Search Results (33)

This paper introduces a non-invasive ultrasonic sensing system for two-phase refrigerant flow characterization in heat pump circuits used in building energy systems, validated through machine learning (ML)-based regression of the acquired signals. Heat pumps play a crucial role in energy-efficient buildings. However, the absence of a low-cost, non-invasive instrument capable of measuring mass flow rate, mixture density, and vapor quality without disrupting the thermodynamics of a refrigerant circuit remains a gap for smart HVAC systems. A carbon dioxide (CO₂) refrigerant circuit was designed to calibrate such a sensing system under representative operating heat pump conditions. Ultrasonic measurements were conducted using piezoelectric transducers clamped onto the refrigerant pipeline. A calibration framework was structured with ground-truth flowmeter labels, establishing a thermodynamic envelope across 10–20 bar and down to −25 °C, and achieving R² = 0.901 for flow rate, 0.997 for density, and 0.971 for quality, with an overall R² = 0.956. The proposed measurement system is a plug-and-play kit enabling more efficient next-generation heat pumps, supporting building energy management and performance monitoring. The labeled dataset and calibration methodology provide a basis for training and validating ML regression models for real-time flow property inference in operational HVAC systems. Full article

Accurate monitoring of temperature variations in fluid flow through porous media is important for numerous geophysical, environmental, and chemical processes. Traditional temperature measurement techniques often suffer from limitations such as invasiveness, restricted spatial coverage, or limited capability for real-time subsurface sensing. Thermoacoustic (TA) methods provide a promising alternative by combining electromagnetic excitation with acoustic detection to enable non-contact subsurface monitoring. In this work, thermoacoustic measurements were conducted over a temperature range of 20–60 °C using a water-saturated sand porous medium, and a multiphysics simulation framework incorporating rock physics models, temperature-dependent material properties, acoustic dispersion, and attenuation calibration was developed to reproduce the experimental observations. A clear relationship between temperature and TA signal amplitude was observed, and strong agreement was achieved between the experimentally measured and simulated thermoacoustic responses after applying the proposed calibration procedures. The results demonstrate the feasibility of using thermoacoustic signals to monitor bulk temperature variations in porous media and establish a foundation for future development of spatially resolved thermoacoustic temperature imaging methods. Full article

The growing need for sustainable wastewater treatment highlights the importance of low-energy solutions such as horizontal subsurface flow constructed wetlands (HSSF CWs). While effective, these systems often face clogging issues that reduce performance and lifespan. This study investigates clogging dynamics in a Water Treatment Plant (Lleida, Spain) using a multidisciplinary approach. Non-invasive geophysical methods such as Electrical Resistivity Tomography (ERT) and Induced Polarization (IP) were combined with high-resolution drone imagery to characterize surface and subsurface indicators of clogging. Drone data captured surface anomalies, while geophysical measurements revealed subsurface obstructions. The integrated analysis identifies clogged zones and shows a strong spatial correlation between surface features and geophysical anomalies. These results validate the use of drone imagery as a rapid, non-invasive diagnostic tool and demonstrate the effectiveness of combining remote sensing with geophysical techniques for wetland assessment. This approach supports improved monitoring, targeted maintenance, and optimized long-term performance of HSSF CWs. Full article

Background: Non-invasive respiratory supports (High-Flow Nasal Oxygen, HFNO; Continuous Positive Airway Pressure, CPAP; Non-Invasive Ventilation, NIV) are frequently used in Acute Hypoxemic Respiratory Failure (AHRF). However, the experience of assisted breathing may profoundly affect patients’ psychological balance, particularly during acute critical illness and subsequent rehabilitation. Aims and objectives: This longitudinal study investigated the psychological burden associated with non-invasive respiratory support use in patients with COVID-19-related AHRF, exploring changes in psychological functioning from acute hospitalization (RICU/ICU) (T0) to follow-up, conducted at a mean of 6.0 ± 3.1 months after respiratory rehabilitation (T1). Methods: Fifty-two patients (mean age = 66.9 ± 9.17 years) were assessed at T0 and T1. Standardized measures evaluated anxiety, psychological distress, post-traumatic stress symptoms, depression, and resilience, in relation to perceived illness severity and subjective experience of non-invasive respiratory support. Results: During acute care, patients reported high levels of fear and anxiety related to illness severity and uncertainty. The experience of non-invasive respiratory support, often perceived as a marker of critical condition, was associated with increased fear and anxiety (t(14) = 2.79, p = 0.014) compared to the recovery phase, leading to feelings of loss of control and diminished psychological well-being (t(17) = 2.35, p = 0.031). However, resilience significantly improved over time (t(16) = −4.78, p < 0.001). Conclusions: Non-invasive respiratory support may represent a psychologically demanding experience, often perceived as challenging to patients’ sense of safety and control. Encouragingly, psychological adaptation and resilience can improve during rehabilitation. Integrating structured psychological support within respiratory rehabilitation pathways may promote recovery and restore psychological balance in patients requiring assisted ventilation. Full article

This article presents an intelligent differential capacitive bioelectronic sensing system that provides an experimental foundation for future AI-assisted reliable microfluidic reagent delivery in automated pathology. The proposed platform integrates a slot-type microfluidic chamber, a differential slot-line capacitive sensor, embedded readout and signal-conditioning electronics, and a supervisory state assessment concept within a unified architecture. Its purpose is to support stable microliter-scale reagent exchange together with non-invasive process observability in automated staining workflows. The experimental study included flow calibration, analysis of feed direction and chamber tilt angle, preliminary vibration-assisted bubble mobilization, and evaluation of the sensing subsystem. The results showed that reliable operation is achieved only within a practically admissible regime in which fluidic stability and sensing informativeness overlap. In the investigated setup, upper-feed delivery and low chamber tilt angles provided the most favorable filling conditions, while the differential capacitive subsystem enabled stable detection of liquid-state changes in narrow microtubes. The reported results establish a foundation for future AI-assisted transport-state recognition and adaptive monitoring in automated pathology platforms. Full article

This study designs a complementary split-ring resonator (CSRR)-based 5 GHz patch-type microwave resonant sensor for measuring the concentrations of glucose solutions under static and dynamic conditions. Circulating glucose solutions were used to simulate blood glucose, and the CSRR sensor was operated over a frequency range of 4.8–5.0 GHz. The planar microstrip configuration of the CSRR creates a highly confined electric field within the sensing area. When glucose solution covers or flows through the sensing region, the dielectric loading changes, altering the resonance condition and inducing perturbations. Identifiable measurement features can be extracted from data on the scattering parameter S₁₁. Glucose solutions with concentrations ranging from 5% to 65% were used to examine the response of the proposed sensor. The concentrations of these solutions were estimated on the basis of resonant frequency shifts, and variation in S₂₁ at the CSRR’s resonant frequency (or at a fixed frequency corresponding to the maximum slope) was also analyzed. Full article

Carotid artery disease, including atherosclerotic stenosis and non-atherosclerotic abnormalities, substantially increases ischemic stroke risk and motivates accessible tools for early screening. Current diagnostic pathways rely on clinic-based imaging and skilled operators, creating barriers to frequent monitoring and scalable deployment. We present a non-invasive diagnostic approach using a wearable MEMS accelerometer patch to capture mechano-acoustic vibrations generated by carotid blood flow at the neck. The miniature device integrates a hermetically sealed wideband accelerometer with out-of-plane sensitivity and micro-g resolution to detect subtle flow-induced vibrations. We validated the approach in a carotid flow phantom and a clinical study of 74 patients. Time–frequency representations were computed using the continuous wavelet transform (CWT), from which interpretable spectral and scalogram-derived candidate biomarkers were extracted. Six non-redundant features were then selected for multivariate classification, distinguishing pathology, defined as 50% or greater stenosis or a non-atherosclerotic abnormality, from non-pathology, defined as less than 50% stenosis. Finally, model interpretability was assessed using SHapley Additive exPlanations (SHAP) to quantify the contribution of each biomarker to predicted disease probability. These findings resulted in an AUROC of 0.97 and AUPR of 0.947, with 81.7% sensitivity and 93.6% specificity at the prespecified threshold (precision 85.4%, F1 83.5%, accuracy 89.8%), highlighting the potential of wearable seismic sensing combined with interpretable machine learning for fast screening and longitudinal monitoring of the right and left carotid arteries. Full article

The measurement of the magnetic field generated by a flowing current constitutes a non-invasive sensing technique for online energy consumption monitoring. In this work, based on the use of low-cost linear Hall effect sensors, a low-form-factor custom contactless ammeter probe is presented. The differential configuration of the sensor module and the subsequent fully digital programmability in range and sensitivity, together with the included self-calibration and compensation circuits for mismatching, managed by a microcontroller, allow for optimum detection for both continuous and mains current with a resolution of 10 mA for input ranges of 2 A. The proposed ammeter power consumption and measurement accuracy in different scenarios are tested, including the power monitoring of an IoT-based device, obtaining results matched to those featured by a commercial oscilloscope current probe, which validates its suitability and reliability as autonomous low-cost probe for portable contactless power monitoring. Full article

Concurrent with the rising consumption of ultra-processed, high-calorie diets and the decline in physical activity, obesity and related cardiovascular conditions among young adults have continued to increase, becoming an important global public health concern. This study integrates non-invasive Internet of Things (IoT) sensing devices, including the TERUMO ES-P2000 blood pressure monitor (Terumo Corp., Tokyo, Japan) and the PhysioFlow PF07 Enduro cardiac hemodynamic analyzer (Manatec Biomedical, Poissy, France), with an artificial neural network (ANN) for cardiac index (CI) prediction. Through appropriate data preprocessing and model training strategies, the generalization ability and stability of the proposed CI prediction model were significantly enhanced. Experimental results demonstrate that, when using three physiological parameters as input, the ANN achieved a classification accuracy of 97.78%, substantially outperforming traditional approaches. Even under two-parameter input conditions, the model maintained strong predictive performance. These findings confirm the effectiveness and practical potential of the proposed framework for real-time, non-invasive CI assessment. Moreover, this research has received rigorous assessment and approval from the Institutional Review Board (IRB) under application number 202501987B0. Full article

In this work, we used a label-free biosensor that provides optical readouts to perform continuous detection of human interleukin 8 (IL-8), which is especially overexpressed in certain cancers and, thus, could be an effective biomarker for cancer prognosis estimation and therapy evaluation. For this purpose, we engineered a compact, portable, and easy-to-assemble biosensing module device. It combines a fluidic chip for reagent flow, a biosensing chip for signal transduction, and an optical readout head based on fiber optics in a single module. The biosensing chip is based on independent arrays of resonant nanopillar transducer (RNP) networks. We integrated the biosensing chip with the RNPs facing down in a simple and rapidly fabricated polydimethyl siloxane (PDMS) microfluidic chip, with inlet and outlet channels for the sample flowing through the RNPs. The RNPs were vertically oriented from the backside through an optical fiber mounted on a holder head fabricated ad hoc on polytetrafluoroethylene (PTFE). The optical fiber was connected to a visible spectrometer for optical response analysis and consecutive biomolecule detection. We obtained a sensogram showing anti-IL-8 immobilization and the specific recognition of IL-8. This unique portable and easy-to-handle module can be used for biomolecule detection within minutes and is particularly suitable for in-line sensing of physiological and biomimetic organ-on-a-chip systems. Cancer biomarkers’ continuous monitoring arises as an efficient and non-invasive alternative to classical tools (imaging, immunohistology) for determining clinical prognostic factors and therapeutic responses to anticancer drugs. In addition, the multiplexed layout of the optical transducers and the simplicity of the monolithic sensing module yield potential high-throughput screening of a combination of different biomarkers, which, together with other medical exams (such as imaging and/or patient history), could become a cutting-edge technology for further and more accurate diagnosis and prediction of cancer and similar diseases. Full article

This article introduces a novel, cost-effective, noninvasive sensing mechanism for measuring water flow rate. It employs two tunneling magnetoresistance (TMR) sensors (analog and bi-polar), a magnet, and a stainless-steel cantilever. The TMR sensors are installed outside the insulating water pipe. A magnet is fixed at the free end of the cantilever and integrated into the pipe system. The cantilever’s deflection corresponds to the flow rate, with an analog TMR sensor measuring the bending angle. This bending angle, in either direction of the cantilever’s deflection, is captured through the analog voltage from the TMR sensor. The output from the analog TMR sensor is an analog voltage that directly reflects the strength of the magnetic field. An ESP32 microcontroller records the voltage from the analog TMR sensor, converts it to flow rates, and utilizes the bi-polar TMR sensor to ascertain the flow direction. A prototype sensor was developed and tested in a laboratory-scale setup to validate the effectiveness of the sensing mechanism. This prototype demonstrated a worst-case accuracy of 1.0% across flow rates of 0 to 1.5 m³/h for both the forward and reverse flow directions. The response and recovery times of the sensor are approximately 470 ms and 592 ms for forward and 487 ms and 625 ms for reverse direction flow. Also, hysteresis errors of 1.84% and 2.06% have been calculated for both flow directions. Notably, the sensing element does not contain any rotating components or require electrical connections to the cantilever for measurement. These attributes potentially lead to lower maintenance requirements and a longer lifespan for the sensor. Full article

Wearable sweat-sensing devices hold significant potential for non-invasive, continuous health monitoring. However, challenges such as ensuring data accuracy, sensor reliability, and measurement stability persist. This study presents the development of a wearable system for the real-time monitoring of human sweat sodium levels, addressing these challenges through the integration of a novel microfluidic chip and a compact potentiostat. The microfluidic chip, fabricated using hydrophilic materials and designed with vertical channels, optimizes sweat flow, prevents backflow, and minimizes sample contamination. The developed wearable potentiostat, as a measurement device, precisely measures electrical currents across a wide dynamic range, from nanoamperes to milliamperes. Validation results demonstrated accurate sodium concentration measurements ranging from 10 mM to 200 mM, with a coefficient of variation below 4% and excellent agreement with laboratory instruments (intraclass correlation = 0.998). During physical exercise, the device measured a decrease in sweat sodium levels, from 101 mM to 67 mM over 30 min, reflecting typical physiological responses to sweating. These findings confirm the system’s reliability in providing continuous, real-time sweat sodium monitoring. This work advances wearable health-monitoring technologies and lays the groundwork for applications in fitness optimization and personalized hydration strategies. Future work will explore multi-biomarker integration and broader clinical trials to further validate the system’s potential. Full article

Soil degradation is a critical challenge to global agricultural sustainability, driven by intensive land use, unsustainable farming practices, and climate change. Conventional soil monitoring techniques often rely on invasive sampling methods, which can be labor-intensive, disruptive, and limited in spatial coverage. In contrast, non-invasive geophysical techniques, particularly ground-penetrating radar, have gained attention as tools for assessing soil properties. However, an assessment of ground-penetrating radar’s applications in agricultural soil research—particularly for detecting soil structural changes related to degradation—remains undetermined. To address this issue, a systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. A search was conducted across Scopus and Web of Science databases, as well as relevant review articles and study reference lists, up to 31 December 2024. This process resulted in 86 potentially relevant studies, of which 24 met the eligibility criteria and were included in the final review. The analysis revealed that the ground-penetrating radar allows the detection of structural changes associated with tillage practices and heavy machinery traffic in agricultural lands, namely topsoil disintegration and soil compaction, both of which are important indicators of soil degradation. These variations are reflected in changes in electrical permittivity and reflectivity, particularly above the tillage horizon. These shifts are associated with lower soil water content, increased soil homogeneity, and heightened wave reflectivity at the upper boundary of compacted soil. The latter is linked to density contrasts and waterlogging above this layer. Additionally, ground-penetrating radar has demonstrated its potential in mapping alterations in electrical permittivity related to preferential water flow pathways, detecting shifts in soil organic carbon distribution, identifying disruptions in root systems due to tillage, and assessing soil conditions potentially affected by excessive fertilization in iron oxide-rich soils. Future research should focus on refining methodologies to improve the ground-penetrating radar’s ability to quantify soil degradation processes with greater accuracy. In particular, there is a need for standardized experimental protocols to evaluate the effects of monocultures on soil fertility, assess the impact of excessive fertilization effects on soil acidity, and integrate ground-penetrating radar with complementary geophysical and remote sensing techniques for a holistic approach to soil health monitoring. Full article

This paper proposes a metrologically interpretable soft sensing method for estimating the liquid flow rates in hydraulic systems from non-invasive vibration frequency power band data. Despite considerable interest in non-invasive flow estimation, state-of-the-art methods provide little to no metrological capabilities. In this work, a dedicated test rig was developed to automatically acquire vibration and flow rate data from a centrifugal pump, in a flow rate range between 0.05 × 10⁻⁵${\mathrm{m}}^3$/$\mathrm{s}$ and 9.11 × 10⁻⁵${\mathrm{m}}^3$/$\mathrm{s}$. The vibration data were processed into power bands, which were subsequently used to optimize and train a multilayer perceptron neural network for flow soft sensing. The trained model was compared with models with different vibration processing methods from literature. The power band processing model resulted in a root mean squared error 75.4% smaller than the second-best model in cross-validation, and 51.5% smaller with test data. The uncertainty of the proposed regression model was estimated using a combination of ensemble learning and Monte Carlo simulations, and combined with the reference flow sensor uncertainty to obtain the total combined uncertainty of the soft sensor, found to be between 3.9 × 10⁻⁶${\mathrm{m}}^3$/$\mathrm{s}$ and 6.1 × 10⁻⁶${\mathrm{m}}^3$/$\mathrm{s}$ throughout the measured flow range. The reference flow sensor accuracy was found to be the largest individual contribution for the final uncertainty, closely followed by the regression model uncertainty. Full article

(1) Background: To develop a novel capillary refill time measurement system and evaluate its reliability and reproducibility. (2) Methods: Firstly, the utilization of electromagnetic pressure technology facilitates the automatic compression and instantaneous release of the finger. Secondly, the employment of pressure sensing technology and photoelectric volumetric pulse wave analysis technology enables the dynamic monitoring of blood flow in distal tissues. Thirdly, the subjects were recruited to compare the average measurement time and the number of measurements required for successful measurements. The satisfaction of doctors and patients with the instrument was investigated through the administration of questionnaires. Finally, 71 subjects were recruited and divided into two groups, A and B. Three doctors repeated the measurement of the right index fingers of the subjects. In Group A, the same measuring instrument was used, and the consistency of the measurements was evaluated using the intragroup correlation coefficient. In Group B, one doctor repeated the measurement of each subject three times using the same measuring instrument, and the reproducibility of the CRT was evaluated using the analysis of variance of the repeated measurement data. (3) Results: The development of the capillary refill time meter was successful, with an average measurement time of 18 s and a single measurement. This study found that doctor–patient satisfaction levels were 98.3% and 100%, respectively. The intraclass correlation coefficient was 0.995 in Group A, and the p-value was greater than 0.05 in Group B. (4) Conclusions: The non-invasive monitoring of microcirculation has been rendered both rapid and effective, thus paving the way for the further mechanization and standardization of this process. The CRT, when measured using the capillary refill time meter test machine, demonstrated consistent and reproducible results, both when assessed by different researchers and when evaluated across varying measurement sets. Full article