The National Genomics Data Center (NGDC) provides a suite of database resources to support worldwide research activities in both academia and industry. With the rapid advancements in higher-throughput and lower-cost sequencing technologies and accordingly the huge volume of multi-omics data generated at exponential scales and rates, NGDC is continually expanding, updating and enriching its core database resources through big data integration and value-added curation. In the past year, efforts for update have been mainly devoted to BioProject, BioSample, GSA, GWH, GVM, NONCODE, LncBook, EWAS Atlas and IC4R. Newly released resources include three human genome databases (PGG.SNV, PGG.Han and CGVD), eLMSG, EWAS Data Hub, GWAS Atlas, iSheep and PADS Arsenal. In addition, four web services, namely, eGPS Cloud, BIG Search, BIG Submission and BIG SSO, have been significantly improved and enhanced. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.

Fuel supply shortages that have emerged in recent hurricane events underscore the weakness of existing emergency logistics planning processes. An effective modeling approach is central to refueling station location and supply planning decisions. This paper documents a research effort based on a simulation–optimization framework integrating a mixed integer program formulation and a mesoscopic simulation model. The mesoscopic simulation model was incorporated with decision rules to select refueling stations, methods to model the impact of stalled vehicles on traffic flow, and a formula to accumulate each vehicle's fuel consumption under various running speed conditions. The mixed integer program formulation is aimed at maximizing the served demand by deciding which stations to operate and how much fuel to supply given limited resources. The interplay between the simulation model and the optimization model continues until convergence. The proposed modeling approach is applied to a case study based on the I-45 corridor between Houston and Dallas, Texas, to highlight the characteristics of the proposed modeling approach.

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In order to reduce the land cost of the warehouse, improve the competitiveness of enterprises. One of the approaches increasing space utilization is to reduce passageway as far as possible. Compared with single-double rack, doubledeep rack can save half passageway space, hence can realize more compact warehousing. Firstly, an interleaving time model, for which both the run time of automated crane and the handling time of stacking crane are taken into consideration; secondly, a dynamic storage optimization method is put forward, which, in relation to random storage method, can shorten interaction time, and reduce reshuffle as far as possible; at last, simulation experiment is carried out, which indicates that the optimized storage method can improve operation efficiency.