Life Review Therapy Using Autobiographical Retrieval Practice for Older Adults With Depressive Symptomatology.

The aim of this study was to examine the efficacy of life review based on autobiographical retrieval practice for treating depressed older adults. Forty-three adults aged 65-93 with clinically significant depressive symptomatology and no dementia were randomly assigned to treatment or to no treatment. The results indicated significant differences between experimental and control groups after 4 weeks of autobiographical retrieval practice. At posttest, those in the treatment condition showed fewer depressive symptoms, less hopelessness, improved life satisfaction, and retrieval of more specific events. The findings suggest that practice in autobiographical memory for specific events may be among the components of life review that account for its effectiveness and could be a useful tool in psychotherapy with older adults. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Increasing Reading Comprehension and Engagement Through Concept-Oriented Reading Instruction.

Based on an engagement perspective of reading development, we investigated the extent to which an instructional framework of combining motivation support and strategy instruction (Concept-Oriented Reading Instruction--CORI) influenced reading outcomes for third-grade children. In CORI, five motivational practices were integrated with six cognitive strategies for reading comprehension. In the first study, we compared this framework to an instructional framework emphasizing Strategy Instruction (SI), but not including motivation support. In the second study, we compared CORI to SI and to a traditional instruction group (TI), and used additional measures of major constructs. In both studies, class-level analyses showed that students in CORI classrooms were higher than SI and/or TI students on measures of reading comprehension, reading motivation, and reading strategies. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Bayesian classification for the selection of in vitro human embryos using morphological and clinical data

In vitro fertilization (IVF) is a medically assisted reproduction technique that enables infertile couples to achieve successful pregnancy. Given the uncertainty of the treatment, we propose an intelligent decision support system based on supervised classification by Bayesian classifiers to aid to the selection of the most promising embryos that will form the batch to be transferred to the woman's uterus. The aim of the supervised classification system is to improve overall success rate of each IVF treatment in which a batch of embryos is transferred each time, where the success is achieved when implantation (i.e. pregnancy) is obtained. Due to ethical reasons, different legislative restrictions apply in every country on this technique. In Spain, legislation allows a maximum of three embryos to form each transfer batch. As a result, clinicians prefer to select the embryos by non-invasive embryo examination based on simple methods and observation focused on morphology and dynamics of embryo development after fertilization. This paper proposes the application of Bayesian classifiers to this embryo selection problem in order to provide a decision support system that allows a more accurate selection than with the actual procedures which fully rely on the expertise and experience of embryologists. For this, we propose to take into consideration a reduced subset of feature variables related to embryo morphology and clinical data of patients, and from this data to induce Bayesian classification models. Results obtained applying a filter technique to choose the subset of variables, and the performance of Bayesian classifiers using them, are presented.     

Exact algorithms for a scheduling problem with unrelated parallel machines and sequence and machine-dependent setup times

A scheduling problem with unrelated parallel machines, sequence and machine-dependent setup times, due dates and weighted jobs is considered in this work. A branch-and-bound algorithm (B&B) is developed and a solution provided by the metaheuristic GRASP is used as an upper bound. We also propose a set of instances for this type of problem. The results are compared to the solutions provided by two mixed integer programming models (MIP) with the solver CPLEX 9.0. We carry out computational experiments and the algorithm performs extremely well on instances with up to 30 jobs.     

Multiplexed spectral coding for simultaneous detection of DNA hybridization reactions based on FRET

Fluorescence resonance energy transfer (FRET) is widely used in spectral codification of information at the molecular level, and can be used to generate several layers of information on a DNA chip. We used two oligonucleotides (probes) labeled with different donor (harvesting) molecules in hybridization experiments with complementary oligonucleotides labeled with four different acceptors (targets). By looking at the fluorescence response of the sample after “specific” excitation of each donor molecule (by “specific” we mean a wavelength where one of the donors is predominantly excited), we inspected the possibility to identify the complementary oligonucleotide hybridized to the probe, in mixtures containing two donor probe/acceptor target pairs. In most samples (13 out of the 16 possible), it is trivial to identify the complementary target that is hybridized to the excited donor probe in the mixtures. The major limitations of the chosen system arise when very different concentrations of donor probe/acceptor target pairs are present in the same sample.     

Interactive tool for analysis of time-delay systems with dead-time compensators

The problem of controlling delayed systems has been widely studied in the control community. The Smith predictor (SP) scheme was the most famous and efficient technique to cope with this problem. Afterwards, robustness problems and those related to the control of integrating and unstable delayed processes were resolved. This work presents an interactive tool for studying and understanding the different alternatives available for controlling systems with large delays. Interactive tools have provided excellent support for the control community during recent years. In this sense, the tool developed here helps to compare easily the performance of PID controllers and SP approaches for processes with delay, as well as other control structures used to solve different problems that the original SP has when integrator and unstable models are employed. Robustness problems due to presence of model uncertainties can also be analyzed. Furthermore, some real industrial processes are also described as motivation in the study of the problems presented in the paper. The tool incorporates a set of typical transfer functions based on these real industrial processes.     

Invariant sets techniques for Youla–Kučera parameter synthesis

This article addresses an invariant sets approach for Youla–Ku?era parameter synthesis using linear matrix inequality (LMI) techniques. Given a linear discrete-time observer-based system affected by bounded disturbances and constraints, the proposed technique furnishes the best Youla parameter in terms of finding an invariant ellipsoidal set satisfying the constraints and having the maximal ellipsoidal projection on the state space. Compared with the results obtained for an observer-based design, the synthesis of a Youla parameter provides a larger ellipsoidal projection and an improved sensitivity function. The price to pay for these achievements in terms of robustness is usually a slow closed-loop performance with degraded complementary sensitivity function. In order to obtain a compromise between robustness and performance two methods are proposed: the first method imposes a new bound on the Lyapunov function decreasing speed and the second refers to the pole placement concept. The aforementioned approaches are finally validated in simulation considering position control of an induction motor.     

Applying classifier systems to learn the reactions in mobile robots

The navigation problem involves how to reach a goal avoiding obstacles in dynamic environments. This problem can be faced considering reactions and sequences of actions. Classifier systems (CSs) have proven their ability of continuous learning, however, they have some problems in reactive systems. A modified CS, namely a reactive classifier system (RCS), is proposed to overcome those problems. Two special mechanisms are included in the RCS: the non-existence of internal cycles inside the CS (no internal cycles) and the fusion of environmental message with the messages posted to the message list in the previous instant (generation list through fusion). These mechanisms allow the learning of both reactions and sequences of actions. This learning process involves two main tasks: first, discriminate between rules and, second, the discovery of new rules to obtain a successful operation in dynamic environments. DiVerent experiments have been carried out using a mini-robot Khepera to find a generalized solution. The results show the ability of the system for continuous learning and adaptation to new situations.     

A family of simple distributed minimum connected dominating set-based topology construction algorithms

This paper considers the problem of topology construction to save energy in wireless sensor networks. The proposed topology construction mechanisms build reduced topologies using the Connected Dominating Set approach in a distributed, efficient, and simple manner. This problem is very challenging because the solution must provide a connected network with complete coverage of the area of interest using the minimum number of nodes possible. Further, the algorithms need to be computationally inexpensive and the protocols simple enough in terms of their message and computation complexity, so they do not consume more energy creating the reduced topology than the energy that they are supposed to save. In addition, it is desirable to reduce or completely eliminate the need of localization mechanisms since they introduce additional costs and energy consumption. To this end, we present the family of A3 distributed topology construction algorithms, four simple algorithms that build reduced topologies with very low computational and message complexity without the need of localization information: A3, A3Cov, A3Lite and A3CovLite. The algorithms are compared in sparse and dense networks versus optimal theoretical bounds for connected-coverage topologies and two distributed heuristics found in the literature using the number of active nodes and the ratio of coverage as the main performance metrics. The results demonstrate that there is no clear winner, and rather, trade offs exist. If coverage is not as critical as energy (network lifetime), it would be better to use A3Lite, as it needs fewer number of nodes and messages. If coverage is very important for the application, then the A3CovLite is the best option mostly because of the lower message complexity.