LPNN-based approach for LASSO problem via a sequence of regularized minimizations

In compressive sampling theory, the least absolute shrinkage and selection operator (LASSO) is a representative problem. Nevertheless, the non-differentiable constraint impedes the use of Lagrange programming neural networks (LPNNs). We present in this article the -LPNN model, a novel algorithm that tackles the LASSO minimization together with the underlying theory support. First, we design a sequence of smooth constrained optimization problems, by introducing a convenient differentiable approximation to the non-differentiable -norm constraint. Next, we prove that the optimal solutions of the regularized intermediate problems converge to the optimal sparse signal for the LASSO. Then, for every regularized problem from the sequence, the -LPNN dynamic model is derived, and the asymptotic stability of its equilibrium state is established as well. Finally, numerical simulations are carried out to compare the performance of the proposed -LPNN algorithm with both the LASSO-LPNN model and a standard digital method.     

RELAP5/3.2 assessment against low pressure onset of flow instability in parallel heated channels

Best Estimate computer codes have been, so far, developed for safety analysis of nuclear power plants and were extensively validated against a large set of separate effects and integral test facilities experimental data relevant to such kind of reactors. Their application to research reactors is not fully straightforward. Modelling problems generally emerge when applying existing models to low pressure and more particularly to subcooled flow boiling situations. The objective of the present work is to investigate the RELAP5/3.2 system code capabilities in predicting phenomena that could be encountered under abnormal research reactor’s operating conditions. For this purpose, the separate effect related to the static onset of flow instability is investigated. The cases considered herein are the flow excursion tests performed at the Oak Ridge National Laboratory thermal hydraulic test loop (THTL) as well as some representative Whittle and Forgan (W & F) experiments. The simulation results are presented and the capabilities of RELAP5/Mod 3.2 in predicting this critical phenomenon are discussed.     

Combination of information retrieval methods with LESK algorithm for Arabic word sense disambiguation

In this paper, we propose to use Harman, Croft and Okapi measures with Lesk algorithm to develop a system for Arabic word sense disambiguation, that combines unsupervised and knowledge based methods. This system must solve the lexical semantic ambiguity in Arabic language. The information retrieval measures are used to estimate the most relevant sense of the ambiguous word, by returning a semantic coherence score corresponding to the context that is semantically closest to the original sentence containing the ambiguous word. The Lesk algorithm is used to assign and select the adequate sense from those proposed by the information retrieval measures mentioned above. This selection is based on a comparison between the glosses of the word to be disambiguated, and its different contexts of use extracted from a corpus. Our experimental study proves that using of Lesk algorithm with Harman, Croft, and Okapi measures allows us to obtain an accuracy rate of 73%.     

Experimental Evidence for Seed Metabolic Allometry in Barrel Medic (Medicago truncatula Gaertn.)

Seed size is often considered to be an important trait for seed quality, i.e., vigour and germination performance. It is believed that seed size reflects the quantity of reserve material and thus the C and N sources available for post-germinative processes. However, mechanisms linking seed size and quality are poorly documented. In particular, specific metabolic changes when seed size varies are not well-known. To gain insight into this aspect, we examined seed size and composition across different accessions of barrel medic (Medicago truncatula Gaertn.) from the genetic core collection. We conducted multi-elemental analyses and isotope measurements, as well as exact mass GC–MS metabolomics. There was a systematic increase in N content (+0.17% N mg⁻¹) and a decrease in H content (–0.14% H mg⁻¹) with seed size, reflecting lower lipid and higher S-poor protein quantity. There was also a decrease in ²H natural abundance (δ²H), due to the lower prevalence of ²H-enriched lipid hydrogen atoms that underwent isotopic exchange with water during seed development. Metabolomics showed that seed size correlates with free amino acid and hexoses content, and anticorrelates with amino acid degradation products, disaccharides, malic acid and free fatty acids. All accessions followed the same trend, with insignificant differences in metabolic properties between them. Our results show that there is no general, proportional increase in metabolite pools with seed size. Seed size appears to be determined by metabolic balance (between sugar and amino acid degradation vs. utilisation for storage), which is in turn likely determined by phloem source metabolite delivery during seed development.     

Modeling the flow-level performance of hierarchical modulation in?OFDMA-based networks

Hierarchical Modulation (HM) is a means to enhance the spectral efficiency of a system by superposing, in terms of modulation, an additional stream for a given user with good radio conditions on a basic stream of a user with worse radio conditions. This, in turn, increases the throughput of the former user and hence the overall performance of the whole system. We consider, in this work, such a performance at the flow level, for a realistic dynamic setting where users come to the system and leave it after a finite duration corresponding, for instance, to the completion of a file transfer. We specifically model and quantify, both analytically and via simulations, the gain thus achieved and propose two extensions to the basic HM algorithm: a first one in which a user with bad radio conditions is also superposed on one with better radio conditions and a second one in which a user of one type is further superposed on a user of the same type as well.     

Electrode roughness effects on the breakdown of air-insulatedapparatus

The dielectric breakdown of air insulating systems is believed to be sensitive to local irregularity of the electric field which may result from the presence of defects such as contaminants adhering to electrode surfaces and surface roughness. Normally metal machining methods are used to eliminate such electrode surfaces irregularities. However, system aging and harsh operating conditions create and sustain such rough surface conditions which may, in turn, lead to the failure of insulation under the resulting enhanced electric stresses. Electrode surface roughness causes a large reduction in the breakdown strengths of gas insulated apparatus. Surface roughness leads to the existence of localized microscopic regions with local field intensities larger than the average field in the gas near the electrodes. This paper models the insulation breakdown mechanism in the presence of such surface roughness, or protrusions, taking into account their random nature which lends the problem to probabilistic treatment. In order to generalize the surface roughness effect on the dielectric withstand of air-insulated systems, surface roughness is simulated by using a random event generator. The perturbations which these protrusions inflict on the field distribution in a nearly-uniform field gap are assessed. The corresponding breakdown voltages are estimated for different patterns of surface roughness. The results are statistically formulated     

Learning curve analysis of concentrated photovoltaic systems

Price declines and volume growth of concentrated photovoltaic (CPV) systems are analysed using the learning curve methodology and compared with other forms of solar electricity generation. Logarithmic regression analysis determines a learning rate of 18% for CPV systems with 90% confidence of that rate being between 14 and 22%, which is higher than the learning rates of other solar generation systems (11% for CSP and 12 to 14% for PV). Current CPV system prices are competitive with PV and CSP, which, when combined with the higher learning rate, indicates that CPV is likely to further improve its marketability. A target price of 1 $/W in 2020 could be achieved with a compound growth rate of 67% for the total deployed volume between 2014 and 2020, which would realize a cumulative deployed volume of 7900 MW. Other projections of deployment volumes from commercial sources are converted using the learning rate into future price scenarios, resulting in predicted prices in the range of 1.1 to 1.3 $/W in 2020. © 2014 The Authors. Progress in Photovoltaics: Research and Applications published by John Wiley & Sons Ltd.     

Validation and improvement of gamma heating calculation methods for the G.A. Siwabessy multipurpose reactor

Nuclear Science and Techniques - This study presents the RF design of a linear accelerator (linac) operated in single-bunch mode. The accelerator is powered by a compressed RF pulse produced from a...     

Progress in Carbon Fiber and Its Polypropylene- and Polyethylene-Based Composites

Due to their lightweight and excellent toughness, carbon fiber (CF) and its reinforced thermoplastic composites are suitable for high-performance applications such as aerospace, aviation, automotive and sport equipments. In this study, comprehensive detail is provided on the production of carbon fiber, its various forms and geometry and their corresponding effects on the mechanical properties of CF and its reinforced polypropylene (PP) and polyethylene (PE) composites. Here we discuss extensively various methods reported in literature on improving the interfacial fiber-matrix adhesion and dispersion in order to achieve better mechanical properties for such composites.