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BACKGROUND: The macro-level corporate social responsibility (CSR) has been researched widely in recent years, however, micro-level CSR has recently gained attention. Employees are more influenced by employee related internal CSR initiatives than the external CSR activities. Internal CSR ensures favorable behavior of employees towards their employer which leads towards better organizational performance. OBJECTIVE: This study investigates the impact of internal corporate social responsibility (CSR) on public sector employees' engagement through Organizational Pride (OP). Further, the study considers moderating role of empathy between internal CSR and employee engagement (EE) METHODS: Quantitative research design was developed and theoretical framework was investigated through a survey of 430 employees of public sector in Pakistan. SmartPLS was used and partial least square structural equation modeling (PLS-SEM) was used for hypothesis testing RESULTS: The results showed that the internal CSR directly influences EE. Secondly, the mediation of OP between internal CSR and EE is also supported. However, the results do not significantly support moderation of empathy between internal CSR and EE. CONCLUSIONS: Public sector organizations should actively contribute in employees well- being through internal CSR activities especially on skills development, work-life balance and employment stability. Public sector organizations lack in emotional and cognitive attachment with employees, and should pay attention to pride over organization through employee focused CSR activities [ABSTRACT FROM AUTHOR]

Global environmental concerns have prompted organizations to adopt green human resource management practices that will eventually lead to green innovations. Given that understanding, this study aims to converge multidimensional research published on green human resource management (GHRM) and green innovation. In addition, it seeks to discuss the topic's research trends in the context of what, how, when, where, and by whom. The data were retrieved from the Scopus database (until March 2023), and VOSviewer was used to investigate the research trends. A total of 312 documents were extracted from the Scopus database, and 46 published articles were finalized using the PRISMA flow approach. The results indicate that there has been an upward trend in terms of published articles on the topic, and the highest number of articles (15) were published in the current year, 2023. The co-occurrence results indicate that GHRM is strongly associated with environmental performance and green innovation. The journals Environmental Science and Pollution Research and Sustainability (Switzerland) stand out in this domain. The Abu Dhabi University of United Arab Emirates is the most cited institution, whereas Saudi Arabia has the highest co-authorship at the country level. Furthermore, this study summarizes the major developments in this research domain and suggests research gaps and possible future directions. [ABSTRACT FROM AUTHOR]

An efficient full-text search is achieved by indexing the raw data with an additional 20 to 30 percent storage cost. In the context of Big Data, this additional storage space is huge and introduces challenges to entertain full-text search queries with good performance. It also incurs overhead to store, manage, and update the large size index. In this paper, we propose and evaluate a method to minimize the index size to offer full-text search over Big Data using an automatic extractive-based text summarization method. To evaluate the effectiveness of the proposed approach, we used two real-world datasets. We indexed actual and summarized datasets using Apache Lucene and studied average simple overlapping, Spearman's rho correlation, and average ranking score measures of search results obtained using different search queries. Our experimental evaluation shows that automatic text summarization is an effective method to reduce the index size significantly. We obtained a maximum of 82% reduction in index size with 42% higher relevance of the search results using the proposed solution to minimize the full-text index size. [ABSTRACT FROM AUTHOR]

Laparoscopic surgery is a relatively new field in developing countries. There is a scarcity of laparoscopically trained doctors due to a lack of training and resources available in hospitals. Training and evaluation of medical professionals to develop laparoscopic surgical skills are important and essential as it improves the success rate and reduces the risk during real surgery. The purpose of this research is to develop a series of training exercises based on virtual reality using HTC Vive headset to emulate real-world training of doctors. This virtual training not only gives the trainee doctors mastery in their profession but also decreases the chances of complications during laparoscopic surgery. After practicing on our simulator for three trials, doctors report enhancement in the following skills: time to complete a task, precision, efficiency of a task, and the recorded movements of surgical instruments using both hands. Our simulator is comparatively low cost as compared to the available simulator in the local market [ABSTRACT FROM AUTHOR]

The accurate calibration of powder diffraction data acquired from area detectors using calibration standards is a crucial step in the data reduction process to attain high‐quality one‐dimensional patterns. A novel algorithm has been developed for extracting Debye–Scherrer rings automatically using an approach based on computer vision and pattern recognition techniques. The presented technique requires no human intervention and, unlike previous approaches, makes no restrictive assumptions on the diffraction setup and/or rings. It can detect complete rings as well as portions of them, and works on several types of diffraction images with various degrees of ring graininess, textured diffraction patterns and detector tilt with respect to the incoming beam. [ABSTRACT FROM AUTHOR]