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Advancements in autonomous ship trajectory tracking: a comparative study of mechanistic and Neural network models with NMPC
No full textThe evolution of autonomous ships marks a significant stride in maritime operations, promising applications across a wide range of industries. These innovations enhance shipping and marine operations by improving safety through the reduction of human error and by enhancing the quality of life for mariners by alleviating tedious or difficult workloads. Whether in commercial shipping, passenger transport, or scientific research, autonomous ships are set to revolutionize the way we navigate the seas, making maritime activities more efficient and safer. Central to advancing this domain is the precise trajectory tracking of Autonomous Surface Vessels (ASVs), which is vital for their safe and efficient navigation. It is simultaneously required to operate within specified timeframes while adhering to maritime regulations and safely maneuvering amidst dynamic marine conditions such as waves, currents, and winds, which pose formidable technical challenges to autonomous trajectory tracking.
Presently, both model-based and data-driven controllers are pivotal in navigating Autonomous Vessels, emphasizing the critical need for accuracy and reliability in trajectory following. However, achieving precise trajectory tracking under real-world conditions remains intricate due to varying ship dynamics and environmental disturbances, necessitating tailored controller designs.
In this study, our principal aim is to develop a controller that comprehensively addresses these challenges while upholding safety constraints. We leverage Nonlinear Model Predictive Control (NMPC) for its suitability in handling nonlinear ship models, accommodating unmodeled dynamics, managing diverse constraints, and ensuring course stability amidst multivariable
systems. An Unscented Kalman Filter (UKF) is integrated with NMPC to mitigate wave-induced disturbances and enhance robustness.
Our NMPC controller with UKF, implemented with mechanistic and Neural Network (NN) ship models, is evaluated through trajectory tracking simulations and experimental trials using the Magne Viking ship model at the National Research Council (NRC) in Canada. Incorporating an Artificial Neural Network captures intricate ship dynamics, exhibiting promising results in simulations and practical experiments. We compare the performances of mechanistic and NN models to validate their efficacy, proposing further enhancements through deep neural network training with natural data.
Integrating NMPC with neural network structures represents a core aspect of this research, aiming to advance autonomous ship trajectory tracking capabilities in real-world scenarios.Includes bibliographical references (pages 160-166
Design and Analysis of Photovoltic System for a House in Model Town Lahore Using Homer Pro
No full textThis paper presents a detailed performance evaluation of an 11 kW grid-connected photovoltaic (PV) system installed at a residential site in Lahore, Pakistan. The study focuses on assessing the system's economic, environmental, and operational benefits, offering insights into its viability for reducing energy costs and carbon emissions. The analysis shows that the PV system achieves a penetration level of 89.6%, providing nearly 90% of the site's energy demand from solar power. Annual energy cost savings amount to 65%, with a favorable payback period of 1.97 years and an internal rate of return (IRR) of 50.7%. Additionally, the system reduces CO2 emissions by 42.4%, aligning with global sustainability goals. These results suggest that rooftop solar PV installations in urban residential areas in Pakistan offer significant financial and environmental advantages
Behind the whistle: understanding mental performance in elite ice hockey officiating
No full textOfficiating elite sport requires focus, confidence, arousal regulation, and decisive decision-making. Thus, mental skills use and training seems imperative for elite sport officials. However, little research has examined mental skills acquisition and use among sport officials. This study explored ice hockey officials’ use of mental skills. A pragmatic paradigm guided the study. 10 elite ice hockey officials participated in semi-structured interviews. Data were analyzed using a six-step thematic analysis. Results showed varying levels of mental skill use, with pre-performance routines, self-talk, visualization, and goal setting being the most common. Specific processes behind skill implementation were identified. Few participants received formal training to acquire their mental skills; instead, they learned from past athletic experiences and peers. All respondents acknowledged the critical role of mental skills in elite sport officiating and advocated for organizations to provide formal educational opportunities, especially early in officials’ careers. The findings highlight the importance of mental skills for elite sport officials and lack of formal mental skills training. This shortfall could prevent sport officials from reaching their full potential. I have offered recommendations for sport officiating organizations including the implementation of early education programs, formal training opportunities, and recommendations for continued organizational support.Includes bibliographical reference
Quantifying microvascular hemodynamics in healthy and type 2 diabetic rats
No full textThe human circulatory system is composed of a branching vascular tree giving rise to the microcirculation which serves as the major site of exchange between the blood and tissues.
Blood flow distribution among microvascular networks is dynamically controlled to match
the supply of oxygen and nutrients, such as glucose, with the demands of the tissues.
Diabetes Canada estimates that 1 in 3 Canadians are living with diabetes or prediabetes, and
up to 90 to 95% of those living with diabetes are type 2 diabetic. Evidence in the literature suggests the response to insulin is blunted in type 2 diabetes (T2D) and insulin resistant individuals due to endothelial dysfunction. We hypothesized that chronic hyperglycemia and
elevated insulin in T2D impairs oxygen mediated blood flow regulation leading to functional defects in capillary network blood flow distribution at rest, in response to oxygen challenges,
and during hyperinsulinemia. To test this hypothesis 15- and 27-week-old Sprague Dawley
(SD) and Zucker Diabetic Sprague Dawley (ZDSD) rats were fed a normal fat, high fat, or
high-sugar high-fat diet, anaesthetized with sodium pentobarbital, mechanically ventilated,
and instrumented for systemic monitoring and fluid resuscitation. The extensor digitorum longus muscle was blunt dissected and reflected over a glass coverslip or a gas exchange chamber set in the stage of an inverted microscope. Intravital video microscopy recordings were made during baseline, hyperinsulinemia, and acute changes in local O₂ concentration
([O₂]). Further, in 7-week-old SD rats we quantified the dynamics of capillary hemodynamic
responses to changes in tissue [O₂] and CO₂ concentration ([CO₂]) analogous to expected
changes at the onset of moderate exercise. We determined that hyperinsulinemic-euglycemic clamp increased capillary hemodynamics and red blood cell oxygen saturation in SD rats;
however, there was no response to systemic hyperinsulinemia in ZDSD rats. 27-week-old
SD and ZDSD rats had impaired capillary hemodynamic responses to changes in [O₂]
following high-fat high-sugar feeding. We quantified profound differences in the dynamics of hemodynamic responses to altered [O₂] and [CO₂] in skeletal muscle which are additive in
young healthy SD rats. This demonstrates that prolonged high-fat feeding and T2D lead to
functional changes in oxygen reactivity in capillary hemodynamic responses.Includes bibliographical references (pages 260-290
Experiences of Iranian women entrepreneurs in the ICT (information and communications technology) sector
No full textDespite the expansion of women’s entrepreneurship in Iran, women still face many challenges that make it difficult to start a business. Barriers such as limited funding, gender biases, limited government support, and family responsibilities have been in the way of women entrepreneurs for many years and women face more barriers than men (Shinnar, Giacomin, & Janssen, 2012). Still, information and communication technologies (ICTs) are appearing as increasingly valuable business tools for women entrepreneurs. That being the case, I investigated the barriers to Iranian women entrepreneurs in the ICT sector as well as their resilience to those barriers. To conduct my research, I collected already published interviews with 12 women entrepreneurs in the media, prepared a codebook, and analyzed them through feminist content analysis. I found that challenges to women’s entrepreneurship in Iran in the ICT sector include some obstacles. These obstacles contain those that are specific to Iran, such as the lack of foreign trade, economic instability, bribery, brain drain, sanctions, decrease in tourism, filtering, low internet speed, lack of cutting-edge technology, some Islamic laws practice in Iran. Obstacles also include the lack of trust in women for activities outside the home, gender role expectations (including family responsibilities and childcare), literacy and education, finance, a lack of professional human resources to be recruited for their companies, and time-consuming and inefficient bureaucracy. In terms of women entrepreneurs’ resilience, I found that women tried to navigate their challenges by working hard, having motivation, perseverance, and self-confidence, conducting time management and responsibility, and acquiring related skills and knowledge. Women had resilience while receiving support from family members, reducing household and childcare pressures, as well as developing cooperation and assistance from others and practicing teamwork. Through a better understanding of their challenges, the findings of my research would benefit Iranian women entrepreneurs, especially in the ICT sector.Includes bibliographical references (pages 103-113
Shrinkage estimators for semi-parametric proportional hazards mixture cure models
No full textSurvival analysis is essential for modelling time-to-event data, particularly in medical
research. Mixture cure models are widely used methods to study patients' latency
and incidence components. This research focuses on mixture model properties in the
semi-parametric estimation of the Cox proportional hazard models in the presence of
the multicollinearity problem, where the explanatory variables are linearly dependent
so that the input design matrix is ill-conditioned. In the mixture of cure models, the
multicollinearity issue can happen in both latency and incidence components, where
the commonly used least squares (LS) method may lead to unreliable estimates for
the coefficients of the underlying model. To address this issue, we propose shrinkage
methods to estimate the coefficient of the underlying model. To do so, we developed
new expectation-maximization (EM) algorithms to incorporate the shrinkage methods
for both components.
Through various simulations, we show that the proposed shrinkage methods cope
with the multicollinearity problem in latency and incidence components and lead to
more reliable estimates in semi-parametric settings. Our findings indicate that Ridge
and Liu-type (LT) shrinkage methods provide more reliable parameter estimates and
outperform the LS estimation method in scenarios with high multicollinearity.
The developed methods are finally applied to a dataset on breast cancer, analyzing
the disease prognosis and survival rates of patients with 10 or more positive lymph
nodes. The results consistently show that the Ridge and LT methods offer better
estimation and survival results compared to the LS method. Our numerical studies
show the practical advantages of our proposed shrinkage methods in medical research.Includes bibliographical references (pages 61-66
Causal inference and interpretable machine learning for multiscale environmental data analysis
No full textEnvironmental data analysis encompasses methods including domain specific environmental modelling, statistics, and data-driven methods (e.g., artificial intelligence) to interpret observational and experimental datasets for tackling environmental issues. The field of environmental data analysis has experienced significant advancement over the last decade, fueled by the exponential increase in data quantity and complexity and the progression of data-driven paradigms alongside artificial intelligence. This field faces several key challenges, including 1) the lack of means for analysis from causal perspectives, especially in complicated multivariable problems, 2) the relatively high computational cost associated with partial-differential-equation-based models that incorporate physics priors, compounded by intrinsic uncertainties in parameter tuning processes, and 3) the frequent situations with limited data available or valid for analysis. This dissertation research aims to bridge the gaps by developing a set of integrated methods that meld the strengths of interpretable machine learning and causal inference with classic tools for environmental data analysis and modelling. It entails the following major tasks: 1) to introduce an interpretable data analysis framework that leverages machine learning and causal inference. This framework can not only promote a deeper understanding of the causal relationships within environmental data but also serve as a testament to the value and potential of applying interpretative analytics in environmental fields. It is exhibited by a case study on the relationships between environmental factors and pandemic severity. 2) to develop a causal-prior embedded neural network, utilizing experimental data and parameters fitted from physics-based models, offering a systematic integration of lab experiments, physics-based simulation, causal inference techniques, and neural network
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modelling. The method is demonstrated through an integrated experimental and modelling study on the fate and transport of metformin, an emerging contaminant, in a porous medium. 3) To propose and test a transfer learning-based method to estimate the occurrences of environmental pollutants released or closely associated with human activities under data-scarce scenarios, supported by a novel neural network architecture and a comprehensive model fine-tuning strategy. The method is exemplified through a global risk assessment of metformin with a special attention on Canadian ecozones and the Arctic and sub-Arctic regions to showcase the method’s effectiveness in enhancing environmental risk evaluation in data-limited contexts.
The dissertation research advances the field of environmental data analysis by developing a set of new methodologies based on causal inference and interpretable machine learning. Those methods deliver benefits including enhanced model interpretability, reduced computational costs, and improved efficiency in dataset utilization, enabling robust analysis of environmental data across diverse scales. The research can offer not only robust and effect methodologies for actionable environmental data analysis and modelling but also enhance our capability to harness vast and complex environmental data for informed decision-making and policy development.Includes bibliographical references (pages 162-197
How do I know a children’s book qualifies as antiracist? The educator’s analytic tool for choosing antiracist books for children.
No full textThis study aimed to develop an educator’s analytic tool for successfully analysing antiracist books for children. The analytic tool was developed through a comparative systematic literature review of literary approaches and themes in modern anti-racist literature from two books: ‘Stamped: (Racism, Antiracism, and You) for kids’ by Jason Reynolds and Ibram Kendi and ‘This Book is Anti-racist’ by Tiffany Jewel. The review of literature chapter explored various authors' perspectives on the role that children's antiracist literature plays in creating a society where children of all races are equitably recognized in books. The consensus amongst these scholarly writers was that children's antiracist books are at the forefront of social justice efforts and creating a democratic environment for children. The methodology chapter portrayed how a qualitative research methodology was used to uncover themes and language in children's antiracist literature. A systematic literature review was used to select, appraise, synthesize, and analyse the two books chosen for this study. The analysis of these two books revealed that the following standards must be met when choosing an antiracist book: Age Appropriateness, Historical Accuracy, Systemic racism exploration, Embracing individuality discourse, Intersectionality, Dominant group exploration, Required antiracist actions, explanation of Harmful stereotypes and Examples of racism. Thus, the analytic tool equips educators with important themes to prioritize when choosing antiracist books for a more inclusive and equitable future.Includes bibliographical references (pages 100-105
Children of their city: migration, resistance, and the construction of a working-class identity in late nineteenth-century Stockholm
No full textFormidable economic and social forces shaped the lives of working-class men and women in Stockholm at the end of the nineteenth century as they confronted difficulty in securing consistent work and faced high food and housing costs as well as class-based prejudice in the popular press. These people responded with unique expressions of self-determination and demonstrated a profound resilience in the form of subtle, mundane, and nearly imperceptible acts of resistance. These are revealed in an examination of their daily lives and struggles and situating their experiences within the overall creation of a working-class identity. E. P. Thompson’s definition of “class” offers a starting point to understanding the creation of this identity as do post-structural approaches that examine the language used to describe these men and women. Here “class” refers to fluid boundaries delineated more by common experiences and behaviours rather than social standing or occupation.
An ephemeral lure of bright lights and a promising future continued to draw men and women to the city. Biographies of three men and three women who moved from Kalmar County to Stockholm in the 1880s reveal the migration histories and the tactics and strategies they and thousands of others employed to try and survive after they arrived. They and their fellow workers responded to efforts to control them and their behaviour. Some men resorted to drinking as a form of resistance while women forced to resort to prostitution devised their own tactics to avoid police scrutiny and compulsory medical examinations.
Men and women of the working class converged within the space of the tavern. It served as the most important locus of socialisation and networking for many men although women’s presence there has been understated but they were integral to the tavern after the passage of the Gothenburg System that required much more stringent scrutiny to cut down on public drunkenness. Workers fought back against this attempt to subdue one of the most important institutions in their lives by re-appropriating this space and using it to construct social networks that transcended occupational and geographic and served as the basis for a working-class identity.Includes bibliographical references (pages 354-378
Analysis of time-to-event data with multi-state models and causal inference methods
No full textAnalyses of disease-free survival data for certain cancer types indicate that cohorts
of patients treated for cancer consist of individuals who are susceptible to experience
cancer related events and individuals who are cured. Cured individuals do not experience
any cancer related event, and eventually die due to other causes. Individuals
who are not cured may die after experiencing cancer recurrence or without experiencing
any recurrence. Cure status is a partially latent variable and is only known if a
disease related event, cancer recurrence or cancer death, is observed. Causes of some
observed deaths may be masked. To model disease progression events, which are cancer
recurrence and cancer death, we consider a multi-state model including partially
latent cured and not cured states. We describe our modeling approach and discuss
an inference method incorporating masked causes of deaths. Our method allows us
to identify factors associated with the risk of experiencing a disease related event and
with timing of disease events after the treatment of cancer.
It is of interest to make inference on direct exposure effects on time-to-event outcomes
in many studies. Traditional survival analysis methods may not reveal direct
exposure effects on time-to-event outcomes when there are indirect exposure effects
through intermediate variables which are confounded by some unmeasured factors. We
propose a mediation analysis method to make inference about direct exposure effects
on time-to-event outcomes under additive hazards model using estimating equations
methodology. We examine properties of the proposed method and compare them
with traditional survival analysis methods and the existing two-stage mediation analysis
method which uses additive hazards model. The results show that our method
provides valid inference about controlled direct exposure effects on time-to-event outcomes
by successfully removing indirect effects through intermediate variables. It is
robust against measured and unmeasured confounding of indirect effects.Includes bibliographical references (pages 109-116