Search Results (633)

Optical fiber technology is becoming essential in modern radiation therapy, enabling precise, real-time, and minimally invasive monitoring. As oncology moves toward patient-specific treatment, there is growing demand for adaptable and biologically compatible sensing tools. Fiber-optic systems meet this need by integrating into clinical workflows with highly localized dosimetric and spectroscopic feedback. Their small size and flexibility allow deployment within catheters, endoscopes, or treatment applicators, making them suitable for both external beam and internal therapies. This paper reviews the fundamental principles and diverse applications of optical fiber sensing technologies in radiation oncology, focusing on dosimetry, spectroscopy, imaging, and adaptive radiotherapy. Implementations such as scintillating and Bragg grating-based dosimeters demonstrate feasibility for in vivo dose monitoring. Spectroscopic techniques, such as Raman and fluorescence spectroscopy, offer real-time insights into tissue biochemistry, aiding in treatment response assessment and tumor characterization. However, despite such advantages of optical fiber sensors, challenges such as signal attenuation, calibration demands, and limited dynamic range remain. This paper further explores clinical application, technical limitations, and future directions, emphasizing multiplexing capabilities, integration and regulatory considerations, and trends in machine learning development. Collectively, these optical fiber sensing technologies show strong potential to improve the safety, accuracy, and adaptability of radiation therapy in personalized cancer care. Full article

As global Marine resource development continues to expand into deep-sea and ultra-deep-sea domains, the intelligent and green transformation of deep-sea aquaculture equipment has become a key direction for high-quality development of the Marine economy. Large deep-sea cages are considered essential equipment for deep-sea aquaculture. However, there are significant challenges associated with ensuring their structural integrity and long-term monitoring capabilities in the complex Marine environments characteristic of deep-sea aquaculture. The present study focuses on large deep-sea cages, addressing their dynamic response challenges and long-term monitoring power supply needs in complex Marine environments. The present study investigates the nonlinear vibration characteristics of flexible net structures under complex fluid loads. To this end, a multi-field coupled dynamic model is constructed to reveal vibration response patterns and instability mechanisms. A self-powered sensing system based on triboelectric nanogenerator (TENG) technology has been developed, featuring a curved surface adaptive TENG array for the real-time monitoring of net vibration states. This review aims to focus on the research of optimizing the design of curved surface adaptive TENG arrays and deep-sea cage monitoring. The present study will investigate the mechanisms of energy transfer and cooperative capture within multi-body coupled cage systems. In addition, the biomechanics of fish–cage flow field interactions and micro-energy capture technologies will be examined. By integrating different disciplinary perspectives and adopting innovative approaches, this work aims to break through key technical bottlenecks, thereby laying the necessary theoretical and technical foundations for optimizing the design and safe operation of large deep-sea cages. Full article

The accurate monitoring of crop water status is critical for optimizing irrigation strategies in winter wheat. Compared with satellite remote sensing, unmanned aerial vehicle (UAV) technology offers superior spatial resolution, temporal flexibility, and controllable data acquisition, making it an ideal choice for the small-scale monitoring of crop water status. During 2023–2025, field experiments were conducted to predict crop water status using UAV images in the North China Plain (NCP). Thirteen vegetation indices were calculated and their correlations with observed crop water content (CWC) and equivalent water thickness (EWT) were analyzed. Four machine learning (ML) models, namely, random forest (RF), decision tree (DT), LightGBM, and CatBoost, were evaluated for their inversion accuracy with regard to CWC and EWT in the 2024–2025 growing season of winter wheat. The results show that the ratio vegetation index (RVI, NIR/R) exhibited the strongest correlation with CWC (R = 0.97) during critical growth stages. Among the ML models, CatBoost demonstrated superior performance, achieving R² values of 0.992 (CWC) and 0.962 (EWT) in training datasets, with corresponding RMSE values of 0.012% and 0.1907 g cm⁻², respectively. The model maintained robust performance in testing (R² = 0.893 for CWC, and R² = 0.961 for EWT), outperforming conventional approaches like RF and DT. High-resolution (5 cm) inversion maps successfully identified spatial variability in crop water status across experimental plots. The CatBoost-RVI framework proved particularly effective during the booting and flowering stages, providing reliable references for precision irrigation management in the NCP. Full article

Forest fire monitoring is of great significance for ecological protection and public safety. This study proposes a monitoring technology based on LoRa-Mesh (Long Range-Mesh) wireless communication, integrating temperature and humidity sensing, image acquisition, fire identification, data transmission, and energy-saving optimization. To address the limitations of traditional LoRa networks in flexibility and energy consumption, a Layered Dynamic Synchronization Energy-saving (LDSE) protocol is designed. By constructing a hierarchical network, employing implicit route exploration, multi-channel and multi-path communication, and time synchronization optimization, the protocol significantly reduces packet loss rate and system energy consumption. Experimental results demonstrate that the LDSE protocol outperforms the traditional Ad hoc On-Demand Distance Vector Routing Protocol (AODV) in terms of packet loss rate, energy consumption, and latency. Additionally, the proposed energy-saving algorithm significantly reduces system power consumption, with the node sleep-relay mode exhibiting optimal energy efficiency. Experimental verification confirms that the system achieves high reliability, low power consumption, and efficient data transmission, providing an effective IoT solution for forest fire prevention. Full article

Zinc oxide (ZnO) thin films have attracted increasing attention as promising materials for sensing applications due to their wide band gap, high exciton binding energy, and remarkable chemical stability. However, the inherent limitations of pure ZnO, such as moderate sensitivity, selectivity, and relatively high operating temperatures, limit its widespread use in advanced sensing technologies. Co-doping, or dual doping with two distinct elements, has emerged as an effective strategy to overcome these challenges by synergistically tailoring the structural, electronic, and surface properties of ZnO thin films. This review provides a comprehensive overview of recent advances in the development of co-doped ZnO thin films for sensing applications. The focus is on the role of different combinations of dopants, including transition metals, rare earth elements, and non-metals, in modulating the charge carrier concentration, oxygen vacancy density, and adsorption dynamics. These effects collectively enhance the sensing properties and long-term stability and reduce detection limits. The analysis highlights the correlations between synthesis methods, dopant incorporation mechanisms, and resulting sensor performance. Key challenges such as dopant clustering, reproducibility, and scalability are discussed, along with emerging opportunities in flexible room-temperature sensor platforms. Overall, it has been demonstrated that co-doped ZnO thin films represent a versatile and tunable class of sensing materials with strong potential for next-generation environmental and biomedical monitoring. Full article

Urban soundscapes are complex due to the interaction of different sound sources and the influence of structures on sound propagation. Moreover, the dynamic nature of sounds over time and space adds to this complexity. Virtual reality (VR) has emerged as a powerful tool to simulate acoustic and visual environments, offering users an immersive sense of presence in controlled settings. This technology facilitates more accurate and predictive assessment of urban environments. It serves as a flexible tool for exploring, analyzing, and interpreting them under repeatable conditions. This study presents a systematic literature review focusing on research that integrates VR technology for the audiovisual reconstruction of urban environments. This topic remains relatively underrepresented in the existing literature. A total of 69 peer-reviewed studies were analyzed in this systematic review. The studies were classified according to research goals, selected urban environments, VR technologies used, technical equipment, and experimental setups. In this study, the relationship between the tools used in urban VR representations is examined, and experimental setups are discussed from both technical and perceptual perspectives. This paper highlights existing challenges and opportunities in using VR to assess soundscapes and offers practical insights for future applications of VR in urban environments. Full article

Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production. Full article

As smart campus construction continues to advance, traditional safety monitoring and environmental sensing systems are increasingly showing limitations in sensitivity, anti-interference capability, and deployment flexibility. Optical fiber sensing (OFS) technology, with its advantages of high sensitivity, passive operation, immunity to electromagnetic interference, and long-distance distributed sensing, provides a novel solution for real-time monitoring and early warning of critical campus infrastructure. This review systematically examines representative applications of OFS technology in smart campus scenarios, including structural health monitoring of academic buildings, laboratory environmental sensing, and intelligent campus security. By analyzing the technical characteristics of various types of optical fiber sensors, the paper explores emerging developments and future potential of OFS in supporting intelligent campus construction. Finally, the feasibility of building data acquisition, transmission, and visualization platforms based on OFS systems is discussed, highlighting their promising roles in campus safety operations, the integration of teaching and research, and intelligent equipment management. Full article

Integrated sensing and communication (ISAC) is a transformative technology for sixth-generation (6G) wireless networks. In this paper, we investigate end-to-end constellation mapping and demapping in ISAC systems, leveraging OFDM-based waveforms and an adaptive DNN architecture for pulse-based transmission. Specifically, we propose an end-to-end autoencoder framework that optimizes the constellation through adaptive symbol distribution shaping via deep learning, enhancing communication reliability with symbol mapping and boosting sensing capabilities with an improved peak-to-sidelobe ratio (PSLR). The autoencoder consists of an autoencoder mapper (AE-Mapper) and an autoencoder demapper (AE-Demapper), jointly trained using a composite loss function to optimize constellation points and achieve flexible performance balance in communication and sensing. Simulation results demonstrate that the proposed DNN-based end-to-end design achieves dynamic balance between PSLR of the autocorrelation function (ACF) and bit error rate (BER). Full article

Intelligent gas sensors are indispensable devices widely used in modern society for environmental monitoring, healthcare, the food industry, and public safety. Recent advancements in wireless communication, cloud storage, computing technologies, and artificial intelligence algorithms have significantly enhanced the intelligence level and performance requirements of these sensors. Particularly in the Internet of Things (IoT) environment, flexible and wearable gas sensors are playing an increasingly important role due to their convenience and real-time monitoring capabilities. This review systematically summarizes the latest progress in intelligent gas sensors, covering conceptual frameworks, working principles, and applications across various fields, as well as the construction of IoT networks using sensor arrays. It provides a comprehensive assessment of recent advancements in intelligent gas sensing technologies, highlighting innovations in device architecture, functional mechanisms, and performance in diverse application environments. Special emphasis is placed on transformative developments in flexible and wearable sensor platforms and the enhanced intelligence achieved through the integration of advanced computational algorithms and machine learning techniques. Finally, a summary and future prospects are presented. Despite significant progress, intelligent gas sensors still face challenges related to sensing accuracy, stability, and cost in future applications. Full article

The advancement of sports science has heightened demands for precise monitoring of athletes’ technical movements, physiological status, and performance. Optical fiber sensing (OFS) technology, with its unique advantages including high sensitivity, immunity to electromagnetic interference, capability for distributed sensing, and strong biocompatibility, demonstrates significant application potential in sports science. This review systematically examines the technical principles, innovative breakthroughs, and practical application cases of optical fiber sensors in various domains: monitoring key human physiological parameters such as respiration, heart rate, and body temperature; capturing motion and analyzing movement covering muscle activity, joint angles, and gait; integrating within smart sports equipment and protective gear; and monitoring sports apparatus and environments. The value of OFS technology is further analyzed in areas including sports biomechanics analysis, training load monitoring, injury prevention, and rehabilitation optimization. Concurrently, current technical bottlenecks such as the need for enhanced sensitivity, advancements in flexible packaging technologies, cost control, system integration, and miniaturization are discussed. Future development trends involving the integration of OFS with artificial intelligence, the Internet of Things, and new materials are explored, aiming to provide a theoretical foundation for sports medicine and training optimization. Full article

Biopolymers have emerged as a transformative class of materials that reconcile high-performance functionality with environmental stewardship. Their inherent capacity for controlled degradation and biocompatibility has driven rapid advancements across electronics, sensing, actuation, and healthcare. In flexible electronics, these polymers serve as substrates, dielectrics, and conductive composites that enable transient devices, reducing electronic waste without compromising electrical performance. Within sensing and actuation, biodegradable polymer matrices facilitate the development of fully resorbable biosensors and soft actuators. These systems harness tailored degradation kinetics to achieve temporal control over signal transduction and mechanical response, unlocking applications in in vivo monitoring and on-demand drug delivery. In healthcare, biodegradable polymers underpin novel approaches in tissue engineering, wound healing, and bioresorbable implants. Their tunable chemical architectures and processing versatility allow for precise regulation of mechanical properties, degradation rates, and therapeutic payloads, fostering seamless integration with biological environments. The convergence of these emerging applications underscores the pivotal role of biodegradable polymers in advancing sustainable technology and personalized medicine. Continued interdisciplinary research into polymer design, processing strategies, and integration techniques will accelerate commercialization and broaden the impact of these lower eCO₂ value materials across diverse sectors. This perspective article comments on the innovation in these sectors that go beyond the applications of biodegradable materials in packaging applications. Full article

With the rapid development of wearable devices and intelligent human–machine interaction technologies, the demand for high-precision pressure sensors has soared. Piezoresistive pressure sensors excel due to their simple structure, low cost, and high sensitivity, among which flexible piezoresistive pressure sensors based on porous polymers have become a research focus, thanks to their unique 3D porous structure and excellent performance. This review summarizes recent advances: it introduces key performance metrics and the piezoresistive sensing mechanism; outlines porous structure preparation methods (phase separation, 3D printing, electrospinning) with their principles, advantages, and limitations; examines conductive fillers (carbon-based, polymer, metal, MXene) with their properties and applications; and highlights flexible substrates (silicone, polyurethane, polyimide, natural polymers) in ensuring mechanical compliance and device integration. Studies show material innovation, structural optimization, and process improvement can significantly enhance sensor accuracy, stability, and durability, helping break traditional performance bottlenecks. Future prospects are broad in tactile sensing, biomedical monitoring, and human–machine interaction, providing references for related research and industrial development. Full article

The convergence of carbon nanomaterials and functional polymers has led to the emergence of smart carbon–polymer nanocomposites (CPNCs), which possess exceptional potential for next-generation sensing and actuation systems. These hybrid materials exhibit unique combinations of electrical, thermal, and mechanical properties, along with tunable responsiveness to external stimuli such as strain, pressure, temperature, light, and chemical environments. This review provides a comprehensive overview of recent advances in the design and synthesis of CPNCs, focusing on their application in multifunctional sensors and actuator technologies. Key carbon nanomaterials including graphene, carbon nanotubes (CNTs), and MXenes were examined in the context of their integration into polymer matrices to enhance performance parameters such as sensitivity, flexibility, response time, and durability. The review also highlights novel fabrication techniques, such as 3D printing, self-assembly, and in situ polymerization, that are driving innovation in device architectures. Applications in wearable electronics, soft robotics, biomedical diagnostics, and environmental monitoring are discussed to illustrate the transformative impact of CPNCs. Finally, this review addresses current challenges and outlines future research directions toward scalable manufacturing, environmental stability, and multifunctional integration for the real-world deployment of smart sensing and actuation systems. Full article

The growing global demand for rapid, sensitive, and cost-effective food safety monitoring has driven the development of nanozyme-based biosensors as alternatives to natural enzyme-based methods. Among various nanozymes, bimetallic gold–platinum (AuPt) nanozymes show superior catalytic performance compared to monometallic and other Au-based bimetallic hybrids. This is due to their synergistic colorimetric, catalytic, geometric, and ensemble properties. This review systematically evaluates AuPt nanozymes in food safety applications, focusing on their synthesis, structural design, and practical uses. Various structural types are highlighted, including plain, magnetic, porous nanomaterial-labeled, and flexible nanomaterial-loaded AuPt hybrids. Key synthesis methods such as seed-mediated growth and one-pot procedures with different reducing agents are summarized. Detection modes covered include colorimetric, electrochemical, and multimodal sensing, demonstrating efficient detection of important food contaminants. Key innovations include core–shell designs for enhanced catalytic activity, new synthesis strategies for improved structural control, and combined detection modes to increase reliability and reduce false positives. Challenges and future opportunities are discussed, such as standardizing synthesis protocols, scaling up production, and integration with advanced sensing platforms. This review aims to accelerate the translation of AuPt nanozyme technology into practical food safety monitoring solutions that improve food security and public health. Full article