Search results

This study aims to investigate the impact of government support on the coupling coordination degree of innovation chain and capital chain in integrated circuit (IC) enterprises and to explore the mechanism for considering talent in the influence path.

This paper uses coupling coordination degree model to estimate the coupling of two chains, and applies dynamic panel system generalized method of moments (system-GMM) to analyze the impact of government support on coupling of two chains and conducts dynamic panel threshold regression to explore the threshold effect of talent in the influence of government support on coupling coordination degree.

Serious imbalance in the coupling of two chains is a major obstacle in IC enterprises. Government support significantly reduces the coupling coordination degree. The talent in IC enterprises has a significant threshold effect. When the number of talent is lower than the threshold value, government support has a negative impact. Once the number of talent reaches a certain level, government support can significantly enhance the coupling of two chains. Compared with state-owned enterprises, government support has a greater negative impact on the coupling of the two chains in non-state-owned enterprises. The former needs more talent to take advantage of government support.

This paper applies the concept of coupling into enterprises and deeply studies the coupling coordination degree of two chains. The influence mechanism of government support and talent on the coupling of two chains is explored, which reveals that government support cannot achieve the expected incentive effect without the support of talent. We also discuss the heterogeneous effect of government support and of talent in enterprises of different ownership types.

This study aims to investigate the relationship between government subsidies, R&D expenditures and overcapacity, and to explore the heterogeneity effects in different time periods and different types of companies. It can provide theoretical and practical guidance for the development of the photovoltaic industry.

This paper constructs a mediation model to explore the impact of government subsidies on overcapacity and on R&D expenditures, and to propose an indirect way to disentangle the impact of government subsidies on the creation of overcapacity from the positive aspect of increased R&D expenditures. A total of 94 listed enterprises in the Chinese photovoltaic industry were selected as the sample over the period 2012–2019.

There was significant overcapacity in the photovoltaic industry. Government subsidies had a positive effect in promoting overcapacity and R&D expenditures. The influence of government subsidies on excess capacity increased and on R&D expenditures decreased over time. Compared with large enterprises, government subsidies the small enterprises received had a greater positive impact on the overcapacity and a smaller positive impact on R&D expenditure. R&D expenditures restrained the influence of government subsidies on overcapacity, but the suppression effect was limited and decreased over time. The indirect effect in small enterprises was greater than that of large enterprises.

This paper studied government subsidies, R&D expenditure and overcapacity in the same framework and used bias-corrected bootstrapping to explore the path of “government subsidies–R&D expenditures–overcapacity”. The heterogeneous effects in different periods and different types of firms are discussed.

This study aims to prevent the sharp decline in the load-carrying capacity of lubricating oil film under harsh conditions of abrupt changes in friction interface temperature, which is a major challenge in lubrication technology.

In this paper, we synthesized a series of silver pyrazole methylpyridine complexes containing a high metal concentration and minimal supporting organic ligands (complex 1 [Ag(LMe)]₂(BF₄)₂, complex 2 [Ag(Li-Pr)n](BF4)n and complex 3 [Ag(LMe)(NO₃)]₂). The thermal decompose behavior of as-prepared silver complex was investigated by thermogravimetric analysis and X-ray photoelectron spectrometry (XPS). Four-ball friction testers were used to evaluate the friction and wear properties of lubricating oil in the temperature ranges associated with the operation of modern heavy machinery.

The complex decomposed silver particles at high-temperature, which could fill the pits on the friction surface, change the wear form of the friction pair and reduce the roughness of the friction surface. Reduction in both friction coefficients and wear scar diameters was obtained by adding silver complexes in oil. The lubricating oil, with the additive content of 1.5 Wt.%, has the best tribological performance, moreover, the lubricating performance of the silver complexes in oil were correlated with their concentration and thermal decomposed temperatures, respectively.

As a result, a series of silver pyrazole methylpyridine complexes as oil additives can support friction and wear reduction under abrupt high-temperature conditions are intended to be a controllable backup lubricant additive.

The purpose of this study is to investigate accurate and effective experimental methods for measuring the frictional loss of bearings (FLB) in mechanical systems and to measure the effect of various operating parameters on the frictional loss of high-speed mechanical systems.

Two novel methods were studied in this paper to measure the FLB: the free-deceleration method and the energy-balance method. A special high-speed motorised spindle and a friction loss test rig were designed and built to measure the effects of rotational speed, lubrication, preload and operating temperature on the FLB.

The experimental results showed that the frictional torque of bearings increases initially but then decreases with an increase in rotational speed. Similarly, the FLB decreases initially and then increases with an increase in temperature because of the influence of the viscosity–temperature relationship of the lubricant and the thermomechanical coupling factor. The optimal lubricant flow was determined, and the effectiveness of a novel preload online adjusting device was verified through experiments.

The research results of this paper provide the basis and methods for the measurement, reduction and prediction of the FLB in mechanical systems.

The primary objective of this study was to recognize critical indicators in predicting blood glucose (BG) through data-driven methods and to compare the prediction performance of four tree-based ensemble models, i.e. bagging with tree regressors (bagging-decision tree [Bagging-DT]), AdaBoost with tree regressors (Adaboost-DT), random forest (RF) and gradient boosting decision tree (GBDT).

This study proposed a majority voting feature selection method by combining lasso regression with the Akaike information criterion (AIC) (LR-AIC), lasso regression with the Bayesian information criterion (BIC) (LR-BIC) and RF to select indicators with excellent predictive performance from initial 38 indicators in 5,642 samples. The selected features were deployed to build the tree-based ensemble models. The 10-fold cross-validation (CV) method was used to evaluate the performance of each ensemble model.

The results of feature selection indicated that age, corpuscular hemoglobin concentration (CHC), red blood cell volume distribution width (RBCVDW), red blood cell volume and leucocyte count are five most important clinical/physical indicators in BG prediction. Furthermore, this study also found that the GBDT ensemble model combined with the proposed majority voting feature selection method is better than other three models with respect to prediction performance and stability.

This study proposed a novel BG prediction framework for better predictive analytics in health care.

This study incorporated medical background and machine learning technology to reduce diabetes morbidity and formulate precise medical schemes.

The majority voting feature selection method combined with the GBDT ensemble model provides an effective decision-making tool for predicting BG and detecting diabetes risk in advance.

The purpose of this paper is to present a novel method for minor fabric defects detection.

This paper proposes a PETM-CNN algorithm. PETM-CNN is designed based on self-similar estimation algorithm and Convolutional Neural Network. The PE (Patches Extractor) algorithm extracts patches that are possible to be defective patches to preprocess the fabric image. Then a TM-CNN (Triplet Metric CNN) method is designed to predict labels of the patches and the final label of the image. The TM-CNN can perform better than normal CNN.

This algorithm is superior to other algorithms on the data set of fabric images with minor defects. The proposed method achieves accurate classification of fabric images whether it has minor defects or not. The experimental results show that the approach is effective.

Traditional fabric defects detection is not effective as minor defects detection, so this paper develops a method of minor fabric images classification based on self-similar estimation and CNN. This paper offers the first investigation of minor fabric defects.

The online crowdsourcing has been widely applied in the practice. The purpose of this paper is to investigate the all-pay auction contest in crowdsourcing, wherein a seeker posts a task online and the solvers decide whether to participate in the contest and in what extent to spend efforts on their submissions.

The authors specifically consider two classic contest formats: simultaneous contest and sequential contest, depending on whether the solver can observe the prior solvers’ submissions before making her own effort investment decision or not. They derive both seeker’s and solver’s equilibrium decisions and payoffs under different contest formats, and show that they vary significantly according to the number and the average skill level of solvers.

The results show that a solver would always invest more on her submission under simultaneous contest than under sequential contest, as she cannot confirm how other solvers’ submissions would be. This subsequently intensifies the market competition and brings down a solver’s average payoff under simultaneous contest. Although the simultaneous contest gives rise to a higher expected highest quality of all submissions, it also requires the seeker to spend more search cost to identify the best submission. Therefore, when the number of solvers is high or the average skill level is low, the seeker prefers sequential contest to simultaneous contest. The results also show an analogous preference over two formats for the platform.

This paper investigates two formats of all-pay auction contest in crowdsourcing and evaluates them from the perspective of solvers, seekers and platforms, respectively. The research offers many interesting insights which do not only explain the incentive mechanisms for solvers under different contest formats, but also make meaningful contributions to the seeker’s or the platform’s adoption strategies between two alternative contest formats in crowdsourcing practice.

Session-based recommendation aims to predict the user's next preference based on the user's recent activities. Although most existing studies consider the global characteristics of items, they only learn the global characteristics of items based on a single connection relationship, which cannot fully capture the complex transformation relationship between items. We believe that multiple relationships between items in learning sessions can improve the performance of session recommendation tasks and the scalability of recommendation models. At the same time, high-quality global features of the item help to explore the potential common preferences of users.

This work proposes a session-based recommendation method with a multi-relation global context–enhanced network to capture this global transition relationship. Specifically, we construct a multi-relation global item graph based on a group of sessions, use a graded attention mechanism to learn different types of connection relations independently and obtain the global feature of the item according to the multi-relation weight.

We did related experiments on three benchmark datasets. The experimental results show that our proposed model is superior to the existing state-of-the-art methods, which verifies the effectiveness of our model.

First, we construct a multi-relation global item graph to learn the complex transition relations of the global context of the item and effectively mine the potential association of items between different sessions. Second, our model effectively improves the scalability of the model by obtaining high-quality item global features and enables some previously unconsidered items to make it onto the candidate list.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

Social coding platforms (SCPs) have been adopted by scores of developers in building, testing and managing their codes collaboratively. Accordingly, this type of platform (site) enables collaboration between enterprises and universities (c-EU) at a lower cost in the form of online team-building projects (repositories). This paper investigates the open collaboration patterns between these two parties on GitHub by measuring their online behaviours. The purpose of this investigation is to identify the most attractive collaboration features that enterprises can offer to increase university students' participation intentions.

The research process is divided into four steps. First, the authors crawled for numerical data for each interactive repository feature created by employees of Alibaba on GitHub and identified the student accounts associated with these repositories. Second, a categorisation schema of feature classification was proposed on a behavioural basis. Third, the authors clustered the aforementioned repositories based on feature data and recognised four types of repositories (popular, formal, normal and obsolete) to represent four open collaboration patterns. The effects of the four repository types on university students' collaboration behaviour were measured using a multiple linear regression model. An ANOVA test was implemented to examine the robustness of research results. Finally, the authors proposed some practical suggestions to enhance collaboration between both sides of SCPs.

Several counterintuitive but reasonable findings were revealed, for example, those based on the “star” repository feature. The actual coding contribution of the repositories had a negative correlation with student attention. This result indicates that students were inclined to imitate rather than innovate.

This research explores the open collaboration patterns between enterprises and universities on GitHub and their impact on student coding behaviour. According to the research analysis, both parties benefit from open collaboration on SCPs, and the allocation or customisation of online repository features may affect students' participation in coding. This research brings a new perspective to the measurement of users' collaboration behaviour with output rates on SCPs.