Job-worker spatial dynamics in Beijing: Insights from Smart Card Data.

As a megacity, Beijing has experienced traffic congestion, unaffordable housing issues and jobs-housing im- balance. Recent decades have seen policies and projects aiming at decentralizing urban structure and job-worker patterns, such as subway network expansion, the suburbanization of housing and firms. But it is unclear whether these changes produced a more balanced spatial configuration of jobs and workers. To answer this question, this paper evaluated the ratio of jobs to workers from Smart Card Data at the transit station level and offered a longitudinal study for regular transit commuters. The method identifies the most preferred station around each commuter's workpalce and home location from individual smart datasets according to their travel regularity, then the amounts of jobs and workers around each station are estimated. A year-to-year evolution of job to worker ratios at the station level is conducted. We classify general cases of steepening and flattening job-worker dynamics, and they can be used in the study of other cities. The paper finds that (1) only temporary balance appears around a few stations; (2) job-worker ratios tend to be steepening rather than flattening, influencing commute patterns; (3) the polycentric configuration of Beijing can be seen from the spatial pattern of job centers identified.Authors appreciate Beijing Transport Information Center for data access. This work is financially supported by Strategic Priority Research Program of Chinese Academy of Sciences (No. XDA19040402) and National Natural Science Foundation of China (No. 41701132 and No. 41722103)

Numerical simulation on structural safety and dynamic response of coal mine rescue ball with gas explosion load using Arbitrary Lagrangian-Eulerian (ALE) algorithm

Coal mine rescue devices, which can supply miners underground with fundamental shelters after gas explosion, are essential for safety production of coal mines. In this paper, a novel and composite structure-rescue antiknock ball for coal mine rescue is designed. Further, the structural safety and dynamic response under gas explosion of the antiknock ball is investigated by ALE algorithm. To achieve this goal, the ALE finite element method is described in dynamic form, and governing equations and the finite element expressions of the ALE algorithm are derived. 3 balls with different structures are designed and dynamic response analysis has been conducted in a semi-closed tunnel with explosive load of pre-mixed gas/air mixture by using ALE algorithm based on explicit nonlinear dynamic program LS-DYNA. Displacement field, stress field and energy transmission laws are analyzed and compared via theoretical calculations. Results show that the cabin door, emergency door and spherical shell are important components of the rescue ball. The 3# composite ball is the optimization structure that can delay the shock effect of the gas explosion load on a coal mine rescue system; the simulation results can provide reference data for coal mine rescue system design

Discover, Explanation, Improvement: Automatic Slice Detection Framework for Natural Language Processing

Current natural language processing (NLP) models such as BERT and RoBERTa have achieved high overall performance, but they often make systematic errors due to bias or certain difficult features to learn. Thus research on slice detection models (SDM) which automatically identifies underperforming groups of datapoints has gradually caught more attention, which aims at both understanding model behaviors and providing insights for future model training and designing. However, there is little systematic research on SDM and quantitative evaluation of its assessment for NLP models. Our paper fills this gap by proposing "Discover, Explanation, Improvement" framework that discovers coherent and underperforming groups of datapoints and unites datapoints of each slice under human-understandable concepts; it also provides comprehensive evaluation tasks and the corresponding quantitative metrics, which enable convenient comparison for future works. Results show that our framework can accurately select error-prone datapoints with informative semantic features that summarize error patterns, based on which it directly boosts model performance by an average of 2.85 points based on trained models without tuning any parameters across multiple datasets.Comment: 15 pages, 5 figure

FM-DBEM Simulation of 3D Microvoid and Microcrack Graphite Models

The graphite is porous medium, and the geometry and size distribution of its structural deficiencies such as microcracks and microvoids at different oxidation degrees have a great influence on the overall performance. In this paper, we adopt the FM-DBEM to study 3D models which contain spheroidal microvoids and circular microcracks. The accuracy of this method is tested by a comparison to the theoretical solution to the problem of 2D microcrack and microvoid interaction problem. Two simulations are conducted: the simulation of graphite model containing a large number of randomly distributed microcracks and microvoids and the simulation of graphite model containing microcracks and growing microvoids. The simulations investigate the effective moduli versus the two microstructures’ density and the effect of microvoid’s growth on the SIF of microcrack

Multimode Process Monitoring Based on Sparse Principal Component Selection and Bayesian Inference-Based Probability

According to the demand for diversified products, modern industrial processes typically have multiple operating modes. At the same time, variables within the same mode often follow a mixture of Gaussian distributions. In this paper, a novel algorithm based on sparse principal component selection (SPCS) and Bayesian inference-based probability (BIP) is proposed for multimode process monitoring. SPCS can be formulated as a just-in-time regression between all PCs and each sample. SPCS selects PCs according to the nonzero regression coefficients which indicate the compact expression of the sample. This expression is necessarily discriminative: amongst all subset of PCs, SPCS selects the PCs which most compactly express the sample and rejects all other possible but less compact expressions. BIP is utilized to compute the posterior probabilities of each monitored sample belonging to the multiple components and derive an integrated global probabilistic index for fault detection of multimode processes. Finally, to verify its superiority, the SPCS-BIP algorithm is applied to the Tennessee Eastman (TE) benchmark process and a continuous stirred-tank reactor (CSTR) process