Miracle-dz/UniFAR-FTUs huggingface dataset:
Beneficios de la actividad física para la promoción de un envejecimiento activo en personas mayores. Revisión bibliográfica

espanolEn las ultimas decadas se observan cambios demograficos significativos, se observa una tendencia progresiva en cuanto alenvejecimiento de la poblacion, la longevidad se trata de una caracteristica presente en nuestra sociedad. Dicho cambio esta propiciandoel desarrollo de diversas politicas orientadas a fomentar un envejecimiento activo y saludable, teniendo en cuenta los beneficios de laactividad fisica para su promocion. El principal objetivo de este estudio se centra en realizar una revision sistematica de estudios sobrela incidencia positiva de la actividad fisica en el bienestar de las personas mayores asociado a un envejecimiento saludable. Para ello, seutilizaron los estudios recogidos en la coleccion principal de la base de datos Web of Science, seleccionando las revistas con mayor indicede impacto. La muestra consta de 15 articulos, los cuales se estudian a fondo para desglosar la informacion mas relevante. Los articuloshan sido seleccionados teniendo en cuenta las siguientes variables: objetivos, diseno, muestra e instrumentos y resultados. Los articulosanalizados muestran que la practica diaria de actividad fisica incrementa los niveles de autoestima e incide en la felicidad de los mayores,ademas de contribuir a mejorar la capacidad para el autocuidado, favorecer la integracion del esquema corporal y facilitar las relacionesintergeneracionales, entre otros aspectos positivos. Es necesario promover un envejecimiento activo y saludable en mayores basado enla promocion de la actividad fisica. EnglishA significant change is taking place nowadays, i.e. the progressive tendency to aging of the elderly population: longevity is avery latent characteristic in our society. This change is favoring the development of various policies for the preparation of the populationtowards an active and healthy aging, taking into account the benefits of physical activity for its promotion. The main objective of thisstudy is to review the bibliography on the positive impact of physical activity on elders’ well-being associated with healthy aging.Studies collected in the Web of Science database were chosen from those journals with the highest impact index. The sample consistedof 15 articles, which were thoroughly studied so to break down the most relevant information. The articles were selected taking intoaccount the following variables: objectives, design, sample and instruments, and results. The analyzed articles showed how daily practiceof physical activity increases self-esteem levels, affects elderly’s happiness, contributes to improving self-care competence, promotescorporal scheme integration, facilitates intergenerational relations, increases social participation, creates positive changes in lifestyle,improves sleep quality, reduces anxiety, stress, depression and insomnia, and reinforces intellectual activity due to good cerebraloxygenation, among other effects. In the third age it is necessary to promote an active and healthy aging based on the promotion ofphysical activity.
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules

A wide variety of deep reinforcement learning (DRL) models have recently been proposed to learn profitable investment strategies. The rules learned by these models outperform the previous strategies specially in high frequency trading environments. However, it is shown that the quality of the extracted features from a long-term sequence of raw prices of the instruments greatly affects the performance of the trading rules learned by these models. Employing a neural encoder-decoder structure to extract informative features from complex input time-series has proved very effective in other popular tasks like neural machine translation and video captioning in which the models face a similar problem. The encoder-decoder framework extracts highly informative features from a long sequence of prices along with learning how to generate outputs based on the extracted features. In this paper, a novel end-to-end model based on the neural encoder-decoder framework combined with DRL is proposed to learn single instrument trading strategies from a long sequence of raw prices of the instrument. The proposed model consists of an encoder which is a neural structure responsible for learning informative features from the input sequence, and a decoder which is a DRL model responsible for learning profitable strategies based on the features extracted by the encoder. The parameters of the encoder and the decoder structures are learned jointly, which enables the encoder to extract features fitted to the task of the decoder DRL. In addition, the effects of different structures for the encoder and various forms of the input sequences on the performance of the learned strategies are investigated. Experimental results showed that the proposed model outperforms other state-of-the-art models in highly dynamic environments.
Purification, Characterization And Immobilization Of Lignin Peroxidase Produced By Ganoderma Lucidum For Industrial Applications

Microorganisms are commonly used in biotechnological and environmental processes through exploitation of their natural catalytic activities.An indigenous white rot fungus Ganoderma lucidum IBL-05 was used for Lignin peroxidase (LiP) production using wheat straw as lignocellulosic substrate in solid state fermentation (SSF). The SSF process for LiP production was improved by optimizing some physical and nutritional parameters at pre-optimized pH 4.5 and temperature 35oC. By optimization different physical and nutritional factors, LiP production by the fungus was substantially enhanced and the maximum LiP activity (1019.6 IU/mL) was obtained after 48h with Medium III, 60% moisture level, 5mL inoculum size, glucose as and urea as a carbon and nitrogen source in 15:1 C/N ratio, 1mL of 2mM Zn2+ as metal ion, 1mL of 4mM 4-MMA as LiP mediator. Surfactants like Tween-80, Tween-20 and SDS suppressed LiP synthesis by G. luucidum IBL-05. LiP produced under optimum conditions was purified by 80% of ammonium sulphate precipitation, dialysis, ion exchange chromatography and Sephadex G-200 gel filtration chromatography to 6.4 fold to get 16.8 % yield and 2860.049 U/mg specific activity.The purified LiP was run on SDS-PAGE to determine its molecular organization and molecular mass.The presence of two bands (47 kDa and 28 kDa) on SDS-PAGE confirmed that the enzyme was an oligomeric protein composed of two polypeptide chains.Immobilization of LiP using xerogels of different hydrophobicity enhanced its activity as compared to free LiP.The enzyme entrapped in 1:5 P/T ratio gel was the most active. Immobilized and free. The immobilized LiP showed optimum pH 3 and optimum temperature 55oC while free LiP showed the same optimum pH 3 but optimum temperature was 40oC. Immobilized LiP was more thermostable as compared to free LiP. The Km values for free and immobilized LiP were 0.83 and 0.5 mM, respectively and their respective Vmax values were 545.6 and 639.7 mM/min, respectively. Immobilized LiP was more efficient decolorizer of selected textile dyes and practical textile industry effluents.
High-Order Control Barrier Functions

We approach the problem of stabilizing a dynamical system while optimizing a cost and satisfying safety constraints and control limitations. For (nonlinear) affine control systems and quadratic costs, it has been shown that control barrier functions (CBFs) guaranteeing safety and control Lyapunov functions (CLFs) enforcing convergence can be used to (conservatively) reduce the optimal control problem to a sequence of quadratic programs (QPs). Existing works in this category have two main limitations. First, with one exception, they are based on the assumption that the relative degree of the system with respect to a function enforcing a safety constraint is one. Second, the QPs can easily become infeasible, in particular for problems with many safety constraints and tight control limitations. We propose high-order CBFs (HOCBFs), which can accommodate systems of arbitrary relative degrees. For each safety constraint, by using Lyapunov-like conditions, we construct a set of controls that renders the intersection of a set of sets forward invariant, which implies the satisfaction of the original constraint. We formulate optimal control problems with constraints given by HOCBF and CLF, and propose two methods—the penalty method and the parameterization method—to address the feasibility problem. Finally, we show how our methodology can be extended for safe navigation in unknown environments with long-term feasibility. We illustrate the proposed framework on adaptive cruise control and robot control problems.
Regular non-semisimple Dubrovin–Frobenius manifolds

We study regular non-semisimple Dubrovin–Frobenius manifolds in dimensions 2, 3, and 4. Our results rely on the existence of special local coordinates introduced by David and Hertling [Ann. Sc. Norm. Super. Pisa, Cl. Sci. 17(5), 1121–1152 (2017)] for regular flat F-manifolds endowed with an Euler vector field. In such coordinates, the invariant metric of the Dubrovin–Frobenius manifold takes a special form, which is the starting point of our construction. We give a complete classification in the case where the Jordan canonical form of the operator of multiplication by the Euler vector field has a single Jordan block, and we reduce the classification problem to a third-order ordinary differential equation and to a system of third-order PDEs in the remaining three-dimensional and four-dimensional cases. In all the cases, we provide explicit examples of Dubrovin–Frobenius potentials.
Sparse Channel Estimation in OFDM System

OFDM is widely used in wireless communications due to its capacity of high data rate transmission over frequency selective channel. For coherent detection of OFDM symbols, channel frequency responses must be estimated, which is usually done with the help of pilot tones. Frequency domain estimation methods are often employed with interpolation methods. Usually, interpolation methods introduce an error floor. The easy solution of increasing the number of pilots decreases the spectral efficiency of the system. Another solution is to work in the time domain. In general, the channel impulse response contains a limited number of significant values having more energy than the noise. In the case of sparse channel, this number is much smaller than that of pilot subcarriers. To improve the sparse channel estimation, some kind of threshold is needed. In this thesis, in the case where the number of pilots is larger than the length of cyclic prefix, a time domain sparse channel estimation method based on an original threshold is proposed. This method with high spectral efficiency, good channel estimation performance, low computational complexity, requires no prior knowledge of both the channel statistics and noise standard deviation. In the case where the channel is sparse with large delay spread, we propose an original channel estimation technique based on compressed sensing theory. The proposed method requires smaller number of pilots than that of classical frequency domain techniques. This work ends with the study of non-sample spaced sparse channel; the idea of smart measurement matrix is proposed to improve the efficiency of the classical CS based estimation methods.
Developing programming skills on digital native children through the interaction with smart devices

Nos dias de hoje o pensamento computacional e uma das habilidades mais importantes que uma pessoa deve desenvolver para se preparar melhor pro futuro proximo. Em poucos anos essa habilidade sera tao importante como ler e escrever, pessoas precisarao aprender a programar e resolver problemas com pensamento computacional desde cedo. Estudos que tentam estimular a introducao dessas habilidades para criancas sao feitos desde 1967 quando o Institudo de Tecnologia de Massachusetts criou a primeira linguagem para esse publico chamada LOGO. Embora os estudos na area de desenvolvimento do pensamento computacional em criancas tenha comecado a mais de seis decadas atras, a importância de ensinar programacao em escolas nao e amplamente difundida, em lugares como Brasil, essa habilidade esta comecando a ser introduzida a criancas com mais de 10 anos de idade. Por outro lado, nos Estados Unidos e em alguns paises europeus diversas abordagens vem sendo usadas para introduzir esses conceitos para criancas de 4 a 12 anos de idade, normalmente sao criados brinquedos e jogos que podem ajudar a desenvolver tais conceitos. Infelizmente a maioria dessas abordagens sao focadas em criancas alfabetizadas, poucas nao requerem a habilidade de leitura, limitando a idade minima a 6 anos de idade. Esse trabalho argumenta que criancas muito novas nao somente sao capazes de desenvolver algoritimos e iniciar o desenvolvimento de habilidades do pensamento computacional, como essa pratica sera bastante proveitosa para o futuro deles. E apresentada uma pesquisa envolvendo 9 criancas com idade entre 4 e 6 anos, onde as criancas selecionadas jogam um jogo desenvolvido especialmente para este trabalho, e a performance deles foi capaz de produzir dados que foram analisados para testar a hipotese principal que e " Criancas muito novas podem desenvolver pensamento algoritimico jogando jogos de programacao ", adicionalmente, enquanto a literatura foi revisada, problemas relacionados aos efeitos de permitir criancas a usar dispositivos moveis e internet sem a supervisao dos responsaveis foram identificados, para defender o uso desse tipo de tecnologia na educacao de criancas as possiveis causas e meios de evitar esses problemas foram levantados, os resultados desse trabalho sao encorajadores, todas as criancas envolvidas foram aptas a jogar o jogo desenvolvido com uma boa performance.
Canarium odontophyllum Miq. (Dabai) Leaf Phytoextracts and Their Medicinal Properties

Canarium odontophyllum Miq. , renowned locally as “dabai,” is an endemic plant in Sarawak, Malaysia. Most people, especially in rural areas, consume this plant to maintain their health. A few medicinal studies have investigated C. odontophyllum Miq. ’s biological properties to substantiate its use as nutraceuticals and health supplements. Crude extracts from C. odontophyllum Miq. demonstrated various medicinal properties, including antibacterial, antimalarial, antioxidant, anticancer, antidiabetic, and antihypertensive. In addition, numerous phytoextracts studies have revealed the existence of a variety of beneficial compounds, including flavonoids, tannins, and terpenoids. However, despite various biological activities of C. odontophyllum Miq. , there is currently no analysis summarizing the medicinal properties of its leaf. Thus, this short communication attempts to narrate the medicinal properties of C. odontophyllum Miq. leaf and their phytoextracts responsible. In conclusion, we summarized C. odontophyllum Miq. leaf promising therapeutic effects with their phytoextracts and a step closer to developing it as potential nutraceuticals and health supplements to fulfill social interest.
Software aging prediction – a new approach

To meet the users’ requirements which are very diverse in recent days, computing infrastructure has become complex. An example of one such infrastructure is a cloud-based system. These systems suffer from resource exhaustion in the long run which leads to performance degradation. This phenomenon is called software aging. There is a need to predict software aging to carry out pre-emptive rejuvenation that enhances service availability. Software rejuvenation is the technique that refreshes the system and brings it back to a healthy state. Hence, software aging should be predicted in advance to trigger the rejuvenation process to improve service availability. In this work, the k-nearest neighbor (k-NN) algorithm-based new approach has been used to identify the virtual machine's status, and a prediction of resource exhaustion time has been made. The proposed prediction model uses static thresholding and adaptive thresholding methods. The performance of the algorithms is compared, and it is found that for classification, the k-NN performs comparatively better, i.e., k-NN showed an accuracy of 97.6. In contrast, its counterparts performed with an accuracy of 96.0 (naïve Bayes) and 92.8 (decision tree). The comparison of the proposed work with previous similar works has also been discussed.
Maximum likelihood estimation of regression models with autoregressive-moving average disturbances

SUMMARY The regression model with autoregressive-moving average disturbances may be cast in a form suitable for the application of Kalman filtering techniques. This enables the generalized least squares estimator to be calculated without evaluating and inverting the covariance matrix of the disturbances. The problem of forecasting future values of the dependent variable is also effectively solved when the Kalman filter technique is applied. Furthermore, the properties of the residuals produced by the filter suggest that they may be useful for diagnostic checking of the model. The Kalman filter algorithm also forins the basis of a method for the exact maximum likelihood estimation of the model. This may well have computational, as well as theoretical, advantages over other methods. In a general formulation of the linear regression model, the disturbances may be assumed to be generated by an autoregressive-moving average process. Most work, however, has been restricted to the estimation of models with purely autoregressive disturbances. Models with autoregressive-moving average, or even pure moving average, errors have been much less popular, the main reason for this being computational difficulties. At first sight, it is necessary to evaluate the covariance matrix of the disturbances, and then to invert it. Since the matrix is n x n, where n is the sample size, this can be time consuming, although, as Akaike (1973) and Galbraith & Galbraith (1974) have shown, the special form of the covariance matrix means that it can be inverted relatively efficiently. Maximum likelihood estimation of the regression model with autoregressive-moving average errors has been considered by Pierce (1971). The method of estimation he adopts is basically an extension of the familiar 'conditional sum of squares' approach employed by Box & Jenkins (1970) in their treatment of autoregressive-moving average time series models. This method avoids any large-scale matrix inversions, but it does lead to an estimation problem which is nonlinear in the vector of regression coefficients as well as in the parameters of the autoregressive-moving average process. Furthermore, the estimators obtained in this way are only approximations to the full maximum likelihood estimators. A number of authors, for example Newbold (1974) and Osborn (1976), have recently stressed the desirability of computing estimators of autoregressive-moving average parameters using the exact likelihood function, and this view is reiterated in the context of regression models
Towards an YouTube Verified Content System based on Blockchain Approach

— YouTube connects people with each other through an online video sharing service platform. With the great development of the entertainment industry, content on YouTube is accessible to many people of different ages. However, verifying the content posted on YouTube is clean or not is a difficult problem. Dirty content is violent, pornographic and vulgar content that causes serious psychological harm to the segment of users under the age of 18, i.e., especially those of an age who are not yet aware of the harmful effects of content. Toxic will bring to the child’s behavior. Agree that Google (i.e., YouTube) has developed a YouTube Kid application where the videos are only for children under the age of 13. However, cultural and educational differences between regions strongly influence the choice of children. Select content for children. Therefore, the content restrictions on the YouTube Kid application have not yet met all the requirements of parents around the world. There have been many development directions to identify videos containing malicious content based on deep learning. However, there is no method to build a tool to support parents of children to share and identify videos with objectionable content (e.g., violence, pornography, obscene words) on the YouTube platform. In this research paper, we introduce YVC, a YouTube-verified content platform by applying blockchain’s distributed, public validation. This tool helps parents validate YouTube content and issue a report to reduce dirty content on YouTube. To demonstrate the effectiveness of our approach, we implement the proof-of-concept in the three most popular EVM platforms: Ethereum, Fantom, and the Binance smart chain. Compared to the YouTube Kids (i.e., the most common shared video platform for the under 13-year-old kid), our approach is able to capture the video preferences of the parents covering the difference areas/countries.
DYNAMIC MODEL UPDATING—A MULTIPARAMETER EIGENVALUE PROBLEM

Abstract Analytical models of linear elastomechanical systems are often updated by model parameter estimation using input–output measurements or modal test results. The structure of the model equations and the parametrisation of the spatially discretised model—often a sum of matrices multiplied each by a dimensionless adjustment parameter—are usually assumed to be consistent with the system under test. Thus, the analytical model can be corrected by estimating the actual values of the dimensionless adjustment parameters. There is, however, a lack of measurement information that is caused by the difference between the number of degrees of freedom (dof) of the analytical model and the number of measured dof. That is not only due to large-scale models but is also due to the fact that rotational dof are generally not measured. This lack of information is also the reason for the non-linear character of this kind of model updating because it is in general a simultaneous parameter and state estimation problem. Due to its intrinsic non-linearity the inverse problem is ill-posed with the latent risk of biased parameter estimates. In this paper it is shown that the problem of updating linear analytical models can be transferred to a multiparameter eigenvalue problem that needs only a minimum set of test data for estimating the actual model parameter values with a negligibly small risk of biased estimates. The presented method is well-founded by its rigorous mathematical deduction.