Qishan Li and Dimitri B. Kececioglu
To design an optimal accelerated degradation test (ADT) plan for light emitting diodes (LEDs).
Abstract
Purpose
To design an optimal accelerated degradation test (ADT) plan for light emitting diodes (LEDs).
Design/methodology/approach
This paper presents a method for the optimum planning of ADTs. The method is applied to the design of an optimal plan for LEDs. An analytical method is developed for obtaining the optimal allocations of test units to minimize the variance of the transformed life estimation at the use stress level; a simulation method is used to help select the optimal test plan and evaluate the test plans' properties. Optimal stress levels, and optimal allocations of test units to the stress levels are determined to minimize the mean squared error (MSE) of the estimated mean life of LEDs at the use stress level.
Findings
Different test plans result in different accuracy. The optimal test plan provides the most efficient use of test resources.
Research limitations/implications
This paper focuses on designing an optimal plan using two test stress levels. Future research may extend to multiple stress levels.
Practical implications
With increasing emphasis on reliability in industry, products are made more robust, and few failures are observed in a reasonable test period. Therefore, assessing product reliability using ADTs becomes very useful. This paper fulfills the need to scientifically design plans for these tests to provide more accurate estimates of the designed‐in and manufactured reliability for the same amount of test resources.
Originality/value
The methodologies developed in this paper can be used for other ADTs. This enables reliability and test engineers to get the most efficient use of their test resources.
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China’s swift economic rise, as symbolized by the first Chinese Olympics and by surpassing Japan to become the world’s second largest economy despite the recent global financial…
Abstract
China’s swift economic rise, as symbolized by the first Chinese Olympics and by surpassing Japan to become the world’s second largest economy despite the recent global financial meltdown, has been accompanied by a transformation of Chinese foreign policy behavior. After spending the last decade emphasizing China’s “peaceful rise” or “peaceful development,” Beijing has begun to expound its policy preferences and territorial claims more forthrightly, even assertively. The purpose of this chapter will be to consider the origins, consequences, and likely future of the new Chinese foreign policy in the wake of the leadership transition at the 18th Party Congress in 2012 and the 12th National People’s Congress in 2013.
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Yong Guo and Songfeng Li
The purpose of this paper is to analyze several measures which the government of the People’s Republic of China (PRC) has adopted to curb corruption and to make recommendations to…
Abstract
Purpose
The purpose of this paper is to analyze several measures which the government of the People’s Republic of China (PRC) has adopted to curb corruption and to make recommendations to curb the spread of corruption.
Design/methodology/approach
The study is based on the analysis of government policy documents and reports, and statistical data on anti-corruption measures in China.
Findings
During the past ten years, the government of the PRC has adopted these anti-corruption measures: first, increasing the ability to handle cases for deterring corrupt officials; second, improving the work style of officials and prohibiting them from enjoying special privileges, and promoting moral behavior among them; third, reforming the economic and political system to reduce corruption opportunities; and fourth, reforming the Central Commission for Discipline Inspection (CCDI) to more effectively handle corruption cases. Nevertheless, in despite of these anti-corruption measures, there remain serious challenges for reducing corruption stemming from an irrational system of administrative reform and balancing the relationship between the CCDI and the judiciary departments to enhance the professionalism and efficiency of the anti-corruption agencies, which continue to constrain China’s current anti-corruption efforts. Therefore, the Chinese government should take a top-down approach, analyze the characteristics and trends of corruption in the new era, strengthen the institutional structures, and strive to suppress the spread of corruption.
Originality/value
This paper will be useful for those scholars, policy-makers and anti-corruption practitioners who are interested in China’s anti-corruption measures.
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Elite politics in China.
Zhensi Lin, Qishan Zhang and Hong Liu
The purpose of this paper is to enhance the forecast precision of GM(1,1) model using an improved artificial fish swarm algorithm.
Abstract
Purpose
The purpose of this paper is to enhance the forecast precision of GM(1,1) model using an improved artificial fish swarm algorithm.
Design/methodology/approach
An optimization model of GM(1,1) model about identifying the parameters is proposed, which takes the minimum of the average relative error as objective function and takes the development coefficient and grey action quantity as decision variables, then an improved artificial fish swarm algorithm is designed to solve the optimization model.
Findings
The results show that the proposed method may enhance the precision of GM(1,1) model, and have better performance than particle swarm optimization.
Practical implications
The method exposed in the paper can be used to optimize the parameters of GM(1,1) model, which is used frequently to solve the economic and management problem.
Originality/value
The paper succeeds in enhancing the forecast precision of GM(1,1) model using an improved artificial fish swarm algorithm.
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Yuling Hong, Yingjie Yang and Qishan Zhang
The purpose of this paper is to solve the problems existing in topic popularity prediction in online social networks and advance a fine-grained and long-term prediction model for…
Abstract
Purpose
The purpose of this paper is to solve the problems existing in topic popularity prediction in online social networks and advance a fine-grained and long-term prediction model for lack of sufficient data.
Design/methodology/approach
Based on GM(1,1) and neural networks, a co-training model for topic tendency prediction is proposed in this paper. The interpolation based on GM(1,1) is employed to generate fine-grained prediction values of topic popularity time series and two neural network models are considered to achieve convergence by transmitting training parameters via their loss functions.
Findings
The experiment results indicate that the integrated model can effectively predict dense sequence with higher performance than other algorithms, such as NN and RBF_LSSVM. Furthermore, the Markov chain state transition probability matrix model is used to improve the prediction results.
Practical implications
Fine-grained and long-term topic popularity prediction, further improvement could be made by predicting any interpolation in the time interval of popularity data points.
Originality/value
The paper succeeds in constructing a co-training model with GM(1,1) and neural networks. Markov chain state transition probability matrix is deployed for further improvement of popularity tendency prediction.
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The crowdfunding market has experienced rapid growth in recent years. However, not all projects are successfully financed because of information asymmetries between the founder…
Abstract
Purpose
The crowdfunding market has experienced rapid growth in recent years. However, not all projects are successfully financed because of information asymmetries between the founder and the providers of external finance. This shortfall in funding has made factors that lead to successful fundraising, a great interest to researchers. This study draws on the social capital theory, human capital theory and level of processing (LOP) theory to predict the success of crowdfunding projects.
Design/methodology/approach
A feature set is extracted and correlations between project success and features are utilized to order the features. The artificial neural network (ANN) is popularly applied to analyze the dependencies of the input variables to improve the accuracy of prediction. However, the problem of overfitting may exist in such neural networks. This study proposes a neural network method based on ensemble machine learning and dropout methods to generate several neural networks for preventing the problem of overfitting. Four machine learning techniques are applied and compared for prediction performance.
Findings
This study shows that the success of crowdfunding projects can be predicted by measuring and analyzing big data of social media activity, human capital of funders and online project presentation. The ensemble neural network method achieves highest accuracy. The investments rose from early projects and another platform by the funder serve as credible indicators for later investors.
Practical implications
The managerial implication of this study is that the project founders and investors can apply the proposed model to predict the success of crowdfunding projects. This study also identifies the most influential features that affect fundraising outcomes. The project funders can use these features to increase the successful opportunities of crowdfunding project.
Originality/value
This study contributes to apply a new machine learning modeling method to extract features from activity data of crowdfunding platforms and predict crowdfunding project success. In addition, it contributes to the research on the deployment of social capital, human capital and online presentation strategies in a crowdfunding context as well as offers practical implications for project funders and investors.
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Construction works, which contributed to the built environment of the primitive, slave and feudal societies in ancient China, constitute an important component of Chinese history…
Abstract
Construction works, which contributed to the built environment of the primitive, slave and feudal societies in ancient China, constitute an important component of Chinese history. This paper discusses the nest and cave dwellings as well as the tools used in the primitive society (before 2100 BC) of China. Construction works in the Slave Society (2100‐500 BC) encompassed the construction of city walls as well as wood and earth structures, covering roofs, wall and floor facing, and drainage facilities. The invention of new building materials and construction tools as well as standardization in working procedures and material consumption are discussed in “Feudal society” (221 BC‐AD 1840). The paper suggests that the more than 5,000 years of rich history of construction works in China should not be ignored.
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Jiangmei Chen, Wende Zhang and Qishan Zhang
The purpose of the paper is to improve the rating prediction accuracy in recommender systems (RSs) by metric learning (ML) method. The similarity metric of user and item is…
Abstract
Purpose
The purpose of the paper is to improve the rating prediction accuracy in recommender systems (RSs) by metric learning (ML) method. The similarity metric of user and item is calculated with gray relational analysis.
Design/methodology/approach
First, the potential features of users and items are captured by exploiting ML, such that the rating prediction can be performed. In metric space, the user and item positions can be learned by training their embedding vectors. Second, instead of the traditional distance measurements, the gray relational analysis is employed in the evaluation of the position similarity between user and item, because the latter can reduce the impact of data sparsity and further explore the rating data correlation. On the basis of the above improvements, a new rating prediction algorithm is proposed. Experiments are implemented to validate the effectiveness of the algorithm.
Findings
The novel algorithm is evaluated by the extensive experiments on two real-world datasets. Experimental results demonstrate that the proposed model achieves remarkable performance on the rating prediction task.
Practical implications
The rating prediction algorithm is adopted to predict the users' preference, and then, it provides personalized recommendations for users. In fact, this method can expand to the field of classification and provide potentials for this domain.
Originality/value
The algorithm can uncover the finer grained preference by ML. Furthermore, the similarity can be measured using gray relational analysis, which can mitigate the limitation of data sparsity.