ATLAS: Accelerated torus-like angular source for fusion neutronics applications

There is increasing demand for fast and accurate neutronics analysis to be conducted on specific ITER components. The ATLAS (accelerated torus-like angular source) algorithm has been developed to accelerate such neutronics calculations by approximating the 3-D plasma volume source as a surface source at the first-wall region of interest. The ATLAS routine rapidly calculates the angular distribution of the unscattered neutron flux at the chosen first wall location, on a discrete angular grid, and writes a surface source for both MCNP and Attila. This discretised source can then be used to accurately reproduce the full 3-D toroidal volume source in angle and energy, and to quickly simulate the irradiation of individual components or regions in the ITER model. While designed for ITER, the method is general in implementation and can be applied to any tokamak.     

Degradation of organic dye using zero-valent iron prepared from by-product of pickling line

In this study, zero-valent iron (ZVI) was produced using iron oxide that is a by-product of a pickling line at a steel works. The reaction activity of the produced ZVI was evaluated through a series of decomposition experiments of Orange II aqueous solution. The size of ZVI particles increased with reduction temperature due to coalescence. Correspondingly, the specific surface area of ZVI decreased with increasing reduction temperature. The decomposition efficiency of synthesized ZVI particles was higher at a lower pH. In particular, no significant decomposition reaction was observed at pH of 4 and higher. The rate of the ZVI-assisted decomposition of Orange II was increased by addition of H2O2 at pH of 3, whereas it was reduced by addition of H2O2 at a higher pH of 6. Nevertheless, simultaneous use of ZVI, UV and H2O2 led to a considerable increase in the decomposition rate even at a high pH condition (pH = 6).     

Solid biofuels in Mexico: a sustainable alternative to satisfy the increasing demand for heat and power

Bioenergy is the largest renewable energy source in Mexico with an estimated 4–9% of total current energy demand. There are large uncertainties and contrasting estimates regarding its current extent and end-uses, particularly with traditional uses. However, a large potential exists to improve the efficiency of existing uses and, at the same time, to diversify the use of SBF in the industrial and power sectors. This paper aims at: providing the first updated and comprehensive estimate of current SBF demand in Mexico including traditional and modern uses; providing a consistent estimate of actual SBF supply potential; estimating the total potential substitution of fossil fuels that could be achieved by SBF considering an integrated “modernization scenario”; and finally describing the main barriers limiting SBF to fully triggering its potential. Results show that current SBF consumption reached 481 PJ/yr in 2015; SBF supply potential reaches 3622 PJ/yr, out of which 883 PJ/yr could be used to substitute up to 29% of current demand of FF, mitigating 66 MtCO_2e/yr of greenhouse gas (GHG) emissions, or near 88 MtCO_2e/yr if mitigation from traditional uses is added.     

Leveraging BPMN particularities to improve traceability links recovery among requirements and BPMN models

Requirements Engineering - Traceability links recovery (TLR) has been a topic of interest for many years. However, TLR approaches are based on the latent semantics of the software artifacts, and...     

A propositional probabilistic logic with discrete linear time for reasoning about evidence

The aim of the paper is to present a sound, strongly complete and decidable probabilistic temporal logic that can model reasoning about evidence. The formal system developed here is actually a solution of a problem proposed by Halpern and Pucella (J Artif Intell Res 26:1–34, 2006).     

Bernstein-type operators which preserve polynomials

In this paper we present the sequence of linear Bernstein-type operators defined for f∈C[0,1] by B_n(f°τ⁻¹)°τ, B_n being the classical Bernstein operators and τ being any function that is continuously differentiable ∞ times on [0,1], such that τ(0)=0, τ(1)=1 and τ^′(x)>0 for x∈[0,1]. We investigate its shape preserving and convergence properties, as well as its asymptotic behavior and saturation. Moreover, these operators and others of King type are compared with each other and with B_n. We present as an interesting byproduct sequences of positive linear operators of polynomial type with nice geometric shape preserving properties, which converge to the identity, which in a certain sense improve B_n in approximating a number of increasing functions, and which, apart from the constant functions, fix suitable polynomials of a prescribed degree. The notion of convexity with respect to τ plays an important role.     

Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules

This paper presents a proposal for the extraction of association rules called G3PARM (Grammar-Guided Genetic Programming for Association Rule Mining) that makes the knowledge extracted more expressive and flexible. This algorithm allows a context-free grammar to be adapted and applied to each specific problem or domain and eliminates the problems raised by discretization. This proposal keeps the best individuals (those that exceed a certain threshold of support and confidence) obtained with the passing of generations in an auxiliary population of fixed size n. G3PARM obtains solutions within specified time limits and does not require the large amounts of memory that the exhaustive search algorithms in the field of association rules do. Our approach is compared to exhaustive search (Apriori and FP-Growth) and genetic (QuantMiner and ARMGA) algorithms for mining association rules and performs an analysis of the mined rules. Finally, a series of experiments serve to contrast the scalability of our algorithm. The proposal obtains a small set of rules with high support and confidence, over 90 and 99% respectively. Moreover, the resulting set of rules closely satisfies all the dataset instances. These results illustrate that our proposal is highly promising for the discovery of association rules in different types of datasets.