Impacts of Forest Management on Forest Bird Occurrence Patterns-A Case Study in Central Europe

The global increase in demand for wood products, calls for a more sustainable management of forests to optimize both the production of wood and the conservation of forest biodiversity. In this paper, we evaluate the status and future trends of forest birds in Central European forests, assuming different forest management scenarios that to a varying degree respond to the demand for wood production. To this end, we use niche models (Boosted Regression Trees and Generalized Linear Models) to model the responses of 15 forest bird species to predictors related to forest stand (e.g., stand volume of specific tree species) and landscape structure (e.g., percentage cover), and to climate (bioclimatic variables). We then define five distinct forest management scenarios, ranging from set-aside to productivity-driven scenarios, project them 100 years into the future, and apply our niche models into these scenarios to assess the birds' responses to different forest management alternatives. Our models show that the species' responses to management vary reflecting differences in their ecological niches, and consequently, no single management practice can benefit all species if applied across the whole landscape. Thus, we conclude that in order to promote the overall forest bird species richness in the study region, it is necessary to manage the forests in a multi-functional way, e.g., by spatially optimizing the management practices in the landscape

Deployment of a smart and predictive maintenance system in an industrial case study

Industrial manufacturing environments are often characterized as being stochastic, dynamic and chaotic, being crucial the implementation of proper maintenance strategies to ensure the production efficiency, since the machines? breakdown leads to a degradation of the system performance, causing the loss of productivity and business opportunities. In this context, the use of emergent ICT technologies, such as Internet of Things (IoT), machine learning and augmented reality, allows to develop smart and predictive maintenance systems, contributing for the reduction of unplanned machines? downtime by predicting possible failures and recovering faster when they occur. This paper describes the deployment of a smart and predictive maintenance system in an industrial case study, that considers IoT and machine learning technologies to support the online and real-time data collection and analysis for the earlier detection of machine failures, allowing the visualization, monitoring and schedule of maintenance interventions to mitigate the occurrence of such failures. The deployed system also integrates machine learning and augmented reality technologies to support the technicians during the execution of maintenance interventions.2411-78B2-7CDB | Pedro Miguel MoreiraN/

A versatile synthesis method of dendrites-free segmented nanowires with a precise size control

We report an innovative strategy to obtain cylindrical nanowires combining well established and low-cost bottom-up methods such as template-assisted nanowires synthesis and electrodeposition process. This approach allows the growth of single-layer or multi-segmented nanowires with precise control over their length (from few nanometers to several micrometers). The employed techniques give rise to branched pores at the bottom of the templates and consequently dendrites at the end of the nanowires. With our method, these undesired features are easily removed from the nanowires by a selective chemical etching. This is crucial for magnetic characterizations where such non-homogeneous branches may introduce undesired features into the final magnetic response. The obtained structures show extremely narrow distributions in diameter and length, improved robustness and high-yield, making this versatile approach strongly compatible with large scale production at an industrial level. Finally, we show the possibility to tune accurately the size of the nanostructures and consequently provide an easy control over the magnetic properties of these nanostructures

Measuring beta-diversity by remote sensing: a challenge for biodiversity monitoring

Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, overall when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this view, airborne or satellite remote sensing allow to gather information over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (beta-diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript we propose novel techniques to measure beta-diversity from airborne or satellite remote sensing, mainly based on: i) multivariate statistical analysis, ii) the spectral species concept, iii) self-organizing feature maps, iv) multi- dimensional distance matrices, and the v) Rao's Q diversity. Each of these measures allow to solve one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating beta-diversity from remotely sensed imagery and potentially relate them to species diversity in the field

Measuring beta-diversity by remote sensing: a challenge for biodiversity monitoring

Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, overall when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this view, airborne or satellite remote sensing allow to gather information over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (beta-diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript we propose novel techniques to measure beta-diversity from airborne or satellite remote sensing, mainly based on: i) multivariate statistical analysis, ii) the spectral species concept, iii) self-organizing feature maps, iv) multi- dimensional distance matrices, and the v) Rao's Q diversity. Each of these measures allow to solve one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating beta-diversity from remotely sensed imagery and potentially relate them to species diversity in the field

The ABC130 barrel module prototyping programme for the ATLAS strip tracker

For the Phase-II Upgrade of the ATLAS Detector, its Inner Detector, consisting of silicon pixel, silicon strip and transition radiation sub-detectors, will be replaced with an all new 100 % silicon tracker, composed of a pixel tracker at inner radii and a strip tracker at outer radii. The future ATLAS strip tracker will include 11,000 silicon sensor modules in the central region (barrel) and 7,000 modules in the forward region (end-caps), which are foreseen to be constructed over a period of 3.5 years. The construction of each module consists of a series of assembly and quality control steps, which were engineered to be identical for all production sites. In order to develop the tooling and procedures for assembly and testing of these modules, two series of major prototyping programs were conducted: an early program using readout chips designed using a 250 nm fabrication process (ABCN-25) and a subsequent program using a follow-up chip set made using 130 nm processing (ABC130 and HCC130 chips). This second generation of readout chips was used for an extensive prototyping program that produced around 100 barrel-type modules and contributed significantly to the development of the final module layout. This paper gives an overview of the components used in ABC130 barrel modules, their assembly procedure and findings resulting from their tests.Comment: 82 pages, 66 figure

Breeding habitat selection of steppe birds in Castro Verde: a remote sensing and advanced statistics approach

La ZEPA de CastroVerde es la principal área pseudo-esteparia de Portugal, con poblaciones de varias especies de aves esteparias amenazadas de importancia nacional e internacional. En esta área se ha desarrollado un programa agro-ambiental para mantener dichas poblaciones. Mientras la comunidad local de aves esteparias ha sido estudiada relativamente bien, el conocimiento sobre su selección de hábitat está basado principalmente en bases de datos limitadas o métodos estadísticos restringidos, lo cual podría obstaculizar la efectividad de las medidas de manejo recomendadas. En este estudio se usaron datos de diferentes fuentes de teledetección (p. ej. SPOTVGT, Landsat TM, LiDAR) para caracterizar rasgos del paisaje a diferentes escalas espaciales, mientras que se usaron imágenes multi-fecha para capturar la dinámica de los cultivos agrícolas. Se reunió una gran base de datos sobre presencia de aves esteparias mediante un diseño combinado y estratificado aleatorio de muestreo de campo. Las asociaciones entre especies y hábitats fueron cuantificadas por medio de regresiones no lineales (MARS) en un marco metodológico robusto. La metodología usada mostró resultados consistentes con la información conocida, confirmando la mayor parte del conocimiento existente sobre uso del hábitat por parte de la comunidad local de aves esteparias, pero también añadió información adicional. La aproximación es por tanto fiable y podría ser usada para estudiar otras comunidades peor conocidas. Se sugiere además que losresultados de este estudio pueden ser incorporados entre las prescripciones de manejo apropiadas. <br/

Testbed Development for the Characterisation of an ASIC for Beam Loss Measurement Systems

A high-performance, radiation-hardened, application-specific integrated circuit (ASIC) is under development at CERN for digitising signals from beam losses monitoring systems in harsh radiation environments. To fully characterise and validate both the analogue and digital parts of these ASICs, an automated testbed has been developed. Here we report on the components used to build this setup, its capabilities as well as the methodology of the data analysis. Focus is given to the data collection, the automation and the efficient computation methods developed to extract the merit factors of two different ASIC designs from prototype manufacturing runs

Hydrodynamic model study of the Ria de Pontevedra under estuarine conditions

13 páginas, 9 figurasThe Ria de Pontevedra is one of the Galician Rias Baixas, partially mixed estuaries on the north-west coast of the Iberian peninsula. The hydrodynamics of the rias is far from being fully understood. In this application, the hydrodynamics of the Ria de Pontevedra is studied by means of a 3-D baroclinic model. Our aim was to establish the circulation pattern driven by tide and the horizontal density gradient between river and shelf waters under estuarine conditions. The spatial variability of tidal velocities and salinities is studied. The residual component of the flow, related to transport, is described and discussed. As expected in a partially mixed estuary, a double-layered residual pattern is observed, with water flowing seaward in a surface layer and upstream in a bottom layer. A cross-channel residual flow, not described up to date, is presented.This work is part of a Ph.D thesis by M. R. Villarreal, who gratefully acknowledges the support of a predoctoral grant from the Xunta de Galicia (Spain). This research was also carried out in relation to the project ‘ La Hidrodina´mica y el Ciclo Biogeoquímico del Silicio’, financed by the Spanish Comisión Interministerial de Ciencia y Tecnología (CICYT) under the number MAR96-1782.Peer reviewe

Hyperspectral satellite data for modelling spatial beta diversity patterns of birds along an environmental gradient

Human-driven reduction in biodiversity is widely acknowledged, with direct impact on ecosystem functioning and provisioning of services. However, existing patterns of biodiversity and most particularly those of community composition turnover, or beta diversity, are little known. While Earth observation missions provide an excellent tool for describing these patterns, the structural complexity of biotic communities is usually difficult to characterise using data from existing satellite sensors. Forthcoming hyperspectral missions will deliver much more detailed descriptions of the Earth's surface, which will greatly enhance our ability to tackle this issue. In the current study we used simulated EnMAP imagery, derived from geometrically and spectrally highly resolved airborne data from a region in southern Portugal. These data were used to describe the turnover of a bird community along an environmental gradient of shrub encroachment, resulting from land abandonment. For describing the turnover in community composition we adopted generalised dissimilarity modelling, while a sparse canonical correlation analysis enabled making full use of the hyperspectral information. The use of hyperspectral data, when compared to broadband multispectral data, such as Landsat TM, improved the explanatory power of the models by over 25%. Our results thus highlight the potential of hyperspectral satellite data for modelling the spatial patterns of biodiversity and ecosystem functioning. Nevertheless, further studies are still needed to validate the generalised usage of these type of data for tackling complex problems of ecosystem research