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In recent decades, the integration of solar energy sources has gradually become the main challenge for global energy consumption. Therefore, it is essential to predict global solar radiation in an accurate and efficient way when estimating outputs of the solar system. Inaccurate predictions either cause load overestimation that results in increased cost or failure to gather adequate supplies. However, accurate forecasting is a challenging task because solar resources are intermittent and uncontrollable. To tackle this difficulty, several machine learning models have been established; however, the forecasting outcomes of these models are not sufficiently accurate. Therefore, in this study, we investigate ensemble learning with square root regularization and intelligent optimization to forecast hourly global solar radiation. The main structure of the proposed method is constructed based on ensemble learning with a random subspace (RS) method that divides the original data into several covariate subspaces. A novel covariate-selection method called square root smoothly clipped absolute deviation (SRSCAD) is proposed and is applied to each subspace with efficient extraction of relevant covariates. To combine the forecasts obtained using RS and SRSCAD, a firefly algorithm (FA) is used to estimate the weights assigned to individual forecasts. To handle the complexity of the proposed ensemble system, a simple and efficient algorithm is derived based on a thresholding rule and accelerated gradient method. To illustrate the validity and effectiveness of the proposed method, global solar radiation datasets of eight locations of Xinjiang province in China are considered. The experimental results show that the proposed RS-SRSCAD-FA achieves the best performances with a mean absolute percentage error, root-mean-square error, Theil inequality coefficient, and correlation coefficient of 0.066, 20.21 W/m2, 0.016, 3.40 s, and 0.98 in site 1, respectively. For the other seven datasets, RS-SRSCAD-FA still outperforms other approaches. Finally, a nonparametric Friedman test is applied to perform statistical comparisons of results over eight datasets. [ABSTRACT FROM AUTHOR]

The accurate, efficient, and reliable forecasting of wind speed is a hot research topic in wind power generation and integration. However, available forecasting models focus on forecasting the wind speed using historical wind speed data and ignore multidimensional meteorological variables. The objective is to develop a hybrid model with multidimensional meteorological variables for forecasting the wind speed accurately. The complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is applied to handle the nonlinearity of the wind speed. Then, the original wind speed will be decomposed into a series of intrinsic model functions with specified numbers of frequencies. A quadratic model that considers the two-way interactions between factors is used to pursue accurate forecasting. To reduce the model complexity, Gram–Schmidt-based feature selection (GSFS) is applied to extract the important meteorological factors. Finally, all the forecasting values of IMFs will be summed by assigning weights that are carefully determined by the whale optimization algorithm (WOA). The proposed forecasting approach has been applied on six datasets that were collected in Qinghai province and is compared with several state-of-the-art wind speed forecasting models. The forecasting results demonstrate that the proposed model can represent the nonlinearity of the wind speed and deliver better results than the competitors. [ABSTRACT FROM AUTHOR]

Objective. The effect of vaginal microbiota on spontaneous preterm birth (sPTB) has not been fully addressed, and few studies have explored the associations between vaginal taxa and sPTB in the gestational diabetes mellitus (GDM) and non-GDM groups, respectively. Study Design. To minimize external interference, a total of 41 pregnant women with sPTB and 308 controls (pregnant women without sPTB) from same regain were enrolled in this case-cohort study. Controls were randomly selected at baseline. With the exception of GDM, other characteristics were not significantly different between the two groups. Vaginal swabs were collected at early second trimester. Using 16S amplicon sequencing, the main bioinformatics analysis was performed on the platform of QIIME 2. Vaginal microbiota traits of the sPTB group were compared with controls. Finally, the effects of binary taxa on sPTB in the GDM group and the non-GDM group were analyzed, respectively. Results. The proportion of GDM in the sPTB (19.51%) was higher than the controls (7.47%, P = 0.018). The vaginal microbiota of pregnant women with sPTB exhibited higher alpha diversity metrics (observed features, P = 0.001 ; Faith's phylogenetic diversity, P = 0.013) and different beta diversity metrics (unweighted UniFrac, P = 0.006 ; Jaccard's distance, P = 0.004), compared with controls. The presence of Lactobacillus paragasseri/gasseri (aOR: 3.12, 95% CI: 1.24-7.84), Streptococcus (aOR: 3.58, 95% CI: 1.68-7.65), or Proteobacteria (aOR: 3.39, 95% CI: 1.55-7.39) was associated with an increased risk of sPTB in the non-GDM group (P < 0.05). However, the relative abundance of novel L. mulieris (a new species of the L. delbrueckii group) was associated with a decreased risk of sPTB (false discovery rate, 0.10) in all pregnant women. Conclusion. GDM may modify the association of vaginal taxa with sPTB, suggesting that maternal GDM should be considered when using vaginal taxa to identify pregnant women at high risk of sPTB. [ABSTRACT FROM AUTHOR]

Due to atmospheric pollution from fossil fuels, the reduction of wind turbine costs, and the rise of the low-carbon economy, wind energy conversion systems have become one of the most significant forms of new energy in China. Therefore, to reduce investment risk and maximize profits, it is necessary to assess wind resources before building large wind farms. This paper develops a comprehensive system containing four steps to evaluate the potential of wind resources at two sites in Xilin Gol League and at additional two sites in Alxa League of Inner Mongolia, China: (1) By calculating the total scores of three indexes, including the effective wind power density (EWPD), wind available time (WAT) and population density (PD), an indexes method is applied to assess the theoretical wind energy potential from 2001 to 2010. (2) To judge the fluctuations in the wind speed, the Fisher optimal partition method and the Jonckheere–Terpstra test are used to analyze the changes in the average monthly and yearly wind speeds from 2001 to 2010. (3) Three probability density functions, i.e., Weibull, Gamma and Lognormal, are used to assess the wind speed frequency distribution in 2010. To enhance the evaluation accuracy, three intelligent optimization parameter estimation algorithms, i.e., the particle swarm optimization algorithm (PSO), differential evolution algorithm (DE) and ant colony algorithm (ACO), are used to estimate the parameters of these distributions. (4) It is helpful to analyze the wind characteristics when assessing wind resources and selecting wind turbines. Therefore, the optimal frequency distribution based on the best parameter estimation method can be chosen to calculate the wind power density, the most probable wind speed and the wind speed carrying the maximum energy. The experimental results show that Site 1 and Site 4 are more suitable for large wind farms than Site 2 or Site 3. [ABSTRACT FROM AUTHOR]

As the energy crisis becomes a greater concern, wind energy, as one of the most promising renewable energy resources, becomes more widely used. Thus, wind energy forecasting plays an important role in wind energy utilization, especially wind speed forecasting, which is a vital component of wind energy management. In view of its importance, numerous wind speed forecasts have been proposed, each with advantages and disadvantages. Searching for more effective wind speed forecasts in wind energy management is a challenging task. As proposed, combined models have desirable forecasting abilities for wind speed. This paper reviewed the combined models for wind speed predictions and classified the combined wind speed forecasting approaches. To further study the combined models, two combination models, the no negative constraint theory (NNCT) combination model and the artificial intelligence algorithm combination model, are proposed. The hourly average wind speed data of three wind turbines in the Chengde region of China are used to illustrate the effectiveness of the proposed combination models, and the results show that the proposed combination models can always provide desirable forecasting results compared to the existing traditional combination models. [ABSTRACT FROM AUTHOR]

With the increasing depletion of fossil fuel and serious destruction of environment, wind power, as a kind of clean and renewable resource, is more and more connected to the power system and plays a crucial role in power dispatch of hybrid system. Thus, it is necessary to forecast wind speed accurately for the operation of wind farm in hybrid system. In this paper, we propose a hybrid model called EEMD-GA-FAC/SAC to forecast wind speed. First, the Ensemble empirical mode decomposition (EEMD) can be applied to eliminate the noise of the original data. After data preprocessing, first-order adaptive coefficient forecasting method (FAC) or second-order adaptive coefficient forecasting method (SAC) can be employed to do forecast. It is significant to select optimal parameters for an effective model. Thus, genetic algorithm (GA) is used to determine parameter of the hybrid model. In order to verify the validity of the proposed model, every ten-minute wind speed data from three observation sites in Shandong Peninsula of China and several error evaluation criteria can be collected. Through comparing with traditional BP, ARIMA, FAC, and SAC model, the experimental results show that the proposed hybrid model EEMD-GA-FAC/SAC has the best forecasting performance. [ABSTRACT FROM AUTHOR]

So far there are limited data on persistent organic pollutants (POPs) in the atmosphere of the Tibetan Plateau. XAD 2-resin based passive air samplers were therefore deployed for 1 year (between July 2007-June 2008) at 16 locations across the Tibetan Plateau. Based on previously reported sampling rates (R) derived in the north and south America, and their correlations with atmospheric temperature and pressure, R values in the present study were in the range of 2.2-3.3 m³ d-1 (average = 2.7 ± 0.3). Derived air concentrations (pg/m³) ranged as follows: DDTs, 5-75; HCHs, 0.1-36; a-endosulfan, 0.1-10; HCB, 2.8-80; sum of 15 PCBs, 1.8-8.2; and sum of 9 PBDEs, 0.1-8.3. The highest DDTs occurred at Qamdo, where the sampling site is near to farm land, indicating the spatial distribution of DDTs across the plateau may be influenced by scattered local usage of DDT. Higher levels of HCHs were observed at sites with high elevation (>4000 m) and close to the China-India border, indicating possible long-range atmospheric transport. The highest levels of HCB, PCBs, and PBDEs were found at a site impacted by forest fire during the sampling campaign. [ABSTRACT FROM AUTHOR]

Background: The objective of the current meta.analysis was to assess the arterial spin labeling. (ASL) perfusion imaging measurement of cerebral blood flow. (CBF) in patients with brain tumors, and assessing preoperative tumor grade in brain. Materials and Methods: PubMed, Web of Science, Embase, China BioMedicine (CBM), CINAHL, Cochrane Library, and China National Knowledge Infrastructure (CNKI) databases were chosen to evaluate the associations between ASL and brain cancer. Two reviewers separately evaluated the quality of the included trials. Standardized mean difference (SMD) at 95% confidence interval (95% CI) was also calculated. Results: Finally, 475 patients were enrolled into this meta-analysis from 12 eligible studies and were selected for statistical analysis. Results showed that relative tumor blood flow (rTBF) and relative cerebral blood flow (rCBF) in high-grade brain cancer patients were faster than those in low-grade brain cancer patients. Subgroup analysis stratified by country implied that ASL may be the main prediction of increased rTBF in high-grade brain cancer patients among USA, Korea and China; and rCBF was faster in high-grade brain cancer using ASL in USA and China. Further reference by tissue-stratified analysis revealed a positive association of rTBF with high-grade brain cancer by utilization of ASL in all the experimental subgroups, while rCBF was only correlated in white subgroups. Conclusion: These results showed that rTBF and rCBF were faster in high-grade brain cancer patients, suggesting that ASL may provide suitable measurement for the differential diagnosis of tumor grade in brain. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes mellitus (T2DM) has been suggested to increase the risk of cancers. The aim of this study was to investigate the risk of common cancers in Chinese patients with T2DM.Methods: A population-based retrospective cohort study including 36,379 T2DM patients was conducted in Minhang District of Shanghai, China, during 2004 to 2010. All T2DM patients were enrolled from the standardized management system based on local electronic information system. Newly-diagnosed cancer cases were identified by record-linkage with the Shanghai Cancer Registry. Standardized incidence ratios (SIR) and 95% confidence interval (CI) were used to estimate the risk of cancers among T2DM patients.Results: Overall crude incidence rate (CIR) of cancers was 955.21 per 105 person-years in men and 829.57 per 105 person-years in women. Increased risk of cancer was found in both gender, with an SIR being 1.28 (95% CI = 1.17-1.38) in men and 1.44 (95% CI =1.32-1.55) in women. Increased risk of colon (SIR = 1.97; 95% CI = 1.49 to 2.46), rectum (1.72; 1.23 to 2.21), prostate (2.87; 2.19 to 3.56), and bladder cancers (1.98, 1.28 to 2.68) were observed in men and elevated risk of colon (1.67; 1.25 to 2.08), breast (1.66; 1.38 to 1.95), and corpus uteri cancers (2.87; 2.03 to 3.71) were observed in women.Conclusions: Our results indicate that Chinese patients with T2DM may have an increased risk of some cancers, and the increase may vary by sub-sites of cancers. [ABSTRACT FROM AUTHOR]

We report rabies virus transmission among solid organ transplantation recipients in Changsha, China, in 2016. Two recipients were confirmed to have rabies and died. Our findings suggest that more attention should be paid to the possibility of rabies virus transmission through organ transplantation for clinical and public health reasons. [ABSTRACT FROM AUTHOR]

Background and Methods: To understand the burden and epidemiology of Streptococcus pneumoniae disease among children between 1 and 59 months of age in China, we conducted a review of literature published between 1980 and 2008 applying standardized algorithms. Because of the absence of population-based surveillance for pneumococcal disease (PD), we identified all-cause pneumonia, bacteremia and meningitis burden, syndromes most commonly associated with S. pneumoniae, and applied the proportion of disease attributable to S. pneumoniae from studies that determined the etiology of these three syndromes to calculate PD burden. Because of the microbiologic difficulties in identifying S. pneumoniae-attributable pneumonia which likely underestimates the pneumonia burden, we also used the proportion obtained from vaccine efficacy trials. Results: Between 1980 and 2008, there were 12,815 cases/100,000/year of all-cause pneumonia among children between 1 month and 59 months, with 526 deaths/100,000 annually. There were 14 meningitis cases/100,000/year. We estimate that as of 2000, there were 260,768 (113,000 to 582,382) and 902 (114-4,463) cases of pneumococcal pneumonia and meningitis, respectively with 10,703 (4,638-23,904) and 75 (9-370) pneumococcal pneumonia and meningitis deaths, respectively. Pneumococcal pneumonia cases and deaths were more than two-fold higher, 695,382 (173,845-1,216,918) and 28,542 (7,136-49,949), respectively, when parameters from efficacy trials were used. Serotypes 19F, 19A and 14 were the most common serotypes obtained from pneumonia/meningitis patients. Currently available vaccines are expected to cover 79.5% to 88.4% of the prevalent serotypes. With high antibiotic resistance, introducing pneumococcal vaccines to the routine immunization program should be considered in China. Population-based studies are warranted. [ABSTRACT FROM AUTHOR]

Previous prognostic factor models for metastatic renal cell carcinoma (mRCC) have not included erythrocyte sedimentation rate (ESR). We designed the present study to evaluate the prognostic value of ESR for mRCC patients treated with sorafenib. Sorafenib was given to 83 patients with clear cell mRCC. Serum ESR was tested before treatment and every 4 weeks after first administration of sorafenib. Oncological evaluation was carried out every 8 weeks. Analyzed factors included age, sex, performance status, method of nephrectomy, number of metastatic organs, anemia, lactate dehydrogenase, corrected calcium, albumin, baseline ESR level and ESR kinetics status. Kaplan-Meier and Cox regression analyses on progression-free survival (PFS) were carried out. Baseline ESR levels ranged from 3 to 154 mm/h, and 43 (41.0%) patients had an ESR level higher than 40 mm/h. Median PFS was 10.0 months (95% CI 7.6-12.4 months). Dividing the cohort into three groups according to ESR kinetics status, median PFS was 27 months in the decreased ESR group, 12 months in the stable ESR group and 6 months in the increased ESR group. Performance status, time from diagnoses to sorafenib treatment, number of metastatic organs and ESR kinetics were independent predictors for PFS in multivariable Cox regression model analysis, with an area under the curve of 0.865 in a binary logistic regression model of 12-month PFS probability. ESR kinetics can be useful to monitor the treatment response and to predict PFS for mRCC patients treated with sorafenib as second-line therapy. [ABSTRACT FROM AUTHOR]