Sharif Digital Repository

This study aimed to identify the influence of workplace cyber ostracism on employee online work engagement and employee mental well-being with the mediating roles of remote work challenges such as loneliness, procrastination, work-home interference, and ineffective communication. As amidst lockdown due to COVID-19, the data was collected from 303 respondents through an online questionnaire that was distributed in virtual groups among friends, relatives, and other fellows who were working in the private sector organizations of Pakistan. The hypotheses were tested using the partial least square structural equation modeling PLS-SEM technique. The findings of this study showed that workplace... 

Iran is the fourth refugee-hosting country and Afghanistan is the second refugee-source country in the world. This research is qualitative and conducted with content analysis method. Twenty-seven individuals participated in the study. They were recruited from two midterm residential drug treatment centers in Tehran. Afghan refugees who use drugs felt and experienced stigma in five areas: family, friends, workplace, neighborhood, and treatment center. The consequences include frequent treatment failure, family relationship disruption, superficial conformity with the host society, and self-stigma. It is necessary to design interventions to reduce stigma and discrimination attached to refugees.... 

Evaluation of workers’ body posture in workstations is a prerequisite to estimate spinal loads and assess risk of injury for the subsequent design of preventive interventions. The Microsoft Kinect™ sensor is, in this regard, advantageous over the traditional skin-marker-based optical motion capture systems for being marker-less, portable, cost-effective, and easy-to-use in real workplaces. While several studies have demonstrated the validity/reliability of the Kinect for posture measurements especially during gait trials, its capability to adequately drive a detailed spine musculoskeletal model for injury risk assessments remains to be investigated. Lumbosacral (L5-S1) load predictions of a... 

Body posture measurement approaches, required in biomechanical models to assess risk of musculoskeletal injuries, are usually costly and/or impractical for use in real workplaces. Therefore, we recently developed three artificial neural networks (ANNs), based on measured posture data on several individuals, to predict whole body 3D posture (coordinates of 15 markers located on body's main joints), segmental orientations (Euler angles of 14 body segments), and lumbosacral (L5-S1) moments during static manual material handling (MMH) activities (ANNPosture, ANNAngle, and ANNMoment, respectively). These ANNs require worker's body height, body weight (only for ANNMoment), hand-load 3D position,...