Trading Network Risk [National Grid]

2015 ◽  
Author(s):  
U. Bryan ◽  
D. Miller ◽  
H. Bain
Keyword(s):  
Trading Network ◽  
Network Risk

Аdvertising in the functioning of the all-ukrainian trading office «TORGSIN» in 1931–1936

2018 ◽  
pp. 144-149
Author(s):  
І. V. Shuisky
Keyword(s):  
Trading Network

The development of advertising affairs of the All-Ukrainian office «Torgsin» was analyzed. The main methods and forms of work of the trading network in the field of advertising are highlighted.

Dynamic Network Risk

2020 ◽  
Author(s):  
Michael Ellington ◽  
Jozef Barunik
Keyword(s):  
Dynamic Network ◽  
Network Risk

Historical Perspectives on Innovation in Indian Business

2018 ◽  
Author(s):  
Claude Markovits
Keyword(s):  
Case Studies ◽  
Technological Innovation ◽  
Historical Perspective ◽  
Specific Context ◽  
Colonial Era ◽  
Industrial Firm ◽  
Historical Perspectives ◽  
Entire World ◽  
Definition Of ◽  
Trading Network

This chapter deals with the question of innovation in Indian business from a historical perspective. After a brief survey of the literature, emphasizing how divided scholarly opinion was regarding the existence of forms of innovation in Indian business prior to the colonial era, the focus shifts to the British period. It is shown that Schumpeter’s definition of innovation equating it with technological innovation cannot be fruitfully applied to the Indian business scene. Two case studies are then proposed: Tata Iron & Steel, the largest Indian industrial firm, is shown to have been innovative in the specific context of India’s backward industrial scene, while the Sindwork merchants of Hyderabad are an instance of an Indian trading network which extended its range to the entire world. Concluding remarks interrogate post-Independence developments and stress the limits of the innovativeness of Indian business, prior to the recent liberal reforms.

Immune-Based Systems on Network Security Situation Awareness and Risk Prediction Model

2014 ◽  
Vol 643 ◽  
pp. 99-104
Author(s):  
Jin Yang ◽  
Yun Jie Li ◽  
Qin Li
Keyword(s):  
Situation Awareness ◽  
Artificial Immune Systems ◽  
Epidemic Spreading ◽  
State Spaces ◽  
Real Time Processing ◽  
Network Intrusion ◽  
Markov Chain Theory ◽  
Proposed Model ◽  
Network Risk

In this paper, the process of the developments and changes of the network intrusion behaviors were analyzed. An improved epidemic spreading model was proposed to study the mechanisms of aggressive behaviors spreading, to predict the future course of an outbreak and to evaluate strategies to control a network epidemic. Based on Artificial Immune Systems, the concepts and formal definitions of immune cells were given. And in this paper, the forecasting algorithm based on Markov chain theory was proposed to improve the precision of network risk forecasting. The data of the Memory cells were analyzed directly and kinds of state-spaces were formed, which can be used to predict the risk of network situation by analyzing the cells status and the classification of optimal state. Experimental results show that the proposed model has the features of real-time processing for network situation awareness.

scholarly journals A Privacy Preserving Approach to Collaborative Systemic Risk Identification: The Use-Case of Supply Chain Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-18
Author(s):  
Tirazheh Zare-Garizy ◽  
Gilbert Fridgen ◽  
Lars Wederhake
Keyword(s):  
Supply Chain ◽  
Structural Information ◽  
Risk Identification ◽  
Use Case ◽  
Supply Chain Network ◽  
Strategic Positioning ◽  
Supply Chain Networks ◽  
Structural Risk ◽  
Main Factors ◽  
Network Risk

Globalization and outsourcing are two main factors which are leading to higher complexity of supply chain networks. Due to the strategic importance of having a sustainable network, it is necessary to have an enhanced supply chain network risk management. In a supply chain network many firms depend directly or indirectly on a specific supplier. In this regard, unknown risks of network’s structure can endanger the whole supply chain network’s robustness. In spite of the importance of risk identification of supply chain network, firms are not willing to exchange the structural information of their network. Firms are concerned about risking their strategic positioning or established connections in the network. The paper proposes to combine secure multiparty computation cryptography methods with risk identification algorithms from social network analysis to address this challenge. The combination enables structural risk identification of supply chain networks without endangering firms’ competitive advantage.

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