Search Results (501)

Structural Health Monitoring (SHM) of large-span bridges requires dense sensor networks to accurately capture dynamic and kinematic behaviors. Traditional commercial systems rely on complex wiring or wireless protocols that frequently suffer from data loss, high power consumption, and synchronization phase errors, which are detrimental to Operational Modal Analysis (OMA). To address these limitations, this study presents the design, development, and field validation of a custom, offline-synchronized multi-sensor Data Acquisition (DAQ) system. Two specialized sensor nodes were developed: (i) an inertial node integrating a low-noise triaxial MEMS accelerometer (ADXL355); and (ii) a displacement node featuring a 24-bit Analog-to-Digital Converter (ADS1220) for displacement sensors. Both nodes share an ultra-low-power microcontroller (STM32L431) and utilize a local microSD storage strategy with an intermediate pseudo-SRAM buffer. To ensure precise temporal alignment without wireless communication overhead, each node incorporates a temperature-compensated Real-Time Clock (DS3231) for offline timestamp synchronization. The system was validated during a field campaign on the Spyckstraße bridge (Germany), deploying a hardware pool of 53 physical DAQ nodes to monitor 118 distinct geometric measurement points (106 inertial, 12 displacement) through a hybrid strategy of fixed and roving setups. The proposed system achieved reliable, low-noise measurements and enabled accurate extraction of operational mode shapes, demonstrating its viability as a robust, cost-effective solution for large-scale infrastructure monitoring. Full article

Radiofrequency ablation (RFA) is commonly modeled using classical heat diffusion equations; however, growing evidence suggests that heat transport in biological tissues exhibits nonlocal and scale-dependent behavior driven by capillary perfusion. In this work, we develop a fractional diffusion framework for the computational modeling of hepatic tumor ablation based on the fractional Laplacian operator. A characteristic length scale is introduced to bridge microscale capillary effects and macroscale heat propagation, enabling the model to capture the superdiffusive thermal transport associated with hepatic vascular networks. Owing to its nonlocal nature, the fractional formulation entails significantly higher computational costs than classical diffusion models. To address this challenge, we investigate the complexity of two temporal discretization strategies: the backward Euler method with uniform time stepping and an adaptive backward–forward Euler scheme. Numerical experiments involving single- and double-probe ablation configurations demonstrate the robustness of the proposed framework and illustrate its applicability to realistic ablation scenarios. Overall, the results indicate that fractional diffusion provides a flexible and physiologically meaningful framework for modeling heat transfer during RFA. Full article

Background/Objectives: Cancer survivors admitted to inpatient rehabilitation suffer from a complex burden of interrelated physical and psychological symptoms. While mean-level improvements during rehabilitation are well-documented, it remains unknown whether rehabilitation modifies the underlying structure of symptom interconnections—the symptom network—beyond reducing individual symptom scores. This study aimed to characterize symptom network structure at admission and discharge of a 21-day inpatient cancer rehabilitation program based on cancer-related physical symptoms and psychosocial functioning, formally compare network topology across timepoints, identify structurally central treatment targets, and assess the transdiagnostic generalizability of findings. Methods: Secondary analysis of routinely collected, electronic patient-reported outcome (PRO) data from 5066 cancer survivors (mean age 60.3 years, SD 12.2; 64.2% female; most frequent diagnoses: breast cancer = 36.9%, hematological malignancies = 10.4%; prostate cancer = 8.5%) admitted to a single-center inpatient rehabilitation program was performed between January 2017 and November 2022. The EORTC QLQ-C30 and the Hospital Anxiety and Depression Scale (HADS) questionnaires were utilized. Bayesian Gaussian Graphical Models were estimated at admission (T0) and discharge (T1) across 17 symptom and functioning domains using Bayesian Model Averaging (15,000 iterations). Edge-level change was quantified via posterior distributions of pairwise differences with 95% Highest Density Intervals. Node-level changes were assessed using Bayesian paired t-tests. Centrality was quantified by Expected Influence and Bridge Expected Influence. Results: Patients showed clinically meaningful improvements across all 17 domains during rehabilitation (all Bayes Factors >10; posterior probability of direction >99.9%). The largest standardized effects were observed for emotional functioning (Cohen’s d = 0.76), global health status (d = 0.69), and fatigue (d = 0.53). These improvements were clinically meaningful for a substantial proportion of patients: 62% improved by at least the minimal important difference in fatigue and 58% in emotional functioning, and the proportion of patients with probable anxiety fell from 15% to 6% and probable depression from 10% to 4%. Emotional functioning and anxiety were the most central domains in the symptom network—most strongly connected to the rest of patients’ symptom burden—at both admission and discharge. Despite the clinical improvements, the overall architecture of symptom interconnections changed little (83% of connections were unchanged). This indicates that the severity of symptoms was mitigated while the structure linking them together remained largely intact. The one connection that strengthened was that between impaired social functioning and financial difficulties (Δ = −0.112). Structural findings were consistent across ten cancer types (leave-one-out r > 0.80 in seven of ten). Conclusions: Over the course of inpatient cancer rehabilitation, patients showed large improvements against a background of largely stable symptom network architecture. Emotional functioning and anxiety occupy structurally central positions at both admission and discharge, identifying them as candidate domains warranting further investigation for network-informed rehabilitation. These findings provide a novel structural perspective on oncological rehabilitation and a framework for developing more targeted intervention strategies. Full article

As a pilot-scale feasibility study, aging bridge infrastructure and limited inspection resources have created an urgent need for automated and reliable bridge condition assessment systems. Most existing deep learning-based inspection approaches detect damage types from images without considering the structural member on which the damage occurs, limiting their practical utility for maintenance decision-making. This study proposes a structure-aware deep learning framework for automated bridge inspection that integrates structural member segmentation, two-class damage detection, and spatial damage-to-member association within a unified pipeline. A SegFormer-based semantic segmentation model was trained on a custom bridge inspection dataset comprising 1339 images to identify three primary structural member classes—main girder, deck slab, and abutment—achieving a test mean Intersection over Union (mIoU) of 0.851. Boundary refinement using the Segment Anything Model (SAM) in mask-prompt mode was applied to improve mask precision during training data preparation. A YOLOv8s object detection model was trained on a custom bridge damage dataset of 9142 annotated images (6531 training, 1740 validation, and 871 test images) to detect two damage classes—crack and corrosion—achieving a mean Average Precision (mAP50) of 0.445 at a confidence threshold of 0.30. The framework associates detected damage with segmented structural members using a region-based spatial assignment strategy, enabling structure-aware outputs such as “crack on main girder” and “corrosion on deck slab.” Manual evaluation on 100 bridge inspection images demonstrated a fully correct damage detection accuracy of 70.0% and a fully correct member assignment accuracy of 62.0%. When partially correct predictions were additionally considered for qualitative analysis, the corresponding accuracies increased to 84.0% and 87.0%, respectively. The main girder class achieved the highest combined accuracy for both damage detection (90.9%) and member assignment (93.9%). These results demonstrate the potential of the proposed framework as a first layer for AI-assisted bridge inspection by associating detected damage with structural members, providing structured inspection information to support subsequent maintenance assessment and infrastructure monitoring. Full article

Amid the global drive for carbon peaking and carbon neutrality, integrating renewable energy into building energy systems to mitigate photovoltaic (PV) intermittency and realize low-carbon energy supply has become a critical research frontier. This study proposes a novel dual-storage renewable energy system integrating solar energy storage system (SES), hydrogen energy storage system (HES), and an alkaline fuel cell (AFC). The model was validated using a two-story single-family residence as the case study, with residential load profiles and Xi’an’s climatic conditions considered under real-world scenarios. An adaptive energy management strategy is developed to dynamically coordinate PV utilization, hydrogen dispatch, and grid interaction, while recovering AFC waste heat to enhance overall efficiency. Targeting minimized lifecycle cost (LCC) and levelized cost of energy (LCOE), the GenOpt multi-objective optimization model optimizes key design parameters. Key results show 74.2% annual renewable energy penetration, 68.5% carbon reduction versus conventional systems, and robust seasonal operation: PV dominates summer supply (81.3% self-sufficiency), while AFC compensates in winter (62.4% hydrogen contribution). The system reduces annual grid dependence by 43.7% with a minimum LCOE of ~ 12.9 USD/MWh, bridging technical feasibility and economic practicality to provide actionable insights for building-scale renewable integration. Full article

Seismic metamaterials (SMs) have emerged as a novel wave-control strategy for earthquake-resistant engineering, offering the potential to manipulate seismic waves via artificially designed periodic/resonant structures. Field and laboratory experiments are critical to bridge theoretical predictions and engineering practice, yet a systematic synthesis focusing on experimental progress remains lacking. This review systematically classifies SMs into buried (BSMs), above-surface (ASMs), and partially embedded (PESMs) configurations, summarizing their structural designs, attenuation mechanisms, experimental performance, and key limitations. Results show that SMs can achieve >70% attenuation in the 0–50 Hz seismic band, with buried periodic barriers reaching 99.7% energy blocking and forest-like ASMs achieving 93–99% Rayleigh wave reduction. PESMs exhibit superior adaptability to shallow soils, with bandgaps concentrated in 1.5–14.5 Hz (building-sensitive range). Current experiments have advanced from single mechanisms to multi-mechanism synergy and from specialized materials to conventional concrete/steel. However, critical gaps remain: scaling-induced deviations, poor complex-geology adaptability, lack of long-term durability, and insufficient multi-waveform control. Finally, we propose a 3–10-year engineering roadmap and outline future directions: multi-waveform regulation, soil–metamaterial dynamic matching, durability design, and full-scale intelligent upgrades. This work aims to provide a critical experimental reference for the practical deployment of SMs. Full article

The aging of social infrastructure, intensively constructed during periods of rapid economic growth, is a pressing challenge facing modern society. Conventional infrastructure asset management has disproportionately emphasized a “managerial financial perspective,” aiming to maintain physical functions within limited budgets. However, the malfunction of road appurtenances such as tunnel lighting facilities induces severe traffic accidents and chronic congestion, resulting in public health risks for users (physical trauma, psychological stress, and the deterioration of Disability-Adjusted Life Years: DALYs) as well as massive socio-economic losses. The primary novelty of this study lies in bridging the gap between stochastic engineering deterioration models—specifically, discrete-time Markov chain models predicting physical degradation—and socio-economic stakeholder value chains. This study constructs a “Social Life Cycle Cost (LCC) Optimization Model” that directly incorporates these social losses and stakeholder risk disparities into the evaluation function, addressing the limitations of conventional financial-centric LCC models. By conducting robust uncertainty and global sensitivity analyses via large-scale Markov Chain Monte Carlo simulations (number of trials $N={10}^5$), we reveal that a corrective maintenance strategy inheres a critical “fat-tail risk” of stochastically incurring catastrophic social losses. Conversely, preventive intervention at State C minimizes the expected total cost with statistical significance ($p<0.001$) and drastically decouples engineering costs from social risks. This research provides quantitative evidence that early infrastructure intervention functions as an indispensable “social investment” for mitigating public health risks under the specific parameters of the proposed model. Full article

Hot Rolling Production Scheduling (HRPS) is a crucial combinatorial optimization problem characterized by severe conflicts between rigid physical rolling rules and strict order due dates. While real-time scheduling is essential for dynamic manufacturing, traditional meta-heuristics suffer from severe computational time bottlenecks. Conversely, standard end-to-end Deep Reinforcement Learning (DRL) models offer rapid inference but typically struggle with spatio-temporal feature entanglement, training instability under extreme penalty landscapes, and poor zero-shot scale generalization. To bridge these gaps, this paper proposes a novel framework named Dual-Stream Group-Optimize Policy Optimization with Multiple Optima (DSGO-POMO). The framework introduces three core innovations: (1) a Dual-Stream intervention network that explicitly decouples and synergistically fuses physical attributes with temporal slacks; (2) a Group Relative Policy Optimization (GRPO) training mechanism to stabilize policy updates; and (3) an Entropy-Aware and Dual-Annealed Differential Active Search (EA-DAS) strategy to seamlessly adapt pre-trained weights to out-of-distribution scales. Extensive computational experiments validate the superiority of the proposed framework. On medium-scale instances (<!-- MathType@Translator@5@5@MathML2 (no namespace).tdl@MathML 2.0 (no namespace)@ --> Full article

Heart failure (HF) is fundamentally a disease of energetic insufficiency, in which impaired mitochondrial efficiency, maladaptive metabolic remodeling, and disrupted intercellular signaling converge at the organ level to limit cardiac performance. Despite advances in pharmacologic and device-based therapies, current treatment paradigms largely modulate hemodynamics or neurohormonal pathways rather than directly restoring myocardial bioenergetic capacity. Emerging evidence positions extracellular vesicles (EVs) as endogenous regulators of cardiac energy homeostasis, capable of orchestrating coordinated metabolic and mitochondrial adaptations across cardiac and non-cardiac cell populations. This review advances a system-level framework in which EVs are conceptualized as bioenergetic therapeutics, i.e., active biological agents that reprogram cellular energy utilization, substrate flexibility, and mitochondrial efficiency, rather than passive carriers of isolated molecular cargo. We synthesize preclinical evidence demonstrating EV-mediated modulation of oxidative phosphorylation, glycolytic balance, redox signaling, and mitochondrial dynamics, and examine how these effects scale from cellular and small-animal models to clinically relevant heart failure phenotypes. Importantly, we highlight organ-level integration, wherein EV signaling interfaces with vascular, immune, and metabolic networks to reshape myocardial energetic demand and supply. By bridging mechanistic insights with translational considerations, this review addresses the central question of how EV-driven bioenergetic reprogramming can be deployed within contemporary HF treatment paradigms. We propose EV-based strategies as complementary or synergistic interventions capable of restoring energetic resilience, reframing heart failure therapy beyond structural repair toward systemic metabolic renewal. Full article

Atmospheric CO₂ concentrations have reached significant levels during the industrial era, necessitating the implementation of effective carbon dioxide removal (CDR) technologies. Enhanced Rock Weathering (ERW) using basalt has emerged as a high-potential strategy, leveraging its mafic composition to sequester CO₂ as stable carbonates. This review analyzes ERW’s geochemical processes, application methods, and multifaceted co-benefits, such as restoring “background fertility” and improving soil structure. The literature indicates that while small-scale applications range from 1.5 to 6 Mg·ha⁻¹·yr⁻¹, intensive agricultural rates typically reach 40–100 Mg·ha⁻¹·yr⁻¹. Global models estimate a sequestration potential of up to 4.9 × 10⁹ Mg CO₂·yr⁻¹ for basalt, although field-scale results vary significantly, reaching uptake rates of up to 4 Mg CO₂·ha⁻¹ depending on pedological conditions and crop types. Despite this promise, transitioning to large-scale deployment faces critical hurdles, including operational difficulties in mechanized spreading and a scarcity of audited, long-term field data. Future research must prioritize standardized protocols and comprehensive economic analyses to bridge the gap between theoretical models and empirical evidence. Ultimately, ERW represents a multifaceted solution for climate stabilization and sustainable food security, provided that sequestration efficacy and environmental safety are rigorously verified through high-application field trials. Full article

Dielectric permittivity describes how a material becomes polarized in response to a time-varying electric field and provides a powerful framework for probing the physical organization of biological systems. This review aims to clarify the origin of dielectric permittivity in plants, offering a conceptually grounded interpretation while keeping mathematical formalism to the level necessary for biological interpretation. We first outline the fundamental mechanisms of polarization, their characteristic time scales, and the frequency-dependent nature of the dielectric response, including the concept of complex permittivity, together with commonly used measurement approaches in biological materials. Particular attention is given to water, whose dielectric properties play a dominant role in plant tissues. We then examine how permittivity varies across different plant organs, including leaves, fruits, and roots, highlighting the relationship between dielectric response and structural and compositional features. Modeling strategies linking microscopic organization to macroscopic dielectric behavior are also discussed. Because dielectric permittivity is intrinsically connected to plant structure and composition, non-invasive measurements offer significant potential for assessing plant physiological status, including the detection of changes induced by abiotic and biotic stresses. By bridging engineering approaches with plant physiology, this review provides a unified framework to interpret dielectric measurements in plants and supports their application in plant science and phenotyping. Full article

This study explores the multifaceted attitudes of high school students toward the use of artificial intelligence (AI) and large language models (LLMs) like ChatGPT in educational contexts. Drawing upon a tripartite model of attitudes, our research evaluates affective, cognitive, and behavioral dimensions to offer a nuanced understanding of students’ perceptions. The affective dimension assesses emotional responses to AI tools, the cognitive dimension examines beliefs about the utility and ethical considerations of AI, and the behavioral dimension evaluates actual usage patterns of AI technologies. Utilizing a newly developed survey instrument tailored for the educational context, data was collected from 93 high school students across different regions of Italy in the period that ranged from February 2024–March 2024. Exploratory factor analysis (EFA) was employed to explore the underlying structure of the survey instrument and identify underlying factors influencing AI acceptance. The analysis reveals three distinct factors—Mindful AI Learning, Embracing AI Effects, and LLM as Learning Companion, highlighting the complexity of students’ attitudes toward AI. Results indicate a cautious but optimistic reception of AI in education, offering crucial insights into Information Intelligence for enhanced learning and the design of personalized learning pathways. The study contributes to the literature by offering a novel scale to measure attitudes toward artificial intelligence, specifically focusing on both general AI and Generative AI large language models, such as ChatGPT. Moreover, it highlights the critical need for AI literacy, ethical digital learning frameworks, and robust institutional policies to bridge the digital divide. Consequently, this work is framed as a preliminary exploratory investigation. Ultimately, these findings advance our knowledge of transformative digital learning processes and inform future strategies for human–machine integration in educational systems. Full article

The Vortex Ring State (VRS) poses a catastrophic aerodynamic threat to coaxial dual-rotor unmanned aerial vehicles (UAVs). Traditional reactive detection mechanisms provide insufficient altitude for recovery, while existing data-driven diagnostics are severely bottlenecked by data leakage, extreme class imbalance, and a lack of physical interpretability. To bridge these gaps, this paper proposes a physics-informed multimodal deep learning framework that transitions from post-occurrence detection to proactive early warning. We establish a 1.5 s precursor window—creating a three-class ordinal state space—to provide the flight control system with critical intervention time for differential rotor recovery. We developed a novel ViT-LSTM architecture (MTSF-Net) to fuse continuous seven-channel onboard-recorded data (comprising three-axis acceleration, three-axis angular velocity, and barometric vertical velocity), which are subsequently transformed into Continuous Wavelet Transform (CWT) spectrograms. To ensure real-time unidirectional inference while preserving absolute physical vibration scales across heterogeneous sensors, a Calibrated Benchmark Normalization (CBN) strategy is introduced. Furthermore, a Hybrid Ordinal Loss is proposed to mitigate the extreme sample imbalance (<0.5%) of the precursor state by penalizing asymmetric aerodynamic degradation. Evaluated under a strict sortie-based isolation protocol, the proposed system achieves an exceptional test accuracy of 98.26% and an unprecedented precursor recall of 100%. Notably, it completely eliminates fatal missed detections (VRS predicted as Normal) and false-positive VRS predictions triggered by precursor states. Finally, Gradient-weighted Class Activation Mapping (Grad-CAM) is utilized to verify that the multimodal sensor processing pipeline successfully anchors onto authentic physical vibration frequencies rather than artifactual noise, laying a rigorous, interpretable foundation for intelligent aviation safety systems. Full article

Early-age temperature rise in mass concrete can generate substantial thermal gradients and increase the risk of cracking. In this study, a temperature-rise suppression concrete was developed by partially replacing conventional coarse aggregate with steel-encapsulated superabsorbent polymer (SAP)–water phase-change aggregates. Semi-adiabatic temperature-rise tests were conducted to characterize the early-age thermal response, and the corresponding adiabatic temperature-rise histories were reconstructed using a heat-loss compensation method. The results showed that the incorporation of steel-encapsulated SAP–water aggregates reduced the temperature rise and delayed the thermal peak under semi-adiabatic conditions. For SAP-15, the peak core temperature in the validated finite element simulation decreased from 51 °C to 44 °C, while the maximum adiabatic temperature rise decreased to 40.5 °C. Engineering-scale simulation of a bridge pile-cap foundation further showed reductions in internal peak temperature, temperature difference, and thermal stress. These findings demonstrate that steel-encapsulated SAP–water phase-change aggregates provide an effective material-based strategy for moderating early-age thermal accumulation and mitigating thermal cracking risk in mass concrete. Full article

The increasing number of extreme events affecting buildings and strategic infrastructures in mountain areas requires reliable approaches for territorial risk assessment with respect to snow avalanches. Considering risk as the combination of hazard, vulnerability, and exposure factors, a simplified framework—recently adopted in Italian national guidelines—is proposed. Avalanche hazard is defined by considering the intrinsic avalanche susceptibility of the territory under investigation, typically described by means of hazard intensity maps. On the other hand, the vulnerability of the construction is determined by considering both the physical, or structural, vulnerability of the building and the functional vulnerability of network systems. Finally, the exposure level accounts for the direct and indirect losses resulting from the hazardous event, based on the typology, use, and potential occupancy of the building or infrastructure. A weighted classification system combining these three factors is adopted to derive risk matrices, in which the risk class of each exposed construction is defined across five levels (high, medium–high, medium, medium–low, low), thus enabling a hierarchical risk classification at the territory scale. The methodology is intended to bridge technical risk assessment and territorial governance, offering an operational decision-support tool for policymakers, emergency planners, and infrastructure operators to support resource allocation and mitigation strategies. Full article