Digital Commons Network^™

A Tribute To The Life And Work Of Jess Chua, James J. Chrisman, Zhenyu Wu, Alfredo De Massis, Pramodita Sharma, Lloyd P. Steier, Franz W. Kellermanns, Josip Kotlar, Emanuela Rondi, Bingbing Ge, Chevy-Hanqing Fang

Business and Information Technology Faculty Research & Creative Works

Jess Chua was a leading scholar in the field of family business and a major contributor to Entrepreneurship Theory and Practice both as an author and editor. His significant contributions to the field were acknowledged by the Web of Science, which listed him among the world's most highly cited researchers in Economics and Business in 2017, 2019, and 2020. In this memorial editorial, we pay tribute to him by discussing his influence on the field through his academic work, mentoring, long-term collaborations with other scholars, and the kindness he and his wife, Eva Kan, showed to his colleagues and students.

Scalable Fabrication Of High-Payload Dendrimer-Based Nanoparticles For Targeted Atherosclerosis Therapy, Huari Kou, Lin Qi, Da Huang, Jiandong Wu, Honglan Shi, Hu Yang

Chemistry Faculty Research & Creative Works

Nanoparticle-based therapeutics hold promise for the treatment of atherosclerosis, but challenges such as low drug-loading capacity and a lack of scalable, controllable production hinder their clinical translation. Flash nanoprecipitation, a continuous synthesis method, offers a potential solution for scalable and reproducible nanoparticle production. In this study, we employed a custom-designed multi-inlet vortex mixer to perform cross-linking reaction-enabled flash nanoprecipitation, facilitating controlled and scalable synthesis of cross-linked Poly amidoamine (PAMAM) dendrimer nanoparticles. Notably, this approach allows simultaneous nanoparticle cross-linking and drug loading in a single step. The mannose moiety enabled specific targeting of macrophages via mannose receptors, enhancing the localization of …

Discontent In The World City Of Singapore, Gordon Tan, Jessie P. H. Poon, Orlando Woods

Research Collection College of Integrative Studies

A burgeoning literature on ‘left behind’ places has emerged that captures the backlash against globalisation and highlights the locales that lag world cities. This paper integrates the ‘left behind’ and world cities literatures through the lens of discontent in the context of Singapore, using sentiment analysis and topic modelling as well as interviews with local professionals to unpack the multidimensional aspects of discontent. Focusing on the Singapore–India Comprehensive Economic Cooperation Agreement that spurred discontent directed at foreign Indian professionals, we show that the worlding generated by transnational flows has accentuated intra-urban inequality through racialisation and spatialisation of financial business and …

Natural Capital Credit Risk Assessment In Agricultural Lending: An Approach Based On The Natural Capital Protocol, Francisco Ascui, Theodor Florian Cojoianu

Research Collection College of Integrative Studies

It is increasingly recognised that financial institutions have significant impacts and dependencies on natural capital – defined as the stock of the world’s renewable and non-renewable natural resources and ecosystems that yield flows of environmental goods and services, which directly and indirectly underpin the global economy and human wellbeing. Historically, many of these impacts and dependencies have been overlooked. The value of goods and services that nature provides ‘for free’ has often been ignored, and resources have typically been priced at their cost of extraction, rather than the cost of their replacement or substitution, which would promote more sustainable long-term …

Meteorology, Weather And War In South East Asia: Malaya C. 1940-1960, Fiona Williamson

Research Collection College of Integrative Studies

This article interrogates the positioning of British colonial meteorology in Malaysia and Singapore from the 1940s to 1960. This period spanned a global conflict and an internecine war, effecting profound sociopolitical changes from which neither Malaysia nor Singapore would emerge the same. The meteorological services were essential to Britain's armed conflicts, providing vital weather information to the army, navy and, especially, the air forces, as well as supporting the aviation and shipping industry often in difficult and dangerous circumstances. This article argues that British military policy in South East Asia and the specific concerns of the colonial government in Malaya …

Landscapes Of State Capitalism: Reconciling The Vernacular, National Development Imaginaries, And Green Capital Accumulation, Sol Andrew Stokols

Research Collection College of Integrative Studies

This article theorizes ‘state capitalist landscapes’ as a unique spatial form shaped by state-owned developers that reconcile economic goals with national development imperatives and symbolic national imaginaries. State capitalist landscapes, I argue, integrate economically productive spaces with natural spaces to symbolize imaginaries of the nation. Through an inductive case study methodology, this article surveys three industrial zones developed and/or under development by state owned developers: Singapore's Jurong, China's Xiong’an New Area, and Thailand's Wangchan Valley. In each of these cases, natural landscapes are integrated into new economic zones in order to construct specific symbolic and/or political imaginaries of the nation: …

Oxidation Behavior Of Silicon Carbide, Matrix Graphite, And Refractory Multi-Principal Element Alloy, Visharad Jalan

Doctoral Dissertations

"The present study focuses on the investigation of the oxidation behavior of silicon carbide, matrix graphite in a water vapor environment. It also examines the oxidation behavior of refractory multi-principal element alloys when exposed to air. The oxidation behavior of silicon carbide and matrix graphite were investigated in conditions pertaining to off-normal incidents in high-temperature gas-cooled nuclear reactors. Understanding this is crucial for the prediction of their performance in such accidents and ensuring that the nuclear reactor is accident tolerant. Upon exposure to water vapor, the two A3-based matrix graphite exhibited different behaviors. The A3-27 type showed oxidation in all …

Leveraging Deep Learning Models And Social Media Data For Enhanced Situation Awareness In Disaster Management, Ademola Abdulganiyu Adesokan

Doctoral Dissertations

"In recent years, social media has become a crucial source of real-time data for disaster management, supporting emergency responses when traditional channels like 911 are overcrowded and overwhelmed. It offers authorities valuable data for developing effective strategies, especially when swift actions are essential to save lives. However, the informal language, ambiguous meanings, and irrelevant content on social media pose challenges to accurate classification and hinder the efficient extraction of disaster-relevant information, leading to inefficiencies in emergency response efforts.

This research focuses on seven key questions: i) How can we detect, classify, and analyze hate and offensive tweet emotions during large-scale …

Mitigating Frost Heave And Thaw Weakening In Roads Using Wicking Geotextile, Javad Galinmoghadam

Doctoral Dissertations

"Excess water is known to be the primary cause of soil and foundation problems. In cold regions, water-related problems are exacerbated by seasonal freezing and thawing cycles. Frost heave and thaw weakening is the leading cause of premature pavement failure and consume considerable budgets of the Department of Transportations in cold regions. The first step to designing mitigation measures is the ability to predict frost heave under different conditions. This study aims to introduce a new method for quantifying frost heave using a prediction model based on the state surface approach. This proposed model addressed many inconsistencies and limitations of …

Defining Forced And Free Labor, 1930–1957, Christopher M. Roberts

Fordham International Law Journal

No abstract provided.

The Re-Criminalization Of Sexual Minorities As A Testing Ground For International Human Rights Law In Africa, Matteo M. Winkler & Ilias Bantekas

Fordham International Law Journal

This Article scrutinizes the recently enacted statutes that re- criminalize sexual minorities in Africa from the standpoint of international human rights law (IHRL). These statutes are currently justified under cultural relativism, that is, the idea that African culture abhors homosexuality, which represents a malicious import from the Western world. We argue that the re-criminalization statutes violate IHRL in multiple stances and that in no way are these violations justifiable under cultural relativism. This does not mean that there is no virtue in cultural relativism, but a limit must be fixed to the dehumanization of minorities which is the intended purpose …

That's One Small Sale For Man: On The Merits Of American Unilateralism In The Evolution Of International Space Law, Sam Grossman

Fordham International Law Journal

In 2020, the United States National Aeronautics and Space Administration (NASA) awarded four contracts to commercial companies to collect lunar resources as part of its Artemis program. This contract was the culmination of a controversial shift in global space policy, as it came shortly after an executive order from President Donald J. Trump. That executive order articulated America’s policy to encourage “public and private recovery and use of resources in outer space,” and to seek international support in doing so. Further, it reaffirmed the 2015 U.S. Commercial Space Launch Competitiveness Act (the “Space Act”), which entitled American individuals and corporations …

Improving System-Level Outcomes Via Artificial Intelligence Decision Support In Kidney Utilization, Casey I. Canfield, Cihan H. Dagli, Daniel Burton Shank, Krista Lentine, Mark Schnitzler, Henry Randall, V. Sriram Siddhardh Nadendla, Brendon Cummiskey

Engineering Management and Systems Engineering Faculty Research & Creative Works

Transplantation provides patients suffering from end-stage kidney disease a better quality of life and long-term survival. However, over 20% of deceased donor kidneys are not utilized and never transplanted. While this is sometimes medically appropriate, this also reflects missed opportunities. We are designing Artificial Intelligence decision support for the kidney offer process to support both demand at the transplant center and supply at the organ procurement organization. This includes (1) developing deep learning models, (2) evaluating the effect of explainable interfaces, (3) improving fairness in the model output, (4) identifying factors that influence adoption decisions, and (5) conducting a randomized …

Spatial Analysis And Machine Learning Integration For Nutritional Status Mapping Using Ann And Random Forest Models, Desi Anis Anggraini, Fachrul Kurniawan, Fresy Nugroho, Meidya Koeshardianto, Mohammad Iqbal Bachtiar

Knowledge Engineering and Data Science

Nutritional problems among children under five remain a major public health challenge. This research seeks to create a spatially oriented system for evaluating and mapping nutritional status utilizing Artificial Neural Network (ANN) and Random Forest (RF) algorithms. Data obtained from the Sumenep District Health Office included age, weight, height, and gender variables. Both models were trained using a 70:30 data ratio and evaluated with accuracy, precision, recall, and F1-score metrics. The ANN model achieved an accuracy of 95.8%, while the RF model reached 97.7%. Classification results were visualized through a Geographic Information System (GIS) to illustrate spatial distribution and identify …

Time Series Forecasting With Lstm: An Extensive Content Analysis, Andri Pranolo, Xiaofeng Zhou, Yingchi Mao

Knowledge Engineering and Data Science

This paper presents a comprehensive bibliometric and content review of the trend, architecture, and application of long short-term memory (LSTM) models for time series forecasting. The study aims to provide insights into the overall statistics and distribution of papers focused on LSTM for forecasting. Additionally, the research questions address the most highly cited papers based on LSTM approaches in forecasting, the most productive journals in this field, and identifying trends, gaps, summary tasks and their performance, datasets availability, and future research directions for LSTM in forecasting. This paper is a comprehensive review of LSTM for forecasting from 2017 to 2023 …

From Data To Insight: A Machine Learning Approach In Classifying Dairy Cow Productivity Level And Identifying Important Influencing Variables, Fatkhurokhman Fauzi, Achmad Fauzan, Rhendy K P Widiyanto, Khairil Anwar Notodiputro, Bagus Sartono

Knowledge Engineering and Data Science

Identifying influential predictor variables is crucial for enhancing model interpretability in supervised classification. This study applies Permutation Variable Importance (PVI), a model-agnostic approach, to evaluate variable relevance after model fitting. Using data from the 2024 Indonesia Dairy Cow Productivity Survey, this research investigates five classification techniques: (1) Support Vector Machine (SVM), (2) Neural Network (NN), (3) k-Nearest Neighbors (kNN), (4) Naïve Bayes Classifier (NB), and (5) Logistic Regression (LR), to identify which method(s) yield the best performance based on evaluation metrics such as accuracy, sensitivity, and specificity. PVI is employed to identify the most influential predictor variables within the best-performing …

A Hybrid Soft Voting And Stacking-Based Meta-Learning Approach For Sentiment Analysis Of Bangkalan Batik, Moh. Imron Wahyudi, Lailil Muflikhah, Rizal Setya Perdana

Knowledge Engineering and Data Science

Sentiment analysis is an important field in Natural Language Processing (NLP) that focuses on processing consumer opinions to gain useful insights. The information generated from sentiment analysis can be used as a basis for business decision-making, service quality evaluation, and the formulation of more effective marketing strategies. In the local context, Bangkalan Batik, as one of Madura's distinctive cultural products, has high economic value and cultural identity. However, consumer reviews available online, for example through Google Maps, are still rarely utilized optimally by MSMEs as a source of strategic information. Therefore, this study was conducted to develop a sentiment classification …

Mapping Of Product Sales Potential Based On Brands In The East Kalimantan Region Using Hybrid Analytical Framework, Achmad F O Gaffar Mr, Mulyanto Mulyanto Mr, Arief Bw Putra Mr, Muhammad Taufiq Sumadi Mr, Emmilya Umma Aziza Gaffar

Knowledge Engineering and Data Science

In geographically dispersed markets, operational costs should be reflected in sales planning to support accurate performance evaluation. However, such considerations are often neglected in practice. This study proposes a hybrid analytical framework to map brand-based product sales potential, with and without operational cost consideration, using historical sales data from PT Karya Inti Total Anugerah (PT KITA) in East Kalimantan. The framework integrates spatial, statistical, and machine learning techniques. Principal Component Analysis (PCA) is used to reduce the dimensionality of variables related to travel distance, total sales, and units sold, where travel distance represents the primary contributor to operational costs. K-Means …

Feature Engineering And Anchor Optimization For Enhancing Faster R-Cnn Detection Of Low-Contrast Steel Surface Defects, Herdianti Darwis, Sitti Nurhalimah, Huzain Azis

Knowledge Engineering and Data Science

Detection of defects on low-contrast steel surfaces, especially crazing and rolled-in-scale, remains a major challenge due to their visual similarity to background patterns. Although state-of-the-art methods have achieved high accuracy through complex architectural adjustments, the contribution of preprocessing techniques has not been thoroughly investigated. This study investigates pre-processing-based improvements to Faster R-CNN by combining Bilateral Filtering to reduce noise, CLAHE to enhance local contrast, CIoU Loss for more effective bounding box regression, and customized anchor settings for irregular defect configurations. Evaluated using the NEU-DET dataset, our BF-CIoU Faster R-CNN model achieved a mAP@50 score of 72.32%, with an AP of …

Comparative Analysis Of Deep Learning Algorithms For Predicting Enso Based On Non-Sequential Sampling Procedure Algorithms, Ria Faulina, Siti Hadijah Hasanah, Nuramaliyah Nuramaliyah, Ika Nur Laily Fitriana

Knowledge Engineering and Data Science

The El Niño–Southern Oscillation (ENSO) is a major climate phenomenon that significantly influences global weather patterns, particularly rainfall and temperature variability across different regions. Accurate ENSO forecasting is therefore essential to support disaster risk mitigation and strategic decision-making in climate-sensitive sectors such as agriculture, fisheries, and water resource management. This study investigates the performance of deep learning approaches for ENSO prediction using a non-sequential sampling procedure on historical climate data. Three models are comparatively evaluated: Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN–LSTM architecture. The results demonstrate that the hybrid CNN–LSTM model outperforms the standalone CNN …

Cognitive Eeg Differentiation With Hypnosis-Based Noise Reduction And K-Harmonic Means For Personalized Brainwave Modeling, Ahmad Azhari, Dimas Chaerul Ekty Saputra

Knowledge Engineering and Data Science

This study investigates the integration of hypnosis-based noise reduction and K-Harmonic Means (KHM) clustering for personalized brainwave modeling using Electroencephalography (EEG) data. EEG signals were collected from 100 participants using a Neurosky Mindset sensor at the FP1 (prefrontal) location, with each subject performing nine standardized cognitive tasks such as breathing, memory recall, and mathematical problem-solving. Hypnosis was applied not as a filtering method but as a behavioral protocol to standardize subject conditions and minimize physiological and environmental noise. The EEG signals were sampled at 128 Hz and analyzed using KHM clustering with K=4K = 4K=4, resulting in a Silhouette Score …

Comparative Performance Of Vgg16 And Efficientnetb0-Based Transfer Learning For Brain Tumor Classification, Huzain Azis, Rizqi Ananda Jalil, Abdul Rachman Manga'

Knowledge Engineering and Data Science

The classification of brain tumors using Magnetic Resonance Imaging (MRI) images is essential for early diagnosis but remains challenging due to tumor diversity. This study evaluates the effectiveness of two distinct architectural approaches for feature extraction: VGG16, representing a classic sequential design, and EfficientNetB0, a modern architecture optimized for parameter efficiency through compound scaling. Using a dataset of 2,870 MRI images categorized into four classes, we implemented a static transfer learning strategy by freezing all pre-trained ImageNet weights to act as fixed feature extractors. Features were extracted from specific layers, the final pooling layer for VGG16 and the Global Average …

Ahp–Python Framework For Multicriteria Modeling Of Rice Production In Asean, Mayang Anglingsari Putri, Risqy Siwi Pradini, Anuraga Jayanegara, Alexander Dimas Yonanta Putra

Knowledge Engineering and Data Science

Rice production is a key indicator of food security and agricultural stability in Southeast Asia, especially among Association of Southeast Asian Nations (ASEAN) countries. Despite shared regional goals, disparities in rice production remain, and previous studies mainly rely on descriptive statistics, lacking structured multicriteria decision-making frameworks and computational tools for cross-country comparisons. This study addresses these gaps by proposing an integrated Analytic Hierarchy Process (AHP)–Python framework to evaluate and rank ASEAN rice production from 2013 to 2022. Three criteria are used: Total Production Volume (K1), Production Growth Trend (K2), and Recent Year Performance (K3), capturing both long-term consistency and short-term …

Stable Numerical Solution Of An Elliptic Pde Inverse Problem Subject To Incomplete Boundary Conditions, Qasim Abd Ali Tayyeh

Knowledge Engineering and Data Science

This study addresses the challenging problem of solving inverse elliptic Partial Differential Equations (PDE) with incomplete boundary data, data available only on a part of the domain boundary. The aim is to develop a robust, effective numerical framework that consistently recovers parameters and/or sources from incomplete, ill-posed data. In the case of a variational problem discretized by the Finite Element Method (FEM) and solved by an adjoint-based optimization strategy, the framework uses Tikhonov regularization. Morozov's Discrepancy Principle is used to determine regularization parameters that achieve the best balance between accuracy and stability. Even with 5% noise in the measurement data, …

A Bounded Custom Gpt Structures Operational Knowledge For Small-Industry Decision Support, Ikhwan Arief, Alizar Hasan, Nilda Tri Putri, Hafiz Rahmann

Knowledge Engineering and Data Science

Small manufacturing and craft-based firms increasingly use Generative Artificial Intelligence (GenAI) through public chat interfaces, low-cost tools, and informal experimentation. However, these firms often make operational decisions with incomplete records, tacit owner knowledge, fragmented spreadsheets, and limited managerial capacity. Under such conditions, open-ended chatbots may generate fluent but unsafe recommendations by overlooking missing information, contradictory evidence, feasibility constraints, and implementation constraints. This study presents Asisten Cerdas Industri Kecil as a bounded Custom GPT artifact for operational diagnosis, priority selection, and short-horizon action planning in small industries. Using a Design Science Research approach, the study develops a documented artifact corpus comprising …

Risk Management For Authorising Railway Vehicles In The Eu Legal Framework, Jan Maciej Siudecki

internetowy Kwartalnik Antymonopolowy i Regulacyjny (internet Quarterly on Antitrust and Regulation)

Safe integration of rolling stock with railway system requires comprehensive hazard identification and application of safety measures. There are different cases when European Union regulations demand carrying out risk assessment before a vehicle may be authorised to be placed on the market for both Rolling stock and Command and control signalling onboard subsystems. Specificities of each case are outlined and conclusions as to their scope and interactions between them are discussed. Practical examples of hazards that should be managed in each case are provided. Subsequently it is described, how conclusions from risk assessment impact results of conformity assessment, the authorisation …

Characterizing Heat Transfer Performance In A Slurry Bubble Column Reactor Equipped With A Real Heat Exchanger, Dalia S. Makki, Hasan Sh Majdi, Amer A. Abdulrahman, Abbas J. Sultan, Bashar J. Kadhim, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

Abstract: This study examines the impact of equipping a real heat exchanger in a slurry bubble column SBC on instantaneous and local heat transfer coefficients IHTC and LHTC, as well as the overall heat transfer coefficient U, employing advanced heat transfer techniques. The experiments were conducted in a 0.15 m inner diameter Plexiglas SBC with varying gas flowrates Ug (0.14–0.35) m/s at several radial sites along the column's diameter (±0.18, ±0.46, and ± 0.74) and three axial locations (H/D = 2, 3 and 4). To simulate the industrial Fischer–Tropsch bubble column reactor FT-BCR, a real heat exchanger consisting of 18 …

Tumor Microenvironment Immunomodulation By Nanoformulated Tlr 7/8 Agonist And Pi3k Delta Inhibitor Enhances Therapeutic Benefits Of Radiotherapy, Mostafa Yazdimamaghani, Oleg V. Kolupaev, Chaemin Lim, Duhyeong Hwang, Sonia J. Laurie, Charles M. Perou, Alexander V. Kabanov, Jonathan S. Serody

Chemical and Biochemical Engineering Faculty Research & Creative Works

Infiltration of immunosuppressive cells into the breast tumor microenvironment (TME) is associated with suppressed effector T cell (Teff) responses, accelerated tumor growth, and poor clinical outcomes. Previous studies from our group and others identified infiltration of immunosuppressive myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs) as critical contributors to immune dysfunction in the orthotopic claudin-low tumor model, limiting the efficacy of adoptive cellular therapy. However, approaches to target these cells in the TME are currently lacking. To overcome this barrier, polymeric micellular nanoparticles (PMNPs) were used for the co-delivery of small molecule drugs activating Toll-like receptors 7 and 8 …

Workable Seams, Jackson Mills Smith

Graduate Student Theses, Dissertations, & Professional Papers

In Jackson Mills Smith’s Workable Seams, readers encounter the stories, landscapes, and lives of rural Knox County, Indiana, from the oscillating vantage of a Hoosier boy raised with a Cajun sensibility. After relocating from Louisiana to the river bottoms of the crossroad state at a young age, the speaker gradually uncovers a local history only previously understood in fragmentary shards and fading glimpses.

Alternating between semi-lyrical embodied experiences and memories, documentary pieces in conversation with the state’s historical archive, and the retelling of exaggerated local yarns, poetry is a means of inquiry in Workable Seams. Topics range from …

Enhanced Biofuel Cells Based On A Hybrid Enzymatic/Bimetallic Composite For Complete Lactate Catalytic Electrooxidation, Jefferson Honorio Franco, João Victor Bonaldo, Shelley D. Minteer, Adalgisa R. De Andrade

Chemistry Faculty Research & Creative Works

We describe complete lactate electrooxidation in an enzymatic biofuel cell that combines the catalytic action of the bimetallic composite Ru@Pt-CNT and the enzyme oxalate oxidase (OxOx). The Ru@Pt-CNT/OxOx hybrid electrode was 2.0-fold more catalytically active than the electrode containing the bimetallic composite only. During chronoamperometric experiments, the hybrid electrode achieved a 35% higher maximum current density (2.65 ± 0.15 mA cm^-2) than the Ru@Pt-CNT electrode. Electrochemical impedance spectroscopy showed that the hybrid electrode had lower charge transfer resistance than the Ru@Pt-CNT electrode, confirming that OxOx had a high affinity for lactate during the bioelectrocatalytic reaction on the electrode …