A Novel and Open-Source Application for Vasculature Dataset Analysis and Visualization

Vascular networks can dictate and indicate states of health and disease. Structural analyses of these networks can facilitate improved understanding of disease states. Recent advances in preclinical imaging techniques and segmentation software have led to the generation of large-scale vasculature datasets. However, these advances have not been accompanied by the development of modernized, open-source analysis software packages. Here we describe VesselVio, an application developed to analyze and visualize pre-segmented 2D and 3D vasculature datasets. Vasculature datasets can be loaded and analyzed with custom parameters to extract numerous quantitative whole-network and individual segment features. Visualization of results and accuracy inspections can be conducted using the interactive visualization tool. The utility and compatibility of VesselVio is demonstrated via the analysis of 3D inferior colliculus segmentations from female and male mice as well as the analysis of 2D retinal fundus images of control, glaucomatous, and diabetic retinopathy patients.

Evaluating traffic operations performance of directional interchange with semi-direct ramp connections with loops

The purpose of this research is to evaluate the performance of the directional interchange with semi-direct ramp connections with loops (DI-SDRL) in terms of traffic operations under a wide range of traffic demand conditions. Towards this end, the performance of this interchange has been compared with that of a conventional one, i.e., directional with loops interchange (DLI). Thirty different traffic scenarios were developed to test their performance using a state-of-the-art traffic microsimulation tool PTV-VISSIM. The results showed that the DI-SDRL interchange design outperforms the conventional DLI interchange in terms of vehicle hours traveled and mean speed. Nevertheless, the DI-SDRL underperforms the DLI in terms of vehicle kilometres traveled. There was no significant difference in terms of mean delay. At the individual-segment level, the analysis showed that the DI-SDRL interchange outperforms the DLI at diverging segments; in contrast, the DLI interchange outperforms the DI-SDRL at merging segments.

Transnational Cybercrime

Organized crime groups are involved in all kinds of transnational crimes. Lawbreakers can victimize people in other countries through international scams and cyber theft of financial information. Moreover, much of the harm from transnational crimes stems from activities of formal criminal organizations or criminal networks that connect individuals and organizations who undertake specific criminal acts together. According to the United Nations, annual proceeds from transnationally organized crime activities amount to more than $870 billion dollars with drug trafficking producing the largest individual segment of that total amount. One of the most difficult forms of transnational crime to combat is cybercrime. This article will review transnational cybercrime and it's technology.

Automated scoring of the Rey-Osterrieth Complex Figure Test using a deep-learning algorithm

Objective To validate a fully automated scoring algorithm for the Rey-Osterrieth Complex Figure Test (ROCFT) by comparing the scoring results of the algorithm to the results of human raters. Method The algorithm consisted of a cascade of deep neural networks which were trained on human rater scores to extract the 18 segments of the figure, and to quantify the patient’s performance. Algorithm results were compared to six expert raters for 303 drawings. We tested whether the average correlation between algorithm scores and scores by all human raters was equivalent to the average inter-rater correlation (with equality bound Δr < .05). The immediate and delayed recall trial were used; the copy trial showed a strong ceiling effect. Results The mean Pearson correlation between raters was .94 (SD = 0.01). The correlation between to algorithm and the raters was .88 (SD = 0.02). A two-one-sided t-tests (TOST) equivalence test showed that these correlations were not strictly equivalent, t(5) = 4.02, p = .995, 95% CI [0.35, 0.52]. Conclusions Although not strictly equivalent to human ratings, the algorithm’s performance is high, approaching a level of reliability found among human raters. We expect that improved individual segment detection will bring the algorithm scoring accuracy on par with that of human raters. Algorithmic scoring of the ROCFT will likely save valuable time and lead to higher levels of standardization in clinical practice.

A Hybrid Deep Learning and Handcrafted Feature Approach for Cervical Cancer Digital Histology Image Classification

Cervical cancer is the second most common cancer affecting women worldwide but is curable if diagnosed early. Routinely, expert pathologists visually examine histology slides for assessing cervix tissue abnormalities. A localized, fusion-based, hybrid imaging and deep learning approach is explored to classify squamous epithelium into cervical intraepithelial neoplasia (CIN) grades for a dataset of 83 digitized histology images. Partitioning the epithelium region into 10 vertical segments, 27 handcrafted image features and rectangular patch, sliding window-based convolutional neural network features are computed for each segment. The imaging and deep learning patch features are combined and used as inputs to a secondary classifier for individual segment and whole epithelium classification. The hybrid method achieved a 15.51% and 11.66% improvement over the deep learning and imaging approaches alone, respectively, with a 80.72% whole epithelium CIN classification accuracy, showing the enhanced epithelium CIN classification potential of fusing image and deep learning features.

Seismic Levee System Fragility considering Spatial Correlation of Demands and Component Fragilities

Seismic levee performance is most readily computed for short segments having consistent geometry, soil conditions, and seismic demands. Spatial variations of seismic demands and of segment capacities significantly influence system risk, which is critical for flood protection because any segment failure within the system can cause inundation. We present a methodology to compute the probability of seismic levee system failure conditional on individual segment fragility and spatial correlations of demands and of capacities. Seismic demands are estimated from ground motion prediction equations; their correlation is available in the literature. Capacities and their correlation are derived from levee damage observations from a levee system in Japan shaken by two earthquakes. We find seismic capacities to exhibit positive correlations over shorter distances than for demands. System fragility is computed using Monte Carlo simulations where segment demand and capacity realizations are generated to account for spatial correlations. We find that the probability of system failure is lower than would be obtained under an assumption of no correlation and that damage probability increases as the number of components in the system increases.

THE COST–TIME CURVE FOR AN OPTIMAL TRAIN JOURNEY ON LEVEL TRACK

In this paper, we show that the cost of an optimal train journey on level track over a fixed distance is a strictly decreasing and strictly convex function of journey time. The precise structure of the cost–time curves for individual trains is an important consideration in the design of energy-efficient timetables on complex rail networks. The development of optimal timetables for busy metropolitan lines can be considered as a two-stage process. The first stage seeks to find optimal transit times for each individual journey segment subject to the usual trip-time, dwell-time, headway and connection constraints in such a way that the total energy consumption over all proposed journeys is minimized. The second stage adjusts the arrival and departure times for each journey while preserving the individual segment times and the overall journey times, in order to best synchronize the collective movement of trains through the network and thereby maximize recovery of energy from regenerative braking. The precise nature of the cost–time curve is a critical component in the first stage of the optimization.

Successive dynamic programming and subsequent spline optimization for smooth time optimal robot path tracking

The time optimal path tracking for industrial robots regards the problem of generating trajectories that follow predefined end-effector (EE) paths in shortest time possible taking into account kinematic and dynamic constraints. The complicated tasks used in industrial applications lead to very long EE paths. At the same time smooth trajectories are mandatory in order to increase the service life. The consideration of jerk and torque rate restrictions, necessary to achieve smooth trajectories, causes enormous numerical effort, and increases computation times. This is in particular due to the high number of optimization variables required for long geometric paths. In this paper we propose an approach where the path is split into segments. For each individual segment a smooth time optimal trajectory is determined and represented by a spline. The overall trajectory is then found by assembling these splines to the solution for the whole path. Further we will show that by using splines, the jerks are automatically bounded so that the jerk constraints do not have to be imposed in the optimization, which reduces the computational complexity. We present experimental results for a six-axis industrial robot. The proposed approach provides smooth time optimal trajectories for arbitrary long geometric paths in an efficient way.