S. Mohammad

Circular economy models in biotechnology: waste-to-resource pathways for biodiversity enhancement

Abstract The transitional shift from a linear production-consumption-disposal model towards a circular economy model leads to ecosystem pressure reduction while maintaining biodiversity protection. This study develops and assesses waste-to-resource biotechnology systems to determine their impact on biodiversity conservation in Malaysia. The research study employed mixed methods which combined library resource analysis with government report evaluation and interviews with 15 experts and conceptual modeling. The researchers gathered data from 111 operational facilities located in Selangor Johor Penang Sabah and Sarawak for the years 2024 to 2025 and used MaxQDA software for their data analysis. The findings showed that the palm oil industry, with 95.6 million tons of waste generated per year, has the largest share (62.4%) in the country’s organic waste. Black soldier fly larvae demonstrated maximum efficiency because they achieved waste reduction of 92.5% while anaerobic digestion reached 78.3% and bioplastic conversion achieved 71.2% waste reduction. The composting process achieved an efficiency rate of 65.8 percent while the biofertilizer production process achieved an efficiency rate of 58.4%. The SMOKAHs from Sarawak and the bamboo revitalization project in Perak showed biodiversity indices of 8.1 and 8.4 which displayed a notable difference when compared to the landfill site biodiversity index of 4.2. The black soldier fly larvae method produced 350 kilograms of carbon emission reduction per ton which accounts for 81.1% less than the in-situ burial method. The results demonstrate that new biotechnologies achieve better waste reduction results which help local communities and boost biodiversity through carbon emission reductions and the development of useful products. The implementation of these findings in Malaysia’s Fifth Plan (2026-2030) policies is necessary for the country to achieve its circular economy objectives and biodiversity protection goals.

The mediating role of green innovation capability between AI-driven green project management and sustainable project performance including MADANI sustainability in Malaysian green projects

Artificial intelligence has become a core enabling factor for raising the standard of sustainable project management, and it can effectively strengthen organizational capacity, innovation levels, and the quality of environmental decision-making. Against this backdrop, this study is an original empirical study focused on organizations implementing green projects in Malaysia. Its core research objective is to explore the impact of AI-driven green project management on sustainable project performance. The study incorporates the following variables: antecedent variable MADANI sustainability orientation, core independent variable AI-driven green project management, mediating variable green innovation capability, moderating variables organizational green agility and green tax incentives, and dependent variable sustainable project performance. This study adopts a quantitative cross-sectional research design, and collected valid samples from 341 practitioners. It uses partial least squares structural equation modeling (PLS-SEM) to analyze the core conceptual model. The results show that the path coefficients and p-values between all core variables reach the threshold for statistical significance. The model’s explained variance for green innovation capability is 41.7%, and its explained variance for sustainable project performance is 49.2%. At the academic level, this study fills the literature gap in the research field of AI-enabled sustainable project management; at the practical level, it provides clear actionable guidance for local Malaysian organizations and policymakers to advance digital transformation and sustainable development initiatives.

Extracellular Vesicles in Neurodegenerative Diseases: A New Frontier in Diagnosis and Therapy.

Neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease are among the progressive disorders of the nervous system that are characterized by the gradual destruction of neurons, the accumulation of misfolded proteins, and the limited effective therapeutic options. In recent years, numerous lines of evidence have emphasized the important role of extracellular vesicles (EVs) in the formation and progression of these diseases. These vesicles are membrane-bound nanoscale structures that are secreted by almost all cell types and play a role in cell-cell communication through the transfer of molecules such as proteins, lipids, and nucleic acids. In neurodegenerative disorders, EVs can facilitate the transport and dissemination of disease-related proteins, including amyloid-β, tau, α-synuclein, mutant huntingtin, SOD1, and TDP-43, thus contributing to the spread of pathological processes in different parts of the nervous system. On the other hand, the ability of these vesicles to cross the blood-brain barrier and reflect molecular changes occurring in the central nervous system makes them valuable candidates for the development of minimally invasive biomarkers. This review reviews the biogenesis, classification, isolation methods, and molecular content of EVs, and analyzes their role in the pathogenesis, diagnosis, and treatment of the most important neurodegenerative diseases. Also, the importance of EV-associated proteins, RNAs, and lipids as emerging diagnostic biomarkers, as well as the therapeutic potential of natural and engineered vesicles as drug delivery systems and regulators of neuroinflammation and neurodegenerative processes, is discussed.

Condition-based maintenance threshold determination for gearbox fault progression using vibration envelope features and accelerated life testing

Gearbox failures represent a critical problem faced by industrial machines, considering the impacts of such failures on machine reliability, efficiency, and maintenance cost. Despite the well-established use of vibration-based condition monitoring techniques for diagnosing the presence of faults, few attempts have been made in transforming information about fault progression into thresholds that could be used in making condition-based maintenance (CBM) decisions. In this work, a CBM system for analysing gearbox fault progression based on vibration envelope features is presented, alongside accelerated life testing. An experimental approach has been adopted, whereby accelerated life testing was performed to induce gearbox degradation progressively. Then, vibration data were analysed through the envelope technique, out of which six vibration envelope features, namely RMS Envelope, Kurtosis, Crest Factor, Peak Amplitude, Envelope Energy, and Sideband Energy Ratio, were derived and analysed based on their sensitivity through correlation, monotonicity, trendability, and separability tests. Thereafter, a composite health index based on the most sensitive features was formulated, and a multilevel maintenance threshold system consisting of Alert, Warning, and Critical levels was created. The findings show that Envelope Energy, Sideband Energy Ratio, and Kurtosis have the highest sensitivity to degradation and can accurately represent the evolution of gearbox faults. The composite health index shows a strong correlation with the extent of degradation (R2 = 0.962) and successfully discriminates between different health states of the gearbox. The developed framework for determining maintenance thresholds achieves an accuracy of 94.9%, which allows accurate identification of maintenance intervention phases.

Net sustainability assessment of AI-driven data centers in the GCC: an MCDA approach framework

The Gulf Cooperation Council (GCC) is witnessing a rapid growth in artificial intelligence (AI)-enabled data center development, thanks to national digital transformation strategies and the region’s strategic location bridging global markets. Yet, no evaluation framework holistically compares the direct impacts of data center operations on the environment with the dispersed sustainability impacts of AI applications across major business sectors. To address this, this study proposes and applies a Multi-Criteria Decision Analysis (MCDA) framework to assess carbon emissions, water use, energy consumption, social impacts and governance structures across three scenarios (business-as-usual (BAU), moderate transition (MOD), and aggressive decarbonization (OPT). Using secondary structured data from peer-reviewed sources, institutional databases (IEA, UNFCCC, Uptime Institute) and independently verified case studies, the analysis applies clearly defined boundary rules and attribution principles. The findings suggest that under the BAU high-growth scenario, modelled emissions increase substantially by 2035, while AI-enabled sustainability applications in buildings, industry, and utilities provide only partial offsets under optimistic adoption assumptions. The NIS indicates that net-positive outcomes are achievable only when substantial renewable-energy procurement, best-practice cooling, and transparent verification of AI benefits occur together. The results extend socio-technical systems theory and ecological modernization theory by addressing the boundary problem in sustainability assessment of digital infrastructure, providing a scalable approach for policymakers and investors in high-carbon, water-scarce regions. The Net Impact Score (NIS) is not to be construed as an absolute causal prediction, but rather as a scenario-conditional decision-support indicator because AI-benefit attribution and scaling are both dependent on the mentioned assumptions and data limits.

AI-Powered Assessment in Physical Education: Rethinking Student Engagement through Social Sciences Perspectives First position