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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Modeling And Solving The Outsourcing Risk Management Problem In Multi-Echelon Supply Chains, Arian A. Nahangi
Modeling And Solving The Outsourcing Risk Management Problem In Multi-Echelon Supply Chains, Arian A. Nahangi
Master's Theses
Worldwide globalization has made supply chains more vulnerable to risk factors, increasing the associated costs of outsourcing goods. Outsourcing is highly beneficial for any company that values building upon its core competencies, but the emergence of the COVID-19 pandemic and other crises have exposed significant vulnerabilities within supply chains. These disruptions forced a shift in the production of goods from outsourcing to domestic methods.
This paper considers a multi-echelon supply chain model with global and domestic raw material suppliers, manufacturing plants, warehouses, and markets. All levels within the supply chain network are evaluated from a holistic perspective, calculating a total …
Analyzing Student Experience On Group Work With The Application Of Different Group Allocation Approaches, An Yee Tan
Analyzing Student Experience On Group Work With The Application Of Different Group Allocation Approaches, An Yee Tan
Management and HR
Working as a group can be as challenging as working by oneself. Common issues like ineffective group work, unequal work contribution, and poor communication are believed to be the reasons why many students preferred to work individually. The purpose of this study is to understand if there is a disparity in student experience on group work by implementing different methods of group formation, which are, intentional group formation and random assignment. Topics around team well-being, team communication, and team effectiveness are the main focus of this study. The second emphasis of this study is students’ opinions on whether or not …
Comparing Radiation Shielding Potential Of Liquid Propellants To Water For Application In Space, John Czaplewski
Comparing Radiation Shielding Potential Of Liquid Propellants To Water For Application In Space, John Czaplewski
Master's Theses
The radiation environment in space is a threat that engineers and astronauts need to mitigate as exploration into the solar system expands. Passive shielding involves placing as much material between critical components and the radiation environment as possible. However, with mass and size budgets, it is important to select efficient materials to provide shielding. Currently, NASA and other space agencies plan on using water as a shield against radiation since it is already necessary for human missions. Water has been tested thoroughly and has been proven to be effective. Liquid propellants are needed for every mission and also share similar …
Clustering Web Users By Mouse Movement To Detect Bots And Botnet Attacks, Justin L. Morgan
Clustering Web Users By Mouse Movement To Detect Bots And Botnet Attacks, Justin L. Morgan
Master's Theses
The need for website administrators to efficiently and accurately detect the presence of web bots has shown to be a challenging problem. As the sophistication of modern web bots increases, specifically their ability to more closely mimic the behavior of humans, web bot detection schemes are more quickly becoming obsolete by failing to maintain effectiveness. Though machine learning-based detection schemes have been a successful approach to recent implementations, web bots are able to apply similar machine learning tactics to mimic human users, thus bypassing such detection schemes. This work seeks to address the issue of machine learning based bots bypassing …
Node Classification On Relational Graphs Using Deep-Rgcns, Nagasai Chandra
Node Classification On Relational Graphs Using Deep-Rgcns, Nagasai Chandra
Master's Theses
Knowledge Graphs are fascinating concepts in machine learning as they can hold usefully structured information in the form of entities and their relations. Despite the valuable applications of such graphs, most knowledge bases remain incomplete. This missing information harms downstream applications such as information retrieval and opens a window for research in statistical relational learning tasks such as node classification and link prediction. This work proposes a deep learning framework based on existing relational convolutional (R-GCN) layers to learn on highly multi-relational data characteristic of realistic knowledge graphs for node property classification tasks. We propose a deep and improved variant, …