Digital Commons Network^™

Nanocatalytic Enhancement Of Local Heat Transfer In Continuous-Flow Thermal Reactors, Nasser Zouli, Nujud Maslamani, Ayman Yousef, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

An experimental investigation was conducted to evaluate the thermal conductivity (TC) and local heat-transfer coefficients (LHTCs) of nanofluids containing alumina (Al₂O₃), hematite (Fe₂O₃), and copper oxide (CuO) nanoparticles dispersed in deionized water. A newly developed non-invasive LHTC probe was integrated into the inner wall of the test section to enable direct quantification of interfacial heat-transfer performance. The measurements were conducted under laminar and turbulent flow conditons across Reynolds numbers ranging from 1000 to 10,000. The selected nanoparticles were chosen based on their high intrinsic thermal conductivity, cost effectiveness, and, in the case …

February 2026 Cafe Newsletter, Missouri University Of Science And Technology

CAFE Faculty Newsletters

Center for Advancing Faculty Excellence (CAFE) newsletter February 2026

The National Security Internet, Anupam Chander

Georgetown Law Faculty Publications and Other Works

In response to widespread foreign surveillance and growing geopolitical distrust, governments are erecting a national security internet. Pioneered by China, national firewalls have gone global. But where firewalls sought to keep information out, they now seek to keep data in. Governments keen to avoid their citizens’ data from falling into foreign hands demand not only that personal data be stored on local servers, but also require that it be stored on local servers by local companies—what this Article calls “data localization squared.” Enforcing this demand requires a new mechanism of transnational control: immunity from foreign jurisdiction. Artificial Intelligence (AI) systems, …

Machine Learning In Peak Demand Forecasting: Foundations, Trends, And Insights, Shuang Dai, Fanlin Meng, Hongsheng Dai, Qian Wang, Xizhong Chen, Wenlei Bai, Peizhi Shi, Richard Allmendinger, Yuchen Zhang, Jian Liu

Electrical and Computer Engineering Faculty Research & Creative Works

Peak demand forecasting involves predicting the maximum electricity demand within a specific period, which plays a key role in maintaining the efficiency and stability of power systems. The rapid evolution of power systems, driven by advanced metering infrastructure, local energy applications such as electric vehicles, and the increasing adoption of intermittent renewable energy, has introduced greater randomness and reduced predictability in peak demand. Given the pressing need to address more diverse implementation requirements across different contexts, accurate and reliable peak demand forecasting has become increasingly important. To the best of our knowledge, this study is the first to provide a …

The Case For An African Investment Court (Aic), Won L. Kidane

Faculty Publications

No abstract provided.

Bibliography For Love Data Week 2026, Annikah Carpio, Sally Park

Library Displays and Bibliographies

A bibliography created to support a display about research data and Love Data Week during February 2026 at the Leatherby Libraries at Chapman University.

Deep Learning Style Transfer For Enhanced Smoke Plume Visibility: A Standardized False Color Composite (Sfcc) In Gems Satellite Imagery, Yemin Jeong, Seung Hee Kim, Menas Kafatos, Jeong-Ah Yu, Kyoung-Hee Sung, Seung-Yeon Kim, Goo Kim, Jae-Jin Kim, Yangwon Lee

Institute for ECHO Articles and Research

Wildfire smoke visualization using geostationary satellite imagery is essential for real-time monitoring and atmospheric analysis; however, inconsistencies in color tone across Geostationary Environment Monitoring Spectrometer (GEMS) images hinder reliable interpretation and model training. This study proposes a Standardized False Color Composite (SFCC) framework based on deep learning style transfer to enhance the visual consistency and interpretability of wildfire smoke scenes. Four tone-standardization methods were compared: the statistical Empirical Cumulative Distribution Function (ECDF) correction and three neural approaches—ReHistoGAN, StyTr2, and Style Injection Diffusion Model (SI-DM). Each model was evaluated visually and quantitatively using six metrics (SSIM, LPIPS, FID, histogram similarity, ArtFID, …

Reconceiving Safety Regulation For Ai And Ml Medical Software, Barbara J. Evans, Eric S. Rosenthal, Azra Bihorac

Vanderbilt Journal of Entertainment & Technology Law

This article explores the challenges of regulating AI and ML clinical decision support tools intended to assist trained health care professionals in delivering clinical care. Two old, twentieth-century regulatory models have dominated discussions of medical AI policy since 2013. Thinking inside these old regulatory boxes has not produced effective regulatory solutions to address the novel risks AI poses in clinical care. The first regulatory box treats software as a medical device, which tasks medical device regulators with making software safe but neglects the crucial roles physicians, nurses, administrators, medical practice regulators, and other health oversight bodies must also play to …

Study Of Nano-Bio Interactions In Biological Fluids: Modelling And Simulations., Beatrice Cipriani

Doctoral

Nanoparticles have attracted significant interest due to their tunable properties and unique physicochemical characteristics, offering promising applications in drug delivery, biosensing, and diagnostics. Their use in medicine has the potential to reshape current paradigms by enabling more efficient, precise, and patient-centric approaches. Although advances in nanomaterial synthesis have rapidly expanded the library of available nanodevices, our understanding of the mechanisms that govern their behaviour at biological interfaces remains limited. Nanoparticles are similar in size to biomolecules found in biological environments, leading to complex, competitive interactions that affect their functional performance, biological fate, and acquired bioidentity. Unlike traditional small molecules, which …

Predicting Water Quality Using Quantum Machine Learning: The Case Of The Umgeni Catchment (U20a) Study Region, Jamal Al-Karaki, Muhammad Al Zafar Khan, Amjad Gawanmeh, Marwan Omar

All Works

The assessment of water quality has become increasingly vital for maintaining the ecological balance and ensuring public safety across global water systems. This study examines the application of Quantum Machine Learning (QML) techniques in a real-world setting to predict water quality in the U20A region of the Umgeni Catchment, Durban, South Africa. We implemented the Quantum Support Vector Classifier (QSVC) and Quantum Neural Network (QNN) on a field-collected dataset. Our results demonstrate that the QSVC is more practical to implement and yields superior performance, achieving 75 % accuracy with polynomial and radial basis function kernels. In contrast, the QNN encountered …

Xgboost-Powered Predictive Analytics For Early Identification Of Thermal Runaway In Lithium-Ion Batteries, Isslam Alhasan, Mohd H.S. Alrashdan

All Works

Lithium-ion batteries are pivotal in powering modern technology, from electric vehicles to portable electronics. However, their safety is challenged by the risk of thermal runaway, a critical failure mode leading to catastrophic consequences such as fires and explosions. This study presents a machine learning framework for the early detection of thermal runaway events using sensor data from over 210 open-source battery tests. The framework utilizes voltage, temperature, and force measurements from experimental mechanical indentation tests, with force data providing additional predictive value beyond standard BMS sensors. Key features such as the rate of temperature change and voltage change were engineered …

Regional Geography And Tuning Systems Of Traditional Georgian Folk Music, Alex Kutubidze

Dissertations, Theses, and Capstone Projects

The project focuses on the traditional folk music of Georgia, mapping the geographical distribution of singing traditions across the country’s historical regions. Because vocal traditions and stylistic features of Georgian folk music vary significantly across historical regions, the project highlights these differences for the audience, which might be interested in learning Georgian folk music, seeking a basic understanding of its structure, or planning to visit the country for cultural or educational purposes related to folk music.  In addition, this work examines the tonal organization of Georgian traditional music, drawing on recent ethnomusicological research. It engages with ongoing scholarly discussions regarding …

Culture And Its Discontents: Versions Of Matthew Arnold From The 1860s To The 1980s, Jarrett Moran

Dissertations, Theses, and Capstone Projects

Between the 1860s and the 1980s, the concept of culture articulated by Matthew Arnold provided a widespread and persistent language for making claims about who was ready for inclusion in British politics and identity and who was not yet ready or even incapable of being included. Scholarship on Arnoldian culture has often focused on presenting just one version of Arnold, whether as a cultural elitist, a progenitor of literary studies, or proponent of “doing away with classes.” This dissertation instead shows that Arnold’s concept of culture was so pervasive and persistent because he could be appropriated and redefined by people …

Online Visual Query System For Real-Time Large-Scale Spatio-Temporal Data Explorations With Error Bounds, Xueqi Huang

Dissertations, Theses, and Capstone Projects

Modern datasets continue to grow in size, dimensionality, and heterogeneity, creating increasing tension between the need for responsive, interactive analysis and the computational cost of accessing, aggregating, and visualizing large volumes of data. Traditional database engines and visualization tools often assume that full data retrieval is feasible or that exact computation is necessary for meaningful insight. In practice, however, analysts frequently benefit from timely, uncertainty-aware approximations than from delayed and exact results. This thesis investigates how data summarization techniques, specifically mergeable sketches can be combined with progressive, out-of-core visualization methods to support interactive exploration of datasets that exceed main memory. …

Protein Allostery Probed By Ligands Across Sites, Virgil A. Woods

Dissertations, Theses, and Capstone Projects

Allostery is a pervasive regulatory principle throughout biology, yet the structural pathways by which distal inputs alter active‑site chemistry within protein structures remain incompletely defined and exploited. This dissertation uses protein tyrosine phosphatase 1B (PTP1B) as a tractable model to map those pathways with complementary experimental and computational tools. I integrate high‑resolution hydrogen-deuterium exchange mass spectrometry (HDX-MS), room-temperature crystallography, NMR, steady-state kinetics, crystallographic pseudo-ensembles, and machine-learning-guided ligand discovery. The working premise is that regulation reflects redistribution within a conformational ensemble rather than a binary switch. That orthogonal perturbations by small molecules, mutations, and protein partners can be used to both …

Research Journal 2026: People And Places, José A. Rivera Ph.D

Faculty Publications

José Rivera narrates his life history from adolescence into college years, university teaching, research career, publications, and ends with post-retirement service as a giving back to community. For most of his term as an academic, his research focus was on community-based organizations that he terms “peoples’ institutions”: acequia associations and mutual help societies. For data, he conducted field work in New Mexico and the American Southwest as well as internationally, primarily in Mexico, Peru, Spain and the Philippines. While at these locations, he interviewed people, collected maps, researched archives, and took photographs as ethnographic evidence of people and places. His …

Watersmart Farms - Desalination: Brine Management Review, V Ravisankar, E Wendell, S Aktar, B Husbands, L Li, J K. Ruprecht

Natural resources commissioned reports

All desalination technologies generate residual wastes. Waste-stream management poses both cost and implementation challenges that may critically impact project viability, environmental health, and proximate land use. Brine management encompasses brine minimisation and concentration, dilution, and disposal techniques. The scope of this report is to:

• Summarise commercially available approaches to aid in the selection of the best available technology (BAT) for future inland, small-scale desalination plants;
• Provide general performance metrics such as system recovery, energy consumption and costs; and
• List alternative desalination technologies to reverse osmosis (RO) that may mitigate specific residual waste management challenges.

The choice of technology for brine …

Utilization Of Plastic Waste In The Concrete Industry, Nancy Abdelmoneim Sakr

Theses and Dissertations

Plastic recycling has emerged as a crucial strategy that aligns with environmental, social and economic sustainability indicators. Currently, substantial volumes of plastic waste are either deposited in landfills or incinerated, neglecting the potential to harness its embodied energy and the energy consumed for producing virgin materials. A key advantage of plastic lies in its promising mechanical properties. Concrete mix design is fundamental to a wide range of construction applications, including brick walls, reinforced concrete slabs and concrete pavements. Despite the adoption of recycled plastic in construction materials in various countries, its widespread implementation remains limited. This is primarily due to …

Vercation: Precise Vulnerable Open-Source Software Version Identification Based On Static Analysis And Llm, Yiran Cheng, Ting Zhang, Lwin Khin Shar, Shouguo Yang, Chaopeng Dong, David Lo, Shichao Lv, Zhiqiang Shi, Limin Sun

Research Collection School Of Computing and Information Systems

Open-source software (OSS) has experienced a surge in popularity, attributed to its collaborative development model and cost-effective nature. However, the adoption of specific software versions in development projects may introduce security risks when these versions bring along vulnerabilities. Current methods of identifying vulnerable versions typically analyze and extract the code features involved in vulnerability patches using static analysis with pre-defined rules. They then use code clone detection to identify the vulnerable versions. These methods are hindered by imprecision due to (1) the exclusion of vulnerability- irrelevant code in the analysis and (2) the inadequacy of code clone detection. This paper …

Efficient Function Orchestration For Large Language Models, Xiaoxia Liu, Peng Di, Cong Li, Jun Sun, Jingyi Wang

Research Collection School Of Computing and Information Systems

Function calling is a fundamental capability of today's large language models, but sequential function calling posed efficiency problems. Recent studies have proposed to request function calls with parallelism support in order to alleviate this issue. However, they either delegate the concurrent function calls to users for execution which are conversely executed sequentially, or overlook the relations among various function calls, rending limited efficiency. This paper introduces LLMOrch, an advanced framework for automated, parallel function calling in large language models. The key principle behind LLMOrch is to identify an available processor to execute a function call while preventing any single processor …

Exploring Jvm Garbage Collector Testing With Event-Coverage, Kai Zheng, Yingquan Zhao, Junjie Chen, Hanmo You, Haoyu Wang, Haoyu Wang, Tianchang Gao

Research Collection School Of Computing and Information Systems

Garbage Collection (GC) in the Java Virtual Machine (JVM) serves as an automatic memory management mechanism, efficiently reclaiming unused memory space in different production scenarios. To optimize JVM performance, developers typically fine-tune the garbage collector by identifying an optimal set of GC configurations for specific scenarios. Despite the sophisticated design of garbage collectors, they still have the potential for bugs in different settings, and these bugs can result in more severe consequences. Hence, comprehensive testing of these garbage collectors is imperative before their release. Code coverage criteria are typically employed to assess the comprehensiveness of a test suite. However, traditional …

Less Is More: Docstring Compression In Code Generation, Guang Yang, Yu Zhou, Wei Cheng, Xiangyu Zhang, Xiang Chen, Terry Yue Zhuo, Xin Zhou, Ke Liu, David Lo, Taolue Chen

Research Collection School Of Computing and Information Systems

The widespread use of Large Language Models (LLMs) in software engineering has intensified the need for improved model and resource efficiency. In particular, for neural code generation, LLMs are used to translate function/method signature and DocString to executable code. DocStrings, which capture user requirements for the code and are typically used as the prompt for LLMs, often contain redundant information. Recent advancements in prompt compression have shown promising results in Natural Language Processing (NLP), but their applicability to code generation remains uncertain. Our empirical study shows that the state-ofthe-art prompt compression methods achieve only about 10% reduction, as further reductions …

Constrained Pricing In Logit-Based Revenue Management, Qian Shao, Tien Mai, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

We consider a dynamic pricing problem in network revenue management in which customer behavior is predicted by a choice model, that is, the multinomial logit model. The problem, even in the static setting (i.e., customer demand remains unchanged over time), is highly nonconcave in prices. Existing studies mostly rely on the observation that the objective function is concave in terms of purchasing probabilities, implying that the static pricing problem with linear constraints on purchasing probabilities can be efficiently solved. However, this approach is limited in handling constraints on prices, noting that such constraints could be highly relevant in some real …

Fcghunter: Towards Evaluating Robustness Of Graph-Based Android Malware Detection, Shiwen Song, Xiaofei Xie, Ruitao Feng, Qi Guo, Sen Chen

Research Collection School Of Computing and Information Systems

Graph-based detection methods leveraging Function Call Graph (FCG) have shown promise for Android malware detection (AMD) due to their semantic insights. However, the deployment of malware detectors in dynamic and hostile environments raises significant concerns about their robustness. While recent approaches evaluate the robustness of FCG-based detectors using adversarial attacks, their effectiveness is constrained by the vast perturbation space, particularly across diverse models and features. To address these challenges, we introduce FCGHunter, a novel robustness testing framework for FCG-based AMD systems. Specifically, FCGHunter employs innovative techniques to enhance exploration and exploitation within this huge search space. Initially, it identifies critical …

Zero-Shot Video Translation Via Token Warping, Haiming Zhu, Yangyang Xu, Jun Yu, Shengfeng He

Research Collection School Of Computing and Information Systems

With the revolution of generative AI, video-related tasks have been widely studied. However, current state-of-the-art video models still lag behind image models in visual quality and user control over generated content. In this paper, we introduce TokenWarping, a novel framework for temporally coherent video translation. Existing diffusion-based video editing approaches rely solely on key and value patches in self-attention to ensure temporal consistency, often sacrificing the preservation of local and structural regions. Critically, these methods overlook the significance of the query patches in achieving accurate feature aggregation and temporal coherence. In contrast, TokenWarping leverages complementary token priors by constructing temporal …

Light Cone Cancellation For Variational Quantum Eigensolver In Solving Noisy Max-Cut, Xinwei Lee, Xinjian Yan, Ningyi Xie, Yoshiyuki Saito, Leo Kurosawa, Nobuyoshi Asai, Dongsheng Cai, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Variational Quantum Eigensolver (VQE) is a quantum-classical hybrid algorithm used to estimate the ground energy of a given Hamiltonian. It consists of a parameterized quantum circuit, which the parameters are optimized using a classical optimizer. With the increasing need in solving large-scale problems in real-world applications, solving those large problems with fewer qubits and fewer gates becomes essential, so that we reduce the simulation difficulty and mitigate the effect of noise in real quantum hardware. In this study, we applied the Light Cone Cancellation (LCC) method to reduce the number of qubits and gates required in a two-local ansatz. LCC …

Grounding Is All You Need? Dual Temporal Grounding For Video Dialog, You Qin, Wei Ji, Xinze Lan, Hao Fei, Xun Yang, Dan Guo, Roger Zimmermann, Lizi Liao

Research Collection School Of Computing and Information Systems

In the realm of video dialog response generation, capturing both the essence of video content and the temporal nuances of conversation history is crucial. While some approaches rely on large-scale pretrained visual-language models, often neglecting temporal dynamics, others emphasize spatial-temporal relationships within videos but demand intricate object trajectory pre-extractions and overlook dialog temporal dynamics. This paper introduces the Dual Temporal Grounding-enhanced Video Dialog model (DTGVD), designed to bridge the gap between these two approaches. DTGVD uniquely integrates the strengths of both by emphasizing dual temporal relationships. It achieves this by predicting dialog turn-specific temporal regions, selectively filtering video content, and …

Learnable Game-Theoretic Policy Optimization For Data-Centric Self-Explanation Rationalization, Yunxiao Zhao, Zhiqiang Wang, Xingtong Yu, Xiaoli Li, Jiye Liang, Ru Li

Research Collection School Of Computing and Information Systems

Rationalization, a data-centric framework, aims to build self-explanatory models to explain the prediction outcome by generating a subset of human-intelligible pieces of the input data. It involves a cooperative game model where a generator generates the most human-intelligible parts of the input (i.e., rationales), followed by a predictor that makes predictions based on these generated rationales. Conventional rationalization methods typically impose constraints via regularization terms to calibrate or penalize undesired generation. However, these methods are suffering from a problem called mode collapse, in which the predictor produces correct predictions yet the generator consistently outputs rationales with collapsed patterns. Moreover, existing …

Youth Development Programs Critical Analysis, Rethinking Real Sustainability Measures. Focus On Middle-Eastern And African Contexts., Rasha As Salem

Theses and Dissertations

Abstract Using a systematic review of 18 research papers, policy documents, and evaluation reports, the study examines youth development programs sustainability through four aspects. First, the review explores how definitions of marginalized youth are applied in Middle East and African contexts. Furthermore, the review focuses on how programmatic suitability is discussed and implemented through the concepts of localization and contextualization. Additionally, the importance of locally grounded evidence as much as rigorous evidence and finally the implementation tactics. The review uses frameworks of ecological systems theory and empowering management approaches to analyze the data. The findings reveal significant variation in how …

Assessing The Effectiveness Of A Habit Recording Tool To Improve Mental Health Of University (Auc) Students, Noran Nounou

Theses and Dissertations

Introduction: According to previous research, the mental health of university students has become an alarming public health concern worldwide. Most of the mental disorders begin among young adults, attributed to the transition to university and increased financial and societal pressure. Despite the important effect of daily habits on mental health, there still remain research gaps in Egypt in exploring their effect among university students and exploring the potential opportunities to support their well-being. Objectives: To assess depression rates among AUC students and to evaluate the effectiveness of a newly designed habit recording board, Make an Impact (MAI) to improve depression …