University of Applied Science and Technology

The current research has proposed an innovative methodology to enhance PVA/CSP/AgNPs hybrid biocomposite films by integrating the CRITIC and CoCoSo multi-criteria decision-making (MCDM) frameworks with machine learning techniques. Bio composite films can be used in medicine, packaging, and environmental applications. The biocomposite films were developed by mixing polyvinyl alcohol (PVA), coconut shell powder (CSP), and silver nanoparticles (AgNPs) to attain better mechanical, thermal, and antibacterial properties. The experimental design comprises 25 distinct trials to assess the tensile strength (TS), Young’s Modulus (YM), percentage elongation (%E), and maximum degradation temperature (T) of the fabricated composite films. CRITIC method was adopted to evaluate the weights of the criteria in an unbiased way, and CoCoSo technique ranked the alternatives. The results were analyzed using supervised machine learning algorithms, including Random Forest, Neural Network, Linear Regression, and AdaBoost. AdaBoost algorithm demonstrated superior performance with an R 2 value greater than 0.98 for all output responses. Decision tree analysis revealed that the composition of silver nitrate significantly influences tensile strength, while coconut shell powder affects Young’s modulus. The optimized film, composed of PVA/20%CSP/4 mM AgNPs, exhibited improved UV-shielding, reduced water absorption (26.2%), and decreased soil weight loss (41.4%). SEM images confirmed the uniform dispersion of AgNPs within the PVA matrix. The integration of CRITIC-CoCoSo with machine learning provides a scalable approach for designing multifunctional bio-composites with enhanced properties for sustainable applications.

Abstract Background: This study assesses Oman's current healthcare financing scheme and explores future directions for sustainable and equitable financing. Method: A mixed method approach was utilized, comprising semi-structured interviews with 25 key stakeholders. Descriptive statistics, compound annual growth rate (CAGR), correlation, and regression modelling were used for quantitative analysis, and thematic analysis was conducted on the qualitative data. Result: The primary source of financing is government revenue, which accounts for 84% of current health expenditure (CHE). There was zero real growth of per capita CHE (CAGR = -0.35%) relative to gross domestic product. Regression analyses found that a decline in out-of-pocket payments (-0.42, p < 0.01) and an increase in voluntary health insurance and enterprise financing (0.38, p < 0.01) had statistically significant effects on financing transition. Stakeholder interviews revealed that affordability is a major issue, particularly for small- and medium-sized enterprises (78% uninsured). Besides that, informal employees and low-income expatriates, combined with regulatory weaknesses, are the major challenge to health financing. Conclusion: Although the system in Oman provides financial protection, it is undermined by fiscal unsustainability. The main recommendations, including compulsory private insurance, subsidies to vulnerable groups, and strengthening regulation, are necessary to achieve Vision 2040.

Human resources are among the most critical assets of large organizations. Human resource management and employee motivation play a key role in organizational performance, presenting a constant challenge for senior management. The Iranian Railway Company, employing over 22,000 staff and transporting nearly 50 million t of cargo annually, is one of the largest state-owned enterprises in Iran. Employee satisfaction significantly influences the company's performance indicators. The equitable allocation of motivational benefits impacts freight and passenger volumes, as well as transportation safety. Failure to allocate these benefits fairly can lead to employee dissatisfaction, undermining motivation and performance.This study presents a method for allocating welfare and motivational benefits based on employee performance. Key performance criteria within the railway industry, such as net tkm, loading and unloading tonnage, wagon travel speed, income, accident rates, and passenger train delays, were identified. The relative importance of these criteria was determined using the pairwise comparison method. Monthly values for each criterion were calculated across 21 regions of the Iranian railway network. Motivational resources were then allocated accordingly through a hierarchical approach. The implementation of this model led to improvements in key productivity indicators, such as freight volumes and transportation efficiency, thereby enhancing overall company performance and employee satisfaction.

Abstract Microplastics (MPs) are pervasive pollutants impacting ecosystems worldwide, causing physical harm and behavioral changes in wildlife. MP toxicity mainly depends on size, porosity, weathering, and surrounding compounds. MPs disrupt feeding and metabolism, resulting in reproductive, neurological, and behavioral disorders. These impacts significantly alter ecological niches and reduce animal survival. Understanding these impacts is crucial for assessing the broader ecological effects of plastic pollution and developing effective mitigation strategies. This review investigated 110 papers on quantifying the behavioral consequences of MPs while considering their physiological basis, emphasizing the significance of these changes and their potential long-term influence on populations and ecosystems. This study thoroughly investigates model species, including plankton, fish, amphibians, crustaceans, pollinators, and mammals. Fish exhibited the most significant behavioral impact, while crustaceans showed the least. Mice, representing mammals, displayed the most severe physiological damage.