Anion exchange membrane electrolyzer (AEMEC) is a promising hydrogen production technology device. An electrochemical model is developed using MATLAB/Simulink to analyze the impact of factors such as anion exchange membrane (AEM) thickness, operating temperature, pressure, and gas diffusion layer (GDL) parameters including GDL thickness, porosity, and pore size. The results showed that as the thickness of AEM, operating pressure, and GDL decreased, the electrolysis efficiency significantly improved, and energy consumption decreased. When the thickness of AEM decreases from 70 microns to 65 microns, it will cause a decrease of 24 mV in cell voltage. This study also found that increasing pressure slightly increases voltage due to higher diffusion overpotential. In addition, changes in GDL porosity and pore size have a significant impact on performance. The lower porosity reduces ohmic loss and improves efficiency. This study highlights the importance of optimizing the design of AEMEC components to improve hydrogen production performance.

Nowadays, reverse osmosis (RO) desalination has become a highly effective and economical solution to address water scarcity worldwide. The membranes used in this type of separation are influenced by both pre-treatment operations and feed water quality, leading to fouling, a complex phenomenon responsible for reducing treatment performance and shortening membrane lifespan. In this study, an autopsy of a RO membrane from the Boujdour plant was performed, and a fouling prediction tool based on transmembrane pressure (TMP) was developed using MATLAB/Simulink (R2015a) with an artificial neural network (ANN) model. The impact of membrane fouling on treatment performance was also examined through one year of monitoring. A detailed analysis of the fouled membrane was conducted using SEM/EDS techniques to characterize the fouling on the membrane’s surface and cross-section. The results revealed significant fractures on the membrane surface, with fouling predominantly consisting of organic deposits (characterized by a high oxygen concentration of 39.69%) and inorganic fouling, including Si (7.99%), Al (2.79%), Mg (1.56%), Fe (1.27%), and smaller quantities of K (0.87%), S (0.36%), and Ca (0.12%). The ANN model for predicting transmembrane pressure was successfully developed, achieving a high R2 value of 92.077% and a low mean square error (MSE) of 0.005657. This predictive model demonstrates the ability to forecast future TMP cycles based on historical data. The research provides a detailed understanding of the types of fouling affecting RO membranes and contributes to the development of preventive strategies to mitigate membrane fouling.

Reverse engineering (RE) is essential in recreating 3D models of existing manufactured parts. It is widely used for repairing damaged components, improving used parts, and designing new items based on older models. One of the most common methods in RE is photogrammetry, which enables 3D reconstruction by capturing multiple images. Therefore, this study aimed to explore the application of mobile photogrammetry to obtain a 3D model of a train brake pad. The process started with capturing images of objects in a quick and professional manner to ensure visualization of data. This was followed by processing 2D images using Agisoft Metashape 2.2.1 software and Artificial Intelligence (AI) algorithms to create a precise 3D model. Subsequently, assessment was performed using feasibility in terms of cost, time, and accuracy. The results show that mobile photogrammetry provided an accessible and cost-effective method, alongside maximum contact stress after reducing optimization by approximately 28.42%, with maximum error value measured by the virtual model with the reference value of 0.30 mm (on Metashape) and 0.46 mm (on AI). This suggested that reverse parameterization significantly accelerated computer-aided design (CAD) model reconstruction and reduced the part redesign development cycle. By using photogrammetry and parametric modeling, engineers could accurately analyze and optimize train brake pads, ensuring safety as well as sustainability in railway operations. Additionally, RE and parametric modeling could assist in creating durable, cost-effective alternatives, and predicting appropriate replacements.

This work aims to explore the magnetohydrodynamic mixed convection boundary layer flow (MHD-MCBLF) on a slanted extending cylinder using Eyring–Powell fluid in combination with Levenberg–Marquardt algorithm–artificial neural networks (LMA-ANNs). The thermal properties include thermal stratification, which has a higher temperature surface on the cylinder than on the surrounding fluid. The mathematical model incorporates essential factors involving mixed conventions, thermal layers, heat absorption/generation, geometry curvature, fluid properties, magnetic field intensity, and Prandtl number. Partial differential equations govern the process and are transformed into coupled nonlinear ordinary differential equations with proper changes of variables. Datasets are generated for two cases: a flat plate (zero curving) and a cylinder (non-zero curving). The applicability of the LMA-ANN solver is presented by solving the MHD-MCBLF problem using regression analysis, mean squared error evaluation, histograms, and gradient analysis. It presents an affordable computational tool for predicting multicomponent reactive and non-reactive thermofluid phase interactions. This study introduces an application of Levenberg–Marquardt algorithm-based artificial neural networks (LMA-ANNs) to solve complex magnetohydrodynamic mixed convection boundary layer flows of Eyring–Powell fluids over inclined stretching cylinders. This approach efficiently approximates solutions to the transformed nonlinear differential equations, demonstrating high accuracy and reduced computational effort. Such advancements are particularly beneficial in industries like polymer processing, biomedical engineering, and thermal management systems, where modeling non-Newtonian fluid behaviors is crucial.

In an era marked by sustainability challenges and digital transformation, organizations face heightened uncertainty in strategic decision-making. This paper applies a conceptual tool, a fuzzy-based decision model, in the appraisal of the attractiveness of electric vehicle acquisition and navigates the multifaceted complexities of integrating economic, environmental, and infrastructural factors. A concise overview of fuzzy principles highlights their relevance to strategic management in uncertain contexts. The study uses a practical example to demonstrate how fuzzy set-based decision models assess EV attractiveness by synthesizing costs, environmental impact, vehicle depreciation, and energy independence variables. The findings reveal the fuzzy set-based decision model’s potential to enhance decision clarity and efficiency, offering managers a simple but robust framework for navigating complex trade-offs. Implications for sustainable strategic management and suggestions for future research on advanced decision support systems are discussed.

Sudden cardiac death (SCD) represents a critical public health challenge, emphasizing the need for predictive techniques that model complex physiological dynamics. Studies indicate that the “V-trough” pattern in sympathetic nerve activity (SNA) could act as an early indicator of potentially fatal cardiac events, which can be effectively modeled using a modified version of Chua’s chaotic system, incorporating the variables of heart rate (HR), SNA, and blood pressure (BP). This paper introduces a Chua circuit with delay, and proposes a novel control design technique based on Lurie-type control systems theory combined with mixed-sensitivity H∞ (S/KS/T) methodology. The proposed controller enables precise regulation of HR in Chua’s circuit, both with and without delay, paving the way for the development of advanced devices capable of preventing SCD. Furthermore, the developed theory allows for the project of robust controllers for delayed Lurie systems within the single-input–single-output (SISO) framework. The presented theoretical framework, supported by numerical simulations, demonstrates the effectiveness of the conceptualization, marking a considerable advance in the understanding and early intervention of SCD through robust and nonlinear control systems.

The effectiveness of polystyrene (PS)/TiO2 nanocomposite coatings in reducing stress–corrosion cracking (SCC) susceptibility of aluminum alloy 2024-T3 (AA2024-T3) was evaluated using an accelerated stress–corrosion test. Polystyrene (PS)-based coatings incorporating TiO2 nanoparticles with three different aspect ratios (ARs) were compared to a bare polystyrene coating. A compact tension (CT) specimen (5 mm thick) was coated for testing in a synergistic stress–corrosion environment. A slow constant displacement rate of 1.25 nm/s was applied in the load-line direction of the specimen to gradually open the crack mouth, while the crack tip was periodically dosed with a 3.5 wt.% NaCl solution. Load-displacement data were recorded and analyzed to calculate the J-integral, according to Standard ASTM E1820, for each coated specimen tested under laboratory-controlled SCC conditions. The fracture toughness, stress intensity, and six other SCC susceptibility indices were further developed to compare the performance of each coating in enhancing SCC resistance. The results revealed a strong dependence of SCC resistance on the nanoparticle aspect ratio, with the nanocomposite coating featuring an AR of 1 performing the best. The SCC behavior was reflected in the fractography of the fractured halves of a specimen, where cleavage was observed during the very slow, stable cracking stage, and dimples formed as a result of fast, unstable cracking toward the end of testing. These findings highlight the potential of tailored nanocomposite coatings to enhance the durability of aerospace-grade aluminum alloys.

Scaling in geothermal production wells poses a critical challenge to sustainable energy production, particularly in vapor-dominated systems where scaling mechanisms are less understood. This study investigates scale treatment planning using the broaching method in Well X at Indonesia’s Kamojang geothermal field. Through well integrity testing, geochemical analysis, and XRD characterization, silica (quartz) scale formations were identified in the production casing. Performance monitoring revealed gradual decreases in steam production and wellhead pressure over a three-year period. The selection of the broaching method was validated through analysis of scale characteristics, well geometry, and economic feasibility, offering a significantly more cost-effective solution compared to conventional methods with a substantially shorter payback period. Broaching has effectively operated on multiple geothermal wells, restoring significant production capacity at approximately half the expense of conventional well workover methods. Our results challenge accepted assumptions on scaling in vapor-dominated systems and provide a methodical framework for scale treatment planning. This study demonstrates how strategic scale management can efficiently preserve well productivity while lowering operating costs, thus enabling sustainable geothermal resource development for operators worldwide.

This study investigates the thermal performance of heat pipes using nanofluids based on silver (Ag), aluminum oxide (Al2O3), and multi-walled carbon nanotubes (MWCNTs) at varying concentrations. Heat pipes, recognized for their efficiency in passive thermal management, face limitations with traditional fluids. Nanofluids, engineered by dispersing nanoparticles in base fluids, were explored as alternatives due to their superior thermal conductivity and convective properties. Nanofluids were prepared using ultrasonication, and their thermal conductivity, viscosity, and stability were evaluated. Experimental tests were conducted under controlled conditions to assess the impact of nanoparticle type, concentration, inclination angle, and fluid filling ratio on performance metrics, including thermal resistance (TR) and heat transfer coefficients (HTCs). The results demonstrated that Ag-based nanofluids outperformed others, achieving a 150% increase in thermal conductivity and an 83% reduction in TR compared to deionized water. HTCs increased by 300% for Ag nanofluids at a 0.5% concentration. Inclination angles and filling ratios also significantly affected performance, with optimal conditions identified at a 70% filling ratio and a 30° inclination angle. The findings highlight the potential of nanofluids in optimizing heat transfer systems and provide a framework for selecting suitable parameters in industrial applications.

Given the significant increase in electricity consumption, especially in the industrial and commercial categories, exploring new energy sources and developing innovative technologies are essential. The fourth industrial revolution (Industry 4.0) and digital transformation are not just buzzwords; they offer real opportunities for energy sustainability, using technologies such as cloud computing, artificial intelligence, and the Internet of Things (IoT). In this context, this study focuses on improving energy efficiency in smart spaces within the context of Industry 4.0 by utilizing the SmartParcels framework. This framework creates a detailed and cost-effective plan for equipping specific areas of smart communities, commonly referred to as parcels. By adapting this framework, we propose an integrated model for planning and implementing IoT applications that optimizes service utilization while adhering to operational and deployment cost constraints. The model considers multiple layers, including sensing, communication, computation, and application, and adopts an optimization approach to meet the needs related to IoT deployment. In simulated industrial environments, it demonstrated scalability and economic viability, achieving high service utility and ensuring broad geographic coverage with minimal redundancy. Furthermore, the use of heuristics for device reuse and geophysical mapping selection promotes cost-effectiveness and energy sustainability, highlighting the framework’s potential for large-scale applications in diverse industrial contexts.