The Earth–Air Heat Exchanger (EAHE), also referred to as an air–soil heat exchanger, represents an effective passive cooling technology that exploits the thermal inertia of the ground. This study presents a combined experimental and analytical investigation of an EAHE system installed at the Faculty of Sciences of Agadir (Morocco). A steady-state analytical model based on convective heat transfer between the airflow within a buried duct and the surrounding soil is developed to describe the axial evolution of air temperature along the exchanger. The model is formulated under a sensible heat transfer framework, where the influence of humidity is accounted for through its effect on the thermophysical properties of moist air, while latent heat transfer and condensation phenomena are neglected. An instrumented experimental setup was implemented to perform continuous measurements of air temperature and relative humidity over a seven-month monitoring period. The experimental results indicate that the outlet air temperature remains stabilized within the range of 23.5–23.8 °C, despite significant variations in ambient temperature (13–38 °C). A parametric analysis is conducted to assess the influence of duct diameter, airflow velocity, and humidity through its effect on moist air properties on the thermal performance of the system. The close agreement between experimental observations and analytical predictions demonstrates the validity and predictive capability of the proposed model. These findings highlight the potential of EAHE systems as an effective passive cooling solution for greenhouse applications in semi-arid climatic conditions.

Ultra-high-performance concrete (UHPC) is widely used due to its strength, toughness, and durability. Shrinkage issues are the primary cause of concrete cracking and one of the main factors limiting the widespread application of UHPC in structural engineering. The shrinkage properties of UHPC vary depending on curing conditions. Research indicates that after thermal curing, the pore structure of UHPC is optimized, resulting in a significant reduction in shrinkage values. Based on the superposition principle, temperature creep coefficients and humidity creep coefficients are introduced to correct the temperature and humidity in the test environment to a constant temperature (20 °C) and humidity (75% relative humidity). The B3 coefficient of variation method was used to compare five different creep prediction models. The CEB-FIP2010 model was selected as the benchmark creep model, and curing condition coefficients were incorporated into the model to establish a comprehensive creep calculation model considering curing conditions. After 550 days of steam curing, the shrinkage strain of the UHPC specimens was approximately 28.9% of that of the uncured specimens. The additional creep deformation caused by temperature and humidity in the uncured and steam-cured specimens accounted for approximately 10% and 20% of the total creep deformation over 550 days, respectively. The strain development rates for both tensile and compressive strains in steam-cured specimens were lower than those in uncured specimens. A ten-year long-term creep simulation of UHPC precast joint beams was conducted using the finite element software Midas-Fea, and the comparison results validated the reliability of the comprehensive creep model.

Detecting drivable areas is a fundamental task in autonomous driving systems. Although semantic segmentation networks have demonstrated strong performance in segmenting drivable regions, two key challenges persist. First, acquiring sufficient contextual information in complex road scenarios remains difficult, often leading to segmentation errors. Second, the coarseness of extracted features may degrade accuracy even when texture information is available in RGB images. To address these issues, we propose an enhanced DeepLabv3+ algorithm called Split Convolution Selective Attention Network (SCSANet), which incorporates the Adaptive Kernel (AK) and Split Convolution Attention (SCA) modules. AK adaptively adjusts the receptive field to accommodate varying road scenarios, while SCA improves boundary clarity by enhancing channel interaction. In addition, we employ surface normals to provide complementary geometric information, thereby strengthening the ability of the network to recognize drivable areas. To compensate for the lack of publicly available datasets for closed or semi-closed scenarios, we introduce XMUROAD, a new dataset of binocular disparity images. Experiments on the XMUROAD dataset demonstrate that the proposed architectural improvements yield an mIoU gain of 1.63% under the same RGB input, and the full pipeline with surface normal input achieves improvements of 1.55% to 2.59% in mF1 and 2.94% to 4.83% in mIoU over state-of-the-art methods. Experiments on the KITTI dataset further verify the generalization capability of SCSANet, with improvements of 1.58% in mF1 and 2.88% in mIoU over state-of-the-art methods. The proposed method provides a practical approach for accurate drivable area detection in closed and semi-closed mobile-robot scenarios.

Fault detection in microgrids is a critical element of system stability and uninterrupted power delivery. Herein, a comparative study using LSTM and bidirectional LSTM networks is performed based on three-phase current data for multi-class fault classification. Five major fault types, namely LG, LL, LLG, LLL, and LLLG, were simulated using a Real-Time Digital Simulator (RTDS) under grid-connected and islanded modes. Collected current signals were preprocessed, normalized, and segmented for sequence learning. Later, both models were trained using the best hyperparameter setting to enhance their capabilities and classify faults. To measure how well they identified faults, evaluation metrics, like accuracy, precision, recall, F1-score, and ROC-AUC, were calculated. The results revealed that the Bi-LSTM outperformed the LSTM and classical machine learning models consistently, with more than 99% accuracy for most fault types. More importantly, the proposed framework also checked classification performance for LLLG faults, with the Bi-LSTM model having a test accuracy of 98.8%. These results confirm that the Bi-LSTM model can robustly and precisely classify and detect faults in real time within specific phases of microgrids; therefore, it provides a scalable foundation for the development of intelligent protection in smart power systems.

The complexity of an extrusion die profile is determined by its geometry. Various metrics such as the complexity index, shape factor, and form factor are used to quantify how geometric intricacy affects production costs, die life, energy consumption, product quality, and overall manufacturability. Bearing geometry plays a critical role in controlling metal flow and tool life in aluminum extrusion. In this study, a simulation-based investigation is performed to investigate the influence of bearing geometry on extrusion behavior using the finite element method. Two extrusion dies are examined: A single-cavity die with uniform bearing geometry and a dual-cavity die with controlled bearing geometry modification in one cavity. The results show that the bearing modification in the dual-cavity die causes severe flow imbalance, with exit velocity deviations. This imbalance leads to localized pressure amplification, increased thermal gradients, and stress concentration in critical die regions. In contrast, the single-cavity die, due to its simple geometry, exhibits uniform flow, stable pressure evolution, and low tool stress. These findings demonstrate the high sensitivity of multi-cavity extrusion dies to bearing geometry and highlight the importance of simulation-driven die design for achieving balanced flow and improved tool performance.

Temperature prediction for partially shaded photovoltaic (PV) modules is essential for ensuring the stability and safety of PV systems. However, existing methods suffer from high computational complexity, limiting their applicability in engineering practice. Aimed at a real-time and portable algorithm that can be embedded in mobile devices for intelligent monitoring of PV stations, a simple and fast method is designed in this work for estimating the thermal behavior of PV modules under partial shading conditions. To the best of our knowledge, this is the first work in this field that achieves computational simplicity without relying on professional commercial software. The experimental results validate the accuracy of the proposed method in comparison with the multiphysics model (which is widely regarded as the benchmark in this field) while significantly improving computational efficiency. Simulations are conducted to explore the effects of shading proportions and environmental conditions. Shading proportions ranging from 6% to 90% are prone to promoting the development of hotspots under conditions that involve partial shading of an individual cell. Higher irradiance, a higher ambient temperature and a lower wind speed result in a higher temperature of the PV module.

In most AFM nanoindentation experiments on soft biological samples, classical contact mechanics models, such as Hertz or Sneddon’s equations, are commonly employed to determine the Young’s modulus. However, biological materials are inherently heterogeneous, and their mechanical properties often depend on the indentation depth. In this work, we present a novel and simple approach to quantify how the apparent modulus varies with increasing indentation depth. The method is based on the general indentation equation for axisymmetric indenters combined with a straightforward polynomial fitting of the force–indentation data. The proposed approach offers significant advantages, as it greatly simplifies the fitting process without requiring any advanced algorithms, while maintaining high accuracy. In addition, it is shown that the depth-dependent mechanical properties of cells can be described by a simple law, E(h)=Cd/h+El , where El is the limiting value of the apparent modulus at large indentations, and Cd/h represents the depth-dependent contribution dominant at the initial stages of the indentation process. Here, Cd is a positive stiffness coefficient, and h is the indentation depth. This is a very important result, indicating that by using the pair of coefficients Cd and El, we can fully describe the mechanical properties of cells, capturing their depth-dependent mechanical behavior. Experiments on fibroblasts and H4 human glioma cells confirm the accuracy of this equation. The proposed methods provide an accessible and reliable framework for nanoscale mechanical characterization, offering insights into the depth-dependent elasticity of heterogeneous soft materials and revealing mechanical patterns in biological samples.

This study introduces a hybrid methodological approach for personalised clinical decision support, integrating SHACL-based deterministic constraints with Bayesian probabilistic models. The primary goal is to validate the model and demonstrate the benefits of combining encoded clinical knowledge with probabilistic uncertainties in managing complex therapeutic scenarios. The framework was applied to recurrent urinary tract infections (UTIs) in postmenopausal patients, a clinical context marked by high frequency, treatment challenges, and potential conflicts among therapeutic guidelines. Realistic simulated case studies were developed, encompassing both simple clinical profiles and complex situations, such as patients with antibiotic resistance. Each profile was modelled in RDF/Turtle, enabling semantic representation of clinical features and therapeutic rules. The system automatically calculates success and failure probabilities for different therapeutic scenarios, dynamically adapting them based on follow-up data. This allows clinicians to assess not only the initial therapy choice (Case study no. 1) but also the potential addition of supplementary interventions during treatment (Case study no. 2). Results highlight that the proposed hybrid SHACL–Bayesian framework enables tightly coupled deterministic–probabilistic reasoning, where SHACL constraints define the admissible clinical decisions and Bayesian inference operates within this validated space. Compared to deterministic or probabilistic approaches, the combined framework more effectively handles uncertainty, guideline conflicts, and temporal updates. The scientific contribution lies in showing that this integration enhances decision support for recurrent UTIs in postmenopausal patients, providing clinically consistent, transparent, and adaptive therapeutic recommendations aligned with the patient’s evolving condition.

Chitosan hydrogels are effective wound dressings that promote healing through the synergy of chitosan’s inherent biological properties and the moist environment they maintain. We previously developed hydrogel microparticles using a highly biocompatible chitosan derivative with superior therapeutic effects. This study aimed to enhance their clinical translation for Endoscopic Sinus Surgery (ESS) by optimizing preparation conditions to achieve an extrusion force of <20 N, facilitating ergonomic, single-handed administration by surgeons. While reducing the particle size alone was insufficient to lower the extrusion force significantly, the introduction of a mechanical “kneading” process to de-agglomerate microparticle aggregates resulted in a substantial reduction in the required force from 213 ± 80 N to approximately 47 N. By further optimizing the polymer concentration to 5.0% (w/v), we successfully reduced the maximum extrusion force to below 20 N (17 ± 1 N). These results demonstrate that the optimized injectable chitosan gel microparticles achieve the practical usability required for precise surgical maneuvers during ESS.

Refractory geopolymers derived from aluminosilicate sources and alkaline activation are a promising alternative to traditional fired bricks, particularly when low-cost, waste-derived raw materials are used. This study improves the workability of a refractory brick formulated with clays (Kaolin and Tepozan–Bauwer), seashell waste, sodium silicate, potassium hydroxide, and water by incorporating sodium lignosulfonate (LS) and polycarboxylate (PC) plasticizers. Clays from Comonfort, Guanajuato, Mexico, and seashells were ground and sieved to pass a 100 Tyler mesh. A base mixture was prepared and evaluated using the Mini Slump Test, varying plasticizer content from 0 to 2% relative to the solid fraction. Based on workability, 0.5% LS and 1% PC (by solids) increased the slump, and a blended plasticizer formulation (1.5% by solids, 80%PC+20%LS) produced the highest workability. These additives act through different mechanisms, with LS primarily promoting electrostatic repulsion and PC steric repulsion. Bricks with and without plasticizers exhibited thermal resistance up to 1200 °C. After four calcination cycles, compressive strength values were 354.74 kgf/cm2 for the brick without plasticizer, 597.25 kgf/cm2 for 1% PC, 433.63 kgf/cm2 for 0.5% LS, and 519.05 kgf/cm2 for 1.5% of the 80%PC+20%LS blend. Strength was consistent with changes in porosity and apparent density, and 1% PC provided a favorable combination of high workability and high compressive strength after cycling. Because the cost of clays and seashells is negligible, formulation selection was based on plasticizer cost per brick. Although 1% PC and the 1.5% of 80%PC+20%LS blend showed statistically comparable strength after cycling, 1% PC was selected as the preferred option due to its lower additive cost ($0.0449 per brick) compared with the blend ($0.0633 per brick). Stereoscopic microscopy indicated pore closure after calcination with no visible cracking, and SEM–EDS identified O, Si, and Al as the significant elements, with traces of S and K. Overall, the study provides an integrated assessment of workability, multi-cycle calcination, microstructure, and performance for refractory bricks produced from readily available clays and seashell waste.