Digital Commons Network^™

Self-Supervised Spatial-Temporal Normality Learning For Time Series Anomaly Detection, Yutong Chen, Hongzuo Xu, Guansong Pang, Hezhe Qiao, Yuan Zhou, Mingsheng Shang

Research Collection School Of Computing and Information Systems

Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is to learn the normal patterns of time series data, thereby identifying deviations in test samples. Most existing TSAD methods focus on modeling data from the temporal dimension, while ignoring the semantic information in the spatial dimension. To address this issue, we introduce a novel approach, called Spatial-Temporal Normality learning (STEN). STEN is composed of a sequence Order prediction-based Temporal Normality learning (OTN) module that captures the temporal correlations within sequences, and a Distance prediction-based Spatial Normality learning …

Putting Gpt-4o To The Sword: A Comprehensive Evaluation Of Language, Vision, Speech, And Multimodal Proficiency, Sakib Shahriar, Brady D. Lund, Nishith Reddy Mannuru, Muhammad Arbab Arshad, Kadhim Hayawi, Ravi Varma Kumar Bevara, Aashrith Mannuru, Laiba Batool

All Works

As large language models (LLMs) continue to advance, evaluating their comprehensive capabilities becomes significant for their application in various fields. This research study comprehensively evaluates the language, vision, speech, and multimodal capabilities of GPT-4o. The study employs standardized exam questions, reasoning tasks, and translation assessments to assess the model’s language capability. Additionally, GPT-4o’s vision and speech capabilities are tested through image classification and object-recognition tasks, as well as accent classification. The multimodal evaluation assesses the model’s performance in integrating visual and linguistic data. Our findings reveal that GPT-4o demonstrates high accuracy and efficiency across multiple domains in language and reasoning …

Paper-Recorded Ecg Digitization Method With Automatic Reference Voltage Selection For Telemonitoring And Diagnosis, Liang Hung Wang, Chao Xin Xie, Tao Yang, Hong Xin Tan, Ming Hui Fan, I. Chun Kuo, Zne Jung Lee, Tsung Yi Chen, Pao Cheng Huang, Shih Lun Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

In electrocardiograms (ECGs), multiple forms of encryption and preservation formats create difficulties for data sharing and retrospective disease analysis. Additionally, photography and storage using mobile devices are convenient, but the images acquired contain different noise interferences. To address this problem, a suite of novel methodologies was proposed for converting paper-recorded ECGs into digital data. Firstly, this study ingeniously removed gridlines by utilizing the Hue Saturation Value (HSV) spatial properties of ECGs. Moreover, this study introduced an innovative adaptive local thresholding method with high robustness for foreground–background separation. Subsequently, an algorithm for the automatic recognition of calibration square waves was proposed …

Developing Empathetic Ai: Exploring The Potential Of Artificial Intelligence To Understand And Simulate Family Dynamics And Cultural Identity, Emily Barnes, James Hutson

Faculty Scholarship

The rapid advancement of Artificial Intelligence (AI) has significantly impacted various domains. Yet, the exploration of AI's potential to develop a deep understanding of family culture and identity remains underexplored. This study introduces the concept of "a love of grandma and apple pie" to symbolize the potential of various AI to internalize and appreciate familial relationships, cultural traditions, and personal identity. The proposed study would investigate how an advanced deep learning model, trained on diverse unstructured datasets—including multimedia data from 100 families-could learn and reflect human-like emotions, values, and cultural understanding. Utilizing Convolutional Neural Networks (CNNs) for visual data processing …

Foliar Diseases And Their Management In Lupins, Department Of Primary Industries And Regional Development, Western Australia

Grains and other field crops factsheets

This factsheet describes symptoms and management of the major fungal and viral diseases affecting lupin foliage, stems, and pods. Several of these diseases have the capacity to cause significant losses if not managed effectively.

Inoculum can be carried in soil, seed, stubble, on green regrowth, or by insect vectors, depending on the disease. Therefore, an integrated approach to disease management is required using tactics such as crop rotation, stubble management, fungicide seed dressing, variety selection, and seed testing

Artificial Intelligence In Orthopaedic Education: A Comparative Analysis Of Chatgpt And Bing Ai’S Orthopaedic In-Training Examination Performance, Clark Chen, Vivek Biololikar, Duncan Vannest, James Raphael, Gene Shaffer

Einstein Health Papers

Background: This study evaluated the performance of generative artificial intelligence (AI) models on the Orthopaedic In-Training Examination (OITE), an annual exam administered to U.S. orthopaedic residency programs. Methods: ChatGPT 3.5 and Bing AI GPT 4.0 were evaluated on standardised sets of multiple-choice questions drawn from the American Academy of Orthopaedic Surgeons OITE online question bank spanning 5 years (2018–2022). A total of 1165 questions were posed to each AI system. The performance of both systems was standardised using the latest versions of ChatGPT 3.5 and Bing AI GPT 4.0. Historical data of resident scores taken from the annual OITE technical …

Draft Final Quarterly Operations And Maintenance Report Butte Treatment Lagoon System – Second Quarter 2024, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Climate Change Prediction Model Using Mcdm Technique Based On Neutrosophic Soft Functions With Aggregate Operators, Kainat Muniba, Muhammad Naveed Jafar, Asma Riffat, Jawaria Mukhtar, Adeel Saleem

Neutrosophic Systems with Applications

The increasing impact of climate change necessitates innovative approaches in modeling and prediction to mitigate its adverse effects. This paper introduces a novel methodology integrating Neutrosophic Soft Functions (NSFs) into climate change prediction frameworks. NSFs, a hybrid of Neutrosophic Set Theory and Soft Set Theory, provide a flexible framework for handling uncertain and imprecise information inherent in climate data. This study explores the application of NSFs in capturing the complex interplay of various climatic variables, including temperature, precipitation, humidity, and atmospheric pressure, thereby enhancing the accuracy and reliability of climate change predictions. By incorporating NSFs into existing predictive models, such …

Deciphering The Interdomain Coupling In A Gram-Negative Bacterial Membrane Insertase, Adithya Polasa, Shadi A. Badiee, Mahmoud Moradi

Chemistry & Biochemistry Faculty Publications and Presentations

YidC is a membrane protein that plays an important role in inserting newly generated proteins into lipid membranes. The Sec-dependent complex is responsible for inserting proteins into the lipid bilayer in bacteria. YidC facilitates the insertion and folding of membrane proteins, both in conjunction with the Sec complex and independently. Additionally, YidC acts as a chaperone during the folding of proteins. Multiple investigations have conclusively shown that Gram-positive bacterial YidC has Sec-independent insertion mechanisms. Through the use of microsecond-level all-atom molecular dynamics (MD) simulations, we have carried out an in-depth investigation of the YidC protein originating from Gram-negative bacteria. This …

Integrating Blockchain Technology Into The Software Development Life Cycle To Satisfy The Software Bill Of Materials Requirement For Government Software Systems, Walter T. Scott Ii

Theses and Dissertations

This thesis explores the integration of Blockchain Technology (BT) into the Software Development Life Cycle (SDLC) to satisfy the Software Bill of Materials (SBOM) requirement for government software systems. This study begins by synthesizing a standard SDLC definition from various government and industry references, which may provide the foundation for future efforts to standardize software development practices across the government software development community. This study proceeds to define working definitions for the software supply chain (SSC) and software supply chain management (SCM) before introducing and detailing the SBOM requirement as well as providing an overview of prior research regarding SBOMs …

Editorial Commentary On The Special Issue “Antioxidant Therapy For Cardiovascular Diseases”—Cutting-Edge Insights Into Oxidative Stress And Antioxidant Therapy In Cardiovascular Health, Guim Kwon, K. Michael Gibson, Lanrong Bi

Michigan Tech Publications

No abstract provided.

A Convenient Synthesis Of Short Α-/Β-Mixed Peptides As Potential Α-Amylase Inhibitors, Naeem Ahmed, Fakhira Razzaq, Muhammad Arfan, Mansour K. Gatasheh, Hammad Nasir, Joham Sarfraz Ali, Hamna Hafeez

Michigan Tech Publications

Over the last decades, the increased incidence of metabolic disorders, such as type two diabetes and obesity, has motivated researchers to investigate new enzyme inhibitors. Inhibition of the α-amylase enzyme is one therapeutic approach in lowering glucose levels in the blood to manage diabetes mellitus. The objective of this study was to synthesize short α-/β-mixed peptides in the solution phase. The Boc-protected α-L-leucine was converted to β-analogue by using Arndt–Eistert synthesis with the advantage of no racemization and retention of configuration. Three novel short peptides were successfully synthesized: N(Boc)-Gly-β-Leu–OCH3(14), N(Boc)-O(Bz)α-Ser-β-Leu–OCH3(16), and N(Boc)-O(Bz)-α-Tyr-α-Gly-β-Leu–OCH3(17), characterized by FTIR and 1H NMR analysis. The …

Getting To The Point: Contrasting Directness And Warmth In Motivational Embodied Conversational Agents, Michael O'Mahony, Cathy Ennis, Robert Ross

Conference papers

Enhancing long-term engagement with conversational agents remains a significant challenge. Controlling the perceived warmth or directness of an agent’s personality through the style of its generated text could be used to increase user likeability. This paper reports an investigation of a Wizard-of-Oz (WoZ) mediated study of two variants of a motivational embodied conversational agent to measure user perception of and attitudes towards warmth in interaction style. Results show a significant effect of users preferring an agent with a "more direct" personality for this scenario, though this effect is in many ways nuanced.

The Impact Of Instrumental Attribution In Ai-Enabled Monitoring On Counterproductive Work Behavior, Qiang Zhang

Dissertations and Theses Collection (Open Access)

AI-enabled monitoring tools are theoretically expected to suppress unethical employee behavior. However, in practice, employees may perceive such monitoring as being driven by leaders' instrumental motives, primarily focused on personal performance evaluation and self-interest. This perception can foster feelings of job insecurity and moral disengagement, ultimately leading to counterproductive work behavior (CWB), which includes unethical employee behavior and turnover. These outcomes may undermine the intended effectiveness of AI-enabled monitoring tools. This study aims to explore the impact of Instrumental Attribution in AIenabled Monitoring (IAAIM) on CWB, specifically focusing on unethical employee behavior and turnover, through both theoretical and empirical lenses. …

Enhancing Stance Classification On Social Media Using Quantified Moral Foundations, Hong Zhang, Quoc-Nam Nguyen, Prasanta Bhattacharya, Wei Gao, Liang Ze Wong, Brandon Siyuan Loh, Joseph J. P. Simons, Jisun An

Research Collection School Of Computing and Information Systems

This study enhances stance detection on social media by incorporating deeper psychological attributes, specifically individuals’ moral foundations. These theoretically-derived dimensions aim to provide an interpretable profile of an individual’s moral concerns which, in recent work, has been linked to behaviour in a range of domains including society, politics, health, and the environment. In this paper, we investigate how moral foundation dimensions can contribute to detecting an individual’s stance on a given target. Specifically, we incorporate moral foundation features extracted from text, along with semantic features, to classify stances at both message-and user-levels using traditional machine learning and Large Language Models …

Draft Final Bpsou Backfill And Cover Material Quality Assurance Project Plan (Qapp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Performance Of 5 Prominent Large Language Models In Surgical Knowledge Evaluation: A Comparative Analysis, Adam M. Ostrovsky, Joshua R. Chen, Vishal N. Shah, Babak Abai

Department of Surgery Faculty Papers

No abstract provided.

Network Resilience Of Plant-Bee Interactions In The Eastern Afromontane Biodiversity Hotspot, Fairo F. Dzekashu, Abdullahi A. Yusuf, Kazuhiro Takemoto, Marcell K. Peters, H. Michael G. Lattorff, Ingolf Steffan-Dewenter, Christian W.W. Pirk

All Peer-Reviewed Publications

Interaction network resilience can be defined as the ability of interacting organisms to maintain their functions, processes or populations after experiencing a disturbance. Studies on mutualistic interactions between plants and pollinators along environmental gradients are essential to understand the provision of ecosystem services and the mechanisms challenging their network resilience. However, it remains unknown to what level ecological changes along climatic gradients constrain the network resilience of mutualistic organisms, especially along elevation gradients. We surveyed bee species and recorded their interactions with plants throughout the four major seasons (i.e. long and short rainy, and long and short dry) on 50 …

A Comparison Of Four Approaches To Modeling Information Insufficiency, Pengya Ai, Sonny Rosenthal

Research Collection College of Integrative Studies

Information insufficiency, or the disparity between the level of knowledge needed to confidently judge an issue and the perceived level of current knowledge, is a key motivator of risk information seeking and processing. This study compared 4 approaches to modeling information insufficiency within the planned risk information seeking model. These approaches included the raw difference score, regression approach, partial variance score, and direct measure. Statistical modeling used data from large samples in Singapore (n = 2,124) and the United States (n = 2,125). The results of ordinary least squares regression analysis and structural equation modeling pointed to several issues. First, …

Evaluation Of Transplant Benefits With The Us Scientific Registry Of Transplant Recipients By Semiparametric Regression Of Mean Residual Life, Ge Zhao, Yanyuan Ma, Huazhen Lin, Yi Li

Mathematics and Statistics Faculty Publications and Presentations

Kidney transplantation is the most effective renal replacement therapy for end stage renal disease patients. With the severe shortage of kidney supplies and for the clinical effectiveness of transplantation, patient’s life expectancy posttransplantation is used to prioritize patients for transplantation; however, severe comorbidity conditions and old age are the most dominant factors that negatively impact posttransplantation life expectancy, effectively precluding sick or old patients from receiving transplants. It would be crucial to design objective measures to quantify the transplantation benefit by comparing the mean residual life with and without a transplant, after adjusting for comorbidity and demographic conditions. To address …

A New Fractional Derivative Extending Classical Concepts: Theory And Applications, Mutaz Mohammad, Mohamed Saadaoui

All Works

In this paper, a novel general definition for the fractional derivative and fractional integral based on an undefined kernel function is introduced. For 0<α≤1, this definition aligns with classical interpretations and is applicable for calculating the derivative in an open negative interval I⊆[a,+∞),a∈R. Additionally, when α=1, the definition coincides with the classical derivative. Fundamental properties of the fractional integral and derivative, including the product rule, quotient rule, chain rule, Rolle's theorem, and the mean value theorem, are derived. These properties are illustrated through various applications to demonstrate their applicability. Furthermore, some applications of solving fractional nonlinear systems of integro-differential equations using framelets are presented.

A Two-Stage Matheuristic For The Home Healthcare Routing And Scheduling Problem With Perishable Products, Aldy Gunawan, Nabila Yuraisyah Salsabila, Vincent F. Yu, Pham Kien Minh Nguyen

Research Collection School Of Computing and Information Systems

This study proposes a home healthcare routing and scheduling problem, where perishable products such as medicines, vaccines, or meals must be provided for some patients’ treatments. This problem is formulated as a mixed integer linear programming (MILP). A two-stage matheuristic is then developed as the solution approach. The first stage is a local search to solve the nurse routing problem, and the second stage is run as the relaxed MILP to solve the scheduling problem. The matheuristic is tested on newly generated instances and compared with the results of CPLEX. The proposed matheuristic is able to obtain CPLEX solutions within …

Solving Fractional Differential Equations On A Quantum Computer: A Variational Approach, Fong Yew Leong, Dax Enshan Koh, Jian Feng Kong, Siong Thye Goh, Jun Yong Khoo, Wei Bin Ewe, Hongying Li, Jayne Thompson, Dario Poletti

Research Collection School Of Computing and Information Systems

We introduce an efficient variational hybrid quantum-classical algorithm designed for solving Caputo time-fractional partial differential equations. Our method employs an iterable cost function incorporating a linear combination of overlap history states. The proposed algorithm is not only efficient in terms of time complexity but also has lower memory costs compared to classical methods. Our results indicate that solution fidelity is insensitive to the fractional index and that gradient evaluation costs scale economically with the number of time steps. As a proof of concept, we apply our algorithm to solve a range of fractional partial differential equations commonly encountered in engineering …

Improving Large Language Models For Clinical Named Entity Recognition Via Prompt Engineering, Yan Hu, Qingyu Chen, Jingcheng Du, Xueqing Peng, Vipina Kuttichi Keloth, Xu Zuo, Yujia Zhou, Zehan Li, Xiaoqian Jiang, Zhiyong Lu, Kirk Roberts, Hua Xu

Faculty, Staff and Student Publications

IMPORTANCE: The study highlights the potential of large language models, specifically GPT-3.5 and GPT-4, in processing complex clinical data and extracting meaningful information with minimal training data. By developing and refining prompt-based strategies, we can significantly enhance the models' performance, making them viable tools for clinical NER tasks and possibly reducing the reliance on extensive annotated datasets.

OBJECTIVES: This study quantifies the capabilities of GPT-3.5 and GPT-4 for clinical named entity recognition (NER) tasks and proposes task-specific prompts to improve their performance.

MATERIALS AND METHODS: We evaluated these models on 2 clinical NER tasks: (1) to extract medical problems, treatments, …

Ensemble Pretrained Language Models To Extract Biomedical Knowledge From Literature, Zhao Li, Qiang Wei, Liang-Chin Huang, Jianfu Li, Yan Hu, Yao-Shun Chuang, Jianping He, Avisha Das, Vipina Kuttichi Keloth, Yuntao Yang, Chiamaka S Diala, Kirk E Roberts, Cui Tao, Xiaoqian Jiang, W Jim Zheng, Hua Xu

Faculty, Staff and Student Publications

OBJECTIVES: The rapid expansion of biomedical literature necessitates automated techniques to discern relationships between biomedical concepts from extensive free text. Such techniques facilitate the development of detailed knowledge bases and highlight research deficiencies. The LitCoin Natural Language Processing (NLP) challenge, organized by the National Center for Advancing Translational Science, aims to evaluate such potential and provides a manually annotated corpus for methodology development and benchmarking.

MATERIALS AND METHODS: For the named entity recognition (NER) task, we utilized ensemble learning to merge predictions from three domain-specific models, namely BioBERT, PubMedBERT, and BioM-ELECTRA, devised a rule-driven detection method for cell line and …

Ord River Irrigation Area Strategy 2024-34, Department Of Primary Industries And Regional Development, Western Australia

Natural resources published reports

The ORIA Strategy 2024–34 sets a collective 10-year vision, identifying key areas for focus as well as defining key roles and responsibilities to drive the achievement of this vision.

It provides direction for government to continue to work alongside and support industry and the community to achieve positive development outcomes. As such, it has been developed in consultation with key stakeholders across local industry, Traditional Owners and community groups.

Multi-Scale Variational Autoencoder For Imputation Of Missing Values In Untargeted Metabolomics Using Whole-Genome Sequencing Data, Chen Zhao, Kuan-Jui Su, Chong Wu, Xuewei Cao, Qiuying Sha, Wu Li, Zhe Luo, Tian Qing, Chuan Qiu, Lan Juan Zhao, Anqi Liu, Lindong Jiang, Xiao Zhang, Hui Shen, Weihua Zhou, Hong-Wen Deng

Faculty, Staff and Student Publications

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies.

Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics …

Image-Guided Patient-Specific Optimization Of Catheter Placement For Convection-Enhanced Nanoparticle Delivery In Recurrent Glioblastoma, Chengyue Wu, David A Hormuth, Chase D Christenson, Ryan T Woodall, Michael R A Abdelmalik, William T Phillips, Thomas J R Hughes, Andrew J Brenner, Thomas E Yankeelov

Faculty, Staff and Student Publications

Background: Proper catheter placement for convection-enhanced delivery (CED) is required to maximize tumor coverage and minimize exposure to healthy tissue. We developed an image-based model to patient-specifically optimize the catheter placement for rhenium-186 (186Re)-nanoliposomes (RNL) delivery to treat recurrent glioblastoma (rGBM).

Methods: The model consists of the 1) fluid fields generated via catheter infusion, 2) dynamic transport of RNL, and 3) transforming RNL concentration to the SPECT signal. Patient-specific tissue geometries were assigned from pre-delivery MRIs. Model parameters were personalized with either 1) individual-based calibration with longitudinal SPECT images, or 2) population-based assignment via leave-one-out cross-validation. The concordance correlation coefficient …

44ti/44sc Generator Development And Radiolabeling Optimization For Medical Applications, Christine E. Schmidt-Kennelty

Dissertations, Theses, and Capstone Projects

In recent years, there has been a growing interest in scandium-44 (⁴⁴Sc) for medical applications due to its favorable decay properties. With a relatively short half-life of 3.97 hours and high positron branching ratio of 94.3%, ⁴⁴Sc is an ideal candidate for positron emission tomography (PET) imaging. However, producing ⁴⁴Sc without an on-site cyclotron limits access, as its short half-life makes it impractical to ship to more remote locations.

To address this issue, ⁴⁴Ti/⁴⁴Sc generators have become a focal point of research, providing an onsite source of⁴⁴Sc. Despite their potential, current …

Advancing Affective Computing: Emotion Recognition And Tracking Across Diverse Contexts (Varied Environments), Shao Liu

Dissertations, Theses, and Capstone Projects

Affective Computing (AC) is an interdisciplinary field that recognizes, interprets, and processes human emotions. Emotions are complex, involving consciousness, physical sensations, and behavioral expressions, and are significant in various domains like mental health, human-computer interaction, and social security. Real-world applications of AC include monitoring drivers’ emotional states to improve road safety and understanding the emotions expressed by artists in visual arts. Traditional methods relying on facial expressions often fall short due to the nuanced nature of emotions, which vary across individuals, cultures, and contexts. Accurate AC systems require sophisticated, multimodal models to handle these variations and external factors like noise …