Physical activity in and out-of-school and academic performance in Spain

[EN] Background: Academic performance in school stems from an interaction of factors associated with students, families and schools. Among these factors, physical activity could play a very relevant role. Objective: The goal of this study was to determine whether students' physical activities in and outside school were related to their academic performance. Design, setting and method: We used the Programme for International Student Assessment (PISA) 2015 database for Spain using ordinary least squares (OLS) regression models to answer our research questions. Results: Results suggest a positive association between the number of days per week doing moderate physical activities and academic performance in science, reading and mathematics. However, a higher number of days per week doing vigorous physical activities was associated with lower scores in reading and science. We also find a negative association between exercising or practising sports before going to school and the scores achieved in the three competences evaluated. Conclusions: Our results show that physical activity can play an important role in academic performance and that the effects of physical activity on children and teenagers should be further investigated.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article. Nerea Gomez-Fernandez is grateful to the Spanish Ministry of Science, Innovation and Universities for financial support under grant FPU16/04571.Gómez-Fernández, N.; Albert, J. (2020). Physical activity in and out-of-school and academic performance in Spain. Health Education Journal. 79(7):788-801. https://doi.org/10.1177/001789692092974378880179

Customer-oriented risk assessment in Network Utilities

For companies that distribute services such as telecommunications, water, energy, gas, etc., quality perceived by the customers has a strong impact on the fulfillment of financial goals, positively increasing the demand and negatively increasing the risk of customer churn (loss of customers). Failures by these companies may cause customer affection in a massive way, augmenting the intention to leave the company. Therefore, maintenance performance and specifically service reliability has a strong influence on financial goals. This paper proposes a methodology to evaluate the contribution of the maintenance department in economic terms, based on service unreliability by network failures. The developed methodology aims to provide an analysis of failures to facilitate decision making about maintenance (preventive/predictive and corrective) costs versus negative impacts in end-customer invoicing based on the probability of losing customers. Survival analysis of recurrent failures with the General Renewal Process distribution is used for this novel purpose with the intention to be applied as a standard procedure to calculate the expected maintenance financial impact, for a given period of time. Also, geographical areas of coverage are distinguished, enabling the comparison of different technical or management alternatives. Two case studies in a telecommunications services company are presented in order to illustrate the applicability of the methodology

Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models

In the field of renewable energy, reliability analysis techniques combining the operating time of the system with the observation of operational and environmental conditions, are gaining importance over time. In this paper, reliability models are adapted to incorporate monitoring data on operating assets, as well as information on their environmental conditions, in their calculations. To that end, a logical decision tool based on two artificial neural networks models is presented. This tool allows updating assets reliability analysis according to changes in operational and/or environmental conditions. The proposed tool could easily be automated within a supervisory control and data acquisition system, where reference values and corresponding warnings and alarms could be now dynamically generated using the tool. Thanks to this capability, on-line diagnosis and/or potential asset degradation prediction can be certainly improved. Reliability models in the tool presented are developed according to the available amount of failure data and are used for early detection of degradation in energy production due to power inverter and solar trackers functional failures. Another capability of the tool presented in the paper is to assess the economic risk associated with the system under existing conditions and for a certain period of time. This information can then also be used to trigger preventive maintenance activities

Analysis of the impact of the Asset Health Index in a Maintenance Strategy

Hosted by the Johannes Kepler University, Linz, Austria. May 23-24, 2019 - European Safety, Reliability & Data Association (ESReDA)During many years, asset management methodologies used in industry were focused on knowing and analysing the operational control of the daily work and the impact of the maintenance on the availability. Later, the costs turn into the priority, and strategies were focused on assesses a longer lifecycle and optimizing processes and contracts. Finally, recent normative have included concepts as “knowing and managing the risks” and the target is to prioritize the maintenance tasks to the critical assets. However, taking a balanced asset management model for the operational environment, quite a lot of facilities of Oil & Gas sector are reaching the end of their initially estimated lifecycle. New challenges are related to extend the life of the main items of the facilities or at least, to find the optimal replacement moment that guarantees that the maintenance strategy is being optimized. Asset Health Index methodology considers a theoretical lifecycle of an item, in which depending on the proximity to the end of the useful life, the probability of failure increases. But take this theoretical lifecycle as a base, different operation location factors or O&M aspects can modify this period. All these factor are quantified and permit us to calculate a new theoretical profile. This paper is about assess the impact of the AHI into the maintenance strategy optimisation. AHI enables us to compare future alternative cost profiles and assess the impact in the failure probability of the item. As a result, we are able to know the risk that is taken when we enlarge the operation of an item, and the impact in the operational costs

A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies

CBM (Condition Based Maintenance) solutions are increasingly present in industrial systems due to two main circumstances: rapid evolution, without precedents, in the capture and analysis of data and significant cost reduction of supporting technologies. CBM programs in industrial systems can become extremely complex, especially when considering the effective introduction of new capabilities provided by PHM (Prognostics and Health Management) and E-maintenance disciplines. In this scenario, any CBM solution involves the management of numerous technical aspects, that the maintenance manager needs to understand, in order to be implemented properly and effectively, according to the company’s strategy. This paper provides a comprehensive representation of the key components of a generic CBM solution, this is presented using a framework or supporting structure for an effective management of the CBM programs. The concept “symptom of failure”, its corresponding analysis techniques (introduced by ISO 13379-1 and linked with RCM/FMEA analysis), and other international standard for CBM open-software application development (for instance, ISO 13374 and OSA-CBM), are used in the paper for the development of the framework. An original template has been developed, adopting the formal structure of RCM analysis templates, to integrate the information of the PHM techniques used to capture the failure mode behaviour and to manage maintenance. Finally, a case study describes the framework using the referred template.Gobierno de Andalucía P11-TEP-7303 M

Efecto de dos dietas hipocalóricas y su combinación con ejercicio físico sobre la tasa metabólica basal y la composición corporal

El Síndrome Metabólico (SMet) se diagnostica por el cumplimiento de al menos tres criterios: hipertrigliceridemia, HDL-C disminuido, hipertensión arterial, glucemia alterada en ayunas y obesidad. Dicha obesidad constituiría el punto inicial para el desarrollo del SMet. Según la evidencia científica, las dietas hipocalóricas, incluyendo la mediterránea y la reducida en grasa con alto contenido en carbohidratos, reducen la masa grasa (MG) de estos pacientes y su efecto se potencia al combinarse con ejercicio físico (EF), pero se desconoce aún su influencia sobre la tasa metabólica basal (TMB). Objetivo: Conocer el efecto de dos dietas hipocalóricas: mediterránea y baja en grasas, combinadas o no con EF, sobre la TMB y la composición corporal (CC) de adultos con SMet. Métodos: 36 voluntarios, > 50 años, ambos sexos, con diagnóstico de SMet. Se asignaron aleatoriamente a uno de los cuatro grupos de intervención: Dieta hipocalórica mediterránea (MED), Dieta hipocalórica baja en grasa (CHO) ó ambas asociadas a EF (MEDE y CHOE respectivamente). Se evaluó CC (antropometría) y TMB (calorimetría indirecta) antes y después de la intervención. Resultados: La adición de EF a los dos tratamientos hipocalóricos produjo mayor pérdida de peso y MG que las dietas por sí solas, siendo esta pérdida en CHOE > MEDE (p CHOE (p MED (p < 0,05). Conclusiones: CHOE fue el tratamiento que mayor pérdida de peso y MG produjo, induciendo menor reducción de TMB y manteniendo un mejor perfil de CC que MEDE

Criticality Analysis for Maintenance Purposes: A Study for Complex In‐service Engineering Assets

The purpose of this paper is to establish a basis for a criticality analysis, considered here as a prerequisite, a first required step to review the current maintenance programs, of complex in‐service engineering assets. Review is understood as a reality check, a testing of whether the current maintenance activities are well aligned to actual business objectives and needs. This paper describes an efficient and rational working process and a model resulting in a hierarchy of assets, based on risk analysis and cost–benefit principles, which will be ranked according to their importance for the business to meet specific goals. Starting from a multicriteria analysis, the proposed model converts relevant criteria impacting equipment criticality into a single score presenting the criticality level. Although detailed implementation of techniques like Root Cause Failure Analysis and Reliability Centered Maintenance will be recommended for further optimization of the maintenance activities, the reasons why criticality analysis deserves the attention of engineers and maintenance and reliability managers are precisely explained here. A case study is presented to help the reader understand the process and to operationalize the mode

How to Engage with Younger Users on Instagram: A Comparative Analysis of HBO and Netflix in the Spanish and US Markets

Social media have become useful tools for audiovisual promotion, especially to reach niche audiences. Twitter promotional strategies have been widely studied, yet other growing platforms such as Instagram have been less analyzed. This paper examines the Instagram promotional strategies of two pay-per-view platforms (HBO and Netflix) in two markets (the United States and Spain). A total of 731 messages, posted between May and November 2017, were analyzed to identify their formal features, objectives, and their content’s emotional and cognitive elements posted on HBO and Netflix’s Spanish and U.S. accounts. The results showed acute differences between how HBO and Netflix use their Instagram accounts in the two markets (Spain and the United States). The Spanish accounts mostly provided information, while the American accounts both provided information and promoted program content. Posts from U.S. accounts more frequently included links to social media, particularly to celebrity accounts. Spanish accounts relied more on diegetic images, whereas U.S. accounts emphasized non-diegetic and off-set images. Netflix used humor more than HBO did, and a stronger focus on celebrities. Humor and positive tone were found to be linked to higher engagement

Capsaicin affects the structure and phase organization of phospholipid membranes

AbstractCapsaicin is a natural compound with pharmacological and toxicological effects, which given its hydrophobicity, can influence the structure of membranes. The interaction of capsaicin with model membranes of dipalmitoylphosphatidylcholine and dielaidoylphosphatidylethanolamine has been studied by using differential scanning calorimetry, fluorescent probe spectroscopy and 31P-nuclear magnetic resonance. Capsaicin remarkably affects the phase transition of dipalmitoylphosphatidylcholine, shifting the transition temperature to lower values, and giving rise, at relatively high capsaicin concentrations, to the appearance of two peaks in the thermogram. These peaks may correspond to separated phases as indicated by the partial phase diagram. Whereas capsaicin did not affect the fluorescence polarization of the probes diphenylhexatriene and trimethylammonium-diphenylhexatriene, it clearly affected that of the probe 2-anthroyloxystearic acid, indicating that the perturbation produced by capsaicin on the membrane would be mainly at the position where this fluorophore is located. On the other hand, capsaicin, at relatively low concentrations, gives rise to immiscible phases in the presence of dielaidoylphoshatidylethanolamine and decrease the temperature of the lamellar to hexagonal HII phase transition. At concentrations of capsaicin higher than 0.3 mol fraction, isotropic phases were detected. The possible implications of the effects of capsaicin on biological membranes are discussed

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

Within the field of soft computing, intelligent optimization modelling techniques include various major techniques in artificial intelligence. These techniques pretend to generate new business knowledge transforming sets of "raw data" into business value. One of the principal applications of these techniques is related to the design of predictive analytics for the improvement of advanced CBM (condition-based maintenance) strategies and energy production forecasting. These advanced techniques can be used to transform control system data, operational data and maintenance event data to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation. One of the systems where these techniques can be applied with massive potential impact are the legacy monitoring systems existing in solar PV energy generation plants. These systems produce a great amount of data over time, while at the same time they demand an important e ort in order to increase their performance through the use of more accurate predictive analytics to reduce production losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of the problems to address. This paper presents a review and a comparative analysis of six intelligent optimization modelling techniques, which have been applied on a PV plant case study, using the energy production forecast as the decision variable. The methodology proposed not only pretends to elicit the most accurate solution but also validates the results, in comparison with the di erent outputs for the di erent techniques