Remote sensing of high energy charged particle current (HEC) for megavoltage therapeutic electron beams.

Objective.We demonstrate detection of high energy particle current (HEC) for MeV therapeutic electron beams. Detection of HEC comprises of remote sensing or acquiring information about HEC inside radiation transport medium from a distance outside of the medium.Approach.HEC is self-propelled motion of charged particles through a radiation transport medium. Remote sensing of HEC is embodied in an experimental setup, which includes homogeneous and heterogeneous phantoms irradiated with 4-15 MeV electron beams and two large area parallel-plane electrodes extraneous to the phantoms providing two-parameter detection. We also introduce a new type of scanning method (depth-scan) for probing object properties along the beamline axis.Main Results.Deterministic radiation transport simulations and measurements agree, considering differences in simulation vs experimental geometry and experimental uncertainties.Significance.This method may be suitable for range detection of charged particle beams, or for probing of radiation opaque objects in non-destructive testing.

Mapping fine-scale seagrass disturbance using bi-temporal UAV-acquired images and multivariate alteration detection.

Seagrasses provide critical ecosystem services but cumulative human pressure on coastal environments has seen a global decline in their health and extent. Key processes of anthropogenic disturbance can operate at local spatio-temporal scales that are not captured by conventional satellite imaging. Seagrass management strategies to prevent longer-term loss and ensure successful restoration require effective methods for monitoring these fine-scale changes. Current seagrass monitoring methods involve resource-intensive fieldwork or recurrent image classification. This study presents an alternative method using iteratively reweighted multivariate alteration detection (IR-MAD), an unsupervised change detection technique originally developed for satellite images. We investigate the application of IR-MAD to image data acquired using an unoccupied aerial vehicle (UAV). UAV images were captured at a 14-week interval over two seagrass beds in Brisbane Water, NSW, Australia using a 10-band Micasense RedEdge-MX Dual camera system. To guide sensor selection, a further three band subsets representing simpler sensor configurations (6, 5 and 3 bands) were also analysed using eight categories of seagrass change. The ability of the IR-MAD method, and for the four different sensor configurations, to distinguish the categories of change were compared using the Jeffreys-Matusita (JM) distance measure of spectral separability. IR-MAD based on the full 10-band sensor images produced the highest separability values indicating that human disturbances (propeller scars and other seagrass damage) were distinguishable from all other change categories. IR-MAD results for the 6-band and 5-band sensors also distinguished key seagrass change features. The IR-MAD results for the simplest 3-band sensor (an RGB camera) detected change features, but change categories were not strongly separable from each other. Analysis of IR-MAD weights indicated that additional visible bands, including a coastal blue band and a second red band, improve change detection. IR-MAD is an effective method for seagrass monitoring, and this study demonstrates the potential for multispectral sensors with additional visible bands to improve seagrass change detection.

Digital innovations for monitoring sustainability in food systems.

Monitoring systems that incentivize, track and verify compliance with social and environmental standards are widespread in food systems. In particular, digital monitoring approaches using remote sensing, machine learning, big data, smartphones, platforms and blockchain are proliferating. The increasing use and availability of these technologies put us at a critical juncture to leverage these innovations for enhanced transparency, fairness and open access, rather than descending into a dystopian landscape of digital surveillance and division perpetuated by a powerful few. Here we discuss opportunities and risks, and highlight research gaps linked to the ongoing digitalization of monitoring approaches.

Rapid estimation of soil water content based on hyperspectral reflectance combined with continuous wavelet transform, feature extraction, and extreme learning machine.

Background: Soil water content is one of the critical indicators in agricultural systems. Visible/near-infrared hyperspectral remote sensing is an effective method for soil water estimation. However, noise removal from massive spectral datasets and effective feature extraction are challenges for achieving accurate soil water estimation using this technology. Methods: This study proposes a method for hyperspectral remote sensing soil water content estimation based on a combination of continuous wavelet transform (CWT) and competitive adaptive reweighted sampling (CARS). Hyperspectral data were collected from soil samples with different water contents prepared in the laboratory. CWT, with two wavelet basis functions (mexh and gaus2), was used to pre-process the hyperspectral reflectance to eliminate noise interference. The correlation analysis was conducted between soil water content and wavelet coefficients at ten scales. The feature variables were extracted from these wavelet coefficients using the CARS method and used as input variables to build linear and non-linear models, specifically partial least squares (PLSR) and extreme learning machine (ELM), to estimate soil water content. Results: The results showed that the correlation between wavelet coefficients and soil water content decreased as the decomposition scale increased. The corresponding bands of the extracted wavelet coefficients were mainly distributed in the near-infrared region. The non-linear model (ELM) was superior to the linear method (PLSR). ELM demonstrated satisfactory accuracy based on the feature wavelet coefficients of CWT with the mexh wavelet basis function at a decomposition scale of 1 (CWT(mexh_1)), with R2, RMSE, and RPD values of 0.946, 1.408%, and 3.759 in the validation dataset, respectively. Overall, the CWT(mexh_1)-CARS-ELM systematic modeling method was feasible and reliable for estimating the water content of sandy clay loam.

Temperature and ST-segment morphology remote monitoring: new perspectives for implantable cardiac monitors in Brugada syndrome.

INTRODUCTION: Patients with Brugada syndrome (BrS) face an increased risk of ventricular arrhythmias and sudden cardiac death. Implantable cardiac monitors (ICMs) have emerged as effective tools for detecting arrhythmias in BrS. Technological advancements, including temperature sensors and improved subcutaneous electrocardiogram (subECG) signal quality, hold promise for further enhancing their utility in this population. METHODS AND RESULTS: We present a case of a 40-year-old man exhibiting a BrS type 2 pattern on 12-lead ECG, who underwent ICM insertion (BIOMONITOR IIIm, BIOTRONIK) due to drug-induced BrS type 1 pattern and a history of syncope, with a negative response to programmed ventricular stimulation. The device contains an integrated temperature sensor and can transmit daily vital data, such as mean heart rate and physical activity. Several months later, remote alerts indicated a temperature increase, along with transmitted subECGs suggesting a fever-induced BrS type 1 pattern. The patient was promptly advised to commence antipyretic therapy. Over the following days, remotely monitored parameters showed decreases in mean temperature, physical activity, and mean heart rate, without further recurrence of abnormal subECGs. CONCLUSION: ICMs offer valuable insights beyond arrhythmia detection in BrS. Early detection of fever using embedded temperature sensors may improve patient management, while continuous subECG morphological analysis has the potential to enhance risk stratification in BrS patients.

Remotely monitored physical activity from older people with cardiac devices associates with physical functioning.

INTRODUCTION: Accelerometer-derived physical activity (PA) from cardiac devices are available via remote monitoring platforms yet rarely reviewed in clinical practice. We aimed to investigate the association between PA and clinical measures of frailty and physical functioning. METHODS: The PATTErn study (A study of Physical Activity paTTerns and major health Events in older people with implantable cardiac devices) enrolled participants aged 60 + undergoing remote cardiac monitoring. Frailty was measured using the Fried criteria and gait speed (m/s), and physical functioning by NYHA class and SF-36 physical functioning score. Activity was reported as mean time active/day across 30-days prior to enrolment (30-day PA). Multivariable regression methods were utilised to estimate associations between PA and frailty/functioning (OR = odds ratio, ß = beta coefficient, CI = confidence intervals). RESULTS: Data were available for 140 participants (median age 73, 70.7% male). Median 30-day PA across the analysis cohort was 134.9 min/day (IQR 60.8-195.9). PA was not significantly associated with Fried frailty status on multivariate analysis, however was associated with gait speed (ß = 0.04, 95% CI 0.01-0.07, p = 0.01) and measures of physical functioning (NYHA class: OR 0.73, 95% CI 0.57-0.92, p = 0.01, SF-36 physical functioning: ß = 4.60, 95% CI 1.38-7.83, p = 0.005). CONCLUSIONS: PA from cardiac devices was associated with physical functioning and gait speed. This highlights the importance of reviewing remote monitoring PA data to identify patients who could benefit from existing interventions. Further research should investigate how to embed this into clinical pathways.

Remote sensing estimation of sugar beet SPAD based on un-manned aerial vehicle multispectral imagery.

Accurate, non-destructive and cost-effective estimation of crop canopy Soil Plant Analysis De-velopment(SPAD) is crucial for precision agriculture and cultivation management. Unmanned aerial vehicle (UAV) platforms have shown tremendous potential in predicting crop canopy SPAD. This was because they can rapidly and accurately acquire remote sensing spectral data of the crop canopy in real-time. In this study, a UAV equipped with a five-channel multispectral camera (Blue, Green, Red, Red_edge, Nir) was used to acquire multispectral images of sugar beets. These images were then combined with five machine learning models, namely K-Nearest Neighbor, Lasso, Random Forest, RidgeCV and Support Vector Machine (SVM), as well as ground measurement data to predict the canopy SPAD of sugar beets. The results showed that under both normal irrigation and drought stress conditions, the SPAD values in the normal ir-rigation treatment were higher than those in the water-limited treatment. Multiple vegetation indices showed a significant correlation with SPAD, with the highest correlation coefficient reaching 0.60. Among the SPAD prediction models, different models showed high estimation accuracy under both normal irrigation and water-limited conditions. The SVM model demon-strated a good performance with a correlation coefficient (R2) of 0.635, root mean square error (Rmse) of 2.13, and relative error (Re) of 0.80% for the prediction and testing values under normal irrigation. Similarly, for the prediction and testing values under drought stress, the SVM model exhibited a correlation coefficient (R2) of 0.609, root mean square error (Rmse) of 2.71, and rela-tive error (Re) of 0.10%. Overall, the SVM model showed good accuracy and stability in the pre-diction model, greatly facilitating high-throughput phenotyping research of sugar beet canopy SPAD.

EcoLiDAR: An economical LiDAR scanner for ecological research.

Despite recent popularization and widespread use in modern electronic devices, LiDAR technology remains expensive for research purposes, in part due to the very high performance offered by commercially available LiDAR scanners. However, such high performance is not always needed, and the expensive price ends up making LiDAR scanners inaccessible for research projects with reduced budget, such as those in developing countries. Here we designed and built a simple ground-based LiDAR scanner, with performance sufficient to fulfil the requirements for a variety of ecological research projects, while being cheap and easy to build. We managed to assemble a LiDAR scanner under 400 USD (as of 2021), and it is simple enough to be built by personnel with minimal engineering background. We also demonstrated the quality of the resulting point clouds by scanning a test site and producing some common LiDAR products. Although not adequate for mapping large area due to its limited range, our LiDAR design is open, customizable, and can produce adequate results while costing ~1% of "low-cost" scanners available in the market. As such, our LiDAR scanner opens a world of new opportunities, particularly for projects in developing countries.

ASG-YOLOv5: Improved YOLOv5 unmanned aerial vehicle remote sensing aerial images scenario for small object detection based on attention and spatial gating.

With the accelerated development of the technological power of society, aerial images of drones gradually penetrated various industries. Due to the variable speed of drones, the captured images are shadowed, blurred, and obscured. Second, drones fly at varying altitudes, leading to changing target scales and making it difficult to detect and identify small targets. In order to solve the above problems, an improved ASG-YOLOv5 model is proposed in this paper. Firstly, this research proposes a dynamic contextual attention module, which uses feature scores to dynamically assign feature weights and output feature information through channel dimensions to improve the model's attention to small target feature information and increase the network's ability to extract contextual information; secondly, this research designs a spatial gating filtering multi-directional weighted fusion module, which uses spatial filtering and weighted bidirectional fusion in the multi-scale fusion stage to improve the characterization of weak targets, reduce the interference of redundant information, and better adapt to the detection of weak targets in images under unmanned aerial vehicle remote sensing aerial photography; meanwhile, using Normalized Wasserstein Distance and CIoU regression loss function, the similarity metric value of the regression frame is obtained by modeling the Gaussian distribution of the regression frame, which increases the smoothing of the positional difference of the small targets and solves the problem that the positional deviation of the small targets is very sensitive, so that the model's detection accuracy of the small targets is effectively improved. This paper trains and tests the model on the VisDrone2021 and AI-TOD datasets. This study used the NWPU-RESISC dataset for visual detection validation. The experimental results show that ASG-YOLOv5 has a better detection effect in unmanned aerial vehicle remote sensing aerial images, and the frames per second (FPS) reaches 86, which meets the requirement of real-time small target detection, and it can be better adapted to the detection of the weak and small targets in the aerial image dataset, and ASG-YOLOv5 outperforms many existing target detection methods, and its detection accuracy reaches 21.1% mAP value. The mAP values are improved by 2.9% and 1.4%, respectively, compared with the YOLOv5 model. The project is available at https://github.com/woaini-shw/asg-yolov5.git.

Characterising illness stages and recovery trajectories of eating disorders in young people via remote measurement technology (STORY): a multi-centre prospective cohort study protocol.

BACKGROUND: Eating disorders (EDs) are serious, often chronic, conditions associated with pronounced morbidity, mortality, and dysfunction increasingly affecting young people worldwide. Illness progression, stages and recovery trajectories of EDs are still poorly characterised. The STORY study dynamically and longitudinally assesses young people with different EDs (restricting; bingeing/bulimic presentations) and illness durations (earlier; later stages) compared to healthy controls. Remote measurement technology (RMT) with active and passive sensing is used to advance understanding of the heterogeneity of earlier and more progressed clinical presentations and predictors of recovery or relapse. METHODS: STORY follows 720 young people aged 16-25 with EDs and 120 healthy controls for 12 months. Online self-report questionnaires regularly assess ED symptoms, psychiatric comorbidities, quality of life, and socioeconomic environment. Additional ongoing monitoring using multi-parametric RMT via smartphones and wearable smart rings ('Oura ring') unobtrusively measures individuals' daily behaviour and physiology (e.g., Bluetooth connections, sleep, autonomic arousal). A subgroup of participants completes additional in-person cognitive and neuroimaging assessments at study-baseline and after 12 months. DISCUSSION: By leveraging these large-scale longitudinal data from participants across ED diagnoses and illness durations, the STORY study seeks to elucidate potential biopsychosocial predictors of outcome, their interplay with developmental and socioemotional changes, and barriers and facilitators of recovery. STORY holds the promise of providing actionable findings that can be translated into clinical practice by informing the development of both early intervention and personalised treatment that is tailored to illness stage and individual circumstances, ultimately disrupting the long-term burden of EDs on individuals and their families.

Internet of Things-Enabled Patch With Built-in Microsensors and Wireless Chip: Real-Time Remote Monitoring of Patch Treatment.

Purpose: We aimed to design, develop, and evaluate an internet of things-enabled patch (IoT patch) for real-time remote monitoring of adherence (or patch wear time) during patch treatment in child participants in clinical trials. This study provides healthcare providers with a tool for objective, real-time, and remote assessment of adherence and for making required adjustments to treatment plans. Methods: The IoT patch had two temperature microsensors and a wireless chip. One sensor was placed closer to the skin than the other, resulting in a temperature difference depending on whether the patch was worn. When the patch was worn, it measured temperatures every 30 seconds and transmitted temperature data to a cloud server via a mobile application every 15 seconds. The patch was evaluated via 2 experiments with 30 healthy adults and 40 children with amblyopia. Results: Excellent monitoring accuracy was observed in both adults (mean delay of recorded time data, 0.4 minutes) and children (mean, 0.5 minutes). The difference between manually recorded and objectively recorded patch wear times showed good agreement in both groups. Experiment 1 showed accurate monitoring over a wide range of temperatures (from 0 to 30°C). Experiment 2 showed no significant differences in wearability (ease-of-use and comfort scores) between the IoT and conventional patches. Conclusions: The IoT patch offers an accurate, real-time, and remote system to monitor adherence to patch treatment. The patch is comfortable and easy to use. The utilization of an IoT patch may increase adherence to patch treatment based on accurate monitoring. Translational Relevance: Results show that the IoT patch can enable real-time adherence monitoring in clinical trials, improving treatment precision, and patient compliance to enhance outcomes.

Combining multisensor images and social network data to assess the area flooded by a hurricane event.

In this study, multisensor remote sensing datasets were used to characterize the land use and land covers (LULC) flooded by Hurricane Willa which made landfall on October 24, 2018. The landscape characterization was done using an unsupervised K-means algorithm of a cloud-free Sentinel-2 MultiSpectral Instrument (MSI) image, acquired during the dry season before Hurricane Willa. A flood map was derived using the histogram thresholding technique over a Synthetic Aperture Radar (SAR) Sentinel-1 C-band and combined with a flood map derived from a Sentinel-2 MSI image. Both, the Sentinel-1 and Sentinel-2 images were obtained after Willa landfall. While the LULC map reached an accuracy of 92%, validated using data collected during field surveys, the flood map achieved 90% overall accuracy, validated using locations extracted from social network data, that were manually georeferenced. The agriculture class was the dominant land use (about 2,624 km2), followed by deciduous forest (1,591 km2) and sub-perennial forest (1,317 km2). About 1,608 km2 represents the permanent wetlands (mangrove, salt marsh, lagoon and estuaries, and littoral classes), but only 489 km2 of this area belongs to aquatic surfaces (lagoons and estuaries). The flooded area was 1,225 km2, with the agricultural class as the most impacted (735 km2). Our analysis detected the saltmarsh class occupied 541 km2in the LULC map, and around 328 km2 were flooded during Hurricane Willa. Since the water flow receded relatively quickly, obtaining representative imagery to assess the flood event was a challenge. Still, the high overall accuracies obtained in this study allow us to assume that the outputs are reliable and can be used in the implementation of effective strategies for the protection, restoration, and management of wetlands. In addition, they will improve the capacity of local governments and residents of Marismas Nacionales to make informed decisions for the protection of vulnerable areas to the different threats derived from climate change.

Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.

Phytoplankton are the world's largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, etc., can retard their growth. With advancements in better hardware, the usage of Artificial Intelligence techniques is rapidly increasing for creating an intelligent decision-making system. Therefore, we attempt to overcome this gap by using supervised regressions on reanalysis data targeting global phytoplankton levels in global waters. The presented experiment proposes the applications of different supervised machine learning regression techniques such as random forest, extra trees, bagging and histogram-based gradient boosting regressor on reanalysis data obtained from the Copernicus Global Ocean Biogeochemistry Hindcast dataset. Results obtained from the experiment have predicted the phytoplankton levels with a coefficient of determination score (R2) of up to 0.96. After further validation with larger datasets, the model can be deployed in a production environment in an attempt to complement in-situ measurement efforts.

Towards a toolkit for global insect biodiversity monitoring.

Insects are the most diverse group of animals on Earth, yet our knowledge of their diversity, ecology and population trends remains abysmally poor. Four major technological approaches are coming to fruition for use in insect monitoring and ecological research-molecular methods, computer vision, autonomous acoustic monitoring and radar-based remote sensing-each of which has seen major advances over the past years. Together, they have the potential to revolutionize insect ecology, and to make all-taxa, fine-grained insect monitoring feasible across the globe. So far, advances within and among technologies have largely taken place in isolation, and parallel efforts among projects have led to redundancy and a methodological sprawl; yet, given the commonalities in their goals and approaches, increased collaboration among projects and integration across technologies could provide unprecedented improvements in taxonomic and spatio-temporal resolution and coverage. This theme issue showcases recent developments and state-of-the-art applications of these technologies, and outlines the way forward regarding data processing, cost-effectiveness, meaningful trend analysis, technological integration and open data requirements. Together, these papers set the stage for the future of automated insect monitoring. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Monitoring aerial insect biodiversity: a radar perspective.

In the current biodiversity crisis, populations of many species have alarmingly declined, and insects are no exception to this general trend. Biodiversity monitoring has become an essential asset to detect biodiversity change but remains patchy and challenging for organisms that are small, inconspicuous or make (nocturnal) long-distance movements. Radars are powerful remote-sensing tools that can provide detailed information on intensity, timing, altitude and spatial scale of aerial movements and might therefore be particularly suited for monitoring aerial insects and their movements. Importantly, they can contribute to several essential biodiversity variables (EBVs) within a harmonized observation system. We review existing research using small-scale biological and weather surveillance radars for insect monitoring and outline how the derived measures and quantities can contribute to the EBVs 'species population', 'species traits', 'community composition' and 'ecosystem function'. Furthermore, we synthesize how ongoing and future methodological, analytical and technological advancements will greatly expand the use of radar for insect biodiversity monitoring and beyond. Owing to their long-term and regional-to-large-scale deployment, radar-based approaches can be a powerful asset in the biodiversity monitoring toolbox whose potential has yet to be fully tapped. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Remote monitoring of amyotrophic lateral sclerosis using wearable sensors detects differences in disease progression and survival: a prospective cohort study.

BACKGROUND: There is an urgent need for objective and sensitive measures to quantify clinical disease progression and gauge the response to treatment in clinical trials for amyotrophic lateral sclerosis (ALS). Here, we evaluate the ability of an accelerometer-derived outcome to detect differential clinical disease progression and assess its longitudinal associations with overall survival in patients with ALS. METHODS: Patients with ALS wore an accelerometer on the hip for 3-7 days, every 2-3 months during a multi-year observation period. An accelerometer-derived outcome, the Vertical Movement Index (VMI), was calculated, together with predicted disease progression rates, and jointly analysed with overall survival. The clinical utility of VMI was evaluated using comparisons to patient-reported functionality, while the impact of various monitoring schemes on empirical power was explored through simulations. FINDINGS: In total, 97 patients (70.1% male) wore the accelerometer for 1995 days, for a total of 27,701 h. The VMI was highly discriminatory for predicted disease progression rates, revealing faster rates of decline in patients with a worse predicted prognosis compared to those with a better predicted prognosis (p < 0.0001). The VMI was strongly associated with the hazard for death (HR 0.20, 95% CI: 0.09-0.44, p < 0.0001), where a decrease of 0.19-0.41 unit was associated with reduced ambulatory status. Recommendations for future studies using accelerometery are provided. INTERPRETATION: The results serve as motivation to incorporate accelerometer-derived outcomes in clinical trials, which is essential for further validation of these markers to meaningful endpoints. FUNDING: Stichting ALS Nederland (TRICALS-Reactive-II).

Device-detected atrial sensing amplitudes as a marker of increased risk for new onset and progression of atrial high-rate episodes.

BACKGROUND: Atrial high-rate episodes (AHREs) are frequent in patients with cardiac implantable electronic devices. A decrease in device-detected P-wave amplitude may be an indicator of periods of increased risk of AHRE. OBJECTIVE: The objective of this study was to assess the association between P-wave amplitude and AHRE incidence. METHODS: Remote monitoring data from 2579 patients with no history of atrial fibrillation (23% pacemakers and 77% implantable cardioverter-defibrillators, of which 40% provided cardiac resynchronization therapy) were used to calculate the mean P-wave amplitude during 1 month after implantation. The association with AHRE incidence according to 4 strata of daily burden duration (≥15 minutes, ≥6 hours, ≥24 hours, ≥7 days) was investigated by adjusting the hazard ratio with the CHA2DS2-VASc score. RESULTS: The adjusted hazard ratio for 1-mV lower mean P-wave amplitude during the first month increased from 1.10 (95% confidence interval [CI], 1.05-1.15; P < .001) to 1.18 (CI, 1.09-1.28; P < .001) with AHRE duration strata from ≥15 minutes to ≥7 days independent of the CHA2DS2-VASc score. Of 871 patients with AHREs, those with 1-month P-wave amplitude <2.45 mV had an adjusted hazard ratio of 1.51 (CI, 1.19-1.91; P = .001) for progression of AHREs from ≥15 minutes to ≥7 days compared with those with 1-month P-wave amplitude ≥2.45 mV. Device-detected P-wave amplitudes decreased linearly during the 1 year before the first AHRE by 7.3% (CI, 5.1%-9.5%; P < .001 vs patients without AHRE). CONCLUSION: Device-detected P-wave amplitudes <2.45 mV were associated with an increased risk of AHRE onset and progression to persistent forms of AHRE independent of the patient's risk profile.

Sticker-Type Remote Monitoring System for Early Risk Detection of Catheter Associated Urinary Tract Infections.

A substantial number of critically ill patients in intensive care units (ICUs) rely on indwelling urinary catheters (IDCs), demanding regular monitoring of urine bags. This process increases the workload for healthcare providers and elevates the risk of exposure to contagious diseases. Moreover, IDCs are a primary cause of catheter-associated urinary tract infections (UTIs) in ICU patients whose delayed detection can have life-threatening complications. To address this, we have developed a Sticker Type Antenna for Remote Sensing (STARS) system capable of measuring urine flow rate and conductivity as early-risk markers for UTIs, alongside tracking patients' urine bag status to facilitate medical automation for healthcare providers. STARS comprises a simple, low-cost, disposable antenna module for contactless measurements of urine volume and conductivity, and a reusable wireless module for real-time data transmission. Systematic studies on STARS revealed its stable performance within physiologically relevant ranges of urine volume (0 to 2000 ml) and conductivity (5 to 40 mS/cm) in urine bags. As a proof-of-concept, STARS was tested in artificially created healthy and infected urine specimens to validate its non-contact sensing performance in detecting the onset of UTIs in catheterized patients within a hospital-like environment. STARS represents the first application of a real-time, contactless, wireless monitoring platform for simultaneous urine bag management and early risk detection of UTIs.

Prognostic impact of residual pulmonary congestion assessed by remote dielectric sensing system in patients admitted for heart failure.

AIMS: Remote dielectric sensing (ReDS) represents a contemporary non-invasive technique reliant on electromagnetic energy to quantify pulmonary congestion. Its prognostic significance within the context of heart failure (HF) patients remains elusive. This study aimed to assess the prognostic implications of residual pulmonary congestion, as gauged by the ReDS system, among patients admitted due to congestive HF. METHODS AND RESULTS: We enrolled hospitalized HF patients who underwent ReDS assessments upon admission and discharge in a blinded manner, independent of attending physicians. We evaluated the prognostic impact of the ReDS ratio between admission and discharge on the primary outcome, which encompassed all-cause mortality and HF-related re-hospitalizations. A cohort of 133 patients (median age 78 [72, 84] years, 78 male [59%]) was included. Over a median observation period of 363 days post-index discharge, an escalated ReDS group (ReDS ratio > 100%), determined through statistical calculation, emerged as an independent predictor of the primary outcome, exhibiting an adjusted hazard ratio of 4.37 (95% confidence interval 1.13-16.81, P = 0.032). The cumulative incidence of the primary outcome was notably higher in the increased ReDS group compared with the decreased ReDS group (50.1% vs. 8.5%, P = 0.034). CONCLUSIONS: Elevated ReDS ratios detected during the index hospitalization could serve as a promising prognostic indicator in HF patients admitted for treatment. The clinical ramifications of ReDS-guided HF management warrant validation in subsequent studies.