Hardness, grainsize and porosity formation prediction on the Laser Metal Deposition of AISI 304 stainless steel

The presented numerical model solves the heat and mass transfer equations in the Laser Metal Deposition process and based on the evolution of the thermal field predicts the grainsize, the resulting hardness and evaluates the pores formation probability in an AISI 304 stainless steel. For this purpose, in a first step, the model calculates the shape of the deposited material and the variations of the temperature field. In a second step, and based on the evolution of the thermal field, the model calculates the resulting hardness of the deposited material, the grainsize and the porosity formation probability after the deposition process. Numerical results are experimentally validated, and good agreement is obtained. Consequently, besides predicting the geometry of the resulting part and the evolution of the thermal field, the developed model enables to evaluate the quality of the deposited material. Therefore, the optimum process conditions and strategy when depositing AISI 304 stainless steel can be determined without initial trial-and-error tests.“LaCaixa” foundation . In addition, this work has been founded by the H2020- FoF13-2016 PARADDISE project (contract No.: 723440). This work has been also carried out in the framework of the DPI2016-79889-R – INTEGRADDI project, funded by the Spanish Ministry of Industry and Competitiveness

Unsupervised Graph-based Rank Aggregation for Improved Retrieval

This paper presents a robust and comprehensive graph-based rank aggregation approach, used to combine results of isolated ranker models in retrieval tasks. The method follows an unsupervised scheme, which is independent of how the isolated ranks are formulated. Our approach is able to combine arbitrary models, defined in terms of different ranking criteria, such as those based on textual, image or hybrid content representations. We reformulate the ad-hoc retrieval problem as a document retrieval based on fusion graphs, which we propose as a new unified representation model capable of merging multiple ranks and expressing inter-relationships of retrieval results automatically. By doing so, we claim that the retrieval system can benefit from learning the manifold structure of datasets, thus leading to more effective results. Another contribution is that our graph-based aggregation formulation, unlike existing approaches, allows for encapsulating contextual information encoded from multiple ranks, which can be directly used for ranking, without further computations and post-processing steps over the graphs. Based on the graphs, a novel similarity retrieval score is formulated using an efficient computation of minimum common subgraphs. Finally, another benefit over existing approaches is the absence of hyperparameters. A comprehensive experimental evaluation was conducted considering diverse well-known public datasets, composed of textual, image, and multimodal documents. Performed experiments demonstrate that our method reaches top performance, yielding better effectiveness scores than state-of-the-art baseline methods and promoting large gains over the rankers being fused, thus demonstrating the successful capability of the proposal in representing queries based on a unified graph-based model of rank fusions

Introduction to Collider Physics

This is a set of four lectures presented at the Theoretical Advanced Study Institute (TASI-09) in June 2009. The goal of the lectures is to introduce students to some of the basic ideas and tools required for theoretical analysis of collider data. Several examples of Standard Model processes at electron-positron and hadron colliders are considered to illustrate these ideas. In addition, a general strategy for formulating searches for physics beyond the Standard Model is outlined. The lectures conclude with a brief survey of recent, ongoing and future searches for the Higgs boson and supersymmetric particles.Comment: 47 pages, 34 figures, contributed to TASI-09 proceedings. One reference added in v

Contributions of plasma physics to chaos and nonlinear dynamics

This topical review focusses on the contributions of plasma physics to chaos and nonlinear dynamics bringing new methods which are or can be used in other scientific domains. It starts with the development of the theory of Hamiltonian chaos, and then deals with order or quasi order, for instance adiabatic and soliton theories. It ends with a shorter account of dissipative and high dimensional Hamiltonian dynamics, and of quantum chaos. Most of these contributions are a spin-off of the research on thermonuclear fusion by magnetic confinement, which started in the fifties. Their presentation is both exhaustive and compact. [15 April 2016