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Data For: Examining The Impacts Of Demographics, Individual Experiences, And Perceptions On Range Anxiety And Electric Vehicle Road Trips, Joshua Ward, Patrick A. Singleton, Antje Graul, Zane Regan

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Electric Vehicle Road Trip Study: These data contain anonymized responses to a survey designed to investigate the experiences, perceptions, barriers, and opportunities identified by electric vehicle (EV) drivers regarding road trips to recreational areas (like national parks) and for recreational activities.

Evolving Uk Biology Education, Helen R. Watson

Peninsula Medical School

School science curriculum reform is underway in the UK. Whilst each of the four devolved nations is at a different stage in this process, it is clear we are entering an era of opportunity for positive change, where curricula can be updated to reflect our current understanding of science and to better prepare learners for further study, work, and modern life more broadly.

From Sampling To Simulating: Single-Cell Multiomics In Systems Pathophysiological Modeling, Alexandra Manchel, Michelle M. Gee, Rajanikanth Vadigepalli

Department of Pathology, Anatomy, and Cell Biology Faculty Papers

As single-cell omics data sampling and acquisition methods have accumulated at an unprecedented rate, various data analysis pipelines have been developed for the inference of cell types, cell states and their distribution, state transitions, state trajectories, and state interactions. This presents a new opportunity in which single-cell omics data can be utilized to generate high-resolution, high-fidelity computational models. In this review, we discuss how single-cell omics data can be used to build computational models to simulate biological systems at various scales. We propose that single-cell data can be integrated with physiological information to generate organ-specific models, which can then be …

Neuvac: Nonlinear Extreme Ultraviolet Irradiance Model For Aeronomic Calculations, Daniel A. Brandt, Aaron J. Ridley

Michigan Tech Publications

The need for reliable estimates of solar extreme ultraviolet (EUV) for operational space weather nowcasting and forecasting has motivated the development of new model of solar irradiance variability in 59 wavelength bins between 1 and 1,750 Å. The model employs a nonlinear parameterization of rebinned daily FISM2 irradiances and is termed NEUVAC: Nonlinear Extreme Ultraviolet Irradiance Model for Aeronomic Calculations. The model is parameterized in terms of F10.7 and 54-day averaged F10.7 in a backwards-looking window. The model takes advantage of the high accuracy and resolution of FISM2, and the consistent availability of F10.7 data in order to provide the …

Harmonic Impedance Modeling And Oscillation Analysis Of Modular Multilevel Converter, Yuhong Wang, Wensheng Chen, Shilin Gao, Jianquan Liao, Yangfan Cheng

Journal of System Simulation

Abstract: To facilitate rapid analysis of the oscillation stability mechanism in modular multilevel converter-based high voltage direct current (MMC-HVDC) systems and streamline the simulation process for determining MMC impedance characteristics, a simplified mathematical simulation model for MMC closed-loop impedance is developed using the harmonic state space method. This model considers various control strategies and includes both AC-side and DC-side impedance models. By applying a Nyquist criterion-based impedance analysis method, the stability mechanisms on the AC and DC sides of the MMC are examined. In addition, a data-driven oscillation stability analysis method is also proposed, leveraging a global sensitivity algorithm based …

Path Planning Of Desert Robot Based On Deep Reinforcement Learning, Ming Li, Wangzhong Ye, Jiehua Yan

Journal of System Simulation

Abstract: Due to the complexity and variability of the desert environment, the key to the high-efficient of mobile robot is how to avoid obstacles and plan its path. To solve the problems of poor search efficiency and slow convergence of deep reinforcement learning algorithm in complex environment, an improved deep reinforcement learning path planning algorithm is proposed. The exploration factor is improved and dynamically adjusted according to the convergence degree of the algorithm, so that the exploration factor dynamically decreases with the increase of the understanding degree of the agent to the environment, thus speeding up the convergence speed of …

Investigating Catalytic Conversion Of Lignin To Valuable Products, Jialiang Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Lignin is a complex, random, and heterogeneous biopolymer in lignocellulosic biomass. It has significant potential for conversion into valuable chemicals and fuels. However, its natural recalcitrant structure has made selective depolymerization challenging. This dissertation presents a detailed investigation into the mechanisms of lignin disassembly and structural evolution under various catalytic and solvent conditions. The research spans three key studies. In Chapter 2, operando magic-angle spinning (MAS) solid-state nuclear magnetic resonance (NMR) spectroscopy was employed to study reaction pathways and kinetics of lignin model polymers during catalytic hydrogenolysis. We used a Ni-Al2O3 catalyst in methanol under high temperature and pressure, and …

Investigating Catalytic Conversion Of Lignin To Valuable Products, Jialiang Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Lignin is a complex, random, and heterogeneous biopolymer in lignocellulosic biomass. It has significant potential for conversion into valuable chemicals and fuels. However, its natural recalcitrant structure has made selective depolymerization challenging. This dissertation presents a detailed investigation into the mechanisms of lignin disassembly and structural evolution under various catalytic and solvent conditions. The research spans three key studies. In Chapter 2, operando magic-angle spinning (MAS) solid-state nuclear magnetic resonance (NMR) spectroscopy was employed to study reaction pathways and kinetics of lignin model polymers during catalytic hydrogenolysis. We used a Ni-Al2O3 catalyst in methanol under high temperature and pressure, and …

Econometric Insights Into Lng Carrier Port Congestion And Energy Inflation: A Data-Driven Approach, Stavros Karamperidis, Konstantinos Melas, Nektarios Michail

Plymouth Business School

We examine how LNG carrier port congestion in European ports, measured via detailed vessel-level AIS data, affects euro area energy inflation. As energy inflation significantly affects headline inflation, this study provides an additional factor that can contribute to inflationary pressures. Overall, the results show that higher port congestion increases natural gas prices with the latter having an impact on energy inflation. The reaction stands at 0.1% per 1% shock in port congestion. These findings underline the relationship between the shipping industry and the real economy and support the view that shipping developments can potentially be used as leading indicators.

Information Management Plan – Delivery Team, Robert Moore

Tools

Information Management Plan – Delivery Team is a support in a spreadsheet format that contains a number of delivery team (tenderers) templates that ISO 19650 recommend the delivery team (tenderers) should consider when responding to a tender to deliver a project for an Asset.

Build Digital has created Industry templates to allow organisations work in accordance with ISO 19650. These templates start to address the information requirements in the ISO 19650 series of standards. The templates are also in accordance with CEN-TR17654-2021” Guideline for the implementation of Exchange Information Requirements (EIR) and BIM Execution Plans (BEP) on European level based …

A New Form Of Greenwashing: Why The Implementation Of Carbon Capture Cannot Be Justified For The Public Good Under The Takings Clause Of The Fifth Amendment, Emma Valiante

Seton Hall Law Review

No abstract provided.

Developing Naturalistic Precision Functional Mapping For High-Density Diffuse Optical Tomography, Aahana Bajracharya

McKelvey School of Engineering Graduate Student Theses & Dissertations

Advancements in non-invasive imaging have enabled extensive research into higher-order cognitive functions of the brain. Functional Magnetic Resonance Imaging (fMRI) has provided valuable insights into brain networks underlying various cognitive processes. However, limitations such as confined environments and scanner noise pose challenges for fMRI to be broadly applicable across all demographics and experimental conditions, especially in speech and language studies. Because of this, there is a need for complementary techniques that can help understand the brain as it functions during everyday scenarios. High Density Diffuse Optical Tomography (HD-DOT) is an emerging functional near-infrared spectroscopy-based technique with comparative advantages over fMRI, …

Bone-Ivd Crosstalk And The Role Of Rage Signaling In An Ivd Injury Model, Remy Walk

McKelvey School of Engineering Graduate Student Theses & Dissertations

Low back pain (LBP) is the number one musculoskeletal cause of disability with a lifetime prevalence of up to 85%. The causes of LBP are multifactorial with potential contributions from factors such as tissue injury and degeneration as well as social economic and psychological factors. Intervertebral disc (IVD) degeneration is associated with 26-40% of LBP cases and in a 2015 meta-analysis, it was estimated that a total 403 million people suffer from symptomatic IVD degeneration. IVD degeneration is characterized by loss of hydration in the nucleus pulposus (NP), damage accumulation in the annulus fibrosus (AF) and calcification of the cartilaginous …

Defining The Chondrocyte “Mechanome”: How Mechanosensitive Ion Channels Trpv4 And Piezo1 Drive Changes In Gene Expression To Affect Cell Behavior And Cartilage Health, Daniel Raymond Palmer

McKelvey School of Engineering Graduate Student Theses & Dissertations

Chondrocytes, the only native cell population of cartilage, utilize mechanosensitive transmembrane ion channels to sense mechanical forces. Transient Receptor Potential Vanilloid 4 (TRPV4) is activated by physiologic levels of load (resulting in ~10% tissue deformation) while PIEZO1 is activated by supraphysiologic levels of load (resulting in >50% tissue deformation). TRPV4 activation promotes the expression of a limited number of chondrogenic genes and the production of vital cartilaginous matrix proteins, while PIEZO1 activation drives cell death and senescence, cytoskeletal weakening, and production of catabolic mediators, which contribute to the development of osteoarthritis (OA). However, the path connecting activation of these channels …

Multimodal Representation Learning Frameworks For Modeling Progression And Heterogeneity In Alzheimer’S Disease, Sayantan Kumar

McKelvey School of Engineering Graduate Student Theses & Dissertations

Alzheimer’s Disease (AD) is the leading cause of dementia, characterised by cognitive and functional impairments that disrupt daily activities. Different clinical modalities such as neuroimaging biomarkers, cognitive assessments, fluid biomarkers and genetic data provide unique and complementary information, contributing to a more comprehensive understanding of disease progression and heterogeneity in disease characteristics. With recent advancements in computational capabilities, particularly in deep learning, multimodal representation learning frameworks aim to integrate diverse clinical modalities into a cohesive framework, capturing the most significant patterns within each modality. Existing data-driven multimodal representation learning frameworks in AD research have two major limitations. First, AD progresses …

Pan-Cancer Analysis Of Rna Dysregulation, Somatic Mutations, And Matrix Stiffness Using Bioinformatics Approaches, Gongyu Tang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Cancer is a complex disease driven by genetic mutations, epigenetic modifications, and alterations in the tumor microenvironment. Understanding these intricate molecular mechanisms is crucial for advancing cancer diagnosis, prognosis, and treatment. RNA sequencing (RNA-seq) has emerged as a powerful tool to study cancer by providing comprehensive insights into gene expression, alternative splicing, and mutations at the transcriptome level. This dissertation leverages RNA-seq data to explore different dimensions of cancer biology through multi-omics integration and computational approaches. The research is divided into three main projects. First, I developed OncoDB, an interactive database to analyze gene expression, methylation patterns, and viral interactions …

The Encoding Of Decision Variables In Orbitofrontal Cortex (Ofc) Is Longitudinally Stable And Varies By Cortical Layer, Manning Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Economic choices entail computing the values of different options and then making choice by comparing values. Work in primates and rodents has shown that these mental processes rely on the OFC. Neurophysiology studies identified in OFC distinct groups of neurons encoding the value of individual options, the binary choice outcome, and the chosen value. These variables capture both the input and the output of the decision process, suggesting a decision circuit existing in OFC. A primary challenge for the field is to assess the structure and mechanisms governing this circuit. A first fundamental question is whether the circuit is stable …

Terpene Bioproduction From Non-Model Yeast: Omics Analysis And Metabolic Modeling For Guiding Scaleup Optimization, Alyssa Worland

McKelvey School of Engineering Graduate Student Theses & Dissertations

Sustainability efforts have driven significant advancements in synthetic biology, particularly through the development of microbial cell factories designed for waste reduction and resource conservation. This dissertation seeks to characterize a microbial biorefinery platform for the biosynthesis of terpene compounds from yeast, with an emphasis on the industrial bioconversion of waste lipids. Terpene compounds play crucial roles in various industrial applications; however, traditional methods of terpene production have sustainability and productivity challenges that could be overcome by using microbial biosynthesis. The yeast Yarrowia lipolytica is a microbial host well suited for converting waste oils into value-added terpenes due to its native …

Quantitative Approaches To Dissect Bivalent Antibody Binding And Detect Viruses From Aerosolized Samples, Ananya Benegal

McKelvey School of Engineering Graduate Student Theses & Dissertations

Influenza A virus (IAV) poses substantial epidemiological burdens around the world, with an estimated annual global mortality rate in the hundreds of thousands. Its rapid rate of mutation gives it a propensity for causing seasonal epidemics and occasional pandemics. As an airborne virus, IAV is most commonly transmitted through droplets and aerosols. Antibodies play a crucial role in the immune defense against IAV infection. By understanding the mechanisms of antibody-mediated defense and the transmission dynamics of IAV, we can better inform strategies for prevention, vaccination, and treatment, ultimately mitigating its impact on public health. The work encompassed in this dissertation …

Integrating Physics And Deep Learning For Transmission-Less Attenuation Compensation In Clinical Spect, Zitong Yu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Attenuation compensation (AC) is beneficial for visual interpretation and serves as a prerequisite for quantification tasks in single-photon emission computed tomography (SPECT) imaging. However, conventional AC methods typically rely on a separate X-ray CT component, leading to multiple challenges. In this context, studies have shown that scatter-window projection data contains information to estimate the attenuation distribution. Furthermore, large amounts of SPECT emission data in both photopeak and scatter-energy windows and corresponding CT scans are available. The ability of deep learning (DL) to model complex relationships by leveraging large datasets motivates its use for transmission-less AC in SPECT. Building upon the …

Development Of Computational Imaging Pipelines To Advance Quantitative Co-Clinical Imaging And Pathology, Kaushik Dutta

McKelvey School of Engineering Graduate Student Theses & Dissertations

Deep learning driven computational methods have profoundly impacted medical imaging by advancing feature extraction, and by establishing a critical foundation for downstream image analysis and interpretation. Co-clinical trials are an emerging area of investigation in which a clinical trial is coupled with a corresponding preclinical study to inform the corresponding clinical trial to enhance translational accuracy and therapeutic development. Within this framework, quantitative imaging and pathology plays a fundamental role in both clinical and preclinical arms of co-clinical trials. Quantitative imaging enables enabling identification and validation of imaging biomarkers which are leveraged to predict therapeutic response, characterize disease phenotypes, and …

Developing Naturalistic Precision Functional Mapping For High-Density Diffuse Optical Tomography, Aahana Bajracharya

McKelvey School of Engineering Graduate Student Theses & Dissertations

Advancements in non-invasive imaging have enabled extensive research into higher-order cognitive functions of the brain. Functional Magnetic Resonance Imaging (fMRI) has provided valuable insights into brain networks underlying various cognitive processes. However, limitations such as confined environments and scanner noise pose challenges for fMRI to be broadly applicable across all demographics and experimental conditions, especially in speech and language studies. Because of this, there is a need for complementary techniques that can help understand the brain as it functions during everyday scenarios. High Density Diffuse Optical Tomography (HD-DOT) is an emerging functional near-infrared spectroscopy-based technique with comparative advantages over fMRI, …

Bone-Ivd Crosstalk And The Role Of Rage Signaling In An Ivd Injury Model, Remy Walk

McKelvey School of Engineering Graduate Student Theses & Dissertations

Low back pain (LBP) is the number one musculoskeletal cause of disability with a lifetime prevalence of up to 85%. The causes of LBP are multifactorial with potential contributions from factors such as tissue injury and degeneration as well as social economic and psychological factors. Intervertebral disc (IVD) degeneration is associated with 26-40% of LBP cases and in a 2015 meta-analysis, it was estimated that a total 403 million people suffer from symptomatic IVD degeneration. IVD degeneration is characterized by loss of hydration in the nucleus pulposus (NP), damage accumulation in the annulus fibrosus (AF) and calcification of the cartilaginous …

Defining The Chondrocyte “Mechanome”: How Mechanosensitive Ion Channels Trpv4 And Piezo1 Drive Changes In Gene Expression To Affect Cell Behavior And Cartilage Health, Daniel Raymond Palmer

McKelvey School of Engineering Graduate Student Theses & Dissertations

Chondrocytes, the only native cell population of cartilage, utilize mechanosensitive transmembrane ion channels to sense mechanical forces. Transient Receptor Potential Vanilloid 4 (TRPV4) is activated by physiologic levels of load (resulting in ~10% tissue deformation) while PIEZO1 is activated by supraphysiologic levels of load (resulting in >50% tissue deformation). TRPV4 activation promotes the expression of a limited number of chondrogenic genes and the production of vital cartilaginous matrix proteins, while PIEZO1 activation drives cell death and senescence, cytoskeletal weakening, and production of catabolic mediators, which contribute to the development of osteoarthritis (OA). However, the path connecting activation of these channels …

Reimagining The Machine Vision Pipeline In Cyber Physical Systems For Trustworthiness And Efficiency, Adith Boloor

McKelvey School of Engineering Graduate Student Theses & Dissertations

Cyber-physical systems (CPS), including autonomous vehicles, drones, and mobile robots, rely on intricate sensors, actuators, and machine learning algorithms to perceive the physical world and execute actions within their surroundings. In the context of vision-driven CPS, achieving this demands processing a substantial volume of visual data captured by on-board cameras. The data is subsequently channeled through digital processors and harnessed by deep neural networks for tasks such as image classification, object detection, and depth perception. This data-centric, machine-vision-infused CPS fosters intelligent decision-making, thereby enhancing overall system performance. Trustworthiness, encompassing the robustness of the entire machine-vision pipeline, and system-level efficiency are …

Multimodal Representation Learning Frameworks For Modeling Progression And Heterogeneity In Alzheimer’S Disease, Sayantan Kumar

McKelvey School of Engineering Graduate Student Theses & Dissertations

Alzheimer’s Disease (AD) is the leading cause of dementia, characterised by cognitive and functional impairments that disrupt daily activities. Different clinical modalities such as neuroimaging biomarkers, cognitive assessments, fluid biomarkers and genetic data provide unique and complementary information, contributing to a more comprehensive understanding of disease progression and heterogeneity in disease characteristics. With recent advancements in computational capabilities, particularly in deep learning, multimodal representation learning frameworks aim to integrate diverse clinical modalities into a cohesive framework, capturing the most significant patterns within each modality. Existing data-driven multimodal representation learning frameworks in AD research have two major limitations. First, AD progresses …

Integrating Physics And Deep Learning For Transmission-Less Attenuation Compensation In Clinical Spect, Zitong Yu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Attenuation compensation (AC) is beneficial for visual interpretation and serves as a prerequisite for quantification tasks in single-photon emission computed tomography (SPECT) imaging. However, conventional AC methods typically rely on a separate X-ray CT component, leading to multiple challenges. In this context, studies have shown that scatter-window projection data contains information to estimate the attenuation distribution. Furthermore, large amounts of SPECT emission data in both photopeak and scatter-energy windows and corresponding CT scans are available. The ability of deep learning (DL) to model complex relationships by leveraging large datasets motivates its use for transmission-less AC in SPECT. Building upon the …

The Encoding Of Decision Variables In Orbitofrontal Cortex (Ofc) Is Longitudinally Stable And Varies By Cortical Layer, Manning Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Economic choices entail computing the values of different options and then making choice by comparing values. Work in primates and rodents has shown that these mental processes rely on the OFC. Neurophysiology studies identified in OFC distinct groups of neurons encoding the value of individual options, the binary choice outcome, and the chosen value. These variables capture both the input and the output of the decision process, suggesting a decision circuit existing in OFC. A primary challenge for the field is to assess the structure and mechanisms governing this circuit. A first fundamental question is whether the circuit is stable …

Terpene Bioproduction From Non-Model Yeast: Omics Analysis And Metabolic Modeling For Guiding Scaleup Optimization, Alyssa Worland

McKelvey School of Engineering Graduate Student Theses & Dissertations

Sustainability efforts have driven significant advancements in synthetic biology, particularly through the development of microbial cell factories designed for waste reduction and resource conservation. This dissertation seeks to characterize a microbial biorefinery platform for the biosynthesis of terpene compounds from yeast, with an emphasis on the industrial bioconversion of waste lipids. Terpene compounds play crucial roles in various industrial applications; however, traditional methods of terpene production have sustainability and productivity challenges that could be overcome by using microbial biosynthesis. The yeast Yarrowia lipolytica is a microbial host well suited for converting waste oils into value-added terpenes due to its native …

Reimagining The Machine Vision Pipeline In Cyber Physical Systems For Trustworthiness And Efficiency, Adith Boloor

McKelvey School of Engineering Graduate Student Theses & Dissertations

Cyber-physical systems (CPS), including autonomous vehicles, drones, and mobile robots, rely on intricate sensors, actuators, and machine learning algorithms to perceive the physical world and execute actions within their surroundings. In the context of vision-driven CPS, achieving this demands processing a substantial volume of visual data captured by on-board cameras. The data is subsequently channeled through digital processors and harnessed by deep neural networks for tasks such as image classification, object detection, and depth perception. This data-centric, machine-vision-infused CPS fosters intelligent decision-making, thereby enhancing overall system performance. Trustworthiness, encompassing the robustness of the entire machine-vision pipeline, and system-level efficiency are …