Miracle-dz/UniFAR-FTUs huggingface dataset:
Midterm Results Comparing Perventricular Device Closure with Surgical Repair for Isolated Congenital Ventricular Septal Defects: A Systematic Review and Meta-Analysis

Background : This systematic review and meta-analysis aimed at comparing the midterm outcomes of perventricular device closure (PDC) with conventional surgical repair (CSR) for VSD. Methods : PubMed, Cochrane Library, and Web of Science databases were searched from January 1, 2005, to October 15, 2020, for English or Chinese language studies comparing outcomes of PDC with CSR for VSD. The midterm results were assessed as a primary outcome. A systematic review and meta-analysis was performed under the frequentist frame with risk ratio (RR) and 95% confidence interval (CI). Results : A total of 4381 patients (PDC = 2016, CSR = 2365) from 15 studies were included. The pooled estimates of success rate favored the CSR compared with the PDC (RR, 0.97; 95% CI, 0.96 to 0.99; p = 0.001). No significant differences in minor complications or severe complications were found between the PDC and CSR (RR, 0.79; 95% CI, 0.50 to 1.23; p = 0.29; RR, 1.43; 95% CI, 0.74 to 2.75; p = 0.29). The pooled estimates of residual shunts favored the PDC compared with the CSR (RR, 9.07; 95% CI, 4.77 to 17.24; p < 0.001), the pooled estimates of aortic regurgitation favored the CSR compared with the PDC (RR, 1.59; 95% CI, 1.05 to 2.39; p = 0.03). Conclusions : PDC is a safe and effective procedure with less surgical injury and shorter perioperative hospital stay. However, aortic regurgitation is a concern during follow-up.
The Analysis of Model for Electronic Commerce - Artificial Intelligent

Now a day many firms has used Artificial intelligent (AI) in their ecommerce processes. Such as Apple, Amazon, Amazon and Intel use Artificial intelligent through both their services and their products, some other company using AI to help company automatic analyze customer data as well as help manager make decision. Electronic Commerce combine with Artificial Intelligent become extremely important for business in the near future. Therefore, this paper try to figure out The Analysis of Model for Electronic Commerce – Artificial Intelligent to help Business man make the best choice model in their business.
Tratamento de lesões nos pés de pessoas com diabetes mellitus no cenário brasileiro: revisão integrativa

Introdução: Pessoas com diabetes mellitus são mais suscetíveis a lesões cutâneas, ulceração e infecção no pé. Essas complicações são causas frequentes de internação hospitalar e elevados custos em saúde. Objetivo: Analisar os tratamentos para o pé diabético na realidade brasileira. Método: Realizou-se revisão integrativa da literatura por meio de busca nas bases de dados PubMed/MEDLINE, Scopus, Web of Science e EMBASE, baseada no método preconizado em seis etapas e construída conforme a metodologia PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Dois pesquisadores independentes realizaram a combinação de descritores Diabetic foot AND Therapeutics AND Brazil, em outubro de 2020. Foram examinados artigos publicados entre 2010 e 2020. Dos artigos selecionados, extraíram-se as informações: base de dados, autor, ano de publicação, desenho de pesquisa, tratamento e principais resultados. Resultados: Dentre os 257 artigos inicialmente identificados, foram selecionados 15. A laserterapia (26,66%), seguida da oxigenoterapia hiperbárica (13,33%) foram as formas de tratamento para pé diabético mais abordadas nos estudos brasileiros. Outras terapêuticas também foram encontradas. Todas as formas de tratamento para o pé diabético mostraram efeitos positivos sobre o processo de cicatrização da ferida, reduzindo sua extensão, área de tecido desvitalizado e a dor referida. Conclusão: a terapia com laser e a oxigenoterapia hiperbárica tem sido as principais formas de tratamento testadas e implementadas na realidade brasileira por pesquisadores e profissionais de saúde.
Influence of Coulomb’s Friction Coefficient in Finite Element Modeling of Orthogonal Cutting of Ti6Al4V

The reliability of the pertinent parameters set of Johnson-Cook constitutive model is highly linked with the friction condition at the tool-chip-workpiece interface. In the present work, a study on the influence of Coulomb’s friction coefficient on the observables such as forces, chip thickness and chip curvature by FE simulation of orthogonal cutting of Ti6Al4V alloy has been carried out. A FE model with an Arbitrary Lagrangian-Eulerian (ALE) approach is employed to simulate the cutting process for different cutting conditions. The simulated results, for a wide range of friction conditions, are analyzed and compared with experimental results. The analysis show that the Coulomb’s friction coefficient has a direct link with the observables. The paper reveals that for accurate prediction of observables an optimized value of the coefficient of friction in correlation with the parameters values of the constitutive model is imperative.
Spectroscopic Quantum Chemical and Molecular Docking Study of 5,7-Dibromo-8-Quinolinol and Its Spectrophotometrically Investigated Niobium (V) Complex

Abstract In order to achieve a sensitive, selective and a reproducible method, different physical parameters were closely examined for Nb(V)-5,7-Dibromo-8-quinolinol (DBQ) complex thus giving rise to a novel method for trace determination of pentavalent niobium. The analytical reagent, DBQ was used to determine Niobium (V), spectrophotometrically. The yellow colored complex thus formed due to colorimetric reaction between the two moieties was extracted into chloroform from hydrochloric acid medium. The complex system followed linearity and hence the Beer’s law range of 0.0–7.0 µg Nb (V) ml−1, with an optimum range of determination 2.042–5.082 µg Nb (V) ml−1. The developed method has been tested for its applicability on a variety of synthetic, natural and industrial samples with commendatory results. The investigated complex along with the used ligand was subjected to spectroscopic quantum chemical studies employing DFT technique, NBO analysis, MEP, Fukui functions and molecular docking. Consequently, the theoretical studies deeply enrooted the structural features, reactivity behaviors and bonding interactions in the ligand as well as the complex molecule.
A Systematic Overview of the Machine Learning Methods for Mobile Malware Detection

With the deployment of the 5G cellular system, the upsurge of diverse mobile applications and devices has increased the potential challenges and threats posed to users. Industry and academia have attempted to address cyber security challenges by implementing automated malware detection and machine learning algorithms. This study expands on previous research on machine learning-based mobile malware detection. We critically evaluate 154 selected articles and highlight their strengths and weaknesses as well as potential improvements. We explore the mobile malware detection techniques used in recent studies based on attack intentions, such as server, network, client software, client hardware, and user. In contrast to other SLR studies, our study classified the means of attack as supervised and unsupervised learning. Therefore, this article aims at providing researchers with in-depth knowledge in the field and identifying potential future research and a framework for a thorough evaluation. Furthermore, we review and summarize security challenges related to cybersecurity that can lead to more effective and practical research.
Association between Functional Connectivity of Entorhinal Cortex and Olfactory Performance in Parkinson’s Disease

The present study aimed to investigate the association between the functional connectivity (FC) of the olfactory cortex and olfactory performance in Parkinson’s disease (PD). Eighty-two early PD patients and twenty-one healthy controls underwent structural and resting-state functional MRI scans, as well as neuropsychological assessments from the Parkinson’s Progression Markers Initiative database. A whole brain voxel-wise regression analysis was conducted to evaluate the relationship between the FC of the entorhinal cortex (EC-FC) and olfactory performance. Then, a one-way ANCOVA, based on the regions of interest, was performed with SPSS to investigate the group differences and correlation analysis that were used to analyze the relationships between the FC and neuropsychological assessments. In addition, regression models were used to evaluate the risk factors for the decreased olfactory function. A significantly negative correlation was observed between the olfactory performance and the left EC-FC in the right dorsal cingulate gyrus (dCC) in patients. The PD patients with anosmia exhibited significantly higher FC values than the PD patients with normal olfaction or the PD patients with mild to moderate microsomia. Except for the olfactory performance, no significant correlation was detected between the neuropsychological assessments and the FC values. A linear regression analysis revealed that the increased FC and Geriatric Depression Scale are independently associated with lower the University of Pennsylvania Smell Identification Test scores. The current findings enhanced the understanding of olfactory dysfunction-related pathophysiological mechanisms in early PD and suggested that the left EC-FC in the right dCC may be a potential neuroimaging biomarker for olfactory performance.
Seasonal and diurnal surface urban heat islands in China: an investigation of driving factors with three-dimensional urban morphological parameters

ABSTRACT Surface urban heat island (SUHI) can considerably influence the urban environment and the quality of life. It is vital to examine how underlying surface properties impact seasonal and diurnal SUHIs. However, the influence of three-dimensional (3D) urban morphological parameters (UMPs) on SUHIs has not been thoroughly studied under varying climatic settings. To fill this knowledge gap, the present study investigated seasonal and diurnal changes in SUHI intensities (ΔT) in 208 cities in China from 2014 to 2016 using moderate-resolution imaging spectroradiometer (MODIS) land surface temperature products. In addition, the influence of potential factors in urban surface energy balance, including two-dimensional (2D) and 3D UMPs, socio-economic indices, urban greening, and surface albedo, on seasonal and diurnal ΔT were assessed under different climatic settings and with different city sizes using the method of Geographically Weighted Regression (GWR). Results show that negative summer daytime ΔT was observed in some cities under dry climates. Generally, in summer, the ΔT during daytime was higher than at nighttime. The 3D UMPs (i.e. building height and volume) yielded more decisive influences on ΔT than 2D UMP (i.e. building coverage). This is particularly true for the summer diurnal cycle and under dry climatic settings. Building height was found to be negatively correlated with surface temperatures, while building volume was positively correlated. Additionally, the 3D UMPs yielded more influences on winter ΔT than summer ΔT. The capability of vegetation to regulate ΔT was more potent in dry climates than in wet climates and in small cities than in large cities. Varying climates and city sizes can modify the significance of the 2D and 3D UMPs on the urban surface energy balance, suggesting that urban thermal mitigation should consider climate background and population size.
Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network

The efficient biological signal processing method can effectively improve the efficiency of researchers to explore the work of life mechanism, so as to better reveal the relationship between physiological structure and function, thus promoting the generation of major biological discoveries; high-precision medical signal analysis strategy can, to a certain extent, share the pressure of doctors' clinical diagnosis and assist them to formulate more favorable plans for disease prevention and treatment, so as to alleviate patients' physical and mental pain and improve the overall health level of the society. This article in biomedical signal is very representative of the two types of signals: mammary gland molybdenum target X-ray image (mammography) and the EEG signal as the research object, combined with the deep learning field of CNN; the most representative model is two kinds of biomedical signal classification, and reconstruction methods conducted a series of research: (1) a new classification method of breast masses based on multi-layer CNN is proposed. The method includes a CNN feature representation network for breast masses and a feature decision mechanism that simulates the physician's diagnosis process. By comparing with the objective classification accuracy of other methods for the identification of benign and malignant breast masses, the method achieved the highest classification accuracy of 97.0% under different values of c and gamma, which further verified the effectiveness of the proposed method in the identification of breast masses based on molybdenum target X-ray images. (2) An EEG signal classification method based on spatiotemporal fusion CNN is proposed. This method includes a multi-channel input classification network focusing on spatial information of EEG signals, a single-channel input classification network focusing on temporal information of EEG signals, and a spatial-temporal fusion strategy. Through comparative experiments on EEG signal classification tasks, the effectiveness of the proposed method was verified from the aspects of objective classification accuracy, number of model parameters, and subjective evaluation of CNN feature representation validity. It can be seen that the method proposed in this paper not only has high accuracy, but also can be well applied to the classification and reconstruction of biomedical signals.
A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation

Brain tissue segmentation is an important component of the clinical diagnosis of brain diseases using multi-modal magnetic resonance imaging (MR). Brain tissue segmentation has been developed by many unsupervised methods in the literature. The most commonly used unsupervised methods are K-Means, Expectation-Maximization, and Fuzzy Clustering. Fuzzy clustering methods offer considerable benefits compared with the aforementioned methods as they are capable of handling brain images that are complex, largely uncertain, and imprecise. However, this approach suffers from the intrinsic noise and intensity inhomogeneity (IIH) in the data resulting from the acquisition process. To resolve these issues, we propose a fuzzy consensus clustering algorithm that defines a membership function resulting from a voting schema to cluster the pixels. In particular, we first pre-process the MRI data and employ several segmentation techniques based on traditional fuzzy sets and intuitionistic sets. Then, we adopted a voting schema to fuse the results of the applied clustering methods. Finally, to evaluate the proposed method, we used the well-known performance measures (boundary measure, overlap measure, and volume measure) on two publicly available datasets (OASIS and IBSR18). The experimental results show the superior performance of the proposed method in comparison with the recent state of the art. The performance of the proposed method is also presented using a real-world Autism Spectrum Disorder Detection problem with better accuracy compared to other existing methods.
Simulation of Wind-Battery Microgrid Based on Short-Term Wind Power Forecasting

The inherently intermittent and highly variable nature of wind necessitates the use of wind power forecasting tools in order to facilitate the integration of wind turbines in microgrids, among others. In this direction, the present paper describes the development of a short-term wind power forecasting model based on artificial neural network (ANN) clustering, which uses statistical feature parameters in the input vector, as well as an enhanced version of this approach that adjusts the ANN output with the probability of lower misclassification (PLM) method. Moreover, it employs the Monte Carlo simulation to represent the stochastic variation of wind power production and assess the impact of energy management decisions in a residential wind-battery microgrid using the proposed wind power forecasting models. The results indicate that there are significant benefits for the microgrid when compared to the naive approach that is used for benchmarking purposes, while the PLM adjustment method provides further improvements in terms of forecasting accuracy.
Sleep Deprivation Increases the Anesthetic Potency of Sevoflurane Regardless of Duration.

BACKGROUND Sleep deprivation reduced the time to induce anesthesia by propofol and isoflurane and prolonged the time to recovery. However, it is unknown whether sleep deprivation affects the potency of inhaled anesthetics. In this study, the effect of sleep deprivation on sevoflurane anesthetic potency was explored. METHODS Ten animals received the following behavioral interventions in turn (ad libitum activity, 24 h sleep deprivation, 48 h sleep deprivation, 72 h sleep deprivation). After each behavioral intervention, the 50% effective dose for loss of righting reflex (LORR ED50) was determined to evaluate the potency of sevoflurane in inducing unconsciousness in mice. Repeated-measures analysis of variance was used to compare our behavioral interventions statistically, post hoc multiple comparisons were made using the Bonferroni test. RESULTS Sleep deprivation decreased the sevoflurane LORR ED50 significantly (p = 0.0003). However, the effect of duration of sleep deprivation on LORR ED50 was not statistically significant (p > 0.9999). CONCLUSIONS Sleep deprivation can increase the anesthetic potency of sevoflurane regardless of duration of sleep deprivation.
Prevalence and Characteristics of Prediabetes and Metabolic Syndrome in Seemingly Healthy Persons at a Health Check-Up Clinic

Purpose This study investigated the prevalence and characteristics of prediabetes (PreDM) and metabolic syndrome (MetS) in seemingly healthy persons attending a health check-up clinic at a tertiary care hospital. Patients and Methods This was a cross-sectional study that enrolled 1213 subjects (339 male, 874 female) who underwent an annual health check-up at Siriraj Hospital, Bangkok, Thailand from 2009 to 2019. Factors that independently related to PreDM were analyzed using unconditional logistic regression analysis with adjustments for age, BMI, and gender. Results The prevalence of PreDM and MetS was 54.3% and 19.7% respectively. Participants with impaired fasting glucose (IFG) and glycated hemoglobin (HbA1c) 38.8–46.4 mmol/mol had significantly higher waist circumference (WC) and blood pressure (BP) compared to those with IFG or HbA1c 38.8–46.4 mmol/mol alone (P < 0.05). Among three PreDM subgroups, the average age was lowest in the HbA1c 38.8–46.4 mmol/mol subgroup (P < 0.001). PreDM participants with MetS were older (p = 0.03), had higher WC, BP, fasting plasma glucose and serum triglyceride level (all P < 0.001) but had lower serum high-density lipoprotein (HDL) cholesterol level (P < 0.001). Multivariate analysis revealed high MetS score, obesity, and low serum HDL cholesterol level to be independently associated with PreDM with odds ratios of 9.02 (95% confidence interval [CI]: 4.03–20.18), 1.8 (95% CI: 1.07–3.04), and 1.42 (95% CI: 1.02–1.96), respectively. Conclusion The prevalence of PreDM and MetS was relatively high in seemingly healthy persons. Distinct PreDM subgroups with or without MetS exhibited diverse clinical and biochemical features suggesting dissimilar pathogenesis.
Prehistoric agricultural decision making in the western Himalayas: ecological and social variables

Abstract The high-altitude landscape of western Tibet is one of the most extreme environments in which humans have managed to introduce crop cultivation. To date, only sparse palaeoeconomic data have been reported from this region. The authors present archaeobotanical evidence from five sites (dating from the late first millennium BC and the early first millennium AD) located in the cold-arid landscape of western Tibet. The data indicate that barley was widely grown in this region by c. 400 BC but probably fulfilled differing roles within local ecological constraints on cultivation. Additionally, larger sites are characterised by more diverse crop assemblages than smaller sites, suggesting a role for social diversity in the development of high-altitude agriculture.
The essential competencies of software professionals: A unified competence framework

Context: Developing high-quality software requires skilled software professionals equipped with a set of basic and essential software engineering competencies (SEC). These competencies and the satisfaction levels derived from them change over a project ’ s lifecycle, or as software professionals move from one project to another. Objective: Previous studies suggest a lack of means enabling SEC stakeholders to identify and assess competencies suitable for different projects. Additionally, previous research has mainly portrayed SEC to be static and over-looked their evolution over time and across projects. We investigate how we could effectively identify and match the competencies of software professionals necessary for different projects. Method: We follow a mixed-method approach to iteratively develop and evaluate a framework for identifying and managing SEC. In so doing, we use the results of an extensive literature review, focus group discussions with experts from academia and industry, and data collected through interviews with 138 individuals with a super-visory role in the software industry. Results: Drawing on the Kano model and Competency Framework for Software Engineers, we propose a Unified Competence Gate for Software Professionals (UComGSP), a framework for identifying and managing SEC. The UComGSP consists of 62 hard competencies, 63 soft competencies
Voltage-Gated Ion Channels and the Variability in Information Transfer

The prerequisites for neurons to function within a circuit and be able to contain and transfer information efficiently and reliably are that they need to be homeostatically stable and fire within a reasonable range, characteristics that are governed, among others, by voltage-gated ion channels (VGICs). Nonetheless, neurons entail large variability in the expression levels of VGICs and their corresponding intrinsic properties, but the role of this variability in information transfer is not fully known. In this study, we aimed to investigate how this variability of VGICs affects information transfer. For this, we used a previously derived population of neuronal model neurons, each with the variable expression of five types of VGICs, fast Na+, delayed rectifier K+, A-type K+, T-type Ca++, and HCN channels. These analyses showed that the model neurons displayed variability in mutual information transfer, measured as the capability of neurons to successfully encode incoming synaptic information in output firing frequencies. Likewise, variability in the expression of VGICs caused variability in EPSPs and IPSPs amplitudes, reflected in the variability of output firing frequencies. Finally, using the virtual knockout methodology, we show that among the ion channels tested, the A-type K+ channel is the major regulator of information processing and transfer.