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2025

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Articles 11641 - 11670 of 12193

Full-Text Articles in Physical Sciences and Mathematics

Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar Jan 2025

Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

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Deepsecure: A Novel Deep Learning Model For Effective Detection Of Attacks On Big Data In Internet Of Urban Things, Laiba Sabir, Nadeem Javaid, Mariam Akbar, Nabil Alrajeh, Safdar Hussain Bouk, Abdulaziz Aldegheishem Jan 2025

Deepsecure: A Novel Deep Learning Model For Effective Detection Of Attacks On Big Data In Internet Of Urban Things, Laiba Sabir, Nadeem Javaid, Mariam Akbar, Nabil Alrajeh, Safdar Hussain Bouk, Abdulaziz Aldegheishem

School of Cybersecurity Faculty Publications

The Internet of Urban Things (IoUTs) regularly generates large amounts of data, making it a focus of cyberthreats such as denial-of-service attacks and malware bot networks. Traditional intrusion detection systems struggle to detect intricate attack patterns, handle class imbalance, capture temporal dependencies, and exhibit transparency. To address these limitations, we introduce a novel deep machine learning model, DeepSecure, a hybrid model that combines Deep Belief Networks (DBN) for hierarchical feature extraction and Deep Neural Networks for attack classification. DBN is used for feature selection through unsupervised learning to extract hierarchical representations in the IoUTs network data. We assess the random …

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Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar Jan 2025

Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

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Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington Jan 2025

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington

School of Cybersecurity Faculty Publications

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …

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Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty Jan 2025

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

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Swatting Flies: Biting Insects As Non-Invasive Samplers For Mammalian Population Genomics, Anubhab Khan, Ryan Carter, Chengetai D. Mpamhanga, Daniel Masiga, Manun Channumsin, Marc Ciosi, Oliva Manangwa, Furaha Mramba, Umer Zeeshan Ijaz, Harriet Auty, Barbara K. Mable Jan 2025

Swatting Flies: Biting Insects As Non-Invasive Samplers For Mammalian Population Genomics, Anubhab Khan, Ryan Carter, Chengetai D. Mpamhanga, Daniel Masiga, Manun Channumsin, Marc Ciosi, Oliva Manangwa, Furaha Mramba, Umer Zeeshan Ijaz, Harriet Auty, Barbara K. Mable

All Peer-Reviewed Publications

Advances in next-generation sequencing have allowed the use of DNA obtained from unusual sources for wildlife studies. However, these samples have been used predominantly to sequence mitochondrial DNA for species identification while population genetics analyses have been rare. Since next-generation sequencing allows indiscriminate detection of all DNA fragments in a sample, technically it should be possible to sequence whole genomes of animals from environmental samples. Here we used a blood-feeding insect, tsetse fly, to target whole genome sequences of wild animals. Using pools of flies, we compared the ability to recover genomic data from hosts using the short-read sequencing (Illumina) …

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An Exploratory Attempt To Incorporate Social Criteria Into Spent Nuclear Fuel Site Screening And Relocation, Julie M. Sorfleet Jan 2025

An Exploratory Attempt To Incorporate Social Criteria Into Spent Nuclear Fuel Site Screening And Relocation, Julie M. Sorfleet

Cal Poly Humboldt theses and projects

Spent nuclear fuel, also known as nuclear waste, is the byproduct of nuclear power generation. The need for safe and responsible long-term storage and disposal of radioactive fuel presents significant technical and social challenges. These challenges are compounded by several factors, including the emerging nuclear renaissance in the United States, climate and coastal hazards, and the lack of consolidated interim or permanent storage. This research explores perceptions of risk, relocation, and siting spent nuclear fuel, predominantly focusing on Humboldt County, California. I used online surveys, the Analytic Hierarchy Process, and geographic information systems (GIS) analysis to inform how social and …

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Snag Failure And Fuel Recovery Following Stand-Replacing Fire In The Klamath Mountains, Joseph R. Nicholas Jan 2025

Snag Failure And Fuel Recovery Following Stand-Replacing Fire In The Klamath Mountains, Joseph R. Nicholas

Cal Poly Humboldt theses and projects

High-severity wildfires can produce extensive patches of standing dead trees (snags), which later contribute to surface fuel loading and present a hazard as they decay and fall. In the Klamath Mountains of northwestern California and southwestern Oregon, recent increases in the occurrence of large stand-replacing wildfires have raised concerns about fuel and vegetation recovery trajectories, the likelihood of future fires, and resulting impacts to local communities. Nevertheless, the post-fire environment may offer opportunities to enhance ecological and community resilience. Estimates of snag failure and fuel loading can support land management planning and risk assessment following wildfire, though such work is …

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Vaim-Cff: A Variational Autoencoder Inverse Mapper Solution To Compton Form Factor Extraction From Deeply Virtual Compton Scattering, Manal Almaeen, Tareq Alghamdi, Brandon Kriesten, Douglas Adams, Yaohang Li, Huey-Wen Lin, Simonetta Liuti Jan 2025

Vaim-Cff: A Variational Autoencoder Inverse Mapper Solution To Compton Form Factor Extraction From Deeply Virtual Compton Scattering, Manal Almaeen, Tareq Alghamdi, Brandon Kriesten, Douglas Adams, Yaohang Li, Huey-Wen Lin, Simonetta Liuti

Computer Science Faculty Publications

We develop a new methodology for extracting Compton form factors (CFFs) from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted …

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A Bibliographic And Topic Modeling Analysis Of The P-Adic Theory Literature Using Latent Dirichlet Allocation, Humberto Llinás, Ismael Gutiérrez, Anselmo Torresblanca, Javier De La Hoz, Brian Llinás Jan 2025

A Bibliographic And Topic Modeling Analysis Of The P-Adic Theory Literature Using Latent Dirichlet Allocation, Humberto Llinás, Ismael Gutiérrez, Anselmo Torresblanca, Javier De La Hoz, Brian Llinás

Computer Science Faculty Publications

P-adic analysis, introduced by Kurt Hensel in the early 20th century, has developed into a fundamental area of mathematical research with broad applications in number theory, algebraic geometry, and mathematical physics. This study aims to examine the thematic evolution and scholarly impact of p-adic research through a comprehensive topic modeling and bibliometric analysis. Using classical bibliometric techniques (e.g., performance analysis, co-authorship, and co-citation networks) combined with Latent Dirichlet Allocation (LDA), we analyzed 7388 peer-reviewed documents published between 1965 and 2024. The computational workflow was conducted using R (version 4.4.1) and VOSviewer (version 1.6.20), which enabled the identification of 20 distinct …

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From Philosophy To Nlu: Evolving Definitions With Research Hypotheses, Jian Wu, Sarah Rajtmajer Jan 2025

From Philosophy To Nlu: Evolving Definitions With Research Hypotheses, Jian Wu, Sarah Rajtmajer

Computer Science Faculty Publications

Over the past decades, alongside advancements in natural language processing, significant attention has been paid to training models to automatically extract, understand, test, and generate hypotheses in open and scientific domains. However, interpretations of the term hypothesis for various natural language understanding (NLU) tasks have migrated from traditional definitions in the natural, social, and formal sciences. Even within NLU, we observe differences defining hypotheses across literature. In this paper, we overview and delineate various definitions of hypothesis. Especially, we discern the nuances of definitions across recently published NLU tasks. We highlight the importance of well-structured and well-defined hypotheses, particularly as …

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Understanding Pii Leakage In Large Language Models: A Systematic Survey, Shuai Cheng, Zhao Li, Shu Meng, Mengxia Ren, Haitao Xu, Shuai Hao, Chuan Yue, Fang Zhang Jan 2025

Understanding Pii Leakage In Large Language Models: A Systematic Survey, Shuai Cheng, Zhao Li, Shu Meng, Mengxia Ren, Haitao Xu, Shuai Hao, Chuan Yue, Fang Zhang

Computer Science Faculty Publications

Large Language Models (LLMs) have demonstrated exceptional success across a variety of tasks, particularly in natural language processing, leading to their growing integration into numerous facets of daily life. However, this widespread deployment has raised substantial privacy concerns, especially regarding personally identifiable information (PII), which can be directly associated with specific individuals. The leakage of such information presents significant real-world privacy threats. In this paper, we conduct a systematic investigation into existing research on PII leakage in LLMs, encompassing commonly utilized PII datasets, evaluation metrics, and current studies on both PII leakage attacks and defensive strategies. Finally, we identify unresolved …

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Energy-Based Deep Incomplete Multi-View Clustering, Ziyu Wang, Yiming Du, Rui Ning, Lusi Li Jan 2025

Energy-Based Deep Incomplete Multi-View Clustering, Ziyu Wang, Yiming Du, Rui Ning, Lusi Li

Computer Science Faculty Publications

Incomplete multi-view clustering (IMVC) deals with real-world scenarios where certain views are partially missing, posing significant challenges to effective clustering. Most existing IMVC approaches face a trade-off: imputation-free methods suffer from information bias and imbalance, while full-imputation methods risk introducing and propagating noise. To overcome these limitations, we propose Energy-Based Deep Incomplete Multi-View Clustering (Energy-DIMC), a novel selective-imputation framework that leverages energy-based models (EBMs) to guide reliable imputations and robust clustering. EBMs assess data compatibility by assigning lower energy to more coherent structures, effectively modeling complex inter-view and inter-sample dependencies. Inspired by EBMs, Energy-DIMC integrates four key components: 1) a …

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Humans Vs. Llms On Open Domain Scientific Claim Verification: A Baseline Study, Benjamin Curtis, Stefania Dzhaman, Matthew Maisonave, Jian Wu Jan 2025

Humans Vs. Llms On Open Domain Scientific Claim Verification: A Baseline Study, Benjamin Curtis, Stefania Dzhaman, Matthew Maisonave, Jian Wu

Computer Science Faculty Publications

Verifying scientific claims is challenging for the general public because most people lack domain knowledge. Manual verification by subject domain experts is accurate, but it is obviously not scalable to meet the rising number of scientific claims on the Web. Whether the emerging large language models and large reasoning models can be used for scientific claim verification, and how their performances compare to humans, are still research questions. To this end, we developed a new benchmark MSVEC2 that consists of 138 claims from credible fact verification websites and science news outlets. Two tasks were given to both human and LLM …

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Privshap: A Finer-Granularity Network Linearization Method For Private Inference, Xiangrui Xu, Zhenzhen Wang, Rui Ning, Chunsheng Xiu, Hongyi Wu Jan 2025

Privshap: A Finer-Granularity Network Linearization Method For Private Inference, Xiangrui Xu, Zhenzhen Wang, Rui Ning, Chunsheng Xiu, Hongyi Wu

Computer Science Faculty Publications

Private inference applies cryptographic techniques like homomorphic encryption, garble circuit and secret sharing to keep both sides privacy in a client-server setting during inference. It is often hindered by the high communication overheads, especially at non-linear activation layers such as ReLU. Hence ReLU pruning has been widely recognized as an efficient way to accelerate private inference. Existing approaches to ReLU pruning typically rely on coarse hypothesis, which assume an inverse correlation between the importance of ReLU and linear layers or shallow activation layers have less importance for universal models, to assign the budgets according to the layer while preserving the …

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Draft Final Silver Bow Creek Conservation Area Repository Data Gap Data Summary Report (Dsr), Pioneer Technical Services, Inc. Jan 2025

Draft Final Silver Bow Creek Conservation Area Repository Data Gap Data Summary Report (Dsr), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

This Silver Bow Creek Conservation Area (SBCCA) Repository Data Gap Data Summary Report (DSR) presents the results from the soil sampling activities conducted as specified in the SBCCA Repository Data Gap Quality Assurance Project Plan (QAPP) (referred to herein as the SBCCA Repository Data Gap QAPP; Atlantic Richfield Company, 2024). The information presented in this DSR includes laboratory analytical results from subsurface soil sampling collected from Buffalo Gulch (BG), Butte Reduction Works (BRW), Diggings East (DE), and Northside Tailings (NST) within the SBCCA. This DSR was prepared by Pioneer Technical Services, Inc. (Pioneer), 1101 S. Montana Street, Butte, Montana 59701 …

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Position: Benchmarking Is Broken - Don't Let Ai Be Its Own Judge, Zerui Cheng, Stella Wohnig, Ruchika Gupta, Samiul Alam, Tassallah Abdullahi, João Alves Ribeiro, Christian Nielsen-Garcia, Saif Mir, Siran Li, Jason Orender, Seyed Ali Bahrainian, Daniel Kirste, Aaron Gokaslan, Carsten Eickhoff, Pramod Viswanath, Ruben Wolff Jan 2025

Position: Benchmarking Is Broken - Don't Let Ai Be Its Own Judge, Zerui Cheng, Stella Wohnig, Ruchika Gupta, Samiul Alam, Tassallah Abdullahi, João Alves Ribeiro, Christian Nielsen-Garcia, Saif Mir, Siran Li, Jason Orender, Seyed Ali Bahrainian, Daniel Kirste, Aaron Gokaslan, Carsten Eickhoff, Pramod Viswanath, Ruben Wolff

Computer Science Faculty Publications

The meteoric rise of Artificial Intelligence (AI), with its rapidly expanding market capitalization, presents both transformative opportunities and critical challenges. Chief among these is the urgent need for a new, unified paradigm for trustworthy evaluation, as current benchmarks increasingly reveal critical vulnerabilities. Issues like data contamination and selective reporting by model developers fuel hype, while inadequate data quality control can lead to biased evaluations that, even if unintentionally, may favor specific approaches. As a flood of participants enters the AI space, this "Wild West" of assessment makes distinguishing genuine progress from exaggerated claims exceptionally difficult. Such ambiguity blurs scientific signals …

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Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu Jan 2025

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu

Computer Science Faculty Publications

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …

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A Real-Time Approach To Capture Ambient And Focal Attention In Visual Search, Gavindya Jayawardena, Yasith Jayawardana, Yasasi Abeysinghe, Bhanuka Mahanama, Sampath Jayarathna, Jacek Gwizdka Jan 2025

A Real-Time Approach To Capture Ambient And Focal Attention In Visual Search, Gavindya Jayawardena, Yasith Jayawardana, Yasasi Abeysinghe, Bhanuka Mahanama, Sampath Jayarathna, Jacek Gwizdka

Computer Science Faculty Publications

During visual search, individuals’ attention shifts between ambient and focal states in response to task demands and stimuli. The ambient/focal coefficient K is a statistically validated measure of these states, computed offline from fixation duration and saccade amplitude data. While current methods compute K offline, real-time computation could enable applications such as monitoring user attention, creating attention-adaptive user interfaces, and optimizing graphics rendering. However, real-time computation of K requires stable estimates for the parameters of fixation duration and saccade amplitude distributions. Since these distributions are heavy-tailed, the real-time estimates exhibit high variance and slow convergence. To overcome this, we propose …

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Icu-Length Of Stay Prediction On Electronic Health Records Using Graph Neural Networks And Homogeneous Similarity Graphs, Ahmad F. Al Musawi, Pratip Rana, Sibtanu Raha, Joshua Braunstein, William C. Sleeman Iv, Rishabh Kapoor, Preetam Ghosh Jan 2025

Icu-Length Of Stay Prediction On Electronic Health Records Using Graph Neural Networks And Homogeneous Similarity Graphs, Ahmad F. Al Musawi, Pratip Rana, Sibtanu Raha, Joshua Braunstein, William C. Sleeman Iv, Rishabh Kapoor, Preetam Ghosh

Computer Science Faculty Publications

Predicting the length of stay (LoS) is important for hospital administration, as it helps allocate proper resources, such as bed management and hospital staffing. Patients' Electronic Health Records (EHRs) contain highly relevant data for LoS prediction; however, their integration and effective use in predictive modeling for accurately estimating LoS remain challenging. To address this, we propose a homogeneous Graph Neural Network (GNN)-based framework for predicting LoS. This method employs a comprehensive data fusion strategy based on the hospital Visit-based Similarity Graph (VSG), which integrates diverse multi-modal clinical features into a coherent, homogeneous graph representation. Next, this VSG is fed into …

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Applying The Matching Law To Major League Baseball (Mlb), Christopher Watkins, Vincent Berardi Jan 2025

Applying The Matching Law To Major League Baseball (Mlb), Christopher Watkins, Vincent Berardi

Psychology Faculty Articles and Research

The application of the generalized matching equation (GME) has been detailed in a variety of sports, including football, basketball, and others. However, only a limited number of studies have focused on Major League Baseball (MLB), and they typically have examined ≤ 5 players and/or focused on a single behavior. This paper increases the generalizability of such work by using newly available, state-of-the-art data from thousands of players to explore the GME in several scenarios within three aspects of a baseball game - defense, pitching and batting. We found that the GME accurately summarized response allocation in most scenarios, with r …

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Generalizations Of Some Q-Product Identities Of Ramanujan And Others, Timothy J. Huber, James Mclaughlin, Dongxi Ye Jan 2025

Generalizations Of Some Q-Product Identities Of Ramanujan And Others, Timothy J. Huber, James Mclaughlin, Dongxi Ye

School of Mathematical & Statistical Sciences Faculty Publications

By considering certain limiting cases of a WP-Bailey chain discovered by Andrews, and also limiting cases of certain classical summation formulae for basic hypergeometric series, we derive new expressions for certain Lambert series in terms of basic hypergeometric series. In some cases, the resulting series involve an arbitrary Bailey pair. This allows for the derivation of new basic hypergeometric expansions for some q-products and series that Ramanujan expressed in terms of Lambert series. Some of Ramanujan’s identities are extended to more general relations containing one or more free parameters.

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Artificial Intelligence In Science And Society: The Vision Of Usern, Tommaso Dorigo, Gary D. Brown, Carlo Casonato, Artemi Cerda, Joseph Ciarrochi, Mauro Da Lio, Nicole D'Souza, Nicolas R. Gauger, Steven C. Hayes, Stefan G. Hofmann, Robert Johansson, Marcus Liwicki, Fabien Lotte, Juan J. Nieto, Giulia Olivato, Peter Parnes, George Perry, Alice Plebe, Idupulapati M. Rao, Nima Rezaei, Fredrik Sandin, Andrey Ustyuzhanin, Giorgio Vallortigara, Pietro Vischia, Niloufar Yazdanpanah Jan 2025

Artificial Intelligence In Science And Society: The Vision Of Usern, Tommaso Dorigo, Gary D. Brown, Carlo Casonato, Artemi Cerda, Joseph Ciarrochi, Mauro Da Lio, Nicole D'Souza, Nicolas R. Gauger, Steven C. Hayes, Stefan G. Hofmann, Robert Johansson, Marcus Liwicki, Fabien Lotte, Juan J. Nieto, Giulia Olivato, Peter Parnes, George Perry, Alice Plebe, Idupulapati M. Rao, Nima Rezaei, Fredrik Sandin, Andrey Ustyuzhanin, Giorgio Vallortigara, Pietro Vischia, Niloufar Yazdanpanah

All Peer-Reviewed Publications

The recent rise in relevance and diffusion of Artificial Intelligence (AI)-based systems and the increasing number and power of applications of AI methods invites a profound reflection on the impact of these innovative systems on scientific research and society at large. The Universal Scientific Education and Research Network (USERN), an organization that promotes initiatives to support interdisciplinary science and education across borders and actively works to improve science policy, collects here the vision of its Advisory Board members, together with a selection of AI experts, to summarize how we see developments in this exciting technology impacting science and society in …

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Global Strategies To Manage Huanglongbing (Hlb) And Its Vectors: Insights And Implications For The Mediterranean Region, Meritxell Pérez-Hedo, Mark S. Hoddle, Fernando Alferez, Lochy Batista, George A.C. Beattie, Shourish Chakravarty, Paul Holford, Fathiya Mbarak Khamis, Inusa Jacob Ajene, Aruna Manrakhan, Paul H. Fourie, Maritza Luis, Eduardo Osorio-Hernández, Mamoudou Sétamou, Alejandro Tena, Pedro Takao Yamamoto, Tara Wade, Changyong Zhou, Nian Wang, Lukasz L. Stelinski, Alberto Urbaneja Jan 2025

Global Strategies To Manage Huanglongbing (Hlb) And Its Vectors: Insights And Implications For The Mediterranean Region, Meritxell Pérez-Hedo, Mark S. Hoddle, Fernando Alferez, Lochy Batista, George A.C. Beattie, Shourish Chakravarty, Paul Holford, Fathiya Mbarak Khamis, Inusa Jacob Ajene, Aruna Manrakhan, Paul H. Fourie, Maritza Luis, Eduardo Osorio-Hernández, Mamoudou Sétamou, Alejandro Tena, Pedro Takao Yamamoto, Tara Wade, Changyong Zhou, Nian Wang, Lukasz L. Stelinski, Alberto Urbaneja

All Peer-Reviewed Publications

Huanglongbing (HLB), also known as citrus greening disease, poses a severe threat to the global citrus industry. This disease is caused by three species of bacteria, ‘Candidatus Liberibacter asiaticus’, ‘C. L. africanus’, and ‘C. L. ameri-canus’. The primary pathogen vectors are the Asian citrus psyllid (Diaphorina citri Kuwayama, Hemiptera: Psyllidae) and the African citrus psyllid (Trioza erytreae Del Guercio, Hemiptera: Triozidae). The disease is incurable, and its management relies on reducing vector densities and the adverse effects of bacterial infections on host trees. Managing psyllid vec-tors, especially D. citri, has proven exceptionally challenging due to its rapid spread and subsequent …

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Advancing Multivariate Time Series Similarity Assessment: An Integrated Computational Approach, Franck B.N. Tonle, Henri E.Z. Tonnang, Milliam M.Z. Ndadji, Maurice Tchoupe Tchendji, Armand Nzeukou, Kennedy Senagi, Saliou Niassy Jan 2025

Advancing Multivariate Time Series Similarity Assessment: An Integrated Computational Approach, Franck B.N. Tonle, Henri E.Z. Tonnang, Milliam M.Z. Ndadji, Maurice Tchoupe Tchendji, Armand Nzeukou, Kennedy Senagi, Saliou Niassy

All Peer-Reviewed Publications

Data mining, particularly multivariate time series data analysis, is crucial in extracting insights from complex systems and supporting informed decision-making across diverse domains. However, assessing the similarity of multivariate time series data presents several challenges, including dealing with large datasets, addressing temporal misalignments, and necessitating efficient and comprehensive analytical frameworks. A novel integrated computational approach, Multivariate Time series Alignment and Similarity Assessment (MTASA) is proposed to address these challenges. MTASA is built upon a hybrid methodology designed to optimise time series alignment, complemented by a multiprocessing engine that enhances the utilisation of computational resources. This integrated approach comprises four key …

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Morphometric Characterization Of Invasive Diaphorina Citri Infesting Citrus And Orange Jasmine In Ghana, Kodwo Dadzie Ninsin, Maxwell Kelvin Billah, Bernard Agyeman Boateng, Jonathan Osei-Owusu, Fred Kormla Ablormeti, Samuel Opuni-Basoa, Aboagye Kwarteng Dofuor, George Edusei, Steve B.S. Baleba, Owusu Fordjour Aidoo Jan 2025

Morphometric Characterization Of Invasive Diaphorina Citri Infesting Citrus And Orange Jasmine In Ghana, Kodwo Dadzie Ninsin, Maxwell Kelvin Billah, Bernard Agyeman Boateng, Jonathan Osei-Owusu, Fred Kormla Ablormeti, Samuel Opuni-Basoa, Aboagye Kwarteng Dofuor, George Edusei, Steve B.S. Baleba, Owusu Fordjour Aidoo

All Peer-Reviewed Publications

Morphometric analysis plays a crucial role in the accurate identification and classification of insect pests, which is essential for implementing effective management strategies. The Asian citrus psyllid (Diaphorina citri Kuwayama; Hemiptera: Liviidae), an invasive species and a known vector of ‘Candidatus Liberibacter asiaticus’ and ‘Ca. Liberibacter americanus,’ the bacterial pathogens associated with Huanglongbing (HLB), has recently emerged as a pest of concern in Ghana. Despite its significance, little is known about the biological traits of field populations in the region. In this study, we examined morphometric variation in D. citri populations infesting citrus and orange jasmine (Murraya paniculata) across different …

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A Review On Knowledge And Information Extraction From Pdf Documents And Storage Approaches, Salvador D. Atagong, Henri Tonnang, Kennedy Senagi, Mark Wamalwa, Komi M. Agboka, John Odindi Jan 2025

A Review On Knowledge And Information Extraction From Pdf Documents And Storage Approaches, Salvador D. Atagong, Henri Tonnang, Kennedy Senagi, Mark Wamalwa, Komi M. Agboka, John Odindi

All Peer-Reviewed Publications

Introduction: Automating the extraction of information from Portable Document Format (PDF) documents represents a major advancement in information extraction, with applications in various domains such as healthcare, law, or biochemistry. However, existing solutions face challenges related to accuracy, domain adaptability, and implementation complexity. Methods: A systematic review of the literature was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to examine approaches and trends in PDF information extraction and storage approaches. Results: The review revealed three dominant methodological categories: rule-based systems, statistical learning models, and neural network-based approaches. Key limitations include the rigidity of rule-based …

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Spatial Analytics To Elucidate The Incubation Period And Drivers Of Visceral Leishmaniasis: Case Of Turkana County In Kenya, Kennedy Senagi, Maureen Nzilani, Evans Omondi, David P. Tchouassi, Tobias Landmann, Damaris Matoke-Muhia, Emmanuel Okunga, Barrington Gesimba, Elfatih M. Abdel-Rahman, Dawn Maranga, Joseph M. Ndungu, Daniel Masiga Jan 2025

Spatial Analytics To Elucidate The Incubation Period And Drivers Of Visceral Leishmaniasis: Case Of Turkana County In Kenya, Kennedy Senagi, Maureen Nzilani, Evans Omondi, David P. Tchouassi, Tobias Landmann, Damaris Matoke-Muhia, Emmanuel Okunga, Barrington Gesimba, Elfatih M. Abdel-Rahman, Dawn Maranga, Joseph M. Ndungu, Daniel Masiga

All Peer-Reviewed Publications

Introduction: Visceral leishmaniasis (VL) is a severe and neglected tropical disease of public health concern. VL is fatal if not treated. Kenya has experienced multiple outbreaks of the disease since 2017. The underlying drivers of the disease risk dynamics, as well as the incubation period, are not well understood. Methods: We implemented statistical (spatial logistic regression and Bayesian spatial) and machine learning (random forest, support vector machine, AdaBoost, logistic regression, and extra trees) models to estimate the incubation period and predict areas of low/high risk in Turkana County, an endemic VL foci in Kenya. Two-year (2019–2020) patient data were sourced …

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Gorenstein Flat Preenvelopes Over Coherent Rings, Alec Moore Jan 2025

Gorenstein Flat Preenvelopes Over Coherent Rings, Alec Moore

College of Graduate Studies: Theses & Dissertations

The existence of precovers and preenvelopes of Gorenstein flat modules is of great interest in the field of Gorenstein homological algebra. We give a sufficient condition in order for the class of Gorenstein flat modules to be preenveloping. More precisely, we prove that if the ring R is coherent such that every injective module has finite flat dimension, then every R-module has a Gorenstein flat preenvelope.

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Evaluating Multimodal Ai Systems: A Comparative Analysis Of Large Languagel Model-Based Models For Text, Image, And Video Generation, Azeezat O. Akinola Jan 2025

Evaluating Multimodal Ai Systems: A Comparative Analysis Of Large Languagel Model-Based Models For Text, Image, And Video Generation, Azeezat O. Akinola

College of Graduate Studies: Theses & Dissertations

In the era of rapid technological advancement, efficient content generation, application development, and data management are crucial for meeting the demands of dynamic digital environments. This thesis uses state-of-the-art models to explore three core areas: AI-driven video content creation, text-to-image-to-text consistency, and automatic text summarization. The first study investigates the potential of AI-powered text-to-video generation to democratize video production and enhance storytelling. By comparing the performance of three models—ModelScope, Text2Video (Zero), and Motion Consistency—this study assessed the quality of generated videos using CLIP scores. It evaluated statistical significance through t-tests and homogeneity tests. Results indicate that ModelScope outperformed the others, …

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