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The purpose of this paper is to study the failures spread in complex power grids, and what topology of power grids is best for preventing or reducing blackouts.

Based on the study of cascading failure models of complex power networks, an extended dynamical cascading failure model is proposed. Based on this model, two representatives of the complex power grids, the small‐world network and the scale‐free network, were simulated for line cascading failure. The power loss caused by cascading failures and the spreading speed of cascading failure are discussed.

Power loss caused by cascading failures in the small‐world network is much larger than that in the scale‐free network, and the speed of cascading failure propagation in the small‐world network is much faster than that in the scale‐free network.

The establishment of the dynamical cascading failure model considering other protection devices needs further study.

The results of this study can be beneficial in system planning and upgrading.

An extended dynamical cascading failure model is proposed and cascading failures in different topology of power grid are discussed.

The purpose of this paper is to review recent contributions to the theoretical and empirical literature on informational cascades.

This paper reviews and synthesises the existing literature, methodologies and evidence on informational cascades.

Many financial settings foster situations where informational cascades and herding are likely. Cascades remain mainly an area of experimental research, leaving the empirical evidence inconclusive. Existing measures have limitations that do not allow for a direct test of cascading behaviour. More accurate models and methods for empirical testing of informational cascades could provide more conclusive evidence on the matter.

Outlined findings have implications for designing policies and regulatory requirements, as well as for the design of collective decisions processes.

The paper reviews and critiques existing theory; it summarises the recent laboratory and empirical evidence and identifies issues for future research. Most of other theoretical work reviews informational cascades as a subsection of herding. This paper focusses on informational cascades specifically. It distinguishes between informational cascade and herding. The paper also reviews most recent empirical evidence on cascades, presents review and synthesis of the theoretical and empirical development on information cascades up to date, and reviews the model of informational cascades with model criticism.

The construction industry is an industry with a high incidence of safety accidents, and the interactions of unsafe behaviors of construction workers are the main cause of accidents. The neglect of the interactions may lead to serious underestimation of safety risks. This research aims to analyze the cascading vulnerability of unsafe behaviors of construction workers from the perspective of network modeling.

An unsafe behavior network of construction workers and a cascading vulnerability analysis model were established based on 296 actual accident cases. The cascading vulnerability of each unsafe behavior was analyzed based on the degree attack strategy.

Complex network with 85 unsafe behavior nodes is established based on the collected accidents in total. The results showed that storing in improper location, does not wear a safety helmet, working with illness and working after drinking are unsafe behaviors with high cascading vulnerability. Coupling analysis revealed that differentiated management strategies of unsafe behaviors should be applied. Besides, more focus should be put on high cascading vulnerability behaviors.

This research proposed a method to construct the cascading failure model of unsafe behavior for individual construction workers. The key parameters of the cascading failure model of unsafe behaviors of construction workers were determined, which could provide a reference for the research of cascading failure of unsafe behaviors. Additionally, a dynamic vulnerability research framework based on complex network theory was proposed to analyze the cascading vulnerability of unsafe behaviors. The research synthesized the results of dynamic and static analysis and found the key control nodes to systematically control unsafe construction behaviors.

The purpose of this paper is to explore how hubs’ social influence on social network decisions can cause the behavior of information cascades in a market.

The authors establish understanding of the fundamental mechanism of information cascades through a computational simulation approach.

Eigenvector centrality, betweenness centrality, and PageRank are statistically correlated with the occurrence of information cascades among agents; the hubs’ incorrect decisions in the early diffusion stage can significantly cause misled shift cascades; and the bridge role of hubs is more influential than their pivotal position role in the process of misled shift cascades.

This implication can be extendable in the field of marketing, sequential voting, and technology, or innovation adoption.

The behavior of the entities in a small and medium-sized enterprise (SME) cooperation network is influenced by the core enterprise. Addressing the problem of how the network vulnerability changes when the core enterprise is attacked is a challenging topic. The purpose of this paper is to reveal the failure process of SME cooperation networks caused by the failure of the core SME from the perspective of cascading failure.

According to the Torch High Technology Industry Development Center, Ministry of Science & Technology in China, 296 SMEs in Jiangsu province were used to construct an SME cooperation network of technology-based SMEs and an under-loading cascading failure model. The weight-based attack strategy was selected to mimic a deliberate node attack and was used to analyze the vulnerability of the SME cooperation network.

Some important conclusions are obtained from the simulation analysis: (1) The minimum boundary of node enterprises has a negative relationship with networks' invulnerability, while the breakdown probability has an inverted-U relationship with networks' invulnerability. (2) The combined effect of minimum boundary and breakdown probability indicates that the vulnerability of networks is mainly determined by the breakdown probability; while, minimum boundary helps prevent cascading failure occur. Furthermore, according to the case study, adapting capital needs and resilience in the cooperation network is the core problem in improving the robustness of SME cooperation networks.

This research proposed an under-loading cascading failure model to investigate the under-loading failure process caused by the shortage of resources when the core enterprise fails or withdraws from the SME cooperation network. Two key parameters in the proposed model—minimum capacity and breakdown probability—could serve as a guide for research on the vulnerability of SME cooperation networks. Additionally, practical meanings for each parameter in the proposed model are given, to suggest novel insights regarding network protection to facilitate the robustness and vulnerability in real SME cooperation networks.

The purpose of this paper is to adopt a three-stage cloud-based management system for optimizing greenhouse gases (GHG) emission and marketing decisions with supplier selection and product family design in a multi-level supply chain with multiple suppliers, one single manufacturer and multiple retailers.

The manufacturer purchases optional components of a certain functionality from his alternative suppliers and customizes a set of platform products for retailers in different independent market segments. To tackle the studied problem, a hierarchical analytical target cascading (ATC) model is proposed, Jaya algorithm is applied and supplier selection and product family design are implemented in its encoding procedure.

A case study is used to verify the effectiveness of the ATC model in solving the optimization problem and the corresponding algorithm. It has shown that the ATC model can not only obtain close optimization results as a central optimization method but also maintain the autonomous decision rights of different supply chain members.

This paper first develops a three-stage cloud-based management system to optimize GHG emission, marketing decisions, supplier selection and product family design in a multi-level supply chain. Then, the ATC model is proposed to obtain the close optimization results as central optimization method and also maintain the autonomous decision rights of different supply chain members.

The purpose of this paper is to propose an optimal predictive model for the short-term forecast of real-time non-stationary machine variables by combining time series prediction with adaptive algorithms to minimize the error and to improve the prediction accuracy.

The proposed model is applied for prediction of speed and controller set point of three-phase induction motor operating on closed loop speed control with AC drive and PI controller. At Stage 1, the trend of the machine variables has been extracted and added to auto-regressive moving average (ARMA) time series prediction. ARMA prediction has been carried out using different combinations of AR and MA methods in order to make prediction with less Mean Squared Error (MSE).

The prediction error indicates the inadequacy of the model to estimate the data characteristics, which has been resolved at the subsequent stage by cascading an adaptive least mean square finite impulse response filter to the time series model. The adaptive filter receives the predicted output including training data and iteratively adjusts its coefficients for zero error convergence.

The componentized data prediction based on time series and cascade adaptive filter algorithm decomposes the non-stationary data characteristics for predictive maintenance. Evaluation of the model with different combination of time series algorithms and parameter settings of adaptive filter has been carried out to illustrate the performance of the prediction model. This prediction accuracy is compared with existing linear adaptive filter prediction using MSE as comparison index. The wide margin in the MSE values substantiates the prediction efficiency of the proposed model for machine data.

This model predicts the dynamic machine data with component decomposition at high accuracy, which enables to interpret the system response under dynamic conditions efficiently.

Costly physical experiments are normally used to evaluate the performance of thrust reverser concepts during the design process. This is because the design geometry is normally so complex that producing a computational fluid dynamics (CFD) model would take longer than running an experiment. Recently, unstructured grid CFD software packages have come onto the market that claim to greatly reduce the amount of time required to produce a grid. One of these packages. RAMPANT from Fluent, Inc., Lebanon, New Hampshire, was used to model an experimental thrust reverser design. A series of varying cascade blades were modelled in less than a day and the analysis results matched well with the experimental data.

Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.

The authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.

Local social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.

The paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.

The purpose of this study is to provide a new convolutional neural network (CNN) model with multi-scale feature extractor to segment and recognize wear particles in complex ferrograph images, especially fatigue and severe sliding wear particles, which are similar in morphology while different in wear mechanism.

A CNN model named DWear is proposed to semantically segment fatigue, severe sliding particles and four other types of particles, that is, chain, spherical, cutting and oxide particles, which unifies segmentation and recognition together. DWear is constructed using four modules, namely, encoder, densely connected atrous spatial pyramid pooling, decoder and fully connected conditional random field. Different from the architectures of ordinary semantic segmentation CNN models, a multi-scale feature extractor using cascade connections and a coprime atrous rate group is incorporated into the DWear model to obtain multi-scale receptive fields and better extract features of wear particles. Moreover, fully connected conditional random field module is adopted for post-processing to smooth coarse prediction and obtain finer results.

DWear is trained and verified on the ferrograph image data set, and experimental results show that the final Mean Pixel Accuracy is 95.6% and the Mean Intersection over Union is 92.2%, which means that the recognition and segmentation accuracy is higher than those of previous works.

DWear provides a promising approach for wear particle analysis and can be further developed in equipment condition monitoring applications.