Environmental testing is essential to certify systems to withstand the harsh dynamic loads they may experience in their service environment or during transport. For example, satel- lites are subjected to large vibration and acoustic loads when transported into orbit and need to be certified with tests that are representative of the anticipated loads. However, tra- ditional certification testing specifications can consist of sequential uniaxial vibration tests, which have been found to severely over- and under-test systems needing certification. The recently developed Impedance-Matched Multi-Axis Test (IMMAT) has been shown in the literature to improve upon traditional environmental testing practices through the use of multi-input multi-output testing and impedance matching. Additionally, with the use of numerical models, predictive simulations can be performed to determine optimal testing pa- rameters. Developing an accurate numerical model, however, requires precise knowledge of the system's dynamic characteristics, such as boundary conditions or material properties. These characteristics are not always available and would also require additional testing for verification. Furthermore, some systems may be extremely difficult to model using numerical methods because they contain millions of finite elements requiring impractical times scales to simulate or because they were fabricated before mainstream use of computer aided drafting and finite element analysis but are still in service. An alternative to numerical modeling is data-driven modeling, which does not require knowledge of a system's dynamic characteris- tics. The Continuous Residue Interpolation (CRI) method has been recently developed as a novel approach for building data-driven models of dynamical systems. CRI builds data- driven models by fitting smooth, continuous basis functions to a subset of frequency response function (FRF) measurements from a dynamical system. The resulting fitted basis functions can be sampled at any geometric location to approximate the expected FRF at that location. The research presented in this thesis explores the use of CRI-derived data-driven models in predictive simulations for the IMMAT performed on a Euler-Bernoulli beam. The results of the simulations reveal that CRI-derived data-driven models of a Euler-Bernoulli beam achieve similar performance when compared to a finite element model and make similar decisions when deciding the excitation locations in an IMMAT. / Master of Science / In the field of vibrations testing, environmental tests are used to ensure that critical devices or structures can withstand harsh vibration environments. For example, satellites experience harsh vibrations and damaging acoustics that are transferred from it's rocket transport vehicle. Traditional environmental tests would require that the satellite be placed on a vibration table and sequentially vibrated in multiple orientations for a specified duration and intensity. However, these traditional environmental tests do not always produce vibrations that are representative of the anticipated transport or operational environment. Newly developed methods, such as the Impedance-Matched Multi-Axis Test (IMMAT) methods achieves representative test results by matching the mounting characteristics of the structure during it's transport or operational environment and vibrating the structure in multiple directions simultaneously. An IMMAT can also be optimized by using finite element models (FEM), which approximate the device to be tested with a discrete number of small volumes whose physics are described by fundamental equations of motion. However, an FEM can only be used if it's dynamic characteristics are sufficiently similar to the structure undergoing testing. This can only be achieved with precise knowledge of the dynamical properties of the structure, which is not always available. An alternate approach to an FEM is to use a data-driven model. Because data-driven models are made using data from the system it is supposed to describe, dynamical properties of the device are pre-built in the model and is not necessary to approximate them. Continuous Residue Interpolation (CRI) is a recently developed data-driven modeling scheme that approximates a structure's dynamic properties with smooth, continuous functions updated with measurements of the input-output response dynamics of the device. This thesis presents the performance of data-driven models generated using CRI when used in predictive simulations of an IMMAT. The results show that CRI- derived data-driven models perform similarly to FEMs and make similar predictions for optimal input vibration locations.

Since the emergence of big data, traditional business intelligence systems have been unable to meet most of the information demands in many data-driven organisations. Nowadays, big data analytics is perceived to be the solution to the challenges related to information processing of big data and decision-making of most data-driven organisations. Irrespective of the promised benefits of big data, organisations find it difficult to prove and realise the value of the investment required to develop and maintain big data analytics. The reality of big data is more complex than many organisations’ perceptions of big data. Most organisations have failed to implement big data analytics successfully, and some organisations that have implemented these systems are struggling to attain the average promised value of big data. Organisations have realised that it is impractical to migrate the entire traditional business intelligence (BI) system into big data analytics and there is a need to integrate these two types of systems. Therefore, the purpose of this study was to investigate a framework for creating a hybrid data-driven decision support system that combines components from traditional business intelligence and big data analytics systems. The study employed an interpretive qualitative research methodology to investigate research participants' understanding of the concepts related to big data, a data-driven organisation, business intelligence, and other data analytics perceptions. Semi-structured interviews were held to collect research data and thematic data analysis was used to understand the research participants’ feedback information based on their background knowledge and experiences. The application of the organisational information processing theory (OIPT) and the fit viability model (FVM) guided the interpretation of the study outcomes and the development of the proposed framework. The findings of the study suggested that data-driven organisations collect data from different data sources and process these data to transform them into information with the goal of using the information as a base of all their business decisions. Executive and senior management roles in the adoption of a data-driven decision-making culture are key to the success of the organisation. BI and big data analytics are tools and software systems that are used to assist a data-driven organisation in transforming data into information and knowledge. The suggested challenges that organisations experience when they are trying to integrate BI and big data analytics were used to guide the development of the framework that can be used to create a hybrid data-driven decision support system. The framework is divided into these elements: business motivation, information requirements, supporting mechanisms, data attributes, supporting processes and hybrid data-driven decision support system architecture. The proposed framework is created to assist data-driven organisations in assessing the components of both business intelligence and big data analytics systems and make a case-by-case decision on which components can be used to satisfy the specific data requirements of an organisation. Therefore, the study contributes to enhancing the existing literature position of the attempt to integrate business intelligence and big data analytics systems. / Dissertation (MIT (Information Systems))--University of Pretoria, 2021. / Informatics / MIT (Information Systems) / Unrestricted

International Telemetering Conference Proceedings / October 25-28, 1993 / Riviera Hotel and Convention Center, Las Vegas, Nevada / A data driven telemetry system is described that responds to the rapid nature in which experimental satellite telemetry content is changed during the development process. It also meets the needs of a diverse experiment in which the many phases of a mission may contain radically different types of telemetry data. The system emphasizes mechanisms for achieving high redundancy of critical data. A practical example of such an implementation, Brilliant Pebbles Flight Experiment Three (FE-3), is cited.

Dans un paysage industriel de plus en plus tourné vers le numérique, les opportunités des entreprises ne manquent pas pour innover et répondre à une demande jusqu’alors inaccessible. C’est dans ce cadre que l’internet des objets apparait comme un élan technologique a fort potentiel. Ce levier d’innovation, basé sur la valorisation de flux de données, sont par nature intangible et c’est pourquoi nous les considérons ici comme des services. Cependant, les concepteurs doivent faire face ici à un univers complexe où de nombreux domaines d’expertise et de connaissance sont engagés. C’est pourquoi nous proposons dans cette thèse un modèle méthodologique de conception mettant en scène le service, l’expertise métier et les technologies de découverte de connaissance de manière optimisé pour concevoir à l’internet des objets. Ce modèle de conception a été éprouvé chez e.l.m. leblanc, entreprise du groupe Bosch, dans le développement d’un appareil de chauffage connecté et de ses services / In a more and more numeric oriented industrial landscape, the business opportunities for companies to innovate and answer needs inaccessible yep are increasing. In this framework, the internet of things appears as a high potential technology. This innovation lever, where the value-creation is principally based on the data, is not tangible by nature and this is the reason why we conceder it as a service in this thesis. However, the designer has to face a complex universe where a high number expertise and knowledge are engaged. This is the reason why we propose in this thesis a design methodology model organizing the service, the domain knowledge and the data discovery technologies in an optimized process to design the internet of things. This model has been experienced at e.l.m. leblanc, company of the Bosch group, in the development of a connected boiler and its services.

Bridge monitoring studies indicate that the quasi-static response of a bridge, while dependent on various input forces, is affected predominantly by variations in temperature. In many structures, the quasi-static response can even be approximated as equal to its thermal response. Consequently, interpretation of measurements from quasi-static monitoring requires accounting for the thermal response in measurements. Developing solutions to this challenge, which is critical to relate measurements to decision-making and thereby realize the full potential of SHM for bridge management, is the main focus of this research. This research proposes a data-driven approach referred to as temperature-based measurement interpretation (TB-MI) approach for structural performance evaluation of bridges based on continuous bridge monitoring. The approach characterizes and predicts thermal response of structures by exploiting the relationship between temperature distributions across a bridge and measured bridge response. The TB-MI approach has two components - (i) a regression-based thermal response prediction (RBTRP) methodology and (ii) an anomaly detection methodology. The RBTRP methodology generates models to predict real-time structural response from distributed temperature measurements. The anomaly detection methodology analyses prediction error signals, which are the differences between predicted and real-time response to detect the onset of anomaly events. In order to generate realistic data-sets for evaluating the proposed TB-MI approach, this research has built a small-scale truss structure in the laboratory as a test-bed. The truss is subject to accelerated diurnal temperature cycles using a system of heating lamps. Various damage scenarios are also simulated on this structure. This research further investigates if the underlying concept of using distributed temperature measurements to predict thermal response can be implemented using physics-based models. The case study of Cleddau Bridge is considered. This research also extends the general concept of predicting bridge response from knowledge of input loads to predict structural response due to traffic loads. Starting from the TB-MI approach, it creates an integrated approach for analyzing measured response due to both thermal and vehicular loads. The proposed approaches are evaluated on measurement time-histories from a number of case studies including numerical models, laboratory-scale truss and full-scale bridges. Results illustrate that the approaches accurately predicts thermal response, and that anomaly events are detectable using signal processing techniques such as signal subtraction method and cointegration. The study demonstrates that the proposed TB-MI approach is applicable for interpreting measurements from full-scale bridges, and can be integrated within a measurement interpretation platform for continuous bridge monitoring.

This report describes the design, development, and evaluation of the Simple Linear Regression Lesson (SLRL), a web-based lesson that uses visual strategies to teach graduate students the principles of simple linear regression. The report includes a literature review on the use of graphics, animations, and data-driven animations in statistics pedagogy and instruction in general. The literature review also summarizes the pertinent instructional design and development theories that informed the creation of the lesson. Following the literature review is a description the SLRL and the methodologies used to develop it. The evaluation section of the report details the methods used during the formative and summative evaluation stages, including results from a small-group implementation of the SLRL. The report concludes with a review of the product's strengths and weaknesses and the process' strengths and weaknesses.

In this thesis, I describe research on teachers' experiences with learner-centered technology. Specifically, this research investigated teachers' experiences with adoption of the learner-centered tools available from Imagine Learning, an online elementary school literacy program. This thesis includes an extended literature review describing learner-centered classrooms, technology integration, and models of technology adoption, followed by a journal-ready article that describes teachers' experiences throughout the process of adopting Imagine Learning. Finally, I provide a description my experiences throughout this project as well as a proposal for future areas of study.

The greatest wealth is health. It is sometimes said your health is a function of what you are not doing, not what you are currently doing. The degree to which individuals can attain, process, and comprehend the necessary health information and services they need to make proper health decisions is vital for optimal health and well-being. This project documents the analysis, design, development, and implementation of a prototype web-based data-driven health & wellness system targeted for college students. The architecture for this system uses business intelligence to develop a smart online platform for real-time analysis based on inputs entered by its users. The objective is to develop modules that can be used to provide meal plan options that dietitians can recommend to students, while also providing a standard wellness health check. This also promotes constant awareness for students with specialized health diets. User-health and wellness history of each Student is collected and stored for generating progress and wellness reports for end users. The dietitian can monitor the user in real time through the data collected and stored in the data server. Users can monitor their own progress. The system incorporates user context and feedback to personalize each user's lifestyle. Implementation of this system provides a complete and easy to use integrated system that promotes the process of analyzing wellness and improving the user’s overall health. The system is designed to be in a non-clinical setting and hence more lifestyle-oriented compared to other health-oriented systems. It is thus more relevant and convenient to student’s everyday life context.

Mitochondria plays a crucial role in cells by maintaining energy metabolism and directing cell death mechanisms by buffering calcium (Ca2+ )from cytosol. Therefore, the Ca2+ overload of mitochondria due to the upregulated cytosolic Ca2+ , observed in many neurological disorders is hypothesized to be a key pathway leading to mitochondrial dysfunction and cell death. In particular, Ca2+ homeostasis disruptions due to Alzheimer’ s disease (AD)-causing presenilins (PS1/PS2) and oligomeric forms of β-amyloid peptides Aβ commonly found in AD patients are presumed to cause detrimental effects on the mitochondria and its ability to function properly. We begin by showing that Familial Alzheimer’s disease (FAD)-causing PS mutants affect intracellular Ca2+ ([Ca2+]i) homeostasis by enhancing the gating of inositol 1,4,5-trisphosphate (IP3) receptor (IP3R) Ca2+ channels on the endoplasmic reticulum (ER), leading to exaggerated Ca2+ release into the cytoplasm. Using experimental IP3R-mediated Ca2+ release data in conjunction with a computational model of mitochondrial bioenergetics, we explore how the differences in mitochondrial Ca2+ uptake in control cells and cells expressing FAD-causing PS mutants affect key variables such as ATP, reactive oxygen species (ROS), NADH, and mitochondrial Ca2+ ([Ca2+ ]m). We find that as a result of exaggerated [Ca2+]i in FAD-causing mutant PS-expressing cells, the rate of oxygen consumption increases dramatically and overcomes the Ca2+ dependent enzymes that stimulate NADH production. This leads to decreased rates of proton pumping due to diminished membrane potential (Ψm) along with less ATP and enhanced ROS production. These results show that through Ca2+ signaling disruption, mutant PS leads to mitochondrial dysfunction and potentially cell death. Next, the model for the mitochondria is expanded to include the mitochondrial uniporter (MCU) that senses Ca2+ in the microdomain formed by the close proximity of mitochondria and ER. Ca2+ concentration in the microdomain ([Ca2+] mic) depends on the distance between the cluster of IP3R channels (r) on ER and mitochondria, the number of IP3R in the cluster (nIP3R), and open-probability (Po) of IP3R. Using the same experimental results for Ca2+ release though IP3R due to FAD-causing PS mutants, in conjunction with a computational model of mitochondrial bioenergetics, a data-driven Markov chain model for IP3R gating, and a model for the dynamics of the mitochondrial permeability transition pore (PTP), we explore the difference in mitochondrial Ca2+ uptake in cells expressing wild type (PS1-WT) and FAD-causing mutant (PS1-M146L) PS. We find that increased mitochondrial [Ca2+]m due to the gain-of-function enhancement of IP3R channels in the cell expressing PS1-M146L leads to the opening of PTP in high conductance state (PTPh), where the latency of opening is inversely correlated with r and proportional to nIP3R. Furthermore, we observe diminished inner mitochondrial Ψm, [NADH], [Ca2+]m, and [ATP] when PTP opens. Additionally, we explore how parameters such as the pH gradient, inorganic phosphate concentration, and the rate of the Na+/ Ca2+ -exchanger affect the latency of PTP to open in PTPh. Intracellular accumulation of oligomeric forms of Aβ are now believed to play a key role in the early phase of AD as their rise correlates well with the early symptoms of the disease. Extensive evidence points to impaired neuronal Ca2+ homeostasis as a direct consequence of the intracellular Aβ oligomers. To study the effect of intracellular Aβ on Ca2+ signaling and the resulting mitochondrial dysfunction, we employed data-driven modeling in conjunction with total internal reflection fluorescence (TIRF) microscopy (TIRFM). High resolution fluorescence TIRFM together with detailed computational modeling provides a powerful approach towards the understanding of a wide range of Ca2+ signals mediated by the IP3R. Achieving this requires a close agreement between Ca2+ signals from computational models and TIRFM experiments. However, we found that elementary Ca2+ release events, puffs, imaged through TIRFM do not show the rapid single-channel opening and closing during x and between puffs using data-driven single channel models. TIRFM also shows a rapid equilibration of 10 ms after a channel opens or closes which is not achievable in simulation using standard Ca2+ diffusion coefficients and reaction rates between indicator dye and Ca2+. Using the widely used Ca2+ diffusion coefficients and reaction rates, our simulations show equilibration rates that are eight times slower than TIRFM imaging. We show that to get equilibrium rates consistent with observed values, the diffusion coefficients and reaction rates have to be significantly higher than the values reported in the literature. Once a close agreement between experiment and model is achieved, we use multiscale modeling in conjunction with patch-clamp electrophysiology of IP3R and fluorescence imaging of whole-cell Ca2+ response, induced by intracellular Aβ42 oligomers to show that Aβ42 inflicts cytotoxicity by impairing mitochondrial function. Driven by patch-clamp experiments, we first model the kinetics of IP3R, which is then extended to build a model for the whole-cell Ca2+ signals. The whole-cell model is then fitted to fluorescence signals to quantify the overall Ca2+ release from the ER by intracellular Aβ42 oligomers through G-protein-mediated stimulation of IP3 production. The estimated IP3 concentration as a function of intracellular Aβ42 content together with the whole-cell model allows us to show that Aβ42 oligomers impair mitochondrial function through pathological Ca2+ uptake and the resulting reduced mitochondrial inner membrane potential, leading to an overall lower ATP and increased production of reactive oxygen species and [H2O2]. We further show that mitochondrial function can be restored by the addition of Ca2+ buffer EGTA, in accordance with the observed abrogation of Aβ42 cytotoxicity by EGTA in our live cells experiments. Finally, our modeling study was extended to other pathological phenomena such as epileptic seizures and spreading depolarizations (SD) and their effects on mitochondria by incorporating conservation of particles and charge, and accounting for the energy required to restore ionic gradients to the neuron. By examining the dynamics as a function of potassium and oxygen we can account for a wide range of neuronal hyperactivity from seizures, normoxic SD, and hypoxic SD (HSD) in the model. Together with a detailed model of mitochondria xi and Ca2+ -release through the ER, we determine mitochondrial dysfunction and potential recovery mechanisms from HSD. Our results demonstrate that HSD causes detrimental mitochondrial dysfunction that can only be recovered by restoration of oxygen. Once oxygen is replenished to the neuron, organic phosphate and pH gradients along the mitochondria determine how rapid the neuron recovers from HSD.

Educators across the country are expected to be data literate. They must be able to systemically collect and analyze student data to make informed instructional decisions. However, many school leaders lack the knowledge about how to transform mountains of data on student achievement into an action plan that will improve instruction and increase student learning (Boudett, et al., 2007). In addition, time constraints make it difficult for educators to effectively and efficiently collaborate around student data consistently. Most of the research on data use describes the importance for educators to use data to improve student achievement. However, limited research has been documented on the role the campus leader employs when creating a culture of data-driven decision-making as it relates to student achievement. Furthermore, the research on data use in Title 1 schools is also limited. Therefore, it is imperative to examine and describe how a Title 1 middle school principal implemented Data Teams on a campus. Consequently, the goal of this research was to determine how school leaders improve student learning through teacher data teams. The four primary questions this research addressed in this single case study were: 1. What is the role of the principal in implementing successful Data Teams? 2. What campus structures foster the Data Team process? 3. What are the perceptions of teachers regarding the effectiveness of the Data Team? 4. What practices contribute to the sustainability of Data Teams? Data was gathered through semi-structured interviews, direct observations, and document reviews which informed the findings. This research study revealed that the principal played a key leadership role in creating a culture of collaboration and data inquiry by implementing teacher Data Teams. Such leadership role is enacted by: communicating a vision for Data Teams, providing for job-embedded professional development, and offering differentiated support. Structured time, structured meetings, student data system, and structured assessments are structures employed by the school. Student-focused collaboration, enhanced teacher trust, and increased student achievement illustrate evidence of Data Team effectiveness. Shared accountability, building school culture, and focused interventions serve to sustain Data Teams. In conclusion, it can be affirmed the principal has the most influence on what will be supported on a campus. Therefore, the leadership role performed by the principal when guiding a faculty through the implementation of Data Teams must be deliberate and thoughtful. The principal should include key stakeholders in the decision-making process and build capacity among teachers to ensure the sustainability of Data Teams. Furthermore, targeted professional development and structures that allow time for teachers to collaborate are necessary. Because the ultimate goal for schools is student learning, it is important that everyone within the school organization understand their role in the Data Team process. / text