Search Results (745)

The quality and freshness of fruits and vegetables are critical factors in consumer acceptance and are significantly affected during transport and storage. This study aimed to evaluate the quality of greenhouse-grown tomatoes stored for 24 days by combining non-destructive image analysis, spectrophotometric assays (including total phenolic content and antioxidant and antiradical activity assessments), and attenuated total reflectance–Fourier transform infrared (ATR-FTIR) spectroscopy. Additionally, water activity, moisture content, total soluble solids, texture, and color were evaluated. Most physicochemical changes occurred between days 14 and 17, without major impact on overall fruit quality. A progressive transition in peel hue from orange to dark orange, and increased surface irregularity of their textural image were noted. Moreover, the combined use of instrumental and image analyses results via multivariate analysis allowed the clear discrimination of tomatoes according to storage days. In this sense, tomato samples were effectively classified by ATR-FTIR spectral bands, linked to carotenoids, phenolics, and polysaccharides. Machine learning (ML) models, including Random Forest and Gradient Boosting, were trained on image-derived features and accurately predicted shelf life and quality traits, achieving R² values exceeding 0.9. The findings demonstrate the effectiveness of combining imaging, spectroscopy, and ML for non-invasive tomato quality monitoring and support the development of predictive tools to improve postharvest handling and reduce food waste. Full article

Background/Objectives: Patients with normal sinus rhythms undergoing cardiac resynchronization therapy defibrillator (CRT-D) implantation may benefit from a novel two-lead CRT-D system (CRT-DX), which features an atrial sensing dipole integrated into the right ventricular lead. This single-arm, international, non-controlled investigation focused on the safety and clinical efficacy of CRT-DX devices in CRT-D candidates who do not require atrial pacing. Methods: Patients indicated for CRT-D implantation (resting heart rates > 40 bpm and ≥100 bpm during exercise, no second or higher-degree AV block, and no history of persistent or permanent atrial fibrillation) were enrolled across 21 sites in four European countries. The primary endpoint was the need for an additional RA lead implantation within 12 months. Secondary endpoints comprised any invasive re-intervention to the CRT-DX system or infection. Results: Among the 110 patients (mean age 62 years, 70% male), 60% had an underlying non-ischemic cardiac disease. During 12 months of follow-up, RA lead implantation was required in two patients for atrial undersensing or chronotropic incompetence (RA lead implantation-free rate: 98.2% (95% CI: 92.7–99.5%)). Atrial sensing amplitudes were stable (mean: 4.7 ± 1.7 mV), AV-synchrony was maintained at >99%, and the median percentage of biventricular pacing exceeded 98%. The left ventricular ejection fraction improved by an absolute 14.7%. Conclusions: Using simple, clinically applicable inclusion criteria, the two-lead CRT-DX system demonstrated a low rate of subsequent RA lead implantations (1.8%) and maintained adequate RA sensing amplitudes throughout the observation period. The two-lead CRT-DX concept appears to be a feasible alternative for patients with preserved chronotropic competence. 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 quantification of solid debris in used lubricating oil is essential for assessing transmission system wear and optimizing maintenance strategies. This study introduces a low-cost capacitive proximity sensor for monitoring total solid particle contamination in lubricants, with a focus on ferrous (Fe), non-ferrous (Al), and non-metallic (SiO₂) debris. Controlled tests were performed using five mixing ratios of large-to-small particles (100:0, 75:25, 50:50, 25:75, and 0:100) at a fixed debris mass of 0.5 g per 25 mL of SAE 85W-140 automotive gear oil. Cubic regression analysis yielded high predictive accuracy, with average R² values of 0.994 for Fe, 0.943 for Al, and 0.992 for SiO₂. Further dimensionality reduction using Principal Component Analysis (PCA), along with Linear Discriminant Analysis (LDA) of multivariate statistical analysis, effectively classifies debris types and enhances interpretability. These results demonstrate the potential of capacitive sensing as an offline, non-invasive alternative to traditional techniques for wear debris monitoring in transmission systems. These results confirm the potential of capacitive sensing, supported by statistical modeling, as a non-invasive, cost-effective technique for offline classification and monitoring of wear debris in transmission systems. Full article

This paper presents a methodology for extracting and simulating arterial pulse waveform signals from Fabry–Perot interferometric measurements, emphasizing a practical approach for noninvasive cardiovascular assessment. A key novelty of this work is the presentation of a complete Python-based processing pipeline, which is made publicly available as open-source code on GitHub (git version 2.39.5). To the authors’ knowledge, no such repository for demodulating these specific interferometric signals to obtain a raw arterial pulse waveform previously existed. The proposed system utilizes accessible Python-based preprocessing steps, including outlier removal, Butterworth high-pass filtering, and min–max normalization, designed for robust signal quality even in settings with common physiological artifacts. Key features such as the rate of change, the Hilbert transform of the rate of change (envelope), and detected extrema guide the signal reconstruction, offering a computationally efficient pathway to reveal its periodic and phase-dependent dynamics. Visual analyses highlight amplitude variations and residual noise sources, primarily attributed to sensor bandwidth limitations and interpolation methods, considerations critical for real-world deployment. Despite these practical challenges, the reconstructed arterial pulse waveform signals provide valuable insights into arterial motion, with the methodology’s performance validated on measurements from three subjects against synchronized ECG recordings. This demonstrates the viability of Fabry–Perot sensors as a potentially cost-effective and readily implementable tool for noninvasive cardiovascular diagnostics. The results underscore the importance of precise yet practical signal processing techniques and pave the way for further improvements in interferometric sensing, bio-signal analysis, and their translation into clinical practice. Full article

We developed a photothermal deflection spectroscopy (PTDS) system for the noninvasive analysis of biological tissue. This system detects heat induced by irradiation with pulse-modulated mid-infrared light as the deflection of a probe laser. The probe light is incident on the sensing element horizontal with respect to its contact surface with the sample. This setup simplifies the optical alignment compared to conventional systems, which require the probe laser to be totally reflected at the prism contact surface and aligned with the point of mid-infrared light irradiation. In this study, we measured the PTDS spectra of biological samples to determine the characteristic features of their infrared absorption. We also compared the measurement reproducibility of two configurations: a horizontal optical path and a total reflection optical path. The horizontal optical path showed greater measurement reproducibility than the total reflection optical path when performing intermittent measurements on the wrist. Full article

Underground cast-in-place pipes (CIPP, Diameter of 2’–5’) are used to transport water for the Phoenix, AZ area. These pipes have developed leaks due to their age and changes in the environment, resulting in a significant waste of water. Currently, leaks can only be identified when water pools above ground occur and are then manually confirmed through the inside of the pipe, requiring the shutdown of the water system. However, many leaks may not develop a puddle of water, making them even harder to identify. The primary objective of this research was to develop an inspection method utilizing drone-based infrared imagery to remotely and non-invasively sense thermal signatures of abnormal soil moisture underneath urban surface treatments caused by the leakage of water pipelines during the regular operation of water transportation. During the field tests, five known leak sites were evaluated using an intensive experimental procedure that involved conducting multiple flights at each test site and a stringent filtration process for the measured temperature data. A detectable thermal signal was observed at four of the five known leak sites, and these abnormal thermal signals directly overlapped with the location of the known leaks provided by the utility company. A strong correlation between ground temperature and shading before sunset was observed in the temperature data collected at night. Thus, a shadow and solar energy model was implemented to estimate the position of shadows and energy flux at given times based on the elevation of the surrounding structures. Data fusion between the metrics of shadow time, solar energy, and the temperature profile was utilized to filter the existing points of interest further. When shadows and solar energy were considered, the final detection rate of drone-based infrared imaging was determined to be 60%. Full article

Farmers face increasing pressure to maintain vital populations of the critically endangered field hamster (Cricetus cricetus) while managing crop damage caused by field mice. This challenge is linked to the UN Sustainable Development Goals (SDGs) 2 and 15, addressing food security and biodiversity. Consequently, the reliable detection of hamster activity in agricultural fields is essential. While remote sensing offers potential for wildlife monitoring, commonly used RGB imagery has limitations in detecting small burrow entrances in vegetated areas. This study investigates the potential of drone-based Light Detection and Ranging (LiDAR) data for identifying field hamster burrow entrances in agricultural landscapes. A geostatistical method was developed to detect local elevation minima as indicators of burrow openings. The analysis used four datasets captured at varying flight altitudes and spatial resolutions. The method successfully detected up to 20 out of 23 known burrow entrances and achieved an F1-score of 0.83 for the best-performing dataset. Detection was most accurate at flight altitudes of 30 m or lower, with performance decreasing at higher altitudes due to reduced point density. These findings demonstrate the potential of UAV-based LiDAR to support non-invasive species monitoring and habitat management in agricultural systems, contributing to sustainable conservation practices in line with the SDGs. Full article

Proper nutrition is a fundamental aspect to maintaining overall health and well-being, influencing both physical and social aspects of human life; an unbalanced or inadequate diet can lead to various nutritional deficiencies and chronic health conditions. In today’s fast-paced world, monitoring nutritional intake has become increasingly important, particularly for those with specific dietary needs. While smartphone-based applications using image recognition have simplified food tracking, they still rely heavily on user interaction and raise concerns about practicality and privacy. To address these limitations, this paper proposes a novel, compact spectroscopic sensing platform for automatic beverage recognition. The system utilizes the AS7265x commercial sensor to capture the spectral signature of beverages, combined with a K-Nearest Neighbors (KNN) machine learning algorithm for classification. The approach is designed for integration into everyday objects, such as smart glasses or cups, offering a noninvasive and user-friendly alternative to manual tracking. Through optimization of both the sensor configuration and KNN parameters, we identified a reduced set of four wavelengths that achieves over 96% classification accuracy across a diverse range of common beverages. This demonstrates the potential for embedding accurate, low-power, and cost-efficient sensors into Internet of Things (IoT) devices for real-time nutritional monitoring, reducing the need for user input while enhancing accessibility and usability. Full article

This study proposes a novel heart rate estimation method that detects subtle cardiac-induced vibrations propagated through the cardiovascular system based on the ballistocardiography (BCG) principle, using a six-axis heart rate sensing device that integrates a three-axis accelerometer and a three-axis gyroscope. To validate the effectiveness of the proposed method, a comparative analysis was conducted against heart rate measurements obtained from photoplethysmography (PPG) sensors, which are widely used in conventional heart rate monitoring. Experiments were conducted on 20 adult participants, and frequency domain analysis was performed using different time windows of 30 s, 20 s, 8 s, and 4 s. The results showed that the 4 s window provided the highest accuracy in heart rate estimation, demonstrating that the proposed method can effectively capture fine cardiac-induced vibrations. This approach offers a significant advantage by utilizing inertial sensors commonly embedded in wearable devices for heart rate monitoring without the need for additional optical sensors. Compared to optical-based systems, the proposed method is more power-efficient and less affected by environmental factors such as ambient lighting conditions. The findings suggest that heart rate estimation using the six-axis heart rate sensing device presents a reliable, continuous, and non-invasive alternative for cardiovascular monitoring. Full article

Structural health monitoring (SHM) is crucial for the longevity of engineering structures, especially under thermal loading conditions. Elevated temperatures pose challenges for sensors in data acquisition and characterization. Once suitable sensors are deployed, temperature data help detect and evaluate potential damage or defects. This work uses a Rectangular Microstrip Antenna (RMSA)-based sensor for temperature sensing, offering non-invasive, accurate, and stable measurements. The antenna resonant frequency is sensitive to changes in temperature, acting as an indicator. Simulations using HFSS analyze the antenna’s reflection characteristics under thermal loading, simplifying sensing systems, reducing sensor count, and minimizing installation efforts work. Simulated results are comparable to the experimental ones, which ascertain the performance of patch antennas as temperature sensors. Full article

Monumental trees, defined by their exceptional size, form, and age, are critical components of both cultural heritage and ecological systems. However, their conservation faces increasing threats from habitat fragmentation, climate change, and inadequate public policies. This review synthesized global research on monumental trees by analyzing 204 peer-reviewed articles published between 1989 and 2024 that were sourced from Web of Science and Scopus. Our bibliometric analysis highlighted Olea europaea and Castanea sativa as the most frequently studied species and identified a surge in publications after 2019, particularly from the USA, Italy, and Spain. Key research themes included conservation, biodiversity, and ecosystem services. The methodological approaches varied globally, encompassing ranking systems; GIS mapping; remote sensing; and non-invasive diagnostic tools, such as acoustic tomography and chlorophyll fluorescence. Conservation strategies discussed included vegetative propagation, cryopreservation, and legal risk management. Despite advances in these techniques, significant gaps remain in effectively addressing environmental pressures and integrating multidisciplinary approaches. We concluded that targeted, interdisciplinary strategies are essential to safeguard monumental trees as vital ecological and cultural landmarks. Full article

In recent years, non-invasive methods for the analysis of biological fluids have attracted growing interest. In this study, we propose a straightforward approach to fabricating silver nanoparticle (AgNP)-coated non-woven polyacrylonitrile substrates for surface-enhanced Raman scattering (SERS). AgNPs were synthesized directly on the substrate using borohydride reduction, ensuring uniform distribution. The optimized SERS substrates exhibited a high enhancement factor (EF) of up to 10⁵ for the detection of 4-mercaptobenzoic acid (4-MBA). To enable glucose sensing, the substrates were further functionalized with glucose oxidase (GOx), allowing detection in the 1–10 mM range. Machine learning classification and regression models based on gradient boosting were employed to analyze SERS spectra, enhancing the accuracy of quantitative predictions (R² = 0.971, accuracy = 0.938, limit of detection = 0.66 mM). These results highlight the potential of AgNP-modified substrates for reliable and reusable biochemical sensing applications. Full article

Effective monitoring of fish passage through river barriers is essential for evaluating fishway performance and supporting adaptive river management. Traditional methods are often invasive, labor-intensive, or too costly to enable widespread implementation across most fishways. Infrared (IR) beam-break counters offer a promising alternative, but their adoption has been limited by high costs and a lack of flexibility. We developed and tested a novel, low-cost infrared beam-break counter—FishTracker—based on open-source Raspberry Pi and Arduino platforms. The system detects fish passages by analyzing interruptions in an IR curtain and reconstructing fish silhouettes to estimate movement, direction, speed, and morphometrics under a wide range of turbidity conditions. It also offers remote access capabilities for easy management. Field validation involved controlled tests with dummy fish, experiments with small-bodied live specimens (bleak) under varying turbidity conditions, and verification against synchronized video of free-swimming fish (koi carp). This first version of FishTracker achieved detection rates of 95–100% under controlled conditions and approximately 70% in semi-natural conditions, comparable to commercial counters. Most errors were due to surface distortion caused by partial submersion during the experimental setup, which could be avoided by fully submerging the device. Body length estimation based on passage speed and beam-interruption duration proved consistent, aligning with published allometric models for carps. FishTracker offers a promising and affordable solution for non-invasive fish monitoring in multispecies contexts. Its design, based primarily on open technology, allows for flexible adaptation and broad deployment, particularly in locations where commercial technologies are economically unfeasible. Full article

The recent advancements in high-frequency, high-power switching devices require the development of non-invasive, cost-effective sensors for signal diagnostics. In this context, planar sensors have emerged as promising candidates for voltage and current sensing due to their compatibility with printed circuit boards (PCBs). However, previously proposed voltage planar sensors exhibit trade-offs between high bandwidths and responsivity, limiting their usage to sub-GHz applications. This study introduces a planar voltage sensor that leverages geometric optimization using software-assisted design to enhance bandwidth without compromising sensitivity. The optimized sensors demonstrate an extended bandwidth response up to 4 GHz and accurate recovery of fast transient signals validated through experimental measurements, which represents a significant step forward in broadband sensing for high-power applications. Full article