scholarly journals An Emergency Supplies Scheduling for Chemical Industry Park: Based on Super-Network Theory

2021 ◽  
Author(s):  
Yu Yuan ◽  
Fei Wang
Keyword(s):  
Chemical Industry ◽  
Network Theory ◽  
Projection Algorithm ◽  
Emergency Relief ◽  
Multiple Networks ◽  
Proposed Model ◽  
Local Relief ◽  
Relationship Network ◽  
Emergency Process ◽  
Optimal Type

Abstract In a concentrated area of Chemical Industry Parks (CIPs), emergency relief efficiency is not only affected by the rescue capability of themselves, but also their relationships with other CIPs. Academic literature suggests the use of multiple networks such as transport network and information network, in the emergency process after unexpected events, but rarely integrates them ideally in practice. This paper utilizes the super network theory to propose a regional emergency scheduling model to bridge the logistic and relation among CIPs. The proposed super-network model is composed of resources flow network and relationship network that fill a gap of only considering emergency logistic supply chain. Therefore, the main problem is to how coordinate all the disaster relief actors including primary relief centers (PRC) local relief centers (LRC) and CIPs. The proposed model provides an optional answer regarding the optimal configuration (one-stage or two-stage), the optimal type, number and transportation direction of resources. We turn the optimization problem into a variational inequality problem and develop a modified projection algorithm to solve the problem and compare the performance under several disaster scenarios. The practicability of the model is proved by the result of the numerical example given.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

scholarly journals Work Motivation in Temporary Organizations: Establishing Theoretical Corpus

2018 ◽  
Vol 5 (3) ◽  
pp. 29
Author(s):  
Ravikiran Dwivedula ◽  
Christophe Bredillet ◽  
Ralf Müller
Keyword(s):  
Network Theory ◽  
Work Motivation ◽  
Actor Network Theory ◽  
Systematic Investigation ◽  
Job Design ◽  
Project Work ◽  
Moderation Effect ◽  
Temporary Organizations ◽  
Proposed Model ◽  
Design Theories

The purpose of this article is to organize this literature, which will facilitate a systematic investigation of work motivation in temporary organizations. First, we highlight the limitations of current theoretical lenses of work motivation specific to temporary organizations. Second, we synthesize three major theories- Event-Systems (E-S) theory, Socio-Technical Systems (STS) Perspective/Job Design, and Actor-Network Theory (ANT) to establish the theoretical corpus for our proposed model of work motivation. Our model conceptualizes project work characteristics as an ‘Event’ capable of producing an ‘event outcome’ which is work motivation. This is explained using E-S and STS/ Job Design theories. Propositions are introduced. The moderation effect is explained using ANT. Third, we present the academic contribution of our proposed model.

scholarly journals A Multiobjective Allocation Model for Emergency Resources That Balance Efficiency and Fairness

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yanyan Wang ◽  
Baiqing Sun
Keyword(s):  
Resource Allocation ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Allocation Model ◽  
Swarm Optimization ◽  
Emergency Relief ◽  
Resource Allocation Model ◽  
Proposed Model ◽  
Emergency Resource

Efficiency and fairness are two important goals of disaster rescue. However, the existing models usually unilaterally consider the efficiency or fairness of resource allocation. Based on this, a multiobjective emergency resource allocation model that can balance efficiency and fairness is proposed. The object of the proposed model is to minimize the total allocating costs of resources and the total losses caused by insufficient resources. Then the particle swarm optimization is applied to solve the model. Finally, a computational example is conducted based on the emergency relief resource allocation after Ya’an earthquake in China to verify the applicability of the proposed model.

Innovativeness in the context of technological and market dynamism

2017 ◽  
Vol 30 (4) ◽  
pp. 548-568 ◽  
Author(s):  
Job Rodrigo-Alarcón ◽  
Pedro Manuel García-Villaverde ◽  
Gloria Parra-Requena ◽  
María José Ruiz-Ortega
Keyword(s):  
Network Theory ◽  
Interactive Effect ◽  
Interactive Effects ◽  
Network Density ◽  
Critical Aspect ◽  
Content Type ◽  
Proposed Model ◽  
The Relationship ◽  
Positive Effect

Purpose Innovativeness is a critical aspect for the survival and success of the company in the long term. The purpose of this paper is to study how the density of the network in which the company is immersed influences the relationship between environment, dynamism and innovativeness. More specifically, the authors analyse whether the network density acts in a heterogeneous way, worsening or improving the effects of technological and market dynamism on innovativeness, respectively. Design/methodology/approach The empirical study was conducted on a sample of 292 companies in the agri-food industry in Spain. In order to test the proposed model, the authors used partial least squares. Findings The results show that technological dynamism has a positive effect on the generation and development of a firm’s innovativeness. However, market dynamism does not influence innovativeness. The authors also observe that the interactive effects between network density and dynamism are significant, but in a divergent way. Whereas the interactive effect between density and technological dynamism is negative, the interaction between density and market dynamism is positive. Originality/value The main contribution of the study is to show how the level of network density alters the effect of technological and market dynamism on innovativeness. The authors highlight the relevance of network theory to explain the contextual background to innovativeness. The authors also stress the importance of differentiating between the market and technological components of dynamism to further elucidate their effects.

scholarly journals From the Difference of Structures to the Structure of the Difference

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Massimiliano Zanin ◽  
Ernestina Menasalvas ◽  
Xiaoqian Sun ◽  
Sebastian Wandelt
Keyword(s):  
Complex System ◽  
Network Theory ◽  
General Framework ◽  
Random Noise ◽  
Time Varying ◽  
Evolving Systems ◽  
Multiple Networks ◽  
Stochastic Fluctuations ◽  
Multidimensional Complex ◽  
The Difference

When dealing with evolving or multidimensional complex systems, network theory provides us with elegant ways of describing their constituting components, through, respectively, time-varying and multilayer complex networks. Nevertheless, the analysis of how these components are related is still an open problem. We here propose a general framework for analysing the evolution of a (complex) system, by describing the structure created by the difference between multiple networks by means of the Information Content metric. Differently from other approaches, which focus on assessing the magnitude of the change, the proposed one allows understanding if the observed changes are due to random noise or to structural (targeted) modifications; in other words, it allows describing the nature of the force driving the changes and discriminating between stochastic fluctuations and intentional modifications. We validate the framework by means of sets of synthetic networks, as well as networks representing real technological, social, and biological evolving systems. We further propose a way of reconstructing network correlograms, which allow converting the system’s evolution to the frequency domain.

scholarly journals An Eigenvector Centrality for Multiplex Networks with Data

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 763 ◽  
Author(s):  
Francisco Pedroche ◽  
Leandro Tortosa ◽  
José F. Vicent
Keyword(s):  
Complex Systems ◽  
Network Theory ◽  
Essential Feature ◽  
Eigenvector Centrality ◽  
Centrality Measure ◽  
Multiplex Networks ◽  
Primitive Matrix ◽  
The Core ◽  
Proposed Model ◽  
Node Classification

Networks are useful to describe the structure of many complex systems. Often, understanding these systems implies the analysis of multiple interconnected networks simultaneously, since the system may be modelled by more than one type of interaction. Multiplex networks are structures capable of describing networks in which the same nodes have different links. Characterizing the centrality of nodes in multiplex networks is a fundamental task in network theory. In this paper, we design and discuss a centrality measure for multiplex networks with data, extending the concept of eigenvector centrality. The essential feature that distinguishes this measure is that it calculates the centrality in multiplex networks where the layers show different relationships between nodes and where each layer has a dataset associated with the nodes. The proposed model is based on an eigenvector centrality for networks with data, which is adapted according to the idea behind the two-layer approach PageRank. The core of the centrality proposed is the construction of an irreducible, non-negative and primitive matrix, whose dominant eigenpair provides a node classification. Several examples show the characteristics and possibilities of the new centrality illustrating some applications.

scholarly journals Development of a simulation model of a cargo customs complex operation as a link of a logistic supply chain

2021 ◽  
Vol 5 (3 (113)) ◽  
pp. 19-29
Author(s):  
Alexander Mazurenko ◽  
Andrii Kudriashov ◽  
Iryna Lebid ◽  
Nataliia Luzhanska ◽  
Irina Kravchenya ◽  
...  
Keyword(s):  
Supply Chain ◽  
Simulation Model ◽  
Simulation Modeling ◽  
Random Number ◽  
Random Number Generators ◽  
Maximum Value ◽  
Proposed Model ◽  
Wide Range ◽  
Simulation Error ◽  
Optimal Type

The main link in the logistics supply chain is the cargo customs complex. It provides customs and logistics services to cargo owners during the export and import of goods, complex services, placement of goods in a customs warehouse and a temporary storage warehouse. To substantiate the choice of the optimal logistics supply chain and optimize the work of the cargo customs complex, it is proposed to use simulation modeling. The model of operation of the logistics chain and the cargo customs complex is presented in a general form. The proposed model is implemented in the GPSS World simulation automation package. Testing the simulation model involved checking its adequacy. Checking the adequacy of the simulation model, which showed the maximum value of the t-statistic of 1.424 with a critical value of 1.85, proved its compliance with the work of a real object. After completing the adequacy check, the simulation error was estimated, which was 3 % with an allowable 5 %, due to the presence of pseudo-random number generators in the simulation model. Thus, the simulation error is insignificant for this study. For the cargo customs complex, an example of the simulation results is given. Based on the results of simulation modeling, it is possible to determine: the optimal type of the logistics supply chain and the optimal structure of the cargo customs complex. A wide range of tasks that the proposed simulation model can solve is presented. Thus, the developed simulation model will make it possible to analyze and improve the modes of operation of the cargo customs complex. In addition, it will allow to get an informed decision regarding the use of a certain type of logistics supply chain

scholarly journals Effective inter-organisational networks for Responsible Research and Innovation and global sustainability: a scoping review

2021 ◽  
Vol 1 ◽  
pp. 144
Author(s):  
Danielle Martine Farrugia ◽  
Silvia Leonor Vilches ◽  
Alexander Gerber
Keyword(s):  
Scoping Review ◽  
Network Theory ◽  
Final Analysis ◽  
Responsible Research And Innovation ◽  
Global Issues ◽  
Research And Innovation ◽  
Development Goals ◽  
Relationship Network ◽  
And Function ◽  
Responsible Research

Background: Achieving the United Nations Sustainable Development Goals (SDGs) is beyond the capacity of any single organisation. The principles of engaging stakeholders suggest that an engaged, multi-sectoral approach, such as described in models of Responsible Research and Innovation (RRI), hold promise to mobilise humanity to solve complex and urgent global issues. Methods: This scoping review explores the characteristics of effective and sustainable inter-organisational networks for fostering RRI in service of the SDGs. The review focuses on strategies to initiate and maintain international communities of practice relevant to the implementation of RRI and/or SDGs. The search began with themes derived from prior network theory, focusing on: (a) the type and function of networks; (b) the aims and vision; and (c) the relationships between networks and network members. In total, 55 articles on inter-organisational network theory were included for the final analysis. Results:  Results are reported under themes of: (1) Effectiveness, Sustainability, and Success; (2) Governance and Management; and (3) Network Relationship. Network structures, forms of management and funding are linked to sustainable networks. Potential threats include power imbalances within networks, and internal and external factors that may affect relationships at network and community levels. Few studies examine diversity or cultural viewpoints. Studies highlight the benefits of networks such as enhancing knowledge sharing among researchers, practitioners, and other stakeholders. Conclusions: The effectiveness of the managerial structure may be observed as outputs of the intention and values of an inter-organisational network. Our review demonstrates that a global inter-organisational network approach is achievable. Such a network would have many benefits, including allowing organisations to be responsive and flexible towards change and innovation.

scholarly journals Web Service Applications and Consumer Environments Based on ICT-Driven Optimization

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Chaozhi Fan ◽  
Law Siong Hook ◽  
Saifuzzaman Ibrahim ◽  
Mohd Naseem Ahmad
Keyword(s):  
Web Service ◽  
Resource Sharing ◽  
Network Embedding ◽  
Service Recommendation ◽  
Multiple Networks ◽  
Relational Networks ◽  
Data Links ◽  
Relationship Network ◽  
Service Application ◽  
Embedding Methods

Networking is the use of physical links to connect individual isolated workstations or hosts together to form data links for the purpose of resource sharing and communication. In the field of web service application and consumer environment optimization, it has been shown that the introduction of network embedding methods can effectively alleviate the problems such as data sparsity in the recommendation process. However, existing network embedding methods mostly target a specific structure of network and do not collaborate with multiple relational networks from the root. Therefore, this paper proposes a service recommendation model based on the hybrid embedding of multiple networks and designs a multinetwork hybrid embedding recommendation algorithm. First, the user social relationship network and the user service heterogeneous information network are constructed; then, the embedding vectors of users and services in the same vector space are obtained through multinetwork hybrid embedding learning; finally, the representation vectors of users and services are applied to recommend services to target users. To verify the effectiveness of this paper’s method, a comparative analysis is conducted with a variety of representative service recommendation methods on three publicly available datasets, and the experimental results demonstrate that this paper’s multinetwork hybrid embedding method can effectively collaborate with multirelationship networks to improve service recommendation quality, in terms of recommendation efficiency and accuracy.

scholarly journals Anchor Link Prediction across Attributed Networks via Network Embedding

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 254 ◽  
Author(s):  
Shaokai Wang ◽  
Xutao Li ◽  
Yunming Ye ◽  
Shanshan Feng ◽  
Raymond Lau ◽  
...  
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
State Of The Art ◽  
User Profiling ◽  
Data Sets ◽  
Network Embedding ◽  
Real World Data ◽  
Intrinsic Structure ◽  
Multiple Networks ◽  
Proposed Model

Presently, many users are involved in multiple social networks. Identifying the same user in different networks, also known as anchor link prediction, becomes an important problem, which can serve numerous applications, e.g., cross-network recommendation, user profiling, etc. Previous studies mainly use hand-crafted structure features, which, if not carefully designed, may fail to reflect the intrinsic structure regularities. Moreover, most of the methods neglect the attribute information of social networks. In this paper, we propose a novel semi-supervised network-embedding model to address the problem. In the model, each node of the multiple networks is represented by a vector for anchor link prediction, which is learnt with awareness of observed anchor links as semi-supervised information, and topology structure and attributes as input. Experimental results on the real-world data sets demonstrate the superiority of the proposed model compared to state-of-the-art techniques.

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