http://hdl.handle.net/10563/1000001 2026-07-16T13:01:59Z http://hdl.handle.net/10563/1012830 Optimized adsorption removal and capacity prediction of anionic pollutants using a hybrid strategy of machine learning algorithms Hamza Ul Haq; Yasir, Muhammad; Aslam Khan, Muhammad Nouman; Gul, Jawad; Zubair, Mukarram; Ali, Hassan; Sedlařík, Vladimír; Ahmad, Nasir. M. Accurate prediction of adsorption performance is crucial for optimizing wastewater treatment systems, however, the complex interactions among operational variables and adsorbent properties often limit conventional modelling approaches. In this study, a machine learning framework was developed to predict the adsorption removal efficiency and kinetic capacity of anionic pollutants in aqueous systems. A comprehensive experimental dataset was generated using four representative pollutants, i.e., bovine serum albumin, methyl orange, sulfate, and nitrate and four adsorbent materials, including powdered activated carbon (PAC), thermally modified PAC, chemically modified PAC, and ion-exchange chitosan beads. Key operational parameters, including pH, contact time, adsorbent dosage, BET surface area, solution volume, and pollutant concentration, were used as input features. Four ML algorithms, i.e., Decision Tree (DT), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Ensemble Learning Tree (ELT), were developed and further optimized using Bayesian optimization to improve predictive performance. Among the evaluated models, the optimized ELT model demonstrated the highest predictive accuracy with a coefficient of determination (R2) of 0.78, indicating its strong capability in capturing nonlinear adsorption behavior. Model interpretation through partial dependence plots revealed significant influences of pH, adsorbent dosage, BET surface area, and initial pollutant concentration on adsorption performance, while Sobol sensitivity analysis confirmed the dominant role of initial concentration. Experimental validation using jojoba-derived biochar for the removal of methyl orange and Eriochrome Black T dyes showed strong agreement with model predictions. The developed ML models provide a reliable tool for predicting adsorption performance and designing efficient adsorbent-based wastewater treatment systems. 2026-01-01T00:00:00Z http://hdl.handle.net/10563/1012829 Aminopropyltriethoxysilane-Enhanced Activated Carbon with Polyether Sulfone-Cellulose Acetate Mixed Matrix Nanofiltration Membranes for Water Purification Batool, Mehwish; Haidar, Usman; Khan, Asim Laeeq; Aslam, Muhammad; Alsubaie Abdullah Saad; Yasir, Muhammad; Zaheen, Aqsa; Asad Abbas, M.; Batool, Iram; Ahmad, Nasir M. 2026-01-01T00:00:00Z http://hdl.handle.net/10563/1012814 Environmental transparency: A catalyst for SMEs’ environmental disclosure and financial outcomes Afful, Charles Randy; Dvorský, Ján Environmental transparency enhances corporate competitiveness, as legal constraints and stakeholder demands have increased global interest in ethical business practices. This study examines 302 Ghanaian manufacturing small and medium-sized enterprises (SMEs) in an emerging market context. This study investigates the impact of environmental disclosure (ED) and corporate reputation on the financial performance (FP) of SMEs. We adopted a quantitative approach through hypothetical testing, using partial least squares structural equation modeling (PLS-SEM) and the purposive sampling technique. Our findings show that ED and business reputation (BR) influence the financial success of SMEs in emerging markets. Additionally, business strategies positively moderate BR and ED. The results indicate that ED practices can guide managerial policies through regulatory compliance in meeting United Nations Sustainable Development Goals targets and improve environmental transparency and reputation demanded by stakeholders. A strategic policy alignment with the firm’s ED framework influences SMEs’ environmental transparency, competitiveness, and FP, offering both theoretical and practical insights for SMEs. © 2026 International Council for Small Business. 2026-01-01T00:00:00Z http://hdl.handle.net/10563/1012818 Early-stage degradation of electrolytic iron particle-based magnetorheological elastomer under natural weathering conditions Viension, Rehnupreya Hentry; Nordin, Nur Azmah; Mazlan, Saiful Amri; Johari, Mohd Aidy Faizal; Wereley, Norman M.; Fatah, Abdul Yasser Abd; Zaini, Nursyafiqah; Sedlačík, Michal Magnetorheological elastomer (MRE) is a smart composite possessing properties that can be tuned by an external magnetic field, making them highly attractive for vibration isolation applications. Their reliable use in outdoor environments, however, requires a clear understanding of how natural weathering influences their performance and durability. While most previous research has addressed long-term or accelerated ageing conditions, the onset of environmental degradation of MRE remains insufficiently explored. Therefore, this study investigated the early-stage degradation of MRE, embedded with irregular electrolytic iron particles (MRE-EIP) over six weeks of natural weathering exposure. Weekly samples (W0-W6) were analysed using vibrating sample magnetometer (VSM), rheometer and low vacuum scanning electron microscope and the results were correlated with weathering data from the Malaysian Meteorological Department, Kuala Lumpur. The saturation magnetization, Ms finding shows minimal change from 111.63 Am2/kg in W0 to 113.79 Am2/kg in W6, likely attributed to the exposure of EIP following the removal of the aged localized surface over the six week exposure. Strain sweep results meanwhile, revealed the progressive stiffening, with the storage modulus (G′) increased from 0.22 MPa (W0) to 0.53 MPa (W6), accompanied by a narrowing linear viscoelastic (LVE) region, indicative of early embrittlement of the samples. Nevertheless, a temporary reduction in G′ for W3 suggested a moisture-induced plasticisation, from increased rainfalls that week. Besides, the absolute MR effect, ΔG′ increased from 0.23 MPa (W0) to 0.34 MPa (W6), indicating greater responsiveness of exposed EIP to the magnetic fields which enhanced the G′ accordingly. Morphological analysis confirmed the development of localized surface depressions suggests combine effects of UV-driven embrittlement and moisture plasticisation from rainfall, leading to localised EIP exposure, while the cross-sectional structure integrity remained intact. These findings provide the first detailed account of early-stage degradation in MRE-EIP under natural weathering, offering valuable insights into early failure mechanisms and guiding durability driven material design for outdoor smart material applications. 2026-01-01T00:00:00Z