Open Access. Powered by Scholars. Published by Universities.®

Digital Commons Network™

Open Access. Powered by Scholars. Published by Universities.®

Discipline
Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 4021 - 4050 of 714544

Full-Text Articles in Entire DC Network

An Emergent Seafood Nationalism Takes Hold In The United States, Owen Temby Jul 2026

An Emergent Seafood Nationalism Takes Hold In The United States, Owen Temby

School of Earth, Environmental, & Marine Sciences Faculty Publications

This paper introduces the concept of ‘seafood nationalism’ to describe how, since 2024–2025, the United States increasingly uses tariffs, trade remedies, food safety enforcement and origin-based rules to favour American seafood over foreign products. Federal actions include Executive Order 14276, Restoring American Seafood Competitiveness, which directs agencies to address unfair trade practices, unsafe imports and to develop a coordinated seafood trade strategy. Additional measures include a tariff package imposing a 10% duty on nearly all seafood imports and 30% on seafood from China, as well as new anti-dumping and countervailing duty orders on warm-water shrimp. This paper presents Gulf …

Go to article

Hsv-1 Us3 Hijacks Conserved Actin Regulatory Complexes To Drive F-Actin Remodeling, Md Imran Hossain, Md Arifuzzaman, Md Mehedi Hasan, Seung Jong Park, Leila Rahimian, Ojasvi Dutta, Vladimir Chouljenko, Harikrishnan Mohan, Reza Ghavimi, Konstantin G. Kousoulas Jul 2026

Hsv-1 Us3 Hijacks Conserved Actin Regulatory Complexes To Drive F-Actin Remodeling, Md Imran Hossain, Md Arifuzzaman, Md Mehedi Hasan, Seung Jong Park, Leila Rahimian, Ojasvi Dutta, Vladimir Chouljenko, Harikrishnan Mohan, Reza Ghavimi, Konstantin G. Kousoulas

Computer Science Faculty Research & Creative Works

The herpes simplex virus 1 (HSV-1) US3 is a multifunctional serine/threonine kinase that promotes HSV-1 replication and spread. But its role and the mechanisms by which US3 regulates actin cytoskeletal remodeling remain poorly defined. We combined flow cytometry, confocal microscopy, immunoprecipitation-mass spectrometry (IP-MS), protein complex mapping, and machine learning to characterize US3-mediated F-actin dynamics. Flow cytometry and confocal microscopy showed that wild-type HSV-1 induces significant F-actin remodeling, while the ΔUS3 mutant displays F-actin levels comparable to uninfected cells, identifying US3 as a key regulator. IP-MS identified 47 high-confidence US3 interactors enriched in conserved actin regulatory complexes, including Arp2/3 nucleation machinery, …

Go to article

New Algorithms For Fully-Dynamic K-Center With Outliers, Junyu Huang, Zhize Li, Zhen Zhang, Xujia Li, Jianxin Wang, Qilong Feng Jul 2026

New Algorithms For Fully-Dynamic K-Center With Outliers, Junyu Huang, Zhize Li, Zhen Zhang, Xujia Li, Jianxin Wang, Qilong Feng

Research Collection School Of Computing and Information Systems

In this paper, we study the fully dynamic k-center with outliers problem, where points are inserted and deleted over time and the goal is to maintain an approximate clustering while discarding up to z outliers. Existing algorithms typically rely on radius guessing to maintain cluster representations, leading to update and query times that depend explicitly on the aspect ratio Δ. We propose a layered-sampling framework that avoids radius guessing by maintaining a hierarchy of sampled structures, which can separate most inliers from potential outliers and refine the remaining uncertain points. The resulting algorithm achieves Õ(k²/ε⁴) update and query time independent …

Go to article

A Refined Generalization Analysis For Extreme Multi-Class Supervised Contrastive Representation Learning, Minh Hieu Nong, Antoine Ledent Jul 2026

A Refined Generalization Analysis For Extreme Multi-Class Supervised Contrastive Representation Learning, Minh Hieu Nong, Antoine Ledent

Research Collection School Of Computing and Information Systems

Contrastive Representation Learning (CRL) has achieved strong empirical success in multiple machine learning disciplines, yet its theoretical sample complexity remains poorly understood. Existing analyses usually assume that input tuples are identically and independently distributed, an assumption violated in most practical settings where contrastive tuples are constructed from a finite pool of labeled data, inducing dependencies among tuples. While one recent work analyzed this learning setting using U-Statistics to estimate the population risk, the techniques used therein require the risk of each class to concentrate uniformly, making excess risk bounds scale in the order of where denotes the probability of the …

Go to article

Production Of Seismic-Resistant Steel From Scrap With Up To 0.6% Copper, Iman El-Mahallawi, Ahmed Ramadan Seada Dr. Jul 2026

Production Of Seismic-Resistant Steel From Scrap With Up To 0.6% Copper, Iman El-Mahallawi, Ahmed Ramadan Seada Dr.

Mechanical Engineering

Earthquake-resistant steel grades of characteristic value defined by the ratio (UTS/YS) greater than 1.25 are verified through controlling the proportion of alloying elements in steel. This work illustrates a novel idea to produce earthquake-resistant steel grade utilizing steel scrap. Different heats were produced containing varying levels of copper, manganese, and silicon, along with deep desulfurization. The heats were hot rolled to diameters of 10, and 16 mm, followed by Tempcore process. JMatPro software was used to predict the formed phases. The chemical composition was determined by optical emission spectrometry. The tensile properties were determined by tensile testing, and bend testing …

Go to article

Draft Final 2023 Insufficiently Reclaimed Sites Sampling: Bres No. 93 – Soudan Dump Site Evaluation Summary Report, Pioneer Technical Services, Inc. Jul 2026

Draft Final 2023 Insufficiently Reclaimed Sites Sampling: Bres No. 93 – Soudan Dump Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Go to article

Draft Final 2023 Unreclaimed Sites Sampling: Ur-42 Site Evaluation Summary Report, Pioneer Technical Services, Inc. Jul 2026

Draft Final 2023 Unreclaimed Sites Sampling: Ur-42 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Go to article

Medical Monitoring Working Group Meeting Minutes, Mike Mcanulty, Eric Hassler Jul 2026

Medical Monitoring Working Group Meeting Minutes, Mike Mcanulty, Eric Hassler

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Go to article

2026 Final Insufficiently Reclaimed Sites Quality Assurance Project Plan (Qapp), Pioneer Technical Services, Inc. Jul 2026

2026 Final Insufficiently Reclaimed Sites Quality Assurance Project Plan (Qapp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Go to article

The Effectiveness Of Writing Professional Development For Instructional Leaders On Student Academic Outcomes, Kourtney Y. Lavergne Jul 2026

The Effectiveness Of Writing Professional Development For Instructional Leaders On Student Academic Outcomes, Kourtney Y. Lavergne

Electronic Theses and Dissertations

The purpose of this qualitative case study was to explore how elementary instructional leaders in a Southeast Texas school district perceived and utilized the R.A.C.E. (Restate, Answer, Cite, Explain) professional development training as an instructional tool to improve student writing performance. The study was guided by two research questions: (a) How do elementary instructional leaders perceive the R.A.C.E. professional development training as an effective English writing curriculum tool to improve student performance? and (b) How do instructional leaders utilize the R.A.C.E. professional development training to improve student writing performance in their classrooms? The study was grounded in adult learning theory …

Go to article

Mechanistic And Spatio-Temporal Modelling Of Cassava Whitefly (Bemisia Tabaci) Risk Under Climate Change: Projections For Africa With Case Evidence From Malawi, Lumbani Benedicto Banda, Frank Thomas Ndjomatchoua, Ritter A. Guimapi, Komi Mensah Agboka, Abdelmutalab G.A. Azrag, Wezi G. Mhango, Trust Kasambala Donga, Chikondi Makwiza, Karl H. Thunes, Elfatih M. Abdel-Rahman Jul 2026

Mechanistic And Spatio-Temporal Modelling Of Cassava Whitefly (Bemisia Tabaci) Risk Under Climate Change: Projections For Africa With Case Evidence From Malawi, Lumbani Benedicto Banda, Frank Thomas Ndjomatchoua, Ritter A. Guimapi, Komi Mensah Agboka, Abdelmutalab G.A. Azrag, Wezi G. Mhango, Trust Kasambala Donga, Chikondi Makwiza, Karl H. Thunes, Elfatih M. Abdel-Rahman

All Peer-Reviewed Publications

The cassava whitefly (Bemisia tabaci) greatly constrains cassava production across Africa due to its role as a vector of viral diseases that cause substantial yield losses. Effective management of this insect pest requires detailed knowledge of its spatio-temporal distribution, however long-term datasets are scarce. Mechanistic models circumvent these long-term data needs by modelling temperature-dependent processes that govern population dynamics. Nevertheless, their application to B. tabaci remains poorly explored. Here, we developed a mechanistic model to derive a risk index (RI) for B. tabaci across Africa, focusing on Malawi. The model integrates the effects of temperature on the life stages of …

Go to article

Environmental And Socioeconomic Effects Of Roundtable On Sustainable Palm Oil Certification: A Systematic Review And Meta-Analysis, Kibrom T. Sibhatu, Matin Qaim Jul 2026

Environmental And Socioeconomic Effects Of Roundtable On Sustainable Palm Oil Certification: A Systematic Review And Meta-Analysis, Kibrom T. Sibhatu, Matin Qaim

All Peer-Reviewed Publications

Palm oil is the most widely produced and consumed vegetable oil worldwide, but its production is associated with deforestation and other environmental and social problems. Roundtable on Sustainable Palm Oil (RSPO) is the only internationally-recognized voluntary certification standard aimed at mitigating such problems. Many studies examine RSPO’s effects on particular outcomes in specific contexts, but a consolidated global assessment is lacking. Here, we systematically review the literature and identify 53 original studies covering RSPO effects in various countries of Asia, Africa, and Latin America. Where data availability permits, we also conduct meta-analysis. Results reveal that RSPO can lead to environmental …

Go to article

Larger Is Not Always Better: Exploring Small Open-Source Language Models In Logging Statement Generation, Renyi Zhong, Yichen Li, Guangba Yu, Wenwei Gu, Jinxi Kuang, Yintong Huo, Michael R. Lyu Jul 2026

Larger Is Not Always Better: Exploring Small Open-Source Language Models In Logging Statement Generation, Renyi Zhong, Yichen Li, Guangba Yu, Wenwei Gu, Jinxi Kuang, Yintong Huo, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Developers use logging statements to create logs that document system behavior and aid in software maintenance. As such, high-quality logging is essential for effective maintenance; however, manual logging often leads to errors and inconsistency. Recent methods emphasize using large language models (LLMs) for automated logging statement generation, but these present privacy and resource issues, hindering their suitability for enterprise use. This paper presents the first large-scale empirical study evaluating small open-source language models (SOLMs) for automated logging statement generation. We evaluate four prominent SOLMs using various prompt strategies and parameter-efficient fine-tuning techniques, such as Low-Rank Adaptation (LoRA) and Retrieval-Augmented Generation …

Go to article

Multicbr: Multi‑View Contrastive Learning For Bundle Recommendation, Yunshan Ma, Yingzhi He, Xiang Wang, Yinwei Wei, Xiaoyu Du, Yuyangzi Fu, Tat‑Seng Chua Jul 2026

Multicbr: Multi‑View Contrastive Learning For Bundle Recommendation, Yunshan Ma, Yingzhi He, Xiang Wang, Yinwei Wei, Xiaoyu Du, Yuyangzi Fu, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Bundle recommendation seeks to recommend a bundle of related items to users to improve both userexperience and the profits of platform. Existing bundle recommendation models have progressed from capturing only user-bundle interactions to the modeling of multiple relations among users, bundles, and items.CrossCBR, in particular, incorporates cross-view contrastive learning into a two-view preference learningframework, significantly improving SOTA performance. It does, however, have two limitations: (1) the twoview formulation does not fully exploit all the heterogeneous relations among users, bundles, and items; and(2) the “early contrast and late fusion” framework is less effective in capturing user preference and difficultto generalize to …

Go to article

Survey On Learning-Based Dynamic Fault Localization: From Traditional Machine Learning To Large Language Models, Chunyan Liu, Yan Lei, Huan Xie, Jinping Wang, Yue Yu, David Lo Jul 2026

Survey On Learning-Based Dynamic Fault Localization: From Traditional Machine Learning To Large Language Models, Chunyan Liu, Yan Lei, Huan Xie, Jinping Wang, Yue Yu, David Lo

Research Collection School Of Computing and Information Systems

Learning-based dynamic fault localization techniques play a crucial role in the field of software engineering. These techniques dynamically execute test cases to meticulously extract useful knowledge from the execution information in the program, with the aim of identifying fault locations by leveraging machine learning, deep learning, and large language models. Currently, there is already a flourishing body of research that is intensely focused on learning-based dynamic fault localization. Research literature can be categorized into two main aspects for learning-based dynamic fault localization: data-based enhancements (i.e., the datasets) and model-based enhancements (i.e., the suspiciousness algorithms). Thus, we conduct an extensive literature …

Go to article

Deep Learning For Video Anomaly Detection: A Review, Peng Wu, Chengyu Pan, Yuting Yan, Guansong Pang, Qingsen Yan, Peng Wang, Yanning Zhang Jul 2026

Deep Learning For Video Anomaly Detection: A Review, Peng Wu, Chengyu Pan, Yuting Yan, Guansong Pang, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

Video anomaly detection (VAD) aims to discover behaviors or events deviating from the normality in videos. As a long-standing task in the field of computer vision, VAD has witnessed much good progress. In the era of deep learning, with the explosion of architectures of continuously growing capability and capacity, a great variety of deep learning-based methods are constantly emerging for the VAD task, greatly improving the generalization ability of detection algorithms and broadening the application scenarios. Therefore, such a multitude of methods and a large body of literature make a comprehensive survey a pressing necessity. In this article, we present …

Go to article

Anomaly Management In Unmanned Aerial Vehicles: A Systematic Literature Review, Ivan Tan Wei Han, Christopher M. Poskitt, Lingxiao Jiang, Lwin Khin Shar Jul 2026

Anomaly Management In Unmanned Aerial Vehicles: A Systematic Literature Review, Ivan Tan Wei Han, Christopher M. Poskitt, Lingxiao Jiang, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical applications such as logistics, surveillance, disaster response, and urban air mobility. While their autonomy enables powerful capabilities, it also introduces vulnerabilities due to hardware faults, software defects, communication failures, and adversarial interference. This survey presents a comprehensive review of research studies closely related to UAV anomalies published between 2015 and 2025, covering 111 papers from academic and industrial sources. We introduce a unified five-pillar taxonomy—anomaly generation, prevention, detection, recovery, and analysis—that organizes existing work across the full anomaly management lifecycle. In contrast to prior surveys that focus primarily on detection algorithms, …

Go to article

Configuring Agentic Ai Coding Tools: An Exploratory Study, Matthias Galster, Seyedmoein Mohsenimofidi, Jai Lal Lulla, Muhammad Auwal Abubakar, Christoph Treude, Sebastian Baltes Jul 2026

Configuring Agentic Ai Coding Tools: An Exploratory Study, Matthias Galster, Seyedmoein Mohsenimofidi, Jai Lal Lulla, Muhammad Auwal Abubakar, Christoph Treude, Sebastian Baltes

Research Collection School Of Computing and Information Systems

Agentic AI coding tools increasingly automate software development tasks. Developers can configure these tools through versioned repository-level artifacts such as Markdown and JSON files. We present a systematic analysis of configuration mechanisms for agentic AI coding tools, covering Claude Code, GitHub Copilot, Cursor, Gemini, and Codex. We identify eight configuration mechanisms spanning from static context to executable and external integrations and, in an empirical study of 2,853 GitHub repositories, examine whether and how they are adopted, with a detailed analysis of Context Files, Skills, and Subagents. First, Context Files dominate the configuration landscape and are often the sole mechanism in …

Go to article

Towards Uniformity And Alignment For Multimodal Representation Learning, Wenzhe Yin, Pan Zhou, Zehao Xiao, Jie Liu, Shujian Yu, Jan-Jakob Sonke, Efstratios Gavves Jul 2026

Towards Uniformity And Alignment For Multimodal Representation Learning, Wenzhe Yin, Pan Zhou, Zehao Xiao, Jie Liu, Shujian Yu, Jan-Jakob Sonke, Efstratios Gavves

Research Collection School Of Computing and Information Systems

Multimodal representation learning aims to construct a shared embedding space in which heterogeneous modalities are semantically aligned. Despite strong empirical results, InfoNCE-based objectives introduce inherent conflicts that yield distribution gaps across modalities. In this work, we identify two conflicts in the multimodal regime, both exacerbated as the number of modalities increases: (i) an alignment–uniformity conflict, whereby the repulsion of uniformity undermines pairwise alignment, and (ii) an intra-alignment conflict, where aligning multiple modalities induces competing alignment directions. To address these issues, we propose a principled decoupling of alignment and uniformity for multimodal representations, providing a conflict-free recipe for multimodal learning that …

Go to article

Air: Improving Agent Safety Through Incident Response, Zibo Xiao, Jun Sun, Junjie Chen Jul 2026

Air: Improving Agent Safety Through Incident Response, Zibo Xiao, Jun Sun, Junjie Chen

Research Collection School Of Computing and Information Systems

Large Language Model (LLM) agents are increasingly deployed in practice across a wide range of autonomous applications. Yet current safety mechanisms for LLM agents focus almost exclusively on preventing failures in advance, providing limited capabilities for responding to, containing, or recovering from incidents after they inevitably arise. In this work, we introduce AIR, the first incident response framework for LLM agent systems. AIR defines a domain-specific language for managing the incident response lifecycle autonomously in LLM agent systems, and integrates it into the agent's execution loop to (1) detect incidents via semantic checks grounded in the current environment state and …

Go to article

Train In Vain: Functionality-Preserving Poisoning To Prevent Unauthorized Use Of Code Datasets, Yuan Xiao, Yuchen Chen, Jiaming Wang, Wei Song, Jun Sun, Shiqing Ma, Yanzhou Mu, Juan Zhai, Chunrong Fang, Jin Song Dong, Zhenyu Chen Jul 2026

Train In Vain: Functionality-Preserving Poisoning To Prevent Unauthorized Use Of Code Datasets, Yuan Xiao, Yuchen Chen, Jiaming Wang, Wei Song, Jun Sun, Shiqing Ma, Yanzhou Mu, Juan Zhai, Chunrong Fang, Jin Song Dong, Zhenyu Chen

Research Collection School Of Computing and Information Systems

The widespread availability of large-scale code datasets has accelerated the development of code large language models (CodeLLMs), raising concerns about unauthorized dataset usage. Dataset poisoning offers a proactive defense by reducing the utility of such unauthorized training. However, existing poisoning methods often require full-dataset poisoning and introduce transformations that break code compilability. In this paper, we introduce FunPoison, a functionality-preserving poisoning approach that injects short, compilable weak-use fragments into executed code paths. FunPoison leverages reusable statement-level templates with automatic repair and conservative safety checking to ensure side-effect freedom, while a type-aware synthesis module preserves type correctness, suppresses static-analysis warnings, and …

Go to article

Dual-Diffusional Generative Fashion Recommendation, Mingzhe Yu, Lei Wu, Qianru Sun, Yunshan Ma Jul 2026

Dual-Diffusional Generative Fashion Recommendation, Mingzhe Yu, Lei Wu, Qianru Sun, Yunshan Ma

Research Collection School Of Computing and Information Systems

Personalized generative recommender systems have emerged as a promising solution for fashion recommendation. However, existing methods primarily rely on implicit visual embeddings from historical interactions, which often contain preference-irrelevant information and result in insufficient user behavior modeling. Moreover, these models typically generate only item images, providing limited interpretability. To address these limitations, we propose DualFashion, a Dual-Diffusional Generative Fashion Recommendation Architecture that jointly models image and text modalities for personalized and explainable recommendation. DualFashion adopts a dual-diffusion Transformer with image and text branches, where structured attribute-level captions and visual outfit information are jointly used as conditioning signals to model user …

Go to article

Avadclip: Audio-Visual Collaboration For Robust Video Anomaly Detection, Peng Wu, Wanshun Su, Guansong Pang, Yujia Sun, Qingsen Yan, Peng Wang, Yanning Zhang Jul 2026

Avadclip: Audio-Visual Collaboration For Robust Video Anomaly Detection, Peng Wu, Wanshun Su, Guansong Pang, Yujia Sun, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

With the increasing adoption of video anomaly detection in intelligent surveillance domains, conventional visual-only detection approaches often struggle with information insufficiency and high false-positive rates in complex environments. To address these limitations, we present a novel weakly supervised framework that leverages audio-visual collaboration for robust video anomaly detection. Capitalizing on the exceptional cross-modal representation learning capabilities of Contrastive Language-Image Pretraining (CLIP) across visual, audio, and textual domains, our framework introduces two major innovations: an efficient audio-visual fusion that enables adaptive cross-modal integration through lightweight parametric adaptation while maintaining the frozen CLIP backbone, and a novel audio-visual prompt that dynamically enhances …

Go to article

Generation-Augmented Video Corpus Moment Retrieval, Mingjin Kuai, Qianyin Xiao, Juncheng Li, Jin Peng, Lizi Liao, Wei Ji Jul 2026

Generation-Augmented Video Corpus Moment Retrieval, Mingjin Kuai, Qianyin Xiao, Juncheng Li, Jin Peng, Lizi Liao, Wei Ji

Research Collection School Of Computing and Information Systems

Video Corpus Moment Retrieval (VCMR) requires models to efficiently retrieve and precisely locate specific moments relevant to natural language queries within a massive, untrimmed video corpus. However, existing discriminative approaches typically rely on shallow visual-textual feature matching mechanisms, which often struggle to capture fine-grained semantic differences. To address this limitation, we propose Video-GAR, a novel framework that reframes the conventional retrieval task from superficial matching to generative understanding, positing that the capability for query reconstruction evidences deep semantic comprehension. Specifically, Video-GAR orchestrates three synergistic components: To overcome the computational efficiency bottleneck, we construct a Bi-Mamba backbone that leverages the linear …

Go to article

Assessing The Imprint Of Eccentricity In Gravitational-Wave Signatures, Natalie Malagon Jul 2026

Assessing The Imprint Of Eccentricity In Gravitational-Wave Signatures, Natalie Malagon

Theses

The gravitational wave signal from merging compact binaries encodes information about their orbital and intrinsic properties. Over the last few years, state-of-the-art waveform models have begun to incorporate the effects of orbital eccentricity into their estimated signal. Over a similar period, many groups have applied these waveforms to characterize whether the imprint of eccentricity is present and, if so, measure this time-evolving property (at a suitably-defined reference point). In this work, we present a comprehensive analysis of 173 confident sources identified in the O1 through O4a observing runs of the International Gravitational Wave Network (LIGO-Virgo-KAGRA). Using the RIFT parameter inference …

Go to article

Learning-Based Multi-Objective Operation Of Microgrid With Renewable Uncertainty And Demand-Side Management, Zeina Bahij Jul 2026

Learning-Based Multi-Objective Operation Of Microgrid With Renewable Uncertainty And Demand-Side Management, Zeina Bahij

Theses

A microgrid is a small-scale power grid that can operate in either the grid-connected mode or the islanded mode. It consists of energy generation and storage devices for both power and thermal demands, where the thermal energy from the exhaust heat that cannot be transmitted over long distances can be utilized locally. The supply side may include dispatchable generation units with adjustable outputs and energy storage, alongside non-dispatchable renewables that depend entirely on resource availability. On the demand side, thermal loads are satisfied through waste heat recovery from exhaust gas (to maximize overall system efficiency) and direct fuel combustion. Electrical …

Go to article

Marketplace Summer 2026, Southern Adventist University Jul 2026

Marketplace Summer 2026, Southern Adventist University

MarketPlace - School of Business Newsletter

The Summer 2026 issue of MarketPlace features an article about Ruth McKee's legacy. Additional articles explore interdisciplinary student exploration of challenges and possibilities in rural Kenya and fundraising projects for local non-profits.

Go to article

Butte Reclamation Evaluation System Corrective Action Plans For Reclaimed Sites 2025 Bres Site Specific Corrective Action Plans For Quadrant 2, Butte-Silver Bow Department Of Reclamation And Environmental Services Jul 2026

Butte Reclamation Evaluation System Corrective Action Plans For Reclaimed Sites 2025 Bres Site Specific Corrective Action Plans For Quadrant 2, Butte-Silver Bow Department Of Reclamation And Environmental Services

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Go to article

Zero-Shot Transfer Of Foundation Time-Series Forecasters To Datacenter Operational Telemetry: Mapping A Domain Nobody Gets To Study, David Pace Jr Jul 2026

Zero-Shot Transfer Of Foundation Time-Series Forecasters To Datacenter Operational Telemetry: Mapping A Domain Nobody Gets To Study, David Pace Jr

LSU New Orleans Theses and Dissertations

This thesis asks how best to forecast real datacenter operational telemetry. Can pretrained foundation time-series forecasters do it zero-shot, or do trained classical baselines perform best? On a single-site benchmark of eight audited targets and four horizons (32 slices), the zero-shot foundation forecasters Moirai, Chronos, and TimesFM win 26 of 32 slices against multivariate classical baselines. However, the foundation wrappers operate per-channel on the target history, while classical baselines consume the full feature tensor. When the same classical models are retrained univariate, the foundation lead persists (27 of 32 slices) and a 3-seed rerun of a classical challenger reproduces it; …

Go to article

Multi-Angle Dynamic Light Scattering Experiments With Real-Time Autocorrelation Using Field-Programmable Gate Arrays, Spiros Simotas Jul 2026

Multi-Angle Dynamic Light Scattering Experiments With Real-Time Autocorrelation Using Field-Programmable Gate Arrays, Spiros Simotas

Theses and Dissertations from DePaul University

Dynamic light scattering (DLS) determines particle size from temporal fluctuations in coherently scattered light. Here a field-programmable gate array (FPGA) computes intensity autocorrelations in real time, and an automated diffractometer with custom 3D-printed cuvette, laser, homing, and GT2-gear mounts enables multi-angle measurements. Square-wave timing tests at 1 Hz, 100 Hz, and 10 kHz verified the correlator response. Calibrated latex spheres were then measured at seven scattering angles from 32.5◦ to 44.5◦. Each autocorrelation function was fit to the standard single-exponential form g(τ ) = B + βe−2Γτ with weights from analytic per-lag Poisson uncertainties σg = pg/N based on coincidence …

Go to article

First Prev Next Last