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The purpose of this paper is to forecast the reliable storage life of a certain kind of equipment under the normal stress level.

Through the stepping stress acceleration life test and the failure mechanism analysis, this paper aims to confirm the stress level for the stepping stress acceleration life test of a certain kind of equipment and establish the data processing mathematical model and storage life forecasting method.

The stress level for the stepping stress acceleration life test of a certain kind of equipment is confirmed and the data processing mathematical model and storage life forecasting method is established.

Availability of data is the main limitation affecting which model will be applied.

Useful advice for products' storage life forecasting.

The paper presents a new approach to product storage life estimation.

The purpose of this paper is to put forward a method which uses a computer to make a large number of simulated tests and determine the reference value of a system reliability index according to a unit reliability index.

At the beginning of conducting an index demonstration and development on a new system, its unit reliability index can already reflect the unit reliability level accurately. Based on this characteristic, this index is used as the confidence lower limit of single side estimated in the unit reliability interval to find the failure distribution law of the unit. On this basis, a great number of simulated tests are made by a computer to calculate the unit failure number and reliability point estimated value every test.

The confidence lower limit of single side estimated in the system reliability interval is found by counting up the simulated test data of these units.

Availability of data is the main limitation regarding which model will be applied.

A useful advice for system reliability analysis and calculation.

This paper presents a new approach of system reliability analysis.

Drawing on the motivated information processing in groups (MIP-G) model, the purpose of this paper is to explore the relationship between team reward interdependence and team performance, treating shared leadership as a mediator and team average job-based psychological ownership as a moderator.

Data were collected from a field sample of 72 knowledge-based work teams comprised of 466 team members and their team leaders. Data were analysed using hierarchical regression analysis and moderated path analysis.

Team reward interdependence was positively related to team performance through shared leadership. Team average job-based psychological ownership moderated both the relationship between team reward interdependence and shared leadership, and the indirect relationship between team reward interdependence and team performance.

The shared leadership literature is extended by exploring the antecedents of shared leadership from the perspective of team incentives and by examining the moderating role of team average job-based psychological ownership.

Organizations and managers should pay attention to team pay system design and be aware of the importance of employees’ psychological ownership toward their jobs in promoting shared leadership in teams.

This study sheds light on the antecedents of shared leadership from motivated information processing perspective and examines antecedent boundary conditions through the moderating role of team average job-based psychological ownership.

As the conventional multistep‐ahead prediction may be unsuitable in some cases, the purpose of this paper is to propose a novel method based on joint probability distributions, which provides the most probable estimation for the predicted trajectory.

Many real‐time series can be modeled in hidden Markov models. In order to predict these time series online, sequential Monte Carlo (SMC) method is applied for joint multistep‐ahead prediction.

The data of monthly national air passengers in China are analyzed, and the experimental results demonstrate that the method proposed and the corresponding online algorithms are effective.

In this paper, SMC method is applied for joint multistep‐ahead prediction. However, with the increasing of prediction step, the number of particles is increasing exponentially, which means that the prediction steps cannot be too large.

A very useful advice for researchers who study time‐series forecasts.

A novel method of multistep‐ahead prediction based on joint probability distribution is proposed and SMC method is applied to prediction time series online. This paper is aimed at those researchers who focus on time‐series forecasts.

The purpose of this study is to investigate the underlying characteristics that influence quality management implementation at manufacturing companies operating in China.

The data of this study were based on 199 manufacturing companies collected from a cross‐sectional survey in China. The cultural profiles of these companies were identified through cluster analysis. Multivariate analysis of variance was conducted to identify the effects of operating characteristics and cultural profile on the implementation level of quality management practices.

The results show that in general, there is no significant difference in implementing quality management practices among companies of different operating characteristics in terms of company size, industry, ownership, and production process. This study reveals that cultural profile is a distinguishing factor to explain the difference in quality management implementation among the companies.

As China is becoming an important supplier of products to the global market, it is necessary to understand how product quality is controlled and managed in China. This study examines the effects of operating and cultural characteristics of companies in China on their implementation of quality management practices. The results contribute to a deeper understanding of how to build an effective quality system at companies in China.

The consideration of the substitution phenomenon in the project portfolio selection problem can improve the robustness of project portfolio selection and help enterprises better achieve their strategic objectives. However, the existence of inter-project risk propagation will have a negative impact on project substitution. This paper proposes a new framework for project portfolio selection and constructs a risk propagation model based on strategic objectives to study the impact of risk propagation on substitution in the project portfolio.

The authors first construct a risk propagation model based on strategic objectives to describe the risk propagation between projects. Then the project substitution phenomenon based on risk propagation is put forward, and the calculation method of substitution loss is given. Finally, a robust project portfolio selection framework based on strategic objectives considering risk propagation is constructed.

The analysis of a case study demonstrates that (1) With the increase of risk intensity, the strategic loss of the same project portfolio increases linearly, and under the same risk intensity, the more projects in the portfolio, the stronger the robustness. (2) Considering risk propagation, the effect of project substitution is significantly weakened, and the strategic loss rate of the project portfolio is significantly increased compared with that of a direct attack.

This study is the first to take the project substitution into account in the project portfolio selection process. Moreover, the authors describe inter-project risk propagation and analyze the impact of risk propagation on the project substitution phenomenon. Finally, the authors extend the evaluation index of robustness. This paper puts forward a new way to solve the problem of project portfolio selection.

The purpose of this paper is to explore a multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm to solve the error increasing problem, caused by the Extended Kalman filtering (EKF) violating the local linear assumption in simultaneous localization and mapping (SLAM) for mobile robots because of strong nonlinearity.

A multi-innovation with forgetting factor-based EKF-SLAM (FMI-EKF-SLAM) algorithm is investigated. At each filtering step, the FMI-EKF-SLAM algorithm expands the single innovation at current step to an extended multi-innovation containing current and previous steps and introduces the forgetting factor to reduce the effect of old innovations.

The simulation results show that the explored FMI-EKF-SLAM method reduces the state estimation errors, obtains the ideal filtering effect and achieves higher accuracy in positioning and mapping.

The method proposed in this paper improves the positioning accuracy of SLAM and improves the EKF, so that the EKF has higher accuracy and wider application range.

The purpose of this paper is to improve the accuracy of short time load forecasting to ensure the economical and safe operation of power systems. The traditional neural network applied in time series like load forecasting, easily plunges into local optimum and has a complicated learning process, leading to relatively slow calculating speed. On the basis of existing literature, the authors carried out studies in an effort to optimize a new recurrent neural network by wavelet analysis to solve the previous problems.

The main technique the authors applied is referred to as echo state network (ESN). Detailed information has been acquired by the authors using wavelet analysis. After obtaining more information from original time series, different reservoirs can be built for each subsequence. The proposed method is tested by using hourly electricity load data from a southern city in China. In addition, some traditional methods are also applied for the same task, as contrast.

The experiment has led the authors to believe that the optimized model is encouraging and performs better. Compared with standard ESN, BP network and SVM, the experimental results indicate that WS‐ESN improves the prediction accuracy and has less computing consumption.

The paper develops a new method for short time load forecasting. Wavelet decomposition is employed to pre‐process the original load data. The approximate part associated with low frequencies and several detailed parts associated with high frequencies components give expression to different information from original data. According to this, suitable ESN is chosen for each sub‐sequence, respectively. Therefore, the model combining the advantages of both ESN and wavelet analysis improves the result for short time load forecasting, and can be applied to other time series problem.

The Library System of China and the Information System of China are the two systems of information organisations in China. This paper discusses the types of information agencies in each, focusing on structural characteristics, and governing regulations and especially on programmes and activities. Included in the discussion are such topics as publications, cooperative undertakings, new services and evaluation procedures.

The purpose of this paper is to introduce trench termination for high power buried‐gate static induction transistor (SIT) comprising three parts, which can inhibit the reverse leakage current substantially and paradisaical current. The simplified step‐etching process will also be discussed in detail.

For power buried‐gate SIT, the trench termination comprises three grooves, gate electrode etching, mesa‐groove etching and the separated groove, respectively. The simplified step‐etching process is proposed to optimize the traditional technical processing.

The tripartite trench termination of power SIT can inhibit the reverse leakage current, improve the gate‐source breakdown and increase the blocking voltage. The step‐etching process which is proposed for the first time, realizes the tripartite trench termination simultaneously which simplifies the traditional processes and is beneficial by protecting the surface of the die. The optimum etched depth of termination is also presented with experimentations.

The tripartite trench termination of power SIT is novel and the step‐etching process is also proposed for the first time.