Topology optimization for crashworthiness of thin-walled structures under axial impact using hybrid cellular automata

Although topology optimization is well established in most engineering fields, it is still in its infancy concerning highly non-linear structural applications like vehicular crashworthiness. One of the approaches recently proposed and based on Hybrid Cellular Automata is modified here such that it can be applied for the first time to thin-walled structures. Classical methods based on voxel techniques, i.e., on solid three-dimensional volume elements, cannot derive structures made from thin metal sheets where the main energy absorption mode is related to plastic buckling, folding and failure. Because the main components of car structures are made from such thin-walled beams and panels, a special approach using SFE CONCEPT was developed, which is presented in this paper.     

Optimization of MRI Acquisition and Texture Analysis to Predict Physico-chemical Parameters of Loins by Data Mining

The main objective of this study was to configure the acquisition and analysis of low-field magnetic resonance imaging (MRI) to predict physico-chemical characteristics of Iberian loin, evaluating the use of different MRI sequences (spin echo, SE; gradient echo, GE; turbo 3D, T3D), computational texture feature methods (GLCM, NGLDM, GLRLM, GLCM + NGLDM + GLRLM), and data mining techniques (multiple linear regression, MLR; isotonic regression, IR). Moderate to very good correlation coefficients and low mean absolute error were found when applying MLR or IR on any method of computational texture features from MRI acquired with SE or GE. For T3D sequence, accurate results are only obtained by applying IR on GLCM or GLCM + NGLDM + GLRLM methods. Considering not only the accuracy of the methodology but also consumed time and required resources, the use of SE sequences for MRI acquisition, GLCM method for MRI texture analysis, and MLR could be indicated for prediction physico-chemical characteristics of loin.     

Automatic categorisation of comments in social news websites

The use of the social web has brought a series of changes in the way how content is created. In particular, social news sites link stories and the different users can comment them. In this paper, we propose a new method based on different features extracted from the text able to categorise the comments. To this end, we use a combination of statistical, syntactic and opinion features and machine-learning classifiers to classify a comment within three different categorisation types: the focus of the comment, the type of information contained in the comment and the controversy level of the comment. We validate our approach with data from ‘Menéame’, a popular Spanish social news site.     

Noncanonical Reactions of Flavoenzymes

Enzymes containing flavin cofactors are predominantly involved in redox reactions in numerous cellular processes where the protein environment modulates the chemical reactivity of the flavin to either transfer one or two electrons. Some flavoenzymes catalyze reactions with no net redox change. In these reactions, the protein environment modulates the reactivity of the flavin to perform novel chemistries. Recent mechanistic and structural data supporting novel flavin functionalities in reactions catalyzed by chorismate synthase, type II isopentenyl diphosphate isomerase, UDP-galactopyranose mutase, and alkyl-dihydroxyacetonephosphate synthase are presented in this review. In these enzymes, the flavin plays either a direct role in acid/base reactions or as a nucleophile or electrophile. In addition, the flavin cofactor is proposed to function as a “molecular scaffold” in the formation of UDP-galactofuranose and alkyl-dihydroxyacetonephosphate by forming a covalent adduct with reaction intermediates.     

Neuro-evolutionary approach applied for optimizing the PEMFC performance

A multi-objective optimization strategy, based on stacked neural network–genetic algorithm (SNN–GA) hybrid approach, was applied to study the C/PBI content on a high temperature PEMFC performance. The operating conditions of PEMFC were correlated with power density and electrochemical active surface area for electrodes. The structure of the stack was determined in an optimal form related to the contribution of individual neural networks, after applying an interpolation based procedure. Multi-objective optimization using SNN as model and GA as solving procedure provides optimal working conditions which lead to a high PEMFC performance. Simulation results were in agreement with experimental data, both for model validation and system optimization (the C/PBI content in the range of 17–21%).     

Characterization of a miniaturized liquid bridge for nL sample infusion: a comparative study of sample flush-out behavior using flow simulations and direct ESI-MS analysis

In this work we shed light on the microfluidics of a miniaturized liquid bridge that forms the central part of a so-called “capillary gap sampler,” a novel device for rapid and seamless injection of nanoliter sample volumes into an electrospray ionization mass spectrometer (ESI-MS). Parameters relevant for sample flush-out at the liquid bridge and in the spray capillary were identified by systematic variation of the capillary dimensions, the linear buffer flow rate (2.1–34 mm/s) and molecular weight of the analytes (0.5–30 kDa). We found that a reduction in capillary wall thickness by a factor of 1.6 significantly influences analyte peak shapes, leads to an inversion of the relationship between peak width and analyte molecular weight, and allows a fivefold decrease in peak width for large molecules down to 5 s. The results could be verified and explained by simulations, in which the presence of diffusion-controlled “dead zones” at the liquid bridge and dispersion in the spray tip that depend on analyte molecular weight were identified as key factors relevant for the sample flush-out process. The merging of simulations and experimental data gives useful hints toward the re-design of a spray tip as built-in ESI-MS interface for an optimized gap sampler performance.     

Metaheuristic optimization frameworks: a survey and benchmarking

This paper performs an unprecedented comparative study of Metaheuristic optimization frameworks. As criteria for comparison a set of 271 features grouped in 30 characteristics and 6 areas has been selected. These features include the different metaheuristic techniques covered, mechanisms for solution encoding, constraint handling, neighborhood specification, hybridization, parallel and distributed computation, software engineering best practices, documentation and user interface, etc. A metric has been defined for each feature so that the scores obtained by a framework are averaged within each group of features, leading to a final average score for each framework. Out of 33 frameworks ten have been selected from the literature using well-defined filtering criteria, and the results of the comparison are analyzed with the aim of identifying improvement areas and gaps in specific frameworks and the whole set. Generally speaking, a significant lack of support has been found for hyper-heuristics, and parallel and distributed computing capabilities. It is also desirable to have a wider implementation of some Software Engineering best practices. Finally, a wider support for some metaheuristics and hybridization capabilities is needed.     

Optimization and CFD modeling of an improved rustic oven for producing bricks

Clean Technologies and Environmental Policy - This paper presents an optimization formulation and a computational fluid dynamic study for a new proposal of a rustic oven that uses natural gas...     

Optimization of Lithium Extraction from Lepidolite by Roasting Using Sodium and Calcium Sulfates

The lithium extraction from a lepidolite concentrate using roasting, followed by water leaching, was studied. Several alternative additives were initially tested. The use of sodium and calcium sulfates as additives was evaluated in more detail. The influence of some process variables, namely the roasting time, roasting temperature and the additive/concentrate mass ratio, was studied applying a design of experiments. The lithium extraction was modelled and the fitted and validated model was used to optimize the process response. The increase in the additive/concentrate mass ratio, roasting time and temperature seems to result in solid state reactions and transformations that lead to phase, morphological and particle size distribution modifications, which were assessed by XRPD, SEM, and particle size analyses. In this process, lithium sodium sulfate formation constitutes a crucial step enabling the Li water leaching. High lithium extractions were estimated for several combinations of factors. At 850°C, lithium extractions over 90% are obtained when the roasting time is above 1.90 hour and the additive/concentrate mass ratios are over 0.77. An increase in the temperature to 875°C also leads to lithium extractions over 90% for a roasting time of 1 hour and an additive/concentrate mass ratio of 0.60.