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Search Results (23,078)

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20 pages, 2022 KB  
Article
Data-Driven Condition Monitoring of Fixed-Turnout Frogs Using Standard Track Recording Car Measurements
by Markus Loidolt, Julia Egger and Andrea Katharina Korenjak
Appl. Sci. 2025, 15(20), 11122; https://doi.org/10.3390/app152011122 - 16 Oct 2025
Abstract
Turnouts are critical components of railway infrastructure, ensuring operational flexibility but also representing a significant share of track maintenance costs. The frog, as the most vulnerable part of a turnout, is subject to severe wear and degradation, requiring frequent inspection and maintenance. Traditional [...] Read more.
Turnouts are critical components of railway infrastructure, ensuring operational flexibility but also representing a significant share of track maintenance costs. The frog, as the most vulnerable part of a turnout, is subject to severe wear and degradation, requiring frequent inspection and maintenance. Traditional manual inspection methods are costly, labour-intensive, and susceptible to subjectivity. This study explores a data-driven approach to condition monitoring of fixed-turnout frogs using standard track recording car measurements. By leveraging over 20 years of longitudinal level and rail surface signal data from the Austrian track-recording measurement car, we assess the feasibility of using existing measurement data for predictive maintenance. Six complementary approaches are proposed to evaluate frog condition, including track geometry assessment, ballast condition analysis, rail surface irregularity detection, and axle box acceleration-based monitoring. Results indicate that data-driven monitoring enhances maintenance decision-making by identifying deterioration trends, reducing reliance on manual inspections, and enabling predictive interventions. The integration of standardised measurement data with advanced analytical models offers a cost-effective and scalable solution for turnout maintenance. Full article
22 pages, 885 KB  
Article
Navigating the Deep Eutectic Solvent Landscape: Experimental and Machine Learning Solubility Explorations of Syringic, p-Coumaric, and Caffeic Acids
by Piotr Cysewski, Tomasz Jeliński, Maciej Przybyłek, Natalia Gliniewicz, Marcel Majkowski and Michał Wąs
Int. J. Mol. Sci. 2025, 26(20), 10099; https://doi.org/10.3390/ijms262010099 - 16 Oct 2025
Abstract
Efficiently identifying suitable solvents for active pharmaceutical ingredients (APIs) is critical in drug formulation, yet the vast number of possible solvent-solute combinations presents a significant experimental challenge. This study addresses this by developing a robust machine learning (ML) model for accurately predicting the [...] Read more.
Efficiently identifying suitable solvents for active pharmaceutical ingredients (APIs) is critical in drug formulation, yet the vast number of possible solvent-solute combinations presents a significant experimental challenge. This study addresses this by developing a robust machine learning (ML) model for accurately predicting the solubility of three phenolic acids (syringic, p-coumaric, and caffeic) in various deep eutectic solvents (DESs), integrating both experimental and computational investigations. Measured solubility data showed that the choline chloride combined with triethylene glycol in a 1:2 molar ratio was the most efficient system for the dissolution of the studied APIs. Different ML models, utilizing nu-Support Vector Regression (nuSVR) as the core regressor and based on descriptor sets derived from COSMO-RS (Conductor-like Screening Model for Real Solvents) computations, were systematically evaluated. A novel methodology termed DOO-IT (Dual-Objective Optimization with ITerative feature pruning) was employed to address the common challenges of model development with limited, high-value datasets. The final optimal 10-descriptor nuSVR model, selected from an exhaustive, multi-run search, demonstrated outstanding predictive power, offering a highly reliable computational tool for guiding experimental screening, significantly accelerating the exploration of DES-based formulations. This research also provides a strong foundation for future machine learning-guided discovery of chemicals, offering an effective and transferable framework for developing QSPR models for various chemical systems. Full article
32 pages, 12557 KB  
Article
Controlling an Industrial Robot Using Stereo 3D Vision Systems with AI Elements
by Jarosław Panasiuk
Sensors 2025, 25(20), 6402; https://doi.org/10.3390/s25206402 (registering DOI) - 16 Oct 2025
Abstract
Robotization of production processes and the use of 3D vision systems are currently becoming more and more popular. It allows for more flexibility in the robotic process as well as expands the possibilities of process control, depending on changes in the parameters of [...] Read more.
Robotization of production processes and the use of 3D vision systems are currently becoming more and more popular. It allows for more flexibility in the robotic process as well as expands the possibilities of process control, depending on changes in the parameters of the object, its pose, and changes in the process itself. Unfortunately, the use of standard solutions is limited to a relatively small space in which the robot’s vision system operates. The use of the latest solutions in the field of Artificial Intelligence (AI) and external vision systems, in combination with the closed structures of industrial robot control systems, provides advantages by enhancing the digital awareness of the environment of robotic systems. This article presents an example of solving the problem of low digital awareness of the environment of robotic systems resulting from the limited field of view of vision systems used in industrial robots, while maintaining high precision of the systems consisting of the combination of a 3D vision system using a stereovision camera and software with AI elements with the control system of an industrial robot from FANUC and an integrated Robot Vision (iRVision) system to maintain the positioning accuracy of the robot tool. Full article
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17 pages, 3785 KB  
Article
Feasibility Study of Microwave Radiometer Neural Network Modeling Method Based on Reanalysis Data
by Xuan Liu, Qinglin Zhu, Xiang Dong, Houcai Chen, Tingting Shu, Wenxin Wang and Bin Xu
Atmosphere 2025, 16(10), 1194; https://doi.org/10.3390/atmos16101194 - 16 Oct 2025
Abstract
To address the challenge of microwave radiometer modeling in regions lacking radiosonde data, this study proposes a neural network retrieval method based on high-resolution the Final Reanalysis (FNL) reanalysis data and validates its feasibility. A microwave radiometer brightness temperature–profiles retrieval model was developed [...] Read more.
To address the challenge of microwave radiometer modeling in regions lacking radiosonde data, this study proposes a neural network retrieval method based on high-resolution the Final Reanalysis (FNL) reanalysis data and validates its feasibility. A microwave radiometer brightness temperature–profiles retrieval model was developed by the Back Propagation (BP) neural network, based on FNL reanalysis data from Qingdao, China. The model’s accuracy was evaluated by comparing retrieval results with synchronous radiosonde data, with an analysis of seasonal variations. Results indicate that the Root Mean Square Error (RMSE) of temperature profiles are 1.15 °C in the near-surface layer (0–2 km) and 2.05 °C in the mid-to-upper layers (>2 km). The comprehensive RMSE for relative humidity, water vapor density, and Integrated Water Vaper (IWV) are 17.27%, 0.96 g/m3, and 1.37 mm, respectively. Overall, the errors are relatively small, and the retrieval results exhibit strong spatiotemporal consistency with radiosonde data. The error increases most rapidly within the lower atmosphere (<2 km), with distinct seasonal differences observed. Temperature and relative humidity retrieval accuracies peak in summer, whereas water vapor density and IWV retrievals perform best in winter and worst in summer. This study confirms that reanalysis data–based modeling effectively addresses the issue of limited radiosonde coverage. This method is applicable to atmospheric remote sensing in regions lacking radiosonde data, such as oceans and plateaus. It provides a feasible solution to the regional limitations of microwave radiometer applications and expands the potential uses of reanalysis data. Full article
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37 pages, 3273 KB  
Article
Model Predictive Control of a Hybrid Li-Ion Energy Storage System with Integrated Converter Loss Modeling
by Paula Arias, Marc Farrés, Alejandro Clemente and Lluís Trilla
Energies 2025, 18(20), 5462; https://doi.org/10.3390/en18205462 (registering DOI) - 16 Oct 2025
Abstract
The integration of renewable energy systems and electrified transportation requires advanced energy storage solutions capable of providing both high energy density and fast dynamic response. Hybrid energy storage systems offer a promising approach by combining complementary battery chemistries, exploiting their respective strengths while [...] Read more.
The integration of renewable energy systems and electrified transportation requires advanced energy storage solutions capable of providing both high energy density and fast dynamic response. Hybrid energy storage systems offer a promising approach by combining complementary battery chemistries, exploiting their respective strengths while mitigating individual limitations. This study presents the design, modeling, and optimization of a hybrid energy storage system composed of two high-energy lithium nickel manganese cobalt batteries and one high-power lithium titanate oxide battery, interconnected through a triple dual-active multi-port converter. A nonlinear model predictive control strategy was employed to optimally distribute battery currents while respecting constraints such as state of charge limits, current bounds, and converter efficiency. Equivalent circuit models were used for real-time state of charge estimation, and converter losses were explicitly included in the optimization. The main contributions of this work are threefold: (i) verification of the model predictive control strategy in diverse applications, including residential renewable energy systems with photovoltaic generation and electric vehicles following the World Harmonized Light-duty Vehicle Test Procedure driving cycle; (ii) explicit inclusion of the power converter model in the system dynamics, enabling realistic coordination between batteries and power electronics; and (iii) incorporation of converter efficiency into the cost function, allowing for simultaneous optimization of energy losses, battery stress, and operational constraints. Simulation results demonstrate that the proposed model predictive control strategy effectively balances power demand, extends system lifetime by prioritizing lithium titanate oxide battery during transient peaks, and preserves lithium nickel manganese cobalt cell health through smoother operation. Overall, the results confirm that the proposed hybrid energy storage system architecture and control strategy enables flexible, reliable, and efficient operation across diverse real-world scenarios, providing a pathway toward more sustainable and durable energy storage solutions. Full article
73 pages, 2702 KB  
Review
Towards an End-to-End Digital Framework for Precision Crop Disease Diagnosis and Management Based on Emerging Sensing and Computing Technologies: State over Past Decade and Prospects
by Chijioke Leonard Nkwocha and Abhilash Kumar Chandel
Computers 2025, 14(10), 443; https://doi.org/10.3390/computers14100443 (registering DOI) - 16 Oct 2025
Abstract
Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing [...] Read more.
Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production. Full article
31 pages, 3812 KB  
Review
Generative Adversarial Networks in Dermatology: A Narrative Review of Current Applications, Challenges, and Future Perspectives
by Rosa Maria Izu-Belloso, Rafael Ibarrola-Altuna and Alex Rodriguez-Alonso
Bioengineering 2025, 12(10), 1113; https://doi.org/10.3390/bioengineering12101113 - 16 Oct 2025
Abstract
Generative Adversarial Networks (GANs) have emerged as powerful tools in artificial intelligence (AI) with growing relevance in medical imaging. In dermatology, GANs are revolutionizing image analysis, enabling synthetic image generation, data augmentation, color standardization, and improved diagnostic model training. This narrative review explores [...] Read more.
Generative Adversarial Networks (GANs) have emerged as powerful tools in artificial intelligence (AI) with growing relevance in medical imaging. In dermatology, GANs are revolutionizing image analysis, enabling synthetic image generation, data augmentation, color standardization, and improved diagnostic model training. This narrative review explores the landscape of GAN applications in dermatology, systematically analyzing 27 key studies and identifying 11 main clinical use cases. These range from the synthesis of under-represented skin phenotypes to segmentation, denoising, and super-resolution imaging. The review also examines the commercial implementations of GAN-based solutions relevant to practicing dermatologists. We present a comparative summary of GAN architectures, including DCGAN, cGAN, StyleGAN, CycleGAN, and advanced hybrids. We analyze technical metrics used to evaluate performance—such as Fréchet Inception Distance (FID), SSIM, Inception Score, and Dice Coefficient—and discuss challenges like data imbalance, overfitting, and the lack of clinical validation. Additionally, we review ethical concerns and regulatory limitations. Our findings highlight the transformative potential of GANs in dermatology while emphasizing the need for standardized protocols and rigorous validation. While early results are promising, few models have yet reached real-world clinical integration. The democratization of AI tools and open-access datasets are pivotal to ensure equitable dermatologic care across diverse populations. This review serves as a comprehensive resource for dermatologists, researchers, and developers interested in applying GANs in dermatological practice and research. Future directions include multimodal integration, clinical trials, and explainable GANs to facilitate adoption in daily clinical workflows. Full article
(This article belongs to the Special Issue AI-Driven Imaging and Analysis for Biomedical Applications)
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27 pages, 10471 KB  
Article
A Dual-Horizon Peridynamics–Discrete Element Method Framework for Efficient Short-Range Contact Mechanics
by Kinan Bezem, Sina Haeri and Stephanie TerMaath
Modelling 2025, 6(4), 131; https://doi.org/10.3390/modelling6040131 - 16 Oct 2025
Abstract
Short-range forces enable peridynamics to simulate impact, yet it demands a computationally expensive contact search and includes no intrinsic damping. A significantly more efficient solution is the coupled dual-horizon peridynamics–discrete element method approach, which provides a robust framework for modeling fracture. The peridynamics [...] Read more.
Short-range forces enable peridynamics to simulate impact, yet it demands a computationally expensive contact search and includes no intrinsic damping. A significantly more efficient solution is the coupled dual-horizon peridynamics–discrete element method approach, which provides a robust framework for modeling fracture. The peridynamics component handles the nonlocal continuum mechanics capabilities to predict material damage and fracture, while the discrete element method captures discrete particle behavior. Whereas existing peridynamics–discrete element method approaches assign discrete element method particles to many or all surface peridynamics points, the proposed method integrates dual-horizon peridynamics with a single discrete element particle representing each object. Contact forces are computed once per discrete element pair and mapped to overlapping peridynamics points in proportion to shared volume, conserving linear momentum. Benchmark sphere-on-plate impact demonstrates prediction of peak contact force, rebound velocity, and plate deflection within 5% of theoretical results found in the literature, while decreasing neighbour-search cost by more than an order of magnitude. This validated force-transfer mechanism lays the groundwork for future extension to fully resolved fracture and fragmentation. Full article
20 pages, 960 KB  
Article
A Machine Learning-Based Hybrid Approach for Safeguarding VLC-Enabled Drone Systems
by Ge Shi, Hongyang Zhou, Huixin Wu, Fupeng Wei and Wei Cheng
Drones 2025, 9(10), 721; https://doi.org/10.3390/drones9100721 (registering DOI) - 16 Oct 2025
Abstract
This paper explores the physical layer security performance of collaborative drone fleets enabled by visible light communication (VLC) in a multi-eavesdropper scenario, where multiple drones leverage VLC to serve terrestrial users. To strengthen system security, we formulate a sum worst-case secrecy rate maximization [...] Read more.
This paper explores the physical layer security performance of collaborative drone fleets enabled by visible light communication (VLC) in a multi-eavesdropper scenario, where multiple drones leverage VLC to serve terrestrial users. To strengthen system security, we formulate a sum worst-case secrecy rate maximization problem. To address the non-convex optimization challenge of this problem, we develop two innovative Q-learning-based position decision algorithms (Q-PDA and Q-PDA-lite) with a dynamic reward mechanism, allowing drones to adaptively optimize their positions. Additionally, we propose an enhanced Tabu Search-based grouping algorithm (TS-GA) to establish the suboptimal user equipment (UE)–drone association by balancing candidate solution exploration and tabu constraint exploitation. Simulation results demonstrate that the proposed Q-PDA and Q-PDA-lite achieve worst-case secrecy rates significantly exceeding those of Random-PDA and K-means-PDA. While Q-PDA-lite exhibits 2% lower performance than Q-PDA, it offers reduced complexity. Additionally, the proposed TS-GA achieves a worst-case secrecy rate that substantially outperforms random grouping, UE-channel-gain-based grouping, and channel-gain-based grouping. Collectively, the hybrid approach integrating Q-PDA and TS-GA achieves 10% near-global optimality with guaranteed convergence, while preserving computational efficiency. Furthermore, this hybrid approach outperforms other combinations in terms of security metrics. Full article
28 pages, 9211 KB  
Article
Effect of Long-Term Immersion in Low-Salinity Seawater on Epoxy Resin Composites Filled with Marine Secondary Raw Materials
by Greta Vicentini, Carlo Santulli, Sara Mattiello, Roberto Matassa, Danilo Nikolić, Slavica Petovic, Ana Pesic, Radmila Gagic, Alberto Felici and Cristiano Fragassa
J. Mar. Sci. Eng. 2025, 13(10), 1985; https://doi.org/10.3390/jmse13101985 - 16 Oct 2025
Abstract
This research explores the potential introduction of marine waste-derived biological fillers within bio-epoxy matrices to mitigate the environmental impact of traditional materials, like fiberglass, in boat construction. However, this raises concerns about biofouling and degradation, issues that have not been extensively investigated in [...] Read more.
This research explores the potential introduction of marine waste-derived biological fillers within bio-epoxy matrices to mitigate the environmental impact of traditional materials, like fiberglass, in boat construction. However, this raises concerns about biofouling and degradation, issues that have not been extensively investigated in composites, especially over a time frame representative of issues that could arise during service. Although protective solutions like biocides and specific coatings exist, degradation remains challenging when attempting to use eco-friendly natural fillers. This study specifically integrates various biological fillers, namely ceramics (mussel, oyster, clam powder) or ligno-cellulosic (i.e., Posidonia oceanica fibers) into epoxy for use in some boat components (bench seats for the bridge deck), aiming to evaluate the biofouling process under extreme (or decommissioning) conditions. In itself, epoxy does represent an ideal enclosing matrix for biomass waste, which ideally needs to be introduced in significant amounts. The development of biofouling in the specific context of Kotor’s Bay, Montenegro, for a duration of six months, and relevant composite degradation were examined. In particular, three situations were reproduced by positioning the samples in a harbor environment: (i) on the bottom of the sea (2 m. depth), (ii) immersed just below the surface (0.5 m. depth), and (iii) on the splashing surface (pier). The concerns identified appear generally limited in the case of the envisaged application, despite some significant wear effect in the case of the samples containing Posidonia. However, this study also offers information and caveats in terms of more ambitious prospective applications (e.g., the boat hull structure). Full article
(This article belongs to the Section Ocean Engineering)
32 pages, 2224 KB  
Article
Fuel Cell–Battery Hybrid Trains for Non-Electrified Lines: A Dynamic Simulation Approach
by Giuliano Agati, Domenico Borello, Alessandro Ruvio and Paolo Venturini
Energies 2025, 18(20), 5457; https://doi.org/10.3390/en18205457 (registering DOI) - 16 Oct 2025
Abstract
Hydrogen-powered hybrid trains equipped with fuel cells (FC) and batteries represent a promising alternative to diesel traction on non-electrified railway lines and have significant potential to support modal shifts toward more sustainable transport systems. This study presents the development of a flexible MATLAB-based [...] Read more.
Hydrogen-powered hybrid trains equipped with fuel cells (FC) and batteries represent a promising alternative to diesel traction on non-electrified railway lines and have significant potential to support modal shifts toward more sustainable transport systems. This study presents the development of a flexible MATLAB-based tool for the dynamic simulation of fuel cell–battery hybrid powertrains. The model integrates train dynamics, rule-based energy management, system efficiencies, and component degradation, enabling both energy and cost analyses over the vehicle’s lifetime. The objective is to assess the techno-economic performance of different powertrain configurations. Sensitivity analyses were carried out by varying two sizing parameters: the nominal power of the fuel cell (parameter m) and the total battery capacity (parameter n), across multiple real-world railway routes. Results show a slight reduction in lifecycle costs as m increases (5.1 €/km for m = 0.50) mainly due to a lower FC degradation. Conversely, increasing battery capacity (n) lowers costs by reducing cycling stress for both battery and FC, from 5.3 €/km (n = 0.10) to 4.5 €/km (n = 0.20). In general, lowest values of m and n provide unviable solutions as the battery discharges completely before the end of the journey. The study highlights the critical impact of the operational profile: for a fixed powertrain configuration (m = 0.45, n = 0.20), the specific cost dramatically increases from 4.44 €/km on a long, flat route to 15.8 €/km on a hilly line and up to 76.7 €/km on a mountainous route, primarily due to severe fuel cell degradation under transient loads. These findings demonstrate that an “all-purpose” train sizing approach is inadequate, confirming the necessity of route-specific powertrain optimization to balance techno-economic performance. Full article
24 pages, 4698 KB  
Article
Cross-Kingdom Enzymatic Strategies for Deoxynivalenol Detoxification: Computational Analysis of Structural Mechanisms and Evolutionary Adaptations
by Francisco J. Enguita and Ana Lúcia Leitão
Microorganisms 2025, 13(10), 2384; https://doi.org/10.3390/microorganisms13102384 - 16 Oct 2025
Abstract
Deoxynivalenol (DON) is a trichothecene mycotoxin produced by Fusarium species that frequently contaminates cereal crops, representing a major threat to food safety, public health, and agricultural productivity. Its remarkable chemical stability during food processing presents significant challenges for effective detoxification. Among the available [...] Read more.
Deoxynivalenol (DON) is a trichothecene mycotoxin produced by Fusarium species that frequently contaminates cereal crops, representing a major threat to food safety, public health, and agricultural productivity. Its remarkable chemical stability during food processing presents significant challenges for effective detoxification. Among the available mitigation strategies, biological approaches have emerged as particularly promising, as they exploit enzymatic systems capable of converting DON into metabolites with substantially reduced toxicity. In this study, we provide a comprehensive analysis of the structural and evolutionary mechanisms underlying DON detoxification across three kingdoms of life. We investigated the fungal glutathione S-transferase Fhb7, the bacterial DepA/DepB epimerization pathway, and the plant SPG glyoxalase using integrative bioinformatics, phylogenetics, molecular modeling, and docking simulations. The selected enzymatic systems employ distinct yet complementary strategies: Fhb7 conjugates DON with glutathione and disrupts its epoxide ring, DepA/DepB converts it into the less toxic 3-epi-DON through stereospecific epimerization, and SPG glyoxalase mediates DON isomerization. Despite their mechanistic differences, these enzymes share key adaptive features that enable efficient DON recognition and detoxification. This work provides an integrative view of cross-kingdom enzymatic strategies for DON degradation, offering insights into their evolution and functional diversity. These findings open avenues for biotechnological applications, including the development of DON-resistant crops and innovative solutions to reduce mycotoxin contamination in the food chain. Full article
(This article belongs to the Special Issue Secondary Metabolism of Microorganisms, 3rd Edition)
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25 pages, 6408 KB  
Review
Application Prospects of Optical Fiber Sensing Technology in Smart Campus Construction: A Review
by Huanhuan Zhang, Xinli Zhai and Jing Sun
Photonics 2025, 12(10), 1026; https://doi.org/10.3390/photonics12101026 - 16 Oct 2025
Abstract
As smart campus construction continues to advance, traditional safety monitoring and environmental sensing systems are increasingly showing limitations in sensitivity, anti-interference capability, and deployment flexibility. Optical fiber sensing (OFS) technology, with its advantages of high sensitivity, passive operation, immunity to electromagnetic interference, and [...] Read more.
As smart campus construction continues to advance, traditional safety monitoring and environmental sensing systems are increasingly showing limitations in sensitivity, anti-interference capability, and deployment flexibility. Optical fiber sensing (OFS) technology, with its advantages of high sensitivity, passive operation, immunity to electromagnetic interference, and long-distance distributed sensing, provides a novel solution for real-time monitoring and early warning of critical campus infrastructure. This review systematically examines representative applications of OFS technology in smart campus scenarios, including structural health monitoring of academic buildings, laboratory environmental sensing, and intelligent campus security. By analyzing the technical characteristics of various types of optical fiber sensors, the paper explores emerging developments and future potential of OFS in supporting intelligent campus construction. Finally, the feasibility of building data acquisition, transmission, and visualization platforms based on OFS systems is discussed, highlighting their promising roles in campus safety operations, the integration of teaching and research, and intelligent equipment management. Full article
(This article belongs to the Special Issue Applications and Development of Optical Fiber Sensors)
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30 pages, 3838 KB  
Article
Multiscale Investigation of Interfacial Behaviors in Rubber Asphalt–Aggregate Systems Under Salt Erosion: Insights from Laboratory Tests and Molecular Dynamics Simulations
by Yun Li, Youxiang Si, Shuaiyu Wang, Peilong Li, Ke Zhang and Yuefeng Zhu
Materials 2025, 18(20), 4746; https://doi.org/10.3390/ma18204746 (registering DOI) - 16 Oct 2025
Abstract
Deicing salt effectively melts ice and snow to maintain traffic flow in seasonal freezing zones, but its erosion effect compromises the water stability and structural integrity of asphalt pavements. To comprehensively explore the impacts of salt erosion on the interfacial behaviors of rubber [...] Read more.
Deicing salt effectively melts ice and snow to maintain traffic flow in seasonal freezing zones, but its erosion effect compromises the water stability and structural integrity of asphalt pavements. To comprehensively explore the impacts of salt erosion on the interfacial behaviors of rubber asphalt–aggregate systems, this study developed a multiscale characterization method integrating a macroscopic mechanical test, microscopic tests, and molecular dynamics (MD) simulations. Firstly, laboratory-controlled salt–freeze–thaw cycles were employed to simulate field conditions, followed by quantitative evaluation of interfacial bonding properties through pull-out tests. Subsequently, the atomic force microscopy (AFM) and Fourier transform infrared spectrometer (FTIR) tests were conducted to characterize the microscopic morphology evolution and chemical functional group transformations, respectively. Moreover, by combining the diffusion coefficients of water molecules, salt solution ions, and asphalt components, the mechanism of interfacial salt erosion was elucidated. The results demonstrate that increasing NaCl concentration and freeze–thaw cycles progressively reduces interfacial pull-out strength and fracture energy, with NaCl-induced damage becoming limited after twelve salt–freeze–thaw cycles. In detail, with exposure to 15 freeze–thaw cycles in 6% NaCl solution, the pull-out strength and fracture energy of the rubber asphalt–limestone aggregate decrease by 50.47% and 51.57%, respectively. At this stage, rubber asphalt exhibits 65.42% and 52.34% increases in carbonyl and sulfoxide indexes, respectively, contrasted by 49.24% and 42.5% decreases in aromatic and aliphatic indexes. Long-term exposure to salt–freeze–thaw conditions promotes phase homogenization, ultimately reducing surface roughness and causing rubber asphalt to resemble matrix asphalt morphologically. At the rubber asphalt–NaCl solution–aggregate interface, the diffusion of Na+ is faster than that of Cl. Meanwhile, compared with other asphalt components, saturates exhibit notably enhanced mobility under salt erosion conditions. The synergistic effects of accelerated aging, salt crystallization pressure, and enhanced ionic diffusion jointly induce the deterioration of interfacial bonding, which accounts for the decrease in macroscopic pull-out strength. This multiscale investigation advances understanding of salt-induced deterioration while providing practical insights for developing durable asphalt mixtures in cold regions. Full article
(This article belongs to the Section Construction and Building Materials)
21 pages, 3303 KB  
Article
Research on Intelligent Early Warning System and Cloud Platform for Rockburst Monitoring
by Tianhui Ma, Yongle Duan, Wenshuo Duan, Hongqi Wang, Chun’an Tang, Kaikai Wang and Guanwen Cheng
Appl. Sci. 2025, 15(20), 11098; https://doi.org/10.3390/app152011098 - 16 Oct 2025
Abstract
Rockburst disasters in deep underground engineering present significant safety hazards due to complex geological conditions and high in situ stresses. To address the limitations of traditional microseismic (MS) monitoring methods—namely, vulnerability to noise interference, low recognition accuracy, and limited computational efficiency—this study proposes [...] Read more.
Rockburst disasters in deep underground engineering present significant safety hazards due to complex geological conditions and high in situ stresses. To address the limitations of traditional microseismic (MS) monitoring methods—namely, vulnerability to noise interference, low recognition accuracy, and limited computational efficiency—this study proposes an intelligent real-time monitoring and early warning framework that integrates deep learning, MS monitoring, and Internet of Things (IoT) technologies. The methodology includes db4 wavelet-based signal denoising for preprocessing, an improved Gaussian Mixture Model for automated waveform recognition, a U-Net-based neural network for P-wave arrival picking, and a particle swarm optimization algorithm with Lagrange multipliers for event localization. Furthermore, a cloud-based platform is developed to support automated data processing, three-dimensional visualization, real-time warning dissemination, and multi-user access. Field application in a deep-buried railway tunnel in Southwest China demonstrates the system’s effectiveness, achieving an early warning accuracy of 87.56% during 767 days of continuous monitoring. Comparative verification further indicates that the fine-tuned neural network outperforms manual approaches in waveform picking and event identification. Overall, the proposed system provides a robust, scalable, and intelligent solution for rockburst hazard mitigation in deep underground construction. Full article
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