Search Results (1,141)

To accurately evaluate the health condition of the cables of a cross-sea cable-stayed bridge under typhoon effects and to improve the efficiency of damage identification, an accurate bridge damage identification method combining convolutional neural network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) is proposed. A numerical model of the cable-stayed bridge was first established in ANSYS. Based on the monitoring data of Super Typhoon Mujigae, a three-dimensional fluctuating wind field was generated by harmonic synthesis. Through transient analysis, the static and dynamic responses of the cable-stayed bridge under typhoon loads were analyzed, and the critical cable locations most susceptible to damage were identified. Subsequently, the acceleration signals of the structural damage states under typhoon were extracted, and the damage-sensitive features were obtained through the Hilbert transform. Finally, an intelligent damage identification model for cable-stayed bridges was established by combining CNN and BiLSTM, and the identification results were compared with those obtained using CNN and BiLSTM individually. The results indicate that the neural network model combining CNN and BiLSTM performs significantly better than either CNN or BiLSTM alone in predicting both the location and degree of damage. Compared with the standalone CNN and BiLSTM models, the proposed hybrid CNN–BiLSTM network improves the accuracy of damage-location identification by 1.6% and 2.42%, respectively, and achieves an overall damage-degree identification accuracy exceeding 98%. The findings of this study provide theoretical and practical support for the intelligent operation and maintenance of cable-stayed bridges in coastal regions. The proposed approach is expected to serve as a valuable reference for evaluating large-span bridge structures under extreme wind conditions. Full article

This study addresses the challenges in health monitoring and safety assessment of retaining structures by developing an innovative damage identification system based on the Frequency-Optimized Wavelet Packet Transform (FOWPT) algorithm. The system introduces the Impulse Response Function (IRF) and optimized energy feature characterization to achieve precise damage localization (error ≤ 5%) and quantitative severity assessment. Recognizing the limitations of traditional dynamic methods in explaining damage mechanisms and spatial specificity, this research proposes a Wavelet Packet–Curvature Fusion (WPCF) model that integrates dynamic response signals with static topographic features. Through experimental validation, the WPCF model demonstrates a strong spatial correlation between terrain curvature and damage indicators, enabling damage prediction based solely on topographic data. The results show that the fusion approach significantly improves the accuracy of damage diagnosis and facilitates a transition from post-diagnosis to pre-prediction, offering a reliable technical framework for the intelligent monitoring and maintenance of retaining structures. Full article

Background: Recreational noise exposure from personal listening devices (PLDs) may lead to hidden hearing loss (HHL), affecting auditory nerve function despite normal pure-tone audiometry (PTA) and otoacoustic emissions (OAE). Subclinical auditory damage at the synaptic level often goes undetected by conventional assessments, emphasizing the need for more sensitive measures. Recorded click ABR in the presence of various levels of ipsilateral maskers for the better identification of auditory damage at the synaptic level. These results could help to develop a better objective diagnostic tool that can detect hidden hearing loss. Objective: To examine the effects of PLD usage on extended high-frequency audiometric thresholds and on click-evoked auditory brainstem responses (ABR) with and without ipsilateral masking in individuals with normal hearing. Materials and Methods: Thirty-five young adults aged 18–35 years (18 PLD users, 17 controls) with clinically normal hearing were recruited. Extended high-frequency audiometry (EHFA) was conducted from 9 to 16 kHz. Click-evoked ABRs were recorded at 80 dB nHL under unmasked and ipsilateral broadband noise-masked conditions at 50, 60, and 70 dB SPL. ABR analyses included absolute and relative amplitude (V/I) and latencies of waves I, III, and V. Results: PLD users demonstrated significantly elevated extended high-frequency thresholds compared to controls. ABR analyses revealed reduced Wave I amplitudes across stimulus conditions in PLD users, while Wave V amplitudes were largely preserved, resulting in consistently higher V/I amplitude ratios under masked conditions. No group differences were observed for Wave III amplitudes or absolute/interpeak latencies, except for a modest prolongation of I–III latency at one masker level in PLD users. Conclusions: Conventional audiological tests may not detect early auditory damage; however, extended high-frequency audiometry and ABR with ipsilateral masking demonstrate greater sensitivity in identifying noise-induced functional changes within the auditory brainstem pathways. Full article

Mining-induced subsidence boundaries, i.e., the surface areas affected by underground mining, play an important role in surface damage assessment and illegal mining identification. Traditional boundary delineation methods rely on field surveys, which restrict their applicability in regions with limited ground observations. Interferometric Synthetic Aperture Radar (InSAR) technology provides a cost-effective and non-contact solution for delineating subsidence boundaries. However, existing InSAR-based methods for subsidence boundary delineation are susceptible to observation noise and other deformation sources, which reduce the accuracy of boundary identification. To this end, this study proposes a novel method for delineating mining-induced subsidence boundaries by integrating both the magnitude and direction of InSAR-derived deformation gradients, referred to as DMSB-DG. First, time-series line-of-sight (LOS) deformation is obtained based on InSAR technology over mining areas. Then, the Roberts operator is employed to compute the magnitude and direction of the deformation gradients, which serve as the basis for boundary delineation. Finally, the ISODATA clustering algorithm is used, incorporating both the magnitude and direction of the deformation gradients as dual constraints to achieve accurate delineation of mining-affected boundaries. The combination of the two features effectively enhances the completeness and accuracy of boundary delineation. The performance of the proposed DMSB-DG method has been verified by simulation and field data. Specifically, compared with the adaptive mining subsidence boundary delimitation (ASBD) method, the proposed method achieved Kappa coefficients of 91.96% and 87.28%, representing improvements of 21.23% and 27.14% in two field tests, respectively. Furthermore, the influence of ascending and descending SAR images, as well as observational noise, on the performance of the proposed algorithm is also evaluated. The results demonstrate that the proposed method effectively suppresses InSAR noise and other interfering deformations, enabling high-precision delineation of mining impact boundaries. Full article

This study examines the effects of hailstorms on photovoltaic (PV) modules, focussing on damage mechanisms, testing standards, numerical simulations, damage detection techniques, and mitigation strategies. A comprehensive review of the recent literature (2017–2025), experimental results, and case studies is complemented by advanced simulation methods such as finite element analysis (FEA) and smoothed particle hydrodynamics (SPH). The research emphasises the crucial role of protective glass thickness, cell type, number of busbars, and quality of lamination in improving hail resistance. While international standards such as IEC 61215 specify test protocols, actual hail events often exceed these conditions, leading to glass breakage, micro-cracks, and electrical faults. Numerical simulations confirm that thicker glass and optimised module designs significantly reduce damage and power loss. Detection methods, including visual inspection, thermal imaging, electroluminescence, and AI-driven imaging, enable rapid identification of both visible and hidden damage. The study also addresses the financial risks associated with hail damage and emphasises the importance of insurance and preventative measures. Recommendations include the use of certified, robust modules, protective covers, optimised installation angles, and regular inspections to mitigate the effects of hail. Future research should develop lightweight, impact-resistant materials, improve simulation modelling to better reflect real-world hail conditions, and improve AI-based damage detection in conjunction with drone inspections. This integrated approach aims to improve the durability and reliability of PV modules in hail-prone regions and support the sustainable use of solar energy amidst increasing climatic challenges. Full article

In a botnet attack, significant damage can occur when an attacker gains control over a large number of compromised network devices. Botnets have evolved from traditional centralized architectures to decentralized Peer-to-Peer (P2P) and hybrid forms. Recently, a steganography-based botnet (Stego-botnet) has emerged, which conceals command and control (C&C) messages within cover media such as images or video files shared over social networking sites (SNS). This type of Stego-botnet can evade conventional detection systems, as identifying hidden messages embedded in media transmitted via SNS platforms is inherently challenging. However, the inherent file size limitations of SNS platforms restrict the achievable payload capacity of such Stego-botnets. Moreover, the centralized characteristics of conventional botnet architectures expose attackers to a higher risk of identification. To overcome these challenges, researchers have explored network steganography techniques leveraging P2P networks such as BitTorrent, Google Suggest, and Skype. Among these, a hidden communication method utilizing Bitfield messages in BitTorrent has been proposed, demonstrating improved concealment compared to prior studies. Nevertheless, existing approaches still fail to achieve sufficient payload capacity relative to traditional digital steganography techniques. In this study, we extend P2P-based network steganography methods—particularly within the BitTorrent protocol—to address these limitations. We propose a novel botnet C&C communication model that employs network steganography over BitTorrent and validate its feasibility through experimental implementation. Furthermore, our results show that the proposed Stego-botnet achieves a higher payload capacity and outperforms existing Stego-botnet models in terms of both efficiency and concealment performance. Full article

Due to the influence of materials, construction quality, environmental conditions, and artificial factors, debonding damage in external thermal insulation composite systems (ETICS) has become a common issue in the construction field. A reliable and efficient method for identifying the debonding is still lacking. In this study, four groups of external insulation specimens with different degrees of debonding were fabricated. A non-contact detection method based on nonlinear vibration characteristics was employed, using a laser Doppler vibrometer to acquire the vibration response signals of the specimens. The results demonstrate that this technique can effectively distinguish specimens with different levels of debonding and accurately identify and locate the damage. Moreover, the relative position of the signal acquisition point with respect to the debonding area has no significant impact on the detection results. Full article

Background: Systemic sclerosis (SSc) is a rare autoimmune disease characterized by progressive fibrosis, systemic inflammation and vascular dysfunction, with manifestations that can affect multiple organs, including the heart. Cardiac involvement in SSc is often underdiagnosed, although it can have serious consequences on the prognosis, especially the occurrence of arrhythmias. These rhythm disturbances can result from direct damage to the myocardium, the conduction system, or the coronary microcirculation. Equally, the medication used can have iatrogenic consequences manifested by severe arrhythmias. Methodology: The aim of this study was to provide a synthesis of incidence, pathogenic mechanisms, diagnostic methods, and therapeutic strategies of arrhythmias associated with SSc. The potential effects of immunomodulatory therapies, such as conventional immunosuppressants and biological therapies, on cardiac electrical function were also analyzed. This narrative review could present the state of the art on arrhythmias associated with SSc, which could serve as a practical guide. In clinical practice, it is necessary to establish a team that includes cardiologists and rheumatologists as well as other specialists to contribute to a correct diagnosis followed by an optimal therapy in patients with SSc. Results: Current data suggest that diffuse myocardial fibrosis, silent ischemia, and inflammatory infiltration may alter the propagation of the electrical impulse in the heart, favoring the occurrence of arrhythmias. Atrioventricular blocks, ventricular tachyarrhythmias, and atrial fibrillation are the most commonly reported rhythm abnormalities in SSc. Also, some therapies used in the treatment of the disease may influence the arrhythmic risk. Conclusions: Cardiac arrhythmias in SSc can have a significant impact on the prognosis of patients, which is why a multidisciplinary approach is essential. Collaboration between rheumatologists, cardiologists, and electrophysiologists is crucial for the early identification and appropriate management of arrhythmic risk in this patient group. Full article

This study aimed to investigate whether knockout of the ApoB48 gene improves lipid metabolism disorders induced by a high-fat diet (HFD) in mice. Clustered regularly interspaced short palindromic repeats–Cas9 gene editing technology was used to knock out the ApoB48 gene in C57BL/6J mice, and genotype identification showed heterozygosity (HE, ApoB48 +/−). Subsequently, eight HE and eight wild-type (WT) mice were fed a HFD for 12 weeks. Fasting blood glucose, and insulin levels were decreased in ApoB48 +/− mice. The intraperitoneal glucose tolerance test and intraperitoneal insulin tolerance test showed mild insulin resistance. Moreover, it delayed the development of atherosclerosis and intestinal tissue damage. Differential metabolites such as ceramide, sphingosine, and sphingosine-1-phosphate were identified using liquid chromatography–mass spectrometry, and differentially expressed proteins, including ceramide synthase 6 (CerS6), protein phosphatase 2A (PP2A), and protein kinase B (AKT), were indicated by the Kyoto Encyclopaedia of Genes and Genomes. Therefore, decreased expression of ApoB48 can ameliorate lipid metabolism disorders induced by an HFD, which may be related to the CerS6/PP2A/AKT pathway. This might represent a new approach for exploring methods to treat hyperlipidaemia. Full article

Background: Hereditary spastic paraplegias (HSPs) are a group of neurodegenerative disorders marked by progressive corticospinal tract dysfunction and wide phenotypic variability. Their genetic heterogeneity has so far limited the identification of biomarkers that are broadly applicable across different subtypes. Objective: We aim to define a balanced review on the use of biomarkers in HSP. Methods: This review focuses on fluid biomarkers already available in clinical or research settings—primarily validated in other neurodegenerative diseases—and assesses their potential translation to the HSP context. Biomarkers such as neurofilament light chain, brain-derived tau, glial fibrillary acidic protein, and soluble TREM2 reflect key converging mechanisms of neurodegeneration, including axonal damage, neuronal loss, and glial activation. These shared downstream pathways represent promising targets for disease monitoring in HSP, independently of the underlying genetic mutation. Results: An integrative framework of fluid biomarkers could assist in defining disease progression and stratify patients in both clinical and research settings. Moreover, recent advances in ultrasensitive assays and remote sampling technologies, such as dried blood spot collection, offer concrete opportunities for minimally invasive, longitudinal monitoring. When combined with harmonized multicenter protocols and digital infrastructure, these tools could support scalable and patient-centered models of care. Conclusions: The integration of already available biomarkers into the HSP field may accelerate clinical translation and offer a feasible strategy to overcome the challenges posed by genetic and clinical heterogeneity. Full article

Seafood fraud, such as mislabeling low-cost rainbow trout as premium salmon, poses serious food safety risks and damages consumer rights. To address this growing concern, this study develops a deep learning-based, smartphone-compatible sensing system for fish meat identification and salmon freshness grading. By providing consumers with real-time, image-based verification tools, the system supports informed purchasing decisions and enhances food safety. The system adopts a two-stage design: first classifying fish meat types, then grading salmon freshness into three levels based on visual cues. An improved DenseNet121 architecture, enhanced with global average pooling, dropout layers, and a customized output layer, improves accuracy and reduces overfitting, while transfer learning with partial layer freezing enhances efficiency by reducing training time without significant accuracy loss. Experimental results show that the two-stage method outperforms the one-stage approach and several baseline models, achieving robust accuracy in both classification and grading tasks. Sensitivity analysis demonstrates resilience to blur and camera tilt, though real-world adaptability under diverse lighting and packaging conditions remains a challenge. Overall, the proposed system represents a practical, consumer-oriented tool for seafood authentication and freshness evaluation, with potential to enhance food safety and consumer protection. Full article

Glazed components in ancient Chinese architecture hold profound historical and cultural value. However, over time, environmental erosion, physical impacts, and human disturbances gradually lead to various forms of damage, severely impacting the durability and stability of the buildings. Therefore, preventive protection of glazed components is crucial. The key to preventive protection lies in the early detection and repair of damage, thereby extending the component’s service life and preventing significant structural damage. To address this challenge, this study proposes a Restoration-Scale Identification (RSI) method that integrates depth information. By combining RGB-D images acquired from a depth camera with intrinsic camera parameters, and embedding a Convolutional Block Attention Module (CBAM) into the backbone network, the method dynamically enhances critical feature regions. It then employs a scale restoration strategy to accurately identify damage areas and recover the physical dimensions of glazed components from a global perspective. In addition, we constructed a dedicated semantic segmentation dataset for glazed tile damage, focusing on cracks and spalling. Both qualitative and quantitative evaluation results demonstrate that, compared with various high-performance semantic segmentation methods, our approach significantly improves the accuracy and robustness of damage detection in glazed components. The achieved accuracy deviates by only ±10 mm from high-precision laser scanning, a level of precision that is essential for reliably identifying and assessing subtle damages in complex glazed architectural elements. By integrating depth information, real scale information can be effectively obtained during the intelligent recognition process, thereby efficiently and accurately identifying the type of damage and size information of glazed components, and realizing the conversion from two-dimensional (2D) pixel coordinates to local three-dimensional (3D) coordinates, providing a scientific basis for the protection and restoration of ancient buildings, and ensuring the long-term stability of cultural heritage and the inheritance of historical value. Full article

The continued use of agrochemicals in Valle de Arista, SLP, Mexico, has generated loss of effectiveness of active ingredients and impacts on public health and the environment. To identify environmental and socioeconomic impacts, a quantification method was designed using the Kovach Environmental Impact Quotient and environmental accounting of pesticides (Leach and Mumford) that included agricultural diagnosis and identification of agrochemical impacts. Producers, technical advisors and agrochemicals dealers were surveyed as key agents of tomato (Solanum lycopersicum) and chili pepper crops (Capsicum annuum) due to their economic importance. Gower quotation coefficients were calculated to measure similarity of quantitative, qualitative and dichotomous variables with continuous, discrete and binary characteristics. The use of fungicides (carbendazim and chlorothalonil) showed the greatest environmental impact, followed by insecticides (endosulfan and thiametoxam) and herbicides. The negative externality averaged US$15.60 ha⁻¹ annually, corresponding to 50% of tomato, 31.25% of poblano pepper and 18.75% of serrano pepper. Estimated damages due to the use of greenhouses were 37.7% to the consumer, 21.2% to the worker, 14.8% to aquatic life, 3.6% to birds, 9.2% to bees and 3.3% to insects. Full article

This article aims to perform system identification of a nearly 30-year-old cable-stayed steel footbridge over the Wisłok River in Rzeszów (Poland). The design documentation of the bridge has been lost, and since its construction, the footbridge has been subject to renovations. The structure is highly susceptible to pedestrian traffic, and before any actions are taken to improve the comfort of use, it is necessary to create and validate a numerical model and assess the force distribution in the structure. Models are often built as mappings of an ideal structure. However, real structures are not ideal. The comparison of numerical and measured data can allow for an indication of potential damage areas. Two main purposes of the article have been formulated: (1)Development of a numerical model of an old footbridge, whose components have been degraded due to long-term use. Changes, compared to the ‘original’, focused on elongation of the cables due to rheology and a decrease in their tension. (2) Demonstrate the challenges in modeling and validating this type of bridge. In the article, the result of the numerical simulation (Finite Element Method and Ansys2024 R2 was applied, the verification was made in RFEM6) for models with different boundary conditions and varied pre-tension in cables was compared with the results of static and dynamic examination of a real object. The dynamic tests showed an uneven distribution of pre-tension in cables. The ratio of the first natural frequencies of inner cables on the north side is as high as 16%. The novelty demonstrated in the article is that static tests are insufficient for proper system identification; the same value of vertical displacement can be obtained for a selected static load, with varied tension in cables. Therefore, dynamic testing is essential. Full model updating requires a multicriteria approach, which will be made in the future. Full article

Red bricks serve as an important material for load-bearing or enclosing structures in traditional architecture and are widely used in construction projects both domestically and internationally. Fujian red bricks, due to geographical, trade, and immigration-related factors, have spread to Taiwan and various regions in Southeast Asia, giving rise to distinctive red brick architectural complexes. To further investigate the types of damage, such as cracking and missing bricks, that occur in traditional red brick buildings due to multiple factors, including climate and human activities, this study takes Fujian red brick buildings as its research subject. It employs the YOLOv12 rapid detection method to conduct technical support research on structural assessment, type detection, and damage localization of surface damage in red brick building materials. The experimental model was conducted through the following procedures: on-site photo collection, slice marking, creation of an image training set, establishment of an iterative model training, accuracy analysis, and experimental result verification. Based on this, the causes of damage types and corresponding countermeasures were analyzed. The objective of this study is to attempt to utilize computer vision image recognition technology to provide practical, automated detection and efficient identification methods for damage types in red brick building brick structures, particularly those involving physical and mechanical structural damage that severely threaten the overall structural safety of the building. This research model will reduce the complex manual processes typically involved, thereby improving work efficiency. This enables the development of customized intervention strategies with minimal impact and enhanced timeliness for the maintenance, repair, and preservation of red brick buildings, further advancing the practical application of intelligent protection for architectural heritage. Full article