PRESS: A Novel Framework of Trajectory Compression in Road Networks

Location data becomes more and more important. In this paper, we focus on the trajectory data, and propose a new framework, namely PRESS (Paralleled Road-Network-Based Trajectory Compression), to effectively compress trajectory data under road network constraints. Different from existing work, PRESS proposes a novel representation for trajectories to separate the spatial representation of a trajectory from the temporal representation, and proposes a Hybrid Spatial Compression (HSC) algorithm and error Bounded Temporal Compression (BTC) algorithm to compress the spatial and temporal information of trajectories respectively. PRESS also supports common spatial-temporal queries without fully decompressing the data. Through an extensive experimental study on real trajectory dataset, PRESS significantly outperforms existing approaches in terms of saving storage cost of trajectory data with bounded errors.Comment: 27 pages, 17 figure

Role-Play Simulations and System Dynamics for Sustainability Solutions around Dams in New England

Research has shown that much of the science produced does not make its way to the decision-making table. This leads to a gap between scientific and societal progress, which is problematic. This study tests a novel science-based negotiation simulation that integrates role-play simulations (RPSs) with a system dynamic model (SDM). In RPSs, stakeholders engage in a mock decision-making process (reflecting real-life institutional arrangements and scientific knowledge) for a set period. By playing an assigned role (different from the participant’s real-life role), participants have a safe space to learn about each other’s perspectives, develop shared understanding about a complex issue, and collaborate on solving that issue. System Dynamic Models (SDMs) are visual tools used to simulate the interactions and feedback with a complex system. We test the integration of the two approaches toward problem-solving with real stakeholders in New Hampshire and Rhode Island via a series of two consecutive workshops in each state. The workshops are intended to engage representatives from diverse groups who are interested in dam related issues to foster dialogue, learning, and creativity. Participants will discuss a hypothetical (yet realistic) dam-decision scenario to consider scientific information and explore dam management options that meet one another\u27s interests. In the first workshop participants will contribute to the design of the fictionalized dam decision scenario and the SDM, for which we have presented drafts based on a literature review, stakeholder interviews, and expert knowledge. In the second workshop, participants will assume another representative\u27s role and discuss dam management options for the fictionalized scenario. We will report results related to the effectiveness to which this new knowledge production process leads to more innovative and collaborative decision-making around New England dams

The Effect of Self-repair on Judged Quality of Consecutive Interpreting: Attending to Content, Form and Delivery

This paper investigates the correlations between self-repair and subjective assessments of student interpreters’ performance in consecutive interpreting(CI). Twelve interpretations from an interpreting contest in China are transcribed, with the self-repairs identified and annotated based on Levelt’s classification (1983), including both overt and covert repairs. In addition to the final scores awarded at the contest, different methods and raters are used to assess the comprising aspects of an overall quality, namely content, form and delivery. Statistical analysis shows that: (1)overt repairs have a strong positive correlation with content, and moderate negative correlations with form and delivery; (2) form and delivery are negatively correlated with covert repairs, in terms of the frequencies of repetitions and pauses, and the mean length of pauses;(3) the judges’ overall assessments are more closely correlated with content than self-repairs. Finally, pedagogical implications for CI training are discussed, as are suggestions for future research

Type-2 diabetes-induced changes in vascular extracellular matrix gene expression: Relation to vessel size

BACKGROUND: Hyperglycemia-induced changes in vascular wall structure contribute to the pathogenesis of diabetic microvascular and macrovascular complications. Matrix metalloproteinases (MMP), a family of proteolytic enzymes that degrade extracellular matrix (ECM) proteins, are essential for vascular remodeling. We have shown that endothelin-1 (ET-1) mediates increased MMP activity and associated vascular remodeling in Type 2 diabetes. However, the effect of Type 2 diabetes and/or ET-1 on the regulation of ECM and MMP gene expression in different vascular beds remains unknown. METHODS: Aorta and mesenteric artery samples were isolated from control, Type 2 diabetic Goto-Kakizaki (GK) rats and GK rats treated with ET(A )antagonist ABT-627. Gene expression profile of MMP-2, MMP-9, MT1-MMP, fibronectin, procollagen type 1, c-fos and c-jun, were determined by quantitative real-time (qRT) PCR. In addition, aortic gene expression profile was evaluated by an ECM & Adhesion Molecules pathway specific microarray approach. RESULTS: Analysis of the qRT-PCR data demonstrated a significant increase in mRNA levels of MMPs and ECM proteins as compared to control animals after 6 weeks of mild diabetes. Futhermore, these changes were comparable in aorta and mesentery samples. In contrast, treatment with ET(A )antagonist prevented diabetes-induced changes in expression of MMPs and procollagen type 1 in mesenteric arteries but not in aorta. Microaarray analysis provided evidence that 27 extracellular matrix genes were differentially regulated in diabetes. Further qRT-PCR with selected 7 genes confirmed the microarray data. CONCLUSION: These results suggest that the expression of both matrix scaffold protein and matrix degrading MMP genes are altered in macro and microvascular beds in Type 2 diabetes. ET(A )antagonism restores the changes in gene expression in the mesenteric bed but not in aorta suggesting that ET-1 differentially regulates microvascular gene expression in Type 2 diabetes

Research on the Competitiveness of Hubei Manufacturing Post Financial Crisis Era——Panel Data of 2008~2011

With Location Quotient and Shift-Share-Method, this paper evaluated the competitiveness of manufacturing industries in Hubei province post Financial Crisis Era based on the panel data from 2008 to 2011of Output Value of Industrial Products Sales and Delivery Value for Export of Large and Medium Scale Industrial Enterprises. On basis of these, this paper also put forward some countermeasures to improve the competitiveness of manufacturing industry of Hubei province

A solver for subsonic flow around airfoils based on physics-informed neural networks and mesh transformation

Physics-informed neural networks (PINNs) have recently become a new popular method for solving forward and inverse problems governed by partial differential equations (PDEs). However, in the flow around airfoils, the fluid is greatly accelerated near the leading edge, resulting in a local sharper transition, which is difficult to capture by PINNs. Therefore, PINNs are still rarely used to solve the flow around airfoils. In this study, we combine physical-informed neural networks with mesh transformation, using neural network to learn the flow in the uniform computational space instead of physical space. Mesh transformation avoids the network from capturing the local sharper transition and learning flow with internal boundary (wall boundary). We successfully solve inviscid flow and provide an open-source subsonic flow solver for arbitrary airfoils. Our results show that the solver exhibits higher-order attributes, achieving nearly an order of magnitude error reduction over second-order finite volume methods (FVM) on very sparse meshes. Limited by the learning ability and optimization difficulties of neural network, the accuracy of this solver will not improve significantly with mesh refinement. Nevertheless, it achieves comparable accuracy and efficiency to second-order FVM on fine meshes. Finally, we highlight the significant advantage of the solver in solving parametric problems, as it can efficiently obtain solutions in the continuous parameter space about the angle of attack.Comment: arXiv admin note: text overlap with arXiv:2401.0720

An Empirical Study on Zero Address Terms Among Chinese College Students

Chinese address terms are the basis of interpersonal communication. On the one hand, a conversation usually begins with address terms which play a important role. On the other hand, choosing different address terms represents the different tone the speaker wants to express. Therefore, accurate and appropriate use of address terms is one of the important symbols of successful communication. However, using zero address terms is still very common because we don’t know how to address appropriately in some occasions. This paper aims to study the common types of zero address terms usage among university students, and try to summarize the main reasons. This study is based on daily conversations of college students in four common campus scenes which are office building, study building, store and playground. The following findings are obtained in this study. There are five common types of zero address terms phenomenon. They are polite expression, personal pronoun, no expression, onomatopoeia and body language. There are three main reasons for using zero address terms. They are occasion, interpersonal purpose and social development.

A complete state-space solution model for inviscid flow around airfoils based on physics-informed neural networks

Engineering problems often involve solving partial differential equations (PDEs) over a range of similar problem setups with various state parameters. In traditional numerical methods, each problem is solved independently, resulting in many repetitive tasks and expensive computational costs. Data-driven modeling has alleviated these issues, enabling fast solution prediction. Nevertheless, it still requires expensive labeled data and faces limitations in modeling accuracy, generalization, and uncertainty. The recently developed methods for solving PDEs through neural network optimization, such as physics-informed neural networks (PINN), enable the simultaneous solution of a series of similar problems. However, these methods still face challenges in achieving stable training and obtaining correct results in many engineering problems. In prior research, we combined PINN with mesh transformation, using neural network to learn the solution of PDEs in the computational space instead of physical space. This approach proved successful in solving inviscid flow around airfoils. In this study, we expand the input dimensions of the model to include shape parameters and flow conditions, forming an input encompassing the complete state-space (i.e., all parameters determining the solution are included in the input). Our results show that the model has significant advantages in solving high-dimensional parametric problems, achieving continuous solutions in a broad state-space in only about 18.8 hours. This is a task that traditional numerical methods struggle to accomplish. Once established, the model can efficiently complete airfoil flow simulation and shape inverse design tasks in approximately 1 second. Furthermore, we introduce a pretraining-finetuning method, enabling the fine-tuning of the model for the task of interest and quickly achieving accuracy comparable to the finite volume method