Virtual Blocks: a serious game for spatial ability improvement on mobile devices

This paper presents a novel spatial instruction system for improving spatial abilities of engineering students. A 3D mobile game application called Virtual Blocks has been designed to provide a 3D virtual environment to build models with cubes that help students to perform visualization tasks to promote the development of their spatial ability during a short remedial course. A validation study with 26 freshman engineering students at La Laguna University (Spain) has concluded that the training had a measurable and positive impact on students’ spatial ability. In addition, the results obtained using a satisfaction questionnaire show that Virtual Blocks is considered an easy to use and stimulating application.     

Locality-Aware Automatic Parallelization for GPGPU with OpenHMPP Directives

The use of GPUs for general purpose computation has increased dramatically in the past years due to the rising demands of computing power and their tremendous computing capacity at low cost. Hence, new programming models have been developed to integrate these accelerators with high-level programming languages, giving place to heterogeneous computing systems. Unfortunately, this heterogeneity is also exposed to the programmer complicating its exploitation. This paper presents a new technique to automatically rewrite sequential programs into a parallel counterpart targeting GPU-based heterogeneous systems. The original source code is analyzed through domain-independent computational kernels, which hide the complexity of the implementation details by presenting a non-statement-based, high-level, hierarchical representation of the application. Next, a locality-aware technique based on standard compiler transformations is applied to the original code through OpenHMPP directives. Two representative case studies from scientific applications have been selected: the three-dimensional discrete convolution and the simple-precision general matrix multiplication. The effectiveness of our technique is corroborated by a performance evaluation on NVIDIA GPUs.     

A comparison of two learning mechanisms for the automatic design of fuzzy diagnosis systems for rotating machinery

In this paper, we approach the problem of automatically designing fuzzy diagnosis rules for rotating machinery, which can give an appropriate evaluation of the vibration data measured in the target machines. In particular, we explain the implementation to this aim and analyze the advantages and drawbacks of two soft computing techniques: knowledge-based networks (KBN) and genetic algorithms (GA). An application of both techniques is evaluated on the same case study, giving special emphasis to their performance in terms of classification success and computation time.A reduced version of this paper first appeared under the title “A comparative assessment on the application of knowledge-based networks and genetic algorithms to the design of fuzzy diagnosis systems for rotating machinery”, published in the book “Soft Computing in Industry—Recent Appliactions” (Springer Engineering).     

Decentralized control and identified-model predictive control of divided wall columns

As a thermal separation method, distillation is one of the most important separation technologies in the chemical industry. Given its importance, it is no surprise that increasing efforts have been made in reducing its energy inefficiencies. A great deal of research is focused in the design and optimization of the divided-wall column (DWC). Its applications are still reduced due to distrust of its controllability and robustness. Previous references have studied the decentralized control of DWC but still few papers deal about model predictive control (MPC) applied to DWC.In this work we present a decentralized control of both a divided-wall column along with its equivalent MPC schema, both approaches are compared. Instead of building a rigorous model or performing the step test to an existing plant, the MPC model is obtained by identification of a rigorous simulation. An ARX model is demonstrated to represent adequately the DWC column behavior. This approach might be very convenient if plant testing is not available.     

Stereo Video Surveillance Multi-agent System: New Solutions for Human Motion Analysis

This article presents a distributed agent-based system that can process the visual information obtained by stereoscopic cameras. The system is embedded within a global project whose objective is to develop an intelligent environment for location and identification within dependent environments that merges with other types of technologies. In this kind of environments, vision algorithms are very costly and require a lot of time to produce a response, which is highly inconvenient since many applications can require action to be taken in real time. A multi-agent system (MAS) can automate the process of analyzing images obtained by cameras, and optimize the procedure. This study presents a MAS that can process stereoscopic images to detect and classify people by combining a series of novel techniques. The article shows in detail the combination of techniques used to perform the detection process. The process can be subdivided into human detection, human tracking, and human behavior understanding. With the addition of a case-based reasoning (CBR) model, the system can also incorporate reasoning capabilities. The system was tested under different conditions and environments.     

MutY catalytic core, mutant and bound adenine structures define specificity for DNA repair enzyme superfamily

The DNA glycosylase MutY, which is a member of the Helix-hairpin-Helix (HhH) DNA glycosylase superfamily, excises adenine from mispairs with 8-oxoguanine and guanine. High-resolution crystal structures of the MutY catalytic core (cMutY), the complex with bound adenine, and designed mutants reveal the basis for adenine specificity and glycosyl bond cleavage chemistry. The two cMutY helical domains form a positively-charged groove with the adenine-specific pocket at their interface. The Watson-Crick hydrogen bond partners of the bound adenine are substituted by protein atoms, confirming a nucleotide flipping mechanism, and supporting a specific DNA binding orientation by MutY and structurally related DNA glycosylases.     

Parent–adolescent conflict in the context of parental support: Families with alcoholic and nonalcoholic fathers.

The authors tested the hypothesis that parental support provides a social context that moderates the effects of parent–adolescent conflict on adolescent problem behavior. They also examined the possible potentiating effects of a family risk factor, paternal alcoholism, on parent–adolescent conflict. Cross-sectional and prospective analyses of 269 adolescents and their parents showed that parent–adolescent conflict was more highly related to adolescent problem behavior when parental support was low than when support was high. Parent–adolescent conflict was related to problem behavior for adolescent children of alcoholics, but not for children of nonalcoholic parents. These findings support the contention that the effects of parent–adolescent conflict need to be understood within the context of other interpersonal and family background characteristics. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Visual analysis of a cold rolling process using a dimensionality reduction approach

The rolling process is a strategical industrial and economical activity that has a large impact among world-wide commercial markets. Typical operating conditions during the rolling process involve extreme mechanical situations, including large values of forces and tensions. In some cases, these scenarios can lead to several kinds of faults, which might result in large economic losses. Thereby, a proper assessment of the process condition is a key aspect, not only as a fault detection mechanism, but also as an economic saving system. In the rolling process, a remarkable kind of fault is the so-called chatter, a sudden powerful vibration that affects the quality of the rolled material. In this paper, we propose a visual approach for the analysis of the rolling process. According to physical principles, we characterize the exit thickness and the rolling forces by means of a large dimensional feature vector, that contains the energies at specific frequency bands. Afterwards, we use a dimensionality reduction technique, called t-SNE, to project all feature vectors on a visual 2D map that describes the vibrational states of the process. The proposed methodology provides a way for an exploratory analysis of the dynamic behaviors in the rolling process and allows to find relationships between these behaviors and the chatter fault. Experimental results from real data of a cold rolling mill are described, showing the application of the proposed approach.     

Bridging challenges of clinical decision support systems with a semantic approach. A case study on breast cancer

The integration of Clinical Decision Support Systems (CDSS) in nowadays clinical environments has not been fully achieved yet. Although numerous approaches and technologies have been proposed since 1960, there are still open gaps that need to be bridged. In this work we present advances from the established state of the art, overcoming some of the most notorious reported difficulties in: (i) automating CDSS, (ii) clinical workflow integration, (iii) maintainability and extensibility of the system, (iv) timely advice, (v) evaluation of the costs and effects of clinical decision support, and (vi) the need of architectures that allow the sharing and reusing of CDSS modules and services. In order to do so, we introduce a new clinical task model oriented to clinical workflow integration, which follows a federated approach. Our work makes use of the reported benefits of semantics in order to fully take advantage of the knowledge present in every stage of clinical tasks and the experience acquired by physicians. In order to introduce a feasible extension of classical CDSS, we present a generic architecture that permits a semantic enhancement, namely Semantic CDSS (S-CDSS). A case study of the proposed architecture in the domain of breast cancer is also presented, pointing some highlights of our methodology.     

Using genetic algorithms to generate test sequences for complex timed systems

The generation of test data for state-based specifications is a computationally expensive process. This problem is magnified if we consider that time constraints have to be taken into account to govern the transitions of the studied system. The main goal of this paper is to introduce a complete methodology, supported by tools, that addresses this issue by representing the test data generation problem as an optimization problem. We use heuristics to generate test cases. In order to assess the suitability of our approach we consider two different case studies: a communication protocol and the scientific application BIPS3D. We give details concerning how the test case generation problem can be presented as a search problem and automated. Genetic algorithms (GAs) and random search are used to generate test data and evaluate the approach. GAs outperform random search and seem to scale well as the problem size increases. It is worth to mention that we use a very simple fitness function that can be easily adapted to be used with other evolutionary search techniques.