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Aiming at the difficulties in the quantitative verification and evaluation of underwater countermeasures for our underwater platform and the lack of a guided maneuvering evasive combat model, a prediction model of underwater countermeasures based on big data learning is designed. Firstly, the underwater countermeasure modeling of the underwater platform is carried out, and the large data set of avoidance probability is obtained by several rounds of simulation based on Monte Carlo method. At the same time, in order to solve the problem of poor time efficiency under massive simulation, BP neural network prediction algorithm is proposed for big data learning to provide accurate, fast and visual antagonistic results. The test results show that under the test environment set in this paper, the average prediction error of BP neural network prediction algorithm is 7.28% respectively, which can effectively predict the avoidance probability of the underwater platform and provide data support for the commander’s command decision.

Crucial to the assessment of future water security is how the land model component of Earth System Models partition precipitation into evapotranspiration and runoff, and the sensitivity of this partitioning to climate. This sensitivity is not explicitly constrained in land models nor the model parameters important for this sensitivity identified. Here, we seek to understand parametric controls on runoff sensitivity to precipitation and temperature in a state‐of‐the‐science land model, the Community Land Model version 5 (CLM5). Process‐parameter interactions underlying these two climate sensitivities are investigated using the sophisticated variance‐based sensitivity analysis. This analysis focuses on three snow‐dominated basins in the Colorado River headwaters region, a prominent exemplar where land models display a wide disparity in runoff sensitivities. Runoff sensitivities are dominated by indirect or interaction effects between a few parameters of subsurface, snow, and plant processes. A focus on only one kind of parameters would therefore limit the ability to constrain the others. Surface runoff exhibits strong sensitivity to parameters of snow and subsurface processes. Constraining snow simulations would require explicit representation of the spatial variability across large elevation gradients. Subsurface runoff and soil evaporation exhibit very similar sensitivities. Model calibration against the subsurface runoff flux would therefore constrain soil evaporation. The push toward a mechanistic treatment of processes in CLM5 have dampened the sensitivity of parameters compared to earlier model versions. A focus on the sensitive parameters and processes identified here can help characterize and reduce uncertainty in water resource sensitivity to climate change. Key Points: Parametric controls on runoff sensitivity to climate in the land component of an Earth System Model are investigated in a snowmelt runoff regimeRunoff sensitivities are dominated by interactions between certain parameters of subsurface, snow, and plant processesHigh sensitivity is found for soil porosity and depth, saturated fraction, snow cover and densification, stomatal conductance, and photosynthetic capacity [ABSTRACT FROM AUTHOR]

Multisource meteorological re-analyses provide the most reliable forcing data for driving hydrological models to simulate streamflow. We aimed to assess different hydrological responses through hydrological modeling in the upper Lancang-Mekong River Basin (LMRB) using two gridded meteorological datasets, Climate Forecast System Re-analysis (CFSR) and the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS). We selected the Pearson's correlation coefficient (R), percent bias (PBIAS), and root mean square error (RMSE) indices to compare the six meteorological variables of the two datasets. The spatial distributions of the statistical indicators in CFSR and CMADS, namely, the R, PBIAS, and RMSE values, were different. Furthermore, the soil and water assessment tool plus (SWAT+) model was used to perform hydrological modeling based on CFSR and CMADS meteorological re-analyses in the upper LMRB. The different meteorological datasets resulted in significant differences in hydrological responses, reflected by variations in the sensitive parameters and their optimal values. The differences in the calibrated optimal values for the sensitive parameters led to differences in the simulated water balance components between the CFSR- and CMADS-based SWAT+ models. These findings could help improve the understanding of the strengths and weaknesses of different meteorological re-analysis datasets and their roles in hydrological modeling. [ABSTRACT FROM AUTHOR]

Land–atmosphere coupling (LAC) is a critical process in the climate system, and has great effects on surface air temperature over the humid–dry transition zones. Using ERA5/ERA5‐land reanalysis datasets, we show that the summer temperature anomalies over Northwest China (NWC), known as one of the most important LAC hotpots in China, are significantly correlated with the LAC strength during 1981–2020. The stronger the coupling strength between air temperature and land surface, the more pronounced decrease in air temperature and extreme hot days over NWC. Among the four pathways of land–air temperature coupling, including soil moisture (SM)/soil temperature–latent heat flux/sensible heat flux–air temperature coupling, SM–sensible heat flux–air temperature pathway corresponds most closely with the temperature changes in summer. By analyzing the possible local and non‐local physical links in this relationship, we show that the wave trains composed of the intensification of Ural ridge and Central–Asia trough stimulate a meridional fluctuation of westerly airflow over the mid–high latitudes of Eurasia. Large moisture, therefore, converges into NWC, leading to excessive precipitation and deficient incident solar radiation reaching the land surface. In such case, wetter and cooler soil states accelerate the cooling of the environment through feedbacks between the lands, land–atmosphere exchanging fluxes and air temperature locally, accompanied by a significant enhancement in LAC strength. Our study provides insight into the physical processes causing variations in summer temperature over a typical LAC hotspot and suggests that the role of land surface–atmosphere interactions needs to be considered when studying temperature extremes under climate change. Plain Language Summary: There exist strong interactions between land surface and air temperature over Northwest China (NWC) in summer. In the meantime, NWC region (especially Xinjiang province) has undergone severe hot extremes recently and suffered great loss of life and property. In this case, this study is presented to investigate the relationship of variation in surface air temperature with the land–air temperature coupling strength using reanalysis‐based datasets. We revealed that the stronger the coupling strength, the more pronounced decrease in air temperature and extreme hot days there. Both local and non‐local factors contribute to this process. The large‐scale anomalous Arctic Oscillation–East Atlantic/West Russia‐like patterns are conducive to extra water vapor convergence in southern Xinjiang, accompanied by reduced net solar radiation. Such changes further result in significant variations in land states, and local land–air temperature interactions had also been exacerbated, collectively leading to air temperature decreases. These findings are expected to deepen our understanding of the variation of surface air temperature and related extreme temperature events in NWC. Key Points: The stronger the land–atmosphere coupling (LAC), the more decrease of air temperature and extreme hot days (HDs) during summer in Northwest China (NWC)Anomalous circulation patterns correlate with the LAC strength and air temperature variations in NWCThe local land–air temperature interactions enhance the impacts from non‐local atmospheric forcings [ABSTRACT FROM AUTHOR]

Objectives: The objective of this study is to evaluate whether PIV (Pan-Immune-Inflammation Value) and PILE [a score derived from PIV, lactate dehydrogenase (LDH), and Eastern Cooperative Oncology Group Performance Status (ECOG PS)] can predict clinical outcome of anti-PD-1/PD-L1 inhibitor combined with chemotherapy in patients with extensive-stage (ES) small cell lung cancer (SCLC). Methods: A total of 53 patients with ES-SCLC in the control group of clinical trial (NCT03041311) were included in this study. PIV was calculated as follows: (neutrophil count × platelet count × monocyte count)/lymphocyte count. The PILE scores were composited based on PIV, LDH levels, and ECOG PS. The Kaplan–Meier method and Cox hazards regression models were used for survival analyses. Moreover, the predictive ability of PIV and PILE was validated in an independent real-world group consisting of 84 patients. Results: Patients in the low PIV group (PIV < median) had longer progression-free survival (PFS) and overall survival (OS) than those in the high PIV group (PIV ≥ median), along with the HR, which was 2.157 and 2.359, respectively (PFS HR 95% CI: 1.181–3.940, p = 0.012; OS HR 95% CI: 1.168–4.762, p = 0.020). High PILE score was observed relating to worse treatment efficacy (disease control rate (DCR): 84.21% vs. 100%, p = 0.047; durable clinical benefit (DCB) rate: 10% vs. 48.5%, p = 0.060) and poor clinical outcome (median PFS: 4.75 vs. 5.53 m, p = 0.043; median OS: 7.13 vs. 15.93 m, p = 0.002). Similar results were obtained about the predictive and prognostic abilities of PIV and PILE scores in the validation group. Conclusions: High PIV and high PILE were correlated with worse clinical outcomes in ES-SCLC patients treated with anti-PD-1/PD-L1 inhibitor combined with chemotherapy, reflecting that PIV and PILE might be useful to identify patients unlikely to benefit from anti-PD-1/PD-L1 therapy. [ABSTRACT FROM AUTHOR]

In the past decades, data-driven machine-learning (ML) models have emerged as promising tools for short-term streamflow forecasting. Among other qualities, the popularity of ML models for such applications is due to their relative ease in implementation, less strict distributional assumption, and competitive computational and predictive performance. Despite the encouraging results, most applications of ML for streamflow forecasting have been limited to watersheds in which rainfall is the major source of runoff. In this study, we evaluate the potential of random forests (RFs), a popular ML method, to make streamflow forecasts at 1 d of lead time at 86 watersheds in the Pacific Northwest. These watersheds cover diverse climatic conditions and physiographic settings and exhibit varied contributions of rainfall and snowmelt to their streamflow. Watersheds are classified into three hydrologic regimes based on the timing of center-of-annual flow volume: rainfall-dominated, transient, and snowmelt-dominated. RF performance is benchmarked against naïve and multiple linear regression (MLR) models and evaluated using four criteria: coefficient of determination, root mean squared error, mean absolute error, and Kling–Gupta efficiency (KGE). Model evaluation scores suggest that the RF performs better in snowmelt-driven watersheds compared to rainfall-driven watersheds. The largest improvements in forecasts compared to benchmark models are found among rainfall-driven watersheds. RF performance deteriorates with increases in catchment slope and soil sandiness. We note disagreement between two popular measures of RF variable importance and recommend jointly considering these measures with the physical processes under study. These and other results presented provide new insights for effective application of RF-based streamflow forecasting. [ABSTRACT FROM AUTHOR]

Predictive modeling of large-scale spatio-temporal data is an important but challenging problem as it requires training models that can simultaneously predict the target variables of interest at multiple locations while preserving the spatial and temporal dependencies of the data. In this paper, we investigate the effectiveness of applying a multi-task learning approach based on supervised tensor decomposition to the spatio-temporal prediction problem. Our proposed framework, known as SMART, encodes the data as a third-order tensor and extracts a set of interpretable, spatial and temporal latent factors from the data. An ensemble of spatial and temporal prediction models are trained using the latent factors as their predictor variables. Outputs from the ensemble model are aggregated to make predictions on test instances. The framework also allows known patterns from the domain to be incorporated as constraints to guide the tensor decomposition and ensemble learning processes. As the data may grow over space and time, an incremental learning version of the framework is given to efficiently update the models. We perform extensive experiments using a global-scale climate dataset to evaluate the accuracy and efficiency of the models as well as interpretability of the latent factors. [ABSTRACT FROM AUTHOR]

El Niño‐Southern Oscillation (ENSO) is an important driver of interannual climate variability with increasing attention for its impacts on water and flood management. The impact of ENSO on basin‐scale floods during the TRMM period (1998–2013) is examined by using the streamflow outputs from the Dominant river Routing Integrated with VIC Environment model (DRIVE). Significant simultaneous correlations between flood indices and Niño 3.4 appear in many flood‐prone river basins during peak flood months across both the tropics and midlatitudes especially for flood frequency and flood duration. Gauged by significant lag‐correlations between floods and Niño 3.4, significant ENSO‐leading‐floods relations are found as well in many river basins in South America, south and southeastern Asia, and northern Africa. These ENSO‐floods‐relations can greatly enhance understanding of physical mechanisms relevant to the ENSO impact and may also improve the skills of basin‐scale monthly‐to‐seasonal flood forecast, thus allowing for better preparedness and management of flood risks. Plain Language Summary: As a naturally occurring phenomenon involving quasi‐periodic variation of air pressure and sea surface temperature, the ENSO impacts global floods through its modulation of atmospheric circulations, weather, and precipitation. Previous studies on ENSO‐floods‐relations are often limited in supporting practical flood disaster preparedness and risk analyses because of the lack of a "complete" reconstruction of past flood events likely due to scattered gauge‐based streamflow data and/or coarse‐resolution retrospective simulations. Here flood events at model grids and for individual river basins are identified using the outputs from the Dominant river Routing Integrated with VIC Environment model (DRIVE) with a high spatial resolution (0.125° × 0.125°) driven by the state‐of‐the‐art satellite precipitation product TMPA. Simultaneous and lagged responses of basin‐scale floods to ENSO are explored to provide an improved knowledge of the ENSO effect on global floods and a guidance for flood disaster planning and risk management for the regions consistently impacted by ENSO. Key Points: Simultaneous correlations between flood indices and Nino 3.4 appear in many flood‐prone river basins in the tropics and midlatitudesENSO‐leading‐floods relations gauged by significant lag‐correlations between floods and Nino 3.4 can also be seen in many river basinsThese ENSO‐floods‐relations can greatly help our efforts of exploring basin‐scale monthly‐to‐seasonal flood predictability [ABSTRACT FROM AUTHOR]

The current research aimed to explore medium chain triglycerides (MCT) incorporation in liposomes to overcome stability challenges when drugs with high molecular weight and payload are loaded within lipid membranes. A model drug clarithromycin was loaded in lipid dispersions with various MCT/phospholipids ratios (R M/P = 0, 0.5, 1.75 and 7.5 w/w). TEM images demonstrated a liposome-to-emulsion structural transformation by MCT incorporation to cause increased particle size (104.3–167.7 nm) but decreased zeta potential (−63.6 to −44.4 mV) of lipid particles. MCT incorporation produced biphasic release in PBS and accelerated released in plasma. The tolerance of liposomes for thermal sterilization, high temperature test and freeze-thaw cycles were significantly improved by MCT incorporation. However, MCT incorporation produced adverse effects on colloidal stability in plasma and pharmacokinetics behavior in vivo to some extent. MCT stabilizing mechanism attributes to the modulation of drug loading area and stability improvement of lipid carriers. MCT incorporated liposomes achieved 2–3 fold cellular uptake level than traditional liposomes without significant cytotoxicity. These results indicated that MCT incorporation could be a promising strategy to apply in liposome production to achieve stable drug loading. [ABSTRACT FROM AUTHOR]

Endorheic and arid regions around the world are suffering from serious drought problems. In this study, a drought forecasting system based on eight state-of-the-art climate models from the North American Multi-Model Ensemble (NMME) and a Distributed Time-Variant Gain Hydrological Model (DTVGM) was established and assessed over the upstream and midstream of Heihe River basin (UHRB and MHRB), a typical arid endorheic basin. The 3-month Standardized Precipitation Index (SPI3) and 1-month Standardized Streamflow Index (SSI1) were used to capture meteorological and hydrological drought, and values below -1 indicate drought events. The skill of the forecasting systems was evaluated in terms of anomaly correlation (AC) and Brier score (BS) or Brier skill score (BSS). The predictability for meteorological drought was quantified using AC and BS with a “perfect model" assumption, referring to the upper limit of forecast skill. The hydrological predictability was to distinguish the role of initial hydrological conditions (ICs) and meteorological forcings, which was quantified by root-mean-square error (RMSE) within the ESP (Ensemble Streamflow Prediction) and reverse ESP framework. The UHRB and MHRB showed season-dependent meteorological drought predictability and forecast skill, with higher values during winter and autumn than that during spring. For hydrological forecasts, the forecast skill in the UHRB was higher than that in MHRB. Predicting meteorological droughts more than 2 months in advance became difficult because of complex climate mechanisms. However, the hydrological drought forecasts could show some skills up to 3-6 lead months due to memory of ICs during cold and dry seasons. During wet seasons, there are no skillful hydrological predictions from lead month 2 onwards because of the dominant role of meteorological forcings. During spring, the improvement of hydrological drought predictions was the most significant as more streamflow was generated by seasonal snowmelt. Besides meteorological forcings and ICs, human activities have reduced the hydrological variability and increased hydrological drought predictability during the wet seasons in the MHRB. [ABSTRACT FROM AUTHOR]

Better quantification of the spatiotemporal distribution of soil moisture across different spatial scales contributes significantly to the understanding of land surface processes on the Earth as an integrated system. While observational data for root-zone soil moisture (RZSM) often have sparse spatial coverage, model-simulated soil moisture may provide a useful alternative. TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) has been widely studied and actively modified in recent years, while a detailed regional application with evaluation currently is still lacking. Thus, TOPLATS was used to generate high-resolution (30 arc s) RZSM based on coarse-scale (0.125°) forcing data over part of the Arkansas–Red River basin. First, the simulated RZSM was resampled to coarse scale to compare with the results of Mosaic, Noah, and VIC from NLDAS. Second, TOPLATS performance was assessed based on the spatial absolute difference among the models. The comparison shows that TOPLATS performance is similar to VIC, but different from Mosaic and Noah. Last, the simulated RZSM was compared with in situ observations of 16 stations in the study area. The results suggest that the simulated spatial distribution of RZSM is largely consistent with the distribution of topographic index (TI) in most instances, as topography was traditionally considered a major, but not the only, factor in horizontal redistribution of soil moisture. In addition, the finer-resolution RZSM can reflect the in situ soil moisture change at most local sites to a certain degree. The evaluation confirms that TOPLATS is a useful tool to estimate high-resolution soil moisture and has great potential to provide regional soil moisture estimates. [ABSTRACT FROM AUTHOR]

Accurate and timely precipitation forecasts are a key factor for improving hydrological forecasts. Therefore, it is fundamental to evaluate the skill of NumericalWeather Prediction (NWP) for precipitation forecasting. In this study, the Global Environmental Multi-scale (GEM) model, which is widely used around Canada, was chosen as the high-resolution medium-term prediction model. Based on the forecast precipitation with the resolution of 0.24° and taking regional differences into consideration, the study explored the forecasting skill of GEM in nine drought sub-regions around China. Spatially, GEM performs better in East and South China than in the inland areas. Temporally, the model is able to produce more precise precipitation during flood periods (summer and autumn) compared with the non-flood season (winter and spring). The forecasting skill variability differs with regions, lead time and season. For different precipitation categories, GEM for trace rainfall and little rainfall performs much better than moderate rainfall and above. Overall, compared with other prediction systems, GEM is applicable for the 0-96 h forecast, especially for the East and South China in flood season, but improvement for the prediction of heavy and storm rainfall and for the inland areas should be focused on as well. [ABSTRACT FROM AUTHOR]