Distributional extensions of Carollia castanea and Micronycteris minuta from Guatemala, Central America

Field expeditions in 2011 that inventoried the terrestrial vertebrate fauna of two wildlife protected areas in the tropical Caribbean of Guatemala have produced the first confirmed records of two bats for the country: the white-bellied big-eared bat, Micronycteris (Schizonycteris) minuta (Gervais 1856) and the Chesnut short-tailed bat Carollia castanea H. Allen, 1890, both of neotropical distribution and with their current northern limit at Lancetilla, Honduras. The record of M. minuta at Sierra de Caral, Guatemala extends the range of this species 137 km to the west, and the record of C. castanea at Cerro San Gil extends its range 147 km to the west

Multistage and adaptive sampling protocols combined with near-infrared spectral sensors for automated monitoring of raw materials in bulk

A near-infrared (NIR) spectroscopy-based real-time monitoring system is proposed to sample and analyse agro-industrial raw materials transported in bulk in a single stage, easing and optimising the evaluation process of incoming lots at reception of agri-food plants. NIR analysis allows rapid and cost-effective analytical results to be obtained, and hence to rethink current sampling protocols. For this purpose, multistage and adaptive sampling designs were tested in this paper, which have been reported (in soil science and ecology) to be more flexible and efficient than conventional strategies to study patterns of clustering or patchiness, which can be the result of natural phenomena. The additional spatial information provided by NIR has also been exploited, using geostatistical analysis to model the spatial pattern of key analytical constituents in Processed Animal Proteins (PAPs). This study addresses the assessment of two kinds of quality/safety issues in PAP lots – moisture accumulation and cross-contamination. After a simulation study, qualitative and quantitative analyses were carried out to make a performance comparison between sampling designs. Results show that sampling densities below 10–15% demonstrated higher estimation errors, failing to represent the actual spatial patterns, while a stratified adaptive cluster sampling design achieved the best performance

Performance comparison of sampling designs for quality and safety control of raw materials in bulk: a simulation study based on NIR spectral data and geostatistical analysis

This study exploits the potential of near infrared (NIR) spectroscopy to deliver a measurement for each sampling point. Furthermore, it provides a protocol for the modelling of the spatial pattern of analytical constituents. On the basis of these two aspects, the methodology proposed in this work offers an opportunity to provide a real-time monitoring system to evaluate raw materials, easing and optimising the existing procedures for sampling and analysing products transported in bulk. In this paper, Processed Animal Proteins (PAPs) were selected as case study, and two types of quality/safety issues were tested in PAP lots —induced by moisture and cross-contamination. A simulation study, based on geostatistical analysis and the use of a set of sampling protocols, made a qualitative analysis possible to compare the representation of the spatial surfaces produced by each design. Moreover, the Root Mean Square Error of Prediction (RMSEP), calculated from the differences between the analytical values and the geostatistical predictions at unsampled locations, was used to measure the performance in each case. Results show the high sensitivity of the process to the sampling plan used — understood as the sampling design plus the sampling intensity. In general, a gradual decrease in the performance can be observed as the sampling intensity decreases, so that unlike for higher intensities, the too low ones resulted in oversmoothed surfaces which did not manage to represent the actual distribution. Overall, Stratified and Simple Random samplings achieved the best results in most cases. This indicated that an optimal balance between the design and the intensity of the sampling plan is imperative to perform this methodology

Infering Air Quality from Traffic Data using Transferable Neural Network Models

This work presents a neural network based model for inferring air quality from traffic measurements. It is important to obtain information on air quality in urban environments in order to meet legislative and policy requirements. Measurement equipment tends to be expensive to purchase and maintain. Therefore, a model based approach capable of accurate determination of pollution levels is highly beneficial. The objective of this study was to develop a neural network model to accurately infer pollution levels from existing data sources in Leicester, UK. Neural Networks are models made of several highly interconnected processing elements. These elements process information by their dynamic state response to inputs. Problems which were not solvable by traditional algorithmic approaches frequently can be solved using neural networks. This paper shows that using a simple neural network with traffic and meteorological data as inputs, the air quality can be estimated with a good level of generalisation and in near real-time. By applying these models to links rather than nodes, this methodology can directly be used to inform traffic engineers and direct traffic management decisions towards enhancing local air quality and traffic management simultaneously.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Experiments on Multidimensional Solitons

This article presents an overview of experimental efforts in recent years related to multidimensional solitons in Bose-Einstein condensates. We discuss the techniques used to generate and observe multidimensional nonlinear waves in Bose-Einstein condensates with repulsive interactions. We further summarize observations of planar soliton fronts undergoing the snake instability, the formation of vortex rings, and the emergence of hybrid structures.Comment: review paper, to appear as Chapter 5b in "Emergent Nonlinear Phenomena in Bose-Einstein Condensates: Theory and Experiment," edited by P. G. Kevrekidis, D. J. Frantzeskakis, and R. Carretero-Gonzalez (Springer-Verlag

Virtual Sensors For Advanced Controllers In Rehabilitation Robotics

In order to properly control rehabilitation robotic devices, the measurement of interaction force and motion between patient and robot is an essential part. Usually, however, this is a complex task that requires the use of accurate sensors which increase the cost and the complexity of the robotic device. In this work, we address the development of virtual sensors that can be used as an alternative of actual force and motion sensors for the Universal Haptic Pantograph (UHP) rehabilitation robot for upper limbs training. These virtual sensors estimate the force and motion at the contact point where the patient interacts with the robot using the mathematical model of the robotic device and measurement through low cost position sensors. To demonstrate the performance of the proposed virtual sensors, they have been implemented in an advanced position/force controller of the UHP rehabilitation robot and experimentally evaluated. The experimental results reveal that the controller based on the virtual sensors has similar performance to the one using direct measurement (less than 0.005 m and 1.5 N difference in mean error). Hence, the developed virtual sensors to estimate interaction force and motion can be adopted to replace actual precise but normally high-priced sensors which are fundamental components for advanced control of rehabilitation robotic devices.This work was supported in part by the Basque Country Governments (GV/EJ) under grant PRE-2014-1-152, UPV/EHU's PPG17/56 project, Basque Country Governments IT914-16 project, Spanish Ministry of Economy and Competitiveness' MINECO & FEDER inside DPI2017-82694-R project, Euskampus, FIK and Spanish Ministry of Science and Innovation PDI-020100-2009-21 project

Wolf habitat selection when sympatric or allopatric with brown bears in Scandinavia

Habitat selection of animals depends on factors such as food availability, landscape features, and intra- and interspecific interactions. Individuals can show several behavioral responses to reduce competition for habitat, yet the mechanisms that drive them are poorly understood. This is particularly true for large carnivores, whose fine-scale monitoring is logistically complex and expensive. In Scandinavia, the home-range establishment and kill rates of gray wolves (Canis lupus) are affected by the coexistence with brown bears (Ursus arctos). Here, we applied resource selection functions and a multivariate approach to compare wolf habitat selection within home ranges of wolves that were either sympatric or allopatric with bears. Wolves selected for lower altitudes in winter, particularly in the area where bears and wolves are sympatric, where altitude is generally higher than where they are allopatric. Wolves may follow the winter migration of their staple prey, moose (Alces alces), to lower altitudes. Otherwise, we did not find any effect of bear presence on wolf habitat selection, in contrast with our previous studies. Our new results indicate that the manifestation of a specific driver of habitat selection, namely interspecific competition, can vary at different spatial-temporal scales. This is important to understand the structure of ecological communities and the varying mechanisms underlying interspecific interactions

Ultrametric spaces of branches on arborescent singularities

Let $S$ be a normal complex analytic surface singularity. We say that $S$ is arborescent if the dual graph of any resolution of it is a tree. Whenever $A,B$ are distinct branches on $S$, we denote by $A \cdot B$ their intersection number in the sense of Mumford. If $L$ is a fixed branch, we define $U_L(A,B)= (L \cdot A)(L \cdot B)(A \cdot B)^{-1}$ when $A \neq B$ and $U_L(A,A) =0$ otherwise. We generalize a theorem of P{\l}oski concerning smooth germs of surfaces, by proving that whenever $S$ is arborescent, then $U_L$ is an ultrametric on the set of branches of $S$ different from $L$. We compute the maximum of $U_L$, which gives an analog of a theorem of Teissier. We show that $U_L$ encodes topological information about the structure of the embedded resolutions of any finite set of branches. This generalizes a theorem of Favre and Jonsson concerning the case when both $S$ and $L$ are smooth. We generalize also from smooth germs to arbitrary arborescent ones their valuative interpretation of the dual trees of the resolutions of $S$. Our proofs are based in an essential way on a determinantal identity of Eisenbud and Neumann.Comment: 37 pages, 16 figures. Compared to the first version on Arxiv, il has a new section 4.3, accompanied by 2 new figures. Several passages were clarified and the typos discovered in the meantime were correcte