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1. Opportunities for Industrial Control

   Witte, Martin ; Sehr, Martin A. ; Ugalde, Ines ; Neidig, Joerg ; Niknami, Mehrdad ; Hoeme, Stephan ; Lee, Edward A. ( July 2020 , Proc. of 21st IFAC World Congress)

   Programmable Logic Controllers are an established platform used throughout industrial automation, but rather poorly understood among researchers in the control systems community. This paper gives an overview of the state of the practice in industrial control systems while presenting a critical analysis of the dominant programming styles used in today's automation systems. We describe the patterns standardized loosely in IEC 61131-3 and, where there are ambiguities in the standard, realized in concrete vendor implementations. Ultimately, we suggest directions for further research towards enabling increasingly complex industrial control applications subject to the novel requirements of Industry 4.0 settings without compromising the safety and reliability guaranteed by the current industrial automation stack. 

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2. Development of a Product Pipeline System to Teach Industrial Manufacturing Automation

   Morcos, M. ; He, S. ; Youssef, M. ( June 2023 , The 130th ASEE Annual Conference and Exposition, June 25-28, 2023, Baltimore, MD)

   Our modern age is being forged by industrialization and automation. Processes that once required tedious handwork can now be completed with higher efficiency and consistent quality by machines and facilities that perform their operations automatically. Examples of automation technology in our daily lives are found in households where washing machines are used, on the streets where traffic lights regulate traffic, or even in buildings that use air-conditioning units and automatic lighting systems. Open-loop control systems or closed-loop control systems are used in all these systems to determine a predefined sequence of processing steps. The Industrial Manufacturing System (IMS) developed at the college intends to address the need for education. This project introduces how the production assembly line develops. The system consists of Sorting, Assembly, Processing, Testing, Storage, and Buffering operations. The Siemens Simatic PLC (Programmable Logic Controller) S7-300 is used in the manufacturing system and TIA (Total Integrated Automation) Portal is used as the programming environment. This project focuses on the automation of an industrial manufacturing system through several tools such as PLC, TIA PORTAL (V16), and PROFIBUS. The control of the whole system is implemented by using Siemens Sematic PLC. The main objective of this project is to create a fully automated production line for college education. The system consists of Buffering, Sorting, Assembly, Processing, Testing, Handling, and Storage to minimize the risk to workers’ health [1] and the occurrence of accidents and increase production efficiency. 

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3. Benchmarking an AI-Guided Reasoning-Based Operator Support System on the Three Mile Island Accident Scenario

   https://doi.org/10.1115/ICONE2020-16400  

   Hanna, Botros N. ; Son, Tran C. ; Dinh, Nam T. ( August 2020 , Proceedings of the 28th Conference on Nuclear Engineering)

   null (Ed.)

   In the Nuclear Power Plant (NPP) control room, the operators’ performance in emergencies is impacted by the need to monitor many indicators on the control room boards, the limited time to interact with dynamic events, and the incompleteness of the operator’s knowledge. Recent research has been directed toward increasing the level of automation in the NPP system by employing modern AI techniques that support the operator’s decisions. In previous work, the authors have employed a novel AI-guided declarative approach (namely, Answer Set Programming (ASP)) to represent and reason with human qualitative knowledge. This represented knowledge is structured to form a reasoning-based operator support system that assists the operator and compensates for any knowledge incompleteness by performing reasoning to diagnose failures and recommend executing actions in real time. A general ASP code structure has been proposed and tested against simple scenarios, e.g., diagnosis of pump failures that result in loss of flow transients and generating the needed plans for resolving the issue of stuck valves in the secondary loop.

   In this work, we investigate the potential of the previously proposed ASP structure by applying ASP to a realistic case study of the Three Mile Island, Unit 2 (TMI-2) accident event sequence (in particular, the first 142 minutes). The TMI scenario presents many challenges for a reasoning system, including a large number of variables, the complexity of the scenario, and the misleading readings. The capability of the ASP-based reasoning system is tested for diagnosis and recommending actions throughout the scenario. This paper is the first work to test and demonstrate the capability of an automated reasoning system by applying it to a realistic nuclear accident scenario, such as the TMI-2 accident.

    

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4. Using Design-based Research Methods to Scale in an Expanding Intervention

   Boyer, D. M. ; & Duncan, L. A. ( July 2021 , ASEE Annual Conference proceedings)

   In this work-in-progress paper, we present design-based methodological work intended to support implementation of a large-scale, ongoing NSF Scholarships in STEM (S-STEM) project. We have created pilot tools and procedures for data collection and analysis that will supply consistent research throughlines during the project lifespan while also providing iterations of formative feedback about participants’ needs and their experiences in the project. While these tools and procedures have allowed us to analyze pilot data, in each year of the project, participant numbers will continue to grow, placing additional demands on our research team and our chosen methods. Will the designs we have created help us to scale effectively while remaining fidelitous to our project goals? The purpose of the S-STEM project is to connect student pathways at state technical colleges to Engineering and Computer Sciences programs at a Research I university in the southeastern United States. Toward this goal, we are implementing communities of practice and cognitive apprenticeship strategies to support student cohorts and create programming aspects to enhance transfer students’ enculturation to the university, completion of STEM-related degrees, and placement in the industrial workforce. Cohorts begin at the technical colleges, guided by a doctoral student mentor who engages program participants in applied research and shepherds them throughout their transition to the university. Now in the second year of the project, the pilot cohort is studying at the university while new cohorts are engaged at the collaborating technical colleges. Each year, the number of students participating in the five-year implementation will accumulate to a proposed total of over 300 students. Our research team will need to scale our efforts toward project fidelity. Our intent with this work-in-progress paper is to share our current status and invite interested colleagues to provide feedback about our pilot analysis work and our plans for future data. We will introduce the design-based methods that inform both research and development in our project. In particular, we will focus on the applicability of these methods for implementation work and how they can be effectively scaled to large interventions. We will describe observation, interview and focus group, and survey methods, along with how these methods complement academic sources and other student-related data. 

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5. Developmental exposure of California mice to endocrine disrupting chemicals and potential effects on the microbiome-gut-brain axis at adulthood

   https://doi.org/10.1038/s41598-020-67709-9  

   Kaur, Sarabjit ; Sarma, Saurav J. ; Marshall, Brittney L. ; Liu, Yang ; Kinkade, Jessica A. ; Bellamy, Madison M. ; Mao, Jiude ; Helferich, William G. ; Schenk, A. Katrin ; Bivens, Nathan J. ; et al ( July 2020 , Scientific Reports)

   Xenoestrogens are chemicals found in plant products, such as genistein (GEN), and in industrial chemicals, e.g., bisphenol A (BPA), present in plastics and other products that are prevalent in the environment. Early exposure to such endocrine disrupting chemicals (EDC) may affect brain development by directly disrupting neural programming and/or through the microbiome-gut-brain axis. To test this hypothesis, California mice (Peromyscus californicus) offspring were exposed through the maternal diet to GEN (250 mg/kg feed weight) or BPA (5 mg/kg feed weight, low dose- LD or 50 mg/kg, upper dose-UD), and dams were placed on these diets two weeks prior to breeding, throughout gestation, and lactation. Various behaviors, gut microbiota, and fecal metabolome were assessed at 90 days of age. The LD but not UD of BPA exposure resulted in individuals spending more time engaging in repetitive behaviors. GEN exposed individuals were more likely to exhibit such behaviors and showed socio-communicative disturbances. BPA and GEN exposed females had increased number of metabolites involved in carbohydrate metabolism and synthesis. Males exposed to BPA or GEN showed alterations in lysine degradation and phenylalanine and tyrosine metabolism. Current findings indicate cause for concern that developmental exposure to BPA or GEN might affect the microbiome-gut-brain axis.

    

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