A Machine Learning Approach to Prediction of the Compressive Strength of Segregated Lightweight Aggregate Concretes Using Ultrasonic Pulse Velocity

Lightweight aggregate concrete (LWAC) is an increasingly important material for modern construction. However, although it has several advantages compared with conventional concrete, it is susceptible to segregation due to the low density of the incorporated aggregate. The phenomenon of segregation can adversely affect the mechanical properties of LWAC, reducing its compressive strength and its durability. In this work, several machine learning techniques are used to study the influence of the segregation of LWAC on its compressive strength, including the K-nearest neighbours (KNN) algorithm, regression tree-based algorithms such as random forest (RF) and gradient boosting regressors (GBRs), artificial neural networks (ANNs) and support vector regression (SVR). In addition, a weighted average ensemble (WAE) method is proposed that combines RF, SVR and extreme GBR (or XGBoost). A dataset that was recently used for predicting the compressive strength of LWAC is employed in this experimental study. Two different types of lightweight aggregate (LWA), including expanded clay as a coarse aggregate and natural fine limestone aggregate, were mixed to produce LWAC. To quantify the segregation in LWAC, the ultrasonic pulse velocity method was adopted. Numerical experiments were carried out to analyse the behaviour of the obtained models, and a performance improvement was shown compared with the machine learning models reported in previous works. The best performance was obtained with GBR, XGBoost and the proposed weighted ensemble method. In addition, a good choice of weights in the WAE method allowed our approach to outperform all of the other models.This research was funded by MCIN/AEI/10.13039/501100011033, grant PID2021-123627OB-C55 and by “ERDF A way of making Europe”

A parallel methodology using radial basis functions versus machine learning approaches applied to environmental modelling

Parallel nonlinear models using radial kernels on local mesh support have been designed and implemented for application to real-world problems. Although this recently developed approach reduces the memory requirements compared with other methodologies suggested over the last few years, its computational cost makes parallelisation necessary, especially for big datasets with many instances or attributes. In this work, several strategies for the parallelisation of this methodology are proposed and compared. The MPI communication protocol and the OpenMP application programming interface are used to implement the algorithm. The performance of this methodology is compared with various machine learning methods, with particular consideration of techniques using radial basis functions (RBF). Different methods are applied to model the daily maximum air temperature from real meteorological data collected from the Agroclimatic Station Network of the Phytosanitary Alert and Information Network of Andalusia, an autonomous community of southern Spain. The obtained goodness-of-fit measures illustrate the effectiveness of this nonlinear methodology, and its training process is shown to be simpler than those of other powerful machine learning methods.This research was supported by the Spanish Ministry of Science, Innovation and Universities Grant RTI2018-098156-B-C54, co-financed by the European Commission (FEDER funds), and by the University of Alicante

Newton additive and multiplicative Schwarz iterative methods

Convergence properties are presented for Newton additive and multiplicative Schwarz (AS and MS) iterative methods for the solution of nonlinear systems in several variables. These methods consist of approximate solutions of the linear Newton step using either AS or MS iterations, where overlap between subdomains can be used. Restricted versions of these methods are also considered. These Schwarz methods can also be used to precondition a Krylov subspace method for the solution of the linear Newton steps. Numerical experiments on parallel computers are presented, indicating the effectiveness of these methods.The Spanish Ministry of Science and Education (TIN2005-09037-C02-02); Universidad de Alicante (VIGROB-020); the U.S. Department of Energy (DE-FG02-05ER25672)

Parallel approach of a Galerkin-based methodology for predicting the compressive strength of the lightweight aggregate concrete

A methodology based on the Galerkin formulation of the finite element method has been analyzed for predicting the compressive strength of the lightweight aggregate concrete using ultrasonic pulse velocity. Due to both the memory requirements and the computational cost of this technique, its parallelization becomes necessary for solving this problem. For this purpose a mixed MPI/OpenMP parallel algorithm has been designed and different approaches and data distributions analyzed. On the other hand, this Galerkin methodology has been compared with multiple linear regression models, regression trees and artificial neural networks. Based on different measures of goodness of fit, the effectiveness of the Galerkin methodology, compared with these statistical techniques for data mining, is shown.This research was supported by the Spanish Ministry of Science, Innovation and Universities Grant RTI2018-098156-B-C54, co-financed by the European Commission (FEDER funds)

ArtEM

Comunicación publicada en las Actas de las IX Jornadas sobre la enseñanza universitaria de la informática (JENUI 03), ISBN: 84-283-2845-5La herramienta ArtEM (Aritmética Entera y Modular) es una aplicación informática programada en Visual Basic y desarrollada con el fin de ser utilizada en las prácticas de cualquier asignatura que incluya como tópicos los relacionados con la aritmética entera y modular. Está estructurada en 5 menús básicos: - Euclides. - Ecuaciones diofánticas. - Números primos. - Aritmética modular. - Aplicación a la criptografía. Los tres primeros menús están dedicados a la aritmética entera, el cuarto menú proporciona cálculos básicos en la aritmética modular como los cálculos del representante de clase, inverso de un elemento, función de Euler y potencias. El quinto menú constituye una aplicación a la criptografía centrándose en dos criptosistemas, uno de clave privada y otro de clave pública. Todos los algoritmos disponibles en ArtEM se desarrollan de tal forma que el usuario es capaz de reconocer los pasos que se han seguido para su ejecución, de manera que se obtiene un importante valor pedagógico

Parallel alternating iterative algorithms with and without overlapping on multicore architectures

We consider the problem of solving large sparse linear systems where the coefficient matrix is possibly singular but the equations are consistent. Block two-stage methods in which the inner iterations are performed using alternating methods are studied. These methods are ideal for parallel processing and provide a very general setting to study parallel block methods including overlapping. Convergence properties of these methods are established when the matrix in question is either M-matrix or symmetric matrix. Different parallel versions of these methods and implementation strategies, with and without overlapping blocks, are explored. The reported experiments show the behavior and effectiveness of the designed parallel algorithms by exploiting the benefits of shared memory inside the nodes of current SMP supercomputers.This research was partially supported by the Spanish Ministry of Science and Innovation under grant number TIN2011-26254, and by the European Union FEDER (CAPAP-H5 network TIN2014-53522- REDT)

Parallel two-stage algorithms for solving the PageRank problem

In this work we present parallel algorithms based on the use of two-stage methods for solving the PageRank problem as a linear system. Different parallel versions of these methods are explored and their convergence properties are analyzed. The parallel implementation has been developed using a mixed MPI/OpenMP model to exploit parallelism beyond a single level. In order to investigate and analyze the proposed parallel algorithms, we have used several realistic large datasets. The numerical results show that the proposed algorithms can speed up the time to converge with respect to the parallel Power algorithm and behave better than other well-known techniques.This research was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Commission (FEDER funds) under Grant Number TIN2015-66972-C5-4-R

Non-Stationary Acceleration Strategies for PageRank Computing

In this work, a non-stationary technique based on the Power method for accelerating the parallel computation of the PageRank vector is proposed and its theoretical convergence analyzed. This iterative non-stationary model, which uses the eigenvector formulation of the PageRank problem, reduces the needed computations for obtaining the PageRank vector by eliminating synchronization points among processes, in such a way that, at each iteration of the Power method, the block of iterate vector assigned to each process can be locally updated more than once, before performing a global synchronization. The parallel implementation of several strategies combining this novel non-stationary approach and the extrapolation methods has been developed using hybrid MPI/OpenMP programming. The experiments have been carried out on a cluster made up of 12 nodes, each one equipped with two Intel Xeon hexacore processors. The behaviour of the proposed parallel algorithms has been studied with realistic datasets, highlighting their performance compared with other parallel techniques for solving the PageRank problem. Concretely, the experimental results show a time reduction of up to 58.4% in relation to the parallel Power method, when a small number of local updates is performed before each global synchronization, outperforming both the two-stage algorithms and the extrapolation algorithms, more sharply as the number of processes increases.This research was supported by the Spanish Ministry of Science, Innovation and Universities Grant RTI2018-098156-B-C54, co-financed by the European Commission (FEDER funds)

Patients with Schizophrenia Showed Worse Cognitive Performance than Bipolar and Major Depressive Disorder in a Sample with Comorbid Substance Use Disorders

Comorbidity of substance use disorders (SUD) and severe mental illness (SMI) is highly frequent in patients, the most common diagnoses being schizophrenia (SZ), bipolar disorder (BD) and major depressive disorder (MDD). Since comorbidity has its own clinical features, and neurocognitive functioning is not always similar to psychiatric symptoms the present study explores the cognitive performance of patients with dual disorders. A neuropsychological battery of tests was used to assess 120 under treatment male patients, 40 for each group considered (SZ + SUD, BD + SUD and MDD + SUD) who were mainly polyconsumers. Significant differences (with premorbid IQ as a covariate) were found among the groups, with SZ + SUD having a worse performance in attention, verbal learning, short term memory and recognition. The consideration of a global Z score for performance evidenced an impaired neurocognitive pattern for SZ + SUD compared with BD + SUD and MDD + SUD. According to norms, all patients showed difficulties in verbal learning, short-term memory and recognition. Our research indicated that the neurocognitive functioning of dual disorder patients was influenced by the comorbid SMI, with SZ + SUD presenting major difficulties. Future studies should thoroughly explore the role of such difficulties as indicators or endophenotypes for dual schizophrenia disorders, and their usefulness for prevention and treatment.This research was funded by the grant PID2020-117767GB-I00 of Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) and the Generalitat de Catalunya (2017SGR-748). Partial funding for open access charge: Universidad de Málag

A heuristic relaxed extrapolated algorithm for accelerating PageRank

The PageRank algorithm for determining the importance of Web pages has become a central technique in Web search. This algorithm uses the Power method to compute successive iterates that converge to the principal eigenvector of the Markov chain representing the Web link graph. In this work we present an effective heuristic Relaxed and Extrapolated algorithm based on the Power method that accelerates its convergence. A hybrid parallel implementation of this algorithm has been designed by combining various OpenMP threads for each MPI process and several strategies of data distribution among nodes have been analyzed. The results show that the proposed algorithm can significantly speed up the convergence time with respect to the parallel Power algorithm.This research was partially supported by the Spanish Ministry of Science and Innovation under Grant Number TIN2011-26254 and Grant Number TIN2015-66972-C5-4-R, and by the European Union FEDER (CAPAP-H5 network TIN2014-53522-REDT)