Electronic Theses and Dissertations

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    The perspectives of childbearing individuals and their families with reduced local access to maternity services in Canadian rural and remote communities: a qualitative evidence synthesis
    (2024-05-01) Magdum, Komal; Brunton, Ginny
    The reduction and discontinuation of maternity services in rural and remote communities in Canada has created health accessibility issues for many childbearing individuals. In this study, the perspectives of individuals and their families who lack local access to maternity care will be assessed using a Qualitative Evidence Synthesis (QES). Previous qualitative research has identified several stressors due to closures. By framing the research after the release of a watershed document - the Family Centered Maternity and Newborn Care (FCMNC) guidelines from 2019 - the implementation of the guidelines will be assessed. The objective of this study is to synthesize the existing evidence on perspectives of childbearing individuals and their families living in rural and remote communities who face lack of local access to maternity services after the FCMNC release. Results of this QES show that childbearing individuals continue to experience accessibility issues, which impacts their health and wellbeing.
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    The acquisition and implementation of risk technologies by Canadian police services
    (2024-05-01) Hill, Dallas; O'Connor, Christopher
    Most Canadian police services have rapidly acquired and implemented a range of technological advancements in recent years. This rapid adoption of technologies has left a significant gap in our empirical and theoretical understanding of how police make decisions about which technologies to acquire. While existing research has focused on technology’s impact at the organizational level (e.g., post-implementation evaluations), the macro-level contexts that shape technological acquisition by the police is undertheorized and underexamined. The current study examines the acquisition and implementation of risk technologies (i.e., all technologies operationally used by police services to collect data in mass volumes for the purpose of immediate or future risk assessment) by Canadian municipal/regional police services through a tri-phased methodological approach, including: 1) a national survey, 2) semi-structured interviews with police personnel implicated in technological decision-making, and 3) a content analysis of 71 police services’ formal strategic plans. Findings revealed a stark disconnect between formal and informal technology acquisition processes within services, alongside a lengthy list of economic, institutional, and societal influences on said decision-making. Second, results highlight a shifting role of police in the era of evidence-based policing (EBP) and rapid technological advancement towards that of knowledge workers who fulfill ever-evolving demands for information and consumers of private sector technologies. Results are then used to substantiate a call for accountability through collaborative decision-making, formal strategic planning, and external research partnerships.
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    Communicating police-involved deaths: a mixed-methods exploration of Canadian police agencies’ Twitter strategies and perceptions of police
    (2024-05-01) Ferrara, Cristina; Emeno, Karla
    Despite increasing demands for police transparency and accountability regarding police-involved deaths (PIDs), minimal research to date has explored how police agencies utilize social media to communicate these events and little research has explored how the public perceives police statements that address publicized videos capturing an excessive use of force incident (EUOFI). This dissertation addresses this gap using a mixed-methods approach. The first study employs qualitative content analysis to examine how Canadian police agencies communicated PIDs via Twitter during a three-year period (January 1, 2020, to December 31, 2022). Of the 177 PIDs that occurred during this time, only slightly more than half were acknowledged via Twitter by the involved police agencies (n = 100). Analysis of these tweets revealed that they were often very brief, provided minimal information, and utilized a neutral tone. Further analysis suggested that many agencies used techniques to possibly obscure the event, with few clearly communicating that a death had occurred and the role that police played. Of the few tweets that employed empathy, most were identified as being defensive in tone. When comparing these communications with agencies’ tweets following a line-of-duty death (LODD) during the same three-year period, a disparity in frequency and emotional tone were observed, with agencies tweeting eight times more often about LODDs than they did for PIDs. The second study empirically analyzed the impact of different types of police communication on public perceptions after viewing a video capturing an EUOFI. Participants were randomly assigned to view a police tweet presenting excuses, justifications, or reconciliation attempts. Results indicate that those who viewed reconciliatory tweets were more satisfied with the police response they viewed compared to the other groups; however, satisfaction with local police was negatively affected by viewing these statements compared to the no comment and control conditions. Results from these studies provide valuable insight into how police communicate PIDs on Twitter and the impact police statements may have on public perceptions of police following an EUOFI shared on social media. This research contributes to ongoing discussions about police transparency and accountability following PIDs, as well as public perceptions of police.
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    New frontiers in population-based multi-objective feature selection
    (2024-04-01) Zanjani Miyandoab, Sevil; Rahnamayan, Shahryar; Makrehchi, Masoud
    Feature selection is a persistent challenge aimed at minimizing the number of features while maximizing accuracy of classification, or any other machine learning and data mining task, by mitigating the curse of dimensionality. We frame feature selection as a multi-objective binary optimization task with the objectives of enhancing accuracy and reducing the feature count. Given that feature selection, along with binary optimization problems in general, is a NP-hard problem since the size of search space increases exponentially by the increase of the number of features, so especially in high-dimensional spaces, it can be very challenging. We propose three innovative approaches to tackle large-scale multi-objective feature selection. The first technique involves an augmentation to the diversity of the population in the well-established multi-objective scheme of the genetic algorithm, NSGA-II, which is achieved through the substitution of the worst individuals with new randomly generated individuals with a limited number of features in each generation. As the second method, a binary Compact NSGA-II (CNSGA-II) algorithm has been introduced for feature selection for the first time, which represents the population as a probability distribution not only to be more memory-efficient but also to accelerate finding a better candidate solution. Additionally, we present a novel binary multi-objective coordinate search (MOCS) algorithm, which, to the best of our knowledge, is the first of its kind, demonstrating effectiveness in solving multi-objective binary optimization problems. A comparative study on the proposed methods and NSGA-II showcases the promising performance of our methods in feature selection tasks involving large-scale datasets, surpassing the renowned NSGA-II algorithm. These methods are not limited to feature selection but can be applied to various binary optimization domains, including the Knapsack problem and training Binary Deep Neural Networks.
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    Experimental investigations of the thermodynamic properties of Nd-C and Ce-C binary systems for TRISO fuel applications
    (2024-04-01) Varga, Ryan Mathew; Atkinson, Kirk; Piro, Markus; Fitzpatrick, Bernie
    TRISO fuels are proposed for modular and niche nuclear power reactor technologies, expanding the global nuclear power inventory to meet increasing energy demands through small scale applications. Expansion is dependent upon the improvements to safety through thorough understanding of fission product behaviour, studied and analyzed here using experimental techniques and comparisons to existing literature. Significant knowledge gaps exist in the thermodynamic behaviour of neodymium carbide and cerium carbide fission products that play significant roles in the qualification of TRISO fuels. Thermodynamic investigations of neodymium and cerium carbide isotopic equivalent fission products were performed to improve the knowledge base of TRISO fuels. Various crucible tests, calibrant experimentation, sample generation and sample preparation techniques, and new thermodynamic measurements have been performed. Boron nitride crucibles worked most effectively, providing useful data for comparison to existing literature. Measurements of varying molar compositions of neodymium carbide and cerium carbide provided new data to expand upon predicted phase change boundaries, with experimental phase changes exhibiting comparatively higher transition temperatures.
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    A study of meta-learning methods on the problem of video matting
    (2024-04-01) Tabaraki, Negin; Qureshi, Faisal; Pu, Ken
    Applying image matting techniques directly to video matting presents challenges, primarily due to the complex temporal dynamics inherent in video data. In this work, we studied two Meta Learning approaches—Boosting with Adapters (BwA) and Boosting using Ensemble (BuE)—to tackle the task of video matting using pre-trained image matting models. BwA refines (image matting) alpha mattes by fine tuning pre-trained segmentation models, which we refer to as adapters, using video frames. BuE, additionally, combines multiple fine-tuned adapters using a convolutional neural network. We introduced a meta-learning architecture that incorporates both adapters and ensemble boosting through an iterative process of expert selection and fine tuning. Based on our evaluation on benchmarks based on a standard video matting dataset (VideoMatte240K), we confirm that the proposed scheme improves the performance of image matting models on the task of video matting. In addition, the proposed approach also improves the performance of VMFormer (c. 2022), a recent video matting method.
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    A hybrid compensation-based misalignment tolerant wireless power transfer system for e-mobility
    (2024-04-01) Shrestha, Niranjan; Williamson, Sheldon
    The thesis focuses on developing a hybrid compensation with a phase shift control strategy, aiming for misalignment tolerant constant current/constant voltage (CC/CV) charging through a wireless power transfer (WPT) system for e-mobility. The thesis proposes a hybrid multi-resonant compensation network (LCC-LCC and LCC-S) for CC/CV charging during perfect alignment, controlled by the secondary side only. Additionally, the thesis introduces a phase shift control technique in the inverter to maintain the corresponding CC and CV charging mode during the misalignment up to 100 mm between primary and secondary coils. Initially, the theoretical analysis of the proposed system is described in detail. Then, simulation results for 3.7 kW and 270 W peak load were carried out in MATLAB Simulink. Lastly, experimental testing and validation were conducted for the proposed hybrid compensated system for 270 W peak load, applicable to the E-bike. The experimental results show good consistency with theoretical and simulation analysis.
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    Development of multi-physics capabilities in coupling computational fluid dynamics and thermodynamics for molten salt reactor applications
    (2024-03-01) Scuro, Nikolas Lymberis; Mohany, Atef; Piro, Markus H.A.
    Existing knowledge gaps in Molten Salt Reactors (MSR), such as understanding fuel salt chemistry and fission product retention, require rigorous investigations to support reactor safety. The objective of this work was to develop a novel computational toolset tailored for multi-physics simulations of MSR studies that could fill the aforementioned knowledge gaps. This toolset studied the intricate dependence between thermal-hydraulics and salt chemistry by coupling the computational fluid dynamics code OpenFOAM with the computational thermodynamics code Thermochimica . This coupling facilitates the simulation of scenarios relevant to MSR reactor safety, which is especially important given that MSRs have a low technical readiness level relative to other reactor technologies. Two demonstration problems exemplify the applications and outcomes of this project, which was in support of the SAMOSAFER Co-ordinated Research Project of the European Commission. The initial problem revolves around the first step of the molten salt clean-up fluorination process, which vaporizes molten fuel components (i.e., UF4) into its volatile form (i.e., UF6). Simulations revealed that the fluorination time is strongly dependent on the molten salt system, salt viscosity, and temperature. Compared with experimental results, the simulations displayed a strong correlation in vaporization rates under steady-state conditions, giving credence to the validity of the local equilibrium hypothesis. The second demonstration problem centred on the molten salt fast reactor. Here, normal operating conditions were examined, focusing on fission product retention and release, such as Cs, La, Xe in promising molten fluoride systems (e.g., LiF – ThF4 – UF4 77.5-20-2.5 mol%). Simulations demonstrated that most fission products are retained by the salt and the evaporation rates of several compounds, such as, LiF, ThF4, UF4, CsF, Cs2, Cs2F2, LaF3, F, F2 posed to be almost insignificant when compared to other known volatile/gaseous fission products, such as xenon. This investigation provided an understanding of how fast the UF4/UF3 molar ratio reaches optimal design limits, which plays a pivotal role in controlling corrosion. In conclusion, the outcomes underscore advancements in computational capabilities, promising to elucidate further the intricacies of designing and testing multifaceted scenarios in the realm of MSRs.
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    Coupled radiation transport and mobile depletion for health physics and environmental impact applications
    (2024-04-01) Sawatzky, Kevin; Atkinson, Kirk D.
    The next generation of nuclear reactors pose a large challenge to existing computational methods. Caribou is a MOOSE-based health physics and environmental impact code under development at Ontario Tech which aims to address some of these challenges. This work developed a discrete ordinates radiation transport solver and a radionuclide trace species transport solver in the MOOSE framework for Caribou. This radiation transport solver is compared to several benchmark problems to determine its accuracy with and without ray effect mitigation measures, finding good agreement with all the problems tested. The trace species transport solver is then verified with the method of manufactured solutions. The coupled solvers are then used to analyze the formation of ⁴¹Ar in a containment volume due to ex-core neutron fields, and the photon fields from a ¹³⁷Cs plume. The conclusions of this work indicate that these methods are a valuable addition to Caribou’s suite of capabilities.
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    Design and development of an LLM-based framework for crime classification and prediction
    (2024-04-01) Sarzaeim, Paria; Mahmoud, Qusay H.; Azim, Akramul
    Large language models (LLMs), as a subset of generative AI, have been used in different domains, such as financial, medical, legal, and agricultural applications. However, adopting LLMs for smart policing applications remains unexplored. This thesis concentrates on developing a framework based on the transformative potential of the BART, BERT, and GPT models in this domain using methods such as zero-shot prompting, few-shot prompting, and fine-tuning. As a prototype, these methods were used to comprehensively assess the performance of LLMs in crime classification and prediction based on state-of-the-art datasets from two major cities: San Francisco and Los Angeles. The main objective is to illuminate the adaptability of LLMs and their capacity to revolutionize crime analysis practices. Additionally, a comparative analysis of the aforementioned methods on the GPT model and BART with machine learning (ML) techniques is provided. The experimental results demonstrate the feasibility of integrating LLMs into smart policing systems and show that GPT models are more suitable than traditional ML models for crime prediction in most experimental scenarios.
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    Artificial intelligence agent for assessing game difficulty
    (2024-04-01) Sansalone, Stevie C. F.; Mirza-Babaei, Pejman
    Balancing game difficulty is a key element of developing games which engage players without causing frustration. Difficulty balancing is an iterative process and game developers frequently rely on user tests to guide them, but the cost of user testing can be prohibitive, particularly for independent developers. In response to this, I seek to determine whether AI can be used to test the difficulty of developing games before setting up a full playtest. For this purpose, I developed ARTemiS, a prototype tool for AI-based playtesting focused on game difficulty arising from precision input tasks such as combat. I then performed a user study with 10 participants who used the tool to assess and modify three demo levels. This research seeks to assess the applicability and utility of AI-based playtesting for difficulty balancing and lay a foundation for the development of tools for assessing other aspects of difficulty with AI.
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    Design and development of a framework for predicting short price jumps in cryptocurrency market
    (2024-04-01) Rajaei, Mohammadjavad; Mahmoud , Qusay H.
    The cryptocurrency market's volatility offers significant profit opportunities despite its risks. This thesis aims to capitalize on these opportunities by designing and developing a framework to predict whether a coin will experience growth in the next trading candle. To achieve this, we constructed a robust framework that incorporated various input features. We conducted comprehensive analyses by leveraging six machine learning models. Our methodology involves training these models on historical daily data from the Binance Exchange. Subsequently, we evaluate their performance using diverse testing datasets from January 2022 to December 2023. Demonstrating notable precision, especially with a growth rate of 1%, the model has proven effective across various scenarios, consistently yielding profits. Regarding the backward testing results, the XGBoost model combined with the trading strategy made a profit in all three test datasets of Oct 2023, Jul to Sep 2023, and LSK/USDT achieved 24%, 47%, and 98% profits, respectively. This thesis underscores the potential for leveraging well-designed machine learning models to earn significant profits, even in bearish market conditions.
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    Initial validity and reliability testing of the SGBA-5
    (2024-03-01) Putman, Andrew; Dogra, Shilpa
    Background: A hurdle to incorporating Sex- and Gender-Based Analysis is a lack of easily implemented measurement tools. To address this, we created the Sex- and Gender-Based Analysis Tool – 5 item [SGBA-5]. Objectives: Assess the validity and reliability of the SGBA-5 for health research where sex or gender are not primary variables. Methods: A Delphi consensus study was conducted with Canadian researchers [n=14]. A 2-arm [students, n=89; older adults, n = 71] test-retest study was then conducted. Results: Agreement was reached for the sex item [93%] and non-agreement for gendered aspect of health items [identity: 64%, expression: 64%, roles: 50%, relations: 57%]. The test-retest study found all items reliable on both arms [sex: κ = 1.00, gendered: ICC(A,1) > .865]. Conclusion: The novel SGBA-5 demonstrated reliability for all items and validity of the sex item; the gendered aspects of health items may be valid. Future research will further assess the SGBA-5.
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    Quadruped robots and the uncanny valley: a study on canine representation
    (2024-04-01) Padilla Velasco, Carolina; Hung, Patrick
    This research analyses how dog-like features in robotic quadrupeds influence their social perception. The initial part of the methodology describes the design and development of three robotic dog prototypes that include key features like a head and a tail. Further, Contrastive Language-Image Pre-Training (CLIP), a neural network that has demonstrated signatures of the uncanny valley effect before, was used to explore how the perception of quadrupeds evolves as their level of canine likeness intensifies. For this purpose, seven models were tested, ranging from a fully robotic quadruped to a living dog, and 252 images were assessed for each. Our findings indicate that the uncanny valley effect also develops in quadruped robots. This novel contribution serves as a reference to select an appropriate level of realism for four-legged robots. This is particularly valuable for robotic applications that look to incorporate human-dog dynamics.
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    Robotic tails in education for enhanced Human-Robot Interaction
    (2024-04-01) Martinez Cano, Marco Antonio; Hung, Patrick
    This thesis presents an articulated tail mechanism to enhance quadruped robots, enabling them to mimic expressive behaviors like dogs. Additionally, this work investigates non-verbal communication in quadruped robots for Human-Robot Interaction (HRI) while exploring the potential of utilizing a robotic tail prototype as an education tool in academia, introducing Science, Technology, Engineering and Mathematics (STEM) students to the development of social robotics. This thesis describes the development of the Do It Yourself (DIY) Robotic Tail Workshop for the “Special Topics in Information Technology-Service Robots + Topics in Technology Management-Service Robots” class at Ontario Tech University, listing the objectives, methods, and considerations presented through the three times the workshop has been executed. Furthermore, an experiment and comparative analysis were conducted using the first and current tail prototypes by performing an accuracy and repeatability parameter testing procedure based on the Robot-Test and Ford methods. The experimental results indicate higher levels of accuracy and a broader range of motion for the latest tail prototype compared to similar commercial products available in the market, such as the Qoobo robot, a companion cushion robot with a moving tail that responds to physical touch. This thesis contributes to establishing robotic tails as an innovative approach to HRI by setting the basis to bring emotional expression to robots.
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    Run, Llama, Run: an educational coding game for assessing tangible and hybrid interfaces
    (2024-04-01) Koornneef, Stacey A.; Bradbury , Jeremy; Miljanovic, Michael
    As the impact of computer science becomes more pervasive across the world, it is increasingly being integrated into curriculum at earlier and earlier grades, including from kindergarten through grade 5 (which is the grades of early elementary school). One of the important concepts in computer science at earlier ages is computational thinking, or the process of how to think algorithmically. It is important when developing new ways to teach computer science that they are collaborative, engaging, and cost-accessible. This thesis proposes a new cost-accessible educational game for K-5 students that teaches computational thinking through an educational coding game called Run, Llama, Run. This game has both fully tangible and hybrid digital/tangible versions, which allow us to assess the impact of the interfaces on engagement and collaboration. Its purpose is to determine the benefits and detriments of a fully tangible educational game for this age group, focusing on collaboration and engagement.
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    Modeling and testing of an improved 8x8 scaled electric combat vehicle
    (2024-04-01) Kim, Junwoo; El-Gindy, Moustafa
    The current 8x8 combat vehicle pertaining to multi-wheeled vehicles indicates a lack of effort in developing advanced multi-steering electric drive vehicle. Therefore, novel steering scenarios for an 8x8 scaled electric combat vehicle that features maintaining individual wheel’s steering and speed are developed in this thesis using a scaled 8x8 compat vehicle to introduce a future steering control system for the current combat vehicles. This thesis explains the mechanical improvement of the scaled vehicle compared to the previously developed model. In addition, validation of the scaled electric vehicle model in comparison with a full-size electric drive vehicle model in TruckSim is presented to prove the advantage of using the scaled vehicle in experimental test. Furthermore, development and validation of steering strategies including traditional, fixed 3rd axle, and all-wheel steering scenarios in the scaled vehicle in terms of both experiment and simulation results are conducted to enhance the limitations imposed by conventional vehicle’s steering control system. Active Steering Controller (ASC) is developed and simulated to overcome the limitations of the complete Ackermann steering condition when the scaled vehicle is driving at a relatively high speed. The outcome of this thesis research work is a novel future electric multi-drive and multi-steer combat vehicle.
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    Modeling and analysis of Regional Haul Steer (RHS) truck tire model
    (2024-04-01) Khosravi, Mehran; El-Gindy, Moustafa; El-Sayegh, Zeinab
    In this thesis, the RHS truck tire size 315/80R22.5 was developed using Finite Element Analysis and several material properties. The tire model was then validated using static and dynamic testing, against physical measurements provided by the manufacturer. A simulation model of flooded and snow terrain was then developed using the Smoothed-Particle Hydrodynamics technique and hydrodynamic elastic-plastic material model. The tire-terrain interaction characteristics were then evaluated over flooded and snow surfaces. The interaction characteristics included the rolling resistance, cornering force, self-aligning moment, and overturning moment. The analysis was performed at different operating conditions including terrain depth, longitudinal speed, and vertical loads. In general, the results from both surfaces exhibited similar trends, even though the values were not the same. Future work involves the utilization of genetic algorithms to generate semi-empirical relationships, as well as the implementation of temperature and wear models for the RHS tire.
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    Autonomous UAV-UGV robot collaboration for exploration and mapping of unknown environments
    (2024-04-01) Khabbaz, Noor; Nokleby, Scott
    This thesis addresses the limitations of existing approaches to autonomous exploration and mapping of unknown environments that use multiple Unmanned Ground Vehicles (UGVs). An Unmanned Aerial Vehicle (UAV) is introduced into the multirobot system to overcome the challenges of relative localization and obstacle detection. A novel method is proposed for autonomously determining the UGVs’ starting poses using ArUco markers visible to the UAV, resulting in the initialization of a global merged map. A second method is developed to overcome UGV obstacle detection limitations. UAV depth camera data is processed to detect and incorporate previously unseen obstacles into the UGVs’ navigation schemes, enabling avoidance. Experimental validation demonstrates the effectiveness of both methods in enhancing system autonomy. The integration of a UAV into multi-robot systems presents a promising solution to address UGVs’ localization challenges and limited field of view to improve their functionality in hazardous environments.
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    Characterization of apoptotic cell death in bovine red blood cells
    (2024-04-01) Kennedy, Bailey; Qadri, Syed
    Red blood cells (RBCs) in mammalian species display phenotypic variations in cellular functions and metabolism ascribed to their compositional and morphological differences. After accruing stress-induced damages, organelle-free human RBCs display a specialized apoptotic cell death process characterized by breakdown of normal phospholipid cell membrane architecture. This process facilitates swift phagocytic recognition and catabolism of apoptotic RBCs, which could significantly curtail their lifespan in circulation. Due to multiple phospholipid anomalies in bovine RBCs, their cell death machinery is not completely understood. Herein, we observed that bovine RBCs display differential cell death patterns in response to various pathophysiologic cell stressors in vitro as compared to human RBCs, which were only partly explained by increased Ca2+ influx and oxidative stress. In conclusion, premature cell death of circulating bovine RBCs could potentially contribute to the pathogenesis of anemia in cattle of varying etiology. The present observations may have relevance to livestock health and productivity.