Time Stamp Research Articles

Human influence on burrow activity of the Chinese pangolin in Nepal

Context People and wildlife are coming into greater contact worldwide because of the increasing human footprint. Although some species tolerate certain levels of human activities, others are vulnerable to human disturbance, potentially altering their spatial or temporal patterns of activity in response to disturbance by humans. Aims We evaluated the influence of human activity on burrow activity of the Chinese pangolin, a Critically Endangered species that is heavily persecuted for subsistence and illegal trade. Methods We deployed remotely triggered camera traps at pangolin burrows located at four sites with contrasting levels of human density and infrastructure in Nepal, and estimated burrow-activity patterns of pangolins by using a kernel-density estimator based on the time stamp of camera trap observations. Key results Our findings did not suggest that anthropogenic disturbance affected pangolin burrow activity, but we acknowledge that these findings were based on a limited number of observations. Peak pangolin burrow activity was observed after midnight in three of the study sites, including those with highest and lowest levels of human activity. The fourth area, which had intermediate levels of human activity, had an earlier peak in burrow activity, possibly caused by prey deficiency owing to intense agriculture. Conclusions We suggest that pangolins may tolerate human activity because of their strictly nocturnal temporal niche, but that this tolerance has made them vulnerable to poaching because it allows them to co-exist spatially with humans. Implications Nocturnal species may be particularly prone to non-conflict-related persecution, because they may be easily accessible targets for illegal hunting activities.

Stochastic Collocation Introduction Into Correlation Functions Method Applied for Underground Objects Detection

A method of space ensemble (SE) and time ensemble (TEs) correlation function technique applied on ground penetration radar (GPR) B-scan raw data proves to result in detailed information for tracking buried objects. These computations help in site allocations by time ensemble correlation functions (TESCs) and time stamps of objects scattering by space ensemble correlation functions (SECFs) which are precisely consistent with the simulation results. While these results are applied for given data stack, randomness in physical parameters of tested ground always occurs. An introduction of randomness to the physical parameters of tested models by creating stochastic collocation (SC) ensembles incorporating randomness to B-scan via successive A-scans, SC-SE, and SC-TE ensembles enables sensitivity analysis (SA) study of GPR raw data which gives a tool to change the physical properties of the ground in search for better ground matching. This method can be added to GPR machines or simulation software to enhance raw data analysis. A field experiment of small non-metallic objects’ allocation was carried out as a platform for assessing the realistic performance of the method.

Edge Detection in Natural Scenes Inspired by the Speed Drawing Challenge

Edge detection is a major step in several computer vision applications. Edges define the shape of objects to be used in a recognition system, for example. In this work, we introduce an approach to edge detection inspired by a challenge for artists: the Speed Drawing Challenge. In this challenge, a person is asked to draw the same figure in different times (as 10[Formula: see text]min, 1[Formula: see text]min and 10[Formula: see text]s); at each time, different levels of details are drawn by the artist. In a short time stamp, just the major elements remain. This work proposes a new approach for producing images with different amounts of edges representing different levels of relevance. Our method uses superpixel to suppress image details, followed by Globalized Probability of Boundary (gPb) and Canny edge detection algorithms to create an image containing different number of edges. After that, an edge analysis step detects whose edges are the most relevant for the scene. The results are presented for the BSDS500 dataset and they are compared to other edge and contour detection algorithms by quantitative and qualitative means with very satisfactory results.

Force Orientation Measurement: Evaluating Ski Sport Dynamics

A modular sensor application for measuring athlete performance in skiing sports was developed. Using inertial measurement units (IMUs) and load cells in a modular system, a force orientation measurement system, FOMS, was developed. A functioning prototype capable of measuring ski sports dynamics was created. Data processing using the system, a validation of the prototype in terms of angle measurement IMU accuracy, example data from in-field athlete testing, and visualization by animations are described. The system developed contains four subsystems: a controller, two pole measuring modules, and a terrain-measuring module. The system structure also allows for additional modules, making the system applicable to different sports. The IMUs use orientation-sensing components to measure pole orientations, which are used to calculate decomposed forces relative to the terrain. Data from different modules are synchronized using wireless communication and saved on SD cards with time stamps. A validation experiment was conducted in which the angles from the modules were compared with the Oqus motion capture system from Qualisys. Examples for athlete testing in both cross country and alpine skiing were calculated from the matrix provided by the different modules and are presented in graphs to evaluate the athlete. In addition, the relative pole/terrain coordinates are visualized in 2D and 3D animations for analyzing the movement pattern in connection with the applied forces, opening up a whole new level of sports analysis.

IoT enabled environmental toxicology for air pollution monitoring using AI techniques

In past decades, the industrial and technological developments have increased exponentially and accompanied by non-judicial and un-sustainable utilization of non-renewable resources. At the same time, the environmental branch of toxicology has gained significant attention in understanding the effect of toxic chemicals on human health. Environmental toxic agents cause several diseases, particularly high risk among children, pregnant women, geriatrics and clinical patients. Since air pollution affects human health and results in increased morbidity and mortality increased the toxicological studies focusing on industrial air pollution absorbed by the common people. Therefore, it is needed to design an automated Environmental Toxicology based Air Pollution Monitoring System. To resolve the limitations of traditional monitoring system and to reduce the overall cost, this paper designs an IoT enabled Environmental Toxicology for Air Pollution Monitoring using Artificial Intelligence technique (ETAPM-AIT) to improve human health. The proposed ETAPM-AIT model includes a set of IoT based sensor array to sense eight pollutants namely NH3, CO, NO2, CH4, CO2, PM2.5, temperature and humidity. The sensor array measures the pollutant level and transmits it to the cloud server via gateways for analytic process. The proposed model aims to report the status of air quality in real time by using cloud server and sends an alarm in the presence of hazardous pollutants level in the air. For the classification of air pollutants and determining air quality, Artificial Algae Algorithm (AAA) based Elman Neural Network (ENN) model is used as a classifier, which predicts the air quality in the forthcoming time stamps. The AAA is applied as a parameter tuning technique to optimally determine the parameter values of the ENN model. In-order to examine the air quality monitoring performance of the proposed ETAPM-AIT model, an extensive set of simulation analysis is performed and the results are inspected in 5, 15, 30 and 60 min of duration respectively. The experimental outcome highlights the optimal performance of the proposed ETAPM-AIT model over the recent techniques.

Online System for Power Quality Operational Data Management in Frequency Monitoring Using Python and Grafana

This article proposes a measurement solution designed to monitor the instantaneous frequency in power systems. It uses a data acquisition module and a GPS receiver for time stamping and traceability. A Python-based module receives data, computes the frequency, and finally transfers the measurement results to a database. The frequency is calculated with two different methods, which are compared in the article. The stored data is visualized using the Grafana platform, thus demonstrating its potential for comparing scientific data. The system as a whole constitutes an efficient, low-cost solution as a data acquisition system.

How long does a transvaginal ultrasound examination for endometriosis take in comparison to a routine transvaginal ultrasound examination?

This study aimed to determine the additional time needed to perform an endometriosis transvaginal ultrasound (eTVUS) compared to routine transvaginal ultrasound (rTVUS). A retrospective case-control study was performed. The study group included 199 eTVUS performed between September 2019 and September 2020. The control group comprised 105 consecutive rTVUS studies performed in the same time period. The time stamps on the ultrasound images of all cases in both groups were reviewed to determine the time taken to perform each study. Mean, median, minimum and maximum scan times for both groups were calculated as was percentage difference between scan times. A two-tailed, unpaired t-test of the normalised data and a Mann-Whitney U test assessing time difference of scans between two groups were performed with P value <0.05 considered statistically significant. Performing eTVUS took significantly longer than rTVUS with increases in the mean (8.4 vs 13.8 min, 64%), median (7 vs 12 min, 71%), minimum (4 vs 7 min, 75%) and maximum (19 vs 42 min, 121%) scan times. The Mann-Whitney U test indicated a statistically significant difference in the median scan times (5.0, CI 4.0-6.0), P < 0.001. An independent t-test of the normalised data revealed a significantly larger mean scan time for eTVUS than rTVUS, Mean = 9.05 95%CI [13.17-4.94], t(302) = 4.327, P < 0.001. R 2 = 0.583. Endometriosis transvaginal ultrasound added an average 5.4 min to rTVUS, which is statistically significant. For ultrasound departments wanting to offer this technique, doubling the scan time allocated to perform a transvaginal ultrasound (TVUS) is suggested.

Development of cost-effective phasor measurement unit for wide area monitoring system applications

&lt;span&gt;Sustained growth in the demand with unprecedented investments in the transmission infrastructure resulted in narrow operational margins for power system operators across the globe. As a result, power networks are operating near to stability limits. This has demanded the electrical utilities to explore new avenues for control and protection of wide area systems. Present supervisory control and data acquisition/energy management systems (SCADA/EMS) can only facilitate steady state model of the network, whereas synchrophasor measurements with GPS time stamp from wide area can provide dynamic view of power grid that enables supervision, and protection of power network and allow the operator to take necessary control/remedial measures in the new regime of grid operations. Construction of phasor measurement unit (PMU) that provide synchrophasors for the assessment of system state is widely accepted as an essential component for the successful execution of wide area monitoring system (WAMS) applications. Commercial PMUs comes with many constraints such as cost, proprietary hardware designs and software. All these constraints have limited the deployment of PMUs at high voltage transmission systems alone. This paper addresses the issues by developing a cost-effective PMU with open-source hardware, which can be easily modified as per the requirements of the applications. The proposed device is tested with IEEE standards.&lt;/span&gt;

Deep Learning Model for Epileptic Seizure Prediction

Prediction of occurrence of a seizure would be of greater help to make necessary precaution for taking care of the patient. A Deep learning model, recurrent neural network (RNN), is designed for predicting the upcoming values in the EEG values. A deep data analysis is made to find the parameter that could best differentiate the normal values and seizure values. Next a recurrent neural network model is built for predicting the values earlier. Four different variants of recurrent neural networks are designed in terms of number of time stamps and the number of LSTM layers and the best model is identified. The best identified RNN model is used for predicting the values. The performance of the model is evaluated in terms of explained variance score and R2 score. The model founds to perform well number of elements in the test dataset is minimal and so this model can predict the seizure values only a few seconds earlier.

Optimal Causal Rate-Constrained Sampling for a Class of Continuous Markov Processes

Consider the following communication scenario. An encoder observes a stochastic process and causally decides when and what to transmit about it, under a constraint on the expected number of bits transmitted per second. A decoder uses the received codewords to causally estimate the process in real time. The encoder and the decoder are synchronized in time. For a class of continuous Markov processes satisfying regularity conditions, we find the optimal encoding and decoding policies that minimize the end-to-end estimation mean-square error under the rate constraint. We show that the optimal encoding policy transmits a 1-bit codeword once the process innovation passes one of two thresholds. The optimal decoder noiselessly recovers the last sample from the 1-bit codewords and codeword-generating time stamps, and uses it to decide the running estimate of the current process, until the next codeword arrives. In particular, we show the optimal causal code for the Ornstein-Uhlenbeck process and calculate its distortion-rate function. Furthermore, we show that the optimal causal code also minimizes the mean-square cost of a continuous-time control system driven by a continuous Markov process and controlled by an additive control signal.

PRIME uncovers circadian oscillation patterns and links with Alzheimer’s disease in untimed genome‐wide gene expression data across multiple regions of human brain

AbstractBackgroundThe disruption of brain circadian rhythm is a major symptom of AD patients. Recent studies and accumulating clinic evidence have suggested that this symptom usually occurs in the early phase of AD and probably precedes cognitive impairment development; however, the full circadian rhythm orchestration of gene expression in the human brain and its inherent associations with AD currently remain unknown. The main barrier to studying circadian rhythmic patterns of the whole‐genome gene expression is the requirement of time labels of brain tissues, which is hard to obtain or inaccurate. Indeed, current existing circadian rhythm analyses rely on the timestamps of samples.MethodThis study aims to explore the relationships between circadian rhythms of brain regions and AD with untimed genome‐wide gene expression data. To this end, we developed and presented an innovative, comprehensive, unsupervised machine‐learning based approach based on principal component analysis (PCA) and matrix factorization (MF), called PRIME, to detect and analyze oscillation patterns in untimed high‐dimensional data across multiple brain regions. PRIME captures oscillatory patterns in the latent space underlying the gene expression data without requiring time stamps of brain tissue samples.ResultTo demonstrate PRIME’s utility, firstly, we validated it by a time course expression dataset from mouse liver (Figs. 1‐2) as a cross‐species and cross‐organ validation, because this dataset has systematically recorded time labels. Then, we applied it to studying oscillation patterns in genome‐wide gene expression from 19 human brain regions of controls and AD patients. Our experiments showed that there exist clear synchronized oscillation patterns in 15 union brain regions of normal controls (Fig. 3). In sharp contrast, the synchronized oscillation patterns became either disappear or weakened for AD‐affected brains (Fig. 4). Additionally, we provided necessary rules to infer AD’s impacts on human brain regions (Table 1).ConclusionWe developed a novel comprehensive approach, called PRIME, to detect and analyze rhythmic oscillation patterns in untimed high‐dimensional gene expression data across multiple datasets. It is worth noting that our approach discovered the circadian rhythmic patterns without the need for the sample’s timestamps.

BusCount: A Provable Replay Protection Solution for Automotive CAN Networks

Information technology has become eminent in the development of modern cars. More than 50 Electronic Control Units (ECUs) realize vehicular functions in hardware and software, ranging from engine control and infotainment to future autonomous driving systems. Not only do the connections to the outside world pose new threats, but also the in-vehicle communication between ECUs, realized by bus systems such as Controller Area Network (CAN), needs to be protected against manipulation and replay of messages. Multiple countermeasures were presented in the past making use of Message Authentication Codes and time stamps and message counters, respectively, to provide message freshness, most prominently AUTOSAR’s Secure Onboard Communication (SecOC). In this paper, we focus on the latter ones. As one aspect of this paper, using an adequate formal model and proof, we will show that the currently considered solutions exhibit deficiencies that are hard if not impossible to overcome within the scope of the respective approaches. We further present a hardware-based approach that avoids these deficiencies and formally prove its freshness properties. In addition, we show its practicability by a hardware implementation. Finally, we evaluate our approach in comparison to counter-based solutions currently being used.

The complexity of finding temporal separators under waiting time constraints

In this work, we investigate the computational complexity of Restless Temporal(s,z)-Separation, where we are asked whether it is possible to destroy all restless temporal paths between two distinct vertices s and z by deleting at most k vertices from a temporal graph. A temporal graph has a fixed vertex set but the edges have (discrete) time stamps. A restless temporal path uses edges with non-decreasing time stamps and the time spent at each vertex must not exceed a given duration Δ.Restless Temporal(s,z)-Separation naturally generalizes the NP-hard Temporal(s,z)-Separation problem. We show that Restless Temporal(s,z)-Separation is complete for Σ2P, a complexity class located in the second level of the polynomial time hierarchy. We further provide some insights in the parameterized complexity of Restless Temporal(s,z)-Separation parameterized by the separator size k.

Tri-Partition Alphabet-Based State Prediction for Multivariate Time-Series

Recently, predicting multivariate time-series (MTS) has attracted much attention to obtain richer semantics with similar or better performances. In this paper, we propose a tri-partition alphabet-based state (tri-state) prediction method for symbolic MTSs. First, for each variable, the set of all symbols, i.e., alphabets, is divided into strong, medium, and weak using two user-specified thresholds. With the tri-partitioned alphabet, the tri-state takes the form of a matrix. One order contains the whole variables. The other is a feature vector that includes the most likely occurring strong, medium, and weak symbols. Second, a tri-partition strategy based on the deviation degree is proposed. We introduce the piecewise and symbolic aggregate approximation techniques to polymerize and discretize the original MTS. This way, the symbol is stronger and has a bigger deviation. Moreover, most popular numerical or symbolic similarity or distance metrics can be combined. Third, we propose an along–across similarity model to obtain the k-nearest matrix neighbors. This model considers the associations among the time stamps and variables simultaneously. Fourth, we design two post-filling strategies to obtain a completed tri-state. The experimental results from the four-domain datasets show that (1) the tri-state has greater recall but lower precision; (2) the two post-filling strategies can slightly improve the recall; and (3) the along–across similarity model composed by the Triangle and Jaccard metrics are first recommended for new datasets.

Novel rotor effective wind speed estimation method for light detection and ranging application

The ‘Cyclops’ effect of the light detection and ranging (LiDAR) system means that the LiDAR system can only measure wind speed at one point in space at a certain moment. However, for the purposes of wind turbine control, considering the wind information at the rotor plane with time stamps can be more precise. The wind speed attenuates due to the induction zone; however, Taylor's frozen hypothesis is commonly used in the application of LiDAR for the rotor effective wind speed estimation. The wind turbine is controlled directly by LiDAR measured data with constant time delay, which weakens LiDAR's effectiveness. In this paper, a novel LiDAR data pre-processing method is demonstrated. The Medici induction zone model is further refined with initial wind speed and different measured distances for accurate estimation. Furthermore, it makes online variable time delay of LiDAR data computation possible with the solution of the separated differential equation. Finally, the novel method is fully verified through field test results. The results show that the initial wind speed affected the attenuation factor within 1.4 times rotor radius and the error between the experimental value and the theoretical value is within 0.1 m/s.

Dance the Night Away: How Automatic TikTok Use Creates Pre-Sleep Cognitive Arousal and Daytime Fatigue

TikTok is one of the most popular apps. TikTok's endless stream of content, the lack time stamps or notifications of ever being “all caught up,” and concealing the phone's clock make it easy to lose track of time on TikTok. However, there is a lack of knowledge about how TikTok use may therefore interfere with our circadian rhythms, particularly our sleep hygiene. By focusing on pre-sleep cognitive arousal, this study aimed to close this knowledge gap by investigating the association between automatic TikTok use and daytime fatigue. We also investigated how individual preferences for sensation seeking and delayed gratification moderated this relationship. Within a sample of 1,050 TikTok/Douyin users in China, automatic TikTok use was associated with increased daytime fatigue that was mediated by higher levels of cognitive arousal before sleep. This relationship was aggravated by a preference for sensation seeking, and attenuated by a preference for delayed gratification. Above and beyond these early empirical insights, we also provide an early explanatory framework that is meant to systematize both existing and future knowledge about the use of TikTok.

Abstract 10337: Initiative for Timely Acquisition of ECGs in Non-ICU Hospital Areas

Introduction: In-hospital patients are a unique patient population with numerous co-morbidities and often atypical presentations for common cardiac problems. Delay in obtaining STAT ECGs on non-ICU (Intensive Care Unit) floors can have consequences ranging from delays in patient care to catastrophic morbidity and mortality for delayed diagnosis and treatment. Hypothesis: We hypothesize that there is high variability in the timely acquisition of obtaining inpatient EKGs in non-ICU areas regardless of whether there is a STAT or routine indication. Methods: We retrospectively reviewed 75 randomly selected non-ICU inpatient encounters for ECGs between January 2020 and March 2021. Of the 75 cases, 46 encounters met the criteria as non-ICU ECG encounters. The final data set included 24 routine ECG orders and 22 STAT ECG orders. Data gathered included time stamps for time of order, intended time for order to be completed, completion time and upload time in the EMR (electronic medical record). Results: STAT ECGs took a median of 1 hour and 42 minutes with times from the first and third quartiles ranging from 36 minutes to 2 hours and 58 minutes. Routine ECGs took a median of 3 hours and 17 minutes with times from the first and third quartiles ranging from 1 hour and 17 minutes to 4 hours and 30 minutes (Graph 1). Conclusion: Our results confirm that the acquisition of inpatient EKGs is highly variable and often delayed. Inpatient acquisition of ECGs should be standardized for patient safety and optimal outcomes. A protocol driven and multidisciplinary approach should be implemented to improve timely ECG acquisition.

5G Narrow Band-IoT Based Air Contamination Prediction Using Recurrent Neural Network

Air pollution plays a vital role among all other pollution; this is because it seriously has dreadful impact over the environment and the health of every individuals. Fine Particulate Matter (PM) 2.5 is the most significant ecological jeopardy feature contributing to global Cardio Vascular (CV) transience and respiratory syndrome. In India, the majority metropolitan spot has a deprived air quality with PM 2.5 levels exceeding the suggested limits. Apparently 60 % of Indian metropolitan areas has PM 2.5 levels greater than the index suggested by National Ambient Air Quality Standards. Hence, it is important to examine the level of PM 2.5 in air. In this paper, PM 2.5 level is monitored at various time stamp through Deep Learning (DL) technique called Long Short Term Memory (LSTM) which is a modification of Recurrent Neural Network (RNN). The proposed model will assist in alerting the dangerous level of PM 2.5 and taking action against it. Here, we use a 5 G protocol named 5 G Narrow Bands-Internet of Things (NB-IoT) communication protocol to alert officials quickly about the dangerous level of PM 2.5. This proposed techniques also helps to monitor the levels of various gaseous such as Carbon Monoxide (CO), Sulphur di Oxide (SO2), Oxide (O3), Hydrogen di Sulphide (H2S), Nitrogen di Oxide (NO2) along with Particulate Matter (PM 2.5). The proposed technology develops a cost effective and accurate stack based air pollution module that will provide a real-time, location based air pollutant statistics. The data is considered in division of one hour with different air quality sensors. The performance is measured using the loss function and the accuracy of predicting the PM 2.5 level.

Developing and exploring a methodology for multi-modal indoor and outdoor gait assessment.

Gait assessment is emerging as a prominent way to understand impaired mobility and underlying neurological deficits. Various technologies have been used to assess gait inside and outside of laboratory settings, but wearables are the preferred option due to their cost-effective and practical use in both. There are robust conceptual gait models developed to ease the interpretation of gait parameters during indoor and outdoor environments. However, these models examine uni-modal gait characteristics (e.g., spatio-temporal parameters) only. Previous studies reported that understanding the underlying reason for impaired gait requires multi-modal gait assessment. Therefore, this study aims to develop a multi-modal approach using a synchronized inertial and electromyography (EMG) signals. Firstly, initial contact (IC), final contact (FC) moments and corresponding time stamps were identified from inertial data, producing temporal outcomes e.g., step time. Secondly, IC/FC time stamps were used to segment EMG data and define onset and offset times of muscle activities within the gait cycle and its subphases. For investigation purposes, we observed notable differences in temporal characteristics as well as muscle onset/offset timings and amplitudes between indoor and outdoor walking of three stroke survivors. Our preliminary analysis suggests a multi-modal approach may be important to augment and improve current inertial conceptual gait models by providing additional quantitative EMG data.

Correlation Between Visual Factors During Patient Transfer and Objective Measures of Operative Risk in Preoperative Risk Assessment

<h3>Introduction and Objectives</h3> Frailty, an important consideration for determining a patient's surgical risk, is usually assessed informally based on clinical gestalt—the "eye-ball test." Surgeons are usually skilled at identifying high-risk patients, however, the factors underlying their unstructured assessments are unknown. The objective of our study was to explore the visual factors that may be used in an "eye-ball test" and understand how they compare to objective measures of frailty, multiple comorbidities, and operative risk. <h3>Methods</h3> A convergent mixed methods design was utilized to compare factors present in video recordings of patients transferring from a chair to exam table during vascular clinic visits to objective measures of surgical risk, including grip strength, modified frailty index-5 (mFI-5), and Fried Frailty Score. The videos were qualitatively evaluated by three independent coders. Patient transfer began in seated position in chair, followed by ambulation and transfer to the exam table, and ended in seated position on the exam table (Figure 1). Transfer length of time was measured in seconds based on time stamps. T-tests were used to compare length of transfer and Fisher's exact test was used to examine associations between visual factors and the objective measures of operative risk.Figure 1Components of a patient transfer from chair to exam table in the clinic setting. This segmentation was utilized for analysis of the video-recorded patient transfers.Figure 1 <h3>Results</h3> 30 patients and their video-taped transfers from two centers were analyzed. Mean age of participants was 71.9±9.45 years, 43.3% were female, and 18% identified with non-white race. Approximately half were categorized as frail by mFI-5 score and 50% were weak by grip strength measurement (65% convergence; P=.37). Frail patients had a longer transfer time (mean 11.6 vs 9.9 seconds; P=.19). A median of 45 coded factors based on visually present patient appearance and movement were coded. Patients who were considered frail more often used their arms to push up from sitting position (69.2% vs 25.0%; P=.04), have a medical device visible (e.g., portable oxygen, orthotic shoe, insulin pump) (38.5% vs 0%; P=.04), have a walking assistive device present (e.g., wheelchair, cane) (23.1% vs 0%; P=.22), and displayed postural instability during transfer from chair to exam table (50% vs 8.3%; P=.07). There appeared to be no difference in the presence of no-lace shoes (46.2% vs 41.7%; P=.82). <h3>Conclusions</h3> This pilot study reveals patient appearance and movement correlate with objective measures of operative risk. These visual factors may underlie unstructured frameworks utilized by vascular surgeons during preoperative risk assessment. Future work will build on this work by exploring surgeon's assessment in comparison to the visually coded factors in this study through case simulations.