Digital Commons Network^™

Fiber-Reinforced Foam Concrete Using Quarry Micro Fines And Sugarcane Bagasse Ash: A Box–Behnken Design Optimization And Performance Assessment, Ravindaran Thangavel, Sanjay Kumar Shukla, Mini K. Madhavan

Research outputs 2022 to 2026

Foam concrete is well-appreciated for its thermal and acoustic benefits and is prepared by introducing foam into cement slurry/mortar. The current research examines the feasibility of Quarry Micro Fines (QMF), a waste generated from the quarries during sand manufacturing, as a substitute for fine aggregate in the preparation of foam concrete. During the preparation of concrete, a portion of cement is replaced with sugarcane bagasse ash (SCBA), while polypropylene (PP) fibers are added to improve the shrinkage resistance and tensile strength of the resulting concrete. A three-factor, three-level Box–Behnken Design (BBD) in Response Surface Methodology (RSM) was used to optimize …

Least Squares As Random Walks: The General Case Of Arbitrary Spacing, Daniel Kestner, Alexander Kostinski

Michigan Tech Publications

Recently, we introduced the notion of a random walk based on a discrete sequence of data samples ( data walk ) and discovered a surprising link between ordinary least squares (OLS) fits to evenly sampled data and random walks. Here we generalize earlier results by showing that the slope of a linear fit to data which annuls the net area under a residual data walk equals that found by OLS for irregularly spaced data sequence. We also discover a deep connection with the orthogonality principle of estimation theory, leading to interpretation of suitably defined scalar products of data vectors as …

Air Mass Trajectories Related To Dust Storms On Mars And A Comparison Study With Spacecraft Images, Mohammad Abdulla Ahli

Theses

The Martian weather system, climate, and surface evolution are strongly influenced by dust storms, which interact with the atmospheric circulation and the planet’s surface features. In this study, selected Martian dust storms are examined using data provided by the Emirates Mars Mission (EMM) and the Mars Climate Database (MCD) to track the movement of air masses and improve understanding of how these storms develop and move across the Martian atmosphere.

The Emirates Mars Mission (EMM), together with the Mars Climate Database (MCD), provides the data needed to track the movement of air masses at different altitudes and times of the …

Removed: When Taxi Drivers Meet Dynamic Pricing: A Lesson From Singapore's Justgrab Program, Shih-Fen Cheng, Wen-Tai Hsu, Jing Li

Research Collection School Of Economics

This paper studies how dynamic pricing influences taxi drivers’ behaviors using a unique event, the inception of the JustGrab program in Singapore in 2017, which introduces dynamic pricing to some, but not all, taxi drivers. This is the first time in history that traditional taxi drivers have access to dynamic pricing. Using data covering the universe of taxi trips before and after the inception of JustGrab, we find that there is spatial reallocation that directs more taxi drivers to the previously less-served areas, that there is also a temporal reallocation that directs more taxi drivers to rush hours, as well …

Efficient Bilateral Trade With Interdependent Values: The Use Of Two-Stage Mechanisms, Takashi Kunimoto, Cuiling Zhang

Research Collection School Of Economics

Efficient, voluntary bilateral trades are generally not implementable in an interdependent values environment with two-sided asymmetric information. To obtain more positive results, we consider two-stage mechanisms in which (i) the outcome (e.g., allocation of the goods) is determined first; (ii) the agents partially learn the state via their own outcome-decision payoffs; and (iii) transfers are finally made. We propose the approximate shoot-the-liar (AS) mechanism and identify Assumption 1, under which “approximately” efficient, voluntary trades are implementable. We further illustrate the permissiveness of the assumption, and characterize it in terms of the nature of interdependence in valuation functions – delineating the …

Final 2022 Unreclaimed Sites Sampling: Ur-20 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

G-Trac: Graph-Textual Representations Alignment For Cold-Start Recommendations, Li Yang Chang, Yuan Fang, Ming Feng Tsai, Chuan Ju Wang

Research Collection School Of Computing and Information Systems

The cold-start problem remains a significant challenge in recommendation systems, particularly for new users or unseen items with little to no historical data. Existing methods, including graph neural networks, often struggle in such scenarios. Inspired by the success of transformer models in natural language processing, we propose G-TRAC (Graph-Textual Representations Alignment for Cold-start Recommendations), a novel approach that integrates transformer-based textual modeling with graph neural networks. By effectively leveraging both textual and structural information, G-TRAC addresses cold-start challenges more effectively. Extensive experiments demonstrate its ability to enhance recommendation quality and generalize well across diverse scenarios.

Sit, Stay, And Teach: Exploring Teachers' Perceptions Of Facility Dogs' Impact On Stress And Retention, Dennis Powers

Graduate Student Dissertations, Theses, Capstones, and Portfolios

The teaching profession remains at a high level of workplace stress, a factor contributing to teacher burnout and turnover. The study examines how teachers in K-12 public education perceived that facility dogs might reduce stress, increase job satisfaction, and aid in teacher retention in public schools of a southeastern state. The study employed a qualitative, cross-sectional survey that was emailed to 112,546 educators from a LISTSERV request. A total of 1,242 individuals provided informed consent and completed the survey, which consisted of Likert-scale measurement items, open-ended questions, and an informational video inserted to achieve a common understanding of the facility …

A Guide To Transcriptomic Deconvolution In Cancer, Yaoyi Dai, Shuai Guo, Yidan Pan, Carla Castignani, Matthew D Montierth, Peter Van Loo, Wenyi Wang

Faculty, Staff and Student Publications

Cancer tissues are heterogeneous mixtures of tumour, stromal and immune cells, where each component comprises multiple distinct cell types and/or states. Mapping this heterogeneity and understanding the unique contributions of each cell type to the tumour transcriptome is crucial for advancing cancer biology, yet high-throughput expression profiles from tumour tissues only represent combined signals from all cellular sources. Computational deconvolution of these mixed signals has emerged as a powerful approach to dissect both cellular composition and cell-type-specific expression patterns. Here, we provide a comprehensive guide to transcriptomic deconvolution, specifically tailored for cancer researchers, presenting a systematic framework for selecting and …

Numerical Investigation Of A Dual-Mode Shape Memory Alloy Stent Enabling Secondary Expansion Via Focused Ultrasound: A Potential Strategy For Correcting In-Stent Restenosis, Stephen Asare, Lucinda Duncan, Josiah Owusu-Danquah, Brian L. Davis

Civil and Environmental Engineering Faculty Publications

Coronary stent implantation is an invasive procedure performed to correct atherosclerosis. The aftermath of this procedure is identified with neointimal hyperplasia, which contributes to in-stent restenosis (ISR), and thus remains a significant clinical challenge. This study introduces a novel, noninvasive conceptual approach for addressing ISR through the thermal activation of shape memory alloy (SMA) stent using focused ultrasound (FU) in a controlled manner to restore luminal patency. COMSOL Multiphysics and ABAQUS finite element software were employed to perform the numerical modeling to simulate the thermal and mechanical responses of the SMA stent integrated into the arterial wall. After 15 s …

Computational Characterization Of A Recently Identified Disinfection Byproduct In Drinking Water: The Chloronitramide Anion And Its Monohydrate Complex, Thufail M. Ismail, Max R. Tucker, Gregory S. Tschumper

Chemistry Faculty Research & Creative Works

The chloronitramide anion (ClNNO₂⁻) was recently identified as disinfection byproduct in drinking water, and various quantum chemistry methods (CCSD(T), MP2, B3LYP, ωB97XD, and M06-2X) are employed in this study to characterize this negatively charged species and its monohydrate complex. Both vertical and adiabatic quantities indicate the excess electron is bound by 3–4 eV. Four ClNNO₂⁻ monohydrate minima were identified, each exhibiting double ionic hydrogen bonding. The computed ClNNO₂⁻⋯H₂O interaction approaches 15 kcal/mol, and it induces pronounced vibrational frequency shifts in both fragments. Natural bond orbital analysis shows a redistribution of …

Electric Dipole Forbidden, Quadrupole Allowed Transitions In The Pure Rotational Spectrum Of Cyclopropylchloromethyldifluorosilane, Alexander R. Davies, Abanob G. Hanna, Alma Lutas, Gamil A. Guirgis, S. A. Cooke, Garry S. Grubbs

Chemistry Faculty Research & Creative Works

In a recent publication, some electric dipole forbidden, quadrupole allowed ΔJ = +2 and x-type transitions were observed in the chirped-pulse Fourier transform microwave spectrum of two conformations of cyclopropylchloromethyldifluorosilane. Many of these transitions arise from a handful of mixed states and mechanisms are proposed through which these transitions become weakly allowed. Observations of electric dipole forbidden, quadrupole allowed transitions in rotational spectra are unusual for molecules which contain a chlorine nucleus owing to the small quadrupole moment of ³⁵Cl and ³⁷Cl; thus, we believe we are the first to observe x-type transitions arising from perturbations caused by …

Atmospheric Circulation Reconstructions Over The Earth (Acre) 2025: Climate Data Recovery, Fiona Williamson

Research Collection College of Integrative Studies

On 15-16 September 2025, Singapore Management University hosted the annual Atmospheric Circulation Reconstructions over the Earth (ACRE, www.met-acre.net/) workshop, ’ACRE 2025: Climate Data Recovery’, chaired by Fiona Williamson, Rob Allan and Praveen Teleti. The 2-day event took place at the College of Integrative Studies and saw around 30 participants from multi-disciplinary backgrounds all working in meteorological data rescue (DaRe, www.community.wmo.int/en/data-rescue-projects-and-initiatives-dare) sharing their latest work and discoveries. Speakers were a mix of climate scientists and historians, archivists and historical climatologists from across the globe. ACRE has been operating for 18 years under Professor Rob Allan, formerly of the UK Meteorological Office …

Hybrid Data-Driven Cement-Stabilized Soil Design: An Integration Of Machine Learning, Multi-Objective Optimization, And Life Cycle Assessment, Chikezie Chimere Onyekwena, Yunli Li, Ikenna J. Okeke, Ubani Obinna Uzodimma, Monday Uchenna Okoronkwo, Wenping Wu

Chemical and Biochemical Engineering Faculty Research & Creative Works

Soil stabilization is crucial in geotechnical engineering, yet conventional methods are often time-consuming, resource-intensive, and environmentally unsustainable. Despite growing interest in Machine Learning (ML) and optimization tools for mix design, few studies integrate these methods with decision-making techniques and environmental assessment to support practical implementation. This study proposes a hybrid data-driven framework for predicting strength, optimizing mix compositions, and evaluating environmental impacts via life cycle assessment of cement-stabilized soft soils. Six ML models were evaluated, and the top-performing eXtreme Gradient Boosting (XGB) model was further improved using the Grey Wolf Optimizer (GWO). The optimized XGB-GWO model, integrated with a polynomial …

Performance And Mechanistic Insights Into Cement Systems Modified With Wastewater-Recovered Struvite, Ugochukwu Ewuzie, Rupack R. Halder, Abdulkareem O. Yusuf, Abiodun A. Saka, Godwin I. Ogbuehi, Titus C. Egbosiuba, Damilola A. Daramola, Monday Uchenna Okoronkwo

Chemical and Biochemical Engineering Faculty Research & Creative Works

Struvite, the stable hydration product and primary strength phase in magnesium ammonium phosphate cement (MAPC), derived from wastewater treatment, has recently been utilized as a sustainable additive to Portland cement (PC). However, its impacts on cement hydration kinetics, pore refinement, rheology, and the mechanisms underlying these processes have not been comprehensively studied. This study developed Portland cement-struvite (PCS) systems by replacing PC with 3–20 % struvite (ST wt.%: PCS3–PCS20) and evaluated these processes using isothermal calorimetry, 3D micro-computed tomography (μXCT), time-dependent rheometry, X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR), and the Krstulović-Dabić (K-D) model. The FTIR/XRD confirmed the coexistence …

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

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 …

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 …

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 …

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 …