Eng, Volume 6, Issue 10 (October 2025) – 34 articles

Accurate prediction of concrete compressive strength is essential for ensuring structural safety in civil engineering, particularly in road and bridge construction, where inadequate strength can lead to deformation, cracking, or collapse. Traditional non-destructive testing (NDT) methods, such as the Rebound Hammer Test, estimate strength using regression-based formulas fitted with measurement data; however, these formulas, typically optimized via the least squares method, are highly sensitive to initial parameter settings and exhibit low robustness, especially for nonlinear relationships. Meanwhile, AI-based models, such as neural networks, require extensive datasets for training, which poses a significant challenge in real-world engineering scenarios with limited or unevenly distributed data. To address these issues, this study proposes a gradient-boosting artificial bee colony (GB-ABC) algorithm for robust regression curve fitting. The method integrates two novel mechanisms: gradient descent to accelerate convergence and prevent entrapment in local optima, and a point density-weighted strategy using Gaussian Kernel Density Estimation (GKDE) to assign higher weights to sparse data regions, enhancing adaptability to field data irregularities without necessitating large datasets. Following data preprocessing with Local Outlier Factor (LOF) to remove outliers, validation on 600 real-world samples demonstrates that GB-ABC outperforms conventional methods by minimizing mean relative error rate (RER) and achieving precise rebound-strength correlations. These advancements establish GB-ABC as a practical, data-efficient solution for on-site concrete strength estimation. Full article

This paper presents Rain-Cloud Condensation Optimizer (RCCO), a nature-inspired metaheuristic that maps cloud microphysics to population-based search. Candidate solutions (“droplets”) evolve under a dual-attractor dynamic toward both a global leader and a rank-weighted cloud core, with time-decaying coefficients that progressively shift emphasis from exploration to exploitation. Diversity is preserved via domain-aware coalescence and opposition-based mirroring sampled within the coordinate-wise band defined by two parents. Rare heavy-tailed “turbulence gusts” (Cauchy perturbations) enable long jumps, while a wrap-and-reflect scheme enforces feasibility near the bounds. A sine-map initializer improves early coverage with negligible overhead. RCCO exposes a small hyperparameter set, and its per-iteration time and memory scale linearly with population size and problem dimension. RCOO has been compared with 21 state-of-the-art optimizers, over the CEC 2022 benchmark suite, where it achieves competitive to superior accuracy and stability, and achieves the top results over eight functions, including in high-dimensional regimes. We further demonstrate constrained, real-world effectiveness on five structural engineering problems—cantilever stepped beam, pressure vessel, planetary gear train, ten-bar planar truss, and three-bar truss. These results suggest that a hydrology-inspired search framework, coupled with simple state-dependent schedules, yields a robust, low-tuning optimizer for black-box, nonconvex problems. Full article

Four tertiary amines (tributylamine, tripentylamine, trihexylamine, triethanolamine) were investigated with a 25 μm platinum disc microelectrode in more organic solvents. In the commonly used inert solvent acetonitrile, the sigmoidal-shaped curves used were recorded, except for triethanolamine, which showed two current plateaus close to each other, indicating temporal blocking in this case. The really surprising results arose from studies in acetic acid and ethyl acetate. Due to the complete protonation in acetic acid, the significant shifts of oxidation potentials led to the acquisition of lower currents only with the rising parts also using the same potential window as in ethyl acetate, where the voltammograms had a sigmoidal shape. Triethanolamine exhibited significant electrode deactivation in ethyl acetate, leading to the appearance of peak-shaped curves, and the difference between the first and second voltammograms was large. The current difference between the first and second scans allowed consequences for acetic acid content in cases where it was small, as the choice of this parameter proved to be the best for the analytical task. On the other hand, the differences in the shape of voltammograms allowed for quantitative approximations. The observed phenomenon could be utilized only for the estimation of acetic acid content. Full article

Background: Operational cycle detection underpins a range of important tasks, such as predictive maintenance, energy consumption prediction, and energy management for mobile equipment in mining. Yet, no review has investigated the landscape of methods that segment mobile mining vehicle telemetry into discrete operating modes—a task termed operational cycle detection. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Scoping Review extension (PRISMA-ScR) framework, we searched The Lens database on 27 June 2025, for records published between 2000 and 2025 that apply cycle detection to mobile mining vehicles. After de-duplication and two-stage screening, 20 empirical studies met all criteria (19 diesel, 1 electric-drive). Due to the sparse research involving battery electric vehicles (BEVs) in mining, three articles performing cycle detection on heavy-duty vehicles in a similar operational context to mining are synthesized. Results: Early diesel work used single-sensor thresholds, often achieving >90% site-specific accuracy, while recent studies increasingly employ neural networks using multivariate datasets. While the cycle detection research on mining BEVs, even supplemented with additional heavy-duty BEV studies, is sparse, similar approaches are favored. Conclusions: Persisting gaps in the literature include the absence of public mining datasets, inconsistent evaluation metrics, and limited real-time validation. Full article

Virtual Machine Introspection (VMI) is a powerful technology used to detect and analyze malicious software inside Virtual Machines (VMs) from the outside. Asynchronous access to the VM’s memory can be insufficient for efficient monitoring of what is happening inside of a VM. Active VMI introduces breakpoints to intercept VM execution at relevant points. Especially for frequently visited breakpoints, and even more so for production systems, it is crucial to keep performance overhead as low as possible. In this paper, we present an empirical study that compares the performance of four VMI breakpoint-implementation variants—EPT switching (SLAT view switching) with and without fast single-stepping acceleration, instruction repair, and instruction emulation—from two VMI applications (DRAKVUF, SmartVMI) with the XEN hypervisor on 20 Intel Core i processors ranging from the fourth to the thirteenth generation. Instruction emulation was the fastest method across all 20 tested platforms. Modern processors such as the Intel Core i7 12700H and Intel Core i9 13900HX achieved median breakpoint-processing times as low as 15 µs for the emulation mechanism. The slowest method was instruction repair, followed by EPT switching and EPT switching with FSS. The order was the same for all measurements, indicating that this is a strong and generalizable result. Full article

The article presents the results of uniaxial fatigue tests for the high-cycle regime on a polymer matrix composite material reinforced with Kevlar and carbon fibers, fabricated with material extrusion (MEX) technology. The samples were manufactured according to the ASTM D638 type-I standard, and the tests were performed under a load inversion factor of 0.1 at room temperature, measuring the number of cycles to failure. Based on previous results, in which different configurations were tested, tests were carried out on specimens subjected to loads ranging from 40% to 91% of the rupture stress for Kevlar and 25.5% to 80.7% for carbon, obtaining a maximum life of 2.5 M cycles for Kevlar and 4.06 M cycles for carbon. The observed failure modes included fiber tearing, matrix cracking, and fiber–matrix pull-out. Full article

The increasing adoption of electric vehicles has driven the development of charging technologies that meet growing demands for power, efficiency, and grid compatibility. This review presents a comprehensive analysis of the EV charging ecosystem, covering Level 3 DC charging stations, power converter topologies, and the role of energy storage systems in supporting grid integration. Commercial solutions and academic prototypes are compared across key parameters such as voltage, current, power, efficiency, and communication protocols. The study highlights trends in charger architectures—including buck, boost, buck–boost, LLC resonant, and full-bridge configurations—while also addressing the integration of stationary storage as a buffer for fast charging stations. Special attention is given to wide-bandgap semiconductors like SiC and GaN, which enhance efficiency and thermal performance. A significant gap persists between the technical transparency of commercial systems and the ambiguity often observed in prototypes, highlighting the urgent need for standardized research reporting. Although converter efficiency is no longer a primary constraint, substantial challenges remain regarding infrastructure availability and the integration of storage with charging stations. This paper seeks to offer a comprehensive perspective on the design and deployment of smart, scalable, and energy-efficient charging systems, with particular emphasis on cascaded and bidirectional topologies, as well as hybrid storage solutions, which represent promising pathways for the advancement of future EV charging infrastructure. Full article

This study reviews and experimentally investigates critical logistics and transport models in stainless-steel (SS) fluid storage tanks, focusing on stainless steel grades 316 and 304L. Conceptual vessel schematics emphasize hygienic drainability, refill uniformity, and thermal control, supported by representative 316L properties for heat-transfer, stress, and fluid–structure analyses. At the logistics scale, modelling integrates component-level simulations, computational fluid dynamics (CFD), and Finite Element Method (FEM) with network-level approaches, such as Continuous Approximation, to address facility location, refilling schedules, and demand variability. Experimental characterization using EDS and XRF confirmed the expected Cr/Ni backbone and grade-consistent Mo in 316, while unexpected C, Mn, and Cu readings were attributed to instrumental limits or statistical variance. Unexpected detection of Europium in 304L highlights the need for further mechanical testing. Overall, combining simulation, logistics modelling, and compositional verification offers a coherent framework for safe, efficient, and thermally reliable stainless-steel tank design. Full article

Deepfake technology, which utilizes advanced AI models such as Generative Adversarial Networks (GANs), has led to the proliferation of highly convincing manipulated media, posing significant challenges for detection. Existing detection methods often struggle with the low-quality or compressed press, which is prevalent on social media platforms. This paper proposes a novel Deepfake detection framework that leverages No-Reference Image Quality Assessment (NRIQA) techniques, specifically, BRISQUE, NIQE, and PIQUE, to extract quality-related features from facial images. These features are then classified using a Support Vector Machine (SVM) with various kernel functions. We evaluate our method under both intra-dataset and cross-dataset settings. For intra-dataset evaluation, we conduct K-fold cross-validation on two benchmark datasets, DFDC and Celeb-DF (v2), including downsampled versions to simulate real-world degradation. The results show that our method maintains high accuracy even under significant quality loss, achieving up to 98% accuracy on the Celeb-DF (v2) dataset and outperforming several state-of-the-art methods. To improve the transferability of the detection models, we introduce an integrated filtering strategy based on NR-IQA thresholding, which enhances performance in cross-dataset transfer scenarios. This approach yields up to 7% improvement in detection accuracy under challenging cross-domain conditions. Full article

Titanium and, in particular, its alloys are widely used in biomedical applications due to their favorable combination of mechanical properties, such as high strength, low density, low elastic modulus, and excellent biocompatibility. In this study, novel titanium-based alloys were developed using powder metallurgy techniques. The chemical composition of the studied alloys was 93%Ti-7%Fe, 90%Ti-7%Fe-3%Al, and 88%Ti-7%Fe-5%Cr. The metallic powders were processed in a planetary mill, uniaxially compacted, and subsequently sintered at 1300 °C during 2 h under an inert atmosphere. The primary objective was to evaluate the corrosion behavior of these alloys in simulated body fluid solutions, as well as to determine some of the properties, such as the relative density, microhardness, and elastic modulus. The resulting microstructures were homogeneous, with micrometer-scale grain sizes and the formation of intermetallic precipitates generated during sintering. Mechanical tests revealed that the Ti-Fe-Cr alloy exhibited the highest microhardness and Young’s modulus values, followed by Ti-Fe and Ti-Fe-Al. These results confirm a strong correlation between hardness and stiffness, showing that Cr enhances mechanical and elastic properties, while Al reduces them. Corrosion tests demonstrated that the alloys possess high resistance and stability in physiological environments, with a low current density, minimal mass loss, and strong performance even under prolonged exposure to acidic conditions. Full article

Fault slips induced by hydraulic fracturing are the primary mechanism of casing de-formation during deep shale gas development in Sichuan’s Luzhou Block, where de-formation rates reach 51% and severely compromise productivity. To address a critical gap in existing research on quantitative risk assessment systems, we developed a probabilistic model integrating pore pressure evolution dynamics with Monte Carlo simulations to quantify slip risks. The model incorporates key operational parameters (pumping pressure, rate, and duration) and geological factors (fault friction coefficient, strike/dip angles, and horizontal stress difference) validated through field data, showing >90% slip probability in 60% of deformed well intervals. The results demonstrate that prolonged high-intensity fracturing increases slip probability by 32% under 80–100 MPa pressure surges. Meanwhile, an increase in the friction coefficient from 0.40 to 0.80 reduces slip probability by 6.4% through elevated critical pore pressure. Fault geometry exhibits coupling effects: the risk of low-dip faults reaches its peak when strike parallels the maximum horizontal stress, whereas high-dip faults show a bimodal high-risk distribution at strike angles of 60–120°; here, the horizontal stress difference is directly proportional to the slip probability. We propose optimizing fracturing parameters, controlling operation duration, and avoiding high-risk fault geometries as mitigation strategies, providing a scientific foundation for enhancing the safety and efficiency of shale gas development. Full article

Frozen loess is extensively distributed across seasonally frozen regions, where its mechanical behavior plays a critical role in the stability of engineering structures such as foundations, tunnels, and slopes. While the temperature-dependent strength characteristics of frozen soils have been widely investigated under conventional triaxial conditions, their response to variations in principal stress direction and intermediate principal stress under complex loading paths remains poorly understood. This study addresses this gap through a series of directional shear tests on frozen loess, examining the effects of principal stress direction angle (α) and intermediate principal stress coefficient (b) at different temperatures. The results demonstrate that lower negative temperatures (−5 °C, −10 °C, and −15 °C) markedly enhance both axial and shear strength compared with normal temperature (20 °C). Increasing α leads to a progressive reduction in axial strength, highlighting the strong influence of stress orientation on deformation characteristics. Higher values of b also reduce axial strength, but their impact on shear strength is limited. Overall, the influence of α and temperature on the strength of frozen loess is considerably more pronounced than that of b. These findings provide new insights into the mechanical behavior of frozen loess under non-traditional stress paths, offering practical implications for the design, safety evaluation, and stability control of geotechnical structures in cold regions. Full article

The aim of this study is to determine the reliability of regular and spatial cross-validation methods in predicting subfield-scale maize yields using phenological measures derived by Sentinel-2. Three maize fields from eastern Croatia were monitored during the 2023 growing season, with high-resolution ground truth yield data collected using combine harvester sensors. Sentinel-2 time series were used to compute two vegetation indices, Enhanced Vegetation Index (EVI) and Wide Dynamic Range Vegetation Index (WDRVI). These features served as inputs for three machine learning models, including Random Forest (RF) and Bayesian Generalized Linear Model (BGLM), which were trained and evaluated using both regular and spatial 10-fold cross-validation. Results showed that spatial cross-validation produced a more realistic and conservative estimate of the performance of the model, while regular cross-validation overestimated predictive accuracy systematically because of spatial dependence among the samples. EVI-based models were more reliable than WDRVI, generating more accurate phenomenological fits and yield predictions across parcels. These results emphasize the importance of spatially explicit validation for subfield yield modeling and suggest that overlooking spatial structure can lead to misleading conclusions about model accuracy and generalizability. Full article

Cell towers play a key role in providing telecommunications infrastructure, especially in remote mountainous regions. This paper presents an approach to the efficient design of 42-metre-high cell towers intended to install high-power equipment in remote mountainous regions of the Carpathians (750 m above sea level). The region requires rapid deployment of many standardized towers adapted to geographical features. The main design challenges were the limited space available for the base, the impact of extreme weather conditions, and the need for a fast project implementation due to the critical importance of ensuring stable communication. Special methodological attention is given to how the transition between pyramidal and prismatic segments in cell tower shafts influences overall structural performance. The effect of this geometric boundary on structural efficiency and material usage has not been addressed in previous studies. A dedicated investigation shows that positioning the transition at a height of 33 m yields the best compromise between stiffness and weight, minimizing a generalized penalty function that accounts for both the horizontal displacement of the tower top and its total mass. Modal analysis confirms that the chosen configuration maintains a natural frequency of 1.68 Hz, ensuring a safe margin from resonance. For the final analysis of the behavior of towers with elements of different cross-sectional shapes, finite element modeling was used for a detailed numerical study of their structural and performance characteristics. This allowed us to assess the impact of geometric constraints of structures and take into account the most unfavorable combinations of static and dynamic loads. The study yields a concise rule of thumb for towers with compact foundations, namely that the pyramidal-to-prismatic transition should be placed at roughly 78–80% of the total tower height. Full article

Efficient irrigation management is crucial for optimizing crop development while minimizing resource use. This study aimed to assess the spatial variability of water distribution under conventional sprinkler irrigation, alongside soil moisture and infiltration dynamics, using multispectral sensors onboard Remotely Piloted Aircraft (RPAs). The experiment was conducted over a 466.2 m² area equipped with 65 georeferenced collectors spaced at 3 m intervals. Soil data were collected through volumetric rings (0–5 cm), auger sampling (30–40 cm), and 65 measurements of penetration resistance down to 60 cm. Four RPA flights were performed at 20 min intervals post-irrigation to generate NDVI and NDWI indices. NDWI values decreased from 0.03 to −0.02, indicating surface moisture reduction due to infiltration and evaporation, corroborated by gravimetric moisture decline from 0.194 g/g to 0.191 g/g. Penetration resistance exceeded 2400 kPa at 30 cm depth, while bulk density ranged from 1.30 to 1.50 g/cm³. Geostatistical methods, including Inverse Distance Weighting and Ordinary Kriging, revealed non-uniform water distribution and subsurface compaction zones. The integration of spectral indices within situ measurements proved effective in characterizing irrigation system performance, offering a robust approach for calibration and precision water management. Full article

Hybrid AC/DC microgrids have emerged as a promising solution for integrating diverse renewable energy sources, enhancing efficiency, and strengthening resilience in modern power systems. However, existing control schemes exhibit critical shortcomings that limit their practical effectiveness. Traditional linear controllers, designed around nominal operating points, often fail to maintain stability under large load and generation fluctuations. Optimization-based methods are highly sensitive to model inaccuracies and parameter uncertainties, reducing their reliability in dynamic environments. Intelligent approaches, such as fuzzy logic and ML-based controllers, provide adaptability but suffer from high computational demands, limited interpretability, and challenges in real-time deployment. These limitations highlight the need for robust control strategies that can guarantee reliable operation despite disturbances, uncertainties, and varying operating conditions. Numerical performance indices demonstrate that the reviewed robust control strategies outperform conventional linear, optimization-based, and intelligent controllers in terms of system stability, voltage and current regulation, and dynamic response. This paper provides a comprehensive review of recent robust control strategies for hybrid AC/DC microgrids, systematically categorizing classical model-based, intelligent, and adaptive approaches. Key research gaps are identified, including the lack of unified benchmarking, limited experimental validation, and challenges in integrating decentralized frameworks. Unlike prior surveys that broadly cover microgrid types, this work focuses exclusively on hybrid AC/DC systems, emphasizing hierarchical control architectures and outlining future directions for scalable and certifiable robust controllers. Also, comparative results demonstrate that state of the art robust controllers—including H∞-based, sliding mode, and hybrid intelligent controllers—can achieve performance improvements for metrics such as voltage overshoot, frequency settling time, and THD compared to conventional PID and droop controllers. By synthesizing recent advancements and identifying critical research gaps, this work lays the groundwork for developing robust control strategies capable of ensuring stability and adaptability in future hybrid AC/DC microgrids. Full article

This study systematically investigated the structural damage and electrochemical performance changes in 18650 cylindrical lithium-ion batteries under multiple impacts through a 10 kg drop-hammer impact test. The experimental results showed that as the state of charge (SOC) increased from 25% to 75%, the battery’s stiffness increased and its impact resistance improved, but the electrolyte leakage intensified, with a higher risk of leakage at high SOCs. An increase in the impact force led to enhanced voltage fluctuations and a continuous increase in deformation. After an impact of 500 mm, the voltage decreased about 0.02 V, while after an impact of 1000 mm, it dropped about 0.04 V. Axial impacts caused a sudden voltage drop to 1.96 V, resulting in permanent failure; compared with planar impacts, cylindrical surface impacts are more likely to cause compression in the middle and warping at both ends, significantly increasing the risk of internal short circuits. CT scans revealed that the battery porosity can reach up to 3.09% under high impact energy, and the deformation rate can reach 28.39%. The research results provide a quantitative experimental basis for the impact-resistant design and safety assessment of power batteries. Full article

The present study examines the effect of a modified oval–round rolling scheme incorporating inclined oval calibers on the mechanical behavior and microstructural evolution of Mn–Si low-alloy steel (25G2S). Cylindrical billets were hot rolled through both classical and modified sequences under identical thermal and kinematic conditions. Tensile testing demonstrated that, relative to the unrolled condition (σ_0.2 ≈ 269 MPa; σᵤ ≈ 494 MPa), the classical route increased yield and ultimate strengths to ~444 MPa and ~584 MPa, respectively, whereas the modified scheme yielded comparable values (~433 MPa and ~572 MPa) while providing superior ductility (δ ≈ 26.8%, ψ ≈ 68.6%). Vickers microhardness decreased systematically from 244 HV (unrolled) to 213 HV (classical) and 184 HV (modified), with the modified scheme exhibiting the lowest scatter (±4.8 HV), confirming enhanced structural uniformity. Scanning electron microscopy revealed ferrite–pearlite refinement under both rolling sequences, with the modified scheme producing finer equiaxed ferrite grains (~3–5 µm) and attenuated longitudinal banding. These features are indicative of shear-assisted dynamic recrystallization, activated by the inclined oval calibers. The findings highlight that the modified rolling strategy achieves a favorable strength–ductility balance and improved homogeneity, suggesting its applicability for advanced thermomechanical processing of low-alloy steels. Full article

As global demand for clean and renewable energy continues to rise, wind power has become a critical component of the sustainable energy transition. However, the increasingly complex operating conditions and structural configurations of modern wind turbines pose significant challenges for system reliability and control. Specifically, accurate load torque estimation is crucial for supporting the long-term stable operation of the wind power system. This paper presents a novel load torque estimation approach based on a nonlinear extended state observer (NLESO) for wind turbines with permanent magnet synchronous generators. In this method, the load torque is estimated using current measurements and observer-derived acceleration, thereby eliminating the need for torque sensors. This not only reduces hardware complexity but also improves system robustness, particularly in harsh or fault-prone environments. Furthermore, the stability of the observer is rigorously proven through Lyapunov theory using the variable gradient method. Finally, simulation results under different wind speed conditions validate the method’s accuracy, robustness, and adaptability. Full article

Dynamic pressure measurement is an important component in the turbo engine testing process. This paper presents a comparative analysis between two types of multichannel electronic pressure measurement systems, commonly known as pressure scanners, used for this purpose: ZOC17/8Px, with analog amplification per channel, and MPS4264, a modern digital system with integrated A/D conversion. The study was conducted in two stages: a metrological verification and validation in static mode, using a high-precision pressure standard, and an experimental stage in dynamic mode, where data was acquired from a turbojet engine test stand, in constant engine speed mode. The signal stability of the pressure scanners was statistically analyzed by determining the coefficient of variation in the signal and the frequency spectrum (FFT) for each channel of the pressure scanners. Furthermore, comprehensive uncertainty budgets were calculated for both systems. The results highlight the superior stability and reduced uncertainty of the MPS4264 pressure scanner, attributing its enhanced performance to digital integration and a higher resilience to external noise. The findings support the adoption of modern digital systems for dynamic applications and provide a robust metrological basis for the optimal selection of measurement systems. Full article

Background: This narrative review summarizes recent progress in artificial intelligence (AI), especially radiomics and deep learning, for non-invasive diagnosis and molecular profiling of gliomas. Methodology: A thorough literature search was conducted on PubMed, Scopus, and Embase for studies published from January 2020 to July 2025, focusing on clinical and technical research. In key areas, these studies examine AI models’ predictive capabilities with multi-parametric Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Results: The domains identified in the literature include the advancement of radiomic models for tumor grading and biomarker prediction, such as Isocitrate Dehydrogenase (IDH) mutation, O6-methylguanine-dna methyltransferase (MGMT) promoter methylation, and 1p/19q codeletion. The growing use of convolutional neural networks (CNNs) and generative adversarial networks (GANs) in tumor segmentation, classification, and prognosis was also a significant topic discussed in the literature. Deep learning (DL) methods are evaluated against traditional radiomics regarding feature extraction, scalability, and robustness to imaging protocol differences across institutions. Conclusions: This review analyzes emerging efforts to combine clinical, imaging, and histology data within hybrid or transformer-based AI systems to enhance diagnostic accuracy. Significant findings include the application of DL to predict cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletion and chemokine CCL2 expression. These highlight the expanding capabilities of imaging-based genomic inference and the importance of clinical data in multimodal fusion. Challenges such as data harmonization, model interpretability, and external validation still need to be addressed. Full article

Underground mining faces unique challenges in equipment maintenance due to extreme operating conditions and intensive use, which limit the effectiveness of traditional methods. This study proposes a predictive maintenance (PdM) framework based on artificial intelligence (AI) to optimize efficiency and reduce costs, focusing on early fault detection. The methodology integrates IoT sensors to monitor key parameters (temperature, pressure, oil analysis, and wear) in real time, combined with machine learning models to identify predictive patterns. The results demonstrate an 8% reduction in maintenance costs and a 10% increase in equipment availability, validating the system’s ability to anticipate failures and minimize unplanned downtime. It is concluded that this approach not only enhances productivity but also raises safety standards, offering a scalable model for critical industrial environments. The findings are supported by empirical data collected from actual operations, with no theoretical extrapolations. Full article

The building sector is under increasing pressure to lower its environmental impact, prompting renewed interest in raw soil as a low-carbon and locally available material. This study investigates the mechanical and thermal properties of clay-based masonry walls through a comprehensive experimental program on earthen mortars, bricks, and their interfaces, considering both stabilized and non-stabilized formulations. Compressive, bending, and shear tests reveal that strength is strongly influenced by mortar composition, hydration time, and the soil-to-sand ratio. The addition of 5–7.5% cement yields modest gains in compressive strength but increases the carbon footprint, whereas extended pre-hydration achieves similar improvements with lower environmental costs. Thermal characterization of the studied samples (SiO₂ ≈ 61.2 wt%, Al₂O₃ ≈ 11.7 wt%, MgO ≈ 5.1 wt%) revealed that SiO₂-enriched compositions significantly enhance thermal conductivity, whereas the presence of Al₂O₃ and MgO contributes to increased heat capacity and improved moisture regulation. These findings suggest that well-optimized clay-based mortars can satisfy the structural and thermal requirements of non-load-bearing applications, offering a practical and sustainable alternative to conventional construction materials. By reducing embodied carbon, enhancing hygrothermal comfort, and relying on locally available resources, such mortars contribute to the advancement of green building practices and the transition towards low-carbon construction. Full article

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, with ischemic heart disease (IHD) being the most prevalent and deadly subtype. The growing burden of IHD underscores the urgent need for effective early detection methods that are scalable and non-invasive. Heart Rate Variability (HRV), a non-invasive physiological marker influenced by the autonomic nervous system (ANS), has shown clinical relevance in predicting adverse cardiac events. This study presents a photoplethysmography (PPG)-based Zhurek IoT device, a custom-developed Internet of Things (IoT) device for non-invasive HRV monitoring. The platform’s effectiveness was evaluated using HRV metrics from electrocardiography (ECG) and PPG signals, with machine learning (ML) models applied to the task of early IHD risk detection. ML classifiers were trained on HRV features, and the Random Forest (RF) model achieved the highest classification accuracy of 90.82%, precision of 92.11%, and recall of 91.00% when tested on real data. The model demonstrated excellent discriminative ability with an area under the ROC curve (AUC) of 0.98, reaching a sensitivity of 88% and specificity of 100% at its optimal threshold. The preliminary results suggest that data collected with the “Zhurek” IoT devices are promising for the further development of ML models for IHD risk detection. This study aimed to address the limitations of previous work, such as small datasets and a lack of validation, by utilizing real and synthetically augmented data (conditional tabular GAN (CTGAN)), as well as multi-sensor input (ECG and PPG). The findings of this pilot study can serve as a starting point for developing scalable, remote, and cost-effective screening systems. The further integration of wearable devices and intelligent algorithms is a promising direction for improving routine monitoring and advancing preventative cardiology. Full article

Indoor sports facilities present unique challenges for air quality management due to high crowd densities and limited ventilation. This study investigated air quality in a municipal athletic facility in Komotini, Greece, focusing on concentrations of airborne particulate matter (PM_1.0, PM_2.5, PM₁₀), humidity, and temperature across spectator zones, under varying mask scenarios. Sensing devices were installed in the stands to collect high-frequency environmental data. The system, based on optical particle counters and cloud-enabled analytics, enabled real-time data capture and retrospective analysis. The main experiment investigated the impact of spectators wearing medical masks during two basketball games. The results show consistently elevated PM levels during games, often exceeding recommended international thresholds in the spectator area. Notably, the use of masks by spectators led to measurable reductions in PM_1.0 and PM_2.5 concentrations, because they seem to have limited the release of human-generated aerosols as well as the amount of movement among spectators, supporting their effectiveness in limiting fine particulate exposure in inadequately ventilated environments. Humidity emerged as a reliable indicator of occupancy and potential high-risk periods, making it a valuable parameter for real-time monitoring. The findings underscore the urgent need for improved ventilation strategies in small to medium-sized indoor sports facilities and support the deployment of low-cost sensor networks for actionable environmental health management. Full article

This study explores the combustion behavior of three biomass pellet types—wood (W), sunflower husk (SH), and a mixture of wood and sunflower husks (W/SH)—in a residential hot water boiler. Experiments were carried out under two air supply regimes (40%/60% and 60%/40% primary to secondary air) to measure flue gas concentrations of oxygen (O₂), carbon monoxide (CO), and nitrogen oxides (NO_x). The results indicate that SH pellets generate the highest emissions (CO: 1095.3 mg/m³, NO_x: 679.3 mg/m³), while W pellets achieve the lowest (CO: 0.3 mg/m³, NO_x: 194.1 mg/m³). The mixed W/SH pellets produce intermediate values (CO: 148.7 mg/m³, NO_x: 201.8 mg/m³). Overall boiler efficiency for all tested fuels ranged from 90.3% to 91.4%. Numerical simulations using ANSYS CFX (2024 R2 (24.2)) were performed to analyze temperature distribution, flue gas composition, and flow fields, showing good agreement with experimental outlet temperature and emission trends. These findings emphasize that both pellet composition and air distribution significantly influence efficiency and emissions, offering guidance for optimizing small-scale biomass boiler operation. Full article

This paper investigates the effects of using 10% v/v (E10) and 30% v/v (E30) ethanol–gasoline blends on spark ignition (SI) engine fuel consumption, brake-specific fuel consumption, brake thermal efficiency, combustion parameters and exhaust gas temperature. The 30% v/v ethanol–gasoline blend was designed not to exceed the octane number (RON and MON) of the regular commercially available reference fuel (E10); therefore, the knock resistance of the reference and research fuel does not differ significantly. The tests were conducted on an AVL internal combustion engine test cell using a four-stroke, four-cylinder, turbocharged SI engine with direct injection and a compression ratio of 12.2:1. The engine was manufactured in 2022, and it is the latest commercially available version currently in production. Engine tests were conducted under stoichiometric conditions (when possible) at loads ranging from 2–20 bar brake mean effective pressure and engine speeds ranging from 1000–6000 rpm, and the fuel consumption, brake-specific fuel consumption, combustion parameters, exhaust gas temperature and brake thermal efficiency were measured using the two different ethanol–gasoline blends. Test results showed that the higher concentration ethanol–gasoline blend—due to its lower density, lower heating value and higher latent heat of vaporization—had increased fuel consumption, brake-specific fuel consumption and decreased brake thermal efficiency, while exhaust gas temperature also decreased (at 2500 rpm 12 bar BMEP, the differences were 11%, 6.6%, −0.78% and −3.7%, respectively). Peak combustion pressures were identical under the same operating conditions, but the peak combustion temperature of E30 was on average 3% lower. Full article

The predictive models performance on embedded devices represents a significant technical challenge for applications for real-time Predicting of Photovoltaic Solar Energy Generation (PPSEG). This study evaluated the computational feasibility of the Hybrid Prediction Method (HPM), focusing on the extraction of nine visual features extracted from 180° hemispheric all-sky images, processed on the Raspberry Pi 4 Model B microcomputer. The experiment, conducted with 100 images at different resolutions, demonstrated that the proposed pipeline is operationally feasible in all tested configurations. Processing times were significantly reduced with decreasing resolution, remaining compatible with embedded applications. However, an increase in normalized absolute error of up to 8% was observed at 25% resolution, especially in the measurement of cloud motion, which is sensitive to the loss of spatial detail. The other measurements remained stable and had low error levels. The main contribution of this work lies in the validation of a pipeline and measurement of embedded computer vision performance for HPM, enabling its actual implementation and promoting advances in the development of short-term PPSEG solutions. Full article

The increasing sophistication of grid-connected photovoltaic (GCPV) systems necessitates advanced fault detection and diagnosis (FDD) methods to ensure operation efficiency and security. In this paper, a novel two-stage hybrid AI architecture is analyzed that couples an autoencoder using Long Short-Term Memory (LSTM) for unsupervised anomaly detection with an RF classifier for focused fault diagnosis. The architecture is critically compared to that of a baseline-only RF baseline on a synthetic dataset. The results of this two-stage hybrid AI show a strong overall accuracy of (83.1%). The hybrid model’s first stage trains only on unlabeled healthy data, reducing the reliance on extensive and often unavailable labeled fault datasets. This design has the safety-critical advantage of marking unfamiliar faults as anomalies instead of committing to a misclassification. By integrating anomaly detection with classification, the architecture enables early stage screening of faults and targeted categorization, even in data-scarce scenarios. This offers a scalable, interpretable solution suitable for deployment in real-world GCPV systems where robustness and early detection are critical. While the method exhibits reduced sensitivity to subtle or recurring faults, it demonstrates strong reliability in confidently detecting distinct and significant anomalies. Additionally, the approach improves interpretability, facilitating clearer identification of performance constraints such as the autoencoder’s moderate fault sensitivity (AUC = 0.61). This study confirms the hybrid approach as a very promising FDD solution, in which the architectural advantages of safety and maintainability offer a more worthwhile proposition to real-world systems than incremental improvements in a single accuracy measure. Full article

With the rapid development of renewable energy and the continuous pursuit of efficient energy utilization, distributed photovoltaic power generation has been widely used in village-level microgrids. As a key platform connecting distributed photovoltaics with users, energy storage systems play an important role in alleviating the imbalance between supply and demand in VMG. However, current energy storage systems rely heavily on lithium batteries, and their frequent charging and discharging processes lead to rapid lifespan decay. To solve this problem, this study proposes a hybrid energy storage system combining supercapacitors and lithium batteries for VMG, and designs a hybrid energy storage scheduling strategy to coordinate the “source–load–storage” resources in the microgrid, effectively cope with power supply fluctuations and slow down the life degradation of lithium batteries. In order to give full play to the active support ability of supercapacitors in suppressing grid voltage and frequency fluctuations, the scheduling optimization goal is set to maximize the sum of the virtual inertia time constants of the supercapacitor. In addition, in order to efficiently solve the high-complexity model, the reason for choosing the snow goose algorithm is that compared with the traditional mathematical programming methods, which are difficult to deal with large-scale uncertain systems, particle swarm optimization, and other meta-heuristic algorithms have insufficient convergence stability in complex nonlinear problems, SGA can balance global exploration and local development capabilities by simulating the migration behavior of snow geese. By improving the convergence effect of SGA and constructing a multi-objective SGA, the effectiveness of the new algorithm, strategy and model is finally verified through three cases, and the loss is reduced by 58.09%, VMG carbon emissions are reduced by 45.56%, and the loss of lithium battery is reduced by 40.49% after active support optimization, and the virtual energy inertia obtained by VMG from supercapacitors during the scheduling cycle reaches a total of 0.1931 s. Full article