BOOK REVIEW: ENGLISH L2 READING: GETTING TO THE BOTTOM (4TH EDITION)

Research in L2 reading, especially the incorporation of insights from reading processing into reading instruction, has gained a standing attention in ELT and recent second language acquisition research has highlighted a complex and dynamic trajectory of reading efficacy development. English L2 Reading: Getting to the Bottom, now in its fourth edition, has timely responded to the psycholinguistic turn in reading studies over decades. With purposefully modified and updated sections on metalinguistic awareness in terms of pedagogical value, the latest edition provides a focused overview of central issues in understanding L2 English reading processors with practical considerations on their direct relevance to pedagogical interventions. This book review will firstly present a brief account of each chapter's underlying concerns and then offer critical comments on the latest version's theoretical implications in relation to current trends in L2 instruction research

The Performance Measuring for Chinese Banking Industry During 2011-2018: Cost Efficiency and Profitability

This dissertation was aimed to identify the cost efficiency and the determinants of profitability in Chinese banking industry over the period 2011-2018. To achieve the aim, Stochastic Frontier Analysis (SFA) and the System Generalized Method of Moments (SGMM) models were adopted. The research involved 105 Chinese commercial banks of different types, including state-owned commercial banks (SOCBs), joint-stock commercial banks (JSCBs), city commercial banks (CCBs), rural commercial banks (RCBs) and foreign commercial banks (FCBs). The research explored mediating influence of three variables, including ownership type, geographical regions and years, on cost efficiency. It was found that macroeconomic factors including the GDP growth rate (GDPGR), GDP per capita growth rate (GDPPCG), inflation rate (INF) and unemployment rate (UNE) had significant impact on Chinese banks’ cost efficiency. In addition, the CCBs were found to have the highest cost efficiency on average, whereas cost efficiency of the SOCBs was found to be the lowest. Moreover, the banks in eastern part of China were found to have the highest efficiency compared with their counterparts in the rest part of China. Furthermore, it was found that the deregulation of financial market (i.e. the interest rate marketization) improved the Chinese commercial banks’ efficiency. As for determinants of profitability, it was found that the profit of commercial Chinese banks was influenced by their previous performance and indicated that the banking sector in China was quite competitive. In addition, the results confirmed internal determinants of profitability, including size, liquidity, stability and efficiency. Moreover, macroeconomic determinants including GDP growth, inflation and unemployment rate all significantly influenced Chinese banks’ profitability. The research offered implications for further researchers and decision-makers

The Performance Measuring for Chinese Banking Industry During 2011-2018: Cost Efficiency and Profitability

This dissertation was aimed to identify the cost efficiency and the determinants of profitability in Chinese banking industry over the period 2011-2018. To achieve the aim, Stochastic Frontier Analysis (SFA) and the System Generalized Method of Moments (SGMM) models were adopted. The research involved 105 Chinese commercial banks of different types, including state-owned commercial banks (SOCBs), joint-stock commercial banks (JSCBs), city commercial banks (CCBs), rural commercial banks (RCBs) and foreign commercial banks (FCBs). The research explored mediating influence of three variables, including ownership type, geographical regions and years, on cost efficiency. It was found that macroeconomic factors including the GDP growth rate (GDPGR), GDP per capita growth rate (GDPPCG), inflation rate (INF) and unemployment rate (UNE) had significant impact on Chinese banks’ cost efficiency. In addition, the CCBs were found to have the highest cost efficiency on average, whereas cost efficiency of the SOCBs was found to be the lowest. Moreover, the banks in eastern part of China were found to have the highest efficiency compared with their counterparts in the rest part of China. Furthermore, it was found that the deregulation of financial market (i.e. the interest rate marketization) improved the Chinese commercial banks’ efficiency. As for determinants of profitability, it was found that the profit of commercial Chinese banks was influenced by their previous performance and indicated that the banking sector in China was quite competitive. In addition, the results confirmed internal determinants of profitability, including size, liquidity, stability and efficiency. Moreover, macroeconomic determinants including GDP growth, inflation and unemployment rate all significantly influenced Chinese banks’ profitability. The research offered implications for further researchers and decision-makers

The Performance Measuring for Chinese Banking Industry During 2011-2018: Cost Efficiency and Profitability

This dissertation was aimed to identify the cost efficiency and the determinants of profitability in Chinese banking industry over the period 2011-2018. To achieve the aim, Stochastic Frontier Analysis (SFA) and the System Generalized Method of Moments (SGMM) models were adopted. The research involved 105 Chinese commercial banks of different types, including state-owned commercial banks (SOCBs), joint-stock commercial banks (JSCBs), city commercial banks (CCBs), rural commercial banks (RCBs) and foreign commercial banks (FCBs). The research explored mediating influence of three variables, including ownership type, geographical regions and years, on cost efficiency. It was found that macroeconomic factors including the GDP growth rate (GDPGR), GDP per capita growth rate (GDPPCG), inflation rate (INF) and unemployment rate (UNE) had significant impact on Chinese banks’ cost efficiency. In addition, the CCBs were found to have the highest cost efficiency on average, whereas cost efficiency of the SOCBs was found to be the lowest. Moreover, the banks in eastern part of China were found to have the highest efficiency compared with their counterparts in the rest part of China. Furthermore, it was found that the deregulation of financial market (i.e. the interest rate marketization) improved the Chinese commercial banks’ efficiency. As for determinants of profitability, it was found that the profit of commercial Chinese banks was influenced by their previous performance and indicated that the banking sector in China was quite competitive. In addition, the results confirmed internal determinants of profitability, including size, liquidity, stability and efficiency. Moreover, macroeconomic determinants including GDP growth, inflation and unemployment rate all significantly influenced Chinese banks’ profitability. The research offered implications for further researchers and decision-makers

A novel processing methodology for traffic-speed road surveys using point lasers

The rapidly increasing traffic volumes using local road networks allied to the implications of climate change drive the demand for cost-effective, reliable and accurate road condition assessment. A particular concern for local road asset managers is the loss of material from the road surface known as fretting which unchecked can lead to potholes. In order to assess the road condition quantitatively and affordably, a system should be designed with low complexity, be capable of operating in a variety of weather conditions and operate at normal traffic-speeds. Many different techniques have been developed for road condition assessment such as ground penetrating radar, visual sensors and mobile scanning lasers. In this work, the use of the point laser technique for scanning the road surface is investigated. It has the advantages of being sufficiently accurate, is relatively unaffected by levels of illumination and it produces relatively low volumes of data. In this work, road fretting/surface disintegration was determined using a novel signal processing approach which considers a number of features of reflected laser signals. The proposed methodology was demonstrated using data collected from the UK's local road network. The experimental results indicate that the proposed system can assess road fretting to an accuracy which is comparable to a visual inspection, and at Information Quality Level (IQL) 3 which is sufficient for tactical road asset management whereby road sections requiring treatment are selected and appropriate treatments identified

LeCaRDv2: A Large-Scale Chinese Legal Case Retrieval Dataset

As an important component of intelligent legal systems, legal case retrieval plays a critical role in ensuring judicial justice and fairness. However, the development of legal case retrieval technologies in the Chinese legal system is restricted by three problems in existing datasets: limited data size, narrow definitions of legal relevance, and naive candidate pooling strategies used in data sampling. To alleviate these issues, we introduce LeCaRDv2, a large-scale Legal Case Retrieval Dataset (version 2). It consists of 800 queries and 55,192 candidates extracted from 4.3 million criminal case documents. To the best of our knowledge, LeCaRDv2 is one of the largest Chinese legal case retrieval datasets, providing extensive coverage of criminal charges. Additionally, we enrich the existing relevance criteria by considering three key aspects: characterization, penalty, procedure. This comprehensive criteria enriches the dataset and may provides a more holistic perspective. Furthermore, we propose a two-level candidate set pooling strategy that effectively identify potential candidates for each query case. It's important to note that all cases in the dataset have been annotated by multiple legal experts specializing in criminal law. Their expertise ensures the accuracy and reliability of the annotations. We evaluate several state-of-the-art retrieval models at LeCaRDv2, demonstrating that there is still significant room for improvement in legal case retrieval. The details of LeCaRDv2 can be found at the anonymous website https://github.com/anonymous1113243/LeCaRDv2

Evaluation of oral Lanzhou lamb rotavirus vaccine via passive transfusion with CD4+/CD8+ T lymphocytes

AbstractLanzhou Lamb derived Rotavirus (RV) Vaccine (namely LLR) for children is only used in China. Since there were no reports on evaluation of LLR, even the data of phase IV clinical trial, we proceed the evaluation of LLR through focusing on T-cell to investigate whether LLR could induce the potential function involving in protection as a vaccine. Four groups of nude mice were transfused with CD4+/CD8+ T-cells isolated from LLR-immunized (primed) and LLR-unimmunized (naïve) mice via intraperitonea (i.p.) respectively. Consequently, the adoption mice were challenged with mice-origin wild rotavirus EDIM (Epizootic Diarrhea of Infant Mice) by intragastric administration. Series of fecal/serum samples were collected and viral shedding, then serum IgA/IgG and secreted IgA were assayed. Compared to the mice transfused with T lymphocytes from naïve mice, the nude mice transfused with CD4+ T lymphocytes from primed mice induce fecal and serum IgA increasing more rapidly, and have a shorter duration of virus shedding too. Whereas, no significant difference in virus clearance was found between the mice transfused with CD8+ T lymphocytes isolated from primed and naïve mice. Therefore, we cleared the distinct roles of transfused CD4+/CD8+ T lymphocytes for rotavirus clearance in nude mice, that the viral clearance conducted by CD4+ T lymphocytes. Meanwhile, it has ability to help induction of LLR specific immunogenicity. Comparing with the transfusion of cell from primed and naïve mice, LLR can induce CD4+ T lymphocytes memory which is a potential index to reflect the immunogenicity and protection, while CD8+ T lymphocytes remove rotavirus by CTL with little memory ability

Identification and analysis of the stigma and embryo sac-preferential/specific genes in rice pistils

The secretion-related genes. (XLS 40 kb

An Intent Taxonomy of Legal Case Retrieval

Legal case retrieval is a special Information Retrieval~(IR) task focusing on legal case documents. Depending on the downstream tasks of the retrieved case documents, users' information needs in legal case retrieval could be significantly different from those in Web search and traditional ad-hoc retrieval tasks. While there are several studies that retrieve legal cases based on text similarity, the underlying search intents of legal retrieval users, as shown in this paper, are more complicated than that yet mostly unexplored. To this end, we present a novel hierarchical intent taxonomy of legal case retrieval. It consists of five intent types categorized by three criteria, i.e., search for Particular Case(s), Characterization, Penalty, Procedure, and Interest. The taxonomy was constructed transparently and evaluated extensively through interviews, editorial user studies, and query log analysis. Through a laboratory user study, we reveal significant differences in user behavior and satisfaction under different search intents in legal case retrieval. Furthermore, we apply the proposed taxonomy to various downstream legal retrieval tasks, e.g., result ranking and satisfaction prediction, and demonstrate its effectiveness. Our work provides important insights into the understanding of user intents in legal case retrieval and potentially leads to better retrieval techniques in the legal domain, such as intent-aware ranking strategies and evaluation methodologies.Comment: 28 pages, work in proces

SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval

Legal case retrieval, which aims to find relevant cases for a query case, plays a core role in the intelligent legal system. Despite the success that pre-training has achieved in ad-hoc retrieval tasks, effective pre-training strategies for legal case retrieval remain to be explored. Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. However, most existing language models have difficulty understanding the long-distance dependencies between different structures. Moreover, in contrast to the general retrieval, the relevance in the legal domain is sensitive to key legal elements. Even subtle differences in key legal elements can significantly affect the judgement of relevance. However, existing pre-trained language models designed for general purposes have not been equipped to handle legal elements. To address these issues, in this paper, we propose SAILER, a new Structure-Aware pre-traIned language model for LEgal case Retrieval. It is highlighted in the following three aspects: (1) SAILER fully utilizes the structural information contained in legal case documents and pays more attention to key legal elements, similar to how legal experts browse legal case documents. (2) SAILER employs an asymmetric encoder-decoder architecture to integrate several different pre-training objectives. In this way, rich semantic information across tasks is encoded into dense vectors. (3) SAILER has powerful discriminative ability, even without any legal annotation data. It can distinguish legal cases with different charges accurately. Extensive experiments over publicly available legal benchmarks demonstrate that our approach can significantly outperform previous state-of-the-art methods in legal case retrieval.Comment: 10 pages, accepted by SIGIR 202