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Aquatic epiphytes as bioindicators of wetland health: a study from the Simcoe County wetlands
(2026) Read-Maney, Kate; Kanavillil, Nanda
Aquatic microscopic epiphytes are increasingly recognized as sensitive bioindicators of aquatic ecosystem health, yet significant knowledge gaps remain regarding their indicator potential within Canadian wetlands. This study investigates the variability and environmental responsiveness of epiphytic communities to water quality changes as well as effects of the host species and structure. Two common macrophytes were used in this study - Typha angustifolia (alive and dead samples) and Nymphaea odorata. Four wetlands were studied, all from within the Lake Simcoe watershed (Ontario, Canada) - Langman’s Marsh, Lagoon City, Holland Marsh, and Victoria Point. Three major gaps motivated this research: the scarcity of Canadian based epiphyte bioindicator research, the limited use of multi-genera epiphyte assemblages as bioindicators rather than single taxa, and the lack of understanding of host - epiphyte relationships in Canadian wetlands. Epiphyte samples were collected seasonally (Summer 2021, Fall 2021, Spring 2022). Standardized scraping, centrifugation, and microscopic analyses with a haemocytometer, were used to quantify epiphyte density, species richness, and diversity. Measurements were taken of phytoplankton communities, water chemistry (e.g., nutrients, DOC, chlorophyll-a), and in-situ parameters (e.g., DO, pH, conductivity) were obtained to evaluate and compare wetland health. Statistical analyses - including Shannon diversity indices, data transformations, and diagnostic modelling - were used to assess spatial and temporal patterns and to determine relationships between epiphyte assemblages, macrophyte hosts, seasons, and wetland conditions. Across sites and seasons, epiphyte communities demonstrated clear, measurable variation associated with macrophyte type, season, and wetland characteristics. Preliminary findings indicate that (1) epiphyte assemblages respond sensitively to environmental gradients, supporting their use as indicators of wetland health in Canadian wetlands; (2) host macrophytes influence epiphyte density and composition, with notable differences between senescent and alive macrophytes; and (3) wetlands with higher anthropogenic stressors exhibited distinct epiphyte community structures compared with less impacted sites. Importantly, several key genera - such as Navicula spp., Nitzschia spp., Eunotia spp., Cymbella spp., and Gomphonema spp., occurred across all wetlands, but their relative abundances differentiated stressed systems from healthier ones: wetlands where Navicula spp. and Nitzschia spp. dominated reflected nutrient pressure, whereas sites where these taxa coexisted alongside diverse, well-represented assemblages indicated more stable ecological conditions. Overall, this research provides a multi-wetland, multi-host assessment of aquatic microscopic epiphytes within the Lake Simcoe region and offers strong evidence supporting their utility as bioindicators in Canadian freshwater wetlands. The findings expand baseline ecological knowledge and contribute a valuable framework for future monitoring and conservation initiatives.
Pyrite trace element chemistry of gold deposits in the Red Lake greenstone belt, Northwestern Ontario, Canada
(2025) Sulyman, Nafiu Omotosho; Hollings, Peter
The Red Lake greenstone belt located in northwestern Ontario, Canada, hosts a world class gold camp that contains the high grade Campbell-Red Lake, Cochenour-Willans, Howey, and Hasaga deposits, as well as several other gold deposits and gold occurrences. This thesis examined textures, trace element chemistry and sulfur isotope evolution of pyrite and its use as a tool to discriminate Red Lake gold deposits, as well as the potential to use of pyrite chemistry as vectoring and fertility assessment tools. Field and petrographic observations, along with results from scanning electron microscopy, LA-ICP-MS analyses and sulfur isotope composition of pyrite, were integrated to achieve the objectives of this study. Petrographic analyses of chemically etched pyrite grains reveal two main groups of pyrites associated with Red Lake deposits namely hydrothermal zoned pyrites (Py1a, Py1b and Py2) and recrystallized pyrites (Py3). At the Campbell-Red Lake (CRL) deposit, Py1a which is a mineral inclusion rich pyrite is enriched in element suite of Sb-Tl-Au-Ag-Te-W ± Hg with average δ34S values of approximately +6.6 ‰ and forms the core of zoned pyrites. It represent a stage of low temperature hydrothermal alteration. The second stage pyrite of pyrite growth at CRL is identified by the formation of arsenian, Au-rich Py1b crystals which represents metamorphic devolatization and mobilization of main stage mineralizing fluids with a narrow range of δ34S (+3‰ to +6.7‰). Py2, interpreted as the last stage of pyrite growth represents retrograde cooling of CRL deposit hydrothermal system resulting in the enrichment of Co, Ni and As and has δ34S value around +6‰ consistent with a metamorphic fluid source. Py3 at the CRL represents recrystallization of early pyrites under high temperature contact metamorphism from plutonic rocks with trace element content of Co, Ni and As around 1000ppm and average δ34S of +1.1‰ whereas Py3 at the Howey deposit with similar trace element contents to the CRL have δ34S of +2.5‰ consistent with association with fluids from intrusive rocks of the Howey diorite complex. In contrast to the CRL deposit, oscillatory zoned P1yb and Py2 are the early pyrite whereas mineral inclusion rich Py1a is the latest pyrite at the East Bay area deposits and gold occurrences. The paragenetic sequence of pyrite at the East Bay area and intrusion related deposits show evidence of coupled dissolution and reprecipitation of early pyrites to form gold-rich late stage Py1a. Sulfur isotope data from the East Bay area deposits suggests that early Py1b and Py2 formed from metamorphic fluid whereas Py1a have magmatic fluid input. The intrusion related Howey and Hasaga deposit have similar pyrite paragenesis and sulfur isotope characteristics compared to East Bay area deposit except the non occurrence of As-rich Py1b. The Sb/Tl, Sb/Bi and Bi/Te ratios of Py1a across several Red Lake deposits and occurrences provide potential fertility indicator tools. The Sb/Bi ratio of pyrite decreases with decreasing deposit size whereas Bi/Te increases. Gold, Te, and Mo concentrations of ore related Py1b is appears to be a good discriminator between the large CRL and the smaller systems in the Red Lake greenstone belt. Various elements in pyrite including Se, Te, Bi and Ni/Co ratio show systematic changes in concentration from the center of the CRL deposit to about 3km away approximately 1-2km more than the footprint of whole rock samples. Pyrite trace element composition from other Red Lake deposits effectively discriminates between deposit proximal or distal signature from a mineralized zone.
Enhancing agricultural semantic segmentation: architectural innovations and strategies for reducing model complexity
(2025) Ling, Chee Mei; Akilan, Thangarajah; Yassine, Abdulsalam; Dzhamal, Amishev; Zhou, Yushi
Agricultural image semantic segmentation plays a vital role in precision agriculture, enabling accurate analysis of visual data to enhance crop management and optimize resource use. However, achieving high segmentation accuracy while maintaining computational efficiency remains challenging, particularly for real-time systems and edge devices. This thesis presents a two-phase research effort toward an efficient and scalable segmentation framework for high-resolution agricultural imagery. In the first phase, an effective model was developed using a novel Dual Atrous Separable Convolution (DAS-Conv) module integrated into a DeepLabV3 backbone. The DAS-Conv module optimizes the balance between dilation rates and padding size to enhance contextual representation without extra computational cost, while a skip connection between encoder and decoder stages improves fine-grained feature recovery. Despite its lightweight design, the model achieved strong results on the Agriculture-Vision benchmark, demonstrating over 66% higher efficiency compared to transformer-based state-of-the-art models. In the second phase, the framework was extended to DAS-SK, which integrates Selective Kernel (SK) attention into the DAS-Conv module to strengthen multi-scale feature learning and adaptability. The enhanced Atrous Spatial Pyramid Pooling (ASPP) module captures both fine local structures and global context, while a dual-backbone design (MobileNetV3-Large and EfficientNet-B3) further improves representation and scalability. Across three benchmark datasets—LandCover.ai, VDD, and PhenoBench—DAS-SK consistently demonstrates superior efficiency–accuracy trade-offs and notable improvements over its predecessor, DAS. On LandCover.ai, DAS-SK achieves 86.25% mIoU, surpassing DAS by +3.17%, using 10.68M parameters and 11.25 GFLOPs. Although it employs slightly more parameters than DAS, the model remains far lighter than hybrid systems such as Ensemble UNet and transformer models like SegFormer MiT-B2. DAS-SK also achieves higher overall efficiency compared with DAS, demonstrating that the added SK attention and dual-backbone design translate directly into improved segmentation quality. A similar trend is observed on the VDD dataset. DAS-SK attains 79.45% mIoU, improving on DAS by +2.25%, while operating with 10.68M parameters and 43.52 GFLOPs. Although the smaller DAS backbone enables slightly higher FPS, DAS-SK delivers the best accuracy–efficiency balance overall, achieving the highest efficiency score of 9.12%, outperforming transformer models whose parameter counts range from 27M to 234M. On the PhenoBench dataset, DAS-SK again provides the highest performance, reaching 85.55% mIoU compared to DAS at 82.53% (+3.02% improvement). The computational cost remains moderate at 45.00 GFLOPs, versus 25.23 GFLOPs for DAS, but the efficiency gain is substantial—10.09% for DAS-SK versus 7.43% for DAS—highlighting the benefits of improved feature selection and multi-scale fusion. Despite introducing a modest computational increase (typically 1.8× more GFLOPs), DAS-SK consistently delivers 2–3% higher mIoU and markedly stronger multi-scale feature discrimination than its ancestor DAS. Combined with parameter counts that remain significantly below those of modern transformer and hybrid models, DAS-SK offers a practical, lightweight, and high-performing solution for real-time agricultural monitoring and remote sensing in resource-limited environments, where accuracy, efficiency, and scalability are equally critical.
Design and optimization of heterogeneous coded distributed computing
(2025) Zhang, Siyu; Deng, Yong; Rathore, M. Mazhar; Akilan, Thangarajah; Zhou, Yushi
The massive increase in data volume in recent years has posed significant challenges for traditional data processing systems. Although distributed computing has been considered as an effective solution, its efficient implementation faces the challenge of the high communication overhead incurred by data exchange (shuffling) between workers. Coded Distributed Computing (CDC) has been proposed by utilizing coded multicasting to reduce the shuffling load. To our best knowledge, existing works on the CDC only consider input files with uniform file size, limiting their practicality in real-world applications. To address this limitation, we propose a Heterogeneous Coded Distributed Computing (HetCDC) scheme to handle input files of nonuniform sizes. We then formulate a joint optimization problem to optimize the file placement and coded shuffling strategies to minimize the shuffling load. Through reformulation, we convert the nonconvex optimization problem into an integer linear programming problem and solve it through the branch-and-cut method. Numerical studies show the proposed HetCDC outperforms existing works. Based on the Het- CDC, we further develop a Heterogeneous TeraSort algorithm to improve the sorting time of traditional TeraSort, which is a key building blocks for many big data processing algorithms.
ADHD and career sustainability: A sustainable career ecosystem perspective
(Emerald, 2025-09-26) Grabarski, Mirit K.; Jameson, Tiffany Payton; Mouratidou, Maria
Purpose: We explore the perceptions of career sustainability of individuals with attention-deficit/hyperactivity disorder (ADHD) in the United States, taking a sustainable career ecosystem perspective that considers multiple sustainability indicators and different interdependent actors. Design/methodology/approach: We conducted semi-structured interviews with 31 participants and analyzed the data using a template approach that allows combining deductive and inductive analysis. Findings: We identify how ADHD impacts different aspects of sustainable careers, namely time, person-related factors and indicators (i.e. happiness, productivity and health). Moreover, our findings identify empirical support for two additional indicators (financial security and growth mindset) as proposed by sustainable career ecosystem theory. We suggest a disproportionate impact of ADHD on the indicators, specifically, productivity, due to contextual workplace barriers. We also identify key actors at the local ecosystem level (e.g. family members, teachers, neighbors, friends, co-workers and therapists) that play an important role in individual careers within the ecosystem, particularly regarding diagnosis and support. Originality/value: We provide empirical insights that support the recently developed sustainable career ecosystem theory and suggest a differential impact of ADHD on the indicators.