Last-mile delivery is one of the most costly and complex stages of logistics, especially in urban areas where travel distances are affected by road direction, traffic conditions, and access restrictions. This paper studies the Asymmetric Traveling Salesman Problem (ATSP) as a routing framework for last-mile delivery optimization. A binary integer programming model is proposed to determine the visiting order of customers and the directed movements between consecutive positions in the tour. The model assigns each customer to exactly one position, ensures that all customers are visited once, and returns the route to the depot while minimizing the total directed travel cost. The proposed formulation is implemented in Python and solved using Gurobi Optimizer. Computational experiments are conducted on ATSP benchmark instances, including four different instances. The results show that the model generates valid ATSP routes and performs efficiently for small instances, achieving a solution time of 0.01 seconds for 4.atsp. However, the solution time increases for larger instances due to the growth of transition variables. The proposed model is compared with classical ATSP formulations, including MTZ, GP, MCF, PQ, and SST. The findings show that the model provides a clear and interpretable route structure, while future work will focus on improving its computational performance for larger-scale instances.

Transformer fires are high-risk events affecting safety, equipment reliability, and power continuity. This paper presents a risk based framework for transformer fire prevention, developed by integrating four analytical tools: Descriptive Analysis, Pareto Analysis, Root Cause Analysis (RCA) using a fishbone diagram, and Failure Mode and Effects Analysis (FMEA). Data from six research papers covering eight failure causes were analysed. Pareto Analysis identified electrical faults, overheating, and insulation degradation as the top three risk drivers. FMEA quantified each root cause using Severity, Occurrence, and Detection ratings, producing Risk Priority Numbers (RPN) before and after proposed controls. Mitigation measures including condition-based maintenance, online dissolved gas analysis, thermal imaging, and operator training significantly reduced RPN values. The integrated framework supports earlier fault detection and decision-making aligned with National Fire Protection Association (NFPA) 70B-2023, offering a practical tool for safer transformer operation.

Early childhood learning environments form the foundation for children’s physical, cognitive, and social-emotional development. While there is a body of research on educational quality and pedagogy in nursery settings, little is known about the impact of technical infrastructure failures that may negatively affect developmental outcomes. Most previous research has focused on environmental quality factors such as indoor air quality, lighting, acoustics, and thermal comfort, without using a systematic engineering approach to identify and assess infrastructure-related risks. The purpose of this study is to determine the risk factors for technical infrastructure failure affecting early childhood development outcomes in nurseries in Sharjah. A structured literature review was performed to extract and classify infrastructure-related failure modes reported in the literature related to nurseries, educational facilities, building performance, and facility management. The identified risks were subsequently categorized by infrastructure systems and categorized into three categories related to physical, cognitive, and socio-emotional developmental outcomes. The results provide a preliminary risk inventory that may support future risk assessment studies and inform evidence-based infrastructure management policies for nursery facilities.

Small- to medium-sized manufacturing industries are challenged by insufficient production systems and resources, leading them to adopt a run-to-failure maintenance strategy. This run-to-failure maintenance strategy was adopted by factory “A” due to higher production output demands, resulting in inefficiencies in preventive maintenance activities. In return, the inefficiencies in preventive maintenance have led to higher frequencies and longer production downtimes, negatively affecting production output. The use of Factory “A” historical downtime data extracted from the Overall Equipment Effectiveness (OEE) software connected to Factory “A” production lines was analysed using FTA to assess failure causes and identify gaps in existing maintenance approaches. The research adopted the Markov processes as a model to extend equipment lifespan and reduce maintenance costs. This research focused on the adoption of Condition-Based Maintenance (CM) strategies in factory “A. The research optimised CM using the Markov processes and models to predict equipment reliability and optimise maintenance schedules. The optimised CM have improved factory “A” operational Key Performance Indicators (KPIs), by lowering Systems Average Interruption Duration Index (SAIDI) and Systems Average Interruption Frequency Index (SAIFI), resulting in a 58.11% reduction in downtime (102.9 hours) and a 28.04% decrease in downtime frequency (60 occurrences) respectively. The improved CM strategy balanced investment, operational, and maintenance costs by enhancing production efficiency and profitability. By incorporating Markov models for reliability predictions, the study demonstrated how CM have optimised maintenance strategies, extended equipment lifespan, and improved cost-effectiveness in manufacturing factory “A”.