RISKY BUSINESS: AN ANALYTICAL APPROACH TO SERVICES SUPPLY CHAIN RISK MANAGEMENT

Cyber threats, economic upheavals, and environmental disasters threaten global supply chains. These vulnerabilities impact the readiness of U.S. forces and their capacity to defend the nation. Consumers and the government need a framework for assessing vulnerabilities and establishing effective supply chains. MITRE’s System of Trust (SoT) serves as a framework to measure trustworthiness and identify risk factors affecting their supply chain security. The SoT develops a taxonomy of risk factors, defines risk measures attributable to those risk factors, and creates a framework for organizations to objectively quantify supply chain risk. Our study validates the services risk factors and identifies techniques and best practices to mitigate risk unique for services. Our research questions are: What are the primary indicators of supply chain risk, and which are unique to Department of Defense services? Furthermore, what are the best practices for preventing, mitigating, and responding to service-specific supply chain risks? This research draws on qualitative interview data to obtain insight into the services aspect of supply chains, systematically evaluate MITRE’s risk factors and risk measures, and identify gaps in available data. Our research results in a Services Supply Chain Risk Management Framework that managers should use to evaluate and mitigate risks within their supply chains.Captain, United States Air ForceCaptain, United States Air ForceApproved for public release. Distribution is unlimited

Convolutional neural network for malware detection in IoT Network

[EN] The network has exploded in popularity in the twenty-first century in the last few years, becoming one of the most extensively utilized and prominent technologies. Nowadays, cyberattacks occurring and the variety, size, and intensity of cyberattacks are increasing. In this work, the machine learning method is used to predict Intrusion in the Internet of Things. Attacks on networks connected to smart cities or on intelligent transportation systems endanger the security of these networks. Studies show that IoT attacks can cost the network millions of dollars. DDoS attacks by malicious botnets and nodes on the IoT network are among the most malicious attacks on the network and can disable IoT application servers

Deep learning neural network for Alzheimer’s disease predictions

[EN] Alzheimer's disease is a dangerous and progressive disease that affects the nervous system and brain of people. An important and effective approach to treating Alzheimer's disease is to diagnose the disease early so that more effective treatments can be offered. One practical way to diagnose Alzheimer's disease is to use magnetic resonance imaging to detect plaque and affected areas. In this paper, a new method based on the Harris Hawks optimization method is presented for Alzheimer’s disease diagnosis. This method uses the best features that obtain from the MRI images and uses it in deep learning to classify the healthy and non-healthy images

Machine learning and metaheuristic based for Alzheimer’s disease diagnosis

[EN] Alzheimer's disease was first described in 1907 by Alois Alzheimer. It is a progressive neurological disorder with a gradual onset of dementia. Although Alzheimer's was initially a rare disease, it is now one of the most common diseases in the elderly and is the fourth most common cause of death in this age group in the rankings. In this paper, we present a new methods of group intelligence to improve image threshold in Alzheimer's diagnosis. The challenge of this method is the uncertainty of metaheuristic methods in solving optimization problem

Flat electrode contacts for vagus nerve stimulation.

The majority of available systems for vagus nerve stimulation use helical stimulation electrodes, which cover the majority of the circumference of the nerve and produce largely uniform current density within the nerve. Flat stimulation electrodes that contact only one side of the nerve may provide advantages, including ease of fabrication. However, it is possible that the flat configuration will yield inefficient fiber recruitment due to a less uniform current distribution within the nerve. Here we tested the hypothesis that flat electrodes will require higher current amplitude to activate all large-diameter fibers throughout the whole cross-section of a nerve than circumferential designs. Computational modeling and in vivo experiments were performed to evaluate fiber recruitment in different nerves and different species using a variety of electrode designs. Initial results demonstrated similar fiber recruitment in the rat vagus and sciatic nerves with a standard circumferential cuff electrode and a cuff electrode modified to approximate a flat configuration. Follow up experiments comparing true flat electrodes to circumferential electrodes on the rabbit sciatic nerve confirmed that fiber recruitment was equivalent between the two designs. These findings demonstrate that flat electrodes represent a viable design for nerve stimulation that may provide advantages over the current circumferential designs for applications in which the goal is uniform activation of all fascicles within the nerve