This study develops a multi-period Mixed Integer Linear Programming model for Closed-Loop Supply Chain (CLSC) network design in the electronics sector. The model explicitly integrates two dynamic drivers of consumer behavior—discounts and environmental awareness—into the return rate formulation. The model defines the flow of production, remanufacturing, shipping, returned product quantity, waste volume, and consumer return rate across multiple planning periods, with the goal of minimizing total costs. The numerical case study is based on synthetic data constructed based on parameter ranges in previous CLSC studies to reflect realistic operational conditions. The numerical results show manufacturing costs as the largest component (68.36%), followed by transportation (8.23%) and distribution handling (6.69%). Sensitivity analysis indicates (i) higher discounts and greater awareness significantly increase the return rate, increase remanufacturing volume, and reduce waste; and (ii) increasing Fd results in significant cost savings, with the highest outcome minimizing combined production and remanufacturing costs. These findings support a targeted strategy of discounts and environmental awareness, aligning remanufacturing capacity with returns, and achieving profitability. For policymakers, this model offers a decisionsupport tool to assess discounts and awareness programs, encouraging sustainable and profitable CLSC operations that align with circular economy principles.

The Boundary Element Method (BEM) has long been recognized as an efficient numerical technique for the solution of linear boundary value problems, particularly those involving unbounded domains, free-surface effects and radiation conditions. However, its application to nonlinear problems poses additional challenges, including the loss of strict boundary-only discretization and the need for domain integration or numerical approximation strategies. As a result, nonlinear extensions of BEM have evolved more slowly than their linear counterparts, and related research remains comparatively fragmented across scientific domains. This paper presents a comprehensive review of nonlinear BEM, synthesizing the mathematical foundations, historical developments and application-specific methodologies reported in the literature. The review is organized around a structured thematic framework spanning solid mechanics, heat transfer, fluid mechanics, acoustics, electromagnetics and emerging multi-physics applications. Bibliometric analysis provides a supporting perspective on publication trends and research evolution. A comparative assessment highlights methodological strategies—such as domain–boundary element formulations, dual reciprocity, local domain methods and hybrid FEM–BEM couplings—and evaluates their applicability across problem classes. The review also identifies research gaps, including the scarcity of benchmark problems, limited software availability and a lack of systematic comparative studies across numerical frameworks. Overall, the survey demonstrates that while nonlinear BEM remains specialized relative to nonlinear FEM, it has matured into a viable and efficient alternative for problems involving unbounded domains, boundary-dominant phenomena and multi-field coupling. The findings of this review aim to support researchers, computational engineers and method developers by clarifying the current status, trends and future opportunities in nonlinear boundary element analysis.

This study applies lean manufacturing techniques to optimise container management in a retail distribution centre’s yard. It addresses inefficiencies, such as processing delays, poor container visibility, outdated equipment and inadequate management techniques. Using a mixed-method approach, including process mapping, direct observation and quantitative data, this study identifies baseline metrics and root causes of delays. Techniques such as the fishbone diagram, Pareto analysis and five why’s were used to analyse these causes. The SWOT Analysis and Prioritisation Matrix were used to assess potential improvements. The results indicated that poor layout design, manual tracking and container congestion were the major delay factors. The structured approach aims to enhance operational efficiency by projecting a 30% improvement in container processing times, vehicle turnaround and visibility. The study concluded that using lean technologies effectively enhances container yard operations, reduces costs and boosts productivity by providing a framework for continuous improvement.

The main advantage of the proposed knuckle joint press mechanism over traditional crank presses is the extended dwell time, which enables better control over the stamping process. This allows for precise deformation, improving product quality. Additionally, the optimized force transmission in the proposed design reduces mechanical losses, leading to improved energy efficiency. These improvements make the press more suitable for high-demand applications where precision and durability are essential, thus providing a significant advantage over existing crank presses. This study focuses on the synthesis of a knuckle joint press utilizing mechanisms with unevenly distributed rods, which belong to the category of variable contour mechanisms. The objective is to develop a systematic method for synthesizing such a mechanism, specifically targeting the task of maintaining the stability of the working link with predetermined parameters. The synthesis approach involves reducing the mechanism’s class and implementing a step-by-step kinematic synthesis method. The reduction in class is based on the original double-rod mechanism of the fourth class, while the kinematic synthesis of the third-class mechanism is performed in two stages: optimal synthesis of the closed kinematic chain and interpolative synthesis of the kinematic linkage. By following this methodology, a third-class mechanism with a prolonged dwell time is successfully synthesized, and a two-rod crank press with similar kinematic characteristics is designed based on its scheme. Additionally, algorithms and programs for kinematic analysis of the synthesized mechanisms are developed, demonstrating the reliability of the results in achieving the desired kinematic characteristics for the crank press. The obtained results have practical significance and can be applied in the development of high-precision presses for metal forming, ensuring improved product quality and energy efficiency in industrial applications.

In this study, activated carbons (ACs) from banana stems and ripped plantain peels were used in the mitigation of iron (Fe2+) and manganese (Mn2+) in aqueous media. The AC were activated by soaking in 20% phosphoric acid (H3PO4) at a ratio of 1:5. The AC was calcined at 500 °C and washed till near neutral pH. The ACs were characterized by SEM, FTIR, and BET analysis. Ripped plantain peel AC (RPPAC), and Banana Stem AC (BSAC) were packaged as bagged and unbagged adsorbents. All adsorption experiments were undertaken using 2 g of adsorbent, pH 6 and at room temperature (25 °C). The maximum removal efficiency of bagged BSAC and RPPAC were 99.95% (for both) at 30 minutes for Mn2+. The bagged and unbagged BSAC resulted in 72.5% removal efficiency value (at 120 minutes) and 99.1% removal efficiency (at 90 minutes) for Fe2+ removal respectively. At a contact time of 30 mins, the unbagged (RPPAC and BSAC) adsorbents had a removal efficiency of 99.95% for both Fe2+ and Mn2+. Adsorbents were evaluated via Isotherm and kinetic models and were found effective in mitigating Fe and Mn contamination in water.

Solar power has become a popular renewable energy source, and the integration of solar power plants into a power grid has various technical, economic, and environmental implications. This research provides a new perspective on investigating the economic and environmental impact on a power grid. The effectiveness of this method is tested in a solar photovoltaic system connected to a grid of 8-bus. Simultaneous results show that the integration increases bus voltage by up to 0.4 kV and reduces transmission losses to 181.23 MWh/year, with total generated power at the slack bus reaching 3266.75 MWh/year. The reduction in transmission losses saves approximately USD 1,468,730.80 in operational costs. The payback period is 3.78 years, and the investment proves profitable, with a positive net present value. This reduction also reduces fuel consumption by about 345.78 kiloliters/year, mitigating climate change by preventing 898.38 tons CO2e/year of greenhouse gas emissions.

The rapid expansion of Internet of Things (IoT) devices has highlighted the shortcomings of traditional centralized identity management systems, which often suffer from single points of failure, high latency, and limited scalability. These challenges are particularly significant in IoT environments with limited resources, where secure and efficient identity management is crucial for real-time data processing and decision-making. This paper introduces a consortium blockchain-based IoT identity management system enhanced by fog computing. By utilizing blockchain’s decentralized and tamper-resistant features, the system secures identity management, while fog computing minimizes latency by processing data closer to its source. Our system is designed to scale effectively, even in densely populated IoT deployments, while maintaining low energy consumption and high throughput. Simulation results show notable reductions in latency and improvements in throughput, with the system achieving an average latency of 175.15 ms for registration operations with a 0.35 MB payload. The system’s robustness is further demonstrated by its resilience to common IoT security threats, such as man-in-the-middle attacks and node compromise. Key contributions include the innovative integration of blockchain and fog computing, enhanced scalability and efficiency, improved security through decentralized identity management, and adaptability to emerging technologies like quantum-resistant cryptography.

Alzheimer’s Disease (AD) is a long-term neurodegenerative condition that demands precise and timely diagnosis to enable early intervention. This study proves that combining deep learning with fuzzy logic along with explainable AI (XAI) can significantly enhance diagnostic accuracy for AD detection and grading. This research presents a new deep learning (DL) methodology for grading the severity of AD based on brain MRI images. The technique combines Swish-activated ResNet for extracting features, fuzzy logic for enhanced decision-making, and XAI methods to increase model transparency. For increased interpretability, Grad-CAM and SHAP methods are used with consistency and clinical applicability. The model is rigorously evaluated on multiple benchmark datasets, including OASIS, ADNI, PDAD, and Alzheimer’s Dataset. Experimental results on several datasets show the better performance of the proposed model. It reports 99.2% accuracy on OASIS’s and other datasets, outperforming DenseNet-121 (92.8%) and XGBoost (89.8%). This research contributes a hybrid CNN–fuzzy–XAI framework that not only improves diagnostic performance but also enhances explainability, making it clinically relevant and scalable for real-world deployment. This research emphasizes the diagnostic accuracy improvement and early detection support provided by CNN–fuzzy–XAI.