1,956 research outputs found

    Critical Teaching Behaviors: Defining, Documenting, and Discussing Good Teaching

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    What does “good” teaching mean, and how can we know it when we see it? Perhaps you have grappled with these questions at some point in your career, either as an instructor wanting to document or grow your teaching effectiveness or as a peer or administrator trying to provide guidance to or assess the teaching of others. This book serves three purposes: a condensed, evidence-based guide to effective teaching; a resource on creating a focused teaching narrative and teaching portfolio; and a toolkit that equips faculty to conduct peer observations, student midterm feedback, and productive conversations related to teaching. The first part of the book offers a rich guide as to what constitutes effective teaching based on a comprehensive review of the research on instructional strategies and behaviors that promote student engagement, learning, and success. It includes practical advice flexible enough to accommodate disciplinary and contextual differences, recognizing that readers will want to adapt effective behaviors based on their values and dispositions. The opening chapters successively cover aligning classroom activities to learning goals; teaching inclusively to account for students’ prior learning and diversity; creating an environment that promotes students’ active engagement in learning and taking responsibility for their intellectual development; assessing students’ progress and adjusting teaching accordingly; using technology effectively; and finally engaging in reflective self-assessment with feedback from peers and students to adjust and develop teaching skills. In the second part of the book, the authors offer structured guidance on developing a focused teaching narrative, gathering peer and student feedback to support that narrative, and curating a portfolio to showcase exemplary practices and achievements. The insights and tools presented also equip readers to facilitate classroom peer observations and gather midterm student feedback. Overall, the second part of the book provides readers with a common language and tools to use when discussing teaching with peers and those who may formally or informally observe their teaching. The book builds to providing the reader with a clear sense of the criteria and evidence needed to document their teaching for the purposes of annual review, promotion, or tenure. The now widely recognized Critical Teaching Behaviors (CTB) framework offers a holistic means of documenting and assessing teaching effectiveness by including a variety of evidence and perspectives. The comprehensive feedback and documentation toolkit aligned to the framework incorporates more of the instructor’s perspective on their own teaching into the evaluation process and substitutes for or supplements student evaluations of teaching (SETs). Administrators will also find the CTB useful as a template and guide for the objective evaluation of teaching. In a single volume, this book offers faculty evidence-based guidance and encouragement to explore effective teaching strategies whether they are just embarking on their college teaching journey or are experienced instructors looking to explore new ideas. The CTB presents instructors a roadmap to both developing teaching skills and demonstrating achievements in promoting student learning to advance their careers. It is designed to be an interactive workbook. While readers can choose to read passively, they will get the most value from this book by completing the prompts and activities along the way

    Serial sectioning PSOCT and 2PM for imaging post-mortem human brain and neurodegeneration

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    The study of aging and neurodegenerative processes in the human brain necessitates a comprehensive understanding of its myeloarchitectonic, cytoarchitectonic, and vascular structures. While recent computational advances have enabled volumetric reconstruction of the human brain using stained slices, the standard histological processing methods have often led to tissue distortions and loss, making deformation-free reconstruction challenging. Therefore, the development of a multi-scale and volumetric imaging technique that can accurately measure multiple structures within the intact brain would be a significant technical breakthrough. In this work, we present the development of an integrated approach that combines serial sectioning Polarization Sensitive Optical Coherence Tomography (PSOCT) and Two Photon Microscopy (2PM) to provide label-free multi-contrast imaging of human brain tissue. Our method allows for the simultaneous visualization of scattering, birefringence, and autofluorescence properties of the post-mortem human brain. By utilizing high-throughput reconstruction of 4x4x2cm3 sample blocks and simple registration of PSOCT and 2PM images, we enable comprehensive analysis of myelin content, cellular information, and vascular structure. PSOCT provides mesoscopic images and enables quantitative measurement of those brain structures, while 2PM provide complementary microscopic validation and enrichment of cellular and capillary information. This combined approach reveals myelin density and structure maps of the whole brain block and supplies intricate vessel and capillary networks as well as lipofuscin-filled cell soma across cortical regions, providing insights into the myeloarchitectural, cellular and vascular changes associated with neurodegenerative diseases such as Alzheimer's disease (AD) and Chronic Traumatic Encephalopathy (CTE)

    2017 GREAT Day Program

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    SUNY Geneseo’s Eleventh Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1011/thumbnail.jp

    The role of the brain extracellular space in diffusion and cell signalling

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    134 p.The extracellular space (ECS) is a highly complex space consisting of narrow interconnected channels and reservoirs. The ECS substructures are usually few nanometers wide and consequently, they are very difficult to visualize. In addition, the brain ECS is a very dynamic structure, that changes at different temporal scales. These structural changes can be physiological or they can have a pathological cause. In fact, astrocytic swelling at the expense of the ECS volume is one of the hallmarks of epilepsy. Particularly, we are interested in how ECS volume changes affect GABAergic inhibition, the main source of inhibition in the brain and one of the most studied processes in the onset of epileptogenesis.On the other hand, most intercellular signalling in the brain occurs by diffusion of particles through the ECS channels. Understanding how diffusion is regulated by the fine geometry of the brain neuropil is becoming the focus of interest for researchers. However, progress in this field is limited by the difficulty to access local ECS diffusion with experimental techniques. Recently developed techniques, such as super-resolution shadow imaging (SUSHI), are opening the doors to understand diffusion of molecules through the brain sub-micron ECS structures. In this study, we aim to investigate how the nano-scale ECS geometry of the live brain tissue shapes the diffusion of transmitters and its impact on cellular communication. To attain this goal, we have developed a novel computational model, based on SUSHI images

    Quantitative and Non‐Quantitative Assessments of Enzymatic Electrosynthesis: A Case Study of Parameter Requirements

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    The integration of enzymatic and electrochemical reactions offers a unique opportunity to optimize production processes. Recently, an increasing number of laboratory-scale enzymatic electrosyntheses have shown impressive performance indicators, leading to scientific interest in technical implementation. However, important process parameters are missing in most of the relevant literature. On one hand, this is due to the large variety of relevant performance indicators. On the other hand, enzyme technologists and electrochemists use different parameters to describe a process. In this article, we review the most important performance indicators in electroenzymatic processes and suggest that in order to allow quantitative comparison, these indicators should be reported in all respective publications. In addition to quantitative parameters, non-quantitative assessments often need to be included in a final evaluation. Examples of such parameters are sustainability, contribution to the UN Sustainable Development Goals or interactions with the overall process. We demonstrate the evaluation of processes using hydrogen peroxide-dependent peroxygenases. The strength of the proposed evaluation system lies in its ability to identify weaknesses in a process at an early stage of development. Finally, it can be concluded that all evaluated enzymatic electrosynthesis do not yet meet typical industrial requirements for an enzyme-based process

    Transfer Learning of Deep Learning Models for Cloud Masking in Optical Satellite Images

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    Los satélites de observación de la Tierra proporcionan una oportunidad sin precedentes para monitorizar nuestro planeta a alta resolución tanto espacial como temporal. Sin embargo, para procesar toda esta cantidad creciente de datos, necesitamos desarrollar modelos rápidos y precisos adaptados a las características específicas de los datos de cada sensor. Para los sensores ópticos, detectar las nubes en la imagen es un primer paso inevitable en la mayoría de aplicaciones tanto terrestres como oceánicas. Aunque detectar nubes brillantes y opacas es relativamente fácil, identificar automáticamente nubes delgadas semitransparentes o diferenciar nubes de nieve o superficies brillantes es mucho más difícil. Además, en el escenario actual, donde el número de sensores en el espacio crece constantemente, desarrollar metodologías para transferir modelos que funcionen con datos de nuevos satélites es una necesidad urgente. Por tanto, los objetivos de esta tesis son desarrollar modelos precisos de detección de nubes que exploten las diferentes propiedades de las imágenes de satélite y desarrollar metodologías para transferir esos modelos a otros sensores. La tesis está basada en cuatro trabajos los cuales proponen soluciones a estos problemas. En la primera contribución, "Multitemporal cloud masking in the Google Earth Engine", implementamos un modelo de detección de nubes multitemporal que se ejecuta en la plataforma Google Earth Engine y que supera los modelos operativos de Landsat-8. La segunda contribución, "Transferring deep learning models for Cloud Detection between Landsat-8 and Proba-V", es un caso de estudio de transferencia de un algoritmo de detección de nubes basado en aprendizaje profundo de Landsat-8 (resolución 30m, 12 bandas espectrales y muy buena calidad radiométrica) a Proba-V, que tiene una resolución de 333m, solo cuatro bandas y una calidad radiométrica peor. El tercer artículo, "Cross sensor adversarial domain adaptation of Landsat-8 and Proba-V images for cloud detection", propone aprender una transformación de adaptación de dominios que haga que las imágenes de Proba-V se parezcan a las tomadas por Landsat-8 con el objetivo de transferir productos diseñados con datos de Landsat-8 a Proba-V. Finalmente, la cuarta contribución, "Towards global flood mapping onboard low cost satellites with machine learning", aborda simultáneamente la detección de inundaciones y nubes con un único modelo de aprendizaje profundo, implementado para que pueda ejecutarse a bordo de un CubeSat (ϕSat-I) con un chip acelerador de aplicaciones de inteligencia artificial. El modelo está entrenado en imágenes Sentinel-2 y demostramos cómo transferir este modelo a la cámara del ϕSat-I. Este modelo se lanzó en junio de 2021 a bordo de la misión WildRide de D-Orbit para probar su funcionamiento en el espacio.Remote sensing sensors onboard Earth observation satellites provide a great opportunity to monitor our planet at high spatial and temporal resolutions. Nevertheless, to process all this ever-growing amount of data, we need to develop fast and accurate models adapted to the specific characteristics of the data acquired by each sensor. For optical sensors, detecting the clouds present in the image is an unavoidable first step for most of the land and ocean applications. Although detecting bright and opaque clouds is relatively easy, automatically identifying thin semi-transparent clouds or distinguishing clouds from snow or bright surfaces is much more challenging. In addition, in the current scenario where the number of sensors in orbit is constantly growing, developing methodologies to transfer models across different satellite data is a pressing need. Henceforth, the overreaching goal of this Thesis is to develop accurate cloud detection models that exploit the different properties of the satellite images, and to develop methodologies to transfer those models across different sensors. The four contributions of this Thesis are stepping stones in that direction. In the first contribution,"Multitemporal cloud masking in the Google Earth Engine", we implemented a lightweight multitemporal cloud detection model that runs on the Google Earth Engine platform and which outperforms the operational models for Landsat-8. The second contribution, "Transferring deep learning models for Cloud Detection between Landsat-8 and Proba-V", is a case-study of transferring a deep learning based cloud detection algorithm from Landsat-8 (30m resolution, 12 spectral bands and very good radiometric quality) to Proba-V, which has a lower{333m resolution, only four bands and a less accurate radiometric quality. The third paper, "Cross sensor adversarial domain adaptation of Landsat-8 and Proba-V images for cloud detection", proposes a learning-based domain adaptation transformation of Proba-V images to resemble those taken by Landsat-8, with the objective of transferring products designed on Landsat-8 to Proba-V. Finally, the fourth contribution, "Towards global flood mapping onboard low cost satellites with machine learning", tackles simultaneously cloud and flood water detection with a single deep learning model, which was implemented to run onboard a CubeSat (ϕSat-I) with an AI accelerator chip. In this case, the model is trained on Sentinel-2 and transferred to theϕSat-I camera. This model was launched in June 2021 onboard the Wild Ride D-Orbit mission in order to test its performance in space

    A Practical Review to Support the Implementation of Smart Solutions within Neighbourhood Building Stock

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    The construction industry has witnessed an increase in the use of digital tools and smart solutions, particularly in the realm of building energy automation. While realising the potential benefits of smart cities, a broader scope of smart initiatives is required to support the transition from smart buildings towards smart neighbourhoods, which are considered critical urban development units. To support the interplay of smart solutions between buildings and neighbourhoods, this study aimed to collect and review all the smart solutions presented in existing scientific articles, the technical literature, and realised European projects. These solutions were classified into two main sections, buildings and neighbourhoods, which were investigated through five domains: building-energy-related uses, renewable energy sources, water, waste, and open space management. The quantitative outcomes demonstrated the potential benefits of implementing smart solutions in areas ranging from buildings to neighbourhoods. Moreover, this research concluded that the true enhancement of energy conservation goes beyond the building’s energy components and can be genuinely achieved by integrating intelligent neighbourhood elements owing to their strong interdependencies. Future research should assess the effectiveness of these solutions in resource conservation

    Hemodynamic Quantifications By Contrast-Enhanced Ultrasound:From In-Vitro Modelling To Clinical Validation

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