Impacts of spatial mismatch on commuting time of urban residents in China

In much of studies on spatial mismatch between residential and employer locations, job accessibility has been measured. However, the apparent disadvantages of the traditional measurement methods on the studies of Chinese cities have been noted.  This paper proposed an optimized method for job accessibility measurement by introducing the weigh coefficient of job opportunity, which quantifies the degree of uneven distribution of job opportunity in the Chinese cities. Take Nanjing city for example, this new method was used to measure the spatial distribution of job opportunity, investigate the spatial patterns and analyze the influences of job accessibility on commuting behavior. The results show that the distribution of job accessibility in Nanjing exhibits the different spatial patterns and mechanisms compared with US cases. <! [endif] --

Flight Time and Frequency-Optimization Model for Multiairport System Operation

This study’s goal is to reduce the number of flights and alleviate congestion in hub airports. It proposes a flight time and frequency-optimization method for multiairport systems. A flight time and frequency-optimization model for multiairport system operation is created to minimize loss of passenger trip time. A k-means clustering algorithm is designed to solve the model and calculate indexes such as flight time and frequency, passenger trip-time loss, and distribution of airplane models and quantity. The calculation results of an example in China are as follows. Under multiairport system operation mode, passenger demands are divided into 7 categories; 11 flights satisfy all passenger demands; passenger trip-time loss is 129,573 min; and the average passenger load factor is 90.1%. Under an independent operation mode, passenger demands are divided into 8 categories; 13 flights satisfy all passenger demands; passenger trip-time loss is 173,705 min; and the average passenger load factor is 87.4%. The multiairport system operation mode not only improves passenger trip efficiency but also benefits airlines by improving the passenger load factor and reducing flights. Moreover, comparative analysis of a genetic algorithm versus a clustering algorithm further proves the accuracy of the clustering algorithm

Evaluation Method for Green Ecological Airports in China Based on Combination Weighting

To scientifically and accurately evaluate the status of the development of green airports in China, evaluation methods of green, ecological airports are established in this paper. To address the shortcomings in subjective and objective weighting methods, we propose a combination weighting method based on Spearman’s rank correlation coefficient and evaluation grades based on interval approximation. At the same time, by taking into account resource conservation, environmental friendliness, operation efficiency, and people-oriented service, we propose an evaluation index system and an interval number for each index. Lastly, the theory is applied to five large airports in different regions of China. Analysis of the evaluation results shows that Shanghai Pudong International Airport (PVG) and Guangzhou Baiyun International Airport (CAN) have the highest scores for the resource conservation and environmental friendliness indexes, thus indicating that the development of a green ecological airport is closely related to its passenger transportation scale and economic strength. All considered airports showed the need for upgrading public  service facilities and constructing intelligent equipment. The method proposed in this paper is reasonable  and reliable; therefore, it can provide guidance for the evaluation and construction of green, ecological  airports

Bilevel Traffic Evacuation Model and Algorithm Design for Large-Scale Activities

This paper establishes a bilevel planning model with one master and multiple slaves to solve traffic evacuation problems. The minimum evacuation network saturation and shortest evacuation time are used as the objective functions for the upper- and lower-level models, respectively. The optimizing conditions of this model are also analyzed. An improved particle swarm optimization (PSO) method is proposed by introducing an electromagnetism-like mechanism to solve the bilevel model and enhance its convergence efficiency. A case study is carried out using the Nanjing Olympic Sports Center. The results indicate that, for large-scale activities, the average evacuation time of the classic model is shorter but the road saturation distribution is more uneven. Thus, the overall evacuation efficiency of the network is not high. For induced emergencies, the evacuation time of the bilevel planning model is shortened. When the audience arrival rate is increased from 50% to 100%, the evacuation time is shortened from 22% to 35%, indicating that the optimization effect of the bilevel planning model is more effective compared to the classic model. Therefore, the model and algorithm presented in this paper can provide a theoretical basis for the traffic-induced evacuation decision making of large-scale activities

Full-length transcriptome analysis provides insights into larval shell formation in Mulinia lateralis

Mollusca is the second largest animal phylum and represents one of the most evolutionarily successful animal groups. Mulinia lateralis, a small bivalve, is a promising model organism to facilitate studies of mollusc development. However, because of the lack of published genomic and transcriptomic resources, integrated research on the formation of larval shells in this species, which is a representative developmental process of molluscs and of great importance for larva survival, is hindered. In this study, the blastula, gastrula, trochophore larva, and D-shaped larva of M. lateralis were utilized for generating a comprehensive full-length transcriptome through Pacific BioSciences (PacBio) isoform sequencing (Iso-seq) and Illumina RNA-Seq. A total of 238,919 full-length transcripts with an average length of 3,267 bp and 121,424 annotated genes were obtained. Illumina RNA-Seq data analysis showed that 4,512, 10,637, and 17,829 differentially expressed genes (DEGs) were obtained between the two adjacent developmental stages. Functional annotation and enrichment analysis revealed the specific function of genes in shell biomineralization during different developmental stages. Twelve genes that may be involved in the formation of the larval shell of M. lateralis were identified, including insoluble shell matrix protein-encoding gene 1 (ISMP1), ISMP2, ISMP5, chitin synthase, tyrosinase, chitin-binding protein, collagen and pu14 involved in shell matrix deposition, and carbonic anhydrase, solute carrier family 4 member 8 (slc4a8), EF-hand, and a calmodulin coding gene C-2442 participated in ion transportation. In addition, calcium ion binding function, calcium signaling pathway, and endocrine and other factor-regulated calcium reabsorption pathways were significantly enriched. Weighted gene correlation network analysis (WGCNA) identified two modules related to biomineralization and larval shell formation, and slc4a8 and ring finger protein 41 (rnf41) were key hub genes that may be involved in this process. Moreover, it could be implied that the process of ion transport occurs earlier than the deposition of the shell matrix. This work provided a clear view of the transcriptome for M. lateralis and will be valuable in elucidating the mechanisms of larval shell formation as well as other developmental processes in molluscs

Optimization of Airport Shuttle Bus Routes Based on Travel Time Reliability

An optimization model of airport shuttle bus routes is constructed by taking operational reliability maximization as a main goal in this paper. Also, a hybrid genetic algorithm is designed to solve this problem. Then the theoretical method is applied to the case of Nanjing Lukou International Airport. During the research, a travel time reliability estimation method is proposed based on back propagation (BP) neural network. Absolute error and regression fitting methods are used to test the measurement results. It is proved that this method has higher accuracy and is applicable to calculate airport bus routes reliability. In algorithm design, the hill-climbing algorithm with strong local search ability is integrated into genetic algorithm. Initial solution is determined by hill-climbing algorithm so as to avoid the search process falling into a local optimal solution, which makes the accuracy of calculation result improved. However, the calculation results show that the optimization process of hybrid genetic algorithm is greatly affected by both the crossover rate and mutation rate. A higher mutation rate or lower crossover rate will decrease the stability of the optimization process. Multiple trials are required to determine the optimal crossover rate and mutation rate. The proposed method provides a scientific basis for optimizing the airport bus routes and improving the efficiency of airport’s external transportation services