EPICS based low-level radio frequency control system in LIPAc

The IFMIF–EVEDA (International Fusion Materials Irradiation Facility – Engineering Validation and Engineering Design Activity) linear accelerator, known as Linear IFMIF Prototype Accelerator (LIPAc), will be a 9 MeV, 125 mA continuous wave (CW) deuteron accelerator prototype to validate the technical options of the accelerator design for IFMIF. The primary mission of such facility is to test and verify materials performance when subjected to extensive neutron irradiation of the type encountered in a fusion reactor to prepare for the design, construction, licensing and safe operation of a fusion demonstration reactor (DEMO). The radio frequency (RF) power system of IFMIF–EVEDA consists of 18 RF chains working at 175 MHz with three amplification stages each. The low-level radio frequency (LLRF) controls the amplitude and phase of the signal to be synchronized with the beam and it also controls the resonance frequency of the cavities. The system is based on a commercial compact peripheral component interconnect (cPCI) field programmable gate array (FPGA) board, provided by Lyrtech and controlled by a Windows host PC. For this purpose, it is mandatory to communicate the cPCI FPGA board from EPICS Channel Access [1]. A software architecture on EPICS framework in order to control and monitor the LLRF system is presented.     

From handheld collaborative tool to effective classroom module: Embedding CSCL in a broader design framework

The field of Computer Supported Collaborative Learning (CSCL) includes designers who emphasize effectiveness, measured via experiments, as well as designers who emphasize context and conduct qualitative research on teaching and learning practices. We conjectured that these two different emphases could be fruitful combined in a research and development process aimed at producing effective CSCL practices. We explored this possibility in a project that adapted a CSCL tool from Chile to serve as the basis of an effective 3-week classroom module for primary school mathematics in the United States. To go from tool to module, we addressed curricular fit, training materials, pedagogical guidance, formative and summative assessments, and logistical support. In conducting the project, we found that effectiveness and contextual research could be conducted simultaneously and yielded complementary insight to this design process, which enabled our project to rapidly move from the base tool towards complete classroom modules. An experiment we conducted after our design iterations showed that students who used the modules learned more about the target content, fractions. A retrospective analysis of our design process suggests that the Integrative Learning Design framework is useful for organizing the complementary components of effectiveness and contextual research in our design process.     

A Closed-Form Formula for the RBF-Based Approximation of the Laplace–Beltrami Operator

In this paper we present a method that uses radial basis functions to approximate the Laplace–Beltrami operator that allows to solve numerically diffusion (and reaction–diffusion) equations on smooth, closed surfaces embedded in \(\mathbb {R}^3\). The novelty of the method is in a closed-form formula for the Laplace–Beltrami operator derived in the paper, which involve the normal vector and the curvature at a set of points on the surface of interest. An advantage of the proposed method is that it does not rely on the explicit knowledge of the surface, which can be simply defined by a set of scattered nodes. In that case, the surface is represented by a level set function from which we can compute the needed normal vectors and the curvature. The formula for the Laplace–Beltrami operator is exact for radial basis functions and it also depends on the first and second derivatives of these functions at the scattered nodes that define the surface. We analyze the converge of the method and we present numerical simulations that show its performance. We include an application that arises in cardiology.     

A practical approach for active camera coordination based on a fusion-driven multi-agent system

In this paper, we propose a multi-agent system architecture to manage spatially distributed active (or pan-tilt-zoom) cameras. Traditional video surveillance algorithms are of no use for active cameras, and we have to look at different approaches. Such multi-sensor surveillance systems have to be designed to solve two related problems: data fusion and coordinated sensor-task management. Generally, architectures proposed for the coordinated operation of multiple cameras are based on the centralisation of management decisions at the fusion centre. However, the existence of intelligent sensors capable of decision making brings with it the possibility of conceiving alternative decentralised architectures. This problem is approached by means of a MAS, integrating data fusion as an integral part of the architecture for distributed coordination purposes. This paper presents the MAS architecture and system agents.     

A semantic-based platform for R&D project funding management

Innovation is one of the keys to success in the business world, particularly within the current economic climate. R&D projects constitute the building blocks of the innovation process, hence the importance of searching for funding for these projects. As ontologies and semantic technologies mature, they provide a consistent and reliable means to represent and aggregate knowledge from different sources. The present work explores the use of ontologies to model R&D grant funding calls and the application of semantic technologies to the development of an enhanced funding management system. Our experiments confirm the success of the proposed approach, and reveal that it may bring considerable benefits to R&D funding.     

Data-Based Models for the Prediction of Dam Behaviour: A Review and Some Methodological Considerations

Predictive models are an important element in dam safety analysis. They provide an estimate of the dam response faced with a given load combination, which can be compared with the actual measurements to draw conclusions about dam safety. In addition to numerical finite element models, statistical models based on monitoring data have been used for decades for this purpose. In particular, the hydrostatic-season-time method is fully implemented in engineering practice, although some limitations have been pointed out. In other fields of science, powerful tools such as neural networks and support vector machines have been developed, which make use of observed data for interpreting complex systems. This paper contains a review of statistical and machine-learning data-based predictive models, which have been applied to dam safety analysis. Some aspects to take into account when developing analysis of this kind, such as the selection of the input variables, its division into training and validation sets, and the error analysis, are discussed. Most of the papers reviewed deal with one specific output variable of a given dam typology and the majority also lack enough validation data. As a consequence, although results are promising, there is a need for further validation and assessment of generalisation capability. Future research should also focus on the development of criteria for data pre-processing and model application.     

A comparison of path planning strategies for autonomous exploration and mapping of unknown environments

To date, a large number of algorithms to solve the problem of autonomous exploration and mapping has been presented. However, few efforts have been made to compare these techniques. In this paper, an extensive study of the most important methods for autonomous exploration and mapping of unknown environments is presented. Furthermore, a representative subset of these techniques has been chosen to be analysed. This subset contains methods that differ in the level of multi-robot coordination and in the grade of integration with the simultaneous localization and mapping (SLAM) algorithm. These exploration techniques were tested in simulation and compared using different criteria as exploration time or map quality. The results of this analysis are shown in this paper. The weaknesses and strengths of each strategy have been stated and the most appropriate algorithm for each application has been determined.     

Structural Analysis of Masonry Historical Constructions. Classical and Advanced Approaches

A review of methods applicable to the study of masonry historical construction, encompassing both classical and advanced ones, is presented. Firstly, the paper offers a discussion on the main challenges posed by historical structures and the desirable conditions that approaches oriented to the modeling and analysis of this type of structures should accomplish. Secondly, the main available methods which are actually used for study masonry historical structures are referred to and discussed. The main available strategies, including limit analysis, simplified methods, FEM macro- or micro-modeling and discrete element methods (DEM) are considered with regard to their realism, computer efficiency, data availability and real applicability to large structures. A set of final considerations are offered on the real possibility of carrying out realistic analysis of complex historic masonry structures. In spite of the modern developments, the study of historical buildings is still facing significant difficulties linked to computational effort, possibility of input data acquisition and limited realism of methods.     

Long-term effect of lovastatin on lipoprotein profile in patients with primary nephrotic syndrome

Eight patients with biopsy-proven primary nephrotic syndrome were included in an open, prospective, two-year study of lovastatin. One patients was withdrawn after 6 months due to an asymptomatic rise in creatinine phosphokinase, which was rapidly reversed after interruption of lovastatin. In the remaining patients, treatment was well-tolerated and produced no side effects. After 2 years of treatment, these 7 patients had decreases in total cholesterol from 446 +/- 165 to 250 +/- 57 mg/dl (p < 0.001), LDL cholesterol from 343 +/- 121 to 174 +/- 49 mg/dl (p < 0.001), Apo B lipoprotein from 162 +/- 60 to 108 +/- 42 mg/dl (p < 0.05), triglycerides from 336 +/- 273 to 182 +/- 71 mg/dl (p < 0.04). There was no change in HDL cholesterol. The LDL/HDL cholesterol and the total/HDL cholesterol ratios fell from 15.0 +/- 12.1 and 19.1 +/- 17.2 mg/dl before the study to 4.4 +/- 1.2 and 6.3 +/- 1.6 mg/dl, respectively, at 2 years. A decrease in proteinuria from 8.6 +/- 4.6 to 5.0 +/- 3.7 g/24 h (p < 0.02) was noted in 4 patients on concomitant ACE inhibitor therapy. Renal function remained stable in all patients throughout the study, except for one whose moderate impairment progressed to end-stage renal failure requiring dialysis 3 months poststudy. We conclude that long-term lovastatin in patients with primary nephrotic syndrome is an effective and generally safe treatment for accompanying dyslipidemia.