An empirical study of power consumption of Web-based communications in mobile phones

Currently, mobile devices are the most popular pervasive computing device, and they are becoming the primer way for Web access. Energy is a critical resource in such pervasive computing devices, being network communication one of the primary energy consuming operations in mobile apps. Indeed, web-based communication is the most used, but also energy demanding. So, mobile web developers should be aware of how much energy consumes the different web-based communication alternatives. The goal of this paper is to measure and compare the energy consumption of three asynchronous Web-based methods in mobile devices. Our experiments consider three different Web applications models that allow a web server to push data to a browser: Polling, Long Polling and WebSockets. The obtained results are analyzed to get more accurate understanding of the impact in energy consumption of a mobile browser for each of these three methods. The utility of these experiments is to show developers what are the factors that influence the energy consumption when different web-based asynchronous communication is used. With this information mobile web developers could reduce the power consumption of web applications on mobile devices, by selecting the most appropriate method for asynchronous server communication.MUniversidad 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

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

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

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

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

Ag&ZnO Obtained by Solvothermal Method for Photocatalytic Applications

In this work is reported the solvothermal synthesis of hybrid nanostructured ZnO&Ag systems starting from zinc nitrate hexahydrate (Zn(NO3)2•6H2O) and silver nitrate (Ag(NO3)2) as precursors. The structural and morphological properties of the obtained hybrid materials were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Subsequently, the photocatalytic behavior of prepared systems was evaluated. The results verify the viability of as-synthesized ZnO&Ag nanocomposites for its application in the removal of contaminants in water. The best results (percentage of pollutant removal > 99 %) are obtained for samples synthesized at low temperature, intermediate times, higher ratios Ag+/Zn2+ and in the presence of CTAB, which controls the final morphology of nanostructures and the dispersion thereof. These results prove that the system morphology is critical to the properties of the obtained material

Influence of nanoscale defects on the improvement of photocatalytic activity of Ag/ZnO

This study presents the advances in the field of ZnO/Ag catalysts from the synthesis of hierarchical ZnO nanowires (NWs) decorated with Ag nanoparticles, prepared by a facile solvothermal method at 120°C. It evaluates the photocatalytic efficiency from studying the time reaction of Ag/Zn concentration ratio and the presence of cetyltrimethylammonium bromide (CTAB) as an organic dispersant. X-ray diffraction, scanning electron microscopy, and analytical/high-resolution transmission electron microscopy results confirmed the presence of homogeneous cylindrical ZnO nanowires and quasi-spherical Ag crystals. ZnO NWs exhibited hexagonal wurtzite structure and cubic FCC symmetry in Ag nanoparticles (NPS). Two types of nanostructures, including homogeneous cylindrical ZnO NWs in the absence of Ag and simultaneous presence of ZnO NWs and Ag NPs, formed depending on experimental conditions. The photocatalytic activity was evaluated by studying methylene blue (MB) degradation time under UV light excitation. Diffuse reflectance UV–Vis spectrophotometry (UV–Vis DRS) allowed identifying the ZnO absorption band at ~393 nm. Crystal size varied depending on the reaction time and the addition of CTAB. Synthesis time increased bandgap values, getting better photocatalytic performance in samples synthesized in intermediate times (6 h), higher Ag+/Zn2+ molar ratio (0.2/1.0), and CTAB. According to HRTEM observations, the presence of silver nanocrystals with high content of defects (twinning, stacking faults) could play an essential role in the photocatalytic response. In this context, the specific synthesis conditions of Ag/ZnO might be more appropriate for their use in organic dyes degradation in water and the potential use in protective treatments against materials biodeterioration processes.This work has been supported by the Innovation and Education Ministry (ref. MAT2013-47460-C5-5-P and MAT2016-80875-C3-3-R), the Autonomous Region Program of Madrid (ref. S2018/NMT-4411 and S2013/MIT-2862), the Geomateriales 2 program (S2013/MIT_2914), the TOP Heritage (P2018/NMT-4372) of the Community of Madrid, the Innovation and Education Ministry (MAT201347460-C5-5-P) and the Ministry of Education, Science and Technological Development of Serbia (projects No. 172035 and 45020). Besides, we would like to thank the Master of “Materials Science” of Carlos III University (Spain) for providing financial and laboratory equipment support

Composición Categórica de Análisis Automáticos para Líneas de Productos Extendidas

This is the live presentation of a conference paper. Please, access and cite the published version: https://biblioteca.sistedes.es/articulo/composicion-categorica-de-analisis-automaticos-para-lineas-de-productos-extendidas/ Para las Líneas de Productos Software (LPS) se necesitan operaciones que nos permitan analizar dicho software y el reúso de sus características. Los razonadores son herramientas que automatizan estas operaciones. Desde la extensión de LPS con diversos tipos atributos de calidad, el tipo y número de operaciones de razonamiento ha crecido más rápido que el desarrollo de los respectivos razonadores. En consecuencia, las operaciones de an´alisis extendido son en el mejor caso parcialmente soportadas por los razonadores estado-del-arte. Para este desafío, podemos aplicar un enfoque de Teoría de Categorías (TC); el álgebra abstracta que capta los componentes comunes de estructuras aparentemente diferentes. Basándonos en la flexibilidad de sus razonamientos, proponemos una metodología donde las operaciones extendidas sean composiciones configurables de un conjunto de operaciones reusables independientes. Por tanto, buscamos definir e implementar un framework de razonamiento funcional de LPS extendidas basado en TC.Este trabajo está financiado por el programa de investigación e innovación H2020 de la Unión Europea bajo el acuerdo de subvención DAEMON 101017109, por los proyectos también co-financiados por fondos FEDER LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 y Rhea P18-FR-1081, y la ayuda PRE2019-087496 del Ministerio de Ciencia e Innovación

Effect of temperature and reaction time of the synthesis of nanocrystalline brucite

Mg(OH)2 nanoparticles has been successfully synthesized by means of the hydrothermal method. The effect of the reaction time and the synthesis temperature on the nanoparticles obtained has also been studied. The physic-chemical properties of the synthesized brucite samples have been characterized by X-Ray-diffraction (XRD), scanning electron microscopy/energy dispersive X-rays analysis (SEM/EDX), transmission electron microscopy (TEM), high-resolution transmission electron microscopy (HRTEM), thermogravimetry/ differential scanning Calorimetry (TG-DSC) and in situ high-temperature X-ray diffraction (XRD). The influence of the synthesis parameters in the brucite samples has been discussed in detail. Furthermore, it has been shown that the increase of temperature from 180 to 200ºC improves the crystallinity degree of Mg(OH)2 nanostructured particles and also, promotes the formation of plates with bigger uniform size. As well, the increase in the time reaction induces the formation of bigger size brucite plates. So, this hydrothermal method has been shown to be a really promising method for the large scale production.Peer Reviewe