Fostering Students\u27 Identification with Mathematics and Science

Book Summary: Interest in Mathematics and Science Learning is the first volume to assemble findings on the role of interest in mathematics and science learning. As the contributors illuminate across the volume’s 22 chapters, interest provides a critical bridge between cognition and affect in learning and development. This volume will be useful to educators, researchers, and policy makers, especially those whose focus is mathematics, science, and technology education. Chapter Summary: The primary purpose of this chapter is to explore the process whereby students transition from a short-term, situational interest in mathematics or science to a more enduring individual interest in which they incorporate performance in mathematics or science into their self-definitions (e.g. I am a scientist ). We do so by examining the research related to domain identification, which is the extent to which students define themselves through a role or performance in a domain, such as mathematics or science. Understanding the process of domain identification is important because it can contribute to an understanding of how individual interest develops over time. The means through which students become highly domain identified involves many factors that are internal (e.g. goals and beliefs) and external (e.g. family environment and educational experiences) to them. Students who are more identified with an academic domain tend to demonstrate increased motivation, effort, perseverance (when faced with failure), and achievement. Importantly, students with lower domain identification tend to demonstrate less motivation, lower effort, and fewer desirable outcomes. Student outcomes in a domain can reciprocally influence domain identification by reinforcing or altering it. This feedback loop can help explain incremental changes in motivation, self-concept, individual interest, and, ultimately, important outcomes such as achievement, choice of college major, and career path. This dynamic model presents possible mechanisms for influencing student outcomes. Furthermore, assessing students\u27 domain identification can allow practitioners to intervene to prevent undesirable outcomes. Finally, we present research on how mathematics and science instructors could use the principles of the MUSIC Model of Academic Motivation to enhance students\u27 domain identification, by (a) empowering students, (b) demonstrating the usefulness of the domain, (c) supporting students\u27 success, (d) triggering students\u27 interests, and (e) fostering a sense of caring and belonging. We conclude that by using the MUSIC model, instructors can intentionally design educational experiences to help students progress from a situational interest to one that is more enduring and integrated into their identities

On the molecular biology and evolution of plant parasitism by nematodes

Plant-parasitic nematodes (PPN) are among the most devastating plant pathogens. However, our understanding of how nematodes adapted to plant parasitism, and the molecular mechanisms that PPN use during infection is limited. Among the most important genomic changes that occurred in the free-living nematode ancestors of PPN were multiple horizontal gene transfer (HGT) events from bacteria. Though it is clear that HGT helped shape the genomes of many PPN, how this process occurred is unknown. Also, it is evident that successful parasitism occurs from the delivery of proteinaceous effectors into plant roots to hijack and modify host cellular processes. The research included in this dissertation aims at addressing several important questions regarding HGT in PPN, and investigates important molecular, cellular and developmental processes that are determined critical for successful parasitism. Of particular emphasis throughout this dissertation is the soybean cyst nematode, Heterodera glycines, due to a highly specialized and agronomically important interaction with its soybean host. Major findings for HGT in PPN include the identification of eighteen new H. glycines effectors, three of which are determined to have been part of more ancient HGT events from rhizosphere bacteria. Additionally, homologs of two of the three HGT genes are shown to have been transferred numerous different times from bacteria to diverse eukaryotes and archaea. The latter findings indicate the likely evolutionary advantages that these genes provided not just to PPN, but many different taxa. Intriguingly, we reveal that a group of retroviruses specific to distal nematode clades is genomically associated with HGT genes in PPN genomes. These retroviruses potentially have all of the elements that would be necessary for HGT to occur in PPN. Thus, we propose the tempting hypothesis that this specific group of retroviruses might have contributed to HGT in these nematodes. We also reveal several novelties for plant-nematode interactions. Major findings include the discovery of a strongly expressed H. glycines effector that is essential for virulence and efficiently targets plant cell nucleoli for suppression of innate immune responses. Also, this H. glycines effector contains marginal, but significant sequence similarity with an immunosuppressive effector found only in Plasmodium spp., the malaria parasites. Extensive database searches, phylogenetic analyses, and functional complementation experiments conclude that the similarities are best explained by sequence convergence due to similar immunosuppressive functions. Furthermore, we determine that a specific microRNA network in soybean that is essential for plant development delineates the formation of the H. glycines feeding site, and interfering with this network renders soybean roots much less susceptible to infection. In conclusion, the major findings included in this dissertation reveal novel insights into how nematodes adapted to plant parasitism, and for how PPN manipulate their host plants during infection to establish compatible interactions. Moreover, these findings will undoubtedly provide foundations for developing novel control measures against these important plant pathogens

The effects of eccentric training on strength and muscle development in pre-pubertal and pubertal boys

It is now generally accepted that strength training in pubertal children will increase strength, but it is unlikely to induce morphological changes. However research in this area is limited as most studies fail to control for the confounding effects of normal growth, or employ appropriate training programs. To overcome these limitations it is suggested that researchers should use a within-subject design employing an exercise regime of sufficient intensity. In adults, eccentric training has been shown to have the greatest effect on hypertrophy and strength. The purpose of the study was to examine the effects of eccentric training on muscle strength and development in children, using a one arm training model. Seventeen boys in grades 6, 7, and 8 participated in an eight week eccentric elbow flexion training program; three training sessions per week. The program consisted of 2 – 5 sets of 6 – 10 reps using progressive resistance. Pre and post test strength (Eccentric and concentric elbow flexion maximal strength by a Biodex System 3 Dynamometer and 1 RM with dumbbells) and bicep thickness measurements were performed. The change in biceps thickness was significantly greater in the training arm versus the non-training arm (7.3 +/- 8.3% vs. 0.7 +/- 7.5%) (p0.05), but isokinetic eccentric strength gain in the training arm was significantly greater than the non-training arm (25.4 +/- 16.6% vs. 2.4% +/- 15.6%) (

FPGA-Based CNN Inference Accelerator Synthesized from Multi-Threaded C Software

A deep-learning inference accelerator is synthesized from a C-language software program parallelized with Pthreads. The software implementation uses the well-known producer/consumer model with parallel threads interconnected by FIFO queues. The LegUp high-level synthesis (HLS) tool synthesizes threads into parallel FPGA hardware, translating software parallelism into spatial parallelism. A complete system is generated where convolution, pooling and padding are realized in the synthesized accelerator, with remaining tasks executing on an embedded ARM processor. The accelerator incorporates reduced precision, and a novel approach for zero-weight-skipping in convolution. On a mid-sized Intel Arria 10 SoC FPGA, peak performance on VGG-16 is 138 effective GOPS

Ecological effects of reservoir operations on Blue Mesa Reservoir

Includes bibliographical references.Annual progress report, May 1, 1997-April 30, 1998

Heat stress adaptations in pigs

Implications • Heat stress is a global issue constraining animal agriculture productivity, negatively affects welfare, and reduces production efficiency in many countries. • The effects of heat stress on pig production will intensify, if climate change continues as predicted. • To date, modifying the environment is the most effective way to mitigate the effects of heat stress. • Identifying additional strategies (nutritional and genetics) to maximize pork production during the warm summer months is necessary to satiate a growing demand for high quality meat for human consumption

Mindboggle: Automated brain labeling with multiple atlases

BACKGROUND: To make inferences about brain structures or activity across multiple individuals, one first needs to determine the structural correspondences across their image data. We have recently developed Mindboggle as a fully automated, feature-matching approach to assign anatomical labels to cortical structures and activity in human brain MRI data. Label assignment is based on structural correspondences between labeled atlases and unlabeled image data, where an atlas consists of a set of labels manually assigned to a single brain image. In the present work, we study the influence of using variable numbers of individual atlases to nonlinearly label human brain image data. METHODS: Each brain image voxel of each of 20 human subjects is assigned a label by each of the remaining 19 atlases using Mindboggle. The most common label is selected and is given a confidence rating based on the number of atlases that assigned that label. The automatically assigned labels for each subject brain are compared with the manual labels for that subject (its atlas). Unlike recent approaches that transform subject data to a labeled, probabilistic atlas space (constructed from a database of atlases), Mindboggle labels a subject by each atlas in a database independently. RESULTS: When Mindboggle labels a human subject's brain image with at least four atlases, the resulting label agreement with coregistered manual labels is significantly higher than when only a single atlas is used. Different numbers of atlases provide significantly higher label agreements for individual brain regions. CONCLUSION: Increasing the number of reference brains used to automatically label a human subject brain improves labeling accuracy with respect to manually assigned labels. Mindboggle software can provide confidence measures for labels based on probabilistic assignment of labels and could be applied to large databases of brain images

Creation, God, and the Coronavirus

This short reflection argues that, in the face of natural crises that occur in the world, responsible Christian speech requires a much fuller and more thickly textured understanding of creation than is often presented. Reading the Bible leads us to avoid speculating on the origins or purposes of such crises. Rather, it bears witness to the divine promise of hope in the healing justice of God, and calls human persons and human communities to participate in that justice through responsible action

Nature-inspired flow-fields and water management for PEM fuel cells

Flow-field design is crucial to polymer electrolyte membrane fuel cell (PEMFC) performance, since non-uniform transport of species to and from the membrane electrode assembly (MEA) results in significant power losses. The long channels of conventional serpentine flow-fields cause large pressure drops between inlets and outlets, thus large parasitic energy losses and low fuel cell performance. Here, a lung-inspired approach is used to design flow-fields guided by the structure of a lung. The fractal geometry of the human lung has been shown to ensure uniform distribution of air from a single outlet (trachea) to multiple outlets (alveoli). Furthermore, the human lung transitions between two flow regimes: 14-16 upper generations of branches dominated by convection, and 7-9 lower generations of space-filling acini dominated by diffusion. The upper generations of branches are designed to slow down the gas flow to a rate compatible with the rate in the diffusional regime (Pé ~ 1), resulting in uniform distribution of entropy production in both regimes. By employing a three-dimensional (3D) fractal structure as flow-field inlet channel, we aim to yield similar benefits from replicating these characteristics of the human lung. The fractal pattern consists of repeating “H” shapes where daughter “H’s” are located at the four tips of the parent “H”. The fractal geometry obeys Murray’s law, much like the human lung, hereby leading to minimal mechanical energy losses. Furthermore, the three-dimensional branching structure provide uniform local conditions on the surface of the catalyst layer as only the outlets of the fractal inlet channel are exposed to the MEA. Numerical simulations were conducted to determine the number of generations required to achieve uniform reactant distribution and minimal entropy production. The results reveal that the ideal number of generations for minimum entropy production lies between N = 5 and 7. Guided by the simulation results, three flow-fields with N = 3, 4 and 5 (10 cm2 surface area) were 3D printed via direct metal laser sintering (DMLS), and experimentally validated against conventional serpentine flow-fields. The fractal flow-fields with N = 4 and 5 generations showed ~20% and ~30% increase in performance and maximum power density over serpentine flow-fields above 0.8 A cm-2 at 50% RH. At fully humidified conditions, though, the performance of fractal N = 5 flow-field significantly deteriorates due to flooding issues. Another defining characteristic of the fractal approach is scalability, which is an important feature in nature. Fractal flow-fields can bridge multiple length scales by adding further generations, while preserving the building units and microscopic function of the system. Larger, 3D printed fractal flow-fields (25 cm2 surface area) with N = 4 are compared to conventional serpentine flow-field based PEMFCs. Performance results show that fractal and serpentine flow-field based PEMFCs have similar polarization curves, which is attributed to the significantly higher pressure drop (~ 25 kPa) of large serpentine flow-fields compared to fractal flow-fields. However, such excessive pressure drop renders the use of a large scale serpentine flow-field prohibitive, thus favouring the fractal flow-field. A major shortcoming of using fractal flow-field is, though, susceptibility to flooding in the gas channels due to slow gas velocity. This problem has led to the development of a nature-inspired water management mechanism that draws inspiration from the ability of the Thorny Devil (Australian lizard) to passively transport liquid water across its skin using capillary pressure. We have recently integrated this strategy with the fractal N = 4 flow-fields and verified the viability of the strategy using neutron imaging at Helmholtz-Zentrum Berlin (HZB). Implementation of this water management strategy is expected to circumvent remaining problems of high-generation fractal flow-fields