Sliding mode controller design for linear stochastic systems with unknown parameters

This paper presents the sliding mode mean-square and mean-module controllers for linear stochastic systems with unknown parameters. In both cases, the controller equations are obtained using the separation principle, whose applicability to the considered problem is substantiated. Performance of the obtained controllers is verified in the illustrative example against the sliding mode mean-square and sliding mode controllers that are optimal for linear systems with known parameters. Simulation graphs demonstrating overall performance and computational accuracy of the designed optimal controller are included.     

Relevance-based language modelling for recommender systems

Relevance-Based Language Models, commonly known as Relevance Models, are successful approaches to explicitly introduce the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models achieve state-of-the-art retrieval performance in the pseudo relevance feedback task. On the other hand, the field of recommender systems is a fertile research area where users are provided with personalised recommendations in several applications. In this paper, we propose an adaptation of the Relevance Modelling framework to effectively suggest recommendations to a user. We also propose a probabilistic clustering technique to perform the neighbour selection process as a way to achieve a better approximation of the set of relevant items in the pseudo relevance feedback process. These techniques, although well known in the Information Retrieval field, have not been applied yet to recommender systems, and, as the empirical evaluation results show, both proposals outperform individually several baseline methods. Furthermore, by combining both approaches even larger effectiveness improvements are achieved.     

Determining the minimum number of passive stretches necessary to alter musculotendinous stiffness

Abstract

In this study, we examined the minimum number of constant-torque passive stretches necessary to reduce musculotendinous stiffness. Thirteen healthy individuals (mean age 22 years, s = 3; stature 1.67 m, s = 0.1; mass 66 kg, s = 13 kg) volunteered to participate in the investigation and underwent four 30-s constant-torque passive stretches of the plantar flexor muscles. Musculotendinous stiffness was examined from the angle–torque curves generated prior to the passive stretches, at the beginning of each 30-s stretch, and immediately following the four 30-s passive stretches. The results indicated that musculotendinous stiffness of the plantar flexors was reduced following two 30-s constant-torque passive stretches (P < 0.05) compared with the pre- musculotendinous stiffness assessment. Musculotendinous stiffness remained depressed following the third and fourth stretches, but did not decrease further. These findings suggest that two 30-s bouts of constant-torque passive stretching may be necessary to cause a significant decrease in musculotendinous stiffness of the plantar flexor muscles.     

Sliding mode optimal control for linear systems

This paper addresses the optimal control problem for a linear system with respect to a Bolza–Meyer criterion with a non-quadratic non-integral term. The optimal solution is obtained as a sliding mode control, whereas the conventional linear feedback control fails to provide a causal solution. Performance of the obtained optimal controller is verified in the illustrative example against the conventional LQ regulator that is optimal for the quadratic Bolza–Meyer criterion. The simulation results confirm an advantage in favor of the designed sliding mode control.     

Building user profiles based on sequences for content and collaborative filtering

Modeling user profiles is a necessary step for most information filtering systems – such as recommender systems – to provide personalized recommendations. However, most of them work with users or items as vectors, by applying different types of mathematical operations between them and neglecting sequential or content-based information. Hence, in this paper we study how to propose an adaptive mechanism to obtain user sequences using different sources of information, allowing the generation of hybrid recommendations as a seamless, transparent technique from the system viewpoint. As a proof of concept, we develop the Longest Common Subsequence (LCS) algorithm as a similarity metric to compare the user sequences, where, in the process of adapting this algorithm to recommendation, we include different parameters to control the efficiency by reducing the information used in the algorithm (preference filter), to decide when a neighbor is considered useful enough to be included in the process (confidence filter), to identify whether two interactions are equivalent (δ-matching threshold), and to normalize the length of the LCS in a bounded interval (normalization functions). These parameters can be extended to work with any type of sequential algorithm.We evaluate our approach with several state-of-the-art recommendation algorithms using different evaluation metrics measuring the accuracy, diversity, and novelty of the recommendations, and analyze the impact of the proposed parameters. We have found that our approach offers a competitive performance, outperforming content, collaborative, and hybrid baselines, and producing positive results when either content- or rating-based information is exploited.     

Some numerical tests for an alternative approach to optimal feedback control

We test a recently proposed approach to optimal feedback control of nonlinear systems leading to an iterative descending strategy [24]. We start by discussing the numerical implementation of this strategy, and propose a number of improvements that can speed up the computation process by up to two orders of magnitude. The resulting algorithm is then applied to a series of test problems of increasing complexity. Results seem to show that this can be a promising strategy to bear in mind for more realistic situations.     

Risk markers for disappearance of pediatric Web resources.

OBJECTIVES: The authors sought to find out whether certain Webometric indexes of a sample of pediatric Web resources, and some tests based on them, could be helpful predictors of their disappearance. METHODS: The authors performed a retrospective study of a sample of 363 pediatric Websites and pages they had followed for 4 years. Main measurements included: number of resources that disappeared, number of inbound links and their annual increment, average daily visits to the resources in the sample, sample compliance with the quality criteria of 3 international organizations, and online time of the Web resources. RESULTS: On average, 11% of the sample disappeared annually. However, 13% of these were available again at the end of follow up. Disappearing and surviving Websites did not show differences in the variables studied. However, surviving Web pages had a higher number of inbound links and higher annual increment in inbound links. Similarly, Web pages that survived showed higher compliance with recognized sets of quality criteria than those that disappeared. A subset of 14 quality criteria whose compliance accounted for 90% of the probability of online permanence was identified. Finally, a progressive increment of inbound links was found to be a marker of good prognosis, showing high specificity and positive predictive value (88% and 94%, respectively). CONCLUSIONS: The number of inbound links and annual increment of inbound links could be useful markers of the permanence probability for pediatric Web pages. Strategies that assure the Web editors' awareness of their Web resources' popularity could stimulate them to improve the quality of their Websites.     

Comparative analysis of academic grades in compulsory secondary education in Spain using statistical techniques

The present study, based on the construct comparability approach, performs a comparative analysis of general points average for seven courses, using exploratory factor analysis (EFA) and the Partial Credit model (PCM) with a sample of 1398 student subjects (M = 12.5, SD = 0.67) from 8 schools in the province of Alicante (Spain). EFA confirmed a one-factor model which explains 74.44% of the variance. Cronbach’s alpha value for this factor was .94. The PCM supported the one-factor model, and an optimal fit was achieved in all of the courses. The analysis of differential item functioning showed no significant differences in any course. Equitable distribution was observed in the evolution of the difficulty indices along the measurement scale for each course. This type of analysis confirms the measurement of a single latent construct in the different topics analysed, despite addressing various theoretical and procedural contents.     

On the use of extended TAM to assess students' acceptance and intent to use third‐generation learning management systems

Service‐oriented e‐learning platforms can be considered as a third generation of learning management systems (LMSs). As opposed to the previous generations, consisting of ad hoc solutions and traditional LMS, this new technology contemplates e‐learning systems as services that can be integrated into different learning scenarios. This paper shows that the flexibility of these systems allows building personal learning environments and proposes the use of an extended technology acceptance model (TAM) to assess the acceptance and intention to use a third generation of LMS. To fit and validate the service‐oriented e‐learning platforms acceptance model, structural equation modeling and path analysis have been implemented. The results point out that the intention to use this third‐generation LMS is determined by the gadgets and container design, both of which are structural elements of this technology. Finally, it is also shown that previous experience does not determine the use intention of this technology.