Local well-posedness and small Deborah limit of a molecule-based QQ-tensor system

In this paper, we consider a hydrodynamic $Q$-tensor system for nematic liquid crystal flow, which is derived from Doi-Onsager molecular theory by the Bingham closure. We first prove the existence and uniqueness of local strong solution. Furthermore, by taking Deborah number goes to zero and using the Hilbert expansion method, we present a rigorous derivation from the molecule-based $Q$-tensor theory to the Ericksen-Leslie theory.Comment: 44 page

A simple and accurate discontinuous Galerkin scheme for modeling scalar-wave propagation in media with curved interfaces

Conventional high-order discontinuous Galerkin (DG) schemes suffer from interface errors caused by the misalignment between straight-sided elements and curved material interfaces. We have developed a novel DG scheme to reduce those errors. Our new scheme uses the correct normal vectors to the curved interfaces, whereas the conventional scheme uses the normal vectors to the element edge. We modify the numerical fluxes to account for the curved interface. Our numerical modeling examples demonstrate that our new discontinuous Galerkin scheme gives errors with much smaller magnitudes compared with the conventional scheme, although both schemes have second-order convergence. Moreover, our method significantly suppresses the spurious diffractions seen in the results obtained using the conventional scheme. The computational cost of our scheme is similar to that of the conventional scheme. The new DG scheme we developed is, thus, particularly useful for large-scale scalar-wave modeling involving complex subsurface structures

Street Stall Economy in China in the COVID-19 Era: Dilemmas and the International Experience of Promoting the Normalization of Street Stall Economy

Compared with those major policies that need to be practiced over the years, the street stall economy is more like a special means after the epidemic, resulting in a “short and brilliant” heat. Nevertheless, the street stall economy revives is facing several dilemmas. This paper reveals the dilemma of the prosperity and development of the stall economy before and after the epidemic, followed by the international experience and enlightenment of promoting the normalization of street stall economy, ranging from street vendor’s legal status and road administrative promotion to street food safety and environmental protection. To sum up, employment is the foundation of people’s livelihood and the source of wealth, hence, stall economy plays an indispensable role to create a win-win working world and promote the formation of a sustainable economic

Intelligent Scheduling Method for Bulk Cargo Terminal Loading Process Based on Deep Reinforcement Learning

Funding Information: Funding: This research was funded by the National Natural Science Foundation of China under Grant U1964201 and Grant U21B6001, the Major Scientific and Technological Special Project of Hei-longjiang Province under Grant 2021ZX05A01, the Heilongjiang Natural Science Foundation under Grant LH2019F020, and the Major Scientific and Technological Research Project of Ningbo under Grant 2021Z040. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Sea freight is one of the most important ways for the transportation and distribution of coal and other bulk cargo. This paper proposes a method for optimizing the scheduling efficiency of the bulk cargo loading process based on deep reinforcement learning. The process includes a large number of states and possible choices that need to be taken into account, which are currently performed by skillful scheduling engineers on site. In terms of modeling, we extracted important information based on actual working data of the terminal to form the state space of the model. The yard information and the demand information of the ship are also considered. The scheduling output of each convey path from the yard to the cabin is the action of the agent. To avoid conflicts of occupying one machine at same time, certain restrictions are placed on whether the action can be executed. Based on Double DQN, an improved deep reinforcement learning method is proposed with a fully connected network structure and selected action sets according to the value of the network and the occupancy status of environment. To make the network converge more quickly, an improved new epsilon-greedy exploration strategy is also proposed, which uses different exploration rates for completely random selection and feasible random selection of actions. After training, an improved scheduling result is obtained when the tasks arrive randomly and the yard state is random. An important contribution of this paper is to integrate the useful features of the working time of the bulk cargo terminal into a state set, divide the scheduling process into discrete actions, and then reduce the scheduling problem into simple inputs and outputs. Another major contribution of this article is the design of a reinforcement learning algorithm for the bulk cargo terminal scheduling problem, and the training efficiency of the proposed algorithm is improved, which provides a practical example for solving bulk cargo terminal scheduling problems using reinforcement learning.publishersversionpublishe

Aberrant resting-state brain activity in Huntington's disease: A voxel-based meta-analysis

IntroductionFunctional neuroimaging could provide abundant information of underling pathophysiological mechanisms of the clinical triad including motor, cognitive and psychiatric impairment in Huntington's Disease (HD).MethodsWe performed a voxel-based meta-analysis using anisotropic effect size-signed differential mapping (AES-SDM) method.Results6 studies (78 symptomatic HD, 102 premanifest HD and 131 healthy controls) were included in total. Altered resting-state brain activity was primarily detected in the bilateral medial part of superior frontal gyrus, bilateral anterior cingulate/paracingulate gyrus, left insula, left striatum, right cortico-spinal projections area, right inferior temporal gyrus area, right thalamus, right cerebellum and right gyrus rectus area. Premanifest and symptomatic HD patients showed different alterative pattern in the subgroup analyses.DiscussionThe robust and consistent abnormalities in the specific brain regions identified in the current study could help to understand the pathophysiology of HD and explore reliable neuroimaging biomarkers for monitoring disease progression, or even predicting the onset of premanifest HD patients

Dual Path Modeling for Semantic Matching by Perceiving Subtle Conflicts

Transformer-based pre-trained models have achieved great improvements in semantic matching. However, existing models still suffer from insufficient ability to capture subtle differences. The modification, addition and deletion of words in sentence pairs may make it difficult for the model to predict their relationship. To alleviate this problem, we propose a novel Dual Path Modeling Framework to enhance the model's ability to perceive subtle differences in sentence pairs by separately modeling affinity and difference semantics. Based on dual-path modeling framework we design the Dual Path Modeling Network (DPM-Net) to recognize semantic relations. And we conduct extensive experiments on 10 well-studied semantic matching and robustness test datasets, and the experimental results show that our proposed method achieves consistent improvements over baselines.Comment: ICASSP 2023. arXiv admin note: text overlap with arXiv:2210.0345