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.

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.

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 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.

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.

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.

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.