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14 pages, 3360 KB  
Article
Localized Electric Field Tailoring to Balance Voltage Reliability, Current Density, and High-Frequency Performance of AlGaN/GaN HEMTs
by Yuxin Wang, Jiangwen Wang, Zilong Dong, Peiran Tian, Yuxiu Liu, Junyi Zhai and Weiguo Hu
Micromachines 2025, 16(11), 1199; https://doi.org/10.3390/mi16111199 - 22 Oct 2025
Abstract
Emerging applications including advanced industrial manufacturing, cutting-edge scientific research and medical equipment demand AlGaN/GaN HEMTs possessing both high-frequency and high-voltage characteristics. However, a persistent trade-off remains between the frequency characteristics and breakdown characteristics of these devices. In this study, we employed localized electric [...] Read more.
Emerging applications including advanced industrial manufacturing, cutting-edge scientific research and medical equipment demand AlGaN/GaN HEMTs possessing both high-frequency and high-voltage characteristics. However, a persistent trade-off remains between the frequency characteristics and breakdown characteristics of these devices. In this study, we employed localized electric field tailoring (LEFT) by introducing materials with different dielectric constants to construct a non-uniform composite gate dielectric layer, aiming to balance the breakdown voltage and cut-off frequency of the device. Device models were developed using APSYS-2018 software and their reliability was experimentally validated. Research data indicates that, compared to traditional uniform high-k (typically with dielectric constants k > 10, such as HfO2 and HfZrO) gate dielectrics, the non-uniform composite gate dielectric structure demonstrates superior transconductance, saturation current density and cut-off frequency, with minimal degradation in breakdown voltage. Specifically, relative to HfO2 and HfZrO uniform devices, the Al2O3/HfO2 and Al2O3/HfZrO non-uniform HEMTs achieved 20.0% and 35.2% increases in cut-off frequency, respectively. Meanwhile, breakdown voltage remained above 97% of their uniform counterparts, saturation current density and transconductance increased by approximately 5%. Therefore, this non-uniform composite gate dielectric layer structure of AlGaN/GaN HEMT with LEFT holds great potential for industrial plasma generators, magnetic resonance imaging systems and biomedical radiofrequency hyperthermia devices. Full article
(This article belongs to the Section D1: Semiconductor Devices)
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22 pages, 3237 KB  
Article
Quantifying Field Soil Moisture, Temperature, and Heat Flux Using an Informer–LSTM Deep Learning Model
by Na Li, Xiaoxiao Sun, Peng Wang, Wenke Wang and Zhitong Ma
Agronomy 2025, 15(11), 2453; https://doi.org/10.3390/agronomy15112453 - 22 Oct 2025
Abstract
Understanding water and heat transport through soils is vital for managing soil and groundwater resources, agricultural irrigation, and ecosystem protection. This paper aims to explore the potential application of deep learning methods in simulating water and heat transport processes within soils. It also [...] Read more.
Understanding water and heat transport through soils is vital for managing soil and groundwater resources, agricultural irrigation, and ecosystem protection. This paper aims to explore the potential application of deep learning methods in simulating water and heat transport processes within soils. It also examines the interactions between soil hydrological processes and environmental factors, including meteorological conditions and groundwater levels. To achieve these, we develop a hybrid model Informer–LSTM by combining two powerful architectures: Informer, a Transformer-based model essentially designed for long-sequence time-series forecasting, and Long Short-Term Memory (LSTM), a neural network that is great at learning short-term patterns in sequential data. The model is applied to field measurements from Henan Township in Ordos, Inner Mongolia, China, for training and testing, to simulate three key variables: soil water content, temperature, and heat flux at different depths in two soil columns with different groundwater levels. Our results confirm that Informer–LSTM is highly effective at simulating the soil water and heat transport. Simultaneously, we evaluate its performance by incorporating various combinations of input data including meteorological data, soil hydrothermal dynamics, and groundwater level. This reveals the relationship between soil hydrothermal processes and meteorological data, as well as coupled processes of soil water and heat transport. Moreover, employing SHapley Additive exPlanations (SHAP) analysis, we identify the most influential factors for predicting heat flux in shallow soils. This research demonstrates that deep learning models are a viable and valuable tool for simulating soil hydrothermal processes in arid and semi-arid regions. Full article
(This article belongs to the Special Issue Agroclimatology and Crop Production: Adapting to Climate Change)
28 pages, 1946 KB  
Article
Efficient Analysis of the Gompertz–Makeham Theory in Unitary Mode and Its Applications in Petroleum and Mechanical Engineering
by Refah Alotaibi, Hoda Rezk and Ahmed Elshahhat
Axioms 2025, 14(11), 775; https://doi.org/10.3390/axioms14110775 - 22 Oct 2025
Abstract
This paper introduces a novel three-parameter probability model, the unit-Gompertz–Makeham (UGM) distribution, designed for modeling bounded data on the unit interval (0,1). By transforming the classical Gompertz–Makeham distribution, we derive a unit-support distribution that flexibly accommodates a wide range of shapes in both [...] Read more.
This paper introduces a novel three-parameter probability model, the unit-Gompertz–Makeham (UGM) distribution, designed for modeling bounded data on the unit interval (0,1). By transforming the classical Gompertz–Makeham distribution, we derive a unit-support distribution that flexibly accommodates a wide range of shapes in both the density and hazard rate functions, including increasing, decreasing, bathtub, and inverted-bathtub forms. The UGM density exhibits rich patterns such as symmetric, unimodal, U-shaped, J-shaped, and uniform-like forms, enhancing its ability to fit real-world bounded data more effectively than many existing models. We provide a thorough mathematical treatment of the UGM distribution, deriving explicit expressions for its quantile function, mode, central and non-central moments, mean residual life, moment-generating function, and order statistics. To facilitate parameter estimation, eight classical techniques, including maximum likelihood, least squares, and Cramér–von Mises methods, are developed and compared via a detailed simulation study assessing their accuracy and robustness under varying sample sizes and parameter settings. The practical relevance and superior performance of the UGM distribution are demonstrated using two real-world engineering datasets, where it outperforms existing bounded models, such as beta, Kumaraswamy, unit-Weibull, unit-gamma, and unit-Birnbaum–Saunders. These results highlight the UGM distribution’s potential as a versatile and powerful tool for modeling bounded data in reliability engineering, quality control, and related fields. Full article
(This article belongs to the Special Issue Advances in the Theory and Applications of Statistical Distributions)
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26 pages, 1271 KB  
Article
Predicting the Forest Fire Duration Enriched with Meteorological Data Using Feature Construction Techniques
by Constantina Kopitsa, Ioannis G. Tsoulos, Andreas Miltiadous and Vasileios Charilogis
Symmetry 2025, 17(11), 1785; https://doi.org/10.3390/sym17111785 - 22 Oct 2025
Abstract
The spread of contemporary artificial intelligence technologies, particularly machine learning, has significantly enhanced the capacity to predict asymmetrical natural disasters. Wildfires constitute a prominent example, as machine learning can be employed to forecast not only their spatial extent but also their environmental and [...] Read more.
The spread of contemporary artificial intelligence technologies, particularly machine learning, has significantly enhanced the capacity to predict asymmetrical natural disasters. Wildfires constitute a prominent example, as machine learning can be employed to forecast not only their spatial extent but also their environmental and socio-economic impacts, propagation dynamics, symmetrical or asymmetrical patterns, and even their duration. Such predictive capabilities are of critical importance for effective wildfire management, as they inform the strategic allocation of material resources, and the optimal deployment of human personnel in the field. Beyond that, examination of symmetrical or asymmetrical patterns in fires helps us to understand the causes and dynamics of their spread. The necessity of leveraging machine learning tools has become imperative in our era, as climate change has disrupted traditional wildfire management models due to prolonged droughts, rising temperatures, asymmetrical patterns, and the increasing frequency of extreme weather events. For this reason, our research seeks to fully exploit the potential of Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (MRMR), and Grammatical Evolution, both for constructing Artificial Features and for generating Neural Network Architectures. For this purpose, we utilized the highly detailed and publicly available symmetrical datasets provided by the Hellenic Fire Service for the years 2014–2021, which we further enriched with meteorological data, corresponding to the prevailing conditions at both the onset and the suppression of each wildfire event. The research concluded that the Feature Construction technique, using Grammatical Evolution, combines both symmetrical and asymmetrical conditions, and that weather phenomena may provide and outperform other methods in terms of stability and accuracy. Therefore, the asymmetric phenomenon in our research is defined as the unpredictable outcome of climate change (meteorological data) which prolongs the duration of forest fires over time. Specifically, in the model accuracy of wildfire duration using Feature Construction, the mean error was 8.25%, indicating an overall accuracy of 91.75%. Full article
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32 pages, 18849 KB  
Article
Research on the Design Parameters of Outdoor Transitional Spaces Based on the Improvement of Thermal Environment
by Guoying Hou, Yiming Kuai, Ping Shu, Xuan Li and Shen Wei
Buildings 2025, 15(21), 3808; https://doi.org/10.3390/buildings15213808 - 22 Oct 2025
Abstract
Global warming and urban expansion impose far-reaching, negative implications on the quality of the outdoor thermal environment in residential areas. Due to its potential for microclimate regulation and easy configuration with less site restrictions, the transitional space is an effective mitigation measure to [...] Read more.
Global warming and urban expansion impose far-reaching, negative implications on the quality of the outdoor thermal environment in residential areas. Due to its potential for microclimate regulation and easy configuration with less site restrictions, the transitional space is an effective mitigation measure to transform existing outdoor spaces for thermal discomfort. The point of this article is to explore the optimum design parameters of the transitional space for increasing outdoor thermal comfort, with a focus on its orientation, aspect ratio (H/W), plan aspect ratio (L/W), and enclosure degree. The ENVI-met micro-meteorological model is adopted to visualize the environmental parameters after field measurement whereas the Ecotect is applied to validate the thermal performance under different design variants. The simulation results show that the thermal performance of geometry and orientation for the transitional space is seasonally discordant. On account of giving consideration to balance the double demands of solar shading in summer and solar gain in winter, a south-oriented transitional space with the windward side enclosure, length-width ratio of 2:1, and aspect ratio with 1.2 produces a greater thermal environment in Tianjin, China. Combined with the previous literature, south-oriented transitional spaces have a comparative advantage in balancing year-round thermal comfort for most cases; the deviation of the preferred orientation in the corresponding cities from the due south orientation is within 90° of a counterclockwise rotation. The lower aspect ratio (approximately below 1), deeper shape, and lower enclosure of the transitional spaces is appropriate for tropical and subtropical areas to avoid excessive sunshine; for temperate climates with hot summers and cold winters, such as in Tianjin, the reverse happens. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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19 pages, 1479 KB  
Article
SANet: A Pure Vision Strip-Aware Network with PSSCA and Multistage Fusion for Weld Seam Detection
by Zhijian Zhu, Haoran Gu, Zhao Yang, Lijie Zhao, Guoli Song and Qinghui Wang
Appl. Sci. 2025, 15(20), 11296; https://doi.org/10.3390/app152011296 - 21 Oct 2025
Abstract
Weld seam detection is a fundamental prerequisite for robotic welding automation, yet it remains challenging due to the elongated shape of welds, weak contrast against metallic backgrounds, and significant environmental interference in industrial scenarios. To address these challenges, we propose a novel deep [...] Read more.
Weld seam detection is a fundamental prerequisite for robotic welding automation, yet it remains challenging due to the elongated shape of welds, weak contrast against metallic backgrounds, and significant environmental interference in industrial scenarios. To address these challenges, we propose a novel deep neural network architecture termed SANet (Strip-Aware Network). The model is constructed upon a U-shaped backbone and integrates strip-aware feature modeling with multistage supervision. It mainly consists of two complementary modules: the Paralleled Strip and Spatial Context-Aware (PSSCA) module and the Multistage Fusion (MF) module. The PSSCA module enhances the extraction of elongated strip-like features by combining parallel strip perception with spatial context modeling, thereby improving fine-grained weld seam representation. In addition, SANet integrates the StripPooling attention mechanism as an auxiliary component to enlarge the receptive field along strip directions and enhance feature discrimination under complex backgrounds. Meanwhile, the MF module performs cross-stage feature fusion by aggregating encoder and decoder features at multiple levels, ensuring accurate boundary recovery and robust global-to-local interaction. The weld seam detection task is formulated as a two-dimensional segmentation problem and evaluated on a self-built dataset consisting of over 4000 weld seam images covering diverse industrial scenarios such as pipe joints, trusses, elbows, and furnace structures. Experimental results show that SANet achieves an IoU of 96.23% and a Dice coefficient of 98.07%, surpassing all compared models and demonstrating its superior performance in weld seam detection. These findings validate the effectiveness of the proposed architecture and highlight its potential as a low-cost, flexible, and reliable pure vision solution for intelligent welding applications. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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22 pages, 4369 KB  
Article
Research on Finite Permeability Semi-Analytical Harmonic Modeling Method for Maglev Planar Motors
by Yang Zhang, Chunguang Fan and Chenglong Yu
Magnetism 2025, 5(4), 27; https://doi.org/10.3390/magnetism5040027 - 21 Oct 2025
Abstract
This study proposes a semi-analytic harmonic modeling method that significantly improves the accuracy and efficiency of complex magnetic field modeling by integrating numerical and analytical approaches. Compared to traditional methods such as the equivalent charge method and finite element method, this approach optimizes [...] Read more.
This study proposes a semi-analytic harmonic modeling method that significantly improves the accuracy and efficiency of complex magnetic field modeling by integrating numerical and analytical approaches. Compared to traditional methods such as the equivalent charge method and finite element method, this approach optimizes the distribution of surface and body charges in the magnetic dipole model and introduces a finite and variable permeability model to accommodate material non-uniformity. Through harmonic expansion and analytical optimization, the method more accurately reflects the characteristics of real magnets, providing an efficient and precise solution for complex magnetic field problems, particularly in the design of high-performance magnets such as Halbach arrays. In this study, the effectiveness of the new modeling method is verified through the combination of simulation and experiment: the magnetic field distribution of the new Halbach array is accurately simulated, and the applicability of the model in the description of complex magnetic fields is analyzed. The dynamic response ability of the optimized model is verified by modeling and simulating the variation of the permeability under actual conditions. The distribution of scalar potential energy with permeability was simulated to evaluate the adaptability of the model to the real physical field. Through the comparative analysis of simulation and experimental results, the advantages of the new method in modeling accuracy and efficiency are clearly pointed out, and the effectiveness of the semi-analytic harmonic modeling method and its wide application potential in the design of new magnetic fields are proved. In this study, a semi-analytic harmonic modeling method is proposed by combining numerical and analytical methods, which breaks through the efficiency bottleneck of traditional modeling methods, and achieves the unity of high precision and high efficiency in the magnetic field modeling of the new Halbach array, providing a new solution for the study of complex magnetic field problems. Full article
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20 pages, 5454 KB  
Article
Investigation of Roadway Anti-Icing Without Auxiliary Heat Using Hydronic Heated Pavements Coupled with Borehole Thermal Energy Storage
by Sangwoo Park, Annas Fiaz Abbasi, Hizb Ullah, Wonjae Ha and Seokjae Lee
Energies 2025, 18(20), 5546; https://doi.org/10.3390/en18205546 - 21 Oct 2025
Abstract
Roadway anti-icing requires low-carbon alternatives to chloride salts and electric heating. This study evaluated a seasonal thermal energy storage system that couples a geothermal hydronic heated pavement (HHPS-G) with borehole thermal energy storage (BTES), operated without auxiliary heat. A coupled transient HHPS-G–BTES model [...] Read more.
Roadway anti-icing requires low-carbon alternatives to chloride salts and electric heating. This study evaluated a seasonal thermal energy storage system that couples a geothermal hydronic heated pavement (HHPS-G) with borehole thermal energy storage (BTES), operated without auxiliary heat. A coupled transient HHPS-G–BTES model was developed and validated against independent experimental data. A continuous cycle was then simulated, consisting of three months of summer pavement heat harvesting and BTES, followed by three months of winter heat discharge. A parametric analysis varied borehole depth (10, 20, and 40 m) and number of units (1, 2, and 4). Results indicated that depth is consistently more effective than unit number. Deeper fields produced larger summer pavement surface cooling with less long-term drift and yielded more persistent winter anti-icing performance. The 40 m 4-unit case lowered the end-of-summer surface temperature by 3.8 °C relative to the no-operation case and kept the surface at or above 0 °C throughout winter. In contrast, the 10 m–1-unit case was near 0 °C by late winter. A depth-first BTES design, supplemented by spacing or edge placement to limit interference, showed practical potential for anti-icing without auxiliary heat. Full article
(This article belongs to the Special Issue Geothermal Energy Heating Systems)
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22 pages, 8185 KB  
Article
A Non-Contact Phosphor Thermometry Technique for Determining the Optical Absorptivity of Materials
by Thomas M. F. Hutchinson, Matthew Davies, Callum Fisk, Hazem Zied, Jon R. Willmott and Matthew J. Hobbs
Materials 2025, 18(20), 4806; https://doi.org/10.3390/ma18204806 - 21 Oct 2025
Abstract
This work presents a bespoke, non-contact, and low-cost Phosphor Thermometry (PT) technique for the measurement of material absorptivity. The approach circumvents the challenges associated with traditional and intrusive calorimetric techniques, which require secure contact with the sample substrate. A thermographic phosphor (TP), Manganese-activated [...] Read more.
This work presents a bespoke, non-contact, and low-cost Phosphor Thermometry (PT) technique for the measurement of material absorptivity. The approach circumvents the challenges associated with traditional and intrusive calorimetric techniques, which require secure contact with the sample substrate. A thermographic phosphor (TP), Manganese-activated Magnesium Fluorogermanate (MFG), was used as a two-colour thermometer utilising the peak intensity ratio technique, enabling an empirical temperature measurement of a given Material Under Test (MUT). The system was calibrated to temperature across a dynamic range of 20°C to 140°C and subsequently assessed in terms of noise and relative sensitivity. A mathematical model describing the thermal behaviour of the samples was subsequently developed and used to infer the absorptivity value of the MUTs. Two paints, Black 3.0® and Avian-B500®, with known but contrasting absorptivities, were analysed, resulting in measured absorptivity values of 0.9385 and 0.0651 within a range of 0.0081 and 0.0127 for the two paints, respectively. Subsequent mixtures of both paints, with inherent unknown absorptivities, provided resolvable and incremental steps between the two extremities. Further measurements at specific narrow-band wavelengths of 600nm and 1550nm of Black 3.0® were performed, yielding median absorptivity values of 0.9598 and 0.9172 within a range of 0.0168 and 0.0396, respectively, therefore demonstrating the technique for the measurement of material absorptivity at discrete wavelengths. The potential of a non-contact calorimetric PT technique could provide a scalable, non-intrusive, and low-cost solution for measuring the wavelength-dependent absorptivity values of materials that are used across engineering and research fields. Full article
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27 pages, 8712 KB  
Article
Assessing NDVI, Climate, and Management to Predict Winter Wheat Yields at Field Scale in Kansas, USA
by Rebecca Lima Albuquerque Maranhão, Marcellus Marques Caldas, Jude Kastens, Jordan Watson and Romulo Pisa Lollato
Remote Sens. 2025, 17(20), 3500; https://doi.org/10.3390/rs17203500 - 21 Oct 2025
Abstract
Accurate crop yield prediction is challenging in environmentally diverse areas. This study evaluated the potential of different satellite sensors to predict winter wheat grain yield at the field level in Kansas, the U.S.’s leading winter wheat producer. Using Landsat NDVI data from late [...] Read more.
Accurate crop yield prediction is challenging in environmentally diverse areas. This study evaluated the potential of different satellite sensors to predict winter wheat grain yield at the field level in Kansas, the U.S.’s leading winter wheat producer. Using Landsat NDVI data from late February to June, a linear regression model was able to reduce the standard deviation of predicted yields by over 20% (with a normalized root mean square error (nRMSE) of 80%). The NDVI during the anthesis and grain fill stages was essential for precise yield estimation. A subregional approach that incorporated weather and management data improved results, accounting for 51%, 63%, and 68% of the nRMSE in W, SC, and NC. Results indicate that NDVI-based yield models at the field scale are environmentally dependent, particularly in south-central and western Kansas, areas prone to heat stress and water deficit, respectively. Our findings showed the benefits of an environmental subregional model integrating remote sensing and field-specific weather and management data to improve yield prediction accuracy, particularly in large, environmentally diverse regions. Full article
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30 pages, 2475 KB  
Article
Can Hydrogen Be Produced Cost-Effectively from Heavy Oil Reservoirs?
by Chinedu J. Okere and James J. Sheng
Energies 2025, 18(20), 5539; https://doi.org/10.3390/en18205539 - 21 Oct 2025
Abstract
The potential for hydrogen production from heavy oil reservoirs has gained significant attention as a dual-benefit process for both enhanced oil recovery and low-carbon energy generation. This study investigates the technical and economic feasibility of producing hydrogen from heavy oil reservoirs using two [...] Read more.
The potential for hydrogen production from heavy oil reservoirs has gained significant attention as a dual-benefit process for both enhanced oil recovery and low-carbon energy generation. This study investigates the technical and economic feasibility of producing hydrogen from heavy oil reservoirs using two primary in situ combustion gasification strategies: cyclic steam/air and CO2 + O2 injection. Through a comprehensive analysis of technical barriers, economic drivers, and market conditions, we assess the hydrogen production potential of each method. While both strategies show promise, they face considerable challenges: the high energy demands associated with steam generation in the steam/air strategy, and the complexities of CO2 procurement, capture, and storage in the CO2 + O2 method. The novelty of this work lies in combining CMG-STARS reservoir simulations with GoldSim techno-economic modeling to quantify hydrogen yields, production costs, and oil–hydrogen revenue trade-offs under realistic field conditions. The analysis reveals that under current technological and market conditions, the cost of hydrogen production significantly exceeds the market price, rendering the process economically uncompetitive. Furthermore, the dominance of oil production as the primary revenue source in both methods limits the economic viability of hydrogen production. Unless substantial advancements are made in technology or a more cost-efficient production strategy is developed, hydrogen production from heavy oil reservoirs is unlikely to become commercially viable in the near term. This study provides crucial insights into the challenges that must be addressed for hydrogen production from heavy oil reservoirs to be considered a competitive energy source. Full article
(This article belongs to the Section B: Energy and Environment)
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20 pages, 5056 KB  
Article
Prediction of Sandstone-Type Uranium Deposits Based on Data from Oilfield Drilling and Its Mineralization Regularity: A Case Study of Jingchuan Uranium Deposit, SW Ordos Basin
by Bo Zhang, Yinhang Cheng, Keyan Xiao, Rengan Yu, Yin Chen, Qiang Zhu and Sibo Wen
Appl. Sci. 2025, 15(20), 11268; https://doi.org/10.3390/app152011268 - 21 Oct 2025
Abstract
A large-scale sandstone-type uranium deposit, recently discovered within the petroleum field of the Jingchuan area on the southwestern margin of the Ordos Basin, exemplifies a classic case of uranium exploration success achieved through the analysis of petroleum geological data including borehole logs. By [...] Read more.
A large-scale sandstone-type uranium deposit, recently discovered within the petroleum field of the Jingchuan area on the southwestern margin of the Ordos Basin, exemplifies a classic case of uranium exploration success achieved through the analysis of petroleum geological data including borehole logs. By synthesizing borehole radioactive logs and seismic surveys, we delineated target sandstone geometry, connectivity, and ore-controlling structures (e.g., paleochannels, redox interfaces). This study establishes a novel methodology for sandstone-type uranium exploration in petroliferous basins, unifying geophysical and geochemical datasets to define drill-validated targets. We integrated detailed core logging, petrography, and assay data to delineate the deposit’s geology. This included the host strata composition, ore-body morphology, mineralogy, and alteration assemblages. Our analysis identified the critical controls on mineralization: sandbody architecture, structural framework, and redox zonation. Based on these constraints, we constructed a genetic metallogenic model. Furthermore, we elucidated the mechanistic role of hydrocarbons in uranium mineralization and demonstrated the strategic potential of repurposing legacy oilfield data for synergistic uranium targeting. The Jingchuan uranium deposit provides both an exploration blueprint and theoretical foundations for uranium targeting in analogous sedimentary basins. Full article
(This article belongs to the Special Issue New Insights into Mineralization and Mining)
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29 pages, 4164 KB  
Review
Multimodal Field-Driven Actuation in Bioinspired Robots: An Emerging Taxonomy and Roadmap Towards Hybrid Intelligence
by Jianping Wang, Xin Wang, Shuai Zhou and Gengbiao Chen
Biomimetics 2025, 10(10), 713; https://doi.org/10.3390/biomimetics10100713 - 21 Oct 2025
Abstract
Rigid–flexible coupled robots hold significant potential for operating in unstructured environments, but a systematic analysis of their actuation strategies across diverse physical fields is notably lacking in the literature. This review addresses this gap by introducing a novel taxonomy based on field-controlled evolutionary [...] Read more.
Rigid–flexible coupled robots hold significant potential for operating in unstructured environments, but a systematic analysis of their actuation strategies across diverse physical fields is notably lacking in the literature. This review addresses this gap by introducing a novel taxonomy based on field-controlled evolutionary pathways—mechanical → electromagnetic → chemical → biohybrid—and critically examining over 100 seminal studies through a six-dimensional framework encompassing design, dynamics, and performance. We demonstrate that hybrid field integration (e.g., pneumatic-chemical synergy) improves grasping robustness by 40% in cluttered environments compared to single-field systems. Notably, biohybrid actuators, which integrate living cells, exhibit over 90% motion similarity to biological models, while phase-transition materials allow for adaptive stiffness tuning (0.1–5 N·mm−1) in medical applications. Radar chart analysis further reveals fundamental trade-offs between energy efficiency, response speed, and scalability across the various fields. These insights provide a clear roadmap for the development of next-generation robots with embodied intelligence, emphasizing multi-field synergies and bio-inspired adaptability. Full article
(This article belongs to the Special Issue Bioinspired Locomotion Control: From Biomechanics to Robotics)
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21 pages, 3058 KB  
Article
Dynamic Identification Method for Highway Subgrade Soil Compaction Based on Embedded Attitude Sensors
by Zhizhou Su, Hao Li, Jiaye Hu, Bin Wu, Fengteng Liu, Peixin Tian and Xukai Ding
Materials 2025, 18(20), 4801; https://doi.org/10.3390/ma18204801 - 21 Oct 2025
Abstract
Compaction quality is a critical factor in ensuring the long-term performance of subgrade structures; however, traditional testing methods are limited by their destructive nature and delayed feedback. To address these shortcomings, this study proposes a dynamic identification method for subgrade compaction based on [...] Read more.
Compaction quality is a critical factor in ensuring the long-term performance of subgrade structures; however, traditional testing methods are limited by their destructive nature and delayed feedback. To address these shortcomings, this study proposes a dynamic identification method for subgrade compaction based on embedded attitude sensors. A customized sensor unit integrated with an inertial measurement module was embedded in soil samples to record triaxial acceleration and attitude angles during the compaction process. Signal processing techniques, including an improved wavelet-based denoising strategy, were employed to separate long-term compaction trends from transient impact disturbances. Attitude features such as cumulative angular change, angular velocity, root mean square values, and a comprehensive inclination index were extracted as predictive variables. Ridge regression, random forest, and XGBoost models were constructed to establish the mapping relationship between attitude features and compaction degree. Experimental results on clay, loam, and sand samples indicate that the yaw angle is most sensitive to vertical settlement, while pitch and roll angles provide complementary information on lateral and rotational behaviors. Comparative analysis of filtering methods shows that the transient masking interpolation (TMI) approach outperforms the traditional asymmetric wavelet thresholding (AWT) method in effectively preserving baseline trends. Among the regression models, XGBoost demonstrated the best predictive performance, achieving an R2 exceeding 0.995 at high compaction levels. The proposed method has been experimentally demonstrated as a laboratory-scale proof of concept, showing strong potential for future real-time field application, offering a novel technological pathway for intelligent quality control in road construction. Full article
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18 pages, 9828 KB  
Article
Study on Surface Charge Inversion and Accumulation Characteristics of DC Pillar Insulators Based on B-Spline Basis Functions
by Xi Yang, Houde Xu, Jie Wang, Jian Zhang, Shun Li and Xinran Fang
Energies 2025, 18(20), 5531; https://doi.org/10.3390/en18205531 - 21 Oct 2025
Abstract
Surface charge accumulation is an important cause of flashover accidents for DC pillar insulators and the failure of DC gas insulation equipment. In this paper, the DC pillar insulator is taken as the research object, and a surface potential measurement system is built. [...] Read more.
Surface charge accumulation is an important cause of flashover accidents for DC pillar insulators and the failure of DC gas insulation equipment. In this paper, the DC pillar insulator is taken as the research object, and a surface potential measurement system is built. The surface potential distribution of the pillar insulator under different voltages is measured. An inversion algorithm based on the B-spline basis function is proposed. The electric field simulation model of the DC pillar insulator considering the gas’s weak ionization and surface conductance is established. The surface charge accumulation characteristics of the pillar insulator under different DC voltages are studied. The results show that the surface potential of the DC pillar insulator presents an oscillating distribution in the axial direction, and the potential distribution is approximately mirror symmetry under positive and negative voltages. The surface charge density is non-uniform in the axial direction, and the surface charge distribution is different under different voltages. In addition, the current density on the solid side gradually approaches and exceeds the current density on the gas side with the increase in the applied voltage, which promotes the accumulation of charges on the insulator surface with the same symbol as the electrode to weaken the field strength and balance the normal electric field components on both sides. Full article
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