Reflexión sobre el colapso y el decrecimiento: dos conceptos de actualidad

En este trabajo se hará un análisis del paradigma dominante de nuestro tiempo, el paradigma del Neoliberalismo, con la finalidad de demostrar como éste, a través del mito de que el crecimiento infinito es el ideal de toda sociedad razonable, ha permitido el surgimiento en la actualidad de los conceptos de Colapso y Decrecimiento, siendo el primero de ellos una advertencia global de los desastrosos hechos que podrían acontecer con este sistema como la emergencia del Cambio Climático, mientras que el segundo abarca una alternativa radical a esta visión del mundo que tiene como finalidad la búsqueda a través de la simplicidad y el cuidado de la Naturaleza de una vida de bienestar y felicidad para todos.Universidad de Sevilla. Grado en Filosofía

A programmable web platform for distributed access, analysis, and visualization of data

© 2023. The authors. This document 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 Fusion Engineering and Design .Daily work of Fusion Data Research (FDR) scientists faces three practical challenges: (i) getting access to vast amounts of validated, curated, and (ideally) annotated discharge data, (ii) applying a wide variety of standard, domain-specific, and home-made analysis and visualization software libraries and routines, and (iii) using fast, specialized, and not easy to obtain hardware and software installations. This paper introduces a novel web platform that addresses these three challenges in a federated way. Based on a client–server architecture, the new platform allows for easy use and exchange of curated data, validated analysis and visualization routines, and even networked hardware and software installations among the FDR community. This exchange goes beyond the mere use of a code repository, but facilitates the creation of an actual ready-to-use network of computers which can be used remotely to configure and perform data analysis. The network functions in a federated way, in which each member of the community contributes, using the same web platform, with its data, programming experience, and hardware and software availability. The platform is open source

Java Simulations of Embedded Control Systems

This paper introduces a new Open Source Java library suited for the simulation of embedded control systems. The library is based on the ideas and architecture of TrueTime, a toolbox of Matlab devoted to this topic, and allows Java programmers to simulate the performance of control processes which run in a real time environment. Such simulations can improve considerably the learning and design of multitasking real-time systems. The choice of Java increases considerably the usability of our library, because many educators program already in this language. But also because the library can be easily used by Easy Java Simulations (EJS), a popular modeling and authoring tool that is increasingly used in the field of Control Education. EJS allows instructors, students, and researchers with less programming capabilities to create advanced interactive simulations in Java. The paper describes the ideas, implementation, and sample use of the new library both for pure Java programmers and for EJS users. The JTT library and some examples are online available on http://lab.dia.uned.es/jtt

Tools for evaluation and prediction of industrial noise sources. Application to a wastewater treatment plant

© 2022 The Authors. This manuscript version is made available under the CC-BY-NC 4.0 license http://creativecommons.org/licenses/by-nc /4.0/ This document is the published manuscript version of a published work that appeared in final form in Journal of Environmental ManagementIn recent years, acoustic pollution caused by noise has considerably increased in many countries. Particularly in Spain, the noisiest country in Europe. It is sometimes difficult to predict the noise levels that a new installation or an expansion of industrial equipment will cause in the surroundings. This work introduces a new methodology for the prediction, evaluation, and analysis of industrial noise sources, as well as a novel tool for predicting and categorizing outdoor noise from its measurement at their sources. A Wastewater Treatment Plant (WWTP) has been used to demonstrate the applicability and validity of this methodology. The continuous level of acoustic pressure equivalent has been measured in different points of the plant using an integrating sound level meter. From these values, noise maps have been built to obtain detailed information of the industrial noise generated in the installation. Also, the typical frequency patterns of each type of source have been used for the calculation of source noise apportionments. To achieve this objective, several noise sources have been selected to provide information about their contribution to the industrial noise in the WWTP surrounding area. Finally, predictions have been validated using actual measurements. This methodology is a useful tool to predict personal exposure to noise and the impact on the environment. This information can be used, in particular, to propose mitigation actions

A Supervised ML Biometric Continuous Authentication System for Industry 4.0

Continuous authentication (CA) is a promising approach to authenticate workers and avoid security breaches in the industry, especially in Industry 4.0, where most interaction between workers and devices takes place. However, introducing CA in industries raises the following unsolved questions regarding machine learning (ML) models: its precision and performance; its robustness; and the issue about if or when to retrain the models. To answer these questions, this article explores these issues with a proposed supervised versus nonsupervised ML-based CA system that uses sensors, applications statistics, or speaker data collected by the operator’s devices. Experiments show supervised models with equal error rates of 7.28% using sensors data, 9.29% with statistics, and 0.31% with voice, a significant improvement of 71.97, 62.14, and 97.08%, respectively, over unsupervised models. Voice is the most robust dimension when adding new workers, with less than 2% of false acceptance rate even if workforce size is doubled

CGAPP: A continuous group authentication privacy-preserving platform for industrial scene

In Industry 4.0, security begins with the workers’ authentication, which can be done individually or in groups. Recently, group authentication is gaining momentum, allowing users to authenticate as group members without the need to specify the particular individual. Continuous authentication and federated learning are promising techniques that might help group authentication by providing privacy, by its own design, and extra security compared to traditional methods based on passwords, tokens, or biometrics. However, these techniques have not previously been combined or evaluated for authenticating workers in Industry 4.0. Thus, this paper proposes a novel continuous group authentication privacy-preserving (CGAPP)platform that is suitable for the industry. The CGAPP platform incorporates statistical data from workers’ smartphones and employs federated learning-based outlier detection for group worker authentication while ensuring the privacy of personal data vectors. A series of experiments were performed to measure the framework’s suitability and address the following research questions: (i) What is the cost of using FL compared to full data access in industrial scenarios? (ii) How robust is federated learning against adversarial attacks, specifically, how much malicious data is required to deceive the model? and (iii) How much noise is required to disrupt the authentication system? The results demonstrate the effectiveness of the CGAPP platform in the industry since it provides factory safety while preserving privacy. This platform achieves an accuracy of 92%, comparable to the 96% obtained by traditional approaches in the literature that do not address privacy concerns. The platform’s robustness is tested against attacks in the second and third experiments, and various countermeasures are evaluated. While the CGAPP platform exhibits certain vulnerabilities to data injection attacks, straightforward countermeasures can alleviate them. Nevertheless, the system’s performance experiences a notable impact in the event of a data perturbation attack, and the countermeasures investigated are ineffective in addressing this issue