Search Results (2,745)

Timely and accurate fault diagnosis in rotary machinery is essential for ensuring system reliability and minimizing unplanned downtime. While deep learning approaches, particularly Convolutional Neural Networks (CNNs), have demonstrated strong performance in vibration-based fault classification, their limited interpretability poses challenges for adoption in safety-critical environments. To address this, the present study introduces a hybrid diagnostic framework that integrates CNN-based transfer learning with interpretable supervised classification, aiming to enhance both predictive accuracy and model transparency. A key innovation of this work lies in the dual-stage feature selection process, combining Analysis of Variance (ANOVA) and Permutation Feature Importance (PFI) to refine deep features extracted from a pre-trained VGG19 network. This strategy improves both dimensionality reduction and classification performance in a statistically grounded, model-agnostic manner. Furthermore, SHapley Additive exPlanations (SHAP) are employed to interpret the predictions, offering insight into the most influential features driving the classification decisions. Experimental evaluation on the Case Western Reserve University (CWRU) bearing dataset confirms the effectiveness of the proposed approach, achieving 100% classification accuracy using ten-fold cross-validation. By uniting high performance with transparent decision-making, the framework demonstrates strong potential for explainable and reliable fault diagnosis in industrial settings. Full article

This paper introduces a method for estimating the Downlink Angle of Departure (DL-AoD) of 5G User Equipment (UE) from measured signal strengths of directionally transmitted synchronization signals. Based on estimated DL-AoD values, from two or more anchor nodes, the position of the UE was estimated. Unlike most prior work, which is simulation-based or relies on custom testbeds, this study uses real measurements from an operational 5G network in an industrial factory environment. A deterministic estimator was derived, but multipath and unknown beam characteristics limit its accuracy. To address this, machine learning was applied to automatically adapt to the environment. Previous simulation studies reported 90th-percentile DL-AoD estimation errors below 2°, while experimental works achieved best-case accuracies of 5–6°. In this study, the experimental DL-AoD estimation error remained below 4° for 90% of the measurements, indicating improved real-world performance. Reported positioning errors in the literature range from 3.8 m to 140 m, whereas the 13.2 m error obtained here lies near the midpoint of this range, confirming the practicality of the proposed method in industrial environments. Compared to existing approaches, this work demonstrates high angular accuracy using only sub-6 GHz beams in a realistic industrial scenario without detailed knowledge of antenna beam patterns and channel state. The findings demonstrate that standard 5G signals can provide accurate indoor localization without additional infrastructure, offering a practical path toward cost-effective positioning in industrial IoT and automation. Full article

Alkali metal-modified M-ZSM-5 zeolites (M: Li⁺, Na⁺, K⁺) were synthesized by cationic exchange and characterized using ICP-MS, XRD, N₂ adsorption–desorption, Py-IR and NH₃-TPD techniques to evaluate their elemental composition, structure, textural and acidic properties. In addition, XPS and DFT calculations were employed to study the effects of metal ion doping on the electronic structure and catalytic behavior. The latter catalytic performance was assessed in the methanol-to-olefin (MTO) reaction. The results showed that alkali metal doping facilitated the enhancement of the zeolite structural stability, adjustment of acid density, and increase in the adsorption energy of light olefins onto the active sites. During the reaction, olefin products shifted from Brønsted acid sites to alkali metal sites, effectively minimizing hydrogen transfer reactions. This change in the active site nature promoted the olefin cycle, resulting in higher yields in propylene and butylenes, reduced coke deposition, and prolonged catalyst lifetime. Among all zeolites, Li-exchanged ZSM-5 exhibited the best and extending the catalyst lifetime by 5 h. Full article

Forests store substantial amounts of aboveground biomass (AGB) and play a critical role in the global carbon cycle. Optical remote sensing offers long-term, large-scale monitoring capabilities; however, spectral saturation in high-biomass regions limits the accuracy of AGB estimation. Although radar and LiDAR data can mitigate the saturation problem, optical imagery remains irreplaceable for continuous, multi-decadal monitoring from regional to global scales. Nevertheless, quantitative analyses of nationwide optical saturation thresholds and compensation mechanisms are still lacking. In this study, we integrated high-accuracy AGB estimates from the Global Ecosystem Dynamics Investigation (GEDI) L4A product, Sentinel-2 optical imagery, and topographic variables to develop a 200 m resolution Light Gradient Boosting Machine (LightGBM) machine learning model for forests in China. Stratified error analysis, locally weighted scatterplot smoothing (LOWESS) curves, and SHapley Additive exPlanations (SHAP) were employed to quantify optical saturation thresholds and the compensatory effects of topographic features. Results showed that estimation accuracy declined markedly when AGB exceeded approximately 300 Mg·ha⁻¹. Red and red-edge bands saturated at around 80 Mg·ha⁻¹, while certain spectral indices delayed the threshold to 100–150 Mg·ha⁻¹. Topographic features maintained stable contributions below 300 Mg·ha⁻¹, providing critical compensation for AGB prediction in high-biomass areas. This study delivers a high-resolution national AGB dataset and a transferable analytical framework for saturation mechanisms, offering methodological insights for large-scale, long-term optical AGB monitoring. Full article

In engineering practice, soils will inevitably experience some rotation of principal stress directions. Recent experimental evidence has highlighted how principal stress axes rotation significantly impacts clay behavior. However, most existing constitutive models accounting for this effect are essentially designed for sand and may not be applicable to clays. This paper introduces an anisotropic bounding surface model to reproduce the response of clay to principal stress axes rotation. The model’s key innovation lies in its incorporation of a secondary mapping procedure in the deviatoric stress ratio plane, which utilizes a relocatable mapping center. This step is a complement to the conventional radial mapping procedure in the meridional plane, which utilizes a fixed mapping center. This constitutive enhancement facilitates the precise modeling of plastic deformation triggered by the rotation of principal stress axes, without introducing additional loading mechanisms or incremental stress–strain nonlinearity. The performance of the model is first evaluated under various conditions and then verified through comparisons between simulation results and experimental data. The results demonstrate the effectiveness of the model and underscore the necessity of incorporating stress rotation effects into the constitutive modeling of clay. Full article

Industrial environments are complex, making the monitoring of gauge meters challenging. This is especially true in confined spaces, underground, or at high altitudes. These difficulties underscore the need for intelligent solutions in the inspection and monitoring of plant assets, such as gauge meters. In this study, we plan to integrate unmanned ground vehicles and unmanned aerial vehicles to address the challenge, but the integration of these heterogeneous systems introduces additional complexities in terms of coordination, interoperability, and communication. Our goal is to develop a multi-domain robotic swarm system for industrial mapping and asset monitoring. We created an experimental setup to simulate industrial inspection tasks, involving the integration of a TurtleBot 2 and a QDrone 2. The TurtleBot 2 utilizes simultaneous localization and mapping (SLAM) technology, along with a LiDAR sensor, for mapping and navigation purposes. The QDrone 2 captures high-resolution images of meter gauges. We evaluated the system’s performance in both simulation and real-world environments. The system achieved accurate mapping, high localization, and landing precision, with 84% accuracy in detecting meter gauges. It also reached 87.5% accuracy in reading gauge indicators using the paddle OCR algorithm. The system navigated complex environments effectively, showcasing the potential for real-time collaboration between ground and aerial robotic platforms. Full article

Glazed components in ancient Chinese architecture hold profound historical and cultural value. However, over time, environmental erosion, physical impacts, and human disturbances gradually lead to various forms of damage, severely impacting the durability and stability of the buildings. Therefore, preventive protection of glazed components is crucial. The key to preventive protection lies in the early detection and repair of damage, thereby extending the component’s service life and preventing significant structural damage. To address this challenge, this study proposes a Restoration-Scale Identification (RSI) method that integrates depth information. By combining RGB-D images acquired from a depth camera with intrinsic camera parameters, and embedding a Convolutional Block Attention Module (CBAM) into the backbone network, the method dynamically enhances critical feature regions. It then employs a scale restoration strategy to accurately identify damage areas and recover the physical dimensions of glazed components from a global perspective. In addition, we constructed a dedicated semantic segmentation dataset for glazed tile damage, focusing on cracks and spalling. Both qualitative and quantitative evaluation results demonstrate that, compared with various high-performance semantic segmentation methods, our approach significantly improves the accuracy and robustness of damage detection in glazed components. The achieved accuracy deviates by only ±10 mm from high-precision laser scanning, a level of precision that is essential for reliably identifying and assessing subtle damages in complex glazed architectural elements. By integrating depth information, real scale information can be effectively obtained during the intelligent recognition process, thereby efficiently and accurately identifying the type of damage and size information of glazed components, and realizing the conversion from two-dimensional (2D) pixel coordinates to local three-dimensional (3D) coordinates, providing a scientific basis for the protection and restoration of ancient buildings, and ensuring the long-term stability of cultural heritage and the inheritance of historical value. Full article

This study presents an integrated approach to processing the heavy fraction of coal tar (HFCT) using oil shale (OS) from Shubarkol Komir JSC to simultaneously increase the yield of valuable hydrocarbon fractions and extract rare and dispersed trace elements. The lack of data on the effect of shale on the process and the kinetics of multi-component “tar + shale” systems limits the development of effective technologies. TG/DTG analysis was combined with the Friedman, Ozawa–Flynn–Wall, and Šesták–Berggren methods for the first time to evaluate the role of oil shale (OS). It was shown that the addition of 13% OS provides a sustained reduction in activation energy (~85–86 kJ/mol) and optimal conditions for hydrometallization. At 420 °C, an initial H₂ pressure of 4 MPa, and a reaction time of 60 min, the yield of light fractions reaches 62.6%, and the solid residue concentrates Ti, Mo, Ge, and other rare and dispersed elements reach up to 66,000 g/t in total. The possibility of extracting Ge using the Purolite C100 sorbent has also been confirmed. The novelty of the study lies in demonstrating the donor–catalytic effect of shale and the practical prospects of solid residue as a secondary mineral raw materials. Full article

As drone-based Light Detection and Ranging (LiDAR) becomes more accessible, it presents new opportunities for automated, geometry-driven classification. This study investigates the use of LiDAR point cloud data and Machine Learning (ML) to classify rooftop solar panels from building surfaces. While rooftop solar detection has been explored using satellite and aerial imagery, LiDAR offers geometric and reflectance-based attributes for classification. Two datasets were used: the University of Southern Queensland (UniSQ) campus, with commercial-sized panels, both elevated and flat, and a suburban area in Newcastle, Australia, with residential-sized panels sitting flush with the roof surface. UniSQ was classified using RANSAC (Random Sample Consensus), while Newcastle’s dataset was processed based on reflectance values. Geometric features were selected based on histogram overlap and Kullback–Leibler (KL) divergence, and models were trained using a Multilayer Perceptron (MLP) classifier implemented in both PyTorch and Scikit-learn libraries. Classification achieved F1 scores of 99% for UniSQ and 95–96% for the Newcastle dataset. These findings support the potential for ML-based classification to be applied to unlabelled datasets for rooftop solar analysis. Future work could expand the model to detect additional rooftop features and estimate panel counts across urban areas. Full article

Today, hydrogen is already widely regarded as up-and-coming source of energy. It is essential to meet energy needs while reducing environmental pollution, since it has a high energy capacity and does not emit carbon oxide when burned. However, for the widespread application of hydrogen energy, it is necessary to search new technical solutions for both its production and storage. A promising effective and cost-efficient method of hydrogen generation and storage can be the use of solid materials, including nanomaterials in which chemical or physical adsorption of hydrogen occurs. Focusing on the recommendations of the DOE, the search is underway for materials with high gravimetric capacity more than 6.5% wt% and in which sorption and release of hydrogen occurs at temperatures from −20 to +100 °C and normal pressure. This review aims to summarize research on hydrogen generation and storage using silicon nanostructures and silicon composites. Hydrogen generation has been observed in Si nanoparticles, porous Si, and Si nanowires. Regardless of their size and surface chemistry, the silicon nanocrystals interact with water/alcohol solutions, resulting in their complete oxidation, the hydrolysis of water, and the generation of hydrogen. In addition, porous Si nanostructures exhibit a large internal specific surface area covered by SiH_x bonds. A key advantage of porous Si nanostructures is their ability to release molecular hydrogen through the thermal decomposition of SiHx groups or in interaction with water/alkali. The review also covers simulations and theoretical modeling of H₂ generation and storage in silicon nanostructures. Using hydrogen with fuel cells could replace Li-ion batteries in drones and mobile gadgets as more efficient. Finally, some recent applications, including the potential use of Si-based agents as hydrogen sources to address issues associated with new approaches for antioxidative therapy. Hydrogen acts as a powerful antioxidant, specifically targeting harmful ROS such as hydroxyl radicals. Antioxidant therapy using hydrogen (often termed hydrogen medicine) has shown promise in alleviating the pathology of various diseases, including brain ischemia–reperfusion injury, Parkinson’s disease, and hepatitis. Full article

Al-SiC composite coatings were successfully fabricated through the process of electrodeposition utilizing an AlCl₃-LiAlH₄-benzene-THF system. This method allows for the incorporation of silicon carbide (SiC) particles into the aluminum matrix, enhancing the coating’s properties. The study examined various factors that influence the coating characteristics, including current density, temperature, and the quantity of SiC particles added to the formula. The findings revealed that these parameters significantly affect the resulting surface morphology, corrosion resistance, and hardness of the Al-SiC composite coatings. Specifically, the analysis demonstrated that the Al-SiC composite coating produced optimal surface morphology, which is crucial for its performance and durability in various applications. when the current density is 50 mA/cm², the bath temperature is at 30 °C, and the addition amount of SiC particles is optimized to 40 g/L. Combined with electrochemical experimental data, the corrosion resistance of the composite coating prepared under this condition was significantly improved. The results of scanning electron microscopy showed that the surface of the composite coating prepared under this process parameter was uniform and dense, without obvious holes and cracks, and the SiC particles were uniformly distributed in the coating with high density. Through the hardness test of composite coatings with different SiC particle contents, it was found that in the research interval, when the SiC particle content was less than 3 wt%, the hardness of the coating changed relatively slowly. As the amount of SiC particles surpassed 4 wt%, there was a notable increase in hardness. At a SiC concentration of 5%, the coating exhibited a hardness level of 152.1 HV. Full article

Conductive hydrogels (CHs) have attracted significant attention in the fields of flexible electronics, human–machine interaction, and electronic skin (e-skin) due to their self-adhesiveness, environmental stability, and multi-stimuli responsiveness. However, integrating these diverse functionalities into a single conductive hydrogel system remains a challenge. In this study, polyvinyl alcohol (PVA) and polyacrylamide (PAM) were used as the dual-network matrix, lithium chloride and MXene were added, and a simple immersion strategy was adopted to synthesize a multifunctional MXene-based conductive hydrogel in a glycerol/water (1:1) binary solvent system. A subsequent investigation was then conducted on the hydrogel. The prepared PVA/PAM/LiCl/MXene hydrogel exhibits excellent tensile properties (~1700%), high electrical conductivity (1.6 S/m), and good self-healing ability. Furthermore, it possesses multimodal sensing performance, including humidity sensitivity (sensitivity of −1.09/% RH), temperature responsiveness (heating sensitivity of 2.2 and cooling sensitivity of 1.5), and fast pressure response/recovery times (220 ms/230 ms). In addition, the hydrogel has successfully achieved real-time monitoring of human joint movements (elbow and knee bending) and physiological signals (pulse, breathing), as well as enabled monitoring of spatial pressure distribution via a 3 × 3 sensor array. The performance and versatility of this hydrogel make it a promising candidate for next-generation flexible sensors, which can be applied in the fields of human health monitoring, electronic skin, and human–machine interaction. Full article

NaNbO₃ (NN), known for the complexity of its phase transition sequence, is antiferroelectric (AFE) at room temperature, while both LiNbO₃ (LN) and KNbO₃ (KN) are ferroelectric (FE). The origin of ferroelectricity in ABO₃ perovskites is believed to lie in the B-O hybridization, but the origin of antiferroelectricity remains unclear. Recent ab initio studies have shown that the same B-O hybridization is necessary in AFE and proposed an additional, anharmonic contribution to the potential of the A-site atom as the crucial difference between FE and AFE perovskites. We used structure factors obtained from X-ray diffraction experiments in combination with the Maximum Entropy Method to obtain electron densities for LN, KN, and NN and identify differences in their bonding behavior. We present experimental evidence for anharmonic A-site contributions of varying strength in alkali niobates, pointing at a new path for the design of (anti-)ferroelectric materials. Full article

The growing demand for radiation-shielded infrastructure highlights the need for materials that balance shielding performance with environmental and economic sustainability. Heavyweight self-compacting concretes (HWSCC), commonly produced with barite (BaSO₄) or magnetite (Fe₃O₄) aggregates, lack systematic life cycle comparisons. The aim of this study is to systematically compare barite- and magnetite-based HWSCC in terms of life cycle environmental impacts, life cycle cost, functional performance (strength and shielding), and end-of-life circularity, in order to identify the more sustainable and cost-effective material for radiation shielding infrastructure. This study applies cradle-to-grave life cycle assessment (LCA) and life cycle cost analysis (LCC), in accordance with ISO 14040/14044 and ISO 15686-5, to evaluate barite- and magnetite-based HWSCC. Results show that magnetite concrete reduces global warming potential by 19% eutrophication by 24%, and fossil resource depletion by 23%, while lowering life cycle costs by ~23%. Both concretes achieve comparable compressive strength (~48 MPa) and shielding efficiency (µ ≈ 0.28–0.30 cm⁻¹), meeting NCRP 147 and IAEA SRS-47 standards. These findings demonstrate that magnetite-based HWSCC offers a more sustainable, cost-effective, and ethically sourced alternative for radiation shielding in healthcare, nuclear, and industrial applications. In addition, the scientific significance of this work lies in establishing a transferable methodological framework that combines LCA, LCC, and performance-normalized indicators. This enables scientists and practitioners worldwide to benchmark heavyweight concretes consistently and to adapt sustainability-informed material choices to their own regional contexts. Full article

Traditional liquid electrolyte batteries face safety concerns such as leakage and flammability, while further optimization has reached a bottleneck. Solid electrolytes are therefore considered a promising solution. Here, a PEO–LiTFSI–LATP (PELT) composite electrolyte was developed by incorporating nanosized Li_1.3Al_0.3Ti_1.7(PO₄)₃ fillers into a polyethylene oxide matrix, effectively reducing crystallinity, enhancing mechanical robustness, and providing additional Li⁺ transport channels. The PELT electrolyte exhibited an electrochemical stability window of 4.9 V, an ionic conductivity of 1.2 × 10⁻⁴ S·cm⁻¹ at 60 °C, and a Li⁺ transference number (tLi+) of 0.46, supporting stable Li plating/stripping for over 600 h in symmetric batteries. More importantly, to address poor electrode–electrolyte contact in conventional layered cells, we proposed an integrated electrode–electrolyte architecture by in situ coating the PELT precursor directly onto LiFePO₄ cathodes. This design minimized interfacial impedance, improved ion transport, and enhanced electrochemical stability. The integrated PELT/LFP battery retained 74% of its capacity after 200 cycles at 1 A·g⁻¹ and showed superior rate capability compared with sandwich-type batteries. These results highlight that coupling LATP-enhanced polymer electrolytes with an integrated architecture is a promising pathway toward high-safety, high-performance solid-state lithium-ion batteries. Full article