Pathological waste generated in healthcare facilities poses a significant risk of infectious disease transmission and therefore requires specialized collection, transportation, and disposal systems. Efficient logistical planning is essential for companies responsible for managing this waste in order to reduce operational costs while ensuring adequate health and environmental standards. In this context, this study develops an optimization strategy based on the Periodic Vehicle Routing Problem (PVRP) to support the planning of pathological waste collection and transportation activities. A mixed-integer linear programming model (MILP) is proposed to determine the visit schedule for healthcare centers, assign vehicles to service days, and design vehicle routes that satisfy demand and service frequency requirements over a weekly planning horizon while minimizing total operational costs. The model is validated through a real case study from Argentina, demonstrating its applicability to real-world waste management systems. Additionally, a benchmark case from the literature is solved to evaluate the impact and performance of the proposed approach under a deterministic demand scenario derived from historical data. Finally, the performance of the proposed model is compared with alternative PVRP formulations using several benchmark instances, showing the reliability and consistency of the solutions obtained.

Presently, attaining sustainability in construction activities by integrating environmental, social, and financial viewpoints, is one of the most difficult objectives for firms, so adopting and implementing sustainable project management (SPM) is fundamental in countering this challenge. Therefore, this present study uses institutional theory to analyze how external factors such as isomorphic pressures contribute to the adoption and implementation of SPM in a developing country. Using a survey research strategy with self-administered questionnaires, data were obtained from 641 construction firms in Ghana. Partial Least Squares Structural Equation Modeling (PLS-SEM) approach was utilized to analyze the measurement and structural model. The outcomes suggest that isomorphic pressures are positively related to SPM with mimetic pressures asserting more impact and coercive pressures with the least influence. These findings offer construction professionals and policymakers important new perspectives that will help them to promote SPM so as to accomplish the Sustainable Development Goals.

The construction industry is acknowledged as one of the major primary energy consumers and contributors to global environmental emissions. To this end, proper selection of building materials is imperative to maintain the sustainability of a built environment. This research study proposed a building information modeling-based framework for lifecycle assessment of building materials in Saudi Arabia. Both Autodesk Revit and Autodesk Quantity Takeoff were adopted to define building materials, components, and their quantities. In addition, Athena Impact Estimator and Ecotect® Analysis software were leveraged to conduct thorough environmental impact assessment and energy simulation of building materials. The conducted lifecycle impact assessment tackled project phases of site preparation, construction, use, and demolition. The environmental dimensions of energy consumption, global warming, air emissions, land emissions, and water emissions were also explored. The capabilities of the developed model were tested using a case study of a college building in Saudi Arabia. The findings from this study can assist in the selection of environmentally friendly building materials that can be employed in Saudi Arabia’s construction market.

Mathematical modeling has become an important tool for understanding and controlling infectious diseases. It allows researchers to simulate and predict the spread of diseases, identify key transmission factors, assess the impact of interventions, and inform decision-making for disease control. Researchers can use mathematical models to explore different scenarios, evaluate the effectiveness of various interventions, and estimate the potential outcomes of different control strategies. This enables policymakers and public health professionals to make informed decisions and implement targeted measures to mitigate the impact of infectious diseases. In addition, mathematical models can be used to examine and evaluate the effectiveness of control strategies such as vaccination, social restrictions, and drug use. The results show that prevention strategies such as population vaccination and social restrictions can significantly help reduce the spread of influenza. This article presents a mathematical model for Influenza A (H1N1), as well as two other models specifically for Influenza A (H1N1) after quarantine and treatment. The purpose of the article is to provide a brief review of these models and compare them.

Air pollution remains a critical public health and environmental issue in megacities, with fine particulate matter (PM2.5) posing significant risks in Tehran, Iran. Traditional modeling methods often fail to capture the non-stationary and multiscale characteristics of urban air pollution dynamics. This study introduces a wavelet-based analytical framework to uncover both long-term seasonal trends and short-lived pollution events using a seven-year dataset (March 2017–March 2024) from nine monitoring stations across Tehran. We applied the Continuous Wavelet Transform (CWT) with Morlet and Daubechies 6 (db6) wavelets to decompose the PM2.5 time series into multiple temporal scales. The Morlet wavelet identified persistent winter peaks associated with temperature inversions and atmospheric stagnation, while db6 revealed abrupt, high-frequency pollution spikes linked to localized traffic emissions and industrial activity. Wavelet coherence analysis further illustrated the relationship between meteorological variables such as temperature, east-west winds, and north-south winds and PM2.5 concentrations, highlighting how these factors modulate air quality across different spatial and temporal scales. While ARIMA demonstrated stronger point-forecasting accuracy, the wavelet-based framework provided superior interpretability by preserving both gradual seasonal cycles and transient anomalies simultaneously structures that conventional models tend to smooth out. This comparison was conducted not to establish forecasting superiority, but to highlight the distinct and complementary role of wavelet analysis as a diagnostic tool for pollution hotspot identification and early-warning systems rather than pure numerical prediction. These findings underscore the value of wavelet analysis as not only an advanced analytical tool but also a decision-support system for real-time air quality monitoring and spatially targeted policy planning. By capturing the full spectrum of PM2.5 dynamics from chronic seasonal exposure to acute pollution spikes, this approach offers a more comprehensive framework for adaptive urban air quality management in complex environments like Tehran.

This study investigates the adoption of Green Construction Finance (GCF) and its determinants within Kenya’s construction industry. Utilizing a quantitative approach, data were collected from 55 registered property developers and analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings reveal a nascent GCF landscape characterized by a stark paradox: while 98% of developers express a conceptual willingness to adopt green practices, actual uptake is restricted to a mere 1.03%. The structural model indicates that the eight theorized determinants, awareness, accessibility, institutional, financial, environmental, technical, risk, and socio-cultural factors, collectively explain only 5.95% (R²=0.0595) of the variance in adoption. Critically, the analysis identifies a "barrier bundle" effect, where a lack of discriminant validity and high multicollinearity among constructs suggest that stakeholders perceive regulatory, financial, and risk-related hurdles as a single monolithic obstacle. Notably, environmental factors exhibit a negative path coefficient (-0.6313), implying they are currently viewed as cost burdens rather than value drivers. The study concludes that piecemeal interventions are insufficient; a holistic, systemic strategy is required to de-risk the sector and move beyond the current state of statistical fragility toward meaningful, sustainable construction uptake.

Tribal heritage in Jharkhand holds immense potential not just as a cultural asset but as a foundation for sustainable, community-led entrepreneurship. This paper examines the major issues that can help the tribal societies transform their cultural heritage into legitimate business enterprises. Based on the domain, the study makes use of the Best-Worst Method (BWM) to rank significant enablers. It applies Interpretive Structural Modelling (ISM) and MICMAC analysis to discover the structural relationship between them. It is indicated that although the availability of natural resources, indigenous knowledge, and governmental support serve as the foundations of tribal enterprise, psychological preparedness and market access are the key factors in the long-term development. Interestingly, cultural values and education are also discovered as highly ingrained factors that determine an entrepreneurial intent and sustainability. The paper illuminates the multifaceted nature of the problems tribal entrepreneurs have to encounter, such as geographical remoteness and the changing demands of consumers, but also shows the opportunity to interfere by making specific changes. Through mapping of these enablers and their linkage, this study will provide a practical framework to be used by policymakers, NGOs, and local stakeholders to enable and expand tribal entrepreneurship in Jharkhand. Finally, it promotes the model of development in which cultural pride and economic empowerment must go hand in hand.

Tribal heritage in Jharkhand holds immense potential not just as a cultural asset but as a foundation for sustainable, community-led entrepreneurship. This paper examines the major issues that can help the tribal societies transform their cultural heritage into legitimate business enterprises. Based on the domain, the study makes use of the Best-Worst Method (BWM) to rank significant enablers. It applies Interpretive Structural Modelling (ISM) and MICMAC analysis to discover the structural relationship between them. It is indicated that although the availability of natural resources, indigenous knowledge, and governmental support serve as the foundations of tribal enterprise, psychological preparedness and market access are the key factors in the long-term development. Interestingly, cultural values and education are also discovered as highly ingrained factors that determine an entrepreneurial intent and sustainability. The paper illuminates the multifaceted nature of the problems tribal entrepreneurs have to encounter, such as geographical remoteness and the changing demands of consumers, but also shows the opportunity to interfere by making specific changes. Through mapping of these enablers and their linkage, this study will provide a practical framework to be used by policymakers, NGOs, and local stakeholders to enable and expand tribal entrepreneurship in Jharkhand. Finally, it promotes the model of development in which cultural pride and economic empowerment must go hand in hand.

This article provides a detailed analysis of the fast fashion (FF) supply chain, focusing on emerging trends and significant challenges. It aims to define fast fashion, identify current supply chain trends, and clarify sustainability issues across environmental, social, and economic areas. Using a systematic literature review, the study examines relevant research published from 2020 to 2025, drawing from various electronic sources. The findings highlight key trends, including increased transparency and traceability enabled by technologies such as blockchain and RFID, sustainable sourcing practices, and greater digitalization and collaboration among stakeholders. However, it also underscores urgent environmental issues, such as water contamination and carbon emissions, as well as social concerns, like worker exploitation, and economic issues, such as overproduction. Technological challenges, such as infrastructure upgrades and resistance to change, are also addressed. The paper recommends that future research explore innovative business models, such as ultra-fast fashion, to better align with consumer preferences and address overproduction. While offering extensive insights into fast-fashion trends and sustainability issues, the study acknowledges some limitations in the scope of innovative business ideas. Practical implications underscore the importance of advanced technologies to improve supply chain transparency and reduce environmental impacts, advocating strategic shifts toward sustainable sourcing and circular-economy principles. Overall, this systematic review consolidates key data on the fast-fashion industry's supply chain, providing valuable insights for stakeholders seeking to adopt more sustainable and fairer practices.

Digital Twin (DT) technology combined with the Internet of Things (IoT) can be used to provide new solutions to real-time monitoring and management of agriculture. This paper introduces an IoT-based digital twin platform that will help in streamlining agricultural operations by incorporating different sensor technologies to monitor vital soil and crop conditions, such as moisture, temperature, pH, and nitrogen concentrations. The system offers predictive analytics to inform irrigation control, pest control, and fertilizer application, to help in making agricultural activities more sustainable. The effectiveness of the model is tested based on real time data integration and predictive modeling with 92% accuracy in monitoring soil moisture and an 87 percent accuracy in predicting crop yields. Although the system shows a high potential in terms of resource optimization and productivity, issues like sensor calibration, network connectivity and scalability to bigger operations exist. The future direction must be aimed at making sensors more reliable, more scalable, and adding AI and automation to make the system even more efficient and applicable in precision farming.