Dynamic learning of the environment for eco-citizen behavior

Le développement de villes intelligentes et durables nécessite le déploiement des technologies de l'information et de la communication (ITC) pour garantir de meilleurs services et informations disponibles à tout moment et partout. Comme les dispositifs IoT devenant plus puissants et moins coûteux, la mise en place d'un réseau de capteurs dans un contexte urbain peut être coûteuse. Cette thèse propose une technique pour estimer les informations environnementales manquantes dans des environnements à large échelle. Notre technique permet de fournir des informations alors que les dispositifs ne sont pas disponibles dans une zone de l'environnement non couverte par des capteurs. La contribution de notre proposition est résumée dans les points suivants : - limiter le nombre de dispositifs de détection à déployer dans un environnement urbain ; - l'exploitation de données hétérogènes acquises par des dispositifs intermittents ; - le traitement en temps réel des informations ; - l'auto-calibration du système. Notre proposition utilise l'approche AMAS (Adaptive Multi-Agent System) pour résoudre le problème de l'indisponibilité des informations. Dans cette approche, une exception est considérée comme une situation non coopérative (NCS) qui doit être résolue localement et de manière coopérative. HybridIoT exploite à la fois des informations homogènes (informations du même type) et hétérogènes (informations de différents types ou unités) acquises à partir d'un capteur disponible pour fournir des estimations précises au point de l'environnement où un capteur n'est pas disponible. La technique proposée permet d'estimer des informations environnementales précises dans des conditions de variabilité résultant du contexte d'application urbaine dans lequel le projet est situé, et qui n'ont pas été explorées par les solutions de l'état de l'art : - ouverture : les capteurs peuvent entrer ou sortir du système à tout moment sans qu'aucune configuration particulière soit nécessaire ; - large échelle : le système peut être déployé dans un contexte urbain à large échelle et assurer un fonctionnement correct avec un nombre significatif de dispositifs ; - hétérogénéité : le système traite différents types d'informations sans aucune configuration a priori. Notre proposition ne nécessite aucun paramètre d'entrée ni aucune reconfiguration. Le système peut fonctionner dans des environnements ouverts et dynamiques tels que les villes, où un grand nombre de capteurs peuvent apparaître ou disparaître à tout moment et sans aucun préavis. Nous avons fait différentes expérimentations pour comparer les résultats obtenus à plusieurs techniques standard afin d'évaluer la validité de notre proposition. Nous avons également développé un ensemble de techniques standard pour produire des résultats de base qui seront comparés à ceux obtenus par notre proposition multi-agents.The development of sustainable smart cities requires the deployment of Information and Communication Technology (ICT) to ensure better services and available information at any time and everywhere. As IoT devices become more powerful and low-cost, the implementation of an extensive sensor network for an urban context can be expensive. This thesis proposes a technique for estimating missing environmental information in large scale environments. Our technique enables providing information whereas devices are not available for an area of the environment not covered by sensing devices. The contribution of our proposal is summarized in the following points: * limiting the number of sensing devices to be deployed in an urban environment; * the exploitation of heterogeneous data acquired from intermittent devices; * real-time processing of information; * self-calibration of the system. Our proposal uses the Adaptive Multi-Agent System (AMAS) approach to solve the problem of information unavailability. In this approach, an exception is considered as a Non-Cooperative Situation (NCS) that has to be solved locally and cooperatively. HybridIoT exploits both homogeneous (information of the same type) and heterogeneous information (information of different types or units) acquired from some available sensing device to provide accurate estimates in the point of the environment where a sensing device is not available. The proposed technique enables estimating accurate environmental information under conditions of uncertainty arising from the urban application context in which the project is situated, and which have not been explored by the state-of-the-art solutions: * openness: sensors can enter or leave the system at any time without the need for any reconfiguration; * large scale: the system can be deployed in a large, urban context and ensure correct operation with a significative number of devices; * heterogeneity: the system handles different types of information without any a priori configuration. Our proposal does not require any input parameters or reconfiguration. The system can operate in open, dynamic environments such as cities, where a large number of sensing devices can appear or disappear at any time and without any prior notification. We carried out different experiments to compare the obtained results to various standard techniques to assess the validity of our proposal. We also developed a pipeline of standard techniques to produce baseline results that will be compared to those obtained by our multi-agent proposal

Context Awareness in Swarm Systems

Recent swarms of Uncrewed Systems (UxS) require substantial human input to support their operation. The little 'intelligence' on these platforms limits their potential value and increases their overall cost. Artificial Intelligence (AI) solutions are needed to allow a single human to guide swarms of larger sizes. Shepherding is a bio-inspired swarm guidance approach with one or a few sheepdogs guiding a larger number of sheep. By designing AI-agents playing the role of sheepdogs, humans can guide the swarm by using these AI agents in the same manner that a farmer uses biological sheepdogs to muster sheep. A context-aware AI-sheepdog offers human operators a smarter command and control system. It overcomes the current limiting assumption in the literature of swarm homogeneity to manage heterogeneous swarms and allows the AI agents to better team with human operators. This thesis aims to demonstrate the use of an ontology-guided architecture to deliver enhanced contextual awareness for swarm control agents. The proposed architecture increases the contextual awareness of AI-sheepdogs to improve swarm guidance and control, enabling individual and collective UxS to characterise and respond to ambiguous swarm behavioural patterns. The architecture, associated methods, and algorithms advance the swarm literature by allowing improved contextual awareness to guide heterogeneous swarms. Metrics and methods are developed to identify the sources of influence in the swarm, recognise and discriminate the behavioural traits of heterogeneous influencing agents, and design AI algorithms to recognise activities and behaviours. The proposed contributions will enable the next generation of UxS with higher levels of autonomy to generate more effective Human-Swarm Teams (HSTs)

Initial Evidence of Construct Validity of Data from a Self-Assessment Instrument of Technological Pedagogical Content Knowledge (TPACK) in 2-Year Public College Faculty in Texas

Technological pedagogical content knowledge (TPACK) has been studied in K-12 faculty in the U.S. and around the world using survey methodology. Very few studies of TPACK in post-secondary faculty have been conducted and no peer-reviewed studies in U.S. post-secondary faculty have been published to date. The present study is the first reliability and validity of data from a TPACK survey to be conducted with a large sample of U.S. post-secondary faculty. The professorate of 2-year public college faculty in Texas will help their institutions meet the goals of the state’s higher education strategic plan, 60x30TX. In order to do reach the 60x30TX goals, Texas community college faculty will need to implement learner-centered strategies as well as more technology in their courses. At present, there is no simple, easy, and effective way for faculty or their institutions to assess the faculty’s readiness to fulfill these goals. A sequential EFA-CFA process is used to test the Community College TPACK Survey for Meaningful Learning (CC-TSML) for reliability, validity, and model fit. The results indicate that the CC-TSML may be a useful initial tool to help Texas community colleges and their faculty determine where to spend their professional development efforts. Comparisons to other studies indicate that the data from Texas 2-year public college faculty in this sample fit well between PK-16 and university faculty in other cultural contexts

ACER Research Conference Proceedings (2018)

There is no shortage of opinion about more and less effective ways of teaching. Schools are continually presented with strategies, programs and approaches that claim to be ‘research-based’, ‘evidence-based’ or even ‘brainbased’. Vocal advocates of particular teaching methods promote their proposed solutions in the media. But how many of these programs and methods have solid foundations in research? And how can teachers and school leaders distinguish exaggerated marketing claims from teaching strategies shown through research to be effective in improving student outcomes? Research Conference 2018 examines research evidence around teaching practices that make a difference. It brings together leading international and Australian researchers to review what is known about more and less effective teaching and discusses the criteria for evaluating the quality of claims made for particular teaching methods

Learning in a digitally connected classroom: Secondary science teachers’ pedagogical reasoning and practices

Despite decades of research surrounding Information Communication Technology (ICT) use in schools, the pedagogical reasoning required to provide meaningful ICT enabled learning opportunities is rarely analysed in the literature. The purpose of this research was therefore to investigate teachers’ pedagogically reasoned practice. This study involved three exemplary Australian secondary science teachers, renowned for their expertise in utilising ICT working in classrooms where students had school issued one-to-one computers and reliable network access. The research utilised qualitative methods, including semistructured interviews, video-based observational data, and an array of lesson artefacts. The study followed a naturalistic multiple-case study design to explore the pedagogical reasoning and actions of these science teachers. The study identified different forms of pedagogical reasoning and action for a digitally connected world. Many aspects of this iterative model bear close resemblance to Shulman’s (1987) original conception of pedagogical reasoning and action. In each case, sophisticated reasoned decision-making drawing upon a range of teacher knowledge bases, most notably technological pedagogical content knowledge took place. The pedagogical reasoning and action model presented demonstrates a backward mapping approach where the use of ICT was directed at supporting the development of scientific content and educational outcomes of the mandated science curriculum. The research also found that these teachers held social constructivist beliefs for the use of ICT and intentionally designed ICT enabled opportunities from a learning affordance perspective. The research also demonstrated a reflexive relationship between the teacher’s beliefs and their pedagogical practices. Teacher activity involved significant preparatory work in the selection and curation of motivating, authoritative and multimodal Internet accessible ICT resources and tools aligned to the mandated science curriculum. In each case, the teachers had purposefully created a customised classroom online presence or website, offering students a flexible learning environment, an uncommon practice at the time of the study. The teachers designed ICT enabled learning opportunities following a guided inquiry model, frequently involving collaborative problem-based strategies. In each case, the students were the dominant users of ICT in the classroom using ICT for discovering knowledge, constructing knowledge and for sharing knowledge. The teachers’ role was predominantly one of orchestration of the learning environment, scaffolding and questioning students as they engaged with guided inquiry-based learning tasks. Ultimately the research revealed the critical role of the teacher in mediating the affordances of ICT for meaningful learning. Overall the findings offer useful insights into how exemplary science teachers’ reason and act about the use of ICT in a digitally connected classroom. An important implication for the development of initial science teacher education programs arose from the study, notably that preservice teachers require ongoing and authentic course opportunities to support the development of the technology, pedagogy, and content knowledge relevant for a digitally connected classroom

Remote access laboratories for preparing STEM teachers: A mixed methods study

Bandura’s self-efficacy theory provided the conceptual framework for this mixed methods investigation of pre-service teachers’ (PSTs) self-efficacy to teach Science, Technology, Engineering and Mathematics (STEM) subjects. The Science Teaching Efficacy Belief Instrument-B (STEBI-B) was modified to create the Technology Teaching Efficacy Belief Instrument (T-TEBI). Pre-test and post-test T-TEBI scores were measured to investigate changes in PSTs’ self-efficacy to teach technology. Interviews and reflections were used to explore the reasons for changes in pre-service teachers’ self-efficacy. This paper reports results from a pilot study using an innovative Remote Access Laboratory system with PSTs

Federal Register

Daily publication of the U.S. Office of the Federal Register contains rules and regulations, proposed legislation and rule changes, and other notices, including "Presidential proclamations and Executive Orders, Federal agency documents having general applicability and legal effect, documents required to be published by act of Congress, and other Federal agency documents of public interest" (p. ii). Table of Contents starts on page iii

Multiscale modelling of intracranial aneurysm evolution: A novel Patient-specific Fluid-Solid-Growth (p-FSG) framework incorporating endothelial mechanobiology

IAs (intracranial aneurysms) affect 2-5% of the adult population with a high fatality rate upon rupture. However, the rupture rate is around 0.1%-1% per year which indicates most aneurysms are stable. This leads to a strong demand for clinicians to have a better understanding of the aneurysm stability for treatment planning. Aneurysm stability is thought to be linked to its mechanical environment from both the blood flow and the pulsatile pressure giving the mechanistic signals to vascular cells. A cascade of subsequently biological reactions through the routine of cellular mechanotransduction within the aneurysm tissue determine the development of aneurysms. It is envisaged that mechanistic modelling of biological processes that govern aneurysm growth may help to distinguish between vulnerable and stable aneurysms. We developed an integrated Patient-specific Fluid-Solid-Growth (p-FSG) framework for simulating the growth of existing intracranial aneurysms. An aneurysm and connected arteries are modelled as fibre-reinforced nonlinear elastic soft-tissue in the commercial software ANSYS. Computational Fluid Dynamics (CFD) simulation quantifies haemodynamic stimuli that act on endothelial cells. Here, we link the morphology of the cells (spindle, hexagonal) to a novel flow metric (Anisotropic Ratio, AR) that characterizes the oscillatory nature of the flow pattern. We then proposed a hypothesis that the endothelial permeability could be regarded as a function of the morphology of endothelial cells which is associated to the growth and remodelling of the aneurysmal tissue. Mass density of elastin and collagen decreases in the region of high endothelial permeability via the inflammatory pathway. Collagen growth (mass changes) is driven by stretch based stimuli of fibroblast cells. Collagen remodelling employs a stress-mediated method that restores the Cauchy stress on collagen fibres to homeostatic levels in the course of the aneurysm enlargement. Principal destructive and self-protective activities during the aneurysm evolution involving elastin, collagen fibres, endothelial cells and fibroblasts are mathematically represented by our p-FSG framework. Our research suggests that the collagen growth function is a vital mechanism for the stability of aneurysms. This is the first framework models the aneurysm evolution on the basis of the patient-specific aneurysm geometry. Also, we incorporated the functionality of endothelial cells quantified by a novel flow metric to the aneurysm growth and remodelling (G&R) model. This automatic p-FSG framework fully integrated into ANSYS engineering software provides a foundational platform for modelling the aneurysm growth and might become a practical tool in the estimation of aneurysm stability

Data-Driven Optimized Operation of Buildings with Intermittent Renewables and Application to a Net-Zero Energy Library

We are at the intersection of three major trends in the built environment where: (i) occupants' comfort, health and safety requirements are needed to support a productive workplace while maintaining a low operating cost, (ii) economic and environmental advantages are favouring an increased use of renewable energy generation and to reduce our reliance on fossil fuels, and (iii) major utilities will require regulation and are gradually shifting towards a more dynamic energy market. This thesis contributes a modelling and control framework that unifies and addresses these three points together. This thesis contributes a methodology for the development of a bootstrapped ensemble-based low-order data-driven grey-box thermal models for supervisory-level optimal controls. The model is integral to a robust sampling-based predictive control (MPC) framework. This approach is directly applicable to most commercial buildings operating on a schedule and can be extended to consider occupant-driven spaces. The methodology is applied to the Varennes Net-Zero Energy Library: Canada's first institutional net-zero energy building. Exogenous inputs are modelled to consider likely probabilistic outcomes for ambient temperature, cloudiness and interior plug loads. Bounding cases are simulated to contrast the proposed approach against conventional methods. MPC is applied to minimize various cost functions and emphasis is placed on a flexible profile-tracking cost function. The profile to track can be an open-market electrical price or a demand response signal thus improving the grid's flexibility while satisfying the building constraints and better utilizing its systems and storage. In a morning peak demand reduction case, given at least a 4-hour notice, our method is able to pre-heat the building, use minimal energy on-peak and yield the full benefits. Considering a profile tracking case to reduce grid interaction, a 10-12% total energy reduction was achieved for winter where the space was gradually heated in the morning and evening while maximizing HVAC utilization during periods of large photovoltaic generation promoting self-consumption. A similar strategy would be near-impossible to handcraft without optimization-based approaches. This proposed methodology can guide later implementations in the development of the next generation of low-cost cloud-connected controllers that are easy to deploy and can be adapted dynamically