Entire DC Network | Open Access Articles | Digital Commons Network™

Jurisdiction Of Vessel-Source Pollution In The Eez Under Unclos : A Case Study On China, Yang Zhang

World Maritime University Dissertations

No abstract provided.

Go to article

Integrating Environmental Impact Assessment Into International Shipping Governance : A Bbnj-Pssa Synergy Framework, Thi Thuy Linh Pham

World Maritime University Dissertations

No abstract provided.

Go to article

An Assessment Of The Impact Of Illegal Sand Mining On The Coastline Of Liberia, Alphena J. Logan

World Maritime University Dissertations

No abstract provided.

Go to article

The Role Of Atmospheric Turbulence On Cloud Droplet Distribution And Droplet Impaction On An Airfoil, Arash Shad, Hashnayne Ahmed, ‪Nadim Zgheib, S. Balachandar, S. A. Sherif

Mechanical Engineering Faculty Publications

We conduct turbulence-informed Euler–Lagrange simulations of droplet-laden flow to examine the effects of atmospheric turbulence on droplet impingement characteristics on a NACA 0012 airfoil. We implement statistical overloading, whereby we inject millions of droplets, ranging in diameter from 1 to 160 microns, upstream of the airfoil at an average streamwise velocity of 120 m/s. Within a fraction of a second, these droplets would have either impinged on the airfoil or continued downstream. While the incoming air flow is maintained uniform, droplet injection is informed by ambient turbulence, affecting both the droplet velocity distribution as well as the size-dependent preferential concentration …

Go to article

Climatic Controls On Soil Production, Transport And Chemical Erosion: Insights From Modelling Topography, Soils And Cosmogenic Nuclides At Little Lake, Oregon, Miles M. Reed, Ken L. Ferrier, Jill A. Marshall, Josh J. Roering, J. Taylor Perron

Geology Faculty Publications and Presentations

Predicting physical and chemical erosion rate responses to climate change are an ongoing challenge in geomorphology. A promising approach for investigating this is by measuring transient variations in physical and chemical erosion rates during climatically variable time periods, which can be accomplished by measuring cosmogenic nuclide concentrations and chemical depletion in sedimentary deposits. Interpreting such measurements warrants applying landscape evolution models that track variations in topography, cosmogenic nuclide concentrations and chemical depletion in soils. We applied a recently developed model that tracks these quantities at Little Lake, Oregon. Previous studies documented variations in cosmogenic nuclide concentrations and chemical depletion in …

Go to article

When Deep Learning Meets Information Retrieval-Based Bug Localization: A Survey, Feifei Niu, Chuanyi Li, Kui Liu, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

Bug localization is a crucial aspect of software maintenance, running through the entire software lifecycle. Information retrieval-based bug localization (IRBL) identifies buggy code based on bug reports, expediting the bug resolution process for developers. Recent years have witnessed significant achievements in IRBL, propelled by the widespread adoption of deep learning (DL). To provide a comprehensive overview of the current state of the art and delve into key issues, we conduct a survey encompassing 61 IRBL studies leveraging DL. We summarize best practices in each phase of the IRBL workflow, undertake a meta-analysis of prior studies, and suggest future research directions. …

Go to article

Do Code Semantics Help? A Comprehensive Study On Execution Trace-Based Information For Code Large Language Models, Jian Wang, Xiaofei Xie, Qiang Hu, Shangqing Liu, Yi Li

Research Collection School Of Computing and Information Systems

Code Large Language Models (Code LLMs) have opened a new era in programming with their impressive capabilities. However, recent research has revealed critical limitations in their ability to reason about runtime behavior and understand the actual functionality of programs, which poses significant challenges for their post-training and practical deployment. Specifically, Code LLMs encounter two principal issues: (1) a lack of proficiency in reasoning about program execution behavior, as they struggle to interpret what programs actually do during runtime, and (2) inconsistent and fragmented representation of semantic information, such as execution traces, across existing methods, which hinders their ability to generalize …

Go to article

Efficient Integration Of External Knowledge To Llm-Based World Models Via Retrieval-Augmented Generation And Reinforcement Learning, Chang Yang, Xinrun Wang, Qinggang Zhang, Qi Jiang, Xiao Huang

Research Collection School Of Computing and Information Systems

World models achieve remarkable success in predicting future states and planning in complex environments and Large Language Models (LLMs) serve as promising foundation to build general world models. However, their performances are usually constrained by the limited external knowledge to specific environments. Existing research attempts to enhance LLM-based world models through prompting or fine-tuning approaches, which are either requiring human knowledge or computationally extensive. Therefore, we introduce Retrieval-Augmented World Models (RAWM), a novel framework that leverages retrieval-augmented generation to efficiently integrate the external knowledge to LLM-based world models. Our main contributions are threefold: (i) We introduce a memory system and …

Go to article

Usefulness And Diminishing Returns: Evaluating Social Information In Recommender Systems, Qing Meng, Huiyu Min, Ming Shan Hee, Roy Ka-Wei Lee, Bing Tian Dai, Shuai Xu

Research Collection School Of Computing and Information Systems

Social recommendation, which leverages users’ social information to predict users’ preferences, is a popular branch of recommender systems. Many existing studies have attempted to advance the performance of collaborative filtering methods by leveraging the user-user matrix to enhance user embedding learning with user’s social connections. While the existing social recommender systems have demonstrated good performance in various recommendation tasks, the extent of social information usefulness in recommender systems remains unclear. This paper addresses the research gap by designing experiments to answer three research questions: (i) How useful is social information in varying user-item data sparsity? (ii) How much social information …

Go to article

Distillcaps: Enhancing Audio-Language Alignment In Captioning Via Retrieval-Augmented Knowledge Distillation, Thinh Pham, Nghiem Diep, Lizi Liao, Binh Nguyen

Research Collection School Of Computing and Information Systems

Automated audio captioning (AAC) benefits from incorporatingexternal context to interpret complex sounds, but doing so withretrieval-augmented generation (RAG) at inference is sometimesinfeasible due to data availability or incurs significant latency andcomplexity. We propose DistillCaps, a novel training-time frame-work that leverages RAG to guide knowledge distillation for im-proved audio-language alignment, while lessening the relianceon retrieval during inference. In our framework, a RAG-equippedteacher model retrieves relevant textual information (e.g., simi-lar captions) for each audio clip and uses it for training to gener-ate context-enriched captions. Simultaneously, a student model istrained to imitate this teacher, learning to produce high-qualitycaptions from audio alone. We further …

Go to article

Unified Molecule Pre-Training With Flexible 2d And 3d Modalities: Single And Paired Modality Integration, Tengwei Song, Min Wu, Yuan Fang

Research Collection School Of Computing and Information Systems

Molecular representation learning plays a crucial role in advancing applications such as drug discovery and material design. Existing work leverages 2D and 3D modalities of molecular information for pre-training, aiming to capture comprehensive structural and geometric insights. However, these methods require paired 2D and 3D molecular data to train the model effectively and prevent it from collapsing into a single modality, posing limitations in scenarios where a certain modality is unavailable or computationally expensive to generate. To overcome this limitation, we propose FlexMol, a flexible molecule pre-training framework that learns unified molecular representations while supporting single-modality input. Specifically, inspired by …

Go to article

Damslnet: Dual-Attention Multi-Scale Lightweight Network For Plant Disease Classification, Linfan Deng, Juan Qin, Kun Li, Jinhua Zhu, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Accurately identifying crop diseases plays a crucial role in advancing intelligent and modern agricultural production. Deep learning techniques have performed robust performance in classifying plant disease images. However, current studies face the challenge that many plant disease datasets are generated in controlled environments, leading to reduced model performance in real-world agricultural settings. This paper aims to provide a lightweight model that can accurately classify plant diseases in natural environments. Specifically, this paper investigates the Dual-Attention Multi-Scale Lightweight Network (DAMSLNet), which combines dual-attention-based multi-scale feature extraction and deep information fusion, to classify plant diseases. At the front end, the model employs …

Go to article

Branch-And-Cut-And-Price For Agile Earth Observation Satellite Scheduling, Guansheng Peng, Jianjiang Wang, Guopeng Song, Aldy Gunawan, Lining Xing, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

The Agile Earth Observation Satellite scheduling selects and sequences satellite observations of possible targets on the Earth’s surface, each with a specific profit and multiple time windows. The objective is to maximize the collected profit of all observations completed under some operational constraints. The problem can be modeled as a variant of the Team Orienteering Problem with Time Windows (TOPTW). The key differences with the regular TOPTW are twofold: first, a time-dependent transition time is required for each pair of consecutive observations to adjust the camera’s look angles. Second, the time windows of each target vary during different observation cycles, …

Go to article

Deep Reinforcement Learning For Solving The Stochastic E-Waste Collection Problem, Dang Viet Anh Nguyen, Aldy Gunawan, Mustafa Misir, Kwan Hui Lim, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

With the growing influence of the internet and information technology, Electrical and Electronic Equipment (EEE) has become a gateway to technological innovations. However, discarded devices, also called e-waste, pose a significant threat to the environment and human health if not properly treated, disposed of, or recycled. In this study, we extend a novel model for the e-waste collection in an urban context: the Heterogeneous VRP with Multiple Time Windows and Stochastic Travel Times (HVRP-MTWSTT). We propose a solution method that employs deep reinforcement learning to guide local search heuristics (DRL-LSH). The contributions of this paper are as follows: (1) HVRP-MTWSTT …

Go to article

The Impact Of Sanctions On Github Developers And Activities, Youmei Fan, Ani Hovhannisyan, Hideaki Hata, Christoph Treude, Raula G. Kula

Research Collection School Of Computing and Information Systems

The GitHub platform has fueled the creation of truly global software, enabling contributions from developers across various geographical regions of the world. As software becomes more entwined with global politics and social regulations, it becomes similarly subject to government sanctions. In 2019, GitHub restricted access to certain services for users in specific locations but rolled back these restrictions for some communities (e.g., the Iranian community) in 2021. We conducted a largescale empirical study, collecting approximately 156 thousand user profiles and their 41 million activity points from 2008 to 2022, to understand the response of developers. Our results indicate that many …

Go to article

Graph Perturbations For Robust Knowledge Discovery And Retrieval, Hanhua Xiao

Dissertations and Theses Collection (Open Access)

Graph perturbation, rooted in classical perturbation theory, studies how small topology edits, i.e., adding or deleting edges, affects graph properties (e.g., density, centrality). This fundamental problem underpins applications like bioinformatics, privacy preservation and system defense. While much prior work targets perturbations that influence global graph statistics or model outputs, comparatively little addresses robustness for knowledge discovery and information retrieval. In these settings, graphs are attributed: nodes carry real-world semantics (e.g., locations, people) and edges encode interactions or relationships. This thesis proposes new formulations and algorithms that generate and leverage graph perturbations to make knowledge discovery and retrieval more robust. Specifically, …

Go to article

When It Comes To Scientific Information Extraction And Llms, Less Is More, Sameer Shaik

Theses and Dissertations from DePaul University

The scientific literature continues to expand rapidly, making manual extraction of structured scientific facts increasingly impractical. Traditional Machine Learning and Natural Language Processing (NLP) pipelines require large expert-annotated datasets, which are costly to produce. Novel Large Language Models (LLMs) face challenges in long-context scientific reasoning, hallucinations, and entity linking. This thesis investigates ELSIE-Blob, a domain-aware preprocessing method that segments scientific articles into compact text “blobs” containing components of entity relations (here, polymer names, melting point indicators, and numerical values). We test whether blob-based input allows lightweight, consumer-hardware-accessible LLMs to extract polymer–melting point (polymer–Tm) pairs accurately without training data. Experiments using …

Go to article

Gw241011 And Gw241110: Exploring Binary Formation And Fundamental Physics With Asymmetric, High-Spin Black Hole Coalescences, A. G. Abac, I. Abouelfettouh, Teviet Creighton, Mario C. Diaz, R. Espinosa, J. Lawrence, Francisco Llamas Villarreal, Soma Mukherjee, Volker Quetschke, Miriam Ramos Arevalo, Wenhui Wang

Physics & Astronomy Faculty Publications

We report the observation of gravitational waves from two binary black hole coalescences during the fourth observing run of the LIGO–Virgo–KAGRA detector network, GW241011 and GW241110. The sources of these two signals are characterized by rapid and precisely measured primary spins, nonnegligible spin–orbit misalignment, and unequal mass ratios between their constituent black holes. These properties are characteristic of binaries in which the more massive object was itself formed from a previous binary black hole merger and suggest that the sources of GW241011 and GW241110 may have formed in dense stellar environments in which repeated mergers can take place. As the …

Go to article

Attorneys And Ai: How Lawyers Use Artificial Intelligence And Analyze Its Impacts, Matthew I. Hall, Christian Turner, Eddie A. Gomez Schieber, Nathaniel Kite, Ari Schlesinger

Scholarly Works

AI systems are testing lawyers' professional ethics obligations of competence, confidentiality, and candor. In the legal profession, the widespread availability of AI systems presents opportunities, like improving the review of documents during the discovery stage of a lawsuit, and challenges, illustrated by the handful of high-profile incidents where lawyers submitted legal briefs in court citing and describing fictitious cases based on AI-generated output. We conducted interviews with 44 legal professionals in the U.S. to understand how attorneys are making sense of AI technology and the impacts these technologies are having on their profession, legal ethics, and legal institutions. We describe …

Go to article

Substituted Piperidines As (Alpha4)3(Beta2)2 Nicotinic Acetylcholine Receptor Positive Allosteric Modulators: In Vitro Pharmacology, Nataly Sanchez, Josue Gaona, Wilder Felix, Nathalia Menegasso, Brisa Cea, Ganesh Thakur, Ayman K. Hamouda

School of Medicine Student Publications and Presentations

Nicotinic acetylcholine receptors (nAChRs) play a crucial role in the pathogenesis of neurodegenerative diseases such as Parkinsonʼs disease (PD). In PD, degeneration of dopaminergic and cholinergic neurons impairs neurotransmitter release, leading to cognitive and motor dysfunction. Current treatments provide only symptomatic relief, highlighting the need for novel therapeutic strategies. The (α4)₃(β2)₂ subtype of nAChRs is expressed on affected neurons, making it a promising druggable target for enhancing neurotransmitter release and providing neuroprotection. To explore the therapeutic potential of positive allosteric modulators (PAMs) of (α4)₃(β2)₂ nAChRs, we evaluated chemical derivatives of CMPI [3-(2-chlorophenyl)-5-(5-methyl-1-(piperidin-4-yl)-1H-pyrazol-4-yl)isoxazole]. We used two-electrode voltage-clamp recordings from Xenopus laevis …

Go to article

Be+ Assisted, Simultaneous Confinement Of More Than 15000 Antihydrogen Atoms, Tim Tharp

Physics Faculty Research and Publications

Antihydrogen, the bound state of a positron and an antiproton, is the only pure anti-atomic system ever studied. It is produced exclusively in the laboratory, as it has never been observed in nature. This unique system is of great interest for searching for tentative differences between matter and antimatter. Antihydrogen has been routinely trapped since 2010 and accumulated since 2017, enabling, for example, the first precision spectroscopic study of the anti-atom in 2018 and the first observation of the influence of gravity in 2023. Here we report an eight-fold increase in the trapping rate of antihydrogen, enabled by sympathetic cooling …

Go to article

Neural Network Ensemble For Computing Cross Sections Of Rotational Transitions In H2O + H2O Collisions, Bikramaditya Mandal, Dmitri Babikov, Phillip C. Stancil, Robert C. Forrey, Roman V. Krems, Naduvalath Balakrishnan

Chemistry Faculty Research and Publications

Water (H₂O) is one of the most abundant molecules in the universe and is found in a wide variety of astrophysical environments. Rotational transitions in H₂O + H₂O collisions are important for modeling environments rich in water molecules but they are computationally intractable using quantum mechanical methods. Here, we present a machine learning (ML) tool using an ensemble of neural networks (NNs) to predict cross sections to construct a database of rate coefficients for rotationally inelastic transitions in collisions of complex molecules such as water. The proposed methodology utilizes data computed with a mixed …

Go to article

Butte Priority Soils Operable Unit Clark Tailings Consolidated Waste Management Area Site Investigation Quality Assurance Project Plan (Qapp) Revision 0, Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Go to article

Spatio-Temporal Gcn With Softmax Classifier For Skeleton-Based Human Action Recognition, Kabul Khudaybergenov, Avazjon Marakhimov, Zahriddin Muminov

Chemical Technology, Control and Management

Skeleton-based human action recognition is an important research area with many practical applications. Most existing methods rely on single representations of skeletal sequences, which cannot totally obtain all the complex features of human movements. This paper presents LFHAR (Latent Features for Human Action Recognition), a new framework that uses multiple spatio-temporal latent representations to improve the extraction of action features. Our method captures how skeletal poses change over time and combines motion information from both individual joints and connected body parts. The proposed approach applies graph-based processing to each skeleton frame in a sequence, then arranges the resulting graph features …

Go to article

Spatio-Temporal Dynamics Of Malaria Vector Niche Overlaps In Africa, Eric Ali Ibrahim, John Odindi, Mark Wamalwa, Henri E.Z. Tonnang

All Peer-Reviewed Publications

Malaria remains a significant public health challenge in sub-Saharan Africa, with transmission heightened by the dynamics of primary and secondary mosquitoes infected with Plasmodium parasites. Regions where both vector types co-exist face heightened likelihood of intensified malaria transmission. Hence, understanding vectors' ecological interactions, especially their niche overlaps in geographic or environmental space, is crucial for targeted malaria control and elimination strategies. We employed a dynamic cellular automata (CA) model to map niche overlaps among primary (Anopheles gambiae complex, An. funestus group) and secondary (An. pharoensis, An. coustani) malaria vectors across African, using open-access environmental and vector occurrence datasets sourced from …

Go to article

Climate-Driven Potential For Tularemia In East Africa: Skill Testing And Ecological Consistency Of A Transferred Risk Model, Komi Mensah Agboka, Allan Muohi Ngángá, Bonoukpoè Mawuko Sokame, Steve Soh Bernard Baleba, Tobias Landmann, Elfatih M. Abdel-Rahman, Chrysantus M. Tanga, Souleymane Diallo

All Peer-Reviewed Publications

Tularemia, a neglected zoonosis, remains underreported in Africa despite growing concern over its climate-driven expansion. This study aims to quantify the specific contribution of climate to tularemia risk using a climate attribution framework. We trained a Least Squares Dummy Variable (LSDV) fixed-effects panel model on United States (U.S.) county-level tularemia incidence data from 2011–2020 (n = 500, R² = 0.90), incorporating only climatic predictors: cumulative temperature, cumulative precipitation, and their respective variabilities. The climate-only model explained 86% of variance in the training data, demonstrating strong climate influence on tularemia disease dynamics. We then applied the model to East Africa, using …

Go to article

Carbon–Neutral Granular Filtration For Sustainable And Safe Rainwater Harvesting In Urban Malaysia, Mohammad Nur-E-Alam, Md Helal Uddin, Saurav Dixit, Boon Kar Yap, Mohammad Tariqul Islam, Nowshad Amin

Research outputs 2022 to 2026

This work aims to design a granular filter-based carbon–neutral rainwater treatment system. Granular filters are capable of cleaning rainwater while leaving a carbon–neutral trace to promote environmentally friendly rainwater collection techniques by examining the filtration performance and impact on the environment. The elimination of pollutants and impurities from collected rainwater through granular filters highlights the significance of meeting water quality requirements. The system uses three filters (F1, F2, and F3) for the treatment process where palm (oil) fruit shell-activated carbon, coconut shell-activated carbon, coarse sand, and fine sand are used as raw materials. The physicochemical quality of rainwater (harvested at …

Go to article

Problems In Extremal Graph Theory And Spectral Graph Theory, Fareeha Jamal

Dissertations

Spectral graph theory is a subfield of algebraic graph theory that studies the matrices associated with graphs. It lives at the nexus of Linear Algebra and Combinatorics. Many intriguing results in the domains of Matrix Theory and Combinatorics have come from studying the eigenvalues of graph matrices; in fact, several open problems in both areas have been resolved. Beyond its theoretical appeal, spectral graph theory has found meaningful applications in theoretical chemistry, particularly in the mathematical classification of chemical graphs. These classifications underpin quantitative structure–property relationships (QSPRs), facilitating the prediction of physicochemical properties such as enthalpy of vaporization, molar refractivity, …

Go to article

Advancing Security Safeguards In Large Language Models Through Multi-Agent Systems, Mohammed Rashed Alnuaimi

Theses

This thesis focused on enhancing the safe use of Large Language Model (LLM) through the innovative use of a Multi-Agent System (MAS). As LLMs like ChatGPT became essential to our everyday interactions, the need to maintain the safe use of these systems increased. This research thoroughly assessed the current security measures in place for LLM, pointed out their limitations and developed new and more effective security strategies. The core of the proposed solution was a MAS designed to ensure that all data processed by LLM met guidelines including Privacy, Confidentiality, and Ethical standards before reaching the user. The system involved …

Go to article

Evaluating Large Language Models For Automated Cv Ranking: A Hybrid Embedding Approach For Enhanced Recruitment, Sarah Mohamed Alhindaassi

Theses

Increasing numbers of applications have revealed limitations in legacy keyword-filtering-based Applicant Tracking Systems (ATS), which commonly overlook candidate potential and ignore contextual or transferable skills. Advances in Natural Language Processing (NLP) and Large Language Models (LLMs) offer an exhilarating alternative, supporting context-sensitive and human-crafted reasoning in candidate evaluation. This thesis systematically evaluates four classes of approaches, lexical models, embedding-based methods, Large Language Models (LLMs), and hybrid ensembles, for automation of Curriculum Vitae (CV) to Job Description (JD) matching without exploiting prior annotations or annotations at match time. Using a combination of publicly available datasets and real-world sample data covering three …

Go to article

Full-Text Articles in Entire DC Network

Jurisdiction Of Vessel-Source Pollution In The Eez Under Unclos : A Case Study On China, Yang Zhang

World Maritime University Dissertations

Integrating Environmental Impact Assessment Into International Shipping Governance : A Bbnj-Pssa Synergy Framework, Thi Thuy Linh Pham

World Maritime University Dissertations

An Assessment Of The Impact Of Illegal Sand Mining On The Coastline Of Liberia, Alphena J. Logan

World Maritime University Dissertations

The Role Of Atmospheric Turbulence On Cloud Droplet Distribution And Droplet Impaction On An Airfoil, Arash Shad, Hashnayne Ahmed, ‪Nadim Zgheib, S. Balachandar, S. A. Sherif

Mechanical Engineering Faculty Publications

Climatic Controls On Soil Production, Transport And Chemical Erosion: Insights From Modelling Topography, Soils And Cosmogenic Nuclides At Little Lake, Oregon, Miles M. Reed, Ken L. Ferrier, Jill A. Marshall, Josh J. Roering, J. Taylor Perron

Geology Faculty Publications and Presentations

When Deep Learning Meets Information Retrieval-Based Bug Localization: A Survey, Feifei Niu, Chuanyi Li, Kui Liu, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

Do Code Semantics Help? A Comprehensive Study On Execution Trace-Based Information For Code Large Language Models, Jian Wang, Xiaofei Xie, Qiang Hu, Shangqing Liu, Yi Li

Research Collection School Of Computing and Information Systems

Efficient Integration Of External Knowledge To Llm-Based World Models Via Retrieval-Augmented Generation And Reinforcement Learning, Chang Yang, Xinrun Wang, Qinggang Zhang, Qi Jiang, Xiao Huang

Research Collection School Of Computing and Information Systems

Usefulness And Diminishing Returns: Evaluating Social Information In Recommender Systems, Qing Meng, Huiyu Min, Ming Shan Hee, Roy Ka-Wei Lee, Bing Tian Dai, Shuai Xu

Research Collection School Of Computing and Information Systems

Distillcaps: Enhancing Audio-Language Alignment In Captioning Via Retrieval-Augmented Knowledge Distillation, Thinh Pham, Nghiem Diep, Lizi Liao, Binh Nguyen

Research Collection School Of Computing and Information Systems

Unified Molecule Pre-Training With Flexible 2d And 3d Modalities: Single And Paired Modality Integration, Tengwei Song, Min Wu, Yuan Fang

Research Collection School Of Computing and Information Systems

Damslnet: Dual-Attention Multi-Scale Lightweight Network For Plant Disease Classification, Linfan Deng, Juan Qin, Kun Li, Jinhua Zhu, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Branch-And-Cut-And-Price For Agile Earth Observation Satellite Scheduling, Guansheng Peng, Jianjiang Wang, Guopeng Song, Aldy Gunawan, Lining Xing, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

Deep Reinforcement Learning For Solving The Stochastic E-Waste Collection Problem, Dang Viet Anh Nguyen, Aldy Gunawan, Mustafa Misir, Kwan Hui Lim, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

The Impact Of Sanctions On Github Developers And Activities, Youmei Fan, Ani Hovhannisyan, Hideaki Hata, Christoph Treude, Raula G. Kula

Research Collection School Of Computing and Information Systems

Graph Perturbations For Robust Knowledge Discovery And Retrieval, Hanhua Xiao

Dissertations and Theses Collection (Open Access)

When It Comes To Scientific Information Extraction And Llms, Less Is More, Sameer Shaik

Theses and Dissertations from DePaul University

Physics & Astronomy Faculty Publications

Attorneys And Ai: How Lawyers Use Artificial Intelligence And Analyze Its Impacts, Matthew I. Hall, Christian Turner, Eddie A. Gomez Schieber, Nathaniel Kite, Ari Schlesinger

Scholarly Works

Substituted Piperidines As (Alpha4)3(Beta2)2 Nicotinic Acetylcholine Receptor Positive Allosteric Modulators: In Vitro Pharmacology, Nataly Sanchez, Josue Gaona, Wilder Felix, Nathalia Menegasso, Brisa Cea, Ganesh Thakur, Ayman K. Hamouda

School of Medicine Student Publications and Presentations

Be+ Assisted, Simultaneous Confinement Of More Than 15000 Antihydrogen Atoms, Tim Tharp

Physics Faculty Research and Publications

Neural Network Ensemble For Computing Cross Sections Of Rotational Transitions In H2O + H2O Collisions, Bikramaditya Mandal, Dmitri Babikov, Phillip C. Stancil, Robert C. Forrey, Roman V. Krems, Naduvalath Balakrishnan

Chemistry Faculty Research and Publications

Butte Priority Soils Operable Unit Clark Tailings Consolidated Waste Management Area Site Investigation Quality Assurance Project Plan (Qapp) Revision 0, Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

Spatio-Temporal Gcn With Softmax Classifier For Skeleton-Based Human Action Recognition, Kabul Khudaybergenov, Avazjon Marakhimov, Zahriddin Muminov

Chemical Technology, Control and Management

Spatio-Temporal Dynamics Of Malaria Vector Niche Overlaps In Africa, Eric Ali Ibrahim, John Odindi, Mark Wamalwa, Henri E.Z. Tonnang

All Peer-Reviewed Publications

Climate-Driven Potential For Tularemia In East Africa: Skill Testing And Ecological Consistency Of A Transferred Risk Model, Komi Mensah Agboka, Allan Muohi Ngángá, Bonoukpoè Mawuko Sokame, Steve Soh Bernard Baleba, Tobias Landmann, Elfatih M. Abdel-Rahman, Chrysantus M. Tanga, Souleymane Diallo

All Peer-Reviewed Publications

Carbon–Neutral Granular Filtration For Sustainable And Safe Rainwater Harvesting In Urban Malaysia, Mohammad Nur-E-Alam, Md Helal Uddin, Saurav Dixit, Boon Kar Yap, Mohammad Tariqul Islam, Nowshad Amin

Research outputs 2022 to 2026

Problems In Extremal Graph Theory And Spectral Graph Theory, Fareeha Jamal

Dissertations

Advancing Security Safeguards In Large Language Models Through Multi-Agent Systems, Mohammed Rashed Alnuaimi

Theses

Evaluating Large Language Models For Automated Cv Ranking: A Hybrid Embedding Approach For Enhanced Recruitment, Sarah Mohamed Alhindaassi

Theses