IS THERE DIVERSIFICATION BENEFIT BETWEEN EMERGING AND DEVELOPED STOCK MARKET: EVIDENCE FROM THE BRIC AND US STOCK MARKET

This paper seeks to investigate the linkage and co-movement relationships between the stock markets of US and BRIC, and determine the degree of diversification benefits among them within the sample period from January 2001 to September 2017. The entire sample period is divided into three phases: pre-crisis, during crisis and post-crisis in order to be more comparative. The empirical results show that there is a strong linkage and co-movement relationship between BRIC and US stock markets, especially after 2007 financial crisis. Also, the upward long run conditional correlations demonstrate that the diversification benefits are weakened substantially. However, there is not any evidence showing the existence of co-integration between BRIC and US market for all three phases, except for the stock market of China during the crisis. Moreover, most of the BRIC stock markets are appeared to have no short term causality to US market

Dried tea residue can alter the blood metabolism and the composition and functionality of the intestinal microbiota in Hu sheep

Ruminant animals face multiple challenges during the rearing process, including immune disorders and oxidative stress. Green tea by-products have gained widespread attention for their significant immunomodulatory and antioxidant effects, leading to their application in livestock production. In this study, we investigated the effects of Dried Tea Residue (DTR) as a feed additive on the growth performance, blood biochemical indicators, and hindgut microbial structure and function of Hu sheep. Sixteen Hu sheep were randomly divided into two groups and fed with 0 and 100 g/d of DTR, respectively. Data were recorded over a 56-day feeding period. Compared to the control group, there were no significant changes in the production performance of Hu sheep fed with DTR. However, the sheep fed with DTR showed a significant increase in IgA (p < 0.001), IgG (p = 0.005), IgM (p = 0.003), T-SOD (p = 0.013), GSH-Px (p = 0.005), and CAT (p < 0.001) in the blood, along with a significant decrease in albumin (p = 0.019), high density lipoprotein (p = 0.050), and triglyceride (p = 0.021). DTR supplementation enhanced the fiber digestion ability of hindgut microbiota, optimized the microbial community structure, and increased the abundance of carbohydrate-digesting enzymes. Therefore, DTR can be used as a natural feed additive in ruminant animal production to enhance their immune and antioxidant capabilities, thereby improving the health status of ruminant animals

Le suicide en milieu pénitentiaire (état des lieux et enquête préliminaire sur la formation du personnel)

Le suicide est un problème de santé publique, tant en population générale qu en milieu carcéral. En prison, le nombre des suicides augmente significativement depuis plusieurs années. Ainsi, l Administration pénitentiaire et les Ministères de la Santé et de la Justice se sont saisis du problème, avec la parution de deux rapports en 2004 et en 2009. Leur mission était d évaluer et de proposer un programme de prévention du suicide des personnes détenues. Ils concluent à une série de recommandations, dont un des axes principaux est la formation spécifique de l ensemble du personnel intervenant en milieu pénitentiaire. En Juin 2012, nous avons réalisé une enquête sur la formation spécifique à la prévention du suicide des personnels intervenant en prison, grâce à un questionnaire, qui a été distribué à l ensemble du personnel de la Maison d Arrêt de Grenoble-Varces. Nos résultats confirment l hypothèse de départ du manque de formation spécifique des personnes travaillant en prison. En effet, moins de la moitié du personnel a reçu cette formation. De plus, certaines personnes y ont assisté il y a plus de dix ans, et aurait besoin d un rappel de formation. Au total, la moitié du personnel ressent un besoin de formation complémentaire, et ne se sent pas bien formé en tant qu acteur de la prévention du suicide. Cependant, les données épidémiologiques (facteurs de risque, périodes à risque et moyens de suicide) sont connues, ce qui est rassurant. Finalement, huit ans après le premier rapport ministériel, on constate que les objectifs prédéfinis en termes de formation des intervenants ne sont pas atteints.Suicide is a real public health problem, by the loss of life it provoked and by the psychological and social problems as reflected in it. It is always a painful event, which returns to the guilt of loved ones and the responsibility of those present. In prison, the number of suicides increased significantly for several years. The Prison Administration and the Ministries of Health and Justice are dealing with the problem. Two reports were published in 2004 and 2009. Their mission was to assess and propose a program for the prevention of suicide of persons detained. These two reports conclude with a series of recommendations. One of the principal axes is the specific training of all staff involved in the prison environment. In June 2012, we conducted an investigation in order to evaluate the specific training of personnel involved in prison suicide prevention. It is a questionnaire of self-evaluation, distributed to all the staff of the house arrest of Grenoble - Varces. The results confirm the hypothesis of the lack of specific training of persons working in prison. Indeed, less than half of the staff received this training. In addition, some people received this training over ten years ago, and would need a reminder. In total, half of the staff feels a need for further training, and feels not well trained as an actor in the prevention of suicide. However, the answers concerning the epidemiological data are reassuring. Suicide risk factors and periods most at risk are identified by stakeholders. Similarly, the means of suicide the most used are known. Finally, eight years after the first report, we found that the targets pre-defined in terms of training of stakeholders are not achieved.GRENOBLE1-BU Médecine pharm. (385162101) / SudocSudocFranceF

Effective dysphonia detection using feature dimension reduction and kernel density estimation for patients with Parkinson's disease.

Detection of dysphonia is useful for monitoring the progression of phonatory impairment for patients with Parkinson's disease (PD), and also helps assess the disease severity. This paper describes the statistical pattern analysis methods to study different vocal measurements of sustained phonations. The feature dimension reduction procedure was implemented by using the sequential forward selection (SFS) and kernel principal component analysis (KPCA) methods. Four selected vocal measures were projected by the KPCA onto the bivariate feature space, in which the class-conditional feature densities can be approximated with the nonparametric kernel density estimation technique. In the vocal pattern classification experiments, Fisher's linear discriminant analysis (FLDA) was applied to perform the linear classification of voice records for healthy control subjects and PD patients, and the maximum a posteriori (MAP) decision rule and support vector machine (SVM) with radial basis function kernels were employed for the nonlinear classification tasks. Based on the KPCA-mapped feature densities, the MAP classifier successfully distinguished 91.8% voice records, with a sensitivity rate of 0.986, a specificity rate of 0.708, and an area value of 0.94 under the receiver operating characteristic (ROC) curve. The diagnostic performance provided by the MAP classifier was superior to those of the FLDA and SVM classifiers. In addition, the classification results indicated that gender is insensitive to dysphonia detection, and the sustained phonations of PD patients with minimal functional disability are more difficult to be correctly identified

Study of the influence of meteorological factors on HFMD and prediction based on the LSTM algorithm in Fuzhou, China

Abstract Background This study adopted complete meteorological indicators, including eight items, to explore their impact on hand, foot, and mouth disease (HFMD) in Fuzhou, and predict the incidence of HFMD through the long short-term memory (LSTM) neural network algorithm of artificial intelligence. Method A distributed lag nonlinear model (DLNM) was used to analyse the influence of meteorological factors on HFMD in Fuzhou from 2010 to 2021. Then, the numbers of HFMD cases in 2019, 2020 and 2021 were predicted using the LSTM model through multifactor single-step and multistep rolling methods. The root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) were used to evaluate the accuracy of the model predictions. Results Overall, the effect of daily precipitation on HFMD was not significant. Low (4 hPa) and high (≥ 21 hPa) daily air pressure difference (PRSD) and low ( 12 °C) daily air temperature difference (TEMD) were risk factors for HFMD. The RMSE, MAE, MAPE and SMAPE of using the weekly multifactor data to predict the cases of HFMD on the following day, from 2019 to 2021, were lower than those of using the daily multifactor data to predict the cases of HFMD on the following day. In particular, the RMSE, MAE, MAPE and SMAPE of using weekly multifactor data to predict the following week's daily average cases of HFMD were much lower, and similar results were also found in urban and rural areas, which indicating that this approach was more accurate. Conclusion This study’s LSTM models combined with meteorological factors (excluding PRE) can be used to accurately predict HFMD in Fuzhou, especially the method of predicting the daily average cases of HFMD in the following week using weekly multifactor data

Representation of fluctuation features in pathological knee joint vibroarthrographic signals using kernel density modeling method

This article applies advanced signal processing and computational methods to study the subtle fluctuations in knee joint vibroarthrographic (VAG) signals. Two new features are extracted to characterize the fluctuations of VAG signals. The fractal scaling index parameter is computed using the detrended fluctuation analysis algorithm to describe the fluctuations associated with intrinsic correlations in the VAG signal. The averaged envelope amplitude feature measures the difference between the upper and lower envelopes averaged over an entire VAG signal. Statistical analysis with the Kolmogorov-Smirnov test indicates that both of the fractal scaling index (. p=. 0.0001) and averaged envelope amplitude (. p=. 0.0001) features are significantly different between the normal and pathological signal groups. The bivariate Gaussian kernels are utilized for modeling the densities of normal and pathological signals in the two-dimensional feature space. Based on the feature densities estimated, the Bayesian decision rule makes better signal classifications than the least-squares support vector machine, with the overall classification accuracy of 88% and the area of 0.957 under the receiver operating characteristic (ROC) curve. Such VAG signal classification results are better than those reported in the state-of-the-art literature. The fluctuation features of VAG signals developed in the present study can provide useful information on the pathological conditions of degenerative knee joints. Classification results demonstrate the effectiveness of the kernel feature density modeling method for computer-aided VAG signal analysis. ? 2014 IPEM

Effective Dysphonia Detection Using Feature Dimension Reduction and Kernel Density Estimation for Patients with Parkinson's Disease

National Natural Science Foundation of China [81101115, 31200769, 81272168]; Natural Science Foundation of Fujian [2011J01371]; Fundamental Research Funds for the Central Universities of China [2010121061]; Program for New Century Excellent Talents in Fujian Province University; Natural Sciences and Engineering Research Council of Canada (NSERC); Canada Research Chairs ProgramDetection of dysphonia is useful for monitoring the progression of phonatory impairment for patients with Parkinson's disease (PD), and also helps assess the disease severity. This paper describes the statistical pattern analysis methods to study different vocal measurements of sustained phonations. The feature dimension reduction procedure was implemented by using the sequential forward selection (SFS) and kernel principal component analysis (KPCA) methods. Four selected vocal measures were projected by the KPCA onto the bivariate feature space, in which the class-conditional feature densities can be approximated with the nonparametric kernel density estimation technique. In the vocal pattern classification experiments, Fisher's linear discriminant analysis (FLDA) was applied to perform the linear classification of voice records for healthy control subjects and PD patients, and the maximum a posteriori (MAP) decision rule and support vector machine (SVM) with radial basis function kernels were employed for the nonlinear classification tasks. Based on the KPCA-mapped feature densities, the MAP classifier successfully distinguished 91.8% voice records, with a sensitivity rate of 0.986, a specificity rate of 0.708, and an area value of 0.94 under the receiver operating characteristic (ROC) curve. The diagnostic performance provided by the MAP classifier was superior to those of the FLDA and SVM classifiers. In addition, the classification results indicated that gender is insensitive to dysphonia detection, and the sustained phonations of PD patients with minimal functional disability are more difficult to be correctly identified

Bivariate distributions of vocal patterns in the kernel principal component analysis (KPCA) mapping feature plane.

<p>Vocal pattern distributions for the healthy controls (CO) and patients with Parkinson’s disease (PD) are displayed with the cold color map (blue for the highest density) and hot color map (red for the highest density), respectively.</p

Details of subject information on the records misclassified by the maximum <i>a posteriori</i> (MAP) and support vector machine (SVM) classifiers.

<p>M: male; F: female; CO: healthy controls; PD: Parkinson’s disease; H&Y stage: Hoehn and Yahr stage; N/A: not applicable for healthy subjects.</p

Figure 6

<p>Scatter plots of the vocal patterns associated with the healthy controls (CO) and patients with Parkinson’s disease (PD) in the two-dimensional feature spaces of (<b>A</b>) MDVP: F0 and MDVP: Jitter (%), (<b>B</b>) MDVP: F0 and detrended fluctuation analysis (DFA), (<b>C</b>) MDVP: F0 and Spread2, (<b>D</b>) MDVP: Jitter (%) and DFA, (<b>E</b>) MDVP: Jitter (%) and Spread2, and (<b>F</b>) DFA and Spread2, respectively.</p