Detecting the Baryons in Matter Power Spectra

We examine power spectra from the Abell/ACO rich cluster survey and the 2dF Galaxy Redshift Survey (2dfGRS) for observational evidence of features produced by the baryons. A non-negligible baryon fraction produces relatively sharp oscillatory features at specific wavenumbers in the matter power spectrum. However, the mere existence of baryons will also produce a global suppression of the power spectrum. We look for both of these features using the false discovery rate (FDR) statistic. We show that the window effects on the Abell/ACO power spectrum are minimal, which has allowed for the discovery of discrete oscillatory features in the power spectrum. On the other hand, there are no statistically significant oscillatory features in the 2dFGRS power spectrum, which is expected from the survey's broad window function. After accounting for window effects, we apply a scale-independent bias to the 2dFGRS power spectrum, P_{Abell}(k) = b^2P_{2dF}(k) and b = 3.2. We find that the overall shapes of the Abell/ACO and the biased 2dFGRS power spectra are entirely consistent over the range 0.02 <= k <= 0.15hMpc^-1. We examine the range of Omega_{matter} and baryon fraction for which these surveys could detect significant suppression in power. The reported baryon fractions for both the Abell/ACO and 2dFGRS surveys are high enough to cause a detectable suppression in power (after accounting for errors, windows and k-space sampling). Using the same technique, we also examine, given the best fit baryon density obtained from BBN, whether it is possible to detect additional suppression due to dark matter-baryon interaction. We find that the limit on dark matter cross section/mass derived from these surveys are the same as those ruled out in a recent study by Chen, Hannestad and Scherrer.Comment: 11 pages of text, 6 figures. Submitted to Ap

Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors

Knowledge bases provide applications with the benefit of easily accessible, systematic relational knowledge but often suffer in practice from their incompleteness and lack of knowledge of new entities and relations. Much work has focused on building or extending them by finding patterns in large unannotated text corpora. In contrast, here we mainly aim to complete a knowledge base by predicting additional true relationships between entities, based on generalizations that can be discerned in the given knowledgebase. We introduce a neural tensor network (NTN) model which predicts new relationship entries that can be added to the database. This model can be improved by initializing entity representations with word vectors learned in an unsupervised fashion from text, and when doing this, existing relations can even be queried for entities that were not present in the database. Our model generalizes and outperforms existing models for this problem, and can classify unseen relationships in WordNet with an accuracy of 75.8%

Development of Container Free Sample Exposure for Synchrotron X-ray Footprinting.

The method of X-ray footprinting and mass spectrometry (XFMS) on large protein assemblies and membrane protein samples requires high flux density to overcome the hydroxyl radical scavenging reactions produced by the buffer constituents and the total protein content. Previously, we successfully developed microsecond XFMS using microfluidic capillary flow and a microfocused broadband X-ray source at the Advanced Light Source synchrotron beamlines, but the excessive radiation damage incurred when using capillaries prevented the full usage of a high-flux density beam. Here we present another significant advance for the XFMS method: the instrumentation of a liquid injection jet to deliver container free samples to the X-ray beam. Our preliminary experiments with a liquid jet at a bending magnet X-ray beamline demonstrate the feasibility of the approach and show a significant improvement in the effective dose for both the Alexa fluorescence assay and protein samples compared to conventional capillary flow methods. The combination of precisely controlled high dose delivery, shorter exposure times, and elimination of radiation damage due to capillary effects significantly increases the signal quality of the hydroxyl radical modification products and the dose-response data. This new approach is the first application of container free sample handling for XFMS and opens up the method for even further advances, such as high-quality microsecond time-resolved XFMS studies

Toward Precision Education: Educational Data Mining and Learning Analytics for Identifying Students’ Learning Patterns with Ebook Systems

Precision education is now recognized as a new challenge of applying artificial intelligence, machine learning, and learning analytics to improve both learning performance and teaching quality. To promote precision education, digital learning platforms have been widely used to collect educational records of students’ behavior, performance, and other types of interaction. On the other hand, the increasing volume of students’ learning behavioral data in virtual learning environments provides opportunities for mining data on these students’ learning patterns. Accordingly, identifying students’ online learning patterns on various digital learning platforms has drawn the interest of the learning analytics and educational data mining research communities. In this study, the authors applied data analytics methods to examine the learning patterns of students using an ebook system for one semester in an undergraduate course. The authors used a clustering approach to identify subgroups of students with different learning patterns. Several subgroups were identified, and the students’ learning patterns in each subgroup were determined accordingly. In addition, the association between these students’ learning patterns and their learning outcomes from the course was investigated. The findings of this study provide educators opportunities to predict students’ learning outcomes by analyzing their online learning behaviors and providing timely intervention for improving their learning experience, which achieves one of the goals of learning analytics as part of precision education

Heated Bridge Deck System and Materials and Method for Constructing the Same

Aheated bridge deck (20) uses electrodes (24,26) embedded within conductive concrete and connected to a power Source to remove Snow and ice accumulation. A cement-based mixture containing optimal amounts of conductive materials is molded into pre-formed slabs (22) placed atop the paved Surface of a bridge deck. Alternatively, the conductive concrete may be cast in place on top of an existing bridge deck. A control unit with temperature and moisture Sensors may be coupled to the heated bridge deck

Using a Summarized Lecture Material Recommendation System to Enhance Students’ Preclass Preparation in a Flipped Classroom

Research has revealed the positive effects of flipped classroom approaches on students’ learning engagement and performance compared with conventional lecture-based classrooms. However, because of a lack of out-of-class learning support, many students fail to comprehensively prepare the provided lecture materials before class. One promising solution to this problem is recommendation systems in the educational area, which have been instrumental in helping learners identify useful and relevant lecture materials that satisfy their learning needs. Thus, in this study, we propose a summarized lecture material recommendation system, which is integrated into an e-book reading system as an enhancement of the flipped classroom approach. This system helps students identify pages that contain essential knowledge that must be thoroughly studied before class. The proposed system was constructed on the basis of our previous work. In this study, a quasi-experiment was conducted in a graduate course that implemented the flipped classroom model: experimental group students learned with the proposed system, whereas the control group students had no access to the additional features. The findings of this study suggest that students who learn with the proposed recommendation system significantly outperform those who learn without the system in a flipped classroom in terms of their learning outcomes and engagement in preclass preparation

Advances and Future Challenges in Adenoviral Vector Pharmacology and Targeting

Adenovirus is a robust vector for therapeutic applications, but its use is limited by our understanding of its complex in vivo pharmacology. In this review we describe the necessity of identifying its natural, widespread, and multifaceted interactions with the host since this information will be crucial for efficiently redirecting virus into target cells. In the rational design of vectors, the notion of overcoming a sequence of viral “sinks” must be combined with re-targeting to target populations with capsid as well as shielding the vectors from pre-existing or toxic immune responses. It must also be noted that most known adenoviral pharmacology is deduced from the most commonly used serotypes, Ad5 and Ad2. However, these serotypes may not represent all adenoviruses, and may not even represent the most useful vectors for all purposes. Chimeras between Ad serotypes may become useful in engineering vectors that can selectively evade substantial viral traps, such as Kupffer cells, while retaining the robust qualities of Ad5. Similarly, vectorizing other Ad serotypes may become useful in avoiding immunity against Ad5 altogether. Taken together, this research on basic adenovirus biology will be necessary in developing vectors that interact more strategically with the host for the most optimal therapeutic effect