Kuwait Technical College

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.

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.

This study explores how brand equity elements (brand awareness, association, perceived quality, and loyalty) and brand identity components (values, vision, positioning, and personality) influence sustainability performance across economic, environmental, and social dimensions. A key contribution is the introduction of cultural disruption as a mediating factor linking branding to sustainability. In the context of Kuwaiti restaurant SMEs, cultural disruption emerges from changing consumer expectations, global food trends, and the growing impact of digital culture. These cultural shifts reshape how brand strategies are perceived and how effectively they support sustainable outcomes. The proposed framework presents several propositions explaining how brand attributes mediated by cultural change affect sustainability performance. The study offers theoretical insights and practical guidance for SMEs aiming to align cultural relevance, brand strength, and sustainable development in a dynamic business landscape.