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Synthesis and Characterization of Donor-Chromophore-Acceptor (D-C-A) Copper(I)-Based Photoelectrodes for Photoredox Catalysis
No full textTo-date, most molecular photosensitizers used in dye-sensitized photoelectrochemical cells are ruthenium-based. The incorporation of copper(I) complexes into such systems has been limited because of their shorter excited-state lifetimes. Furthermore, molecular donor-chromophore-acceptor systems offer vectorization of charge movement within the excited state to yield spatially separated cation and anion radicals at opposite ends of the molecule to slow charge recombination.
First, this thesis reports a copper(I)-based donor-chromophore-acceptor triad bearing a 1,8-napthalenemonoimide electron accepting moiety and a carbazole electron donating moiety serving to generate a charge separated state under visible light irradiation. This molecular assembly was integrated onto a zinc oxide nanowire surface on a conductive glass slide. Upon photoexcitation the generated oxidizing equivalents are transferred to a copper (II) water oxidation catalyst in aqueous solution oxidizing water with a Faradaic efficiency of 76%. As an improvement on this initial system, this thesis also reports a copper(I)-based donor-chromophore-acceptor triad that bears a triphenylamine electron donor and a phenazine electron acceptor. This triad was also surface grafted onto zinc oxide nanowires, and the as constructed photoelectrodes were used for photodriven alcohol oxidation. The final evolution of these systems is reported in the form of a copper(I)-based donor-chromophore-acceptor triad containing 1,8-napthalenemonoimide as the electron acceptor moiety and triphenylamine as the electron donor. The final charge-separated state formed on photoexcitation of this triad has a long (18 ns) excited state lifetime in acetonitrile, which is one of the longest reported to-date for copper(I)-based donor-chromophore-acceptor systems and exceeds the first two systems reported in this work.
As the photocathode complement to the previous systems that were used as photoanodes, three copper(I) complexes with progressively extended ligand conjugation through terminal phenazine moieties have been synthesized. Acting as dyads, these have been surface grafted onto nickel oxide and their transient photocurrent properties have been investigated. It was found that even though their electronic properties can be modulated in solution through ligand variations, all three complexes generate the same photocurrent. The results provide evidence of hole injection to nickel oxide and spectroscopic investigations confirm that this is happening in the femtosecond time regime even before the formation of final excited state.
In all systems, the generation of photocurrent under white light illumination has validated the utility of these molecular architectures as photosensitizers for dye-sensitized photoelectrochemical cells
Development of Predictive Analytics for Demand Forecasting and Inventory Management in Supply Chain using Machine Learning Techniques
Get PDFForecasting demand effectively and managing inventories efficiently are critical components of modern supply chain management. By understanding full scope of demand possibilities, businesses gain ability to fine-tune inventory levels, navigate situations involving stockouts and overstock, and move toward a more resilient and precise supply chain. This thesis focuses on strategies to enhance these critical functions.
We start with examining impact of customer segmentation on forecasting precision by introducing a novel cluster-based demand forecasting framework that harnesses ensemble learning techniques. Our results showcase the effectiveness of the clustered-ensembled approach with minimal forecast errors. However, the constraints related to data availability and segmentation indicate areas that warrant further investigation in future research.
The significance of demand accuracy becomes most apparent when we consider its impact on safety stock. In second objective, we explore multivariate time series forecasting for optimal safety stock and inventory management, utilizing deep learning models and a cost optimization framework. This strategy outperforms individual models, demonstrating enhanced forecasting accuracy and stability across diverse product domains. Calculating safety stock based on proposed demand prediction framework leads to optimized safety stock levels. This not only prevents costly stockouts but also minimizes surplus inventory, resulting in reduced overall holding costs and improved inventory efficiency.
Although the first two objectives provided optimized results, relying on point predictions to calculate safety stock is not ideal. Unlike traditional point forecasting, distribution forecasting aims to cover the entire range of potential demand outcomes, essentially creating a comprehensive map of possibilities. The third objective of this thesis introduces recurrent mixture density networks (RMDNs) for refined distribution demand forecasting and safety stock estimation. These innovative models consistently outperform traditional LSTM models, offering more precise stockout and overstock predictions. This approach not only reduces inventory costs but also enhances supply chain efficiency.
In summary, this thesis provides valuable insights and methodologies for businesses aiming to enhance demand forecasting accuracy and optimize inventory management practices in the retail industry. By leveraging customer segmentation, ensemble deep learning, and distribution forecasting techniques, organizations can enhance decision-making processes, reduce operational costs, and thrive in the dynamic landscape of supply chain operations
Sexualized Representations of Female High School Teachers: Reflecting on Popular Television Portrayals and Teachers’ Lived Experiences
Get PDFPopular representations of the teaching profession can affect the lives of real teachers. This qualitative study explores the implications of sexualized representations of female high school teachers. From the beginnings of the feminization of teaching at the start of the 19th century, female teachers have been consistently evaluated and defined by sexuality. As women are linked to the body in the mind/body split, female teacher bodies are surveilled in the classroom. Female teacher identities, such as the spinster teacher and seductress teacher, are linked to sexuality. In turn, popular representations of female high school teachers on television are often unrealistically sexualized; one common storyline is the teacher having a sexual relationship with a teenage male student. The first phase of this study, a text analysis, investigated the following: How is female teacher sexuality portrayed on television and how does this portrayal reinforce feminist backlash? Six television storylines depicting a sexual relationship between a female high school teacher and male student were analyzed through the lens of Susan Faludi’s (1991/2020) feminist backlash. It was found that these storylines perpetuate backlash myths by reinforcing traditional gender roles for women, by diminishing the teacher while enhancing the male student, by subjecting the teacher to excessive violence, and by unnecessarily casting women as abusers. Using focus groups and individual interviews with 10 participants, the second phase of the study investigated: What are female high school teachers’ lived experiences pertaining to the broader context of the sexualization of female teachers? The teachers responded to the six fictional representations and discussed the binaries concerning female teacher sexuality in reference to their own experiences in school. The fictional representations present problematic teacher-student power dynamics, normalize the sexualization of female teachers, and show inadequate consequences of the relationship. In discussing the binaries concerning female teacher sexuality, it was found that managing the body in the classroom is a daily conflict, that female teachers feel restricted when defined by sexuality, and that fictional representations are unrelatable and flawed. Findings from both phases show that sexualizing the female teacher degrades her and ignores the complexities and importance of the profession
The Effects of Cannabidiol on Interleukin-2 Production and Viability of Human T Lymphocytes
Get PDFThe legalization of cannabis in Canada resulted in an increased consumption of cannabis and cannabis-related products by the Canadian population. Cannabidiol (CBD) is one of the active chemicals within the cannabis plant. It is of particular interest because it is thought to suppress T cells, which are an integral part of the adaptive immune system. Suppressing T cells is not the desired effect for most individuals consuming cannabis. One of the problems with commercially available CBD is a lack of information on safe or effective amounts. More research needs to be conducted to determine if CBD at various doses causes health problems or can be therapeutic in patients with autoimmunity. The objectives were: (1) to determine the most effective way to deliver CBD in vitro by testing different solvents, (2) to investigate whether CBD alters the amount of the IL-2 cytokine produced by T cells, and (3) to examine the effects of CBD dose on T cell death. The findings demonstrated (1) glycerol was determined to be a better solvent compared to DMSO, (2) CBD causes cell death and decreased Il-2 production in T cells and PBMCs at higher doses.
The relevance of these findings is to better understand the interplay between CBD and the immune system by elucidating what a safe and effective dose of CBD might be for suppressing cytokine production in T cells. The results of thesis also pave the ground for future studies on the mechanism of action of CBD on T cells
Detonation Cell Size Prediction based on Artificial Neural Networks with Chemical Kinetics and Thermodynamic Parameters
Get PDFIn this paper, we develop a series of Artificial Neural Networks (ANN) using different chemical kinetic and thermodynamic input parameters to predict detonation cell sizes. The feedforward neural networks are trained and validated using available experimental data from the Caltech detonation database covering a wide variety of gaseous combustible mixtures at different initial conditions. For each combination of input parameters, a multiple-stage process is followed, which is described in detail, to first determine the best hyperparameters of the ANN (hidden layers, nodes per layer, etc.) and secondly to establish through a fitting process the optimal parameters for each specific network. The performance of the artificial neural networks with different input features is assessed using data from the same source, but that is kept independent and separate from the training and validation process of the ANN. It is found that ANN with three features can provide an accurate estimation of detonation cell size, while increasing the number of features does not improve the accuracy of the ANN. It is also found that the input parameters with the best performance relate indirectly to the stability parameter χ
Security Weaknesses in IoT Management Platforms
Get PDFA diverse set of Internet of Things (IoT) devices are becoming an integrated part of daily lives, and playing an increasingly vital role in various industry, enterprise and agricultural settings. The current IoT ecosystem relies on several IoT management platforms to manage and operate a large number of IoT devices, their data, and their connectivity. Considering their key role, these platforms must be properly secured against cyber attacks. In this work, we first explore the core operations/features of leading platforms to design a framework to perform a systematic security evaluation of these platforms. Subsequently, we use our framework to analyze a representative set of 52 IoT management platforms, including 42 web-hosted and 10 locally-deployable platforms. We discover a number of high-severity unauthorized access vulnerabilities in 9/52 evaluated IoT management platforms, which could be abused to perform attacks such as remote IoT SIM deactivation, IoT SIM overcharging, and IoT device data forgery. More seriously, we also uncover instances of broken authentication in 13/52 platforms, including complete account takeover on 8/52 platforms along with remote code execution on 2/52 platforms. In effect, 17/52 platforms were affected by vulnerabilities that could lead to platform-wide attacks. 28 platforms responded to our responsible disclosure. We were also assigned 11 CVEs and awarded bounty for our findings
System-Level Analysis and Design of Safety-Critical Cyber Physical Systems
Get PDFThe reduction in size and cost of hardware together with the accelerating innovation and advancement in sensor and computational technologies have opened the door for cyber physical systems into all types of applications. While most early systems involved varying degrees of human involvement, the various success stories are encouraging designers to develop cyber physical systems for autonomous control.
The trustworthiness of a cyber-physical system is essential for it to be qualified for utilization in most real-life deployments. This is especially critical for systems that deal with precious human lives. which can be engaged directly as in biomedical systems or indirectly as in automotive systems. Although use-cases for biomedical and automotive systems are considered, the proposed generalized framework can be used to analyze the safety of various cyber-physical systems.
These safety-critical systems can be investigated using both experimental testing and model-based verification. Accurate models have the potential to permit investigating the system behavior under abnormal scenarios. Also, appropriate modeling can speed-up the development process by evaluating candidate designs at an early stage of the design cycle.
Model-based verification can be conducted using the less-exhaustive simulation testing or the resources-greedy model checking. As a trade-off, statistical model checking bears a feasible approach where statistical guarantees can be examined with a specific level of confidence. This research addresses the problem of utilizing accurate system-level models to analyze and design safety-critical cyber-physical systems.
The behavioral descriptions of cyber physical systems are modelled by constructing equivalent formal models. These system-level models are used to conduct statistical model checking to verify properties written using metric interval temporal logic and to provide statistical guarantees on the system safety. This approach is applied on biomedical and automotive systems to verify their safety with consideration for some distortions resulting from unintentional or intentional sources. The proposed verification approach enlightens the development process by providing feedback that can help elect the designs. Moreover, new robust and safe control techniques are proposed to enhance the safety of a closed-loop glucose controller system. Also, a systematic approach is proposed for safety analysis of cyber physical systems. This approach processes systems described using SysML diagrams and applies a new proposed automatic algorithm to construct equivalent formal models. This research work is a step towards bridging the gap between system-level models and formal models so that analysis can be conducted efficiently to enhance the safety and robustness of cyber-physical systems
Corporate Resilience During Crises: Evidence from the COVID-19 Pandemic
Get PDFIn this paper, we examine the role of environmental, social, and governance (ESG) factors in enhancing the resilience of firms during the COVID-19 pandemic. By analyzing the impact of ESG scores on stock prices during three significant events that occurred in connection with the pandemic in the United States – each with varied market responses – we aim to shed light on whether and how ESG factors affect investor trading in different firms in times of crisis. Although we find that the overall market response to COVID-specific events is significant, our findings indicate that ESG factors offer little to no explanatory power with respect to individual stock price returns. The results are robust when we employ a propensity score matching technique rather than a full-sample analysis and other robustness tests. The results suggest that investors may not prioritize a firm’s ESG performance during periods of economic turbulence, but instead focus on more traditional and shorter-term factors including a firm’s ex-ante financial health
Efficient Computational Methodologies for Multi-Objective Optimization of Distributed Energy Resources (DER) Inverters
Get PDFThe paralleling of power converters connected to the grid for power-sharing is a widely used technique. In this context, the design framework for a low-cost, lightweight, compact, and high-performance optimum configuration is an open research problem. This thesis proposes an innovative Multi-Objective Hierarchical Optimization Design Framework (MO-HO-DF) for an Alternating Current (AC) grid interface with N interleaved H-bridges, each with M parallel ``to-be-determined'' switches, connected through coupling inductances (Lf). A total of eight Figures of Merit (FOMs) were identified for the design framework optimization. A rigorous model of the power electronic system is presented. Next, a highly computationally efficient algorithm for the estimation of the required frequency modulation ratio (mf) to meet current harmonic performance requirements for any given configuration is proposed. Then, the concept and implementation of the algorithm are presented for the MO-HO-DF. The effectiveness of the design optimization framework is demonstrated by comparing it to a base case solution. Finally, the design calculations are validated via Piecewise Linear Electrical Circuit Simulation (PLECS) software with manufacturer-provided Three-Dimensional (3D) power semiconductor models that include thermal modelling.
In particular, when an H-bridge is interfaced with a single-phase grid, it requires controllers to regulate the voltages and currents in the system. In this context, the static optimization of controllers responsible for Direct Current (DC) bus voltage regulation and AC regulation, considering time-domain and frequency-domain behaviours, is an open research problem. Firstly, this thesis proposes a method to obtain FOMs with the use of inbuilt functions in MATLAB software. Then for the Type-II Proportional+Integral (PI) controller, a single-variable two-objective convex optimization is proposed. Next, for the Proportional+Multi-Resonant (PMR) controller, three-variable five-objective convex optimization is proposed. The design of the PMR controller is a multi-variable problem that can inherit the principles of a hierarchical framework and leverage the effect of a design variable on the final optimization result. Thus, the work on PMR controller design optimization is extended to a three-level hierarchical design framework and evaluates all six possible paths for optimization. Finally, enhanced macro-model-based MATLAB simulation results are provided to verify the performance of controller designs and generate statistical insights
Generative Models Based on the Bounded Asymmetric Student’s t-Distribution
Get PDFGaussian mixture models (GMMs) are a very useful and widely popular approach for clustering,
but they have several limitations, such as low outliers tolerance and assumption of data normality. Another problem in relation to finite mixture models in general is the inference of an optimal number of mixture components. An excellent approach to solve this problem is model selection, which is the process of choosing the optimal number of mixture components that ensures the best clustering performance. In this thesis, we attempt to tackle both aforementioned issues: we propose using minimum message length (MML) as a model selection criterion for multivariate bounded
asymmetric Student’s t-mixture model (BASMM). In fact, BASMM is chosen as an alternative to improve the GMM’s limitations, as it provides a better fit for the real-world data irregularities. We
formulate the definition of MML and the BASMM, and we test their performance through multiple experiments with different problem settings.
Hidden Markov models (HMMs) are popular methods for continuous sequential data modeling and classification tasks. In such applications, the observation emission densities of the HMM hidden states are typically modeled by elliptically contoured distributions, namely Gaussians or Student’s t-distributions. In this context, this thesis proposes BAMMHMM: a novel HMM with Bounded Asymmetric Student’s t-Mixture Model (BASMM) emissions. This HMM is destined to sufficiently fit skewed and outlier-heavy observations, which are typical in many fields, such as financial or signal processing-related datasets. We demonstrate the improved robustness of our model
by presenting the results of different real-world applications