Promoting a Patient-Centered Understanding of Safety in Acute Mental Health Wards: A User-Centered Design Approach to Develop a Real-Time Digital Monitoring Tool

BACKGROUND

Acute mental health services report high levels of safety incidents that involve both patients and staff. The potential for patients to be involved in interventions to improve safety within a mental health setting is acknowledged, and there is a need for interventions that proactively seek the patient perspective of safety. Digital technologies may offer opportunities to address this need.

OBJECTIVE

This research sought to design and develop a digital real-time monitoring tool (WardSonar) to collect and collate daily information from patients in acute mental health wards about their perceptions of safety. We present the design and development process and underpinning logic model and programme theory.

METHODS

The first stage involved a synthesis of the findings from a systematic review and evidence scan, interviews with patients (n=8) and health professionals (n=17), and stakeholder engagement. Cycles of design activities and discussion followed with patients, staff, and stakeholder groups, to design and develop the prototype tool.

RESULTS

We drew on patient safety theory and the concepts of contagion and milieu. The data synthesis, design, and development process resulted in three prototype components of the digital monitoring tool (WardSonar): (1) a patient recording interface that asks patients to input their perceptions into a tablet computer, to assess how the ward feels and whether the direction is changing, that is, "getting worse" or "getting better"; (2) a staff dashboard and functionality to interrogate the data at different levels; and (3) a public-facing ward interface. The technology is available as open-source code.

CONCLUSIONS

Recent patient safety policy and research priorities encourage innovative approaches to measuring and monitoring safety. We developed a digital real-time monitoring tool to collect information from patients in acute mental health wards about perceived safety, to support staff to respond and intervene to changes in the clinical environment more proactively.

Fast high-resolution prospective motion correction for single-voxel spectroscopy

PURPOSE

To develop a fast high-resolution image-based motion correction method using spiral navigators with multislice-to-volume registration.

METHODS

A semi-LASER sequence was modified to include a multislice spiral navigator for prospective motion correction (∼305 ms including acquisition, processing, and feedback) as well as shim and frequency navigators for prospective shim and frequency correction (∼100 ms for each). MR spectra were obtained in the prefrontal cortex in five healthy subjects at 3 T with and without prospective motion and shim correction. The effect of key navigator parameters (number of slices, image resolution, and excitation flip angle) on registration accuracy was assessed using simulations.

RESULTS

Without prospective motion and shim correction, spectral quality degraded significantly in the presence of voluntary motion. In contrast, with prospective motion and shim correction, spectral quality was improved (metabolite linewidth = 6.7 ± 0.6 Hz, SNR= 67 ± 9) and in good agreement with baseline data without motion (metabolite linewidth = 6.9 ± 0.9 Hz, SNR = 73 ± 9). In addition, there was no significant difference in metabolites concentrations measured without motion and with prospective motion and shim correction in the presence of motion. Simulations showed that the registration precision was comparable when using three navigator slices with 3 mm resolution and when using the entire volume (all slices) with 8 mm resolution.

CONCLUSION

The proposed motion correction scheme allows fast and precise prospective motion and shim correction for single-voxel spectroscopy at 3 T. With 3 mm resolution, only a few navigator slices are necessary to achieve excellent motion correction performance.

Development and Validation of Three Automated High-Throughput Molecular Tests to Detect Monkeypox Virus Infections

BACKGROUND

The 2022 outbreak of the clade IIb monkeypox virus and subsequent global spread lead to an urgent need for the development of high-throughput, sensitive, and reproducible diagnostic tests.

METHODS

We developed 3 assays to detect monkeypox virus, 2 (MPXV+ and MPXV) for m2000 RealTime and 1 (MPXV) for Alinity m platforms. Dual targets in E9L and B6R (MPXV+) and J2L and B7R (MPXV) increased mutation resistance. In silico prediction indicates MPXV+ cross-reactivity with orthopox viruses and specific monkeypox virus detection with MPXV.

RESULTS

m2000 RealTime MPXV+ and MPXV assay sensitivity was determined to be 3.2 plaque-forming units/mL using a reference virus culture diluted into universal transport medium (UTM). Alinity m MPXV lower limit of detection was 200 copies/mL using monkeypox virus plasmids in pooled UTM matrix. m2000 RealTime MPXV+ and MPXV assays were validated with lesion swabs in UTM and 1:1 saliva to UTM mixtures. Commercially available and remnant clinical lesion specimens in UTM were tested with RealTime MPXV+, RealTime MPXV and Alinity m MPXV assays and demonstrated high agreement to known mpox (MPX)-positive specimens.

CONCLUSIONS

RealTime MPXV+, RealTime MPXV, and Alinity MPXV are high throughput and sensitive assays used for the detection of monkeypox virus. These assays maybe useful during MPX outbreaks.

Real-Time Tracking of Carbon Dioxide Concentration Using an Optical Microsphere Resonator Sensor

Whispering gallery mode resonator sensors are nondisruptive optical sensors that can detect and monitor perturbations in a gaseous environment. Through its resonant properties of peak wavelength, amplitude, and quality factor (Q factor), changes in concentration can be quantified within seconds and monitored over days with great stability. In addition, the small footprint, low cost, and high sensitivity are ideal properties for a disposable sensor that can be utilized in extreme environments. The large Q factor of the resonant cavity enables long interaction lengths and amplifies the effect of small changes in the background refractive index, which is detectable in picometer shifts of the resonance wavelength. However, this measurement is susceptible to changes in other environmental factors such as temperature, pressure, and humidity, which manifest on the picometer wavelength scale, reinforcing the need to decouple the variables. In this work, we compare the spectral response of different diameter resonators to carbon dioxide, nitrogen, and its mixtures, observing the spectral shifting and broadening of the cavity resonance near 1550 nm. In addition, the effect of environmental temperature on spectral shifting due to the thermo-optic effect is characterized and quantified. Lastly, the gas concentrations are changed in real time to showcase the tracking and recovery capabilities of the resonator sensor.

A Novel Method of Real-Time Assessment for Coronary Artery Anastomosis Skill

PURPOSE

Coronary anastomosis is the most key factor to accomplish coronary artery bypass grafting, which is one of the largest areas in cardiovascular surgery. Although we have organized on-site simulator training courses of coronary anastomosis using BEAT YOUCAN, it became difficult to continue it because of COVID-19. Therefore, we established a real-time evaluation sheet instead of an Objective Structured Assessment of Technical Skills (OSATS) evaluation sheet. The purposes of this study was to develop the real-time assessment system and to prove the correlation between the score obtained by the OSATS and the score obtained by the real-time evaluation system.

SUBJECTS AND METHODS

A total of 22 videos from the qualifying round of real-time coronary anastomosis competition evaluated by both the modified OSATS and the real-time evaluation system were utilized in this study. The global rating score of OSATS was compared with the global rating score of real-time evaluation system.

RESULTS

When examined the relationship between the OSATS total score and the real-time total score, there was a significant correlation (R = 0.752, p <0.001). The OSATS general definition score and the real-time total score also showed a strong correlation (R = 0.733, p <0.001).

CONCLUSIONS

We developed a real-time assessment sheet to evaluate coronary anastomosis. This assessment sheet had a good correlation with the OSATS evaluation sheet.

Efficient Human Violence Recognition for Surveillance in Real Time

Human violence recognition is an area of great interest in the scientific community due to its broad spectrum of applications, especially in video surveillance systems, because detecting violence in real time can prevent criminal acts and save lives. The majority of existing proposals and studies focus on result precision, neglecting efficiency and practical implementations. Thus, in this work, we propose a model that is effective and efficient in recognizing human violence in real time. The proposed model consists of three modules: the Spatial Motion Extractor (SME) module, which extracts regions of interest from a frame; the Short Temporal Extractor (STE) module, which extracts temporal characteristics of rapid movements; and the Global Temporal Extractor (GTE) module, which is responsible for identifying long-lasting temporal features and fine-tuning the model. The proposal was evaluated for its efficiency, effectiveness, and ability to operate in real time. The results obtained on the Hockey, Movies, and RWF-2000 datasets demonstrated that this approach is highly efficient compared to various alternatives. In addition, the VioPeru dataset was created, which contains violent and non-violent videos captured by real video surveillance cameras in Peru, to validate the real-time applicability of the model. When tested on this dataset, the effectiveness of our model was superior to the best existing models.

Real-time automatic image-based slice tracking of gadolinium-filled balloon wedge catheter during MR-guided cardiac catheterization: A proof-of-concept study

PURPOSE

MR-guided cardiac catheterization procedures currently use passive tracking approaches to follow a gadolinium-filled catheter balloon during catheter navigation. This requires frequent manual tracking and repositioning of the imaging slice during navigation. In this study, a novel framework for automatic real-time catheter tracking during MR-guided cardiac catheterization is presented.

METHODS

The proposed framework includes two imaging modes (Calibration and Runtime). The sequence starts in Calibration mode, in which the 3D catheter coordinates are determined using a stack of 10-20 contiguous saturated slices combined with real-time image processing. The sequence then automatically switches to Runtime mode, where three contiguous slices (acquired with partial saturation), initially centered on the catheter balloon using the Calibration feedback, are acquired continuously. The 3D catheter balloon coordinates are estimated in real time from each Runtime slice stack using image processing. Each Runtime stack is repositioned to maintain the catheter balloon in the central slice based on the prior Runtime feedback. The sequence switches back to Calibration mode if the catheter is not detected. This framework was evaluated in a heart phantom and 3 patients undergoing MR-guided cardiac catheterization. Catheter detection accuracy and rate of catheter visibility were evaluated.

RESULTS

The automatic detection accuracy for the catheter balloon during the Calibration/Runtime mode was 100%/95% in phantom and 100%/97 ± 3% in patients. During Runtime, the catheter was visible in 82% and 98 ± 2% of the real-time measurements in the phantom and patients, respectively.

CONCLUSION

The proposed framework enabled real-time continuous automatic tracking of a gadolinium-filled catheter balloon during MR-guided cardiac catheterization.

Quantification of Neuronal Cell-Released Hydrogen Peroxide Using 3D Mesoporous Copper-Enriched Prussian Blue Microcubes Nanozymes: A Colorimetric Approach in Real Time and Anticancer Effect

Despite the effectiveness and selectivity of natural enzymes, their instability has paved the way for developing nanozymes with high peroxidase activity using a straightforward technique, thereby expanding their potential for multifunctional applications. Herein, meso-copper-Prussian blue microcubes (Meso-Cu-PBMCs) nanozymes were successfully prepared via a cost-effective hydrothermal route. It was found that the Cu-PBMCs nanozymes, with three-dimensional (3D) mesoporous cubic morphologies, exhibited an excellent peroxidase-like property. Based on the high affinity of Meso-Cu-PBMCs toward H2O2 (Km = 0.226 μM) and TMB (Km = 0.407 mM), a colorimetric sensor for in situ H2O2 detection was constructed. On account of the high catalytic activity, affinity, and cascade strategy, the Meso-Cu-PBMCs nanozyme generated rapid multicolor displays at varying H2O2 concentrations. Under optimized conditions, the proposed sensor exhibits a preferable sensitivity of 18.14 μA μM-1, a linear range of 10 nM-25 mM, and a detection limit of 6.36 nM (S/N = 10). The reliability of the sensor was verified by detecting H2O2 in spiked human blood serum and milk samples, as well as by detecting in situ H2O2 generated from the neuron cell SH-SY5Y. Besides, the Meso-Cu-PBMCs nanozyme facilitated the catalysis of H2O2 in cancer cells, generating •OH radicals that induce the death of cancer cells (HCT-116 colon cancer cells), which holds substantial potential for application in chemodynamic therapy (CDT). This proposed strategy holds promise for simple, rapid, inexpensive, and effective intracellular biosensing and offers a novel approach to improve CDT efficacy.

Toward Real-Time Animal Tracking with Integrated Stimulus Control for Automated Conditioning in Aquatic Eco-Neurotoxicology

Aquatic eco-neurotoxicology is an emerging field that requires new analytical systems to study the effects of pollutants on animal behaviors. This is especially true if we are to gain insights into one of the least studied aspects: the potential perturbations that neurotoxicants can have on cognitive behaviors. The paucity of experimental data is partly caused by a lack of low-cost technologies for the analysis of higher-level neurological functions (e.g., associative learning) in small aquatic organisms. Here, we present a proof-of-concept prototype that utilizes a new real-time animal tracking software for on-the-fly video analysis and closed-loop, external hardware communications to deliver stimuli based on specific behaviors in aquatic organisms, spanning three animal phyla: chordates (fish, frog), platyhelminthes (flatworm), and arthropods (crustacean). The system's open-source software features an intuitive graphical user interface and advanced adaptive threshold-based image segmentation for precise animal detection. We demonstrate the precision of animal tracking across multiple aquatic species with varying modes of locomotion. The presented technology interfaces easily with low-cost and open-source hardware such as the Arduino microcontroller family for closed-loop stimuli control. The new system has potential future applications in eco-neurotoxicology, where it could enable new opportunities for cognitive research in diverse small aquatic model organisms.

Cluster-Disjoint Multipath Routing Protocol for Real-Time and Reliable Packet Transmission in Wireless Sensor Networks

Multipath routing protocol, which is one of the typical schemes to improve end-to-end transmission success ratio in Wireless Sensor Networks (WSNs), exploits two or more paths. However, collisions and interference might occur when each path is adjacent. To prevent this problem, they construct node- or link-disjointed paths. Although the paths could prevent the above-mentioned problem, it still has an issue in the area of time delay. To exploit the disjointed paths, pre-path construction is required. In addition, a lot of time is incurred to recover the whole path when the part of the path is damaged. This phenomenon adversely affects the end-to-end transmission success ratio and transmission time. To alleviate this problem, we propose a cluster-disjoint multipath routing protocol. The proposed scheme divides the whole network into grid clusters of a certain size in the network initialization phase. Each sensor could transmit packets through the pre-constructed cluster structure without the path construction phase. Also, even if any node fails, it could be easily replaced by other nodes within the cluster region. The simulation results show that the proposed scheme has an advantage in terms of transmission delay and end-to-end transmission success ratio compared to existing multipath routing protocols.

Clinical Decision Support System for All Stages of Gastric Carcinogenesis in Real-Time Endoscopy: Model Establishment and Validation Study

BACKGROUND

Our research group previously established a deep-learning-based clinical decision support system (CDSS) for real-time endoscopy-based detection and classification of gastric neoplasms. However, preneoplastic conditions, such as atrophy and intestinal metaplasia (IM) were not taken into account, and there is no established model that classifies all stages of gastric carcinogenesis.

OBJECTIVE

This study aims to build and validate a CDSS for real-time endoscopy for all stages of gastric carcinogenesis, including atrophy and IM.

METHODS

A total of 11,868 endoscopic images were used for training and internal testing. The primary outcomes were lesion classification accuracy (6 classes: advanced gastric cancer, early gastric cancer, dysplasia, atrophy, IM, and normal) and atrophy and IM lesion segmentation rates for the segmentation model. The following tests were carried out to validate the performance of lesion classification accuracy: (1) external testing using 1282 images from another institution and (2) evaluation of the classification accuracy of atrophy and IM in real-world procedures in a prospective manner. To estimate the clinical utility, 2 experienced endoscopists were invited to perform a blind test with the same data set. A CDSS was constructed by combining the established 6-class lesion classification model and the preneoplastic lesion segmentation model with the previously established lesion detection model.

RESULTS

The overall lesion classification accuracy (95% CI) was 90.3% (89%-91.6%) in the internal test. For the performance validation, the CDSS achieved 85.3% (83.4%-97.2%) overall accuracy. The per-class external test accuracies for atrophy and IM were 95.3% (92.6%-98%) and 89.3% (85.4%-93.2%), respectively. CDSS-assisted endoscopy showed an accuracy of 92.1% (88.8%-95.4%) for atrophy and 95.5% (92%-99%) for IM in the real-world application of 522 consecutive screening endoscopies. There was no significant difference in the overall accuracy between the invited endoscopists and established CDSS in the prospective real-clinic evaluation (P=.23). The CDSS demonstrated a segmentation rate of 93.4% (95% CI 92.4%-94.4%) for atrophy or IM lesion segmentation in the internal testing.

CONCLUSIONS

The CDSS achieved high performance in terms of computer-aided diagnosis of all stages of gastric carcinogenesis and demonstrated real-world application potential.

Effects of Real-Time Pressure Map Feedback on Confidence in Pressure Management in Wheelchair Users With Spinal Cord Injury: Pilot Intervention Study

BACKGROUND

Wheelchair users with a spinal cord injury (SCI) are at a high risk for developing pressure injuries (PIs). Performing weight shifts is a primary method of pressure management for PI prevention; however, individuals with SCI may lack confidence in their abilities to perform adequate pressure relief due to their lack of sensation. Real-time seat interface pressure mapping feedback may provide partial substitution for sensory feedback such that an individual's confidence is improved.

OBJECTIVE

We aim to examine how confidence for pressure management by wheelchair users with SCI was impacted by providing access to real-time, on-demand seat interface pressure mapping feedback.

METHODS

Adults with SCI (N=23) completed self-efficacy questions addressing confidence around 4 factors related to performing weight shifts in this longitudinal, repeated-measures study. We evaluated the impact of providing standard PI prevention education and access to live pressure map feedback on confidence levels for performing weight shifts.

RESULTS

Access to live pressure map feedback while learning how to perform weight shifts resulted in significantly higher confidence about moving far enough to relieve pressure at high-risk areas. Confidence for adhering to the recommended weight shift frequency and duration was not significantly impacted by in-clinic education or use of pressure map feedback. Confidence that performing weight shifts reduces PI risk increased most following education, with slight additional increase when pressure map feedback was added.

CONCLUSIONS

Access to live pressure mapping feedback improves confidence about performing weight shifts that relieve pressure when provided in the clinical setting and demonstrates potential for the same in the home. This preliminary exploration of a smartphone-based pressure mapping intervention highlights the value of access to continuous pressure mapping feedback to improve awareness and confidence for managing pressure.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03987243; https://clinicaltrials.gov/study/NCT03987243.

Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation

A common problem with segmentation of medical images using neural networks is the difficulty to obtain a significant number of pixel-level annotated data for training. To address this issue, we proposed a semi-supervised segmentation network based on contrastive learning. In contrast to the previous state-of-the-art, we introduce Min-Max Similarity (MMS), a contrastive learning form of dual-view training by employing classifiers and projectors to build all-negative, and positive and negative feature pairs, respectively, to formulate the learning as solving a MMS problem. The all-negative pairs are used to supervise the networks learning from different views and to capture general features, and the consistency of unlabeled predictions is measured by pixel-wise contrastive loss between positive and negative pairs. To quantitatively and qualitatively evaluate our proposed method, we test it on four public endoscopy surgical tool segmentation datasets and one cochlear implant surgery dataset, which we manually annotated. Results indicate that our proposed method consistently outperforms state-of-the-art semi-supervised and fully supervised segmentation algorithms. And our semi-supervised segmentation algorithm can successfully recognize unknown surgical tools and provide good predictions. Also, our MMS approach could achieve inference speeds of about 40 frames per second (fps) and is suitable to deal with the real-time video segmentation.

Real-time imaging as visual biofeedback in active second stage of labor among nulliparas: a randomized controlled trial

BACKGROUND

A prolonged second stage of labor increases the risk of severe perineal laceration, postpartum hemorrhage, operative delivery, and poor Apgar score. The second stage is longer in nulliparas. Maternal pushing during the second stage of labor is an important contributor to the involuntary expulsive force developed by uterine contraction to deliver the fetus. Preliminary data indicate that visual biofeedback during the active second stage hastens birth.

OBJECTIVE

This study aimed to evaluate if visual feedback focusing on the perineum reduced the length of the active second stage of labor in comparison with the control.

STUDY DESIGN

A randomized controlled trial was conducted in the University Malaya Medical Centre from December 2021 to August 2022. Nulliparous women about to commence the active second stage, at term, with singleton gestation, reassuring fetal status, and no contraindication for vaginal delivery were randomized to live viewing of the maternal introitus (intervention) or maternal face (sham/placebo control) as visual biofeedback during their pushing. A video camera Bluetooth-linked to a tablet computer display screen was used; in the intervention arm, the camera was focused on the introitus, and in the control arm, on the maternal face. Participants were instructed to watch the display screen during their pushing. The primary outcomes were the intervention-to-delivery interval and maternal satisfaction with the pushing experience assessed using a 0-to-10 visual numerical rating scale. Secondary outcomes included mode of delivery, perineal injury, delivery blood loss, birthweight, umbilical cord arterial blood pH and base excess at birth, Apgar score at 1 and 5 minutes, and neonatal intensive care unit admission. Data were analyzed with the t test, Mann-Whitney U test, chi-square test, and Fisher exact test, as appropriate.

RESULTS

A total of 230 women were randomized (115 to intervention and 115 to control arm). The active second stage duration (intervention-to-delivery interval) was a median (interquartile range) of 16 (11-23) and 17 (12-31) minutes (P=.289), and maternal satisfaction with the pushing experience was 9 (8-10) and 7 (6-7) (P<.001) for the intervention and control arm, respectively. Women randomized to the intervention arm were more likely to agree to recommend their management to a friend (88/115 [76.5%] vs 39/115 [33.9%]; relative risk, 2.26 [95% confidence interval, 1.72-2.97]; P<.001) and more likely to have less severe perineal injury (P=.018).

CONCLUSION

Real-time viewing of the maternal introitus as visual biofeedback during pushing resulted in higher maternal satisfaction compared with the sham control of viewing the maternal face; however, the time to delivery was not significantly shortened.

Long-lived SERS Matrix for Real-Time Biochemical Detection Using "Frozen" Transition State

For the long-time tracking of biological events, maintaining the bioactivity of the analytes during the detection process is essential. Here, we show a versatile surface-enhanced Raman Scattering (SERS) platform, termed a superwettable-omniphobic lubricous porous SERS (SOLP-SERS) substrate. The SOLP-SERS substrate could generate a three-dimensional liquid "hotspots" matrix with an ultra-long lifetime (tens of days) by confining tiny amounts of liquids within the gaps between nanoparticles. Then, the analytes are trapped in the uniform liquid "hotspots", whose bioactivity can be well maintained over a long period of time during SERS detection. Limits of detection down to femtomolar levels were achieved for various molecules. More importantly, SERS signals were uniform within the substrate and remained stable for more than 30 days. As a proof-of-concept experiment, the dynamic detection of the polymerization of Aβ peptides into amyloids was monitored by the SOLP-SERS substrate within 48 h. Moreover, the exosomes secreted by breast cancer cells, an important biomarker of cancer, were also measured. These results demonstrate that the SOLP-SERS platform will provide new insights into the development of real-time biochemical sensors with ultrahigh sensitivity.

Online Video Anomaly Detection

With the popularity of video surveillance technology, people are paying more and more attention to how to detect abnormal states or events in videos in time. Therefore, real-time, automatic and accurate detection of abnormal events has become the main goal of video-based surveillance systems. To achieve this goal, many researchers have conducted in-depth research on online video anomaly detection. This paper presents the background of the research in this field and briefly explains the research methods of offline video anomaly detection. Then, we sort out and classify the research methods of online video anomaly detection and expound on the basic ideas and characteristics of each method. In addition, we summarize the datasets commonly used in online video anomaly detection and compare and analyze the performance of the current mainstream algorithms according to the evaluation criteria of each dataset. Finally, we summarize the future trends in the field of online video anomaly detection.

Development of effective model for non-destructive detection of defective kiwifruit based on graded lines

The accurate detection of external defects in kiwifruit is an important part of postharvest quality assessment. Previous studies have not considered the problems posed by the actual grading environment. In this study, we designed a novel approach based on improved Yolov5 to achieve real-time and efficient non-destructive detection of multiple defect categories in kiwifruit. First, a kiwifruit image acquisition device based on grading lines was developed to enhance the image acquisition. Subsequently, a kiwifruit dataset was constructed based on the external defect characteristics and a new data enhancement method was proposed to augment the kiwifruit samples. Thereafter, the SPD-Conv and DW-Conv modules were combined to improve Yolov5s, with EIOU as the loss calculation function. The results demonstrated that the improved model training loss value was 0.013 lower, the convergence was accelerated, the number of parameters was reduced, and the computational effort was increased. The detection accuracies of the samples in the test set, which included healthy, leaf-rubbing damaged, healed cuts or scarred, and sunburned samples, were 98.8%, 98.7%, 97.6%, and 95.9%, respectively, with an overall detection accuracy of 97.7%. The detection time was 8.0 ms, thereby meeting real-time sorting demands. The average detection accuracy and model size of SSD, Yolov5s, Yolov7, and Yolov5-Ours were compared. When the confidence threshold was 0.5, the detection accuracy of Yolov5-Ours was 10% and 6.4% higher than that of SSD and Yolov5s, respectively. In terms of the model size, Yolov5-Ours was approximately 6.5- and 4-fold smaller than SSD and Yolov7, respectively. Thus, Yolov5-Ours achieved the highest accuracy, adaptability, and robustness for the detection of all kiwifruit categories as well as a small volume and portability. These results can provide technical support for the non-destructive detection and grading of agricultural products in the future.

The Effect of the Peristimulus α Phase on Visual Perception through Real-Time Phase-Locked Stimulus Presentation

The α phase has been theorized to reflect fluctuations in cortical excitability and thereby impose a cyclic influence on visual perception. Despite its appeal, this notion is not fully substantiated, as both supporting and opposing evidence has been recently reported. In contrast to previous research, this study examined the effect of the peristimulus instead of prestimulus phase on visual detection through a real-time phase-locked stimulus presentation (PLSP) approach. Specifically, we monitored phase data from magnetoencephalography (MEG) recordings over time, with a newly developed algorithm based on adaptive Kalman filtering (AKF). This information guided online presentations of masked stimuli that were phased-locked to different stages of the α cycle while healthy humans concurrently performed detection tasks. Behavioral evidence showed that the overall detection rate did not significantly vary according to the four predetermined peristimulus α phases. Nevertheless, the follow-up analyses highlighted that the phase at 90° relative to 180° likely enhanced detection. Corroborating neural parietal activity showed that early interaction between α phases and incoming stimuli orchestrated the neural representation of the hits and misses of the stimuli. This neural representation varied according to the phase and in turn shaped the behavioral outcomes. In addition to directly investigating to what extent fluctuations in perception can be ascribed to the α phases, this study suggests that phase-dependent perception is not as robust as previously presumed, and might also depend on how the stimuli are differentially processed as a result of a stimulus-phase interaction, in addition to reflecting alternations of the perceptual states between phases.

MFAFNet: A Lightweight and Efficient Network with Multi-Level Feature Adaptive Fusion for Real-Time Semantic Segmentation

Currently, real-time semantic segmentation networks are intensely demanded in resource-constrained practical applications, such as mobile devices, drones and autonomous driving systems. However, most of the current popular approaches have difficulty in obtaining sufficiently large receptive fields, and they sacrifice low-level details to improve inference speed, leading to decreased segmentation accuracy. In this paper, a lightweight and efficient multi-level feature adaptive fusion network (MFAFNet) is proposed to address this problem. Specifically, we design a separable asymmetric reinforcement non-bottleneck module, which designs a parallel structure to extract short- and long-range contextual information and use optimized convolution to increase the inference speed. In addition, we propose a feature adaptive fusion module that effectively balances feature maps with multiple resolutions to reduce the loss of spatial detail information. We evaluate our model with state-of-the-art real-time semantic segmentation methods on the Cityscapes and Camvid datasets. Without any pre-training and post-processing, our MFAFNet has only 1.27 M parameters, while achieving accuracies of 75.9% and 69.9% mean IoU with speeds of 60.1 and 82.6 FPS on the Cityscapes and Camvid test sets, respectively. The experimental results demonstrate that the proposed method achieves an excellent trade-off between inference speed, segmentation accuracy and model size.

Saliency-Aided Online RPCA for Moving Target Detection in Infrared Maritime Scenarios

Moving target detection (MTD) is a crucial task in computer vision applications. In this paper, we investigate the problem of detecting moving targets in infrared (IR) surveillance video sequences captured using a steady camera in a maritime setting. For this purpose, we employ robust principal component analysis (RPCA), which is an improvement of principal component analysis (PCA) that separates an input matrix into the following two matrices: a low-rank matrix that is representative, in our case study, of the slowly changing background, and a sparse matrix that is representative of the foreground. RPCA is usually implemented in a non-causal batch form. To pursue a real-time application, we tested an online implementation, which, unfortunately, was affected by the presence of the target in the scene during the initialization phase. Therefore, we improved the robustness by implementing a saliency-based strategy. The advantages offered by the resulting technique, which we called "saliency-aided online moving window RPCA" (S-OMW-RPCA) are the following: RPCA is implemented online; along with the temporal features exploited by RPCA, the spatial features are also taken into consideration by using a saliency filter; the results are robust against the condition of the scene during the initialization. Finally, we compare the performance of the proposed technique in terms of precision, recall, and execution time with that of an online RPCA, thus, showing the effectiveness of the saliency-based approach.

Real-Time Detection and Classification of Scirtothrips dorsalis on Fruit Crops with Smartphone-Based Deep Learning System: Preliminary Results

This study proposes a deep-learning-based system for detecting and classifying Scirtothrips dorsalis Hood, a highly invasive insect pest that causes significant economic losses to fruit crops worldwide. The system uses yellow sticky traps and a deep learning model to detect the presence of thrips in real time, allowing farmers to take prompt action to prevent the spread of the pest. To achieve this, several deep learning models are evaluated, including YOLOv5, Faster R-CNN, SSD MobileNetV2, and EfficientDet-D0. EfficientDet-D0 was integrated into the proposed smartphone application for mobility and usage in the absence of Internet coverage because of its smaller model size, fast inference time, and reasonable performance on the relevant dataset. This model was tested on two datasets, in which thrips and non-thrips insects were captured under different lighting conditions. The system installation took up 13.5 MB of the device's internal memory and achieved an inference time of 76 ms with an accuracy of 93.3%. Additionally, this study investigated the impact of lighting conditions on the performance of the model, which led to the development of a transmittance lighting setup to improve the accuracy of the detection system. The proposed system is a cost-effective and efficient alternative to traditional detection methods and provides significant benefits to fruit farmers and the related ecosystem.

EEG-based real-time diagnostic system with developed dynamic 2TEMD and dynamic ApEn algorithms

In real-time electroencephalography (EEG) analysis, the problem of observing dynamic changes and the problem of binary classification is a promising direction. EEG energy and complexity are important evaluation metrics in brain death determination in the field of EEG analysis. We developed two algorithms, dynamic turning tangent empirical mode decomposition to compute EEG energy and dynamic approximate entropy to compute EEG complexity for brain death determination. The developed algorithm is applied to analyze 50 EEG data of coma patients and 50 EEG data of brain death patients. The validity of the dynamic analysis is confirmed by the accuracy rate derived from the comparison with turning tangent empirical mode decomposition and approximate entropy algorithms. We evaluated the EEG data of three patients using the built diagnostic system. The experimental results visually showed that the EEG energy ratio was higher in a coma state than that in brain death, while the complexity was lower than that in brain death.

Pulmonary Nodule Detection and Classification Using All-Optical Deep Diffractive Neural Network

A deep diffractive neural network (D2NN) is a fast optical computing structure that has been widely used in image classification, logical operations, and other fields. Computed tomography (CT) imaging is a reliable method for detecting and analyzing pulmonary nodules. In this paper, we propose using an all-optical D2NN for pulmonary nodule detection and classification based on CT imaging for lung cancer. The network was trained based on the LIDC-IDRI dataset, and the performance was evaluated on a test set. For pulmonary nodule detection, the existence of nodules scanned from CT images were estimated with two-class classification based on the network, achieving a recall rate of 91.08% from the test set. For pulmonary nodule classification, benign and malignant nodules were also classified with two-class classification with an accuracy of 76.77% and an area under the curve (AUC) value of 0.8292. Our numerical simulations show the possibility of using optical neural networks for fast medical image processing and aided diagnosis.

Development of an Open-Source Injection Mold Monitoring System

In the highly competitive injection molding industry, the ability to effectively collect information from various sensors installed in molds and machines is of the utmost relevance, enabling the development of data-based Industry 4.0 algorithms. In this work, an alternative to commercially available monitoring systems used in the industry was developed and tested in the scope of the TOOLING 4G project. The novelty of this system is its affordability, simplicity, real-time data acquisition and display in an intuitive Graphical User Interface (GUI), while being open-source firmware and software-based. These characteristics, and their combinations have been present in previous works, but, to the authors' knowledge, not all of them simultaneously. The system used an Arduino microcontroller-based data acquisition module that can be connected to any computer via a USB port. Software was developed, including a GUI, prepared to receive data from both the Arduino module and a second module. In the current state of development, data corresponding to a maximum of six sensors can be visualized, at a rate of 10 Hz, and recorded for later usage. These capabilities were verified under real-world conditions for monitoring an injection mold with the objective of creating the basis of a platform to deploy predictive maintenance. Mold temperature, cavity pressure, 3-axis acceleration, and extraction force data showed the system can successfully monitor the mold and allowed the clear distinction between normal and abnormal operating patterns.

NanoB2 to monitor interactions of ligands with membrane proteins by combining nanobodies and NanoBRET

The therapeutic potential of ligands targeting disease-associated membrane proteins is predicted by ligand-receptor binding constants, which can be determined using NanoLuciferase (NanoLuc)-based bioluminescence resonance energy transfer (NanoBRET) methods. However, the broad applicability of these methods is hampered by the restricted availability of fluorescent probes. We describe the use of antibody fragments, like nanobodies, as universal building blocks for fluorescent probes for use in NanoBRET. Our nanobody-NanoBRET (NanoB2) workflow starts with the generation of NanoLuc-tagged receptors and fluorescent nanobodies, enabling homogeneous, real-time monitoring of nanobody-receptor binding. Moreover, NanoB2 facilitates the assessment of receptor binding of unlabeled ligands in competition binding experiments. The broad significance is illustrated by the successful application of NanoB2 to different drug targets (e.g., multiple G protein-coupled receptors [GPCRs] and a receptor tyrosine kinase [RTK]) at distinct therapeutically relevant binding sites (i.e., extracellular and intracellular).

Convolutional Neural Networks for the Real-Time Monitoring of Vital Signs Based on Impulse Radio Ultrawide-Band Radar during Sleep

Vital signs provide important biometric information for managing health and disease, and it is important to monitor them for a long time in a daily home environment. To this end, we developed and evaluated a deep learning framework that estimates the respiration rate (RR) and heart rate (HR) in real time from long-term data measured during sleep using a contactless impulse radio ultrawide-band (IR-UWB) radar. The clutter is removed from the measured radar signal, and the position of the subject is detected using the standard deviation of each radar signal channel. The 1D signal of the selected UWB channel index and the 2D signal applied with the continuous wavelet transform are entered as inputs into the convolutional neural-network-based model that then estimates RR and HR. From 30 recordings measured during night-time sleep, 10 were used for training, 5 for validation, and 15 for testing. The average mean absolute errors for RR and HR were 2.67 and 4.78, respectively. The performance of the proposed model was confirmed for long-term data, including static and dynamic conditions, and it is expected to be used for health management through vital-sign monitoring in the home environment.

A Laboratory Evaluation of the New Automated Pollen Sensor Beenose: Pollen Discrimination Using Machine Learning Techniques

The monitoring of airborne pollen has received much attention over the last decade, as the prevalence of pollen-induced allergies is constantly increasing. Today, the most common technique to identify airborne pollen species and to monitor their concentrations is based on manual analysis. Here, we present a new, low-cost, real-time optical pollen sensor, called Beenose, that automatically counts and identifies pollen grains by performing measurements at multiple scattering angles. We describe the data pre-processing steps and discuss the various statistical and machine learning methods that have been implemented to distinguish different pollen species. The analysis is based on a set of 12 pollen species, several of which were selected for their allergic potency. Our results show that Beenose can provide a consistent clustering of the pollen species based on their size properties, and that pollen particles can be separated from non-pollen ones. More importantly, 9 out of 12 pollen species were correctly identified with a prediction score exceeding 78%. Classification errors occur for species with similar optical behaviour, suggesting that other parameters should be considered to provide even more robust pollen identification.

Road User Position and Speed Estimation via Deep Learning from Calibrated Fisheye Videos

In this paper, we present a deep learning processing flow aimed at Advanced Driving Assistance Systems (ADASs) for urban road users. We use a fine analysis of the optical setup of a fisheye camera and present a detailed procedure to obtain Global Navigation Satellite System (GNSS) coordinates along with the speed of the moving objects. The camera to world transform incorporates the lens distortion function. YOLOv4, re-trained with ortho-photographic fisheye images, provides road user detection. All the information extracted from the image by our system represents a small payload and can easily be broadcast to the road users. The results show that our system is able to properly classify and localize the detected objects in real time, even in low-light-illumination conditions. For an effective observation area of 20 m × 50 m, the error of the localization is in the order of one meter. Although an estimation of the velocities of the detected objects is carried out by offline processing with the FlowNet2 algorithm, the accuracy is quite good, with an error below one meter per second for urban speed range (0 to 15 m/s). Moreover, the almost ortho-photographic configuration of the imaging system ensures that the anonymity of all street users is guaranteed.

Performance Analysis of Real-Time GPS/Galileo Precise Point Positioning Integrated with Inertial Navigation System

The integration of global navigation satellite system (GNSS) precise point positioning (PPP) and inertial navigation system (INS) is widely used in navigation for its robustness and resilience, especially in case of GNSS signal blockage. With GNSS modernization, a variety of PPP models have been developed and studied, which has also led to various PPP/INS integration methods. In this study, we investigated the performance of a real-time GPS/Galileo zero-difference ionosphere-free (IF) PPP/INS integration with the application of uncombined bias products. This uncombined bias correction was independent of PPP modeling on the user side and also enabled carrier phase ambiguity resolution (AR). CNES (Centre National d'Etudes Spatiales) real-time orbit, clock, and uncombined bias products were used. Six positioning modes were evaluated, including PPP, PPP/INS loosely coupled integration (LCI), PPP/INS tightly coupled integration (TCI), and three of these with uncombined bias correction through a train positioning test in an open sky environment and two van positioning tests at a complex road and city center. All of the tests used a tactical-grade inertial measurement unit (IMU). In the train test, we found that ambiguity-float PPP had almost identical performance with LCI and TCI, which reached an accuracy of 8.5, 5.7, and 4.9 cm in the north (N), east (E) and up (U) direction, respectively. After AR, significant improvements on the east error component were achieved, which were 47%, 40%, and 38% for PPP-AR, PPP-AR/INS LCI, and PPP-AR/INS TCI, respectively. In the van tests, frequent signal interruptions due to bridges, vegetation, and city canyons make the IF AR difficult. TCI achieved the highest accuracies, which were 32, 29, and 41 cm for the N/E/U component, respectively, and also effectively eliminated the solution re-convergence in PPP.

Real-Time Ratiometric Optical Nanoscale Thermometry

All-optical nanothermometry has become a powerful, remote tool for measuring nanoscale temperatures in applications ranging from medicine to nano-optics and solid-state nanodevices. The key features of any candidate nanothermometer are brightness, sensitivity, and (signal, spatial, and temporal) resolution. Here, we demonstrate a real-time, diamond-based nanothermometry technique with excellent sensitivity (1.8% K^-1) and record-high resolution (5.8 × 10⁴ K Hz^-1/2 W cm^-2) based on codoped nanodiamonds. The distinct performance of our approach stems from two factors: (i) temperature sensors─nanodiamonds cohosting two group IV color centers─engineered to emit spectrally separated Stokes and anti-Stokes fluorescence signals under excitation by a single laser source and (ii) a parallel detection scheme based on filtering optics and high-sensitivity photon counters for fast readout. We demonstrate the performance of our method by monitoring temporal changes in the local temperature of a microcircuit and a MoTe₂ field-effect transistor. Our work advances a powerful, alternative strategy for time-resolved temperature monitoring and mapping of micro-/nanoscale devices such as microfluidic channels, nanophotonic circuits, and nanoelectronic devices, as well as complex biological environments such as tissues and cells.

The Independent Walking for Brain Health Intervention for Older Adults: Protocol for a Pilot Randomized Controlled Trial

BACKGROUND

Extensive research suggests that physical activity (PA) is important for brain and cognitive health and may help to delay or prevent Alzheimer's disease and related dementias. Most PA interventions designed to improve brain health in older adults have been conducted in laboratory, gym, or group settings that require extensive resources and travel to the study site or group sessions. Research is needed to develop novel interventions that leverage mobile health (mHealth) technologies to help older adults increase their engagement in PA in free-living environments, reducing participant burden and increasing generalizability of research findings. Moreover, promoting engagement in moderate-to-vigorous PA (MVPA) may be most beneficial to brain health; thus, using mHealth to help older adults increase time spent in MVPA in free-living environments may help to offset the burden of Alzheimer's disease and related dementias and improve quality of life in older age.

OBJECTIVE

We developed a novel PA intervention that leverages mHealth to help older adults achieve more minutes of MVPA independently. This pilot study was a 12-week randomized controlled trial to investigate the feasibility of providing just-in-time (JIT) feedback about PA intensity during free-living exercise sessions to help older adults meet current PA recommendations (150 minutes per week of MVPA).

METHODS

Participants were eligible if they were cognitively healthy English speakers aged between 65 and 80 years without major cardiovascular, neurologic, or mental health conditions; could ambulate independently; and undergo magnetic resonance imaging. Enrollment occurred from October 2017 to March 2020. Participants randomized to the PA condition received an individualized exercise prescription and an mHealth device that provided heart rate-based JIT feedback on PA intensity, allowing them to adjust their behavior in real time to maintain MVPA during exercise sessions. Participants assigned to the healthy aging education condition received a reading prescription consisting of healthy aging topics and completed weekly quizzes based on the materials.

RESULTS

In total, 44 participants were randomized to the intervention. A follow-up manuscript will describe the results of the intervention as well as discuss screening, recruitment, adverse events, and participants' opinions regarding their participation in the intervention.

CONCLUSIONS

The long-term goal of this intervention is to better understand how MVPA affects brain and cognitive health in the real world and extend laboratory findings to everyday life. This pilot randomized controlled trial was conducted to determine the feasibility of using JIT heart rate zone feedback to help older adults independently increase time spent in MVPA while collecting data on the plausible mechanisms of change (frontal and medial temporal cerebral blood flow and cardiorespiratory fitness) that may affect cognition (memory and executive function) to help refine a planned stage 2 behavioral trial.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03058146; https://clinicaltrials.gov/ct2/show/NCT03058146.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/42980.

RANSAC for Robotic Applications: A Survey

Random Sample Consensus, most commonly abbreviated as RANSAC, is a robust estimation method for the parameters of a model contaminated by a sizable percentage of outliers. In its simplest form, the process starts with a sampling of the minimum data needed to perform an estimation, followed by an evaluation of its adequacy, and further repetitions of this process until some stopping criterion is met. Multiple variants have been proposed in which this workflow is modified, typically tweaking one or several of these steps for improvements in computing time or the quality of the estimation of the parameters. RANSAC is widely applied in the field of robotics, for example, for finding geometric shapes (planes, cylinders, spheres, etc.) in cloud points or for estimating the best transformation between different camera views. In this paper, we present a review of the current state of the art of RANSAC family methods with a special interest in applications in robotics.

An Online Rail Track Fastener Classification System Based on YOLO Models

In order to save manpower on rail track inspection, computer vision-based methodologies are developed. We propose utilizing the YOLOv4-Tiny neural network to identify track defects in real time. There are ten defects covering fasteners, rail surfaces, and sleepers from the upward and six defects about the rail waist from the sideward. The proposed real-time inspection system includes a high-performance notebook, two sports cameras, and three parallel processes. The hardware is mounted on a flat cart running at 30 km/h. The inspection results about the abnormal track components could be queried by defective type, time, and the rail hectometer stake. In the experiments, data augmentation by a Cycle Generative Adversarial Network (GAN) is used to increase the dataset. The number of images is 3800 on the upward and 967 on the sideward. Five object detection neural network models-YOLOv4, YOLOv4-Tiny, YOLOX-Tiny, SSD512, and SSD300-were tested. The YOLOv4-Tiny model with 150 FPS is selected as the recognition kernel, as it achieved 91.7%, 92%, and 91% for the mAP, precision, and recall of the defective track components from the upward, respectively. The mAP, precision, and recall of the defective track components from the sideward are 99.16%, 96%, and 94%, respectively.

Proposal of a Real-Time Test Platform for Tactile Internet Systems

This work aimed to develop a real-time test platform for systems associated with the tactile internet area. The proposal comprises a master device, a communication channel and a slave device. The master device is a tactile glove (wearable technology) that works as a tactile interface based on vibratory feedback. The master device can interact with virtual elements (local or remote). The Matlab/Simulink environment and a robotics toolbox form the communication channel and the slave device. The communication channel introduces a bidirectional connection of variable latency, and the slave device is defined as a robotic phantom omni manipulator emulated in Matlab/Simulink. The virtual robotic manipulator, the slave device, can generate different types of tactile sensations in the tactile glove, that is, in the master device. The platform can model tactile sensations such as coarse roughness, fine roughness, smoothness, dripping and softness. The proposed platform presented adequate results and can be used to test various algorithms and methods correlated to the tactile internet.

Experimental Study of the Thermally Grown Oxide and Interface of Thermal Barrier Coatings Using TEM In-Situ Heating

Thermal barrier coating (TBC) materials play important roles in gas turbine engines to protect the Ni-based superalloys from high-temperature airflow damage. In this work, the nano-mechanism of TBC failure is analyzed. A scanning transmission electron microscopy-energy dispersive spectrometer (STEM-EDS)-based analysis method was used to study the influence of element migration on the deformation behavior of the bond-coat (BC) layer during heating. The content of elements in the same region varied greatly at different temperatures, which could prove the contribution of element migration to the deformation of the BC layer. TEM in-situ heating experiments were designed and carried out to study the deformation behavior near the ceramic topcoat (TC)/thermally grown oxide (TGO) and the TGO/BC interface. The TC/TGO interface was deformed violently during heating, and obvious deformation occurred at 100 °C, while the TGO/BC interface was relatively stable. A subset geometric phase analysis method was used for full field-strain measurement. The strain value near the TGO/BC interface was relatively small and did not change significantly at lower temperatures. The TC/TGO interface is more unstable and easier to deform than the TGO/BC interface. The stress and strain evolution in the internal region of TGO at high temperatures was quantitatively analyzed. The TGO layer has a tensile stress of GPa magnitude along the interface direction at the peak position, and the shear stress is small.

Reversible Immunosensor for the Continuous Monitoring of Cortisol in Blood Plasma Sampled with Microdialysis

Cortisol is a steroid hormone involved in a wide range of medical conditions. The level of the hormone fluctuates over time, but with traditional laboratory-based assays, such dynamics cannot be monitored in real time. Here, a reversible cortisol sensor is reported that allows continuous monitoring of cortisol in blood plasma using sampling by microdialysis. The sensor is based on measuring single-molecule binding and unbinding events of tethered particles. The particles are functionalized with antibodies and the substrate with cortisol-analogues, causing binding and unbinding events to occur between particles and substrate. The frequency of binding events is reduced when cortisol is present in the solution as it blocks the binding sites of the antibodies. The sensor responds to cortisol in the high nanomolar to low micromolar range and can monitor cortisol concentrations over multiple hours. Results are shown for cortisol monitoring in filtered and in microdialysis-sampled human blood plasma.

A Wireless Autonomous Real-Time Underwater Acoustic Positioning System

Recent acoustic telemetry positioning systems are able to reconstruct the positions and trajectories of organisms at a scale of a few centimeters to a few meters. However, they present several logistical constraints including receiver maintenance, calibration procedures and limited access to real-time data. We present here a novel, easy-to-deploy, energy self-sufficient underwater positioning system based on the time difference of arrival (TDOA) algorithm and the Global System for Mobile (GSM) communication technology, capable of locating tagged marine organisms in real time. We provide an illustration of the application of this system with empirical examples using continuous and coded tags in fish and benthic invertebrates. In situ experimental tests of the operational system demonstrated similar performances to currently available acoustic positioning systems, with a global positioning error of 7.13 ± 5.80 m (mean ± SD) and one-third of the pings can be localized within 278 m of the farthest buoy. Despite some required improvements, this prototype is designed to be autonomous and can be deployed from the surface in various environments (rivers, lakes, and oceans). It was proven to be useful to monitor a wide variety of species (benthic and pelagic) in real time. Its real-time property can be used to rapidly detect system failure, optimize deployment design, or for ecological or conservation applications.

(Near) Real-Time Snow Water Equivalent Observation Using GNSS Refractometry and RTKLIB

Global navigation satellite system (GNSS) refractometry enables automated and continuous in situ snow water equivalent (SWE) observations. Such accurate and reliable in situ data are needed for calibration and validation of remote sensing data and could enhance snow hydrological monitoring and modeling. In contrast to previous studies which relied on post-processing with the highly sophisticated Bernese GNSS processing software, the feasibility of in situ SWE determination in post-processing and (near) real time using the open-source GNSS processing software RTKLIB and GNSS refractometry based on the biased coordinate Up component is investigated here. Available GNSS observations from a fixed, high-end GNSS refractometry snow monitoring setup in the Swiss Alps are reprocessed for the season 2016/17 to investigate the applicability of RTKLIB in post-processing. A fixed, low-cost setup provides continuous SWE estimates in near real time at a low cost for the complete 2021/22 season. Additionally, a mobile, (near) real-time and low-cost setup was designed and evaluated in March 2020. The fixed and mobile multi-frequency GNSS setups demonstrate the feasibility of (near) real-time SWE estimation using GNSS refractometry. Compared to state-of-the-art manual SWE observations, a mean relative bias below 5% is achieved for (near) real-time and post-processed SWE estimation using RTKLIB.

End-of-Shift Monitoring of Respirable Crystalline Silica: A Critical Review of Measurement Techniques and Factors Influencing Accurate Measurements

This article presents the findings of a systematic literature review focusing on the impact of interferences in host materials on Respirable Crystalline Silica (RCS) measurement, and the development of a validated method of calibration for RCS instrumentation for in-field use. The impact of interferences in host materials on RCS measurement has been shown to be critical information required for in-field use of portable RCS devices. Portable RCS instruments such as FTIR and XRD have been established to have good sensitivity for measurement but relatively low specificity due to interference in host materials. It has been shown that correction factors are needed to be applied to account for known interferences, especially when calibrating portable RCS instrumentation in-field. The potential to obtain reliable and repeatable results from RCS analysis instrumentation has also been demonstrated. There was significant evidence that interference materials could be present in host materials, and they influence RCS measurement. There is the potential and possibility of using portable RCS instrument for in-field measurement however the challenge is the calibration of such instrumentation in the field to account for changing host materials and thus potential interferences.

Development of a Noninvasive Real-Time Ion Energy Distribution Monitoring System Applicable to Collisional Plasma Sheath

As the importance of ion-assisted surface processing based on low-temperature plasma increases, the monitoring of ion energy impinging into wafer surfaces becomes important. Monitoring methods that are noninvasive, real-time, and comprise ion collision in the sheath have received much research attention. However, in spite of this fact, most research was performed in invasive, not real-time, and collisionless ion sheath conditions. In this paper, we develop a noninvasive real-time IED monitoring system based on an ion trajectory simulation where the Monte Carlo collision method and an electrical model are adopted to describe collisions in sheaths. We technically, theoretically, and experimentally investigate the IED measurement with the proposed method, and compared it with the result of IEDs measured via a quadrupole mass spectrometer under various conditions. The comparison results show that there was no major change in the IEDs as radio-frequency power increased or the IED gradually became broad as gas pressure increased, which was in a good agreement with the results of the mass spectrometer.

Visualizing fluid transport inside orally disintegrating tablets and changes in tablets using real-time X-ray radiography and X-ray computed tomography

OBJECTIVE

To investigate the disintegration of wet- and dry-compressed orally disintegrating (OD) tablets, with synchrotron radiation as the X-ray source.

SIGNIFICANCE

Pharmaceutical tablets are vital for treatment of various diseases. Therefore, they are constantly developed to ensure desirable characteristics. In particular, OD tablets need to disintegrate immediately after absorbing saliva. How these tablets absorb saliva is key to enhancing rapid product development. Recently, absorption processes have been investigated using various non-invasive techniques, including X-ray radiography and X-ray computed tomography. However, X-ray radiography studies on how water without a contrast agent is absorbed, moves, and causes a tablet to swell are scarce. Use of a contrast agent is associated with some shortcomings, including complex data analysis in some instances, alterations in the viscosity of water, and potential influence on fluid transport inside the tablet, thus possibly affecting the disintegration process.

METHODS

Real-time X-ray radiography was used to monitor the disintegration of various tablets, while X-ray computed tomography and software were used to create 3D images.

RESULTS

We demonstrated how pure water penetrated the wet-compressed tablet faster than inside the dry-compressed tablet, and how the latter swelled more. X-ray computed tomography showed the presence of voids in the tablets following water absorption.

CONCLUSION

Our methods are promising for non-destructive fluid absorption and transport investigations inside OD tablets.

Drug Resistance, Rather than Low Tenofovir Levels in Blood or Urine, Is Associated with Tenofovir, Emtricitabine, and Efavirenz Failure in Resource-Limited Settings

The high cost of viral load (VL) testing limits its use for antiretroviral therapy (ART) adherence support. A low-cost lateral flow urine tenofovir (TFV) rapid assay predicts pre-exposure prophylaxis breakthroughs, but has not yet been investigated in HIV treatment. We therefore evaluated its utility in a pilot cross-sectional study of TFV-containing ART recipients at an increased risk of virologic failure (VF). Participants who had a treatment interruption ≥30 days or had ≥1 episode of viremia (VL ≥400 copies/mL) in the previous year were recruited from a public health setting in Cape Town, South Africa. Self-reported adherence data were collected, the urine TFV assay performed, and concurrent TFV-diphosphate analyzed in dried blood spots. VL testing was done concurrently and, if viremic, genotypic HIV drug resistance testing was performed. Of 48 participants, 18 (37.5%) had VL (>400 copies/mL) at the time of the study, including 16 of 39 receiving efavirenz (EFV), 2 of 6 receiving protease inhibitors, and 0 of 3 receiving dolutegravir. Resistance testing succeeded in 17/18, of which 14 had significant mutations compromising ≥2 agents of the current EFV-based regimen. Of these 14, all had detected urine TFV. Urine TFV was undetectable in two out of three without regimen-relevant resistance; p = .02. In participants on EFV-based regimens returning to care, VF was largely due to viral resistance, where detectable urine TFV had 100% sensitivity (14/14 participants) in predicting resistance. Conversely, when undetectable, the urine-based assay could be used to preclude participants with poor adherence from undergoing costly HIV drug resistance testing.

Real-Time Assessment of Mandarin Crop Water Stress Index

The use of plant-based indicators and other conventional means to detect the level of water stress in crops may be challenging, due to their difficulties in automation, their arduousness, and their time-consuming nature. Non-contact and non-destructive sensing methods can be used to detect the level of water stress in plants continuously and to provide automatic sensing and controls. This research aimed at determining the viability, efficiency, and swiftness in employing the commercial Workswell WIRIS Agro R infrared camera (WWARIC) in monitoring water stress and scheduling appropriate irrigation regimes in mandarin plants. The experiment used a four-by-three randomized complete block design with 80−100% FC water treatment as full field capacity and three deficit irrigation treatments at 70−75% FC, 60−65% FC, and 50−55% FC. Air temperature, canopy temperature, and vapor pressure deficits were measured and employed to deduce the empirical crop water stress index, using the Idso approach (CWSI(Idso)) as well as baseline equations to calculate non-water stress and water stressed conditions. The relative leaf water content (RLWC) of mandarin plants was also determined for the growing season. From the experiment, CWSI(Idso) and CWSI were estimated using the Workswell Wiris Agro R infrared camera (CWSIW) and showed a high correlation (R2 = 0.75 at p < 0.05) in assessing the extent of water stress in mandarin plants. The results also showed that at an altitude of 12 m above the mandarin canopy, the WWARIC was able to identify water stress using three modes (empirical, differential, and theoretical). The WWARIC’s color map feature, presented in real time, makes the camera a suitable device, as there is no need for complex computations or expert advice before determining the extent of the stress the crops are subjected to. The results prove that this novel use of the WWARIC demonstrated sufficient precision, swiftness, and intelligibility in the real-time detection of the mandarin water stress index and, accordingly, assisted in scheduling irrigation.

Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks

In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. (1) Background: In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. Recently, a specific class of these models called Temporal Convolutional Networks (TCNs) has been successfully applied to this task. (2) Methods: In this paper, we approach electromyography-based hand gesture recognition as a sequence classification problem using TCNs. Specifically, we investigate the real-time behavior of our previous TCN model by performing a simulation experiment on a recorded sEMG dataset. (3) Results: The proposed network trained with data augmentation yields a small improvement in accuracy compared to our existing model. However, the classification accuracy is decreased in the real-time evaluation, showing that the proposed TCN architecture is not suitable for such applications. (4) Conclusions: The real-time analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.

Building a Precision Medicine Delivery Platform for Clinics: The University of California, San Francisco, BRIDGE Experience

Despite an ever-expanding number of analytics with the potential to impact clinical care, the field currently lacks point-of-care technological tools that allow clinicians to efficiently select disease-relevant data about their patients, algorithmically derive clinical indices (eg, risk scores), and view these data in straightforward graphical formats to inform real-time clinical decisions. Thus far, solutions to this problem have relied on either bottom-up approaches that are limited to a single clinic or generic top-down approaches that do not address clinical users’ specific setting-relevant or disease-relevant needs. As a road map for developing similar platforms, we describe our experience with building a custom but institution-wide platform that enables economies of time, cost, and expertise. The BRIDGE platform was designed to be modular and scalable and was customized to data types relevant to given clinical contexts within a major university medical center. The development process occurred by using a series of human-centered design phases with extensive, consistent stakeholder input. This institution-wide approach yielded a unified, carefully regulated, cross-specialty clinical research platform that can be launched during a patient’s electronic health record encounter. The platform pulls clinical data from the electronic health record (Epic; Epic Systems) as well as other clinical and research sources in real time; analyzes the combined data to derive clinical indices; and displays them in simple, clinician-designed visual formats specific to each disorder and clinic. By integrating an application into the clinical workflow and allowing clinicians to access data sources that would otherwise be cumbersome to assemble, view, and manipulate, institution-wide platforms represent an alternative approach to achieving the vision of true personalized medicine.

Intelligent Electromagnetic Sensors for Non-Invasive Trojan Detection

Rapid growth of sensors and the Internet of Things is transforming society, the economy and the quality of life. Many devices at the extreme edge collect and transmit sensitive information wirelessly for remote computing. The device behavior can be monitored through side-channel emissions, including power consumption and electromagnetic (EM) emissions. This study presents a holistic self-testing approach incorporating nanoscale EM sensing devices and an energy-efficient learning module to detect security threats and malicious attacks directly at the front-end sensors. The built-in threat detection approach using the intelligent EM sensors distributed on the power lines is developed to detect abnormal data activities without degrading the performance while achieving good energy efficiency. The minimal usage of energy and space can allow the energy-constrained wireless devices to have an on-chip detection system to predict malicious attacks rapidly in the front line.

Gesture-Based Human Machine Interaction Using RCNNs in Limited Computation Power Devices

The use of gestures is one of the main forms of human machine interaction (HMI) in many fields, from advanced robotics industrial setups, to multimedia devices at home. Almost every gesture detection system uses computer vision as the fundamental technology, with the already well-known problems of image processing: changes in lighting conditions, partial occlusions, variations in color, among others. To solve all these potential issues, deep learning techniques have been proven to be very effective. This research proposes a hand gesture recognition system based on convolutional neural networks and color images that is robust against environmental variations, has a real time performance in embedded systems, and solves the principal problems presented in the previous paragraph. A new CNN network has been specifically designed with a small architecture in terms of number of layers and total number of neurons to be used in computationally limited devices. The obtained results achieve a percentage of success of 96.92% on average, a better score than those obtained by previous algorithms discussed in the state of the art.

Real-Time Stress Level Feedback from Raw Ecg Signals for Personalised, Context-Aware Applications Using Lightweight Convolutional Neural Network Architectures

Human stress is intricately linked with mental processes such as decision making. Public protection practitioners, including Law Enforcement Agents (LEAs), are forced to make difficult decisions during high-pressure operations, under strenuous circumstances. In this respect, systems and applications that assist such practitioners to take decisions, are increasingly incorporating user stress level information for their development, adaptation, and evaluation. To that end, our goal is to accurately detect and classify the level of acute, short-term stress, in real time, for the development of personalized, context-aware solutions for LEAs. Deep Neural Networks (DNNs), and in particular Convolutional Neural Networks (CNNs), have been gaining traction in the field of stress analysis, exhibiting promising results. Furthermore, the electrocardiogram (ECG) signals, have also been widely adopted for estimating levels of stress. In this work, we propose two CNN architectures for the stress detection and 3-level (low, moderate, high) stress classification tasks, using ultra short-term raw ECG signals (3 s). One architecture is simple and with a low memory footprint, suitable for running in wearable edge-computing nodes, and the other is able to learn more complex features, having more trainable parameters. The models were trained on the two publicly available stress classification datasets, after applying pre-processing techniques, such as data pruning, down-sampling, and data augmentation, using a sliding window approach. After hyperparameter tuning, using 4-fold cross-validation, the evaluation on the test set demonstrated state-of-the-art accuracy both on the 3- and 2-level stress classification task using the DriveDB dataset, reporting an accuracy of 83.55% and 98.77% respectively.

Contributing to Understand the Crosstalk between Brain and Periphery in Methylmercury Intoxication: Neurotoxicity and Extracellular Vesicles

Human exposure to methylmercury (MeHg) is currently high in regions such as the Amazon. Understanding the molecular changes associated with MeHg-induced neurotoxicity and the crosstalk with the periphery is essential to support early diagnoses. This work aimed to evaluate cellular and molecular changes associated with behavioral alterations in MeHg acute exposure and the possible changes in extracellular vesicles (EVs) number and S100β content. Adults male Wistar rats were orally treated with 5 mg/kg for four days. Behavioral performance, molecular and histological changes in the cerebellum, and plasma EVs were assessed. MeHg-intoxicated animals performed significantly worse in behavioral tests. MeHg increased the number of GFAP+ cells and GFAP and S100β mRNA expression in the cerebellum but no change in NeuN+ or IBA-1+ cells number was detected. The number of exosomes isolated from plasma were decreased by the metal. S100B mRNA was detected in circulating plasma EVs cargo in MeHg exposure. Though preliminary, our results suggest astrocytic reactivity is displaying a protective role once there was no neuronal death. Interestingly, the reduction in exosomes number could be a new mechanism associated with MeHg-induced neurotoxicity and plasma EVs could represent a source of future biomarkers in MeHg intoxication.

Estimating the Impact of Statewide Policies to Reduce Spread of Severe Acute Respiratory Syndrome Coronavirus 2 in Real Time, Colorado, USA

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitated rapid local public health response, but studies examining the impact of social distancing policies on SARS-CoV-2 transmission have struggled to capture regional-level dynamics. We developed a susceptible-exposed-infected-recovered transmission model, parameterized to Colorado, USA‒specific data, to estimate the impact of coronavirus disease‒related policy measures on mobility and SARS-CoV-2 transmission in real time. During March‒June 2020, we estimated unknown parameter values and generated scenario-based projections of future clinical care needs. Early coronavirus disease policy measures, including a stay-at-home order, were accompanied by substantial decreases in mobility and reduced the effective reproductive number well below 1. When some restrictions were eased in late April, mobility increased to near baseline levels, but transmission remained low (effective reproductive number <1) through early June. Over time, our model parameters were adjusted to more closely reflect reality in Colorado, leading to modest changes in estimates of intervention effects and more conservative long-term projections.