Traffic Problems of Linjiao’s Turning Passage in Lhasa City from the Perspective of Traffic Sociology

2021 ◽  
Vol 7 (5) ◽  
pp. 4672-4681
Author(s):  
Shuai Lai ◽  
Jinfeng Wu
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Traffic Data ◽  
Learning Network ◽  
Accurate Judgment ◽  
Congestion Prediction ◽  
The Common ◽  
Deep Learning Network

Objectives: The transportation problem of Linjiao transit passage in Lhasa City from the perspective of traffic sociology is studied. Methods: Firstly, the research history and current situation of experts in the field of traffic congestion prediction are studied. The common parameters and models of congestion prediction are analyzed. Results: Combined with the complexity of road traffic structure and the possession of a large number of high-dimensional traffic data records, the use of a prediction model is finally determined based on RNN-RBM deep learning network. Through the research and analysis of all-day road traffic flow data, accurate judgment and prediction of traffic congestion status are made. Conclusion: In this paper, the role of the RNN model on the time axis and the state judgment of the RBM network are used to predict the traffic congestion based on the characterization of the congestion sequence.

scholarly journals Common Coherence Witnesses and Common Coherent States

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1136
Author(s):  
Bang-Hai Wang ◽  
Zi-Heng Ding ◽  
Zhihao Ma ◽  
Shao-Ming Fei
Keyword(s):  
Coherent State ◽  
Coherent States ◽  
State Problem ◽  
Properties And Characterization ◽  
The Common ◽  
High Level

We show the properties and characterization of coherence witnesses. We show methods for constructing coherence witnesses for an arbitrary coherent state. We investigate the problem of finding common coherence witnesses for certain class of states. We show that finitely many different witnesses W1,W2,⋯,Wn can detect some common coherent states if and only if ∑i=1ntiWi is still a witnesses for any nonnegative numbers ti(i=1,2,⋯,n). We show coherent states play the role of high-level witnesses. Thus, the common state problem is changed into the question of when different high-level witnesses (coherent states) can detect the same coherence witnesses. Moreover, we show a coherent state and its robust state have no common coherence witness and give a general way to construct optimal coherence witnesses for any comparable states.

scholarly journals Urban Wasteful Transport and Its Estimation Methods

2018 ◽  
Vol 10 (12) ◽  
pp. 4562 ◽  
Author(s):  
Xiangyang Cao ◽  
Bingzhong Zhou ◽  
Qiang Tang ◽  
Jiaqi Li ◽  
Donghui Shi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Transport Theory ◽  
Road Traffic ◽  
Urban Traffic ◽  
Flow Density ◽  
Urban Road ◽  
Road Capacity ◽  
Density Relationship

The paper studies urban road traffic problems from the perspective of resource science. The resource composition of urban road traffic system is analysed, and the road network is proved as a scarce resource in the system resource combination. According to the role of scarce resources, the decisive role of road capacity in urban traffic is inferred. Then the new academic viewpoint of “wasteful transport” was proposed. Through in-depth research, the paper defines the definition of wasteful transport and expounds its connotation. Through the flow-density relationship analysis of urban road traffic survey data, it is found that there is a clear boundary between normal and wasteful transport in urban traffic flow. On the basis of constructing the flow-density relationship model of road traffic, combined with investigation and analysis, the quantitative estimation method of wasteful transport is established. An empirical study on the traffic conditions of the Guoding section of Shanghai shows that there is wasteful transport and confirms the correctness of the wasteful transport theory and method. The research of urban wasteful transport also reveals that: (1) urban road traffic is not always effective; (2) traffic flow exceeding road capacity is wasteful transport, and traffic demand beyond the capacity of road capacity is an unreasonable demand for customers; (3) the explanation that the traffic congestion should apply the comprehensive theory of traffic engineering and resource economics; and (4) the wasteful transport theory and method may be one of the methods that can be applied to alleviate traffic congestion.

scholarly journals Beyond the Binary: Rethinking Gender Neutrality in Indian Rape Law

2016 ◽  
Vol 11 (2) ◽  
pp. 367-397
Author(s):  
Harshad PATHAK
Keyword(s):  
Human Rights ◽  
Rape Law ◽  
The Common ◽  
Definition Of ◽  
Human Rights Based Approach ◽  
Gender Neutrality ◽  
Gender Specific ◽  
Indian Penal Code

AbstractDespite expanding the definition of rape under the Indian Penal Code to include non-penile-vaginal acts of penetration, the said definition continues to conform to a gender-specific notion of rape, based on a predetermined characterization of the victim-perpetrator framework on the basis of their genders. Herein, I will critique this idea of gender specificity in Indian rape law on the grounds that it reinforces a binary notion of gender, and results in gross underinclusion. Instead, it is more appropriate to adopt a human-rights-based approach in defining the offence of rape, and negate the role of gender in identifying the victims and perpetrators of an act of rape. The argument is pillared on a state’s obligation to not discriminate on the basis of sex, the recognition of transgender rights, and an assessment of the common grounds for opposing gender neutrality in Indian rape law.

CTL-DNNet: Effective Circular Traffic Light Recognition with a Deep Neural Network

2017 ◽  
Vol 31 (11) ◽  
pp. 1750037 ◽  
Author(s):  
Di Wang ◽  
Hong Bao ◽  
Feifei Zhang
Keyword(s):  
Neural Network ◽  
Autonomous Vehicles ◽  
Deep Neural Network ◽  
Road Traffic ◽  
High Accuracy ◽  
Activation Functions ◽  
Traffic Light ◽  
Traffic Lights ◽  
Learning Network ◽  
Deep Learning Network

This paper proposed an algorithm for a deep learning network for identifying circular traffic lights (CTL-DNNet). The sample labeling process uses translation to increase the number of positive samples, and the similarity is calculated to reduce the number of negative samples, thereby reducing overfitting. We use a dataset of approximately 370[Formula: see text]000 samples, with approximately 20[Formula: see text]000 positive samples and approximately 350[Formula: see text]000 negative samples. The datasets are generated from images taken at the Beijing Garden Expo. To obtain a very robust method for the detection of traffic lights, we use different layers, different cost functions and different activation functions of the depth neural network for training and comparison. Our algorithm has evaluated autonomous vehicles in varying illumination and gets the result with high accuracy and robustness. The experimental results show that CTL-DNNet is effective at recognizing road traffic lights in the Beijing Garden Expo area.

scholarly journals Detection of Road Traffic Anomalies Based on Computational Data Science

2022 ◽  
Author(s):  
Jamal Raiyn
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Data Science ◽  
Road Traffic ◽  
Smart Devices ◽  
Traffic Data ◽  
Forecasting Accuracy ◽  
Traffic Efficiency ◽  
Computational Data

Abstract The development of 5G has enabled the autonomous vehicles (AVs) to have full control over all functions. The AV acts autonomously and collects travel data based on various smart devices and sensors, with the goal of enabling it to operate under its own power. However, the collected data is affected by several sources that degrade the forecasting accuracy. To manage large amounts of traffic data in different formats, a computational data science approach (CDS) is proposed. The computational data science scheme introduced to detect anomalies in traffic data that negatively affect traffic efficiency. The combination of data science and advanced artificial intelligence techniques, such as deep leaning provides higher degree of data anomalies detection which leads to reduce traffic congestion and vehicular queuing. The main contribution of the CDS approach is summarized in detection of the factors that caused data anomalies early to avoid long- term traffic congestions. Moreover, CDS indicated a promoting results in various road traffic scenarios.

scholarly journals Comparison of Logistics Time of R3A and R3B for Transportation of Lychee Product

2021 ◽  
pp. 15-25
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Prediction Method ◽  
Transportation Safety ◽  
Traffic Density ◽  
Traffic Data ◽  
Management Application ◽  
Heavy Burden ◽  
Chinese Markets ◽  
Local Farmers

This research presents the logistics management information system (LMIS) for the supply chain of lychee products of Phayao Province, Thailand. The main aim of this research is to develop a management application for Phayao’s agricultures to improve their competitive abilities on Chinese markets by utilizing a prediction method for traffic congestion based on both real-time and anticipated road traffic. The loss of productivity caused by traffic congestion has become a huge and increasingly heavy burden on Phayao farmers. Therefore, the prediction of urban road network traffic flow and the rapid and accurate evaluation of traffic congestion is of great significance to solve this problem. By using traffic data obtained by distance, road conditions, transportation safety, traffic density, and customs clearance, the local farmers in Phayao can deliver lychee products on time and reduce the loss of high emissions and environmental pollution caused by traffic congestion effectively.

scholarly journals Role of Multislice Computed Tomography and Local Contrast in the Diagnosis and Characterization of Choanal Atresia

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Khaled Al-Noury ◽  
Alsaid Lotfy
Keyword(s):  
Computed Tomography ◽  
Multislice Computed Tomography ◽  
Choanal Atresia ◽  
Charge Syndrome ◽  
Local Contrast ◽  
Head Region ◽  
The Common ◽  
Pyriform Aperture

Objective. To illustrate the role of multislice computed tomography and local contrast instillation in the diagnosis and characterization of choanal atresia. To review the common associated radiological findings.Methods. We analyzed 9 pediatric patients (5 males and 4 females) with suspected choanal atresia by multislice computed tomography. We recorded the type of atresia plate and other congenital malformations of the skull.Results. Multislice computed tomography with local contrast installed delineated the posterior choanae. Three patients had unilateral mixed membranous and bony atresia. Three patients had unilateral pure bony atresia. Only 1 of 7 patients have bilateral bony atresia. It also showed other congenital anomalies in the head region. One patient is with an ear abnormality. One patient had congenital nasal pyriform aperture stenosis. One of these patients had several congenital abnormalities, including cardiac and renal deformities and a hypoplastic lateral semicircular canal. Of the 6 patients diagnosed to have choanal atresia, 1 patient had esophageal atresia and a tracheoesophageal fistula. The remaining patients had no other CHARGE syndrome lesions.Conclusions. Local Contrast medium with the application of the low-dose technique helps to delineate the cause of the nasal obstruction avoiding a high radiation dose to the child.

scholarly journals Characterization of the Role of a Non-GPCR Membrane-Bound CFEM Protein in the Pathogenicity and Germination of Botrytis cinerea

2020 ◽  
Vol 8 (7) ◽  
pp. 1043
Author(s):  
Gulab Chand Arya ◽  
Dhruv Aditya Srivastava ◽  
Eswari P. J. Pandaranayaka ◽  
Ekaterina Manasherova ◽  
Dov Bernard Prusky ◽  
...  
Keyword(s):  
Botrytis Cinerea ◽  
Pathogenic Fungi ◽  
Phytopathogenic Fungi ◽  
Fungal Virulence ◽  
Wide Range ◽  
Membrane Bound ◽  
The Common ◽  
G Protein Coupled

The necrotrophic fungus Botrytis cinerea, is considered a major cause of postharvest losses in a wide range of crops. The common fungal extracellular membrane protein (CFEM), containing a conserved eight-cysteine pattern, was found exclusively in fungi. Previous studies in phytopathogenic fungi have demonstrated the role of membrane-bound and secreted CFEM-containing proteins in different aspects of fungal virulence. However, non-G protein-coupled receptor (non-GPCR) membrane CFEM proteins have not been studied yet in phytopathogenic fungi. In the present study, we have identified a non-GPCR membrane-bound CFEM-containing protein, Bcin07g03260, in the B. cinerea genome, and generated deletion mutants, ΔCFEM-Bcin07g03260, to study its potential role in physiology and virulence. Three independent ΔCFEM-Bcin07g03260 mutants showed significantly reduced progression of a necrotic lesion on tomato (Solanum lycopersicum) leaves. Further analysis of the mutants revealed significant reduction (approximately 20–30%) in conidial germination and consequent germ tube elongation compared with the WT. Our data complements a previous study of secreted ΔCFEM1 mutants of B. cinerea that showed reduced progression of necrotic lesions on leaves, without effect on germination. Considering various functions identified for CFEM proteins in fungal virulence, our work illustrates a potential new role for a non-GPCR membrane CFEM in pathogenic fungi to control virulence in the fungus B. cinerea.

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