Kuwait Technical College

Digital transformation that occurs within the work environment is changing the competencies that are needed to remain employable and job security for the long term. The purpose of this study is to explore the antecedent factors of job security. This study also explores the mediating role of the behaviour of digital citizenship and the metaverse mindset. The study participants consist of Kuwait’s 406 employees, and the collected data were analysed via Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings show that digital leadership and literacy have a significant impact in improving the adaptability of the workforce and job security by fostering responsible digital behaviour and willingness to embrace immersive technologies. Employees with relevant certification have increased agility, digital readiness and engagement, which highlights that continuous upskilling is critical. This study positions itself in Kuwait’s Vision 2035, which provides insights that are applicable to human capital development through explaining the core components of HRM, such as digital agility and ethical tech engagement within the digital era.

Lumpy Skin Disease (LSD) threatens cattle health and productivity, imposing economic burdens on livestock production and highlighting the need for reliable image-based classification methods. Although deep learning has demonstrated strong potential for disease-related image classification, distinguishing localized and fine-grained visual patterns remains challenging. In this study, we propose LANet, a Lesion-Aware Global–Local Network for multi-class cattle LSD classification. The proposed framework employs a pretrained ResNet50 backbone followed by two complementary feature branches: a global branch based on adaptive average pooling and a local branch that learns a spatial attention mask to emphasize informative feature regions before adaptive max pooling. The resulting global and local representations are integrated through a feature interaction module and subsequently refined using adaptive feature gating before classification. Experiments on an 8012-image, five-class cattle disease dataset demonstrate that LANet achieves 98.93% accuracy, 97.67% macro-precision, 98.23% macro-F1, and 98.43% Cohen’s kappa against the dataset annotations. The model has 59.18 M parameters, requires 6.621 GFLOPs under standard inference, and achieves an inference latency of 0.533 ms per image with a throughput of 1875.30 images/s. These results demonstrate the effectiveness of LANet’s explicit global–local feature-learning and integration strategy, which combines holistic feature aggregation, spatially guided local feature extraction, feature interaction, and adaptive gating for multi-class classification of cattle disease images based on the available dataset annotations.

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition that significantly affects children’s educational performance and psychological well-being. In Kuwait, the increasing prevalence of ADHD among students in public elementary and middle schools underscores the need for culturally validated screening tools. This study aimed to validate the CHAOS Scale as a screening instrument for ADHD among students in Kuwaiti public schools and to assess its reliability and factor structure. A cross-sectional study was conducted using a cluster sampling approach, involving 565 guardians of students enrolled in public elementary and middle schools across Kuwait. Participants completed the CHAOS Scale on behalf of their children. Principal Component Analysis (PCA) was employed to evaluate the factor structure of the instrument, while reliability analyses assessed internal consistency, test–retest reliability, and interrater reliability. The PCA confirmed the validity of the CHAOS Scale’s factor structure, identifying four distinct components consistent with the theoretical construct. The scale demonstrated high internal consistency across all subscales (Cronbach’s α > 0.80 ), along with moderate to highPrincipal Component Analysis revealed a four-factor structure explaining 52.3

Oral cancer represents a critical global health challenge with rising incidence rates and poor prognosis when diagnosed late. This research presents a novel hybrid meta-ensemble framework for automated Oral tissue phenotype classification using multispectral imaging data from the OC-MSI dataset. The proposed methodology integrates spectral feature extraction, metabolic profiling, and hierarchical ensemble learning to achieve superior classification performance. The framework combines five advanced ensemble techniques including stacked ensemble with isotonic calibration, weighted soft voting, gradient boosting cascade, neural ensemble with uncertainty quantification, and a hybrid meta-ensemble architecture. Experimental validation on 950 multispectral images demonstrates exceptional performance with accuracy of 99.47

Photovoltaic (PV) panels are vital renewable energy sources that convert solar radiation into electricity. However, dust and snow accumulation significantly reduce their efficiency, particularly in arid or cold regions. Existing cleaning systems—whether manual, semi-automated, or robotic—often consume excessive water or fail to adapt to changing environmental conditions. Moreover, current reinforcement learning (RL) approaches such as Q-learning and Deep Q-Network (DQN) lack the flexibility required for real-time optimization. This study proposes a Deep Deterministic Policy Gradient (DDPG)-based intelligent wiper system capable of dynamically adjusting air-blowing and brushing speeds according to debris levels, completely eliminating water use. Simulations were conducted under diverse environmental conditions, including variable dust accumulation rates and solar irradiance levels, to assess system performance. Results show that the DDPG-based model achieved a 35% improvement in panel efficiency, 97% dust cleaning effectiveness, and the lowest mean squared error (0.05) among all tested methods. Operating within a flexible wiper speed range of 1.0–2.5 m/s, it also yielded the greatest reduction in cleaning frequency. The proposed system demonstrates superior adaptability, sustainability, and efficiency, providing a practical, water-free intelligent cleaning solution for maximizing the long-term performance of photovoltaic panels.