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Keywords = damage self-sensing

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14 pages, 636 KB  
Review
Innate Immune Surveillance and Recognition of Epigenetic Marks
by Yalong Wang
Epigenomes 2025, 9(3), 33; https://doi.org/10.3390/epigenomes9030033 - 5 Sep 2025
Viewed by 752
Abstract
The innate immune system protects against infection and cellular damage by recognizing conserved pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). Emerging evidence suggests that aberrant epigenetic modifications—such as altered DNA methylation and histone marks—can serve as immunogenic signals that activate pattern [...] Read more.
The innate immune system protects against infection and cellular damage by recognizing conserved pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). Emerging evidence suggests that aberrant epigenetic modifications—such as altered DNA methylation and histone marks—can serve as immunogenic signals that activate pattern recognition receptor (PRR)-mediated immune surveillance. This review explores the concept that epigenetic marks may function as DAMPs or even mimic PAMPs. I highlight how unmethylated CpG motifs, which are typically suppressed using host methylation, are recognized as foreign via Toll-like receptor 9 (TLR9). I also examine how cytosolic DNA sensors, including cGAS, detect mislocalized or hypomethylated self-DNA resulting from genomic instability. In addition, I discuss how extracellular histones and nucleosomes released during cell death or stress can act as DAMPs that engage TLRs and activate inflammasomes. In the context of cancer, I review how epigenetic dysregulation can induce a “viral mimicry” state, where reactivation of endogenous retroelements produces double-stranded RNA sensed by RIG-I and MDA5, triggering type I interferon responses. Finally, I address open questions and future directions, including how immune recognition of epigenetic alterations might be leveraged for cancer immunotherapy or regulated to prevent autoimmunity. By integrating recent findings, this review underscores the emerging concept of the epigenome as a target of innate immune recognition, bridging the fields of immunology, epigenetics, and cancer biology. Full article
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20 pages, 3967 KB  
Article
A Flexible Frequency-Coded Electromagnetic Sensing Array for Contactless Biological Tissues Health Monitoring
by Angelica Masi, Danilo Brizi, Eliana Canicattì, Sabrina Rotundo and Agostino Monorchio
Appl. Sci. 2025, 15(16), 9015; https://doi.org/10.3390/app15169015 - 15 Aug 2025
Viewed by 768
Abstract
In this study, we present a wearable sensing system for monitoring the physiological status of damaged biological tissues based on a flexible, frequency-coded electromagnetic spiral resonator array. The physiological parameter evaluation is performed in a contactless way, avoiding the placing of electronically active [...] Read more.
In this study, we present a wearable sensing system for monitoring the physiological status of damaged biological tissues based on a flexible, frequency-coded electromagnetic spiral resonator array. The physiological parameter evaluation is performed in a contactless way, avoiding the placing of electronically active elements directly upon the patient’s skin, thus ensuring safety and comfort. Firstly, we report in detail the physical principles behind the sensing strategy: a passive array is interrogated through an actively fed external single-loop probe that is inductively coupled with the double-layer spiral unit cells. The variation in the physiological parameters influences the array response, thus providing sensing information, due to the different complex dielectric permittivity values related to the tissue status. Moreover, the proposed frequency-coded approach allows for spatial information on the lesion to be retrieved, thus increasing the sensing ability. In order to prove the validity of this general methodology, we created a numerical test case, designing a practical implementation of the wearable sensing system working at a radiofrequency regime (10–100 MHz). In addition, we also fabricated prototypes, exploiting PCB technology, and realized stratified phantoms by incorporating opportune additives to control the dielectric properties. The numerical results and the experimental verification demonstrated the validity of the developed sensing strategy, showing satisfying agreement and, thus, proving the good sensibility and spatial resolution of the frequency-coded array. These results can open the path to a radically novel approach for self-care and monitoring of inflamed status and, more generally, for wearable sensing devices in biomedical applications. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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28 pages, 48169 KB  
Article
Advancing Self-Supervised Learning for Building Change Detection and Damage Assessment: Unified Denoising Autoencoder and Contrastive Learning Framework
by Songxi Yang, Bo Peng, Tang Sui, Meiliu Wu and Qunying Huang
Remote Sens. 2025, 17(15), 2717; https://doi.org/10.3390/rs17152717 - 6 Aug 2025
Viewed by 924
Abstract
Building change detection and building damage assessment are two essential tasks in post-disaster analysis. Building change detection focuses on identifying changed building areas between bi-temporal images, while building damage assessment involves segmenting all buildings and classifying their damage severity. These tasks play a [...] Read more.
Building change detection and building damage assessment are two essential tasks in post-disaster analysis. Building change detection focuses on identifying changed building areas between bi-temporal images, while building damage assessment involves segmenting all buildings and classifying their damage severity. These tasks play a critical role in disaster response and urban development monitoring. Although supervised learning has significantly advanced building change detection and damage assessment, its reliance on large labeled datasets remains a major limitation. In contrast, self-supervised learning enables the extraction of meaningful data representations without explicit training labels. To address this challenge, we propose a self-supervised learning approach that unifies denoising autoencoders and contrastive learning, enabling effective data representation for building change detection and damage assessment. The proposed architecture integrates a dual denoising autoencoder with a Vision Transformer backbone and contrastive learning strategy, complemented by a Feature Pyramid Network-ResNet dual decoder and an Edge Guidance Module. This design enhances multi-scale feature extraction and enables edge-aware segmentation for accurate predictions. Extensive experiments were conducted on five public datasets, including xBD, LEVIR, LEVIR+, SYSU, and WHU, to evaluate the performance and generalization capabilities of the model. The results demonstrate that the proposed Denoising AutoEncoder-enhanced Dual-Fusion Network (DAEDFN) approach achieves competitive performance compared with fully supervised methods. On the xBD dataset, the largest dataset for building damage assessment, our proposed method achieves an F1 score of 0.892 for building segmentation, outperforming state-of-the-art methods. For building damage severity classification, the model achieves an F1 score of 0.632. On the building change detection datasets, the proposed method achieves F1 scores of 0.837 (LEVIR), 0.817 (LEVIR+), 0.768 (SYSU), and 0.876 (WHU), demonstrating model generalization across diverse scenarios. Despite these promising results, challenges remain in complex urban environments, small-scale changes, and fine-grained boundary detection. These findings highlight the potential of self-supervised learning in building change detection and damage assessment tasks. Full article
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14 pages, 483 KB  
Article
Silence as a Quiet Strategy: Understanding the Consequences of Workplace Ostracism Through the Lens of Sociometer Theory
by Jun Yang, Bin Wang, Yijing Liao, Feifan Yang and Jing Qian
Behav. Sci. 2025, 15(8), 1022; https://doi.org/10.3390/bs15081022 - 28 Jul 2025
Viewed by 963
Abstract
Existing research has predominantly framed defensive silence as an avoidance response to interpersonal mistreatments. Moving beyond this view, this study theorizes defensive silence as a proactive strategy for managing interpersonal relationships through the lens of sociometer theory. We posit that workplace ostracism will [...] Read more.
Existing research has predominantly framed defensive silence as an avoidance response to interpersonal mistreatments. Moving beyond this view, this study theorizes defensive silence as a proactive strategy for managing interpersonal relationships through the lens of sociometer theory. We posit that workplace ostracism will reduce employees’ organization-based self-esteem (OBSE), which in turn increases their subsequent defensive silence to avert further damage to relationships. In addition, we also expect a moderating role of the sense of power in mitigating the negative impact of workplace ostracism on OBSE. Based on the multi-wave, multi-source data of 345 employees and their 82 immediate supervisors, we tested all the hypotheses. Results from multilevel modeling indicated that OBSE mediated the indirect effect of workplace ostracism on defensive silence, and also supported the moderation role of sense of power. Our theoretical model provides a novel perspective that deepens the understanding of defensive silence and suggests implications for managerial practices. Full article
(This article belongs to the Section Organizational Behaviors)
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23 pages, 20415 KB  
Article
FireNet-KD: Swin Transformer-Based Wildfire Detection with Multi-Source Knowledge Distillation
by Naveed Ahmad, Mariam Akbar, Eman H. Alkhammash and Mona M. Jamjoom
Fire 2025, 8(8), 295; https://doi.org/10.3390/fire8080295 - 26 Jul 2025
Viewed by 1179
Abstract
Forest fire detection is an essential application in environmental surveillance since wildfires cause devastating damage to ecosystems, human life, and property every year. The effective and accurate detection of fire is necessary to allow for timely response and efficient management of disasters. Traditional [...] Read more.
Forest fire detection is an essential application in environmental surveillance since wildfires cause devastating damage to ecosystems, human life, and property every year. The effective and accurate detection of fire is necessary to allow for timely response and efficient management of disasters. Traditional techniques for fire detection often experience false alarms and delayed responses in various environmental situations. Therefore, developing robust, intelligent, and real-time detection systems has emerged as a central challenge in remote sensing and computer vision research communities. Despite recent achievements in deep learning, current forest fire detection models still face issues with generalizability, lightweight deployment, and accuracy trade-offs. In order to overcome these limitations, we introduce a novel technique (FireNet-KD) that makes use of knowledge distillation, a method that maps the learning of hard models (teachers) to a light and efficient model (student). We specifically utilize two opposing teacher networks: a Vision Transformer (ViT), which is popular for its global attention and contextual learning ability, and a Convolutional Neural Network (CNN), which is esteemed for its spatial locality and inductive biases. These teacher models instruct the learning of a Swin Transformer-based student model that provides hierarchical feature extraction and computational efficiency through shifted window self-attention, and is thus particularly well suited for scalable forest fire detection. By combining the strengths of ViT and CNN with distillation into the Swin Transformer, the FireNet-KD model outperforms state-of-the-art methods with significant improvements. Experimental results show that the FireNet-KD model obtains a precision of 95.16%, recall of 99.61%, F1-score of 97.34%, and mAP@50 of 97.31%, outperforming the existing models. These results prove the effectiveness of FireNet-KD in improving both detection accuracy and model efficiency for forest fire detection. Full article
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18 pages, 4250 KB  
Article
A Novel Carbon Fiber Composite Material for the Simulation of Damage Evolution in Thick Aquifers
by Bozhi Zhao, Xing Gao, Weibing Zhu, Jiaxing Ding and Pengjun Gao
Appl. Sci. 2025, 15(13), 7314; https://doi.org/10.3390/app15137314 - 29 Jun 2025
Cited by 1 | Viewed by 464
Abstract
Simulation experiments are a crucial method for investigating overburden failure, strata movement, and strata control during coal mining. However, traditional similar materials struggle to effectively monitor internal damage, fracturing, and dynamic development processes within the strata during mining. To address this issue, carbon [...] Read more.
Simulation experiments are a crucial method for investigating overburden failure, strata movement, and strata control during coal mining. However, traditional similar materials struggle to effectively monitor internal damage, fracturing, and dynamic development processes within the strata during mining. To address this issue, carbon fibers were introduced into the field of similar material simulation experiments for mining. Leveraging the excellent conductivity and the sensitive feedback of resistivity changes in response to damage of this composite material enabled real-time monitoring of internal damage and fracture patterns within the mining strata during similar simulation experiments, leading to the development of a carbon fiber similar simulation composite material with damage self-sensing properties. This study found that as the carbon fiber content increased, the evolution patterns of the electrical resistance change rate and the damage coefficient of the similar material tended to coincide. When the carbon fiber content in the similar material exceeded 2%, the electrical resistance change rate and the damage coefficient consistently exhibited synchronized growth with identical increments. A similar simulation experiment revealed that after the completion of workface mining, the thick sandstone aquifer did not develop significant cracks and remained stable. In the early stages of mining, damage rapidly accumulated at the bottom of the thick aquifer, approaching the failure threshold. In the middle layers, a step-like increase in the damage coefficient occurred after mining reached a certain width, while the top region was less affected by mining activities, resulting in less significant damage development. The research findings offer new experimental insights into rock layer movement and control studies, providing theoretical guidance for the prediction, early warning, and prevention of dynamic disasters in mines with thick key layers. Full article
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19 pages, 4140 KB  
Article
Assessing the Effect of Damage and Steel Fiber Content on the Self-Sensing Ability of Coal Gangue-Cemented Composite by Electrochemical Impedance Spectroscopy (EIS)
by Meng Xiao, Feng Ju, Zequan He, Pai Ning, Tengfei Wang and Dong Wang
Materials 2025, 18(11), 2467; https://doi.org/10.3390/ma18112467 - 24 May 2025
Cited by 1 | Viewed by 610
Abstract
Steel fibers (SFs) can form stable conductive networks in coal gangue-cemented composites (CGCCs), endowing CGCCs with excellent mechanical, electrical and self-sensing properties. Meanwhile, electrochemical impedance spectroscopy (EIS) provides a potential approach to evaluate the damage situation of SF-reinforced CGCC. In this paper, EIS [...] Read more.
Steel fibers (SFs) can form stable conductive networks in coal gangue-cemented composites (CGCCs), endowing CGCCs with excellent mechanical, electrical and self-sensing properties. Meanwhile, electrochemical impedance spectroscopy (EIS) provides a potential approach to evaluate the damage situation of SF-reinforced CGCC. In this paper, EIS responses of CGCCs with different SF content and damage levels were determined. An equivalent circuit was then explored, and the effect of the SF content and damage levels on its parameters was investigated. It was observed that CGCC with 0.8% SFs yielded the best result in terms of mechanical and self-sensing ability. In addition, damage such as microcracks primarily affects the conductive pathways induced by pores rather than those induced by SFs. More importantly, as a non-destructive method, the EIS technique is practical and promising for monitoring damage conditions of SF-reinforced CGCC in underground engineering. Full article
(This article belongs to the Section Advanced Composites)
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26 pages, 8161 KB  
Review
Recent Progress in Self-Healing Triboelectric Nanogenerators for Artificial Skins
by Guoliang Li, Zongxia Li, Haojie Hu, Baojin Chen, Yuan Wang, Yanchao Mao, Haidong Li and Baosen Zhang
Biosensors 2025, 15(1), 37; https://doi.org/10.3390/bios15010037 - 10 Jan 2025
Cited by 10 | Viewed by 3807
Abstract
Self-healing triboelectric nanogenerators (TENGs), which incorporate self-healing materials capable of recovering their structural and functional properties after damage, are transforming the field of artificial skin by effectively addressing challenges associated with mechanical damage and functional degradation. This review explores the latest advancements in [...] Read more.
Self-healing triboelectric nanogenerators (TENGs), which incorporate self-healing materials capable of recovering their structural and functional properties after damage, are transforming the field of artificial skin by effectively addressing challenges associated with mechanical damage and functional degradation. This review explores the latest advancements in self-healing TENGs, emphasizing material innovations, structural designs, and practical applications. Key materials include dynamic covalent polymers, supramolecular elastomers, and ion-conductive hydrogels, which provide rapid damage recovery, superior mechanical strength, and stable electrical performance. Innovative structural configurations, such as layered and encapsulated designs, optimize triboelectric efficiency and enhance environmental adaptability. Applications span healthcare, human–machine interfaces, and wearable electronics, demonstrating the immense potential for tactile sensing and energy harvesting. Despite significant progress, challenges remain in scalability, long-term durability, and multifunctional integration. Future research should focus on advanced material development, scalable fabrication, and intelligent system integration to unlock the full potential of self-healing TENGs. This review provides a comprehensive overview of current achievements and future directions, underscoring the pivotal role of self-healing TENGs in artificial skin technology. Full article
(This article belongs to the Section Nano- and Micro-Technologies in Biosensors)
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35 pages, 2583 KB  
Review
A Review of Soft Robotic Actuators and Their Applications in Bioengineering, with an Emphasis on HASEL Actuators’ Future Potential
by Osura Perera, Ranjith Liyanapathirana, Gaetano Gargiulo and Upul Gunawardana
Actuators 2024, 13(12), 524; https://doi.org/10.3390/act13120524 - 18 Dec 2024
Cited by 6 | Viewed by 9479
Abstract
This review will examine the rapidly growing field of soft robotics, with a special emphasis on soft robotic actuators and their applications in bioengineering. Bioengineering has increasingly utilized soft robotics due to their mechanical adaptability and flexibility, with applications including drug delivery, assistive [...] Read more.
This review will examine the rapidly growing field of soft robotics, with a special emphasis on soft robotic actuators and their applications in bioengineering. Bioengineering has increasingly utilized soft robotics due to their mechanical adaptability and flexibility, with applications including drug delivery, assistive and wearable devices, artificial organs, and prosthetics. Soft robotic applications, as well as the responsive mechanisms employed in soft robotics, include electrical, magnetic, thermal, photo-responsive, and pressure-driven actuators. Special attention is given to hydraulically amplified self-healing electrostatic (HASEL) actuators due to their biomimetic properties and innovative combination of dielectric elastomer actuators (DEAs) and hydraulic actuators, which eliminates the limitations of each actuator while introducing capabilities such as self-healing. HASEL actuators combine the fast response and self-sensing features of DEAs, as well as the force generation and adaptability of hydraulic systems. Their self-healing ability from electrical damage not only makes HASELs a unique technology among others but also makes them promising for long-term bioengineering applications. A key contribution of this study is the comparative analysis of the soft actuators, presented in detailed tables. The performance of soft actuators is assessed against a common set of critical parameters, including specific power, strain, maximum actuation stress, energy efficiency, cycle life, and self-healing capabilities. This study has also identified some important research gaps and potential areas where soft robotics may still be developed in the future. Future research should focus on improvements in power supply design, long-term material durability, and enhanced energy efficiency. This review will serve as an intermediate reference for researchers and system designers, guiding the next generation of advancements in soft robotics within bioengineering. Full article
(This article belongs to the Special Issue Soft Robotics in Biomedical Application)
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20 pages, 4297 KB  
Article
Precision and Efficiency in Dam Crack Inspection: A Lightweight Object Detection Method Based on Joint Distillation for Unmanned Aerial Vehicles (UAVs)
by Hangcheng Dong, Nan Wang, Dongge Fu, Fupeng Wei, Guodong Liu and Bingguo Liu
Drones 2024, 8(11), 692; https://doi.org/10.3390/drones8110692 - 19 Nov 2024
Cited by 2 | Viewed by 1781
Abstract
Dams in their natural environment will gradually develop cracks and other forms of damage. If not detected and repaired in time, the structural strength of the dam may be reduced, and it may even collapse. Repairing cracks and defects in dams is very [...] Read more.
Dams in their natural environment will gradually develop cracks and other forms of damage. If not detected and repaired in time, the structural strength of the dam may be reduced, and it may even collapse. Repairing cracks and defects in dams is very important to ensure their normal operation. Traditional detection methods rely on manual inspection, which consumes a lot of time and labor, while deep learning methods can greatly alleviate this problem. However, previous studies have often focused on how to better detect crack defects, with the corresponding image resolution not being particularly high. In this study, targeting the scenario of real-time detection by drones, we propose an automatic detection method for dam crack targets directly on high-resolution remote sensing images. First, for high-resolution remote sensing images, we designed a sliding window processing method and proposed corresponding methods to eliminate redundant detection frames. Then, we introduced a Gaussian distribution in the loss function to calculate the similarity of predicted frames and incorporated a self-attention mechanism in the spatial pooling module to further enhance the detection performance of crack targets at various scales. Finally, we proposed a pruning-after-distillation scheme, using the compressed model as the student and the pre-compression model as the teacher and proposed a joint distillation method that allows more efficient distillation under this compression relationship between teacher and student models. Ultimately, a high-performance target detection model can be deployed in a more lightweight form for field operations such as UAV patrols. Experimental results show that our method achieves an mAP of 80.4%, with a parameter count of only 0.725 M, providing strong support for future tasks such as UAV field inspections. Full article
(This article belongs to the Special Issue Advances in Detection, Security, and Communication for UAV)
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17 pages, 7769 KB  
Article
Smart Carbon Fiber-Reinforced Polymer Composites for Damage Sensing and On-Line Structural Health Monitoring Applications
by Cláudia Lopes, Andreia Araújo, Fernando Silva, Panagiotis-Nektarios Pappas, Stefania Termine, Aikaterini-Flora A. Trompeta, Costas A. Charitidis, Carla Martins, Sacha T. Mould and Raquel M. Santos
Polymers 2024, 16(19), 2698; https://doi.org/10.3390/polym16192698 - 24 Sep 2024
Cited by 5 | Viewed by 3798
Abstract
High electrical conductivity, along with high piezoresistive sensitivity and stretchability, are crucial for designing and developing nanocomposite strain sensors for damage sensing and on-line structural health monitoring of smart carbon fiber-reinforced polymer (CFRP) composites. In this study, the influence of the geometric features [...] Read more.
High electrical conductivity, along with high piezoresistive sensitivity and stretchability, are crucial for designing and developing nanocomposite strain sensors for damage sensing and on-line structural health monitoring of smart carbon fiber-reinforced polymer (CFRP) composites. In this study, the influence of the geometric features and loadings of carbon-based nanomaterials, including reduced graphene oxide (rGO) or carbon nanofibers (CNFs), on the tunable strain-sensing capabilities of epoxy-based nanocomposites was investigated. This work revealed distinct strain-sensing behavior and sensitivities (gauge factor, GF) depending on both factors. The highest GF values were attained with 0.13 wt.% of rGO at various strains. The stability and reproducibility of the most promising self-sensing nanocomposites were also evaluated through ten stretching/relaxing cycles, and a distinct behavior was observed. While the deformation of the conductive network formed by rGO proved to be predominantly elastic and reversible, nanocomposite sensors containing 0.714 wt.% of CNFs showed that new conductive pathways were established between neighboring CNFs. Based on the best results, formulations were selected for the manufacturing of pre-impregnated materials and related smart CFRP composites. Digital image correlation was synchronized with electrical resistance variation to study the strain-sensing capabilities of modified CFRP composites (at 90° orientation). Promising results were achieved through the incorporation of CNFs since they are able to form new conductive pathways and penetrate between micrometer-sized fibers. Full article
(This article belongs to the Section Polymer Applications)
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23 pages, 2709 KB  
Review
Advanced Micro/Nanocapsules for Self-Healing Coatings
by Ioannis A. Kartsonakis, Artemis Kontiza and Irene A. Kanellopoulou
Appl. Sci. 2024, 14(18), 8396; https://doi.org/10.3390/app14188396 - 18 Sep 2024
Cited by 13 | Viewed by 8566
Abstract
The concept of intelligence has many applications, such as in coatings and cyber security. Smart coatings have the ability to sense and/or respond to external stimuli and generally interact with their environment. Self-healing coatings represent a significant advance in improving material durability and [...] Read more.
The concept of intelligence has many applications, such as in coatings and cyber security. Smart coatings have the ability to sense and/or respond to external stimuli and generally interact with their environment. Self-healing coatings represent a significant advance in improving material durability and performance using microcapsules and nanocontainers loaded with self-healing agents, catalysts, corrosion inhibitors, and water-repellents. These smart coatings can repair damage on their own and restore mechanical properties without external intervention and are inspired by biological systems. Properties that are affected by either momentary or continuous external stimuli in smart coatings include corrosion, fouling, fungal, self-healing, piezoelectric, and microbiological properties. These coating properties can be obtained via combinations of either organic or inorganic polymer phases, additives, and pigments. In this article, a review of the advancements in micro/nanocapsules for self-healing coatings is reported from the aspect of extrinsic self-healing ability. The concept of extrinsic self-healing coatings is based on the use of capsules or multichannel vascular systems loaded with healing agents/inhibitors. The result is that self-healing coatings exhibit improved properties compared to traditional coatings. Self-healing anticorrosive coating not only enhances passive barrier function but also realizes active defense. As a result, there is a significant improvement in the service life and overall performance of the coating. Future research should be devoted to refining self-healing mechanisms and developing cost-effective solutions for a wide range of industrial applications. Full article
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21 pages, 1388 KB  
Systematic Review
Applications of Smart and Self-Sensing Materials for Structural Health Monitoring in Civil Engineering: A Systematic Review
by Ana Raina Carneiro Vasconcelos, Ryan Araújo de Matos, Mariana Vella Silveira and Esequiel Mesquita
Buildings 2024, 14(8), 2345; https://doi.org/10.3390/buildings14082345 - 29 Jul 2024
Cited by 12 | Viewed by 6344
Abstract
Civil infrastructures are constantly exposed to environmental effects that can contribute to deterioration. Early detection of damage is crucial to prevent catastrophic failures. Structural Health Monitoring (SHM) systems are essential for ensuring the safety and reliability of structures by continuously monitoring and recording [...] Read more.
Civil infrastructures are constantly exposed to environmental effects that can contribute to deterioration. Early detection of damage is crucial to prevent catastrophic failures. Structural Health Monitoring (SHM) systems are essential for ensuring the safety and reliability of structures by continuously monitoring and recording data to identify damage-induced changes. In this context, self-sensing composites, formed by incorporating conductive nanomaterials into a matrix, offer intrinsic sensing capabilities through piezoresistivity and various conduction mechanisms. The paper reviews how SHM with self-sensing materials can be applied to civil infrastructure while also highlighting important research articles in this field. The result demonstrates increased dissemination of self-sensing materials for civil engineering worldwide. Their use in core infrastructure components enhances functionality, safety, and transportation efficiency. Among nanomaterials used as additions to produce self-sensing materials in small portions, carbon nanotubes have the most citations and, consequently, the most studies, followed by carbon fiber and steel fiber. This highlight identifies knowledge gaps, benchmark technologies, and outlines self-sensing materials for future research. Full article
(This article belongs to the Section Building Materials, and Repair & Renovation)
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18 pages, 7996 KB  
Article
Development of a Device for Monitoring Erosion in the Field
by Thiago Augusto Mendes, Juan Félix Rodriguez Rebolledo, Sávio Aparecido dos Santos Pereira, Marcus Vinicius Miguel de Oliveira and Klebber Teodomiro Martins Formiga
Micromachines 2024, 15(7), 880; https://doi.org/10.3390/mi15070880 - 4 Jul 2024
Viewed by 1419
Abstract
Monitoring erosion is an important part of understanding the causes of this geotechnical and geological phenomenon. In order to monitor them, it is necessary to develop equipment that is sophisticated enough to resist the sun and water without damage, that is self-mechanized, and [...] Read more.
Monitoring erosion is an important part of understanding the causes of this geotechnical and geological phenomenon. In order to monitor them, it is necessary to develop equipment that is sophisticated enough to resist the sun and water without damage, that is self-mechanized, and that can support the amount of data collected. This article introduces a rain-triggered field erosion monitoring device composed of three main modules: control, capture, and sensing. The control module comprises both hardware and firmware with embedded software. The capture module integrates a camera for recording, while the sensing module includes rain sensors. By filming experimental soil samples under simulated rain events, the device demonstrated satisfactory performance in terms of activation and deactivation programming times, daytime image quality without artificial lighting, and equipment protection. The great differences about this monitoring device are its ease of use, low cost, and the quality it offers. These results suggest its potential effectiveness in capturing the progression of field erosive processes. Full article
(This article belongs to the Topic Innovation, Communication and Engineering)
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28 pages, 112056 KB  
Article
Spatiotemporal Analysis of Ecological Security Based on Landscape Patterns
by Huaidan Zhang, Ke Nie and Xueling Wu
ISPRS Int. J. Geo-Inf. 2024, 13(6), 204; https://doi.org/10.3390/ijgi13060204 - 16 Jun 2024
Cited by 1 | Viewed by 1676
Abstract
With rapid urbanization, environmental problems such as soil erosion and resource shortages have emerged. Ecological environmental quality is decreasing, and ecological security issues are becoming increasingly prominent; thus, relevant research is particularly urgent. The ecological security issue is complex due to many influencing [...] Read more.
With rapid urbanization, environmental problems such as soil erosion and resource shortages have emerged. Ecological environmental quality is decreasing, and ecological security issues are becoming increasingly prominent; thus, relevant research is particularly urgent. The ecological security issue is complex due to many influencing factors. The transformation of landscape type is the most important factor affecting ecological security. Therefore, there is an urgent need to optimize and screen for the indicator factors that affect ecological security, carry out a dynamic evaluation of ecological security based on landscape pattern analysis, and analyze the driving forces behind ecological security changes. Song County is located in the ecological core area of the Funiu Mountains in western Henan, with complex topography and geomorphology; large changes in landscape patterns in recent years; frequent geological disasters, which have posed a greater threat to people’s life and property safety; and significant ecological security problems. This paper takes Song County as the research area, using the decision tree model to obtain the land use classification results of four periods in Song County in 2005, 2010, 2015, and 2020 based on remote sensing images. Landscape pattern analysis is conducted from two aspects: patch level and landscape level. On this basis, ecological security evaluation indicators are constructed from three levels: pressure, state, and response, and the comprehensive index model is used to obtain the results of four ecological security evaluations. Exploratory spatial data analysis (ESDA) is used to conduct research and prediction on spatiotemporal differentiation. Finally, the spatial heterogeneity relationship between the ecological security level and its driving factors in Song County is quantitatively analyzed using a geographic detector model. The results clearly show that the overall landscape form gradually tends to develop in the direction of complex irregularity. Due to frequent geological disasters and strong human engineering activities near the core areas of the Luhun Reservoir and Yi River basin, as well as Baihejie Village in Baihe Township and Che Village in Muzhijie Township, the landscape pattern is changing considerably. The self-restoration ability of the land’s ecosystem is gradually weakening, and the degree of ecological damage is gradually accelerating. The ecological security level is unsafe, the area of unsafe security is gradually increasing, and the ecological security index (ESI) will continue to decrease in the future. To improve ecological security, we recommend paying attention to land conservation and rational utilization while pursuing economic development. Full article
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