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performance analysis of discrete networked control systems (NCSs) based on the FlexRay protocol (FRP) when confronted with randomly occurring cyber attacks (ROCAs). In order to deal with network congestion due to the limited bandwidth, the FRP is used to schedule the information exchange. Besides, a comprehensive attack model is built by simultaneously considering false data injection (FDI) attacks and denial-of-service (DoS) attacks. Then, a mode-dependent output feedback controller is designed on this basis. Furthermore, sufficient conditions for the MSES and

H_{\infty}

performance of the considered system are derived under the mode-dependent Lyapunov function and average dwell time (ADT) constraints. Subsequently, the controller gains of two modes are determined by solving the recursive linear matrix inequalities (RLMIs) and the FRP-based MSES algorithm is also presented. The simulation verifies that the proposed algorithm maintains the system stability with good robustness and

H_{\infty}

performance under ROCAs. Full article

(This article belongs to the Section E: Applied Mathematics)

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24 pages, 14236 KB

Open AccessArticle

Ni-Based Coatings on Molybdenum: Influence of Current Density and Basalt on Mechanical Properties and Forensic Relevance

by Ivana O. Mladenović, Vladislav Jovanov, Željko Radovanović, Vera Obradović, Rastko Vasilić, Radmila Jančić-Heinemann and Nebojša D. Nikolić

Metals 2025, 15(11), 1219; https://doi.org/10.3390/met15111219 (registering DOI) - 2 Nov 2025

Abstract

Ni and Ni/basalt (Ni/Bst) coatings prepared by the electrodeposition on Mo substrate were analyzed with the aim of their potential application in forensics. The coatings of Ni and Ni/Bst are produced galvanostatically from the sulfamate electrolyte at different current densities and characterized by scanning electron microscope (morphology), X-ray diffraction (structure) and Vickers microindentation (microhardness). The wettability of Ni and Ni/Bst coatings was also investigated. While morphology and microhardness of the coatings strongly depended on the current density of electrodeposition and the presence of basalt particles in the electrolyte, the effect of basalt addition on structure of the coatings was not observed. The microhardness of Ni coatings was in the (1.6951–5.7246) GPa range, while the addition of basalt particles increased the range to (5.8206–10.7981) GPa. Both Ni and Ni/Bst coatings were hydrophilic, whereas comparison of the coatings obtained at the same current density showed that incorporation of the basalt particles in the coating decreases the degree of hydrophilicity, as observed by the increase in the water contact angle (WCA). The largest WCA, i.e., the smallest hydrophilicity, showed Ni/Bst coating produced at 30 mA cm⁻² (WCA ≈ 75.5°), and was about 46.7% larger than that of Mo substrate (WCA ≈ 51.5°). This coating also showed the best development of latent fingerprints with clearly visible ridge details, indicating that there is strong correlation between fingerprint development and the wettability of the coatings. Full article

(This article belongs to the Section Powder Metallurgy)

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29 pages, 1323 KB

Open AccessArticle

Research on Surface Water State for Rivers in Western Ukraine Using Time Series Forecasting Methods

by Leonid Bytsyura, Nina Szczepanik-Scislo, Oksana Desyatnyuk, Natalya Shakhovska, Lukasz Scislo, Anatoliy Sachenko, Olena Lototska, Ihor Shevchuk and Oksana Sofinska

Water 2025, 17(21), 3148; https://doi.org/10.3390/w17213148 (registering DOI) - 2 Nov 2025

Abstract

This study presents a data-driven forecasting framework for surface water state trends using time-series modelling based on hydrochemical monitoring data from the Ikva River (Ukraine). The monitoring campaign, conducted between 2021 and 2023, involved monthly sampling of 19 hydrochemical indicators at two sites. We applied the Prophet time series forecasting algorithm, a decomposable additive model, to predict key indicators, including water hardness and bicarbonate concentration. The approach provides a transparent and adaptable method for forecasting water state in data-limited contexts. Key contributions include the integration of high-resolution hydrochemical monitoring with an explainable machine learning model, enabling early warning insights in under-monitored river basins. The case study of best-performing models for hydrocarbonate and hardness confirmed that Prophet offered well-calibrated prediction intervals with rapid deployment, high interpretability, and dependable uncertainty estimation, though its forecasts were comparatively less accurate. Analysis of computational performance shows that Prophet enables faster implementation and quick insights, while ARIMA and LSTM achieve higher predictive accuracy at the cost of longer execution times. Results demonstrate strong predictive skill: for hardness, MAE = 1.64 and RMSE = 1.73; for bicarbonate, MAE = 54.82 and RMSE = 62.00. Coverage accuracy of 95% prediction intervals exceeded 91% for both indicators. The proposed approach provides a practical foundation for implementing early-warning systems and supporting evidence-based water resource management in regions lacking real-time monitoring infrastructure. Full article

(This article belongs to the Special Issue Water Quality Monitoring and Prediction Using New Sensors, Machine Learning and Big Data)

23 pages, 1910 KB

Open AccessFeature PaperReview

High-Dimensional Numerical Methods for Nonlocal Models

by Yujing Jia, Dongbo Wang and Xu Guo

Mathematics 2025, 13(21), 3512; https://doi.org/10.3390/math13213512 (registering DOI) - 2 Nov 2025

Abstract

Nonlocal models offer a unified framework for describing long-range spatial interactions and temporal memory effects. The review briefly outlines several representative physical problems, including anomalous diffusion, material fracture, viscoelastic wave propagation, and electromagnetic scattering, to illustrate the broad applicability of nonlocal systems. However, the intrinsic global coupling and historical dependence of these models introduce significant computational challenges, particularly in high-dimensional settings. From the perspective of algorithmic strategies, the review systematically summarizes high-dimensional numerical methods applicable to nonlocal equations, emphasizing core approaches for overcoming the curse of dimensionality, such as structured solution frameworks based on FFT, spectral methods, probabilistic sampling, physics-informed neural networks, and asymptotically compatible schemes. By integrating recent advances and common computational principles, the review establishes a dual “problem review + method review” structure that provides a systematic perspective and valuable reference for the modeling and high-dimensional numerical simulation of nonlocal systems. Full article

(This article belongs to the Special Issue Advances in High-Dimensional Scientific Computing)

18 pages, 1831 KB

Open AccessArticle

Decentralized Robust Direct MRAC via e-Modification for the Pose of a Quadrotor UAV

by Francisco Jurado and Edmundo Javier Ollervides-Vazquez

Appl. Sci. 2025, 15(21), 11713; https://doi.org/10.3390/app152111713 (registering DOI) - 2 Nov 2025

Abstract

In this work, a decentralized robust direct model reference adaptive controller (MRAC) via e-modification is suggested for the pose control of a quadrotor to prevent parameter drift. The governing equations of motion referred to the rotational system of the quadrotor are parameterized involving matched uncertainties through the control input channel in a decentralized way from the angles of motion, where bounded perturbations are also considered. An error-dependent damping term in the update law is added to enforce robustness. Uniform ultimate boundedness of the tracking error signal is ensured. The translational dynamics are governed through a linear proportional–integral–derivative (PID) control. The performance of the decentralized robust MRAC scheme proposed here is assessed via simulation and compared with that from decentralized robust MRACs using smooth dead-zone modification and

σ

-modification. Full article

(This article belongs to the Section Robotics and Automation)

16 pages, 487 KB

Open AccessReview

Antidiabetic Properties of the Tropical Tree Schinus molle L. (pirul): A Comprehensive Review

by Rosa María Fonseca and Maira Huerta-Reyes

Pharmaceuticals 2025, 18(11), 1661; https://doi.org/10.3390/ph18111661 (registering DOI) - 2 Nov 2025

Abstract

The need for new medications to treat diabetes mellitus (DM) is a global health concern due to the cost and impact on patients and their families, health systems, and society. Recent approaches in drug development have focused on multitarget therapy for DM, considering its multifactorial and complex pathophysiology. The present work contributes to the review of the plant species Schinus molle L. (pirul), a tropical tree native to South America but now widespread worldwide, which has demonstrated anticancer, analgesic, antibacterial, and insecticidal properties. According to traditional uses, pirul has been employed as a food condiment, in the preparation of beverages and chewing gums, and in the treatment of DM. The antidiabetic effects of pirul appear to act through several mechanisms involved in DM. The methanolic extract of S. molle fruits collected in Tunisia exhibited a dose-dependent inhibition on both α-amylase and α-glucosidase enzymes (77.49% and 86.45%, respectively). A dose-dependent anti-inflammatory effect was also observed at 1, 2, 3, 4, and 5 h, in the carrageenan-induced rats’ paw edema model. Furthermore, in both the H₂O₂ and the superoxide radical assays, the pirul extract demonstrated moderate antioxidant activity (IC₅₀ = 0.22 mg/mL). Isomasticadienonic acid and Masazino-flavanone, the major components of active fractions and extracts of S. molle represent promising antidiabetic agents. Although pirul appears to be safe in in vivo acute and subchronic administrations, toxicological studies and clinical trials in individuals with DM are still pending. Full article

(This article belongs to the Special Issue Advances in Natural Products: Basic, Therapeutic, and Computational Approaches in Chronic and Infectious Diseases)

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17 pages, 703 KB

Open AccessReview

Neuroplasticity Across the Autism–Schizophrenia Continuum

by Evangelia Kesidou, Nikolaos Mitsoudis, Olympia Damianidou, Charilaos Taloumtzis, Marianna Tsakiridou, Eleni Polyzoidou, Eleni Grigoriadou, Christos Bakirtzis, Evangelia Spandou and Constantina Simeonidou

Biomedicines 2025, 13(11), 2695; https://doi.org/10.3390/biomedicines13112695 (registering DOI) - 2 Nov 2025

Abstract

Plasticity is a fundamental property of the brain that enables the nervous system to respond appropriately to internal and external stimuli. It primarily involves changes at the synaptic level, mediated by a wide array of molecules, ultimately leading to cognitive and behavioral changes. This review critically contrasts the developmental timing and mechanisms of plasticity in Autism spectrum disorder (early hyperplasticity and excitation–inhibition imbalance) versus Schizophrenia (adolescent overpruning and NMDAR hypofunction) and evaluates evidence for interventions that harness plasticity to improve cognitive and behavioral outcomes. Preclinical and small clinical studies suggest that interventions targeting plasticity-related pathways may improve specific cognitive and behavioral domains. However, effects appear to be symptom-domain-specific and protocol-dependent and larger randomized controlled trials are needed to confirm efficacy. Cognitive remediation for Schizophrenia has been associated with improved executive function and increased hippocampal volume, while virtual reality-based training for Autism spectrum disorder has shown gains in attention and planning skills. By highlighting both molecular mechanisms and therapeutic strategies, this review aims to provide an integrated perspective on how plasticity-targeted interventions could be optimized across neurodevelopmental and neuropsychiatric disorders. Full article

(This article belongs to the Special Issue Multidisciplinary Approaches to Neurodegenerative Disorders)

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18 pages, 670 KB

Open AccessArticle

Social Control vs. Energy Management and Civilization Normotype from the Perspective of Sociocybernetics

by Joanna Marta Wyleżałek

Energies 2025, 18(21), 5786; https://doi.org/10.3390/en18215786 (registering DOI) - 2 Nov 2025

Abstract

The purpose of the article is to present the processes of social control in relation to energy management, including the energy transition, and the processes of forming the normotype of civilization as an important activity that is part of social control. The basis of consideration is sociocybernetics as knowledge that allows a unified methodological approach to the study of many areas of the functioning of society. The present article assumes that the processes of controlling energy access and distribution are linked to the formation of cognitive norms, which is an essential aspect of social control, facilitating changes in the structure and functions of the globalizing society. To clarify assumptions about the systemic nature of society and control processes, the article presents the foundation of the cybernetic theory, in which democratic society is treated as an independent organized system, and various types of deformation of the democratic system which close the system, as externally controlled systems, dependent on the organizer. The actions of an organizer who is economically strong and systemically independent enough to shape the social structure according to the adopted model of action are crucial for considering the shape of the global society. The economic interests and power of influence of the beneficiaries of the global system are part of the variants of the global structure identified by Roland Robertson that refer to the affirmation of common goals or the instrumental treatment of the social structure for the realization of individual goals. The public mood resulting from the processes described is illustrated by the results of five surveys conducted by the Institut Public de Sondage d’Opinion Secteur (IPSOS) in dozens of countries around the world. The conclusions drawn from the considerations treated of can contribute to a broad discussion about the direction of social processes in a globalizing society. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy in Light of Energy Transition Processes—2nd Edition)

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19 pages, 2259 KB

Open AccessArticle

A Sensor Localization and Orientation Method for OPM-MEG Based on Rigid Coil Structures and Magnetic Dipole Fitting Models

by Weinan Xu, Wenli Wang, Fuzhi Cao, Nan An, Wen Li, Min Xiang, Xiaolin Ning, Ying Liu and Baosheng Wang

Bioengineering 2025, 12(11), 1198; https://doi.org/10.3390/bioengineering12111198 (registering DOI) - 2 Nov 2025

Abstract

High-precision sensor co-registration is a critical prerequisite for achieving high-resolution imaging in Optically Pumped Magnetometer–Magnetoencephalography (OPM-MEG) systems. The conventional magnetic dipole fitting method, essentially a multipole expansion approximation of a finite-size coil, exhibits accuracy that strongly depends on spatial geometric factors such as coil–sensor distance, dipole orientation, and the projection angle of the sensor’s sensitive axis. Moreover, the approximation error increases significantly when sensors are placed either too close to the coils or at an unfavorable angular coupling. To address this issue, we propose a sensor localization and orientation method that combines magnetic dipole-equivalent modeling with a rigid coil structure (RCS). The RCS provides stable geometric constraints and eliminates uncertainties introduced by scalp-attached coils. In addition, three objective functions (the standard Frobenius norm, a weighted Frobenius norm and the structural similarity index (SSIM)) are formulated to mitigate the imbalance caused by near-field strong signals and to improve stability under noise and error propagation. Simulation results demonstrate that both under ideal conditions and with assembly perturbations, the weighted Frobenius norm and SSIM methods consistently achieve position errors below 1 mm and orientation errors below 1°, which effectively suppress large outlier deviations and achieve better performance than the standard Frobenius norm. The results confirm the effectiveness of the proposed method in achieving both high accuracy and robustness. Beyond clarifying the primary factors influencing magnetic dipole approximation errors, this study provides a geometry-constrained and optimization-based framework, offering a feasible pathway toward the practical implementation of high-precision, multi-channel OPM-MEG systems. Full article

(This article belongs to the Section Biosignal Processing)

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19 pages, 887 KB

Open AccessArticle

Exploratory In Vitro Evaluation of Maternal–Infant Bifidobacterium Strains for Microbiota Modulation in a Pediatric Cystic Fibrosis Context

by Maria Esteban-Torres, Isabel Blanco, Andrea Asensio-Grau, Nuria Ruiz, Manuel Bernabeu and Joaquim Calvo-Lerma

Microorganisms 2025, 13(11), 2523; https://doi.org/10.3390/microorganisms13112523 (registering DOI) - 2 Nov 2025

Abstract

This study explores the potential of novel Bifidobacterium isolates as targeted probiotic supplements for children with cystic fibrosis (CF), a condition often associated with gut dysbiosis. Five strains of Bifidobacterium genus (B. animalis IATA01, B. pseudocatenulatum IATA35, B. longum IATA02, B. bifidum IATA13, and B. longum IATA05) isolated from maternal–infant fecal samples were assessed in vitro following the FAO/WHO guidelines. Their probiotic potential was evaluated through simulated gastrointestinal digestion in the CF context, their adhesion to mucin, and their carbohydrate fermentation capacity. Additionally, their impact on colonic microbiota modulation was analyzed using static in vitro colonic fermentation with fecal inocula from four pediatric patients with CF to assess the presence of different bacterial groups associated with dysbiosis via qPCR and short-chain fatty acid production by GC-MS. Three strains (IATA01, IATA35, and IATA05) demonstrated survival after gastrointestinal digestion, with IATA01 exhibiting the highest adhesion to mucin but limited carbohydrate fermentation capacity. All strains increased the Bifidobacterium levels after colonic fermentation, while their effects on reducing pathogenic groups and promoting beneficial bacteria such as Akkermansia and Faecalibacterium varied depending on the strain and the individual inoculum. These findings highlight the strain-specific effects of Bifidobacterium and evidence a specific impact on colonic microbiota, depending on the composition of the basal inoculum, highlighting individual-specific responses. Full article

(This article belongs to the Section Gut Microbiota)

20 pages, 909 KB

Open AccessArticle

GRU-Based Stock Price Forecasting with the Itô-RMSProp Optimizers

by Mohamed Ilyas El Harrak, Karim El Moutaouakil, Nuino Ahmed, Eddakir Abdellatif and Vasile Palade

AppliedMath 2025, 5(4), 149; https://doi.org/10.3390/appliedmath5040149 (registering DOI) - 2 Nov 2025

Abstract

This study introduces Itô-RMSProp, a novel extension of the RMSProp optimizer inspired by Itô stochastic calculus, which integrates adaptive Gaussian noise into the update rule to enhance exploration and mitigate overfitting during training. We embed this optimizer within Gated Recurrent Unit (GRU) networks for stock price forecasting, leveraging the GRU’s strength in modeling long-range temporal dependencies under nonstationary and noisy conditions. Extensive experiments on real-world financial datasets, including a detailed sensitivity analysis over a wide range of noise scaling parameters (

ε

), reveal that Itô-RMSProp-GRU consistently achieves superior convergence stability and predictive accuracy compared to classical RMSProp. Notably, the optimizer demonstrates remarkable robustness across all tested configurations, maintaining stable performance even under volatile market dynamics. These findings suggest that the synergy between stochastic differential equation frameworks and gated architectures provides a powerful paradigm for financial time series modeling. The paper also presents theoretical justifications and implementation details to facilitate reproducibility and future extensions. Full article

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24 pages, 4424 KB

Open AccessArticle

A Hybrid Experimental and Computational Framework for Evaluating Wind Load Distribution and Wind-Induced Response of Multi-Span UHV Substation Gantries

by Feng Li, Yiting Wang, Lianghao Zou, Xiaohan Jiang, Xiaowang Pan, Hui Jin and Lei Fan

Sustainability 2025, 17(21), 9767; https://doi.org/10.3390/su17219767 (registering DOI) - 2 Nov 2025

Abstract

The structural safety of multi-span ultra-high-voltage (UHV) substation gantries is a cornerstone for the reliability and resilience of sustainable energy grids. The wind-resistant design of the structures is complicated by their complex modal behaviors and highly non-uniform wind load distributions. This study proposes a novel hybrid framework that integrates segmented high frequency force balance (HFFB) testing with a multi-modal stochastic vibration analysis, enabling the precise assessment of wind load distribution and dynamic response. Five representative segment models are tested to quantify both mean and dynamic wind loads, a strategy rigorously validated against whole-model HFFB tests. Key findings reveal significant aerodynamic disparities among structural segments. The long-span beam, Segment 5, exhibits markedly higher and direction-dependent responses. Its mean base shear coefficient reaches 4.34 at β = 75°, which is more than twice the values of 1.74 to 2.27 for typical tower segments. Furthermore, its RMS wind force coefficient peaks at 0.65 at β = 60°, a value 2.5 to 4 times higher than those of the tower segments, all of which remained below 0.26. Furthermore, a computational model incorporating structural modes, spatial coherence, and cross-modal contributions is developed to predict wind-induced responses, validated through aeroelastic model tests. The proposed framework accurately resolves spatial wind load distribution and dynamic wind-induced response, providing a reliable and efficient tool for the wind-resistant design of multi-span UHV lattice gantries. Full article

(This article belongs to the Special Issue Sustainable Power System Security: Automation, Monitoring, and Intelligent Defense Technology)

15 pages, 1286 KB

Open AccessArticle

PhaseNet: A Deep Learning Framework for Reflectarray Antenna Gain Prediction by Integrating 2D Phase Maps and Angular Embeddings

by Seoyeon Oh, Seongmin Pyo and Haneol Jang

Mathematics 2025, 13(21), 3509; https://doi.org/10.3390/math13213509 (registering DOI) - 2 Nov 2025

Abstract

Reflectarray antenna design traditionally depends on computationally intensive full-wave simulations and experimental measurements, which significantly increase design time and cost. To address these limitations, we propose PhaseNet, an end-to-end deep learning framework that leverages phase maps and radiation angles as inputs to predict reflectarray antenna gain values. PhaseNet integrates spatial features extracted by a convolutional neural network (CNN) backbone with an angle-embedding module, and employs a regression head to enable efficient forward prediction. After training, the model achieves near-real-time inference with a single forward pass, facilitating rapid exploration of high-dimensional design spaces and providing immediate design feedback. In addition, we introduce two novel evaluation metrics, Half-Power Beamwidth (HPBW) and Main-Lobe Root Mean Square Error (RMSE), which allow for a multidimensional evaluation of prediction performance across the full radiation pattern and within the critical main-lobe region. These metrics provide refined criteria for reflectarray antenna design optimization beyond conventional error measures. In particular, PhaseNet achieved the best performance compared to existing models on these newly proposed evaluation metrics, recording up to 0.45 lower HPBW RMSE than existing methods, thereby validating both the relevance of the metrics and the effectiveness of the model. To further enhance practicality, we present a rapid generation of diverse bit-encoded datasets, substantially reducing the time and cost associated with data acquisition. Overall, the proposed framework effectively reduces prediction errors in reflectarray antenna design. Full article

(This article belongs to the Special Issue Deep Neural Networks: Theory, Algorithms and Applications)

28 pages, 474 KB

Open AccessArticle

Estimated Amounts of β-Carotene, Vitamin B6, Riboflavin and Niacin in the Daily Diet of Older Subjects Associate Negatively with ADP-Induced Aggregation of Blood Platelets Independently of Cardiovascular Risk Factors

by Kamil Karolczak, Agnieszka Guligowska, Bartłomiej K. Sołtysik, Joanna Kostanek, Tomasz Kostka and Cezary Watala

Nutrients 2025, 17(21), 3461; https://doi.org/10.3390/nu17213461 (registering DOI) - 2 Nov 2025

Abstract

Background/Objectives: Platelet-dependent thrombotic risk increases with age. Little is known, however, about the potential effect of vitamins on platelet reactivity in older subjects. Methods: Therefore, in the present study we examined the dependencies of whole blood platelet aggregability (in response to arachidonic acid (AA), collagen (COL) or adenosinediphosphate (ADP), using impedance aggregometry) in older men and women (60–65 yr, n = 246) on the intakes of vitamins (vitamins A, E, C, B₆, B₁₂ and D, niacin, thiamine, riboflavin, retinol, β-carotene and folates) with a typical daily diet (vitamin contents estimated using Dieta 5.0 software). Results: Overall, significant negative bootstrap-boosted partial correlation coefficients, adjusted for selected cardiovascular risk factors, were revealed for AA and β-carotene, and ADP and β-carotene, riboflavin, vitamin B₆ and niacin. These findings were further validated by the outcomes of the bootstrap-boosted canonical analysis, confirming the relationships revealed for ADP, and to a lesser extent for AA. COL-dependent platelet aggregation appeared to not be associated with the amount of vitamins in the subjects’ daily diet. Conclusions: Hence, we conclude that the intake of vitamins in the daily diet of older subjects is negatively associated with platelet aggregability in an agonist- and vitamin-specific manner. Full article

(This article belongs to the Section Geriatric Nutrition)

24 pages, 8412 KB

Open AccessArticle

Damage Identification of Gas Station Double Layer Grid Structure Based on Time Domain Response Sensitivity Analysis

by Yan Wang, Yan Shi, Tao-Yuan Yang, Wei-Nan Wang, Yu-Qi Zhang and Wei Xi

Buildings 2025, 15(21), 3959; https://doi.org/10.3390/buildings15213959 (registering DOI) - 2 Nov 2025

Abstract

Gas station canopy grid structures develop local damage during service life, necessitating regular inspection and maintenance to prevent structural collapse. However, conventional field inspection remains inefficient and highly dependent on manual operation. This paper proposes a time domain response sensitivity methodology for damage assessment of structural members in gas station canopy grid structures. The proposed methodology advances time-domain sensitivity analysis to handle spatially complex grid structures with dense spectral characteristics, while proposing a calculation method for implementing intelligent sensing technology in field inspections that enables automated damage localization in practical canopy structures. Through analyzing time domain response sensitivity matrix, an optimal sensor placement method for spatial grid structures is presented. A double-layer spatial grid structure model is constructed to validate the time domain response sensitivity damage identification method and the optimal sensor placement method based on sensitivity analysis. The results show that the time domain response sensitivity damage identification method identifies the member damage in gas station canopy grid structural numerical model with satisfactory accuracy and efficiency, the optimal sensor placement methodology is suitable for damage identification of structural members. Full article

(This article belongs to the Special Issue Intelligent Materials and Structural Health: Mechanics, Damage Detection, Performance Enhancement, and AI-Driven Techniques)

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