This study introduces a novel strategy for optimizing hybrid drone-and-truck delivery systems in remote areas by leveraging geospatial analytics. Geospatial methods are employed to identify optimal depot and drone nest locations, which serve as critical nodes for efficient delivery operations. After determining these locations, a customized Vehicle Routing Problem (VRP) model is applied to solve the routing problem. We use Network Analyst (NA) from ArcGIS Pro to solve the VRP problem and improve the solution by customizing the algorithm so that all delivery orders for a vehicle are geographically clustered within the service area. Comparative analysis between truck-only and hybrid truck-and-drone scenarios reveals significant efficiency gains, including reductions in delivery routes, on-road minutes, and total miles traveled. A case study conducted in parts of Wyoming, Idaho, Nevada, Utah, and Colorado validates these findings. The results demonstrate a 10.5% reduction in delivery routes, a 15% reduction in on-road minutes, and a 28% decrease in total miles. Further improvements were achieved through spatial clustering, optimizing delivery routes by grouping orders geographically. These findings emphasize the potential of hybrid delivery systems to improve logistics in remote areas, providing actionable insights for supply chain decision-makers, highlighting the robustness of the proposed method.

Purpose: In the current global market, textile industries are facing relentless pressure to optimize efficiency and effectiveness to maintain a competitive edge and ensure long-term viability. Acknowledging such a situation, this study aims to identify and implement critical Total Productive Maintenance (TPM)-driven strategies to enhance Overall Equipment Effectiveness (OEE) in the textile dyeing section. It further explores how these improvements contribute to long-term economic sustainability, especially in emerging economies. Design/methodology/approach: By utilizing both quantitative and qualitative analysis by an integrated TPM and interval-valued q-rung ortho-pair fuzzy Decision-Making Trial and Evaluation Laboratory (IVQROF-DEMATEL) method, this study identified the significant TPM-driven strategies by determining their prominence ranking, followed by the implementation of the vital strategies with the goal to enhance OEE and promote economic sustainability. Findings: Among the strategies, “Quality maintenance through precision dosing with closed-loop feedback control (S2)” and “Planned monitoring of water flow control with IoT sensors (S12)” were most prominent. After implementing the prioritized strategies, OEE improved from 68.6% to 76.3%, reflecting a 7.7% overall increase. This improvement stemmed from increases in availability (5.58%), performance (2.45%), and quality (1.43%), emphasizing the importance of TPM’s planned, quality, and autonomous maintenance pillars. Originality/value: This study is the first attempt to develop a decision-support model for enhancing machine maintenance strategies for the textile industry dyeing section by integrating the IVQROF-DEMATEL method with the TPM framework. It also contributes to the performance management literature, indicating that a strategic approach to TPM can not only enhance OEE but also promote economic sustainability in emerging-economy manufacturing environments. The outcomes of this study may serve as a guiding framework for future industry-oriented research conducted by noble scholars.

Artificial Intelligence (AI) offers significant potential to transform wastewater treatment by enhancing reliability, affordability, and sustainability. However, the adoption of AI in rural wastewater management remains limited due to unique challenges, including constrained resources, fragmented infrastructure, and variable water quality. These issues significantly impede the effectiveness of wastewater treatment, intensifying environmental pollution and public health threats in rural communities. This review systematically analyzes literature published between 2006 and 2024 on AI-driven wastewater monitoring and management, emphasizing machine learning (ML) and deep learning (DL) techniques tailored for urban and rural contexts. Relevant peer-reviewed studies were identified using targeted keyword searches across ScienceDirect and Elsevier databases, prioritizing comprehensive methodology and transparent reporting. Findings demonstrate that existing AI approaches predominantly address urban wastewater systems by optimizing chemical usage, energy efficiency, and operational effectiveness. Conversely, rural systems continue to face barriers such as data scarcity, incompatible infrastructure, and limited interpretability of ML and DL models, hindering AI implementation. To bridge these critical gaps, this paper recommends a modular, interpretable AI framework incorporating hierarchical input decomposition, adaptive data augmentation, and real-time monitoring strategies tailored explicitly to rural conditions. Furthermore, future research directions are also proposed to advance energy efficient, cost-effective, and privacy-preserving federated learning methodologies. Enhancing interpretability, addressing rural-specific data challenges, and promoting collaborative policy frameworks with active community participation are essential steps. Ultimately, scalable AI interventions emphasizing adaptive, interpretable strategies are urgently needed to mitigate environmental risks, safeguard public health, and promote sustainable wastewater infrastructure in rural communities.

Waste stabilization ponds (WSPs) in humid, subtropical climates rely on stable temperatures and mechanical aeration to promote microbial activity. These critical infrastructures can lack operational resources to ensure efficient treatment, which can impact downstream communities. This study aims to use remote water quality sensor data to establish trends in a yearly dataset and correlate various water quality parameters for simplistic identification of pond health. A facultative WSP was monitored in two stages: the primary settling over a period of 14 months to evaluate partially treated water, and the secondary treatment pond for a period of 11 months to monitor final stage water quality parameters. A statistical analysis was performed on the measured parameters (dissolved oxygen, temperature, conductivity, pH, turbidity, nitrate, and ammonium) to establish a comprehensive yearly, seasonal, and monthly dataset to show fluctuations in water parameter correlations. Standard relationships in dissolved oxygen, conductivity, pH, and temperature were traced during the seasonal fluctuations, which provided insight into nitrogen processing by microbial communities. During this study, the summer period showed the most variability, specifically a deviation in the dissolved oxygen and temperature relationship from a yearly moderate negative correlation (−0.593) to a moderate positive correlation (0.459), indicating a direct relationship. The secondary treatment pond data showed more nitrogen species correlation, which can indicate final cycling during seasonal transitions. Understanding pond dynamics can lead to impactful, proactive operational decisions to address pond imbalance or chemical dosing for final treatment. By establishing parameter correlations, facilities with WSPs can strategically integrate sensor networks for real-time pond health and treatment efficiency monitoring during seasonal fluctuations.

Purpose: Bangladesh's legal system struggles with major challenges like delays, complexity, high costs, and millions of unresolved cases, which deter many from pursuing legal action due to lack of knowledge or financial constraints. This research seeks to develop a specialized Large Language Model (LLM) to assist in the Bangladeshi legal system. Methods: We created UKIL-DB-EN, an English corpus of Bangladeshi legal documents, by collecting and scraping data on various legal acts. We fine-tuned the GPT-2 model on this dataset to develop GPT2-UKIL-EN, an LLM focused on providing legal assistance in English. Results: The model was rigorously evaluated using semantic assessments, including case studies supported by expert opinions. The evaluation provided promising results, demonstrating the potential for the model to assist in legal matters within Bangladesh. Conclusion: Our work represents the first structured effort toward building an AI-based legal assistant for Bangladesh. While the results are encouraging, further refinements are necessary to improve the model's accuracy, credibility, and safety. This is a significant step toward creating a legal AI capable of serving the needs of a population of 180 million.