career coaching reduces work anxiety and depression among Nigerian public employees.METHODS A total of 120 public employees(n=60)suffering from severe anxiety and depression were randomly assigned to the treatment or control groups in this study.Cognitive behavioral coaching was provided twice a week to those in the treatment group,whereas no treatment was given to those in the control group.As part of the study,the Hamilton Anxiety Rating Scales and Beck Depression Inventory were used to collect data.RESULTS Analysis of covariance of the data from participants indicates a significant effect of cognitive-behavioral career coaching on work anxiety and depression.CONCLUSION Insights into the underlying mechanisms by which cognitive behavior career coaching exerts its effects have been gained from this study.Also,the study has gathered valuable data that can inform future practice and guide the development of strategies for supporting mental health at work.

Research on Work-Life Balance of Female Employees

Nowadays,the pace of work and life is accelerating rapidly,accompanied by significant changes in work content and family structure.Enterprises and employees are encountering various pressures and conflicts.Female employees,in particular,are experiencing increasing pressure in both social labor and family affairs,resulting in ongoing conflicts between work and life.Achieving a balance between these two domains is crucial for female employees.This article aims to analyze the influencing factors of work-life balance for female employees and to propose useful strategies to help them improve it.

Knowledge-Based Efficient N-1 Analysis Calculation Method for Urban Distribution Networks with CIM File Data

The N-1 criterion is a critical factor for ensuring the reliable and resilient operation of electric power distribution networks.However,the increasing complexity of distribution networks and the associated growth in data size have created a significant challenge for distribution network planners.To address this issue,we propose a fast N-1 verification procedure for urban distribution networks that combines CIM file data analysis with MILP-based mathematical modeling.Our proposed method leverages the principles of CIM file analysis for distribution network N-1 analysis.We develop a mathematical model of distribution networks based on CIM data and transfer it into MILP.We also take into account the characteristics of medium voltage distribution networks after a line failure and select the feeder section at the exit of each substation with a high load rate to improve the efficiency of N-1 analysis.We validate our approach through a series of case studies and demonstrate its scalability and superiority over traditional N-1 analysis and heuristic optimization algorithms.By enabling online N-1 analysis,our approach significantly improves the work efficiency of distribution network planners.In summary,our proposed method provides a valuable tool for distribution network planners to enhance the accuracy and efficiency of their N-1 analyses.By leveraging the advantages of CIM file data analysis and MILP-based mathematical modeling,our approach contributes to the development of more resilient and reliable electric power distribution networks.

The Effects of Job Insecurity, Emotional Exhaustion, and Met Expectations on Hotel Employees’ Pro-Environmental Behaviors: Test of a Serial Mediation Model

There are a plethora of empirical pieces about employees’pro-environmental behaviors.However,the extant literature has either ignored or not fully examined various factors(e.g.,negative or positive non-green workplace factors)that might affect employees’pro-environmental behaviors.Realizing these voids,the present paper proposes and tests a serial mediation model that examines the interrelationships of job insecurity,emotional exhaustion,met expectations,and proactive pro-environmental behavior.We used data gathered from hotel customer-contact employees with a time lag of one week and their direct supervisors in China.After presenting support for the psychometric properties of the measures via confirmatory analysis in LISREL 8.30,the abovementioned linkages were gauged using the PROCESS plug-in for statistical package for social sciences.The findings delineated support for the hypothesized associations.Specifically,emotional exhaustion and met expectations partly mediated the effect of job insecurity on proactive pro-environmental behavior.More importantly,emotional exhaustion and met expectations serially mediated the influence of job insecurity on proactive pro-environmental behavior.These findings have important theoretical implications as well as significant implications for diminishing job insecurity,managing emotional exhaustion,increasing met expectations,and enhancing ecofriendly behaviors.

Making Sense of Anything thru Analytics: Employees Provident Fund (EPF)

Employees Provident Fund (EPF) has always been a hot topic, from the previous Government permitted “withdrawals” during the pandemic to the present Government disallowing further withdrawals (afraid it would lead to a financial crisis) and allowing emergency loans through an EPF collateral agreement. The latter used median studies to “contribute” MYR 500 to EPF members with less than RM 10,000 in their accounts. Baffled/bewildered by the current Government’s generosity and highlighted in this paper, I used Monte Carlo simulation, a method used by analysts when determining the size of a client’s portfolio to support their desired retirement lifestyle, to establish a reference-type table displaying ideal savings when reaching 50. The Government could use the table mentioned above to give more meaningful contributions to EPF members, apart from EPF active and inactive members, knowing where they stand concerning their current EPF savings.

Research on Accounting Issues of the Pension Insurance Fund for Urban Employees

Pension insurance is a vital component of China's social security system,ensuring the welfare of the general publie.This study centers on the revenue and expenses of City S's urban employee basic pension insurance fund.It introduces and analyzes the fund's accounting status from four key perspectives:fund raising.expenditure,financial statement system,and accounting information disclosure.The research identifes existing problems in City S's accounting system and conducts in-depth research on these issues.Furthermore,an empirical analysis is conducted on the management of the pension insurance fund.Through this empirical study,the paper aims to offer insights and recommendations for promoting stable and healthy development measures for City S'8 urban employee basic pension insurance fund.

Knowledge-based bridge detection from SAR images

Automatic bridge detection is an important application of SAR images. Differed from the classical CFAR method, a new knowledge-based bridge detection approach is proposed. The method not only uses the backscattering intensity difference between targets and background but also applies the contextual information and spatial relationship between objects. According to bridges＇ special characteristics and scattering properties in SAR images, the new knowledge-based method includes three processes： river segmentation, potential bridge areas detection and bridge discrimination. The application to AIRSAR data shows that the new method is not sensitive to rivers＇ shape. Moreover, this method can detect bridges successfully when river segmentation is not very exact and is more robust than the radius projection method.

Structural Topology Design of Container Ship Based on Knowledge-Based Engineering and Level Set Method

Knowledge-Based Engineering （KBE） is introduced into the ship structural design in this paper. From the implementation of KBE, the design solutions for both Rules Design Method （RDM） and Interpolation Design Method （IDM） are generated. The corresponding Finite Element （FE） models are generated. Topological design of the longitudinal structures is studied where the Gaussian Process （GP） is employed to build the surrogate model for FE analysis. Multi-objective optimization methods inspired by Pareto Front are used to reduce the design tank weight and outer surface area simultaneously. Additionally, an enhanced Level Set Method （LSM） which employs implicit algorithm is applied to the topological design of typical bracket plate which is used extensively in ship structures. Two different sets of boundary conditions are considered. The proposed methods show satisfactory efficiency and accuracy.

New Knowledge-based Genetic Algorithm for Excavator Boom Structural Optimization

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the conflgurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, arc taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Coping Strategies Adopted by Construction Employees to Deal with the Causes and Effects of Occupational Psychological Disorders: A Study in Ghana

Coping strategies adopted to deal with psychological health issues could have influences on the general health, productivity and task performance of the employee. This study sought to investigate the coping strategies adopted by construction employees to deal with the causes and effects of occupational psychological disorders such as burnout and workaholism. To achieve this aim, the methods of focus group discussions were first employed. A total of 16 semi-structured focus group discussions were held in Ghana, with 90 construction employees. The focus group study revealed 25 coping strategies adopted as efforts to mitigate the causes and 22 coping strategies adopted as responses to moderate the effects of occupational psychological disorders. A quantitative study involving 150 construction professionals and 150 construction trade workers were also conducted in Ghana to investigate the coping strategies that were highly adopted by the two construction working groups. The findings from the study revealed that the construction professionals adopted delegating complicated tasks and seeking medical attention as the most common coping strategies to manage the causes and effects respectively. The construction trade workers were also revealed to adopt withdrawing from work duties or changing jobs and taking in more caffeinated drinks as the most common coping strategies to manage the causes and effects respectively. Exploratory factor analysis was employed, and the findings were put into the main constructs. The causes focused coping strategies were categorized under avoidance, alteration, adaptation, and acceptance. The effects focused coping strategies were also categorized under healthy and unhealthy coping strategies. This study recommends appropriate coping strategies and interventions in the construction industry such as education of construction personnel on the consequences of various coping strategies.

Exploring Personal Factors That Might Influence the Vulnerability of Construction Employees to Occupational Psychological Disorders

Personal factors could act as intervening variables between the causes and effects of psychological health conditions of employees. This study focused on six personal intervening factors that were associated with the risks of occupational psychological disorders namely: low self-esteem, negative personality trait, unproductive core beliefs, poor self-concept evaluation on performance, poor relationship with others and poor time management skills. An investigation on the level of severity of these personal intervening factors was conducted involving 150 construction professionals and 150 construction trade workers, who were purposively selected in Ghana. The personal factors that were found to be prevalent among the construction employees were low self-esteem and poor time management skills. Correlation analysis and regression analysis were used to determine the relationships between the personal intervening factors and demographic factors such as the age, educational levels and marital status of the research participants. The results of the study indicated that the age and marital status of the respondents had no significant relationship with any of the personal intervening factors. Personal factors such as personality trait and relationship with others also had no relation with any of the demographic factors analyzed. The educational level of the respondents, however, had a significant relationship with the factors of self-esteem, productive core beliefs, self-evaluation on performance and time management skills. An independent two-sample T-test was used to compare the means of the factors with significant relations. This study revealed that construction employees who were highly educated had better self-esteem and time management skills than those who were less educated. The findings from this study broaden the view of moderators on influential sources of psychological health conditions of employees.

FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.

A Knowledge-based System for the Analysis of the Ability of Paying back Loans

This paper describes the development of a knowledgebased system (KBS) for determining whether or not, and under what conditions, a bank Ioan officer should grant a business loan to a company. The prototype system developed focuses on what is bank loans risks management, how to prevent risk by the analysis of the ability of paying back loans. The paper makes the structural analysis involved in the system's decision situation, the structured situation diagram or model, dependency diagram and the document needed by the KBS prototype system thus are developed. Through testing the samples from loan business, the quality for the analysis of the ability of paying back loans can be effectively evaluated by the KBS prototype system.

Effects of Job Burnout on Employees Satisfaction in Selected Health Service Sector in Southwestern Nigeria

This paper examined the effects of job burnout on job satisfaction among selected health service employees in Southwestern Nigeria. The issue of job burnout has been researched, with limited attention to the health service employees in Southwestern Nigeria. The state of health of the people is vital to the survival of any economy as well as the development of any Nation. Data were obtained from the administration of questionnaires to 400 employees who were Doctors, Nurses, Pharmacists & Administrators with permanent employees’ status. Multiple regression was used to analyze the data. Major causes of burnout discovered include emotional exhaustion, depersonalisation while personal achievement increases employee job satisfaction. Results of the study showed that insufficient motivation, low organisational support and high job demand could lead to job burnout. The study revealed an inverse relationship between job burnout and employee satisfaction which makes them to perform below expectations. The study concluded that since job burnout inhibits performance and manifests significantly in their service delivery, employers should put policies in place to mitigate it.

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.

The Investigation of Jordanian Education Ministry Employees’ Attitude toward the Using of Cloud ERP

ERP systems have become an optimal solution for companies to perform their works with maximum advantages of Enterprise Resources Planning (ERP). Moreover, even though the cloud computing has many obstacles which need to be solved, but the enterprises always embrace the cloud. Many enterprises tend to adopt the cloud computing paradigm in order to get its leverage in successful and benefits. With the development of cloud computing technology, there is a growing orientation to move the ERP from inside boundaries of organizations into the cloud computing technologies. In addition, there were many studies that covered the cloud ERP without concentrating on acceptance the end user for this technology. Thus, this study comes to increase the understanding the attitude of the end user for cloud ERP technology. The main aim of this study is to investigate the employees’ attitude towards use of cloud ERP. The conceptual research framework of cloud ERP is prepared based on the technology acceptance model (TAM). The employees of Queen Rania Center (QRC) in Jordanian Education Ministry used the cloud ERP system which implemented according to this study. The questionnaires are distributed online to the sample. The results indicate that the QRC employees enjoyed using the cloud ERP system.

Knowledge-based potentials in bioinformatics: From a physicist's viewpoint

Biological raw data are growing exponentially, providing a large amount of information on what life is. It is believed that potential functions and the rules governing protein behaviors can be revealed from analysis on known native structures of proteins. Many knowledge-based potentials for proteins have been proposed. Contrary to most existing review articles which mainly describe technical details and applications of various potential models, the main foci for the discussion here are ideas and concepts involving the construction of potentials, including the relation between free energy and energy, the additivity of potentials of mean force and some key issues in potential construction. Sequence analysis is briefly viewed from an energetic viewpoint.

Predictors of Metabolic Syndrome among Employees: A Study from Jordan

Background: Metabolic syndrome (MetS) is a major health problem around the world including Jordan and it has received notable consideration from epidemiologists in the latest years. The objective of this study was to estimate MetS prevalence among employees and its associated factors including socio-demographic, lifestyle, and dietary. Methods: A total of 491 employees (344 men and 147 women aged 20 - 65 years) participated in this study. Data concerning fasting plasma glucose level, lipid profile, blood pressure, anthropometric measurements and 24-dietary recalls were obtained. Results: MetS prevalence was 36.3% (38.7% among men and 30.6% among women). Multiple logistic regression analysis results showed that the odds ratio of MetS was increased in older ages (OR: 3.0;95% CI: 1.43 - 6.47;p < 0.01) and smokers (OR: 2.6;95% CI: 2.58 - 4.22;p < 0.01). With elevation 1 unit in the body mass index (BMI), risk of MetS increased by 30% (95% CI: 22.9% - 38.4%;p < 0.01) and an increase1 mg/day in dietary iron intake, the risk of MetS increased 2.4% (95% CI: 0.07% - 4.3%;p < 0.01). Conclusions: MetS prevalence is considered to be high among Jordanian employees which highlights the urgent need to develop comprehensive national strategies for prevention and treatment of MetS.

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.

MY PERSONAL STUDY ON KNOWLEDGE-BASED ECONOMY

The Financial Crisis in Asia is having a negative impacion the economic development of China, but it also enlightens us. It makes us consider and take measures to avoid such a crisis. I have put forward six measures, one of which is to promote the transformation of S&T knowledge into productive forces.