Digital Commons Network^™

Shocking Words And Helper Spirit, Joel E. Kok

Pro Rege

No abstract provided.

A Kuyperian Solution To The Contemporary Problem Of Individualism Manifested In Eucharistic Practice, Isaac Krommendyk

Pro Rege

Issac Krommendyk, a senior at Dordt University majoring in History and Spanish, submitted this winning paper to the Lambertus Verberg Prize for Excellence in Kuyperian Scholarship, 2026

A Model For A Christian Business, John Wassenaar

Pro Rege

No abstract provided.

Resisting A Consumeristic Approach To Reading In Our Lives (And Our Classrooms), Elise Kamp

Pro Rege

Reviwed Title: Deep Reading: Practices to Subvert the Vices of Our Distracted, Hostile, and Consumeristic Age by Rachel Griffis, Julie OOms, and Rachel De Smith Roberts. Baker Academic, 2024. 230 pp. ISBN: 9781493446023.

Finding Home: How Richard Mouw And Dordt's Community Healed My Heart, Brad D. Hickey

Pro Rege

No abstract provided.

Brooding Upon The Waters: A Memoir Of Farming, Fishing, And Failure In America's Lost Landscape (Book Review), Abby De Groot

Pro Rege

Reviewed Title: Brooding Upon the Waters: A Memoir of Farming, Fishing, and Failure in America's Lost Landscape by Howard Schaap. Slant Books, 2025. 238 pp. ISBN: 9781639822096.

Inosine-Triphosphate-Pyrophosphatase Activity As A Potential Predictor Of Methotrexate Remission In Juvenile Idiopathic Arthritis., Sofia Sindici Forgiarini, Marianna Lucafò, Debora Curci, Davide Selvestrel, Valentina Moressa, Sofia Pagarin, Barbara Bellich, Martina Franzin, Alberto Tommasini, Mara L. Becker, Laura Ramsey, Susan D. Thompson, Carl D. Langefeld, Federica Corba, Edoardo Marrani, Gabriele Simonini, Gabriele Stocco, Giuliana Decorti, Andrea Taddio, Serena Pastore

Manuscripts, Articles, Book Chapters and Other Papers

OBJECTIVE: Methotrexate (MTX) is the first-line therapy for juvenile idiopathic arthritis (JIA), but up to 40% of patients do not respond to it. Low inosine triphosphate pyrophosphatase (ITPA) activity has been associated with reduced clinical remission. We investigated the role and underlying mechanisms of ITPA in vitro.

METHODS: ITPA enzymatic activity was measured by high-performance liquid chromatography with ultraviolet detection in erythrocyte lysates from patients with JIA (enrolled in Trieste, Italy; Florence, Italy; and Kansas City, Missouri) receiving weekly MTX (10-15 mg/m²) for at least 12 months. Remission was evaluated by Wallace et al criteria. ITPA rs1127354, which …

Posaconazole Target Attainment In Critically Ill Children: A 15-Year Single-Center Retrospective Analysis., Kathryn Pavia, Addison M. Pritchett Ms, Jennifer Goldman

Manuscripts, Articles, Book Chapters and Other Papers

Posaconazole is a triazole antifungal agent used for prophylaxis and treatment of invasive fungal diseases. Posaconazole has high inter-individual variability in dose-exposure relationship and benefits from therapeutic drug monitoring (TDM). Adult studies report particularly high rates of subtherapeutic exposure among intensive care unit (ICU) patients related to changes in physiology during critical illness. The rates of subtherapeutic posaconazole exposure among critically ill children in ICU's are unknown. This single-center retrospective analysis at a large freestanding children's hospital used TDM results from 2010 to 2025 obtained while patients were undergoing care in a pediatric ICU. Therapeutic targets were ≥ 1.0 mg/L …

Hla-Dr Expression At Diagnosis Is Not Predictive Of Relapse Risk Post Allogeneic Hematopoietic Stem Cell Transplantation For Pediatric Acute Myeloid Leukemia: A Report From The Children's Oncology Group., Vasant Chinnabhandar, Michael R. Verneris, Kirk R. Schultz, Alan S. Gamis, Richard Aplenc, Chad A. Hudson, Todd A. Alonzo, Robert B. Gerbing, John T. Horan, Joseph H. Chewning

Manuscripts, Articles, Book Chapters and Other Papers

No abstract provided.

Girls’ Education Roadmap: 2026 Report, Meredith Kozak, Miriam Temin, Lina López-Lalinde, Amaranta Martinez Martinez

Adolescents and Young People

This Roadmap highlights where the gender and education field can align on coordinated action and investment to close critical gender gaps in education and improve learning for all. Building on findings from the original 2021 Girls’ Education Roadmap, it brings together data on the greatest gender and education needs, evidence on evaluated approaches, and where current practice reaches or misses vulnerable learners. From this foundation, the Roadmap organizes insights around 18 gender-related barriers to education, giving decision makers a framework for diagnosing context specific challenges and identifying proven approaches to address them.

Single Immunoglobulin Interleukin1-Related Receptor-Twist1 Axis Regulates Barrier Function In Neonatal Intestine., Aparna Venkatraman, Wei Yu, Heather Menden, Sherry M. Mabry, Joshua Wheatley, Shahid Umar, Susana Chavez-Bueno, Venkatesh Sampath

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND & AIMS: The genetic basis of impaired intestinal barrier function in preterm infants is poorly understood. Variants in single immunoglobulin interleukin1-related receptor, a negative regulator of Toll-like receptor signaling, have been identified in preterm infants with necrotizing enterocolitis. We hypothesized that single immunoglobulin interleukin1-related receptor variants associated with necrotizing enterocolitis impair gut barrier function to pathobionts. The aim of this study was to determine how single immunoglobulin interleukin1-related receptor genetic variants disrupt neonatal intestinal barrier integrity and promote susceptibility to Gram-negative bacteria implicated in sepsis and necrotizing enterocolitis pathogenesis.

METHODS: Transgenic mice and preterm infant-derived enteroids expressing SIGIRR variants …

Front Matter

Journal of Science & Technology Law

No abstract provided.

Autonomy, Adolescents, And Artificial Intelligence, Michael J. S. Beauvais

Journal of Science & Technology Law

In this essay, I explore the potential implications of adolescent use of AI assistants on the development of adolescents’ capacities for autonomy. Thinking through these issues from the perspective of first principles is, I believe, needed in order to sharpen our understanding of some of the issues AI assistants pose for young people. A clear analysis will suggest which legal or policy prescriptions should follow.

I first give an overview of adolescent use of AI assistants, noting their popularity and the variety of use contexts. Drawing upon the work of Catriona Mackenzie, I then outline a relational conception of autonomy …

Central Bank Financial Strength, Credibility, And Global Market Risk: Evidence From Southeast Asian Economies, Mega Fauziah, Hermanto Siregar, Solikin M. Juhro, Tanti Novianti, Sigit Wibowo

Bulletin of Monetary Economics and Banking

This paper examines central bank credibility in the context of its financial strength, independence, and global market uncertainty in five Southeast Asian countries between 2005 and 2023. We find that the global market risk indicated by VIX reveals that these Southeast Asian economies, despite achieving considerable central bank independence and institutional development, remain fundamentally constrained by global market conditions in maintaining their credibility, measured by the difference between the inflation rate and its target in the same year. We argue that in emerging economies exposed to global uncertainty, central bank profitability, conditional on its independence, is vital for central banks …

Inflation-Differential Rule Versus (De Facto Soft-Peg) Discretion: Exchange-Rate Management Approach In Developing Economies, Behnam Ebrahimi, Mohammad Vaez Barzani Dr.

Bulletin of Monetary Economics and Banking

Many developing and emerging economies officially report a managed floating exchange-rate regime tailored with a fundamental exchange rate management approach, while in practice operating a de facto soft peg, characterized by discretionary, near-constant exchange-rate depreciation aimed at dampening inflation expectations. This discretionary approach—often justified as a pragmatic response to inflationary pressure—produces a hybrid regime that lacks the transparency and credibility emphasized in IMF policy guidance. This study evaluates whether substituting Discretionary Soft-Peg Exchange Rate Management approach with an Inflation-Differential Rule of Exchange Rate Management approach, which links exchange-rate adjustments to the inflation differential between domestic and rest of the world, …

Fiscal Decentralization, Local Government Size, And Regional Economic Growth: Evidence From Indonesia, Faizul Mubarok, Hasta Dwi Pradana, Etty Puji Lestari

Bulletin of Monetary Economics and Banking

This study investigates the dynamic relationship between fiscal decentralization, local government size, and regional economic growth in Indonesia. Integrating fiscal federalism theory with the Armey curve framework, the study examines whether decentralization reshapes the scale of subnational governments and how this influences growth outcomes. Using balanced panel data from 26 Indonesian provinces over the period 1995–2020, the analysis employs dynamic panel estimators based on the Generalized Method of Moments (GMM) to address endogeneity, persistence, and unobserved heterogeneity. The results reveal a nuanced relationship between fiscal decentralization and government size. Greater revenue autonomy is associated with a contraction in government size …

Oil Futures Reactions To Monetary Policy Surprises: A High-Frequency Analysis, Syed Mujahid Hussain Dr., Basel Awartani, Farrukh Javed, Iqbal Khadaroo Dr., Nader Virk Dr.

Bulletin of Monetary Economics and Banking

This study examines how oil futures respond to monetary policy shocks arising from FOMC and ECB target-rate announcements, using high-frequency data. The results indicate that both sets of announcements significantly influence oil futures prices. However, FOMC decisions have a more pronounced effect on oil futures, whereas ECB interest rate surprises predominantly have a limited impact on the oil futures return and volatility. The analysis further uncovers an asymmetric response to positive versus negative interest rate shocks. These findings enhance the understanding of how monetary policy surprises are transmitted to oil futures markets. Therefore, these results have important implications for policymakers …

The Nusantara Capital City (Ikn) Relocation: Autocratic Legalism And The Marginalization Of Local Communities, Imam Syafi'i, Dian Aulia

The Indonesian Journal of Socio-Legal Studies

The first phase (2022–2024) of the Nusantara Capital City (IKN) relocation prioritizes the construction of the city's physical infrastructure under the “Nusa Rimba Raya” concept. During this phase, the massive development of supporting infrastructure, including government buildings, official residences, toll roads, ports, and the VVIP airport, has driven increased demand for materials and land, impacting areas beyond the delineation of IKN. However, the power-driven approach in the capital relocation policy has led to socio-ecological injustices for indigenous and local communities by disregarding meaningful public participation and environmental sustainability. Land acquisition through the Land Bank (Bank Tanah), which prioritizes investment, along …

Comparative Legal Study On Corruption In The Construction Sector: Indonesia And China, Seng Hansen, Liang Qi

The Indonesian Journal of Socio-Legal Studies

This study aims to compare the legal approaches to corruption in the construction sector in Indonesia and China. The construction sector in both countries is often fertile ground for corruption, which harms the economy and creates social inequality. This study employs a comparative legal analysis method to examine regulations, policies, and the effectiveness of law enforcement in combating corruption within the construction sector in both countries. The primary focus of this study is a comparison of applicable anti-corruption laws and the roles of law enforcement agencies, such as the Corruption Eradication Commission (KPK) in Indonesia and the Central Commission for …

Medical Liability In The Age Of Ai-Driven Healthcare: Ethical Challenges And Legal Framework Recommendations, Rani Tiyas Budiyanti, Roro Isyawati Permata Ganggi, Penggalih Mahardika Herlambang

The Indonesian Journal of Socio-Legal Studies

The integration of artificial intelligence (AI) in healthcare is progressing rapidly and is applied to various aspects of medical practice, including diagnosis, treatment planning, and patient care. However, the integration of AI in healthcare raises complex legal and ethical issues, particularly regarding medical liability. The growing development of AI technology is blurring the concepts of responsibility and accountability in medical practice. Furthermore, with AI systems becoming increasingly autonomous in their decision-making processes, it becomes increasingly difficult to determine responsibility in cases of medical errors or actions that harm patients. In view of this situation this article argues that it is …

Molecular Detection Of Staphylococcus Aureus: A Performance Comparison Of Colony Pcr And Conventional Pcr Targeting The Icab Gene, Farah Badri Abed, Shahrazad Hazim Muhi, Sahar Mustafa Ibrahim, Riyam Alaa Mohammed

Muthanna Medical Journal

Background: Staphylococcus aureus is an important nursing disease that requires a prompt and effective diagnosis to implement a strategy to curb its transmission and spread. Although the gold standard is the conventional PCR using column-purified genomic DNA, Colony PCR with crude cell lysates saves a lot of time and cost of the test and its performance features have not been well established.
Objectives: This paper will compare Colony PCR (boiling method) and Conventional PCR (ABIOpuretm kit extraction) to amplify icaB gene (141 bp), which is a major determinant of biofilm formation in S. aureus and then assess their application with …

The Application Of Machine Learning And Deep Learning On Demand Forecasting Across Time-Critical Industries: A Systematic Review, Asmaa Seyam, Sujith Samuel Mathew, May El Barachi, Cheng Zhang, Jun Shen

All Works

The applications of machine learning and deep learning in demand forecasting have attracted increasing attention, as they offer remarkable predictive capabilities that help automate forecasting processes and achieve higher accuracy. While numerous review studies have examined solutions within specific industries, there is a lack of comprehensive literature review investigating these solutions across different sectors. Therefore, this study overviews machine learning and deep learning applications in demand forecasting across time-critical industries, including power, tourism, water, transportation, and food. A two-tier classification framework is proposed to categorize demand forecasting studies by both application industry and methodological architecture. In addition, the most popular …

Uncovering Customer Sentiment And Brand Perception By Leveraging Large Language Models: A Case Study In The Automotive Industry, Sujith Samuel Mathew, Kadhim Hayawi, Faseela Abdullakutty, Neethu Venugopal, May El Barachi

All Works

Analyzing customer perceptions has become increasingly important in the automotive industry as it provides actionable insights into consumer satisfaction, preferences, and areas requiring improvement. This study proposes a novel perception analysis framework for automotive reviews using advanced Large Language Models (LLMs), including BERT, FLAN-XXL, and Mistral 7B, leveraging zero-shot learning to categorize reviews without task-specific training data. The framework follows a two-stage evaluation process, beginning with zero-shot perception classification and followed by a detailed topic-wise perception analysis. Model performance was evaluated using accuracy, precision, recall, and F1-score across reviews from five major automotive brands—Toyota, Kia, Honda, Nissan, and Hyundai. While …

Dustmambanet: A Hybrid Inceptionv3–State-Space Network For Robust Solar Panel Dust Detection, Kadhim Hayawi, Sakib Shahriar

All Works

To develop a robust, scalable vision-based model for automatic detection and quantification of dust accumulation on solar photovoltaic panels, overcoming limitations of existing convolutional and attention-based methods and supporting proactive maintenance. We propose DustMambaNet, a hybrid model that consists of a pretrained InceptionV3 convolutional neural network as a feature extractor and two selective state space sequence modules. The state space modules use gated depthwise convolutions to represent long-range spatial dependencies that are of linear complexity, after rearranging spatial features to form sequences. The network provides a binary classification of dust with a severity index (DSI) and a continuous one. All …

Comparative Risk Metrics For U.S. Commercial Aviation, September 2026, Mihhail Berezovski

BCAAS Reports

No abstract provided.

Gene-Environment Interactions In Cardiovascular Aging: Volunteering, Polygenic Risk, And Biomarker Change In European And African Ancestry Populations, Seoyoun Kim, Byeongju Ryu, Erin Ware, Koichiro Shiba, Cal J. Halvorsen

Brown School Faculty Publications

Volunteering has been associated with better cardiovascular health, but whether this association varies by genetic susceptibility to cardiovascular disease remains unclear. This study examined associations between volunteering and cardiovascular biomarkers, assessed associations between biomarker-specific polygenic scores (PGSs) and these outcomes, and tested whether polygenic risk moderated volunteering–biomarker associations among U.S. adults aged 51 years and older.

Prune: A Patching Based Repair Framework For Certifiable And Privacy-Robust Unlearning Of Neural Networks, Xuran Li, Jingyi Wang, Xiaohan Yuan, Peixin Zhang

Research Collection School Of Computing and Information Systems

Machine unlearning has emerged as a key mechanism for enabling the “right to be forgotten” in neural network models, allowing the selective removal of specific training data upon request. Existing approaches typically rely on retraining models with the remaining data, which is computationally expensive and difficult to verify, especially when deployed models are distributed or resource-constrained. To address this challenge, our prior conference work introduced PRUNE, a patching-based framework that formulates unlearning as a neural network repair problem. PRUNE achieves targeted forgetting by learning lightweight patch networks that redirect model predictions on the data to be unlearned while preserving performance …

Neural Symphony Of Flow Experience: Evidence For High-Dimensional Metastable Dynamics, Abdelrahman B. M. Eldaly, Kris Zhangguang Kang, Fiona Fui-Hoon Nah, Leanne Lai-Hang Chan, Keng Siau, Xiao Fan Liu, Richard Huskey, Langtao Chen, Tejaswini Yelamanchili, Rene Weber

Research Collection School Of Computing and Information Systems

Flow, an optimal experience characterized by deep immersion and engagement in an activity, has been extensively studied in behavioral research. However, its neural dynamic mechanism remains poorly understood. In a within-subject video gaming experiment, we captured neural activity underlying flow, boredom, and anxiety using a 64-channel electroencephalogram (EEG) system. Compared to boredom and anxiety, flow exhibits the highest global functional connectivity, metastability, and dimensionality of dynamic functional connectivity patterns, suggesting that flow is a highly adaptable process that is supported by high-dimensional neural dynamics. Unlike previous studies that focused on identifying static or localized brain activity, we examine the neural …

Generalized Logit Adjustment: Improved Fine-Tuning By Mitigating Label Bias In Zero-Shot Vision Models, Beier Zhu, Qianru Sun, Xun Yang, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Foundation models like CLIP allow zero-shot transfer on various tasks without additional training data. Yet, the zero-shot performance is less competitive than a fully supervised one. Thus, fine-tuning and ensembling are also commonly adopted to better fit the downstream tasks. However, we argue that such prior work has overlooked the inherent biases in foundation models. Due to the highly imbalanced Web-scale training set, foundation models are inevitably skewed toward frequent semantics, and thus the subsequent fine-tuning or ensembling is still biased. In this study, we systematically examine the biases in foundation models and demonstrate the efficacy of our proposed Generalized …

Smart: When Is It Actually Worth Expanding A Speculative Tree?, Lifu Wang, Pan Zhou

Research Collection School Of Computing and Information Systems

Tree-based speculative decoding accelerates autoregressive generation by verifying a branching tree of draft tokens in a single targetmodel forward pass. However, existing methods prioritize maximizing token-level likelihood or the number of accepted tokens while ignoring a critical “efficiency paradox”: the computational overhead of drafting and verifying big trees can grow super-linearly, particularly at scale. This often leads to negative wall-clock speedup when batch sizes increase or hardware saturation limits are reached. To address this, we propose SMART, a System-aware Marginal Analysis framework for Runtime Tree construction. SMART reformulates tree expansion as a hardware-aware optimization problem that directly maximizes end-toend speedup. …