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External stakeholder risks (ESRs) caused by unfavorable behaviors hinder the success of project portfolios (PPs). However, due to complex project dependency and numerous risk causality in PPs, assessing ESRs is difficult. This research aims to solve this problem by developing an ESR-PP two-layer fuzzy Bayesian network (FBN) model.

A two-layer FBN model for evaluating ESRs with risk causality and project dependency is proposed. The directed acyclic graph (DAG) of an ESR-PP network is first constructed, and the conditional probability tables (CPTs) of the two-layer network are further presented. Next, based on the fuzzy Bayesian network, key variables and the impact of ESRs are assessed and analyzed by using GeNIe2.3. Finally, a numerical example is used to demonstrate and verify the application of the proposed model.

The proposed model is a useable and effective approach for ESR assessment while considering risk causality and project dependency in PPs. The impact of ESRs on PP can be calculated to determine whether to control risk, and the most critical and heavily contributing risks and project(s) in the developed model are identified based on this.

This study extends prior research on PP risk in terms of stakeholders. ESRs that have received limited attention in the past are explored from an interaction perspective in the PP domain. A new two-layer FBN model considering risk causality and project dependency is proposed, which can synthesize different dependencies between projects.

Due to the cross-network effect, two-sided users communicate with each other, producing a coupling network. To study the spread of platform self-operation in two-sided users' marketing and purchasing tactics, this paper considers the differences in reputation acquired by platform-owned and third-party operating channels.

This study proposes a two-layer network with cross-network links: one layer represents the social network of consumers, while the other layer represents the competitive network of buyers. A closed system of differential equations, based on the binary dynamics of the stochastic network, is developed to study the trend and stability points of the platform self-operation dissemination. Then the overall benefits of platform are analyzed to unify the platform diffusion and pricing strategies.

The degree of difference in social influence and cross-network effects affect diffusion synergistically. Cross-network effects hinder diffusion when there is a significant difference of social influence between consumers and sellers but promote diffusion when there is little difference of social influence between consumers and sellers. Additionally, the network weights and reputation gap exhibit a nonlinear correlation with diffusion. For pricing strategy of the platform, it can achieve maximum profit when the pricing of self-operated goods and third-party-operated goods is equal.

This study considers the complex network architecture created by bilateral markets and the dynamic influence of group interactions on product. Additionally, this study takes reputation into account when considering the price and dissemination tactics of various operating channels, offering guidelines for platforms to control merchants and mediate disputes between various operating channels.

This study describes the structure and function of a new financial modeling technique, namely, the Artificial Neutral Network (ANN) in predicting financial markets’ behavior. With the advancement of the computer technology to date, ANN allows us to imitate human reasoning and thought processes in identifying the optimal trading strategies in the financial markets. The paper identifies the theory and steps involved in performing ANN and Generic Alogorithm in financial markets, the accuracy of the computer learning process, and the appropriate ways to use this process in developing trading strategies. It further discusses the superiority of ANN over traditional methodologies. The study concludes with the description of successful use of ANN by various financial institutions.

Artificial Neural Networks (ANNs) have many potential applications virtually in wide areas ranging from engineering to management. Recently, a great deal of interest (and effort) has been directed towards using ANNs in business practice. In particular, they have been used in areas which were once reserved for multivariate statistical analysis. Owing to this they are often considered to be statistical methods. Marketing researchers and managers who are not aware of the conceptual differences between these two methods cannot use this new “cutting‐edge” technology effectively. Discusses the conceptual differences and similarities between the two methods, having in mind market researchers and managers who are looking for new tools to support their decision making.

The rapid proliferation of mobile context aware applications has resulted in an increased research interest towards developing specialized context data management strategies for mobile entities. The purpose of this paper is to aim to develop a new way to model mobile entities and manage their contexts accordingly.

This paper proposes the concept of “Mobile Space” to model mobile entities and presents strategies to manage the various contexts associated therein. To handle availability related issues, two system services are designed: the “Availability Updating Service” which is an identifier based mechanism and is designed to keep track of mobile objects and handle availability related issues, and the “Application Callback Service” which is a publish/subscribe based mechanism to handle application disruptions and interruptions arising due to mobility.

The paper presents a detailed study of the proposed framework and a description of the underlying services and the components therein to validate the framework. Experimental results carried out in WiFi and 3G environments indicate that the proposed techniques can support mobile applications and minimize application disruptions with minimal overhead.

The proposed context management framework is generic in nature and is not designed for a specific class of applications. Any mobile context aware application can leverage on the framework and utilize the provided functionalities to manage application disruptions. Also, the decoupling of mobile application layer and the underlying context data management layer renders context data management layer transparent to the application design.

Non-financial corporate debt is one of the important sources of systematic risk in the real economy. Assessing a measure of systematic risk in corporation debt is currently a key challenge. In this regard, we propose a two-tier risk contagion networks model.

Assessing a measure of systematic risk in corporation debt is currently a key challenge. In this regard, we propose a two-tier risk contagion networks model based on four dimensions: concept definition, data structure, risk contagion network construction, and risk measurement indicators construction. We take the Jiangsu bond issuer guarantee network as a sample area.

Taking the Jiangsu bond issuer guarantee network as a sample area, we find that there is a strong correlation between the debts of non-financial corporation in China, and it is easy to become a potential regional systematic risk source. In addition, our empirical research also reveals that external risk exposure and node degree of network are two key indicators when identifying key risk-contagion enterprises.

The main contributions of this study are two-fold. First, this article proposes a two-tier risk contagion networks model to measure systematic risk in non-financial corporation. Second, this article describes the structure of the corporate risk contagion network.

This paper aims to presents a novel application of neural network (NN) pattern recognition to ore rock sorting using inductive electromagnetic (EM) sensors.

The impedance of a metallic rock can be measured with an inductive method based on Faraday’s law and eddy current theory. A virtual rock model is then created for the simulation of the EM measurements. An NN is trained to differentiate between waste and useful ore samples (containing high amount of minerals) based on the EM sensor signals produced by the rocks.

The NN solution showed high accuracy of rock classification and produced relatively robust results from signals with noise.

A pattern recognition NN was applied to classify low- and high-grade ore samples. It has the potential to determine the approximate amount of conductive materials inside ore rocks through multiple classes. This method can be used to improve the performance of EM-based ore sorting for mineral pre-concentration.

With the development of the modern economy, vehicles are no longer a luxury for people, which greatly facilitate people’s daily life, but at the same time bring traffic congestion. How to relieve traffic congestion and improve its capacity is a hot research area. This paper aims to propose a new simulation framework for crowd transportations to ease traffic congestion.

This paper establishes related simulation models such as vehicles, traffic lights and advisers. Then the paper describes their relationships, gives their interaction mechanism and solidifies the above into a software implementation framework.

This paper proposes a simulation framework for crowd transportations.

In this framework, traffic lights are used as a control method to control the road network and road conditions are used as an Affecter to influence individual behavior. The vehicle passing rate is defined by the correlation between endowment and the start time of the traffic lights. In this framework, members are related, dynamically adjusted according to road conditions and dynamically optimized member decisions. The optimal path is dynamic and real-time adjustments are made for each step forward. It is different from the traditional optimal path in which there is only one fixed one and it is different from the macroscopic optimal path that does not exist.

The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing.

The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN.

The effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models.

The combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/

The purpose of this paper is to present an approach to evaluating contributions in collaborative authoring environments, and in particular, Wikis using social network measures.

A social network model for Wikipedia has been constructed, and metrics of importance such as centrality have been defined. Data has been gathered from articles belonging to the same topic using a web crawler, in order to evaluate the outcome of the social network measures in the articles.

Finds that the question of the reliability regarding Wikipedia content is a challenging one and as Wikipedia grows, the problem becomes more demanding, especially for topics with controversial views such as politics or history.

It is believed that the approach presented here could be used to improve the authoritativeness of content found in Wikipedia and similar sources.

This work tries to develop a network approach to the evaluation of Wiki contributions, and approaches the problem of quality Wikipedia content from a social network point of view.