Digital Commons Network^™

Llamoco: Instruction Tuning Of Large Language Models For Optimization Code Generation, Zeyuan Ma, Yue-Jiao Gong, Hongshu Guo, Jiacheng Chen, Yining Ma, Zhiguang Cao

Research Collection School Of Computing and Information Systems

Recently, combining the strength of large language models (LLMs) and Evolutionary Computation (EC) has shown promising results for addressing optimization problems. It typically involves either iterative next-step solution seeking or directly prompting LLMs to generate critical optimization codes. However, these methods often suffer from low computational efficiency, high sensitivity to prompt design, and a lack of domain-specific knowledge. We introduce LLaMoCo, the first instruction-tuning framework designed to adapt LLMs for solving optimization problems in a code-to-code manner. LLaMoCo features a comprehensive instruction set that includes code-style problem descriptions as input prompts and robust optimization codes from expert EC optimizers as …

No Groundwater, No Fish: The Critical Role Of Groundwater In Supporting Non-Glacial, Salmon-Bearing Rivers In South-Central Alaska, Tyelyn Brigino, Kai Rains, Edgar Guerron-Orejuela, Jacob Argueta, Syverine Bentz, Coowe Walker, Mark Rains

School of Geosciences Faculty and Staff Publications

Groundwater discharge plays an important role in the hydrologic and ecologic functioning of rivers including sustaining streamflow and related habitat year-round. Simultaneously, groundwater supports the increasing demands of people as the global population continues to grow. Balancing the needs of users becomes increasingly important as climate change introduces greater uncertainty in water resources and fisheries, especially for economically important anadromous species that depend on freshwater resources. We investigated the seasonal and regional variability of groundwater contributions to six non-glacial mainstem salmon-bearing rivers in south-central Alaska. We hypothesized that groundwater contributes more than half of the annual streamflow, and nearly all …

Non-Destructive Characterization Of Variously Colored Gypsum And Aragonite/Calcite Speleothems From The Cigalère Cave (Ariège, France), Martin Vlieghe, Johan Wouters, Gérald Fanuel, Jean-François Drion Du Chapois, Anne Gallez, Stéphane Pire-Stevenne, Gaëtan Rochez, Johan Yans

International Journal of Speleology

The Cigalère Cave is a 21 km-long karstic cave located in the Ariège Department, in the French Pyrenees, and underlies directly the Bentaillou Pb-Zn-Fe sulfide ores. The cave hosts abundant gypsum mineralizations, some of them exhibiting various colorations including blue, yellow, purple, orange and black. Due to strict preservation policies, these colored mineralizations have not been studied much. Here we propose a non-destructive characterization of five distinct gypsum or carbonate speleothem structures from the Cigalère, exhibiting different colorations. To comply with the preservation policies, no sample was taken from the cave, and all speleothems were analyzed in situ using portable …

Green Space: Investigation Into The Role Pocket Parks Play In Biodiversity And Human Well-Being, Jacalyn Speicher

Antioch University Dissertations & Theses

Natural communities and the birds that inhabit them are essential to the physical and emotional well-being of human residents in urban settings. The action research undertaken in this study addresses the social and environmental justice issues of equity, accessibility, and diversity afforded avian and human communities in the design of pocket parks within the urban landscape. As urbanization consumes the natural landscape, people and birds must adapt to the novel challenges of a changing environment. I studied 10 pocket parks identified in Center City, Philadelphia, Pennsylvania between January and June 2026. This time frame spanned the seasons from winter to …

Aquatic Invasive Species Survey And Treatment On Lake Umatilla And Lake Celilo 2024-2025 Report, Jacob Rose, Gabriel E. Campbell

Center for Lakes and Reservoirs Publications and Presentations

Flowering Rush (Butomus umbellatus) is an invasive aquatic plant in the Pacific Northwest that threatens salmon habitat. The Center for Lakes and Reservoirs staff surveyed for this and other aquatic species from 2024 and 2025 in the Columbia River in Lake Umatilla and Lake Celilo. This document summarizes their survey efforts including their protocols, data, and small-scale removal efforts.

First Record Of Arrhenophagus Chionaspidis Aurivillius (Hymenoptera: Encyrtidae) Parasitizing White Mango Scale In Kenya: Its Potential Distribution And Prospects In Biocontrol, Francis Obala, Abdelmutalab G.A. Azrag, Rehemah Gwokyalya, Shepard Ndlela, Ingo Grass, Georg Petschenka, Sunday Ekesi, Samira A. Mohamed

All Peer-Reviewed Publications

The white mango scale, Aulacaspis tubercularis Newstead (Hemiptera: Diaspididae), is one of the most destructive pests of mango worldwide. Its current management in most of the invaded areas largely depends on the intensive use of chemical insecticides. Biological control using parasitoids represents one of the most effective and environmentally sustainable management options. However, in Kenya, one of Africa's leading mango-producing countries, no effective parasitoid species associated with A. tubercularis had previously been recorded. This study aimed to identify encyrtid parasitoids associated with A. tubercularis in Kenya, and to assess how bioclimatic factors influence their occurrence and habitat suitability. Mango leaves …

Reinterpreting Stochastic Optimal Control Under Ecological Uncertainty: Inferring Decision Urgency From Vegetation Biomass Dynamics, Komi Mensah Agboka, Tobias Landmann, Elfatih M. Abdel-Rahman

All Peer-Reviewed Publications

Stochastic optimal control provides a rigorous framework for systems subject to uncertainty, yet its operational use in ecological crisis contexts remains limited by interpretability. We reinterpreted a stochastic control formulation in which selected parameters emerged as indicators of decision urgency rather than normative preferences. Vegetation biomass was modeled as a stochastic stock subject to nonlinear loss driven by feeding pressure (e.g., desert locust activity) and multiplicative noise, with uncertainty represented by a time-varying volatility term that integrated extreme rainfall anomalies and conflict-related disruption. Rather than prescribing an optimal policy, we inverted the closed-form solution of the control problem to infer …

Impact Of Environmental Microparticles On Insect Olfaction, Steve B.S. Baleba, Danube K.N. Wandji, Yves H. Tchiechoua, Komi Mensah Agboka, Iman B. Hassaballa, Victor O. Omondi, Beatrice T. Nganso, Saliou Niassy, Souleymane Diallo, Merid N. Getahun

All Peer-Reviewed Publications

Terrestrial insects underpin key ecosystem services, including pollination, herbivory regulation, decomposition, nutrient cycling, and disease control. These functions depend on chemical communication that guides insects to food, mates, hosts, shelters, and oviposition sites while helping them avoid threats. Environmental microparticles, such as micro- and nanoplastics, tyre wear particles, soot, mineral dust, and agricultural residues, are now widespread across air, soil, vegetation, and indoor environments, exposing insects through contact, deposition, and ingestion. Growing evidence shows that these particles disrupt insect olfaction by adsorbing volatile compounds, blocking antennal sensilla, and interfering with receptor and neuronal processes. These disruptions impair foraging, mating, oviposition, …

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 …

Biosecure-Llm Framework: Protecting Llms From Cyberbiosecurity Threats And The Case For Independent Ai Safety Governance, Xavier-Lewis Palmer, Lucas Potter, Srdjan Lesaja, Sotirios Karathanasis, Mohammad Ghasemigol

Computer Science Faculty Publications

Large Language Models (LLMs) are becoming critical infrastructure in scientific, healthcare, and governmental contexts. As frontier AI laboratories increasingly partner with government agencies, a fundamental question arises: Who should control the safety and policy-enforcement layers that constrain model behavior? Current safety mechanisms (LLM guardrails) are typically designed for generic "harmlessness" and operate by detecting semantic patterns and refusing requests. However, they are inadequate governance instruments because they cannot implement auditable, domain-specific controls tied to external regulatory policy objects (e.g., control lists or rules governing personally identifying information). Even a perfectly aligned model is not able to express institution-specific policy without …

Attf-Gnn: An Attention-Based Multi-Omics Graph Neural Network With Modality Learning For Disease Subtyping, Sovon Chakraborty, Eleni Adam, Terry Stilwell, Harold Riethman, Desh Ranjan, Pratip Rana

Computer Science Faculty Publications

We propose AttF-GNN, an attention-based graph fusion strategy for diseases classification and subtyping. In multiomics analysis, not all types of molecular data are equally relevant for disease subtyping and considering all modalities equally may obscure discriminative signals and limit the effectiveness of predictive models by overlooking modality-specific contributions. Therefore, we design an attention-based multimodal GraphSAGE framework that can automatically emphasize the modalities providing the most relevant information for classification. At first, we have constructed three graphs using mRNA, RNA-seq and DNA methylation modalities, and train each omics with individual GraphSAGE encoders. Next, a unified intersection graph is formed using an …

An Investigation Of Federated Gnns Under Aggregation, Data Poisoning, And Differential Privacy For Icu Length-Of-Stay Prediction, Shakib Mahmud Dipto, Soumya Banerjee, Sandip Roy, Ahmad F. Al Musawi, Preetam Ghosh, Sachin Shetty, Pratip Rana

Computer Science Faculty Publications

Accurate prediction of ICU Length of Stay (LoS) is essential for clinical decision-making and healthcare resource management. Graph Neural Networks (GNNs), such as GraphSAGE, offer a natural fit by capturing patient data from Electronic Health Records (EHRs) through graph structures. However, the distributed and sensitive nature of this data raises both privacy and legal concerns regarding the aggregation and training of GNN models. This additionally leads to issues with data imbalance and model robustness. In this study, we perform an analysis of the Federated Graph Neural Network (GNN-FL) framework to enable decentralized learning on EHRs derived from the MIMIC-III dataset. …

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 …

Fumigant Toxicity Of Essential Oils From Chemotypes Of Curry Tree (Murraya Koenigii) Against Adult African Malaria Mosquito (Anopheles Gambiae Sensu Stricto), Clarence M. Mang’Era, Fathiya M. Khamis, Ahmed Hassanali, Paul O. Mireji

All Peer-Reviewed Publications

The toxicity of fumigant formulations of essential oils (EOs) from curry tree (Murraya koenigii (L.) Spreng (Rutaceae)) against African malaria adult mosquito (Anopheles gambiae sensu stricto (Giles, 1902) (Diptera: Culicidae: Culicini)) was evaluated to identify most effective chemotype of the curry tree against the mosquitoes. Ninety-two compounds (91.78%–96.47% coverage) in the EO profiles were identified, among which EO isolated from curry tree chemotype from Kibwezi was most toxic. This chemotype had a median lethal dose (LD50) of 3.6 × 10⁻³ mg/cm³ (95% CI: 2.70–4.58) and a lethal time (Ti50) of 1.91 ± 0.086 h (p < 0.001). The equivalent LD50 of EOs of chemotypes from Mombasa, Makindu, and Malindi in Kenya was 4.9 × 10⁻³, 6.6 …

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 …

Untrained Position-Encoded Multilayer Perceptron Network For Structured Illumination Microscopy Reconstruction, Sahil Sharma, Leonidas Zimianitis, Krishnendu Samanta, Balpreet Singh Ahluwalia, Joby Joseph, Dushan N. Wadduwage

Computer Science Faculty Publications

Structured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image …

Adaptive Self-Attention For Enhanced Segmentation Of Adult Gliomas In Multi-Modal Mri, Evan P. Savaria, Jiangwen Sun

Computer Science Faculty Publications

Every year there are an estimated 80,000–90,000 new glioma cases, highlighting the need for reliable imaging-based decision support. Although deep learning has improved tumor sub-region segmentation, many state-of-the-art models fail to fully capture complementary information across T1, T1Gd, T2, and FLAIR MRI modalities and often operate as “black boxes,” limiting physician trust when precise delineation is critical for surgical planning, radiation targeting, and treatment monitoring. To address these limitations, we propose AIMS, an Adaptive Integrated Multi-Modal Segmentation framework that maintains modality-specific feature streams and employs adaptive self-attention within a hierarchical CNN-Transformer architecture to prioritize and fuse multi-modal MRI features. We …

Sage: Spatially Aware Gene Selection And Dual-View Embedding Fusion For Domain Identification In Spatial Transcriptomics, Yi He, Yunpei Xu, Liqing Ding, Hong-Dong Li, Yaohang Li, Shaokai Wang

Computer Science Faculty Publications

Despite enabling high-resolution mapping of gene expression within tissues, spatial transcriptomics (ST) still faces challenges in accurately segmenting spatial domains due to complex tissue architecture and limitations of current methods. Most approaches rely on local spatial priors, lack gene-level interpretability, and fall short in capturing structure-discriminative genes or long-range functional relationships, limiting their ability to resolve biologically meaningful architectures. We present Spatially Aware Gene selection and dual-view Embedding fusion (SAGE), a unified and reproducible framework for domain identification in spatial transcriptomics that combines topic-driven gene selection with dual-view embedding fusion to address these gaps. SAGE integrates non-negative matrix factorization (NMF)-based …

Integration Of Hybrid Quantum-Neuromorphic Ai With Cloud, Edge, And High-Performance Computing Environments, Arun B. Prasad, Ajay Prasad, Dineshkumar Rajendran, Anurag Tiwari, T. Akilan, Islombek Khushvaktov

Computer Science Faculty Publications

The convergence of quantum computing, neuromorphic learning, and distributed cloud infrastructures has occurred very rapidly, and intelligent systems are now providing new opportunities, but the challenge of instability, complexity of orchestration, and noise sensitivity remains in the way of practical integration. The proposed work is based on a hybrid quantum and neuromorphic architecture, which is the integration of event-based neuromorphic adaptation and quantum-assisted global optimization, orchestrated by cloud-HPC. The architecture presents the thermodynamically regularized learning and resourceful task scheduling to the probabilistic search and the continuous local adaptation. Experimental evaluation across financial modeling, medical imaging, and physical system prediction shows …

Modeling Joint Visual Attention In Naturalistic Dyadic Interactions, Kuushini Thennakoon, Yasasi Abeysinghe, Bhanuka Mahanama, Vikas Ashok, Sampath Jayarathna

Computer Science Faculty Publications

Joint visual attention (JVA) provides important insight into how individuals coordinate attention during social interaction. Egocentric eye tracking enables the study of JVA in natural, multi-user settings. This work presents a multi-stage framework to identify and analyze JVA using egocentric video and gaze data. The approach consists of three steps: spatiotemporal tube-based visual similarity, gaze-guided object detection, and attention pattern analysis using the ambient–focal coefficient K. Results show that object-focused collaborative activities exhibit high JVA, with object detection capturing higher joint attention than visual similarity, whereas conversation-based or independent activities show lower and more fragmented joint attention. Analysis of K …

Analysis Theories On Artificial Intelligence, Chatgpt, Data Science, And Metaverse: The Case Of Digital Medicine, Yin Yang, Xingyun Liu, Jorge Luis Cuyubamba Dominguez, Yuan Fang, Wen Xie, Bairong Shen, Keng Siau

Research Collection School Of Computing and Information Systems

Healthcare organizations are increasingly adopting digital technologies, with Artificial Intelligence (AI), Data Science, and the metaverse driving significant advancements in smart healthcare. Al facilitates personalized medicine and efficient drug development, while Data Science enables predictive analytics and big data management, enhancing patient outcomes and healthcare quality. The metaverse introduces immersive training and telemedicine platforms, revolutionizing patient engagement and healthcare research. This study conducts' a scoping review of 6,171 articles, analyzing the transformational impact of AI, ChatGPT, Data Science, and the metaverse on healthcare. It highlights the benefits and risks of these technologies, identifies research gaps in their application within the …

Nondeterministic Polynomial-Time Problem Challenge: An Ever-Scaling Reasoning Benchmark For Llms, Chang Yang, Ruiyu Wang, Junzhe Jiang, Qi Jiang, Qinggang Zhang, Yanchen Deng, Shuxin Li, Shuyue Hu, Bo Li, Florian T. Pokorny, Xiao Huang, Xinrun Wang

Research Collection School Of Computing and Information Systems

Reasoning is the fundamental capability of large language models (LLMs). Due to the rapid progress of LLMs, there are two main issues of current benchmarks: i) these benchmarks can be crushed in a short time (less than 1 year), and ii) these benchmarks may be easily hacked. To handle these issues, we propose the ever-scalingness for building the benchmarks which are scaling over complexity against crushing, instance against hacking and exploitation, oversight for easy verification, and coverage for real-world relevance. This paper presents Nondeterministic Polynomial-time Problem Challenge (NPPC), an ever-scaling reasoning benchmark for LLMs. Specifically, the NPPC has three main …

Understanding Climate Change And Wellbeing Through Bhutan's Pursuit Of Gross National Happiness, Tashi Dorji

Theses: Doctorates and Masters

Climate change has evolved into one of the biggest global challenges of the twenty first century reshaping ecological systems and social conditions with wide-ranging implications for human wellbeing. Bhutan offers a distinct context to explore interactions between climate change and wellbeing as it remains guided by its wellbeing-centred development philosophy of Gross National Happiness (GNH). At the same time, it remains highly vulnerable to climate change despite being the only carbon negative country in the world. Nonetheless, the intersections between climate change and GNH have not been systematically explored.

This research aims to explore the relationship between climate change and …

Land Use And Land Cover Simulation Via Integrated Modelling With Gis Techniques For Sustainable Land Utilization Development In The Northeast Khong Subwatershed Of Thailand, ฺBanchongsak Faksomboon, Thipphaphone Keoviyavong

Applied Environmental Research

Land utilization is an important indicator of socioeconomic and environmental changes caused by both natural and man-made factors. Land use and land cover (LULC) simulation is a critical tool for monitoring and predicting LULC and is essential for sustainable development, land resource management and planning. The cellular automata (CA) Markov model is the basis for the current study’s prediction of LULC changes in the Northeast Khong Sub Watershed (NKSW). Landsat data from 2013 to 2023 were used to investigate LULC classification and determine the spatiotemporal distributions of LULC. In addition, LULC data from 2013 and 2023 were used to generate …

Heavy Metals And Organic Carbon In Sediments Of Seagrass Sediments Of Trang Province, Thailand, Siriporn Pradit, Pornthep Wirachwong, Thongchai Nitiratsuwan, Sujaree Bureekul, Supraewpan Lohalaksanadech, Thawanrat Kobkeatthawin, Monticha Jirajaras, Prakrit Noppradit, Sanya Sirivithayapakorn

Applied Environmental Research

In this study, the accumulation of heavy metals (Cd, Cr, Cu, Fe, and Pb) in water, suspended sediments, sediments, and seagrass in Kalase Bay, Trang Province, Thailand, during the 2024 dry season was investigated. These findings indicate that the enrichment factor (EF) for all the metals was less than 1, suggesting that anthropogenic contamination is not a significant concern in the area. The translocation factor (TF) values were less than 1 for all the metals except Cu, whose TF was greater than 1; however, these values were not statistically significant, indicating limited phytoextraction capacity. The bioconcentration factor (BCF) values for …

Carbon Stock Assessment In Sonneratia Apetala Afforested Mangroves : A Case Study From Cox’S Bazar, Bangladesh, Mohammad Ismail, Sayed Abu Johany, Tanmoy Dey, Zakia Sultana Teasa, Trishna Das, Prabal Barua, Tonima Hossain, Sajib Ahmed

Applied Environmental Research

Sonneratia apetala is a key species for mangrove afforestation projects in Bangladesh and plays a crucial role in ecosystem restoration and carbon sequestration. The research on how the total carbon stock (TCS) varies across S. apetala plantations in Cox’s Bazar remains limited. Thus, in this study, the biomass and soil carbon stock were quantified across nine S. apetala afforested sites in Cox’s Bazar to assess spatial variation and influencing factors. The total biomass carbon (TBC) was calculated by summing the aboveground (AGBC) and belowground (BGBC) biomass carbon, while the soil organic carbon stock (SOCS) was determined from the 0–40 cm …

Seasonal Wastewater Monitoring And Quantitative Microbial Risk Assessment Of Escherichia Coli In A University Wastewater System, Pathum Thani, Central Thailand, Natsima Tokhun, Natagarn Tongphanpharn, Weerawat Ounsaneha, Montip Jankeaw, Wiriyabhorn Klomsungcharoen, Patsara Wongsudi, Yawanart Ngamnon, Cheerawit Rattanapan, Kwang Mo Yang

Applied Environmental Research

In this study, the physicochemical properties of wastewater were characterized, and a quantitative microbial risk assessment (QMRA) of E. coli was conducted in the wastewater treatment system of the Faculty of Science and Technology, Valaya Alongkorn Rajabhat University, under the Royal Patronage, Pathum Thani, Thailand (VRU-SciTech). This study was conducted because the current water-quality assessment does not include the QMRA or evaluate the occupa-tional and incidental public risk of infection. Over a one-year monitoring period, this study quantified wastewater generation (~6.09 L person-1 day-1; ~3.21 × 103 m3 year-1) and analyzed its physicochemical parameters and E. coli concentrations. High concentrations …

Evaluation Of Soil Moisture Content, Ph, And Dominant Microorganisms In Crude Oil–Contaminated Soil Treated With Cow Dung, Indole-3-Acetic Acid (Iaa), And Simulated Microgravity, Amenze Ovenseri, Tawari-Fufeyin P.

Applied Environmental Research

Crude oil negatively affects soil physicochemical properties and microbial activity, thereby hindering plant growth and posing notable environmental and agricultural issues. The combined effects of cow dung, indole-3-acetic acid (IAA), and simulated microgravity on the recovery of soil degraded by crude oil were explored using Zea mays as the test plant. Soil was contaminated with crude oil at different concentrations (0%, 1%, 3%, and 5% v/w), with each concentration comprising eight treatments: cow dung, Zea mays seeds exposed to microgravity, and IAA alone or in combination. Changes in plant height, soil pH, soil moisture content, and soil microbial diversity were …

Investigation Of Mercury Bioaccessibility In Subsistence Fishes From The Canadian Subarctic, Kiran Mateo Sharma Mr

Theses and Dissertations (Comprehensive)

Fish consumption is the primary pathway for human exposure to methylmercury (MeHg), a global contaminant of concern known to cause adverse health effects. Fort Albany First Nation, a remote Indigenous community in northern Ontario that relies on fish for both subsistence and cultural practices, has expressed concern regarding the safety of local fish consumption. Community members and leaders have communicated several priorities for scientific research that are the foundation of this thesis. Specifically, consumers of wild-caught fish have asked how traditional cooking methods may affect mercury concentrations in fish, and how mercury concentrations in little-studied organs, such as liver and …