Perovskite solar cells (PSCs) continue to advance toward higher efficiencies, yet the geometrical design of functional layers remains a critical bottleneck for device optimization and manufacturability. This work establishes a hybrid physics-data framework that integrates three-dimensional finite-element modeling with machine-learningbased surrogate prediction to accelerate PSC thickness optimization. A full 3D COMSOL Multiphysics model was developed to resolve charge-transport behavior, spatial electric fields, and recombination profiles within TiO₂/MAPbI₃/Spiro-OMeTAD architectures. Systematic variations in electron transport layer (ETL), perovskite absorber, and hole transport layer (HTL) thicknesses reveal that device power conversion efficiency (PCE) is governed by a trade-off between optical absorption, interface recombination, and resistive losses. A multi-layer perceptron regressor was trained using simulation data and achieved strong predictive fidelity (R2 ≈ 0.98) with a mean absolute error below 0.3%. The resulting surrogate model rapidly identifies optimal structural configurations without requiring additional high-cost simulations, demonstrating a reduction of design time by more than an order of magnitude. The proposed workflow provides a transferable route toward digital-twin-driven photovoltaic design and offers practical guidance for high-performance PSC engineering with reduced material consumption and enhanced computational efficiency.

Functional plate is one of the most typical materials used for strengthening of reinforced concrete (RC) structures. This article focuses on using functional plates internally to improve the flexural response of RC beams. For this purpose, experimental and numerical investigations on the flexural behavior and ductility of steel-plated RC beams were conducted. Nine RC beams were cast and cured for 28 days. The steel plates were located at the tension side of the RC beams to investigate their effect on the flexural performance of the tested beams. To achieve the research objective, three configurations of the shape of steel plates were proposed, flat, curved, and rounded. The results demonstrate that using embedded steel plates is effective and significantly enhanced the flexural performance of concrete beams. The strengthening delayed the first cracking appearance and increasing of ultimate load up to 45% compared to the reference beam. Further, there was an improvement in ductility and stiffness behaviours by 202% and 46%, respectively, particularly for beams with constrained flat steel plates, which exhibited the highest performance gain. The experimental and finite element (FE) results showed a good agreement in terms of cracking behavior and with approximately 6% maximum ultimate load difference.

This work provides a comprehensive evaluation of the effect of shot peening (SP) time on the mechanical, electrochemical, and surface properties of AA6061-T6 aluminum alloy tested in an alkaline chloride medium (pH = 9). The specimens were subjectively peened for varying durations from 0 to 12 min. The subsequent effects on tensile strength, fatigue life, corrosion resistance, surface roughness, and microhardness were studied. The results showed that a SP time of 9 min increased the tensile strength and hardness through strain hardening, dislocation accumulation, and establishment of compressive residual stress. The formation of a strong passive layer and delayed crack initiation also help make the material more resistant to corrosion and fatigue. However, peening for more than 9 min resulted in rough and localized damage and slightly reduced the mechanical performance. The results show that a 9-minute SP duration is the ideal method to strengthen the surface and maintain a strong structure, which makes AA6061-T6 parts last longer under harsh conditions.

This article presents a mathematical analysis of 3D Williamson nanofluid flow over a stretching Riga plate in a Darcy-Forchheimer porous medium. The model incorporates thermal radiation, heat generation/absorption, and the Buongiorno nanofluid framework with Cattaneo-Christov double flux. Similarity transformations reduce the governing PDEs to ODEs, solved using Mathematica's NDSolve. Graphs and tables illustrate the effects of key parameters on velocity, temperature, concentration, skin friction, Nusselt number, and Sherwood number. The x-direction velocity increases with the modified Hartmann number ($Ha$ = 0.5–2.0), enhancing skin friction by 20–30%. Higher thermophoresis ($Nt$ = 0.1–0.5) elevates temperature and concentration by 15–20% and 10–14%, respectively. Brownian motion ($Nb$ = 0.1–0.5) boosts mass transfer, increasing Sherwood number by 7–9%. Increasing heat and mass relaxation parameters ($\gamma_1$, $\gamma_2$ = 0.1–0.5) accelerates Nusselt and Sherwood numbers by 5–10%. Results correlate well with prior studies, providing a basis for magnetohydrodynamic (MHD) cooling systems, polymer processing, and biomedical simulations involving non-Newtonian fluids.

The Internet of Things (IoT) consists of a large interconnected system of devices that automatically gather, analyze, and transfer data. Securing the integrity and privacy of these devices is a significant challenge due to their distributed and heterogeneous nature. To address this issue, this paper presents a hybrid security framework that is designed in two phases: Node Topology Measures-based Vulnerable Node Detection (NTMVND) and Adoption-based Differential Evolution (ADE) with Elicited Genetic Algorithm (ADE2GA). The NTMVND component detects vulnerable nodes using important topological measures such as node degree, betweenness, clustering coefficient, and centrality to remove potential risks in the communication network. The ADE2GA component produces optimal and secure paths for data transmission by leveraging the adaptive exploration characteristics of Differential Evolution (DE) and the exploitative learning capabilities of the Genetic Algorithm (GA). The simulation results in Network Simulator-2 shows that the ADE2GA model performs best, resulting in 39% reduction in the end-to-end delay and 26% savings in energy consumption, while producing a 41% increase in throughput and a 10% increase in packet delivery ratio compared to standard Particle Swarm Optimization (PSO) and Differential Evolution with Genetic Algorithm (DEGA) models. The results substantiate the proposed framework's capability for promoting improved integrity, privacy, and efficiency in IoT settings.

This paper experimentally and analytically investigated the effect of pipe wall roughness on hydraulic loss generation in turbulent flow. The study used straight circular pipes with same geometrical dimensions (length = 5 m and internal diameter = 4 cm). Four different metals used such as cast iron, galvanized steel, stainless steel, and copper. Experiments were performed at a flow Reynolds number ($Re$) ranging from approximately 3.4 × 10$^4$ to 5.1 × 10$^4$. The volumetric flow rates were in a range of 80–120 L/min with pressure drop and head loss measured by using a calibrated laboratory setup. The analytical prediction of the head‐loss was conducted using Darcy-Weisbach equation with Colebrook-White friction factor correlations. From the literature data, roughness heights were used to predict the head loss. Experimental and theoretical results can be directly compared, providing an evaluation of model predictiveness accuracy. The frictional head losses depended on the pipe roughness; and it increased from cooper (2.4–2.8 m) to cast iron (3.8–5.2 m), with intermediate values for galvanized and stainless steel, respectively. The friction coefficient ratio measured for the cast iron of 0.031 and copper of 0.018, and this is to indicate different surface roughness. Observed and predicted head losses were in agreement, with errors up to 6–7% relative deviation for smooth-lined pipes, and higher than 8% for roughed ones. The results emphasized the importance of relative roughness in turbulent flow and substantiate the validity of established friction-loss relationships for better engineering design. Model selection and friction loss prediction principles can be practically exploited to aid energy-efficient pipe network design, as well as encourage the recognition of predictive uncertainty. Overall, the study bridges experimental validation and analytical modeling, offering benchmarks for accurate hydraulic analysis under realistic operating conditions.

Increasing energy requirements in hot regions highlight the need for sustainable thermal management technologies. A promising passive cooling solution is Earth-Air Heat Exchanger (EAHE) that use the relatively stable temperature of the subsurface soil to pre-cool air supplied to indoor spaces. This study investigates the thermal and hydraulic performance of an EAHE coupled to a greenhouse in severe summer conditions in Nasiriyah city, southern Iraq, experimentally. The system was designed to evaluate its effectiveness in moderating ambient temperature extremes and reducing mechanical cooling energy requirements. Experimental results during July–October showed that the EAHE outlet air temperature stayed between 31 and 38°C despite ambient temperature exceeding 50°C, indicating a stable thermal response of the buried exchanger. The greenhouse air temperature was maintained at 34–40°C during peak daytime hours and decreased to about 29–33°C during the remaining operating periods, confirming improved internal thermal conditions. The thermal effectiveness ($\varepsilon$) ranged from 0.68 to 0.75, and the average temperature drop ($\Delta{T}$) was above 13°C throughout the test period. Furthermore, pressure drop and fan power increased with airflow velocity, consistent with a turbulent flow regime. The EAHE delivered 2011–2942 W of cooling with 42–144 W of fan power; an indicative baseline from a comparable 50 Hz mini-split (8500–11000 Btu/h) shows a rated electrical input of ~880–1070 W, highlighting the low electrical demand of the EAHE fan (catalog-based context, not a side-by-side test). In summary, the EAHE–greenhouse system demonstrates a viable, energy-efficient pilot-scale option for passive cooling in hot climates, with potential for agricultural applications subject to site-specific sizing and installation constraints.

Electricity theft is a major contributor to non-technical losses (NTL) in distribution networks, causing substantial revenue losses and degrading grid reliability. Conventional approaches such as periodic inspections and smart-meter-only monitoring often fail to detect sophisticated theft behaviors, including meter tampering and illegal line tapping. This paper presents a low-cost IoT-cloud dual-meter system for real-time electricity theft detection and response. The proposed design measures energy at two points: (i) the incoming supply line and (ii) the consumer-side meter output. An embedded controller acquires both streams and uploads the readings to a cloud platform, where a discrepancy-based detection module continuously compares delivered and metered energy within a configurable tolerance to account for normal losses and sensor uncertainty. When persistent abnormal discrepancies are identified, the system issues immediate alerts to utility operators and can optionally trigger a local relay to disconnect the load. A prototype implementation was evaluated under controlled scenarios that emulate normal operation and theft conditions. The system achieved approximately 95% detection accuracy, maintained false alarms below 5%, and detected small theft levels down to about 10 W in the test setup. The bill of materials indicates an estimated unit cost of approximately USD 30–50, supporting scalable deployment in cost-sensitive environments.

Mobile ad hoc networks (MANETs) are inherently susceptible to malicious packet-dropping attacks, including black holes, gray holes and selective forwarding attacks that greatly decrease the reliability and performance of MANETs. Current solutions for detecting malicious attacks have large false-positive rates because they often confuse an intended malicious drop with an unintended loss caused by limited resources, traffic congestion and/or the impairment of wireless channels. In addition, current solutions can be vulnerable to acknowledgment (ACK) forgery attacks and consume considerable amounts of energy in continuously monitoring packets. The authors present a comprehensive four-phase framework that synergistically combines route qualification based on available resources, password-based mutual authentication using chaotic map Diffie Hellman password authenticated key exchange (CMDH-PAKE), authenticated digested ACKs based on counters and selectively activating promiscuous monitors to accurately detect and mitigate malicious packet-dropping attacks in MANETs at low power. The authors’ solution identifies and excludes honest-but-constrained nodes during route discovery by estimating buffer congestion using exponentially weighted moving average (EWMA) and modeling energy feasibility, thus reducing false positives by up to 73%. Binding keys between cryptographic sessions reduces the potential for ACK forgery and impersonation attacks, and aggregated window-based ACKs reduce energy use by 85%, relative to per-packet ACKs. Selectively activating monitors on demand using only cryptographic evidence of anomalies minimizes the energy used while still maintaining a high level of detection accuracy (above 96%). Simulation results using Network Simulator 3 (NS-3) indicate that the authors’ solution has a higher packet delivery ratio (94.2%), shorter end-to-end delay (127 ms), and much lower false-positive rate (3.1%) than other approaches; in addition, the authors’ solution uses about 42% less energy than always-on monitoring approaches.

The increasing adoption of electric vehicles with fast-charging capability has intensified thermal challenges in power cables, potentially leading to localized overheating and reduced system reliability and service life. To address this issue, an integrated framework combining experimental measurement and data-driven analysis was developed to identify and predict thermal behaviour in critical electrical components of electric vehicles. A laboratory-scale electric vehicle powertrain was constructed to replicate representative operating conditions. Infrared thermography was employed together with synchronized electrical measurements to capture the coupled electrical–thermal response of the system. The powertrain was tested under variable mechanical loads ranging from 0% to over 80% and under constant-current operation at 15 A for 30 minutes. Passive thermal management using phase change materials, specifically beeswax and paraffin, was evaluated to assess its effectiveness in mitigating temperature rise. In addition, a lightweight artificial neural network (ANN) model was developed to predict the temperatures of thermally critical components. Thermographic results showed that thermal stress was spatially concentrated at specific interfaces. At maximum load, the temperatures of the power cable downstream of the main switch, the motor cable, and the connector reached 41.2 ℃, 39.7 ℃, and 47 ℃, respectively. Analysis of battery charging behaviour revealed a strong correlation between battery temperature and charging current, with a correlation coefficient of 0.95. When phase change materials were applied, the battery temperature rise was reduced to approximately 35.2–35.5 ℃, compared with 36.7 ℃ in the absence of phase change materials, and voltage stability was improved. The ANN demonstrated high predictive accuracy, with R² values of 0.978 for main switch temperature, 0.969 for connector temperature, and 0.962 for motor cable temperature, corresponding to prediction errors within ±2.5 ℃ across all load conditions. These findings indicate that an integrated experimental and predictive diagnostic approach can effectively support thermal management and early risk identification in high-current electric vehicle power cable systems.

This study investigates whole-body vibration (WBV) exposure in commuters on Iraqi roads. Measurement of vertical, lateral and longitudinal acceleration was obtained on urban arterials, rural two-lane roads and intercity highways using a low-cost measurement setup that incorporates an MPU6050 accelerometer and ESP32 microcontroller. Data was analyzed in accordance with ISO 2631-1, i.e. frequency-weighted root mean square (RMS) acceleration, vibration dose values (VDV) or power spectral density analysis. The results show that vertical vibration (z-axis) predominates in WBV, with maximum energy occurring within the 3–6 Hz frequency range, which is known to correspond to the most responsive frequency range of the human body. The International Roughness Index (IRI), a measure of pavement texture, was highly associated with RMS (r = 0.66), with speed having an additional enhancing influence. Cars and SUVs on intercity highways stayed in “comfort” or “light comfort” zones, whereas heavy trucks on rural roads often encountered “uncomfortable” levels, with VDV up to 16.9 m/s$^{1.75}$. These results reveal a growing need for pavement rehabilitation of the Iraqi arterial and rural road networks, better enforcement of axle-load limits and the adoption of WBV monitoring in sensor-based management of road infrastructure. The research outcomes can serve as a useful reference for enhancing transport and occupational health protection policy making in Iraq.

External dynamic circumstances, especially vertical vibrations brought on by uneven road surfaces and engine activity, have a major impact on a vehicle’s radiator performance. To increase cooling effectiveness and improve thermal management in automobiles, it is essential to comprehend how these vibrations affect heat transmission. This paper proposes using methods to enhance vibration-enhanced heat transfer and increase volumetric flows on a finned-tube car radiator. The radiator’s thermal performance is improved through heat dissipation. The experiment was conducted at volumetric flows (0.5, 0.75, 1, and 1.25 liters per minute (LPM)) and frequencies (0, 5, 10, and 15 Hz). In terms of enhancing vibration-enhanced thermal performance, this study varies from other experimental investigations, particularly with regard to the frequency range employed and volumetric flow. We investigated the impact of vibration coinciding with volumetric flow and pellet behavior under operating settings more similar to those in which cooling systems function. This topic has not been fully explored before and does not constitute redundancy; instead, it solves limits by experimentally examining how vibration and realistic operating circumstances work together to improve thermal performance. The highest increase in Nusselt number enhancement was 23.4% observed on the water side, while the highest enhancement was 12.99% observed on the air side. Increased vibration led to increased heat flow, reaching its maximum 773.85 W/m² at frequency 15 Hz and volumetric flow 1.25 LPM. The vibrational disturbance further enhanced heat exchange between adjacent surfaces.