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Flavonoids can sensitize and quench the photolysis of pesticides such as profenofos in surface water. Profenofos has been frequently detected in surface and underground water. The present study investigated the photolysis of profenofos under various conditions, including natural and artificial light illumination, with and without cyanidin-3-O-glucoside (Cy3G) and in pure and surface water. The degradation half-lives of profenofos in distilled water with 10 equivalents Cy3G of profenofos were 21.7 min, 9.5 h, 12.5 h and 180 h under high-pressure mercury light, UV, Xenon lamp and solar irradiation, respectively, while those without Cy3G were 8.1 min, 6.1 h, 8.2 h and 89.9 h, respectively. The photolysis rate of profenofos under sunlight and artificial light was reduced by 1.5-2.7 times due to Cy3G, compared to the Cy3G-free control. Under sunlight irradiation, the effects of Cy3G on profenofos photolysis were larger than those under high-pressure mercury lamp irradiation. Cy3G also significantly reduced the photolysis rate of profenofos under different pH conditions and in natural water. In addition, Cy3G exhibited a significant capacity of scavenging hydroxyl radicals and quenching

Study Objectives Post-traumatic stress disorder (PTSD) and rapid eye movement (REM) sleep behavior disorder (RBD) share some common features including prominent nightmares and sleep disturbances. We aimed to comparatively analyze REM sleep without atonia (RSWA) between patients with chronic PTSD with and without dream enactment behavior (DEB), isolated RBD (iRBD), and controls. Methods In this retrospective study, we comparatively analyzed 18 PTSD with DEB (PTSD+DEB), 18 PTSD without DEB, 15 iRBD, and 51 controls matched for age and sex. We reviewed medical records to determine PTSD clinical features and quantitatively analyzed RSWA. We used nonparametric analyses to compare clinical and polysomnographic features. Results PTSD patients, both with and without DEB, had significantly higher RSWA than controls (all p < .025, excepting submentalis phasic duration in PTSD+DEB). Most RSWA measures were also higher in PTSD+DEB than in PTSD without DEB patients (all p < .025). Conclusions PTSD patients have higher RSWA than controls, whether DEB is present or not, indicating that REM sleep atonia control is abnormal in chronic PTSD. Further prospective studies are needed to determine whether neurodegenerative risk and disease markers similar to RBD might occur in PTSD patients.

The biases in the Global Circulation Models (GCMs) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non-stationary bias correction model, termed residual-based bagging tree (RBT) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non-stationarities into the modelling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared with the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios (RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44, 2.59, and 4.71 °C by the end of the century, respectively, when compared with the average temperature during 1970–1999.

The global circulation models (GCMs) are useful tools for simulating climate change, projecting future temperature changes, and therefore, supporting the preparation of national climate adaptation plans. However, different GCMs are not always in agreement with each other over various regions. The reason is that GCMs’ configurations, module characteristics, and dynamic forcings vary from one to another. Model ensemble techniques are extensively used to post-process the outputs from GCMs and improve the variability of model outputs. Root-mean-square error (RMSE), correlation coefficient (CC, or R) and uncertainty are commonly used statistics for evaluating the performances of GCMs. However, the simultaneous achievements of all satisfactory statistics cannot be guaranteed in using many model ensemble techniques. In this paper, we propose a multi-model ensemble framework, using a state-of-art evolutionary multi-objective optimization algorithm (termed MOSPD), to evaluate different characteristics of ensemble candidates and to provide comprehensive trade-off information for different model ensemble solutions. A case study of optimizing the surface air temperature (SAT) ensemble solutions over different geographical regions of China is carried out. The data covers from the period of 1900 to 2100, and the projections of SAT are analyzed with regard to three different statistical indices (i.e., RMSE, CC, and uncertainty). Among the derived ensemble solutions, the trade-off information is further analyzed with a robust Pareto front with respect to different statistics. The comparison results over historical period (1900–2005) show that the optimized solutions are superior over that obtained simple model average, as well as any single GCM output. The improvements of statistics are varying for different climatic regions over China. Future projection (2006–2100) with the proposed ensemble method identifies that the largest (smallest) temperature changes will happen in the South Central China (the Inner Mongolia), the North Eastern China (the South Central China), and the North Western China (the South Central China), under RCP 2.6, RCP 4.5, and RCP 8.5 scenarios, respectively.

In the development of a satellite-based precipitation product, two important aspects are sufficient precipitation information in the satellite-input data and proper methodologies, which are used to extract such information and connect it to precipitation estimates. In this study, the effectiveness of the state-of-the-art deep learning (DL) approaches to extract useful features from bispectral satellite information, infrared (IR), and water vapor (WV) channels, and to produce rain/no-rain (R/NR) detection is explored. To verify the methodologies, two models are designed and evaluated: the first model, referred to as the DL-IR only method, applies deep learning approaches to the IR data only; the second model, referred to as the DL-IR+WV method, incorporates WV data to further improve the precipitation identification performance. The radar stage IV data are the reference data used as ground observation. The operational product, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Cloud Classification System (PERSIANN-CCS), serves as a baseline model with which to compare the performances. The experiments show significant improvement for both models in R/NR detection. The overall performance gains in the critical success index (CSI) are 21.60% and 43.66% over the verification periods for the DL-IR only model and the DL-IR+WV model compared to PERSIANN-CCS, respectively. In particular, the performance gains in CSI are as high as 46.51% and 94.57% for the models for the winter season. Moreover, specific case studies show that the deep learning techniques and the WV channel information effectively help recover a large number of missing precipitation pixels under warm clouds while reducing false alarms under cold clouds.

The small-strain damping ratio (Dmin) is a key parameter in site response models and using values from laboratory tests tends to overpredict the site response because laboratory tests canno...

Despite the advantage of global coverage at high spatiotemporal resolutions, satellite remotely sensed precipitation estimates still suffer from insufficient accuracy that needs to be improved for weather, climate, and hydrologic applications. This paper presents a framework of a deep neural network (DNN) that improves the accuracy of satellite precipitation products, focusing on reducing the bias and false alarms. The state-of-the-art deep learning techniques developed in the area of machine learning specialize in extracting structural information from a massive amount of image data, which fits nicely into the task of retrieving precipitation data from satellite cloud images. Stacked denoising autoencoder (SDAE), a widely used DNN, is applied to perform bias correction of satellite precipitation products. A case study is conducted on the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) with spatial resolution of 0.08° × 0.08° over the central United States, where SDAE is used to process satellite cloud imagery to extract information over a window of 15 × 15 pixels. In the study, the summer of 2012 (June–August) and the winter of 2012/13 (December–February) serve as the training periods, while the same seasons of the following year (summer of 2013 and winter of 2013/14) are used for validation purposes. To demonstrate the effectiveness of the methodology outside the study area, three more regions are selected for additional validation. Significant improvements are achieved in both rain/no-rain (R/NR) detection and precipitation rate quantification: the results make 33% and 43% corrections on false alarm pixels and 98% and 78% bias reductions in precipitation rates over the validation periods of the summer and winter seasons, respectively.

Summary This paper evaluates how post-processing can enhance raw precipitation forecasts made by different numerical weather prediction (NWP) models archived in TIGGE (THORPEX Interactive Grand Global Ensemble) database. Ensemble Pre-Processor (EPP), developed at U.S. National Weather Service, is used to post-process raw precipitation forecasts. EPP involves several major steps: (1) deriving the joint distributions of raw forecasts and observations corresponding to different canonical events; (2) obtaining the probability distributions of observations given the raw forecasts; and (3) constructing ensemble forecasts from the conditional probability distributions given the raw forecasts. Raw precipitation forecasts from five NWP models (CMA, ECMWF, JMA, NCEP and UKMO) during the summer-fall period (rainy season) from 2007 to 2011 were evaluated over the Huai river basin in China. The lead time for the precipitation forecasts is set to 9 days, which are divided into 11 canonical events (defined as daily precipitation events or aggregate precipitation events over a period of several consecutive days). Our experiments show that post-processed precipitation forecasts shows substantial improvement over the raw forecasts. Post-processing reduces both the biases and the root mean squared error of the raw forecasts significantly. In terms of ensemble spread, both the Brier skill scores and continuous ranked probability skill score are improved appreciably after post-processing. Reliability diagrams and rank histograms also confirm that post-processed ensemble forecasts possess better ensemble spread property compared to the raw forecasts. Among the five NWP models, ECMWF and JMA have the best overall performance in both raw and post-processed forecasts. The raw and post-processed UKMO and NCEP forecasts outperform other models in certain events. Post-processing can improve the CMA raw forecasts substantially, but still its performance is consistently worse than that of the other models.

Typhoon Haiyan, which struck Southeast Asia in November 2013, might be the strongest storm on record, with a 10-minute sustained wind speed of 230 kilometers per hour. In the Philippines alone, the damage was immense—the storm killed more than 6000 and completely leveled cities and towns, particularly on the island of Leyte.

Diarrhea causes more than 500,000 deaths of children younger than 5 years, globally, each year. A combination of oral rehydration salts (ORS) and zinc is regarded as the most effective treatment to prevent diarrhea-related deaths among children. We study the effectiveness of a program in Nigeria aimed at increasing usage of ORS and zinc. Peer detailers (educators) reinforce the benefits of the treatment to patent and proprietary medicine vendors (PPMVs), a main source of healthcare in the country. Two aspects of program effectiveness are analyzed: 1) PPMV knowledge that this combination is the most effective treatment and 2) PPMV inventory of ORS and zinc. Hypothesis tests reveal that the percentage of PPMVs with knowledge of the most effective treatment increases significantly in most of the tested Nigerian states after peer detailing. Surprisingly, no significant patterns were detected regarding the percentage of PPMVs with inventory of ORS and zinc. Logistic regression results suggest that PPMVs that were detailed have significantly higher odds ratios for both knowledge and inventory. We discuss our findings regarding the overall effectiveness of the program and the limitations of the study, as well as suggestions for future activities to support information dissemination.