PHYSICAL ACTIVITY STATUS AND ITS ASSOCIATION WITH NONCOMMUNICABLE DISEASES AMONG ADULT POPULATION OF SOUTH PUNJAB

Despite well-appreciated benefits of physical activity (PA), a huge number ofpeople do not indulge in sufficient PA, which is a well-known risk factor of theleading non-communicable diseases (NCDs) such as hypertension, diabetes,hypertension, cardiovascular disease and cancer. The study aimed to assessPA status and its association with NCDs among adult population of southPunjab. The cross-sectional analytical study was conducted. A total of 385adults of both genders, and residents of South Punjab were enrolled by clusterrandom sampling. The total of 385 participants, majority of the cases werevery young (18 – 25) years and the median age was 24.0 years. The frequencyof males was three times higher than females (77.1 % vs. 22.9 %). HTN wasfound in 4.9 % participants, DM in 7.5 %, hypercholesterolemia in 6.0 %, andCVD in 3.1 %. The frequency of individuals reporting work related PA ofvigorous intensity was 22.9 %, work related PA of moderate intensity was 51.9%, leisure time PA of vigorous-intensity was 21.6 %, leisure time PA ofmoderate intensity was 46.5 %, and travel related PA was 70.9 %. Gendermale, urban residence, being married, no formal education, and beingemployed were significantly related with HTN. Similarly, being married,occupation homemaker, and travelling through personal car were significantlyrelated with DM. Differently gender male, rural residence, being married,higher education, occupation homemaker, and smoking were significantlyrelated with hypercholesterolemia. Furthermore, being married, occupationhomemaker, and traveling through personal bike were significantly relatedwith CVD. It was concluded that those having any kind of PA at work orduring sports or even using bicycle or walk as activity had minimum chancesof any NCDs like HTN, DM, CVD or hypercholesterolemia and beingphysically active also causes to avoid obesity, which is base for many NCDs

The Research on Organizational Justice in Scopus Indexed Journals: A Bibliometric Analysis of Seven Decades

The organizational justice terminology has had a long journey to become one of the significant contributors to organizational success. Recently, an intense global upsurge in the use of organizational justice terms in publications has forced us for this bibliometric analysis in order to look at the overall publications on organizational justice. The objective of the current research is to advance knowledge about organizational justice research trends using Scopus database and bibliometric analysis research. The analysis was performed to see the publication trends between the years 1941 and 2018; it used authors, journals, countries, academic discipline, research institutes/universities, and various keywords related to organizational justice as search words. After careful consideration and using multiple checkpoints for eliminating irrelevant studies, 5,650 research articles were analyzed. In the realm of organizational justice, procedural justice was the most frequently occurred among other dimensions. Moreover, variables such as organizational trust, job satisfaction, organizational commitment, citizenship behavior, ethics, and turnover are major concepts that occurred within organizational justice research. Some variables with infrequent occurrences, along with future recommendations and study limitations, are also discussed

A bibliometric analysis of psychological contract literature: current status, advancements and future research trends

In the current competitive and dynamic environment, employees' psychological contract is an important psychological factor through which organizations can achieve competitive advantage. Numerous studies have shown that psychological contract is a significant predictor of various organizational outcomes. Despite the growing interest in exploring the psychological contract aspects among scholars, review articles and thorough analysis have been limited. The present study fills in this gap by identifying and evaluating the research development of psychological contract topic. Using 1333 journal articles from the Elsevier Scopus database published between 1973-2019, a bibliometric analysis was employed to assess the patterns of global psychological contract research based on the number of publications, co-authorship analysis amongst affiliated countries and authors, and co-occurrence of author keywords. The results revealed that the number of publications increased every year; subsequently, cumulative total publications also steadily increases till the present time. The current research findings show that researchers from the United States, United Kingdom, Australia, and China contributed about 61% of worldwide publications, leading the other 64 countries/territories. Moreover, four of the world's top 100 universities were among the most productive universities in each of the ten leading countries selected for the analysis. By providing a detailed overview of the research studies in the area of psychological contract, this study highlights the insights and directions for the future researchers, practitioners, academicians, and scholars in related business, management, social sciences, and psychology fields

What makes articles cited highly? An analysis of top 100 highly cited articles on organizational citizenship behavior

Organizational citizenship behavior (OCB) is amongst the main contributors to organizational performance and a significant outcome of various work-related behaviors. This study aims to evaluate the top 100 highly cited articles published on OCB in the Scopus database to assess the reasons why these articles are highly cited. A total of 3,096 articles on OCB, published from 1983-2018, were retrieved from the database, in which 100 highly cited articles were selected for further analysis. The findings revealed that a 40% contribution in the field of OCB research is due to these articles, and this contribution is expected to increase rapidly. Additionally, meta-analytical articles are frequently cited, followed by the review articles and then empirical research articles. Among various reasons, the highly cited articles are either pioneering studies in the field, proposing a new concept, or scale development studies. This study proposes important implications for practitioners and researchers

Impact of servant leadership on employee work engagement: mediating role of psychological climate

This study examined the influence of servant leadership (SL) on the faculty’s work engagement. It also examined psychological climate (PC) as mediating variable in this relationship. Moreover, this study has considered social exchange theory (SET) as a base theory to explain the relationship between SL, PC, and work engagement. A quantitative research design was applied and data was collected using questionnaires from faculty members of Pakistani universities. A total of 276 datasets were analyzed through Structural Equation Modeling using SPSS version 27.0 and Smart-PLS 3. The findings of this study provide evidence that SL is the key leadership style for Pakistani universities. In addition, the PC significantly mediates the relationship between SL and faculty members’ work engagement. In conclusion, the current study has extended the SET by incorporating PC as a mediator between the relationship of SL and the work engagement of faculty members in Pakistani universities. The findings are useful to the universities of Pakistan to consider the SL practices that are important in shaping the powerful motivational process of PC to enhance the level of work engagement among faculty members

A comprehensive bibliometric survey of micro-expression recognition system based on deep learning

Micro-expressions (ME) are rapidly occurring expressions that reveal the true emotions that a human being is trying to hide, cover, or suppress. These expressions, which reveal a person's actual feelings, have a broad spectrum of applications in public safety and clinical diagnosis. This study provides a comprehensive review of the area of ME recognition. A bibliometric and network analysis techniques is used to compile all the available literature related to ME recognition. A total of 735 publications from the Web of Science (WOS) and Scopus databases were evaluated from December 2012 to December 2022 using all relevant keywords. The first round of data screening produced some basic information, which was further extracted for citation, coupling, co-authorship, co-occurrence, bibliographic, and co-citation analysis. Additionally, a thematic and descriptive analysis was executed to investigate the content of prior research findings, and research techniques used in the literature. The year wise publications indicated that the published literature between 2012 and 2017 was relatively low but however by 2021, a nearly 24-fold increment made it to 154 publications. The three topmost productive journals and conferences included IEEE Transactions on Affective Computing (n = 20 publications) followed by Neurocomputing (n = 17) and Multimedia tools and applications (n = 15). Zhao G was the most proficient author with 48 publications and the top influential country was China (620 publications). Publications by citations showed that each of the authors acquired citations ranging from 100 to 1225. While publications by organizations indicated that the University of Oulu had the most published papers (n = 51). Deep learning, facial expression recognition, and emotion recognition were among the most frequently used terms. It has been discovered that ME research was primarily classified in the discipline of engineering, with more contribution from China and Malaysia comparatively

Predictive evaluation of solar energy variables for a large-scale solar power plant based on triple deep learning forecast models

The advanced development of large-scale solar power plants (LSSPs) has made it necessary to improve accurate forecasting models for the output of solar energy. Solar energy is still hampered by the lack of predictability in its output, which remains a major hurdle in the solar industry. This paper focuses on triple deep learning (DL) techniques such as Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Convolutional Neural Network- Long-Short Term Memory CNN-LSTM to address this problem. These techniques are utilized in solar energy variables (SEVs) such as power generation (MWh), soiling loss (%), and performance ratio (PR %) to determine the optimal forecast model. The novelty of this research is that it is the first time that important solar system parameters such as PR and soiling loss have been studied to predict a feasible forecast model using a different DL scheme. The SEVs real-time dataset is procured from the largest solar plant in Pakistan, titled “Quaid-e-Azam Solar Park” (QASP). The main significance of the study is that ANN, RNN, and CNN-LSTM-based models were developed in the DL process through feature generation, data scaling, training, and testing steps to predict the optimal model. The prediction values were compared with the solar plant's actual values over the last 7 years, and then a comparison was made to predict the future forecast trend over the next 20 years. The aim and goal is to develop three models to investigate the accurate results of time-series forecasting on the SEVs dataset, as well as to evaluate the performance measure errors to determine the appropriate model. Based on the forecasting/prediction graphic and error results, it was demonstrated that the CNN-LSTM hybrid model is a more capable forecasting model for the output of power generation and PR values and a more accurate predictor of the future trend over the ANN and RNN models. However, the ANN model is slightly better performed in predicting of soling loss value than CNN-SLTM and RNN. Thus, the CNN-LSTM hybrid model is an optimal model for SEVs, which can guarantee a variety of LSSPs of similar nature in the following time-series forecasting investigations which shows key findings and its importance for the solar industrial forecast issue