Digital Commons Network^™

A Bayesian Optimisation With Segmentation Approach To Optimising Liquid Handling Parameters, Estefania Yap, Viet Huynh, Calvin Vong, Peter Vogel, Viv Louzado, Thomas Barnes, Buser Say, Michael Burke, Dana Kulić, Aldeida Aleti

Research outputs 2022 to 2026

The automation of liquid handling has become integral in speeding up pharmaceutical development for faster drug development and more affordable treatments. However, the optimal parameters which define the aspirate and dispense procedures vary between liquids and liquid volumes, limiting transfer accuracy and precision. Even state-of-the-art liquid handling devices offer predefined parameters for only a handful of liquids and volumes, resulting in novel parameter sets being defined via a manual, time-consuming process. In this study, we propose an experimental framework for automating the optimisation of liquid class parameters for arbitrary liquids. Within our framework, we propose an optimisation and segmentation algorithm, …

Application Of Plastic Waste As A Sustainable Bitumen Mixture—A Review, Nuha S. Mashaan, Thakur Chamlagai

Research outputs 2022 to 2026

Plastic waste is growing rapidly, while asphalt binders remain heavily reliant on petroleum bitumen. Incorporating recycled plastics into bitumen can divert waste and enhance pavement performance. This review compiles 251 experimental records from 56 studies to evaluate how plastic type, dosage, and processing conditions affect softening point, penetration, and viscosity. Across studies, plastics (PET, LDPE/HDPE/LLDPE, PP, and hybrids) consistently stiffen binders, reducing penetration and increasing softening point and viscosity, thereby improving rutting resistance while potentially raising mixing/compaction demands. Using grouped cross-validated machine-learning models (median baseline, ridge, random forest, XGBoost), we quantify the predictability of binder properties and show that nonlinear …

Defeating Evasive Malware With Peekaboo: Extracting Authentic Malware Behavior With Dynamic Binary Instrumentation, Matthew Gaber, Mohiuddin Ahmed, Helge Janicke

Research outputs 2022 to 2026

The accuracy of Artificial Intelligence (AI) in malware detection is dependent on the features it is trained with, where the quality and authenticity of these features is dependent on the dataset and the analysis tool. Evasive malware, that alters its behavior in analysis environments, is challenging to extract authentic features from where widely used static and dynamic analysis tools have several limitations. However, Dynamic Binary Instrumentation (DBI) allows deep and precise control of the malware sample, thereby facilitating the extraction of authentic behavior from evasive malware. Considering the limitations of malware analysis for use with AI, this research had two …

Estimation Of 3d Facial Dynamics With Nonlinear Filters For Position Tracking, Thoa Thieu, Roderick Melnik

School of Mathematical & Statistical Sciences Faculty Publications

This study presents a comparative evaluation of three nonlinear state estimation filters, the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF), for the task of 3D facial landmark tracking. Using a publicly available dataset, we assess each filter's performance under both deterministic (noise-free) and stochastic (noisy) conditions. Metrics such as mean squared error (MSE), convergence rates of state and covariance estimates, and consistency over time are used to quantify tracking performance. Results show that the EKF consistently outperforms the UKF and PF, achieving faster convergence and lower estimation error, particularly in scenarios characterized by mild nonlinearity. …

Insecticide-Treated Net Use And Elimination Of Malaria In Sub-Saharan African Countries: Assessing The Global Technical Strategy Using An Evolutionary Game Approach, Laxmi, Tamer Oraby, Michael G. Tyshenko, Ina Danquah, Samit Bhattacharyya

School of Mathematical & Statistical Sciences Faculty Publications

Background: Malaria continues to be a major public health challenge in Sub-Saharan Africa (SSA), where the majority of the countries have not met the World Health Assembly's endorsed Global Technical Strategy (GTS) milestones in 2020 for malaria reduction. Insecticide-treated net (ITN) usage is a well-established and effective intervention, often outperforming other measures such as indoor residual spraying (IRS). However, multiple survey studies have reported improper use of ITNs across various SSA countries. This misuse likely poses an important barrier to the intervention's success, although it remains a largely untested hypothesis.

Methods: We developed a behaviour-incidence model and statistical analysis of …

Quantization For The Mixtures Of Overlap Probability Distributions, Asha Barua, Angelina Chavera, Ivan Djordjevic, Valerie Manzano, Sergio Soto Quintero, Mrinal Kanti Roychowdhury, Hilda Tejeda

School of Mathematical & Statistical Sciences Faculty Publications

Optimal quantization for mixed distributions has emerged as a compelling area of study. In this work, we have focused on a mixed distribution formed from two uniform distributions with partially overlapping supports. For this class of distributions, we have examined the structure of optimal sets of n-means and the corresponding nth quantization errors for all positive integers n. Initially, we explicitly determined the optimal sets and quantization errors for 1 < = n < = 6. Subsequently, we established several key lemmas and propositions and proposed an algorithm that facilitates the computation of optimal n-means and quantization errors for all n >= 5. Numerical results are also presented to illustrate the application of the algorithm in deriving these quantities. The findings of this study offer valuable insight and serve as a …

Variants Of Conway Checkers And K-Nacci Jumping, Glenn Bruda, Joseph Cooper, Kareem Jaber, Raul Marquez, Steven J. Miller

School of Mathematical & Statistical Sciences Faculty Publications

Conway Checkers is a game played with a checker placed in each square of the lower half of an infinite checkerboard. Pieces move by jumping over an adjacent checker, removing the checker jumped over. Conway showed that it is not possible to reach row 5 in finitely many moves by weighting each cell in the board by powers of the golden ratio such that no move increases the total weight.

Other authors have considered the game played on many different boards, including generalizing the standard game to higher dimensions. We work on a board of arbitrary dimension, where we allow …

Is Noise Exposure Associated With Impaired Extended High Frequency Hearing Despite A Normal Audiogram? A Systematic Review And Meta-Analysis, Sajana Aryal, Monica Trevino, Hansapani Rodrigo, Srikanta K. Mishra

School of Mathematical & Statistical Sciences Faculty Publications

Understanding the initial signature of noise-induced auditory damage remains a significant priority. Animal models suggest the cochlear base is particularly vulnerable to noise, raising the possibility that early-stage noise exposure could be linked to basal cochlear dysfunction, even when thresholds at 0.25-8 kHz are normal. To investigate this in humans, we conducted a meta-analysis following a systematic review, examining the association between noise exposure and hearing in frequencies from 9 to 20 kHz as a marker for basal cochlear dysfunction. Systematic review and meta-analysis followed PRISMA guidelines and the PICOS framework. Studies on noise exposure and hearing in the 9 …

Studies On Curcumin-Glucoside In The Prevention Of Alpha-Synuclein Aggregation, Lakshmi Sowmya Emani, Jayanth K. Rao, Jagadeesha Kumar Dasappa, Marisín Pecchio, Johant Lakey-Beitia, Hansapani Rodrigo, Jessica Cruz-Mora, Priya Narayan, Nikhilesh Anand, Bharathi Gadad

School of Medicine Publications

Background: α-synuclein (α-syn) deposition in the mid-brain region is one of the hallmark pathologies of Parkinson's disease (PD). The key steps involve the transformation of α-synuclein into a toxic oligomer and insoluble fibrillar aggregates.

Objective: To understand the role of curcumin-glucoside in the prevention of α-syn aggregation, a mechanistic approach.

Methods: In the present study, we synthesized a novel molecule, curcumin-glucoside (Curc-gluc), to improve the water solubility and partition coefficient, making the molecule with high bioavailability. The present study is focused on understanding the α-syn aggregation kinetics in the presence and absence of Curc-gluc, curcumin (Cur), copper (Cu), and iron …

Should The Army Be Involved In Divorce? Re-Examining The Pre-Divorce Defaults For Spousal Support, Artem M. Joukov, Thomas Godfrey

MC Law Review

Army Regulation 608-99 assigns a large portion of a Soldier’s paycheck to the spouse upon marital separation. The assignment occurs without a hearing, discovery, or consideration of critical evidence which might moderate the amount of support. The obligation rests on the Soldier to obtain a court order or spousal agreement to reduce the financial burden. The adversarial nature of modern divorces renders these requirements unrealistic. Amending the regulation would ease the burden of the Soldier, which is desirable in light of the ongoing recruitment crisis and the need to attract recruits and retain those already in the service.

Climatology, Seasonality, And Extremes Of Fires In Alaska's Boreal And Tundra Regions Using The Initial Spread Index, Jacob Coffey

Atmospheric Sciences

The Alaskan fire season is an increasingly consequential aspect of the state’s summer climate, with recent years frequently exceeding one million acres burned. Among the key factors influencing fire behavior, wind and moisture play a critical role in driving fire spread. The Initial Spread Index (ISI), a component of the Canadian Forest Fire Danger Rating System, quantifies the expected rate of fire spread following ignition. ISI is currently used operationally in Alaska. Despite its significance, ISI remains under-studied in terms of its historical behavior, trends, and environmental sensitivity. This study evaluates the climatology, variability, and trends of ISI across Alaska …

Lidar And Radar Investigations Of Gravity Wave Activity In The Middle Atmosphere At Pfrr, Chatanika, Alaska, Satyaki Das

Atmospheric Sciences

The middle atmosphere, spanning the stratosphere and mesosphere, plays a critical role in global atmospheric circulation, particularly in the Arctic, where phenomena like Sudden Stratospheric Warmings (SSW) significantly perturb the circulation patterns. This dissertation investigates the dynamics of the polar middle atmosphere using four-year (20218-2022) temperature and wave activity measurements collected by the three-channel Rayleigh Density Temperature Lidar, Sodium Resonance Wind-Temperature Lidar, and Poker Flat Meteor Wind Radar at Poker Flat Research Range, Chatanika, Alaska (650N,1470W). The primary contributions are the development of a new lidar signal retrieval technique to combine the signal from three-channels and improve the lidar signal …

Design Of A Novel Robust Adaptive Fractional-Order Model Predictive Controller For Boost Converter Using Grey Wolf Optimization Algorithm, Chao Peng, Seyyed Morteza [email protected] Ghamari, Hasan Mollaee, Omid Rezaei

Research outputs 2022 to 2026

Boost converters play a crucial role in power electronics but present control challenges due to their non-minimum phase behavior and nonlinear dynamics at high switching frequencies. To address these issues, this work proposes a Fractional-order adaptive Model Predictive Control (FO-MPC) framework incorporating Exponential Regressive Least Squares (ERLS) for system identification. Traditional MPC frameworks often rely on accurate mathematical models, which are difficult to obtain in real-world scenarios. This adaptive modelling approach based on ERLS identification method eliminates the need for precise system models, improving robustness and adaptability under parameter variations. Additionally, a FO derivative term enhances damping, stability, and noise …

Time-Resolved 3d Momentum Spectroscopy In Continuous Wave Atomic Photoionization Experiments, K. L. Romans, B. P. Acharya, A. H.N.C.De Silva, K. Foster, O. Russ, Daniel Fischer

Physics Faculty Research & Creative Works

An experimental continuous-wave (cw) pump-probe scheme is demonstrated by investigating the population and photoionization dynamics of an atomic system. In particular, 6Li atoms are initially prepared in optically pumped 22S1/2 and 22P3/2 states before being excited via multi-photon absorption from a tunable femtosecond laser. The subsequent cascade back to the ground state is analyzed by ionizing the atoms in the field of a cw optical dipole trap laser. Conventional spectroscopic methods, such as standard cold-target recoil ion momentum spectroscopy or velocity map imaging, cannot provide simultaneous momentum and time-resolved information on an event-by-event basis for the system investigated here. The …

Mando-Llm: Heterogeneous Graph Transformers With Large Language Models For Smart Contract Vulnerability Detection, Nhat Minh Nguyen, Huu Hoang Nguyen, Long Le Thanh, Zahra Ahmadi, Thanh Nam Doan, Daoyuan Wu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Detecting vulnerabilities in smart contracts is vital for the security and reliability of decentralized apps. To facilitate vulnerability detection, contract codes, including bug patterns, are represented as heterogeneous graphs with various nodes and edges, like control-flow and function-call graphs. However, existing graph learning techniques struggle with large, complex graphs. This paper presents MANDO-LLM, a novel framework that combines heterogeneous graph transformers (HGTs) with large language models (LLMs) for detecting vulnerabilities in smart contracts represented as heterogeneous contract graphs built upon control-flow and call graphs. MANDO-LLM uses LLMs to capture code features from control-flow and call data, customizes HGTs to learn …

Iostom: Offline Imitation Learning From Observations Via State Transition Occupancy Matching, Quang Anh Pham, Brahmanage Janaka Chathuranga Thilakarathna, Tien Mai, Akshat Kumar

Research Collection School Of Computing and Information Systems

Offline Learning from Observations (LfO) focuses on enabling agents to imitate expert behavior using datasets that contain only expert state trajectories and separate transition data with suboptimal actions. This setting is both practical and critical in real-world scenarios where direct environment interaction or access to expert action labels is costly, risky, or infeasible. Most existing LfO methods attempt to solve this problem through state or state-action occupancy matching. They typically rely on pretraining a discriminator to differentiate between expert and non-expert states, which could introduce errors and instability—especially when the discriminator is poorly trained. While recent discriminator-free methods have emerged, …

No Experts, No Problem: Avoidance Learning From Bad Demonstrations, Minh Huy Hoang, Tien Mai, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

This paper addresses the problem of learning avoidance behavior within the context of offline imitation learning. In contrast to conventional methodologies that prioritize the replication of expert or near-expert demonstrations, our work investigates a setting where expert (or desirable) data is absent, and the objective is to learn to eschew undesirable actions by leveraging demonstrations of such behavior (i.e., learning from negative examples).To address this challenge, we propose a novel training objective grounded in the maximum entropy principle. We further characterize the fundamental properties of this objective function, reformulating the learning process as a cooperative inverse Q-learning task. Moreover, we …

Rising From Ashes: Generalized Federated Learning Via Dynamic Parameter Reset, Jiahao Wu, Ming Hu, Yanxin Yang, Xiaofei Xie, Zekai Chen, Chenyu Song, Mingsong Chen

Research Collection School Of Computing and Information Systems

Although Federated Learning (FL) is promising for privacy-preserving collaborative model training, it suffers from low inference performance due to heterogeneous client data. Due to heterogeneous data across clients, FL training easily learns client-specific overfitting features. Existing FL methods adopt coarsegrained averaging, which can easily cause the global model to get stuck in local optima, leading to poor generalization. Specifically, this paper presents a novel FL framework, FedPhoenix, to address this issue. It stochastically resets partial parameters in each round to destroy some features of the global model, guiding FL training to learn multiple generalized features for inference rather than specific …

Efskip: A New Error Feedback With Linear Speedup For Compressed Federated Learning With Arbitrary Data Heterogeneity, Hongyan Bao, Pengwen Chen, Ying Sun, Zhize Li

Research Collection School Of Computing and Information Systems

Due to the communication bottleneck in distributed and decentralized federated learning applications, algorithms using compressed communication have attracted significant attention. The Error Feedback (EF) is a widely-studied compression framework for convergence with biased compressors such as top-k sparsification. Although various improvements have been obtained in recent years, the theoretical guarantee for EF-type framework is still limited. Previous works either 1) rely on strong assumptions such as bounded gradient/dissimilarity assumptions, thus can not deal with arbitrary data heterogeneity and also slow the convergence speed, or 2) can not enjoy linear speedup in the number of clients. In this work, we propose …

Generalization Bounds For Rank‑Sparse Neural Networks, Antoine Ledent, Rodrigo Alves, Yunwen Lei

Research Collection School Of Computing and Information Systems

It has been recently observed in much of the literature that neural networks exhibit a bottleneck rank property: for larger depths, the activation and weights of neural networks trained with gradient-based methods tend to be of approximately low rank. In fact, the rank of the activations of each layer converges to a fixed value referred to as the “bottleneck rank”, which is the minimum rank required to represent the training data. This perspective is in line with the observation that regularizing linear networks (without activations) with weight decay is equivalent to minimizing the Schatten p quasi norm of the neural …

Design Principles For Customer‑Engaging Digital Service Systems: An Action Research Study, Keng Siau, Xiaofeng Chen, Xin Tan

Research Collection School Of Computing and Information Systems

Digital services represent a business approach employed by organizations to operate in the digital environment. However, systematic development guidelines for developing quality digital service systems are lacking in the literature. The authors identified four general challenges for developing and implementing customer-engaging digital service systems (CEDSS). By employing the method of canonical action research in a digital service system project, they derived 10 design principles for developing high-quality CEDSS. They empirically evaluated the design principles in the development project and through follow-up focus group sessions. The design principles provide applicable and actionable guidelines for the development of CEDSS.

Bias Testing And Mitigation In Llm-Based Code Generation, Dong Huang, Jie M. Zhang, Qingwen Bu, Xiaofei Xie, Junjie Chen, Heming Cui

Research Collection School Of Computing and Information Systems

As the adoption of LLMs becomes more widespread in software coding ecosystems, a pressing issue has emerged: does the generated code contain social bias and unfairness, such as those related to age, gender, and race? This issue concerns the integrity, fairness, and ethical foundation of software applications that depend on the code generated by these models but are underexplored in the literature. This paper presents a novel bias testing framework that is specifically designed for code generation tasks. Based on this framework, we conduct an extensive empirical study on the biases in code generated by five widely studied LLMs (i.e., …

A Rate-Dependent Coreset Selector For Continual Learning On Time-Varying Data Distributions, Zilin Luo, Zichen Tian, Yaoyao Liu, Qianru Sun

Research Collection School Of Computing and Information Systems

In this paper we review the concept of “phase” defined in Class-Incremental Learning (CIL), i.e., learning new classes while not forgetting old ones. Due to this design, classic CIL algorithms are mostly offline or can handle only intensive data distribution shifts across the phases. However, real-world data streams are often online, usually with uncertain or untraceable changes in their data distributions. To this end, we design the per-step distribution shifts by modeling the class sampling weights using bell-shaped curves. Such a design respects the rise-and-fall nature and presents realistic but underexplored challenges for CIL: 1) The data non-stationarity across steps …

Genscore: Agent-Based Short-Answer Question Generation And Scoring In Software Engineering Courses, Nguyen Binh Duong Ta, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Short-answer questions are commonly used in educational assessments, as they are often viewed as a more effective way than multiple-choice questions to determine whether students have achieved the intended learning outcomes. However, manually creating appropriate questions targeting different cognitive levels such as those defined by the Bloom’s Taxonomy, and grading text answers from students are not trivial tasks for instructors. Existing work on auto-question generation and scoring in computing education typically targets coding-based questions. However, in software engineering courses, assessments can extend beyond coding to understanding of processes, DevOps methodologies, system design, etc. This work aims to address the dual …

Pilot-C: Physics-Informed Low-Distortion Optimal Trajectory Compression, Kefei Wu, Baihua Zheng, Weiwei Sun

Research Collection School Of Computing and Information Systems

Location-aware devices continuously generate massive volumes of trajectory data, creating demand for efficient compression. Line simplification is a common solution but typically assumes 2D trajectories and ignores time synchronization and motion continuity. We propose PILOT-C, a novel trajectory compression framework that integrates frequency-domain physics modeling with error-bounded optimization. Unlike existing line simplification methods, PILOT-C supports trajectories in arbitrary dimensions, including 3D, by compressing each spatial axis independently. Evaluated on four real-world datasets, PILOT-C achieves superior performance across multiple dimensions. In terms of compression ratio, PILOT-C outperforms CISED-W, the current state-of-the-art SED-based line simplification algorithm, by an average of 19.2%. For …

Performance Study Of A Woody Downdraft Gasifier: Numerical Investigation And Experimental Validation, Md Sanowar Hossain, Showmitro Bhowmik, Mujahidul Islam Riad, Md Golam Kibria, Barun K. Das, Sanjay Paul

Research outputs 2022 to 2026

Biomass gasification is an established and widely utilized renewable energy system. The research work aims to develop and construct a downdraft gasifier to investigate gasifier performance. The gasifier’s performance and cold gas efficiency were calculated for three volumetric airflow rates: 7.16 m3/h, 5.97 m3/h, and 4.78 m3/h. The efficiency was found maximum of 69.6% for an airflow rate of 7.16 m3/h. The oxidation zone and neck region of the gasifier reactor had the maximum recorded temperatures of 845 °C and 823 °C for Swietenia macrophylla (SM) and Mangifera indica (MI), respectively. A two-dimensional computational fluid dynamics (CFD) model for a …

Perception Of School Climate And Its Impact On The Language Acquisition Of El Students, John A. Starr Iii

LSU New Orleans Theses and Dissertations

Educators are grappling with the best way to support the influx of English Learners enrolling in schools. As the numbers of EL students continues to rise, school administrators are seeking the best approaches to address the needs of this subset of students. Additionally, research has shown the impact that school bonding has on the academic performance of native language students. Specifically, research has shown that students with a positive emotional connection to the teachers, peers, and the institution outperform those with less attachment to the school. The purpose of this study is to see if school climate and school bonding …

Predicting Cryptocurrency Prices Using Stochastic Modeling, Reem Hani Al Omari

Theses

Cryptocurrencies are digital currencies that operate independently of central banks and governments. They were designed to overcome the limitations of traditional financial systems through a decentralized, peer-to-peer electronic cash mechanism. Trading in cryptocurrencies offers several advantages, including decentralized and efficient transactions, reduced costs through the elimination of intermediaries, investment opportunities across exchanges, and seamless cross-border remittances. Modeling cryptocurrency prices is therefore essential, not only due to these advantages but also because of the substantial market capitalization of cryptocurrencies, estimated to exceed 900 billion dollars according to CoinMarketCap [6]. The main objective of this thesis is to propose a predictive framework …

Developing American Indian/Alaska Native Children As Leaders In The Climate Movement, Joseph Burns, Alessandra Angelino, Danielle Heims-Waldron, Allison Empey, Jason Deen

Journal of Youth Development

American Indian/Alaska Native (AI/AN) voices are critical in the climate movement, as numerous social drivers have rendered these communities particularly vulnerable to the consequences of environmental change. In recent years, specific events, including the Dakota Access Pipeline, have galvanized AI/AN youth, who have been increasingly involved as leaders in the climate movement both in the United States and internationally. This yields a significant opportunity to promote leadership development for Indigenous youth, both through local, national, and international organizing and through curricular development to spark interests in environmental science. This review aims to discuss the role of AI/AN youth leadership in …

Leptinotarsa Texana Schaeffer And Gargaphia Arizonica Drake & Carvalho As Potential Biocontrol Agents For The Noxious Weed Solanum Elaeagnifolium Cav, Samikshya Subedi, Stephanie L. Kasper, Alexis Racelis, Greg Lefoe, Rupesh R. Kariyat

School of Earth, Environmental, & Marine Sciences Faculty Publications

Silverleaf nightshade (Solanum elaeagnifolium Cav.; SLN) is a perennial forb native to the southern United States, Mexico and South America that has become a serious agricultural weed across the world. Biological control has emerged as a significant alternative for the management of (SLN) due to the challenges and high costs associated with chemical and mechanical controls. In this study, we used a combination of field and laboratory studies to (1) explore the fundamental and realized host ranges of two North American insects, Leptinotarsa texana Schaeffer and Gargaphia arizonica Drake & Carvalho and (2) assess their suitability as potential biological …