Talking with spies: From naive to distrustful researcher

Intelligence officers - generally known as 'spies' - will not openly admit, even within their own family circles, that they work for an intelligence service. These agencies have neither local offices nor facilities, nor do they visit schools to talk about the daily life and work of their officers. Public media appearances are extremely rare, and it is usually only the director of the agency whose face is seen in public. Innate secretiveness and the inaccessibility of many official archives are obstacles to transparent research on intelligence services. In 1998, I commenced my doctoral thesis on the Spanish intelligence services. Far from being a passing adventure, the topic became my principal line of research for over two decades, during which time I completed almost 250 interviews, including 100 with members and ex-members of the intelligence services and another 50 with heads of government, Spanish and foreign intelligence service directors, military staff, politicians, and diplomats. In this chapter, I present my fieldwork experience of interviewing intelligence officers and the evolution of a young and somewhat naïve researcher into a more mature and distrustful one

Quality assurance mechanisms in agrifood: The case of the Spanish fresh meat sector

The largest fresh meat brand names in Spain are analyzed here to study how quality is signaled in agribusiness and how the underlying quality -assurance organizations work. Results show, first, that organizational form varies according to the specialization of the brand name. Publicly-controlled brand names are grounded on market contracting with individual producers, providing stronger incentives. In contrast, private brands rely more on hierarchy, taking advantage of its superiority in solving specific coordination problems. Second, the seemingly redundant coexistence of several quality indicators for a given product is explained in efficiency terms. Multiple brands are shown to be complementary, given their specialization in guaranteeing different attributes of the product.Quality assurance, co-branding, agriculture, vertical integration, contracts

A Framework for Understanding the Strategies of Openness of the Intelligence Services

The relationship of the intelligence services with openness has been elusive and erratic, changing at the path of the scandals that shaked politics and public opinion. At different rhythms and marked by their national contexts, different intelligence services have embarked over the last two decades in different initiatives to promote societal awareness and a better understanding among society on the intelligence function. In this paper, a theoretical framework is proposed for understanding those openness strategies implemented by the intelligence agencies. The paper discusses two potential approaches to openness with a spectrum of mixed approaches in-between them. The first consists of generating and maintaining a (good) image/reputation and, the second is to legitimize its existence and its role within the State. © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC

El Cornucopiae de Ravisius Textor: edición y estudio

El trabajo que presentamos es un estadio inicial, si bien muy avanzado, de un trabajo en marcha que se pretende concluir como trabajo final en el Máster en Textos de la Antigüedad Clásica y su Pervivencia. El presente trabajo ofrece una edición crítica del Cornucopiae de Joannes Ravisius Textor, un catálogo de los productos en los que abundaban en la Antigüedad todas las regiones del mundo, con testimonios de autores clásicos y neolatinos. Se incluye también un apéndice con la traducción del prefacio y un estudio estadístico de los testimonios.Departamento de Filología ClásicaGrado en Estudios Clásico

A Class of Computationally Fast First Order Finite Volume Solvers: PVM Methods

In this work, we present a class of fast first order finite volume solvers, named as PVM (Polynomial Viscosity Matrix), for balance laws or, more generally, for nonconservative hyperbolic systems. They are defined in terms of viscosity matrices computed by a suitable polynomial evaluation of a Roe matrix. These methods have the advantage that they only need some information about the eigenvalues of the system to be defined, and no spectral decomposition of Roe Matrix is needed. As consequence, they are faster than Roe method. These methods can be seen as a generalization of the schemes introduced by Degond et al. in [12] for balance laws and nonconservative systems. The first-order path conservative methods to be designed here are intended to be used as the basis for higher order methods for multi-dimensional problems. In this work, some well known solvers as Rusanov, Lax-Friedrichs, FORCE (see [30], [8]), GFORCE (see [31], [8]) or HLL (see [18]) are redefined under this form, and then some new solvers are proposed. Finally, some numerical tests are presented and the performance of the numerical schemes are compared among them and with Roe schem

Acorns for fattening free-range pigs (OK-Net Ecofeed Practice Abstract)

- The fattening performance is very much influenced by the age of pigs and their compensatory growth; hence, pigs should be as old as possible (≥1 year) and adapted to grazing. - Grass is necessary as a source of protein to compensate for the low protein levels in acorns. - The food conversion rate is 10.5 kg of whole acorns of Q. i. rotundifolia to gain 1 kg, besides the contribution of grass; to establish the stocking rate, consider that an adult evergreen oak produces ≈11 kg of acorns/year). - Iberian pigs peel acorns to avoid the high content of tannins in the shell. However, during peeling, approxi-mately 20% of the kernel can be wasted

A Study of the Usefulness of Physical Models and Digital Models for Teaching Science to Prospective Primary School Teachers

©2023. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/ This document is the Published, version of a Published Work that appeared in final form in Education Sciences. To access the final edited and published work seehttps://doi.org/10.3390/educsci13040343This study focuses on the impact of the use of teaching resources on future teachers in different formats, physical and digital. We worked with a single task dealing with nutrition in humans with two groups of students, but one group worked with a version of the task that used physical resources, and the other group used digital resources as tools. Analyzing the work carried out and the answers given by the future teachers, it has been possible to observe the advantage of using digital resources over physical ones, although it did not generate significant differences between the two groups of participating students. This study shows how convenient it is to increase the use of digital models because of their lower cost, greater availability and ease of use. In short, they argue that the teaching of scientific knowledge should be complemented by the use of resources and models that facilitate learning, regardless of the format of the resource used

Structural health and intelligent monitoring of wind turbine blades with a motorized telescope

Currently, wind energy plays a fundamental role in the process of generating energy in a sustainable and environmentally friendly manner. However, their infrastructures require ongoing maintenance tasks that involve considerable risk. This is why a predictive maintenance system for the surface inspection of wind turbine blades based on machine learning techniques has been developed. Specifically, convolutional neural networks have been applied to detect and classify turbines and their blades, as well as the surface defects that may appear on them. The system comprises a mobile application that makes use of a telescope to take pictures with certain precision, a computing edge node responsible for processing the images that are captured, and a motorized mount that allows the telescope to move. The objective of this open-source project is to detect and classify different surface defects on the blades of wind turbines and carry out the maintenance of these infrastructures. The system is responsible for undertaking a complete sweep of the surface of the turbine blades in an autonomous way and finally presents the defects found to the user. The deep neural networks also help the system to decide which movements the motorized mount has to make together with the telescope to perform the inspection. Accuracies of around 97% for label predictions and 90% for bounding box coordinate predictions have been achieved for the convolutional deep learning models. Two possible approaches have been considered for the project: the first is to carry out all the necessary computation on a mobile phone to have a portable solution, and the second option considers a edge node to balance the load and thus not overload the mobile device. Tests show that the edge node approach gives better results overall. The proposed system for detecting surface damage on blades was experimentally validated on a wind farm.This work is funded by the Spanish projects UMA-CEIATECH-19 ("Wind Turbine Preventive Maintenance based on Deep Learning Techniques in the Fog"), RT2018-099777-B-100 ("rFOG: Improving Latency and Reliability of Offloaded Computation to the FOG for Critical Services"), PY20 00788 ("IntegraDos: Providing Real-Time Services for the Internet of Things through Cloud Sensor Integration"), and UMA18FEDERJA-215 ("Advanced Monitoring System Based on Deep Learning Services in Fog")

Portable motorized telescope system for wind turbine blades damage detection

Wind turbines are among the fastest-growing sources of energy production and the maintenance operations include regular inspection of their blades, causing considerable downtime and cost. In addition, the manual inspection process involves a great risk. To address this challenge, in this article a preventive maintenance system for wind turbines based on deep computational learning techniques is presented. This open-source project aims to detect and classify possible surface damages on wind turbine blades to facilitate and improve the inspection of such infrastructures. The system consists of a stand-alone Android application that makes use of convolutional neural networks for image processing, a portable telescope to take precise photographs of the turbine blades, and a motorized mount that allows the movement of the telescope. The application tries to carry out a complete sweep of the surface of the wind turbine blades in an autonomous way based on the predictions of neural network models and finally presents the defects found to the user. Thanks to this, maintenance time would be reduced and the risk of manual intervention would be avoided. Accuracies of around 97% for label predictions and 90% for bounding box coordinate predictions have been achieved on the validation dataset. The proposed low-cost inspection system for detecting surface damages on blades has been experimentally validated in a real wind farm.5G+TACTILE: Digital vertical twins for B5G/6G networks, Grant/Award Number: TSI-063000-2021-116; IntegraDos: Providing Real-Time Services for the Internet of Things through Cloud Sensor Integration, Grant/Award Number: PY20_00788; rFOG: Improving Latency and Reliability of Offloaded Computation to the FOG for Critical Services, Grant/Award Number: RT2018-099777-B-100; Wind Turbine Preventive Maintenance based on Deep Learning Techniques in the Fog, Grant/Award Number: UMA-CEIATECH-1