Well-balanced finite difference WENO schemes for the blood flow model

The blood flow model maintains the steady state solutions, in which the flux gradients are non-zero but exactly balanced by the source term. In this paper, we design high order finite difference weighted non-oscillatory (WENO) schemes to this model with such well-balanced property and at the same time keeping genuine high order accuracy. Rigorous theoretical analysis as well as extensive numerical results all indicate that the resulting schemes verify high order accuracy, maintain the well-balanced property, and keep good resolution for smooth and discontinuous solutions

Social Networks, Sectors and Occupational Attainment in Urban China

Since the late 1970s, the People’s Republic of China has experienced a progressive market transition that has led to profound changes in organizations. The development of product market has nurtured the emergence and growth of the private sector, two parallel while competing sectors, the state sector and the private sector have coexisted since then. China has been a “relationship based” society since ancient times, social networks as an efficient channel have been frequently used during job searching process. Do social networks still have effects on job attainment during the transition to a market economy? From macro structural perspective, do social networks have distinctive influence on job attainment across different sectors, namely state sector and private sector? Based on the dataset of “Job Searching and Social Networks” (JSNet 2009), which is drawn from eight big cities, Xi’an, Changchun, Jinan, Shanghai, Xiamen, Guangzhou, Tianjin, and Lanzhou. I assess the variation of social networks in different sectors by splitting the dataset into two parts (the state sector and the private sector), and organizing occupational attainment into three categories: administrative/managerial positions, professional positions and ordinary workers. The findings show that there have been continuity and significant effects of social networks in obtaining occupations in both state sector and private sector across the transition period. Taking Chinese cultural background into consideration, strong ties play a more imperative role in job attainment as compared to weak ties. Significant variations of social networks exist across different sectors: network mechanism works more effectively in the state sector than the private sector; regarding state sector, job applicants who use network methods have greater probabilities to secure administrative/managerial jobs and ordinary jobs in comparison to professional positions, while in private sector, social networks only have effects on searching for administrative/managerial occupations, reflecting the functions of both the persistence of institutions and emerging market forces

Strong Optical and UV Intermediate-Width Emission Lines in the Quasar SDSS J232444.80-094600.3: Dust-Free and Intermediate-Density Gas at the Skin of Dusty Torus ?

Emission lines from the broad emission line region (BELR) and the narrow emission line region (NELR) of active galactic nuclei (AGNs) are extensively studied. However, between these two regions emission lines are rarely detected. We present a detailed analysis of a quasar SDSS J232444.80-094600.3 (SDSS J2324$-$0946), which is remarkable for its strong intermediate-width emission lines (IELs) with FWHM $\approx$ 1800 \kmps. The IEL component is presented in different emission lines, including the permitted lines \lya\ $\lambda$1216, \civ\ $\lambda$1549, semiforbidden line \ciii\ $\lambda$1909, and forbidden lines \oiii\ $\lambda\lambda$4959, 5007. With the aid of photo-ionization models, we found that the IELs are produced by gas with a hydrogen density of $n_{\rm H} \sim 10^{6.2}-10^{6.3}~\rm cm^{-3}$, a distance to the central ionizing source of $R \sim 35-50$ pc, a covering factor of CF $\sim$ 6\%, and a dust-to-gas ratio of $\leq 4\%$ times of SMC. We suggest that the strong IELs of this quasar are produced by nearly dust-free and intermediate-density gas located at the skin of the dusty torus. Such strong IELs, served as a useful diagnose, can provide an avenue to study the properties of gas between the BELR and the NELR

Sample-Based Online Generalized Assignment Problem with Unknown Poisson Arrivals

We study an edge-weighted online stochastic \emph{Generalized Assignment Problem} with \emph{unknown} Poisson arrivals. In this model, we consider a bipartite graph that contains offline bins and online items, where each offline bin is associated with a $D$-dimensional capacity vector and each online item is with a $D$-dimensional demand vector. Online arrivals are sampled from a set of online item types which follow independent but not necessarily identical Poisson processes. The arrival rate for each Poisson process is unknown. Each online item will either be packed into an offline bin which will deduct the allocated bin's capacity vector and generate a reward, or be rejected. The decision should be made immediately and irrevocably upon its arrival. Our goal is to maximize the total reward of the allocation without violating the capacity constraints. We provide a sample-based multi-phase algorithm by utilizing both pre-existing offline data (named historical data) and sequentially revealed online data. We establish its performance guarantee measured by a competitive ratio. In a simplified setting where $D=1$ and all capacities and demands are equal to $1$, we prove that the ratio depends on the number of historical data size and the minimum number of arrivals for each online item type during the planning horizon, from which we analyze the effect of the historical data size and the Poisson arrival model on the algorithm's performance. We further generalize the algorithm to the general multidimensional and multi-demand setting, and present its parametric performance guarantee. The effect of the capacity's (demand's) dimension on the algorithm's performance is further analyzed based on the established parametric form. Finally, we demonstrate the effectiveness of our algorithms numerically

Cross-corpus Readability Compatibility Assessment for English Texts

Text readability assessment has gained significant attention from researchers in various domains. However, the lack of exploration into corpus compatibility poses a challenge as different research groups utilize different corpora. In this study, we propose a novel evaluation framework, Cross-corpus text Readability Compatibility Assessment (CRCA), to address this issue. The framework encompasses three key components: (1) Corpus: CEFR, CLEC, CLOTH, NES, OSP, and RACE. Linguistic features, GloVe word vector representations, and their fusion features were extracted. (2) Classification models: Machine learning methods (XGBoost, SVM) and deep learning methods (BiLSTM, Attention-BiLSTM) were employed. (3) Compatibility metrics: RJSD, RRNSS, and NDCG metrics. Our findings revealed: (1) Validated corpus compatibility, with OSP standing out as significantly different from other datasets. (2) An adaptation effect among corpora, feature representations, and classification methods. (3) Consistent outcomes across the three metrics, validating the robustness of the compatibility assessment framework. The outcomes of this study offer valuable insights into corpus selection, feature representation, and classification methods, and it can also serve as a beginning effort for cross-corpus transfer learning.Comment: 14 pages,17 figure

Chinese Language Teacher Competency: A Literature Review for a Study Series

This literature study reviews the definition and the most significant research studies of teacher competency. A chronological order, from the 20th to the 21st century, is followed to introduce the development of the teacher competency. The current status of the Chinese (as a foreign language) teacher competency research is also revealed, which shows a big gap that needs to be filled