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Temperature-mediated biodegradation of plastics in marine environments
No full textThe global ocean accumulates massive plastic waste, raising concerns over environmental impacts. Plastic biodegradation is a promising solution; however, the efficiency of this process is highly temperature-dependent. Despite its importance, the comprehensive understanding of how temperature affects microbial dynamics in plastic degradation across diverse marine climates, particularly in colder regions, remains limited.
This study begins with a literature review on temperature-mediated biodegradation of plastics in marine environments. Evidence suggests that elevated temperatures generally promote biofilm growth and enzymatic activity. Cold-tolerant bacteria produce extracellular polymeric substances (EPS) to stabilize biofilms at lower temperatures. At moderate temperatures, Proteobacteria dominate the initial degradation phase, while Actinobacteria, Firmicutes, and Cyanobacteria contribute to various stages of degradation. Psychrophilic and thermophilic bacteria facilitate degradation in extreme climates. Enzymes such as cutinases, lipases, and depolymerases facilitate partial degradation of hydrolyzable plastics, while non-hydrolyzable plastics remain recalcitrant, relying on enzyme-generated reactive oxygen species (ROS) for gradual breakdown.
Additionally, controlled laboratory experiments were conducted to evaluate the biodegradation of petroleum-based low-density polyethylene (LDPE), bio-based polylactic acid (PLA), and polyhydroxyalkanoates (PHAs) at various temperatures (4, 15, and 22 °C) using a cold-tolerant Alcanivorax strain isolated from North Atlantic Ocean. Compared to LDPE and PLA, results showed that PHA films supported substantial
bacterial growth, displayed considerable morphological damage, and released more microplastics (MPs) and dissolved organic carbon (DOC) across all temperatures. Notably, degradation by-products of PHA at 22°C exhibited the highest toxicity to Vibrio fischeri, highlighting temperature’s role in biodegradation rates and associated ecological risks. These findings from both the literature review and experiment studies underscore the critical influence of temperature on plastic biodegradation and provide fundamental knowledge for mitigating plastic pollution in diverse marine climates.Includes bibliographical references (pages 114-144
Investigating the biochemical and genetic basis for the production of specialized metabolites in Streptomyces pratensis ATCC 33331
No full textStreptomyces are recognized for producing 70% of clinically used antibiotics, including β-
lactam antibiotics and β-lactamase inhibitors. However, most specialized metabolite (SM)
synthesizing biosynthetic gene clusters (BGCs) are silent or cryptic. Previous studies in the Tahlan
Lab have revealed that Streptomyces pratensis ATCC 33331 synthesize a β-lactamase inhibitor
active against Klebsiella pneumoniae in the presence of penicillin G (PenG). However, the
metabolite remains unknown. The objective of this study was to identify potential BGCs and SMs
contributing to this bioactivity. Genomics analysis of S. pratensis predicted the presence of 30
BGCs, two of which have similarities to the clavulanic acid (CA) and carbapenem MM4550 BGCs
in Streptomyces clavuligerus and Streptomyces argenteolus, respectively. CA is a potent β-
lactamase inhibitor, and carbapenem MM4550 possesses both β-lactam antibiotic and β-lactamase
inhibitory properties. Furthermore, the One Strain MAny Compounds (OSMAC) approach
revealed that S. pratensis produced the β-lactamase inhibitor in Maltose-Yeast extract medium
(MYM) and soy media. S. pratensis like environmental isolate, JAC18, exhibited both β-lactamase
inhibitory and β-lactam antibiotic activities. Transcriptomics analysis of S. pratensis showed the
potential role of NRPS1.1, carbapenem MM4550-like and CA-like BGCs in producing β-
lactamase inhibitors. Gene disruption analysis confirmed that NRPS1.1 and CA-like BGC in S.
pratensis and both carbapenem MM4550-like and CA-like BGC in JAC18, contribute to β-
lactamase inhibitor production. Additionally, metabolomics analysis of mutants defective in
carbapenem MM4550-like and CA-like BGCs in S. pratensis and wt JAC18 indicated that only
early intermediates of the CA pathway, deoxyguanidinoproclavaminic acid and
guanidinoproclavaminic acid, were detected in the bioactive samples. Furthermore, this study
reports the complete metabolomics analysis of S. pratensis and JAC18
Identifying the ecological drivers of total mercury concentrations in Brook Charr (Salvelinus fontinalis) populations across Western Newfoundland, Canada
No full textThe transformation of mercury to the toxic methylmercury in anoxic lake sediment, along with its bioaccumulation and biomagnification in lacustrine food webs, makes it a potent environmental toxin with implications for both ecosystem and human health. Multiple ecological factors operating at different scales contribute to the movement of mercury into freshwater systems and its subsequent methylation and concentration in aquatic organisms. This thesis aims to identify ecological factors driving the bioaccumulation and biomagnification of mercury in lacustrine populations of brook charr (Salevelinus fontinalis) from western Newfoundland, Canada. The total mercury (THg) concentrations were measured in brook charr from 34 headwater lakes. The study examined several variables, including individual morphology, stable isotope compositions, and lake productivity, alongside topographical and land cover data derived from geographical information systems. A structural equation model (SEM) was developed to discern the direct and indirect effects of fish growth and size, chlorophyll-a concentration, catchment topography and land cover on mercury accumulation in brook charr. The analysis revealed a strong influence of catchment-scale factors on mercury bioaccumulation in brook charr. These findings highlight the need for employing optimal catchment management practices alongside continuous monitoring of water quality and ecosystem health in remote freshwater sources
Investigating the harmonic variations of high vowels patterns across Yoruba dialects: a correspondence approach
No full textThis study examines Advanced Tongue Root (ATR) harmony of high vowels [i] and [u] in three Yoruba dialects: Standard Yoruba (SY), Ife Yoruba (IY), and Ekiti Yoruba (EY). Previous analyses, such as alignment-based approach in Grounded Phonology and Optimality Theory (Archangeli & Pulleyblank, 1989, 1994), provide directionality rules for vowel harmony but struggle to account for dialectal differences. Orie (2003) proposes a dual-framework model that combines alignment with prosodic licensing to account for these variations. However, this approach introduces theoretical redundancies and assumes the prosodic head status of the final vowel despite the lack of independent evidence supporting this. To offer a more streamlined explanation, this research adopts Krämer (2003) Correspondence Approach, which emphasises feature preservation through constraints like R-ANCHOR and S-IDENT(ATR) alongside markedness constraints such as *[+high, -ATR]. Though effective, Krämer's framework is not without its limitations, such as its reliance on local conjunction of constraints introduces unnecessary complexity. This study refines his model by replacing local conjunction with new markedness constraints such as *[+high, -ATRright(root)], enhancing both the clarity and generalizability of the analysis.
With the underlying assumption that only [-ATR] is specified and directly associated with all vowels in the input, the findings of this study reveal that the interaction between faithfulness and markedness constraints varies across the dialects. In Ekiti, faithfulness constraints dominate initial and medial root positions, preserving the retracted [-ATR] feature of high vowels despite markedness violations. Conversely, SY and IY prioritize markedness, realizing high vowels as [+ATR] in similar positions to avoid retracted high
vowels. However, all three dialects converge in final root positions, where markedness constraints like *[+high, -ATRright(root)] override faithfulness constraints, ensuring final high vowels surface as [+ATR]. Through the Correspondence Theory, this study offers a cohesive explanation for ATR harmony in Yoruba dialects, contributing to broader phonological theories of vowel harmony in African languages
Electric load forecasting using deep neural networks
No full textShort-Term Load Forecasting (STLF) is a critical and complex task that plays a vital role in the
efficient management of electricity generation, transmission, and distribution. Recent research has
made strides in this field through the application of advanced deep learning techniques to enhance
the accuracy and reliability of load predictions. The first study introduces a novel deep neural
network tailored for STLF at Memorial University of Newfoundland (MUN). This model
integrates electric load data with meteorological information and features a 1D Convolutional
Neural Network followed by an Encoder-Decoder Network with an attention mechanism, showing
superior performance compared to traditional Gated Recurrent Unit (GRU) and Long Short Term
Memory (LSTM) models. The study also focuses on optimizing the input horizon using the
algorithm.
The second study focuses on Multi-Energy Systems (MES) and presents a Multi-Task Learning-based approach for load forecasting. It features a cutting-edge deep learning architecture designed
to forecast multiple loads simultaneously. Applied to the University of Austin Tempe Campus,
this approach employs a Deep Temporal Convolutional Neural Network (D-TCNet) to effectively
capture spatial and temporal correlations in the data, resulting in improved forecasting accuracy
across different energy types and seasons.
The third study compares various Recurrent Neural Network (RNN)-based time-series forecasting
algorithms, including LSTM, GRU, Bi-directional GRU, and Bi-directional LSTM, on electric
load data from MUN. The Bi-directional GRU model emerged as the top performer, achieving the
highest R2 score and the lowest Mean Squared Error (MSE) and Mean Absolute Error (MAE) for
day-ahead predictions.
Collectively, these studies demonstrate the power of deep learning in enhancing the precision and
effectiveness of short-term load forecasting, offering promising avenues for optimizing energy
system operations.Includes bibliographical reference
Mapping accessibility zones to salmon fishing in Newfoundland and assessing the potential impacts of spruce budworm outbreaks using GIS
No full textFishing in Newfoundland represents both a cultural tradition and an important contributor to community well-being and local economies. In this project, we endeavoured to understand salmon fishing as a cultural ecosystem service and explore the relationships and the influence of natural disturbances, specifically Spruce Budworm (SBW) outbreaks. Our primary objectives included (1) mapping accessibility zones to salmon fishing in Newfoundland and (2) Identifying and assessing areas of SBW defoliation impact on salmon fishing areas. Road networks, towns, HydroSHEDs data, SBW defoliation data, scheduled salmon river networks and flow direction raster acted as foundation data for our comprehensive analyses. The study area covered Newfoundland's diverse watersheds, emphasizing the 14 salmon fishing areas designated by the Angler's Guide of Newfoundland and Labrador. By identifying zones of access and overlapping with SBW defoliation data, we aimed to uncover potential impacts on salmon fishing and fishers in the region. The first study mapped out accessibility zones to salmon fishing using a Multi-Criteria Decision-Making (MCDM) approach with GIS, integrating proximity to towns, roads, and scheduled rivers. The weighted overlay model revealed that 13.66% of the area was highly suitable, 31.32% moderately suitable, and 33.46% suitable for salmon fishing accessibility. The second study assessed spatially the potential impacts of SBW defoliation on our ecosystem service using a predicted SBW defoliation model by Zhang et al. (2023) and HydroSHED data resulting in a potential impact matrix. Results indicated that high impact zones represented 3.63% of the study area but included key salmon rivers like the Humber and Exploits that contribute the most to salmon fishing indicating that the continued studied impacts and effects of SBW defoliation on hydrological bodies will most likely have a high occurrence on these rivers. These outcomes aim to inform fisheries management strategies in response to SBW outbreaks and contribute valuable insights for broader ecosystem services quantification and conservation in Newfoundland. Through this approach, we aspired to contribute to sustainable management of Newfoundland's invaluable fishing resources, ensuring their resilience amidst a changing environmental landscape by providing actionable knowledge for stakeholders, policymakers, and the community
Assessing the potential of cover crop mixtures in a faba bean cropping system under boreal climatic conditions
No full textPoor soil conditions and boreal climatic conditions in Newfoundland and Labrador (NL) are the major constraints in the establishment of cover crop (CC) mixtures. CC mixtures were grown as an additional forage source, which not only provided biomass but also balanced soil C:N ratio, scavenged N and improved soil health. An experiment (June 2022- September 2023) was conducted in Pasadena, NL, where fourteen CCs mixtures were introduced in a faba bean cropping system. The combinations composed of two-way and three-way CC species of legumes [red clover (RC), berseem clover (BC), hairy vetch (HV) and birdsfoot trefoil (BT)] and cereals [fall rye (CR), annual ryegrass (AR) and triticale (TR)] in a randomized complete block design. The results showed that CC mixtures were successfully established and highest DMY was observed for HVCR and RCCR. While BCAR and RCAR exhibited better fodder quality as compared to other CC mixtures. CC mixtures also improved the forage quality of the faba bean, weed suppression and benefit-cost-ratio. However, CC mixtures have a non-significant impact on faba bean yield stability. CC mixtures had a significant impact on SMBC while no significant effects on POX-C, POM-N, POM-C, MBN, and soil mineral N. PLFA analysis showed that CC mixtures have significant impact on G⁻, G⁺, total bacterial population and total PLFA content while had no significant effects on fungi and protozoa population. This research concluded that CC mixtures showed good DMY and forage quality along with improvement in weed suppression, benefit-cost-ratio, SMBC, and bacterial population.Includes bibliographical reference
Evaluation of the influence of an adaptive instructional system on participants’ performance in a ship’s bridge simulator
No full textEffective ice management training is necessary for safe and efficient operations in sea ice environments, especially for offshore energy industries that experience seasonal incursions of pack and multi-year ice. Traditional training methods in sea ice management are predominantly through classroom courses, simulator-based training, and experiential learning on-the-job. However, traditional forms of training are non-adaptive, have limited scalability, and lack consistency in skill acquisition. This study evaluates the effectiveness of an Adaptive Instructional System (AIS) as a potential solution for improving ice management performance in simulation-based training, addressing a gap by providing adaptive, tailored feedback for learners. The AIS in this study incorporates a learner model using Decision Trees and an instructor model that integrates feedback from experienced seafarers with the goal of enhancing skill acquisition in a simulated environment. The study compares the performance of participants trained with AIS to those trained without it. Participants completed three training scenarios and one test scenario in a simulator, with key performance metrics used to assess training effectiveness, such as the changes in ice concentration for a specified zone. Statistical analyses, including normality assessments and independent samples t-tests at a significance level of p < 0.05, were conducted to assess performance differences. The findings demonstrate AIS's transformative potential to enhance ice management performance.Includes bibliographical references (pages 109-114
A multisensor fusion-based framework for 3D motion control of a smart AUV for tracking subsea catenary risers
No full textSteel Catenary Risers (SCRs) and Steel Lazy Wave Risers (SLWRs) are integral components of deepwater subsea infrastructure, enabling the transport of hydrocarbons from the seabed to floating production facilities. Their operation in harsh marine environments subjects them to dynamic loads, wave-induced fatigue, and vortex-induced vibrations, particularly in the touchdown zone. Repeated contact with the seabed causes the surrounding soil to remould and form a trench, progressively altering the riser's geometric profile. These changes significantly affect the fatigue life of the structure. Accurately capturing the actual three-dimensional profile of the riser in this region provides a valuable input for numerical models used in fatigue life assessment and structural integrity analysis, thus necessitating accurate three-dimensional profile tracking for structural integrity assessment. This thesis investigates a cost-effective approach for real-time tracking and three-dimensional profile reconstruction of SCRs and SLWRs using Autonomous Underwater Vehicles (AUVs) equipped with cameras and echosounders.
A comprehensive image processing pipeline was developed, incorporating bilateral filtering, edge detection using the Canny method, Hough transform, and clustering algorithms to precisely locate the riser center despite challenges like asymmetric edge detection due to noise. The system was adapted to track the complex curvature and dynamic oscillations of SLWRs, leveraging sensor fusion to capture riser geometry variations in both horizontal and vertical planes. Simulations were conducted in the Unmanned Underwater Vehicle (UUV) Simulator environment, where a torpedo-shaped AUV was deployed for efficient and stable operation. Results demonstrated the robust performance of the proposed methodology in accurately tracking and reconstructing riser profiles, even under dynamically changing conditions.
This work highlights the critical importance of developing automated and efficient systems for accurate riser tracking and profile reconstruction, providing essential inputs for fatigue life modeling, enhancing operational safety, and supporting long-term structural integrity management in offshore oil and gas operations
Weighted accelerated failure time model
No full textThe accelerated failure time (AFT) model is widely used in survival analysis and
auxiliary information can be used to improve the efficiency of the model. We developed
a weighted AFT model by using empirical likelihood probabilities as weights based
on information from previous studies. The proposed model effectively overcomes
the challenges associated with managing censored observations, resulting in more
reliable and accurate estimates. Theoretical justifications of the proposed model are
developed.
A comprehensive simulation study was conducted to assess the effectiveness of the
proposed weighted models, incorporating both partial and complete auxiliary information.
Both the Standard Accelerated Failure Time (AFT) and AFT with Generalized
Estimating Equations (AFTGEE) models were employed for this comparative analysis.
The simulation results suggest that when estimating coefficients, weighted models
incorporating complete or partial auxiliary information on the linked covariate provide
more accurate estimates compared to the model without any weights. Finally,
the proposed method was implemented on a real dataset, illustrating its ability to
accurately determine coefficients, minimize standard errors, and enhance significance
levels by incorporating auxiliary information.Includes bibliographical references (pages 62-66