School Principals in Spain: an Unstable Professional Identity

The article proposes an emerging approach in research on school leadership, within the framework of the “International Successful School Principalship Project(ISSPP)”, where one of the three key research strands is “Principals’ identities”. It formulates, first, the theoretical framework for the professional identity from a narrative approach, linked -at the same time- to the practice of leadership, as an interactive relationship with the other members of the school. Successful leadership practices depend to a large degree on strong principals’ identities. Second, it analyzes the biographical interviews of 15 school principals, through a process of structuring and categorizing the data collected, applying content analysis. The dimension of the principals’ identities emerges in different categories: a) Personal identity; b) Professional identity (internal perspective); c) Professional identity (external perspective); d) Social identity; e) Professionalization; [and] d) Dual identity. Finally, the results are discussed, and lines are proposed to articulate and strengthen the identity of school principals in Spain

Self-supervised pre-training of CNNs for flatness defect classification in the steelworks industry

Classification of surface defects in the steelworks industry plays a significant role in guaranteeing the quality of the products. From an industrial point of view, a serious concern is represented by the hot-rolled products shape defects and particularly those concerning the strip flatness. Flatness defects are typically divided into four sub-classes depending on which part of the strip is affected and the corresponding shape. In the context of this research, the primary objective is evaluating the improvements of exploiting the self-supervised learning paradigm for defects classification, taking advantage of unlabelled, real, steel strip flatness maps. Different pre-training methods are compared, as well as architectures, taking advantage of well-established neural subnetworks, such as Residual and Inception modules. A systematic approach in evaluating the different performances guarantees a formal verification of the self-supervised pre-training paradigms evaluated hereafter. In particular, pre-training neural networks with the EgoMotion meta-algorithm shows classification improvements over the AutoEncoder technique, which in turn is better performing than a Glorot weight initialization

SOM-based behavioral analysis for virtualized network functions

In this paper, we propose a mechanism based on Self-Organizing Maps for analyzing the resource consumption behaviors and detecting possible anomalies in data centers for Network Function Virtualization (NFV). Our approach is based on a joint analysis of two historical data sets available through two separate monitoring systems: system-level metrics for the physical and virtual machines obtained from the monitoring infrastructure, and application-level metrics available from the individual virtualized network functions. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, highlight some of the capabilities of our system to identify interesting points in space and time of the evolution of the monitored infrastructure

Behavioral Analysis for Virtualized Network Functions : A SOM-based Approach

In this paper, we tackle the problem of detecting anomalous behaviors in a virtualized infrastructure for network function virtualization, proposing to use self-organizing maps for analyzing historical data available through a data center. We propose a joint analysis of system-level metrics, mostly related to resource consumption patterns of the hosted virtual machines, as available through the virtualized infrastructure monitoring system, and the application-level metrics published by individual virtualized network functions through their own monitoring subsystems. Experimental results, obtained by processing real data from one of the NFV data centers of the Vodafone network operator, show that our technique is able to identify specific points in space and time of the recent evolution of the monitored infrastructure that are worth to be investigated by a human operator in order to keep the system running under expected conditions

a smartphone application for supporting the data collection and analysis of the cultural heritage damaged during natural disasters

The adverse impacts of natural disasters on lives and livelihoods, as well as regional and local economies, are increasingly evident, and losses to both tangible and intangible cultural heritage due to these disasters pay an important role in the total amount. In fact, damages to sites, structures and artifacts of cultural and historical value, as well as impacts to cultural tourism and the financial resources, produce a strong competitive disadvantage on local communities. Emergency decision making, based on awareness of the suffered damages, can play a crucial role in the attempts of improving resilience of the strategic elements; however, this process typically requires a fast overview on large territories. In this work, we propose a novel framework for obtaining an agile solution to quickly collect and analyze picture galleries and information provided by both internal staff and citizens through commercially available mobile devices. This solution virtually generates a network of information sources during emergency time (e.g., a seismic sequence), and allows to produce a situation map in GIS environment, hence supporting the health status analysis of cultural heritage over time. This paper presents the prototype system composed of: (1) a smartphone application for the acquisition of new information and the examination of existing one; (2) a web-service for exchanging data with databases; and (3) a local service that makes use of a proper piece of software for obtaining a 3D reconstruction from new picture galleries. The proposed system results in a scalable, exportable and modular tool useful during the emergency and for preserving memories of local communities

Forecasting Operation Metrics for Virtualized Network Functions

Network Function Virtualization (NFV) is the key technology that allows modern network operators to provide flexible and efficient services, by leveraging on general-purpose private cloud infrastructures. In this work, we investigate the performance of a number of metric forecasting techniques based on machine learning and artificial intelligence, and provide insights on how they can support the decisions of NFV operation teams. Our analysis focuses on both infrastructure-level and service-level metrics. The former can be fetched directly from the monitoring system of an NFV infrastructure, whereas the latter are typically provided by the monitoring components of the individual virtualized network functions. Our selected forecasting techniques are experimentally evaluated using real-life data, exported from a production environment deployed within some Vodafone NFV data centers. The results show what the compared techniques can achieve in terms of the forecasting accuracy and computational cost required to train them on production data

Programs for students at risk best practices in Secondary Schools. A look from the experience

El presente trabajo se enmarca dentro de un proyecto I+D+I Estudiantes en riesgo de exclusión educativa en la ESO: situación, programas y buenas prácticas del que está dando cuenta este Monográfico. Tiene como objetivo general analizar las políticas educativas, las prácticas y los resultados que se están alcanzando con aquellos estudiantes que encuentran dificultades acusadas de seguir el currículo y la enseñanza regular en la ESO y que están, por tanto en situación extrema de riesgo de exclusión educativa y social. Este artículo, que se inicia con una breve conceptualización teórica del concepto de buenas prácticas, presenta el análisis de los casos tanto de la Comunidad Autónoma de Murcia como de Andalucía, concretamente, a partir de entrevistas en profundidad y observaciones de aulas, analizadas desde la perspectiva de buenas prácticas.En concreto, se pone de manifiesto qué acciones, estrategias y metodologías alimentan las tareas que se llevan a cabo en institutos de educación secundaria con alumnado en riesgo de exclusión social. Son definidas y consideradas como buenas prácticas por sus resultados, sistematización, valoración, aceptación y difusión social y educativa.This work is framed within an R & D project financed by the Ministry of Education, students at risk of educational exclusion in Secondary School: status, programs and best practices, which aims to analyze education policy, practices and outcomes achieved with students who have difficulty following the curricular and regular education in Secondary School, therefore being in extreme risk of social exclusion. This article, which begins with a brief theoretical conceptualization on the concept of best practices, showcases the analysis of the cases for both the Autonomous Community of Murcia and Andalusia, particulary the analysis of interviews and classroom observation regarding the concept of best practices. Specifically, it shows which actions, strategies and methodologies carry out the tasks in Secondary Schools with pupils at risk of social exclusion, and are defined and considered as best practices in terms of performance, organization, evaluation, acceptance and social and educational broadcasting.Grupo de Investigación FORCE (Formación Centrada en la Escuela) Universidad de Granad

High-Resolution 3D Fabrication of Glass Fiber-Reinforced Polymer Nanocomposite (FRPN) Objects by Two-Photon Direct Laser Writing

This paper reports on the nanofabrication of a fiber-reinforced polymer nanocomposite (FRPN) by two-photon direct laser writing (TP-DLW) using silica nanowires (SiO2 NWs) as nanofillers, since they feature a refractive index very close to that of the photoresist used as a polymeric matrix. This allows for the best resolution offered by the TP-DLW technique, even with high loads of SiO2 NWs, up to 70 wt %. The FRPN presented an increase of approximately 4 times in Young's modulus (8.23 GPa) and nanohardness (120 MPa) when compared to those of the bare photoresist, indicating how the proposed technique is well-suited for applications with higher structural requirements. Moreover, three different printing configurations can be implemented thanks to the use of silicon chips, on which the SiO2 NWs are grown, as fabrication substrates. First, they can be effectively used as an adhesive layer when the laser beam is focused at the interface with the silicon substrate. Second, they can be used as a sacrificial layer, when the laser beam is focused in a plane inside the SiO2 NW layer. Third, only the outer shell of the object is printed so that the SiO2 NW tangle acts as the internal skeleton for the structure being fabricated in the so-called shell and scaffold printing strategy

Indirect daylight oxidative degradation of polyethylene microplastics by a bio-waste modified TiO2-based material

Microplastics are recognized as an emerging critical issue for the environment. Here an innovative chemical approach for the treatment of microplastics is proposed, based on an oxidative process that does not require any direct energy source (irradiation or heat). Linear low-density polyethylene (LLDPE) was selected as target commodity polymer, due to its widespread use, chemical inertness and inefficient recycling. This route is based on a hybrid material coupling titanium oxide with a bio-waste, rosin, mainly constituted by abietic acid, through a simple sol-gel synthesis procedure. The ligand-to-metal charge transfer complexes formed between rosin and Ti4+ allow the generation of reactive oxygen species without UV irradiation for its activation. In agreement with theorical calculations, superoxide radical ions are stabilized at ambient conditions on the surface of the hybrid TiO2. Consequently, an impressive degradation of LLDPE is observed after 1 month exposure in a batch configuration under indirect daylight, as evidenced by the products revealed by gas chromatography-mass spectrometry analysis and by chemical and structural modifications of the polymer surface. In a context of waste exploitation, this innovative and sustainable approach represents a promising cost-effective strategy for the oxidative degradation of microplastics, without producing any toxic by-products