Digital Commons Network^™

Thermal Requirements And Phenology Modelling Of A Drosophila Suzukii Population: Implications For Pest Risk Under A Changing Climate, Shepard Ndlela, Abdelmutalab G.A. Azrag, Esther Owino Awuor, Ibrahim Maholidy Farid, Samira Abuelgasim Mohamed

All Peer-Reviewed Publications

Drosophila suzukii (spotted-wing drosophila) is an invasive pest causing significant economic losses in soft-skinned fruit crops globally. This study presents comprehensive temperature-dependent life table data and phenology models for an African D. suzukii population collected in Kenya. Developmental time, mortality, fecundity, and adult longevity were assessed under constant temperatures from 12 to 30 °C. Using Insect Life Cycle Modelling (ILCYM) software, linear and nonlinear models were applied to characterise the effects of temperature on life history traits. Additionally, spatial risk indices including Establishment Risk Index (ERI), Generation Index (GI), and Activity Index (AI) were mapped globally for current and future …

Food Recognition With Visual Language Models: Search Re-Ranking Or Retrieval-Augmented Generation?, Kian Yu Gan, Phuong Anh Nguyen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Despite the rapid advances in Visual Language Models (VLMs), these models struggle to recognize culture-specific food items. While VLMs are effective in recognizing popular cultural dishes, their performance is suboptimal for dishes that are unique but not widely known internationally. Specifically, VLMs often generate either generic labels or hallucinated names for dishes that are localized to a particular culture. As a result, retrieval-augmented generation (RAG), which retrieves relevant recipes as references for VLMs, emerges as a promising approach. Nevertheless, recipe retrieval, which is itself imperfect, could mislead VLMs into generating inaccurate or culturally inappropriate dish names. This paper presents a …

A Note On The Novel Passive Sampling In Environmental Dna (Edna) Collection Technique To Detect Yellow Mud Turtle (Kinosternon Flavescens): Field And Laboratory Approaches In Turtle Edna, Sakib Tahmid Rishan, Richard J. Kline, Md. Saydur Rahman

School of Integrative Biological & Chemical Sciences Faculty Publications

Environmental DNA (eDNA) is a powerful and sensitive method for monitoring aquatic and terrestrial organisms, offering insights into their distribution. Usually, eDNA is collected using a filter membrane through water filtration. However, the passive eDNA method can simplify sampling and broaden its scope, offering new insights for ecological surveillance. This study examined the efficacy of six different membrane filters in detecting the yellow mud turtle (Kinosternon flavescens) through a novel passive eDNA method. After being immersed in water for 12 h, only the mixed cellulose ester (MCE) membrane filter consistently demonstrated specific polymerase chain reaction (PCR) amplification using …

Geographic Variation In Proteomic Responses To Ocean Acidification In A Cold-Water Coral (Balanophyllia Elegans), Keana A. Richmond

Cal Poly Humboldt theses and projects

In the face of a rapidly changing climate, assessing organismal responses to future stressors in the context of current, natural exposure to stress could provide key insights to understanding marine ecosystem resilience. I used Balanophyllia elegans, a cold-water, solitary, azooxanthellate coral as a model to better understand how varying oceanographic conditions across its geographic range have shaped its ability to tolerate and potentially adapt to current and future ocean acidification conditions. I collected B. elegans individuals from four sites across 2,500km of their range and subjected them to two pH treatments to investigate site-specific protein expression in response to …

Early Successional Response To Stream Daylighting In Redwood National And State Parks, Orick, California, Rylee Rawson

Cal Poly Humboldt theses and projects

In-channel stream restoration projects can act as both disturbance and recovery mechanisms, disrupting existing conditions to reestablish the hydrologic and ecological processes that sustain stream ecosystems. These interventions initiate successional trajectories in which channel form and biological communities reorganize over time. In Redwood National and State Parks (California, USA), the Redwoods Rising initiative restores buried headwater channels by recontouring legacy logging roads and daylighting streams buried by Humboldt crossings. Using the National Park Service Klamath Network wadable stream monitoring protocol, I evaluated nine restored reaches spanning one to five years post-restoration, alongside old-growth reference reaches and available pre-treatment data. I …

Explainable Convolutional Neural Network Model Provides An Alternative Genome-Wide Association Perspective On Mutations In Sars-Cov-2, Parisa C. Hatami, Richard Annan, Luis Miranda, Jane L. Gorman, Mengjun Xie, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Identifying informative genomic features in SARS-CoV-2 can help clarify patterns of viral evolution. In this study, we developed an explainable convolutional neural network (CNN) model to classify SARS-CoV-2 genomic sequences into the WHO-designated Variants of Concern (VOCs), Alpha, Beta, Gamma, Delta, and Omicron. Using a balanced dataset of genomes, the classification CNN achieved 99.96% accuracy on the held-out test set. To interpret the model’s predictions, we applied SHapley Additive exPlanations (SHAP) to estimate the contribution of each nucleotide position to VOC-label prediction and compared aggregated attributions with a chi-square GWAS baseline applied to the same categorical labels. SHAP prioritized several …

Privacy-Preserving Federated Learning With Optimized Ensemble Weighting And Knowledge Distillation For Covid-19 Detection From Non-Iid Medical Imaging Data, Richard Annan, Hong Qin, Robert Newman, Madhuri Siddula, Letu Qingge

Computer Science Faculty Publications

Medical imaging enables rapid and accurate diagnosis of COVID-19, with CT scans proving especially effective. However, data privacy concerns limit collaborative model development across hospitals. To address this issue, we introduce a novel federated learning framework. It is referred to as Independent Knowledge Distillation with post-Ensemble Federated Learning (IKDEFL). Differential Privacy (DP) is integrated into the framework to improve privacy guarantees. Three DP mechanisms are evaluated. These include Fixed Gaussian, Gaussian Adaptive, and Tree Adaptive. The evaluation has been conducted on heterogeneous and Non-Independent and Identically Distributed (Non-IID) datasets. These datasets reflect real-world hospital scenarios. Results show that IKDEFL significantly …

Larval Crowding In Culex Pipiens Generates Cumulative Cascading Effects Under Changing Environmental Conditions, Sengul Talay, Zafer Sakaci, Michele C. Weigle, Bugrahan Regaip Kilinc, Jenah Parman, Holly Gaff, Deniz Sirin, Bulent Alten, Dennis Bente, Sirri Kar

Computer Science Faculty Publications

Density-dependent population regulation is a fundamental driver in the population dynamics of living systems, ensuring a balanced and sustainable maintenance in nature. It has been suggested that the adverse developmental and morphological effect of high larval density in container mosquitoes represents a significant limiting factor for population density, and the need to investigate this possible importance within the framework of field-based principles has been emphasized. This semi-field study, conducted under the natural thermal regime and using a natural population in Turkish Thrace, aimed to examine the effects of non-food-limiting larval density (crowding effect) in the mosquito species, Culex pipiens ( …

Attention-Based Multi-Omics Fusion For Drug Synergy Prediction, Kusal Debnath, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

Drug combination therapy in disease management gained popularity in the last few decades. Computational modeling of such combinations is an active area of research in the drug discovery domain. While earlier approaches solely emphasized on the structural features of participating drugs for designing synergistic models, they lack other crucial factors directly linked with drug administration - omics expressions. As differential omics expression is a downstream consequence of the administered drug combinations, utilizing such expressions while designing synergistic models promises robust and dynamic modeling. In this work, we propose SynergyLM that fuses multi-omics features with drug embeddings to build an omics-aware …

Predicting Oil Contamination In Water Using Machine Learning On Microbial Compositions, Tong Gao, Isaac Bigcraft, Stephen Techtmann, Issei Nakamura

Michigan Tech Publications

We present a compact and generative machine-learning framework that predicts oil contamination based on microbial community compositions from experimental samples. Our method combines dimensionality reduction with data augmentation and generative modeling to address high-dimensional, non-linear, and sparse microbial data. To reduce the 503-dimensional bacterial composition dataset, we compared three dimensionality reduction techniques: feature importance from random forest, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE). Feature importance outperformed PCA and t-SNE, improving predictive performance and identifying microbial species most strongly correlated with oil contamination. To mitigate data scarcity, we augmented the training data using an augmented data neural …

Dietary Ecology Of Waterfowl Nest Predators In The Prairie Pothole Region: Evaluating Resource Use From Species To Individual Using Stable Isotope Analysis, Grace Barthelmess

EWU Masters Thesis Collection

Nest predation is a primary factor influencing waterfowl productivity in the Prairie Pothole Region (PPR) of North America. Although this system is intensely managed and studied, research has largely focused on how predators affect waterfowl populations, with comparatively little attention to how predators actually rely on waterfowl as a resource. Understanding predator feeding ecology is therefore critical for informing effective management strategies and interpreting the complexity of predator-prey interactions on this landscape. My objective overall was to elucidate the feeding ecology of nine focal predator species commonly associated with waterfowl nest predation in the Aspen Parkland Ecoregion of the PPR: …

Relationships Between Benthic Macroinvertebrate Communities And Environmental Conditions In Headwater Tributaries Of The Pend Oreille Lake Watershed, Idaho, Devlin Mee

EWU Masters Thesis Collection

Benthic macroinvertebrates are widely used indicators of stream condition, and their communities respond to thermal and land-use conditions in headwater streams. This study characterized macroinvertebrate biomass, community composition, and diversity across forested headwater tributaries of the Pend Oreille lake watershed in northern Idaho, and examined their relationships with summer temperature, land development, and road proximity. Samples from thirteen streams collected in 2024 were identified to genus, measured, and converted to estimated biomass using published length–mass regressions; ten streams had recovered temperature loggers. Because environmental variables were characterized once per stream, all analyses were conducted at the stream level.

Estimated total …

Microgravity-Induced Alterations In Left Atrial Hemodynamics And Thrombogenic Risk: Insights From Healthy And Atrial Fibrillation Models, Grace M. Hoeppner

Dissertations, Master's Theses and Master's Reports

Background: Microgravity exposure alters cardiovascular loading, yet its impact on left atrial flow dynamics and thrombotic risk remains poorly understood. This study investigates how spaceflight-relevant microgravity-induced changes in cardiac outflow affect left atrial hemodynamics in healthy individuals and patients with atrial fibrillation.

Methods: Patient-specific left atrial models were generated for three healthy individuals and three AF patients. Computational fluid dynamics (CFD) simulations were performed using each patient’s baseline mitral outflow waveform and two modified waveforms representing short- and long-duration post-flight cardiac loading changes derived from echocardiographic observations. Hemodynamic metrics included left atrial velocity, time averaged wall shear stress, oscillatory shear …

Intelligent Torque Control Of Switched Reluctance Motors Based On Ditc And Wavelet Neural Network, Ameer L. Saleh, László Számel

Mansoura Engineering Journal

Switched Reluctance Motors (SRMs) have been considered a high-performance and environmentally friendly solution for Electric Vehicle (EV) applications owing to their simpler construction, robust design, and high efficiency. However, it suffers from high torque ripple and acoustic noise due to its highly nonlinear magnetic characteristics and double-salient structure.  This paper introduces a nonlinear torque control based on the Direct Instantaneous Torque Control (DITC) scheme and a Wavelet Neural Network (WNN)  to achieve better dynamic response and mitigate torque ripple.  The proposed WNN is employed as a nonlinear torque controller, inserted between the torque error and the hysteresis torque controller within …

Markov Chain Wave Generative Adversarial Network For Bee Bioacoustic Signal Synthesis, Kumudu Samarappuli, Iman Ardekani, Mahsa Mohaghegh, Abdolhossein Sarrafzadeh

Electrical & Computer Engineering Faculty Publications

This paper presents a framework for synthesizing bee bioacoustic signals associated with hive events. While existing approaches like WaveGAN have shown promise in audio generation, they often fail to preserve the subtle temporal and spectral features of bioacoustic signals critical for event-specific classification. The proposed method, MCWaveGAN, extends WaveGAN with a Markov Chain refinement stage, producing synthetic signals that more closely match the distribution of real bioacoustic data. Experimental results show that this method captures signal characteristics more effectively than WaveGAN alone. Furthermore, when integrated into a classifier, synthesized signals improved hive status prediction accuracy. These results highlight the potential …

Photo And Spatial Control Of Rna Structures And Functions: Exploring Positional Effects And Photo-Responsiveness Of Azobenzene Modified Nucleotides, Jinxi Du

Electronic Theses & Dissertations (2024 - present)

RNA molecules perform many important biological functions by forming complex three-dimensional structures stabilized by base pairing, tertiary interactions, and metal ion coordination. Because RNA function depends strongly on its structure, methods that allow reversible control of RNA conformation and activity are important for understanding RNA behavior and for potential therapeutic applications. However, achieving reversible and site-specific control of RNA structure, especially at the single-nucleotide level, remains challenging. In this dissertation, I developed and applied azobenzene-modified nucleotides as a chemical tool to enable optical and spatial control of RNA structure and function.

The first part of this dissertation focuses on establishing …

The Chemistry Of Red Lipstick: The Impact Red Lipstick Has On Society, Lilliana Weldeslassie

Electronic Theses & Dissertations (2024 - present)

Cosmetic chemistry is deeply embedded in everyday life, shaping the products people use from morning to night. Among these, red lipstick stands out as a formulation that blends complex chemistry with a cultural meaning. This thesis examines the scientific foundations of red lipstick through its chemical composition, pigment structure, toxicological history, analytical methods, and evolving sustainability practices. Historically, red lipsticks relied on high-risk ingredients such as lead, mercury, and other toxic metals, reflecting an era before chemical safety and regulatory oversight were incorporated.²¹ Modern formulations have established safer synthetic dyes, natural waxes, plant-based oils, and stabilizers created to optimize …

New Fossil Mygalomorph Spiders (?Idiopidae, Pycnothelidae) From The Lower Cretaceous Crato Formation Of Brazil, Elis Maria Gomes Santana

Graduate Theses, Dissertations, and Problem Reports (ETD)

The infraorder Mygalomorphae represents one of the earliest-diverging lineages of spiders, but its fossil record remains sparse relative to the modern diversity of the group. This is a reflection of both ecological and preservational biases, such as the predominantly fossorial habits of many mygalomorphs and the strong bias of the spider fossil record toward amber deposits. Compression fossils from rock deposits are therefore especially important for documenting the evolutionary history of the group. Here, two new mygalomorph fossils from the Lower Cretaceous Crato Formation, Araripe Basin (Brazil) are described. The first specimen is tentatively referred to ?Idiopidae based on male …

Multi-Omic Characterization Of Immune And Metabolic Responses To Inflammatory Challenges In Sheep, Samanthia Raceine Johnson

Graduate Theses, Dissertations, and Problem Reports (ETD)

To meet the demands of the growing population, it is imperative to develop sustainable and applicable ways to improve animal health and productivity. The first step in doing so is understanding the mechanisms that govern health and immune response and find ways to appropriately modulate them to improve animal health and productivity. The objective of this study is to use multi-omic techniques to understand breed-specific immune responses in sheep that may contribute to their resistance and susceptibility to parasites and to a greater extent, immunocompetence. Known parasite-resistant breed, St. Croix (STC) were compared to parasite susceptible Suffolk (SUF) sheep for …

Swarm Of One: Self-Organization In Multicellular Robots, Trevor Riley Smith

Graduate Theses, Dissertations, and Problem Reports (ETD)

Designing robots traditionally relies on centralized, top-down methods that constrain adaptability and resiliency, producing systems optimized for specific tasks. However, such systems often fail in complex environments, such as extraterrestrial, disaster, or natural settings, where designers cannot foresee every scenario a robot may encounter. Achieving autonomy in these contexts requires robots that are resilient and capable of adapting to the unknown. Multicellular organisms exhibit this adaptability through self-organization, where complex structures and behaviors emerge from local interactions among their cells. Such organisms can be viewed simultaneously as a single entity and a swarm of cooperating cells, i.e., a Swarm-of-One.

This …

Does Diverse Seeding Adoption Reduce Production Costs? The Case Of Diverse Forage Seeding Strategies In West Virginia., Ivan Andres Wong

Graduate Theses, Dissertations, and Problem Reports (ETD)

Feed and harvested forage represent a major share of production costs in Appalachian cow-calf systems, where a pronounced summer forage gap forces producers to rely on purchased hay. This thesis evaluates whether adopting diverse forage seeding strategies reduces production costs relative to the existing pasture management practice, accounting for the spatial heterogeneity of West Virginia’s production environments. The analysis proceeds in two steps. First, an unsupervised K-means clustering of 14 farms enrolled in the NRCS Conservation Innovation Grant field trial groups the farms into two agroclimatic clusters, Highland-Cool and LowlandWarm, using elevation, growing-season Growing Degree Days, growing-season precipitation, and soil …

Dielectrophoretic Biomarkers For Cancer Detection And Cell Analysis: Integrating Microfluidics And Ai, Raphael Olumide Oladokun

Graduate Theses, Dissertations, and Problem Reports (ETD)

Pancreatic ductal adenocarcinoma (PDAC) carries a five-year survival rate of 13%, driven largely by late-stage diagnosis and the absence of sensitive noninvasive screening tools. Current diagnostic methods lack the sensitivity to detect disease-associated systemic changes at early and precancerous stages, representing a critical clinical gap. This dissertation addresses the central hypothesis that PDAC and its pathological subtypes induce measurable, disease-associated electrophysiological changes in circulating PBMCs that can serve as correlative liquid-biopsy biomarkers. To test this hypothesis, this work asks: can dielectrophoresis (DEP), impedance and zeta potential analysis with AI-assisted classification distinguish benign pancreatic conditions (non-malignant control), precancerous lesions (primarily IPMNs), …

Preliminary Analysis Of Soil Volatile Organic Compounds As Soil Health Indicators, Thorne Taul

Graduate Theses, Dissertations, and Problem Reports (ETD)

Over the past decade, soil health (SH) has gained popularity due to the novelty of sustainable soil use related to human wellbeing. Soil health describes the overall quality of soil and its capacity to function over time. As interest in SH grows, researchers continue to explore new soil health indicators that can improve knowledge on how soil health is assessed and managed. Some indicators directly reflect soil health status, and some indirectly relate to processes and functions occurring within the soil. Volatile organic compounds (VOCs) are naturally occurring compounds that play a role in multiple soil processes. They are associated …

Development Of Enhanced Dingo Optimization Algorithm For Distribution Network Reconfiguration, Samson Oladayo Ayanlade, Ignatius Kema Okakwu, Richard Oladayo Olarewaju, Ayodeji Akinsoji Okubanjo, Joseph Bukola Samson, Oluwadare Adepeju Adebisi, Emmanuel Idowu Ogunwole, Oluwadare Olatunde Akinrogunde

Mansoura Engineering Journal

This paper presents the Enhanced Dingo Optimization Algorithm (EDOA) to reconfigure the radial distribution network with the long-standing problem of active power loss minimization while maintaining a stable and acceptable voltage profile. EDOA enhances the standard Dingo Optimization Algorithm (DOA) through the integration of adaptive search control, elite-guided local refinement, and crossover-based diversity preservation mechanisms tailored for distribution network reconfiguration problems. The proposed method has been tested on two test systems: the IEEE 33-bus standard as well as the real-world Ayepe 34-bus distribution feeder in Ibadan, Nigeria. In the IEEE 33-bus test system, EDOA reduces active power loss by 56.00%; …

Finite Element Analysis Of Oxygen Transport In Tissue And Capillaries With A Second Order Metabolic Consumption, Vikash Ramcharitar, Sreedhara Rao Gunakala, Reddappa Bandi, Donna M. G. Dyer, Jagdesh Ramnanan

Mansoura Engineering Journal

This study presents a numerical investigation into the oxygen transport through a Krogh-type capillary-tissue model with second-order tissue metabolism. The governing non-linear equations are solved by implementing a Galerkin finite element method with a fixed-point iteration scheme. The resulting code and solution algorithm were successfully validated against available analytical solutions for zeroth- and first-order kinetics, showing very good agreement. Numerical simulations indicated, that under the parameters investigated, relative to zeroth- and first-order kinetics, a second-order metabolism leads to less overall oxygen consumption within the tissue phase. This yields higher oxygen partial pressures at the outer tissue boundary and higher oxygen …

The Effect Of Second-Order Slip In Hydromagnetic Eyring-Powell Flow: A Bvp4c Study, Ch. Maheswari, B. Naga Lakshmi, G. Dharmaiah, R. Mohana Ramana, B. Reddappa

Mansoura Engineering Journal

The steady-state Eyring–Powell boundary layer fluid flow past a moving surface is examined, considering various parameter implications. These include the wall mass transfer parameter (0.5  C   2.0), the magnetic field parameter (5  Mn 20), the velocity ratio parameter (0.1  λ1  0.7), Eyring-Powell fluid parameters (0.5 K 2 & 0.1 E 0.4), as well as the first-order velocity slip (0.1 γ 1.1) and second-order velocity slip (0.1 δ  0.4). By employing appropriate similarity transformations, the PDEs are reduced to a set of non-linear ODEs, which are subsequently solved using the bvp4c numerical technique and, an implication of key factors on shear …

Finite Element Analysis Of Multiscale Fgm Plate Using Modified Third Order Shear Deformation Theory, Ravi Kumar, Ajay Kumar

Mansoura Engineering Journal

Present paper aimed to examine the static and dynamic response of a carbon nanotube (CNT) infused multiscale functionally graded (FGM) rectangular plate structure. A third-order shear deformation theory (TSDT) based on Reddy’s formulation with enforcing negligible transverse shear stresses at the outermost surface of the plate, is used. A nine-noded isoparametric element with 13 displacement variables per node is numerically investigated for finite element analysis. Using the Halpin-Tsai and homogeneous equation the elastic behaviour of the final material is obtained. The current result is first checked against a previously published result to validate the present mathematical model. The finite element …

Optimal Design Of Concrete Canal Section For Minimizing Overall Costs Using Artificial Ecosystem Optimization, Aya M. Elkhouly, Hamdy A. El-Ghandour, Tharwat Sarhan, Mahmoud E. Abd-Elmaboud

Mansoura Engineering Journal

The optimal design of concrete-lined canals represents a critical challenge in hydraulic engineering, requiring simultaneous minimization of construction and operational costs while satisfying hydraulic performance constraints. This study presents a novel optimization framework that utilizes the Artificial Ecosystem-based Optimization (AEO) algorithm for determining optimal canal cross-sectional dimensions. The proposed methodology comprehensively considers excavation costs, lining expenses, and water losses due to seepage and evaporation while ensuring hydraulic efficiency through velocity and Froude number constraints. The AEO algorithm mimics natural ecosystem processes through production, consumption, and decomposition phases to efficiently explore the design space and converge to global optima. The framework …

Frequent Pattern Mining With Optimized Gated Recurrent Units For Automated Waste Classification Systems In Smart Cities, Anupriya Garg, Ratish Kumar

Mansoura Engineering Journal

Efficient waste segregation into biodegradable and non-biodegradable categories is a crucial requirement for sustainable smart city development. While Convolutional Neural Network (CNN)-based approaches have demonstrated promising performance, their high architectural complexity and limited adaptability often restrict real-time deployment. This paper proposes BnBCNet, a lightweight waste classification framework that reformulates image-based waste segregation as a sequential learning problem using Gated Recurrent Units (GRUs). To further enhance classification performance, Grey Wolf Optimization (GWO) is employed for hyperparameter optimization, while Adam optimizer is used for network weight updates. Experiments conducted on a publicly available waste segregation image dataset demonstrate that the proposed BnBCNet …

Federated And Explainable Spiking Neural Networks For Fair And Privacy-Preserving Nail Disease Diagnostics, Ch Pavani Reddy, Krishnanaik Vankdoth

Mansoura Engineering Journal

Automated nail disease diagnostics provide a non-invasive pathway for identifying underlying systemic health conditions; however, conventional centralized deep learning approaches often raise concerns related to privacy, fairness, and interpretability. Although the original NeuroNail-SNN framework demonstrated an energy-efficient and edge-ready diagnostic solution, its broader clinical adoption remained limited by unresolved trust, transparency, and ethical considerations. In this study, we propose the Federated and Explainable NeuroNail-SNN, which extends the original spiking neural architecture by integrating federated learning (FL), explainable artificial intelligence (XAI), fairness evaluation, and uncertainty quantification within a unified framework. Federated learning enables decentralized model training across hospitals and mobile clinics …