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New Book: Research and Reflections on Southeast Asian American Education and Advancement
Get PDFThis book is in commemoration of the twentieth anniversary of the Journal of Southeast Asian American Education and Advancement (JSAAEA) and the fiftieth anniversary of Southeast Asian American refugee resettlement in the United States. Pivotal research articles, reviews, and creative works from past issues of JSAAEA have been selected for this volume to document the history and experiences of Cambodian, Laotian, Hmong, and Vietnamese Americans since initial refugee resettlement began in the United States in 1975, as well as the experiences of more recent Southeast Asian immigrant and refugee groups. Reviews of academic books, novels, memoirs, children’s books, and motion pictures further highlight Southeast Asian American perspectives and experiences. Creative works, including poetry and short stories by Cambodian, Laotian, Hmong, Vietnamese, Thai, and Burmese American writers, provide additional and often intimate insights and reflections on the Southeast Asian American experience
History in Motion: Applying the Archival Collections of Frank and Lillian Gilbreth’s Time and Motion Studies to Business Education
No full textEmerging and Re-Emerging Organizational Features on Safe Working Condition in the Construction Industry.
Get PDFIn contemporary organizational design, the process of accomplishing an integrated effort through the arrangement of duties, authority, and labour has received more attention. Organizational features in the construction sector need to take into consideration the requirement for joint ventures between subcontractors or specialty contractors. This study\u27s focus was on emerging and reemerging organizational features related to safe working conditions in developing countries\u27 construction industries. This paper, which is centred on a traditional review, provides an overview of different kinds of organization features within the construction industry that have been proposed and researched by many researchers globally. The contribution of organization features to safety in the construction industry were measured using factors such as Procure safety equipment acquisition and maintenance, effective enforcement of safety scheme, Design effective communication, set clear and realistic goals, and provide sufficient resources allocation. The Organization features constructs suggested have the potential to significantly improve working condition and productive work environments which will provide several advantages for both organization and employees. These constructs are appropriate for creative and efficient implementation of working conditions in the construction industry. The contributions that were received demonstrate how important it is to research the emerging phenomena for worker welfare and health in relation to organization features. These results aid in the creation of a more productive, healthier, and sustainable workplace. By using this framework in line with Sustainable Development Goals 3 and 9 (SDGs), in developing nations, the construction sector must be able to produce safer projects, which translates into a practical solution to the industry\u27s hazardous nature
Inappropriate Attire: Fashion, Nationality, and Shame in \u3cem\u3eQuicksand\u3cem\u3e and \u3cem\u3ePachinko\u3cem\u3e
Get PDFIn her article, Minyoung Park examines how fashion in Quicksand by Nella Larsen and Pachinko by Min Jin Lee functions not simply as a visual strategy for passing but as a site where the wearers’ conflicting feelings—shame, desire, and defiance—emerge and persist. Rather than evaluating the success or failure of passing, the article shifts focus to the process of dressing—selecting, maintaining, and reflecting on clothing—through which identity is negotiated and feelings are embodied. The protagonists, Helga and Sunja, often dismissed as failures, are reconsidered as figures whose attention to their inappropriate or excessive fashion reveals their emotional labor and strenuous efforts both to pass and not to pass. Drawing on affect theory and fashion studies, Park foregrounds shame as a key affect in passing narratives—an emotion that is at once protective and exposing. In this context, fashion is framed not as a mask but as an intimate and affective mode of expression. By placing Pachinko alongside Quicksand, the article expands the genealogy of American passing narratives beyond racial binaries to incorporate questions of nationality. Park argues that this comparative framework and a double-take at the protagonists’ fashion choices enable a broader, transnational understanding of passing and self-fashioning, and calls for a reconsideration of American literature not from within fixed national boundaries but through the layered textures of global, embodied experience
Employing a longitudinal study design in a post-conflict zone: strategies and lessons learned from the field
Get PDFLongitudinal methodology is a powerful study design that focuses on processes and patterns of change; yet it is rarely deployed in research to understand post-conflict circumstances. This article describes experiences and lessons learned from a longitudinal study of an education intervention programme in a post-conflict setting. It illustrates both challenges and successful mitigation strategies for conducting a longitudinal study in a fragile and demanding research environment. Our study identified both methodological (participant recruitment, attrition, contextual variability, instrument modifications and record-keeping) and contextual (community, environmental, security and civil perturbations) challenges that impact longitudinal studies in post-conflict societies. By describing the challenges and successful strategies employed in this longitudinal research study, the article illustrates how researchers and practitioners can utilise this methodology to capture individual change over time, identify impacts and outcomes, and gain a deeper understanding of social phenomena and individual development in post-conflict societies
A Deep Learning Framework with Explainable AI for Atmospheric Blocking Detection and Interpretation
Get PDFAtmospheric blocking is a large-scale quasi-stationary phenomenon in mid-latitude circulation, characterized by persistent high-pressure systems that disrupt the typical west-to-east flow of the jet stream. These systems can cause extreme weather events—such as heatwaves, cold spells, or droughts—that persist for days or even weeks. This study proposes a deep learning framework to predict and interpret the occurrence of atmospheric blocking by integrating geophysical precursors such as geopotential height (Z500), stream function (SF200), and potential vorticity. These features, which are dynamically linked to blocking onset and persistence, serve as inputs to a Convolutional Neural Network model trained on the CESM Large Ensemble (LENS) dataset. After initial training, we apply transfer learning to adapt the model to the ERA5 reanalysis dataset, preserving the feature extractor while retraining the classifier to improve generalization on real-world atmospheric data. To improve model transparency and interpretability, we apply eXplainable AI techniques. These tools provide spatial insights into the CNN\u27s decision-making process, highlighting which atmospheric features are most important for the model’s blocking predictions. By advancing the understanding of mechanisms and indicators of blocking through deep learning and XAI, this work contributes toward more accurate and interpretable weather forecasting models—vital for anticipating and mitigating the societal impacts of extreme weather events
Improving Echocardiographic Aortic Aneurysm Assessment in Marfan Syndrome Patients
Get PDFMarfan syndrome (MFS) is a genetic connective tissue disorder caused by a variant of the fibrillin-1 (FBN1) gene, leading to abnormalities in organs reliant on tissue elasticity. Proximal thoracic aortic aneurysms (TAAs) are a major clinical concern, particularly in pediatric MFS patients, due to their risk of dissection or rupture. Diagnosis and monitoring of TAAs often depend on manual measurements of aortic root diameters from the parasternal long axis views of transthoracic echocardiograms. However, this method is prone to interobserver variability (IOV), which impacts consistency in clinical assessment. To mitigate these limitations, we developed a graphical user interface (GUI) that incorporates a novel feature tracking algorithm for the aortic root boundaries and extracts the diameter values across time at physiologically significant locations from anatomical M-mode images, along with Green-Lagrange Circumferential Strain (GLCS) from standard echocardiographic image data. We evaluated the GUI on a cohort of 28 children between 5 to 10 years of age (14 with MFS and 14 controls). Maximum diameters at the annulus, sinus of valsalva (SoV), sinotubular junction (STJ), and ascending aorta were compared against manual measurements made by a board-certified pediatric cardiologist, with intraclass correlation coefficient (ICC) and linear regression coefficient (R²) values of 0.721/0.5802, 0.977/0.969, 0.528/0.7399, 0.779/0.6302, respectively. As pathophysiologically expected, MFS patients generally showed lower GLCS than controls. This tool enhances the reliability of aortic root assessment by improving precision. Future work will include clinical validation on a larger cohort and integration of deep learning for automated aortic root annotation
Unveiling Food Waste Patterns at Purdue University
Get PDFEver wondered how much food waste is generated at Purdue University—or how we compare to other Big Ten schools? This project aims to both evaluate existing practices and behaviors across major universities, and administer a survey to collect data on Purdue’s campus. AI-assisted photo deconstruction and demographic data collection is used to analyze Purdue’s food waste composition and behavior patterns. Concurrently, a comparative analysis of food waste strategies - including categorization of effectiveness, reach, and transparency of waste management efforts while focusing on composting, diversion programs, and dining hall operations - across Big Ten institutions was conducted. To date, the project has consisted of gaining survey approval and report compilation. Future goals include deploying the survey once approved and applying insights from both facets of this project to reduce Purdue’s environmental footprint through improved food waste handling and policy recommendations
Discovering and Designing Novel Perovskite Photovoltaic Materials via Machine Learning
Get PDFPerovskite semiconductors are promising materials for high-efficiency photovoltaics due to their outstanding optoelectronic properties, emerging as a sustainable energy source through solar cell applications. Perovskites with the ABX₃ composition (A, B = metal or organic cations with varying oxidation states; X = chalcogen or halogen anions) have gained interest for their excellent phase stability and compositional tunability. However, combinatorial possibilities arising from the many choices of A, B, and X site species, and their respective mixing fractions, a large number of possible ABX₃ perovskites remain undiscovered. In this work, we used machine learning (ML) methods to design new stable and synthesizable ABX3 compounds for photovoltaic (PV) applications. Hypothetical charge-neutral ABX₃ compounds were generated based on the oxidation states of the constituent ions, their associated chemical properties were used as numerical descriptors to train ML regression models for predicting stability and electronic properties. The synthesizability of the generated compounds was predicted using positive-unlabeled (PU) classification learning, referencing experimentally synthesized compounds collected from the literature using a large language model (LLM). Regression models were trained on a computational dataset of perovskite properties, namely the decomposition energy and the electronic band gap. Predictions were made for thousands of novel compositions, and screening was performed to yield many new materials with stability against decomposition to alternative phases, high probability of synthesis, and PV-suitable band gaps. To ensure the practicality of the proposed materials, future studies will involve DFT calculations of optoelectronic properties and the realization of hypothetical materials through targeted synthesis and characterization
AI-Based Walkability Ecosystem: A Personalized, Social and Adaptive Solution to Urban Mobility and Public Health
Get PDFUrban environments pose significant barriers to physical activity and public health, often due to a lack of motivation, insufficient adaptability to environmental factors, the uniformity of features in existing fitness apps, and limited opportunities for meaningful interpersonal interaction. This project proposes an AI-powered Walkability Assistant — a mobile application designed to foster healthier, more active lifestyles by addressing these challenges through personalized, adaptive, and community-oriented solutions. This project adopts two research methods—text mining and open-ended surveys—to identify latent user needs and areas for improvement in walkability applications. We conducted large-scale text mining and analyzed over two million user reviews from 26 leading walking and fitness apps using Latent Dirichlet Allocation (LDA) topic modeling. This analysis revealed seven core themes in user review: gamification and incentives, general satisfaction, device sync and technical issues, motivation and activity tracking, features and integration, fun and social play, and support and troubleshooting. In addition, the ongoing results from the open-ended survey are expected to offer deeper qualitative insights that cannot be captured through large-scale data alone. Building on these findings, the proposed prototype incorporates personalized route recommendations, real-time adaptation to environmental conditions, and gamified incentives to sustain user engagement. The Walkability Assistant aims to reduce sedentary behavior, enhance physical and mental well-being, and build inclusive urban communities through features that adapt dynamically to users and their environments. By uniting scalable AI analysis with human-centered design, this project contributes a robust, adaptive tool for promoting public health and sustainable urban mobility