Human HUNK peptide

A new tomographic technique for magnetic data inversion in near-surface geophysical investigations is presented and discussed. It represents a powerful tool for analysing magnetic profiles and/or maps obtained during geophysical surveys carried out for environmental and engineering applications. The inversion technique is based on the cross-correlation integral between the magnetic field measured on earth surface and the theoretical magnetic field produced by an unit scanner dipole positioned in the subsoil at the nodes of a regular grid. The cross-correlation values are then plotted in a 2D map, giving as a result a probability distribution of finding the magnetic dipoles at the selected depth, named dipolar occurrence probability (DOP). The skill of the tomographic technique has been evaluated using theoretical simulations, laboratory experiments with controlled magnetic sources and field measurements for environmental monitoring. Our findings demonstrated that the DOP probabilistic function allows us to localize the presence of metallic buried bodies ( hunks), or objects characterized by high magnetic susceptibility contrasts.

Context Version Control Systems, such as Subversion, are standard repositories that preserve all of the maintenance changes undertaken to source code artifacts during the evolution of a software system. The documented data of the version history are organized as commits; however, these commits do not keep a tag that would identify the purpose of the relevant undertaken change of a commit, thus, there is rarely enough detail to clearly direct developers to the changes associated with a specific type of maintenance. Objective: This work examines the version histories of an open source system to automatically classify version commits into one of two categories, namely adaptive commits and non-adaptive commits. Method: We collected the commits from the version history of three open source systems, then we obtained eight different code change metrics related to, for example, the number of changed statements, methods, hunks, and files. Based on these change metrics, we built a machine learning approach to classify whether a commit was adaptive or not. Results: It is observed that code change metrics can be indicative of adaptive maintenance activities. Also, the classification findings show that the machine learning classifier developed has approximately 75% prediction accuracy within labeled change histories. Conclusion: The proposed method automates the process of examining the version history of a software system and identifies which commits to the system are related to an adaptive maintenance task. The evaluation of the method supports its applicability and efficiency. Although the evaluation of the proposed classifier on unlabeled change histories shows that it is not much better than the random guessing in terms of F-measure, we feel that our classifier would serve as a better basis for developing advanced classifiers that have predictive power of adaptive commits without the need of manual efforts.