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Previous research has demonstrated positive relations between intrinsic reading motivation and reading competence. However, the causal direction of these relations and the moderating role of relevant background variables (e.g., students' achievement level) are not well understood. In the present study, a cross-lagged panel model was applied to academic track and nonacademic track fifth grade students (N = 396) to test whether intrinsic reading motivation and reading competence are reciprocally related depending on the students' achievement level (indicated by school track affiliation). According to expectations, the cross-lagged effect of intrinsic reading motivation on reading competence was only significant for academic track students. In the nonacademic track group, neither the effect of intrinsic reading motivation on reading competence nor the reverse effect proved to be significant. Thus, the nature of the relation between intrinsic reading motivation and reading competence seems to depend on students' school track affiliation.
This survey on the theme of Geometry Education (including new technologies) focuses chiefly on the time span since 2008. Based on our review of the research literature published during this time span (in refereed journal articles, conference proceedings and edited books), we have jointly identified seven major threads of contributions that span from the early years of learning (pre-school and primary school) through to post-compulsory education and to the issue of mathematics teacher education for geometry. These threads are as follows: developments and trends in the use of theories; advances in the understanding of visuo spatial reasoning; the use and role of diagrams and gestures; advances in the understanding of the role of digital technologies; advances in the understanding of the teaching and learning of definitions; advances in the understanding of the teaching and learning of the proving process; and, moving beyond traditional Euclidean approaches. Within each theme, we identify relevant research and also offer commentary on future directions.
We report the results from an eye-movement monitoring study investigating how native and non-native speakers of English process temporarily ambiguous sentences such as While the gentleman was eating the burgers were still being reheated in the microwave, in which an initially plausible direct-object analysis is first ruled out by a syntactic disambiguation (were) and also later on by semantic information (being reheated). Both participant groups showed garden-path effects at the syntactic disambiguation, with native speakers showing significantly stronger effects of ambiguity than non-native speakers in later eye-movement measures but equally strong effects in first-pass reading times. Ambiguity effects at the semantic disambiguation and in participants’ end-of-trial responses revealed that for both participant groups, the incorrect direct-object analysis was frequently maintained beyond the syntactic disambiguation. The non-native group showed weaker reanalysis effects at the syntactic disambiguation and was more likely to misinterpret the experimental sentences than the native group. Our results suggest that native language (L1) and non-native language (L2) parsing are similar with regard to sensitivity to syntactic and semantic error signals, but different with regard to processes of reanalysis.
We recently found that macrophages from RhoA/RhoB double knockout mice had increased motility of the cell body, but severely impaired retraction of the tail and membrane extensions, whereas RhoA-or RhoB-deficient cells exhibited mild phenotypes. Here we extended this work and investigated the roles of Rho signaling in primary human blood monocytes migrating in chemotactic gradients and in various settings. Monocyte velocity, but not chemotactic navigation, was modestly dependent on Rho-ROCK-myosin II signaling on a 2D substrate or in a loose collagen type I matrix. Viewed by time-lapse epi-fluorescence microscopy, monocytes appeared to flutter rather than crawl, such that the 3D surface topology of individual cells was difficult to predict. Spinning disk confocal microscopy and 3D reconstruction revealed that cells move on planar surfaces and in a loose collagen matrix using prominent, curved planar protrusions, which are rapidly remodeled and reoriented, as well as resorbed. In a dense collagen type I matrix, there is insufficient space for this mode and cells adopt a highly Rho-dependent, lobular mode of motility. Thus, in addition to its role in tail retraction on 2D surfaces, Rho is critical for movement in confined spaces, but is largely redundant for motility and chemotaxis in loose matrices.
The excitation of localized surface plasmons in noble metal nanoparticles (NPs) results in different nanoscale effects such as electric field enhancement, the generation of hot electrons and a temperature increase close to the NP surface. These effects are typically exploited in diverse fields such as surface-enhanced Raman scattering (SERS), NP catalysis and photothermal therapy (PTT). Halogenated nucleobases are applied as radiosensitizers in conventional radiation cancer therapy due to their high reactivity towards secondary electrons. Here, we use SERS to study the transformation of 8-bromoadenine (8BrA) into adenine on the surface of Au and AgNPs upon irradiation with a low-power continuous wave laser at 532, 633 and 785 nm, respectively. The dissociation of 8BrA is ascribed to a hot-electron transfer reaction and the underlying kinetics are carefully explored. The reaction proceeds within seconds or even milliseconds. Similar dissociation reactions might also occur with other electrophilic molecules, which must be considered in the interpretation of respective SERS spectra. Furthermore, we suggest that hot-electron transfer induced dissociation of radiosensitizers such as 8BrA can be applied in the future in PTT to enhance the damage of tumor tissue upon irradiation.
Over the past decades, rapid and constant advances have motivated GNSS technology to approach the ability to monitor transient ground motions with mm to cm accuracy in real-time. As a result, the potential of using real-time GNSS for natural hazards prediction and early warning has been exploited intensively in recent years, e.g., landslides and volcanic eruptions monitoring. Of particular note, compared with traditional seismic instruments, GNSS does not saturate or tilt in terms of co-seismic displacement retrieving, which makes it especially valuable for earthquake and earthquake induced tsunami early warning. In this thesis, we focus on the application of real-time GNSS to fast seismic source inversion and tsunami early warning.
Firstly, we present a new approach to get precise co-seismic displacements using cost effective single-frequency receivers. As is well known, with regard to high precision positioning, the main obstacle for single-frequency GPS receiver is ionospheric delay. Considering that over a few minutes, the change of ionospheric delay is almost linear, we constructed a linear model for each satellite to predict ionospheric delay. The effectiveness of this method has been validated by an out-door experiment and 2011 Tohoku event, which confirms feasibility of using dense GPS networks for geo-hazard early warning at an affordable cost.
Secondly, we extended temporal point positioning from GPS-only to GPS/GLONASS and assessed the potential benefits of multi-GNSS for co-seismic displacement determination. Out-door experiments reveal that when observations are conducted in an adversary environment, adding a couple of GLONASS satellites could provide more reliable results. The case study of 2015 Illapel Mw 8.3 earthquake shows that the biases between co-seismic displacements derived from GPS-only and GPS/GLONASS vary from station to station, and could be up to 2 cm in horizontal direction and almost 3 cm in vertical direction. Furthermore, slips inverted from GPS/GLONASS co-seismic displacements using a layered crust structure on a curved plane are shallower and larger for the Illapel event.
Thirdly, we tested different inversion tools and discussed the uncertainties of using real-time GNSS for tsunami early warning. To be exact, centroid moment tensor inversion, uniform slip inversion using a single Okada fault and distributed slip inversion in layered crust on a curved plane were conducted using co-seismic displacements recorded during 2014 Pisagua earthquake. While the inversion results give similar magnitude and the rupture center, there are significant differences in depth, strike, dip and rake angles, which lead to different tsunami propagation scenarios. Even though, resulting tsunami forecasting along the Chilean coast is close to each other for all three models.
Finally, based on the fact that the positioning performance of BDS is now equivalent to GPS in Asia-Pacific area and Manila subduction zone has been identified as a zone of potential tsunami hazard, we suggested a conceptual BDS/GPS network for tsunami early warning in South China Sea. Numerical simulations with two earthquakes (Mw 8.0 and Mw 7.5) and induced tsunamis demonstrate the viability of this network. In addition, the advantage of BDS/GPS over a single GNSS system by source inversion grows with decreasing earthquake magnitudes.
The high rate GPS velocity determination technology which is based on the broadcast ephemeris and epoch differenced model can retrieve displacement of ground motion with the precision of a few centimetres to decimetres in real time. Moreover, the precision of the recovered displacement can be improved if the un-modelled errors such as broadcast ephemeris residuals, atmospheric residuals, multipath effects and high frequency noise are tackled more accurately. In this paper, we propose a method to improve the precision of the recovered displacement by appropriately making use of reference station corrections. For the reference stations, the coordinates are highly constrained to extract the error corrections that are to be broadcast via a communication link to the rover. After correcting the rover’s observations, some errors such as ephemeris residuals and atmospheric residuals are effectively eliminated or at least reduced. This improves the accuracy of the observations and thus enhances the reliability of the velocity estimation. The displacement can be recovered by integrating the estimated velocity after de-trending using a linear trend that is caused by the un-corrected residuals. The series of validation results in the experiment have shown that the displacement of the simulated motion can be real time recovered with a precision of 1–2 cm, and is thus applicable for real time monitoring of the ground motion.
Classification of clouds, cirrus, snow, shadows and clear sky areas is a crucial step in the pre-processing of optical remote sensing images and is a valuable input for their atmospheric correction. The Multi-Spectral Imager on board the Sentinel-2's of the Copernicus program offers optimized bands for this task and delivers unprecedented amounts of data regarding spatial sampling, global coverage, spectral coverage, and repetition rate. Efficient algorithms are needed to process, or possibly reprocess, those big amounts of data. Techniques based on top-of-atmosphere reflectance spectra for single-pixels without exploitation of external data or spatial context offer the largest potential for parallel data processing and highly optimized processing throughput. Such algorithms can be seen as a baseline for possible trade-offs in processing performance when the application of more sophisticated methods is discussed. We present several ready-to-use classification algorithms which are all based on a publicly available database of manually classified Sentinel-2A images. These algorithms are based on commonly used and newly developed machine learning techniques which drastically reduce the amount of time needed to update the algorithms when new images are added to the database. Several ready-to-use decision trees are presented which allow to correctly label about 91% of the spectra within a validation dataset. While decision trees are simple to implement and easy to understand, they offer only limited classification skill. It improves to 98% when the presented algorithm based on the classical Bayesian method is applied. This method has only recently been used for this task and shows excellent performance concerning classification skill and processing performance. A comparison of the presented algorithms with other commonly used techniques such as random forests, stochastic gradient descent, or support vector machines is also given. Especially random forests and support vector machines show similar classification skill as the classical Bayesian method.
Despite a large number of dated glacial landforms in the Himalaya, the ice extent during the global Last Glacial Maximum (LGM) from 19 to 23 ka is only known to first order. New cosmogenic Be-10 exposure ages from well-preserved glacially polished surfaces, combined with published data, and an improved production rate scaling model allow reconstruction of the LGM ice extent and subsequent deglaciation in the Chandra Valley of NW India. We show that a >1000 m thick valley glacier retreated >150 km within a few thousand years after the onset of LGM deglaciation. By comparing the recession of the Chandra Valley Glacier and other Himalayan glaciers with those of Northern and Southern Hemisphere glaciers, we demonstrate that post-LGM deglaciation was similar and nearly finished prior to the Bolling/Allerod interstadial. Our study supports the view that many Himalayan glaciers advanced during the LGM, likely in response to global variations in temperature.