Digital Commons Network^™

The Impact Of International Baccalaureate (Ib) Courses: Experience And Outcome Differences For Full Ib Diploma Earners Vs. Non-Diploma Earners In An Urban School In The Us, Christopher Jennens, Nilufer Guler

Research, Publications & Creative Work

This study examines differences in educational experiences and outcomes for high school(HS) students who participated in the International Baccalaureate (IB) Diploma Program and earned full diplomas compared to those who participated in the IB Diploma Program but did not earn the diploma. ACT scores, after-high school placement, and HS graduation rates of 256 participants were collected. 122 participants completed an online survey, and 12 participated in interviews. The results revealed that the educational outcomes and experiences for diploma and non-diploma earners were vastly similar. Diploma earners had significantly higher ACT scores, but their college entrance and other academic outcomes were …

Hearing From Working Nurses: Incorporating Real-World Knowledge Practices Into The Framework To Better Instruct Tomorrow’S Healthcare Professionals, Anne R. Diekema, Elizabeth (Betsy) S. Hopkins, Britt Fagerheim, Brandon Patterson, Nena Schvaneveldt

Library Faculty & Staff Publications

There is a certain duality to the role of librarians in higher education as the job requires preparing students for their current academic work, such as doing research for writing papers, while at the same time preparing students for their future professional information work.¹ Librarians seeking guidance on teaching information literacy more comprehensively are left to study the professional standards of the targeted professional group,² and find or carry out research on these practitioners.³ Synthesizing and translating these findings into instructional approaches while staying true to existing information literacy standards, however, can be challenging.⁴ In this …

Privacy-Preserving Collaborative Population Stratification With Dynamic Algorithm And Hyperparameter Selection, Maryam Ghasemian, Lynette Hammond Gerido, Erman Ayday

Faculty, Staff and Student Publications

We present a privacy-preserving selection layer for collaborative population stratification under 𝜖-local differential privacy (LDP). Rather than fixing a single pipeline (e.g., PCA+K-Means with preset 𝐾), our framework lets parties choose among three DP pipelines: PCA→Noise, Noise→PCA, and Noise-Only, according to their resources, and has an honest-but-curious server aggregate only DP shares to automatically select the clustering algorithm (K-Means, GMM, or Hierarchical) and 𝐾 that maximize internal metrics (Silhouette, Calinski–Harabasz, Davies–Bouldin). Because selection operates on DP data, it adds no further privacy loss. On openSNP (942 samples, 28,396 SNPs), the PCA-augmented pipelines yield higher utility …

Privacy-Preserving Verification Of Ml Preprocessing Via Model Behavior Indicators, Wenbiao Li, Anisa Halimi, Jaideep Vaidya, Xiaoqian Jiang, Erman Ayday

Faculty, Staff and Student Publications

We present a privacy-preserving framework to verify whether a declared data preprocessing pipeline was correctly applied before training a machine learning model on sensitive data. The verifier has only black-box query access to the model and combines three behavior indicators: shift in prediction accuracy, Kullback-Leibler (KL) divergence between output distributions, and explanation vectors from Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). The method requires neither the original training records nor ground-truth labels. It supports two tasks: (i) a binary decision on correctness and (ii) a multi-class diagnosis identifying which step is missing. Experiments on three tabular datasets …

Explainable Diagnosis Prediction Through Neuro-Symbolic Integration, Qiuhao Lu, Rui Li, Elham Sagheb, Andrew Wen, Jinlian Wang, Liwei Wang, Jungwei W Fan, Hongfang Liu

Faculty, Staff and Student Publications

Diagnosis prediction is a critical task in healthcare, where timely and accurate identification of medical conditions can significantly impact patient outcomes. Traditional machine learning and deep learning models have achieved notable success in this domain but often lack interpretability which is a crucial requirement in clinical settings. In this study, we explore the use of neuro-symbolic methods, specifically Logical Neural Networks (LNNs), to develop explainable models for diagnosis prediction. Essentially, we design and implement LNN-based models that integrate domain-specific knowledge through logical rules with learnable weights and thresholds. Our models, particularly Mmulti-pathway and Mcomprehensive, demonstrate superior performance over traditional models …

Rank Aggregation Methods And Tools In Genomic Data Analysis, Wenping Zou, Savannah Mwesigwa, Sayed-Rzgar Hosseini, Zhongming Zhao

Faculty, Staff and Student Publications

Rank aggregation (RA) is the process of consolidating disparate rankings into a single unified ranking. It holds immense potential in the field of genomics. RA has applications in diverse research areas, such as gene expression analysis, meta-analysis, gene prioritization, and biomarker discovery. However, there are many challenges in the application of the RA approach to biological data, such as dealing with heterogeneous data sources, rankings of mixed quality, and evaluating the consolidated rankings. In this review, we present an overview of the existing RA methods with an emphasis on those that have been tailored to the complexities of genomics research. …

Temporal Ensemble Logic For Integrative Representation Of The Entirety Of Clinical Trials, Xiaojin Li, Yan Huang, Rashmie Abeysinghe, Zenan Sun, Hongyu Chen, Pengze Li, Xing He, Shiqiang Tao, Cui Tao, Jiang Bian, Licong Cui, Guo-Qiang Zhang

Faculty, Staff and Student Publications

Clinical trials are typically specified with protocols that define eligibility criteria, treatment regimens, follow-up schedules, and outcome assessments. Temporality is a hallmark of all clinical trials, reflected within and across trial components, with complex dependencies unfolding across multiple time points. Despite their importance, clinical trial protocols are described in free-text format, limiting their semantic precision and the ability to support automated reasoning, leverage data across studies and sites, or simulate trial execution under varying assumptions using Real-World Data. This paper introduces a formalized representation of clinical trials using Temporal Ensemble Logic (TEL). TEL incorporates metricized modal operators, such as “always …

Challenging Assumptions: Ai And Teacher Assessment Identity Formation, Nadia Ollington

Australian Journal of Teacher Education

This article explores the ways in which teacher educators can strengthen the developing teacher assessment identity of preservice teachers who are at the precipice of Industry 4.0, where work with Artificial Intelligence (AI) and assessment will require flexible thinking. This reflective case study narrative outlines a teacher educator’s experiences in a novel assessment task using Generative AI for technology enhanced learning in an Australian postgraduate teaching program. In the task, those who held positive beliefs and emotions about AI were more likely to engage in flexible thinking skills than those who were more critical of the AI. To ensure that …

Raising Information Literacy Of Biology Pre-Service Teachers: Study On Three Problem Solving Methods, Muhammad Saefi, Hadi Suwono, Rifka Fachrunnisa, Widi Cahya Adi, Herawati Susilo, Ahmad Kamal Sudrajat

Australian Journal of Teacher Education

Integrating information literacy teaching methods with subject-content curricula to develop prospective biology teacher students' abilities is still problematic. This quasi-experimental research, with a nonrandomized pretest-posttest control group design, intends to compare the influences of challenge-based learning (CBL), problem-based learning (PBL), and problem-solving through classroom discussion (PCD) – as the control- in improving information literacy. This study involved 183 students from three classes of freshmen and three sophomores in the biology education program. The results showed that CBL significantly improved students' information literacy compared to PBL and PCD across five indicators: 1) identifying the required information, 2) attaining the required information …

A Qualitative Exploration Of Changes In English Language Teachers’ Perceptions Of Their Agency Through Engaging In Action Research, Khadije Aqajani Delavar, Mohammad R. Hashemi, Mohammad Nabi Karimi

Australian Journal of Teacher Education

While research on language teachers’ engagement in action research (AR) has received a surge of attention, the influence of AR programs on English language teacher agency remains under-examined. This qualitative study explored how ten English language teachers’ perceptions of their agency changed as they participated in an action research engagement program (AREP). Data were collected using semi-structured interviews and reflective journals. The findings revealed that participation in the AR program changed the teachers’ perceptions of their agency and empowered them to enact higher levels of agency in their classroom practice. The study also highlights the important role of institutional support …

Advanced Fruit Quality Assessment Using Deep Learning And Transfer Learning Technique, Walid Abdullah

Sustainable Machine Intelligence Journal

Ensuring the quality of fruits is essential for consumer satisfaction and food industry standards. Accurately identifying and classifying high-quality fruits is essential for maintaining good health and nutrition. The subjective and labor-intensive nature of traditional techniques of evaluating quality highlights the need for automated solutions. This study utilized transfer learning with Four pre-trained convolutional neural network (CNN) models—MobileNetV2, ResNet50, VGG16, and EfficientNetB0 —to detect the quality of five fruits: apple, banana, strawberry, orange, and mango. All models were trained and tested with a public labeled fruit images dataset, and their performance was evaluated in terms of accuracy, precision, recall, and …

A Hybridized Cnn-Lstm-Mlp-Knn Model For Short-Term Solar Irradiance Forecasting, Doaa El-Shahat, Ahmed Tolba

Sustainable Machine Intelligence Journal

The accurate prediction of solar irradiance is essential for maximizing renewable energy production. Despite some researchers struggling to achieve sufficient prediction accuracy and model quality, we are working on developing a forecasting model for solar radiation using advanced artificial intelligence techniques. Solar energy offers a sustainable alternative to fossil fuels and has a wide range of applications, making the development of an effective forecasting model crucial. This paper proposes a hybrid model that combines deep learning and machine learning methods, denoted as CNN-LSTM-MLP-KNN. By combining Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN), …

Effect Of Malt Barley Cluster Farming On Food Security In Amhara Region, Ethiopia, Betelhem Tsedalu Adane, Irene Susan Egyir, Edward Ebo Onumah, Ama Asantewah Ahene-Codjoe, Taeyoon Kim

All Rsif Scholars' Publications

Cluster farming is the practice of merging neighbouring farms into a unified production unit to grow specific crops collaboratively. This is a promising method to enhance food security by improving agricultural productivity through coordinated resource use and enabling access to inputs, technologies, and markets. Despite this benefit, no empirical studies have examined how malt barley cluster farming affects smallholder farmers’ food security. This study adopts descriptive and econometric analysis, including the endogenous switching regression model (ESRM) and control function approach to account for selection bias and endogeneity in estimating the causal effect of malt barley cluster farming. Cross-sectional data were …

Using Public Nuisance Litigation To Address Industrywide Misconduct: Common-Law Statutes, Nondelegation, And Regulation, Hillel Y. Levin, Timothy D. Lytton

Scholarly Works

In this Article, we assert that broad, open-ended public nuisance statutes offer a legitimate legal tool to curb public health harms caused by the unreasonable design, marketing, and distribution practices of product manufacturers. We argue that these statutes are "common-law statutes" by which legislatures authorize courts to apply the concept of public nuisance to new situations using common-law-style reasoning. We demonstrate that this understanding of public nuisance statutes is consistent with well-established interpretive traditions regarding the codification of common law. Statutory delegation of broad interpretive discretion to courts concerning the application of public nuisance law constitutes a legislative choice to …

Charting New Frontiers: A Legal Literature Review Of Social Entrepreneurship And Impact Investing 2018–2023, Anne M. Tucker, Deborah Burand

Scholarly Works

This Article surveys 177 articles published in U.S. law reviews and journals between 2018–2023 that contribute to the fields of social enterprise, social finance and impact investing. The Article extends our earlier legal literature review of the same fields from 2007-2017.Our collective 17-year review of this span of legal literature documents the legal issues examined by scholars with respect to the development of sophisticated alternative business forms, like the Delaware Public Benefit Corporation; the growth in impact investing; the enduring academic interest in corporate purpose; and the effects that developments related to ESG and corporate sustainability are having on the …

Food Finding Ability Does Not Predict Annual Survival In Winter Resident Birds, Aidan Hand

Honors Program: Senior Projects (Public)

Small non-migratory birds in temperate locations face many challenges to survive the winter. They must be able to find enough food, which is in decreased supply, to fuel the energetic demands of staying warm. Prior work has demonstrated that birds show individual variation in their foraging behaviors that could contribute to the discovery of new foraging patches, such as novel environment exploration. A better ability to discover new food sources could increase a bird’s chance of surviving the winter by increasing its access to new food patches. To test this hypothesis, we used radio frequency identification (RFID)-enabled bird feeders to …

All About Over-The-Counters: The Use Of Public Health Principles In Health Education, Himanshu Gandhi

Honors Program: Senior Projects (Public)

The transition from high school to college can be extremely tough on students. There is a massive restructuring in schedules, academics, social lives, and even health. While many students come to college prepared to tackle their respective majors and courses, many of them aren’t prepared to handle the challenges of independent living. One of these challenges is self-healing and preventative care. As part of the University Honors Program, I have had the privilege of taking the first step of many in tackling this challenge. My project consisted of researching several common over-the-counter medications, utilizing my pharmaceutical and biochemical background to …

What Lawyers Could Learn From The Corporate Practice Of Medicine, Melissa D. Mortazavi

Faculty Articles

Proponents of the United States’ domestic experiments in non-lawyer ownership (NLO) of legal practice justify them in terms of increasing “access to justice.” But what if opening ownership of legal practice to investment capital only exacerbates market failures (leading to further consolidation and increased costs to clients), while also compromising professional autonomy and fiduciary duty? The legal profession must be clear-eyed about who are the real winners and losers of such a shift and avoid being blinded by wishful thinking. Prominent legal scholars have repeatedly touted NLO as a solution to the access to justice crisis. However, there is scant …

The First Amendment, Global Corporate Responsibility Standards, & The Quest For Online "Speech Nirvana", Evelyn Aswad

Faculty Articles

With large and powerful social media companies operating as worldwide speech regulators, it is unsurprising that governments have attempted to not only regulate how these companies address platform speech but also pressure them to deliver preferred speech outcomes. In 2024, the Supreme Court decided two cases addressing both themes in the U.S. context. In Moody v. NetChoice, the Court explained how legislation regulating private platform curation of usergenerated content runs afoul of First Amendment protections. And, in Murthy v. Missouri, the Court appeared to erect significant hurdles to challenging alleged governmental coercion of such platforms. These cases have left many …

Confessions Of A National Security Lawyer, Noah C. Chauvin

Faculty Articles

In part one of this paper, an unidentified national security lawyer tells all. In part two, I explain why the overclassification issues part one satirizes have important implications for the democratic accountability of intelligence agencies, using examples of classification abuses related to Section 702 of the Foreign Intelligence Surveillance Act.

High Crimes In Low Airspace: The Rise Of Drones In Cross-Border Drug Trafficking, Hannah Haight

Journal of Air Law and Commerce

Can the United States effectively regulate its airspace when criminal actors exploit it without entering traditional territorial channels? Mexican drug trafficking organizations increasingly deploy drones to deliver narcotics across the U.S.–Mexico border. These flights often occur at low altitudes, at night, and in remote areas, which renders them nearly undetectable by conventional enforcement methods. Yet, no statute squarely addresses the use of drones in cross-border drug trafficking. The Federal Aviation Administration (FAA) regulates drones under the FAA Modernization and Reform Act of 2012, but its mandate is safety, not interdiction. The result? A growing technological asymmetry: criminal cartels innovate, while …

Acc 362: Cryptoassets & Accounting Implications, Sean Stein Smith

Open Educational Resources

AI Policy and course materials (case studies) for course ACC 362: Cryptoassets & Accounting Implications.

Energy Efficiency In The Building Sector In Burkina Faso: Literature Review, Swot Analysis, And Recommendations, Bazam Amonet Ouoba Nebie, Abdou Lawane, Césaire Hema, Monica Siroux

All Rsif Scholars' Publications

In Burkina Faso, increasing access to electricity requires the optimized management of energy consumption in buildings and the implementation of regional and national regulations. These regulations have been implemented to promote energy efficiency in the building sector. However, they do not yet seem to have produced the expected results. Conducting a study could help identify the shortcomings related to energy efficiency and guide future research toward sustainable solutions. To date, no such study has been carried out. Therefore, this research aims to analyze the causes of these shortcomings, understand the gaps in the literature, and identify prospects for implementing energy …

Assessment Of Bio-Security Knowledge, Attitude And Hygienic Practices Among Smallholder Chicken Farmers In Southern Mozambique, Noémia André Mugabe, Jamal Kussaga, Abdulsudi Issa-Zacharia

All Rsif Scholars' Publications

The production of broiler chickens in intensive systems struggles to maintain hygiene in production, despite contributing to combating malnutrition, generating employment, and increasing family income, especially in developing countries. Using a structured questionnaire survey, a cross-sectional study was carried out among 395 smallholder farmers to evaluate their knowledge, attitudes, and practices related to farm biosecurity in urban and peri-urban areas of Mozambique and determine the factors affecting KAP (knowledge, attitude, and practice) levels. Data were analyzed using Statistical Package for Social Science (SPSS) (IBM version 27). The findings indicate that education level and training significantly influence (p < 0.001) food safety in terms of biosecurity knowledge, attitudes, and practices. Smallholders who attended food safety training exhibited positive attitudes (95.5%, mean score of 89.71 ± 10.1) in contrast to those who did not participate in training (74.8%, mean score of 79.91 ± 9.68). Additionally, washing hands with soap before and after using the toilet and wearing protective clothing during chicken husbandry were significantly associated with education level (χ² = 15.345, P = 0.018); those with informal education were 34% less likely to have a negative attitude toward food safety compared to those with high education. A significant correlation (p < 0.01) was observed between residence area, training, and biosecurity knowledge, attitude, and practice. Because smallholders lack training, hygiene expertise, and biosecurity practices on their farms, this study concluded that they pose a risk to public health and food safety. To maintain consumer health and guarantee food safety, farmers should properly dispose of waste, including dead chickens, and receive continuous training in excellent biosecurity measures on the farm

Almanet: A Hybrid Online Learning Ids For Real-Time Iot Security, Promise Ricardo Agbedanu, Shanchieh (Jay) Yang, Richard Musabe, Ignace Gatare, James Rwigema

All Rsif Scholars' Publications

Although some modern Intrusion Detection Systems (IDSs) for Internet of Things (IoT) have explored online machine learning (ML) approaches to build these IDSs, most IoT-based IDSs are designed using offline ML techniques. IDSs built with offline ML approaches cannot adapt to rapidly changing IoT network conditions. They need continuous retraining and require a lot of computational power. To address these limitations, we propose ALMANET (ALMA+NET), a hybrid intrusion detection approach combining Approximate Large Margin Algorithm (ALMA) with Stochastic Weight Averaging (SWA) and an online neural network (NET). ALMANET leverages the power of online learning, which updates models incrementally and allows …

A Scalable Approach To Internet Of Things And Industrial Internet Of Things Security: Evaluating Adaptive Self-Adjusting Memory K-Nearest Neighbor For Zero-Day Attack Detection, Promise Ricardo Agbedanu, Shanchieh Jay Yang, Richard Musabe, Ignace Gatare, James Rwigema

All Rsif Scholars' Publications

The Internet of Things (IoT) and Industrial Internet of Things (IIoT) have drastically transformed industries by enhancing efficiency and flexibility but have also introduced substantial cybersecurity risks. The rise of zero-day attacks, which exploit unknown vulnerabilities, poses significant threats to these interconnected systems. Traditional signaturebased intrusion detection systems (IDSs) are insufficient for detecting such attacks due to their reliance on pre-defined attack signatures. This study investigates the effectiveness of Adaptive SAMKNN, an adaptive k-nearest neighbor with self-adjusting memory (SAM), in detecting and responding to various attack types in Internet of Things (IoT) environments. Through extensive testing, our proposed method demonstrates …

Antibiotic Use And Antimicrobial Resistance: Knowledge, Attitudes, And Practices Among Smallholder Chicken Farmers In Southern Mozambique, Noemia Andre Mugabe, Jamal Kussaga, Abdulsudi Issa-Zacharia

All Rsif Scholars' Publications

Intensive production systems face challenges in treating individual chickens due to the large number of animals, resulting in the indiscriminate use of antibiotics as the only alternative. The use of antibiotics in poultry production has adverse effects on humans, as prolonged consumption of contaminated meat can lead to the development of antibiotic resistance in intestinal bacteria. A cross-sectional survey involving 395 smallholder farmers was conducted through face-to-face interviews to assess knowledge, attitudes, and practices (KAP) regarding antibiotic use in urban and peri-urban areas of Southern Mozambique. Data were analyzed using SPSS version 27. The findings revealed that 60% of respondents …

Clinical Application Of Chambo Tilapia Fish Skin Xenograft For Skin Tissue Regeneration: A Case Report In A Partial-Thickness Burns Pediatric Patient, Bhahat Lawlley Zimba, Dennis Mtunduwatha, David Hara, Mukondwedi Mchazime, Aaron Stima, Mwemezi Rwiza, Elingarami Sauli

All Rsif Scholars' Publications

WHO reports that burns account for more than 180,000 deaths annually with the majority of cases coming from low and middle-income countries due to limited resources in the management of burns. Currently, burns are being treated using silver sulphadiazine and gauze dressings, but this method has demonstrated to have some shortfalls. Alternative methods are being researched to find a better treatment modality with a faster healing duration. This case report describes the novel application of Chambo tilapia (Oreochromis lidole) fish skin xenograft in the treatment of partial thickness burns in a patient. The research was conducted on a 5-year-old female …

Epidemiology Of Patient Record Duplication, Onur Sahin, Audrey Zhao, Reuben Joseph Applegate, Todd R Johnson, Elmer V Bernstam

Faculty, Staff and Student Publications

Objectives: Duplicate patient records can increase costs and medical errors. We assessed the association between demographic factors, comorbidities, health care usage, and duplicate electronic health records.

Methods: We analyzed the association between duplicate patient records and multiple demographic variables (race, Hispanic ethnicity, sex, and age) as well as the Charlson Comorbidity Index (CCI), number of diagnoses, and number of health care encounters. The study population included 3,018,413 patients seen at a large urban academic medical center with at least one recorded diagnosis. Duplication of patient medical records was determined by using a previously validated enterprise Master Person Index.

Results: Unknown …

Quantitatively Assessing The Impact Of The Quality Of Snomed Ct Subtype Hierarchy On Cohort Queries, Xubing Hao, Xiaojin Li, Yan Huang, Jay Shi, Rashmie Abeysinghe, Cui Tao, Kirk Roberts, Guo-Qiang Zhang, Licong Cui

Faculty, Staff and Student Publications

Objective: SNOMED CT provides a standardized terminology for clinical concepts, allowing cohort queries over heterogeneous clinical data including Electronic Health Records (EHRs). While it is intuitive that missing and inaccurate subtype (or is-a) relations in SNOMED CT reduce the recall and precision of cohort queries, the extent of these impacts has not been formally assessed. This study fills this gap by developing quantitative metrics to measure these impacts and performing statistical analysis on their significance.

Material and methods: We used the Optum de-identified COVID-19 Electronic Health Record dataset. We defined micro-averaged and macro-averaged recall and precision metrics to assess the …