Eng

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

Cement-based materials are essential construction components, yet their complex microstructures critically govern mechanical performance and durability. This study investigates the micro-textural characteristics and mechanical properties of cement paste modified with grinding aids (triethanolamine, TEA; maleic acid triethanolamine ester, MGA) and polycarboxylate-based superplasticizer (PCA). Moving beyond qualitative SEM limitations, we employ advanced image-based quantitative techniques: grayscale-based texture analysis for statistical evaluation and fractal dimension analysis for geometric quantification of microstructural irregularity. Results demonstrate that grinding aids enhance particle dispersion and reduce agglomeration, resulting in a more uniform micro-texture characterized by lower grayscale variability and reduced fractal dimensions. PCA superplasticizers further significantly enhance fluidity and compressive strength. The optimal formulation (MGA + PCA) achieved a 20% increase in 28-day compressive strength compared to control samples. The fractal dimension D_B exhibits a positive correlation with compressive strength, while energy and correlation values show a negative correlation; in contrast, entropy and contrast values demonstrate a positive correlation. This research advances quantitative microstructure characterization in cementitious materials, offering insights for tailored additive formulations to enhance sustainability and efficiency in concrete production. Full article

Conventional slurry wall protection exhibits reduced film performance upon exposure to water in saturated sand layers with high permeability, frequently resulting in hole wall instability. Optimizing the slurry ratio to enhance film performance is thus critical for borehole stability. A multiple regression model was developed to determine the optimal slurry ratio for saturated sand. Slurry permeability tests assessed filtration loss, film formation time, and film morphology changes. Scanning electron microscopy (SEM) further elucidated the film formation mechanism. Bentonite, clay, Na₂CO₃, and sodium carboxymethyl cellulose (CMC) significantly affected the slurry’s properties: specific gravity and sand content increased with bentonite/clay; viscosity increased with CMC; and pH increased with Na₂CO₃. The optimized slurry (water–bentonite–Na₂CO₃–clay–CMC = 1000:220:32:110:1; specific gravity, 1.20 g/cm³; viscosity, 29 s) demonstrated low filtration loss and stable film morphology. SEM revealed that simultaneous CMC and clay addition (ratio of 1:110) improved film surface flatness, reduced porosity and pore size, enhanced formation surface filling, and produced a denser film. The optimized slurry ratio significantly enhanced film performance in saturated sand layers. The findings provide a theoretical and engineering framework for bored pile wall protection slurry design and film formation mechanisms. Full article