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Food insecurity hinders individuals from the healthy and sustainable life they truly deserve. Unfortunately, food insecurity and chronic health diseases affect millions of people across the United States. Food banks are constantly fighting the uphill battle against food insecurity to supply adequate, relevant, healthy meals to those who need them. Oftentimes hunger relief organizations lack data and software tools that could aid strategic decision-making. A local food bank faces this exact problem and is struggling to find clients that face chronic health diseases in their service area. This study uses data from the local food bank to develop visualizations that investigate the health considerations of the population they serve. The results easily found a specific county with the largest number of health considerations and a zip code with the highest number of individuals facing hypertension. Dominant chronic health considerations highlight the importance for food banks to diversify their food selections. It is important for the local food bank to know what foods are essential within each county and zip code area to provide services that will be valuable to those who need them. This study benefits food banks so they can yield better service to the community. 

Food insecurity is a serious problem in America and the pandemic makes the problem even worse. Feeding America has more than 200 food banks. that These food banks and their partner agencies are the key players in the battle against food insecurity. Partner agencies may vary in size and location depending on the service area and the variety of the partner agencies and the complexities of their operations make equitable food distribution very challenging. There is a need for a meaningful to group those partner agencies to assist food bank operations managers to make informed decisions. This study uses data from a local food bank and its partner agencies. Each agency is unique in terms of its behavior. Therefore, k-means clustering was used to categorize agencies into groups based on the number of persons served and the amount of food received. The results of the study will provide evidence-based information to assist the food bank in making informed decisions. 

In this paper, we present a co-design study with teachers to contribute towards the development of a technology-enhanced Artificial Intelligence (AI) curriculum, focusing on modeling unstructured data. We created an initial design of a learning activity prototype and explored ways to incorporate the design into high school classes. Specifically, teachers explored text classification models with the prototype and reflected on the exploration as a user, learner, and teacher. They provided insights about learning opportunities in the activity and feedback for integrating it into their teaching. Findings from qualitative analysis demonstrate that exploring text classification models provided an accessible and comprehensive approach for integrated learning of mathematics, language arts, and computing with the potential of supporting the understanding of core AI concepts including identifying structure within unstructured data and reasoning about the roles of human insight in developing AI technologies. 

Maintaining an appropriate staffing level is essential to providing a healthy workplace environment at nursing homes and ensuring quality care among residents. With the widespread Covid-19 pandemic, staff absenteeism frequently occurs due to mandatory quarantine and providing care to their inflicted family members. Even though some of the staff show up for work, they may have to perform additional pandemic-related protection duties. In combination, these changes lead to an uncertain reduction in the quantity of care each staff member able to provide in a future shift. To alleviate the staff shortage concern and maintain the necessary care quantity, we study the optimal shift scheduling problem for a skilled nursing facility under probabilistic staff shortage in the presence of pandemic-related service provision disruptions. We apply a two-stage stochastic programming approach to our study. Our objective is to assign staff (i.e., certified nursing aids) to shifts to minimize the total staffing cost associated with contract staff workload, the adjusted workload for the changing resident demand, and extra workload due to required sanitization. Thus, the uncertainties considered arise from probabilistic staff shortage in addition to resident service need fluctuation. We model the former source of uncertainty with a geometric random variable for each staffer. In a proof-of-the-concept study, we consider realistic COVID-19 pandemic response measures recommended by the Indiana state government. We extract payment parameter estimates from the COVID-19 Nursing Home Dataset publicly available by the Centers for Medicare and Medicaid Services (CMS). We conclude with our numerical experiments that when a skilled nursing facility is at low risk of the pandemic, the absenteeism rate and staff workload increase slightly, thus maintaining the current staffing level can still handle the service disruptions. On the other hand, under high-risk circumstances, with the sharp increase of the absence rate and workload, a care facility likely needs to hire additional full-time staff as soon as possible. Our research offers insights into staff shift scheduling in the face of uncertain staff shortages and service disruption due to pandemics and prolonged disasters. 

In this paper, we present a co-design study with teachers to contribute towards development of a technology-enhanced Artificial Intelligence (AI) curriculum, focusing on modeling unstructured data. We created an initial design of a learning activity prototype and explored ways to incorporate the design into high school classes. Specifically, teachers explored text classification models with the prototype and reflected on the exploration as a user, learner, and teacher. They provided insights about learning opportunities in the activity and feedback for integrating it into their teaching. Findings from qualitative analysis demonstrate that exploring text classification models provided an accessible and comprehensive approach for integrated learning of mathematics, language arts, and computing with the potential of supporting the understanding of core AI concepts including identifying structure within unstructured data and reasoning about the roles of human insight in developing AI technologies. 

Background. It is well recognized that current graduate education is too narrowly focused on thesis research. Graduate students have a strong desire to gain skills for their future career success beyond thesis research. This obvious gap in professional skill training in current graduate study also leads to the common student perception that professional skills beyond academic knowledge should only be gained after completion of thesis research. Purpose. A new program is being developed to rigorously integrate professional skills training with thesis research. The approach is to establish learning communities of Graduates for Advancing Professional Skills (GAPS) to incorporate project management skill training from industry into academic research. The GAPS program seeks to address two fundamental education research questions: How can project management skill training be integrated with thesis research in graduate education? What is the role/value of learning communities in enhancing the training and retention of professional skills and the effectiveness of thesis research? Our proposed solution is that graduate student learning communities engaging in a blended online and classroom approach will promote learning of professional skills such as project and time management in thesis research activities. The purpose of this session is to establish the connection between project management and thesis research, and demonstrate the beginning progress of the GAPS program towards. Methodology/approach. The following progress is being made to establish GAPS learning communities through which to teach and practice professional skills. A website has been developed to introduce the program, recruit participants, provide information on the online modules, and survey results of participants’ current levels of knowledge and skills related to project management. A new course, “Introduction of Project Management for Thesis Research”, has been added to the course catalog and open to enrollment for students from different majors. In addition, learning modules including project charter, scheduling, communication, teamwork, critical path method, and lean concept are developed. Case studies and examples have been developed to help students learn how to utilize project management skills in their thesis research. Conclusions. The concept of integrating professional skills training with thesis research through learning communities has been demonstrated. There are multiple advantages of this approach, including efficient utilization of the current resources, and faculty buy-in. Preliminary data from the first cohort are being collected and analyzed to identify students’ needs, benefits of the program, and areas of improvement for future cohort iterations. Implications. The GAPS program will improve professional skill training for graduate students through communities of practice. This new learning model has the potential to fundamentally change the culture of graduate education. We believe the method demonstrated here can be broadly applied to different engineering majors, and even broadly to all thesis research. 

As technology advances, data driven work is becoming increasingly important across all disciplines. Data science is an emerging field that encompasses a large array of topics including data collection, data preprocessing, data visualization, and data analysis using statistical and machine learning methods. As undergraduates enter the workforce in the future, they will need to “benefit from a fundamental awareness of and competence in data science”[9]. This project has formed a research practice partnership that brings together STEM+C instructors and researchers from three universities and an education research and consulting group. We aim to use high frequency monitoring data collected from real-world systems to develop and implement an interdisciplinary approach to enable undergraduate students to develop an understanding of data science concepts through individual STEM disciplines that include engineering, computer science, environmental science, and biology. In this paper, we perform an initial exploratory analysis on how data science topics are introduced into the different courses, with the ultimate goal of understanding how instructional modules and accompanying assessments can be developed for multidisciplinary use. We analyze information collected from instructor interviews and surveys, student surveys, and assessments from five undergraduate courses (243 students) at the three universities to understand aspects of data science curricula that are common across disciplines. Using a qualitative approach, we find commonalities in data science instruction and assessment components across the disciplines. This includes topical content, data sources, pedagogical approaches, and assessment design. Preliminary analyses of instructor interviews also suggest factors that affect the content taught and the assessment material across the five courses. These factors include class size, students’ year of study, students’ reasons for taking class, and students’ background expertise and knowledge. These findings indicate the challenges in developing data modules for multidisciplinary use. We hope that the analysis and reflections on our initial offerings has improved our understanding of these challenges, and how we may address them when designing future data science teaching modules. These are the first steps in a design-based approach to developing data science modules that may be offered across multiple courses. 

The superτ-charm facility (STCF) is an electron–positron collider proposed by the Chinese particle physics community. It is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of 0.5 × 10³⁵cm⁻²·s⁻¹or higher. The STCF will produce a data sample about a factor of 100 larger than that of the presentτ-charm factory — the BEPCII, providing a unique platform for exploring the asymmetry of matter-antimatter (charge-parity violation), in-depth studies of the internal structure of hadrons and the nature of non-perturbative strong interactions, as well as searching for exotic hadrons and physics beyond the Standard Model. The STCF project in China is under development with an extensive R&D program. This document presents the physics opportunities at the STCF, describes conceptual designs of the STCF detector system, and discusses future plans for detector R&D and physics case studies.

 

In the fight against hunger, Food Banks must routinely make strategic distribution decisions under uncertain supply (donations) and demand. One of the challenges facing the decision makers is that they tend to rely heavily on their prior experiences to make decisions, a phenomenon called cognitive bias. This preliminary study seeks to address cognitive bias through a visual analytics approach in the decision-making process. Using certain food bank data, interactive dashboards were prepared as an alternative to the customary spreadsheet format. A preliminary study was conducted to evaluate the effectiveness of the dashboard and results indicated dashboards reduced the amount of confirmation bias.