Application of Technological Control Measures on Vehicle Pollution: A Cost-Benefit Analysis in China

For the past two decades, China has experienced strong, continuous economic growth. At the same time, the number of motor vehicles in China has rapidly increased. As a direct result of such a phenomenon, China has been registering significant increases in air pollution. In spite of recent advances in air pollution control, it remains a serious problem for Chinas major cities, and constitutes an important issue in the agenda of its policy makers. The object of this paper is to explore the use of cost-benefit analysis (CBA) to evaluate and rank alternative policy scenarios regarding the control of air pollution emitted by motor vehicles. The empirical analysis carried out relates specifically to the Chinese context, over a twenty year period, from 2001 to 2020, and focuses on emission changes of the following three principal pollutants: CO, HC and NOx

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Big Data-Driven Based Real-Time Traffic Flow State Identification and Prediction

With the rapid development of urban informatization, the era of big data is coming. To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Traffic flow state quantification, the basis of traffic flow state identification, is achieved by a SAGA-FCM (simulated annealing genetic algorithm based fuzzy c-means) based traffic clustering model. Considering simple calculation and predictive accuracy, a bilevel optimization model for regional traffic flow correlation analysis is established to predict traffic flow parameters based on temporal-spatial-historical correlation. A two-stage model for correction coefficients optimization is put forward to simplify the bilevel optimization model. The first stage model is built to calculate the number of temporal-spatial-historical correlation variables. The second stage model is present to calculate basic model formulation of regional traffic flow correlation. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods

Automation Countermeasure System for Intersection Optimization

To satisfy the demand of congestion problem solving in intersections, this paper studies the method of automation countermeasure system for intersection optimization (ACSIO). Taking into account the extensive contents and objectives of intersection optimization, this paper puts forward the functions and architecture of ACSIO based on intersection optimization problem statement. Seeking optimal design of intersection channelization and signal control, the main goal of ACSIO is to achieve dynamic and coordination management of intersection. The problem is formulated as a multiobjective program, with each objective corresponding to a different player in the system. Moreover, it presents system design of ACSIO. A case study based on a realworld intersection is implemented to test the efficiency and applicability of the proposed modeling and computing methods

Automation Countermeasure System for Intersection Optimization

To satisfy the demand of congestion problem solving in intersections, this paper studies the method of automation countermeasure system for intersection optimization (ACSIO). Taking into account the extensive contents and objectives of intersection optimization, this paper puts forward the functions and architecture of ACSIO based on intersection optimization problem statement. Seeking optimal design of intersection channelization and signal control, the main goal of ACSIO is to achieve dynamic and coordination management of intersection. The problem is formulated as a multiobjective program, with each objective corresponding to a different player in the system. Moreover, it presents system design of ACSIO. A case study based on a real-world intersection is implemented to test the efficiency and applicability of the proposed modeling and computing methods

Real-Time Corrected Traffic Correlation Model for Traffic Flow Forecasting

This paper focuses on the problems of short-term traffic flow forecasting. The main goal is to put forward traffic correlation model and real-time correction algorithm for traffic flow forecasting. Traffic correlation model is established based on the temporal-spatial-historical correlation characteristic of traffic big data. In order to simplify the traffic correlation model, this paper presents correction coefficients optimization algorithm. Considering multistate characteristic of traffic big data, a dynamic part is added to traffic correlation model. Real-time correction algorithm based on Fuzzy Neural Network is presented to overcome the nonlinear mapping problems. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling methods