Eng

This study investigates the use of machine learning techniques to predict the unconfined compressive strength (UCS) of both stabilized and unstabilized soils. This research focuses on analyzing key soil parameters that significantly impact the strength of earth materials, such as grain size distribution and Atterberg limits. Machine learning models, specifically Support Vector Regression (SVR) and Decision Trees (DT), were employed to predict UCS. Model performance was evaluated using key metrics, including the Pearson coefficient of correlation (r²), coefficient of determination (R²), mean absolute error, and root mean square error. The findings reveal that, for unstabilized soils, both SVR and DT models exhibit remarkable performance with r² values of 0.9948 and 0.9947, respectively, with the DT model surpassing the SVR model in estimating UCS. Validation was conducted using data from four types of locally available soils in the Najd region of Saudi Arabia, although some disparities were noted between actual and predicted results due to limitations in the training data. The analysis indicates that, for unstabilized soil, grain size distribution and moisture content during testing are primary influencers of strength, whereas, for stabilized soil, factors such as stabilizer type and content, as well as density and moisture during testing, are pivotal. This research demonstrates the potential of machine learning for developing a robust classification system to enhance earth material utilization. Full article

Brain tumors pose a significant challenge in medical research due to their associated morbidity and mortality. Magnetic Resonance Imaging (MRI) is the premier imaging technique for analyzing these tumors without invasive procedures. Recent years have witnessed remarkable progress in brain tumor detection, classification, and progression analysis using MRI data, largely fueled by advancements in deep learning (DL) models and the growing availability of comprehensive datasets. This article investigates the cutting-edge DL models applied to MRI data for brain tumor diagnosis and prognosis. The study also analyzes experimental results from the past two decades along with technical challenges encountered. The developed datasets for diagnosis and prognosis, efforts behind the regulatory framework, inconsistencies in benchmarking, and clinical translation are also highlighted. Finally, this article identifies long-term research trends and several promising avenues for future research in this critical area. Full article

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. Full article

Hydrogen (H₂) is a key energy vector in the global transition toward clean and sustainable energy systems. Among the various production methods, water electrolysis presents a promising pathway for zero-emission hydrogen generation when powered by renewables. This review provides a comprehensive evaluation of water electrolysis technologies, including alkaline (AWE), proton exchange membrane (PEMWE), solid oxide (SOEC), anion exchange membrane (AEMWE), and microbial electrolysis cells (MEC). It critically examines their material systems, catalytic strategies, operational characteristics, and recent performance advances. In addition to reviewing experimental progress, the study presents a finite element modeling (FEM) case study that evaluates thermal and mechanical responses in PEM and AWE configurations—illustrating how FEM supports design optimization and performance prediction. To broaden methodological insight, other simulation frameworks such as computational fluid dynamics (CFD), response surface methodology (RSM), and system-level modeling (e.g., Aspen Plus^®) are also discussed based on their use in recent literature. These are reviewed to guide future integration of multi-scale and multi-physics approaches in electrolyzer research. By bridging practical design, numerical simulation, and material science perspectives, this work provides a resource for researchers and engineers advancing next-generation hydrogen production systems. Full article

The use of polyester and polyamide fabrics for parachute constructions has a great advantage because, in comparison with classical silk-based parachutes, they are more durable and suitable for absorbing higher mechanical shocks. Because polyester and polyamides are thermoplastics, they are sensitive to sudden increases in temperature due to mechanical shocks and high-speed friction. It is known that the local surface temperature of these parachute fabrics may exceed the melting point of the canopy for a short time period during parachute opening, which would have irreversible effects on parachute functionality and could lead to catastrophic parachute rupture. The main aim of this article is to enhance the surface heat resistance of the parachute fabrics from polyamide and polyester filaments through surface coating combined with super-fine TiO₂ particles and silanization. This coating is also selected to increase the frictional heat loss and enhance the mechanical stability of parachute fabrics constructed from polyamide and polyester filaments. The changes in air permeability, bending rigidity, and friction of surface-coated parachute fabrics are evaluated as well. The new method based on laser irradiation by a pulsed laser is used for the prediction of these fabrics’ short-time surface thermal resistance. Full article

This study presents the design, development, and evaluation of a multifunctional stepladder that integrates four functionalities: a stepladder, Pilates chair, wheelchair, and walking aid. Unlike existing research that focuses on single-function assistive devices, this study uniquely integrates a stepladder, wheelchair, walking aid, and Pilates chair into one multifunctional device, offering a compact, space-saving solution that addresses multiple daily needs in a single design. Building upon previous research, which conceptualized a multifunctional stepladder by synthesizing ideas, features, and functions from patent literature, existing products, and scientific articles, this study focuses on the design and testing phases to refine and validate the concept. Using sustainable materials like mild steel and aluminium, the design was optimized through structural simulations, ensuring durability under loads of up to 100 kg. Usability tests revealed that the invention significantly reduced task completion times, saved five times the space compared to single-function products, and provided enhanced versatility. Cost analysis highlighted its affordability, with a retail price of MYR 1392—approximately 35% lower than the combined cost of its single-function counterparts. Participant feedback noted strengths such as eco-friendliness, practicality, and ergonomic design, alongside areas for improvement, including portability, armrests, and storage. Future work includes enhanced portability for stair navigation, outdoor usability tests, and integration of smart technologies. This multifunctional stepladder significantly contributes to caregivers by reducing the physical burden of managing multiple assistive devices, enhancing efficiency in daily caregiving tasks, and providing a safer, more convenient tool that supports both mobility and exercise for elderly users. This multifunctional stepladder also offers a sustainable, cost-effective, and user-centric solution, addressing usability gaps while supporting global sustainability and accessibility initiatives. Full article

In the face of the global depletion of natural resources and increasing demand for sustainable development, processing industrial waste, such as tailings from processing plants, is becoming essential. This study focuses on combined processing technologies, including flotation concentration and concentrate processing, allowing the efficient recovery of valuable components. This study aims to investigate the possibility of thermochemical enrichment and the opening of low-grade copper tailings of processing plants with the transfer of silicon and rhenium in the form of silicate-ions and perrhenate-ions into a solution with the output of a multifactor multiplicative model and obtaining tabular nomograms. Multifactor experiments on the thermochemical enrichment of rough copper concentrates made it possible to construct partial dependences of silicon and rhenium extraction into a solution and to obtain multiplicative Protodyakonov–Malyshev models of these processes and multifactor nomograms over a wide range of temperatures, durations, and alkali-to-concentrate ratios to determine the maximum recovery rates. The developed multifactor models made it possible to establish the optimal intervals of changes in the concentrate sintering parameters, providing high recovery rates (over 85% of silicon and 98% of rhenium) during subsequent water leaching. Optimal sintering conditions (temperature of 350 °C, the duration of 90 min, and the ratio of NaOH to concentrate = 1:2) ensured a recovery of up to 85% of silicon and 98% of rhenium from the concentrate into the solution. This recovery rate reduces the need for primary raw materials and positively affects the production’s environmental performance because it minimizes the amount of industrial waste disposal. Full article

This paper presents laboratory results on the physical–mechanical properties of frozen rocks from Russia’s Yamal Peninsula, aiming to improve foundation design in permafrost. Samples from various geological profiles underwent compression and shear tests along the freezing surface at −3 °C, following standard protocols. Strength and deformation characteristics were established for prevalent frozen rock types (sands, sandy loams, clay loams, clays), revealing links between physical properties and mechanical behavior. The study specifically investigated how salinity and the degree of pore filling with ice/unfrozen water influence the deformation modulus, crucial for foundation reliability in permafrost. Results demonstrated significant property variability related to granulometry, plasticity, porosity, and salinity. Deformation modulus generally decreased with increasing dispersion, ranging from approximately 44 MPa for saline sands down to 6–14 MPa for clays. Shear resistance varied from 0.05 to 0.20 MPa (clays) to 0.20–0.30 MPa (sands). The influence of pore filling on deformation modulus depended complexly on rock type, porosity, and salinity. These findings provide valuable data for geomechanical modeling and bearing capacity assessments of pile foundations in Arctic regions, particularly the Yamal Peninsula. Full article

This work presents a method for classifying EEG (Electroencephalogram) signals generated when a person concentrates on specific words, defined as “Imagined Speech”. Imagined speech is essential to enhance problem-solving, memory, and language development. In addition, imagined speech is beneficial because of its applications in therapy fields like managing anxiety or improving communication skills. EEG measures the electrical activity of the brain. EEG signal classification is difficult as the machine learning (ML) algorithm has to learn how to categorize the signal linked to the imagined word. This work proposes a novel method to generate a specific feature vector to achieve classification with superior accuracy results to those found in the state of the art. The method leverages a genetic algorithm to create an optimal feature combination for the classification task and machine learning model. This algorithm can efficiently explore ample feature space and identify the most relevant features for the task. The proposed method achieved an accuracy of 96% using eight electrodes for EEG signal recordings. Full article

The recycling of cable scrap, particularly from discarded electrical wiring, is gaining significant attention due to the rising demand for copper and the need for sustainable management of electronic waste. Traditionally, mechanical and thermal processings have been used to recover copper and plastic from cables. However, these approaches are often energy-intensive, time-consuming, and costly in terms of equipment and labor. In this study, we present a simple and effective method for recovering materials from cable scrap using a domestic microwave oven. Cable pieces (2–2.5 cm long) were exposed to 700 W of microwave irradiation under rotation for 30 s, enabling the rapid and efficient separation of high-quality copper metal from the core wire, and activated carbon from the carbonized plastic sheath. Microwaves facilitate this process through Ohmic heating, which induces electrical resistance in the metal, generating heat that mechanically loosens the metal and carbonized plastic components. The process demonstrates high efficiency, achieving an 80% reduction in energy consumption compared to conventional processings. This fast and energy-efficient method shows strong potential for scaling up to industrial recycling, offering a cost-effective and environmentally friendly way to recover high-quality materials for further use or repurposing. Full article

Ensuring the overall efficiency of hydraulic fracturing treatment depends on the ability to forecast bottomhole pressure. It has a direct impact on fracture geometry, production efficiency, and cost control. Since the complications present in contemporary operations have proven insufficient to overcome inherent uncertainty, the precision of bottomhole pressure predictions is of great importance. Achieving this objective is possible by employing machine learning algorithms that enable real-time forecasting of bottomhole pressure. The primary objective of this study is to produce sophisticated machine learning algorithms that can accurately predict bottomhole pressure while injecting guar cross-linked fluids into the fracture string. Using a large body of work, including 42 vertical wells, an extensive dataset was constructed and meticulously packed using processes such as feature selection and data manipulation. Eleven machine learning models were then developed using parameters typically available during hydraulic fracturing operations as input variables, including surface pressure, slurry flow rate, surface proppant concentration, tubing inside diameter, pressure gauge depth, gel load, proppant size, and specific gravity. These models were trained using actual bottomhole pressure data (measured) from deployed memory gauges. For this study, we carefully developed machine learning algorithms such as gradient boosting, AdaBoost, random forest, support vector machines, decision trees, k-nearest neighbor, linear regression, neural networks, and stochastic gradient descent. The MSE and R² values of the best-performing machine learning predictors, primarily gradient boosting, decision trees, and neural network (L-BFGS) models, demonstrate a very low MSE value and high R² correlation coefficients when mapping the predictions of bottomhole pressure to actual downhole gauge measurements. R² values are reported as 0.931, 0.903, and 0.901, and MSE values are reported at 0.003, 0.004, and 0.004, respectively. Such low MSE values together with high R² values demonstrate the exceptionally high accuracy of the developed models. By illustrating how machine learning models for predicting pressure can act as a viable alternative to expensive downhole pressure gauges and the inaccuracy of conventional models and correlations, this work provides novel insight. Additionally, machine learning models excel over traditional models because they can accommodate a diverse set of cross-linked fracture fluid systems, proppant specifications, and tubing configurations that have previously been intractable within a single conventional correlation or model. Full article

The transition to clean energy has accelerated the pursuit of hydrogen as a sustainable fuel. Among various production methods, proton exchange membrane electrolysis cells (PEMECs) stand out due to their ability to generate ultra-pure hydrogen with efficiencies exceeding 80% and current densities reaching 2 A/cm². Their compact design and rapid response to dynamic energy inputs make them ideal for integration with renewable energy sources. This review provides a comprehensive assessment of PEMEC technology, covering key internal components, system configurations, and efficiency improvements. The role of catalyst optimization, membrane advancements, and electrode architectures in enhancing performance is critically analyzed. Additionally, we examine state-of-the-art numerical modelling, comparing zero-dimensional to three-dimensional simulations and single-phase to two-phase flow dynamics. The impact of oxygen evolution and bubble dynamics on mass transport and performance is highlighted. Recent studies indicate that optimized electrode architectures can enhance mass transport efficiency by up to 20%, significantly improving PEMEC operation. Advancements in two-phase flow simulations are crucial for capturing multiphase transport effects, such as phase separation, electrolyte transport, and membrane hydration. However, challenges persist, including high catalyst costs, durability concerns, and scalable system designs. To address these, this review explores non-precious metal catalysts, nanostructured membranes, and machine-learning-assisted simulations, which have demonstrated cost reductions of up to 50% while maintaining electrochemical performance. Future research should integrate experimental validation with computational modelling to improve predictive accuracy and real-world performance. Addressing system control strategies for stable PEMEC operation under variable renewable energy conditions is essential for large-scale deployment. This review serves as a roadmap for future research, guiding the development of more efficient, durable, and economically viable PEM electrolyzers for green hydrogen production. Full article

The increasing demand for sustainable road infrastructure necessitates alternative materials that enhance soil stabilization while reducing environmental impact. This study investigated the application of organosilane-based nanotechnology to improve the structural performance and durability of road corridors in Peru, offering a viable alternative to conventional stabilization methods. A comparative experimental approach was employed, where modified soil and asphalt mixtures were evaluated against control samples without nanotechnology. Laboratory tests showed that organosilane-treated soil achieved up to a 100% increase in the California Bearing Ratio (CBR), while maintaining expansion below 0.5%, significantly reducing moisture susceptibility compared to untreated soil. Asphalt mixtures incorporating nanotechnology-based adhesion enhancers exhibited a Tensile Strength Ratio (TSR) exceeding 80%, ensuring a superior resistance to moisture-induced damage relative to conventional mixtures. Non-destructive evaluations, including Dynamic Cone Penetrometer (DCP) and Pavement Condition Index (PCI) tests, confirmed the improved long-term durability and load-bearing capacity. Furthermore, statistical analysis of the International Roughness Index (IRI) revealed a mean value of 2.449 m/km, which is well below the Peruvian regulatory threshold of 3.5 m/km, demonstrating a significant improvement over untreated pavements. Furthermore, a comparative reference to IRI standards from other countries contextualized these results. This research underscores the potential of nanotechnology to enhance pavement resilience, optimize resource utilization, and advance sustainable construction practices. Full article

The aim of this work is to evaluate the influence of the surface deformations of an open inflatable wing section on aerodynamic performance and boundary layer separation phenomena. The inflation/deflation processes are allowed by an air intake placed on the bottom side of the model. Due to its low rigidity, non-contact measurements are required. Therefore, an infrared thermography technique was applied in order to detect local surface deformations and local separation phenomena. Additionally, the inflation and deflation of the whole wing were studied through an innovative approach, introduced by the authors, based on a piezoelectric sensor. It is important to note that open and closed wing sections exhibit very different aerodynamic behavior. For these reasons, both cases were investigated in the following research. The impact of deformation on the wing’s aerodynamic performance was assessed by means of wind tunnel tests. The inflatable wing presented lower lift and higher drag than the corresponding rigid wing due to the fabric’s deformations. Furthermore, the lift and moment coefficient curves were strongly related to the wing’s inflation. In particular, there was a change in the slope of the lift curve and a drop in the moment coefficient when the wing inflated. Lastly, the results provided evidence that a thermographic approach can be used to qualitatively detect local deformations of an inflatable wing and that a piezoelectric sensor can be used feasibly in detecting the inflation and deflation phases of a wing. Full article

The United Kingdom seeks to achieve net-zero emissions by 2050, mostly via the shift to an electrical system exclusively powered by zero-carbon sources. Microgrids (MGs) can be seen as an effective system for integrating renewables into the energy portfolio. Nonetheless, MGs face the acknowledged obstacle of backup power generation due to the intermittent nature of renewable energy sources, necessitating the establishment of backup power generation capacity. This paper contrasts selfish power generation, where the MG pursues complete energy autonomy, with an alternative influenced by lean principles (Heijunka production), which seeks to stabilise power transactions within the national electricity supply chain, reduce emissions, and tackle the backup generation challenge. This study proposes a pre-contractual order update (COU) strategy for the operation of hybrid collaborative MG where a forward order update to the utility grid is placed, in contrast to selfish MG, which uses a spot order update strategy. The COU strategy was defined, and two simulation models (for selfish and collaborative MG) were developed, each incorporating four backup generation scenarios to illustrate the method’s efficacy by assessing the system’s critical performance metrics. It has been found that the collaborative MG model reduced the carbon emissions by 62% and the volatility of unplanned orders to the grid by 61% compared to the selfish model in the first scenario (grid-dependent MG). Furthermore, the MG achieved zero volatility and a 33% reduction in carbon content in the collaborative MG when using the H₂ burner as backup generation compared to the first scenario. Indicating that sustainability encompasses not only the use of renewable resources but also the stability of their outputs through the implementation of collaborative MGs. Full article

The aviation sector faces a significant challenge in balancing the rising demand for air travel with the need to reduce its environmental impact. Because air travel accounts for approximately 2.5% of global carbon emissions, there is a need to find sustainable solutions to reduce its environmental impact. Improving aerodynamic performance is a crucial area for reducing fuel consumption and emissions. Nowadays, more focus is given to commercial aviation, which contributes to global aviation emissions. The A380 is the largest passenger aircraft in the world at the moment. It was observed in real life that the wake turbulence from the A380 led to a sudden loss of the Challenger aircraft’s control and a rapid descent of more than 10,000 feet. This Challenger incident is a wake-up call to address the A380’s wake turbulence. Hence, this research focuses on designing and analysing blended winglets for the Airbus A380 to reduce wake turbulence. With the use of modern computational fluid dynamics tools, the current A380 winglets’ performance was evaluated to identify the level of lift, drag and wake vortex patterns. To address these challenges, the performance of newly designed blended winglets with different cant angles, i.e., 0, 15, 45 and 80, was analysed computationally using the K-ω SST turbulent model in the software ANSYS Fluent 2024 R1. It resulted in a decrease in the wake vortex size accompanied by a 1.724% decrease in drag. This research project evidenced that addressing the wake turbulence issue on a large aircraft could improve aerodynamic performance and thus contribute towards sustainable aviation. Full article

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. Full article

This review paper investigates the comprehensive impact of various nanomaterials on the mechanical properties and durability of pavement-quality concrete (PQC) with a specific focus on compressive strength, flexural strength, split tensile strength, permeability, abrasion resistance, fatigue performance, and crack relief performance. Despite significant advancements in the use of nanomaterials in concrete, existing research lacks a comprehensive evaluation of their comparative effectiveness, optimal dosages, and long-term durability in PQC. While conventional PQC faces challenges such as low fatigue resistance, high permeability, and susceptibility to abrasion, studies on nanomaterials have largely focused on individual properties rather than a holistic assessment of their impact. Nano SiO₂ and graphene oxide (GO) emerged as the most effective, with optimal dosages of 2% and 0.03%, respectively, leading to substantial improvements in compressive strength (up to 48.88%), flexural strength (up to 60.7%), and split tensile strength (up to 78.6%) through improved particle packing, reduced permeability, and refined microstructure. Nano TiO₂, particularly at a 1% dosage, significantly enhanced multiple properties, including a 36.30% increase in compressive strength, over 100% improvement in abrasion resistance, and a 475% increase in fatigue performance. However, a critical research gap exists in understanding the combined effects of multiple nanomaterials, their interaction mechanisms within cementitious systems, and their real-world performance under prolonged environmental and loading conditions. Most studies have been limited to laboratory-scale investigations, with minimal large-scale validation for pavement applications. The findings indicate that nanomaterials like nano TiO₂, nano CaCO₃, nano Al₂O₃, nano clay, and carbon nanomaterials play crucial roles in improving characteristics like permeability, abrasion resistance, and fatigue performance, with notable gains observed in many cases. This review systematically analyzes the influence of these nanomaterials on PQC, identifies key research gaps, and emphasizes the need for large-scale field validation to enhance their practical applicability. Full article