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In this paper, a series of samples La0.7Sr0.3Mn1-xFexO3 (x = 0, 0.05, 0.10, 0.15, 0.2) were prepared. Among these samples, La0.7Sr0.3Mn0.85Fe0.15O3 showed better catalytic activity than the samples with undoped and other doped ones, so undoped iron La0.7Sr0.3MnO3 and doped iron La0.7Sr0.3Mn0.85Fe0.15O3 were characterized by X-ray diffraction (XRD), UV–Vis diffuse reflection analysis (UV–Vis DRS), vibrating sample magnetometer (VSM), transmission electron microscopy (TEM), scanning electron microscope (SEM), specific surface analysis (BET), and X-ray photoelectron spectroscopy (XPS). In addition, it takes methyl orange (MO) as the object of degradation, and the photocatalysis, Fenton-like reaction, and their synergistic catalytic effects as well as the catalytic mechanisms of La0.7Sr0.3Mn0.85Fe0.15O3 have been studied. The results show that La0.7Sr0.3Mn0.85Fe0.15O3 exhibits high solar-driven photocatalytic and Fenton-like catalytic activities for MO degradation. Moreover, there is an obvious synergistic catalytic effect of photocatalysis and Fenton-like catalytic activity. In addition, according to the free radical capture experiment, the hole, •OH and •O2− are the active substances in the synergistic catalytic process. The possible synergistic catalytic mechanism of La0.7Sr0.3Mn0.85Fe0.15O3 was also proposed. And the average pore size of the catalyst is 20.91 nm by BET. The near-superparamagnetic properties of La0.7Sr0.3Mn0.85Fe0.15O3 enable it to achieve simple separation in an external magnetic field, avoiding the disadvantages of ordinary catalysts that are difficult to separate. Therefore, La0.7Sr0.3Mn0.85Fe0.15O3 is an advanced multifunctional nanocatalyst and could be widely used in the field of wastewater treatment.The magnetic La0.7Sr0.3Mn0.85Fe0.15O3 was synthesized by hydrothermal method successfully, the photocatalysis, Fenton-like reaction and their synergistic catalytic effects as well as the catalytic mechanisms of La0.7Sr0.3Mn0.85Fe0.15O3 have been studied, It is not only a high efficient solar-driven photocatalyst, but also has Fenton -like catalytic properties, and both of them have synergistic catalytic effect. And those indicate that La0.7Sr0.3Mn0.85Fe0.15O3 is an advanced multi-functional catalyst.

Despite numerous assessments of satellite-based and reanalysis precipitation across the globe, few studies have been conducted based on the precipitation linear trend (LT), particularly during daytime and nighttime, when there are different precipitation mechanisms. Herein, we first examine LTs for the whole day (LTwd), daytime (LTd), and nighttime (LTn) over mainland China (MC) in 2003–2017, with sub-daily observations from a dense rain gauge network. For MC and ten Water Resources Regions (WRRs), annual and seasonal LTwd, LTd, and LTn were generally positive but with evident regional differences. Subsequently, annual and seasonal LTs derived from six satellite-based and six reanalysis popular precipitation products were evaluated using metrics of correlation coefficient (CC), bias, root-mean-square-error (RMSE), and sign accuracy. Finally, metric-based optimal products (OPs) were identified for MC and each WRR. Values of each metric for annual and seasonal LTwd, LTd, or LTn differ among products; meanwhile, for any single product, performance varied by season and time of day. Correspondingly, the metric-based OPs varied among regions and seasons, and between daytime and nighttime, but were mainly characterized by OPs of Tropical Rainfall Measuring Mission (TRMM) 3B42, ECMWF Reanalysis (ERA)-Interim, and Modern Era Reanalysis for Research and Applications (MERRA)-2. In particular, the CC-based (RMSE-based) OPs in southern and northern WRRs were generally TRMM3B42 and MERRA-2, respectively. These findings imply that to investigate precipitation change and obtain robust related conclusions using precipitation products, comprehensive evaluations are necessary, due to variation in performance within one year, one day and among regions for different products. Additionally, our study facilitates a valuable reference for product users seeking reliable precipitation estimates to examine precipitation change across MC, and an insight (i.e., capacity in detecting LTs, including daytime and nighttime) for developers improving algorithms.

As an essential variable in linking water, carbon, and energy cycles, evapotranspiration (ET) is difficult to measure. Remote sensing, reanalysis, and land surface model-based ET products offer comprehensive alternatives at different spatio-temporal intervals, but their performance varies. In this study, we selected four popular ET global products: The Global Land Evaporation Amsterdam Model version 3.0a (GLEAM3.0a), the Modern Era Retrospective-Analysis for Research and Applications-Land (MERRA-Land) project, the Global Land Data Assimilation System version 2.0 with the Noah model (GLDAS2.0-Noah) and the EartH2Observe ensemble (EartH2Observe-En). Then, we comprehensively evaluated the performance of these products over China using a stratification method, six validation criteria, and high-quality eddy covariance (EC) measurements at 12 sites. The aim of this research was to provide important quantitative information to improve and apply the ET models and to inform choices about the appropriate ET product for specific applications. Results showed that, within one stratification, the performance of each ET product based on a certain criterion differed among classifications of this stratification. Furthermore, the optimal ET (OET) among these products was identified by comparing the magnitudes of each criterion. Results suggested that, given a criterion (a stratification classification), the OETs varied among stratification classifications (the selected six criteria). In short, no product consistently performed best, according to the selected validation criterion. Thus, multi-source ET datasets should be employed in future studies to enhance confidence in ET-related conclusions.

Satellite-based precipitation products, especially those with high temporal and spatial resolution, constitute a potential alternative to sparse rain gauge networks for multidisciplinary research and applications. In this study, the validation of the 30-year Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) daily precipitation dataset was conducted over the Huai River Basin (HRB) of China. Based on daily precipitation data from 182 rain gauges, several continuous and categorical validation statistics combined with bias and error decomposition techniques were employed to quantitatively dissect the PERSIANN-CDR performance on daily, monthly, and annual scales. With and without consideration of non-rainfall data, this product reproduces adequate climatologic precipitation characteristics in the HRB, such as intra-annual cycles and spatial distributions. Bias analyses show that PERSIANN-CDR overestimates daily, monthly, and annual precipitation with a regional mean percent total bias of 11%. This is related closely to the larger positive false bias on the daily scale, while the negative non-false bias comes from a large underestimation of high percentile data despite overestimating lower percentile data. The systematic sub-component (error from high precipitation), which is independent of timescale, mainly leads to the PERSIANN-CDR total Mean-Square-Error (TMSE). Moreover, the daily TMSE is attributed to non-false error. The correlation coefficient (R) and Kling–Gupta Efficiency (KGE) respectively suggest that this product can well capture the temporal variability of precipitation and has a moderate-to-high overall performance skill in reproducing precipitation. The corresponding capabilities increase from the daily to annual scale, but decrease with the specified precipitation thresholds. Overall, the PERSIANN-CDR product has good (poor) performance in detecting daily low (high) rainfall events on the basis of Probability of Detection, and it has a False Alarm Ratio of above 50% for each precipitation threshold. The Equitable Threat Score and Heidke Skill Score both suggest that PERSIANN-CDR has a certain ability to detect precipitation between the second and eighth percentiles. According to the Hanssen–Kuipers Discriminant, this product can generally discriminate rainfall events between two thresholds. The Frequency Bias Index indicates an overestimation (underestimation) of precipitation totals in thresholds below (above) the seventh percentile. Also, continuous and categorical statistics for each month show evident intra-annual fluctuations. In brief, the comprehensive dissection of PERSIANN-CDR performance reported herein facilitates a valuable reference for decision-makers seeking to mitigate the adverse impacts of water deficit in the HRB and algorithm improvements in this product.

The purpose of a battery thermal management system (BTMS) is to maintain the battery safety and efficient use as well as ensure the battery temperature is within the safe operating range. The traditional air-cooling-based BTMS not only needs extra power, but it could also not meet the demand of new lithium-ion battery (LIB) packs with high energy density, while liquid cooling BTMS requires complex devices to ensure the effect. Therefore, phase change materials (PCMs)-based BTMS is becoming the trend. By using PCMs to absorb heat, the temperature of a battery pack could be kept within the normal operating range for a long time without using any external power. PCMs could greatly improve the heat dissipation efficiency of BTMS by combining with fillers such as expanded graphite (EG) and metal foam for their high thermal conductivity or coordinating with fins. In addition, PCMs could also be applied in construction materials, solar thermal recovery, textiles and other fields. Herein, a comprehensive review of the PCMs applied in thermal storage devices, especially in BTMS, is provided. In this work, the literature concerning current issues have been reviewed and summarized, while the key challenges of PCM application have been pointed out. This review may bring new insights to the PCM application.

A full analysis of 3-month Standardized Precipitation-Evapotranspiration index (SPEI-3) changes and attribution analyses are of significance for deeply understanding dryness/wetness evolutions and thus formulating specific measures to sustain regional development. In this study, we analyze monthly and annual SPEI-3 changes over Southwest China (SWC; including Sichuan (SC), Chongqing (CQ), Guizhou (GZ), Yunnan (YN), and west Guangxi (wGX)) during 1961–2012, using the SPEI model and routine meteorological measurements at 269 weather sites. For SWC and each subregion (excluding wGX), annual SPEI-3 during 1961–2012 tends to decrease, and drying is at most of months in January and September–December, but wetting is in February–August (excluding March for wGX). Additionally, more than 50% of sites show declined and increased SPEI-3 in January, April, June, and August–December and the remaining months, respectively. Except for wGX with dominant of ET0, annual SPEI-3 changes in SWC and other four subregions have dominant of precipitation. Spatially, annual SPEI-3 changes at 59% of sites are because of precipitation, generally located in southeast SC, south YN, CQ, GZ, and south and northeast wGX. Nevertheless, dominants at regional and site scales vary among months, e.g., SWC, SC, CQ, and GZ, having dominant of precipitation (ET0) during September–December (most of months during January–August), YN always with dominant of precipitation, and wGX with dominant of precipitation (ET0) in February–April and July–December (January, May, and June). Importantly, this study provides a reference for quantitatively evaluating spatiotemporal dryness/wetness variations with climate change, especially for regions with significant drying/wetting.

Background Prognostic classification approaches are commonly used in clinical practice to predict health outcomes. However, there has been limited focus on use of the general approach for predicting costs. We applied a grouping algorithm designed for large-scale data sets and multiple prognostic factors to investigate whether it improves cost prediction among older Medicare beneficiaries diagnosed with prostate cancer. Methods We analysed the linked Surveillance, Epidemiology and End Results (SEER)-Medicare data, which included data from 2000 through 2009 for men diagnosed with incident prostate cancer between 2000 and 2007. We split the survival data into two data sets (D 0 and D 1 ) of equal size. We trained the classifier of the Grouping Algorithm for Cancer Data (GACD) on D 0 and tested it on D 1 . The prognostic factors included cancer stage, age, race and performance status proxies. We calculated the average difference between observed D 1 costs and predicted D 1 costs at 5 years post-diagnosis with and without the GACD. Results The sample included 110,843 men with prostate cancer. The median age of the sample was 74 years, and 10 % were African American. The average difference (mean absolute error [MAE]) per person between the real and predicted total 5-year cost was US$41,525 (MAE US$41,790; 95 % confidence interval [CI] US$41,421–42,158) with the GACD and US$43,113 (MAE US$43,639; 95 % CI US$43,062–44,217) without the GACD. The 5-year cost prediction without grouping resulted in a sample overestimate of US$79,544,508. Conclusion The grouping algorithm developed for complex, large-scale data improves the prediction of 5-year costs. The prediction accuracy could be improved by utilization of a richer set of prognostic factors and refinement of categorical specifications.

Land–atmosphere (LA) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat andmoisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. In this study, the authors examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled Weather Research and Forecasting Model (WRF) forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through calibration of the Noah land surface model using the new optimization and uncertainty estimation subsystems in NASA’s Land Information System (LIS-OPT/LIS-UE). The impact of the calibration on the 1) spinup of the land surface used as initial conditions and 2) the simulated heat and moisture states and fluxes of the coupled WRF simulations is then assessed. In addition, the sensitivity of this approach to the period of calibration (dry, wet, or average) is investigated. Results show that the offline calibration is successful in providing improved initial conditions and land surface physics for the coupled simulations and in turn leads to systematic improvements in land–PBL fluxes and near-surface temperature and humidity forecasts. Impacts are larger during dry regimes, but calibration during either primarily wet or dry periods leads to improvements in coupled simulations due to the reduction in land surface model bias. Overall, these results provide guidance on the questions of what, how, and when to calibrate land surface models for coupled model prediction.

The monthly 3‐month Standardized Precipitation‐Evapotranspiration Index (SPEI‐3) over southwest China (SWC) shows obvious inter‐annual and inter‐decadal variability during 1961–2012. By examining the isolated contributions of precipitation and reference evapotranspiration (ET0) alone to the monthly SPEI‐3 anomalies, we find out that although anomalous precipitation plays a predominant role, ET0 impacts cannot be neglected, particularly during February–May. The monthly dryness/wetness sensitivity to precipitation (Sp; Set0 for ET0), that is, SPEI‐3 changes by per millimetre of precipitation (ET0) anomalies, is quantified as the slope of a linear regression, which has precipitation (ET0) contributions and anomalies during 1961–2012 as the dependent and the independent variables, respectively. Both SWC Sp and Set0 have evident intra‐annual differences, decreasing during February–August and increasing afterwards. Moreover, their magnitudes are dependent on climatological precipitation and generally decrease with precipitation increase. To sum up, the estimated respective contributions of monthly precipitation and ET0 anomalies are favourable for thoroughly understanding SWC dry/wet condition evolutions during the past 52 years. More importantly, Sp and Set0 are of significance for drought forecasting and emergency system over SWC. Over southwest China (SWC), determinants (i.e., precipitation and reference evapotranspiration [ET0]) of monthly dryness/wetness anomalies are identified. Monthly dryness/wetness sensitivity to precipitation (ET0) has evident intra‐annual differences and decreases during February–August and afterwards increases. Dryness/wetness sensitivity is dependent on monthly climatological precipitation.

Mobile ah hoc network (MANET) is wireless, mobile network that can be deployed anywhere and anytime without constant infrastructure. The mobile nodes perform both as a host and a router by forwarding packets to other nodes. Owing to traditional wired routing protocols are not fit for wireless network, a number of routing protocols have been proposed for ad hoc network. In this paper, we focus on on-demand routing protocols, including Ad hoc On-demand Distance Vector (AODV) and Dynamic Source Routing (DSR), which are simulated to compare the performance of packet delivery fraction, average end-to-end delay and normalized routing load. (5 pages)