Biodegradability and improved mechanical performance of polyhydroxyalkanoates/agave fiber biocomposites compatibilized by different strategies

In this work, biocomposites made of polyhydroxyalkanoates (PHA) with natural fibers were produced via compression molding. In particular, polyhydroxybutyrate (PHB) and polyhydroxybutyrate-co-hydroxyvalerate (PHBV) were reinforced with 20 wt% of agave fibers. Different compatibilization strategies were investigated to improve the fiber-matrix interaction: fiber surface treatment in PHA solution, fiber surface treatment in maleated PHA solution, fiber propionylation, and extrusion with maleated PHA. The biocomposites were characterized in terms of morphology, mechanical properties, water absorption, and biodegradability by CO₂ production tracking. In general, fiber propionylation was the best strategy for mechanical properties enhancement and water uptake decreasing. Biocomposites with propionylated fibers showed improved flexural strength (170% for PHB and 84% for PHBV). The flexural modulus was also enhanced with propionylated fibers up to 19% and 18% compared to uncompatibilized biocomposites (PHB and PHBV, respectively). Tensile strength increased by 16% (PHB) and 14% (PHBV), and the water absorption was reduced using propionylated fibers going from 6.6% to 4.4% compared with biocomposites with untreated fibers. Most importantly, the impact strength was also improved for all biocomposites by up to 96% compared with the neat PHA matrices. Finally, it was found that the compatibilization did not negatively modify the PHA biodegradability.     

Enhanced Oxygen Diffusion Along Internal Oxide–Metal Matrix Interfaces in Ni–Al Alloys During Internal Oxidation

A study of the internal oxidation of dilute Ni–Al alloys in an NiO/Ni Rhines pack was performed at 800, 1000, and 1100°C. Considerable deviations from the classical internal oxidation model have been observed. The rate of internal oxidation depends not only on the concentration of the alloying element but also on its nature, which contributes to determining the size, shape, orientation and distribution of the internal oxide precipitates. For instance, the precipitates in the Ni–Al alloys are continuous rods, arranged in a cone-shaped configuration that extends from the surface to the internal oxide front. The observed depths of internal oxidation for the various concentrations of aluminum are discussed and related to the morphologies of the internal oxide precipitates. The apparent N^(s) _oD_o values determined from internal oxide penetrations increase with increasing solute content in the alloy. It is postulated that diffusivity of oxygen is enhanced along the internal oxide–metal matrix interface compared with that in the metal matrix.     

Decision support system for classifying archaeological pottery profiles based on Mathematical Morphology

We present a decision support system to help archaeologists in classifying wheel-made pottery pieces by its profile. A novel shape characterization method, using Mathematical Morphology, is introduced for this purpose. Each profile is represented as a vector, obtained by sampling the so called morphological curves (erosion, dilation, opening and closing), and Euclidean Distance is used as a similarity measure. We show results of our method applied to a profile database of Iberian Pottery from the upper valley of Guadalquivir River (Spain).     

A Review of Element-Based Galerkin Methods for Numerical Weather Prediction: Finite Elements,Spectral Elements,and Discontinuous Galerkin

Numerical weather prediction (NWP) is in a period of transition. As resolutions increase, global models are moving towards fully nonhydrostatic dynamical cores, with the local and global models using the same governing equations; therefore we have reached a point where it will be necessary to use a single model for both applications. The new dynamical cores at the heart of these unified models are designed to scale efficiently on clusters with hundreds of thousands or even millions of CPU cores and GPUs. Operational and research NWP codes currently use a wide range of numerical methods: finite differences, spectral transform, finite volumes and, increasingly, finite/spectral elements and discontinuous Galerkin, which constitute element-based Galerkin (EBG) methods. Due to their important role in this transition, will EBGs be the dominant power behind NWP in the next 10 years, or will they just be one of many methods to choose from? One decade after the review of numerical methods for atmospheric modeling by Steppeler et al. (Meteorol Atmos Phys 82:287–301, 2003), this review discusses EBG methods as a viable numerical approach for the next-generation NWP models. One well-known weakness of EBG methods is the generation of unphysical oscillations in advection-dominated flows; special attention is hence devoted to dissipation-based stabilization methods. Since EBGs are geometrically flexible and allow both conforming and non-conforming meshes, as well as grid adaptivity, this review is concluded with a short overview of how mesh generation and dynamic mesh refinement are becoming as important for atmospheric modeling as they have been for engineering applications for many years.     

Binarised regression tasks: methods and evaluation metrics

Some supervised tasks are presented with a numerical output but decisions have to be made in a discrete, binarised, way, according to a particular cutoff. This binarised regression task is a very common situation that requires its own analysis, different from regression and classification—and ordinal regression. We first investigate the application cases in terms of the information about the distribution and range of the cutoffs and distinguish six possible scenarios, some of which are more common than others. Next, we study two basic approaches: the retraining approach, which discretises the training set whenever the cutoff is available and learns a new classifier from it, and the reframing approach, which learns a regression model and sets the cutoff when this is available during deployment. In order to assess the binarised regression task, we introduce context plots featuring error against cutoff. Two special cases are of interest, the \( UCE \) and \( OCE \) curves, showing that the area under the former is the mean absolute error and the latter is a new metric that is in between a ranking measure and a residual-based measure. A comprehensive evaluation of the retraining and reframing approaches is performed using a repository of binarised regression problems created on purpose, concluding that no method is clearly better than the other, except when the size of the training data is small.     

Standard goal programming with fuzzy hierarchies: a sequential approach

This paper proposes a pragmatic model for multi-objective decision-making processes involving clusters of objectives which have a decisional meaning for the decision maker (DM). We provide the DMs with a comfortable tool that allows them to express their preferences both by comparing criteria of the same cluster and via the comparison between the different clusters. In standard goal programming the importance of the goals is modeled by the introduction of preferential weights or/and the incorporation of pre-emptive priorities. However, in many cases the DM is not able to establish a precise preference structure. Even in the case of precise weights the solution does not match necessarily the relative weights or, in the case of precise pre-emptive priority, the result could be very restrictive. In order to overcome these drawbacks, in this paper the normalized unwanted deviations are interpreted in terms of achievement degrees of the goals and fuzzy relations are used to model the relative importance of the goals. Thus, we show how several methodologies from the fuzzy goal programming literature can be tailored for solving standard GP problems. We apply this new modeling to problems where there is a “natural” clustering between goals of the same class. We address this situation by solving two phases; in the first one each class is handled separately taking into account the hierarchy of their goals and, in the second phase, we integrate the results of the first phase and the imprecise hierarchy of the different classes. We formulate a new goal programming model called as sequential goal programming with fuzzy hierarchy model. Because many real situations involve decision making in this environment, our proposal can be a useful tool of broad application. A numerical example illustrates the methodology.     

A novel methodology to predict urban traffic congestion with ensemble learning

Urban traffic congestion prediction is a very hot topic due to the environmental and economical impacts that currently implies. In this sense, to be able to predict bottlenecks and to provide alternatives to the circulation of vehicles becomes an essential task for traffic management. A novel methodology, based on ensembles of machine learning algorithms, is proposed to predict traffic congestion in this paper. In particular, a set of seven algorithms of machine learning has been selected to prove their effectiveness in the traffic congestion prediction. Since all the seven algorithms are able to address supervised classification, the methodology has been developed to be used as a binary classification problem. Thus, collected data from sensors located at the Spanish city of Seville are analyzed and models reaching up to 83 % are generated.     

Parallel simulation of Population Dynamics P systems: updates and roadmap

Population Dynamics P systems are a type of multienvironment P systems that serve as a formal modeling framework for real ecosystems. The accurate simulation of these probabilistic models, e.g. with Direct distribution based on Consistent Blocks Algorithm, entails large run times. Hence, parallel platforms such as GPUs have been employed to speedup the simulation. In 2012, the first GPU simulator of PDP systems was presented. However, it was able to run only randomly generated PDP systems. In this paper, we present current updates made on this simulator, involving an input modu le for binary files and an output module for CSV files. Finally, the simulator has been experimentally validated with a real ecosystem model, and its performance has been tested with two high-end GPUs: Tesla C1060 and K40.