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Background The aging society posits new socioeconomic challenges to which a potential solution is active and assisted living (AAL) technologies. Visual-based sensing systems are technologically among the most advantageous forms of AAL technologies in providing health and social care; however, they come at the risk of violating rights to privacy. With the immersion of video-based technologies, privacy-preserving smart solutions are being developed; however, the user acceptance research about these developments is not yet being systematized. Objective With this scoping review, we aimed to gain an overview of existing studies examining the viewpoints of older adults and/or their caregivers on technology acceptance and privacy perceptions, specifically toward video-based AAL technology. Methods A total of 22 studies were identified with a primary focus on user acceptance and privacy attitudes during a literature search of major databases. Methodological quality assessment and thematic analysis of the selected studies were executed and principal findings are summarized. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines were followed at every step of this scoping review. Results Acceptance attitudes toward video-based AAL technologies are rather conditional, and are summarized into five main themes seen from the two end-user perspectives: caregiver and care receiver. With privacy being a major barrier to video-based AAL technologies, security and medical safety were identified as the major benefits across the studies. Conclusions This review reveals a very low methodological quality of the empirical studies assessing user acceptance of video-based AAL technologies. We propose that more specific and more end user– and real life–targeting research is needed to assess the acceptance of proposed solutions.

Entre los campos del saber existe solapamiento debido a que las clases de objetos gnoseológicos que abarca cada uno de ellos ha sido establecida con criterios arbitrarios, frecuentemente basados en los efectos que producen dichos objetos. Ese proceder da lugar a taxonomías entre las clases de conocimiento que complican la resolución de los problemas, esto es, establecer la composición de los objetos. Las estrategias "divide et vinces" pueden resultar ineficaces. Criterios que, basados en las características de los objetos —causalidad—, establezcan clasificaciones disjuntas entre las clases de conocimiento, permiten descomponer los problemas en subproblemas de menor talla y que son independientes entre sí, al menos en los aspectos relacionados con las características clasificatorias —nótese, por ejemplo, la superposición casi completa que existe entre Automática, Telecomunicación e Informática; así como los conflictos de jurisprudencia entre los poderes legislativo y judicial, o los conflictos de vigilancia penitenciaria entre el judicial y el ejecutivo—. Como quiera que el establecimiento de taxonomías sobre el conocimiento se remonta al platonismo, corresponde retrotraerse al método socrático para superarlas. La clasificación disjunta causal se convierte así en receta universal para crear conocimiento, ya sea para resolver problemas científicos, problemas técnicos de diseñar ingenios, humanísticos de establecer conceptos, sociológicos de regular las relaciones e incluso conocimiento fantástico, pudiendo a su vez ser este artístico ―surgido de la inspiración intuitiva― o revelado, e incluso instintivo. El estudio de la precedencia entre los subproblemas proporciona el criterio para establecer el procedimiento de su resolución. El método para crear el conocimiento consiste, pues, en clasificar las características del problema para descomponerlo en partes más sencillas y ordenar esas partes para establecer el procedimiento resolutivo: clasificar para descomponer seguido de ordenar para procesar, recurrentemente. La resolución de los problemas consiste en tomar decisiones que transformen el enunciado inicial cuyo significado es insuficiente —problema— en otro enunciado con significado suficiente —solución—. Dichas decisiones resultan socráticamente de responder a las preguntas sobre el problema hasta su esclarecimiento. El método formal causal, aquí propuesto, consta de cinco etapas que son universales, es decir, independientes de problema a resolver: Etapa 1. Descompone procedimentalmente el problema en la secuencia de subproblemas: Análisis → Síntesis → Verificación. Etapa 2. Descompone el problema analítico en: Formal → Factual → Inspirado → Inmanente. Etapa 3. Descompone el problema factual en el conocido método orientado a modelo: Modelo → Instrumental → Contextual. Etapa 4. Descompone el problema instrumental mediante un método orientado a la arquitectura, de arriba hacia abajo: Arquitectura → Estructura → Tecnología. Etapa 5. Descompone el problema contextual en: Selección → Planificación → Contingencias. El paradigma "clasificar las entidades y ordenar las acciones" proporciona un método universal de resolución de problemas con estrategia "divide et vinces" de arriba hacia abajo, orientado a modelo y a la arquitectura, que tiene máxima potencia resolutoria y optimiza la credibilidad de la coherencia causal para satisfacer los objetivos. El método completo consiste en la siguiente secuencia: Formal → Modelo → Arquitectura → Estructura → Tecnología → Selección → Planificación → Contingencias → Inspirado → Inmanente → Síntesis → Verificación. En términos del cuestionamiento socrático, el método formal causal consiste en responder ordenadamente a la secuencia de preguntas: ¿Por qué el problema? → ¿Qué es? → ¿Para qué el resultado? → ¿Cómo es? → ¿Con qué se hace? → las demás. El resultado conceptual es elevar la categoría del método experimental, desde la condición de empírica que venía ostentando, hasta la de método formal. El método formal causal aborda primero la caracterización del problema desde el punto de vista externo ―"por qué", "qué" y "para qué"―, seguidamente, acomete la obtención arquitectural de la solución desde el punto de vista interno ―"cómo y "con que´"― y, finalmente, aborda el contexto operacional del nivel de saber hacer la solución mediante la respuesta a las demás preguntas. La especificación del problema adquiere el formato de una expresión lógica compuesta por la conjunción del modelo y los objetivos y, por lo tanto, el enunciado es una expresión aritmético-lógica. Es decir, el método proporciona una especificación funcional del problema. La causalidad de la secuencia de resolución hace posible la verificación progresiva del proceso de resolución: verificación de objetivos, simulación estructural, simulación local de cada módulo y replanteo. La formalidad del método proporciona un camino hacia la resolución automática de problemas. Hemos llamado Mayéutica a nuestro prototipo básico de asistente digital en homenaje a Sócrates, el maestro de los maestros helenos que sentó las bases de la causalidad. Surge una definición para "arquitectura" de una solución: la capacidad funcional de la solución. La estructura del libro consiste en tres bloques, el primero de los cuales contiene el cuerpo de toda la investigación en seis capítulos. Está escrito en lenguaje natural y acompañado de figuras explicativas porque la finalidad es que esta parte puedan leerla los expertos de cualquier campo del saber, tanto los familiarizados con el rigor del conocimiento causal ―formal o factual―, como los que profesan las disciplinas sociales y del comportamiento humano, como los que se dedican a la creación artística o los partidarios del conocimiento revelado. El segundo bloque es para que los que prefieren el rigor matemático puedan cerciorarse de la solvencia formal que soporta al método causal. Consta de catorce anexos dedicados a proponer la axiomática observacional del conocimiento que subyace al método formal causal y a las demostraciones algebraicas de la existencia de las relaciones de equivalencia que dividen el problema iterativamente y de las relaciones de orden que conforman la secuencia metodológica formal de resolución del problema. El último bloque está dedicado a la resolución de cuatro problemas, unos científico-técnicos y otros del ámbito de los estudios sociales y humanos: el diseño del prototipo del asistente digital de resolución de problemas mediante el método formal causal ―Mayéutica―, la definición del término "enseñar", la elaboración de un plan de estudios universitarios y la especificación de un parque científico. La finalidad es doble: evidenciar la universalidad del método y aportar material empírico para facilitar al lector la comprensión. El contenido ha sido defendido como tesis doctoral el día 22 de noviembre de 2022.

Evidence accumulated to date about the relationship between cognitive impairments and adults who are overweight and obese suggests that excess weight has a great impact on memory function. Nevertheless, most of the literature has focused only on studying the influences on working memory and episodic memory. This study aimed to examine the potential associations of clinical and anthropometric measures [body mass index (BMI), WHR, body fat, visceral fat, muscle mass, and hypertension] with six memory domains, including contextual memory, short-term visual memory, short-term memory, non-verbal memory, short-term phonological memory, and working memory, in a sample of 124 individuals classified as overweight (n = 33), obese (n = 53), and normal weight (n = 38). The results obtained showed that, after controlling for employment situations, people classified as obese had poorer short-term phonological memory and working memory than those with normal weights. Bivariate correlations showed that measures of weight, BMI, waist–hip ratio index, body fat, and visceral fat were inversely associated with memory function. However, muscle mass was not a significant predictor of memory function. Higher systolic blood pressure was also associated with worse memory function. The study provides evidence of the importance of adiposity in health and memory function. This work was partially funded by the Spanish Government (TIN2017-89069-R and PID2020-119144RB-100).

The chess game provides a very rich experience in neighborhood types. The chess pieces have vertical, horizontal, diagonal, up/down or combined movements on one or many squares of the chess. These movements can associate with neighborhoods. Our work aims to set a behavioral approximation between calculations carried out by means of traditional computation tools such as ordinary differential equations (ODEs) and the evolution of the value of the cells caused by the chess game moves. Our proposal is based on a grid. The cells’ value changes as time pass depending on both their neighborhood and an update rule. This framework succeeds in applying real data matching in the cases of the ODEs used in compartmental models of disease expansion, such as the well-known Susceptible-Infected Recovered (SIR) model and its derivatives, as well as in the case of population dynamics in competition for resources, depicted by the Lotke-Volterra model. This research is funded by Generalitat Valenciana, Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Spain. Project AICO 2021-331.

The population decline of small villages is a very serious problem for our society. This situation is not easy to reverse. The challenge is to generate consensus among the inhabitants of small villages to develop projects that have both a link with social and cultural heritage and the aid of the regional and local authorities. This framework can be successful when it also has the capability to provide new lines of development growing from this initial seed that can attract new inhabitants. In this paper, we present research that follows these requirements. Our proposal is based on a traditional agriculture resource, which is the art of building dry stone walls. We study the case of Tàrbena (642 inhabitants in the province of Alicante, Spain). Stone artifacts are recovered: some of them are still useful for agriculture, and others are cataloged and transformed into a product for cultural tourism. This project is expected to develop local, manual, and specialized work through the development of workshops, crafts, and small businesses. This will provide more income for the municipality and the private sector and more opportunities to attract new inhabitants. This research was funded by the government of Generalitat Valenciana, Spain (Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital). Project AICO 2021-331.

This study aimed to assess the effectiveness of a two-year intervention based on the Mediterranean diet for the treatment of overweight and obesity in a sample of 51 older people from the Mediterranean city of Alicante (Spain). We also examined the effects of the intervention on psychological well-being. The participants were randomly assigned to the experimental and control groups. The experimental group received group nutritional education sessions, an individualized dietary–nutritional treatment based on a Mediterranean diet, and a physical activity program; the control group received Mediterranean nutritional education in a written format. The experimental group showed a greater loss in weight (p = 0.017) and percentage of fat mass (p = 0.049), and a greater reduction in body mass index (BMI) (p = 0.014) and waist circumference (p = 0.010). Both groups improved their depression scores using the PHQ-9; however, no significant improvement was seen in adherence to the Mediterranean diet (PREDIMED) and anxiety level (GAD-7). These results suggest that a two-year intervention based on the Mediterranean diet allows an older population with overweight or obesity to achieve greater weight loss and a greater decrease in BMI, waist circumference, and fat mass percentage. In relation to psychological well-being, depression levels improved at the end of said intervention. This research was funded by the Office of the Vice President of Research of the University of Alicante (GRE19-20).

Human skin detection has been remarkably incorporated in different computer vision and biometric systems. It has been receiving increasing attention in face analysis, human tracking and recognition, and medical image analysis. For many human-related recognition tasks, using skin detection cue could be a proper choice. Despite the vast area of usage and applications for skin detection, not many large or reliable skin detection datasets are available, and many of the existing ones, are originally created for other tasks such as hand tracking or face analysis. In this paper, we propose a methodology for extracting skin pixels from garment segmentation and recognition datasets. This is achieved by using deep learning methods to generate automatic skin label masks from them by exploiting human body and hair segmentation and provided garment masks. Following this approach, a large human skin segmentation dataset is introduced. A validation set is also manually segmented in order to evaluate the accuracy of the output skin masks. Finally, usual methods for skin detection and segmentation are evaluated on this new dataset. This work is part of the visuAAL project on Privacy-Aware and Acceptable Video-Based Technologies and Services for Active and Assisted Living (https://www.visuaal-itn.eu/). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 861091.

Background: Nowadays, there is a growing interest in the relationship among lifestyle, reproductive health, and fertility. Recent investigations highlight the influence of environmental and lifestyle factors such as stress, diet, and nutritional status on reproductive health. The aim of this review was to determine the influence of nutritional status on ovarian reserve in order to improve the reproductive health of women of childbearing age. Methods: A systematic literature review was carried out following the PRISMA method. The quality of the studies was assessed using the Cochrane Collaboration Risk of Bias tool. Data were extracted, and the results were summarized into two blocks: according to the technique used to assess ovarian reserve and nutritional status; according to the results found in the relationship between ovarian reserve and nutritional status. Results: A total of 22 articles involving 5929 women were included. In 12 of the included articles (54.5%), a relationship between nutritional status and ovarian reserve was demonstrated. In seven publications (31.8%), the increased body mass index (BMI) led to a decrease in ovarian reserve, two of them (0.9%) in patients with polycystic ovary syndrome, showing a decrease only if BMI > 25. In two articles (0.9%), there was a negative relationship between ovarian reserve and waist-to-hip ratio, and in one (0.45%), a positive relationship was shown between ovarian reserve and testosterone levels, the latter being related to body mass index. In five articles (22.7%), body mass index was used as a confounder and was negatively related to ovarian reserve, and in another four (18%), no correlation was found. Conclusions: Ovarian reserve appears to be influenced by nutritional status. A high body mass index has a negative impact on the ovary, decreasing antral follicle count and anti-Müllerian hormone. Oocyte quality is compromised, increasing the rate of reproductive problems and the demand for assisted reproductive techniques. Further studies are needed to understand which dietary factors have the greatest effect on ovarian reserve in order to promote reproductive health.

Facilities based on the Internet of Things and embedded systems along with the application of ambient intelligence paradigms offer new scenarios for optimization services in agronomic processes, specifically in the hemp industry. The traceability of products and activities demonstrates the scope of these technologies. However, the technologies themselves introduce integration-related problems that can affect the planned benefits. This article proposes a model that balances agricultural expert knowledge (user-centered design), value chain planning (through blockchain implementation), and digital technology (Internet of Things protocols) for providing tamper proof, transparent, and secure traceability in this agricultural sector. The proposed approach is backed by a proof-of-concept implementation in a realist scenario, using embedded devices and a permissioned blockchain. The model and its deployment fully integrate a set of services that other proposals only partially integrate. On one hand, the design creates a permissioned blockchain that contemplates the different actors in the value chain, and on the other hand, it develops services that use applications with human-machine interfaces. Finally, it deploys a network of embedded devices with Internet of Things protocols and control algorithms with automated access to the blockchain for traceability services. Combining digital systems with interoperable human tasks it has been possible to deploy a model that provides a new approach for the development of value-added services.

Life expectancy has increased, so the number of people in need of intensive care and attention is also growing. Falls are a major problem for older adult health, mainly because of the consequences they entail. Falls are indeed the second leading cause of unintentional death in the world. The impact on privacy, the cost, low performance, or the need to wear uncomfortable devices are the main causes for the lack of widespread solutions for fall detection and prevention. This work present a solution focused on bedtime that addresses all these causes. Bed exit is one of the most critical moments, especially when the person suffers from a cognitive impairment or has mobility problems. For this reason, this work proposes a system that monitors the position in bed in order to identify risk situations as soon as possible. This system is also combined with an automatic fall detection system. Both systems work together, in real time, offering a comprehensive solution to automatic fall detection and prevention, which is low cost and guarantees user privacy. The proposed system was experimentally validated with young adults. Results show that falls can be detected, in real time, with an accuracy of 93.51%, sensitivity of 92.04% and specificity of 95.45%. Furthermore, risk situations, such as transiting from lying on the bed to sitting on the bed side, are recognized with a 96.60% accuracy, and those where the user exits the bed are recognized with a 100% accuracy., La esperanza de vida ha aumentado, por lo que también crece el número de personas que necesitan cuidados y atención intensivos. Las caídas son un problema importante para la salud de los adultos mayores, principalmente por las consecuencias que conllevan. Las caídas son, de hecho, la segunda causa principal de muerte no intencional en el mundo. El impacto en la privacidad, el coste, el bajo rendimiento o la necesidad de llevar dispositivos incómodos son las principales causas de la falta de soluciones generalizadas para la detección y prevención de caídas. Este trabajo presenta una solución centrada en la hora de dormir que aborda todas estas causas. La salida de la cama es uno de los momentos más críticos, especialmente cuando la persona sufre un deterioro cognitivo o tiene problemas de movilidad. Por ello, este trabajo propone un sistema que monitorice la posición en la cama con el fin de identificar situaciones de riesgo lo antes posible. Este sistema también se combina con un sistema automático de detección de caídas. Ambos sistemas trabajan en conjunto, en tiempo real, ofreciendo una solución integral para la detección y prevención automática de caídas, que es de bajo costo y garantiza la privacidad del usuario. El sistema propuesto fue validado experimentalmente con adultos jóvenes. Los resultados muestran que las caídas se pueden detectar, en tiempo real, con una precisión del 93,51 %, una sensibilidad del 92,04 % y una especificidad del 95,45 %. Además, las situaciones de riesgo, como pasar de estar tumbado en la cama a sentarse al lado de la cama, se reconocen con un 96,60 % de precisión, y aquellas en las que el usuario sale de la cama se reconocen con un 100 % de precisión. El sistema propuesto fue validado experimentalmente con adultos jóvenes. Los resultados muestran que las caídas se pueden detectar, en tiempo real, con una precisión del 93,51 %, una sensibilidad del 92,04 % y una especificidad del 95,45 %. Además, las situaciones de riesgo, como pasar de estar tumbado en la cama a sentarse al lado de la cama, se reconocen con un 96,60 % de precisión, y aquellas en las que el usuario sale de la cama se reconocen con un 100 % de precisión. El sistema propuesto fue validado experimentalmente con adultos jóvenes. Los resultados muestran que las caídas se pueden detectar, en tiempo real, con una precisión del 93,51 %, una sensibilidad del 92,04 % y una especificidad del 95,45 %. Además, las situaciones de riesgo, como pasar de estar tumbado en la cama a sentarse al lado de la cama, se reconocen con un 96,60 % de precisión, y aquellas en las que el usuario sale de la cama se reconocen con un 100 % de precisión

Artificial intelligence techniques have been increasingly applied in healthcare to help in many areas, from assisting clinical diagnoses to preventing diseases. In this paper, a machine learning approach to predict cholesterol levels using non-invasive and easy-to-collect data is presented. Specifically, it uses clinical and anthropometric data gathered by nutritionists during weight loss intervention (dieting) periods. The prediction power analysis of different patient variables is aimed at improving both non-invasive diagnosis quality and screening of associated diseases. Moreover, a clustering analysis has been carried out to identify different groupings of patients that might share some characteristics that have so far remained inconspicuous but might contain a valuable diagnosis or prognosis information for clinical experts.The experiments show a mean absolute percentage error rate (MAPE) of 4.39% in cholesterol estimation via regression, as well as clustering of patients within four profiles in which variable values share commonalities among cluster members. This work was supported by the Spanish State Research Agency (AEI) under grant PID2020-119144RB-I00 funded by MCIN/AEI/10.13039/501100011033.

IntroductionPregnancy is a transcendent period for the mother and the fetus, characterized by an increase on energy requirements. Mediterranean diet (MD) is considered a healthy eating pattern that can provide the nutritional requirements of pregnancy and protect from the development of obstetric pathologies.ObjectiveTo know the relationship between adherence to the MD and its maternal-fetal benefits.MethodologyA systematic review was conducted by identifying articles in the PubMed and Cochrane databases. The publication date of the studies was between 2010 and 2020, and the inclusion criteria established were that the articles were written in English and Spanish and were accessible in full text. Studies concerning assisted reproduction, gene modulation, conference abstracts, systematic reviews, and pilot studies were excluded.ResultsFinally, a total of 14 studies were included in the review. The association between the MD and the reduction of some pathologies of pregnancy, such as gestational diabetes, overweight or obesity, sleep quality, complications of childbirth, urinary tract infections (UTIs), and alterations in fetal growth was demonstrated, as well as perinatal problems, including birth weight, prematurity, gastroschisis, and other childhood problems.ConclusionThe MD is an optimal diet to consume during pregnancy.

La necesidad de mejorar el proceso de enseñanza-aprendizaje se hace crítica cuando se trata del estudiantado de primer curso en los grados. Este sector estudiantil es, generalmente, en el que se produce una tasa mayor de abandono y, por tanto, es en él donde se deben realizar tantos esfuerzos como se requieran con el fin de aumentar la motivación. En el caso de estudio analizado en este trabajo, se cuenta con un grupo en el Primer Curso del Grado en Ingeniería Informática. Concretamente, tomando en consideración la asignatura Fundamentos de Computadores, se cuenta con una característica adicional del grupo, que consiste en que la sesión de teoría y la de prácticas se concentran en la misma aula y en una franja horaria integrada para ambos apartados docentes. Esta configuración es muy apropiada para implantar en dicha asignatura una metodología de clase invertida (flipped classroom), en la que la estructura del proceso de aprendizaje sufre una profunda transformación. Esta metodología, relativamente novedosa, se contrapone al esquema tradicional consistente en la impartición de una clase teórica (magistral). El aprendizaje se inicia en casa: el/la estudiante toma como referencia el material audiovisual o los enlaces que son suministrados por el profesor y, posteriormente, es el/la docente quien promueve actividades en el aula para consolidar los conocimientos adquiridos en casa. De esta forma, se facilitan y potencian otros procesos de adquisición y práctica de conocimientos dentro del aula que resultan más complejos. La aplicación de la metodología de la clase invertida en Fundamentos de Computadores se orienta a la mejora de los resultados académicos del estudiantado a través del aumento de su motivación. Otro de los objetivos, no menos importante, consiste en integrar los conceptos teóricos con su aplicación práctica. La investigación desarrollada se realizó en varias etapas. En primer lugar, se analizaron las características del grupo de estudiantes sobre el que aplicar la metodología, a fin de asegurar la viabilidad de la propuesta con una alta probabilidad de éxito. A continuación, se concibió un proyecto integral a desarrollar por el estudiantado, de modo que dicho proyecto quedó imbuido tanto en las clases teóricas, para dar una base formal al mismo, como en las sesiones prácticas, para la implementación de las distintas etapas del proyecto. De esta manera el proyecto, de naturaleza compleja, se realizó de forma presencial en el aula (sin establecer diferenciación entre horas correspondientes a teoría o a práctica) mientras que en casa, previamente, se incorporaron los conocimientos básicos a través de píldoras educativas especialmente diseñadas en esta investigación. Las etapas del proyecto permiten ir poniendo en práctica los contenidos teóricos de la asignatura, que siguen un orden cronológico en su calendario de impartición. Se compararon los resultados académicos de este grupo con otros de la misma asignatura que adoptaron una metodología de aprendizaje tradicional. Los resultados académicos indican que la propuesta de clase invertida aplicada ha logrado inspirar un mayor interés en el estudiantado, así como una comprensión más profunda de los conceptos teóricos de la asignatura.

Journal of ambient intelligence and humanized computing (2022). doi:10.1007/s12652-022-04486-5, Published by Springer, Heidelberg

Desde la Universidad de Alicante se promueve la implantación de estrategias de enseñanza-aprendizaje centradas en los estudiantes, en las que se refuerza el carácter activo del estudiantado. En el Grado en Ingeniería Informática, se imparte la asignatura Fundamentos de Computadores que resulta apropiada para ser impartida mediante una metodología de clase invertida (flipped classroom). Esta metodología se contrapone al esquema tradicional caracterizado por la impartición de clases teóricas magistrales. El objetivo principal de la propuesta consistió en implementar esta nueva metodología para aumentar el interés del estudiante por la asignatura y su rendimiento académico. Para ello, se concibió el rediseño completo de la clase tradicional haciendo que la adquisición de conocimientos se iniciara en casa, a través del visionado de píldoras educativas. Además, se diseñó un proyecto práctico integral que quedó imbuido tanto en las clases teóricas como en las sesiones prácticas. Se compararon los resultados académicos del grupo en el que se utilizó la clase invertida con otros grupos control que adoptaron una metodología de aprendizaje tradicional. Los resultados indican que la propuesta de clase invertida ha logrado aumentar notablemente el rendimiento académico del grupo experimental que se ha traducido en una comprensión más profunda de los conceptos teóricos de la asignatura, así como inspirar una alta motivación en el estudiantado.

Several research studies have investigated the human activity recognition (HAR) domain to detect and recognise patterns of daily human activities. However, the accurate and automatic assessment of activities of daily living (ADLs) through machine learning algorithms is still a challenge, especially due to limited availability of realistic datasets to train and test such algorithms. The dataset contains data from 52 participants in total (26 women, and 26 men). The data for these participants was collected in two phases: 33 participants initially, and 19 further participants later on. Participants performed up to 5 repetitions of 24 different ADLs. Firstly, we provide an annotated description of the dataset collected by wearing a wrist-worn measurement device, Empatica E4. Secondly, we describe the methodology of the data collection and the real context in which participants performed the selected activities. Finally, we present some examples of recent and relevant target applications where our dataset can be used, namely lifelogging, behavioural analysis and measurement device evaluation. The authors consider the dissemination of this dataset can highly benefit the research community, and specially those involved in the recognition of ADLs, and/or in the removal of cues that reveal identity. The support of the More Years Better Lives JPI, the Italian Ministero dell’Istruzione, Università e Ricerca (CUP: I36G17000380001) and the Spanish Agencia Estatal de Investigación (grant no: PCIN-2017-114), for this research activity carried out within the project PAAL – Privacy-Aware and Acceptable Lifelogging services for older and frail people, (JPI MYBL award number: PAAL JTC2017) is gratefully acknowledged.

Background Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)—infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. Objective This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. Methods This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. Results We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. Conclusions This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590

Tobacco, alcohol and cannabis use are important health problems that contribute greatly to causes of death in worldwide. Early onset of substance use increases rapidly during adolescence and it has been associated with a wide range of adverse events. Because substance use is associated with dramatic consequences, delaying the initiation of substance use among adolescents remains a major public priority. This study examined the effectiveness of a school-based intervention program based on the application of the Reasoning and Rehabilitation V2 (R&amp, R2) program for preventing substance use among adolescents at risk of academic failure. A sample of 142 participants (aged 13–17 years old) who were studying alternative education provision in Spain were randomly allocated to two conditions (68 experimental group, 74 control group). A pre-test survey assessing past and current substance use was conducted prior the implementation of the program, while a post-test survey was conducted about 12 months after the pre-test. The results showed a significant effect of the R&amp, R program in the reduction of cigarette smoking, episodes of drunkenness, alcohol consumption and cannabis use. However, for daily smoking and episodes of drunkenness such benefits showed a reduction over time. These findings offer additional evidence of the effectiveness of the Reasoning and Rehabilitation V2 program in Spanish adolescent students who are exposed to substance use and suggest areas of future research.

This article belongs to the Section Nutrition Methodology & Assessment., On behalf of the Spanish Nutrition Society (SEÑ)., The health-transitions humans have delivered during the 20th Century associated with the nutrition is that from undernutrition to obesity, which perseveres in the current years of the 21st Century. Energy intake (EI) is a contributing factor and therefore a fascination in nutritional sciences. However, energy expenditure (EE) has not been usually considered as a conjoint factor. Thus, this study aimed to review if studies on adults consider data on dietary intake, specifically EI, and included data on EE and physical activity (PA). A search of MEDLINE from 1975 to December 2015 was managed. Our scoping review consisted of keywords related to EI, dietary allowances, and nutritional requirements. From 2229 acknowledged articles, 698 articles were finally taken fulfilling inclusion and quality criteria. A total of 2,081,824 adults (53.7% females) were involved, and most studies had been conducted in EEUU (241), Canada (42), Australia (30), Japan (32), and Brazil (14). In Europe, apart from UK (64), the Netherlands (31) and France (26) led the classification, followed by Sweden (18), Denmark (17), and France (26). Mediterranean countries are represented with 27 studies. A total of 76.4% did not include EE and 93.1% did not include PA. Only 23.6% of the studies contained both EI and EE. A large methodological diversity was perceived, with more than 14 different methods regarding EI, and more than 10 for EE. PA was only analyzed in scarce articles, and scarcely considered for interpretation of data and conclusions. Moreover, PA was often measured by subjective questionnaires. Dietary surveys show a large diversity regarding methodology, which makes comparability of studies difficult. EE and PA are missing in around 80% of studies or are not included in the interpretation of results. Conclusions regarding EI or diet adequacy in adults should not be taken without analyzing EE and PA., Support from the Spanish Nutrition Society (SEÑ) is highly acknowledged.

Purpose This paper aims to present a framework to address the impact of people’s behaviour in the dissemination of information through mobile social networks. Design/methodology/approach This approach follows the epidemical compartmental models and uses a grid to model the nodes’ (people) behaviour in the dissemination process. The nodes’ status is determined by binary rules that update and define the flow of information between neighbour nodes. An improved stacked-layer grid model is used to implement modulations in the application of the rules and neighbourhoods to model the impact of people’s attitude, which may improve or jeopardize the efficiency of the process. Findings This proposal shows how grid architecture is a valuable tool to model different causes of malfunction of data dissemination. Combining different grids with different neighbourhoods and different local rules provides a wide range of possibilities to depict the impact of human awareness and decision on the dissemination of data. Originality/value This works develops a new approach for the analysis of dissemination of information which add new features to traditional methods for modelling local interactions and describing the dynamics of the communication patterns in the population.

Physical traits such as the shape of the hand and face can be used for human recognition and identification in video surveillance systems and in biometric authentication smart card systems, as well as in personal health care. However, the accuracy of such systems suffers from illumination changes, unpredictability, and variability in appearance (e.g. occluded faces or hands, cluttered backgrounds, etc.). This work evaluates different statistical and chrominance models in different environments with increasingly cluttered backgrounds where changes in lighting are common and with no occlusions applied, in order to get a reliable neural network reconstruction of faces and hands, without taking into account the structural and temporal kinematics of the hands. First a statistical model is used for skin colour segmentation to roughly locate hands and faces. Then a neural network is used to reconstruct in 3D the hands and faces. For the filtering and the reconstruction we have used the growing neural gas algorithm which can preserve the topology of an object without restarting the learning process. Experiments conducted on our own database but also on four benchmark databases (Stirling’s, Alicante, Essex, and Stegmann’s) and on deaf individuals from normal 2D videos are freely available on the BSL signbank dataset. Results demonstrate the validity of our system to solve problems of face and hand segmentation and reconstruction under different environmental conditions. This work has been supported by the Spanish Government TIN2016-76515R Grant, supported with FEDER funds, the University of Alicante Project GRE16-19, the Valencian Government Project GV-2018-022, and the UK Dunhill Medical Trust Grant RPGF1802\37.

Mobile Cloud Computing is one of today's more disruptive paradigms of computation due to its effects on the performance of mobile computing and the development of Internet of Things. It is able to enhance the capabilities of devices by outsourcing the workload to external computing platforms deployed along the network, such as cloud servers, cloudlets, or other edge platforms. The research described in this work presents a computational model of a multilayer architecture for increasing the performance of devices using the Mobile Cloud Computing paradigm. The main novelty of this work lies in defining a comprehensive model where all the available computing platforms along the network layers are involved to perform the outsourcing of the application workload. This proposal provides a generalization of the Mobile Cloud Computing paradigm which allows handling the complexity of scheduling tasks in such complex scenarios. The behaviour of the model and its ability of generalization of the paradigm are exemplified through simulations. The results show higher flexibility for making offloading decisions. This work was supported by the Spanish Research Agency (AEI) and the European Regional Development Fund (ERDF), under Project CloudDriver4Industry TIN2017-89266-R, and by the Conselleria de Educación, Investigación, Cultura y Deporte, of the Community of Valencia, Spain, within the program of support for research under Project AICO/2017/134.

Purpose The purpose of this paper is to study the unexpected consequences in the operation of urban environments. Prediction within the urban planning process often presents difficulties and unintended consequences. It is not enough to develop a good project. Unexpected consequences are possible because of the environment. The authors argue that these problems of uncertainty can be minimized with citizen participation and the use of new technologies. Design/methodology/approach The problem of how urban planning initiatives result in unexpected consequences is described. These effects are determined by studying a series of cities and real urban environments. A case study on urban accessibility is developed for a better understanding of the problem. Findings Avoiding unexpected consequences in the operation of urban environments is strongly linked to the concept of Smart City 3.0. This concept is based on the co-creation. In this line to address the problem, a citizen-centric methodology using the latest information and communications technologies and internet of things technologies is presented. As a practical application, different categories of unexpected events related to the Faculty of Education building at the University of Alicante have been identified as a consequence of the impact of its environment. An uncomfortable or non-accessible environment causes unforeseen behaviour of individuals. Originality/value There are no analytical tools to investigate how aspects of the urban environment cause uncertainty about the acceptance of projects by future citizen users. This work takes a step forward in that direction.

We aim to assess the beneficial effects of the Mediterranean style-diet before and after the period of confinement due to COVID-19 in a sample of 51 older patients who were part of a clinical trial of the Instituto de Investigación sanitaria y Biomédica de Alicante (ISABIAL, CEIM). Participants were randomly assigned to two conditions: experimental vs. a control group. A pre-test survey assessment was conducted before confinement, while a post-test survey was conducted after the confinement period. Adherence to Mediterranean Diet and nutritional status were evaluated through self-reported questionnaires. Individuals who initiated the Mediterranean Diet intervention program before confinement increased 3.5% their level of adherence to the Mediterranean Diet and maintained their nutritional status after the confinement. In the case of BMI, there no were statistically significant differences between groups before and after confinement. These results suggest that adherence to the Mediterranean Diet may play an important role in the establishment of appropriate dietary guidelines in confinement situations such as the COVID-19. This research was funded by the Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL) (CEIm: PI2019/057).

Evidence accumulated to date suggests that excess weight in the adult population is associated with a wide range of impairments in executive function. However, most studies have only examined the influence of body mass index (BMI) on the cognitive function of individuals with overweight and obesity. This study examined the potential associations of markers of adiposity (BMI, body fat, and visceral fat) with five domains of executive function including cognitive flexibility, inhibition, monitoring, planning, and working memory in a sample of 87 adult with overweight (n = 34) and obesity (n = 53). The results show that obese people had poorer working memory than those with overweight. After controlling for educational levels and physical activity, the results suggest that neither the waist-hip index not visceral fat were associated with cognitive function. In overweight, body fat was negatively associated with executive components of inhibition (p = 0.05) and monitoring (p = 0.02). In the obesity subgroup, body fat was negatively associated with inhibition (0.02) and working memory (0.04). The results provide evidence of the importance of adiposity for cognitive function. The implications for understanding the influence of markers of adiposity in adults with overweight and obesity are discussed.

Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease’s progress for patients receiving care at home. Some sleep disturbances, such as obstructive sleep apnea syndrome, can increase the risk for COVID-19 patients. This paper proposes an approach to evaluating patients’ sleep quality with the aim of detecting sleep disturbances caused by pneumonia and other COVID-19-related pathologies. We describe a non-invasive sensor network that is used for sleep monitoring and evaluate the feasibility of an approach for training a machine learning model to detect possible COVID-19-related sleep disturbances. We also discuss a cloud-based approach for the implementation of the proposed system for processing the data streams. Based on the preliminary results, we conclude that sleep disturbances are detectable with affordable and non-invasive sensors. A.D., E.Z., P.L., and V.T. acknowledge the partial funding by the Ss. Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering. This work was also partially funded by FCT/MEC through national funds and co-funded by FEDER—PT2020 partnership agreement under the project UIDB/50008/2020 (Este trabalho é parcialmente financiado pela FCT/MEC através de fundos nacionais e cofinanciado pelo FEDER, no âmbito do Acordo de Parceria PT2020 no âmbito do projeto UIDB/50008/2020). This work was also partially funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project UIDB/00742/2020. Furthermore, I.P. would like to thank the Politécnico de Viseu for their support. This article is based upon work from Sheldon COST Action CA16226 Indoor Living Space Improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). COST is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. www.cost.eu (accessed on 1 April 2021).

This paper presents a new evolutionary approach, EvoSplit, for the distribution of multi-label data sets into disjoint subsets for supervised machine learning. Currently, data set providers either divide a data set randomly or using iterative stratification, a method that aims to maintain the label (or label pair) distribution of the original data set into the different subsets. Following the same aim, this paper first introduces a single-objective evolutionary approach that tries to obtain a split that maximizes the similarity between those distributions independently. Second, a new multi-objective evolutionary algorithm is presented to maximize the similarity considering simultaneously both distributions (labels and label pairs). Both approaches are validated using well-known multi-label data sets as well as large image data sets currently used in computer vision and machine learning applications. EvoSplit improves the splitting of a data set in comparison to the iterative stratification following different measures: Label Distribution, Label Pair Distribution, Examples Distribution, folds and fold-label pairs with zero positive examples.

Planes are the core geometric models present everywhere in the three-dimensional real world. There are many examples of manual constructions based on planar patches: facades, corridors, packages, boxes, etc. In these constructions, planar patches must satisfy orthogonal constraints by design (e.g. walls with a ceiling and floor). The hypothesis is that by exploiting orthogonality constraints when possible in the scene, we can perform a reconstruction from a set of points captured by 3D cameras with high accuracy and a low response time. We introduce a method that can iteratively fit a planar model in the presence of noise according to three main steps: a clustering-based unsupervised step that builds pre-clusters from the set of (noisy) points; a linear regression-based supervised step that optimizes a set of planes from the clusters; a reassignment step that challenges the members of the current clusters in a way that minimizes the residuals of the linear predictors. The main contribution is that the method can simultaneously fit different planes in a point cloud providing a good accuracy/speed trade-off even in the presence of noise and outliers, with a smaller processing time compared with previous methods. An extensive experimental study on synthetic data is conducted to compare our method with the most current and representative methods. The quantitative results provide indisputable evidence that our method can generate very accurate models faster than baseline methods. Moreover, two case studies for reconstructing planar-based objects using a Kinect sensor are presented to provide qualitative evidence of the efficiency of our method in real applications. This work was supported by the French ANR project LIVES (ANR-15-CE23-0026-03) and the Spanish State Research Agency (AEI) and the European Regional Development Fund (FEDER) under projects TIN2017-89069-R and PID2020-119144RB-I00.

The potential benefits of recognising activities of daily living from video for active and assisted living have yet to be fully untapped. These technologies can be used for behaviour understanding, and lifelogging for caregivers and end users alike. The recent publication of realistic datasets for this purpose, such as the Toyota Smarthomes dataset, calls for pushing forward the efforts to improve action recognition. Using the separable spatio-temporal attention network proposed in the literature, this paper introduces a view-invariant normalisation of skeletal pose data and full activity crops for RGB data, which improve the baseline results by 9.5% (on the cross-subject experiments), outperforming state-of-the-art techniques in this field when using the original unmodified skeletal data in dataset. Our code and data are available online. This work is part of the PAAL—“Privacy-Aware and Acceptable Lifelogging services for older and frail people” project: The support of the Joint Programme Initiative “More Years, Better Lives” (JPI MYBL, award number: PAAL_JTC2017) and the Spanish Agencia Estatal de Investigación (grant no: PCIN-2017-114) is gratefully acknowledged.

This paper presents a discrete compartmental Susceptible–Exposed–Infected–Recovered/Dead (SEIR/D) model to address the expansion of Covid-19. This model is based on a grid. As time passes, the status of the cells updates by means of binary rules following a neighborhood and a delay pattern. This model has already been analyzed in previous works and successfully compared with the corresponding continuous models solved by ordinary differential equations (ODE), with the intention of finding the homologous parameters between both approaches. Thus, it has been possible to prove that the combination neighborhood-update rule is responsible for the rate of expansion and recovering/death of the disease. The delays (between Susceptible and Asymptomatic, Asymptomatic and Infected, Infected and Recovered/Dead) may have a crucial impact on both height and timing of the peak of Infected and the Recovery/Death rate. This theoretical model has been successfully tested in the case of the dissemination of information through mobile social networks and in the case of plant pests.

Active and assisted living technologies are much needed, but some aspects of them cause user rejection due to concerns on privacy. This is even more concerning to users when visual information is used, processed, and transmitted. To respond to these concerns, and maximise user acceptance, visual privacy protection measures have to be put in place. In the past, human detection and object segmentation in video were constrained by technological limitations, and could only run with specific hardware and sensors. This paper introduces a proposal for an RGB-only based visual privacy preservation filter, which capitalises on ‘deep learning’-based segmentation and pose detectors. A background update scheme is presented, which limits leakage of sensitive information when detection fails. Dilation of the mask can further prevent information leakage, but a trade-off is necessary to correctly update background information. This is achieved via a specific study which is also presented. A comparative study is performed to determine the best configuration for privacy preservation. Results show that union of dilated masks from different deep networks achieves the best overall result. This work is part of the PAAL – “Privacy-Aware and Acceptable Lifelogging services for older and frail people” project: The support of the Joint Programme Initiative “More Years, Better Lives” (JPI MYBL, award number: PAAL_JTC2017) and the Spanish Agencia Estatal de Investigación (grant no: PCIN-2017-114) is gratefully acknowledged.

Increasing numbers of older individuals in the societies pose great challenges for countries affected by the demographic change. The rapid development in the technological sector, on the other hand, enables various applications to make everyday life easier for older and disabled people and to maintain their autonomy for longer. This study examines the acceptance and privacy perceptions of a video-based technology for lifelogging in home environments among German and Turkish users, using a multi-method empirical research approach. Results expose an overall differing acceptance of using lifelogging cameras between German and Turkish participants and suggest that the consideration of the varying culture-bound demands is necessary. The findings of this study support the understanding of requirements for a successful implementation of a video-based assistive technology in private environments to optimally address the needs of the future users, drawing attention to the important cultural influences that affect its acceptance. This work is part of the PAAL-project (“Privacy-Aware and Acceptable Lifelogging services for older and frail people”). The support of the Joint Programme Initiative “More Years, Better Lives” (award number: PAAL_JTC2017), the German Federal Ministry of Education and Research (Grant no: 16SV7955), and the Spanish Agencia Estatal de Investigacion (PCIN-2017-114) is gratefully acknowledged.

Anthropometrics are a set of direct quantitative measurements of the human body’s external dimensions, which can be used as indirect measures of body composition. Due to a number of limitations of conventional manual techniques for the collection of body measurements, advanced systems using three-dimensional (3D) scanners are currently being employed, despite being a relatively new technique. A systematic review was carried out using Pubmed, Medline and the Cochrane Library to assess whether 3D scanners offer reproducible, reliable and accurate data with respect to anthropometrics. Although significant differences were found, 3D measurements correlated strongly with measurements made by conventional anthropometry, dual-energy X-ray absorptiometry (DXA) and air displacement plethysmography (ADP), among others. In most studies (61.1%), 3D scanners were more accurate than these other techniques; in fact, these scanners presented excellent accuracy or reliability. 3D scanners allow automated, quick and easy measurements of different body tissues. Moreover, they seem to provide reproducible, reliable and accurate data that correlate well with the other techniques used. TIN2017-89069-R funded by the Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund (FEDER).

La presente red tiene como objetivo analizar la influencia de la evaluación colaborativa en la mejora de los procesos de enseñanza-aprendizaje en el contexto de la educación superior. Para ello, se ha desarrollado una experiencia de innovación educativa a través de la inclusión de esta metodología de evaluación en dos asignaturas: Orientación Profesional del Máster en Profesorado y Procesos Psicológicos y Gastronomía del Grado en Gastronomía y Artes Culinarias de la Universidad de Alicante. Tras el desarrollo de la experiencia educativa, se analizaron las actitudes del estudiantado acerca de la evaluación por pares, así como su satisfacción con diferentes características de la misma. Para ello, se utilizó la versión española del BPFQ (Huisman et al., 2020) y un cuestionario elaborado ad-hoc. Participaron un total de 93 estudiantes (66 de la asignatura Procesos Psicológicos y Gastronomía y 27 de la asignatura de Orientación Profesional). Los resultados obtenidos indican que el estudiantado muestra actitudes positivas y una elevada satisfacción con la inclusión de la evaluación por pares en ambas asignaturas, aunque es ligeramente superior en el caso del alumnado de máster. Futuros estudios deberían profundizar en los efectos que este tipo de evaluación tiene sobre aspectos específicos del proceso de enseñanza-aprendizaje.

La gestión y seguimiento del desarrollo de proyectos o trabajos fin de grado TFG requiere de una gran cantidad de tiempo y suele realizarse de manera individual sin colaboración ni retroalimentación entre alumnos. Debido al desarrollo y presentación individual se ha perdido el espíritu colaborativo y de trabajo en equipo en una asignatura tan relevante. Los objetivos concretos del trabajo son: 1. Diseñar una metodología colaborativa y ágil para el seguimiento de los TFG, 2. Proponer y evaluar herramientas colaborativas en tareas concretas del TFG y 3. Comparar la nueva metodología con metodologías convencionales. Se utilizarán cuestionarios a los alumnos tutelados por miembros de la red como instrumentos para la recogida de información. En la fase de diseño de la experiencia se plantearán las herramientas y metodología a seguir con el grupo de alumnos tutelados. En concreto se plantea un enfoque innovador en la gestión de los estudiantes de TFG mediante el uso de herramientas colaborativas. Se van a diseñar una serie de materiales y recursos disponibles para los estudiantes.

Purpose The purpose of this paper is to present a discrete compartmental susceptible-asymptomatic-infected-dead (SAID) model to address the expansion of plant pests. The authors examined the case of Xylella fastidiosa in almond trees in the province of Alicante (Spain) to define the best eradication/contention protocol depending on the environmental parameters such as climatic factors, distance between trees, isolation of the plots, etc. Design/methodology/approach This approach considers the expansion of the disease among the almond trees orchards by means of a grid model. The cells of the grid represent a tree (or even a group of trees) that can be susceptible (healthy), asymptomatic (infected by the bacterium but without symptoms), infected or dead. When time passes, the status of the cells is determined by binary rules that update following both a neighborhood and a delay pattern. The model assumes that the environmental parameters have a crucial impact on the expansion of the disease, so a grid is assigned to each parameter to model the single effect caused by this parameter. The expansion is then the weighted sum of all the grids. Findings This proposal shows how the grid architecture, along with an update rule and a neighborhood pattern, is a valuable tool to model the pest expansion. This model has already been analyzed in previous works and has been compared with the corresponding continuous models solved by ordinary differential equations, coming to find the homologous parameters between both approaches. Thus, it has been possible to prove that the combination neighborhood-update rule is responsible for the rate of expansion and recovering/death of the illness. The delays (between susceptible and asymptomatic, asymptomatic and infected, infected and recovered/dead) may have a crucial impact on both the peak of infected and the recovery/death rate. This theoretical model has been successfully tested in the case of the dissemination of information through mobile social networks and is also currently under study in the case of expansion of COVID-19. Originality/value This work develops a new approach for the analysis of expansion of plant pests. This approach provides both behavioral variability at the cell level (by its capability to modify the neighborhood and/or the update rule and/or the delays) and modularity (by easy scaling the number of grids). This provides a wide range of possibilities to deal with realistic scenarios.

Suicide is one of the main premature causes of death in patients with schizophrenia. However, little is known about the relationship between neurocognitive functioning and suicidality in violent offenders with schizophrenia who have been sentenced to psychiatric treatment after committing violent crimes. We examined the neurocognitive functioning of a sample of 61 violent offenders, most of them murderers with schizophrenia who were classified as suicide attempters (n = 26) and non-attempters (n = 35). We compared the neurocognitive functioning of both groups using a neuropsychological battery. Suicide attempters showed similar performance to non-attempters in a neuropsychological test across all domains of cognitive functioning, memory, attention, verbal fluency, and executive functioning. However, after controlling for demographic and clinical variables, suicide attempters performed better than non-attempters in two planning-related tasks: the Tower of London (p &lt, 0.01) and the Zoo Map (p &lt, 0.01). Suicide attempters were also characterized as having more family histories of suicidality and as displaying more depressive symptoms and negative symptoms of psychopathology on the Positive and Negative Syndrome Scale (PANSS) scale. These results suggest that suicide attempters have a greater ability to formulate plans and initiate goals directed at making a suicide attempt.

Registration is the process that computes the transformation that aligns sets of data. Commonly, a registration process can be divided into four main steps: target selection, feature extraction, feature matching, and transform computation for the alignment. The accuracy of the result depends on multiple factors, the most significant are the quantity of input data, the presence of noise, outliers and occlusions, the quality of the extracted features, real-time requirements and the type of transformation, especially those ones defined by multiple parameters, like non-rigid deformations. Recent advancements in machine learning could be a turning point in these issues, particularly with the development of deep learning (DL) techniques, which are helping to improve multiple computer vision problems through an abstract understanding of the input data. In this paper, a review of deep learning-based registration methods is presented. We classify the different papers proposing a framework extracted from the traditional registration pipeline to analyse the new learning-based proposal strengths. Deep Registration Networks (DRNs) try to solve the alignment task either replacing part of the traditional pipeline with a network or fully solving the registration problem. The main conclusions extracted are, on the one hand, 1) learning-based registration techniques cannot always be clearly classified in the traditional pipeline. 2) These approaches allow more complex inputs like conceptual models as well as the traditional 3D datasets. 3) In spite of the generality of learning, the current proposals are still ad hoc solutions. Finally, 4) this is a young topic that still requires a large effort to reach general solutions able to cope with the problems that affect traditional approaches., Published in Applied Sciences

AE-20L es el acrónimo para “Área Empresarial 2050”, cuya finalidad es potenciar el desarrollo de las áreas industriales de la provincia alicantina. Mejorar la competitividad y aumentar las oportunidades vertebrando estratégicamente las áreas industriales es la clave que ha movido a nuestro empresariado a asumir el protagonismo de esta iniciativa con la mirada puesta en la integración territorial, en la cooperación sectorial y en la efectividad operativa. En la figura empresarial concurren, pues, el rol de promotor del Proyecto AE-20L y de beneficiario en primera instancia: siendo los que saben de hacer negocio quienes deciden cuáles son las condiciones para hacerlo, la expectativa es el éxito. Con carácter general, el beneficio es para toda la sociedad de la provincia alicantina, que mucho antes del año MML habrá alcanzado, entre otros empeños, el pleno empleo e incluso volverá a ser territorio importador de mano de obra y también de conocimiento. A este respecto, percibimos la Ley 14/2018, de 5 de junio, de gestión, modernización y promoción de las áreas industriales de la Comunitat Valenciana como una oportunidad coyuntural para que los territorios empresariales y las empresas en ellos ubicadas saquen provecho de los cambios que la innovación sistemática y el progreso tecnológico nos van a reportar durante los próximos años. A la pregunta ¿cómo se hace esto?, la respuesta que le corresponde es “andando y con el paso ligero”. En octubre firmaron convenio de colaboración FEPEVAL y la UCIE Ars Innovatio de la UA. A finales de ese mes se incorporó el primer grupo de directivos y responsables de las entidades de conservación de las áreas industriales. El 11 de diciembre, la Jornada Técnica sobre el Proyecto AE-20L ha reunido a cuarenta expertos y profesionales en una sesión de dos horas y media trabajo intensivo encaminada a identificar las utilidades y los servicios que tienen mayor interés para los empresarios de las áreas industriales, según el criterio de ellos mismos. Este evento, celebrado en las dependencias que la Cámara de Comercio tiene en Elx Parc Empresarial, ha consistido en tres acciones ágiles: presentación del proyecto durante media hora, una hora de aportación de iniciativas para que empresarios y expertos enuncien propuestas específicas mediante siete mesas temáticas, y valoración de las propuestas elaboradas por las mesas técnicas, en menos de otra hora. Hacemos hincapié en los tiempos porque, este proyecto no es que sea cuestión de ir con prisas, sino con vocación de imprimir diligencia, el ingrediente de la efectividad. Sabemos que detectar los factores y las acciones más pertinentes, solamente puede hacerse involucrando activamente a todo el empresariado, así como a los demás protagonistas de la producción. Junto a la participación directa de las empresas (representadas por el 25% de los asistentes) esta jornada técnica ha compilado equilibradamente la diversidad territorial industrial, la variedad de sectores productivos y los agentes del ámbito de los negocios (gerentes de polígonos industriales, parques empresariales, entornos logísticos, parques científicos, organismos promotores de la innovación empresarial, entidades financieras, ayuntamientos, institutos tecnológicos, sector energético, fundaciones empresariales, universidades, oficinas municipales de gestión industrial, federaciones y asociaciones empresariales), e incluso los destinos objetivo de la producción de nuestra provincia. A partir de las propuestas categorizadas que han proporcionado los participantes en la jornada técnica, el Laboratorio de Ingeniería de Procesos de la UCIE Ars Innovatio, ha elaborado este informe para, después de darlo a conocer a los participantes en la Jornada Técnica, llevar a la práctica el Proyecto AE-20L. De entre las aproximadamente ciento cincuenta aportaciones muy relevantes que ha alumbrado la jornada técnica, algunas especialmente sugerentes entroncan con la autosuficiencia energética limpia, la conciliación de la vida laboral, el trabajo decente y la calidad de vida, el transporte inteligente y sostenible, la eficiencia de las telecomunicaciones, la calidad urbanística, el soporte institucional de las administraciones públicas, la protección y seguridad de los bienes, las personas, las instalaciones, los procesos y los recursos naturales, inteligencia ambiental y big data, economía circular, actualización profesional, instrumentos de innovación de las áreas industriales, sistemas conectados, servicios mancomunados, innovación en tecnología escalable, la creación de centrales de compras, etc. Complementariamente, consecuencia de la experiencia en metodología de grandes proyectos transdisciplinares que acumula la UCIE AI, algunos criterios encaminados a garantizar el éxito del proyecto son las estrategias para hacer el seguimiento del grado de cumplimiento del propio Proyecto AE-20L, los planes de contingencias para atender la incidencias y casos sobrevenidos, la promoción de incentivos (bono ecológico, etc.) y sellos de calidad para las áreas industriales y para las empresas por alinearse con la Ley 14/18, los ODS u otros objetivos de interés común. Así mismo, iniciativas encaminadas a crear cultura proactiva, contrarrestar equívocos o revertir malentendidos endémicos o de otro tiempo, promoviendo el acercamiento entre los miembros de los distintos estamentos empresariales, para enriquecer la visión y la comprensión que, entre trabajadores y empresarios, entre proveedores y clientes, entre fabricantes y consumidores, etc., tienen los unos de los otros y, mutuamente, cada uno de todos los demás: crear fundaciones, desarrollar iniciativas solidarias, valorización antroponímica de personajes ilustres de la industria y la producción, etc. El programa de desarrollo de la conurbación empresarial establece cuáles son los factores esenciales (resultados, estructura e instrumentos) que corresponde implantar y los encaja dentro del compendio de los factores contextuales (plazos, financiación, agentes, etc.) para sustanciar el Proyecto AE-20L. Este proyecto ha sido financiado por la Agencia Valenciana de la Innovación, AVI, a través de la Unidad Científica de Innovación Empresarial Ars Innovatio, de la Universidad de Alicante

This research aims to improve dietetic-nutritional treatment using state-of-the-art RGB-D sensors and virtual reality (VR) technology. Recent studies show that adherence to treatment can be improved using multimedia technologies. However, there are few studies using 3D data and VR technologies for this purpose. On the other hand, obtaining 3D measurements of the human body and analyzing them over time (4D) in patients undergoing dietary treatment is a challenging field. The main contribution of the work is to provide a framework to study the effect of 4D body model visualization on adherence to obesity treatment. The system can obtain a complete 3D model of a body using low-cost technology, allowing future straightforward transference with sufficient accuracy and realistic visualization, enabling the analysis of the evolution (4D) of the shape during the treatment of obesity. The 3D body models will be used for studying the effect of visualization on adherence to obesity treatment using 2D and VR devices. Moreover, we will use the acquired 3D models to obtain measurements of the body. An analysis of the accuracy of the proposed methods for obtaining measurements with both synthetic and real objects has been carried out. This work has been partially funded by the Spanish Government TIN2017-89069-R grant supported with Feder funds.

Many complex systems, such as the product life cycle (PLC), can be described in terms of networks, with elements interacting and based on terms of graph theory. In this article, we intend to analyze the PLC from the point of view of network theory and chaos. To do this we will use the Smarta software, developed by our research group (but not yet in a commercial phase), based on network analysis and that allows us to interpret the virtuous circles that the system possesses, and analyze the causalities by observing the plot of existing relationships between the system’s attractors. Smarta allows us to manage the uncertainty of whether or not the product is ecological, and to be proactive, in the sense of designing strategies that anticipate it. A case study is the PLC of a smartphone, where Smarta will act as a filter since it will allow us to associate the concepts of ecological and attractor, so that if the product is recyclable an attractor will be found, and if it is not recyclable it will lack attractors. This work was supported in part by the Ministry of Economy and Business, Government of Spain under Grant RTI2018-094653-B-C22, in part by the Spanish Research Agency (AEI) and the European Regional Development Fund (FEDER) under Grant CloudDriver4Industry TIN2017-89266-R, and in part by the Regional Ministry of Education, Research, Culture, and Sports (Valencian Community) under Grant GV/ 2018//061.

E-learning is a response to the new educational needs of society and an important development in information and communication technologies because it represents the future of the teaching and learning processes. However, this trend presents many challenges, such as the processing of online forums which generate a huge number of messages with an unordered structure and a great variety of topics. These forums provide an excellent platform for learning and connecting students of a subject but the difficulty of following and searching the vast volume of information that they generate may be counterproductive. The main goal of this paper is to review the approaches and techniques related to online courses in order to present a set of learning analytics techniques and a general architecture that solve the main challenges found in the state of the art by managing them in a more efficient way: 1) efficient tracking and monitoring of forums generated; 2) design of effective search mechanisms for questions and answers in the forums; and 3) extraction of relevant key performance indicators with the objective of carrying out an efficient management of online forums. In our proposal, natural language processing, clustering, information retrieval, question answering, and data mining techniques will be used. This work was supported in part by the Spanish Ministry of Economy and Competitiveness through the Project SEQUOIA-UA under Grant TIN2015-63502-C3-3-R, the Project RESCATA under Grant TIN2015-65100-R, and the Project PROMETEO/2018/089, and in part by the Spanish Research Agency (AEI) and the European Regional Development Fund (FEDER) through the Project CloudDriver4Industry under Grant TIN2017-89266-R.

The development of Internet technologies and their application to commerce environments has favored new business strategies for industries. These allow including in the design phase the experience of use that the clients have of the product. However, this new element has not been considered in formal terms. A fundamental problem in product design is that it has not been modeled in mathematical terms, which means that their characteristics do not appear in rigorous and short properties, but in long developments that from an economic point of view maintain their meaning but that from a mathematical point of view are not sufficiently manageable. Therefore, since these properties have not been axiomatically formalized, we cannot work with them mathematically. For this reason, we propose analyzing the design of products through a network and discrete chaos theory perspective, which will allow us to use important mathematical tools such as graph theory and concepts, such as coverage, invariability, orbits, attractors, and the structural function. This paper also draws attention to the importance of circular flow in the general systems theory and its application to the design phase of products. Finally, the Intel case study is analyzed, locating the current attractor and its relationship with the success of the company’s products. This work was supported in part by the Spanish Research Agency (AEI) and the European Regional Development Fund (FEDER) through project CloudDriver4Industry under Grant TIN2017-89266-R, and in part by the Valencian Government under Grant GV/2018/061.

Diseño de mascarilla antipatógeno basada en OZONO El trabajo se ha realizado desde el laborartorio de la Unidad Científica de Innovación Empresarial de la Universidad de Alicante, financiado por la Agencia Valenciana de Innovación.

Background: In preterm infants, it is important to ensure adequate nutritional intake to accomplish foetal growth requirements. This study evaluated clinical practice regarding the prescription of parenteral nutrition in preterm infants in the neonatology unit of a tertiary hospital. Methods: It was a retrospective observational study of a sample of preterm infants (n = 155) born between January 2015 and December 2017 who were prescribed parenteral nutrition. Compliance with the hospital&rsquo, s protocol and with the guidelines of the scientific societies American Society for Parenteral and Enteral Nutrition (ASPEN), European Society for Clinical Nutrition and Metabolism (ESPEN) and Spanish Society of Clinical Nutrition and Metabolism (SENPE) was evaluated. The differences in macronutrient intake and total duration of parenteral nutrition were analysed according to gestational age and birth weight. Results: The established protocol was followed in a high percentage (95.5%&ndash, 100%) except with respect to the initiation of supplying established trace elements (64.9%). Compliance with the recommendations set forth in the guidelines was between 82.1% and 100%, with the exception of the initial carbohydrate intake recommended by ASPEN and ESPEN, for which compliance was 8.3%. Lower gestational age and birth weight were correlated with longer duration of parenteral nutrition (p &lt, 0.001). Conclusions: A lower gestational age and birth weight are related to a longer duration of parenteral nutrition. The results of this study demonstrate the importance of developing and evaluating protocols in clinical practice.

Risky sexual behaviors, such as not using a condom for preventing sexually transmittable diseases and unwanted pregnancies, are associated with emotional and psychological problems in adolescence, including higher levels of depression, anxiety, stress, and low self-esteem. Adolescents with a history of violence victimization are also at increased risk of engaging in sexual risk behaviors. In this study, we examined the associations between mental health disturbances and partner violence victimization and the non-use of condoms among young people in Spain. We also examined self-esteem as a mediator of mental health problems and unprotected sexual relationships. A cross-sectional study was conducted on a sample of 831 adolescents randomly selected from 25 high schools in Spain. From the initial sample, 285 students (144 men and 141 women) from high schools in Alicante (Spain) who reported having experienced sexual activity with at least one partner were approached. The results showed that one in three adolescents between 14 and 19 years old did not use condoms during sexual intercourse. The factors associated with the non-use of condoms in the estimated models of Poisson robust variance were having a history of physical, psychological, or sexual violence; having been expelled from school because of behavioral problems; and having higher levels of depression, anxiety, and stress and lower levels of self-esteem. Self-esteem was independently associated with the non-use of condoms. Having lower levels of self-esteem increased the risk of not using a condom during participants’ last sexual intercourse. These findings suggest the importance of developing public health strategies for mental health promotion to increase condom use among adolescents. This study was funded by Concejalía de Acción Social, Plan Municipal sobre Drogodependencias; Ayuntamiento de Alicante, Spain (AYTOALICANTE3-13I); Delegación del Gobierno para el Plan Nacional sobre Drogas. This study was also funded by GE-PPUA2013–2017 PA PROGRAMA DE REDES DE INVESTIGACIÓN EN DOCENCIA UNIVERSITARIA. Insituto Ciencias de la Educación, Universidad de Alicante.

Nowadays malware is a major threat to the security of cyber activities. The rapid development of the Internet and the progressive implementation of the Internet of Things (IoT) increase the security needs of networks. This research presents a theoretical model of malware propagation for mobile computer devices. It is based on the susceptible-exposed-infected-recovered-susceptible (SEIRS) epidemic model. The scheme is based on a concrete connection pattern between nodes defined by both a particular neighbourhood which fixes the connection between devices, and a local rule which sets whether the link is infective or not. The results corroborate the ability of our model to perform the behaviour patterns provided by the ordinary differential equation (ODE) traditional method.

Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we formulate the semantic segmentation problem and define the terminology of this field as well as interesting background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. We also devote a part of the paper to review common loss functions and error metrics for this problem. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques. This work has been funded by the Spanish Government TIN2016-76515-R funding for the COMBAHO project, supported with Feder funds. It has also been supported by a Spanish national grant for PhD studies FPU15/04516 (Alberto Garcia-Garcia). In addition, it was also funded by the grant Ayudas para Estudios de Master e Iniciacion a la Investigacion from the University of Alicante.

Distinguishing axons from central or peripheral nervous systems (CNS or PNS, respectively) is often a complicated task. The main objective of this work was to facilitate and support the process of automatically distinguishing the different types of nerve fibres by analysing their morphological characteristics. Our approach was based on a multi-level hierarchical classifier architecture that can handle the complexity of directly identifying nerve-fibre groups belonging to either the CNS or the PNS. The approach adopted comprises supervised methods (multilayer perceptron and decision trees), which are responsible for distinguishing the origin of the axons (CNS or PNS), whereas the unsupervised method (K-means clustering) performs nerve fibre clustering based on similar characteristics for both the CNS and PNS. Our experiments produced results with an accuracy higher than 88%. Our findings suggest that the development and implementation of a multi-level system improves automation capabilities and increases accuracy in the classification of nerves. Furthermore, our architecture allows for generalisation and flexibility, which can subsequently be extended to other biological control systems. This work was partially supported by the following grants: The Office of the Vice Chancellor for Research, Development, and Innovation, University of Alicante, Spain, (Grant Vigrob-137 to JDJ); the Chair of Reproductive Medicine, University of Alicante-Bernabeu Institute of Reproductive medicine (Grant 4-12I to JDJ). The Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) under the granted Project SEQUOIA-UA (Management requirements and methodology for Big Data analytics) TIN2015-63502-C3-3-R, by the University of Alicante, within the program of support for research, under project GRE14-10, and by the Conselleria de Educación, Investigación, Cultura y Deporte, Comunidad Valenciana, Spain, within the program of support for research, under project GV/2016/087.

The digital transforms are intensive in multiplication and accumulation operations which have a high computational cost. Advances in computer arithmetic and digital technologies allow simplifying the processing of complex algorithms when they are implemented in modern circuits. New computation techniques can be explored to provide efficient operational methods for implementing algorithms that avoid much of the complex and costly mathematical operations. This work aims to design a high-performance architecture for computing some common digital transforms. The proposed architecture has been compared to other methods. The transform used as example in this work is the discrete cosine transform. The results show that the proposal offers high-performance results comparable or better than best-known methods. This work was supported by the Spanish Research Agency (AEI) and the European Regional Development Fund (FEDER) under project “CloudDriver4Industry” TIN2017-89266-R.