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For many optimization problems such as optimal techniques, compositions, producing process, the optimizing objectives in systems are complex relations with respect to a great deal of parameters. Generally, the objective function is hardly obtained, even the searching objective is unquantifiable. So it is difficult to model and optimize the complex systems to some extent.

To the above purpose, a novel approach is presented in this paper. It firstly utilizes the excellent fitting performance of neural network (NN) combined with expert knowledge (EK) to obtain the objective function, and secondly searches the optimal influential parameters with genetic algorithm (GA).

Peaks function inside Matlab and the acural application of comprehensive performance optimization in analog PID control system are studied, respectively. The results of simulation and the actual experiment both show that the modeling and optimizing method presented in the paper are effective.

Expert knowledge is needed for the fuzzy/unquantifiable objective.

The paper presents a useful way to model and optimize complex systems.

The combined approach based on NN, EK and GA is firstly presented and is effectively used in modeling and optimizing complex systems.

The purpose of this paper is to describe a general purpose localization system, GRAIL. GRAIL provides real‐time, adaptable, indoor localization for wireless devices.

In order to localize as diverse a set of devices as possible, GRAIL utilizes a centralized, anchor‐based approach. GRAIL defines an abstract data model for various system components to support different physical modalities. The scalable architecture of GRAIL provides maximum flexibility to integrate various localization algorithms.

The authors show through real deployments that GRAIL functions over a variety of physical modalities, networks, and algorithms. Further, the authors found that a centralized solution has critical advantages over distributed implementations for handling privacy concerns.

A key contribution of this system is its universal approach: it can integrate different hardware and software capabilities within a single localization framework. The deployment of such a system in academic and research environments allows researchers to explore issues beyond algorithms and investigate effects in real deployments.

The purpose of this paper is to analyze the effect and mechanism of platform incentives on users’ knowledge collaboration performance (KCP) and the configuration leading to high KCP in online knowledge communities (OKCs) in the post-COVID-19 pandemic era from a cross-culture perspective.

A survey method and a standard questionnaire were applied. The data was analyzed using multiple regression and fuzzy set qualitative comparative analysis.

The results indicate that, for both kinds of users, self-enhancement and communication positively affect the KCP. User engagement significantly mediates the relationship between communication and KCP and knowledge absorptive capacity moderates the relationship between user engagement and KCP. In contrast, material incentive positively affects the KCP of Chinese users, while hurting the cross-cultural sample. And the promotion of KCP for cross-cultural samples does not require a higher engagement and knowledge absorptive capacity, while paying more attention to short-term interests, such as communication and self-enhancement.

The study only divides users into Chinese and cross-cultural foreign users, without a distinction between foreign users in different countries. In addition, the research is based on cross-sectional data and failed to try to explore the long-term effects of these incentives from the time dimension.

This study explores the incentive mechanism and configuration of OKC platforms to achieve high KCP for different users from a cross-cultural perspective. It provides new ideas and solutions for precise incentives for users of OKC platforms.

The provision of high-quality e-Government services requires efficient and collaborative sharing of data across varied types of government agencies. However, interagency government data sharing (IDS) is not always spontaneous, active and unconditional. Adopting a stickiness theory, this paper reports on a research study, which explores the causes of data stickiness in IDS.

This study employed an inductive case study approach. Twenty-three officials from the government of City M in Hubei Province, Central China, were approached and interviewed using a semi-structured question script.

The analysis of the interview data pointed to 27 causes of data stickiness in five main themes: data sharing willingness; data sharing ability; data articulatability; data residence; and data absorptive capacity. The analysis revealed that interagency tensions and lack of preparedness of individual agencies are the main causes of data stickiness in IDS.

The case setting is based on China's Government, but the findings offer useful insights and indications that can be shared across international borders.

Green technology, characterized by its environmentally friendly attributes and sustainable practices, has emerged as a crucial tool in harmonizing the economic and ecological benefits. However, the challenge lies in selecting the most effective strategies for acquiring green technology. This paper aims to explore how chemical enterprises choose green technology acquisition strategies across diverse scenarios.

Considering the influence of competition effects, spillover effects and their interactions on selecting green technology acquisition strategies, this paper develops three decision models (independent R&D, cooperative R&D and technology introduction). Drawing on the duopoly game theory as its theoretical framework, this paper delves into the examination of the economic and environmental benefits within distinct scenarios.

Cooperative R&D excels in promoting green technology R&D when spillover effects are strong, while independent R&D demonstrates superiority when spillover effects are weak. The threshold for the strength of spillover effects is related to competition effects. Additionally, cooperative R&D typically yields greater financial advantages than independent R&D and technology introduction. Moreover, the economic and environmental benefits may not be optimized simultaneously. Only enterprises that satisfy low competition and spillover effects as well as high competition and spillover effects, can achieve win-win economic and environmental benefits.

Although green technology R&D and introduction are alternative strategies, they have typically been considered separately in prior literature. This study attempts to incorporate green technology R&D and introduction into a strategic system to investigate the selection of green technology acquisition strategies, taking into account competition effects, spillover effects and their interactions.

Using unique trade credit insurance data from China, we examine whether trade insurance claims are associated with audit efforts and audit quality.

The paper is based on a sample of Chinese firms to study insurance claims of trade credit insurance that affects abnormal audit fees.

In this study, we find that firms with high insurance claims pay higher abnormal audit fees. Further, our findings indicate that firms with high insurance claims have a short audit report lag and tend to select local audit firms.

To the best of our knowledge, this is the first study to investigate the association between trade credit insurance claims and audit efforts. In addition, we contribute to the literature on the agency cost of abnormal audit fees.

The purpose of this paper is to re-examine the effectiveness of price limits on stock volatilities in China over a more recent time period spanning from 2007 to 2012. The motivation stems from the fact that very high stock market volatilities are observed in China and we are sceptical of the volatility mitigating effect claimed by advocates of price limits.

The effectiveness of price limits on volatilities is examined using an event study methodology and within an expanded framework of volatility-volume relationships. The sample stocks include the 300 component stocks of the CSI300 Index.

Both event study and regression analysis suggest that price limits exaggerate market volatilities by causing volatility spillovers. The destabilising effect is much more pronounced for small firm stocks and when the market falls. In addition to the informational source of volatilities (represented by volume), price limits create another non-trivial frictional source of volatilities in China’s stock market.

This research is the first to re-examine the price limit effect in China’s stock market in an expanded framework of volatility-volume relationships. It identifies price limits, in addition to information, as another non-trivial frictional source of volatilities. The findings derived from a recent sample period confirm the conventional view of inefficiency of price limits raised by Fama (1989) and provide evidence in support of the pervasive trend of stock market deregulations.

Grounding the research in the stimulus-organism-resource (S-O-R) framework, this study aims to address the research gap of explaining and predicting the relationship between price discounts, interactivity and professionalism on college students’ purchasing intention in live-streaming shopping. It also attempts to understand if trust plays the role of mediator in the effect of these relationships.

This study collected data using a questionnaire protocol adapted and refined from the original scales in existing studies. The partial least squares structural equation modeling was used to analyze data collected from 258 college students in China. Other than assessing the path model’s explanatory power, this study examined the model’s predictive power toward predicting new cases using PLS predict.

Results indicated that all three predictors have a positive significant relationship with trust, while only price discounts demonstrate a significant relationship with purchase intention. Simultaneously, the mediation results provide support to the S-O-R framework demonstrating that external factors (professionalism, interactivity and price discounts) can arouse organism (trust), which in return, generate a behavioral outcome (purchase intention).

This study is the first few studies that focus on college students’ behavioral responses in an online shopping environment. At the same time, this is the first study supplement the explanatory perspective with a predictive focus, which is of particular importance in making sound recommendations on managerial decision-making.

This study tested a holistic model that investigated the interaction effect of negative mentoring experiences and moqi (pronounced “mò-chee”) with a mentor—where moqi refers to a situated state between two parties in which one party understands and cooperates well with the other party without saying a word—on the protégés’ turnover intention, along with the mediating role of protégés’ harmonious work passion.

Data were collected from 281 protégés through a three-wave questionnaire survey with a 1-month lag between waves. We used a hierarchical multiple regression and bootstrapping analysis to test our hypotheses.

Our results support the mediating effect of harmonious work passion on the positive relationship between protégés’ negative mentoring experiences and turnover intention. In addition, our analysis confirmed that moqi with the mentor amplifies both the impact of protégés’ negative mentoring experiences on harmonious work passion and the indirect effect of negative mentoring experiences on protégés’ turnover intention via harmonious work passion.

By demonstrating the interaction effect of protégés’ negative mentoring experiences and moqi with their mentor on turnover intention, as well as the mediating role of harmonious work passion, this study expands our understanding of the mechanism and boundary condition of the effect of negative mentoring experiences and provides inspiration and guidance for mentoring practices.

This study aims to investigate the effect of perceived insider status to employees' innovative behavior, the mediating role of knowledge sharing and the moderating role of organizational innovation climate in the relationship between knowledge sharing and employees' innovative behavior.

This study adopted questionnaires to gather data. The sample of 341 employees working in diverse organizations in China was applied to examine the hypotheses.

The results indicate that perceived insider status is positively related to employees' innovative behavior and knowledge sharing mediates the relationship between perceived insider status and employees' innovative behavior. In addition, organizational innovation climate enhances the relationship of knowledge sharing and employees' innovative behavior.

This study builds a system from psychological aspect to behavior, which includes the mechanism of the influence of perceived insider status on employees' innovative behavior and a cross-level analysis of the influence of organizational innovation climate on employees' innovative behavior, breaking through the previous research paradigm of a single level of climate and employee behavior.