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Este artículo se encuentra disponible en la siguiente URL: https://www.mdpi.com/1660-4601/19/9/5546 Este artículo pertenece la número especial "Exclusive Papers Collection of Editorial Board Members (and Invited Scholars) in Section Public Health Statistics and Risk Assessment". Background: Forecasting the behavior of epidemic outbreaks is vital in public health. This makes it possible to anticipate the planning and organization of the health system, as well as possible restrictive or preventive measures. During the COVID-19 pandemic, this need for prediction has been crucial. This paper attempts to characterize the alternative models that were applied in the first wave of this pandemic context, trying to shed light that could help to understand them for future practical applications. Methods: A systematic literature search was performed in standardized bibliographic repertoires, using keywords and Boolean operators to refine the findings, and selecting articles according to the main PRISMA 2020 statement recommendations. Results: After identifying models used throughout the first wave of this pandemic (between March and June 2020), we begin by examining standard data-driven epidemiological models, including studies applying models such as SIR (Susceptible-Infected-Recovered), SQUIDER, SEIR, time-dependent SIR, and other alternatives. For data-driven methods, we identify experiences using autoregressive integrated moving average (ARIMA), evolutionary genetic programming machine learning, short-term memory (LSTM), and global epidemic and mobility models. Conclusions: The COVID-19 pandemic has led to intensive and evolving use of alternative infectious disease prediction models. At this point it is not easy to decide which prediction method is the best in a generic way. Moreover, although models such as the LSTM emerge as remarkably versatile and useful, the practical applicability of the alternatives depends on the specific context of the underlying variable and on the information of the target to be prioritized. In addition, the robustness of the assessment is conditioned by heterogeneity in the quality of information sources and differences in the characteristics of disease control interventions. Further comprehensive comparison of the performance of models in comparable situations, assessing their predictive validity, is needed. This will help determine the most reliable and practical methods for application in future outbreaks and eventual pandemics.

This paper aims to study the evolution of CO2 concentrations and emissions on a conventional farm with weaned piglets between 6.9 and 17.0 kg live weight based on setpoint temperature, outdoor temperature, and ventilation flow. The experimental trial was conducted during one transition cycle. Generally, the ventilation flow increased with the reduction in setpoint temperature throughout the cycle, which caused a reduction in CO2 concentration and an increase in emissions. The mean CO2 concentration was 3.12 g m–3. Emissions of CO2 had a mean value of 2.21 mg s−1 per animal, which is equivalent to 0.195 mg s−1 kg−1. A potential function was used to describe the interaction between 10 min values of ventilation flow and CO2 concentrations, whereas a linear function was used to describe the interaction between 10 min values of ventilation flow and CO2 emissions, with r values of 0.82 and 0.85, respectively. Using such equations allowed for simple and direct quantification of emissions. Furthermore, two prediction models for CO2 emissions were developed using two neural networks (for 10 min and 60 min predictions), which reached r values of 0.63 and 0.56. These results are limited mainly by the size of the training period, as well as by the differences between the behavior of the series in the training stage and the testing stage This research was funded by Consellería de Educación, Universidade e Formación Profesional and Consellería de Economía, Emprego e Industria from the Galician Government (Xunta de Galicia). Granted with reference ED431B 2018/12-GPC SI

Este artículo se encuentra disponible en la siguiente URL: https://www.mdpi.com/1424-8247/15/11/1386 Este artículo pertenece a la sección "Natural Products". Diarrhea diseases caused by the intestinal protozoan parasite Giardia intestinalis are a major global health burden. Moreover, there is an ongoing need for novel anti-Giardia drugs due to drawbacks with currently available treatments. This paper reports on the isolation and structural elucidation of six new flavonoids (1–6), along with twenty-three known ones (7–29) from the Piper species. Their structures were established by spectroscopic and spectrometric techniques. Flavonoids were tested for in vitro antiprotozoal activity against Giardia intestinalis trophozoites. In addition, structure-activity relationship (SAR) and in silico ADME studies were performed to understand the pharmacophore and pharmacokinetic properties of these natural compounds. Eight flavonoids from this series exhibited remarkable activity in the micromolar range. Moreover, compound 4 was identified as having a 40-fold greater antiparasitic effect (IC50 61.0 nM) than the clinical reference drug, metronidazole (IC50 2.5 M). This antiprotozoal potency was coupled with an excellent selectivity index (SI 233) on murine macrophages and in silico drug-likeness. SAR studies revealed that the substitution patterns, type of functional group, and flavonoid skeleton played an essential role in the activity. These findings highlight flavonoid 4 as a promising candidate to develop new drugs for the treatment of Giardia infections.

Institutional investors often have to decide which strategy to use across international business cycles. This is especially important during economic and financial crises. The exogenous nature of the outbreak of the dramatic COVID-19 crisis represents a unique opportunity to understand the performance of risk factors during severe economic times across international stock markets. Even more important is to analyze how these factors behave across very different economic crises, such as the COVID-19 pandemic and the Great Recession. Although, the overall results show that the momentum and quality factors are the winners, with the value factor as the loser, this research also reports different responses of factors across crises and countries. The size, value, and defensive factors tend to perform worse during the health crisis relative to the Great Recession, while the momentum factor shows a poor performance during the financial crisis, but a positive one during the outbreak of COVID-19. The quality factor is an extraordinary defensive factor in both crises. Similarly, this paper reports heterogeneous responses of option-implied expected market risk premia across alternative stock market indices, and between the Great Recession and the COVID-19 crisis. The authors acknowledge financial support from the Ministry of Science, Innovation, and Universities through grant PGC2018-095072-B-I00. In addition, they acknowledge financial support from Generalitat Valencia grant Prometeo/2017/158.

Este artículo se encuentra disponible en la siguiente URL: https://www.mdpi.com/2076-3417/12/10/5105 Este artículo de investigación pertenece al número especial "STEAM Education and the Innovative Pedagogies in the Intelligence Era". The objective of our research is the implementation of STEAM (Science Technology Engineering Art Mathematics) learning in the bachelor of Engineering in industrial design and product development at CEU Cardenal Herrera University through the EXPLORIA project. This article implements and develops the proposal for the first year of this bachelor, which includes 24 students aged 18–20. This article focuses on how to integrate STEAM learning within the EXPLORIA project for the improvement in the learning of the physics subject, and in particular, regarding the part of the syllabus related to mechanical engineering through different projects, challenges and milestones that allow the student to see the use in the design and development of products. The EXPLORIA project connects the competencies of the different STEAM subjects included in the bachelor, designing a learning process as a logical, sequential and incremental itinerary. Through concepts on which the fundamentals of design are based: shape, volume, color, space and structure. In particular, this article shows the adaptation made in the physical part to be able to teach the integrated mechanics part in this learning process. The complete learning was carried out through several challenges and two milestones the students had to overcome through the application of the physical knowledge learned in class. To validate the effectiveness of the proposed methodology, at the end of the paper, an ad hoc questionnaire is carried out showing the students’ assessment regarding the new teaching methodology.

The increased use of distributed energy resources, especially electrical energy storage systems (EESS), has led to greater flexibility and complexity in power grids, which has led to new challenges in the operation of these systems, with particular emphasis on frequency regulation. To this end, the transmission system operator in Great Britain has designed a control scheme known as Enhanced Frequency Response (EFR) that is especially attractive for its implementation in EESS. This paper proposes a Type-2 fuzzy control system that enables the provision of EFR service from a battery energy storage system in order to improve the state-of-charge (SoC) management while providing EFR service from operating scenarios during working and off-duty days. The performance of the proposed controller is compared with a conventional FLC and PID controllers with similar features. The results showed that in all scenarios, but especially under large frequency deviations, the proposed controller presents a better SoC management in comparison without neglecting the EFR service provision.

This paper proposes a new damage index named degree of health (DoH) to efficiently tackle structural damage monitoring in real-time. As a key contribution, the proposed index relies on a pattern matching methodology that measures the time-of-flight mismatch of sequential ultrasonic guided-wave measurements using fuzzy logic fundamentals. The ultrasonic signals are generated using the transmission beamforming technique with a phased-array of piezoelectric transducers. The acquisition is carried out by two phased-arrays to compare the influence of pulse-echo and pitch-catch modes in the damage assessment. The proposed monitoring approach is illustrated in a fatigue test of an aluminum sheet with an initial notch. As an additional novelty, the proposed pattern matching methodology uses the data stemming from the transmission beamforming technique for structural health monitoring. The results demonstrate the efficiency and robustness of the proposed framework in providing a qualitative and quantitative assessment for fatigue crack damage., European Commission 721455, Aernnova Engineering Division S.A., University of Basque Country

In a circular economy (CE) environment, it is important to make good and efficient use of resources and consider that the waste generated in production processes can be a valuable resource. However, the tools and methodologies conventionally used to analyze and evaluate production systems are based on techniques focused on linear production management models, where the primary purpose is to reduce the treatment and management of waste as much as possible and where productive and environmental efficiency are not evaluated simultaneously. Changing the paradigm from a linear to a circular economy requires that a new strategy for production systems be defined, one that makes production processes simultaneously circular and efficient (in terms of quality and productivity). In this context, a holistic vision is needed when implementing CE strategies. Therefore, the main aim of this paper is to provide evidence, through two real case studies in the use of water, that the management of this resource without considering systemic thinking may not be the most circular solution. Main results showed that improvements based on the traditional approach of reducing resource use cannot provide the best results if they are supported only by current process consumption without considering the circularity of resources.

Medicine is the main means to reduce cancer mortality. However, some medicines face various risks during transportation and storage due to the particularity of medicines, which must be kept at a low temperature to ensure their quality. In this regard, it is of great significance to evaluate and select drug cold chain logistics suppliers from different perspectives to ensure the quality of medicines and reduce the risks of transportation and storage. To solve such a multiple criteria decision-making (MCDM) problem, this paper proposes an integrated model based on the combination of the SWARA (stepwise weight assessment ratio analysis) and CoCoSo (combined compromise solution) methods under the probabilistic linguistic environment. An adjustment coefficient is introduced to the SWARA method to derive criteria weights, and an improved CoCoSo method is proposed to determine the ranking of alternatives. The two methods are extended to the probabilistic linguistic environment to enhance the applicability of the two methods. A case study on the selection of drug cold chain logistics suppliers is presented to demonstrate the applicability of the proposed integrated MCDM model. The advantages of the proposed methods are highlighted through comparative analyses., This article belongs to the Special Issue Artificial Intelligence in Health Care, The research was funded by the National Natural Science Foundation of China under Grant 71771156, 71971145 and the APC was funded by the National Natural Science Foundation of China under Grant 71971145.

This paper looks at the opinions of 241 European academics (who provide pharmacy education), and of 258 European community pharmacists (who apply it), on competences for pharmacy practice. A proposal for competences was generated by a panel of experts using Delphi methodology. Once finalized, the proposal was then submitted to a large, European-wide community of academics and practicing pharmacists in an additional Delphi round. Academics and community pharmacy practitioners recognized the importance of the notion of patient care competences, underlining the nature of the pharmacist as a specialist of medicines. The survey revealed certain discrepancies. Academics placed substantial emphasis on research, pharmaceutical technology, regulatory aspects of quality, etc., but these were ranked much lower by community pharmacists who concentrated more on patient care competences. In a sub-analysis of the data, we evaluated how perceptions may have changed since the 1980s and the introduction of the notions of competence and pharmaceutical care. This was done by splitting both groups into respondents < 40 and > 40 years old. Results for the subgroups were essentially statistically the same but with some different qualitative tendencies. The results are discussed in the light of the different conceptions of the professional identity of the pharmacist.