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Text-based open-ended questions in academic formative and summative assessments help students become deep learners and prepare them to understand concepts for a subsequent conceptual assessment. However, grading text-based questions, especially in large courses, is tedious and time-consuming for instructors. Text processing models continue progressing with the rapid development of Artificial Intelligence (AI) tools and Natural Language Processing (NLP) algorithms. Especially after breakthroughs in Large Language Models (LLM), there is immense potential to automate rapid assessment and feedback of text-based responses in education. This systematic review adopts a scientific and reproducible literature search strategy based on the PRISMA process using explicit inclusion and exclusion criteria to study text-based automatic assessment systems in post-secondary education, screening 838 papers and synthesizing 93 studies. To understand how text-based automatic assessment systems have been developed and applied in education in recent years, three research questions are considered. All included studies are summarized and categorized according to a proposed comprehensive framework, including the input and output of the system, research motivation, and research outcomes, aiming to answer the research questions accordingly. Additionally, the typical studies of automated assessment systems, research methods, and application domains in these studies are investigated and summarized. This systematic review provides an overview of recent educational applications of text-based assessment systems for understanding the latest AI/NLP developments assisting in text-based assessments in higher education. Findings will particularly benefit researchers and educators incorporating LLMs such as ChatGPT into their educational activities. Comment: 27 pages, 4 figures, 6 tables

Introduction and Aim: Pain is a common problem that can negatively affect patients' daily life and impair the quality of life of patients. This systematic review evaluates ginger's analgesic effects and underlying mechanisms in postoperative pain. Methods: An extensive search was undertaken in various databases, including Cochrane Library, Pub- Med, Embase, Web of Science, and Scopus. After considering the study's inclusion and exclusion criteria, 12 records were retrieved. The raw data were extracted and entered into an Excel form, and the study outcomes were analyzed. A PRISMA 2020 flow diagram illustrates the direct search approach used for this systematic review. Results: The reviewed studies mainly examined ginger's analgesic effects and other chemical analgesics, such as ibuprofen. Ginger and its bioactive compounds, such as gingerols and shogaols, can reduce postoperative pain by relieving nociceptive, mechanical, and neuropathic pain inflammatory pain by activating the various descendent inhibitory pathways of pain. Ginger induces its postoperative analgesic effects by involving and changing thinly myelinated A-delta, unmyelinated C-fibers, and myelinated A-beta-fibers, Transient receptor potential vanilloid 1 (TRPV1), and inhibiting inflammatory process and oxidant activities. Conclusion: Ginger is emerging as promising analgesic effects through various nociceptive pathways on postoperative pain in patients. Additional rigorous clinical trials are warranted to investigate these results further.

Introduction: An increase in obesity-related diseases is becoming an alarming worldwide problem. Therefore, new therapeutic methods are constantly sought to prevent, treat, and alleviate symptoms of the diseases associated with obesity. Method: This study investigates the effects of two natural compounds (indole-3-carbinol, I3C, a bioactive indolic compound found in cruciferous vegetables; cannabidiol, CBD, the active ingredient derived from the hemp plant) on the fatty acid accumulation in the human liver cell line HepaRG, a well-established model for non-alcoholic fatty liver disease (NAFLD) and in human pre-adipocytes (adipose-derived mesenchymal stem cells, MSC). Results: EC50s of each compound were in the high µM range (approximately 30 mg/L), showing the low toxicity of these compounds. Determination of the selected compounds in cell media showed no significant differences during the exposure, suggesting that no significant metabolism or degradation happened during the exposure time. Quantification of the bioaccumulation of lipid droplets on exposed HepaRG revealed a significant reduction and mitigation of fatty acid accumulation when exposed to 1 nM of I3C and 100 nM of CBD.). On MSC cells a significant inhibition of lipogenesis and adipocyte differentiation was observed in cells exposed to 0.1 nM of I3C and 1 nM of CBD. Conclusion: This study provides a significant contribution to advancing the understanding of preventative dietary strategies that target adipocyte differentiation and NAFLD.

"Compactness," or the use of shape as a proxy for fairness, has been a long-running theme in the scrutiny of electoral districts; badly-shaped districts are often flagged as examples of the abuse of power known as gerrymandering. The most popular compactness metrics in the redistricting literature belong to a class of scores that we call contour-based, making heavy use of area and perimeter. This entire class of district scores has some common drawbacks, outlined here. We make the case for discrete shape scores and offer two promising ideas: a cut score and a spanning tree score. We use recent United States redistricting history as a source of examples. No shape metric can work alone as a seal of fairness, but we argue that discrete metrics are better aligned both with the grounding of the redistricting problem in geography and with the computational tools that have recently gained significant traction in the courtroom. Comment: 32 pages, 15 figures; substantially revised

Abstract A wide range of empirical scholarship has documented a partisan gap in health behaviors during the COVID-19 pandemic in the United States, but the political foundations and temporal dynamics of these partisan gaps remain poorly understood. Using an original six-wave individual panel study (n = 3,000) of Americans throughout the course of the COVID-19 pandemic, we show that at the individual level, partisan differences in health behavior grew rapidly in the early months of the pandemic and are explained almost entirely by individual support for or opposition to President Trump. Our results comprise powerful evidence that Trump support (or opposition), rather than ideology or simple partisan identity, explains partisan gaps in health behavior in the United States. In a time of populist resurgence around the world, public health efforts must consider the impact of charismatic authority in addition to entrenched partisanship.

Emotional Expression Recognition (EER) and Facial Expression Recognition (FER) are active research areas in the affective computing field, which involves studying human emotion, recognition, and sentiment analysis. The main objective of this research is to develop algorithms that can accurately interpret and estimate human emotions from portrait images. The emotions depicted in a portrait can reflect various factors such as psychological and physiological states, the artist’s emotional responses, social and environmental aspects, and the period in which the painting was created. This task is challenging because (i) the portraits are often depicted in an artistic or stylized manner rather than realistically or naturally, (ii) the texture and color features obtained from natural faces and paintings differ, affecting the success rate of emotion recognition algorithms, and (iii) it is a new research area, where practically we do not have visual arts portrait facial emotion estimation models or datasets. To address these challenges, we need a new class of tools and a database specifically tailored to analyze portrait images. This study aims to develop art portrait emotion recognition methods and create a new digital portrait dataset containing 927 images. The proposed model is based on (i) a 3-dimensional estimation of emotions learned by a deep neural network and (ii) a novel deep learning module (3DEmo) that could be easily integrated into existing FER models. To evaluate the effectiveness of the developed models, we also tested their robustness on a facial emotion recognition dataset. The extensive simulation results show that the presented approach outperforms established methods. We expect that this dataset and the developed new tools will encourage further research in recognizing emotions in portrait paintings and predicting artists’ emotions in the painting period based on their artwork.

There are gaps in the historical record of Purdue University as evidenced in the lack of source materials in the University Archives. In particular, researching history on Black alumni, faculty, and staff and other people of color in Purdue's past is challenging due to the scarcity of source material. This presentation discusses gaps or archival silences in the University Archives and measures being taken to preserve and share access to Black history at Purdue.

Natural language processing (NLP), a technology that translates human language into machine-readable data, is revolutionizing numerous sectors, including cancer care. This review outlines the evolution of NLP and its potential for crafting personalized treatment pathways for cancer patients. Leveraging NLPs ability to transform unstructured medical data into structured learnable formats, researchers can tap into the potential of big data for clinical and research applications. Significant advancements in NLP have spurred interest in developing tools that automate information extraction from clinical text, potentially transforming medical research and clinical practices in radiation oncology. Applications discussed include symptom and toxicity monitoring, identification of social determinants of health, improving patient-physician communication, patient education, and predictive modeling. However, several challenges impede the full realization of NLPs benefits, such as privacy and security concerns, biases in NLP models, and the interpretability and generalizability of these models. Overcoming these challenges necessitates a collaborative effort between computer scientists and the radiation oncology community. This paper serves as a comprehensive guide to understanding the intricacies of NLP algorithms, their performance assessment, past research contributions, and the future of NLP in radiation oncology research and clinics.

Forging the Future: A History of the John Martinson Honors College, 2013–2023 is the story of a collaborative effort to build a visionary place: an academic-residential college that would bring together students from across disciplines and differences to rethink the goals and practices of a college education. Designed to be a hub for interdisciplinary learning and innovative pedagogy at Purdue University and a national leader in honors education, the John Martinson Honors College (JMHC) was first and foremost a dream of the future. How that collective dream took shape—from the first, speculative discussions of a college to the literal construction of its buildings and the arrival of its students—is a tale researched, written, and published by the students and alumni of the JMHC. Part institutional history, part biography of a place and its people, Forging the Future is a record of what hope and imagination can accomplish in ten years. https://docs.lib.purdue.edu/founders/1002/thumbnail.jpg