Prediction of surface roughness in low speed turning of AISI316 austenitic stainless steel

Surface roughness is an important quality in manufacturing, as it affects the product’s tribological, frictional and assembly characteristics. Turning stainless steel at low cutting speeds may result in a rougher surface due to built up edge formation, where as speed increases the surface roughness improves, due to the low contact time between the chip and the tool to allow bonding to occur.However, this increase in cutting speed produces higher tool wear rates, which increases the machining costs. Previous studies have indicated that savings in cost and manufacturing time are obtained when predicting the surface roughness, prior to the machining process. In this paper, experimental data are used to develop prediction models using Multiple Linear Regression and Artificial Neural Network methodologies. Results show that the neural network outperforms the linear model by a fair margin (1400%). Moreover, the developed Artificial Neural Network model has been integrated with an optimisation algorithm, known as Simulated Annealing (SA),this is done in order to obtain a set of cutting parameters that result in low surface roughness. A low value of surface roughness and the set of parameters resulting on it, are successfully yielded by the SA algorithm

Systematic Collaborative Reanalysis of Genomic Data Improves Diagnostic Yield in Neurologic Rare Diseases

Altres ajuts: Generalitat de Catalunya, Departament de Salut; Generalitat de Catalunya, Departament d'Empresa i Coneixement i CERCA Program; Ministerio de Ciencia e Innovación; Instituto Nacional de Bioinformática; ELIXIR Implementation Studies (CNAG-CRG); Centro de Investigaciones Biomédicas en Red de Enfermedades Raras; Centro de Excelencia Severo Ochoa; European Regional Development Fund (FEDER).Many patients experiencing a rare disease remain undiagnosed even after genomic testing. Reanalysis of existing genomic data has shown to increase diagnostic yield, although there are few systematic and comprehensive reanalysis efforts that enable collaborative interpretation and future reinterpretation. The Undiagnosed Rare Disease Program of Catalonia project collated previously inconclusive good quality genomic data (panels, exomes, and genomes) and standardized phenotypic profiles from 323 families (543 individuals) with a neurologic rare disease. The data were reanalyzed systematically to identify relatedness, runs of homozygosity, consanguinity, single-nucleotide variants, insertions and deletions, and copy number variants. Data were shared and collaboratively interpreted within the consortium through a customized Genome-Phenome Analysis Platform, which also enables future data reinterpretation. Reanalysis of existing genomic data provided a diagnosis for 20.7% of the patients, including 1.8% diagnosed after the generation of additional genomic data to identify a second pathogenic heterozygous variant. Diagnostic rate was significantly higher for family-based exome/genome reanalysis compared with singleton panels. Most new diagnoses were attributable to recent gene-disease associations (50.8%), additional or improved bioinformatic analysis (19.7%), and standardized phenotyping data integrated within the Undiagnosed Rare Disease Program of Catalonia Genome-Phenome Analysis Platform functionalities (18%)

Revista Actualidad Criminológica nº3

Nuestra revista nace con vocación de servir a dos objetivos: a) divulgativo, dar a conocer la ciencia criminológica al público en general y al estudiante universitario en particular y, b) generador de un espacio de encuentro y reflexión de profesores y alumnos de la Universidad Camilo José Cela. A través de sus páginas pretendemos servir de escaparate de una realidad que se escapa al ciudadano de a pié, del fenómeno de la criminalidad en todas sus manifestaciones, de su cruda realidad, de la lucha contra el delito, del estudio y la investigación criminal, del método científico como herramienta básica de trabajo