The multi-agent model of language choice: National planning and individual volition in China

Language choice is often studied as choices made by the state at the level of national language planning or as individual choice of language or variety in language use. There has been little research to directly connect these two aspects of language choice. This paper attempts to incorporate the two aspects and other related phenomena in a multi-agent model of language choice and illustrates the proposed model with some data on circumstances in China. The agents involved are: policy-makers, educators, family members, learners and other language users. After outlining the model, the discussion focuses on how choices made by policy-makers in China at the language planning level relate to choices made by individual learners or language users at the level of personal language development in China. It draws upon findings in the Language Education in China Project based on three main types of data: policy statements, survey statistics (on 415 Han Chinese learners, the majority ethnic group in China, and 60 learners from different minorities in China) as well as interviews of 35 Han Chinese learners and 17 learners from ethnic minorities. The paper presents brief accounts of the policies and some survey trends before focusing on interview data on six Han Chinese learners and three minority learners. It is argued that realization of national language planning goals depends on whether individual learners abide by the choices made by the state and whether the intermediary agents such as teachers and parents cooperate; it is also suggested that learners learn well if they choose to do so, where choice assumes active investment of learning time and energy into learning or using the target language(s).postprin

Review: Pharmacological effects of Capparis spinosa L.

Medicinal plants have been known as one of the most important therapeutic agents since ancient times. During the last two decades, much attention has been paid to the health-promoting effects of edible medicinal plants, because of multiple beneficial effects and negligible adverse effects. Capparis spinosa L. is one of the most common medicinal plants, used widely in different parts of the world to treat numerous human diseases. This paper aims to critically review the available scientific literature regarding the health-promoting effects of C. spinosa, its traditional uses, cultivation protocols and phytochemical constituents. Recently, a wide range of evidence has shown that this plant possesses different biological effects, including antioxidant, anticancer and antibacterial effects. Phytochemical analysis shows that C. spinosa has high quantities of bioactive constituents, including polyphenolic compounds, which are responsible for its health-promoting effects, although many of these substances are present in low concentrations and significant changes in their content occur during processing. In addition, there is negligible scientific evidence regarding any adverse effects. Different health promotion activities, as well as tremendous diversity of active constituents, make C. spinosa a good candidate for discovering new drugs. However these findings are still in its infancy and future experimental and clinical studies are needed

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis

Heart Rate Variability Dynamics for the Prognosis of Cardiovascular Risk

Statistical, spectral, multi-resolution and non-linear methods were applied to heart rate variability (HRV) series linked with classification schemes for the prognosis of cardiovascular risk. A total of 90 HRV records were analyzed: 45 from healthy subjects and 45 from cardiovascular risk patients. A total of 52 features from all the analysis methods were evaluated using standard two-sample Kolmogorov-Smirnov test (KS-test). The results of the statistical procedure provided input to multi-layer perceptron (MLP) neural networks, radial basis function (RBF) neural networks and support vector machines (SVM) for data classification. These schemes showed high performances with both training and test sets and many combinations of features (with a maximum accuracy of 96.67%). Additionally, there was a strong consideration for breathing frequency as a relevant feature in the HRV analysis