Search Results (437)

This study proposes an enhanced physics-informed neural network (PINN) framework for predicting seepage fields around deeply buried tunnels with limited field measurements. Hard-constrained boundary enforcement via distance-function trial functions is introduced to exactly satisfy Dirichlet conditions on both the ground surface and tunnel perimeter, and Bayesian optimization automates loss weight tuning to replace costly manual calibration. A systematic evaluation of 15 sensor placement schemes demonstrates that the hydraulic head variance across monitoring points, governed by radial coverage distance, is the primary determinant of prediction accuracy—not the number of sensors or angular density. Remarkably, a strategically designed 12-point configuration outperforms 100 randomly distributed points under the idealized conditions studied, confirming that placement quality can dominate over quantity when physics-informed optimization is applied. Transfer learning experiments across 132 geometric configurations reveal a previously unreported geometric transition zone at D/R ≈ 13–15, where prediction errors exhibit a distinct non-monotonic peak. Finite element benchmarking confirms that this error peak stems from the learning characteristics of PINNs under competing boundary influences rather than from the physical complexity of the problem itself. High-density sampling effectively suppresses this peak error by 32% compared with sparse sampling. These findings establish quantitative sensor deployment guidelines for tunnel seepage monitoring and identify fundamental performance boundaries of physics-informed machine learning under geometry–physics coupling. Full article

The railway track system is a complex assembly of rails, sleepers, and fastenings designed to ensure operational stability and safety. Within this framework, rail pads play a critical role in load transfer, vibration attenuation, and noise control. This study provides a comprehensive bibliometric analysis of research on railway components published between 2015 and 2024, based on 288 documents retrieved from Scopus, Elicit, and Web of Science. Publication trends reveal a steady increase in research output over the study period, primarily driven by Spain and China. Keyword co-occurrence analysis yielded 51 keywords organized into seven thematic clusters, with the highest frequency terms being “rail pad”, “noise”, “dynamic property”, and “MTHDRP”. The analysis highlights a significant focus on materials such as TPEs, EPDM, and EVA, with static preload and stiffness identified as the most scrutinized performance factors. Findings indicate a clear thematic shift from traditional field testing toward advanced material science and sensor-integrated monitoring technologies. Ultimately, this review outlines future research trajectories emphasizing sustainability, smart sensor integration, and predictive maintenance. By synthesizing a decade of academic contributions, this study serves as a strategic roadmap for optimizing the long-term durability and efficiency of modern railway infrastructure. Full article

This review paper presents a comprehensive and system-oriented analysis of advanced non-destructive testing (NDT) technologies for metal additive manufacturing (AM), including X-ray computed tomography (XCT), ultrasonic testing (UT), infrared thermography, acoustic emission (AE), and electromagnetic techniques. While the existing literature often focuses on the physical principles of individual NDT methods, this work addresses a critical knowledge gap by analyzing NDT as a digitally integrated “quality intelligence layer” rather than a standalone post-process inspection tool. The primary motivation is to bridge the disconnect between raw inspection data and cyber–physical production systems. Particular focus is given to NDT data analytics and digitalization, where machine learning (ML) and digital twin (DT) integration are discussed as fundamental enablers of intelligent manufacturing. The review systematically examines image and signal processing pipelines required for quantitative defect characterization, highlighting challenges related to voxel resolution, signal-to-noise ratio, anisotropic microstructures, and operator dependency. It further analyzes supervised learning, deep learning, and multi-sensor data fusion approaches for automated defect classification and predictive quality assessment. Furthermore, the role of digital twins in coupling in situ monitoring data, ex situ NDT results, and physics-based models is discussed as a transformative pathway toward closed-loop process control and evidence-based certification. By synthesizing NDT science with digital manufacturing architectures, this review contributes a unique framework for transitioning from traditional inspection-centric quality control to a predictive, adaptive, and digital twin-enabled quality assurance paradigm. The work concludes by identifying key research gaps in data standardization and computational scalability, providing a strategic roadmap for the future of smart AM production. Full article

This work explores the key capabilities of emerging sensing technologies in the context of Structural Health Monitoring (SHM) of civil infrastructures, aiming to contribute to research on integrated and intelligent systems for more accessible and efficient monitoring solutions. As a case study, it focuses on the analysis of the static and dynamic behavior of the Edgar Cardoso stay-cable bridge during its rehabilitation, using fully customized transducers and equipment. The developed system integrates sensors capable of measuring accelerations, displacements, and temperature, which are connected to an autonomous data acquisition and transmission network. A digital interface was also developed to store, process, and visualize the collected data, enabling remote access for subsequent interpretation and analysis. The main contribution of this research lies in the use of optimized wireless monitoring systems with extended autonomy. This is achieved by employing edge computing techniques to minimize energy consumption during data transmission, as well as by managing the sleep modes of the sensor nodes. At same time, a methodology was proposed for the automatic and real-time estimation of axial forces in cables. This approach relies on the use of innovative edge computing tools, combined with the taut string theory as a simplified modelling framework. The results confirm the effectiveness of the developed system in achieving long-term operation without compromising monitoring performance. In addition, the developed system enabled the identification of the structure’s dynamic properties, particularly natural frequencies. The temperature profiles in critical sections, as well as displacements in the expansion joint were also measured and evaluated. The results demonstrate the potential of customized sensing solutions as effective tools for the management, maintenance, and long-term preservation of strategic infrastructures. Full article

Wireless sensor networks (WSNs) are the backbone of IoT-enabled smart manufacturing, environmental monitoring, and industrial automation. However, their broadcast nature makes communication links vulnerable to eavesdropping, routing manipulation, and denial-of-service attacks. Strategically placing monitor nodes to check each link is an effective approach to protect against attacks, but energy, connectivity, and capacity constraints should be considered while picking monitor nodes. In this paper, we tackle the Minimum-Weighted Connected Capacitated Vertex Cover (MWCCVC) problem, which minimizes monitoring costs, ensures backbone connectivity, and adheres to per-node capacity constraints. Unlike prior works that consider weighted vertex cover, connectivity constraints, or capacitated variants separately, the proposed MWCCVC model jointly integrates all three dimensions within a single vertex cover-based monitoring framework. We first provide a Branch-and-Bound (B&B) solver with linear programming relaxation bounds and constraint-based pruning strategies that produces optimum solutions. Three constructive greedy heuristics (GD, GR, GW) and two hybrid genetic algorithms (HGA, HGA-v2) that combine parameterized greedy decoders with evolutionary search are proposed; all methods guarantee full edge coverage, induced-subgraph connectivity, and max-flow-validated capacity feasibility. Tests on 130 small, 160 medium, and 19 large benchmark instances show that HGA matches B&B optima on every small instance, beats the time-limited B&B by 6.6% on medium instances, where the percentage is computed based on the relative difference in average total weight with respect to B&B, and stays the best on large graphs with up to 1000 nodes. The HGA-v2 tries to balance the quality and speed, with only a 3.1% difference at $10\times$ faster execution. Full article

Smart insoles equipped with pressure sensor matrices are increasingly used for gait analysis, yet high-density arrays compromise battery life and data throughput. This study aims to identify the optimal sparse sensor layout required to accurately estimate the Center of Pressure (CoP) by analyzing the trade-off between sensor number, spatial placement, and reconstruction error. Plantar pressure data were collected from twelve healthy participants walking at a self-selected speed using 16-sensor connected insoles. A combinatorial algorithm evaluated all $2^{16}-1$ possible sensor combinations to minimize the Root Mean Square Error (RMSE) in the antero-posterior, medio-lateral, and global Euclidean directions. Results reveal a non-linear convergence of accuracy that depends on the spatial axis. For longitudinal and global progression, a clear inflection point achieving sub-centimetric accuracy (RMSE < 5 mm) is reached at seven sensors. In contrast, medio-lateral tracking shows its largest discrete error reduction at five sensors, followed by gradual improvements at higher densities. Anatomical frequency analysis highlights distinct spatial requirements: the posterior heel is consistently selected for medio-lateral accuracy, while the lateral arch and metatarsal regions are critical for longitudinal progression. These findings suggest that while a minimum of seven strategically placed sensors enables robust CoP tracking across all spatial axes, optimal hardware design should remain task-specific. This work provides a data-driven framework for the development of energy-efficient wearable gait monitoring systems. Full article

With the deep integration of AI and IoT technologies, smartwatches have become core terminals for health management. However, research on the use mechanisms among “digital native” college students remains limited. Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and selected constructs from the Health Action Process Approach (HAPA), this study uncovers the drivers and barriers of youths’ smartwatch health function adoption to propose targeted design strategies. A mixed-methods approach was employed, collecting semi-structured questionnaire data from 226 Chinese college students. Quantitative analysis was conducted (n = 106) using Partial Least Squares Structural Equation Modeling (PLS-SEM), complemented by qualitative text mining of open-ended feedback from non-users and churned users. The model demonstrated robust predictive power, supporting five hypotheses. Habit and action planning emerged as core antecedents of use intention, which significantly promoted actual use behavior. Effort expectancy acted as a baseline hygiene factor positively influencing performance expectancy. Qualitative findings confirmed that insufficient sensor accuracy and “health data anxiety” are critical psychological barriers. Validating the integrated model’s effectiveness, we propose three strategic interventions: enhancing data precision to build trust, implementing tiered pricing, and designing anxiety-alleviating visual interfaces, offering theoretical and empirical foundations for optimizing smart health products. Full article

Ship collision accidents remain a critical concern in maritime safety because of their potential to cause operational disruption as well as environmental and economic damage in areas with dense shipping activity. Complex traffic interactions, differences in vessel characteristics, and dynamic environmental conditions make collision risk increasingly difficult to manage using traditional navigation measures alone. This paper presents a structured review of ship collision research, focusing on collision impacts, collision avoidance strategies, risk assessment methodologies, and safety index development. The review synthesizes reported collision cases and their environmental consequences, examines commonly used analytical frameworks including probabilistic, data-driven, and multicriteria approaches, and discusses recent developments in AIS-based analysis, sensor-based monitoring, and intelligent prediction techniques. The analysis identifies several methodological gaps in existing studies. Collision avoidance methods and risk assessment models are often developed independently, while their integration with safety index frameworks remains limited. In addition, safety index formulations differ considerably in terms of indicator selection and modeling approaches, which reduces comparability between studies conducted in different waterways. The findings highlight how different analytical approaches contribute to maritime safety evaluation at strategic, operational, and real-time levels and provide insights for developing more integrated safety assessment frameworks to support navigation risk monitoring in high-traffic maritime environments. Full article

Background: Sensors and mass spectrometry (MS) are frequently used in combination for food safety and quality assessment, yet their functional integration lacks a formal methodological framework. This review categorizes the synergies between these technologies into distinct Relational Connections. Methodology: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 155 original research articles published between 2015 and 2025 were systematically analyzed. Records were identified via the Scopus database within the food science domain. Experimental meta-data, including extraction protocols, instrumental configurations (ionization source, mass analyzer, cost tier), and chemometric strategies, were extracted to identify core methodological patterns. Statistical associations were quantified using chi-squared tests with Cramer’s V effect sizes. Results: Five Relational Connections were identified: (1) MS as reference for sensor validation (25.2%); (2) MS-sensor correlative analysis (10.3%); (3) MS quantifying data to train predictive sensor models (6.5%); (4) MS identifying targets for sensor detection (7.1%); and (5) MS enabling sensor classification models (51.0%). Technology pairing is governed by a three-level hierarchy: analyte polarity determines the ionization source (V = 0.69), required precision determines the mass analyzer (V = 0.64), and cost/availability constraints shape the practical integration strategy. Gas Chromatography (GC)-MS is predominantly coupled with Electronic Noses for volatile profiling (86% of classification studies), while Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) pairs with biosensors for contaminant analysis (74% of reference validation studies). Systematic analysis of the full pairing matrix reveals that 75% of theoretically possible MS-sensor combinations remain unexplored or underrepresented, identifying both technical boundaries and innovation frontiers. Discussion: The findings clarify the strategic logic behind technology pairings, demonstrating that MS provides the quantitative molecular data required for sensor training. The hierarchical decision framework and identification of underexplored pairings provide an evidence-based guide for designing future integrated food analysis systems. Full article

Glyphosate is the most widely used herbicide worldwide, but many current detection methods rely on lab-based chromatography, requiring costly equipment and expert users. Here, we describe a low-cost, field-deployable fluorescence biosensing platform for glyphosate detection in water and soil. An engineered variant of the Escherichia coli periplasmic binding protein PhnD was optimized through strategic fluorophore placement to produce a robust fluorescence signal increase upon glyphosate binding. The biosensor was integrated into a self-contained, 3D-printed device that functions as a miniature fluorometer, providing a simple yes-or-no output for non-expert users while retaining access to raw fluorescence data. The device exhibits nanomolar fluorescence sensitivity with results comparable to a benchtop fluorometer. Using this platform, glyphosate was reliably detected in buffered solutions, commercial herbicides, tap water, and soil extracts. To mitigate false positives arising from phosphate interference, we developed a dual-sensor strategy incorporating an independent phosphate biosensor and a second-generation device capable of multi-wavelength fluorescence detection. Together, these results demonstrate an affordable and versatile biosensing platform with strong potential for field-based environmental monitoring. Full article

Vulnerabilities in conventional agri-food supply chains (CAFSCs) necessitate a shift toward resilient, localized production models. Within the Agri-Food 4.0 landscape, urban Controlled-Environment Hydroponic Agriculture (CEHA) systems address these challenges by shortening supply chains and mitigating climate-induced breakdowns. However, structurally aligning Triple Bottom Line (TBL)-oriented stakeholder needs with complex technical specifications remains a critical challenge in sustainable CEHA system design. To address this challenge, the present study proposes a novel framework integrating the Full Consistency Method (FUCOM) and Quality Function Deployment (QFD) within a Finite Interval Type-2 (FIT2) Gaussian fuzzy environment. This approach systematically translates TBL-oriented priorities into precise engineering specifications, mapping 17 stakeholder needs (SNs) to 30 technical design requirements (TDRs) while capturing linguistic uncertainty and hesitation. The findings reveal a clear strategic focus on environmental and social sustainability. Specifically, high product quality, food safety and traceability, consumer acceptance, and minimization of environmental impacts emerge as the primary drivers of CEHA adoption. The QFD translation identifies scalable IoT infrastructure, sensor maintenance and calibration, and AI-enabled decision support as the most critical TDRs. The framework’s reliability and structural robustness were rigorously validated through comprehensive analyses, including Kendall’s W test to confirm expert consensus, alongside a Leave-One-Out (LOO) approach, weight perturbations, and a structural evaluation of TDR intercorrelations. These findings provide a scientifically grounded roadmap for designing sustainable, intelligent urban agricultural systems. Ultimately, this framework offers actionable managerial implications for agribusiness stakeholders to bridge strategic TBL-oriented goals with practical engineering, significantly enhancing agri-food supply chain resilience. Full article

Industrial maintenance and predictive diagnostics constitute fundamental pillars of modern manufacturing that prevent equipment failures, minimize operational downtime, and optimize maintenance costs across diverse industrial environments. Vibration-based fault classification plays an important role in industrial operations, necessitating highly sophisticated diagnostic methodologies. This research addresses these industrial imperatives through a comprehensive investigation of novel hybrid deep learning architectures for vibration-based fault classification. This study introduces a strategic integration of Quadratic Neural Networks (QNNs), which demonstrate superior non-linear feature extraction capabilities on a vibration signal compared to traditional convolutional approaches. A systematic evaluation of seven sophisticated architectures establishes a clear performance hierarchy, with QuCNN-LSTM-Transformer emerging as the optimal model achieving 99.26% average accuracy. All proposed models demonstrate excellence, with test accuracies consistently surpassing 95% across all evaluated scenarios. The data analyzed is emprical utilizing sensor data collected from an experimental rig and shows exceptional performance consistency on CWRU and HUST datasets. This investigation establishes a new paradigm in intelligent diagnostics, offering functional guidance and definitive analysis of hybrid architectures that advance industrial fault classification applications. Full article

As wildfires intensify globally due to climate change, accurate wildfire danger forecasting systems have become essential for effective disaster management and early warning. Fuel Moisture Content (FMC), defined as the ratio of water mass to dry fuel mass, plays a critical role in determining ignition probability and fire spread dynamics. This study conducts a comparative analysis of five major national wildfire danger rating systems: the National Fire Danger Rating System (NFDRS, USA), Canadian Forest Fire Danger Rating System (CFFDRS), European Forest Fire Information System (EFFIS), Australian Fire Danger Rating System (AFDRS), and the Korean Forest Fire Danger Rating System (KFDRS). Using a multi-criteria comparative framework, the systems were evaluated based on fuel classification structure, input variables, modeling approach, and spatiotemporal prediction resolution. The results reveal substantial disparities in spatial resolution (100 m to district-level), temporal resolution (hourly vs. daily), and fuel moisture modeling approaches (physics-based, index-based, and hybrid systems). Specifically, NFDRS and AFDRS provide high-frequency forecasting with hourly temporal resolution, operating at spatial resolutions of 1 km and 100 m, respectively, and incorporating dynamic fuel moisture modeling. In contrast, CFFDRS and KFDRS primarily rely on daily index-based predictions. Furthermore, while many global systems increasingly leverage remote sensing and machine learning for real-time FMC estimation, South Korea’s KFDRS remains predominantly empirical and weather-driven. The analysis identifies critical limitations in the KFDRS, including coarse spatial resolution (district-level), limited integration of Live Fuel Moisture Content (LFMC) modeling, and the lack of AI-augmented hybrid approaches. Accordingly, this study proposes a phased three-stage policy roadmap (2026–2035), emphasizing sensor-network expansion, AI–physics fusion modeling, and high-resolution (10 m) FMC mapping to enhance forecasting accuracy in complex terrains. These findings provide strategic insights for improving wildfire risk management and supporting the transition from reactive response to predictive wildfire forecasting under increasing climate variability. Full article

This review focuses on electromagnetic imaging methods widely used in urban geophysics and civil engineering. The rapid growth of the urban population and the increase in the frequency of extreme events related to climate change make novel approaches to the geophysical monitoring of urban areas and civil infrastructures essential in the context of programs for the sustainability and resilience of cities. In this scenario, there is a growing interest in using ground-based electromagnetic methods to investigate strategic infrastructures such as bridges, tunnels, dam embankments, power plants, energy plants and pipelines in a non-invasive way. The development of cost-effective, user-friendly sensor arrays, robust methodologies for tomographic data inversion, and AI-based and machine learning techniques has rapidly transformed these methods. This review critically analyzes the results relating to the application of ground-based electromagnetic methods in infrastructure monitoring and surveillance over the past 20 years by presenting a selection of best practice examples and studies planned to support programs for the resilience and maintenance of engineering infrastructures. The analysis reveals that these methods are highly effective in addressing a broad spectrum of monitoring issues in view of effective maintenance of civil infrastructures. In fact, these methods are essential for detecting the geometry of buried objects (e.g., bars and voids), enabling the early detection of degradation phenomena, and mapping water infiltration processes inside structures, as well as many other challenging applications. Finally, prospectives for development are identified in terms of using soft robot technologies, miniaturized sensors, and AI-based methods to acquire, process and interpret data as well as to design smart operational guidelines for infrastructure management. Full article

Rare earth elements (REEs) play an important role in emerging renewable energy technology, the production of advanced materials, energy conservation, and high-end manufacturing industries, making them an irreplaceable strategic resource. The diagnostic spectral absorption features of REEs in the visible and near-infrared spectrum can be effectively used for identifying the occurrences of REEs on the Earth’s surface. This study systematically compared three airborne hyperspectral sensors—HyMap, CASI-1500h, and AisaFENIX 1K—for detecting REEs in the Bayan Obo area of Inner Mongolia, China. The CASI-1500h imagery performed most effectively in identifying the locations of REEs among the three sensors evaluated here. Additionally, this study proposed a hyperspectral workflow for REE identification, which enabled the detection of REE-bearing minerals regardless of the host rock types—including carbonatites and associated dikes, fenite-syenites, and metamorphic feldspar-quartz sandstone. Laboratory-based spectroscopy and mineral chemistry analyses indicated that the absorption features of the REE-bearing mineral monazite within the 400–1000 nm range can be ascribed to Nd³⁺. This study demonstrates the potential of airborne hyperspectral technology for efficient and large-scale exploration of REE deposits. Full article