Proactive Traffic Information Control in Emergency Evacuation Network

Traffic demand in emergency evacuation is usually too large to be effectively managed with reactive traffic information control methods. These methods adapt to the road traffic passively by publishing real-time information without consideration of the routing behavior feedback produced by evacuees. Other remedy measures have to be prepared in case of nonrecurring congestion under these methods. To use the network capacity fully to mitigate near-future evacuation traffic congestion, we propose proactive traffic information control (PTIC) model. Based on the mechanism between information and routing behavior feedback, this model can change the route choice of evacuees in advance by dissipating strategic traffic information. Generally, the near-future traffic condition is difficult to accurately predict because it is uncertain in evacuation. Assume that the value of traffic information obeys certain distribution within a range, and then real-time traffic information may reflect the most-likely near-future traffic condition. Unlike the real-time information, the proactive traffic information is a selection within the range to achieve a desired level of the road network performance index (total system travel time). In the aspect of the solution algorithm, differential equilibrium decomposed optimization (D-EDO) is proposed to compare with other heuristic methods. A field study on a road network around a large stadium is used to validate the PTIC

Route Restoration Method for Sparse Taxi GPS trajectory based on Bayesian Network

In order to improve the availability of taxi GPS big data, we restore the chosen route for the sparse taxi GPS trajectory in this work. A trajectory restoration method based on Bayesian network is proposed. Compared with the traditional research solely based on time-spatial variables, this method additionally considers the characteristics of empty/heavy taxi status, weather conditions, drivers, vehicle running and other factors to carry out route restoration. A field case of grid network in Ningbo is taken to verify the applicability of the method, using the taxi GPS trajectory data from Ningbo Taxi Information Management Platform. The case results show that the accuracy of Bayesian network method based on multiple factors reaches 91.4%. Its performance is superior to the Multivariate logistic regression model. In addition, the proposed method is especially suitable for scenarios with a high missing rate of track data, such as a scene with timespan of about 5 min between neighbour trajectories

Vernier Ring Based Pre-bond Through Silicon Vias Test in 3D ICs

Defects in TSV will lead to variations in the propagation delay of the net connected to the faulty TSV. A non-invasive Vernier Ring based method for TSV pre-bond testing is proposed to detect resistive open and leakage faults. TSVs are used as capacitive loads of their driving gates, then time interval compared with the fault-free TSVs will be detected. The time interval can be detected with picosecond level resolution, and digitized into a digital code to compare with an expected value of fault-free. Experiments on fault detection are presented through HSPICE simulations using realistic models for a 45 nm CMOS technology. The results show the effectiveness in the detection of time interval 10 ps, resistive open defects 0.2 kΩ above and equivalent leakage resistance less than 18 MΩ. Compared with existing methods, detection precision, area overhead, and test time are effectively improved, furthermore, the fault degree can be digitalized into digital code

Clinical and molecular profiling of EGFR-mutant lung adenocarcinomas transformation to small cell lung cancer during TKI treatment

IntroductionSmall cell lung cancer (SCLC) transformation serves as a significant mechanism of resistance to tyrosine kinase inhibitors (TKIs) in advanced non-small cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutations. To address this clinical challenge, we conducted a retrospective analysis at Zhejiang University School of Medicine, the First Affiliated Hospital, focusing on patients with EGFR sensitizing mutations.MethodsA total of 1012 cases were included in this retrospective analysis. The cohort primarily consisted of patients with EGFR sensitizing mutations. Biopsy-confirmed small cell transformation was observed in seven patients, accounting for 0.7% of the cases. All patients in this subset were initially diagnosed with stage IV adenocarcinoma (ADC), with four cases classified as poorly differentiated and three as moderately to poorly differentiated ADC. EGFR exon 19 deletions were identified in five of these cases. Next-generation sequencing (NGS) was performed on seven cases, revealing mutations in the tumor protein p53 (TP53) gene in four cases and loss of the retinoblastoma1 (RB1) gene in three cases.ResultsThe median duration from the initial diagnosis to small cell transformation was 35.9 months (interquartile range: 12.1–84 months). Following small cell transformation during EGFR inhibition, all patients received etoposide/platinum-based treatment, leading to a median progression-free survival (PFS) of 4.7 months (interquartile range: 2.7–10.1 months). Notably, most patients in this series had poorly differentiated adenocarcinomas at the outset. TP53 mutations and RB1 loss were common genetic alterations observed in patients with small cell transformation in this cohort.DiscussionThe findings underscore the clinical significance of SCLC transformation as a resistance mechanism to EGFR TKIs in NSCLC with EGFR mutations. The observed genetic alterations, including TP53 mutations and RB1 loss, suggest potential associations with the transformation process and warrant further investigation. Understanding the genetic landscape and clinical outcomes in patients experiencing small cell transformation can contribute to improved strategies for managing resistance in EGFR-mutant NSCLC

Proactive Traffic Information Control in Emergency Evacuation Network

Traffic demand in emergency evacuation is usually too large to be effectively managed with reactive traffic information control methods. These methods adapt to the road traffic passively by publishing real-time information without consideration of the routing behavior feedback produced by evacuees. Other remedy measures have to be prepared in case of nonrecurring congestion under these methods. To use the network capacity fully to mitigate near-future evacuation traffic congestion, we propose proactive traffic information control (PTIC) model. Based on the mechanism between information and routing behavior feedback, this model can change the route choice of evacuees in advance by dissipating strategic traffic information. Generally, the near-future traffic condition is difficult to accurately predict because it is uncertain in evacuation. Assume that the value of traffic information obeys certain distribution within a range, and then real-time traffic information may reflect the most-likely near-future traffic condition. Unlike the real-time information, the proactive traffic information is a selection within the range to achieve a desired level of the road network performance index (total system travel time). In the aspect of the solution algorithm, differential equilibrium decomposed optimization (D-EDO) is proposed to compare with other heuristic methods. A field study on a road network around a large stadium is used to validate the PTIC

Calibration of C-Logit-Based SUE Route Choice Model Using Mobile Phone Data

Theoretically speaking, the data of a stated preference survey could be suggested for the calibration of a stochastic route choice model. However, it is unrealistic to implement the questionnaire survey for such a large number of alternative routes. Engineers generally determine the parameter empirically. This experienced choice of perception parameter may cause higher errors in the route flows. In our calibration model of the perception parameter, the data of the cellular network is set as the input. This model consists of two levels. The upper level is to minimize the gap squares of the route choice ratio between the C-logit model and the cellular network data. The stochastic user equilibrium (SUE) in terms of the C-logit model is used as the lower level. The simulated annealing (SA) algorithm is used to solve the model, where the route-based gradient projection (GP) algorithm is used to solve the inner SUE. A case study is used to validate the convergence of the model calibration. A real-world road network is used to demonstrate the objective advantage of an equilibrium constraint over a nonequilibrium constraint and explain the feasibility of the candidate routes assumption

Optimizing Bus Frequencies under Uncertain Demand: Case Study of the Transit Network in a Developing City

Various factors can make predicting bus passenger demand uncertain. In this study, a bilevel programming model for optimizing bus frequencies based on uncertain bus passenger demand is formulated. There are two terms constituting the upper-level objective. The first is transit network cost, consisting of the passengers’ expected travel time and operating costs, and the second is transit network robustness performance, indicated by the variance in passenger travel time. The second term reflects the risk aversion of decision maker, and it can make the most uncertain demand be met by the bus operation with the optimal transit frequency. With transit link’s proportional flow eigenvalues (mean and covariance) obtained from the lower-level model, the upper-level objective is formulated by the analytical method. In the lower-level model, the above two eigenvalues are calculated by analyzing the propagation of mean transit trips and their variation in the optimal strategy transit assignment process. The genetic algorithm (GA) used to solve the model is tested in an example network. Finally, the model is applied to determining optimal bus frequencies in the city of Liupanshui, China. The total cost of the transit system in Liupanshui can be reduced by about 6% via this method

Improvement of neurological recovery in the insomnia rats by Warming Yang Strategy through targeting SIRT4 by inhibiting inflammation and apoptosis

Abstract The incidence rate of insomnia is increasing, but the mechanism of it remains unclear. Warming Yang Strategy (WY) is a kind of Traditional Chinese Medicine, and it is proved to be effective in treating insomnia patients. The insomnia animal was established with chlorophenylalanine (PCPA). Morris water maze and open field test were performed to evaluate the influence of WY on the neurological recovery of insomnia rats. TUNEL staining and flow cytometry were used to measure apoptosis level. WY promoted the neurological recovery in the insomnia rats through Morris water maze and open field test evaluation. The increase of γ‐aminobutyric acid, dopamine, 5‐hydroxytryptamine, and norepinephrine caused by WY was suppressed by siSIRT4. The decrease of apoptosis and inflammation factors expression induced by WY was promoted by siRNA‐SIRT4 (siSIRT4). WY improve neurological recovery in the insomnia rats through SIRT4 by inhibiting inflammation and apoptosis. This research might provide a novel insight for the prevention and treatment of insomnia through targeting SIRT4

Assessing the Impact of Urbanization and Eco-Environmental Quality on Regional Carbon Storage: A Multiscale Spatio-Temporal Analysis Framework

Understanding the mechanisms, intensity, and spatio-temporal heterogeneity of the impacts of urbanization and eco-environmental quality on carbon storage is crucial for achieving carbon neutrality goals. This study constructed a multiscale spatio-temporal analysis framework using multi-source remote sensing data, the InVEST model, and the multiscale geographically weighted regression (MGWR) model. Then, the effects of multiple factors on regional carbon storage were assessed in an empirical study involving 199 counties in Beijing-Tianjin-Hebei. The results showed that the carbon storage loss in the Beijing-Tianjin-Hebei region from 2010 to 2018 was 58.87 Tg C, with an annual relative loss rate of 0.16%. The MGWR model used in this study explained more than 98% of the spatial variation in regional carbon storage. In contrast, the impacts of various urbanization and eco-environmental indicators on regional carbon storage varied with the spatial and temporal variation. Overall, urban land structure and vegetation growth strongly influenced regional carbon storage resulting from urbanization and eco-environmental quality, respectively. In addition, based on an analysis of spatial context, MGWR suggests that the northwestern mountains in the Beijing-Tianjin-Hebei region have a greater potential to store more carbon than the other regions. This study also details the impact of future sustainable land use on regional carbon storage. Our findings can provide a scientific reference for formulating relevant carbon storage conservation policies