Traffic and Related Self-Driven Many-Particle Systems

Since the subject of traffic dynamics has captured the interest of physicists, many astonishing effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by so-called ``phantom traffic jams'', although they all like to drive fast? What are the mechanisms behind stop-and-go traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction of the traffic volume cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize in lanes, while similar systems are ``freezing by heating''? Why do self-organizing systems tend to reach an optimal state? Why do panicking pedestrians produce dangerous deadlocks? All these questions have been answered by applying and extending methods from statistical physics and non-linear dynamics to self-driven many-particle systems. This review article on traffic introduces (i) empirically data, facts, and observations, (ii) the main approaches to pedestrian, highway, and city traffic, (iii) microscopic (particle-based), mesoscopic (gas-kinetic), and macroscopic (fluid-dynamic) models. Attention is also paid to the formulation of a micro-macro link, to aspects of universality, and to other unifying concepts like a general modelling framework for self-driven many-particle systems, including spin systems. Subjects such as the optimization of traffic flows and relations to biological or socio-economic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are discussed as well.Comment: A shortened version of this article will appear in Reviews of Modern Physics, an extended one as a book. The 63 figures were omitted because of storage capacity. For related work see http://www.helbing.org

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Oblique view of preoperative lymphoscintigraphy improves detection of sentinel lymph nodes in esophageal cancer

Because sentinel Lymph nodes(SLNs) of esophageal cancer can be widely located between the neck and the upper abdomen, lymphoscintigraphy plays an important role in their detection, but some modifications are required to clearly visualize their locations. Recently, we applied the stereoscopic imaging method by adding the 10-degree oblique view the the conventional lymphoscintigraphy for SLNs, so that we could better determine SLN locations on the basis of depth information. In this report, we describe a case in which the oblique view of the lymphoscintigram contributed to improving the visualization of a mediastinal SLN of esophageal cancer. Evaluation of the patient\u27s chest CT image validated the notion that gamma rays from SLN are less absorbed by the surrounding soft tissues and the sternum in acquisition from the oblique view than from the true anterior view. The additional oblique view of the lymphoscintigram is useful for evaluation of the SLNs of esophageal cancer

Stereoscopic Scintigraphic Imaging of Breast Cancer Sentinel Lymph Nodes

Background: Lymphoscintigraphy is used preoperatively to identify sentinel lymph nodes(SLNs). Conventional planar scintigraphy cannot provide three-dimensional(3D) information for SLN biopsy. We applied stereoscopic imaging to preoperative lymphoscintigraphy to obtain 3D information and evaluated its usefulness.Methods: Forty-four clinical stage I breast cancer patients(1 male, 43 females; age, 59.4+/-11.4 years) were enrolled in this study. Three hours after the injection of Tc-99m, 10 degrees of oblique images and routine anterior and lateral images were acquired. Anterior and lateral stereoscopic images were obtained in all studies, except for 2 patients; only lateral views were done for those. Two experienced radiologists enumerated the visualized hot nodes.Result: Stereoscopic imaging delineated more hot axillary lymph nodes compared to routine planar imaging in 8 of 42 patients(19.0%) on anterior view, 5 of 44 patients(11.4%) on lateral view, and 11 of 44 patiens(25.0%) on either the anterior or lateral view. Statistically significant differences were observed between stereoscopic and routine planar imaging method on the anterior (p=0.012) and the lateral views(p=0.043). The stereoscopic imaging provided 3D information and effectively separated closely located hot nodes that were viewed as one hot node on conventional planar images. Thirty-eight out of 42 cases(90%) with anterior stereoscopic images identified the same number or more axillary hot nodes compared with lateral stereoscopic images.Conclusion: The stereoscopic imaging method could improve the preoperative identification of SLNs. This method is technically simple, and could be a powerful diagnostic tool for SLN imaging breast cancer

Impact of a combination of quantitative indices representing uptake intensity, shape, and asymmetry in DAT SPECT using machine learning: comparison of different volume of interest settings

Abstract Background We sought to assess the machine learning-based combined diagnostic accuracy of three types of quantitative indices obtained using dopamine transporter single-photon emission computed tomography (DAT SPECT)—specific binding ratio (SBR), putamen-to-caudate ratio (PCR)/fractal dimension (FD), and asymmetry index (AI)—for parkinsonian syndrome (PS). We also aimed to compare the effect of two different types of volume of interest (VOI) settings from commercially available software packages DaTQUANT (Q) and DaTView (V) on diagnostic accuracy. Methods Seventy-one patients with PS and 40 without PS (NPS) were enrolled. Using SPECT images obtained from these patients, three quantitative indices were calculated at two different VOI settings each. SBR-Q, PCR-Q, and AI-Q were derived using the VOI settings from DaTQUANT, whereas SBR-V, FD-V, and AI-V were derived using those from DaTView. We compared the diagnostic value of these six indices for PS. We incorporated a support vector machine (SVM) classifier for assessing the combined accuracy of the three indices (SVM-Q: combination of SBR-Q, PCR-Q, and AI-Q; SVM-V: combination of SBR-V, FD-V, and AI-V). A Mann-Whitney U test and receiver-operating characteristics (ROC) analysis were used for statistical analyses. Results ROC analyses demonstrated that the areas under the curve (AUC) for SBR-Q, PCR-Q, AI-Q, SBR-V, FD-V, and AI-V were 0.978, 0.837, 0.802, 0.906, 0.972, and 0.829, respectively. On comparing the corresponding quantitative indices between the two types of VOI settings, SBR-Q performed better than SBR-V (p = 0.006), whereas FD-V performed better than PCR-Q (p = 0.0003). No significant difference was observed between AI-Q and AI-V (p = 0.56). The AUCs for SVM-Q and SVM-V were 0.988 and 0.994, respectively; the two different VOI settings displayed no significant differences in terms of diagnostic accuracy (p = 0.48). Conclusion The combination of the three indices obtained using the SVM classifier improved the diagnostic performance for PS; this performance did not differ based on the VOI settings and software used