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  1. Job stressors and turnover intention of IT executives in Malaysia: The mediating role of employee well-being
    Langove N, Javaid MU, Ayyasamy RK, Ibikunle AK, Sabir AA
    Work, 2024;77(1):295-305.
    PMID: 37483056 DOI: 10.3233/WOR-230103
    BACKGROUND: Fear of losing psychological resources can lead to stress, impacting psychological health and behavioral outcomes like burnout, absenteeism, service sabotage, and turnover.

    OBJECTIVE: The study examined the impact of job stressors (time pressure, role ambiguity, role conflict) on employee well-being and turnover intentions. The study also investigated the mediating role of employee well-being between job stressors and turnover intention based on the conservation of resources (COR) theory.

    METHODS: Data from 396 IT executives in Malaysian IT firms were analyzed using the Partial Least Squares - Structural Equation Modeling (PLS-SEM) technique.

    RESULTS: Results confirmed a significant negative correlation between time pressure (-0.296), role ambiguity (-0.423), role conflict (-0.104), and employee well-being. Similarly, employee well-being showed a significant negative relationship with turnover intentions (-0.410). The mediation analysis revealed that employee well-being mediates the relationship between time pressure (0.121), role ambiguity (0.173), role conflict (0.043), and turnover intentions.

    CONCLUSION: This paper aims to manifest the importance of designing employee well-being policies by firms to retain employees. Findings reflect the role of the managerial approach towards ensuring employee well-being for employee retention, thereby reducing recruitment and re-training costs.

  2. Fulltext Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis
    Arulananth TS, Kuppusamy PG, Ayyasamy RK, Alhashmi SM, Mahalakshmi M, Vasanth K, et al.
    PLoS One, 2024;19(4):e0300767.
    PMID: 38578733 DOI: 10.1371/journal.pone.0300767
    Semantic segmentation of cityscapes via deep learning is an essential and game-changing research topic that offers a more nuanced comprehension of urban landscapes. Deep learning techniques tackle urban complexity and diversity, which unlocks a broad range of applications. These include urban planning, transportation management, autonomous driving, and smart city efforts. Through rich context and insights, semantic segmentation helps decision-makers and stakeholders make educated decisions for sustainable and effective urban development. This study investigates an in-depth exploration of cityscape image segmentation using the U-Net deep learning model. The proposed U-Net architecture comprises an encoder and decoder structure. The encoder uses convolutional layers and down sampling to extract hierarchical information from input images. Each down sample step reduces spatial dimensions, and increases feature depth, aiding context acquisition. Batch normalization and dropout layers stabilize models and prevent overfitting during encoding. The decoder reconstructs higher-resolution feature maps using "UpSampling2D" layers. Through extensive experimentation and evaluation of the Cityscapes dataset, this study demonstrates the effectiveness of the U-Net model in achieving state-of-the-art results in image segmentation. The results clearly shown that, the proposed model has high accuracy, mean IOU and mean DICE compared to existing models.
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