Repositorio Universidad Europea del Atlántico
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Asparagus saponins: effective natural beneficial ingredient in functional foods, from preparation to applications
No full textAsparagus species is recognized as a perennial herb with several valuable functional ingredients, and has been widely used as medicine and food since ancient times. Among its main chemical constituents, saponins play a vital role in the health benefits and biological activities including anti-cancer, antioxidant, immunomodulatory, anti-microbial, anti-inflammatory, and hypoglycemic. This review summarizes the preparation methods, structure and classification, biological functions, as well as the food and non-food applications of asparagus saponins, with a special emphasis on its anti-cancer effects in vitro and in vivo. Further, the main challenges and limitations of the current research trends in asparagus saponins are highlighted after a detailed analysis of the recent research information. This review bridges the gap between bioactive components and human health and aids current research on functional and health-promoting foods and medicinal application of Asparagus saponins
Influencia de la presencia física del entrenador en el rendimiento del entrenamiento deportivo.
No full textEl objeto de la investigación se concreta en cuantificar la diferencia de rendimiento del entrenamiento deportivo presencial (el modelo tradicional) versus el entrenamiento deportivo administrado de manera remota (a distancia, virtual), presentando este último como una alternativa eficiente, accesible y rentable para el desarrollo del deporte competitivo gracias al desarrollo de las TIC´s en la actualidad
Validity of a new tracking device for futsal match
No full textPurpose: The purpose of this study was to to evaluate the validity of a new IMU device that allows measuring different actions in futsal real game situations.
Methods: 10 high elite futsal players performed a typical futsal training task, this is, a 4v4 in 28x20m with a duration of 180 seconds, where players worn two tracking devices, the new one (OLIVER) and the already validated device (WIMU). Data recorded by the OLIVER and WIMU PRO systems were compared after the training process. Descriptive analysis was performed for each variable, and a one-way ANOVA was developed to calculate the validity of OLIVER compared with WIMU report.
Results: The results reported good validity for most of the variables analyzed, such as total distance, distance covered in different splits, as well as number of accelerations and decelerations and maximal speed (P > .05). However, distance covered at low velocity (0-6 km/h) and high acceleration quantity (>2m/s2) reported statistical differences from OLIVER to WIMU.
Conclusion: The OLIVER system can be stated as a valid technology for monitoring external load in specific training tasks in futsal, which ensures an improvement in the monitoring training proces
A Comprehensive Review on Techno-Economic Analysis and Optimal Sizing of Hybrid Renewable Energy Sources with Energy Storage Systems
No full textRenewable energy solutions are appropriate for on-grid and off-grid applications, acting as a supporter for the utility network or rural locations without the need to develop or extend costly and difficult grid infrastructure. As a result, hybrid renewable energy sources have become a popular option for grid-connected or standalone systems. This paper examines hybrid renewable energy power production systems with a focus on energy sustainability, reliability due to irregularities, techno-economic feasibility, and being environmentally friendly. In attaining a reliable, clean, and cost-effective system, sizing optimal hybrid renewable energy sources (HRES) is a crucial challenge. The presenters went further to outline the best sizing approach that can be used in HRES, taking into consideration the key components, parameters, methods, and data. Moreover, the goal functions, constraints from design, system components, optimization software tools, and meta-heuristic algorithm methodologies were highlighted for the available studies in this timely synopsis of the state of the art. Additionally, current issues resulting from scaling HRES were also identified and discussed. The latest trends and advances in planning problems were thoroughly addressed. Finally, this paper provides suggestions for further research into the appropriate component sizing in HRES
Optimal Sizing and Power System Control of Hybrid Solar PV-Biogas Generator with Energy Storage System Power Plant
No full textIn this paper, the electrical parameters of a hybrid power system made of hybrid renewable energy sources (HRES) generation are primarily discussed. The main components of HRES with energy storage (ES) systems are the resources coordinated with multiple photovoltaic (PV) cell units, a biogas generator, and multiple ES systems, including superconducting magnetic energy storage (SMES) and pumped hydro energy storage (PHES). The performance characteristics of the HRES are determined by the constant power generation from various sources, as well as the shifting load perturbations. Constant power generation from a variety of sources, as well as shifting load perturbations, dictate the HRES’s performance characteristics. As a result of the fluctuating load demand, there will be steady generation but also fluctuating frequency and power. A suitable control strategy is therefore needed to overcome the frequency and power deviations under the aforementioned load demand and generation conditions. An integration in the environment of fractional order (FO) calculus for proportion-al-integral-derivative (PID) controllers and fuzzy controllers, referred to as FO-Fuzzy-PID controllers, tuned with the opposition-based whale optimization algorithm (OWOA), and compared with QOHSA, TBLOA, and PSO has been proposed to control the frequency deviation and power deviations in each power generation unites. The results of the frequency deviation obtained by using FO-fuzzy-PID controllers with OWOA tuned are 1.05%, 2.01%, and 2.73% lower than when QOHSA, TBLOA, and PSO have been used to tune, respectively. Through this analysis, the algorithm’s efficiency is determined. Sensitivity studies are also carried out to demonstrate the robustness of the technique under consideration in relation to changes in the sizes of the HRES and ES system parameters
Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight
No full textObesity and overweight has increased in the last year and has become a pandemic disease, the result of sedentary lifestyles and unhealthy diets rich in sugars, refined starches, fats and calories. Machine learning (ML) has proven to be very useful in the scientific community, especially in the health sector. With the aim of providing useful tools to help nutritionists and dieticians, research focused on the development of ML and Deep Learning (DL) algorithms and models is searched in the literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol has been used, a very common technique applied to carry out revisions. In our proposal, 17 articles have been filtered in which ML and DL are applied in the prediction of diseases, in the delineation of treatment strategies, in the improvement of personalized nutrition and more. Despite expecting better results with the use of DL, according to the selected investigations, the traditional methods are still the most used and the yields in both cases fluctuate around positive values, conditioned by the databases (transformed in each case) to a greater extent than by the artificial intelligence paradigm used. Conclusions: An important compilation is provided for the literature in this area. ML models are time-consuming to clean data, but (like DL) they allow automatic modeling of large volumes of data which makes them superior to traditional statistics
Trastorno por atracón y terapia cognitivo conductual.
No full textSe ha llevado a cabo una revisión sobre los Trastornos de Conducta Alimentaria (TCA), específicamente sobre el Trastorno por Atracón (TA) y su intervención a través de la Terapia Cognitivo Conductual (TCC). El objetivo principal fue conocer la eficacia de la TCC frente a otras terapias, a la vez que se observaba la validez de la combinación entre la terapia citada y otras terapias o técnicas. También se centró en los beneficios de la terapia online. Metodología: Se realizó una revisión sistemática a través de la búsqueda de artículos publicados en las bases de datos “PubMed”, “Redalyc” y “APA Psycnet” a partir del año 2015 que estudiaban los efectos de la TCC combinando o comparando con otras terapias y en función de su formato online o presencial. Resultados: La terapia que ha presentado una alta tasa de mejora para el TA ha sido la TCC. Los resultados de la TCC en formato online han demostrado una mayor satisfacción por parte de los pacientes. Conclusiones: La TCC es la técnica más utilizada, eficaz y validada para llevar a cabo la intervención sobre el TA ya que reduce la frecuencia de los episodios de atracón, la posible sintomatología depresiva y la calidad de vida de los pacientes
Valorization of food products using natural functional compounds for improving organoleptic and functional chemistry
No full textFunctional foods have emerged as an attractive option for many consumers, given their wide-ranging and long-term benefits. The functional food market size was valued at USD 177,770 Million in 2019 and is estimated to reach USD 267,924.4 Million by 2027, registering a CAGR of 6.7% from 2021 to 2027. Various natural products/compounds exert significant functional activity, and could also added value to food products alone or in combination, provided functional activity. The use of natural compounds in preparation of functional foods is important due to its higher safety, superior organoleptic properties, and functional attributes, resulted in wider consumer acceptance. Also, the use of advanced technologies in formulation of functional foods provides a better means of utilizing natural compounds for organoleptic and functional attributes
Bioactive Properties of Tagetes erecta Edible Flowers: Polyphenol and Antioxidant Characterization and Therapeutic Activity against Ovarian Tumoral Cells and Caenorhabditis elegans Tauopathy
No full textTagetes erecta is an edible flower deeply rooted in traditional Mexican culture. It holds a central role in the most popular and iconic Mexican celebration, “the Day of the Dead”. Furthermore, it is currently receiving interest as a potential therapeutic agent, motivated mainly by its polyphenol content. The present study aims to evaluate the biological activity of an extract synthesized from the petals of the edible flower T. erecta. This extract showed significant antioxidant scores measured by the most common in vitro methodologies (FRAP, ABTS, and DPPH), with values of 1475.3 μM trolox/g extr, 1950.3 μM trolox/g extr, and 977.7 μM trolox/g extr, respectively. In addition, up to 36 individual polyphenols were identified by chromatography. Regarding the biomedical aspects of the petal extract, it exhibited antitumoral activity against ovarian carcinoma cells evaluated by the MTS assay, revealing a lower value of IC50 compared to other flower extracts. For example, the extract from T. erecta reported an IC50 value half as low as an extract from Rosa × hybrida and six times lower than another extract from Tulbaghia violacea. This antitumoral effect of T. erecta arises from the induction of the apoptotic process; thus, incubating ovarian carcinoma cells with the petal extract increased the rate of apoptotic cells measured by flow cytometry. Moreover, the extract also demonstrated efficacy as a therapeutic agent against tauopathy, a feature of Alzheimer’s disease (AD) in the Caenorhabditis elegans experimental model. Treating worms with the experimental extract prevented disfunction in several motility parameters such as wavelength and swimming speed. Furthermore, the T. erecta petal extract prevented the release of Reactive Oxygen Species (ROS), which are associated with the progression of AD. Thus, treatment with the extract resulted in an approximate 20% reduction in ROS production. These findings suggest that these petals could serve as a suitable source of polyphenols for biomedical applications
Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance
Get PDFMonitoring tool conditions and sub-assemblies before final integration is essential to reducing processing failures and improving production quality for manufacturing setups. This research study proposes a real-time deep learning-based framework for identifying faulty components due to malfunctioning at different manufacturing stages in the aerospace industry. It uses a convolutional neural network (CNN) to recognize and classify intermediate abnormal states in a single manufacturing process. The manufacturing process for aircraft factory products comprises different phases; analyzing the components after the integration is labor-intensive and time-consuming, which often puts the company’s stake at high risk. To overcome these challenges, the proposed AI-based system can perform inspection and defect detection and alleviate the probability of components’ needing to be re-manufacturing after being assembled. In addition, it analyses the impact value, i.e., rework delays and costs, of manufacturing processes using a statistical process control tool on real-time data for various manufactured components. Defects are detected and classified using the CNN and teachable machine in the single manufacturing process during the initial stage prior to assembling the components. The results show the significance of the proposed approach in improving operational cost management and reducing rework-induced delays. Ground tests are conducted to calculate the impact value followed by the air tests of the final assembled aircraft. The statistical results indicate a 52.88% and 34.32% reduction in time delays and total cost, respectively