The COVID-19 crisis has disrupted when, where, and how employees work. Drawing on a sample of 5452 Finnish employees, this study explores the factors associated with employees' abrupt adjustment to remote work. Specifically, this study examines factors (i.e., work independence and the clarity of job criteria), factors (i.e., interpersonal trust and social isolation), factors of work (i.e., change in work location and perceived disruption), and dynamics (i.e., organizational communication quality and communication technology use (CTU)) as mechanisms underlying adjustment to remote work. The findings demonstrate that structural and contextual factors are important predictors of adjustment and that these relationships are moderated by communication quality and CTU. Contrary to previous research, trust in peers and supervisors does not support adjustment to remote work. We discuss the implications of these findings for practice during and beyond times of crisis.
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http://dx.doi.org/10.3390/ijerph18136966 | DOI Listing |
Sensors (Basel)
January 2025
Department of Environmental Remote Sensing and Geoinformatics, Trier University, Universitätsring 15, 54296 Trier, Germany.
Assessing vines' vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Civil Engineering, Myongji College, Seoul 03656, Republic of Korea.
Conventional approaches for the structural health monitoring of infrastructures often rely on physical sensors or targets attached to structural members, which require considerable preparation, maintenance, and operational effort, including continuous on-site adjustments. This paper presents an image-driven hybrid structural analysis technique that combines digital image processing (DIP) and regression analysis with a continuum point cloud method (CPCM) built on a particle-based strong formulation. Polynomial regressions capture the boundary shape change due to the structural loading and precisely identify the edge and corner coordinates of the deformed structure.
View Article and Find Full Text PDFInt J Environ Res Public Health
January 2025
Faculty of Health Sciences, Durban University of Technology, Durban 4001, South Africa.
The COVID-19 pandemic led to a rapid shift to remote working, which affected ergonomic conditions and increased the risk of upper body musculoskeletal pain (MSP). This study assessed the prevalence and impact of upper body MSP (affecting the head, neck, shoulders, and back) among academic staff at a University of Technology during the pandemic. Data were collected from 110 participants through an online, descriptive, cross-sectional survey adapted from the Dutch Musculoskeletal Questionnaire, the Standardized Nordic Questionnaire, and the McCaffrey Initial Pain Assessment Tool.
View Article and Find Full Text PDFHealthcare (Basel)
January 2025
Department of Orthopedic Surgery, Rush University Medical Center, 1611 W Harrison Street, Suite 201, Chicago, IL 60612, USA.
Background/objectives: Gait retraining is widely used in orthopedic rehabilitation to address abnormal movement patterns. However, retaining walking modifications can be challenging without guidance from physical therapists. Real-time auditory biofeedback can help patients learn and maintain gait alterations.
View Article and Find Full Text PDFSci Rep
January 2025
Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument, Beijing Information Science and Technology University, Beijing, 100192, China.
Object detection is crucial for remote sensing image processing, yet the detection of small objects remains highly challenging due to factors such as image noise and cluttered backgrounds. In response to this challenge, this paper proposes an improved network, named SED-YOLO, based on YOLOv5s. Firstly, we leverage Switchable Atrous Convolution (SAC) to replace the standard convolutions in the original C3 modules of the backbone network, thereby enhancing feature extraction capabilities and adaptability.
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