K&C局部损伤混凝土材料模型在精细有限元模拟中的应用

This paper presents a study on concrete fracture and the associated mesh sensitivity using the finite element (FE) method with a local concrete model in both tension (Mode I) and compression.To enable the incorporation of dynamic loading, the FE model is developed using a transient dynamic analysis code LS-DYNA Explicit.A series of investigations have been conducted on typical fracture scenarios to evaluate the model performances and calibration of relevant parameters.The K&C damage model was adopted because it is a comprehensive local concrete model which allows the user to change the crack band width, fracture energy and rate dependency of the material.Compressive localisation modelling in numerical modelling is also discussed in detail in relation to localisation.An impact test specimen is modelled.

Temporal Spatial-Keyword Top-k publish/subscribe

Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale. These geo-textual data cover a wide range of topics. Users are interested in receiving up-to-date tweets such that their locations are close to a user specified location and their texts are interesting to users. For example, a user may want to be updated with tweets near her home on the topic “food poisoning vomiting.” We consider the Temporal Spatial-Keyword Top-k Subscription (TaSK) query. Given a TaSK query, we continuously maintain up-to-date top-k most relevant results over a stream of geo-textual objects (e.g., geo-tagged Tweets) for the query. The TaSK query takes into account text relevance, spatial proximity, and recency of geo-textual objects in evaluating its relevance with a geo-textual object. We propose a novel solution to efficiently process a large number of TaSK queries over a stream of geotextual objects. We evaluate the efficiency of our approach on two real-world datasets and the experimental results show that our solution is able to achieve a reduction of the processing time by 70-80% compared with two baselines.

Beyond the Academy: Applying Anthropological Research, A Case Study of Demonstrating Impact in the U.K. 2014 REF

The 2014 Research Excellence Framework sought for the first time to assess the impact that research was having beyond the boundaries of the university and the wider academic sphere. While the REF continued the approach of previous research assessment exercises in attempting to measure the overall quality of research and teaching within the higher-education sector, it also expected institutions to evidence how some of their research had had ‘an effect on, change or benefit to the economy, society, culture, public policy or services, health, the environment or quality of life, beyond academia’ (REF 2012: 48). This article provides a case study in how researchers in one U.K. anthropology department were able to demonstrate the impact of their work in the public sphere successfully as part of this major audit exercise.

5.9-keV Mn K-shell X-ray luminosity from the decay of 55Fe in Type Ia supernova models

We show that the X-ray line flux of the Mn K�� line at 5.9 keV from the decay of 55Fe is a promising diagnostic to distinguish between Type Ia supernova (SN Ia) explosion models. Using radiation transport calculations, we compute the line flux for two three-dimensional explosion models: a near-Chandrasekhar mass delayed detonation and a violent merger of two (1.1 and 0.9 M⊙) white dwarfs. Both models are based on solar metallicity zero-age main-sequence progenitors. Due to explosive nuclear burning at higher density, the delayed-detonation model synthesizes ˜3.5 times more radioactive 55Fe than the merger model. As a result, we find that the peak Mn K�� line flux of the delayed-detonation model exceeds that of the merger model by a factor of ˜4.5. Since in both models the 5.9-keV X-ray flux peaks five to six years after the explosion, a single measurement of the X-ray line emission at this time can place a constraint on the explosion physics that is complementary to those derived from earlier phase optical spectra or light curves. We perform detector simulations of current and future X-ray telescopes to investigate the possibilities of detecting the X-ray line at 5.9 keV. Of the currently existing telescopes, XMM-Newton/pn is the best instrument for close (≲1-2 Mpc), non-background limited SNe Ia because of its large effective area. Due to its low instrumental background, Chandra/ACIS is currently the best choice for SNe Ia at distances above ˜2 Mpc. For the delayed-detonation scenario, a line detection is feasible with Chandra up to ˜3 Mpc for an exposure time of 106 s. We find that it should be possible with currently existing X-ray instruments (with exposure times ≲5 × 105 s) to detect both of our models at sufficiently high S/N to distinguish between them for hypothetical events within the Local Group. The prospects for detection will be better with future missions. For example, the proposed Athena/X-IFU instrument could detect our delayed-detonation model out to a distance of ˜5 Mpc. This would make it possible to study future events occurring during its operational life at distances comparable to those of the recent supernovae SN 2011fe (˜6.4 Mpc) and SN 2014J (˜3.5 Mpc).

k-fold Subsampling based Sequential Backward Feature Elimination

We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid featureselection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimalfeature vector that well represents the shapes of the subjects in the images. In detail, the proposed featureselection algorithm adopts the k-fold subsampling and sequential backward elimination approach, while thestandard linear support vector machine (SVM) is used as the classifier for human detection. We apply theproposed algorithm to the publicly accessible INRIA and ETH pedestrian full image datasets with the PASCALVOC evaluation criteria. Compared to other state of the arts algorithms, our feature selection based approachcan improve the detection speed of the SVM classifier by over 50% with up to 2% better detection accuracy.Our algorithm also outperforms the equivalent systems introduced in the deformable part model approach witharound 9% improvement in the detection accuracy

Triangular Objects and Systematic K</i>-Theory

We investigate modules over “systematic” rings. Such rings are “almost graded” and have appeared under various names in the literature; they are special cases of the G-systems of Grzeszczuk. We analyse their K-theory in the presence of conditions on the support, and explain how this generalises and unifies calculations of graded and filtered K-theory scattered in the literature. Our treatment makes systematic use of the formalism of idempotent completion and a theory of triangular objects in additive categories, leading to elementary and transparent proofs throughout.