No abstract

The fused deposition modeling (FDM) technique is used today by companies engaged in the fabrication of traffic signs for the manufacture of light-emitting diode LED spotlights. In this sector, the surface properties of the elements used (surface finish, hydrophobic features) are decisive because surfaces that retain little dirt and favor self–cleaning behavior are needed. A design of experiments (L27) with five factors and three levels has been carried out. The factors studied were: Layer height (LH), print temperature (T), print speed (PS), print acceleration (PA), and flow rate (F). Polyethylene terephthalate glycol (PETG) specimens of 25.0 × 25.0 × 2.4 mm have been printed and, in each of them, the surface roughness (Ra,0, Ra,90), sliding angle (SA0, SA90), and contact angle (CA0, CA90) in both perpendicular directions have been measured. Taguchi and ANOVA analysis shows that the most influential variables in this case are printing acceleration for Ra, 0 (p–value = 0.052) and for SA0 (p–value = 0.051) and flow rate for Ra, 90 (p–value = 0.001) and for SA90 (p–value = 0.012). Although the ANOVA results for the contact angle are not significant, specimen 8 (PA = 1500 mm/s2 and flow rate F = 110%) and specimen 10 (PA =1500 mm/s2 and F = 100%) have reached contact angle values above or near the limit value for hydrophobia, respectively.

3D printing using fused deposition modeling (FDM) includes a multitude of control parameters. It is difficult to predict a priori what surface finish will be achieved when certain values are set for these parameters. The objective of this work is to compare the models generated by decision tree algorithms (C4.5, random forest, and random tree) and to analyze which makes the best prediction of the surface roughness in polyethylene terephthalate glycol (PETG) parts printed in 3D using the FDM technique. The models have been created using a dataset of 27 instances with the following attributes: layer height, extrusion temperature, print speed, print acceleration, and flow rate. In addition, a dataset has been created to evaluate the models, consisting of 15 additional instances. The models generated by the random tree algorithm achieve the best results for predicting the surface roughness in FDM parts.

The modeling of ecological data that include both abiotic and biotic factors is fundamental to our understanding of ecosystems. Repositories of biodiversity data, such as GBIF , iD igBio, Atlas of Living Australia, and SNIB (Mexico's National System of Biodiversity Information), contain a great deal of information that can lead to knowledge discovery about ecosystems. However, there is a lack of tools with which to efficiently extract such knowledge. In this paper, we present SPECIES , an open, web‐based platform designed to extract implicit information contained in large scale sets of ecological data. SPECIES is based on a tested methodology, wherein the correlations of variables of arbitrary type and spatial resolution, both biotic and abiotic, discrete and continuous, may be explored from both niche and network perspectives. In distinction to other modeling systems, SPECIES is a full stack exploratory tool that integrates the three basic components: data (which is incrementally growing), a statistical modeling and analysis engine, and an interactive visualization front end. Combined, these components provide a powerful tool that may guide ecologists toward new insights. SPECIES is optimized to support fast hypothesis prototyping and testing, analyzing thousands of biotic and abiotic variables, and presenting descriptive results to the user at different levels of detail. SPECIES is an open‐access platform available online ( http://species.conabio.gob.mx ), that is, powerful, flexible, and easy to use. It allows for the exploration and incorporation of ecological data and its subsequent integration into predictive models for both potential ecological niche and geographic distribution. It also provides an ecosystemic, network‐based analysis that may guide the researcher in identifying relations between different biota, such as the relation between disease vectors and potential disease hosts.

In this work a non-homogeneous Poisson model is considered to study noise exposure. The Poisson process, counting the number of times that a sound level surpasses a threshold, is used to estimate the probability that a population is exposed to high levels of noise a certain number of times in a given time interval. The rate function of the Poisson process is assumed to be of a Weibull type. The presented model is applied to community noise data from Messina, Sicily (Italy). Four sets of data are used to estimate the parameters involved in the model. After the estimation and tuning are made, a way of estimating the probability that an environmental noise threshold is exceeded a certain number of times in a given time interval is presented. This estimation can be very useful in the study of noise exposure of a population and also to predict, given the current behavior of the data, the probability of occurrence of high levels of noise in the near future. One of the most important features of the model is that it implicitly takes into account different noise sources, which need to be treated separately when using usual models.