This study introduces and investigates a novel phase-specific waste utilization strategy for cement-based materials, aiming to concurrently enhance engineering performance and environmental efficiency. Marble dust (MD) was strategically employed as a partial cement replacement in paste systems, while waste glass (WG) served as a natural sand replacement in mortar. Initial findings indicated a reduction in workability for both MD- and WG-incorporated mixtures, with respective declines reaching up to 48.6% and 44.4%. Early-age compressive strength in MD-added mixtures decreased by up to 9.2%, primarily attributed to dilution effects, while WG-containing mixtures exhibited only minor reductions in early strength. Crucially, significant strength recovery was observed at later ages (>28 days). Compressive strengths ultimately increased by up to 3.8% with MD and 5.1% with WG compared to control mixtures, while flexural strengths saw improvements of 6.8% for MD and a notable 13.8% for WG mixtures. Further analysis revealed improved pore refinement at later ages (>28 days). Porosity decreased substantially, by up to 30.1% for MD-containing mixtures and 22.4% for WG-containing mixtures. Similarly, water absorption was reduced by up to 29.5% for MD and 21.8% for WG, attributing these enhancements to MD's filler and nucleation effects and WG's pozzolanic reactivity. From an environmental perspective, MD incorporation led to a significant reduction in CO2 emissions, up to 10.87%. Conversely, WG generally caused minor increases (up to 0.59%), though a 10% replacement level achieved a 1.43% reduction. These results underscore that cement replacement with MD offers superior environmental benefits compared to aggregate replacement. The study highlights the successful balancing of mechanical properties with environmental sustainability through this phase-specific approach, emphasizing the critical influence of transportation distance on the overall carbon footprint.
This study, grounded in the transactional theory of stress (TTS), investigates how job-related hindrance stressors mediate the effects of technostress on employees' career-related outcomes, including career insecurity, career plateau, and career dissatisfaction. The study analyzed data from 283 airline ground staff in Tehran, Iran, using structural equation modeling and bias-corrected bootstrap confidence intervals to infer indirect effects. Results show that technostress significantly increases hindrance stressors, which in turn heighten career insecurity, career plateau, and career dissatisfaction. Mediation analyses confirm that hindrance stressors act as a critical mechanism linking technostress to negative affective outcomes. By highlighting hindrance stressors as the pathway through which technostress undermines employees' career development, this study extends the service marketing literature and underscores the hidden career costs of technology-induced stress in the airline industry.
Accurate demand forecasting remains a critical challenge in retail operations, where imprecise predictions lead to inventory overstocking, stockouts, and suboptimal pricing strategies. Conventional forecasting models frequently struggle to model the complex, nonlinear interactions between demand and a multitude of influencing factors. To address this limitation, the present work proposes a hybrid model combining the Grey Wolf Optimizer with an Extreme Learning Machine (GWO-ELM) for high-accuracy retail demand forecast prediction. The GWO algorithm is utilized to systematically determine the optimal input weights and biases of the ELM, overcoming the drawbacks of random initialization and enhancing generalization performance. The model is rigorously evaluated using 5-fold cross-validation and 20 independent runs on a real-world retail dataset, demonstrating superior predictive accuracy with a test coefficient of determination (R2) of 0.992 and significantly reduced error metrics compared to standard ELM and other metaheuristic variants. To ensure model transparency and actionable insights, SHAP (SHapley Additive exPlanations) analysis is integrated to interpret feature contributions, revealing that Units Sold, Price, and Competitor Pricing are the most influential predictors. The results confirm that the GWO-ELM framework delivers superior forecasting accuracy compared to existing benchmarks, while simultaneously generating interpretable outputs that support strategic decision-making in inventory management and dynamic pricing. This study advances the body of knowledge on intelligent forecasting systems by combining optimization, machine learning, and explainability into a cohesive and practical solution for retail analytics.
Understanding the composition and amount of waste is crucial for the health and development of communities. Panic and the unpredictable situation of COVID-19 caused significant demands for food, which resulted in high pressure on food waste and waste management systems. To determine the change in waste composition in Northern Cyprus during the COVID-19 pandemic, questionnaires were prepared and distributed through the media and via email. This study found that household waste generation per capita was 0.91 kg with a 6% error when compared with a conventional waste composition study performed by the European Union in 2016. According to the results, the quantity of domestic waste decreased during the pandemic, while garden waste increased. Additionally, the results show that 27% of plastic waste came from cleaning purposes. As face mask usage and tea consumption increased during the pandemic, these materials were incorporated as additives into marble-dust-modified cement paste to develop sustainable construction composite. The mechanical performance of the proposed material was evaluated by measuring the flexural and compressive strengths of specimens cured for 7, 28, and 56 days. Eco-efficiency metrics derived directly from mechanical data provided strong environmental engineering insight. When assessed per unit of compressive function, cement intensity increased with mask dosage, indicating reduced binder efficiency despite batch-level cement savings. Furthermore, waste diversion per unit strength increased with mask content, but progressively larger compressive penalties accompanied this benefit. Within this trade-off, low to intermediate mask dosages offered the most validified balance between waste diversion and mechanical performance.
Potentially toxic elements (PTEs) are under-recognized environmental contributors to non-communicable diseases (NCDs). Emerging evidence suggests that PTEs exert systemic effects by disrupting autonomic regulation and cellular energy metabolism. This systematic review and evidence map examined how toxic metals impair vagal nerve function and mitochondrial bioenergetics to drive NCD pathogenesis. Following PRISMA guidelines and a PECO framework, we searched PubMed, Web of Science, Scopus, and Google Scholar (inception to December 2025). Eligible studies included human epidemiological, animal, and in vitro models examining associations between PTE exposure (Pb, Hg, Cd, As, etc.), autonomic dysfunction, and mitochondrial/bioenergetic stress. Of 313 records identified, 27 studies met inclusion criteria. Lead (63.0 PTEs and NCDs: Toxic elements (Pb, Cd, Hg) are major, under-recognized triggers for non-communicable diseases (NCDs). Vagal Impairment: Chronic exposure significantly reduces vagal tone and disrupts autonomic nervous system balance. Energy Crisis: Metals cause “energy crises” by damaging mitochondria and inhibiting ATP production in cells. Integrated Pathways: Neuro-energetic disruption links environmental pollution directly to hypertension and arrhythmias. Clinical Need: Environmental risk assessment is vital for improving NCD prevention and clinical outcomes.
