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Background: COVID-19 pandemic resulted in lockdown affecting all businesses globally. Everyone was forced to work from home (WFH) leading to challenges in productivity and motivation.
Methodology: One thousand working professionals who worked from home participated in the online survey with semi-structured questionnaire using nonprobability Snowball sampling technique. Descriptive statistics was used to analyze the findings and to collect data method.
Results: Participants were asked about their biggest worries during lockdown COVID-19 situation and their biggest worry was infection to COVID-19/death. Professionals were asked whether they were affected or not affected due to "WFH" in COVID situation. The questionnaire items were clubbed into six major categories of job role overload, lifestyle choices, family distraction, occupational discomfort, job performance, and distress, and majority categories were affected.
Conclusion: Thus, it is observed that the increase in work commitments leads to distress among employees while distractions from family members disrupt the quality of work. While good job performance contributes to life satisfaction, distress significantly diminished it. This paves the way for more studies to be done on work-life balance under WFH arrangements for as long as the pandemic of COVID-19 is prevalent.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8686950 | PMC |
http://dx.doi.org/10.4103/jpbs.jpbs_247_21 | DOI Listing |
Mult Scler Relat Disord
September 2025
Department of Psychology, Wayne State University, Detroit, MI, 48202, USA; Institute of Gerontology, Wayne State University, Detroit, MI, 48202, USA; Translational Neuroscience Program, Wayne State University, Detroit, MI, 48201, USA. Electronic address:
The ability to navigate through one's environment is crucial for maintaining independence in daily life and depends on complex cognitive and motor functions that are vulnerable to decline in persons with Multiple Sclerosis (MS). While previous research suggests a role for mobility in the physical act of navigation, it remains unclear to what extent mobility impairment and perceptions of mobility constraints may modify wayfinding and the recall of environment details in support of successful navigation. Therefore, this study examined the relations among clinical mobility function, concern about falling, and recall of environment details in a clinical sample of MS.
View Article and Find Full Text PDFNeural Netw
September 2025
School of Electronic Science and Engineering, Nanjing University, China. Electronic address:
The Segment Anything Model (SAM) is a cornerstone of image segmentation, demonstrating exceptional performance across various applications, particularly in autonomous driving and medical imaging, where precise segmentation is crucial. However, SAM is vulnerable to adversarial attacks that can significantly impair its functionality through minor input perturbations. Traditional techniques, such as FGSM and PGD, are often ineffective in segmentation tasks due to their reliance on global perturbations that overlook spatial nuances.
View Article and Find Full Text PDFNeural Netw
September 2025
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China.
3D shape defect detection plays an important role in autonomous industrial inspection. However, accurate detection of anomalies remains challenging due to the complexity of multimodal sensor data, especially when both color and structural information are required. In this work, we propose a lightweight inter-modality feature prediction framework that effectively utilizes multimodal fused features from the inputs of RGB, depth and point clouds for efficient 3D shape defect detection.
View Article and Find Full Text PDFNeural Netw
September 2025
Shanghai Maritime University, Shanghai, 201306, China. Electronic address:
Cross-modal hashing aims to leverage hashing functions to map multimodal data into a unified low-dimensional space, realizing efficient cross-modal retrieval. In particular, unsupervised cross-modal hashing methods attract significant attention for not needing external label information. However, in the field of unsupervised cross-modal hashing, there are several pressing issues to address: (1) how to facilitate semantic alignment between modalities, and (2) how to effectively capture the intrinsic relationships between data, thereby constructing a more reliable affinity matrix to assist in the learning of hash codes.
View Article and Find Full Text PDFComput Biol Med
September 2025
Department of Electrical and Computer Engineering and the Institute of Biomedical Engineering, University of New Brunswick, Fredericton, E3B 5A3, NB, Canada.
Pattern recognition-based myoelectric control is traditionally trained with static or ramp contractions, but this fails to capture the dynamic nature of real-world movements. This study investigated the benefits of training classifiers with continuous dynamic data, encompassing transitions between various movement classes. We employed both conventional (LDA) and deep learning (LSTM) classifiers, comparing their performance when trained with ramp data, continuous dynamic data, and an LSTM pre-trained with a self-supervised learning technique (VICReg).
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