Management & Science University (abbreviated as MSU Malaysia or simply MSU) is a private university in Malaysia located in Shah Alam, Selangor. The university was founded in 2001 as University College of Technology & Management Malaysia before officially becoming a full fledge university in October 2007 as Management & Science University.Management & Science University is a member of the MSU Holdings which comprises MSU College, Management & Science Institute, MSU Kids, MSU Medical Centre, MSU Foundation, Sekolah Bina Insan MSU Foundation, Jakarta Institute of Technology and Health, and Ilmu Ekonomi Penguji High School.MSU has been accorded ‘Excellent Status University’ twice on the national university-rating system, and named the ‘Best Entrepreneurial Private University’ by the Ministry of Higher Education Malaysia. As an applied and enterprise university, MSU offers programmes of study at postgraduate, undergraduate, and foundation levels, through connected pathways that admit students from all walks of life and personal backgrounds.A graduate tracer study by the Ministry of Higher Education Malaysia (MoHE) shows 98.6% of MSU graduates secure employment within six months of their graduation. MSU was ranked at 541 to 550 in the 2020 QS World University Rankings and 271 in the 2019 QS Asia University Rankings that makes MSU among top 51% world's best universities and top 1.8% Asia's best universities respectively. MSU was also ranked at 301+ in the 2019 Times Higher Education World University Impact Rankings.
Polyethylene glycol (PEG) is extensively utilized and its persistence in wastewater, soils, and aquatic environments has raised concerns regarding environmental contamination and ecological impacts. Understanding and predicting PEG’s heat capacity is therefore not only critical for material design but also for assessing its stability, transformation, and degradation under environmental conditions. This study develops advanced machine learning models including Gradient Boosting Decision Tree (GBDT) combined with Evolutionary Strategies (ES), Bayesian Probability Improvement (BPI), Batch Bayesian Optimization (BBO), and Gaussian Process Optimization (GPO) to forecast PEG’s molar heat capacity, using a databank of 528 experimental observations. Analysis of correlations demonstrated that temperature dominates the predictive framework (score 0.69), while molar mass provides a secondary but meaningful effect (score 0.51). Monte Carlo-based outlier detection validated data reliability, while sensitivity and SHapley Additive exPlanations (SHAP) analyses reinforced the primacy of temperature in determining heat capacity. The comparative analysis demonstrated that GBDT-ES provided the most accurate predictions, achieving an R2 of 0.998 and an average absolute relative error of 12.825
Human Action Recognition (HAR) is a significant research area in computer vision with applications in surveillance, healthcare, and human–computer interaction. This study proposes a hybrid deep learning framework that integrates the lightweight MobileNetV1 architecture with the scalable EfficientNetB3 network to achieve a balance between computational efficiency and recognition accuracy. The hybrid design leverages the complementary strengths of MobileNetV1 for efficient low-level feature extraction and EfficientNetB3 for enhanced high-level feature representation, enabling improved discriminative capability while maintaining reduced computational complexity. The model is evaluated on the UCF101 dataset, comprising 13,320 video clips across 101 action classes. A frame-based classification strategy with uniform sampling is adopted to reduce computational overhead, and data augmentation techniques such as rotation, shifting, shearing, and brightness adjustment are applied to improve generalization. Experimental results demonstrate that the proposed model achieves an accuracy of 89.18
Early detection of vision problems in children is essential for preventing developmental delays and academic challenges. Conventional vision screening methods are widely implemented; however, various barriers limit their effectiveness and accessibility. This systematic review aims to identify and analyse the barriers and limitations associated with conventional vision screening methods in children. A systematic search was conducted across the Scopus, Web of Science, and PubMed databases using predefined keywords and Boolean operators. Eligible studies included empirical investigations that examined barriers and limitations in paediatric vision screening. The review adhered to PRISMA 2020 guidelines, and quality assessment was performed using the Mixed Methods Appraisal Tool (MMAT). Of the studies screened, 25 met the inclusion criteria. Employing thematic analysis, five key barriers and limitations were discovered: (i) methodological limitations, (ii) resource constraints, (iii) competency gaps, (iv) socioeconomic and psychological barriers, and (v) policy and systemic challenges. Future research should focus on evaluating novel screening approaches that can overcome current limitations and enhance early detection rates for a broader range of paediatric vision conditions.
Chronic wounds are characterized by persistent infection, excessive oxidative stress, and dysregulated inflammation, which together hinder tissue repair and promote non-healing states. In this context, pH/ROS-responsive hydrogels have emerged as promising smart wound-dressing platforms because they can exploit local acidic and oxidative cues for site-specific structural change, controlled cargo release, and microenvironment-adaptive therapy. This review summarizes the pathological basis for pH/ROS-responsive intervention and discusses recent advances in hydrogel design for infection control, ROS scavenging, and immune microenvironment modulation. We further highlight key translational challenges, including manufacturing complexity, degradation-product safety, storage stability, and the limitations of current preclinical models. Overall, pH/ROS-responsive hydrogels represent a promising strategy for chronic wound management, but future progress will depend on achieving a better balance between multifunctional performance and translational practicality.
PurposeThis study investigates how knowledge management (KM) practices influence the financial performance and performance management of higher education institutions (HEIs) in Libya. It examines the roles of human, structural and relational capital, highlighting KM's mediating effect on financial stability, operational efficiency and systematic performance monitoring. By addressing gaps in the literature, the study provides actionable insights for enhancing financial oversight, accountability and institutional outcomes through effective KM practices.Design/methodology/approachA quantitative design was adopted, collecting data via a structured questionnaire from 347 financial stakeholders, including accountants, auditors and finance managers in Libyan HEIs. Purposive sampling targeted individuals directly involved in financial decision-making. Data were analyzed using Smart PLS to assess human, structural and relational capital, KM practices and financial performance, emphasizing KM's role in supporting performance management processes.FindingsHuman capital (HC), structural capital (SC) and relational capital (RC) were found to positively influence financial performance, both directly and indirectly through KM. KM partially mediates these relationships, acting as a strategic mechanism that operationalizes intellectual capital to enhance decision-making, accountability and institutional performance management.Originality/valueThe study advances intellectual capital theory by demonstrating how HC, SC and RC, mediated by KM, enhance both financial performance and performance management in resource-constrained and post-conflict HEIs. This mediation model is novel in North African nonprofit HEIs and offers a framework for policymakers and administrators to strategically leverage KM for sustainable institutional outcomes.
