Pre-/Post-Knowledge Assessment of an Earth Science Course for Elementary/Middle School Education Majors

This article discusses the benefits of experiencing the process of Earth Science through active learning strategies, and how a simple assessment instrument was developed and used to evaluate a specific course in Earth Science for educators. The course itself was broad in scope and designed specifically to fulfill science requirements for elementary and middle-level education majors. The assessment instrument consisted of statements to which students responded true-false or "I don't know." Based on pre- and post-course assessments of 108 education majors who took the class over a period of 5 semesters, the researchers reported an average increase in their content knowledge of 30 percent. Educational levels: Graduate or professional, Graduate or professional

Remember EMBeRS: Model-based Reasoning, Collaborative Teams and Much More!

Studies of interdisciplinary research teams indicate that team members struggle to achieve knowledge integration across disciplines. Knowledge integration across disciplines is at the heart of addressing important research challenges, such as impacts of global change, trade-offs between water, food, and energy production, and the need for sustainable cities. The EMBeRS Project is testing a new model for integrating knowledge across disciplines based on cognitive science theories of model-based reasoning. The project will create educational materials to train students to overcome the barriers to integrating knowledge across disciplines. Issues arise due to the inability of team members to work collaboratively in a research team with others who may hold very different perspectives. Collaboration is a critical aspect of preparing today\u27s students to meet future workforce demands. Providing opportunities for students to explicitly develop collaboration skills is an important attribute of EMBeRS and the UNL Environmental Studies program. To address the challenge of developing collaboration skills, the Environmental Studies program used a backward curriculum design, multiple modalities of experiential learning, and a reflective action research approach to develop collaboration and teamwork skills in undergraduate students. The ES program partnered with Target Training International Ltd. (TTI), to gain insights into the use of their instruments as boundary objects to help student’s understand self and create interdisciplinary teams. Through the use of an instrument, the TriMetrix®, the UNL-ES program is taking a page from the business world and partnering with it to help students understand themselves, and adapt their behaviors to more effectively work in a team. These assessments played a positive role in the dynamics of each group, some more than others. The analyses of these data have informed us about how to improve the use of the assessment output in class. (Abstract only

Phase-sensitive transport at a normal metal-superconductor interface close to a Josephson junction

Phase- and voltage bias-sensitive quasiparticle transport at a double $NIS_1IS_2$ interface is considered. The barriers $I$ range from tunnel to transparent, and the intermediate region $S_1$ has a width comparable to the superconducting coherence length. A phase difference $\varphi$ is applied to the Josephson junction $S_1IS_2$. The normal and Andreev reflections at the $NIS_1$ interface become $\varphi$-sensitive, and transport is governed by interferences within the narrow $S_1$ region, both in the normal and anomalous channels. The subgap conductance is separately (energy $E$)- and (phase $\varphi$)- symmetric. Above the superconducting gap, the conductance is in general not symmetric even if $(E,\varphi)$ is changed in $(-E,-\varphi)$, but the symmetry is restored by averaging Fermi oscillations. The Tomasch oscillations are amplified by the phase difference. The subgap conductance exhibits a resonant structure at the energy of the Andreev bound states (ABS) of the $S_1IS_2$ junction, providing a side-spectroscopy of such states. Depending on the relative transparencies of the junctions, the resonance can increase or reduce the conductance, and it can even vanish for $\varphi=\pi$, featuring total reflection of quasiparticles at $NS_1$ by the ABS at $S_1S_2$.Comment: 8 pages, 10 figures, 1 tabl

Estrogen Receptor Transrepresses Brain Inflammation

Estrogen receptors (ERs) have long been implicated in the etiology of multiple sclerosis, but no clear molecular mechanisms have linked ERs to the disease's pathology. Now Saijo et al. (2011) provide evidence that ERβ activates a transrepression pathway that suppresses inflammation and inhibits progression of pathology in a mouse model of multiple sclerosis

Nebraska Earth Science Education Network: Enhancing the NASA, University, and Pre-College Science Teacher Connection with Electronic Communication

The primary goals of this project were to: 1. Promote and enhance K-12 earth science education; and enhance the access to and exchange of information through the use of digital networks in K-12 institutions. We have achieved these two goals. Through the efforts of many individuals at the University of Nebraska-Lincoln (UNL), Nebraska Earth Science Education Network (NESEN) has become a viable and beneficial interdisciplinary outreach program for K-12 educators in Nebraska. Over the last three years, the NASA grant has provided personnel and equipment to maintain, expand and develop NESEN into a program that is recognized by its membership as a valuable source of information and expertise in earth systems science. Because NASA funding provided a framework upon which to build, other external sources of funding have become available to support NESEN programs

Automated fluvial hydromorphology mapping from airborne remote sensing

Mapping fluvial hydromorphology is an important part of defining river habitat. Mappingvia field sampling or hydraulic modelingis however time consuming, and mappinghydromorphology directly from remote sensing data may offer an efficient solution.Here, we present a system for automated classification of fluvial hydromorphologybased on a deep learning classification scheme applied to aerial orthophotos. Usingselected rivers in Norway, we show how surface flow patterns (smooth or rippled sur-faces vs. standing waves) can be classified in imagery using a trained convolutional neu-ral network (achieving a training and validation accuracy of >95%). We show howintegration of these classified surface flow patterns with information on channel gradi-ent, obtained from airborne topographic LiDAR data, can be used to compartmentalizethe rivers into hydromorphological units(HMUs) that represent the dominant flow fea-tures. Automated classifications were broadly consistent with manual classifications thathad been made in previous ground surveys, with equivalency in automated and manu-ally derived HMU classes ranging from 61.5% to 87.7%, depending on the river stretchconsidered. They were found to be discharge-dependent, showing the temporallydynamic aspect of hydromorphology. The proposed system is quick, flexible, generaliz-able, and provides consistent classifications free from interpretation bias. The deeplearning approach used here can be customized to provide more detailed information onflow features, such as delineating between standing waves and advective diffusion ofair bubbles/foam, to provide a more refined classification of surface flow patterns, andthe classification approach can be furtheradvanced by inclusion of additional remotesensing methods that provide further information on hydromorphological features.publishedVersio

A Unified framework for local visual descriptors evaluation

International audienceLocal descriptors are the ground layer of recognition feature based systems for still images and video. We propose a new framework to explain local descriptors. This framework is based on the descriptors decomposition in three levels: primitive extraction, primitive coding and code aggregation. With this framework, we are able to explain most of the popular descriptors in the literature such as HOG, HOF, SURF. We propose two new projection methods based on approximation with oscillating functions basis (sinus and Legendre polynomials). Using our framework, we are able to extend usual descriptors by changing the code aggregation or adding new primitive coding method. The experiments are carried out on images (VOC 2007) and videos datasets (KTH, Hollywood2 and UCF11), and achieve equal or better performances than the literature