A Precise Electrical Disturbance Generator for Neural Network Training with Real Level Output

Power Quality is defined as the study of the quality of electric power lines. The detection and classification of the different disturbances which cause power quality problems is a difficult task which requires a high level of engineering expertise. Thus, neural networks are usually a good choice for the detection and classification of these disturbances. This paper describes a powerful tool, developed by the Institute for Natural Resources and Agrobiology at the Scientific Research Council (CSIC) and the Electronic Technology Department at the University of Seville, which generates electrical patterns of disturbances for the training of neural networks for PQ tasks. This system has been expanded to other applications (as comparative test between PQ meters, or test of effects of power-line disturbances on equipment) through the addition of a specifically developed high fidelity power amplifier, which allows the generation of disturbed signals at real levels.Ministerio de Ciencia y Tecnología DPI2006-15467-C02-0

Designing personalised mHealth solutions: An overview

Introduction: Mobile health, or mHealth, is based on mobile information and communication technologies and provides solutions for empowering individuals to participate in healthcare. Personalisation techniques have been used to increase user engagement and adherence to interventions delivered as mHealth solutions. This study aims to explore the current state of personalisation in mHealth, including its current trends and implementation. Materials and Methods: We conducted a review following PRISMA guidelines. Four databases (PubMed, ACM Digital Library, IEEE Xplore, and APA PsycInfo) were searched for studies on mHealth solutions that integrate personalisation. The retrieved papers were assessed for eligibility and useful information regarding integrated personalisation techniques. Results: Out of the 1,139 retrieved studies, 62 were included in the narrative synthesis. Research interest in the personalisation of mHealth solutions has increased since 2020. mHealth solutions were mainly applied to endocrine, nutritional, and metabolic diseases; mental, behavioural, or neurodevelopmental diseases; or the promotion of healthy lifestyle behaviours. Its main purposes are to support disease self- management and promote healthy lifestyle behaviours. Mobile applications are the most prevalent technological solution. Although several design models, such as user-centred and patient-centred designs, were used, no specific frameworks or models for personalisation were followed. These solutions rely on behaviour change theories, use gamification or motivational messages, and personalise the content rather than functionality. A broad range of data is used for personalisation purposes. There is a lack of studies assessing the efficacy of these solutions; therefore, further evidence is needed. Discussion: Personalisation in mHealth has not been well researched. Although several techniques have been integrated, the effects of using a combination of personalisation techniques remain unclear. Although personalisation is considered a persuasive strategy, many mHealth solutions do not employ it. Conclusions: Open research questions concern guidelines for successful personalisation techniques in mHealth, design frameworks, and comprehensive studies on the effects and interactions among multiple personalisation techniques

OLIMPO, An Ad-Hoc Wireless Sensor Network Simulator for Public Utilities Applications

This paper introduces OLIMPO, an useful simulation tool for researchers who are developing wireless sensor communication protocols. OLIMPO is a discreteevent simulator design to be easily recon gured by the user, providing a way to design, develop and test communication protocols. In particular, we have designed a self-organizing wireless sensor network for low data rate. Our premise is that, due to their inherent spread location over large areas, wireless sensor networks are well-suited for SCADA applications, which require relatively simple control and monitoring. To show the facilities of our simulator, we have studied our network protocol with OLIMPO, developing several simulations. The purpose of these simulations is to demonstrate, quantitatively, the capability of our network to support this kind of applications

VICARED: A Neural Network Based System for the Detection of Electrical Disturbances in Real Time

The study of the quality of electric power lines is usually known as Power Quality. Power quality problems are increasingly due to a proliferation of equipment that is sensitive and polluting at the same time. The detection and classification of the different disturbances which cause power quality problems is a difficult task which requires a high level of engineering knowledge. Thus, neural networks are usually a good choice for the detection and classification of these disturbances. This paper describes a powerful system for detection of electrical disturbances by means of neural networks

SABIO: Soft Agent for Extended Information Retrieval

In the current study, an integrated system called SABIO is presented. The current system applies Information Retrieval (IR) techniques developed for collections of textual documents to nontextual corpa. SABIO integrates a fuzzy logic-based procedure for IR. Its search algorithm improves the IR efficiency and decreases the computational burden by using a fuzzy logic-based procedure for IR. This procedure is integrated in a flexible and fault-tolerant, human-reasoning-based search algorithm. The Accumulated Knowledge Set (AKS) of the system is sorted in a hierarchic multilevel tree-structure-like ontology. The objects in the AKS are represented using a novel human-reasoning-based-method. This representation takes into account the occurrence of related terms. The system uses a novel fuzzy logic-based term-weighting (TW) method. The developed fuzzy logic method improves the classical term frequency–inverse document frequency (TF=IDF) method, generally used for TW. The abovementioned system is the core of a wizard for search into the website of the University of Seville, www.us.es, which is currently in testing

A Method for the Access to the Contents in a Set of Knowledge Using a Fuzzy Logic Based Intelligent Agent

This paper proposes a method for the classification of the contents in a set of knowledge in order to answer to user consultations using natural language. The system is based on a fuzzy logic engine, which takes advantage of its flexibility for managing sets of accumulated knowledge. These sets can be built in hierarchic levels by a tree structure. A method of consultation based on a fuzzy logic application provided with an interface that one may interact with in natural language is also proposed. The eventual aim of this system is the implementation of an intelligent agent to manage the information contained in an internet portalMinisterio de Educación y Ciencia DPI2006-15467-C02-0

Information Extraction in a Set of Knowledge Using a Fuzzy Logic Based Intelligent Agent

A method for Information Extraction (IE) in a set of knowledge is proposed in this paper in order to answer to user consultations using natural language. The system is based on a fuzzy logic engine, which takes advantage of its flexibility for managing sets of accumulated knowledge. These sets can be built in hierarchic levels by a tree structure. A method of consultation based on a fuzzy logic application provided with an interface that one may interact with in natural language is also proposed. The eventual aim of this system is the implementation of an intelligent agent to manage the information contained in an internet portal

A Fuzzy Logic intelligent agent for Information Extraction: Introducing a new Fuzzy Logic-based term weighting scheme

In this paper, we propose a novel method for Information Extraction (IE) in a set of knowledge in order to answer to user consultations using natural language. The system is based on a Fuzzy Logic engine, which takes advantage of its flexibility for managing sets of accumulated knowledge. These sets may be built in hierarchic levels by a tree structure. The aim of this system is to design and implement an intelligent agent to manage any set of knowledge where information is abundant, vague or imprecise. The method was applied to the case of a major university web portal, University of Seville web portal, which contains a huge amount of information. Besides, we also propose a novel method for term weighting (TW). This method also is based on Fuzzy Logic, and replaces the classical TF–IDF method, usually used for TW, for its flexibility

A Proposal for a Robust Validated Weighted General Data Protection Regulation-based Scale to Assess the Quality of Privacy Policies of Mobile Health Applications: an eDelphi Study

Healthcare services are undergoing a digital transformation in which the Participatory Health Informatics field has a key role. Within this field, studies aimed to assess the quality of digital tools, including mHealth apps, are conducted. Privacy is one dimension of the quality of a mHealth app. Privacy consists of several components, including organizational, technical and legal safeguards. Within legal safeguards, giving transparent information to the users on how their data is handled is crucial. This information is usually disclosed to users through the privacy policy document. Assessing the quality of a privacy policy is a complex task and several scales supporting this process have been proposed in the literature. However, these scales are heterogeneous and even not very objective. In our previous study, we proposed a checklist of items guiding the assessment of the quality of a mHealth app privacy policy, based on the General Data Protection Regulation. Objective: To refine the robustness of our General Data Protection Regulation-based privacy scale to assess the quality of a mHealth app privacy policy, to identify new items, and to assign weights for every item in the scale. Methods: A two-round modified eDelphi study was conducted involving a privacy expert panel. Results: After the Delphi process, all the items in the scale were considered „important“ or „very important“ (4 and 5 in a 5-point Likert scale, respectively) by most of the experts. One of the original items was suggested to be reworded, while 8 tentative items were suggested. Only 2 of them were finally added after Round 2. 11 of the 16 items in the scale were considered „very important“ (weight of 1), while the other 5 were considered „important“ (weight of 0.5). Conclusions: The Benjumea privacy scale is a new robust tool to assess the quality of a mHealth app privacy policy, providing a deeper and complementary analysis to other scales that assesses the general quality. Also, this robust scale provides a guideline for the development of high-quality privacy policies of mHealth apps.Universidad de Sevilla and the Ministerio de Universidades of the Spanish Government under the Requalification of Spanish University System Program funded by European Union –NextGenerationEUCátedra de Telefónica “Inteligencia en la red“ of the Universidad de SevillaCátedra Indra “Sociedad Digital” of the Universidad de Sevill