Health Monitoring Technology Research Articles

Abstract 4137465: Estimating Arterial Stiffness Using a Smart Ring and the Impact of HIIT Exercise: A Pilot Study

Introduction: Arterial stiffness is an important marker of cardiovascular health, but measuring it requires specialized equipment and trained personnel. This study aimed to evaluate a novel method for estimating arterial stiffness using photoplethysmography (PPG) signals recorded by a commercially available device (Oura Ring) and to study the impact of a high-intensity interval training (HIIT) intervention on arterial stiffness. Research Questions: 1. Can a smart ring provide accurate estimates of arterial stiffness comparable to the reference device (Sphygmocor)? 2. Does a three-month supervised HIIT cycling protocol reduce arterial stiffness? Aims: 1. Evaluate a PPG-based arterial stiffness estimation method using a smart ring. 2. Assess the efficacy of HIIT in reducing arterial stiffness and improving VO2max. Methods: The study comprised two parts: 1. A cross-sectional study (N=300, age 18-78) for collecting arterial stiffness data using the Sphygmocor and the Smart Ring to evaluate the PPG-based algorithm. 2. A randomized exercise intervention pilot study (N=80, a subset of the cross-sectional study), assigned to either a three-month HIIT protocol or a control group. Arterial stiffness was measured pre- and post-intervention using both devices. Pearson’s correlation coefficients assessed the agreement between the novel method and reference device, and effects of the intervention were analyzed using ANCOVA. Results: The novel PPG-based method demonstrated good correlation with the Sphygmocor device (R = 0.77 and 0.80 in pre- and post-timepoints, respectively). However, despite improving the participants’ aerobic fitness (average increase in VO2max of 1.41 ml/min/kg; 95% CI: [0.15, 2.67], P = 0.029) the HIIT intervention did not significantly improve arterial stiffness, as measured by both methods. Conclusions: The developed smart ring method provides a reliable, non-invasive, and continuous estimation of arterial stiffness. This innovative approach could significantly impact how arterial stiffness is monitored in an everyday setting. The null results of the HIIT intervention suggest that HIIT may not be effective in reducing arterial stiffness, highlighting the need for further research into different exercise modalities for cardiovascular health improvement. This study underscores the potential of wearable technology in cardiovascular health monitoring and the importance of evaluating diverse exercise protocols for arterial health.

Bioinspired Ultra‐Stretchable and Highly Sensitive Structural Color Electronic Skins

AbstractOrganisms possess remarkably adaptive ability to complex environments. For example, chameleons can alter their skin color to adapt to varying environments, which has inspired significant advances in bioinspired soft electronic skins (E‐skins), and their wide applications in wearable sensors, intelligent robots, and health monitoring. However, current bioinspired E‐skins face challenges in ultra‐stretchability, high sensitivity, and long‐term stability owing to the intrinsic limitations associated with their mismatched interface between soft matrix and hard conductive fillers, hindering their practical applications. Here, it is reported that bioinspired structural color electronic skins (SC E‐skins) that consist of liquid metal particles (LMPs), periodical ordered colloidal crystal arrays, and ultra‐stretchable hydrogel, imparting synergistic and durable electrical–optical sensing capabilities. Such SC E‐skins demonstrate outstanding performances including superior flexibility (elongation at break > 1100%), high sensitivity (gauge factor = 3.26), fast synergetic electric–optical response time (≈100 ms), outstanding durability (over 1500 cycles), and high accuracy (R2 > 99.5%). These bioinspired SC E‐skins with excellent capability of converting mechanical signals into synergetic electrical–optical outputs hold great promise for smart wearable devices, affording a new horizon in developing advanced health monitoring technologies.

Ultraflexible Vertical Corbino Organic Electrochemical Transistors for Epidermal Signal Monitoring.

Skin-conformal organic electrochemical transistors (OECTs) have attracted significant attention for real-time physiological signal monitoring and are vital for health diagnostics and treatments. However, mechanical harmonization amid the inherent dynamic nature of the skin surface and the acquisition of intrinsic physiological signals are significant challenges that hinder the integration of the ultimate skin interface. Thus, this study proposes a novel 4-terminal (4-T) vertical Corbino OECT, exhibiting high transconductance (>400 mS) and offering remarkable resilience and operational stability at an extremely low voltage of 10mV (1.9% of minimal current change after 104 biasing cycles and endurance up to 103 cycles of repetitive deformation with a 5µm bending radius). Consequently, ultralow-power, motion-resistant epidermal electrocardiogram, electromyogram, and electrooculogram sensors are developed with an exceptional signal-to-noise ratio of 40.1dB. The results of this study present a significant stride in non-invasive, skin-interfaced health-monitoring technologies and herald a new era in integrative health technologies.

Humidity Sensing Using Polymers: A Critical Review of Current Technologies and Emerging Trends

In the post-pandemic era, human demand for a healthy lifestyle and a smart society has surged, leading to vibrant growth in the field of flexible electronic sensor technology for health monitoring. Flexible polymer humidity sensors are not only capable of the real-time monitoring of human respiration and skin moisture information but also serve as a non-contact human–machine interaction method. In addition, the development of moist-electric generation technology is expected to break free from the traditional reliance of flexible electronic devices on power equipment, which is of significant importance for the miniaturization, reliability, and environmentally friendly development of flexible devices. Currently, flexible polymer humidity sensors are playing a significant role in the field of wearable electronic devices and thus have attracted considerable attention. This review begins by introducing the structural types and working principles of various humidity sensors, including the types of capacitive, impedance/resistive, frequency-based, fiber optic, and voltage-based sensors. It mainly focuses on the latest research advancements in flexible polymer humidity sensors, particularly in the modification of humidity-sensitive materials, sensor fabrication, and hygrosensitivity mechanisms. Studies on material composites including different types of polymers, polymers combined with porous nanostructured materials, polymers combined with metal oxides, and two-dimensional materials are reviewed, along with a comparative summary of the fabrication and performance mechanisms of related devices. This paper concludes with a discussion on the current challenges and opportunities faced by flexible polymer humidity sensors, providing new research perspectives for their future development.

Dual‐Modal Sensing Skin Adaptive to Daylight, Darkness, and Ultraviolet Light for Simultaneous Full‐Field Deformation Measurement and Mechanoluminescence Responses

AbstractMechanoluminescence (ML) and digital image correlation (DIC) have emerged as promising optical methods to visualize and measure deformation fields. In this study, a dual‐modal sensing skin, called the ML‐DIC skin is introduced, that is capable of emitting ML and facilitating DIC measurements under various lighting conditions, including daylight, night or darkness, and UV irradiation. Four ML‐DIC skins are fabricated with or without carbon nanotubes (CNTs) using a composite powder consisting of SrAl2O4: Eu,Dy (SAO), and acrylic resin, with CNT milling times of 48, 72, and 96 h for three of four skins, respectively. DIC measurements are performed under multiple lighting conditions for measuring photoluminescence, persistence luminescence, and reflection. Uniaxial tension tests demonstrate the superior performance of ML‐DIC skins with CNTs compared with pristine SAO skins, with the skin subjected to 48 h of CNT dispersion exhibiting optimal performance. Further investigations focus on ML emission and DIC measurements near the crack‐tip vicinity of static and propagating cracks as well as on surfaces above subsurface cracks. The integration of ML and DIC techniques offers a versatile approach for comprehensive deformation analysis applicable to diverse environments, with implications for materials science, engineering, and structural health monitoring.

Enhanced Multi‐Objective Optimization Model for Bridge Performance Assessment and Prediction, Based on Improved PCA, K‐Means Clustering, and Kaplan–Meier Survival Algorithm

ABSTRACTThe research proposes a hybrid algorithm model that combines model‐driven and data‐driven approaches for the direct application of bridge health monitoring technology in bridge management. This comprehensive study encompasses a series of analytical techniques and methodologies to build a multi‐objective optimization model for bridge performance assessment and prediction. It focuses on the processing of multi‐source heterogeneous data, selection of key sub‐parameters using Principal Component Analysis (PCA), enhanced K‐means clustering analysis, determination of structural component target thresholds, time‐dependent survival probability analysis, regression fitting, and timing prediction of the bridge system for both the components of double‐layer truss arch bridge and the bridge system. The initial phase of the study concentrates on the diversification and decentralization of monitored data from various sources, integrating and cleaning data obtained from different sources to ensure data quality and consistency. PCA technique is applied to identify key sub‐parameters that have significant impacts on the performance of structural components. Enhanced K‐means clustering analysis is carried out to effectively group and classify the identified key sub‐parameters. Numerical simulations, including structural nonlinear analysis, are conducted to determine the target thresholds of bridge structure, providing important benchmarks for performance evaluation. Finally, a multi‐parameter regression model is used to evaluate and update the performance of the bridge structure, taking into account survival probability (using the Kaplan–Meier method), maintenance history, and material deterioration to estimate the most critical time for the bridge system. A case study is conducted to validate the suggested comprehensive algorithms for a double‐layer truss arch combination bridge, which contributes to enhancing performance evaluation and predicting the most critical time for structural components and bridge system in the bridge management and maintenance practices. It should not be ignored that, the accuracy and reasonability of bridge structure system performance evaluation and prediction depend largely on the selection of target thresholds.

Directional Characteristic Enhancement of an Omnidirectional Detection Sensor Enabled by Strain Partitioning Effects in a Periodic Composite Hole Substrate.

An omnidirectional stretchable strain sensor with high resolution is a critical component for motion detection and human-machine interaction. It is the current dominant solution to integrate several consistent units into the omnidirectional sensor based on a certain geometric structure. However, the excessive similarity in orientation characteristics among sensing units restricts orientation recognition due to their closely matched strain sensitivity. In this study, based on strain partition modulation (SPM), a sensitivity anisotropic amplification strategy is proposed for resistive strain sensors. The stress distribution of a sensitive conductive network is modulated by structural parameters of the customized periodic hole array introduced underneath the elastomer substrate. Meanwhile, the strain isolation structures are designed on both sides of the sensing unit for stress interference immune. The optimized sensors exhibit excellent sensitivity (19 for 0-80%; 109 for 80%-140%; 368 for 140%-200%), with nearly a 7-fold improvement in the 140%-200% interval compared to bare elastomer sensors. More importantly, a sensing array composed of multiple units with different hole configurations can highlight orientation characteristics with amplitude difference between channels reaching up to 29 times. For the 48-class strain-orientation decoupling task, the recognition rate of the sensitivity-differentiated layout sensor with the lightweight deep learning network is as high as 96.01%, superior to that of 85.7% for the sensitivity-consistent layout. Furthermore, the application of the sensor to the fitness field demonstrates an accurate recognition of the wrist flexion direction (98.4%) and spinal bending angle (83.4%). Looking forward, this methodology provides unique prospects for broader applications such as tactile sensors, soft robotics, and health monitoring technologies.

Recent Advancements in Nanomaterials for Near‐Infrared to Long‐Wave Infrared Photodetectors

AbstractNanomaterials have superior electronic, optical, and mechanical properties making them highly suitable for a range of applications in optoelectronics, biomedical fields, and photonics. Nanomaterials‐based IR detectors are rapidly growing due to enhanced sensitivity, wide spectral range, and device miniaturization compared to commercial photodetectors. This review paper focuses on the significant role of nanomaterials in infrared detection, an area critical for enhancing night vision and health monitoring technologies. The latest advancements in IR photodetectors that employ various nanomaterials and their hybrids are discussed. The manuscript covers the operational mechanisms, device designing, performance optimization strategies, and material challenges. This review aims to provide a comprehensive overview of the current developments in nanomaterial‐based IR photodetectors and to identify key directions for future research and technological advancements.

Embroidered textile antenna with dual‐band capability for body‐centric communication systems

AbstractThe integration of health monitoring and wearable antenna technologies has enhanced patient assessment and the provision of healthcare. Traditional health monitoring methods have evoked a surge in interest toward embroidered antennas as a viable solution for constant physiological monitoring in a noninvasive way. This study examines the performance of an embroidered wearable textile (EWT) antenna intended for breathing rate applications. The antenna is seamlessly integrated into a commercially available T‐shirt, extending the confines of “smart” textiles. The compact (45 mm × 26 mm) EWT antenna operates in 2.4 and 5.8 GHz with gains of 2.07 and 5.85 dBi, respectively. Crumpling and stretching analysis have also been investigated to ensure antenna's mechanical stability and reliability. Subsequently a specific absorption rate analysis has also been performed to quantify the radiation within specified limits.

Textile Organic Electrochemical Transistor for Non-Invasive Glucose Sensing

The global rise in diabetes has highlighted the urgent need for continuous, non-invasive health monitoring solutions. Traditional glucose monitoring methods, which are invasive and often inconvenient, have created a demand for alternative technologies that can offer comfort, accuracy, and real-time data. In this study, the development of a textile-based organic electrochemical transistor (OECT) is presented, designed for non-invasive glucose sensing, aiming to integrate this technology seamlessly into everyday clothing. The document details the design, optimization, and testing of a one-component textile-based OECT, featuring a porous PEDOT:PSS structure and a glucose oxidase-modified electrolyte for effective glucose detection in sweat. The research demonstrates the feasibility of using this textile-based OECT for non-invasive glucose monitoring, with enhanced sensitivity and specificity achieved through the integration of glucose oxidase within the electrolyte and the innovative porous PEDOT:PSS design. These findings suggest a significant advancement in wearable health monitoring technologies, providing a promising pathway for the development of smart textiles capable of non-invasively tracking glucose levels. Future work should focus on refining this technology for clinical use, including individual calibration for accurate blood glucose correlation and its integration into commercially available smart textiles.

Wearable Sensors for Healthcare of Industrial Workers: A Scoping Review

Background and Objectives: This scoping review evaluates the use of wearable sensor technologies for workplace safety and health monitoring in industrial settings. The aim is to synthesize evidence on the impact of these sensors and their application in high-risk environments. Materials and Methods: Following the PRISMA guidelines, a systematic search across four international electronic databases yielded 59 studies, of which 17 were included in the final review. The selection criteria involved studies that specifically utilized wearable sensors to monitor various health and environmental parameters relevant to industrial workers. Results: The analysis categorizes wearable technologies into five distinct groups based on their function: gas monitoring technologies, heart rate and physiological data collection, fatigue and activity monitoring, comprehensive environmental and physiological monitoring, and advanced sensing and data collection systems. These devices demonstrated substantial benefits in terms of early detection of health risks and enhancement of safety protocols. Conclusions: The review concludes that wearable sensor technologies significantly contribute to workplace safety by providing real-time, data-driven insights into environmental hazards and workers’ physiological status, thus supporting proactive health management practices in industrial settings. Further research is recommended to address the challenges of data privacy, sensor reliability, and cost-effective integration to maximize their potential in occupational health safety.

A multi-scale deep residual network-based guided wave imaging evaluation method for fatigue crack quantification

Abstract As a promising structural health monitoring (SHM) technology, guided wave (GW) imaging is gaining increasing attention for crack monitoring of aircraft structures. However, actual fatigue crack propagation is a complex dynamically evolving process affected by various variabilities. It is still challenging to accurately track and quantify the dynamic fatigue crack propagation with GW imaging methods. Therefore, in order to achieve more accurate fatigue crack quantification, this paper proposes a multi-scale deep residual network-based GW imaging evaluation method. A convolutional neural network (CNN) is utilized to evaluate the entire pixel distribution of GW imaging maps to fuse damage-related information from multiple GW monitoring paths. By designing multi-scale convolutional kernels and deep residual learning, a robust quantitative image feature extraction is ensured with the dynamic evolution process of fatigue crack growth and the performance degradation is avoided as the CNN goes deeper, thereby improving the quantification accuracy. The method is validated on a fatigue test of landing gear beams, which are important load-carrying aircraft structural components. The results demonstrate that the proposed method can extract multi-scale crack length-related features and accurately track fatigue crack propagations. For batch specimens, the maximum quantification error is reduced from the original 6.1mm to 1.6mm, marking a significant improvement.

Preterm birth risk stratification through longitudinal heart rate and HRV monitoring in daily life

Preterm birth (PTB) remains a global health concern, impacting neonatal mortality and lifelong health consequences. Traditional methods for estimating PTB rely on electronic health records or biomedical signals, limited to short-term assessments in clinical settings. Recent studies have leveraged wearable technologies for in-home maternal health monitoring, offering continuous assessment of maternal autonomic nervous system (ANS) activity and facilitating the exploration of PTB risk. In this paper, we conduct a longitudinal study to assess the risk of PTB by examining maternal ANS activity through heart rate (HR) and heart rate variability (HRV). To achieve this, we collect long-term raw photoplethysmogram (PPG) signals from 58 pregnant women (including seven preterm cases) from gestational weeks 12–15 to three months post-delivery using smartwatches in daily life settings. We employ a PPG processing pipeline to accurately extract HR and HRV, and an autoencoder machine learning model with SHAP analysis to generate explainable abnormality scores indicative of PTB risk. Our results reveal distinctive patterns in PTB abnormality scores during the second pregnancy trimester, indicating the potential for early PTB risk estimation. Moreover, we find that HR, average of interbeat intervals (AVNN), SD1SD2 ratio, and standard deviation of interbeat intervals (SDNN) emerge as significant PTB indicators.

Characterising Skin Electrical Impedance Using Tape Stripping Methods: A Bioelectrical Study of a Porcine Model.

Background Recent advancements in ultra-low power electronics and wireless devices have facilitated the widespread adoption of wearable technology for fitness and health monitoring, paving the way for personalized medicine. Microneedle-based devices, comprising small epidermal patches that penetrate the skin's stratum corneum to potentially access biomarkers in the extracellular fluid of the viable epidermis, represent a promising innovation in this field. Objectives This project aimed to develop and validate a novel method to evaluate microneedle engagement in the skin in real-time. To our knowledge, there are no studies published to date that have characterized the electrical impedance of stratum corneum and epidermis using the tape stripping method to selectively remove cell layers. Additionally, no studies have been published comparing the electrical impedance of fresh to frozen-thawed porcine skin. The objective of this study was to develop and validate a novel method to evaluate microneedle engagement in skin, in real-time, that does not require processing of the tissue. Methods A tape stripping technique was employed to selectively remove the stratum corneum from fresh and frozen-thawed porcine skin samples which were then electrically characterized using an excitation frequency of 5 kHz with a peak Voltage of 1 V. Results This study demonstrated a mean impedance reduction of 97.08 ± 1.3 % for fresh porcine skin, and 98.04 ± 0.3 % for frozen-thawed porcine skin when transitioning from the surface stratum corneum to the viable epidermis. The correlation between the reduction of impedance and the number of tape strips across all 18 test sites was significant (r = 0.98, p < 0.00001). However, comparing the skin impedance of the fresh and frozen-thawed specimens showed poor equivalence, with the frozen-thawed sites approximately 5.5 times the impedance of the fresh sites before any tape stripping, and 4.19 times greater after 30 tape strips. Conclusions These findings suggest that monitoring for an interelectrode impedance decrease of greater than 95% between two projections of a microneedle device could provide a rapid and effective evaluation of skin engagement, crucial for advancing the development and clinical application of microneedle-based technologies in personalized medicine. The study also underscores the impact of the freeze-thaw process on the mechanical and electrical properties of skin, which is crucial for standardizing testing protocols.

A Temporal Fusion Memory Network-Based Method for State-of-Health Estimation of Lithium-Ion Batteries

As energy storage technologies and electric vehicles evolve quickly, it becomes increasingly difficult to precisely gauge the condition (SOH) of lithium-ion batteries (LiBs) during rapid charging scenarios. This paper introduces a novel Time-Fused Memory Network (TFMN) for SOH estimation, integrating advanced feature extraction and learning techniques. Both directly measured and computationally derived features are extracted from the charge/discharge curves to simulate real-world fast-charging conditions. This comprehensive process captures the complex dynamics of battery behavior effectively. The TFMN method utilizes one-dimensional convolutional neural networks (1DCNNs) to capture local features, refined further by a channel self-attention module (CSAM) for robust SOH prediction. Long short-term memory (LSTM) modules process these features to capture long-term dependencies essential for understanding evolving battery health patterns. A multi-head attention module enhances the model by learning varied feature representations, significantly improving SOH estimation accuracy. Validated on a self-constructed dataset and the public Toyota dataset, the model demonstrates superior accuracy and robustness, improving performance by 30–50% compared to other models. This approach not only refines SOH estimation under fast-charging conditions but also offers new insights for effective battery management and maintenance, advancing battery health monitoring technologies.

Self-Powered Wearable Pressure Sensor System for Smart Healthcare Applications

Wearable sensors mark a groundbreaking advancement in the realm of health monitoring technology. However, traditional wearable devices lack comprehensive solutions for tracking sports exercise behaviors and providing feedback on rehabilitation for those undergoing home-based recovery. To bridge this gap, a self-powered triboelectric nanogenerator (TENG)-based wearable sensing system has been developed with in-house monitoring capabilities. The wearable sensor system is composed of triboelectric pressure sensors with an electro-conductive silicone sheet and patterned Ecoflex film as the contact layers. Highly sensitive TENG sensors have been successfully embedded into a bicycle saddle and human shoe insole for real-time pressure monitoring. Experimental results demonstrate that biomechanical energy, originating from both the pelvic-saddle interactions on bicycle and human gait movements, has been successfully converted into electrical energy. The generated sensor voltage remains stable under frequency variations and external environmental conditions, such as temperature and humidity variations. Utilizing distinctive information contained in individual body mass imbalances and gait characteristics, a smart healthcare monitoring system is envisioned to extract essential biomechanical insights via artificial intelligence, facilitating a pivotal role in in-house monitoring and tracking of sports rehabilitation exercises. The as-designed low-cost, highly scalable self-powered wearable sensor system offers an accessible, user-friendly point-of-care solution for gait detection and rehabilitation monitoring for individuals with various physical impairments, with considerable potential for commercialization. Figure 1

Percolation network and piezoresistive properties of ultraductile cementitious monitoring coating with A-POSS modified carbon-based nanoconductors

Conventional cement-based sensors are large and often embedded in cast-in-place concrete structures, resulting in poor flexibility, low sensing accuracy, and inability to be replaced after damage. This paper reports a promising health monitoring technology, namely the composite coated sensor (sensing skin) prepared by anionic waterborne polyurethane (WPU)-modified cement, in which N-(2-aminoethyl)-3-amino propyl trime thoxy silane (NAEAPTMS) silsesquioxane modified ettringite (A-POSS) acts as a core modifier. The results showed that a continuous interpenetration polymer network was formed between the WPU and the cement, which greatly improved the crack resistance and balanced the polarisation reaction field. The agglomerated GO-CNT particles self-dispersed into micron/nanometer-sized particles under the co-precipitation of Ca2+/Al3+ and the semi-cage-like POSS structure formed by NAEAPTMS, using AFt as the supporting skeleton. The toughness and bond strength of the sensing skins were improved in the presence of A-POSS and, in particular, the maximum deflection angle exceeded 110°. Meanwhile, the electrical resistivity was reduced by more than 2 orders of magnitude. The effective conductivity inside and outside the interface can be precisely delineated when GO-CNT was perfectly arranged inside WPU-CEMENT. The agreement between the theoretical and experimental results of effective conductivity indicates that the sensing skin had a percolation network with imperfect and tunnel-assisted interfaces. The synergistic effect of WPU and A-POSS gave the sensing skin excellent performance of high sensitivity (80 times larger than strain gauges), low relaxation time (50 ms), and monitoring stability.

Genetic Algorithm-based Convolutional Neural Network Feature Engineering for Optimizing Coronary Heart Disease Prediction Performance.

This study aimed to optimize early coronary heart disease (CHD) prediction using a genetic algorithm (GA)-based convolutional neural network (CNN) feature engineering approach. We sought to overcome the limitations of traditional hyperparameter optimization techniques by leveraging a GA for superior predictive performance in CHD detection. Utilizing a GA for hyperparameter optimization, we navigated a complex combinatorial space to identify optimal configurations for a CNN model. We also employed information gain for feature selection optimization, transforming the CHD datasets into an image-like input for the CNN architecture. The efficacy of this method was benchmarked against traditional optimization strategies. The advanced GA-based CNN model outperformed traditional methods, achieving a substantial increase in accuracy. The optimized model delivered a promising accuracy range, with a peak of 85% in hyperparameter optimization and 100% accuracy when integrated with machine learning algorithms, namely naïve Bayes, support vector machine, decision tree, logistic regression, and random forest, for both binary and multiclass CHD prediction tasks. The integration of a GA into CNN feature engineering is a powerful technique for improving the accuracy of CHD predictions. This approach results in a high degree of predictive reliability and can significantly contribute to the field of AI-driven healthcare, with the possibility of clinical deployment for early CHD detection. Future work will focus on expanding the approach to encompass a wider set of CHD data and potential integration with wearable technology for continuous health monitoring.

Applications of Smart and Self-Sensing Materials for Structural Health Monitoring in Civil Engineering: A Systematic Review

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.

Diagnosis And Accurate Classification of Apple Leaf Diseases Using Vision Transformers

In agriculture, plant pests and diseases are one of the major factors that reduce fruit and vegetable yields. Early detection of foliar diseases, which cause significant yield losses in apple production, is of great importance for the sustainability of agricultural production. The subjective and inefficient nature of traditional manual and tool-based diagnostic methods is insufficient to provide solutions that meet scientific and production standards. In this context, pattern recognition and deep learning techniques offer more effective alternatives for automatic image analysis and classification. In this study, Vision Transformers are used for disease classification from apple leaf images. The dataset consists of images of healthy apple leaves and apple leaves with different diseases. The results show that Vision Transformers is effective in classifying apple leaf diseases with high accuracy. This study makes an important contribution to increasing the use of digital technologies in agriculture and plant health monitoring.