The construction and application of an atomistic J-integral via Hardy estimates of continuum fields

In this work we apply a Lagrangian kernel-based estimator of continuum fields to atomic data in order to estimate the J-integral for the analysis of cracks and dislocations. We show that this method has the properties of: consistency between the energy, stress and deformation fields; path independence of the contour integrals of the Eshelby stress; and excellent correlation with linear elastic fracture mechanics theory for appropriately constructed simulations. We discuss the appropriate reference configuration and reference energy for this type of analysis. Lastly, we use canonical examples to demonstrate that the proposed method is a direct and rational approach for estimating the configurational forces on atomic defects.     

Regularized gap functions and error bounds for split mixed vector quasivariational inequality problems

In this paper, we consider a class of split mixed vector quasivariational inequality problems in real Hilbert spaces and establish new gap functions by using the method of the nonlinear scalarization function. Further, we obtain some error bounds for the underlying split mixed vector quasivariational inequality problems in terms of regularized gap functions. Finally, we give some examples to illustrate our results. The results obtained in this paper are new.     

New Interfaces and Approaches to Machine Learning When Classifying Gestures within Music

Interactive music uses wearable sensors (i.e., gestural interfaces—GIs) and biometric datasets to reinvent traditional human–computer interaction and enhance music composition. In recent years, machine learning (ML) has been important for the artform. This is because ML helps process complex biometric datasets from GIs when predicting musical actions (termed performance gestures). ML allows musicians to create novel interactions with digital media. Wekinator is a popular ML software amongst artists, allowing users to train models through demonstration. It is built on the Waikato Environment for Knowledge Analysis (WEKA) framework, which is used to build supervised predictive models. Previous research has used biometric data from GIs to train specific ML models. However, previous research does not inform optimum ML model choice, within music, or compare model performance. Wekinator offers several ML models. Thus, we used Wekinator and the Myo armband GI and study three performance gestures for piano practice to solve this problem. Using these, we trained all models in Wekinator and investigated their accuracy, how gesture representation affects model accuracy and if optimisation can arise. Results show that neural networks are the strongest continuous classifiers, mapping behaviour differs amongst continuous models, optimisation can occur and gesture representation disparately affects model mapping behaviour; impacting music practice.     

Some Useful Integral Representations for Information-Theoretic Analyses

This work is an extension of our earlier article, where a well-known integral representation of the logarithmic function was explored and was accompanied with demonstrations of its usefulness in obtaining compact, easily-calculable, exact formulas for quantities that involve expectations of the logarithm of a positive random variable. Here, in the same spirit, we derive an exact integral representation (in one or two dimensions) of the moment of a nonnegative random variable, or the sum of such independent random variables, where the moment order is a general positive non-integer real (also known as fractional moments). The proposed formula is applied to a variety of examples with an information-theoretic motivation, and it is shown how it facilitates their numerical evaluations. In particular, when applied to the calculation of a moment of the sum of a large number, n, of nonnegative random variables, it is clear that integration over one or two dimensions, as suggested by our proposed integral representation, is significantly easier than the alternative of integrating over n dimensions, as needed in the direct calculation of the desired moment.     

Strong cliques in vertex-transitive graphs

A clique (resp, independent set) in a graph is strong if it intersects every maximal independent set (resp, every maximal clique). A graph is clique intersect stable set (CIS) if all of its maximal cliques are strong and localizable if it admits a partition of its vertex set into strong cliques. In this paper we prove that a clique $C$ in a vertex-transitive graph $\mathrm{\Gamma }$ is strong if and only if $\text{◂=▸}\text{◂⋅▸}\mid C\mid \mid I\mid =\mid V\left(\mathrm{\Gamma }\right)\mid$ for every maximal independent set $I$ of $\mathrm{\Gamma }$. On the basis of this result we prove that a vertex-transitive graph is CIS if and only if it admits a strong clique and a strong independent set. We classify all vertex-transitive graphs of valency at most 4 admitting a strong clique, and give a partial characterization of 5-valent vertex-transitive graphs admitting a strong clique. Our results imply that every vertex-transitive graph of valency at most 5 that admits a strong clique is localizable. We answer an open question by providing an example of a vertex-transitive CIS graph which is not localizable.     

强点火作用下C3HF7对甲烷-空气爆炸的抑制

为解决瓦斯输送过程中的爆炸安全问题,探索寻找绿色环保且阻火性能优越的新型抑爆剂,开展了当量比下甲烷-空气预混气体爆炸传播过程中的七氟丙烷抑爆效果研究。实验采用长径比L/D=108的水平管道爆炸特性测试系统,研究了在强点火作用下不同体积分数的七氟丙烷对9.5%甲烷-空气预混气体最大爆炸压力、最大压力上升速率和火焰传播速度的影响。实验结果显示:将2.5 m长的管段作为七氟丙烷抑爆区时,七氟丙烷阻断9.5%甲烷-空气预混气体爆炸火焰传播的最小体积分数为5%;当七氟丙烷的体积分数为1%~4%时,不仅无法阻断爆炸火焰的传播,而且与对照组相比,会使火焰传播速度加快;当七氟丙烷的体积分数为1%~6%时,爆炸源及管道末端处的爆炸压力峰值随着七氟丙烷体积分数的增加而逐渐减小;当七氟丙烷的体积分数为3%时,抑爆区处的爆炸压力峰值与对照组相比增幅为10.9%。