EDZs and Firms’ Environment Performance: Empirical Evidence From Chinese Firms

Economic Development Zones have been proven to be an economic success and have been pursued by many governments around the world over the past several decades, but there is still a research gap on their impact on the environment. This paper documents the effect of national-level EDZs on the environmental performance of firms. Combining rich firm and administrative data in China from 1998–2012, we exploit the relationship between the foundation of EDZs and firms’ pollution emissions with a multi-period differences-in-differences model. We find that the establishment of national EDZs can effectively reduce the pollution emission intensity of firms within it. Moreover, this pollution reduction effects vary across industries, firms, and EDZs types. We also find that the establishment of EDZs can improve firm environmental performance by improving energy efficiency, optimizing the business environment, and upgrading technology

GBG++: A Fast and Stable Granular Ball Generation Method for Classification

Granular ball computing (GBC), as an efficient, robust, and scalable learning method, has become a popular research topic of granular computing. GBC includes two stages: granular ball generation (GBG) and multi-granularity learning based on the granular ball (GB). However, the stability and efficiency of existing GBG methods need to be further improved due to their strong dependence on $k$-means or $k$-division. In addition, GB-based classifiers only unilaterally consider the GB's geometric characteristics to construct classification rules, but the GB's quality is ignored. Therefore, in this paper, based on the attention mechanism, a fast and stable GBG (GBG++) method is proposed first. Specifically, the proposed GBG++ method only needs to calculate the distances from the data-driven center to the undivided samples when splitting each GB instead of randomly selecting the center and calculating the distances between it and all samples. Moreover, an outlier detection method is introduced to identify local outliers. Consequently, the GBG++ method can significantly improve effectiveness, robustness, and efficiency while being absolutely stable. Second, considering the influence of the sample size within the GB on the GB's quality, based on the GBG++ method, an improved GB-based $k$-nearest neighbors algorithm (GB$k$NN++) is presented, which can reduce misclassification at the class boundary. Finally, the experimental results indicate that the proposed method outperforms several existing GB-based classifiers and classical machine learning classifiers on $24$ public benchmark datasets

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

The Influence of Finite Element Meshing Accuracy on a Welding Machine for Offshore Platform’S Modal Analysis

The purpose objective of this study was to investigate the influence of finite element meshing accuracy on modal analysis which is one of the basic factors affecting the accuracy of finite element analysis and mostly preoccupies the working staff in pre-processing finite element simulation models. In this paper, we established several finite element models of a welding machine for offshore platform, with the meshing accuracy as the variable and workbench software as the platform for modal analysis, as the same time, comparing the analysis results. The results indicated that for some specific structures and simulation types, mesh refinement alone does not achieve desired results, and the authors indicate that mesh refinement is rarely related to the equipment’s low-frequency modal analysis but it’s great related to the equipment’s high-frequency modal analysis. The findings of this study may serve as breaking the opinion that smaller mesh size means higher calculation precision and provides references for mesh division practices in low frequency modal analysis

Search-based QoS ranking prediction for web services in cloud environments

Unlike traditional quality of service (QoS) value prediction, QoS ranking prediction examines the order of services under consideration for a particular user. To address this NP-Complete problem, greedy strategy-based solutions, such as CloudRank algorithm, have been widely adopted. However, they can only produce locally approximate solutions. In this paper, we propose a search-based prediction framework to address the QoS ranking problem. The traditional particle swarm optimization (PSO) algorithm has been adapted to optimize the order of services according to their QoS records. In real situations, QoS records for a given consumer are often incomplete, so the related data from close neighbour users is often used to determine preference relations among services. In order to filter the neighbours for a specific user, we present an improved method for measuring the similarity between two users by considering the occurrence probability of service pairs. Based on the similarity computation, the top- neighbours are selected to provide QoS information support for evaluation of the service ranking. A fitness function for an ordered service sequence is defined to guide search algorithm to find high-quality ranking results, and some additional strategies, such as initial solution selection and trap escaping, are also presented. To validate the effectiveness of our proposed solution, experimental studies have been performed on real-world QoS data, the results from which show that our PSO-based approach has a better ranking for services than that computed by the existing CloudRank algorithm, and that the improvement is statistically significant, in most cases

Constructing Governance Framework of a Green and Smart Port

Developing a green and smart port is a significant progress in the specific application of energy conservation and emission reduction as well as intelligent technologies in global ports and maritime shipping sectors. The paper aims to analyze the inherent relationships among different structural factors and proposes specific countermeasures and governance policies for green and smart port construction. It uses interpretive structural modeling analysis to divide the factors into different levels, and draws a model map of green and smart port construction structure. The research result contributes to providing a theoretical basis for governments to formulate a green and smart port policies and establishing effective method systems and technical means for the port industry and stakeholders to leverage intelligent port technologies for the port development

Phosphodiesterase 3/4 inhibitor zardaverine exhibits potent and selective antitumor activity against hepatocellular carcinoma both in vitro and in vivo independently of phosphodiesterase inhibition.

Hepatocellular carcinoma (HCC) is the fifth common malignancy worldwide and the third leading cause of cancer-related death. Targeted therapies for HCC are being extensively developed with the limited success of sorafinib. In the present study, we investigated the potential antitumor activity of zardaverine, a dual-selective phosphodiesterase (PDE) 3/4 inhibitor in HCC cells both in vitro and in vivo. Although all zardaverine, PDE3 inhibitor trequinsin and PDE4 inhibitor rolipram increased intracellular cAMP levels through inhibiting PDE activity, only zardaverine significantly and selectively inhibited the proliferation of certain HCC cells, indicating that the antitumor activity of zardaverine is independent of PDE3/4 inhibition and intracellular cAMP levels. Further studies demonstrated that zardaverine induced G0/G1 phase cell cycle arrest of sensitive HCC cells through dysregulating cell cycle-associated proteins, including Cdk4, Cdk6, Cdk2, Cyclin A, Cyclin E, p21 and Rb. Notably, Rb expression was reversely related to the cell sensitivity to zardaverine. The present findings indicate that zardaverine may have potential as targeted therapies for some HCC, and the likely mechanism of action underlying its selective antitumor activity may be related to its regulation of Rb or Rb-associated signaling in cell cycles

Design of a Moisture Content Detection System for Yinghong No. 9 Tea Leaves Based on Machine Vision

The moisture content of Yinghong No. 9 tea leaves is an important indicator for their processing. The traditional method used to detect the moisture content of tea leaves is not suitable for large-scale production. To improve the efficiency of tea processing, a moisture content detection system for Yinghong No. 9 tea leaves based on machine vision was developed, and the relationship between the moisture content and the fresh tea leaves was researched. Firstly, nine color features and five texture features of the tea leaves images were extracted, and two different tea leaves databases were constructed based on linear discriminant analysis (LDA) and principal component analysis (PCA). Secondly, two models of moisture prediction for fresh tea leaves were built using a backpropagation (BP) neural network, which were then optimized by particle swarm optimization (PSO) and a genetic algorithm (GA), respectively. After, the two preprocessing methods and the two optimization algorithms were cross-combined to optimize the models for moisture content prediction. Finally, the models above were filtered using segmental analysis for the segmental moisture content prediction. It was verified by experiments that the coefficient of determination (R2) of the combined model of PCA-GA-BP and PCA-PSO-BP was 94.1073%, the RMSE was 1.1490%, and the MAE was 0.9982%. The results of this paper can help in the instantaneous detection of the moisture content of fresh tea leaves during processing, improving the production efficiency of Yinghong No. 9 tea