Al-Nahrain Journal for Engineering Sciences
Engineering Determinants of Artificial Intelligence Adoption in Indian Healthcare Systems: A Regression-Validated Institutional Readiness Model
The introduction of Artificial Intelligence (AI) in medical care is becoming an acknowledged challenge in the framework of engineering systems, in which the level of computational readiness, the integration of the infrastructure, the level of user competence, and the limitations of the ethical aspect play a role. The paper analyzes the most important engineering predictors of a readiness to use AI in Indian hospitals, which are assessed using correlation and multiple linear regression frameworks._x000D_ There was a structured survey on 120 healthcare workers and 20 deep interviews with experts to evaluate system-level perceptions and institutional preparedness. The results of regression showed that the model is well fitted (R 2 = 0.61, p < 0.001) with Perceived Usefulness (= 0.43), Infrastructure Availability (= 0.38) and AI Training Exposure ( = 0.26) identified as significant and positive predictors of AI adoption readiness. The Ethical Concern Scores (β = -0.32) had a significant negative impact, highlighting the importance of governance as an important constraint of the system._x000D_ These findings were reinforced by qualitative thematic analysis, which identified interoperability challenges, lack of computational training, data governance oversights, and infrastructure vulnerability as significant engineering limitations. The respondents positively indicated the conditional acceptance of AI on the basis of the explainable model transparency, as well as the system of institutional AI training._x000D_ The research adds a new regression-tested engineering preparedness pathway to AI implementation in Low- and Middle-Income Country (LMIC) hospital setting, suggesting system interface, capacity formation, and ethical algorithm control as the main keystones on the sustainable AI implementation in Indian healthcare.
Copyright (c) 2026 Al-Nahrain Journal for Engineering Sciences
Standardizing Cervical Spine MRI for AI: A Mathematically Formalized Preprocessing and Harmonization Pipeline
The standardization and preprocessing decisions determine the performance, reproducibility and generalizability of Artificial Intelligence (AI) models in cervical spine Magnetic Resonance Imaging (MRI). In this research, the author suggests a six-step standardized preprocessing pipeline that can help with the reliable analysis of heterogeneous MRI data using AI. It is obtained by the pipeline based on recurrent methodological patterns found in the literature, focusing on the operations reported to stabilize image intensity distribution and reduce unwanted sources of variability. A systematic literature review was screened on 2,882 records, and 43 articles were included per the inclusion criteria. Due to the large variability of imaging procedures, preprocessing plans, and reported outcomes metrics, no quantitative meta-analysis was performed and a qualitative synthesis was done. Basic methods such as segmentation, denoising, intensity normalization, augmentation, bias-field correction were consistently reported to improve AI performance. Studies evaluating tasks such as normalization and automated segmentation reported accuracies of 94% to 99.95%. Moreover, less-specific harmonization methods like Combatting Batch Effects (ComBat), z-score normalization and histogram matching, and Generative Adversarial Network (GAN)-based ones were also often attributed with lower scanner- and site-based variations. Regardless of these improvements, the AI workflow remains unstable because of the irregularity of predefined parameters, unequal standards of acquisition, and lack of methodological reporting. In efforts to deal with such challenges, the current work proposes a methodical six-stage preprocessing model that consists of quality control, noise removal, intensity normalization, anatomical segmentation, harmonization and validation. The suggested workflow model offers a practical and transparent basis of clinically translatable AI usage in cervical spine MRI based on conventional mathematical equations and clear documentation.
Good Health and Well-being (Sustainable Development Goal- SDG 3)
Robust, Policy-Tunable Optimization of Diesel Plastic Fuel/α-Terpineol Blends Across the Duty Map
This study establishes a risk aware, multi objective methodology to select diesel plastic fuel α-terpineol blends that deliver robust engine performance across operating loads in a common rail direct injection diesel engine. Heterogeneous responses brake thermal efficiency, brake specific fuel consumption, oxides of nitrogen, carbon monoxide, hydrocarbons, smoke, and exhaust gas temperature are mapped to a unitless desirability scale and aggregated by load. Conditional Value at Risk across loads then prioritizes worst case behavior. By Conditional Value at Risk at level 0.20, the ranking is: diesel 0.242, diesel plastic fuel with 15% α-terpineol 0.197, 10% α-terpineol is 0.177, 5% α-terpineol is 0.157, diesel plastic fuel is 0.141, waste plastic fuel is 0.081. Relative to neat diesel plastic fuel, worst-case performance improves by 11% at 5% α-terpineol, 26% at 10% α-terpineol, and 40% at 15% α-terpineol, with the 100% load consistently governing the tail. Average brake specific fuel consumption decreases from 0.4553 to 0.4217 kg/kWh at 15% α-terpineol, average exhaust gas temperature falls from 321.4 to 293.0°C. Duty map mean oxides of nitrogen reduce from 512 to 474 ppm, hydrocarbons fall from 42.67 to 35.73 ppm. At full load, carbon monoxide declines from 0.52 to 0.46 %vol., and smoke opacity decreases from 52.28 to 49.71%. Within the tested window, 15% α-terpineol in diesel plastic fuel is the risk aware first choice, with 10% α-terpineol a near optimal alternative. The methodology remains stable under risk aversion and weighting changes at high-load calibration.
A Digital Systems and Infrastructure Assessment for Resilient Primary Healthcare Delivery in Paschim Bardhaman, India
Primary healthcare institutions are highly important socio-technical systems, where infrastructure, digital technologies, and service delivery processes collaborate to establish service resilience and performance. Digital systems and sustainable infrastructure may not be integrated as well in a limited environment with resources, which may lead to reduced system robustness and efficiency. This paper is a systematic digital systems and infrastructure evaluation of the process of providing primary healthcare in Paschim Bardhaman District, India, through an engineering-based, multi-criteria analysis framework._x000D_ Cross-sectional engineering analysis was carried out on 25 Primary Health Centers (PHCs) chosen based on the rural and semi-urban settings. They have developed a composite system performance index by incorporating five domains of engineering, namely infrastructure robustness, operational workflow efficiency, outcome performance, digital systems readiness (ICT), and environmental sustainability systems. Information was gathered based on formal auditing of facilities, check of readiness of ICT equipment and software, field observation of operations and review of documents. Domain-specific scores were also summed up to create a Composite Quality and Resilience Index (CQRI). Statistical analyses were made to provide measures of description, correlation analysis and comparative testing between rural and semi-urban facilities._x000D_ Findings showed that there was moderate system performance (mean CQRI = 1.21 ± 0.24) and there was high inter-facility difference. The positive correlation of infrastructure robustness and the efficiency of operational workflow were found to be strong with composite system performance (r = 0.67 and r = 0.74, respectively). The readiness to digital systems, and environmental sustainability scored relatively low, which points out to the underutilization of ICT infrastructure, lack of telemedicine adoption, and the integration of renewable energy sources. The composite performance of semi-urban PHCs was much better than that of rural facilities (p < 0.05), which highlights the importance of infrastructure accessibility and digital connectivity to system resilience._x000D_ The results show that integration of digital systems and infrastructure design that is sustainable are important engineering determinants of resilient primary healthcare delivery. The suggested composite assessment framework can be used as a scalable engineering-based tool to estimate and optimize the performance of low-resource healthcare infrastructure in supporting the resilience of the system and its long-term sustainability and efficiency in delivering the services.
Analysis of Free Vibration Features in FGM Plates
Functionally graded materials (FGM) are a category of composite materials distinguished by specific design attributes that allow them to serve various purposes and applications. This study used FGM samples made from carbon particles and a polyester matrix to manufacture micro-shapes and study the changes in vibration characteristics at different parameters. The manufactured samples contained different weight percentages, ranging from 0% to 20%, and a subsequent step involved manufacturing two different grades of FGM samples: one consisting of five layers and the other of eleven layers. The free vibration characteristics of the FGM structure were determined experimentally and numerically. The effect of varying several parameters on the natural frequency was monitored, including the number of FGM layers, thickness, and gradient coefficient. The Ansys Workbench (version 2022 R1) was used to analyze free vibration to verify the experimental results. Vibration testing of square FGM panels showed that the natural frequency increased with the number of layers. Improving the thickness of the panels also resulted in increased natural frequency values, when changing the thickness from 5mm to 10mm increases the natural frequency by an amount of 173.34 Hz at 11 layers. Model analysis using finite element analysis (FEA) tools confirmed the validity of the experimental solution, with a maximum discrepancy of 9.72%. Based on numerical calculations, the natural frequency coefficient decreased with increasing power law index under different boundary conditions and increased with increasing number of constraints in the sample.
Synthesis Eco-friendly Biochar from Spent Coffee for Using to Remove Acidic and Basic Dyes from Aqueous Media
This study focuses on reuse of biowaste material locally available as spend coffee to synthesis high surface area biochar and evaluate its potential to remove dyes with acidic and basic in nature, as Acid Fuchsin and Methylene Blue dyes. The biochar was fabricated using chemical activation with various concentrating (20, 30, 50) vol. % of phosphoric acid (H3PO4). The pyrolysis process carried out in a tubular furnace under flow of nitrogen (N2) at 600°C for 3 hours. The final prepped biochar was analyzed using various techniques such as XRD, SEM, FTIR, TGA, EDX, N2-adsorption, BET, and pore volume (Vpore). Furthermore, batch adsorption experiments were conducted to remove Acid Fuchsin and Methylene Blue dyes from aqueous solutions under different variables of pH of solution (2-8), contact time (0-240) min, initial dye concentration (20-200) ppm, and weight of dosage (0.1-0.5) g/L. The results show that the best surface area and pore volume has been archived for the prepared biochar at 30% H3PO4 concentration were (1385.269 m2/g and 1.12 cm3/g) respectively. As well as, the maximum removal percentage was 92.5% for Fuchsin dye and 90% for Methylene Blue dye achieved at 180 minutes of adsorbing time, with maximum removal achieved with initial dye concentration of 20 ppm, 6.5 pH solution, and 0.3 g/L adsorbent. The study found that Freundlich isotherm and kinetic adsorption models fit well with the batch experimental data, which indicates homogenous distribution and limiting active sites with adsorption capacity (384.61) mg/g and (357.14) mg/g for Acid Fuchsin and Methylene Blue dyes, respectively. While, the pseudo-second-order kinetic model indicates a physic-sorption is the limiting step.
Synergistic Effects of Hybrid Nano-Calcium Carbonate and Nano-Talc Fillers on the Electrical, Thermal, and Morphological Properties of Epoxy-Glass Fiber Composites
This research investigates the influence of single and hybrid mineral nanofillers on the morphological, thermal, and electrical properties of epoxy-based glass fiber composites. Nanocomposites were fabricated using hand lay-up technique, incorporating nano-calcium carbonate (CaCO₃) and nano-talc with particle size of 50 to 100 nm into an epoxy matrix with 5-ply E-glass/S-glass fiber. The study evaluates composites with 2-8 wt% of nano-CaCO₃, and hybrid systems containing nano-CaCO₃ (2-8 wt%) and nano-talc (1-4 wt%). Morphological analysis via Scanning Electron Microscopy (SEM) assessed nanofiller dispersion, while thermal properties were analyzed using Differential Scanning Calorimetry (DSC) for glass transition temperature (Tg). Electrical performance was evaluated through dielectric breakdown voltage (ASTM D149) and surface resistivity (ASTM D257). SEM analysis showed filler dispersion depended on concentration, with optimal distribution at 4-6 wt% before significant agglomeration at higher concentrations. DSC results revealed increased Tg with nanofillers, peaking for the composite with 6 wt% CaCO₃ and 3 wt% talc, indicating enhanced thermal stability. The dielectric breakdown voltage peaked at 17.6 kV for 2 wt% CaCO₃ and 1 wt% talc composite, while surface resistivity was highest for 4 wt% CaCO₃ and 2 wt% talc (73.74×1012Ω). All formulations maintained UL 94 V-1 flammability rating. The study concludes that the synergistic interaction between nano-CaCO₃ and nano-talc enables significant tailoring of thermal and electrical properties for advanced applications.
Comparative Performance Evaluation of P and O, Grey Wolf Optimization, and Teaching-Learning- Based Optimization Algorithms for MPPT in Photovoltaic Systems
Photovoltaic (PV) systems have gained importance as one of the major renewable energy technologies because of its clean and sustainable characteristics; however, PV nonlinear current-voltage and power-voltage characteristics are a significant factor in limiting the maximum available power determined by the amount of available power under different environmental and load conditions. Maximum Power Point Tracking (MPPT) algorithms are one of the most important performance enhancements to help make PV energy conversion systems more efficient and reliable. Conventional MPPT techniques such as Perturb and Observe (P&O) have been widely used due to their simple and easy implementation in terms of control strategies, however, they exhibit steady-state oscillations, slow convergence, and poor dynamic performance under fast changing conditions of irradiation and load. To overcome these limitations, validation of intelligent and bio-inspired optimization and MPPT algorithms have attracted a great deal of interest. This paper includes a comprehensive comparative analysis of three MPPT techniques that are: conventional P&O algorithm, Grey Wolf Optimization algorithm (GWO), and Teaching-Learning-Based Optimization algorithm (TLBO). A detailed model of PV system coupled with dc-dc boost converter is built in Matlab/Simulink and the algorithms are tested with a constant irradiation and variable load and with simultaneous irradiation and load variation. Performance criteria like tracking performance, speed of convergence, steady state oscillations and robustness under dynamic conditions are analyzed. Based on the simulation results it is proven that both GWO and TLBO clearly outperforms the conventional P&O algorithm. Amongst all the optimization-based approaches, TLBO provides good performance with near-instantaneous convergence, minimum oscillations and consistent tracking efficiency is close to 100% for all the tested scenarios. The results prove that TLBO-based MPPT offers solid and computationally efficient solution for real world applications of PV systems especially in those with frequent and random operating condition variation environment.
Plant Extracts Mediated Synthesis of Nanoparticles: A Review of Innovations for Dentistry
Lately, nanotechnology has made significant advancements in various scientific and medical fields, including dentistry, by developing nanoparticles with distinct chemical and physical properties. Green synthesis is considered an eco-friendly alternative and a safe and sustainable method compared to conventional chemical and physical synthesis methods, which often contain toxic and environmentally harmful materials. This method is also cost-effective, biocompatible, and consumes less energy, thereby reducing pollution and environmental hazards, and making it suitable for various biomedical applications. Many nanoparticles have been employed in dentistry to enhance preventive treatments, including dental implants, orthodontics, the treatment of tooth decay bacteria, and endodontics. Despite their numerous benefits in the dental field, nanoparticles can exhibit toxicity risks, long-term impacts, and limited stability that require further investigation before their use in clinical applications. This review analyzes and classifies over 70 studies from the past 10 years based on plant extract source, nanoparticle types, and dental applications. We offer a new classification framework that organizes nanoparticles synthesized using green methods according to their manufacturing process and functional applications in dentistry. Comparative analysis reveals that, for instance, zinc oxide and silver nanoparticles synthesized through a green process exhibit the highest antibacterial activity with low toxicity. This review highlights current research gaps in biostability and toxicity, and provides insights into potential clinical applications and future research directions. It focuses on the role and benefits of the green method in improving the properties of nanoparticles, as well as its impact and effectiveness in developing dental materials and mitigating biological risks, opening up broad and new horizons in advanced dental treatments.
AI-Optimized Wake-Up Radio Systems for 6G IoT
Battery life is the hard constraint for massive Internet-of-Things (IoT) at 6G scale. Wake-up radios (WuRs)—ultra-low-power auxiliary receivers that “listen” for short wake-up signals while the main transceiver sleeps—offer orders-of-magnitude energy savings, but suffer from false wake-ups, long tail latency under bursty traffic, and sensitivity/coverage limits. This paper proposes an end-to-end AI-optimized WuR stack that combines (i) a TinyML classifier embedded in the WuR path to suppress false triggers and adapt detection thresholds, and (ii) a reinforcement-learning (RL) scheduler at the base station or gateway that co-optimizes wake-up signaling with 3GPP NR DRX/C-DRX timers. The proposed method, tested using a trace-driven simulator calibrated with published WuR power/latency figures, the approach reduces node-average energy by 41–72% versus strong baselines, while meeting 99% latency targets and cutting false wake-ups by >80%.
Towards Next-Generation Autonomous Vehicle Communication in Smart Cities: A Blockchain and AI Perspective
Autonomous vehicles (AVs) rely on continuous vehicle-to-vehicle (V2V) and vehicle-to-everything (V2E) communication to support shared perception, traffic direction, and decision making. Integrating real-time data, connectivity, and precise navigation will revolutionize urban mobility by facilitating sustainable, secure, and highly efficient transportation options. The rapid evolution of AVs within smart cities necessitates robust and secure data transmission methods to ensure efficient and safe operations. This paper proposes a next-generation communication framework that integrates blockchain with artificial intelligence (AI) to enable secure, decentralized, and adaptive AVs network. Blockchain is employed as a distributed trust layer to ensure data immutability, decentralized identity management, and transparent transaction validation among vehicles and roadside infrastructure. AI techniques are incorporated to support intelligent threat detection, dynamic resource allocation, anomaly identification, and context-aware decision making. The integration between blockchain and AI enhances trust establishment while maintaining system scalability and real-time responsiveness. The proposed architecture introduces a layered design that separates communication, consensus, and intelligence components, allowing efficient integration with edge computing and 5G-enabled vehicular environments. This research contributes a unified conceptual framework for secure and intelligent autonomous vehicle communication, highlighting how blockchain and AI can jointly address critical limitations in next-generation vehicular networks and support the evolution of trustworthy, resilient, and scalable smart mobility ecosystems.
Performance Evaluation of Hybrid Porous Asphalt Containing Recycled Waste Plastic
Plastic pollution represents a significant global environmental challenge, with millions of tons of plastic waste accumulated annually in landfills and natural ecosystems. The COVID-19 pandemic intensified this issue due to the widespread use of single-use personal protective equipment. As a response to this problem, this study examines the use of recycled polyethylene terephthalate and single-use face masks as additives in porous asphalt mixtures to improve the mixtures’ properties and promote sustainability in pavement engineering. The research aimed to evaluate the combined effect of incorporating recycled polymers into a hybrid system, rather than separately modified mixtures of polyethylene terephthalate and single-use face mask fibers. Laboratory tests indicated that the hybrid mixture exhibited optimal performance, achieving a 34% increase in Marshall stability, a 19% decrease in flow, and a 20% reduction in air-void content. Additionally, permeability decreased by 36%, while remaining within the accepted limit, improving moisture susceptibility, and cantabro abrasion losses decreased by up to 11%, suggesting improvements in cohesion, structure, and durability. The findings demonstrate that hybrid polymer modification offers an environmentally sustainable and high-performance approach for future porous asphalt pavements.
Mechanical Characterization of Multilayer Fiber-Reinforced Composite Materials
The mechanical behavior of multilayer FRCMs was tested, and the effects of fibers, types of fiber, and laminates on the tensile, flexural, and impact properties were discussed. The vacuum bagging method is a composite manufacturing process that uses atmospheric pressure to consolidate laminate layers, remove excess resin, and get rid of air bubbles. This makes high-quality, void-free composites. Four composite systems were made using this method: natural fiber composites reinforced with sheep wool and goat hair fibers, and synthetic fiber composites reinforced with carbon and glass fibers, all with an epoxy resin (LR620) and hardener (LH620) system. All composite assemblies have been systematically characterized at different layer configurations (3-6 layers considered) for the best structural response. The tensile, flexural, and impact properties were tested by following the corresponding ASTM (D638, D790, D6110) standards, respectively. The tensile and flexural results of the carbon fiber composite (GCT, GCB series) showed excellent mechanical performance (maximum tensile strength of 283 MPa for GCT4 and flexural strength of 164 MPa for GCB5) with high load-carrying capacity, but the failure was brittle. Goat hair composites exhibited excellent impact resistance (GHI4: 1.255 J) and moderate tensile strength (36 MPa), which indicated superior energy absorption capacity. The glass fiber composites showed a good balance of mechanical properties with increased ductility, where the tensile strength was 245 MPa, and a large deflection capacity was reached (14.683 mm). The study demonstrates that 4-layer setups usually result in the highest tensile properties, while 5–6-layer setups improve the flexural strength. These results add to the knowledge of optimization of multilayer composite design, and they are very useful for materials selection in aerospace, automotive, and structural applications.
Functionally Graded Materials of Stainless Steel 316L-Inconel 625: Optimization of the study characteristics through Porosity, Microstructure, and Mechanical Performance
Functionally graded materials were created using laser-directed energy deposition technology. This work examines how different mixing ratios of Stainless Steel 316L and Inconel 625 affect the relative density and porosity of these materials. Twelve samples were created using a constant laser power of 600 W, three different laser scan speeds (20, 25, and 30 mm/s), and four different SS316L/IN625 transition ratios (85%/15%, 60%/40%, 40%/60%, and 15%/85%). To determine the volumetric distribution across the compositional gradients, the porosity and relative density measurements were taken. Optical and scanning electron microscopy were used for microstructural analytical characterization to differentiate between compositional gradients in grain shape and phase distribution. The mechanical performance was examined using microhardness measures, namely the Vickers method. This study applied to prove the process parameters and compositional transformations to the resulting microstructural features and mechanical properties, providing insight into optimizing the laser-directed energy deposition-manufactured functionally graded materials for advanced performance. The best graded composition was found that gives the best overall performance based on experimental data.
Laser Directed Energy Deposition (LDED) technique
Effect of Date Seeds Ash Incorporation with Micro Silica on the Cement Mortar Behavior and Mix Integrity
This study evaluates the performance of cement mortar incorporating micro silica (MS) and thermally treated date seed ash (DS) as sustainable partial cement replacements, in line with the demand for improved construction materials that utilize available resources. In the absence of an understanding of the behavior resulting from combining MS and DS within a hybrid replacement system particularly regarding how different blending ratios influence mortar performance and mix integrity. To achieve this, five mixtures were prepared with a total replacement level of 20% including three hybrid blends and were assessed through flowability, compressive strength, sorptivity, dry density, water absorption, and Ultrasonic Pulse Velocity (UPV) tests. Predictive models have also been developed to estimate chloride and sulfate penetration based on physical and non-destructive indicators. The results showed that the hybrid mixture MS15–DS5 demonstrated the most notable enhancement compared to the control mix, achieving a 6% increase in compressive strength a 42% reduction in sorptivity and clear increases in dry density and pulse velocity indicating a more compact internal structure. The MS10–DS10 mixture also exhibited improved behavior, though to a lesser degree. In contrast, increasing the DS content led to performance deterioration, where MS5–DS15 showed reduced strength and higher sorptivity and DS20 presented the weakest response, with an 8% reduction in compressive strength and a 32% increase in sorptivity. The predictive models also demonstrated high accuracy achieving R2 of 0.989 and 0.983 for chloride and sulfate penetration, respectively, supporting their ability to estimate future behavior based on physical properties. These results provide a clearer understanding of how combining MS and DS influences mortar behavior and mix integrity, and they contribute to the development of sustainable cementitious mixtures with improved performance when appropriate replacement levels are selected.
Behavior of R.C Slab-Column Connections with Square and Circular Columns under Gravity and Lateral Cyclic Loading Conditions
This experimental investigation evaluates the structural behavior of reinforced concrete slab-column connections incorporating columns with square and circular cross-sectional geometries under gravity and lateral cyclic loading. Four interior slab-column specimens were designed, fabricated, and tested, with identical slab dimensions of 1050×1050×80 mm and a column height of 500 mm. The circular columns were intended to have an equivalent second moment of inertia to the square column, ensuring a fair comparison of geometric effects. One specimen identified as SC-G, incorporating a square column, was tested under a progressively increasing vertical load to determine its maximum gravity load-bearing capacity. The remaining three specimens SC-2 with a square tied column, SC-9 with a circular spirally-reinforced column, and SC-10 with a circular column incorporating a column capital were tested under a constant gravity load equivalent to 60% of SC-G’s ultimate capacity (75 kN), combined with a lateral displacement protocol conforming to ACI 374 guidelines. Results indicated that the shape of the column (circular) and reinforcement (spiral) in SC-9 produces a comparatively higher ultimate load, stiffness, and ductility than SC-2 of the square column and tied reinforcement. The ultimate lateral load increase was +13.3%, -26.1%. Circular spiral columns with column capital in slab-column connections led to a noticeable rise in ultimate lateral load by about +62%, -80%. It was also shown that incorporating a column capital in SC-10 significantly enhanced the punching shear resistance and energy dissipation capacity. Circular columns demonstrated more stable hysteretic behavior and improved ductility compared to the square column specimen.
Vehicle’s Traffic Flow Control Using Cloud Based IoT
This paper proposes Smart Adjustable Traffic light Time System (SATTS), a cloud-based IoT network model with a central management architecture by using cloud based IoT. The model innovates an algorithm to gather data from IoT devices connected to the sensors network to enable efficient traffic management in smart city appliances. SATTS scheme adjusts the green light period at intersections to alleviate traffic congestion and minimize delays and keep vehicle’s traffic conservation. The mentioned model has been proposed to modifying the timing of green lights at the four ways intersection based on the vehicles arrival rate. The results verified by comparing SATTS with Fixed time Traffic light System (FTS) and Variable Traffic light System (VTS) models, and showed that the proposed schemes can effectively decreases delay and traffic congestion. On average, there is a reduction in the rate of number of waiting cars compared to the arriving cars of 33% in cycle 2 and 50% in cycle 3 for VTS, and 52% in cycle 2 and 75% in cycle 3 for SATT compared to FTS. SATTS minimizes delay time between the four lines of the intersection.
Shear Strengthening of RC Beam Using FRP Composite in Various Techniques: A Review
This research reviews existing literature on RC beams strengthened techniques for shear. Research indicates that fiber-reinforced polymers (FRP) are best utilized and effective materials to enhance RC beams. These materials have corrosion resistance and exceptional strength. The strengthening of reinforced beams by FRP depends on the nature of fiber, arrangement, materials, and manner of strengthening. The current article analyzes the properties, advantages, and applications the FRP composites. In addition to the mechanisms of failure, properties, and behaviors of FRP-strengthened reinforced beams undergoing shear with various strengthening techniques such as EB, NSM, and ETS.
Leveraging Machine Learning in Modeling andOptimization of Laser Hardening of Aluminum Alloys
The current study focuses on utilizing machine learning (ML) models in optimizing and predicting laser-induced surface cold hardening in aluminum alloy 6061-O using a pulsed fiber laser. Three input features were utilized: power density (Pd), frequency (f), and pulse overlap (OV), with surface hardness as the matching aim. A set of ML models, including XGBoost and Adaptive Ensemble, was employed. The dataset was preprocessed for normalization and outlier handling, hyperparameter tuning, and assessment through the grid search and cross-validation, and model performance was evaluated using the coefficient of determination (R²) and mean square error (MSE) metrics. The Linear regression and Random Forest regression models were excluded due to their weakness in performance, exhibiting noticeable enhancement in performance of the ensemble. After excluding weak models, the results of XGBoost and Adaptive Ensemble models demonstrated great results of R2/MSE of 0.970/0.774 and 0.949/1.319, respectively. The XGBoost model occupied the first order, followed by the Adaptive Ensemble model, in improving the predictive accuracy to around 96% compared to other models.
Review of Enzyme-Induced Calcite Precipitation as Enhancement Technique for Weak Soils
A rapidly expanding global population has heightened the need for engineering technologies aimed to enhancing the mechanical properties of weak soil, raising concerns about the sustainability of engineering practices that depend on energy-intensive materials and methods from earlier times. Traditional ground improvement techniques like compaction, preloading, vibration, and chemical grouting are typically costly and frequently have very high energy and CO2 footprints. Soil stabilization employing bio-enzymes has been viewed as a resilient and ecologically beneficial method for modifying the soil characteristics. Calcite-induced precipitation methods have recently become potential techniques in geotechnical engineering for improving the shear strength of soils. One of the most promising methods among them is enzyme-induced calcite precipitation (EICP). Enzyme induced calcite precipitation (EICP) is a bio-inspired technique based on the precipitation of calcium carbonate for enhancing the geo-mechanical properties of soils. In this technique, calcium carbonate acts as a cementitious agent that binds the soil particles together at the points of contact hence, increasing the strength and stiffness of treated soils, while relatively reducing the soil permeability and porosity. The achieved enhancements make EICP useful for applications such as ground improvement, construction materials, and erosion control over traditional binders. It is an environmental friendly technique that has generated great interest to geotechnical engineers. EICP particularly, has proven to be more effective since its application to all soils coarse and fine which have small pore size. This review thoroughly assesses the use of the EICP approach as a soil stabilization strategy conducted by various researchers. This review article studies urease and its implication on the characteristics of treated soil such as shear strength, permeability and micro-structural changes.
Utilizing of using blocks in the stilling basin and their effects on characteristics of the hydraulic jump
The stilling basin is a frequently used energy dissipators that converts supercritical flow from a dam's spillway into subcritical flow that is in compatible with the downstream river regime. They serve to prevent scouring, which happens when high-velocity water enters the dam's downstream reach. This scouring not only seriously erodes the downstream area but also damages the dam's foundation. In this research, the behavior of blocks of stilling basin and spillway toe in reducing energy losses and the longitudinal hydraulic jump toe position is evaluated. This research includes two cases ; case1 is a spillway and stilling basin without blocks, and case2 which is a spillway and stilling basin with chute blocks and middle blocks. By increasing the discharge from 0.010 m3/s to 0.020 m3/s, the difference in the dissipation energy for case1 and case2 are (17.6%, 24.7%) respectively, and that the percentage of decrease in the longitudinal hydraulic jump toe position is (83.94%, 85.90%, 86.30%, 87.20%, 88.39%, 87.58%, 80.64%, 77.94%) for the discharges tests from ( 0.010 m3/s to 0.017 m3/s). As a results, The using of blocks in stilling basin and spillway led to a rise in relative energy dissipation and decrease the longitudinal hydraulic jump toe position of stilling basins.
YOLOv11 with spatial attention and preprocessing enhancements for accurate skin cancer classification
This work suggests a Deep Learning (DL) architecture based on You Only Look Once YOLOv11 for Skin Cancer (SC) detection. The similarity between malignant and benign lesions makes visual inspection a failure to distinguish between them. To solve this problem, the proposed approach uses a 3-step pre-processing stage, namely hair removal, color normalization, and Contrast Limited Adaptive Histogram Equalization (CLAHE) contrasts, has been conducted to eliminate artifacts and improve image quality. Balanced data augmentation on the training set of the PROVe-AI dataset. In this process, YOLOv11 with C3k2 module and C2PSA module showed significant results in optimized multi-scale feature collection and spatial interest. The experimental outcome demonstrates that the proposed model has a classification accuracy of 93.09% and led the baseline models, such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN). The proposed optimized YOLOv11 architecture allows for skin cancer detection in a computationally efficient framework with promising preliminary results so that the proposed approach can be a beneficial Artificial intelligence (AI) tool for early diagnosis, particularly in a lack of high-tech medical facilities.
Auto Fish: Leveraging AI for fish species identification in natural habitats
Identifying fish species in natural aquatic environments remains challenging due to changing light conditions, turbid water, and complex underwater scenes. Most current deep-learning models rely on controlled datasets, which limits their use in real-world settings. This study presents Auto Fish, a mobile deep-learning system for real-time, offline fish species identification on Android devices. The system uses the MobileNetV2 architecture, optimized with TensorFlow Lite for processing on the device. This approach ensures high accuracy while keeping computational costs low. We trained and evaluated the model on a balanced dataset of 8,000 annotated images, including nine marine species: Sea bass, Red sea bream, Horse mackerel, Gilt-head bream, Shrimp, Black sea sprat, Trout, Red mullet, and Striped red mullet. Extensive preprocessing, image enhancement, and stratified sampling helped the model perform well despite variations in lighting and background conditions. The experimental results showed a validation accuracy of 99.2%, with both macro and micro Precision, Recall, and F1-scores around 99.3%, and an average False Positive Rate (FPR) of 0.09%. The system supports offline recognition, cloud syncing via Firebase, and delivers real-time results within 4.2 seconds per image on mid-range smartphones. These findings show that Auto Fish can effectively classify fish species in the field while remaining efficient and easy to use. This work offers a practical AI-based solution that connects research with ecological monitoring, empowering citizen scientists and conservationists to document biodiversity using mobile technology.
Cyber security for smart inverters and distributed energy resources (DER)
The growing use of “distributed energy resources (DER)” will result in a significant increase in the total number of gadgets or devices that users and third parties own and control. These gadgets rely largely on digital communication and control, placing them in danger due to cyber threats. This study presents a comprehensive framework that is resistant to attacks for defending integrated DER and major power grid infrastructure from hostile cyber-attacks, ensuring the safe integration of DER without jeopardizing system dependability and stability. This research focuses on the development of a cyber-physical power system that incorporates a significant integration of DER and analyses the particular cyber security problems brought about by DER integration. Following that, we provide a systematic DER resilience analysis approach, in addition to effective and measurable resilience measurements and concepts concerning design, and we summarize important DER assault scenarios. In conclusion, we suggest preventive, detective, and responsive measures against cyber-attacks, specifically tailored for integrating Distributed Energy Resources (DER) throughout the physical, cyber device, and regulatory levels of an eventual smart grid.
Improvement and computational complexity reduction in estimating the direction of arrival using switched active switched parasitic antenna arrays for next- generation networks
This research studies the capabilities of antenna arrays known as switched active switched parasitic antennas (SASPA) in enhancing the direction of arrival (DOA) estimation of received signals. In these arrays, a single antenna element operates in an active state while the other antenna elements are parasitic at one time of measurement. In the next measurement time, a parasitic element is switched to an active state while the active element is switched to a parasitic state, and the procedure is quickly repeated for each array element. By a straightforward arrangement of SASPA measurements, a functional steering matrix can be produced without any unitary transformation. This steering matrix results from a real-valued matrix that contains the information on the DOAs of the received signals, multiplied by a vector that represents the mutual coupling. The advantages of this steering matrix contribute to obtaining high-resolution DOA estimation with considerably reduced processing time compared to the conventional antenna arrays where all antenna elements are in an active state. The simulations conducted in this study evidently demonstrate that the resolution of DOA estimation with SASPA arrays is considerably superior, irrespective of the array’s compact size and the directions and proximity of received signals to each other. Additionally, other simulations in this work depict that the processing time for DOA estimation using SASPA arrays is significantly reduced (approximately one-third less on average) in comparison to traditional all-active antenna arrays.
Switched Active Switched Parasitic Antenna Array
Automation in loading/unloading semi-knockdown vehicles in assembly plants for intelligent navigation
Manual handling of semi-knockdown vehicles in assembly plants is unsafe, time-consuming, inefficient, and prone to quality irregularities. To intervene in addressing these problems, this study develops a prototype of an automated load carrier intelligent navigator. The work centre is analysed for space, material type and handling requirements. This is followed by design and testing, whereby software, hardware and mechanical engineering are integrated in the context of process optimisation. The prototype was tested on rough and smooth surfaces, for no-obstacle and obstacle avoidance conditions. On rough and smooth surfaces with no obstacles, the minimum distance considered is 0.5m, and the average speed and time determined are 0.08m/s and 6.23s, 0.17m/s and 2.97s, respectively. For the maximum distance of 3.0m, the average speeds and times determined are 0.081 m/s and 37.42s, and 0.18 m/s and 17.35s, respectively. The average distance considered for both rough and smooth surfaces is 1.75 m, and the average speed and time at each scenario are 0.081 m/s, 21.78s, and 0.17 m/s, 10.26s. The voltage of the battery drops, with a corresponding decrease in the speed of the motors. The automated carrier prototype makes the best decisions when it encounters an obstacle, giving the best outputs. This paper contributes by providing real-time intelligent navigation data and accurate regulation of the automated carrier for automotive assembly plants. Its novelty lies in conducting experimental investigations using the automated loading/unloading intelligent navigator to explore its advantages compared to manual loading/unloading in automotive assembly plants. In conclusion, building a carrier for assembly operations enhances assembly operational performance, correcting inefficient and unsafe loading and unloading processes.
Development of a flexible strain sensor from graphite-treated fabric for smart applications
This research is based on developing a flexible strain sensor from graphite-treated fabric using knife coating technology. Three sensors were formed, differing in the number of coating layers (2, 4, 6). The results of studying their properties had shown that with increasing a number of coating layers, the electrical conductivity value of the treated samples increased, reaching a value of (21.8×10-3 S/cm). The treated layer was superficial, as the penetration of the coating into the structure did not increase significantly. It was also shown that the treatment did not affect the fabric properties such as hardness and tear strength. When studying the sensor's performance, it was found that the sensor's resistance value changes with the change in its bending angle. The change rate was higher for the six-layer sample, and the response time was shorter, faster (0.8s), than the other samples. Then, a working system was applied to the sensor to give a command to turn the LED on or off by bending the sensor and it showed good performance. This, in turn, confirms the effectiveness of applying this sensor in smart wearable textiles.
IoT-enabled proactive women’s safety wearable with long-range fail-safe alerts
Women’s safety remains an urgent challenge, particularly in moments when conventional panic button devices fail due to a victim’s inability to act or poor network coverage. To overcome these shortcomings, TRIAD-Lite is introduced as an IoT-enabled wearable framework that unites multimodal physiological sensing with lightweight deep learning for proactive distress identification. The system captures heart rate, blood pressure, galvanic skin response, and motion patterns, while incorporating a triple-tap gesture to confirm user intent, all processed locally on a Raspberry Pi for real-time inference. Unlike reactive mechanisms, this design anticipates danger by analyzing variations in physiological signals that often precede visible distress. Communication reliability is reinforced through a hybrid strategy: alerts are transmitted via GSM or Wi-Fi under normal conditions, but in the event of limited connectivity, a LoRa-based backup ensures long-range transmission. Experimental analysis using simulated datasets yielded an AUC of 1.000 with flawless precision and recall, highlighting the model’s reliability and calibration. Further field evaluation demonstrated that LoRa maintained connectivity across 5.7 kilometers with complete packet delivery, proving effective for both rural and urban environments. By combining predictive analytics, gesture-based confirmation, and dual communication layers, TRIAD-Lite offers a scalable, privacy-conscious, and highly resilient framework that strengthens women’s safety and extends protective technology into regions where conventional systems often fail.
AI-driven crop disease detection with efficient NetB3 hybrids for sustainable agriculture
In precision agriculture, crop disease detection can be a highly valuable undertaking in which scalable and correct solutions may save considerable amounts of money and loss of yield. This paper introduces a comparative analysis of state-of-the-art deep learning models with special attention to EfficientNetB3 hybrids, which are trained on a balanced subsample of the PlantVillage dataset with 33 classes based on nine crops. To overcome the shortcomings of the previous studies, which used unbalanced sample, a leakage-free balancing approach was used, resulting in 13,200 training and 3,300 validation samples. Custom head transfer learning was used where it was tested using two strategies; FreezeUnfreeze fine-tuning, and Singlephase training. MobileNetV2, InceptionV3, DenseNet121, GhostNet, in addition to other baseline CNNs, were compared to baseline Convolutional Neural Networks (CNNs). The findings indicate that EfficientNetB3 hybrids are superior with an accuracy of ≥99.5% and 99.9% Area Under the Curve (AUC) and specificity than the previous CNN-based systems. The paper logically defines a performance ladder between model options and real-life deployment demands, such as lightweight mobile applications to precision agriculture systems, and points out future trends in the field-based validation.
Natural dyeing of cotton fabrics with licorice extract: the role of mordants in enhancing color fastness and protection from ultraviolet rays
The usage of non-toxic, eco-friendly natural dyes on textiles has achieved notable attention due to increased environmental attention about avoiding hazardous synthetic dyes. This has prompted a return to natural dyes and the search for new sources, especially locally available ones like licorice. In this study, Glycyrrhiza glabra extract (70 g/l), prepared using ultrasound assistance, was used to dye cotton samples. The natural dyeing process employed a simultaneous mordanting method with zinc chloride and alum as mordants, in many concentrations (1, 3, 5, 7 and 10 g/l). The color fastness of the dyed samples was evaluated using a scanner and ImageJ. The fastness of the dyed fabrics was tested against washing and rubbing, and samples with licorice extract showed excellent stability. A tear strength test was also conducted to assess the impact of licorice extract dyeing on the mechanical properties of the samples. It was observed that dyeing with licorice reduced the tear strength, but increasing the concentration of mordants improved the resistance to tearing, making the mordanted samples stronger than the mordanted ones.