Electric vehicle routing models are typically deterministic. However, stochastic vehicle routing models rarely include a battery constraint. This study addresses that gap with a two-stage stochastic mixed-integer programme for electric vehicle routing with time windows, in which routing, load, and load-dependent energy are first-stage decisions and travel-time-driven lateness is a second-stage recourse. Load and battery charge are proved scenario-invariant given any fixed route, collapsing the recourse to a timing sub-problem and yielding an exact multi-cut Benders decomposition with finite-convergence and complexity results. The formulation was implemented and solved with an open-source solver; this process identified and corrected three formulation defects, each documented. On Solomon-benchmark instances scaling from 3 to 20 customers, the implementation reproduces an independent hand calculation exactly, remains solver-certified optimal throughout, and shows empirical solve time scaling approximately with the square of the decision variable count (R²=0.99). This study also reports a value-of-the-stochastic-solution analysis, a battery-capacity threshold effect, and a robustness finding: time-window width, not spatial distribution, drives resilience to travel-time uncertainty. Scope limitations are stated explicitly, with full-scale benchmarking left as future work.

Road is the vital connection for communication between different places in our daily life. The periodical maintenance of road surface, particularly detection of potholes in a prior basis is very important and a major challenge not only for transportation safety but also important for preventing several accidents and human death. Conventional pothole detection methods depend heavily on manual or public surveys and reporting, which is a very delayed process of maintenance and inefficient monitoring. This paper proposes RoadGuard, an intelligent real-time pothole detection and severity assessment framework using YOLO-based deep learning models that help to create smart road infrastructure management by enabling the opportunities to establish secure communication through road. The proposed framework explores three different YOLO variants including YOLOv8n, YOLOv8s, and YOLOv8m for efficient real-time inference that integrates automatic pothole detection and counting, severity classification, and maintenance related record for smart city infrastructure management. Experimental observations indicate that the proposed approach improves automation, scalability, and practical deployment feasibility compared to traditional inspection methods. Results demonstrate that all three variants achieve mAP@0.5 above 89%, confirming reliable pothole detection under varied road and lighting conditions.

Citrus canker, driven by Xanthomonas citri subsp. citri (Xcc), severely compromises global citrus productivity through debilitating lesions that reduce yield and fruit marketability. Conventional management, predominantly reliant on copper-based bactericides and antibiotics, faces diminishing efficacy due to the proliferation of copper-resistant Xcc strains, heavy metal soil accumulation, and stringent regulatory constraints on antibiotic usage. Consequently, there is an urgent imperative for sustainable, high-efficacy alternatives that minimize ecological footprints while ensuring robust disease suppression. Chitosan-based nanocomposites have emerged as a transformative solution, leveraging synergistic interactions between the cationic polysaccharide chitosan and antimicrobial metal or metal oxide nanoparticles, including zinc oxide (ZnO), copper oxide (CuO), and silver (Ag). This review critically evaluates the synthesis, physicochemical properties, and multifaceted antibacterial mechanisms of these nano-formulations against Xcc. We analyze recent advancements in green synthesis methodologies utilizing plant extracts, the development of smart stimuli-responsive delivery systems, and hybrid formulations that offer improved stability and reduced phytotoxicity. Despite promising laboratory and greenhouse results, significant translational hurdles remain, including limited field-scale validation, variability in nanoparticle characterization, and unresolved biosafety concerns regarding long-term ecological impacts. By synthesizing evidence from microbiology, nanotechnology, and plant pathology, this review identifies critical knowledge gaps and proposes future research directions. These include the integration of chitosan-based nanocomposites into integrated pest management (IPM) programs, the establishment of robust regulatory frameworks, and the application of omics technologies to decipher molecular mechanisms. Ultimately, chitosan-based nanocomposites hold substantial potential to revolutionize citrus canker management, offering a high-efficacy, environmentally compatible solution that aligns with the principles of sustainable agriculture and global food security.

Research on the implementation of sustainable project management (SPM) in the building industry is still in its early stages, with only a few noteworthy studies completed. Particularly interesting is how developing countries are responding to development difficulties and strategically using SPM. SPM is one way in which the building industry influences sustainable development. Therefore, this study seeks to identify, categorize and evaluate the influence of the drivers of SPM in Ghana's public sector construction projects. By the usage of a questionnaire survey, the opinions of construction firms on the drivers of SPM were assessed. 641 applicable answers were used for the exploratory factor analysis and four groups named project management-related factors, social sustainability-related factors, financial sustainability-related factors and environmental sustainability-related factors were revealed to significantly influence SPM. The building sector is growing and as a result of its devastating impacts on society, economy and environment, the development of sustainable facilities is imperative.

Space cooling is the primary driver of energy consumption in office buildings in Tanzania; however, architects currently lack simulation-validated geometric guidance tailored to Dar es Salaam’s hot-humid equatorial climate (Köppen Aw). This study quantified the combined effects of plan shape, building orientation, and window-to-wall ratio (WWR) on the annual site energy use intensity (EUI). Five plan forms (square, rectangle, circle, L-shape, and U-shape) were simulated in DesignBuilder v7 (EnergyPlus v9.6) for a 30-story, 50,000 m² prototype using TMYx 2009–2023 weather data, with validation against TANESCO metered billing data (NMBE = +1.21%, CV(RMSE) = 13.32%). The L-shaped plan achieved the lowest EUI of 291.31 kWh/m²/yr, representing a 20.8% reduction compared to the rectangular plan (367.83 kWh/m²/yr), primarily due to façade self-shading rather than compactness alone. An east-west orientation minimizes solar heat gain, whereas a WWR of 20–40% optimally balances daylight and cooling loads. These findings provide locally calibrated, evidence-based design guidelines for energy-efficient office buildings in Tanzania.

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.