Digital Commons Network^™

An Investigation Of Paleoclimate Through A Sedimentological Lens In The Gulf Of Mexico And Western North Pacific, Sarah Monica

Dissertations

This dissertation explores the use of sediment cores as proxy data for the reconstruction of paleoclimatic and environmental conditions. Human-caused climate change is leading to dramatic shifts in the global climate. As the instrumental record of climate is relatively short compared to the amount of time Earth has experienced weather, this work aims to extend our knowledge of climate beyond the instrumental record, thereby improving our holistic understanding of the climate system. In Chapter one, sediment cores from the central Texas inner shelf are used to produce a record of intense tropical cyclone (TC) activity over a ~4500-year period. X-Ray …

Evaluation Of The Suitability Of Tomato (Solanum Lycopersicum L.) Accessions Grown In A Vertical Container System, Darel Kenth S. Antesco, Lourdes Edano Ma., Renerio Gentallan Jr, Roselyn F. Paelmo, Annalissa L. Aquino

The Philippine Agricultural Scientist

Growing tomatoes in vertical container systems such as hanging pots presents a promising production system for urban gardening. However, horticultural selections of tomato with traits suited for this system are still limited. The success of different genotypes grown in this system depends on the combination of morphological characters, including plant form, vegetative growth, and root development, as well as yield. This study assessed the morphological characters of 10 tomato accessions to identify those with ideal traits for this system. Results showed that some accessions possess unique traits that enhance their performance in hanging pots. Among the 10 accessions, T2 from …

Gastropod Assemblage And Biofilm Abundance On Rocky Shores Of Southern Antique, Panay, Philippines, Mia Jane D. Geneblaza, Karen A. Villarta-Lane

The Philippine Agricultural Scientist

The gastropod assemblage and biofilm abundance, measured as chlorophyll a levels, were examined at 3 rocky shore sites in southern Antique, Panay, Philippines, in February 2024 to address significant gaps in current knowledge and help better understand the ecological dynamics of rocky intertidal zones. A total of 14 families comprising 22 genera and 31 species were identified from the area. The family Littorinidae was the most speciesrich, comprising 9 species. Among these, the scabra periwinkle Littoraria scabra (Philippi 1846) was the most abundant, comprising on average 50.17% of the total abundance across sites. Other identified gastropod families included Muricidae (5 …

Non-Target Organisms And Terrestrial Arthropod Biodiversity In The Corn Agroecosystem: Bt Corn Beyond Acb Pest Management [Review Article], Ireneo L. Lit Jr., Merdelyn T. Caasi-Lit, Kristine O. Abenis

The Philippine Agricultural Scientist

Assessments of risks of modern biotechnology products to non-target organisms (NTOs) are required before a product can be registered and allowed for commercial production. For Bt-engineered plants, data from such studies become part of environmental risk or impact assessments that are reviewed before a hybrid can be registered and allowed for commercial planting. The approaches and methods vary among countries and researchers. Consequently, the choice of species and groups of organisms or guilds that are monitored or studied may range from narrow to broad and encompassing. The inevitability of adverse changes in agroecosystems, including those that involve the corn crop, …

Characterization Of Adult Emergence Pattern And Eclosion Behavior Of The Asian Corn Borer, [Ostrinia Furnacalis (Guenée)] (Lepidoptera: Crambidae), Under Laboratory And Field Conditions In The Philippines, Marcela M. Navasero, Gelyn D. Sapin, Merdelyn T. Caasi-Lit, Wilson N. De Panis, Randolph N. Candano, Maricon Dp Javier, Jethro M. Navasero, Meijei T. Bangangao

The Philippine Agricultural Scientist

This study focuses on understanding the emergence pattern of Asian Corn Borer (ACB), Ostrinia furnacalis (Guenee), in the Philippines vital in crafting integrated pest management schemes, including insect resistance management against pesticides and Bt protein. Although ACB adults are known to be nocturnal and primarily emerge at night, identifying critical windows of emergence for targeted management actions is essential. In the experiments conducted, the scotophase period is the preferred time for adult emergence under laboratory conditions with 248 adults (80.52%) and 60 adults (19.48%) in the photophase period. A similar trend was also observed under field cage conditions with ACB …

Movement Of The Different Larval Stages Of Asian Corn Borer, Ostrinia Furnacalis (Guenée), On Conventional Corn Hybrid Under Field Caged Conditions, Merdelyn T. Caasi-Lit, Mark Bryan A. Lontoc, Victor V. Alpuerto, Abigail P. De Leon, Jenifer Bigkas-Naungayan, John David C. Cortez, Edwin A. Benigno

The Philippine Agricultural Scientist

Larval dispersal is crucial in pest population establishment in crop fields. The movement of Ostrinia furnacalis (ACB) larvae within corn plots was observed to determine which factors affect larval dispersal in the field. Conventional corn plots (3 rows, 0.70 m apart x 9 plants, 0.20 m apart) were artificially infested with different combinations of larval age and pest density. The distributions of larvae were recorded through destructive sampling at 4, 8, 12, and 16 days after infestation (DAI). The behavior of ACB from the time the hatching larvae break open the egg mass shell to ballooning to start actual movement …

Hardening The Economical Acquisition Of Intersection Data To Improve System Integrity, Anuska Das, Walt Fehr, Andrew Balmos, Mark Goodfriend, Yaguang Zhang, Richard O. Ajagu, James V. Krogmeier, Samuel Labi

Center for Connected and Automated Transportation

Modern traffic management systems increasingly depend on real-time Signal Phase and Timing (SPaT) data generated by Traffic Signal Controllers (TSCs) to support safety, mobility, and emerging connected-vehicle applications. The applications of SPaT outputs require timely and reliable access to such data from the traffic signal controllers. However, these controllers are safety-critical infrastructure and exposing them to external networks introduces significant cybersecurity and operational risks. Thus, there is a need for mechanisms that provide secure, low-latency access to SPaT data without compromising controller integrity. The information flow methods that are used traditionally for connected and automated vehicle (CAV) environments have several …

News From Hope College, Volume 57.2: Winter 2025, Hope College

News from Hope College

No abstract provided.

Advancements In Sinkhole Remediation: Field Data-Driven Sinkhole Grout Volume Prediction Model Via Machine Learning-Based Regression Analysis, Bubryur Kim, Yuvaraj Natarajan, K. R. Sri Preethaa, V. Danushkumar, Ryan Shamet, Jiannan Chen, Rui Xie, Timothy Copeland, Boo Hyun Nam, Jinwoo An

Civil Engineering Faculty Publications

Sinkhole formation poses a significant geohazard in karst regions, where unpredictable subsurface erosion often necessitates costly grouting for stabilization. Accurate estimation of grout volume remains a persistent challenge due to spatial variability, site-specific conditions, and the limitations of traditional empirical methods. This study introduces a novel machine learning-based regression model for grout volume prediction that integrates cone penetration test (CPT)-derived Sinkhole Resistance Ratio (SRR) values, spatial correlations between CPT and grouting points (GPs), and field-recorded grout volumes from six sinkhole sites in Florida. Three data transformation methods, the Proximal Allocation Method (PAM), the Equitable Distribution Method (EDM), and the Threshold-based …

Upright-Net+: Enhanced Learning Of Upright Orientation For 3d Point Clouds, Xufang Pang, Feng Li, Hongjie Zhuang, Ning Ding, Xiaopin Zhong, Shengfeng He, Wenxi Liu, Bo Jiang

Research Collection School Of Computing and Information Systems

Automatic 3D shape analysis is heavily influenced by the pose of input 3D models, as the continuous nature of pose space introduces complexities that usually exceed the encoding capacities of standard deep learning frameworks. To tackle this challenge, we present Upright-Net+, an enhancement of our previous model, Upright-Net, specifically developed for estimating upright orientation in 3D point clouds. Our approach is grounded in the design principle that ”form ever follows function,” treating the natural base of an object as a functional structure that stabilizes it in its typical pose, influenced by physical laws and geometric properties. We reformulate the continuous …

Iostom: Offline Imitation Learning From Observations Via State Transition Occupancy Matching, Quang Anh Pham, Brahmanage Janaka Chathuranga Thilakarathna, Tien Mai, Akshat Kumar

Research Collection School Of Computing and Information Systems

Offline Learning from Observations (LfO) focuses on enabling agents to imitate expert behavior using datasets that contain only expert state trajectories and separate transition data with suboptimal actions. This setting is both practical and critical in real-world scenarios where direct environment interaction or access to expert action labels is costly, risky, or infeasible. Most existing LfO methods attempt to solve this problem through state or state-action occupancy matching. They typically rely on pretraining a discriminator to differentiate between expert and non-expert states, which could introduce errors and instability—especially when the discriminator is poorly trained. While recent discriminator-free methods have emerged, …

Cssa-Fusion: Channel Selective And Spatial Alignment Infrared-Visible Image Fusion, Zhen Li, Zhi Zeng, Zhongrui Xiao, Ming Wen, Zhiyuan Zhang, Yibin Tian

Research Collection School Of Computing and Information Systems

Infrared-visible image fusion aims to integrate complementary information from two modalities to generate images with enriched semantic content. However, existing methods often neglect two critical aspects: the design of a local–global feature enhancement architecture and spatial alignment. To address these challenges, we propose Channel Selective and Spatial Alignment Fusion (CSSA-Fusion), a novel framework composed of two synergistic modules. The first is a selective channel and redundancy suppression module, which introduces a dual-branch selective channel attention mechanism to jointly capture local saliency and global channel importance for enhanced feature representation, and an informativeness–redundancy separation strategy to suppress redundant information while preserving …

Contx: Scene Context Prediction Via Context Bank And Layout Perception, Jingxin Liang, Yangyang Xu, Haorui Song, Yuan Lu, Yuhui Deng, Yiyi Long, Yan Huang, Shengxin Liu, Jianbo Jiao, Shengfeng He

Research Collection School Of Computing and Information Systems

Scene context prediction, which seeks to infer unknown contextual information from isolated object properties, currently faces limitations due to predominant reliance on pixel-wise supervision that overlooks real-world context priors. To address this, we present ContX, a context-prior-driven, coarse-to-fine model. ContX distinctively integrates explicit linguistic-contextual knowledge in two key ways. First, it proposes a linguistic guided context bank, leveraging linguistic-statistical contextual data to guide the rationality of segmentation shapes and foster meaningful inter-class contextual interactions. Second, ContX augments contextual comprehension by correlating layouts with linguistic descriptions, enhancing layout perception through a multi-modal strategy. Comprehensive experiments demonstrate ContX's superiority and versatility, outperforming …

Dendrimer Gel Nanoparticles With Dual-Targeting Of Upar And Ribonucleotide Reductase R2 Influence Necrosis, Apoptosis, And Cell Cycle Arrest In Triple-Negative Breast Cancer, Hsin Yin Chuang, Yue-Wern Huang, Hu Yang

Biological Sciences Faculty Research & Creative Works

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with limited treatment options due to the absence of estrogen (ER), progesterone (PR), and human epidermal growth factor receptor 2 (HER2) receptors. We previously developed highly adaptable polyamidoamine (PAMAM) dendrimer-based gel nanoparticles (GDP-uPA/GTI) with dual-targeting capabilities against urokinase-type plasminogen activator receptor (uPAR) and ribonucleotide reductase R2 (R2) to achieve selective antitumor effects. In this study, we investigated the underlying mechanisms of GDP-uPA/GTI-induced tumor inhibition. In vivo, GDP-uPA/GTI significantly downregulated R2 expression and activated caspase 8 and caspase 3, indicating caspase-dependent apoptosis. Histological evaluation of major organs revealed no observable toxicity. …

General Test-Time Backdoor Detection In Split Neural Network-Based Vertical Federated Learning, Shunjie Yuan, Xinghua Li, Xuelin Cao, Haiyan Zhang, Robert H. Deng

Research Collection School Of Computing and Information Systems

As a new distributed machine learning framework, vertical federated learning (VFL) has been widely applied in the industry. However, recent studies have demonstrated that VFL faces serious challenges from backdoor attacks, which significantly hinder its further development. Although a few studies have focused on defending against VFL backdoor attacks, these defenses either do not consider the latest attack methods or show limited effectiveness. Moreover, most existing backdoor defense efforts primarily focus on backdoor attacks in horizontal federated learning (HFL) and centralized learning. Due to the unique architecture of VFL models, these methods cannot be directly applied to backdoor defense in …

Kpiroot+: An Efficient Integrated Framework For Anomaly Detection And Root Cause Analysis In Large-Scale Cloud Systems, Wenwei Gu, Renyi Zhong, Guangba Yu, Xinying Sun, Jinyang Liu, Yintong Huo, Zhuangbin Chen, Jianping Zhang, Jiazhen Gu, Yongqiang Yang, Michael R. Lyu

Research Collection School Of Computing and Information Systems

To ensure the reliability of cloud systems, their runtime status reflecting the service quality is periodically monitored with monitoring metrics, i.e., KPIs (key performance indicators). When performance issues happen, root cause localization pinpoints the specific KPIs that are responsible for the degradation of overall service quality, facilitating prompt problem diagnosis and resolution. To this end, existing methods generally locate root-cause KPIs by identifying the KPIs that exhibit a similar anomalous trend to the overall service performance. While straightforward, solely relying on the similarity calculation may be ineffective when dealing with cloud systems with complicated interdependent services. Recent deep learning-based methods …

Fl-Cdf: Collaborative Defense Framework For Backdoor Mitigation In Federated Learning, Haiyan Zhang, Xinghua Li, Yinbin Miao, Shunjie Yuan, Mengyao Zhu, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Federated learning (FL) is vulnerable to backdoor attacks due to its distributed nature. Existing unilateral defense mechanisms often fail against persistent attack strategies, primarily due to their limited perspectives. To address the challenge of model misclassification on the server side caused by overlooked model similarity drift, and gradient misjudgment on the client side caused by semantic learning imbalances across classes, this paper proposes a collaborative defense framework for federated learning, termed FL-CDF. FL-CDF establishes an end-to-end defense through a bidirectional client-server collaboration mechanism. Specifically: (1) On the client side, an adversarial perturbation-based malicious neuron detection module is introduced. This module …

Towards Inclusive Digital Futures Of Cultural Heritage: Insights From A Critical Discourse Analysis Of Unesco Dialogues, Shiqing Huang, Keng Siau, Xiaoting Chen

Research Collection School Of Computing and Information Systems

Digital technologies are shaping many aspects of cultural heritage, but very little research has examined the implications of digital transformation. Drawing on concepts from Fairclough’s three-dimensional critical discourse analysis, this research examines the discourse using seven online dialogues (available on the UNESCO website) between 18 professionals who have different backgrounds and cultures to identify social practices related to the digital transformation of cultural heritage. We identify four digital transformation discourse types in professional dialogues: documentation, management, interpretation, and interaction. We also identify seven main groups: memory institutions including libraries, archives, and museums (LAMs), governments, international organizations, art and creative supporters, …

Cropcapsnet: Enhanced Capsule Network For Crop Disease Classification, Juan Qin, Linfan Deng, Cong Li, Junjie He, Haibo Pen, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

The prevention and treatment of crop diseases are crucial for the development of smart agriculture. The classification of crop diseases based on deep learning for early disease monitoring and control has become the mainstream direction of research. This paper proposes a novel deep learning model called ”CropCapsNet”, which combines Squeeze-and-Excitation Inception (SE-Inception) module and has improved capsule structure for crop disease classification. The network first extracts shallow features of input samples through double-layer convolution, then uses SE-Inception to achieve deep multi-scale feature acquisition, and finally outputs classification results through an improved capsule structure. SE-Inception adds Squeeze-and-Excitation(SE) attention after each multi-scale …

Bone Void Filler Materials For Augmentation In Comminuted Fractures: A Comprehensive Review, S. Sina Mohammadi, Sunjeev S. Phull, B. Sonny Bal, Mark R. Towler

Chemical and Biochemical Engineering Faculty Research & Creative Works

Comminuted fractures, characterized by multiple bone fragments, present significant challenges in orthopedic surgery. Effective treatment often requires augmentation techniques to enhance fixation stability and promote bone regeneration. This review explores the application of bone void filler materials, including autografts, allografts, polymethyl methacrylate (PMMA), and synthetic bone substitutes such as calcium phosphate ceramics and bioactive glass, in managing comminuted fractures. Autografts are the gold standard due to their superior osteogenic potential but are limited by donor site morbidity and availability. Allografts mitigate these issues but face concerns regarding immunogenicity and reduced biological activity. PMMA, widely used for structural augmentation, provides immediate …

Resolving The Energy Efficiency Paradox: Leveraging Benefits For Saudi Arabia’S Building Sector, Fateh Belaïd, Mahelet G. Fikru

Economics Faculty Research & Creative Works

This article examines the economics of energy efficiency in Saudi Arabia's building sector, centering on the "energy efficiency paradox," the observed under adoption of cost-effective technologies despite their clear benefits—such as reducing energy use and associated emissions. We show that a mix of market barriers and behavioral biases—including split incentives, informational gaps, and the value-action gap—constrains adoption of energy-efficient solutions. Recent modeling suggests that improved air-conditioning labels could save up to 80 TWh of electricity annually, though rebound effects may erode some of these gains. Our analysis shows that tackling market failures is necessary but not enough. Strong building codes …

Analysis Of Progressive Failure From Hydrofracturing Tests In Boise Sandstone, Alejandra Sanchez Arizmendi

Boise State University Theses and Dissertations

Sealed borehole laboratory hydrofracturing tests with acoustic emission monitoring were used to investigate the acoustic emission activity, damage thresholds, progressive failure, and tensile strength as a function of injection rate for twenty Boise Sandstone specimens. Four injection rates 15.7, 31.4, 62.8, and 125.6 mL/minute were used in the testing.

Increasing the injection rate initially increased and then decreased acoustic emission activity. The highest injection rate resulted in the lowest number of AE hits. The acoustic emission data was collected via two ultrasonic sensors. The highest average AE hits at the 31.4 mL/min injection rate was 613 and the lowest average …

Integrating Neural Control And Finite Element Muscle Modeling: A Predictive Neuromusculoskeletal Framework For Investigating Neurodegenerative Disease, Colton Babcock

Boise State University Theses and Dissertations

Neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) disrupt the interplay between neural control and musculoskeletal function, leading to progressive motor decline and loss of force control and production. Traditional experimental and clinical approaches are limited in their ability to capture the multiscale, nonlinear mechanisms driving these impairments. This dissertation presents a novel predictive neuromusculoskeletal (NMS) modeling framework that integrates physiologically accurate motor neuron dynamics with three-dimensional finite element (FE) muscle models. Using subject-specific muscle geometry and fiber architecture of the tibialis anterior, the model simulates realistic force generation under both healthy and ALS-affected conditions. The neural component incorporates a …

Harnessing The Mechanical And Gelation Behavior Of Tyramine-Substituted Silk Fibroin Bioinks Through Integration Into A Modular And Low-Cost 3d Bioprinting Platform, Sourjjadeepta Biswas

Boise State University Theses and Dissertations

3D printing has become an indispensable tool in research labs across the globe. Yet, when it comes to bioprinting with custom or sensitive bioinks, accessible options remain limited. Commercial systems are often prohibitively expensive and restricted to proprietary materials, while DIY setups frequently lack the precision and compatibility needed for biologically relevant printing. Moreover, tunable bioinks that align with real-world biological constraints are still hard to come by. To address these gaps, we developed a modular, low-cost bioprinting platform tailored for tyramine-substituted silk fibroin (TA-SF) hydrogels-biocompatible materials known for their rapid gelation, tunable stiffness, and potential to support cell differentiation …

Deep Reinforcement Learning For Adaptive Mesh Refinement: A Training Parameter Study, Antone Chacartegui

Boise State University Theses and Dissertations

Adaptive mesh refinement (AMR) enables efficient solution of partial differential equations by dynamically allocating computational resources to regions requiring higher resolution. It has been demonstrated that deep reinforcement learning agents could learn effective mesh adaptation policies. However, practical deployment requires a systematic understanding of how training parameters affect performance across diverse computational scenarios.

This thesis presents a parameter optimization study analyzing the relationship between deep reinforcement learning for adaptive mesh refinement (DRL-AMR) training parameters and model performance. We implement an independent DRL-AMR system with a discontinuous Galerkin solver and actor-critic (A2C) algorithm for one-dimensional linear advection. The study explores four …

A Machine Learning Approach To Estimating Agricultural Return Flows: A Case Study Of The Twin Falls Canal Company In Idaho, Kendra Alise Figgins

Boise State University Theses and Dissertations

Idaho is the third largest water user in the United States due to its extensive agriculture industry (Murray, 2018). Irrigation has allowed farmers to take advantage of the warm climate and rich soils of the Eastern Snake River Plain to develop one of the nation’s most productive agricultural regions. This area now contains one-third of Idaho’s irrigated acres (Idaho Department of Water Resources, 2024). It also contains critical hydrologic connections between the Snake River and the Eastern Snake Plain Aquifer.

Farming practices in Idaho have gradually shifted over time. Warming temperatures and increased irrigation efficiency are allowing an expansion of …

Understanding Students’ Psychological Needs Satisfaction And Frustration In A Gamified Biology I Course: A Mixed Method Study, Shakiyla Huggins

Boise State University Theses and Dissertations

The purpose of this study was to examine how gamification impacts student motivation in higher education through the framework of Self-Determination Theory (SDT). While previous research has linked gamification to engagement, this study addressed a gap by directly measuring students' satisfaction and frustration with their basic psychological needs — autonomy, competence, and relatedness — in a gamified undergraduate Biology I course. A convergent parallel mixed methods design was employed. Quantitative data were collected using the Basic Psychological Need Satisfaction and Frustration Scale (BPNSFS) from 22 students who completed both pre- and post-course surveys. Qualitative data were drawn from student reflection …

Sustainability Assessment Of Ground Improvement Techniques: A Life Cycle Analysis Of Lime, Cement, Geopolymers And Biopolymers For Roadway Application, Jillian R. Frost

Boise State University Theses and Dissertations

Ground improvement design (GID) is a vital part of constructing safe, long-lasting infrastructure. Traditionally, this is done with a chemical additive such as lime or cement that is mixed with in-situ soil to improve soil parameters such as compressibility, and strength. These stabilizers produce high amounts of carbon emissions. As a response, engineers and researchers are beginning to develop new stabilization methods using biopolymers and geopolymers in hopes of achieving comparable soil stability results as well as reduced environmental impacts.

This thesis work integrates a life-cycle analysis (LCA) and life-cycle-cost-analysis (LCCA) framework to evaluate and compare four soil stabilizer types: …

A Comparative Study Of Novel Methods For Variances, Taoreed Muritala

Boise State University Theses and Dissertations

Understanding variability is fundamental to knowledge advancement across various disciplines, including manufacturing, clinical research, biology and genetics. Population variances play a crucial role in processes such as quality control, treatment evaluation and the interpretation of biological mechanisms. However, statistical procedures for comparing variances across multiple populations remain less developed than those for comparing means. Classical omnibus tests, such as Bartlett's and Levene's, can detect overall variance heterogeneity but fail to identify which populations differ, thereby limiting their usefulness in multi-population analyses and increasing the risk of inflated family-wise error rates through repeated testing.

This thesis develops and evaluates three multiple …

The Effect Of Loading Conditions And Specimen Size On The Tensile Strength Of Ring-Shaped Specimens, Kolby Scott Mcclure

Boise State University Theses and Dissertations

This study introduces a custom-built direct tension testing apparatus designed for ring-shaped rock specimens. The apparatus applies a uniform axial load to the inner surface of the ring and is compatible with standard geotechnical testing equipment. Using this device, 176 granite specimens sourced from Horseshoe Bend, Idaho, were tested under three configurations: Brazilian, indirect ring tension, and direct ring tension. Only specimens exhibiting valid diametrical splitting failure modes (126 out of 176) were included in the final analysis. Direct tension tests yielded the lowest tensile strengths, followed by Brazilian tests, with indirect tension tests producing the highest values. While scatter …