Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of the Potential Risk of Mortality from SARS-CoV-2 Infection in Hospitalized Patients According to the Charlson Comorbidity Index

Background: The pandemic of COVID-19 has represented a major threat to global public health in the last century and therefore to identify predictors of mortality among COVID-19 hospitalized patients is widely justified. The aim of this study was to evaluate the possible usefulness of Charlson Comorbidity Index (CCI) as mortality predictor in patients hospitalized because COVID-19. Methods: This study was carried out in Zacatecas, Mexico, and it included 705 hospitalized patients with suspected of SARS-CoV-2 infection. Clinical data were collected, and the CCI score was calculated online using the calculator from the Sociedad Andaluza de Medicina Intensiva y Unidades Coronarias; the result was evaluated as mortality predictor among the patients with COVID-19. Results: 377 patients were positive for SARS-COV-2. Obesity increased the risk of intubation among the study population (odds ratio (OR) = 2.59; 95 CI: 1.36–4.92; p = 0.003). The CCI values were higher in patients who died because of COVID-19 complications than those observed in patients who survived (p < 0.001). Considering a CCI cutoff > 31.69, the area under the ROC curve was 0.75, with a sensitivity and a specificity of 63.6% and 87.7%, respectively. Having a CCI value > 31.69 increased the odds of death by 12.5 times among the study population (95% CI: 7.3–21.4; p < 0.001). Conclusions: The CCI is a suitable tool for the prediction of mortality in patients hospitalized for COVID-19. The presence of comorbidities in hospitalized patients with COVID-19 reflected as CCI > 31.69 increased the risk of death among the study population, so it is important to take precautionary measures in patients due to their condition and their increased vulnerability to SARS-CoV-2 infection.
A bundler prototype for forestry and agricultural residue management for energy production

ABSTRACT In recent years, a large amount of wood biomass has been produced for energy use by the agricultural and forestry sectors. The goal of this study was to set up a chip bundler prototype which can work with different feedstocks (agricultural and forestry residues). During tests, the prototype was able to produce bundles with a weight between 18 and 20 kg requiring an energy input of between 0.54 and 0.58 MJ kg−1, respectively. The production cost of a single bundle was €2.43, independent of the loading type (mechanical or manual) adopted. The bundler tested seems ideal for the agro-forestry sector because it shows a high versatility: the form and size of the biomass processed does not interfere with productivity or cost. In addition, the prototype requires a small amount of power and a small investment. Because of these reasons, the bundler can be used by individual farmers and small businesses.
Modeling of a Symmetric Five-Bar Displacement Amplification Compliant Mechanism Using Energy Methods

We present a symmetric five-bar compliant mechanism for the displacement amplification of mechanical vibration. When the proposed mechanism is connected to an energy harvester, amplification of the input excitation vibration amplitude leads to an increase in the harvested power. Displacements in the compliant mechanism are caused by deflections in its flexure hinges. The flexure hinges we use are either of the right-circular, or the corner-filleted types. The mechanism is analyzed using energy methods. The displacement amplification was verified analytically and numerically using a finite element model. Through our model we present relations governing the displacement amplification in terms of the design parameters, such as the geometry of the mechanism, the flexure hinges dimensions, in addition to the load caused by the harvester. The effects of the flexure hinge dimensions on displacement amplification, are also presented.Copyright © 2015 by ASME
Simulating the Spatial Heterogeneity of Housing Prices in Wuhan, China, by Regionally Geographically Weighted Regression

Geographically weighted regression (GWR) is an effective method for detecting spatial non-stationary features based on the hypothesis of proximity correlation. In reality, especially in the social and economic fields, research objects not only have spatial non-stationary characteristics, but also spatial discrete heterogeneity characteristics. Therefore, how to improve the accuracy of GWR estimation in this case is worth studying. In this paper, a regionally geographically weighted regression (RGWR) is proposed. Using incoming dummy variables, the zoning discrimination is added to the spatial kernel function of GWR, the spatial kernel function is modified, the spatial weight is optimized, and the influence of “near heterogeneous” observation points is reduced. In this paper, the residential sale price in Wuhan City is taken as an example in the analysis of three aspects: model performance, fitting effect and influencing factors. The results show that the introduction of a zoning dummy variables can significantly improve the model accuracy of a fixed bandwidth and adaptive bandwidth. Under a fixed bandwidth, compared with the GWR model, RGWR increases R2 and R2adj from 0.6776 and 0.6732 to 0.777 and 0.7746, respectively, and the Akaike information criterion, corrected (AICc) standard decreases by 37.4006 compared with GWR, which proves the effectiveness of the method.
UgameFeature: Automatic Code Generation for Unity Game Projects

: Computer games are complex software systems, which means that their development requires some level of programming skills. However, their design also involves the creation of game objects (characters, scenarios, etc.), animations and story lines, which are designed by game domain experts, who in general have minimal (or no) programming skills. Game engines have been developed to facilitate game development by reducing programming efforts and enhancing productivity, but we observed that most of these engines still require programming skills in order to be used. In this paper, we discuss how Model-Driven Engineering technologies, particularly metamodelling and model transformations, can be used to facilitate game development. We deﬁne a Domain Speciﬁc Language called UGameFeature to be used by game designers to deﬁne games that can be automatically transformed into scripts that can be executed by the Unity game engine. In order to facilitate the code generation step, we deﬁned an intermediate metamodel, so that structural differences between the UGameFeature metamodel and the Unity engine scripts can be accommodated by an intermediate model-to-model transformation. We claim that with this approach we could deﬁne a streamlined process to go from game design to game implementation, in this way surpassing the beneﬁts already offered by game engines. We also discuss some practical obstacles of applying MDE techniques and give recommendations to practitioners who want to apply them in their projects.
Evaluación de la Ivermectina más Anticoagulación versus Anticoagulación en Pacientes Críticos no Ventilados, con COVID-19: Ensayo Clínico Multicentrico en Cochabamba, Bolivia

Objetivos: evaluar la ivermectina vs la anticoagulación en los pacientes críticos con COVID-19 no ventilados, y su efecto en la clínica del paciente, parámetros laboratoriales, evolución y complicaciones en Hospitales de 2do y 3er nivel (Hospital Sud y Hospital Obrero Nro. 2 de la Caja Nacional de Salud-CNS), gestión 2020-2021. Material y métodos: ensayo clínico, aleatorizado; julio 2020 a enero 2021, rama con ivermectina (0,2 mg/k) +ANCO y otra ANCO; con dos grupos de variables y tres evaluaciones. Resultados: 44 pacientes inscritos, 27 al Hospital Sud y 17 CNS; 64% al grupo de ivermectina+ANCO, 57% sexo masculino. Media de edad 57,3 años; de hospitalización 13 días; obesidad 40%; posibilidad de recuperación con ivermectina 10 días y ANCO > de 20 días. La media de la saturación de oxigeno inicial (SatO2) 79% (PaO2/FiO2=60) en el grupo con ivermectina+ ANCO y 75% (55) en el grupo ANCO; en una medición final 86% (138) y 83% (120)respectivamente. La primera evaluación, mostro Rho Spearman=-0,7 entre SatO2 y frecuencia respiratoria (FR) y un Rho=-0,7 entre pH y FiO2 en la tercera evaluación. Sobrevida de 26 días, RR=0,98, IC 95% (0,49-1,98), sin diferencias en ambas ramas (p=0,94); 27% intubados con ivermectina+ANCO y 21% ANCO; >60% ingresaron a la terapia intensiva; probabilidad de necesitar Intubación, con RR de 0,76, IC 95% (0,42-1,40), (p=0,4). Conclusiones: Ivermectina no reduce la mortalidad ni posibilidad de intubación. Evolución depende de gravedad, si están polimedicados, días de progresión y uso de oxígeno. Es necesario estandarizar combinaciones, dosis, muestras grandes y homogéneas; por el momento no podemos recomendar el uso de la ivermectina.
From Magnitudes to Geometry and Back: De Zolt's Postulate

Abstract A crucial trend of nineteenth‐century mathematics was the search for pure foundations of specific mathematical domains by avoiding the obscure concept of magnitude. In this paper, we examine this trend by considering the “fundamental theorem” of the theory of plane area: “If a polygon is decomposed into polygonal parts in any given way, then the union of all but one of these parts is not equivalent to the given polygon. ” This proposition, known as De Zolt's postulate, was conceived as a strictly geometrical expression of the general principle of magnitudes “the whole is greater than the part. ” On the one hand, we illustrate this striving for purity in the foundations of geometry by analysing David Hilbert's classical proof of De Zolt's postulate. On the other hand, we connect this geometrical problem with the first axiomatizations of the concept of magnitude by the end of the nineteenth century. In particular, we argue that a recent result in the logical analysis of the concept of magnitude casts new light on Hilbert's proof. We also outline an alternative development of a theory of magnitude that includes a proof of De Zolt's postulate in an abstract setting.
The Impact of Social Media on the Academic Life and Performance of High School Students in Eastern Province, Saudi Arabia

Social media has a vital part of an individual's life that allows people to communicate with one another. In recent days the prevalence of addiction to social media among students is a serious issue. The objective of this study was to assess the impact of social media on the academic life and performance of high school students in Saudi schools, in Eastern Province Saudi Arabia. This was a cross-sectional questionnaire-based study conducted in a Saudi school over 3 months (Nov 2020 Jan 2021), where 203 high school students were participated in the study. Data collected from the questionnaire included the academic performance in terms of school marks, the duration of social media use, time spend for studying, and social media addiction. Data analysis was performed using a statistical package for social science version 26 (SPSS) was used. Most of the participants were male students 131 (64%) and half of them 82 (40.4%) were in their academic second year. The mean time spent by students studying in a week was 18.50(M=18.50, SD = 8.33), while 3.79 mean hours per day were spent by students to use social media (M= 3.79, SD=2.65). A large majority of the participants using WhatsApp 191(94%) followed by snap chat 176 (86.7%). About 171 (84.2%) agreed that the time spend on social networks takes up their time for studying. Based on Internet distraction, 81.3% of the participants agreed that social network is the main reason for their distraction in studies. The time spend on studying per week were significantly associated with gender (P= 0.0001), age (P= 0.0001). Also there was significant difference between male and female in the final exam scoring (P= 0.0001). The results found that the use of social media among Saudi school students’ found high. Additionally, findings reported that the use of social media has distracted students from studying.
Assessment of Appropriate Reference Genes
for Quantitative Real-Time Polymerase Chain
Reaction Normalisation in Magnolia sieboldii
K. Koch across Various Experimental
Conditions

Magnolia sieboldii K. Koch is a famous ornamental plant. The deep dormancy of the seeds makes breeding difficult and prevents cultivation for economic purposes. Gene expression profiles of germination-related genes can provide clues for understanding the molecular mechanism of dormancy breaking. The accuracy of results acquired through quantitative real-time polymerase chain reaction (RT-qPCR), which was developed for investigating the gene expression profiles, is determined by the suitability of the selected reference gene. Nevertheless, appropriate RT-qPCR reference genes have not been determined in M. sieboldii. In the present study, seven potential reference genes in diverse M. sieboldii sample subgroups of different organ tissues, seed developmental stages, and seed subjected to imbibition and stratification treatments were checked through RT-qPCR. The gene expression stability was assessed and analysed through three statistical algorithms, namely, geNorm, NormFinder, and BestKeeper. Cyclophilin (CYC) and ubiquitin-conjugating enzyme (UCE) were identified as the best reference genes for diverse organs. CYC and UCE were evaluated as suitable reference genes in seeds in diverse periods of development. Copper/zinc superoxide dismutase (Cu/Zn-SOD) and manganese superoxide dismutase (Mn-SOD) served as optimal reference genes during seed imbibition treatment, whereas Cu/Zn-SOD and 30S ribosomal protein S13 (RPS13) were optimal during seed stratification treatment. In M. sieboldii, Cu/Zn-SOD was recommended for RT-qPCR normalisation, whereas DBP was inappropriate for gene expression analysis. The expression pattern of DELAY OF GERMINATION1 (DOG1) was analysed for validating reference gene creditability better. The present study provides *e-mail: zhanglin_25683921@hotmail.com **e-mail: lxjsyau@syau.edu. cn DOI: 10.15244/pjoes/143508 ONLINE PUBLICATION DATE:
Postural Stability Analysis of Compass-like Biped Robot Model

This paper focuses on the problem of postural stability of a biped robot. We consider a compass-like biped model and analyze its postural stability based on the energy dissipation when the idling leg collides against the ground surface. We calculate the amount of energy dissipation by using rigid dynamics. We propose a novel criterion PSC (Postural Stability Criterion) that judges postural stability of the robot. Finally the effectiveness of the proposed criterion with two other indexes is demonstrated by computer simulation.
Disaggregating residential hot water use. Part 2

A major obstacle to gathering detailed data on end-use hot water consumption within residences and commercial buildings is the cost and complexity of the field tests. An earlier study by the authors presented a methodology that could accurately disaggregate hot water consumption into individual end-uses using only information on the flow of hot water from the water heater. The earlier methodology can be extended to a much larger population of buildings, without greatly increasing the cost and complexity of the data collection and analysis, by monitoring the temperature of the hot water lines that go to different parts of the building. For the three residences studied here, thermocouples /monitored the temperatures of four hot water lines at each site. The thermocouple readings provide a positive indication of when hot water starts to flow in a line. Since the end-uses served by each hot water line are known, the uncertainty in assigning a draw to a particular end-use is greatly reduced. Benefits and limitations for the methodology are discussed in the paper. Using the revised methodology, hot water usage in three residences is disaggregated into the following end-uses: showers, baths, clothes washing, dishwashing, kitchen sink, and bathroom sink. For two residences,more » the earlier methodology--which does not use the thermocouple data--is also used to disaggregate the same draw data. « less
Path-Analysis-Based Reinforcement Learning Algorithm for Imitation Filming

Imitation filming has been applied to autonomous filming by mimicking human operators. To imitate the operation of cameramen when filming multiple human actions, existing methods plan the camera motion through time series prediction or train multiple models to handle a particular style in a specific situation. As a result, these methods require various settings to adapt to different scenarios. In this work, we overcome such limitations and propose an end-to-end imitation learning framework for drone cinematography systems. The framework consists of two main components: (1) an efficient motion feature extraction module for generating a compact motion feature space, (2) a path-analysis-based reinforcement learning (PABRL) algorithm for imitating multiple filming styles from demonstrations and incorporating aesthetical features for improved perspective shots. Our PABRL method is based on the actor–critic network, which regards multiple human motion variables, camera translations, and image composition as inputs and then outputs an aesthetical filming strategy related to the subject motion. In addition, we propose an attention mechanism and a long–short-term rewarding function to enhance the motion feature space and the integrity of the generated trajectory, respectively. Extensive experimental results in simulated and real outdoor environments demonstrate that compared with state-of-the-art methods, our method can achieve 69.8% higher performance in terms of trajectory planning accuracy while successfully incorporating aesthetical features into the captured videos.
Scan2Part: Fine-grained and Hierarchical Part-level Understanding of Real-World 3D Scans

: We propose Scan2Part, a method to segment individual parts of objects in real-world, noisy indoor RGB-D scans. To this end, we vary the part hierarchies of objects in indoor scenes and explore their effect on scene understanding models. Speciﬁcally, we use a sparse U-Net-based architecture that captures the ﬁne-scale detail of the underlying 3D scan geometry by leveraging a multi-scale feature hierarchy. In order to train our method, we introduce the Scan2Part dataset, which is the ﬁrst large-scale collection providing detailed semantic labels at the part level in the real-world setting. In total, we provide 242,081 correspondences between 53,618 PartNet parts of 2,477 ShapeNet objects and 1,506 ScanNet scenes, at two spatial resolutions of 2cm 3 and 5cm 3 . As output, we are able to predict ﬁne-grained per-object part labels, even when the geometry is coarse or partially missing. Overall, we believe that both our method as well as newly introduced dataset is a stepping stone forward towards structural understanding of real-world 3D environments.
A Supervised Progressive Growing Generative Adversarial Network for Remote Sensing Image Scene Classification

Remote sensing image scene classification is a challenging task. With the development of deep learning, methods based on convolutional neural networks (CNNs) have made great achievements in remote sensing image scene classification. Since the training of a CNN requires a large number of labeled samples, a generative adversarial network (GAN) for sample generation represents a new opportunity to solve the problem of the limited samples. However, most of the existing GAN-based sample generation methods can only generate unlabeled samples, instead of samples labeled with the corresponding scene category. In this article, to solve the problem, a supervised progressive growing generative adversarial network (SPG-GAN) is proposed for remote sensing image scene classification. The proposed method can generate labeled samples for the remote sensing image scene classification, significantly improving the classification accuracy in the case of limited samples. The SPG-GAN method has two main improvements. First, a conditional generative framework for labeled samples is proposed, in which the label information is added in the channel dimension as the input. By considering the constraints of the label information in the loss function, the network can be trained in the direction of a specific category. As a result, the network can generate remote sensing image scene classification samples with label categories. Second, a progressive growing sample generation method is introduced. In order to ensure that the generated samples have more spatial details, they are generated by progressively adding modules to the generator and discriminator, thereby ensuring that the generated sample is of better quality. After testing on two benchmark datasets and carrying out a large-scale experiment in the central area of the city of Wuhan in China, it was found that the proposed method can obtain a superior scene classification accuracy in the case of limited samples.
IMPLEMENTAÇÃO DE ESTRATÉGIA EMPREENDEDORA INTERNACIONAL NO SETOR DE VINHOS: O CASO DA VINÍCOLA MIOLO Implementing of International Entrepreneurial Strategy in Wine's Market: The Vinicola Miolo's Case

In terms of theoretical development there is in the strategy field study a significant gap between strategy formulation process and strategy implementation process. Although these factors are interconnected and fundamental to the strategic management, the formulation has received more attention academically and professionally. As well as the internationalization companies process, the rapprochement between strategy and entrepreneurship fields also need to be focused on the literature of strategic management. Based on this academic and business context, this paper aims to explore theoretical elements having as object the implementation process of the internationalization strategy of Miolo company. This work is characterized as a descriptive and exploratory qualitative case study. Based on case analysis it has been possible to identify aspects that illustrate the strategy implementation, the internationalization strategy and entrepreneurship, such as the role of learning, knowledge, and development of resources and capabilities.