This study investigated the correlation between surfactant protein-A (SP-A) polymorphism and the susceptibility of chronic obstructive pulmonary disease (COPD) in Xinjiang Uighurs. Genomic DNA was extracted from peripheral blood of 194 COPD smokers and 201 healthy smokers of Uighur who were hospitalized in or paid a visit to one of the four Xinjiang-based hospitals involved in the study, from March 2009 to December 2010. Single nucleotide polymorphisms (SNPs) were studied at aa62 (CCA/CCG rs1136451) and aa219 (CGG/TGG, rs4253527) in SP-A. Genotypes were determined by using the TaqMan polymerase chain reaction (PCR). Our results showed that genotype frequencies were different between the COPD and normal smokers in aa62 (x (2)=6.852, P=0.033). There were also significant differences in allele genotype frequencies between the COPD and the control and allele G might decrease the risk COPD (x (2)=6.545, P=0.011; OR=0.663; 95% CI: 0.484-0.909). The result suggested that polymorphism of aa62 (CCA/CCG, rs1136451) of SP-A may be associated with the susceptibility to COPD in Xinjiang Uighurs.

Accurate medium- and long-term electricity peak load forecasting is critical for power system operation, planning, and electricity trading. However, peak load forecasting is challenging because of the complex and nonlinear relationship between peak load and related factors. Here, we propose a hybrid LSTM–BPNN-to-BPNN model combining a long short-term memory network (LSTM) and back propagation neural network (BPNN) to separately extract the features of the historical data and future information. Their outputs are then concatenated to a vector and inputted into the next BPNN model to obtain the final prediction. We further analyze the peak load characteristics for reducing prediction error. To overcome the problem of insufficient annual data for training the model, all the input variables distributed over various time scales are converted into a monthly time scale. The proposed model is then trained to predict the monthly peak load after one year and the maximum value of the monthly peak load is selected as the predicted annual peak load. The comparison results indicate that the proposed method achieves a predictive accuracy superior to that of benchmark models based on a real-world dataset.

Intermittent demand items dominate service and repair inventories in many industries and they are known to be the source of dramatic inefficiencies in the defense sector. However, research in forecasting such items has been limited. Previous work in this area has been developed upon the assumption of a Bernoulli or a Poisson demand arrival process. Nevertheless, intermittent demand patterns may often deviate from the memory-less assumption. In this work we extend analytically previous important results to model intermittent demand based on a compound Erlang process, and we provide a comprehensive categorisation scheme to be used for forecasting purposes. In a numerical investigation we assess the benefit of departing from the memory-less assumption and we provide insights into how the degree of determinism inherent in the process affects forecast accuracy. Operationalised suggestions are offered to managers and software manufacturers dealing with intermittent demand items.

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