Alzheimer’s disease (AD) is the most frequent cause of dementia, linked to morbidity and mortality among elderly patients. Recently, several clinical studies suggested that depression is a potential risk factor for cognitive decline and AD. A review of meta-analyses was performed, calculating pooled odds ratios to estimate the risk of AD in people with a prior diagnosis (or clinically significant symptoms) of depression. A total of six meta-analyses which represented 28 individual studies were analyzed. A significant association between depression and AD was found (OR = 1.54, 95% CI [1.02–2.31]; p = 0.038). The results showed that heterogeneity across studies was substantial. We found a significant positive effect size for clinical measures of depression, but not for symptomatic rating scales, in the association of depression with risk of AD. The type of rating scale used to assess depression and the cut-off criteria selected also moderated the relationship between depression and AD risk. We found that studies that used clinically significant criteria for diagnosis of depression had more consistent and significant results than studies that used symptomatic scales.

Given an undirected graph, a clique is a subset of vertices in which the induced subgraph is complete; that is, all pairs of vertices of this subset are adjacent. Clique problems in graphs are very important due to their numerous applications. One of these problems is the clique partitioning problem (CPP), which consists of dividing the set of vertices of a graph into the smallest number of cliques possible. The CPP is an NP-hard problem with many application fields (timetabling, manufacturing, scheduling, telecommunications, etc.). Despite its great applicability, few recent studies have focused on proposing specific resolution methods for the CPP. This article presents a resolution method that combines multistart strategies with tabu search. The most novel characteristic of our method is that it allows unfeasible solutions to be visited, which facilitates exploration of the solution space. The computational tests show that our method performs better than previous methods proposed for this problem. In fact, our method strictly improves the results of these methods in most of the instances considered while requiring less computation time.

In this study, a model for the selection of investment portfolios is proposed with three objectives. In addition to the traditional objectives of maximizing profitability and minimizing risk, maximization of social responsibility is also considered. Moreover, with the purpose of controlling transaction costs, a limit is placed on the number of assets for selection. To the best of our knowledge, this specific model has not been considered in the literature to date. This model is difficult (NP-Hard), and therefore, only very small instances may be solved in an exact way. This paper proposes a method based on tabu search and multiobjective adaptive memory programming (MOAMP) strategies. With this method it is possible to obtain sets of nondominated solutions in short computational times. To check the performance of our method it is compared with adaptations of the nondominated sorting genetic algorithm (NSGA-II), strength Pareto evolutionary algorithm (SPEA-II) and multiobjective particle swarm optimization (MOPSO). The results of different computational experiments show that our tabu search-MOAMP method performed best. The quality of the sets of solutions that were obtained and the speed of execution mean that our tabu search-MOAMP can be used as a tool for financial assessment and analysis (including online services). This tool, as we can see in this work with some examples, can take into account the social concerns of many clients and their overall risk profile (very conservative, conservative, moderate, or fearless). This approach is also in line with current legal regulations that oblige financial advisors to take the client profile into account to provide greater protection and propose good financial advice.

El objetivo de este trabajo es analizar el efecto de los escenarios presencial, mixto y on-line en los resultados académicos de los estudiantes considerando la interacción con plataformas e-learning. Los resultados muestran que los resultados académicos no se ven afectados por el escenario de aprendizaje, mientras que el grado de interacción con las plataformas e-learning se ve afectado por el escenario de aprendizaje. Además, el modelo de Efectos de Tratamiento se ha utilizado para estudiar el escenario de aprendizaje y la interacción con las plataformas de aprendizaje de manera conjunta. En este caso, los resultados académicos se ven afectados por el escenario on-line frente al presencial, pero no se ven afectados por el escenario mixto frente al presencial. En concreto, sobre un valor medio de 4,14 puntos, obtenido de los resultados académicos de todos los alumnos, con un tratamiento on-line los resultados bajan 1,01 puntos (24,41%), mientras que con un tratamiento mixto los resultados bajan 0,38 puntos (9,13%). Finalmente, utilizando la Regresión Extendida Polinomial Fraccionaria, se proponen modelos de predicción para cada uno de los escenarios.