Digital Commons Network^™

Probing Shielding Tensor Components Of Amino Acids Using Nuclear Magnetic Resonance, Shiva Agarwal

Dissertations

Chirality is fundamental to terrestrial life. While most amino acids exist as nonsuperimposable mirror images, amino acids in terrestrial life are homochiral, with the L-enantiomer being ubiquitous. The detection of an excess of L-amino acids in carbonaceous meteorites suggests that extraterrestrial processes may have contributed to this enantiomeric excess (ee). One proposed mechanism, the magnetochiral model, provides a potential explanation for this phenomenon in stellar environments characterized by strong magnetic and electric fields and the presence of relativistic leptons. According to this model, subtle differences in the electronic environments of chiral amino acids under such conditions …

The Role Of Smart Actuarial Calculations In Improving The Quality Of Financial Reports Of The Iraqi Insurance Company, Zena Abdulstar Allayla, Waheed Mahmood Al-Ibrahimi

Journal of Economics and Administrative Sciences

The research aims to explore the role of actuarial calculations supported by artificial intelligence technologies (smart actuarial calculations) in enhancing the accuracy and quality of financial reporting. Companies face challenges related to the accuracy, transparency, and speed of preparing financial reports, necessitating innovative solutions based on smart technologies. The research analyzed the literature and employed an exploratory methodology to study the impact of smart actuarial calculations on the integrity of financial reports. The association between the application of artificial intelligence and professional standards and their impact on the performance of insurance companies and financial reporting practices was also examined. The …

A Nonparametric Estimator Of The Reliability Function For A Carbon Fiber-Reinforced Polymer Under Cyclical Stress, Haider K. Raheem, Alaa H. Jalob

Journal of Economics and Administrative Sciences

This paper introduces a method for measuring the Reliability Function of Carbon Fiber Reinforced Polymer (CFRP) under cyclic stress using nonparametric estimators. The study's goal is to gain a better understanding of material deterioration by combining microscopic and macroscopic approaches while accounting for the uncertainty associated with the data and models. Several statistical techniques were used to estimate the reliability function from failure data generated under cyclic stress, such as Nadaraya-Watson, spline estimation, kernel density estimation, Bayesian estimation, and Gaussian process regression. Real data will be simulated as a result of experiments conducted in Stanford structures and the vehicle laboratory …

Solving Multi-Objective Machine Scheduling Problem Using The Meerkat Clan Algorithm, Tahani Jabbar, Bayda Attia Khalaf, Erum Rehman

Journal of Economics and Administrative Sciences

Machine scheduling problems have become increasingly complex and dynamic. The complexity and size of the problems require the development of methods and solutions whose efficiency is measured by their ability to find acceptable results within a reasonable amount of time. Therefore, this paper addresses to propose a new mathematical model for multi objective function based on Single-machine scheduling problems  by minimizing  the discounted total weighted completion time the number of tardy jobs the maximum earliness and the maximum weighted tardiness  with release date denoted   +  which are an NP-hard. To achieve efficient solutions, a metaheuristic method (Meerkat clan algorithm (MCA)) …

The Performance Analysis Of Dynamic Investment Portfolio Insurance Strategies Based On Value At Risk, Fatima Faisal Kadim Alkhalidi, Ayad Tahir Mohammed

Journal of Economics and Administrative Sciences

The research aims to address the fundamental problem by reducing the systematic risks to which investments in the stock market are exposed and to benefit from the emerging potential provided by investment portfolio insurance strategies, through Value at Risk Based Portfolio Insurance (VBPI), using the comparative analytical approach. The research community was represented by the (ISX60) index, The data was collected through the annual reports of the Iraq Stock Exchange to form three intentional yearly conditional samples for the period (2022-2024), and several financial models were relied upon, such as Simple Ranking, Single Index, Sharpe, and Treynor Models. Several results …

Classifying Detrital Zircon U-Pb Age Distributions Using Automated Machine Learning, Jack W. Fekete, Glenn R. Sharman, Xiao Huang

Geosciences Faculty Publications and Presentations

The prodigious use of detrital zircon U-Pb geochronology for provenance studies in recent decades has led many researchers to amass extensive datasets (>100,000 dates). When displayed as age distributions, individual samples are traditionally compared using visual inspection and statistical methods, which can become timeconsuming and challenging when using large datasets. We propose that machine learning (ML) can more efficiently classify a sample by its source using detrital zircon U-Pb age distributions. Specifically, we hypothesize that automated machine learning (AutoML), which optimizes algorithm selection and hyperparameters, will outperform an unoptimized Random Forest (RF) classifier and the cross-correlation coefficient (R-2), a …

Proton Exchange Membrane Fuel Cells In Electric Vehicles: Innovations, Challenges, And Pathways To Sustainability, Tarek Abedin, Jagadeesh Pasupuleti, Johnny Koh Siaw Paw, Yaw Chong Tak, Monowar Mahmud, Md Pauzi Abdullah, Mohammad Nur-E-Alam

Research outputs 2022 to 2026

Proton exchange membrane fuel cells are leading the shift to sustainable energy, especially in fuel-cell electric vehicles. In PEMFCs, hydrogen is converted into electricity which contributes to reduced greenhouse gas emissions and decreased dependence on fossil fuels. Many works have been conducted to improve the power density and longevity of the whole system to be adopted in vehicles. The power density was considerably increased by a stack design incorporating lightweight material such as graphene-coated Ni form and weak carbon nanofiber films. Hydrogen refueling failures such as overfilling with hydrogen, hydrogen flow overfill, and hydrogen leakage need mitigation by developing advanced …

Robust Adaptive Fractional-Order Pid Controller Design For High-Power Dc-Dc Dual Active Bridge Converter Enhanced Using Multi-Agent Deep Deterministic Policy Gradient Algorithm For Electric Vehicles, Seyyed Morteza Ghamari, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

The Dual Active Bridge converter (DABC), known for its bidirectional power transfer capability and high efficiency, plays a crucial role in various applications, particularly in electric vehicles (EVs), where it facilitates energy storage, battery charging, and grid integration. The Dual Active Bridge Converter (DABC), when paired with a high-performance CLLC filter, is well-regarded for its ability to transfer power bidirectionally with high efficiency, making it valuable across a range of energy applications. While these features make the DABC highly efficient, they also complicate controller design due to nonlinear behavior, fast switching, and sensitivity to component variations. We have used a …

Building The Australian Counselling Profession's Research Capacity For The Future: A Collaborative Strategic Approach, Nathan Beel, E. A. Cocodia, Katrina Andrews, Stephani Stephens, Sonam Pelden, Shannon Hodges

Research outputs 2022 to 2026

Background: This paper presents an analysis of the current research landscape within the Australian counselling profession, identifying key challenges and proposing a strategic approach to building research capacity. Problem Statement: Written by a taskforce of educational leaders, it reports their perceptions and experiences of the multifaceted challenges hindering research development. These challenges encompass the limited pipeline of future doctoral-trained counselling educators, the barriers to promotion opportunities for current counsellor educators, and the impact these deficits may have on the profession overall. Proposed Solutions: To address these challenges, the paper proposes a series of targeted strategies aimed at stakeholders across the …

Spatial Patterns In Urban Water Consumption: The Role Of Local Climate Zones And Temperature Dynamics, Mohammad Maleki, Amirbahador Damroodi, Mahsa Mostaghim, Amir Reza Bakhshi Lomer, Samira Sadat Saleh, Junye Wang, Nabi Moradpour, Iain D. Stewart, Kanglin (Connie) Chen, Fatemeh Kazemi

Research outputs 2022 to 2026

Urban Water Consumption (UWC) is a major challenge in arid regions, intensified by urbanization, population growth, and resource scarcity, prompting debates on relocating Iran's capital to address resource scarcity and sustainability. This study analyzed the relationship between Local Climate Zones (LCZ), Land Surface Temperature (LST), and water usage in Tehran (2015–2019) to inform urban water management. UWC data was spatially matched to urban areas to calculate per capita consumption. An LCZ map for the base year 2017 was generated using the Random Forest (RF) algorithm, achieving an accuracy of 88.88 %. LST data for the five years was derived using …

Biological Age Acceleration Associated With The Progression Trajectory Of Cardio-Renal–Metabolic Multimorbidity: A Prospective Cohort Study, Yixing Tian, Jinqi Wang, Tianyu Zhu, Xia Li, Haiping Zhang, Xiaoyu Zhao, Xinghua Yang, Yanxia Luo, Lixin Tao, Zhiyuan Wu, Xiuhua Guo

Research outputs 2022 to 2026

Objectives: Previous studies have confirmed that biological age (BA) acceleration is associated with single cardio-renal–metabolic diseases (CRMDs), typically including type 2 diabetes mellitus, cardiovascular disease, and chronic kidney disease. However, its association with progression to cardio-renal–metabolic multimorbidity (CRMM, coexistence of ≥2 CRMDs) and subsequent mortality remains unexplored. Methods: Using the multi-state model, we analyzed 278,927 UK Biobank participants free of CRMDs at baseline to investigate the association between BA acceleration—measured by phenotypic age (PhenoAge) and Klemera–Doubal method age (KDMAge)—and CRMM progression trajectory, from health to the first CRMD and then to CRMM and death. BA acceleration was the residual from …

Measuring The Relationship Between Oil Revenues And The Parallel Exchange Rate In Iraq For The Period (2003-2023): An Empirical Study, Saif Nihad Salim, Safaa Ali Hussein Al-Bakri

Journal of Economics and Administrative Sciences

This study is an empirical investigation into the relationship between oil revenues and the parallel exchange rate in Iraq during the period from 2003 to 2023, highlighting the susceptibilities of a rentier economy dependent overwhelmingly on oil exports. The study adopted a deductive procedure coupled with econometric analysis using the Autoregressive Distributed Lag (ARDL) method to analyze the impacts of oil revenues and GDP without oil on the parallel exchange rate. Annual data were gathered from official Iraqi agencies and subjected to unit root tests, bounds cointegration tests, and error correction model (ECM) tests. The results indicate a statistically significant …

Enabling Automatic Solar Pv Array Identification Using Big Satellite Imagery, Qi Li, Keyang Yu, Carson Snow, Dong Chen

Computer Science Faculty Research and Publications

Recently, there has been a growing interest in automatically collecting distributed solar photovoltaic (PV) installation information in smart grid systems, including the quantity and locations of solar PV deployments, as well as their profiling information across a given geospatial region. Most recent approaches are still suffering low detection accuracy due to insufficient sample and principal feature learning when building their models and also separation of rooftop object segmentation and identification during their detection processes. In addition, they cannot report accurate multi-deployment results. To address these problems, we design a new system-SolarDetector+, which can automatically and accurately detect and profile distributed …

On-Site Water Management Field Sampling Plan (Fsp), 2025 Update, Mark Thompson, Rampart Solutions

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Revised Draft Final Butte Priority Soils Operable Unit (Bpsou) Unreclaimed Sites: Ur-03 Site Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final Butte Priority Soils Operable Unit (Bpsou) Unreclaimed Sites: Ur-03 Site Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Exploring New Mexico 4-H Volunteers’ Acceptance And Use Of Technology: A Qualitative Approach, Kayla Dawn Hinrichs

Electronic Theses and Dissertations

Integrating technology into youth development programs like 4-H is increasingly vital for enhancing communication, education, and engagement. However, technology adoption among 4-H volunteers remains inconsistent. This qualitative study explored the barriers to technology adoption among New Mexico 4-H volunteers. The researcher aimed to understand the factors influencing their willingness and ability to integrate digital tools into their volunteer roles. Guided by Rogers’s (2003) diffusion of innovation theory and framed by Creswell and Poth’s (2024) qualitative research design, this study employed semistructured interviews with a purposive sample of 4-H volunteers across the state. Following Braun and Clarke’s (2006) six- phase approach, …

Testing For Broad Alternatives In Stratified Contingency Tables, Nan Mi

Dissertations

In medical and social sciences fields, data are measured in terms of discrete categories. The primary question of interest involves the relationship between a set of factors and a set of response variables under studies. Moreover, the distribution of the response variables may be influenced by another set of variables called confounders. The data from such studies are summarized in 3-way tables. The hypothesis we are interested in can be expressed in terms of "no partial association" between the sub-populations and the response levels.

The methods for testing the association or independence in a 2x2 contingency table have been developed, …

Runtime Backdoor Detection For Federated Learning Via Representational Dissimilarity Analysis, Xiyue Zhang, Xiaoyong Xue, Xiaoning Du, Xiaofei Xie, Yang Liu, Meng Sun

Research Collection School Of Computing and Information Systems

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detection, existing methods face challenges with detection accuracy and runtime effectiveness, particularly when dealing with complex model architectures. In this work, we propose a novel approach to detecting malicious clients in an accurate, stable, and efficient manner. Our method utilizes a sampling-based network representation method to quantify …

Nexusgs: Sparse View Synthesis With Epipolar Depth Priors In 3d Gaussian Splatting, Yulong Zheng, Zicheng Jiang, Shengfeng He, Yandu Sun, Junyu Dong, Huaidong Zhang, Yong Du

Research Collection School Of Computing and Information Systems

Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) have noticeably advanced photo-realistic novel view synthesis using images from densely spaced camera viewpoints. However, these methods struggle in few-shot scenarios due to limited supervision. In this paper, we present NexusGS, a 3DGS-based approach that enhances novel view synthesis from sparse-view images by directly embedding depth information into point clouds, without relying on complex manual regularizations. Exploiting the inherent epipolar geometry of 3DGS, our method introduces a novel point cloud densification strategy that initializes 3DGS with a dense point cloud, reducing randomness in point placement while preventing over-smoothing and overfitting. Specifically, …

A Multimodal Fusion Model Leveraging Mlp Mixer And Handcrafted Features-Based Deep Learning Networks For Facial Palsy Detection, Heng Yim Nicole Oo, Min Hun Lee, Jeong Hoon Lim

Research Collection School Of Computing and Information Systems

Algorithmic detection of facial palsy offers the potential to improve current practices, which usually involve labor-intensive and subjective assessments by clinicians. In this paper, we present a multimodal fusion-based deep learning model that utilizes an MLP mixer-based model to process unstructured data (i.e. RGB images or images with facial line segments) and a feed-forward neural network to process structured data (i.e. facial landmark coordinates, features of facial expressions, or handcrafted features) for detecting facial palsy. We then contribute to a study to analyze the effect of different data modalities and the benefits of a multimodal fusion-based approach using videos of …

Search For Gravitational Waves Emitted From Sn 2023ixf, A. G. Abac, R. Abbott, Teviet Creighton, Mario C. Diaz, Francisco Llamas Villarreal, Soma Mukherjee, Volker Quetschke, Wenhui Wang

Physics & Astronomy Faculty Publications

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19, during the LIGO–Virgo–KAGRA 15th Engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered ∼14% of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave …

Meta-Learning Hyperparameters For Foundation Model Adaptation In Remote-Sensing Imagery, Zichen Tian, Yaoyao Liu, Qianru Sun

Research Collection School Of Computing and Information Systems

Training large foundation models of remote-sensing (RS) images is almost impossible due to the limited and long-tailed data problems. Fine-tuning natural image pre-trained models on RS images is a straightforward solution. To reduce computational costs and improve performance on tail classes, existing methods apply parameter-efficient fine-tuning (PEFT) techniques, such as LoRA and AdaptFormer. However, we observe that fixed hyperparameters -- such as intra-layer positions, layer depth, and scaling factors, can considerably hinder PEFT performance, as fine-tuning on RS images proves highly sensitive to these settings. To address this, we propose MetaPEFT, a method incorporating adaptive scalers that dynamically adjust module …

Chatgpt’S Performance Evaluation In Spreadsheets Modeling To Inform Assessments Redesign, Michelle L. F. Cheong

Research Collection School Of Computing and Information Systems

Background: Increasingly, students are using ChatGPT to assist them in learning and even completing their assessments, raising concerns of academic integrity and loss of critical thinking skills. Many articles suggested educators to redesign assessments which are more “Generative-AI-resistant” and to focus on assessing students on higher order thinking skills. However, there is a lack of articles that attempt to quantify assessments at different cognitive levels to provide empirical study insights on ChatGPT’s performance at different levels, which will affect how educators redesign their assessments.Objectives: Educators need new information on how well ChatGPT performs to redesign future assessments to assess their …

Alayadb: The Data Foundation For Efficient And Effective Long-Context Llm Inference, Yangshen Deng, Zhengxin You, Long Xiang, Qilong Li, Peiqi Yuan, Zhaoyang Hong, Yitao Zheng, Wanting Li, Runzhong Li, Haotian Liu, Kyriakos Mouratidis, Man Lung Yiu, Huan Li, Qiaomu Shen, Rui Mao, Bo Tang

Research Collection School Of Computing and Information Systems

AlayaDB is a cutting-edge vector database system natively architected for efficient and effective long-context inference for Large Language Models (LLMs) at AlayaDB AI. Specifically, it decouples the KV cache and attention computation from the LLM inference systems, and encapsulates them into a novel vector database system. For the Model as a Service providers (MaaS), AlayaDB consumes fewer hardware resources and offers higher generation quality for various workloads with different kinds of Service Level Objectives (SLOs), when compared with the existing alternative solutions (e.g., KV cache disaggregation, retrieval-based sparse attention). The crux of AlayaDB is that it abstracts the attention computation …

Lessons Learned From Sandboxing, Piloting And Policy Experimentation With Ai And Other Digital Initiatives: Part 2, Ten In-Depth Interviews, Steven M. Miller

Research Collection School Of Computing and Information Systems

This report, "Lessons Learned from Sandboxing, Piloting and Policy Experimentation with AI and Other Digital Initiatives," captures insights and experiences from project experts involved in recent digital innovation initiatives with the governments of Bangladesh, Maldives, and Kazakhstan, and from project experts actively involved with the use of AI for delivering government digital services in the EU, New Zealand, Rwanda, Singapore, United States, and Uzbekistan. The ten in-depth interview write-ups produced from these nine different country settings provide a small but highly informative sample of rich descriptions of some of the important realities, approaches, nuances, issues and challenges related to testing …

Large Language Model For Vulnerability Detection And Repair: Literature Review And The Road Ahead, Xin Zhou, Sicong Cao, Xiaobing Sun, David Lo

Research Collection School Of Computing and Information Systems

The significant advancements in Large Language Models (LLMs) have resulted in their widespread adoption across various tasks within Software Engineering (SE), including vulnerability detection and repair. Numerous studies have investigated the application of LLMs to enhance vulnerability detection and repair tasks. Despite the increasing research interest, there is currently no existing survey that focuses on the utilization of LLMs for vulnerability detection and repair. In this paper, we aim to bridge this gap by offering a systematic literature review of approaches aimed at improving vulnerability detection and repair through the utilization of LLMs. The review encompasses research work from leading …

Ntire 2025 Challenge On Event-Based Image Deblurring: Methods And Results, Lei Sun, Et. Al.

Research Collection School Of Computing and Information Systems

This paper presents an overview of NTIRE 2025, the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results. The primary goal of the challenge is to design an event-based method that achieves high-quality image deblurring, with performance quantitatively assessed using Peak Signal-toNoise Ratio (PSNR). Notably, there are no restrictions on computational complexity or model size. The task focuses on leveraging both events and images as inputs for singleimage deblurring. A total of 199 participants registered, among whom 15 teams successfully submitted valid results, offering valuable insights into the current state of eventbased image deblurring. We anticipate …

Group-And-Match Vs. Route-Then-Insert: Order Dispatching In Vehicle-Based Dual Services (Vedus), Yue Lin, Hai Yang, Hai Wang

Research Collection School Of Computing and Information Systems

Rapid urban transportation and delivery demand and relevant resource constraints have driven the need for more efficient vehicle utilization. An innovative concept, “Vehicle-based MultiServices” (VeMuS), is a service model in which a single vehicle offers multiple services simultaneously in an urban mobility system. Similarly, “Vehicle-based Dual Services” (VeDuS) refers to a vehicle that provides two services simultaneously (Sun et al., 2023).

On Lexicographic Proof Rules For Probabilistic Termination, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný, Jiří Zárevucký, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider the almost-sure (a.s.) termination problem for probabilistic programs, which are a stochastic extension of classical imperative programs. Lexicographic ranking functions provide a sound and practical approach for termination of non-probabilistic programs, and their extension to probabilistic programs is achieved via lexicographic ranking supermartingales (LexRSMs). However, LexRSMs introduced in the previous work have a limitation that impedes their automation: all of their components have to be non-negative in all reachable states. This might result in a LexRSM not existing even for simple terminating programs. Our contributions are twofold. First, we introduce a generalization of LexRSMs that allows for some …