Digital Commons Network^™

Reference Correlations For The Density And Viscosity Of Molten Alkali And Alkaline Earth Fluoride Salts, A. Birri, R. Chesser, N. Termini, J. C. Numbers, Kevin Garland, M. A. Rose

Michigan Tech Publications

While there is a significant body of literature pertaining to thermophysical property measurements of molten salts, there is often a wide degree of variability among independent measurements of the same compounds. As such, the scientific community benefits greatly from an unbiased, independent assessment of duplicate datasets, so that reference correlations which describe these thermophysical properties as functions of temperature can be determined and then commonly used by researchers, scientists, and engineers. With regard to molten fluoride compounds, a significant time has elapsed since density and viscosity reference correlations have been determined; Janz conducted the most recent effort, in 1988, to …

Exploring The Quality Of Professional Development In Addressing Challenges With Behavior Management Of Special Needs Students In Inclusive Classrooms: An Instrumental Case Study, Reem Hussein Abuwatfa

Theses

Inclusive education has become a major educational priority globally and within the United Arab Emirates (UAE), driven by policies promoting the full participation of students with special educational needs (SEN) in inclusive classrooms. This study explores how elementary-level general education teachers perceive the quality of professional development (PD) in addressing their challenges with behavior management of students with SEN in inclusive classrooms. A qualitative case study approach was used, utilizing semi-structured interviews. The findings indicated that teachers face challenges from the behavioral issues of SEN students in inclusive classrooms, affecting both typical peers and teachers. Moreover, teachers had mixed perceptions …

Data-Driven Machine Learning Applications For Predictive Modeling Of Petrochemical And Ecofriendly Systems, Noora Saleh Al Mansoori

Theses

Traditional experimental approaches in industrial processes, such as Fourier Transform Infrared Spectroscopy (FTIR) spectroscopy, thermogravimetric analysis (TGA), and well-drilling operations, are often constrained by time, cost, and operational limitations. This research explores the application of data-driven Machine Learning (ML)-based predictive modeling to improve efficiency and reduce dependency on resource-intensive experimentation. The study develops ML models for three distinct processes: FTIR intensity prediction of bitumen thermal cracking products, thermal degradation of Medium-Density Fibreboard (MDF) using TGA data, and Rate of Penetration (ROP) prediction in petrochemical industry. Six algorithms: Linear Regression (LinReg), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Gradient …

State And Regulatory Agency Approaches To Limiting Deepfakes In Political Advertising, Mary Margaret Burniston J.D. Candidate

Vanderbilt Journal of Entertainment & Technology Law

With recent advancements in artificial intelligence (AI), regulators have turned their attention to the issue of how—and whether—to regulate the use of AI in political advertisements. While nineteen states have passed legislation regulating AI in political advertising, such regulations may be challenged as violations of the First Amendment. Furthermore, federal agencies also dispute which regulatory agency has jurisdiction to address the problem, with the Federal Election Commission (FEC) and the Federal Communications Commission (FCC) both claiming authority. Beyond issues of jurisdiction, agency action is also limited by the US Supreme Court’s recent decision in Loper Bright Enterprises v. Raimondo.

As …

Demystifying Memorization In Llm-Based Program Repair Via A General Hypothesis Testing Framework, Jiaolong Kong, Xiaofei Xie, Shangqing Liu

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have achieved remarkable success in various applications, particularly in code-related tasks such as code generation and program repair, setting new performance benchmarks. However, the extensive use of large training corpora raises concerns about whether these achievements stem from genuine understanding or mere memorization of training data—a question often overlooked in current research. This paper aims to study the memorization issue within LLM-based program repair by investigating whether the correct patches generated by LLMs are the result of memorization. The key challenge lies in the absence of ground truth for confirming memorization, leading to various ad-hoc methods …

Hd-Epic: A Highly-Detailed Egocentric Video Dataset, Toby Perrett, Ahmad Darkhalil, Saptarshi Sinha, Omar Emara, Sam Pollard, Kranti Kumar Parida, Kaiting Liu, Prajwal Gatti, Siddhant Bansal, Kevin Flanagan, Jacob Chalk, Zhifan Zhu, Rhodri Guerrier, Fahd Abdelazim, Bin Zhu, Davide Moltisanti, Michael Wray, Hazel Doughty, Dima Damen

Research Collection School Of Computing and Information Systems

We present a validation dataset of newly-collected kitchenbased egocentric videos, manually annotated with highly detailed and interconnected ground-truth labels covering: recipe steps, fine-grained actions, ingredients with nutritional values, moving objects, and audio annotations. Importantly, all annotations are grounded in 3D through digital twinning of the scene, fixtures, object locations, and primed with gaze. Footage is collected from unscripted recordings in diverse home environments, making HDEPIC the first dataset collected in-the-wild but with detailed annotations matching those in controlled lab environments. We show the potential of our highly-detailed annotations through a challenging VQA benchmark of 26K questions assessing the capability to …

Dupin: A Parallel Framework For Densest Subgraph Discovery In Fraud Detection On Massive Graphs, Jiaxin Jiang, Siyuan Yao, Yuchen Li, Qiange Wang, Bingsheng He, Min Chen

Research Collection School Of Computing and Information Systems

Detecting fraudulent activities in financial and e-commerce transaction networks is crucial. One effective method for this is Densest Subgraph Discovery (DSD). However, deploying DSD methods in production systems faces substantial scalability challenges due to the predominantly sequential nature of existing methods, which impedes their ability to handle large-scale transaction networks and results in significant detection delays. To address these challenges, we introduce Dupin, a novel parallel processing framework designed for efficient DSD processing in billion-scale graphs. Dupin is powered by a processing engine that exploits the unique properties of the peeling process, with theoretical guarantees on detection quality and efficiency. …

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 …

A Knowledge Enhanced Large Language Model For Bug Localization, Yue Li, Bohan Liu, Ting Zhang, Zhiqi Wang, David Lo, Lanxin Yang, Jun Lyu, He Zhang

Research Collection School Of Computing and Information Systems

A significant number of bug reports are generated every day as software systems continue to develop. Large Language Models (LLMs) have been used to correlate bug reports with source code to locate bugs automatically. The existing research has shown that LLMs are effective for bug localization and can increase software development efficiency. However, these studies still have two limitations. First, these models fail to capture context information about bug reports and source code. Second, these models are unable to understand the domain-specific expertise inherent to particular projects, such as version information in projects that are composed of alphanumeric characters without …

Unlocking The Power Of Socio-Knowledge Association For Enterprise Risk Identification In Stock Market, Zhenghao Liu, Keng Siau, Shaochen Yang, Feicheng Ma

Research Collection School Of Computing and Information Systems

Potential risk signals reflected in supply chain and equity connections between enterprises and social connections between investors are becoming crucial to identifying enterprise risks in addition to basic financial indicators. Traditional risk management systems face challenges in adapting to these complexities, highlighting the need for a proactive paradigm shift in risk management. Leveraging graph models such as social networks and knowledge graphs offers a promising approach to identifying and managing potential associated risks effectively. To bridge existing research gaps, a novel risk identification framework driven by social-knowledge graphs has been proposed, integrating graph deep learning and reinforcement learning techniques guided …

Deepvec: State-Vector Aware Test Case Selection For Enhancing Recurrent Neural Network, Zhonghao Jiang, Meng Yan, Li Huang, Weifeng Sun, Chao Liu, Song Sun, David Lo

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNN) have realized significant achievements across various application domains. There is no doubt that testing and enhancing a pre-trained DNN that has been deployed in an application scenario is crucial, because it can reduce the failures of the DNN. DNN-driven software testing and enhancement require large amounts of labeled data. The high cost and inefficiency caused by the large volume of data of manual labeling, and the time consumption of testing all cases in real scenarios are unacceptable. Therefore, test case selection technologies are proposed to reduce the time cost by selecting and only labeling representative test …

Ef21 With Bells & Whistles: Six Algorithmic Extensions Of Modern Error Feedback, Ilyas Fatkhullin, Igor Sokolov, Eduard Gorbunov, Zhize Li, Peter Richtarik

Research Collection School Of Computing and Information Systems

First proposed by Seide (2014) as a heuristic, error feedback (EF) is a very popular mechanism for enforcing convergence of distributed gradient-based optimization methods enhanced with communication compression strategies based on the application of contractive compression operators. However, existing theory of EF relies on very strong assumptions (e.g., bounded gradients), and provides pessimistic convergence rates (e.g., while the best known rate for EF in the smooth nonconvex regime, and when full gradients are compressed, is O(1/T2/3), the rate of gradient descent in the same regime is O(1/T)). Recently, Richtàrik et al. (2021) proposed a new error feedback mechanism, EF21, based …

Less Is More: On The Importance Of Data Quality For Unit Test Generation, Junwei Zhang, Xing Hu, Shan Gao, Xin Xia, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Unit testing is crucial for software development and maintenance. Effective unit testing ensures and improves software quality, but writing unit tests is time-consuming and labor-intensive. Recent studies have proposed deep learning (DL) techniques or large language models (LLMs) to automate unit test generation. These models are usually trained or fine-tuned on large-scale datasets. Despite growing awareness of the importance of data quality, there has been limited research on the quality of datasets used for test generation. To bridge this gap, we systematically examine the impact of noise on the performance of learning-based test generation models. We first apply the open …

The Financial Effects Of Enrolling Students With Disabilities In Catholic High Schools In States That Offer Vouchers Or Scholarships For Students With Disabilities, Susan M. Noonan

Electronic Theses and Dissertations

This study investigated the financial effect of enrolling students with disabilities in Catholic high schools located in states that offer vouchers or scholarships for these students. Furthermore, the research aimed to understand the school’s experience in collaborating with the U.S. Department of Education to meet the criteria for voucher acceptance. It is important to note that enrollment and tuition revenue is the key to a school’s financial stability. Using an exploratory case study approach, information was gathered from a variety of stakeholders including the superintendent, administration, business manager, admissions coordinator, teachers, and counselors. The findings indicated that parents of children …

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

Research Collection School Of Economics

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

Handling Socio-Institutional Traps–Inertia Towards Australia’S Resilient International Development Aid Programs In The Solomon Islands, Mark Opoku Amankwa, Kwadwo Adusei-Asante, Alina Novak

Research outputs 2022 to 2026

This study critically examines how donor’s aid delivery approach, entrenched socio-institutional dynamics, and the Solomon Islands’ complex political economy create persistent barriers to Australia’s aid programming. Through a desktop research approach, institutional trap–inertia theory is applied to analyze how Australia’s aid efforts and political structures perpetuate aid inertia. The study critiques both Australia’s aid delivery approach and the internal governance challenges in a cohesive society of the SI, highlighting their intertwined roles in impeding Australia’s aid programming. The article proffers context-driven, locally adaptive strategies aimed at overcoming institutional traps and fostering more resilient and effective aid interventions.

Working With Patients Who Self-Injure: An Open Label Study Of An Educational Intervention To Upskill Emergency Nurses On Non-Suicidal Self-Injury, Penelope Hasking, Amanda Aiyana, Jonathon Burcham, Samantha Carey, Joanne M. Dickson, Daniel Fatovich, Logan Hopkins, Eme John, Shirley Mcgough, Chloe Mclean, Irene Ngune, Alex Wheeler

Research outputs 2022 to 2026

Non-suicidal self-injury (NSSI), deliberate damage to body tissue with no intent to die, is not engaged with suicidal intent but is the most reliable predictor of later suicidal behaviour. This makes efforts to reduce self-injury critical. Emergency departments can be the gateways to care for many people who have self-injured and optimal sites for interventions that promote recovery. However, emergency nurses have anecdotally reported that they lack training and feel ill-equipped to provide care for patients who self-injure. In this open label trial (where both participants and researchers were aware who received the intervention), all nurses in a metropolitan emergency …

Interpretation As Creation: Article Vi Of The Outer Space Treaty, Charles Stotler

Chicago Journal of International Law

From the launch of Sputnik I in 1957 to proposals for In-Space Servicing, Assembly and Manufacturing (ISAM) and new lunar activities such as resource utilization, advancing technology has always been a driving factor in the creation of space law. From a legal-historical perspective, the notion of law as creation should be contextualized in a broader legal-philosophical transition that began with the rise of positivism. Article VI of the Outer Space Treaty orbits unsteadily between international obligations and national implementation measures, rendering significant States’ understandings of those provisions. Our understanding of Article VI turns on perhaps the most creative legal endeavor: …

From Human Mapping To Machine Embedding: Uncovering Key Legal Drivers And Deterrents Of Isds Filing Frequencies, Sangchul Park

Chicago Journal of International Law

International investment agreements (IIAs), while intended to prop cross-border investment, have faced persistent criticism for potentially undermining the regulatory sovereignty of developing countries. Various mechanisms have been proposed as alternatives to traditional bilateral investment treaty (BIT) models, often with the goal of curbing investor-state dispute settlement (ISDS) filings. While existing research has uncovered the impact of nonlegal factors, such as macroeconomic crises, little has been done to systematically examine how legal provisions in either major model BITs or ISDS reform toolboxes influence ISDS filing patterns. To address this gap, this Article analyzes the interplay between (i) legal texts of 2,148 …

Battlefield Evidence In The Age Of Artificial Intelligence-Enabled Warfare, Winthrop Wells

Chicago Journal of International Law

A number of emerging technologies increasingly prevalent on contemporary battlefields—notably unmanned autonomous systems (UAS) and various military applications of artificial intelligence (AI)—are working a sea change in the way that wars are fought. These technological developments also carry major implications for the investigation and prosecution of serious crimes committed in armed conflict, including for an under-examined yet potentially valuable form of evidence: information and material collected or obtained by military forces themselves.

Such “battlefield evidence” poses various legal and practical challenges. Yet it can play an important role in justice and accountability processes, in which it addresses the longstanding obstacle …

Two Terribles: A Day Without Space And Ai Enabled Synthetic Biological Weapons A Warning Order, James E. Baker

Chicago Journal of International Law

A day without space, a term used to describe the loss or destruction of America’s space assets, and the potential generation of novel biological threats using AI and synthetic biology present catastrophic and potentially existential threats to U.S. security in a way that nuclear weapons did before and continue to do so today. Yet they have not received the level of attention from national security lawyers or commentators they warrant. This article describes the threats. It describes the current and inchoate nature of the law to address these threats. And it makes initial recommendations to policymakers and lawyers about how …

Distinction, Proportionality, And Precautions In Attacks At Sea In The New Era Of The Law Of Naval Warfare, James Kraska

Chicago Journal of International Law

China has upset the security balance in East Asia through the development of a long- range strike complex composed of anti-ship ballistic missiles, drones and cruise missiles, and hypersonic missiles that put U.S. naval fleets at risk. Beijing’s innovative approach to sea control through the projection of power from land-based fires highlights three important differences between the law applicable to naval warfare and the law of armed conflict (LOAC) as it is implemented on land.1 These legal distinctions are subtle in law, but they shape concrete choices available to naval commanders and could determine the outcome of war at sea. …

Decommissioning Offshore Oil And Gas Infrastructure: Report Of Proceedings, May 2, 2025 Offshore Decommissioning Workshop, Martin Lockman, Ashwin Murthy, Romany M. Webb

Sabin Center for Climate Change Law

On May 2, 2025, Ocean Conservancy and the Sabin Center for Climate Change Law organized a workshop at Columbia Law School on offshore oil and gas decommissioning in the United States. The overarching goal of the workshop was to bring together offshore law and policy experts to outline a research and policy agenda for offshore decommissioning. Within this broad goal, we sought to (1) identify key opportunities to reform offshore decommissioning law in the United States, both in the short term and over the next decade, (2) identify knowledge gaps and highlight areas for future research, and (3) begin to …

Directory Of Licensed Foresters: Belknap County, Unh Extension

UNH Cooperative Extension

No abstract provided.

Directory Of Licensed Foresters: Cheshire County, Unh Extension

UNH Cooperative Extension

No abstract provided.

Directory Of Licensed Foresters: Sullivan County, Unh Extension

UNH Cooperative Extension

No abstract provided.

Directory Of Licensed Foresters: Strafford County, Unh Extension

UNH Cooperative Extension

No abstract provided.

Directory Of Licensed Foresters: Rockingham County, Unh Extension

UNH Cooperative Extension

No abstract provided.

Directory Of Licensed Foresters: Merrimack County, Unh Extension

UNH Cooperative Extension

No abstract provided.

Directory Of Licensed Foresters: Grafton County, Unh Extension

UNH Cooperative Extension

No abstract provided.