Physical Sciences and Mathematics Commons^™

Noncontact Diffuse Reflectance Spectroscopy Of Synovial Fluid Samples For Rapid Identification Of Infections, Erin E. Drewke, Robert L. Brand, Caroline G. Geels, Hanna K. Jensen, Kevin Wong, Jarret D. Sanders, Narasimhan Rajaram

Biomedical Engineering Faculty Publications and Presentations

Severe joint infections, such as septic arthritis, require rapid diagnostic testing of the synovial fluid aspirated from joints level so that a surgical team can be assembled quickly. We present a diffuse reflectance spectroscopy (DRS) system for noncontact determination of infection. Using a light-tight syringe holder and fiber optic probe, diffusely reflected light from 475 to 655 nm was acquired from 18 patient samples through the wall of a syringe in a noncontact and sterile manner. We determined the reflectance ratios at two different wavelengths—R ₄₉₀/R ₆₀₀ and R ₅₈₀/R ₆₀₀ and found statistically significant …

Final West Side Soils Operable Unit Remedial Investigation Sampling Data Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final Materials Management Plan Silver Bow Creek Conservation Area, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final 2022 Insufficiently Reclaimed Sites Sampling: Bres No. 158 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final Quarterly Operations And Maintenance Report Butte Treatment Lagoon System – Fourth Quarter 2022, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

September 2024 Berkeley Pit Migratory Waterfowl Protection Monthly Report, Mark Thompson, Loren Burmeister

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Tritrophic Interactions Mediated By Zoophytophagous Predator-Induced Host Plant Volatiles, Bashiru Adams, Abdullahi Ahmed Yusuf, Baldwyn Torto, Fathiya Mbarak Khamis

All Peer-Reviewed Publications

The zoophytophagous mirid predator Nesidiocoris tenuis and the ectoparasitoid Stenomesius japonicus are important biological control agents for several agricultural pests including the invasive leafminer, Phthorimaea absoluta, a destructive pest of Solanaceous crops especially tomato in sub-Saharan Africa. However, little is known about how feeding by N. tenuis can influence the tritrophic interactions in the tomato plant. Here, we tested the hypothesis that N. tenuis phytophagy would influence the tritrophic olfactory interactions between the host plant tomato and pest, predator, and parasitoid. In olfactometer assays, P. absoluta females and N. tenuis adults were both attracted to constitutive volatiles released by the …

Against The Substitutive Approach To Ai In Healthcare, Rand Hirmiz

Research Collection School of Social Sciences

Paul Bloom has famously argued against the need for empathy in clinicians, while Sally Dalton-Brown has argued that AI need not be capable of empathy to be a good carer. In this paper, the capacity for AI to substitute for human clinicians is assessed from a bioethical perspective, primarily through the evaluation of the arguments put forth by Dalton-Brown and Bloom concerning empathy in healthcare. In opposition to both Bloom and Dalton-Brown, this paper argues that (1) empathy is essential to providing good care or deep care (that is, care that goes beyond the mere fulfilment of medical tasks), (2) …

Getting On Board With Incentivising Esg, Shai Ganu

Asian Management Insights

What gets rewarded gets measured and done. Shai Ganu, Managing Director and Global Leader of the Executive Compensation and Board Advisory business at WTW, discusses the increasing adoption of Environmental, Social, and Governance (ESG) metrics in structuring executive compensation. He explores how incorporating ESG goals into incentive plans drives accountability and aligns leadership performance with long-term sustainability objectives.

Perspectives Through The Lens Of Educators And Students: Teaching Flood Mitigation And Other Controversial Food, Energy, Water Nexus Issues In The Classroom, Sumaiya Tabassum

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Science and society are inseparably linked, shaping each other in ways that define our world, emphasizing the need to teach science for empowering students capable of taking meaningful action toward building a sustainable and just society. Developing sustainability competencies in students is an important step toward this goal, with controversial socio-scientific issues (SSIs) providing a valuable teaching context, that encourage students in analyzing interdisciplinary evidence while considering the social, economic impacts of potential solutions. However, recognizing the unique perspectives individuals bring to the classroom is prerequisite for tailoring effective, inclusive instruction, as perspectives often shape learning and classroom-engagement. This study …

The Effects Of Wildfires On The Mental And Physical Health Of School-Age Children In North America: A Scoping Review, Sarah Oerther, Sarah Manspeaker, Alison Wix, Daniel Oerther, Carmen Marsit

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Background: Wildfires are occurring more often and with greater intensity, leading to longer and harsher fire seasons. As a result, children are more frequently exposed to wildfire smoke, which increases their risk of mental and physical health effects. Purpose: The purpose of our scoping review is to explore the current literature on what is known about the direct and indirect impacts of wildfires on the mental and physical health of school-age children in North America (5 to 18-year-olds). Methods: A health sciences librarian-assisted literature search, with input from the research team, was performed. Results: A total of 16 studies were …

Neutrosophic Logic-Based Crop Yield Prediction And Risk Assessment Using Least Squares Regression, M. Srikanth, R.N.V. Jagan Mohan, M. Chandra Naik

Neutrosophic Systems with Applications

Agriculture faces significant challenges due to climate change and unpredictable environmental factors, which impact crop yields and threaten food security. This study proposes a novel approach to crop yield prediction and risk assessment using neutrosophic logic and least squares regression. By integrating these methods, we aim to improve accuracy in predicting crop losses under uncertain conditions. The model classifies crops based on profitability and environmental risks, utilizing the independence test to evaluate the relationships between crop attributes. Our approach leverages deep learning techniques, such as restricted Boltzmann machines (RBM), to enhance the analysis of crop data and provide farmers with …

Addressing Governance Challenges Of Digitalisation And Sustainability: The Case Of Central Bank Digital Currency, Heng Wang

Research Collection Yong Pung How School Of Law

Digitalisation and environmental sustainability are widely discussed topics. However, their nexus remains underexplored and can pose significant challenges for governments and industries alike. The environmental implications of digitalisation are becoming increasingly pertinent with the advent of central bank digital currencies (CBDCs) and their inherent energy consumption and production of e-waste. On the other hand, digitalisation could potentially support sustainability efforts. This begs the question of how systems of governance, such as regulatory frameworks and internal organisational governance, should harmonise digitalisation and sustainability goals. Such harmonisation entails ensuring that digitalisation processes are environmentally responsible while exploring how the application and features …

Media Use, Interpersonal Communication, And Personal Relevance As External And Internal Representations Of Climate Change, Sonny Rosenthal, Pengya Ai

Research Collection College of Integrative Studies

Personal relevance is a key driver of individual climate action. It is also related to media use and interpersonal communication, which the current study examines from two perspectives. First, individuals may find climate change personally relevant because they experience it vicariously through the media and other information sources. Second, they may engage with climate change information because the issue is personally relevant. This study tested these models using structural equation modeling of online survey data from representative samples in Singapore (n = 1,997) and the United States (n = 2,009). Findings supported both models, albeit the first one more strongly. …

Enhancedbert: A Python Software Tailored For Arabic Word Sense Disambiguation, Sanaa Kaddoura, Reem Nassar

All Works

EnhancedBERT is a software framework designed to disambiguate Arabic polysemous terms using advanced natural language processing techniques. It integrates transformer architectures with ensemble methods to achieve high performance in understanding and processing Arabic text. The framework provides a flexible pipeline that can be directly utilized or fine-tuned according to specific needs. EnhancedBERT stands out for its ease of use, leveraging transformer-based models combined with ensemble strategies to provide superior contextual understanding. This contextual awareness makes it an invaluable tool for researchers and practitioners tackling complexities in Arabic language processing.

Uncovering Merchants’ Willingness To Wait In On-Demand Food Delivery Markets, Jian Liang, Ya Zhao, Hai Wang, Zuopeng Xiao, Jintao Ke

Research Collection School Of Computing and Information Systems

While traditional on-demand food delivery services help restaurants reach more customers and enable doorstep deliveries, they also come with drawbacks, such as high commission fees and limited control over the delivery process. White-label food delivery services have emerged as an alternative, ready-to-use platform for restaurants to arrange delivery for customer orders received through their applications or websites, without the constraints imposed by traditional on-demand food delivery platforms or the need to develop an in-house delivery operation. Although several studies have investigated consumer behavior when using traditional on-demand food delivery services, there is limited research on merchants’ behavior when adopting white-label …

Badfl: Backdoor Attack Defense In Federated Learning From Local Model Perspective, Haiyan Zhang, Xinghua Li, Mengfan Xu, Ximeng Liu, Tong Wu, Jian Weng, Robert H. Deng

Research Collection School Of Computing and Information Systems

There is substantial attention to federated learning with its ability to train a powerful global model collaboratively while protecting data privacy. Despite its many advantages, federated learning is vulnerable to backdoor attacks, where an adversary injects malicious weights into the global model, making the global model's targeted predictions incorrect. Existing defenses based on identifying and eliminating malicious weights ignore the similarity variation of the local weights during iterations in the malicious model detection and the presence of benign weights in the malicious model during the malicious local weight elimination, resulting in a poor defense and a degradation of global model …

Don’T Just Say “I Don’T Know”! Self-Aligning Large Language Models For Responding To Unknown Questions With Explanations, Yang Deng, Yong Zhao, Moxin Li, See-Kiong Ng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Despite the remarkable abilities of Large Language Models (LLMs) to answer questions, they often display a considerable level of overconfidence even when the question does not have a definitive answer. To avoid providing hallucinated answers to these unknown questions, existing studies typically investigate approaches to refusing to answer these questions. In this work, we propose a novel and scalable self-alignment method to utilize the LLM itself to enhance its response-ability to different types of unknown questions, being capable of not only refusing to answer but also providing explanation to the unanswerability of unknown questions. Specifically, the Self-Align method first employ …

A Survey Of Ontology Expansion For Conversational Understanding, Jinggui Liang, Yuxia Wu, Yuan Fang, Hao Fei, Lizi Liao

Research Collection School Of Computing and Information Systems

In the rapidly evolving field of conversational AI, Ontology Expansion (OnExp) is crucial for enhancing the adaptability and robustness of conversational agents. Traditional models rely on static, predefined ontologies, limiting their ability to handle new and unforeseen user needs. This survey paper provides a comprehensive review of the state-of-the-art techniques in OnExp for conversational understanding. It categorizes the existing literature into three main areas: (1) New Intent Discovery, (2) New Slot-Value Discovery, and (3) Joint OnExp. By examining the methodologies, benchmarks, and challenges associated with these areas, we highlight several emerging frontiers in OnExp to improve agent performance in real-world …

Consecutive Batch Model Editing With Hook Layers, Shuaiyi Li, Yang Deng, Deng Cai, Hongyuan Lu, Liang Chen, Wai Lam

Research Collection School Of Computing and Information Systems

As the typical retraining paradigm is unacceptably time- and resource-consuming, researchers are turning to model editing to find an effective way that supports both consecutive and batch scenarios to edit the model behavior directly. Despite all these practical expectations, existing model editing methods fail to realize all of them. Furthermore, the memory demands for such sequential model editing approaches tend to be prohibitive, frequently necessitating an external memory that grows incrementally over time. To cope with these challenges, we propose CoachHooK, a model editing method that simultaneously supports sequential and batch editing. CoachHooK is memory-friendly as it only needs a …

Navigating Weight Prediction With Diet Diary, Yinxuan Gui, Bin Zhu, Jingjing Chen, Chong-Wah Ngo, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Current research in food analysis primarily concentrates on tasks such as food recognition, recipe retrieval and nutrition estimation from a single image. Nevertheless, there is a significant gap in exploring the impact of food intake on physiological indicators (e.g., weight) over time. This paper addresses this gap by introducing the DietDiary dataset, which encompasses daily dietary diaries and corresponding weight measurements of real users. Furthermore, we propose a novel task of weight prediction with a dietary diary that aims to leverage historical food intake and weight to predict future weights. To tackle this task, we propose a model-agnostic time series …

Irl For Restless Multi-Armed Bandits With Applications In Maternal And Child Health, Gauri Jain, Pradeep Varakantham, Haifeng Xu, Aparna Taneja, Prashant Doshi, Milind Tambe

Research Collection School Of Computing and Information Systems

Public health practitioners often have the goal of monitoring patients and maximizing patients’ time spent in “favorable” or healthy states while being constrained to using limited resources. Restless multi-armed bandits (RMAB) are an effective model to solve this problem as they are helpful to allocate limited resources among many agents under resource constraints, where patients behave differently depending on whether they are intervened on or not. However, RMABs assume the reward function is known. This is unrealistic in many public health settings because patients face unique challenges and it is impossible for a human to know who is most deserving …

Defending Large Language Models Against Jailbreak Attacks Via Layer-Specific Editing, Wei Zhao, Zhe Li, Yige Li, Jun Sun, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) are increasingly being adopted in a wide range of realworld applications. Despite their impressive performance, recent studies have shown that LLMs are vulnerable to deliberately crafted adversarial prompts even when aligned via Reinforcement Learning from Human Feedback or supervised fine-tuning. While existing defense methods focus on either detecting harmful prompts or reducing the likelihood of harmful responses through various means, defending LLMs against jailbreak attacks based on the inner mechanisms of LLMs remains largely unexplored. In this work, we investigate how LLMs respond to harmful prompts and propose a novel defense method termed Layer-specific Editing (LED) …

Adversarial Learning For Coordinate Regression Through K-Layer Penetrating Representation, Mengxi Jiang, Yulei Sui, Yunqi Lei, Xiaofei Xie, Cuihua Li, Yang Liu, Ivor W. Tsang

Research Collection School Of Computing and Information Systems

Adversarial attack is a crucial step when evaluating the reliability and robustness of deep neural networks (DNNs) models. Most existing attack approaches apply an end-to-end gradient update strategy to generate adversarial examples for a classification or regression problem. However, few of them consider the non-differentiable DNN models (e.g., coordinate regression model) that prevent end-to-end backpropagation resulting in the failure of gradient calculation. In this paper, we present a new adversarial example generation approach for both untargeted and targeted attacks on coordinate regression models with non-differentiable operations. The novelty of our approach lies in a k-layer penetrating representation, on which we …

Efficient Multiplicative-To-Additive Function From Joye-Libert Cryptosystem And Its Application To Threshold Ecdsa, Haiyang Xue, Ho Man Au, Mengling Liu, Yin Kwan Chan, Handong Cui, Xiang Xie, Hon Tsz Yuen, Chengru Zhang

Research Collection School Of Computing and Information Systems

Threshold ECDSA receives interest lately due to its widespread adoption in blockchain applications. A common building block of all leading constructions involves a secure conversion of multiplicative shares into additive ones, which is called the multiplicative-to-additive (MtA) function. MtA dominates the overall complexity of all existing threshold ECDSA constructions. Specifically, O(n2) invocations of MtA are required in the case of n active signers. Hence, improvement of MtA leads directly to significant improvements for all state-of-the-art threshold ECDSA schemes.In this paper, we design a novel MtA by revisiting the Joye-Libert (JL) cryptosystem. Specifically, we revisit JL encryption and propose a JL-based …

Developing Policies For Digital Twin Data Quality And Security Controls, Ahmad Abdelbaset Hassan

Theses

This thesis is concerned with the data quality and security of the digital twin and how it is going to impact its adoption, trustworthiness, and potential for real-world applications. By addressing the potential vulnerabilities and ensuring the integrity of data, this research aims to contribute to the development of robust and trustworthy digital twin policies that to complement the existing international standards across different domains. Moreover, it underscores the important need to establish robust policies to ensure the successful and secure deployment of digital twins across industries. Previous research, while valuable, may not have fully addressed the critical interplay between …

Advancing Crispr-Based Solutions For Covid-19 Diagnosis And Therapeutics, Roaa Hadi, Abhishek Poddar, Shivakuma Sonnaila, Venkata Suryanarayana Murthy Bhavaraju, Shilpi Agrawal

Chemistry & Biochemistry Faculty Publications and Presentations

Since the onset of the COVID-19 pandemic, a variety of diagnostic approaches, including RT-qPCR, RAPID, and LFA, have been adopted, with RT-qPCR emerging as the gold standard. However, a significant challenge in COVID-19 diagnostics is the wide range of symptoms presented by patients, necessitating early and accurate diagnosis for effective management. Although RT-qPCR is a precise molecular technique, it is not immune to false-negative results. In contrast, CRISPR-based detection methods for SARS-CoV-2 offer several advantages: they are cost-effective, time-efficient, highly sensitive, and specific, and they do not require sophisticated instruments. These methods also show promise for scalability, enabling diagnostic tests. …

Optimizing Uncertainty Estimation On Scientific Visualizations Using Learning Models, Clara Almeida, David Guerrero-Pantoja, Erik Pautsch, Silvio Rizzi, George Thiruvathukal, Maria Pantoja

Computer Science: Faculty Publications and Other Works

Scientific visualizations (SciVis) translate numerical and spatial data as images, enabling scientists to better understand the phenomena described by the data and gain insights that may be overlooked by statistical methods alone. In recent years, machine learning, particularly Deep Learning (DL), has driven significant advancements in SciVis. By combining classical numerical techniques with DL methods, we can create models that potentially achieve the accuracies needed for real-world industrial applications and simulations. However, DL models often exhibit undue confidence, particularly when faced with inputs that differ significantly from the data they were trained on, known as out-of-distribution (OOD) inputs. This can …

Puccinellia (Puccinellia Ciliata) For Dryland Salinity Management, Department Of Primary Industries And Regional Development, Western Australia

Natural resources factsheets

Puccinellia (Puccinellia ciliata) is a potentially valuable fodder on waterlogged saline land. Puccinellia, Distichlis and marine couch are the most salt tolerant of the commercially available grasses. It is best suited to areas with more than 400mm annual rainfall and where the watertable is shallow over summer.

The Department of Primary Industries and Regional Development recommends that any dryland salinity management is part of a whole farm, and preferably a whole catchment, water management plan.

Roaded Catchments In Wa, Department Of Primary Industries And Regional Development, Western Australia

Natural resources factsheets

Roaded catchments are earthwork structures designed to increase the amount of run-off from the catchment above a receiving farm dam.

Roaded catchments are lined with clay and compacted to make a smooth surface that reduces infiltration and increases run-off. Roaded catchments should discharge into a silt trap and then into a dam (excavated earth tank). This combined structure should be fenced to exclude livestock.

The Department of Primary Industries and Regional Development recommends that roaded catchments are part of an integrated water and salinity management program.

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