Traditional Crafts And Rural Economic Development: Case Study Of Traditional Rural Handicraft Industry In Yunnan

The Baishapo Yi village in Yunnan China is located in the middle of the mountainous area. Due to the drought and low precipitation in successive years, the vegetation gradually degenerates and the land is rocky desertification, a sharp decline in crop yields year after year. Large numbers of young and middle-aged farmers have given up on farming and have gone to cities to find new jobs. Local farmers, especially the poor left-behind women, need to find new approach to increase their income. Therefore, relying on rich ethnic cultural resources, Baishapo Yi village has tried to develop the handicraft industry with female craftsmen as its core elements. This study takes case analysis as an entry point and attempts to explain the feasibility of using the handcraft industry to cover the shortage of agricultural productio

Research on people’s willingness to pay for the construction of Quanzhou ecological civilization city based on CVM

The city of Quanzhou city was investigated by CVM and Logistic model. The results show that the average willingness to pay is RMB 110.7/year. Six factors, including the length of registered residence, the length of residence, the income of the residents, the degree of understanding of the construction of Quanzhou’s ecological civilization city, and the influence of the public on the ecological civilization’s influence on their own lives, have a significant impact on the willingness to pay. Gender, age and occupation have little effect on willingness to pay

Genetic algorithm-neural network: feature extraction for bioinformatics data.

With the advance of gene expression data in the bioinformatics field, the questions which frequently arise, for both computer and medical scientists, are which genes are significantly involved in discriminating cancer classes and which genes are significant with respect to a specific cancer pathology. Numerous computational analysis models have been developed to identify informative genes from the microarray data, however, the integrity of the reported genes is still uncertain. This is mainly due to the misconception of the objectives of microarray study. Furthermore, the application of various preprocessing techniques in the microarray data has jeopardised the quality of the microarray data. As a result, the integrity of the findings has been compromised by the improper use of techniques and the ill-conceived objectives of the study. This research proposes an innovative hybridised model based on genetic algorithms (GAs) and artificial neural networks (ANNs), to extract the highly differentially expressed genes for a specific cancer pathology. The proposed method can efficiently extract the informative genes from the original data set and this has reduced the gene variability errors incurred by the preprocessing techniques. The novelty of the research comes from two perspectives. Firstly, the research emphasises on extracting informative features from a high dimensional and highly complex data set, rather than to improve classification results. Secondly, the use of ANN to compute the fitness function of GA which is rare in the context of feature extraction. Two benchmark microarray data have been taken to research the prominent genes expressed in the tumour development and the results show that the genes respond to different stages of tumourigenesis (i.e. different fitness precision levels) which may be useful for early malignancy detection. The extraction ability of the proposed model is validated based on the expected results in the synthetic data sets. In addition, two bioassay data have been used to examine the efficiency of the proposed model to extract significant features from the large, imbalanced and multiple data representation bioassay data

Lithium facilitates apoptotic signaling induced by activation of the Fas death domain-containing receptor

BACKGROUND: Lithium, a mood stabilizer widely used to treat bipolar disorder, also is a neuroprotectant, providing neurons protection from apoptosis induced by a broad spectrum of toxic conditions. A portion of this neuroprotection is due to lithium's inhibition of glycogen synthase kinase-3. The present investigation examined if the neuroprotection provided by lithium included apoptosis induced by stimulation of the death domain-containing receptor Fas. RESULTS: Instead of providing protection, treatment with 20 mM lithium significantly increased apoptotic signaling induced by activation of Fas, and this occurred in both Jurkat cells and differentiated immortalized hippocampal neurons. Other inhibitors of glycogen synthase kinase-3, including 20 μM indirubin-3'-monoxime, 5 μM kenpaullone, and 5 μM rottlerin, also facilitated Fas-induced apoptotic signaling, indicating that the facilitation of apoptosis by lithium was due to inhibition of glycogen synthase kinase-3. CONCLUSIONS: These results demonstrate that lithium is not always a neuroprotectant, and it has the opposite effect of facilitating apoptosis mediated by stimulation of death domain-containing receptors

A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging

Single-pixel imaging is an alternate imaging technique particularly well-suited to imaging modalities such as hyper-spectral imaging, depth mapping, 3D profiling. However, the single-pixel technique requires sequential measurements resulting in a trade-off between spatial resolution and acquisition time, limiting real-time video applications to relatively low resolutions. Compressed sensing techniques can be used to improve this trade-off. However, in this low resolution regime, conventional compressed sensing techniques have limited impact due to lack of sparsity in the datasets. Here we present an alternative compressed sensing method in which we optimize the measurement order of the Hadamard basis, such that at discretized increments we obtain complete sampling for different spatial resolutions. In addition, this method uses deterministic acquisition, rather than the randomized sampling used in conventional compressed sensing. This so-called ‘Russian Dolls’ ordering also benefits from minimal computational overhead for image reconstruction. We find that this compressive approach performs as well as other compressive sensing techniques with greatly simplified post processing, resulting in significantly faster image reconstruction. Therefore, the proposed method may be useful for single-pixel imaging in the low resolution, high-frame rate regime, or video-rate acquisition

On moving-average models with feedback

Moving average models, linear or nonlinear, are characterized by their short memory. This paper shows that, in the presence of feedback in the dynamics, the above characteristic can disappear.Comment: Published in at http://dx.doi.org/10.3150/11-BEJ352 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm