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

Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data

Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on predicting taxi demand in the near future by learning patterns from historical data. However, they fail in case of abnormality because dispersal events with abnormally high demand are non-repetitive and violate common assumptions such as smoothness in demand change over time. Instead, in this paper we argue that dispersal events follow a complex pattern of trips and other related features in the past, which can be used to predict such events. Therefore, we formulate the dispersal event prediction problem as a survival analysis problem. We propose a two-stage framework (DILSA), where a deep learning model combined with survival analysis is developed to predict the probability of a dispersal event and its demand volume. We conduct extensive case studies and experiments on the NYC Yellow taxi dataset from 2014-2016. Results show that DILSA can predict events in the next 5 hours with F1-score of 0.7 and with average time error of 18 minutes. It is orders of magnitude better than the state-ofthe-art deep learning approaches for taxi demand prediction.Comment: To appear in AAAI-19 proceedings. The reason for the replacement was the misspelled author name in the meta-data field. Author name was corrected from "Ynahua Li" to "Yanhua Li". The author list in the paper was correct and remained unchange

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

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