A methodology to predict community college STEM student retention and completion

Numerous government reports point to the multifaceted issues facing the country\u27s capacity to increase the number of STEM majors, while also diversifying the workforce. Community colleges are uniquely positioned as integral partners in the higher education ecosystem. These institutions serve as an access point to opportunity for many students, especially underrepresented minorities and women. Community colleges should serve as a major pathway to students pursuing STEM degrees; however student retention and completion rates are dismally low. Therefore, there is a need to predict STEM student success and provide interventions when factors indicate potential failure. This enables educational institutions to better advise and support students in a more intentional and efficient manner. The objective of this research was to develop a model for predicting success. The methodology uses the Mahalanobis Taguchi System as a novel approach to pattern recognition and gives insight into the ability of MTS to predict outcomes based on student demographic data and academic performance. The method accurately predicts institution-specific risk factors that can be used to better retain STEM students. The research indicates the importance of using community college student data to target this distinctive student population that has demonstrated risk factors outside of the previously reported factors in prior research. This methodology shows promise as a mechanism to close the achievement gap and maximize the power of open-access community college pathways for STEM majors --Abstract, page iv

Improving Traffic Density Forecasting in Intelligent Transportation Systems Using Gated Graph Neural Networks

This study delves into the application of graph neural networks in the realm of traffic forecasting, a crucial facet of intelligent transportation systems. Accurate traffic predictions are vital for functions like trip planning, traffic control, and vehicle routing in such systems. Three prominent GNN architectures Graph Convolutional Networks (Graph Sample and Aggregation) and Gated Graph Neural Networks are explored within the context of traffic prediction. Each architecture's methodology is thoroughly examined, including layer configurations, activation functions,and hyperparameters. The primary goal is to minimize prediction errors, with GGNNs emerging as the most effective choice among the three models. The research outlines outcomes for each architecture, elucidating their predictive performance through root mean squared error and mean absolute error (MAE). Hypothetical results reveal intriguing insights: GCNs display an RMSE of 9.10 and an MAE of 8.00, while GraphSAGE shows improvement with an RMSE of 8.3 and an MAE of 7.5. Gated Graph Neural Networks (GGNNs) exhibit the lowest RMSE at 9.15 and an impressive MAE of 7.1, positioning them as the frontrunner

A Retention Model for Community College STEM Students

The number of students attending community colleges that take advantage of transfer pathways to universities continues to rise. Therefore, there is a need to engage in academic research on these students and their attrition in order to identify areas to improve retention. Community colleges have a very diverse population and provide entry into science, technology, engineering, and math (STEM) programs, regardless of student high school preparedness. It is essential for these students to successfully transfer to universities and finish their STEM degrees to meet the global workforce demands. This research develops a predictive model for community college students for degree completion using the Mahalanobis Taguchi System and regression. Data collected from a Midwest community college over a five-year period in three specific associate degree programs will be used for the study. The study identified 92 students that completed a STEM degree within three years, while 730 students were not able to complete the degree within that period or at all. The research illuminates specific areas of concern related to community college students and better informs transfer institutions about this important sector of transfer students. Especially revealing is the important predictive factors traditionally found in research for STEM retention had very low correlation for this set of community college students. This research reinforces the need to investigate community college students more closely and through a different lens

Weighting indicators of employee performance evaluation using Taguchi experimental design approach

Performance evaluation is a prerequisite for many activities in the field of human resource whose accurate implementation results in efficiency and effectiveness of performance feedback, training and development, employee promotion, human resource planning, etc. So accurate weighting of evaluation indicators is of great importance and can affect evaluation results. Therefore, using Taguchi experimental design, this study tries to weight indicators realistically taking into account interaction and synergy force among indicators. Moreover, differences in the importance of different levels of indicators were investigated. The result of using Taguchi experimental design as weight indicators showed that this approach can be a proper way to weight performance evaluation indicators

Vendor managed forecasting: A case study of small enterprise

Enterprises use supply chain management practices for improving business or supply chain performance. It is observed that supply chain technologies like VMI are now becoming an integral part of enterprise’s strategy. Even small and medium enterprises can adopt this practice and improve the performance of supply chain. This paper discusses vendor managed forecasting with the help of case study. It shows how a small enterprise improves supply chain performance by using demand related information obtained from retailer. The results obtained in the study shows that vendor managed forecasting in supply chain reduces the demand variation and improves inventory management significantly.Peer Reviewe

Prediction of machining parameters for optimum Surface Roughness in turning SS 304

In any machining process, apart from obtaining the accurate dimensions, achieving a good surface quality is also of utmost importance. A machining process involves many process parameters which directly or indirectly influence the surface quality of the product. Surface roughness and waviness in turning process are caused due to various parameters of which feed, speed, depth of cut are important ones. A precise knowledge of these optimum parameters would facilitate reduce the machining costs and improve product quality. Extensive study has been conducted in the past to optimize the process parameters in any machining process to have the best product. Current investigation on turning process is a Taguchi optimization technique applied on the most effective process parameters i.e. feed, cutting speed and depth of cut while machining SS 304 as the work piece with brazed cutting tool. Main effect plots are generated and analyzed to find out the relationship between them. The details of experimentation and analysis are given in the following context

Cyber-physical business systems modelling : advancing Industry 4.0

Abstract: The dynamic digital age drives contemporary multinationals to focus on delivering world-class business solutions with the use of advanced technology. Contemporary multinationals relate to a present-day business primarily engaged to generate profits. These complex multinationals offer value through the manufacture, sale, and management of products and services. Disruptive strategies in operations driven by emerging technological innovations demand continuous business improvements. These insightful opportunities are inclusive of operations, enterprise systems, engineering management, and research. Business sustainability is a strategic priority to deliver exceptional digital solutions. The Fourth Industrial Revolutions (4IR) offer significant technological advancements for total business sustainability. The underlying 4IR technologies include Cyber-Physical Systems (CPS). The collective challenges of a large global business are not easy to predict. CPS protocols deliver sustainable prospects required to integrate and model physical systems in real-time driven by the 4IR implementations. The goal of this thesis is to develop a model (CPS) suitable for self-predicting and to determine ideal operational practice driven by technologies of the 4IR. The model (CPS) seeks a novel tool effective for comprehensive business evaluation and optimisation. The competence of the anticipated tool includes suitability to collaborate current operations and predict the impact of change on a complex business. ..D.Phil. (Engineering Management

Sensor data-based decision making

Increasing globalization and growing industrial system complexity has amplified the interest in the use of information provided by sensors as a means of improving overall manufacturing system performance and maintainability. However, utilization of sensors can only be effective if the real-time data can be integrated into the necessary business processes, such as production planning, scheduling and execution systems. This integration requires the development of intelligent decision making models that can effectively process the sensor data into information and suggest appropriate actions. To be able to improve the performance of a system, the health of the system also needs to be maintained. In many cases a single sensor type cannot provide sufficient information for complex decision making including diagnostics and prognostics of a system. Therefore, a combination of sensors should be used in an integrated manner in order to achieve desired performance levels. Sensor generated data need to be processed into information through the use of appropriate decision making models in order to improve overall performance. In this dissertation, which is presented as a collection of five journal papers, several reactive and proactive decision making models that utilize data from single and multi-sensor environments are developed. The first paper presents a testbed architecture for Auto-ID systems. An adaptive inventory management model which utilizes real-time RFID data is developed in the second paper. In the third paper, a complete hardware and inventory management solution, which involves the integration of RFID sensors into an extremely low temperature industrial freezer, is presented. The last two papers in the dissertation deal with diagnostic and prognostic decision making models in order to assure the healthy operation of a manufacturing system and its components. In the fourth paper a Mahalanobis-Taguchi System (MTS) based prognostics tool is developed and it is used to estimate the remaining useful life of rolling element bearings using data acquired from vibration sensors. In the final paper, an MTS based prognostics tool is developed for a centrifugal water pump, which fuses information from multiple types of sensors in order to take diagnostic and prognostics decisions for the pump and its components --Abstract, page iv