Search Results (7,739)

Carbon fiber-reinforced polymer (CFRP) laminates have been widely coated on aged and damaged structures for recovering or enhancing their structural performance. The health conditions of the coated composite structures have been given high attention, as they are critically important for assessing operational safety and residual service life. However, the current problem is the lack of an efficient, long-term, and stable monitoring technique to characterize the structural behavior of coated composite structures in the whole life cycle. For this reason, bare and packaged fiber Bragg grating (FBG) sensors have been specially developed and designed in sensing networks to monitor the structural performance of CFRP-coated composite beams under different loads. Some optical fibers have also been inserted in the CFRP laminates to configure the smart CFRP component. Detailed data interpretation has been conducted to declare the strengthening process and effect. Finite element simulation and simplified theoretical analysis have been conducted to validate the experimental testing results and the deformation profiles of steel beams before and after the CFRP coating has been carefully checked. Results indicate that the proposed FBG sensors and sensing layout can accurately reflect the structural performance of the composite beam structure, and the CFRP coating can share partial loads, which finally leads to the downward shift in the centroidal axis, with a value of about 10 mm. The externally bonded sensors generally show good stability and high sensitivity to the applied load and temperature-induced inner stress variation. The study provides a straightforward instruction for the establishment of a structural health monitoring system for CFRP-coated composite structures in the whole life cycle. Full article

Silicon-based on-chip light sources are important since they can provide a compact solution for various applications in the field of high-speed optical communications, high-precision sensing, quantum information processing, and so on. We review the progress of silicon-based on-chip light sources in various materials. We provide some key parameters like pump thresholds, output powers, and pump schemes of on-chip lasers based on various materials. Finally, we point out the existing issues in the current investigations and possible solutions in the future. Full article

As naturally derived composite materials, flexible transparent wood and bamboo (FTW, FTB) present notable advantages, such as straightforward preparation, high light transmittance, exceptional environmental sustainability, superior mechanical properties, low thermal conductivity, and multifunctional capabilities. Their high conductivity and sensing capabilities provide viable alternatives to conventional materials in flexible electronics. This article reviews the preparation of FTW and FTB through a top-down approach, beginning with examining how the microstructure of wood and bamboo affects the properties of these materials. It subsequently summarizes various manufacturing techniques and explores potential applications across diverse sectors. Finally, the article addresses current challenges and emphasizes the necessity for further research and innovation to promote the sustainable development of FTW and FTB in advanced applications. Full article

Point-of-care (POC) diagnostic technologies have become essential for the real-time monitoring and management of chronic wounds, where maintaining a moist environment and controlling pH levels are critical for effective healing. In this study, a flexible pH sensor based on a graphene/molybdenum disulfide (graphene/MoS₂) composite interdigitated electrode (IDE) structure was fabricated using pulsed laser ablation. The pH sensor, with an active area of 30 mm × 30 mm, exhibited good adhesion to the polyethylene terephthalate (PET) substrate and maintained structural integrity under repeated bending cycles. Precise ablation was achieved under optimized conditions of 4.35 J/cm² laser fluence, a repetition rate of 300 kHz, and a scanning speed of 500 mm/s, enabling the formation of defect-free IDE arrays without substrate damage. The influence of laser processing parameters on the surface morphology, electrical conductivity, and wettability of the composite thin films was systematically characterized. The fabricated pH sensor exhibited high sensitivity (~4.7% change in current per pH unit) across the pH 2–10 range, rapid response within ~5.2 s, and excellent mechanical stability under 100 bending cycles with negligible performance degradation. Moreover, the sensor retained > 95% of its stable sensitivity after 7 days of ambient storage. Furthermore, the pH response behavior was evaluated for electrode structures with different pitches, demonstrating that structural design parameters critically impact sensing performance. These results offer valuable insights into the scalable fabrication of flexible, wearable pH sensors, with promising applications in wound monitoring and personalized healthcare systems. Full article

Asphalt pavement cracking represents a prevalent form of deterioration that significantly compromises road performance and safety under the combined effects of environmental factors and traffic loading. Crack sealing has emerged as a widely adopted and cost-effective preventive maintenance strategy that restores the pavement’s structural integrity and extends service life. This paper presents a systematic review of the development of crack sealing technology, conducts a comparative analysis of conventional sealing materials (including emulsified asphalt, hot-applied asphalt, polymer-modified asphalt, and rubber-modified asphalt), and examines the existing performance evaluation methodologies. Critical failure mechanisms are thoroughly investigated, including interfacial bond failure resulting from construction defects, material aging and degradation, hydrodynamic scouring effects, and thermal cycling impacts. Additionally, this review examines advanced sensing methodologies for detecting premature sealant failure, encompassing both non-destructive testing techniques and active sensing technologies utilizing intelligent crack sealing materials with embedded monitoring capabilities. Based on current research gaps, this paper identifies future research directions to guide the development of intelligent and sustainable asphalt pavement crack repair technologies. The proposed research framework provides valuable insights for researchers and practitioners seeking to improve the long-term effectiveness of pavement maintenance strategies. Full article

In reality, some influencers who publicly share failures can gain more attention, which defies common sense. Existing research primarily focuses on the depth of self-disclosure by influencers, which fails to explain the current phenomena. Here, we focus on negative self-disclosure and explore whether an influencer’s proactive disclosure of failures can enhance purchase intentions, along with the underlying mechanisms and boundary conditions of this strategy. We conducted three online experiments on the Credamo platform. Study 1 (N = 94) explored the main and mediating effect, whereas Study 2 (N = 238) and Study 3 (N = 238) investigated the moderating effects of observer and influencer characteristics, respectively. The following conclusions are drawn: (1) Influencers’ proactive disclosures of failures can boost purchase intentions when recommending products; (2) perceived authenticity plays a mediating role in this process; (3) the degree of viewers’ self-discrepancy moderates the mediating effect of perceived authenticity; and (4) influencers’ follower scale moderates the impact on purchase intentions. This study offers practical implications for influencers on how to enhance marketing effectiveness through self-disclosure. Full article

The non-metallic armored optoelectronic cable (NAOC) serves as a critical component in deep-sea scientific winch systems. Due to its low density and excellent corrosion resistance, it has been widely adopted in marine exploration. However, as the operational water depth increases, the NAOC is subjected to multi-layer winding on the drum, resulting in a cumulative temperature rise that can severely impair insulation performance and compromise the safety of deep-sea operations. To address this issue, this paper conducts temperature rise experiments on NAOCs using a distributed temperature sensing test rig to investigate the effects of the number of winding layers and current amplitude on their temperature rise characteristics. Based on the experimental results, an electromagnetic thermal multi-physics field coupling simulation model is established to further examine the influence of these factors on the maximum operation time of the NAOC. Finally, a multi-variable predictive model for maximum operation time is developed, incorporating current amplitude, the number of winding layers, and ambient temperature, with a fitting accuracy of 97.92%. This research provides theoretical and technical support for ensuring the safety of deep-sea scientific operations and improving the reliability of deep-sea equipment. Full article

What can hybridity teach us? The answer I posit is–like water–to embrace movement in all its forms. We are currently experiencing the sixth mass extinction event on earth and yet few scholars give prolonged attention to how we are to sustainably move our human inheritances forward on this increasingly uninhabitable planet. Reflecting on the work of transnational poets, specifically South Korean poet Kim Hyesoon translated through Korean American poet Don Mee Choi and Lebanese American poet Etel Adnan, we are reminded of the myriad ways in which humans can move and survive in a foreign yet familiar world. This paper finds its support in the confluence of transnational contemporary feminist poetics, formalism, environmentalism, and posthumanism. Through the use of various critical discourses, this paper considers how movement and its hybrid capacities offer a new understanding of contemporary North American poetics. In this sense, a poem should be viewed as a dynamic temporal cybernetic system, a vessel, full of energy, simultaneously pulsing with the changing movements and constrictions of everyday life. Full article

Silk fibroin (SF)-based intelligent wearable systems represent a frontier research direction in artificial intelligence and precision medicine. Their core efficacy stems from the inherent advantages of silk fibroin, including excellent mechanical properties, interfacial compatibility, and tunable structure. This article systematically reviews conductive modification strategies for silk fibroin and its research progress in the smart wearable field. It elaborates on the molecular structural basis of silk fibroin for use in smart wearable devices, critically analyzes five conductive functionalization strategies, compares the advantages, disadvantages, and applicable domains of different modification approaches, and summarizes research achievements in areas such as bioelectrical signal sensing, energy conversion and harvesting, and flexible energy storage. Concurrently, an assessment was conducted focusing on the priority performance characteristics of the materials across diverse application scenarios. Specific emphasis was placed on addressing the long-term functional performance (temporal efficacy) and degradation stability of silk fibroin-based conductive materials exhibiting high biocompatibility in implantable settings. Additionally, the compatibility issues arising between externally applied coatings and the native substrate matrix during conductive modification processes were critically examined. The article also identifies challenges that silk fibroin-based smart wearable devices currently face and suggests potential future development directions, providing theoretical guidance and a technical framework for the functional integration and performance optimization of silk fibroin-based smart wearable devices. Full article

Biomimetic robotics and sensor technologies are reshaping the landscape of healthcare and rehabilitation. Despite significant progress across various domains, many areas within healthcare still demand further bio-inspired innovations. To advance this field effectively, it is essential to synthesize existing research, identify persistent knowledge gaps, and establish clear frameworks to guide future developments. This systematic review addresses these needs by analyzing 89 peer-reviewed sources retrieved from the Scopus database, focusing on the application of biomimetic robotics and sensing technologies in healthcare and rehabilitation contexts. The findings indicate a predominant focus on enhancing human mobility and support, with rehabilitative and assistive technologies comprising 61.8% of the reviewed literature. Additionally, 12.36% of the studies incorporate intelligent control systems and Artificial Intelligence (AI), reflecting a growing trend toward adaptive and autonomous solutions. Further technological advancements are demonstrated by research in bioengineering applications (13.48%) and innovations in soft robotics with smart actuation mechanisms (11.24%). The development of medical robots (7.87%) and wearable robotics, including exosuits (10.11%), underscores specific progress in clinical and patient-centered care. Moreover, the emergence of transdisciplinary approaches, present in 6.74% of the studies, highlights the increasing convergence of diverse fields in tackling complex healthcare challenges. By consolidating current research efforts, this review aims to provide a comprehensive overview of the state of the art, serving as a foundation for future investigations aimed at improving healthcare outcomes and enhancing quality of life. Full article

Termites pose significant threats to the structural integrity of embankments due to their nesting and tunneling behavior, which leads to internal voids, water leakage, or even dam failure. This review systematically classifies and evaluates current termite detection techniques in the context of embankment maintenance, focusing on physical sensing technologies and biological characteristic-based methods. Physical sensing methods enable non-invasive localization of subsurface anomalies, including ground-penetrating radar, acoustic detection, and electrical resistivity imaging. Biological characteristic-based methods, such as electronic noses, sniffer dogs, visual inspection, intelligent monitoring, and UAV-based image analysis, are capable of detecting volatile compounds and surface activity signs associated with termites. The review summarizes key principles, application scenarios, advantages, and limitations of each technique. It also highlights integrated multi-sensor frameworks and artificial intelligence algorithms as emerging solutions to enhance detection accuracy, adaptability, and automation. The findings suggest that future termite detection in embankments will rely on interdisciplinary integration and intelligent monitoring systems to support early warning, rapid response, and long-term structural resilience. This work provides a scientific foundation and practical reference for advancing termite management and embankment safety strategies. Full article

The water regime in Pakistan’s northern region has experienced significant changes regarding hydrological extremes like floods because of climate change. Coupling hydrological models with remote sensing data can be valuable for flow simulation in data-scarce regions. This study focused on simulating the snow- and glacier-melt runoff using the snowmelt runoff model (SRM) in the Gilgit and Kachura River Basins of the upper Indus basin (UIB). The SRM was applied by coupling it with in situ and improved cloud-free MODIS snow and glacier composite satellite data (MOYDGL06) to simulate the flow under current and future climate scenarios. The SRM showed significant results: the Nash–Sutcliffe coefficient (NSE) for the calibration and validation period was between 0.93 and 0.97, and the difference in volume (between the simulated and observed flow) was in the range of −1.5 to 2.8% for both catchments. The flow tends to increase by 0.3–10.8% for both regions (with a higher increase in Gilgit) under mid- and late-21st-century climate scenarios. The Gilgit Basin’s higher hydrological sensitivity to climate change, compared to the Kachura Basin, stems from its lower mean elevation, seasonal snow dominance, and greater temperature-induced melt exposure. This study concludes that the simple temperature-based models, such as the SRM, coupled with improved satellite snow cover data, are reliable in simulating the current and future flows from the data-scarce mountainous catchments of Pakistan. The outcomes are valuable and can be used to anticipate and lessen any threat of flooding to the local community and the environment under the changing climate. This study may support flood assessment and mapping models in future flood risk reduction plans. Full article

Optical sensors have undergone significant evolution, transitioning from discrete optical microsystems toward sophisticated photonic integrated circuits (PICs) that leverage artificial intelligence (AI) for enhanced functionality. This review systematically explores the integration of optical sensing technologies with AI, charting the advancement from conventional optical microsystems to AI-driven smart devices. First, we examine classical optical sensing methodologies, including refractive index sensing, surface-enhanced infrared absorption (SEIRA), surface-enhanced Raman spectroscopy (SERS), surface plasmon-enhanced chiral spectroscopy, and surface-enhanced fluorescence (SEF) spectroscopy, highlighting their principles, capabilities, and limitations. Subsequently, we analyze the architecture of PIC-based sensing platforms, emphasizing their miniaturization, scalability, and real-time detection performance. This review then introduces the emerging paradigm of in-sensor computing, where AI algorithms are integrated directly within photonic devices, enabling real-time data processing, decision making, and enhanced system autonomy. Finally, we offer a comprehensive outlook on current technological challenges and future research directions, addressing integration complexity, material compatibility, and data processing bottlenecks. This review provides timely insights into the transformative potential of AI-enhanced PIC sensors, setting the stage for future innovations in autonomous, intelligent sensing applications. Full article

The Wyrtki Jet (WJ), a pivotal surface circulation system in the equatorial Indian Ocean, exerts significant regulatory control over regional climate dynamics through its intense eastward transport characteristics, which modulate water mass exchange, thermohaline balance, and cross-basin energy transfer. To address the scarcity of in situ observational data, this study developed a satellite remote sensing-driven multi-parameter coupled model and reconstructed the WJ’s seasonal variations using the XGBoost machine learning algorithm. The results revealed that wind stress components, sea surface temperature, and wind stress curl serve as the primary drivers of its seasonal dynamics. The XGBoost model demonstrated superior performance in reconstructing WJ’s seasonal variations, achieving coefficients of determination (R²) exceeding 0.97 across all seasons and maintaining root mean square errors (RMSE) below 0.2 m/s across all seasons. The reconstructed currents exhibited strong consistency with the Ocean Surface Current Analysis Real-time (OSCAR) dataset, showing errors below 0.05 m/s in spring and autumn and under 0.1 m/s in summer and winter. The proposed multi-feature integrated modeling framework delivers a high spatiotemporal resolution analytical tool for tropical Indian Ocean circulation dynamics research, while simultaneously establishing critical data infrastructure to decode monsoon current coupling mechanisms, advancing early warning systems for extreme climatic events, and optimizing regional marine resource governance. Full article

Remote sensing visual grounding, a pivotal technology bridging natural language and high-resolution remote sensing images, holds significant application value in disaster monitoring, urban planning, and related fields. However, it faces critical challenges due to the inherent scale heterogeneity, semantic complexity, and annotation scarcity of remote sensing data. This paper first reviews the development history of remote sensing visual grounding, providing an overview of the basic background knowledge, including fundamental concepts, datasets, and evaluation metrics. Then, it categorizes methods by whether they employ large language models as a pedestal, and provides in-depth analyses of the innovations and limitations of Transformer-based and multimodal large language model-based methods. Furthermore, focusing on remote sensing image characteristics, it discusses cutting-edge techniques such as cross-modal feature fusion, language-guided visual optimization, multi-scale, and hierarchical feature processing, open-set expansion and efficient fine-tuning. Finally, it outlines current bottlenecks and proposes valuable directions for future research. As the first comprehensive review dedicated to remote sensing visual grounding, this work is a reference resource for researchers to grasp domain-specific concepts and track the latest developments. Full article