Journals
2026 EN
Du Yunfeng · Wang Qingbao · Zhou Delai
+4 more
This article presents a comprehensive multi-field analysis on the analysis of hybrid sandwich curved shell composed of a metal matrix core reinforced with tunable three-dimensional origami-enabled reinforcement. The characteristics and governing equations are derived using the energy-based formulation and a minimization process. The hybrid modeling is performed with accounting the multi-loading structures as an attachment. The characterized material properties are evaluated using the experimental and statistical-based analysis in the material science works. The stress and strain analyses of the composite sandwich curved shell are presented with changes of volume fraction and foldability parameters as well as multi-filed loading along the thickness direction. These materials and structures can be used in electromechanical systems and structures.
Journals
2026 EN
Ibrahim Zana H. · Hussein Ridha H. · Maulood Ismail M.
Cardiovascular diseases are often driven by oxidative stress and endothelial dysfunction, particularly under heavy metal exposure such as HgCl 2 . It disrupts NO signaling and RAS balance, impairing vascular function. L-arginine (LA) and tetrahydrobiopterin (BH 4 ) as essential regulators of eNOS, are potential therapeutic agents for restoring vascular reactivity. This study aimed to evaluate the protective effects of LA and BH 4 , individually and in combination, on Ang II-induced vascular reactivity in isolated rat aortic rings under normal and HgCl 2 -induced oxidative stress conditions. The interaction of Ang-II receptors (AT 1 , AT 2 ) and ACE2 activity were explored. Aortic rings were pretreated with LA, BH 4 , or both, followed by stimulation with Ang II (10 −11 –10 −6 M). Pharmacological inhibitors were used to assess the roles of AT 1 (1 µM), AT 2 (10 µM), and ACE2 (1 µM) receptors. Vascular responsiveness was analyzed through Emax, pD 2 , and AUC values in the presence and absence of HgCl 2 (1 µM). HgCl 2 significantly impaired Ang II-induced vasoconstriction. LA and BH 4 partially restored vascular responsiveness, with the combination producing the most substantial improvements, indicating synergistic NO-mediated effects. AT 1 receptor blockade abolished Ang II responses, confirming its central role, while AT 2 inhibition increased contraction, revealing its vasodilatory function. ACE2 inhibition enhanced Ang II-induced contraction, particularly after HgCl 2 exposure. Co-treatment with LA and BH 4 mitigated this effect, restoring balance. LA and BH 4 can reverse HgCl 2 -induced vascular dysfunction by enhancing NO signaling and modulating Ang II receptor pathways. Their combined use offers therapeutic promise in conditions involving oxidative stress and RAS dysregulation.
Journals
2026 EN
Hamza Billel · Slamene Amir · Mokhtari Mohamed
+3 more
Understanding the behavior of tubular elements, particularly elbows, is pivotal in the design and analysis of pressure systems, as these components exhibit intricate responses under cyclic loading and high internal pressure. This study explores the dynamic performance of pressurized tubular structures constructed from functionally graded materials (FGMs), specifically focusing on a bend transitioning from steel grade X52 to X65, seamlessly integrated with straight tubular sections. We emphasize the critical necessity for advanced numerical analysis to accurately capture the unique geometric and loading complexities associated with these structures. Our research investigates the effects of material grading and the volume fraction index (β) on structural integrity under plane bending loads that lead to failure. We employ a sophisticated combined isotropic and kinematic hardening model, calibrated through the Voce approach based on experimental data and implemented in ABAQUS. Additionally, we utilize the Extended Finite Element Method (XFEM) for comprehensive damage prediction, integrating this with our hardening model to simulate crack initiation and propagation guided by Von Mises stress flow theory. The results, depicted through detailed force-displacement hysteresis curves, underscore a significant correlation between structural response and damage progression, intricately influenced by the innovative material grading concept and the volume fraction index. This research not only enriches the understanding of functionally graded tubular structures under high-cyclic loading but also contributes critical insights for optimizing designs and enhancing reliability in various engineering applications, setting the stage for future advancements in structural mechanics.
Journals
2026 EN
Ghernaout Djamel · Boumaaza Messaouda · Belaadi Ahmed
+3 more
This study utilized cellulose fibers from waste leaves of Yucca treculeana L. to reinforce a high-density polyethylene (HDPE) matrix, aligning with a sustainable production approach that recovers agricultural waste. After complete extraction, the fibers underwent a mild chemical treatment (3% sodium bicarbonate for 4 hr) to improve bonding with the polymer matrix and remove surface contaminants. The primary aim was to examine how different fiber percentages (10, 15, 20, and 30%) affect the dynamic behavior of biocomposites, with particular focus on their absorption kinetics and associated diffusion mechanisms. The absorption characteristics were comprehensively analyzed and modeled using two innovative techniques: response surface methodology (RSM) and artificial neural networks (ANN), enhanced by genetic algorithm optimization. Results indicate that the ANN method surpasses RSM in accuracy and robustness, achieving very high correlation coefficients (0.9979 in training, 0.9777 in testing, and 0.9986 in validation) between experimental results and model predictions. This demonstrates the potential for natural fiber/HDPE composites in manufacturing industrial biocomposites, utilizing plant waste, and supporting more environmentally friendly production processes.
Journals
2026 EN
Ghernaout Djamel · Boumaaza Messaouda · Belaadi Ahmed
+3 more
The water absorption (WA) behavior of hybrid biocomposites reinforced with Syagrus romanzoffiana palm fibers ( Sr PFs) and Syagrus romanzoffiana palm waste (biochar, Sr PW) shows a nonlinear relationship with immersion time and biochar content. This study explores the use of response surface methodology (RSM) and artificial neural networks (ANN) for multi-objective optimization and predictive modeling of this behavior. A hybrid model combining ANN with a genetic algorithm (GA) and RSM was developed to predict WA over immersion periods from 24 to 720 hours and Sr PW contents from 0.5% to 2%. The model was further enhanced using a Multi-Criteria Decision-Making (MCDM) approach with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The ANN-GA model predicted WA with reasonable accuracy (2.72% at 672 hours), closely matching experimental results, as confirmed by validation tests. At 615 hours, RSM optimization resulted in a slightly lower WA (2.68%). These findings demonstrate the reliability of the proposed modeling framework in reducing experimental work and guiding material design. The biocomposite exhibits potential for eco-friendly applications, particularly in the automotive sector.
Journals
2026 EN
Bulgurcuoglu Kuran Sibel · Hocaoglu Meryem · Altun Ayse
+9 more
Background: Pregnancies following in vitro fertilization (IVF) are associated with higher rates of intrahepatic cholestasis of pregnancy (ICP) compared to spontaneous conceptions. This study aimed to investigate oxidative stress biomarkers, endothelial nitric oxide synthase (eNOS) expression, and placental histopathology in ICP women undergoing intracytoplasmic sperm injection (ICSI). Methods: Placental samples from four groups—healthy spontaneous conception (non-ICP-SC, n = 7), ICP spontaneous conception (ICP-SC, n = 7), healthy ICSI (non-ICP-ICSI, n = 7), and ICP-ICSI (n = 7)—were analyzed. eNOS immunostaining, total antioxidant status (TAS), total oxidant status (TOS), oxidative stress index (OSI), and syncytial knots were evaluated. Results: The ICP ICSI group had the highest TAS and lowest eNOS levels (p < 0.05). Non-ICP-ICSI showed higher TOS and syncytial knots compared to non-ICP-SC (p = 0.026, p = 0.01). eNOS expression was lower in ICP-ICSI versus ICP-SC (p = 0.021). Conclusion: These findings suggest that ICP-ICSI placentas experience increased oxidative stress and decreased eNOS expression. ICSI may affect placental oxidative balance regardless of ICP status.
Journals
2026 EN
Wuni Ibrahim Yahaya
Artificial intelligence (AI) technologies are deployed in construction projects to analyze a large corpus of data, identify patterns in the data, infer from the data, and provide relevant insights to inform decisions in construction projects. However, the scientific literature recognized multiple risks of AI in construction projects. Therefore, this study investigated the critical risk factors for implementing AI in construction projects. The findings revealed forty-nine (49) critical risk factors for AI in construction projects, which have data, technical, knowledge, management, financial, and legal dimensions. It is discovered that the risk factors are disproportionately discussed in theoretical studies and underrepresented in decision-making models/frameworks. It is also established that the existing studies failed to leverage the sociotechnical perspective to jointly optimize risk management for AI deployment in construction projects. Thus, the study provides better insights and establishes a fertile ground for future investigation into the critical risk factors for AI in construction projects.
Journals
2026 EN
Kelvin Ibrahim Makoji · Aliu John Ogbeleakhu
Digital transformation is revolutionizing industries worldwide by streamlining operations, automating repetitive processes and reducing costs. The construction industry is no exception, as there is increasing interest in adopting innovative technologies to enhance efficiency, sustainability and project management. This paper investigates the benefits of digital technologies in the UK construction sector, identifies key barriers to their adoption and proposes strategies for effective integration, with a particular focus on East London. A qualitative methodology was employed, combining an extensive literature review with structured interviews. The study involved 10 senior professionals—architects, engineers, project managers and quantity surveyors—with 10 to 25 years of industry experience. Contextual analysis of the interview data revealed persistent challenges hindering digital adoption. These include workforce skill gaps, resistance to change and entrenched reliance on traditional construction practices. Despite these challenges, the study highlights several enablers of digital transformation, such as targeted upskilling initiatives, sector-wide digital literacy programs and supportive government policies. The findings suggest that, without intentional efforts from key stakeholders, especially in policy, training and organizational culture reform, the UK construction industry risks falling behind in global innovation and sustainability efforts. The study concludes that a proactive and collaborative approach is critical to fostering digital integration and securing long-term industry resilience.
Journals
2026 EN
Daoud Ahmed Osama · KhairEldin Mohamed · Ibrahim Ahmed Hussein
+1 more
Construction waste (CW) significantly impacts environmental degradation, resource depletion, and construction costs, especially in rapidly developing regions. However, data on CW generation remain scarce in many developing countries, hindering effective waste management. This study presents an artificial intelligence (AI)-based predictive model to estimate CW quantities—specifically, concrete, bricks, and steel—using a case study in Egypt. Data were collected from 25 construction sites, incorporating variables such as total area, design consistency, worker experience, and waste reuse. The model employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) to analyze the dynamic nature of construction activities and predict waste generation. Model accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the a20-index. The concrete model achieved R 2 = 0.95, RMSE = 0.077, MAE = 0.071, a20 = 0.500; the bricks model showed R 2 = 0.96, RMSE = 0.063, MAE = 0.043, a20 = 0.400; and the steel model resulted in R 2 = 0.93, RMSE = 0.044, MAE = 0.042, a20 = 0.500. The results demonstrate the model’s potential to support data-driven waste estimation where data is limited. These findings can inform strategies and policies for CW management by enabling the forecasting of waste volumes during construction phases.
Journals
2026 EN
Fathy Ismail · Abd-Elaty Ismail · Eltarabily Mohamed G.
+6 more
This study aims to develop an integrated solution that mitigates the adverse impacts of flash floods while capturing excess runoff to recharge groundwater aquifer. A case study was conducted in Tenth of Ramadan City, Egypt. The research approach combined field-based monitoring of groundwater levels through observation wells and automated loggers with advanced hydrological modeling. Watershed delineation was performed using WMS, and subsurface flow were simulated using MODFLOW. Modeling results for a flood event with a 100-year return period estimated a total runoff volume of approximately 8 million cubic meters. Subsequent recharge led to a rise in groundwater levels, reaching a depth of 5.0 meters. Recharge, which involves infiltration of surplus surface water proved effective in reducing flood risk and enhancing groundwater reserves. The findings contribute to the growing extreme hydrological events, when effectively managed, can be transformed into strategic opportunities for sustainable water resource development under different climate change scenarios.