Search Results (16,680)

Fractures are widely developed in deep coal-mine surrounding rocks. They weaken the load-bearing capacity and energy-storage capacity of rock specimens, which may induce surrounding-rock deformation, roof collapse, and other hazards. Current studies on fractured rock masses mainly focus on a single parameter, such as fracture number or fracture dip angle. However, their coupled effects remain unclear. Integrated analyses of mechanical behavior, crack propagation, and energy evolution are also limited. In this study, uniaxial compression simulations of intact sandstone, single-fracture sandstone, and double-fracture sandstone were conducted using PFC^2D. The effects of fracture number and fracture dip angle on mechanical properties and failure characteristics were investigated. The results show that fractures reduced the peak stress and modulus of elasticity. A stronger weakening effect was observed with increasing fracture number. With increasing fracture dip angle, both peak stress and modulus of elasticity showed a V-shaped trend. The minimum peak stress occurred at 15°, while the minimum modulus of elasticity occurred at 45°. Sandstone failure was mainly dominated by tensile cracks. At 15°, the total crack number was the lowest, with 932 and 818 cracks for single-fracture and double-fracture specimens, respectively. Energy analysis showed that increasing fracture number reduced elastic strain energy and promoted dissipated energy. The weakest energy-storage capacity was observed at 30°. Overall, fracture number and fracture dip angle jointly controlled strength degradation, crack propagation, and energy evolution. This study provides a reference for fracture–damage assessment and disaster prevention in deep coal-bearing sandstone. Full article

This review analyses and logically structures modern intelligent sensor technologies in the context of animal husbandry, feed production, and veterinary medicine. The main research discussed in the article focuses on machine learning based on modern neural network models, computer vision, and sensor systems that are transforming the methods for assessing the health, behavior, and nutrition of farm animals. The first part examines modern approaches to quality control and optimization of mineral and vitamin premixes, including visual inspection using visual sensors and neural networks. Key roles are played by precise dosing, component stability (minerals, vitamins), and the transition to more bioefficient organic forms of micronutrients to reduce environmental impact. Improvements in feed and premix production are analyzed, including automation, energy management, and the use of machine learning for non-destructive quality control, defect detection, mixing homogeneity assessment, and vitamin stability prediction. The second part analyzes methods for animal location and behavior detection. This article presents computer vision-based systems, including modifications of YOLO, for automatically tracking and classifying key behavioral patterns (lying down, standing, feeding, and aggression) in cattle and pigs, even in crowded conditions. It also discusses the use of ultra-wideband (UWB) systems and accelerometers combined with machine learning for high-precision positioning and detection of specific behavioral anomalies, such as lameness and playfulness. The third section focuses on the application of machine learning in veterinary diagnostics, including the automated interpretation of medical images (X-ray, ultrasound, and MRI) as sensor data streams for the diagnosis of cardiovascular, oncological, and orthopedic diseases in farm and small animals. Furthermore, the article examines the use of machine learning models for proactive disease diagnosis in farm animals and poultry based on multimodal data and image analysis. Considerable attention is given to methods and tools for radiometric diagnosis of animal diseases at an early stage using microwave sensors, as well as laser therapy and surgery in veterinary medicine. The review concludes that the integration of intelligent systems enables a transition to data-driven livestock management, significantly improving animal welfare and, consequently, the efficiency and sustainability of agricultural production. Full article

The thermomechanical deformation behavior of high-entropy alloys (HEAs) is governed by complex interactions among strain, strain rate, and deformation temperature, necessitating robust constitutive models for accurate flow stress prediction and process optimization. In this study, a novel Zerilli–Armstrong (NZA) constitutive model was developed to characterize the hot deformation behavior of FeCoCrNi HEA. The proposed NZA model incorporates enhanced descriptions of strain hardening and deformation-temperature coupling to improve prediction accuracy. The predictability of the proposed NZA model was systematically evaluated and compared with that of the original Zerilli–Armstrong (ZA) and modified Zerilli–Armstrong (MZA) models using key statistical indicators, including the correlation coefficient (R), average absolute relative error (AARE), and root mean square error (RMSE). The findings demonstrate that the NZA model exhibits superior predictive performance, achieving an excellent correlation coefficient (R) of 0.997, a low AARE of 4.22%, and an RMSE of 5.82 MPa. These results confirm the reliability and effectiveness of the proposed constitutive framework in accurately describing the thermomechanical flow behavior of FeCoCrNi HEA over a wide range of deformation conditions. The proposed NZA model provides a robust framework for optimizing hot-forming processes and improving the manufacturing performance of HEA-based components while promoting sustainable manufacturing through reduced material consumption, enhanced energy efficiency, and support for SDGs 9 and 12. Full article

In light of the rapid development of the electric vehicle market, permanent magnet synchronous motors (PMSMs) are becoming essential components of propulsion systems. This is due to their high efficiency, remarkable power density, and ability to deliver high torque over a wide speed range. However, the optimal control of these motors under dynamic conditions remains a major challenge due to system nonlinearities, parameter variations, and external disturbances. Conventional strategies such as field-oriented control (FOC), direct torque control (DTC), and fuzzy logic control (FLC) show variable performance in terms of current quality, robustness, and energy efficiency. To overcome these limitations, this study proposes an intelligent control strategy based on artificial neural networks (ANNs), which ensures efficient operation and high control performance under various operating conditions. This approach leverages the learning capabilities of deep neural networks to improve control accuracy, system stability, and overall energy performance. The results obtained show a significant reduction in the current’s total harmonic distortion (THD) as well as an improvement in the stator’s current quality and the electromagnetic torque’s dynamic behavior compared to conventional methods. This improvement reduces overall losses in the electric drive system, thereby contributing to increased vehicle energy efficiency. As a result, the electric vehicle’s range is extended, and the dynamic performance of the PMSM is optimized. These results confirm the potential of artificial intelligence techniques for developing intelligent, robust, and adaptive control systems designed for modern electric propulsion applications. Full article

Respiratory mortality in Europe is associated with interacting environmental, infrastructural, climatic, and energy-related conditions. This study investigates country–year patterns of respiratory disease mortality by integrating panel-data econometrics, clustering analysis, and machine-learning prediction. The econometric results indicate that agricultural land use and coal-based electricity generation are positively associated with respiratory mortality, while access to electricity and freshwater withdrawals show negative associations. Cooling degree days capture a heat-related environmental-health dimension, although some coefficients become weaker under robust specifications. Sanitation and renewable energy display heterogeneous and specification-sensitive patterns, suggesting that they may partly reflect broader development gradients, infrastructure transitions, and regional heterogeneity rather than direct causal mechanisms. Hierarchical clustering identifies 10 country–year environmental-health profiles, highlighting differentiated combinations of energy systems, land use, infrastructure, climatic exposure, and respiratory mortality. This approach avoids treating countries as fixed homogeneous units and allows environmental-health profiles to vary over time. The selected hierarchical solution provides a balanced and interpretable structure relative to more polarized clustering alternatives. Machine-learning models are used as a complementary predictive exercise rather than as substitutes for econometric inference. Within the adopted validation framework, K-nearest neighbors achieves the strongest predictive performance. Additional stability checks and local additive explanations improve transparency regarding model tuning and prediction behavior, while confirming that machine-learning outputs should be interpreted as predictive rather than causal evidence. Overall, the findings support integrated and region-sensitive policy approaches combining air-quality management, infrastructure resilience, energy transition, climate adaptation, and public-health planning. Full article

Hydrogen-compatible elastomeric seals are critical for the reliability and safety of high-pressure hydrogen infrastructure. However, hydrogen exposure can alter the mechanical response and surface condition of elastomeric materials through coupled transport–mechanical interactions. This study presents a comparative experimental and data-driven investigation of the pressure-dependent degradation behavior of ethylene propylene diene monomer (EPDM), nitrile butadiene rubber (NBR), and fluorocarbon elastomer (FKM) O-ring seals following 192 h exposure to hydrogen pressures ranging from 800 to 7000 psi at room temperature. Tensile testing was performed directly on complete O-ring geometries, and descriptor-based analysis was used to quantify peak-response behavior, energy absorption, stiffness evolution, and normalized deformation characteristics. Multivariate statistical methods, principal component analysis (PCA), clustering analysis, and Random Forest regression were applied to identify material-specific degradation patterns. NBR maintained the highest overall load-bearing capability and stiffness-related response across the investigated pressure range, whereas EPDM exhibited more compliant and non-monotonic deformation behavior. FKM showed the strongest pressure sensitivity, with substantial increases in force- and stiffness-related descriptors at elevated hydrogen pressures. Optical image analysis revealed pronounced increases in defect density and defect area fraction for NBR, while FKM exhibited comparatively stable surface-state behavior. PCA and clustering analyses identified distinct material-dependent degradation trajectories, and Random Forest regression achieved an R² value of 0.888 for energy-absorption prediction. The results demonstrate that hydrogen-induced degradation emerges through coupled interactions among stiffness evolution, deformation progression, energy absorption, and surface-state changes, providing a comparative framework for assessing elastomer performance in hydrogen environments. Full article

In this study, the microstructural changes and coarsening behavior of Ag₃Sn in Sn-6.4Sb-3.9Ag-0.25Ni-0.003Ge (mass%) during high-temperature aging were investigated. Additionally, low-cycle fatigue tests were conducted to compare the fatigue behavior of Sn-6.4Sb-3.9Ag-0.25Ni-0.003Ge with that of Sn-3.0Ag-0.5Cu. At room temperature, SbSn phases are dispersed in the β-Sn matrix. As the temperature rises, Sb atoms dissolve in the β-Sn phase; thus, the SbSn phases disappear, and some of the atoms aggregate. The activation energy was 45 kJ/mol for the coarsening of Ag₃Sn in Sn-6.4Sb-3.9Ag-0.25Ni-0.003Ge due to aging. Ag₃Sn coarsening was estimated to be controlled by the lattice diffusion of Ag atoms in the β-Sn phase. Furthermore, it was confirmed that the solid solution of Sb atoms in the β-Sn phase reduces the solubility limit of Ag atoms in the β-Sn phase, which delays the coarsening of Ag₃Sn. Regarding fatigue properties, while both alloys exhibited comparable low-cycle fatigue behavior at room temperature, the fatigue ductility exponent’s increase was confirmed to be suppressed for the Sn-6.4Sb-3.9Ag-0.25Ni-0.003Ge alloy at 175 °C. This trend suggests that the delayed coarsening of Ag₃Sn maintains the cyclic strain-hardening exponent, thereby influencing high-temperature fatigue behavior. Full article

Although garnet-type Li₇La₃Zr₂O₁₂ (LLZO) solid electrolytes are promising candidates for high-energy-density all-solid-state batteries, their practical applications are limited by high-voltage oxidation instability and interfacial degradation. To address these limitations, Al-doped LLZO (Al-LLZO) solid electrolytes were synthesized via a conventional solid-state reaction method, and the effects of PE-ALD-derived Al₂O₃ nanocoatings on electrochemical properties and interfacial stability were investigated. Al₂O₃ nanocoatings with different structures (5 and 10 nm single-side, and 5 nm double-side) were deposited on Al-LLZO pellets using plasma-enhanced atomic layer deposition. The Al₂O₃ coating reduced electronic conductivity by approximately one order of magnitude while maintaining similar ionic conductivity. Linear sweep voltammetry revealed that initial oxidation onset voltage increased from ~4.2 V (bare Al-LLZO) to ~5.0 V (5 nm-coated samples), while the 10 nm-coated sample exhibited the most delayed anodic current response (~5.2 V). The 5 nm double-side coated sample showed the best Li plating/stripping stability with a critical current density of 1.10 mA/cm² and stable long-term galvanostatic cycling behavior over 200 h at 0.05 mA/cm². Thus, ALD-based Al₂O₃ interfacial engineering can simultaneously improve the high-voltage oxidation and Li metal interfacial stabilities of garnet-type Al-LLZO solid electrolytes for practical all-solid-state batteries. Full article

This study presents a comparative investigation of the microstructure, phase composition, microhardness, tribological behavior, and corrosion resistance of heterogeneous coatings deposited on St3 steel by twin-wire electric arc spraying (TWEAS). Three wire combinations were examined: ER309LSi + Steel 70, Sv-08G2S + Steel 70, and 30KhGSA + ER309LSi. The coatings were characterized using X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS), Vickers microhardness testing, ball-on-disc tribological measurements, and potentiodynamic polarization in 3.5 wt.% NaCl solution. All coatings exhibited a characteristic lamellar structure with a thickness of 340–360 μm and hardness values significantly higher than those of the steel substrate. The 30KhGSA + ER309LSi coating demonstrated the highest cross-sectional microhardness (532 ± 13 HV) and the lowest specific wear rate (0.411 × 10⁻⁴ mm³/(N·m)), which was more than five times lower than that of the substrate. The enhanced wear resistance was associated with the formation of the Cr₇C₃ and Cr₂₃C₆ carbide phases, as identified by XRD. The Sv-08G2S + Steel 70 coating exhibited the lowest corrosion rate among the investigated coatings due to its more homogeneous ferritic structure and reduced electrochemical contrast between lamellae. The results demonstrate that the phase composition and distribution of alloying elements play a decisive role in determining the functional properties of heterogeneous TWEAS coatings. Full article

Indoor Air Quality (IAQ) monitoring, especially in multi-zone smart buildings, is typically limited by the high computational and energy requirements of continuous sensor processing, which makes event-driven methods desirable for efficiency. Energy proportionality, in this context, refers to a system whose computational cost scales with the significance of detected environmental changes rather than with the fixed sampling rate. This paper presents a spike-driven neuromorphic sensing framework for decentralized IAQ monitoring that combines adaptive Kalman filter preprocessing, dynamic threshold-based asynchronous spike encoding, and a Leaky Integrate-and-Fire neural network with Spike-Timing-Dependent Plasticity (STDP) learning. Multiple-parameter IAQ data including PM₁, PM_2.5, PM₁₀, CO₂, CO, TVOCs, and O₃ were sampled from nine functionally differing zones of an educational building in Metro Manila, Philippines. The neuromorphic model yielded a mean Sparse Firing Ratio of 10.94%, a Mean Response Time of 10.62 timesteps, and an energy efficiency proxy score of 9.28. Neuron population scaling and parameter robustness analyses revealed that the four neurons per parameter were enough to saturate the performance, and FLOP-based estimation indicated an 8.9-fold computational reduction (approximately 89% fewer FLOPs) compared to LSTM inference. In addition, the revised Performance Efficiency Index and composite efficiency score corroborated the stable and energy-proportional nature of behavior in all zones. These results illustrate that spike-based neuromorphic computation is an energy-efficient and scalable way for decentralized smart-building IAQ monitoring, though hardware-level validation on dedicated neuromorphic processors remains necessary for absolute power saving verification. Multi-seed validation (five seeds) with expanded baselines including GRU, Temporal CNN, XGBoost, and Logistic Regression confirmed the robustness and repeatability of reported metrics. Full article

Alfalfa densification is a critical step in feed utilization and biomass energy conversion because it directly affects the transport efficiency, storage stability, and energy consumption of biomass processing systems. However, the thermodynamic behavior of the densification process remains poorly understood, especially under open-die conditions without external heating. This study investigated the thermo-mechanical characteristics of alfalfa pellet open-die densification without external heating by combining experimental measurements with ANSYS macro-continuum simulation. Stress transmission and temperature field distributions were analyzed. The results showed that the pellet quality index under different process conditions remained above 800, meeting the requirements for pelleted feed. Moisture content had a more significant effect on forming pressure than other factors; as moisture content increased, the forming pressure decreased. At an aspect ratio of 5.0, the forming pressure was below 45 kN. Simulation results further indicated that aspect ratio had a stronger influence on frictional behavior during densification. Under an aspect ratio of 5.0, the energy consumption was 888.53 J, and the heat flux reached 0.0062 W/mm². These results indicate that frictional dissipation driven by radial force is the dominant mechanism governing thermo-mechanical coupling. Moisture content and aspect ratio significantly affected both peak compression force and coupling intensity. Although reducing moisture content or increasing aspect ratio improved pellet quality, it also increased die load due to enhanced radial force. The coupling intensity followed the order: peak pressure stage > moving stage > compression stage. These findings reveal the evolution of stress and temperature fields during alfalfa densification, offering critical theoretical guidance for optimizing densification process parameters. Full article

The blast furnace–basic oxygen furnace long process is the dominant steel production route in China. Increasing the scrap ratio is an effective way to reduce cost and carbon emissions, and scrap preheating is a key technology to achieve a high scrap ratio. To improve the low thermal efficiency and poor deep-bed melting performance of converter gas-based scrap preheating, an innovative process using oxygen-enriched combustion in a hot metal ladle is proposed. Numerical simulation is essential for capturing the complex multiphysics phenomena, as real-time monitoring of melting inside the packed scrap bed is extremely difficult. In this study, a novel multiphysics approach based on a User-Defined Function (UDF) is developed to dynamically track the progressive melting of the scrap skeleton, overcoming the key limitation of conventional enthalpy–porosity models that cannot capture the feedback between phase change and porous medium property evolution. A three-dimensional transient model was established, integrating turbulent combustion, gas–solid convective heat transfer in porous media, and solid–liquid phase change. The effects of impact pit depth, scrap porosity, and converter gas flow rate on temperature distribution, melting behavior, and thermal efficiency were systematically investigated. Results showed that porosity had the strongest influence; thermal efficiency increased from 33.92% to 65.59% as porosity rose from 0.6 to 0.8, due to a transition from conduction-dominated to coupled convection–conduction heat transfer. Converter gas flow rate exhibited a non-monotonic effect, peaking at 3688.14 m³·h⁻¹, highlighting a trade-off between energy input and gas residence time, while impact pit depth showed a limited effect with diminishing returns. A 600 s full-process simulation revealed stage-dependent melting, and the initial phase was crucial for process optimization. The optimal condition, with a pit depth of 64 cm, porosity of 0.8, and converter gas flow rate of 3688.14 m³·h⁻¹, achieved a 1.23% melting fraction and 65.59% thermal efficiency within 120 s. These findings clarify the combined roles of geometric confinement, permeability, and energy-residence time interactions, providing guidance for industrial scrap preheating design. Full article

This review synthesizes findings from more than 100 journal articles, reports, and design standards on the design, simulation, and testing of steel beam-to-column connections, with emphasis on semi-rigid bolted extended end-plate (EEP) joints. The core objective of this study is to highlight the critical importance of accurately capturing this semi-rigid behavior, given the significant implications of improper modeling for the global response, safety, and design reliability of steel frames. While connections are often idealized as fully rigid or pinned, EEP connections typically exhibit a semi-rigid response governed by nonlinear moment–rotation (M–θ) behavior. The reviewed literature is organized around: (i) mechanical response and key failure mechanisms (end-plate yielding, bolt fracture, and prying action); (ii) analytical and numerical prediction methods, including component-based models and finite-element approaches capable of representing contact, bolt pretension, and cyclic degradation; and (iii) system-level implications for steel frames. Approaches used in major standards (AISC and Eurocode 3) for classifying connection stiffness and strength are compared, and experimental programs are summarized to identify the dominant parameters controlling resistance, ductility, and failure mode. Translating these component-level findings to the structural-system level, the review highlights how appropriately detailed semi-rigid EEP connections can enable moment redistribution, reduce member demands, and support stable inelastic deformation under seismic actions. Key research gaps include three-dimensional and multiaxial loading, impact and other high-rate actions, and the performance of alternative materials such as stainless steel. Full article

High-entropy alloys (HEAs) are a transformative class of materials with remarkable structural and functional properties. Solid-state processing techniques, such as high-energy ball milling, are being increasingly used for their production. In these processes, the use of a process control agent (PCA) seems to be essential to prevent excessive cold welding and agglomeration; however, the influence of different PCAs on alloy formation remains insufficiently understood. This study systematically examined the effects of the PCA type, milling configuration, and elemental substitution on HEAs mechanosynthesis. A non-equiatomic alloy, Al₁₀Cr₁₂Fe₃₅Mn₂₃Ni₂₀ (selected for its known single-phase Face Center Cubic (FCC) behavior), was used to explore the PCA and mill-type effects. The alloy was synthesized in a planetary mill (Fritsch Pulverisette 7) and a vibratory mill (SPEX 8000M) using diverse PCAs, including liquid (methanol, ethanol, isopropyl, and n-heptane) and solid (stearic acid and sodium chloride) agents. In addition, lightweight equiatomic alloys MgAlTiNi(Co,Cr,Fe) were used to explore the influence of different PCAs and the effect of elemental substitution under similar PCA conditions as those used with the equiatomic alloy. The products were characterized using X-ray diffraction, scanning electron microscopy, thermogravimetric analysis, and differential thermal analysis techniques. The results highlighted that the PCA selection, milling configuration, and alloy chemistry influenced the phase evolution, particle size distribution, and thermal behavior. The results provide insights into the mechanosynthesis of selected high-entropy alloys produced under different PCA and milling conditions. Full article

This paper advances pump energy optimization by shifting the analytical focus from nominal efficiency to energy-optimal operating areas derived directly from in-field measurements. A structured experimental methodology is presented for reconstructing pump performance under real hydraulic and electrical conditions using existing systems and variable frequency drives. High-resolution datasets obtained from in-field testing are densified and normalized to map the operational area of pumps across flow, head, and rotational speed. The Best Electricity Use Point (BEUP) is identified as an energy-optimal area rather than a single operating point, accounting for system-level losses. Application to a municipal water supply pumping station on Kos Island (Greece) demonstrates that real operating behavior deviates substantially from manufacturer specifications and that BEUP-oriented control enables systematic reductions in energy consumption while improving hydraulic stability and mechanical stress conditions. The proposed framework supports a transition from static efficiency concepts to adaptive, measurement-driven pump operation. Full article