Estándares internacionales de justicia juvenil

La justicia juvenil, aplicable a menores de dieciocho años, es una justicia específica y diferenciada de la justicia aplicable a los adultos, y para las Organizaciones Internacionales (Naciones Unidas, Organización de Estados Americanos, Consejo de Europa, Unión Europea) debe ser una justicia restaurativa, que pone el acento en la reparación del daño causado a la víctima, frente a la tradicional justicia distributiva, basada en el castigo al ofensor. Para que una justicia juvenil de tales características sea eficaz, las Organizaciones Internacionales consideran necesario que los Estados apliquen unos estándares internacionales (orientaciones comunes en forma de tipo, modelo, norma, patrón, nivel o referencia), extraídos de la Convención sobre los Derechos del Niño y demás normas, reglas y directrices que dichas Organizaciones han emitido. En este TFG formulamos y analizamos veiticinco de esos estándares y los clasificamos en cuatro grupos según se refieran a la prevención de la delincuencia, al proceso judicial, a las medidas sancionadoras y alternativas o a la reinserción social de los menores infractores de la ley.Grado en Criminologí

A Highly Available Cluster of Web Servers with Increased Storage Capacity

Ponencias de las Decimoséptimas Jornadas de Paralelismo de la Universidad de Castilla-La Mancha celebradas el 18,19 y 20 de septiembre de 2006 en AlbaceteWeb servers scalability has been traditionally solved by improving software elements or increasing hardware resources of the server machine. Another approach has been the usage of distributed architectures. In such architectures, usually, file al- location strategy has been either full replication or full distribution. In previous works we have showed that partial replication offers a good balance between storage capacity and reliability. It offers much higher storage capacity while reliability may be kept at an equivalent level of that from fully replicated solutions. In this paper we present the architectural details of Web cluster solutions adapted to partial replication. We also show that partial replication does not imply a penalty in performance over classical fully replicated architectures. For evaluation purposes we have used a simulation model under the OMNeT++ framework and we use mean service time as a performance comparison metric.Publicad

Detecting feature influences to quality attributes in large and partially measured spaces using smart sampling and dynamic learning

Emergent application domains (e.g., Edge Computing/Cloud/B5G systems) are complex to be built manually. They are characterised by high variability and are modelled by large Variability Models (VMs), leading to large configuration spaces. Due to the high number of variants present in such systems, it is challenging to find the best-ranked product regarding particular Quality Attributes (QAs) in a short time. Moreover, measuring QAs sometimes is not trivial, requiring a lot of time and resources, as is the case of the energy footprint of software systems — the focus of this paper. Hence, we need a mechanism to analyse how features and their interactions influence energy footprint, but without measuring all configurations. While practical, sampling and predictive techniques base their accuracy on uniform spaces or some initial domain knowledge, which are not always possible to achieve. Indeed, analysing the energy footprint of products in large configuration spaces raises specific requirements that we explore in this work. This paper presents SAVRUS (Smart Analyser of Variability Requirements in Unknown Spaces), an approach for sampling and dynamic statistical learning without relying on initial domain knowledge of large and partially QA-measured spaces. SAVRUS reports the degree to which features and pairwise interactions influence a particular QA, like energy efficiency. We validate and evaluate SAVRUS with a selection of likewise systems, which define large searching spaces containing scattered measurements.Funding for open access charge: Universidad de Málaga / CBUA. This work is supported by the European Union’s H2020 re search and innovation programme under grant agreement DAEMON H2020-101017109, by the projects IRIS PID2021-12281 2OB-I00 (co-financed by FEDER funds), Rhea P18-FR-1081 (MCI/AEI/ FEDER, UE), and LEIA UMA18-FEDERIA-157, and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación, Spain

Extended Variability Models, Algebra, and Arithmetic

Although classic variability models have been traditionally used to specify members of a product-line, their level of expressiveness was quite limited. Several extensions have been proposed, like numerical features, complex cardinalities and feature and configuration attributes. However, modern tools often provide limited support to these extensions. Imposing variability modelling restrictions into general theories enables off-the-self automated reasoners to analyse extended variability models. While one could argue that those general theories are less reasoning efficient, in practice happen the same if we extend traditional solvers. In contrast, general theories provide new properties with the potential to a) improve reasoning efficiency above extending traditional solvers, and b) provide exotic analyses that uncover new properties of the variability models and feature and configuration spaces. Examples of this could be the functions commutativity property, (reasoning) functors composition, and the fundamental theorem of calculus applied to feature or configuration space.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Detecting Feature Influences to Quality Attributes in Large and Partially Measured Spaces using Smart Sampling and Dynamic Learning

Publicación Journal First siendo el original: Munoz, D. J., Pinto, M., & Fuentes, L. (2023). Detecting feature influences to quality attributes in large and partially measured spaces using smart sampling and dynamic learning. Knowledge-Based Systems, 270, 110558.Emergent application domains (e.g., Edge Computing/Cloud /B5G systems) are complex to be built manually. They are characterised by high variability and are modelled by large \textit{Variability Models} (VMs), leading to large configuration spaces. Due to the high number of variants present in such systems, it is challenging to find the best-ranked product regarding particular Quality Attributes (QAs) in a short time. Moreover, measuring QAs sometimes is not trivial, requiring a lot of time and resources, as is the case of the energy footprint of software systems -- the focus of this paper. Hence, we need a mechanism to analyse how features and their interactions influence energy footprint, but without measuring all configurations. While practical, sampling and predictive techniques base their accuracy on uniform spaces or some initial domain knowledge, which are not always possible to achieve. Indeed, analysing the energy footprint of products in large configuration spaces raises specific requirements that we explore in this work. This paper presents SAVRUS (Smart Analyser of Variability Requirements in Unknown Spaces), an approach for sampling and dynamic statistical learning without relying on initial domain knowledge of large and partially QA-measured spaces. SAVRUS reports the degree to which features and pairwise interactions influence a particular QA, like energy efficiency. We validate and evaluate SAVRUS with a selection of likewise systems, which define large searching spaces containing scattered measurements.Trabajo financiado por el programa de I+D H2020 de la UE bajo el acuerdo DAEMON 101017109, por los proyectos también co-financiados por fondos FEDER \emph{IRIS} PID2021-122812OB-I00, y \emph{LEIA} UMA18-FEDERIA-157, y la ayuda PRE2019-087496 del Ministerio de Ciencia e Innovación. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Professional skills development within the framework of the EHEA. The case of Communication studies.

Desde el Espacio Europeo de Educación Superior (EEES) se potencia el desarrollo de destrezas profesionales desde la Universidad y esta situación se plantea como uno de los retos de este nuevo contexto educativo, caracterizado por cambios en la metodología docente y en el rol que estudiantes y docentes desempeñan en el aula. En el presente trabajo profundizamos en las posibilidades que ofrecen las herramientas docentes basadas en el aprendizaje colaborativo, para potenciar las competencias profesionales en los Estudios de Comunicación.European Higher Education Area (EHEA) enhances professional skills development from the University and this situation is seen as one of the challenges of this new educational context, characterized by changes in the teaching methodology and the role played by students and teachers. This paper goes into the potential of educational tools based on collaborative learning, in order to enhance professional skills in Communication Studies

Asphalt solar collectors: A literature review

Asphalt pavements subject to solar radiation can reach high temperatures causing not only environmental problems such as the heat island effect on cities but also structural damage due to rutting or hardening as a result of thermal cycles. Asphalt solar collectors are doubly effective active systems: as they solve the previously mentioned problems and, moreover, they can harness energy to be used in different applications. The main findings of the existing research on asphalt solar collectors are gathered together in this review paper. Firstly, the main heat transfer mechanisms involved in the solar energy collection process are identified and the most important parameters and variables are presented. After analyzing the theoretical foundations of the heat transfer process, this review focuses on the types of studies carried out so far on asphalt’s thermal behavior, different methodologies employed by other authors to study asphalt solar collectors and influence of the variables involved in thermal energy harvesting

Defining Categorical Reasoning of Numerical Feature Models with Feature-Wise and Variant-Wise Quality Attributes

Automatic analysis of variability is an important stage of Software Product Line (SPL) engineering. Incorporating quality information into this stage poses a significant challenge. However, quality-aware automated analysis tools are rare, mainly because in existing solutions variability and quality information are not unified under the same model. In this paper, we make use of the Quality Variability Model (QVM), based on Category Theory (CT), to redefine reasoning operations. We start defining and composing the six most commonoperations in SPL, but now as quality-based queries, which tend to be unavailable in other approaches. Consequently, QVM supports interactions between variant-wise and feature-wise quality attributes. As a proof of concept,we present, implement and execute the operations as lambda reasoning for CQL IDE – the state-of-theart CT tool.Munoz, Pinto and Fuentes work is supported by the European Union’s H2020 research and innovation programme under grant agreement DAEMON 101017109, by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 and Rhea P18-FR-1081 and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación

Transforming numerical feature models into propositional formulas and the universal variability language

Real-world Software Product Lines (SPLs) need Numerical Feature Models (NFMs) whose features have not only boolean values that satisfy boolean constraints but also have numeric attributes that satisfy arithmetic constraints. An essential operation on NFMs finds near-optimal performing products, which requires counting the number of SPL products. Typical constraint satisfaction solvers perform poorly on counting and sampling. Nemo (Numbers, features, models) is a tool that supports NFMs by bit-blasting, the technique that encodes arithmetic expressions as boolean clauses. The newest version, Nemo2, translates NFMs to propositional formulas and the Universal Variability Language (UVL). By doing so, products can be counted efficiently by #SAT and Binary Decision Tree solvers, enabling finding near-optimal products. This article evaluates Nemo2 with a large set of synthetic and colossal real-world NFMs, including complex arithmetic constraints and counting and sampling experiments. We empirically demonstrate the viability of Nemo2 when counting and sampling large and complex SPLs.Munoz, Pinto and Fuentes work is supported by the European Union’s H2020 research and innovation programme under grant agreement DAEMON 101017109, by the projects co-financed by FEDER, Spain funds LEIA UMA18-FEDERJA-15, IRIS PID2021- 122812OB-I00 (MCI/AEI), and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación. Funding for open access charge: Universidad de Málaga / CBUA

ANXIETY TOWARDS MATHEMATICS IN THE STUDENTS OF THE CONALEP CAMPUS VERACRUZ-1: A COMPARATIVE STUDY OF THE MORNING AND AFTERNOON SHIFTS IN VERACRUZ, MÉXICO

The present study had as aim to measure the anxiety level towards mathematics in the students of the College of Technical Professional Study in the State of Veracruz High School (CONALEP for its acronym in Spanish) on Veracruz I campus in the morning and afternoon shifts. For this purpose, the Muñoz and Mato-Vázquez (2007) scale was used; it is formed by 25 items grouped in five dimensions: anxiety towards evaluation, temporality, understanding of mathematical problems, number and mathematical operations and real life situations. The study was also based on the recent empirical findings of García-Santillán, Escalera and Venegas (2013, 2014, 2015). It was approached from a non-experimental transversal-cut quantitative paradigm using the exploratory factor analysis with component extraction as the technique for data measurement (KMO, X2, α= 0.05). In both cases the value of X2 showed enough evidence to reject the null hypothesis (Ho). The internal consistency of the test showed, for the gathered data of the morning shift students α= 0.849 (grouped) and 0.793 individually, and for the afternoon shift α= 0.849 (grouped) and 0.793 individually, which are very acceptable in theoretical terms (Hair, Yerson, Tatham and Black, 1999).