Scopus Research — Khidhair Jasim Mohammed Al- Jubouri

The imperative to produce lightweight-components has intensified the need for fabrication of Mg-matrix composites. This investigation addresses this exigency by using friction stir processing (FSP) as a solid-state route. This study's goal was to find out how vibration affects the tribological and mechanical properties of AZ31/CeO2+h-BN surface composites that were made using FSP and FSVP. The attained data showed that FSVP resulted in better homogeneity of CeO2+h-BN particles across the matrix. The findings also showed that adding vibration to FSP leads to improved hardness and increased shear punch strength (SPT), but also leads to higher wear resistance. The 43 % reduction in grain size in FSVPed composites resulted in a 16 % increase in hardness, while the SPT also improved by approximately 33 %. These changes also resulted in a 25 % decrease in wear rate and a 25 % reduction in average friction coefficient for FSVP composites compared to the FSP. These results suggest that FSVP had a great potential in the mechanical and tribological properties enhancement of AZ31Mg alloy composites. © 2025 Elsevier Ltd

Microalgae, such as Chlorella vulgaris and Scenedesmus obliquus, are highly efficient at capturing carbon dioxide through photosynthesis, converting it into valuable biomass. This biomass can be further processed into carbon materials with applications in various fields, including water treatment. The reinforcement learning (RL) method was used to dynamically optimize environmental conditions for microalgae growth, improving the efficiency of biodiesel production. The contributions of this study include demonstrating the effectiveness of RL in optimizing biological systems, highlighting the potential of microalgae-derived materials in various industrial applications, and showcasing the integration of renewable energy technologies to enhance sustainability. The study demonstrated that Chlorella vulgaris and Scenedesmus obliquus, cultivated under controlled conditions, significantly improved absorption rates by 50% and 80%, respectively, showcasing their potential in residential heating systems. Post-cultivation, the extracted lipids were effectively utilized for biodiesel production. The RL models achieved high predictive accuracy, with R2 values of 0.98 for temperature and 0.95 for oxygen levels, confirming their effectiveness in system regulation. The development of activated carbon from microalgae biomass also highlighted its utility in removing heavy metals and dyes from water, proving its efficacy and stability, thus enhancing the sustainability of environmental management. This study underscores the successful integration of advanced machine learning with biological processes to optimize microalgae cultivation and develop practical byproducts for ecological applications. © The Author(s), under exclusive licence to Korean Carbon Society 2025.

In this research, we have investigated how pure boron nitride biphenylene (BPN) could be used as a negative electrode material for calcium-ion batteries (CIBs) by employing dispersion-corrected density functional theory (DFT). Precise interaction energies play a vital role in battery and energy storage uses as they have been closely linked to fundamental electrochemical features, including storage capacities overall and specifically open-circuit voltages (OCVs). Our discovery shows that calcium tends to bind above the central point of six-membered rings within BPN, with an adsorption energy of approximately −2.24 eV. By placing a BPN monolayer on both sides, we can create Ca5B6N6, offering a storage capacity reaching 989 mAh g−1 alongside an average OCV of 0.48 V. Our results demonstrate superior outcomes concerning storage capacity, reduced OCV, and lower diffusion energies when compared to frequently researched 2-D materials. This positions thiol-functionalized BPN as a promising contender for deployment as a negative electrode material in CIBs. © 2024 Elsevier Ltd

Ca-ion batteries (CIBs) have become widely acknowledged in the field of energy storage research because of their exceptional qualities. Theoretical analysis of the effectiveness of unadulterated graphdiyne analogue (GDY) is being explored for its potential use as an anode substance in CIBs. In this situation, density functional theory (DFT) computations have been utilized to examine the electrochemical characteristics of Ca@GDY and Ca2+@GDY complexes. The adsorption energy (Ead) of the Ca ion in the Ca2+@GDY complex is recorded at −4.97 eV. The GDY anode attained a specific capacity of 749 mAhg−1. Through Climbing Image-Nudged Elastic Band (CI-NEB) calculations, it is illustrated that adatoms can easily migrate through the anode with a low energy barrier of 74 meV, accompanied by a diffusion coefficient (D) of 9.23 × 10−10 cm2/s. An operating voltage as low as 0.53 V is achieved, supporting safe usage and excellent cycling performance. Therefore, the theoretical exploration reveals promising results for the potential utilization of GDY as an advanced anode in future CIB technologies. © 2024 Elsevier B.V.

The development of polymer composites enhanced with carbon-based additives has been investigated to reinforce their applicability in energy-related systems. Conductive fillers, such as graphene, Carbon Nano Tubes (CNTs), and Short-cut Carbon Fibers (SCFs), were incorporated into a Polydimethylsiloxane (PDMS) matrix to enhance electrical conductivity and thermal performance. Experimental evaluations included four-probe electrical conductivity testing and self-heating measurements under varied input voltages (8–12 V). Results showed that composites containing 6 mm Carbon Fibers (CF), (Long-CNT) achieved the highest electrical conductivity of 1.8 S/m, significantly outperforming the CNT-only control (Base-CNT, 0.1 S/m). Correspondingly, the Long-CNT samples exhibited the fastest thermal response, with heating time constants (τg) as low as 70.01 s and peak surface temperatures exceeding 150 °C under 12 V input. To guide composite optimization, a hybrid Machine Learning (ML) framework combining Random Forest Regression (RFR) and Support Vector Regression (SVR) was developed. This stacked model was trained on 60 samples and achieved high predictive accuracy across all key outputs, including Coefficient of Determination (R²) = 0.985 for conductivity and R² > 0.95 for heating rate (Hr+c), τg, τd, and temperature. Feature importance analysis revealed that carbon fiber length and input voltage were the dominant factors influencing thermal-electrical performance. The model was also used to simulate untested CF–voltage configurations, identifying optimal engineering windows (e.g., 4.5 mm CF at 11 V) that balanced high heating efficiency with manageable power consumption. The integration of data-driven modeling with experimental validation enabled the accurate prediction and strategic tuning of composite properties. This work provides a scalable framework for designing high-performance, self-healing polymer nanocomposites for thermal management, sensing, and energy conversion applications. © 2025

Carbon aerogels including graphite and graphene have unique properties such as lightweight, strong, and insulative to roofing applications. Carbon aerogels offer innovative solutions in building management by enhancing thermal and acoustic insulation while reducing structural weight, aligning with the focus on economic and business analysis driven by machine learning. Traditional building materials often fail to meet contemporary energy efficiency and sustainability demands, underscoring the necessity for more advanced solutions. This project is dedicated to integrating carbon aerogels into roofing systems and employs Deep Neural Networks (DNNs) to optimize their performance and integration. The novelty of this study lies in its application of carbon aerogel technology—a cutting-edge, lightweight, and highly insulative material—specifically within roofing to analyze the practical evaluation of carbon aerogels’ thermal properties and economic viability in the construction industry. This study aims to rigorously assess carbon aerogels’ performance and financial impact on roofing applications. By conducting the thermal guard test and economic lifecycle evaluation, the study seeks to validate carbon aerogels’ enhanced energy efficiency and cost-effectiveness compared to traditional roofing materials. The study demonstrates that carbon aerogels offer superior thermal insulation in roofing applications, with a thermal conductivity of 0.02 W/m·K, significantly outperforming traditional materials. Economically, the high initial cost of carbon aerogels is effectively offset by substantial energy savings, estimated at $300 annually per square meter, resulting in a payback period of approximately 1.05 years. These findings are supported by rigorous testing and optimization through DNN, highlighting the material’s potential to enhance energy efficiency and sustainability in building practices. © The Author(s), under exclusive licence to Korean Carbon Society 2024.

Boehmite-derived Al2O3 offers a sustainable route for developing efficient, scalable catalysts, enabling continuous wastewater treatment under semi-industrial conditions with reduced environmental impact and enhanced process viability. Herein, the influence of various pore-forming agents, including polyethylene glycol (PEG) with molecular weights of 6000 (PEG-6000) and 8000 (PEG-8000), starch (ST), and cetyltrimethylammonium bromide (CTAB), on the surface area of synthesized Al2O3 was systematically investigated. Among the tested agents, PEG-8000 demonstrated the most effective performance in producing mesoporous Al2O3 derived from boehmite. Subsequently, SrTiO3 was impregnated onto the Al2O3 support at loadings of 5, 10, and 15 wt%, and the photocatalytic degradation of cefixime (CF) under varying conditions was evaluated. The effects of liquid hourly space velocity (LHSV) values of 1, 3, and 5 h−1 and initial pH ranging from 3 to 11 were examined. Optimal photocatalytic activity, achieving 99.3 % CF degradation, was observed at 10 wt% SrTiO3 loading, LHSV of 1 h−1, and pH of 7. The selected photocatalyst was further characterized using XRD, FE-SEM, and TEM analyses. These findings are pivotal in optimizing mesoporous catalyst design, thereby enhancing photocatalytic performance in continuous wastewater treatment systems. © 2025 Elsevier B.V.

The demand for developing sensors capable of precisely and selectively identifying harmful substances such as fluorides and cyanides is steadily increasing. Within this piece of research, the detection capability of a carbon nitride nanosheet (referred to as C3N3NS) was investigated for the accurate identification of C2H4 and C2H2 through the implementation of a DFT methodology. The interaction energies of the most stable structures of C2H2 and C2H4 over C3N3NS were calculated at about −53.46 and – 21.92 kJ mol−1, respectively. Based on the analysis of interaction energies, C2H2 formed a strong bond with C3N3NS, while C2H4 was adsorbed onto the C3N3NS through weak van der Waals forces. The complexation of C2H2 and C2H4 with C3N3NS was thoroughly examined using non-covalent interactions (NCIs), frontier molecular orbitals (FMOs), and natural bond orbital charge transfer (QNBOs). Significant differences in bandgap energy were observed in the FMO analysis for C2H2 with C3N3NS, ranging from 3.59 to 2.37 eV, highlighting the presence of strong non-covalent interactions (NCIs). Furthermore, the presence of non-covalent bonds between complexes was verified through the non-covalent interactions-low density gradient (NCI-RDG) analyses. The recovery time in the range for C2H2@C3N3NS and C2H4@C3N3NS was 5.71 × 10−5 s and 3.32 × 10−7 s, respectively. The significant specificity of the monolayers towards analytes and the potential of all discoveries can offer researchers practical recommendations for constructing highly sensitive sensors for C2H2 and C2H4 utilizing C3N3NS. © 2025 Elsevier B.V.

Innovative PCM-hemp concrete formulations integrate Phase Change Materials (PCMs) with natural hemp fibers, boosting building thermal energy storage efficiency. By harnessing the thermal properties of PCM and the renewable benefits of hemp, these advanced materials pave the way for greener, energy-efficient infrastructure. This study aims to enhance the thermal conductivity and dispersion of Methyl hexadecanoate PCM in hemp-lime concrete by integrating nano-scale additives like graphene oxide and enzymatic modification techniques. The objective is to improve thermal performance while minimizing any potential impact on mechanical strength. This study also employs advanced computational methods, including Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS), to optimize the thermal properties and behavior of the PCM. This study investigates the enzymatic modification of Methyl hexadecanoate PCM to enhance its thermal properties for integration into hempcrete. The modified PCM demonstrated a 10 % increase in latent heat capacity and an 8.6 % improvement in thermal conductivity, contributing to more stable indoor temperatures and a potential 25 % reduction in energy consumption. However, the PCM integration led to a 55 % decrease in Compressive Strength (CS), highlighting a trade-off between thermal enhancement and mechanical performance. Future research should optimize mechanical properties and ensure long-term durability in various environmental conditions. © 2025

This study involves a comparison of the experimental findings obtained from testing conducted in the Mode Stirred Reverberation Chamber (MSRC) and the Anechoic Chamber (AC). Directly comparing the reactions of different items under test proved challenging due to variations in the electromagnetic surroundings for both procedures. The tests conducted in both rooms have exhibited varying responses based on the equipment's directivity. Furthermore, the outcomes derived from this examination exhibit variability contingent upon the conditions under which the test is conducted. Hence, the test results obtained from the two chambers exhibit similar error biases. The error bias refers to the proportion of a measured response obtained under specified test conditions compared to the maximum possible reaction. The paper examines the coupling uncertainty and anticipated error bias for both test procedures, analyzing how they vary with apparent directivity. The measured AC data is utilized to ascertain the magnitude and configuration of the apparent directivity of equipment responses. © 2025, Semarak Ilmu Publishing. All rights reserved.

Enhancing the seismic resilience of reinforced concrete frame systems depends on an accurate understanding of beam-column joint behavior under cyclic and dynamic loading. Traditional design methods often rely on broad empirical equations, which may overlook important interactions within the joint core and result in unreliable safety margins. In this study, a tailored framework is introduced, utilizing geometry-sensitive calibration methods to improve the reliability of joint shear strength predictions. Two new approaches, the Geometry-Based Scaling Factor (GBSF) and the Area Ratio Scaling Factor (ARSF) were developed and validated using a comprehensive set of joint tests covering a range of materials and configurations. These statistically derived factors were incorporated into widely used design codes, leading to a noticeable reduction in prediction errors and variability. Overall, these advancements support the design of safer, more resilient structures in earthquake-prone regions and offer greater protection for critical infrastructure against brittle joint failure. © 2025

As Reinforced Concrete (RC) bridge infrastructure ages, its seismic resilience diminishes due to material degradation, such as steel corrosion and concrete deterioration. This study presents a time-dependent probabilistic framework for evaluating the seismic performance of RC bridge columns, considering the aging effects on material properties. Unlike traditional methods that assume a constant structural capacity, the proposed approach incorporates Sobol Sequence Sampling to represent uncertainties in geometry, material, and corrosion parameters. It utilizes Random Forest Regression (RFR) to predict the degradation of drift capacity over time. Column configurations, varying in reinforcement type, axial load, and confinement, were simulated using over 17,000 nonlinear pushover analyses in OpenSees. Results show that columns reinforced with carbon steel experience a significant decline in drift capacity with age. Median serviceability drift limits dropped from ∼2.5 % to ∼1.5 %, and failure-level Serviceability Drift Limit State (SDLS) drift from ∼6.0 % to ∼3.2 % over 50 years. Factors such as reinforcement slenderness and axial load were found to influence drift behavior strongly. Stainless steel reinforcement significantly improved performance. Austenitic stainless-steel columns retained high drift capacities (>7%) with minimal sensitivity to axial load or age. Duplex stainless steel provided moderate resilience but with greater sensitivity to axial compression. Failure modes transitioned with age from ductile yielding in new columns to concrete crushing and bar buckling in aged specimens. Fracture of reinforcement was not a governing failure mechanism in any case. A predictive tool based on RFR was developed, enabling engineers to estimate age-dependent drift capacities using key input parameters. This framework supports the performance-based design and life-cycle resilience assessment of aging RC bridge columns, enabling better-informed retrofit and maintenance strategies for infrastructure in seismic regions. © 2025

Efficient routing of multi-compartment vehicles is a critical challenge in logistics, as it involves optimizing travel distance and load distribution while considering multiple constraints. Traditional vehicle routing algorithms often fail to address the complexities of compartmentalized cargo, leading to inefficiencies in delivery operations and increased costs. This study aims to bridge this gap by introducing a Three-Dimensional Ant Colony Algorithm (3D-ACA) for optimizing Multi-Compartment Vehicle Routing (MCVR). This research aims to minimize total travel distance while ensuring proper allocation of goods to vehicle compartments and adherence to delivery time constraints. The approach involves formulating the Multi-Compartment Vehicle Routing Problem (MCVRP) as an optimization model and applying 3D-ACA to generate efficient routing solutions. Unlike conventional methods, which primarily focus on shortest-path algorithms, this study incorporates three key factors, route efficiency, compartment constraints, and time windows, into a single optimization framework. The novelty of this research lies in the three-dimensional adaptation of the ant colony algorithm, which extends the standard routing problem to include compartment-based decision-making. This approach provides a more realistic and practical solution for logistics companies aiming to improve fleet utilization, reduce costs, and enhance operational efficiency. The proposed 3D-ACA significantly improved key performance metrics, including a 15.6% reduction in travel distance compared to Ant Colony Optimization (ACO), up to 17% lower operational costs, and over 95% feasibility rates across datasets. It also demonstrated superior scalability by solving large-scale problems (150 nodes) under 45 minutes, outperforming traditional methods such as GA and MIP in efficiency and constraint satisfaction. © 2025 Techno-Press, Ltd.

Steel-Reinforced Concrete (SRC) panels with self-centering capability are increasingly applied in sports facility structures to withstand dynamic impacts; however, the interaction behavior of angle shear connectors under three-dimensional dynamic loading remains insufficiently explored, limiting optimization for impact resistance. This study analyses the dynamic 3D response of angle shear connector SRC panel systems under drop-weight impact, introducing a novel integration of selfcentering design into computational interaction modelling for sports facility applications. A detailed Finite Element (FE) model was developed incorporating nonlinear temperature-dependent material properties, explicit contact definitions, and realistic dynamic loading scenarios. Input parameters included panel geometry, connector dimensions, and impact velocity; outputs comprised displacement histories, connector stress distribution, and energy dissipation characteristics. Results show that selfcentering panels reduced residual displacement by 42 58% compared to conventional designs, with self-centering efficiencies (ψₛ) consistently above 0.55 and reaching 0.82 under low-energy impacts. Connector stress utilization remained within ductile limits, peaking at 0.95 in the most severe cases without brittle fracture. Larger connectors decreased peak deflection by up to 12% but increased local concrete bearing stresses by ~15%. Elevated temperature exposure (θ = 550 °C) reduced yield strength by 22 29%, increasing peak displacement by 6 9% and slightly lowering ψₛ. Energy dissipation accounted for 58 65% of initial kinetic energy, with 35 45% from steel plasticity, 25 35% from concrete damage, and the remainder from frictional slip. Boundary restraint stiffness had a more substantial influence on ψₛ than connector size, with clamped supports improving recovery by up to 0.10. The findings confirm that self-centering SRC panels maintain high impact resistance and rapid postevent recovery even under combined fire and impact conditions. Additionally, a Machine Learning (ML)-based predictive framework was integrated to complement the FE analysis, enabling rapid estimation of dynamic response parameters and optimization of connector geometry under varying impact and post-fire conditions. © 2025, Techno-Press. All rights reserved.

Minor and moderate seismic damage were found to dominate across all analyzed Reinforced Concrete (RC) frame configurations, with probabilities approaching 99 % in some cases. Severe or collapse-level failures remained rare, except when irregularities appeared in the ground storey, where vulnerability increased sharply. In contrast, irregularities located in upper storeys had only limited consequences. To explain these trends, two-dimensional RC moment-frame models with systematically varied stiffness and strength irregularities were studied using both elastic and nonlinear analyses. The proposed Multi Storey Drift and Strength Irregularity Detection Method (MSD-SIDM) outperformed existing criteria in codes such as IS 1893–1, NBCC, and ASCE 7–16, showing that inter storey drift is a dependable measure for stiffness deficiencies, while lateral strength is more effective in identifying strength irregularities. The findings confirm that stiffness and strength anomalies can occur independently, highlighting the need for more nuanced design checks. The study concludes with practical recommendations for code improvement and identifies directions for 3D modeling and experimental validation to enhance the seismic resilience of irregular structures. © 2025 Published by Elsevier Ltd on behalf of Institution of Structural Engineers.

This study proposes a novel energy-based framework that draws an explicit analogy between aeroelastic instabilities, such as Seismic response focus, and seismic energy dissipation in flexible bridge decks. The key innovation lies in interpreting nonlinear aerodynamic energy transfer as a conceptual model for understanding hysteretic damping observed in seismic systems. A seismic dynamic model with damping model is used to simulate self-excited wind-induced responses, with outputs validated against experimental amplitude, energy, and frequency data. Simulations across Structural Damping Configurations (SDC1-SDC3) and wind speeds ((Formula presented) ) reveal a critical transition at (Formula presented), where net energy input (Formula presented) becomes positive. At (Formula presented), the system accumulates over + 3.5 J of torsional energy, with vertical amplitudes reaching 22 mm and torsional amplitudes peaking at 13.5∘[jls-end-space/]. These findings confirm the onset and stabilization of Seismic response focus, validating the model's nonlinear force representation. The study also shows that torsional aerodynamic components dominate the energy input, while the evolution of damping ratio and damping analysis closely replicate seismic-like energy dissipation. These dynamics are presented as an analog to transient seismic excitation, supporting a multi-hazard interpretation of structural stability. This work contributes a simplified but validated modeling approach that captures key nonlinear behaviors relevant to both wind and seismic loading. It provides a foundation for the future development of multi-hazard resilient bridge designs and offers a novel methodology for anticipating dynamic instability using unified energy criteria. © 2025 Published by Elsevier Ltd on behalf of Institution of Structural Engineers.

This study presents a novel approach to enhance the longevity and seismic resilience of ‎ Concrete-Filled Steel Tubular (CFST) frame towers commonly used in ‎onshore wind turbines. Fatigue-related failures at welded joints pose significant challenges to the durability of these towers. This research investigates the impact of optimized ring stiffener ‎configurations and concrete infill on reducing stress concentrations and improving ‎fatigue resistance under simulated seismic loading. The study utilizes with samples including CFST-gusset plate joints and non-concrete-filled joints, with different configurations of concrete infill and ring stiffeners. ‎These samples were evaluated under static loading to assess the influence of stiffener width, thickness, and spacing on Stress Concentration Factors (SCFs). Subsequently, cyclic loading tests were conducted to observe crack initiation and progression. The results show that ‎optimized configurations of both concrete fill and ring stiffeners significantly reduce ‎SCFs, improve stress distribution, and delay crack initiation, enhancing the seismic ‎fatigue performance of CFST joints. Moreover, the study integrates Artificial Intelligence ‎‎(AI) techniques, specifically Support Vector Machines (SVM), to predict the fatigue ‎behavior of these joints under different loading conditions. The SVM model was trained on experimental data and demonstrated high predictive accuracy in forecasting the fatigue life of CFST joints, achieving an R² value of 0.95, an RMSE of 0.11, and an MAE of 0.08. These results validate the AI model's ‎ability to optimize design parameters and predict fatigue life, contributing to more ‎efficient and reliable CFST joint design. This integration of AI enhances design ‎optimization by providing precise predictions based on complex variables like stiffener ‎geometry and concrete filling. Overall, the findings suggest that incorporating ‎optimized stiffener configurations, concrete infill, and AI-based design tools can ‎significantly improve the seismic resilience and fatigue life of CFST frame towers joints, ‎providing valuable insights for future engineering practices. © 2025

A growing urban population, rapid urbanisation and industrialisation have exacerbated the challenge of pollution worldwide. Besides threatening environmental sustainability, they also pose a risk to public health and the overall quality of urban life. This analysis of urban pollution focused on its primary forms – air, water, soil and noise pollution – and identifies their diverse sources, such as vehicular emissions, industrial discharges, improper waste management and energy consumption. These pollutants are interconnected by nature and lead to respiratory and cardiovascular diseases, natural resource contamination and urban ecosystem degradation. This study explored several innovative solutions to address these issues, focusing on sustainable urban planning, including compact city designs and expanded green spaces, as well as the use of green technologies such as electric vehicles, renewable energy systems and waste management methods. A key component of urban resilience is community involvement in sustainable practices, demonstrated through a combination of policy reforms and public participation. A comprehensive analysis of successful initiatives worldwide showed that integrated approaches are effective in reducing pollution and promoting a healthier lifestyle. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Urban tunnel infrastructure, crucial for societal well-being, depends on reliable Tunnel Electromechanical Equipment (TEE), including ventilation, drainage, and lighting systems. A key challenge is these systems’ proactive and efficient maintenance, particularly under limited resources. This study introduces a novel deep learning-based multi-output prediction model developed to enhance the understanding and predictive accuracy Tunnel Boring Machine (TBM) performance, with a specific focus on machine wear and tear (y1) and adapting to ground conditions and geotechnical data (y2) in complex underground environments. The model employs an advanced deep learning approach, att-GCN, which innovatively integrates Graph Convolutional Networks (GCN) with a scaled dot-product attention mechanism. This combination notably improves model performance and interpretability. Experimental results indicate that att-GCN model achieves a Mean Absolute Percentage Error (MAPE) of 17.1% for y1 and 16.8% for y2, outperforming other established algorithms, including the Deep Neural Network (DNN)-Genetic algorithm hybrid. Furthermore, an online learning variant of att-GCN was developed that integrates real-time data during tunneling operations. This version demonstrated enhanced predictive accuracy, with a MAPE of 8.7% for y1 and 8.1% for y2. Applying att-GCN for real-time TBM performance estimation based on dynamic monitoring data offers significant insights for intelligent TBM control, improving construction efficiency and reliability. © 2024

The burgeoning demand for durable and eco-friendly road infrastructure necessitates the exploration of innovative materials and methodologies. This study investigates the potential of Graphene Oxide (GO), a nano-material known for its exceptional dispersibility and mechanical reinforcement capabilities, to enhance the sustainability and durability of concrete pavements. Leveraging the synergy between advanced artificial intelligence techniques—Artificial Neural Networks (ANN), Genetic Algorithms (GA), and Particle Swarm Optimization (PSO)—it is aimed to delve into the intricate effects of Nano-GO on concrete's mechanical properties. The empirical analysis, underpinned by a comparative evaluation of ANN-GA and ANN-PSO models, reveals that the ANN-GA model excels with a minimal forecast error of 2.73%, underscoring its efficacy in capturing the nuanced interactions between GO and cementitious materials. An optimal concentration is identified through meticulous experimentation across varied Nano-GO dosages that amplify concrete's compressive, flexural, and tensile strengths without compromising workability. This optimal dosage enhances the initial strength significantly, and positions GO as a cornerstone for next-generation premium-grade pavement concretes. The findings advocate for the further exploration and eventual integration of GO in road construction projects, aiming to bolster ecological sustainability and propel the adoption of a circular economy in infrastructure development. © 2024

The waste heat generated by vehicles, especially heavy-duty engines, has motivated the current research to propose a novel thermal design for a ship's engine. Hence, the suggested system comprises an organic-flash power plant, a bi-evaporator cooling subsystem, a thermal desalination cycle, a water electrolysis-based hydrogen generation unit, and a Claude unit for liquefying hydrogen. The innovative aspects associated with the proposed design include utilizing a novel cascade heat recovery process aimed at reduced irreversibility for the engine's waste and the integration of bi-evaporator technology and Claude unit to facilitate the process of waste-to-liquefied hydrogen generation for a marine engine for improved handling. In addition, the research includes an intelligent study that comprehensively analyzes and optimizes the proposed system in terms of long-term sustainability and economic feasibility. For this purpose, a data-driven optimization technique is implemented using artificial neural networks in combination with multi-objective grey wolf optimization. The aims of the research encompass the optimization of the sustainability index and unit cost of liquefied hydrogen. The research findings indicate that the vapor generator's terminal temperature difference exerts the most substantial influence on performance metrics, as evidenced by a mean sensitivity index of 0.368. Also, the mentioned objective functions exhibit values of 1.139 and 7.60 $/kg, respectively. Besides, the optimum exergy destruction rate is 124.3 kW, the total investment cost rate is 4.94 $/year, the specific cost of products is 74.87 /GJ, the payback period is 2.2 years, and the net present value is 7.48 M$. © 2024 The Institution of Chemical Engineers

This study develops Titanium (Ti) and Magnesium (Mg)-based nano-alloys to enhance the earthquake resilience of steel structures using machine learning (SVM) and sensor technology. Embedding Ti and Mg into steel at the nanoscale creates a lightweight, durable, and flexible material capable of withstanding seismic forces. Ti enhances tensile strength and flexibility, while Mg reduces weight, lowering seismic loads on buildings. The performance of these nano-alloys was assessed through shake table tests, cyclic load testing, and dynamic response testing, showing that nano-alloy-enhanced steel structures experienced 60% less displacement and 40% lower acceleration than traditional steel, demonstrating superior energy absorption and stress distribution. Fatigue tests revealed that the nano-alloy could endure 20, 000 loading cycles, outperforming the 8, 000 cycles of conventional steel. Integrated sensor technology, including strain gauges and accelerometers, provided real-time stress and deformation data, confirming the material’s effectiveness in stress distribution and vibration damping. The SVM model optimized alloy composition, achieving 94% prediction accuracy in assessing seismic performance, highlighting the nano-alloys' durability and resilience. This study suggests that Ti and Mg nano-alloys could greatly improve earthquake-resistant construction. Copyright © 2024 Techno-Press, Ltd.

In this study, an investigation was conducted on friction stir spot-welded AA7075 aluminum alloy with mild steel. Fusion welding of these two materials presents challenges because of differences in melting points and metallurgical incompatibility. To overcome these challenges, friction stir spot welding was employed for joining these materials. Trial runs were conducted based on a central composite rotatable design matrix, which encompassed four factors at five levels: tool rotational speed, plunge rate, dwell time, and tool diameter ratio. Shear tests were conducted to evaluate the joint strength, and subsequently, an empirical equation was developed via analysis of variance. Notably, a joint fabricated under specific conditions demonstrated exceptional strength, with the highest fracture load of 9.56 kN. These optimal parameters included the tool rotational speed, plunge ratio, dwell time and diameter ratio of 1000 rpm, 4 mm/min, 5 sec and 3.0. This achievement underscores the critical role of meticulous parameter optimization in achieving superior weld quality and mechanical properties in dissimilar material joints. © 2024 SAE International.

Ionic liquids (ILs), defined as low-melting organic salts, are a novel class of compounds with unique properties and a combinatorially great chemical diversity. ILs are utilized as synthesis and dispersion media for nanoparticles as well as for surface functionalization. ILs are able to offer outstanding properties as media for the synthesis of nanoparticles. The low surface tension of many ILs leads to high nucleation rates and, in consequence, to small particles. The ILs themselves can act as an electronic as well as a steric stabilizer and depress particle growth. As highly structured liquids, ILs have a strong effect on the morphology of the particles formed. In this paper, the methods of modifying surfaces with ILs through covalent bonding, physical absorption, polymerization or sol–gel methods, metal nanoparticles in ionic liquids, and ionic liquids in catalysis, as well as the application of these modified surfaces in various fields of chemistry, have been investigated. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Liquid crystal materials are a suitable environment for the synthesis of nanostructures of uniform size and shape due to their order and yet mobility at the molecular level. Dense liquid crystal phases allow nanoparticles to self-assemble, resulting in larger organized nanostructures. Although both types of lyotropic liquid crystal and thermotropic liquid crystal have been used for the synthesis and self-assembly of nanoparticles, the use of lyotropic liquid crystal types is more in the synthesis of nanoparticles, while the use of thermotropic liquid crystal phases is mainly related to the self-assembly of nanoparticles. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

This paper investigates the global dynamics of a new 3-dimensional fractional-order (FO) system that presents just cross-product nonlinearities. Firstly, the FO forced Lorenz-84 system is introduced and the stability of its equilibrium points, as well as the chaos control for their stabilization, are addressed. Secondly, dynamical behavior is further analyzed and bifurcation diagrams, phase portraits, and largest Lyapunov exponent (LE) are discussed. Then, the global Mittag-Leffler attractive sets (MLASs) and Mittag-Leffler positive invariant sets (MLPISs) of the FO forced Lorenz-84 system are presented. Finally, the Hamilton energy function (HEF) of the Lorenz-84 system is calculated by using the Helmholtz theorem. The calculation of the Hamilton energy has an essential role on the estimation of chaos in dynamical systems, the guidance of orbits, and stability. In fact, any control action on the dynamical system completely changes the HEF. Numerical simulations are presented for illustrating the theoretical findings. © 2023 Elsevier Ltd

The use of titanium dioxide (TiO2) nanoparticles in many biological and technical domains is on the rise. There hasn't been much research on the toxicity of titanium dioxide nanoparticles in biological systems, despite their ubiquitous usage. In the current investigation, samples were exposed to various dosages of TiO2 nanoparticles for 4 days, 1 month, and 2 months following treatment. ICP-AES was used to dose TiO2 into the tissues, and the results showed that the kidney had a significant TiO2 buildup. On the other hand, apoptosis of renal tubular cells is one of the most frequent cellular processes contributing to kidney disease (KD). Nevertheless, the impact of macroalgal seaweed extract on KD remains undetermined. In this work, machine learning (ML) approaches have been applied to develop prediction algorithms for acute kidney injury (AKI) by use of titanium dioxide and macroalgae in hospitalized patients. Fifty patients with (AKI) and 50 patients (non-AKI group) have been admitted and considered. Regarding demographic data, and laboratory test data as input parameters, support vector machine (SVM), and random forest (RF) are utilized to build models of AKI prediction and compared to the predictive performance of logistic regression (LR). Due to its strong antioxidant and anti-inflammatory powers, the current research ruled out the potential of using G. oblongata red macro algae as a source for a variety of products for medicinal uses. Despite a high and fast processing of algorithms, logistic regression showed lower overfitting in comparison to SVM, and Random Forest. The dataset is subjected to algorithms, and the categorization of potential risk variables yields the best results. AKI samples showed significant organ defects than non-AKI ones. Multivariate LR indicated that lymphocyte, and myoglobin (MB) ≥ 1000 ng/ml were independent risk parameters for AKI samples. Also, GCS score (95% CI 1.4–8.3 P = 0.014) were the risk parameters for 60-day mortality in samples with AKI. Also, 90-day mortality in AKI patients was significantly high (P < 0.0001). In compared to the control group, there were no appreciable changes in the kidney/body weight ratio or body weight increases. Total thiol levels in kidney homogenate significantly decreased, and histopathological analysis confirmed these biochemical alterations. According to the results, oral TiO2 NP treatment may cause kidney damage in experimental samples. © 2022

Sport has no age limit and can be done anywhere and in any condition with minimal equipment. The existence of sports spaces in all parts of the world is considered a citizen's right. One of the activities carried out in this field is installing sports equipment and structures in parks and encouraging citizens to use this equipment for physical health with the least cost and facilities. Installing sports structures in open spaces such as parks is a practical step for developing citizens' sports. Although using devices in parks is acceptable, it is more critical to meet scientific and technical standards. The components of these structures must have high strength and endurance against changes in environmental conditions such as humidity, temperature difference, and corrosion. Among the various causes of material degradation, corrosion has always been one of several fundamental causes of metal equipment failure. Sports structures in open spaces are not safe from corrosion. Uniform corrosion is the most common type of corrosion. This corrosion usually occurs uniformly through a chemical or electrochemical reaction across the surface exposed to the corrosive environment. Rust and corrosion of outdoor sports structures are examples of this corrosion. For this reason, in this research, with the green synthesis of silica nanoparticles and its application in outdoor sports structures, the life span of these structures can be increased for the use of physical exercises as well as their quality. © 2023 Techno-Press, Ltd.

In response to the expanding industrial landscape across various sectors, scientists and researchers have dedicated their efforts to innovating new materials possessing specific technical attributes without compromising cost-effectiveness. In this study, a focus was placed on the development of composite materials involving aluminium alloy (6063), wherein the alloy's strength was augmented by incorporating nickel additives in varying weight percentages: 0%, 5%, 1%, and 1.5% of Ni. Employing a stir-casting technique, the samples were meticulously prepared. The investigation delved into the intricate impact of nickel addition and subsequent heat treatments on the tensile properties, Brinell hardness, and microstructure of AA6063. Following the casting process, a comprehensive array of tests was conducted on the composite materials, coupled with applying distinct heat treatment methods (namely, artificial aging T6 and retrogression and re-aging) on AA6063 specimens, each containing differing proportions of nickel content. By utilizing the retrogression and re-aging treatment, better results were obtained than with the T6 treatment, and by adding nickel to AA6063, a smoother microstructure and an improvement in tensile characteristics and Brinell hardness were produced for the examined alloys. The heat treatment of type T6 improved the mechanical properties of AA6063 without any addition of nickel. It showed increment even more when applying the retrogression and re-aging heat treatment, where yield stress, tensile strength, and Brinell hardness increased by 61.8%, 61.4%, and 67.2%, respectively. Heat treatments also made the crystal structure of AA6063 smoother and reduced the size of the grains. © School of Engineering, Taylor’s University.

Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability © 2023 Techno-Press, Ltd.

The usage of conventional fossil fuels has aided fast economic growth while also having negative consequences, such as increased global warming and the destruction of the ecosystem. This paper proposes a novel swarm-based metaheuristic method called Chimp Optimization Algorithm (ChOA) to tackle the environmental, economic dispatch issue and reducing the waste nonrenewable materials. In this regard, two objective functions named fuel cost function and emission cost function are proposed. Unique constrained handling also solves the challenge of multi-objective optimization. Standard IEEE 30 bus with six generators and a 10-unit system are used to demonstrate the usefulness of ChOA. The result of ChOA is compared with Individual Best Memory Penalty Factor Grey Wolf Optimizer (IBMPF-GWO), Improved Whale Trainer (IWT), Chaotic Biogeography-Based Optimizer (CBBO), Non-Linear Migration BBO (NLBBO), Hybrid Gravitational Search Algorithm Particle Swarm Optimization (GSAPSO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Differential Evolution-Crossover Quantum Particle Swarm Optimization (DE-CQPSO), Salp Swarm Algorithm (SSA), Dragonfly Algorithm (DA), and Fuzzy Grasshopper Optimization Algorithm (FGOA) to confirm its efficiency. For both single- and multi-objective optimization, ChOA's assessment index and convergence rate are superior to other benchmark algorithms, regardless of whether the goal is to reduce emissions or to reduce fuel costs. The efficacy and robustness of the ChOA in handling environmental economic dispatch problems have been shown by discovering a good compromise value. © 2022 Elsevier Ltd

Dye-sensitized solar cells (DSSCs) are among the third generation of solar cells which require further development to find practical applications. One reason that prevents the widespread use of DSSCs is the high cost of their counter electrode (CE). This study proposes a new CE based on WS2/MoS2 material, which demonstrates an excellent performance due to its high electrocatalytic performance and charge transport properties. These qualities are achieved due to the increased active surface area of the composite material. Our champion cell achieved an efficiency of 7.30%, which shows respective improvements of 25% and 18% from the DSSCs fabricated from WS2 and MoS2 CEs. It also displays an efficiency improvement of 3.5% in comparison with the expensive platinum CE. These efficiency enhancements correspond to the increased current densities of the DSSCs as a result of better CE properties. © 2022 International Solar Energy Society

Hundreds of decisions are taken at various phases and with diverse stakeholders along the building design processing, including the select of alternate components, materials, systems, assemblies, and building forms. Also, sustainability in the building sector is important since this business has a big influence on the environment and contributes significantly to socioeconomic growth specifically in Commercial Building. In terms of building sustainability, environmental issues are important issues in the early design stage, in which the principles of safety of structures, probabilistic reliability and durability are involved. A new integrated-design method that permits building analysis from a multi-performance view is regarded necessary to advance the sustainability. In this scenario, the environmental methodologies and footprint schemes for determining building sustainability are investigated using only a decision-making (DM) process on the basis of sustainable triple bottom line structure, which incorporates economic efficiency, resource conserving, and design for human adaption. The framework would enable design teams to achieve an optimal balance between social, environmental, and economic challenges, altering the path of construction practitioners' thought about the information used while appraising building projects, thereby aiding the sustainability of building industry. Finally, the technique of DM utilized in those decisions would influence the final building design, and hence the project's environmental, economic and social results. Copyright © 2022 Techno-Press, Ltd.

This work includes in its theoretical review, some information about such type of constructural alloyed steel which has wide range of applications, referring to its chemical composition, hardness, UTS, yield point and ductility (elongation) in the hot rolled case. However in the practical part, several specimens were made in two forms, circular discs for hardness test after each heat treatment process, and specimens for mechanical test, machined to dimensions near the final standard dimensions in order to complete machining to accurate size after heat treatments to use them in testing UTS, yield point, elongation and contractions in cross section area.We used in this work the following heat treating: Full annealing, normalizing, oil quenching hardening, high and low tempering. These were done to determine the tested properties of this steel to enable the choose of suitable properties to demand or in case of looking for replacement material in production of given parts to help both designer and producer. The research included many tables and graphs and discussion of results. © Springer International Publishing Switzerland 2014.