This book provides a technical approach to a Business Resilience System with its Risk Atom and Processing Data Point based on fuzzy logic and cloud computation in real time. Its purpose and objectives define a clear set of expectations for Organizations and Enterprises so their network system and supply chain are totally resilient and protected against cyber-attacks, manmade threats, and natural disasters. These enterprises include financial, organizational, homeland security, and supply chain operations with multi-point manufacturing across the world. Market shares and marketing advantages are expected to result from the implementation of the system. The collected information and defined objectives form the basis to monitor and analyze the data through cloud computation, and will guarantee the success of their survivability's against any unexpected threats. This book will be useful for advanced undergraduate and graduate students in the field of computer engineering, engineers that work for manufacturing companies, business analysts in retail and e-Commerce, and those working in the defense industry, Information Security, and Information Technology.

This book provides a technical approach to a Business Resilience System with its Risk Atom and Processing Data Point based on fuzzy logic and cloud computation in real time. Its purpose and objectives define a clear set of expectations for Organizations and Enterprises so their network system and supply chain are totally resilient and protected against cyber-attacks, manmade threats, and natural disasters. These enterprises include financial, organizational, homeland security, and supply chain operations with multi-point manufacturing across the world. Market shares and marketing advantages are expected to result from the implementation of the system. The collected information and defined objectives form the basis to monitor and analyze the data through cloud computation, and will guarantee the success of their survivability's against any unexpected threats. This book will be useful for advanced undergraduate and graduate students in the field of computer engineering, engineers that work for manufacturing companies, business analysts in retail and e-Commerce, and those working in the defense industry, Information Security, and Information Technology.

The last several years have seen a dramatic increase in the number of neurobiologists building or using computer-based models as a regular part of their efforts to understand how different neural systems function. As experimental data continue to amass, it is increasingly clear that detailed physiological and anatomical data alone are not enough to infer how neural circuits work. Experimentalists appear to be recognizing the need for the quantitative approach to explore the functional consequences of particular neuronal features that are provided by modeling. This combination of modeling and experimental work has led to the creation of the new discipline of computational neurosciences. More than the use of models per se, the authors believe that computational neuroscience is most distinguished from classical neurobiology due to an explicit focus on how the nervous system computes. Thus, instead of obtaining experimental information about the structure of the nervous system for its own sake, a computational approach involves collecting the information most relevant to the advancement of functional understanding. In our hands, models, especially those based on the detailed physiology and anatomy of the brain region in question, capture what is known about this region while also directing further experimental investigations. These same models can then provide an interpretation for the obtained data. Thus, the interaction between experiments and computer modeling is increasingly iterative and interdependent.