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Addressing the demand for low-frequency noise control in ships and aiming to overcome the limitations imposed by the mass law of traditional materials, a sandwich thin-plate acoustic metamaterial (SPAM) was developed in this study. This structure is characterized by its simplicity and superior sound insulation performance. A sound insulation test was conducted to validate the exceptional low-frequency acoustic suppression capability of SPAM, and the effectiveness of the analytical model was also demonstrated. Subsequently, based on this model, a sensitivity analysis of the sound insulation characteristics of the SPAM relative to structural parameters was carried out. For the optimization process, the Logistic-Sine fusion chaotic mapping was employed to refine the dung beetle optimizer (DBO), enhancing the initial population distribution. The improved dung beetle optimizer (IDBO) was then used to further optimize and elevate the sound insulation performance of the SPAM. The outcomes indicate that the IDBO boasts superior optimization proficiency and accelerated convergence, while the optimized SPAM exhibits more remarkable sound insulation capabilities. Within the frequency range of 100–315 Hz, the average sound transmission loss (STL) has increased by 11.94 dB when compared to the pre-optimization values. [ABSTRACT FROM AUTHOR]

Two models with a wax module tree structure were employed for directional solidification simulation. An experimental alloy of a second-generation nickel-based single-crystal superalloy, DD5, was set for this investigation. The temperature gradient value of the conventional model at 80 mm and 160 mm was less than 3.8 K/mm, and the temperature gradient value of the new model structure at the same height could reach more than 5.0 K/mm. The paste–liquid interface paste zone of the new model structure in the directional solidification process was narrower. The solidification interface was more stable than that of the conventional model. The declination angle between the primary dendrite and the principal stress axis was smaller, and the average crystal orientation was about 6.0°. Under the new model, the integrity of the single crystal of the edge plate was good, and no heterocrystalline defects were formed. At the sharp corner of the end face of the edge plate, the solidified dendrites grew staggered, which hindered the effective contraction of other parts of the edge plate and produced micro-looseness. The whole simulation was in line with the test results. [ABSTRACT FROM AUTHOR]

Global warming has profound effects on precipitation patterns, leading to more frequent and extreme precipitation events over the world. These changes pose significant challenges to the sustainable development of socio-economic and ecological environments. This study evaluated the performance of the new generation of the mesoscale Weather Research and Forecasting (WRF) model in simulating long-term extreme precipitation events over the Minjiang River Basin (MRB) of China from 1981 to 2020. We calculated 12 extreme precipitation indices from the WRF simulations and compared them with observations. The spatio-temporal variations of extreme precipitation were further analyzed in terms of intensity, frequency, and duration. The results indicated that the WRF model can appropriately reproduce the spatial distribution of extreme precipitation indices with acceptable biases. The performance is significantly better for intensity and frequency indices compared to duration indices. Except for PRCPTOT and R10mm, WRF accurately captures the interannual variations of extreme precipitation. Meanwhile, the results of the pre-whitening Mann-Kendall (PWMK) test suggested that WRF can identify significant increasing trends in extreme precipitation, particularly for R95p, R99p, and R50mm. This study provides valuable insights for extreme precipitation forecasting and warning in other mountainous regions. [ABSTRACT FROM AUTHOR]

Accurately predicting the state of surface water quality is crucial for ensuring the sustainable use of water resources and environmental protection. This often requires a focus on the range of factors affecting water quality, such as physical and chemical parameters. Tree models, with their flexible tree-like structure and strong capability for partitioning and selecting influential water quality features, offer clear decision-making rules, making them suitable for this task. However, an individual decision tree model has limitations and cannot fully capture the complex relationships between all influencing parameters and water quality. Therefore, this study proposes a method combining ensemble tree models with voting algorithms to predict water quality classification. This study was conducted using five surface water monitoring sites in Qingdao, representing a portion of many municipal water environment monitoring stations in China, employing a single-factor determination method with stringent surface water standards. The soft voting algorithm achieved the highest accuracy of 99.91%, and the model addressed the imbalance in original water quality categories, reaching a Matthews Correlation Coefficient (MCC) of 99.88%. In contrast, conventional machine learning algorithms, such as logistic regression and K-nearest neighbors, achieved lower accuracies of 75.90% and 91.33%, respectively. Additionally, the model's supervision of misclassified data demonstrated its good learning of water quality determination rules. The trained model was also transferred directly to predict water quality at 13 monitoring stations in Beijing, where it performed robustly, achieving an ensemble hard voting accuracy of 97.73% and an MCC of 96.81%. In many countries' water environment systems, different water qualities correspond to different uses, and the magnitude of influencing parameters is directly related to water quality categories; critical parameters can even directly determine the quality category. Tree models are highly capable of handling nonlinear relationships and selecting important water quality features, allowing them to identify and exploit interactions between water quality parameters, which is especially important when multiple parameters together determine the water quality category. Therefore, there is significant motivation to develop tree model-based water quality prediction models. [ABSTRACT FROM AUTHOR]

Global warming is assumed to accelerate the global water cycle. However, quantification of the acceleration and regional analyses remain open. Accordingly, in this study, we address the fundamental hydrological question: Is the water cycle regionally accelerating/decelerating under global warming? For our investigation, we have implemented the age-weighted regional water tagging approach into the Weather Research and Forecasting (WRF) Model, namely, WRF-age, to follow the atmospheric water pathways and to derive atmospheric water residence times defined as the age of tagged water since its source. We apply a three-dimensional online budget analysis of the total, tagged, and aged atmospheric water into WRF-age to provide a prognostic equation of the atmospheric water residence times and to derive atmospheric water transit times defined as the age of tagged water since its source originating from a particular physical or dynamical process. The newly developed, physics-based WRF-age model is used to regionally downscale the reanalysis of ERA-Interim and the Max Planck Institute Earth System Model (MPI-ESM) representative concentration pathway 8.5 scenario exemplarily for an East Asian monsoon region, i.e., the Poyang Lake basin (the tagged water source area), for historical (1980–89) and future (2040–49) times. In the warmer (+1.9°C for temperature and +2% for evaporation) and drier (−21% for precipitation) future, the residence time for the tagged water vapor will regionally decrease by 1.8 h (from 14.3 h) due to enhanced local evaporation contributions, but the transit time for the tagged precipitation will increase by 1.8 h (from 12.9 h) partly due to slower fallout of precipitating moisture components. Significance Statement: Global warming is assumed to accelerate the global water cycle. However, quantification of the acceleration and regional analyses remain open. This study presents the newly developed WRF-age model that allows us to follow the atmospheric water pathways, to derive atmospheric water residence times and atmospheric water transit times. We exemplarily show for an East Asian monsoon region that global warming leads to regionally decreased residence time for the tagged water vapor in the atmosphere due to enhanced local contributions, but increased transit time for the tagged precipitation over the land surface that is partly attributed to slower fallout of precipitating moisture components in the atmosphere. These findings reveal the physical mechanisms behind dry-getting-drier at regional scales. [ABSTRACT FROM AUTHOR]

Understanding the role of anthropogenic activities in the hydrological cycle is critical to support sustainable water management for the Yangtze River Basin (YRB), which experiences extensive dam operation, irrigation and water withdrawal. However, this remains challenging due to insufficient accuracies of existing process‐based models for fully depicting anthropogenic activities as part of the hydrological cycle. To this end, this study enhances a national‐scale coupled land surface‐hydrologic‐hydrodynamic model (CLHMS) with a dynamic irrigation scheme for distinct crops, an extended reservoir operation scheme incorporating both water storage anomalies and water demand anomalies, and a cost‐function‐based approach to link water demands with reservoir operation. The enhanced model is extensively validated against historical streamflow, water storage of 90 reservoirs, and irrigation water withdrawal in the YRB, and the water level and storage of the Poyang Lake (PYL). By setting up controlled experiments in the YRB, we show that the streamflow decreases by 2%–6% due to irrigation and water withdrawal, and manifests an attenuated seasonality due to reservoir operation. At the basin scale, the increasing trend of extreme flood peaks exhibits a reversal under human activities, with the flood mitigation effect of irrigation and water withdrawal accounting for up to 50% of that of reservoir operation. The hydrodynamics of the PYL also exhibits considerable human‐induced alterations, with a 1.79 m‐decrease in the water level at the end of flood season. Our study sheds light on quantifying anthropogenic hydrologic impacts at basin scales, with important implications for understanding the co‐evolution between anthropogenic activities and the hydrological cycle. Key Points: The human‐induced alterations of streamflow, extreme floods, and lake water level and storage are comprehensively analyzedA dynamic irrigation module for distinct crops is developed and coupled to reservoir operation with a cost‐function‐based approachA conceptual reservoir operation scheme is extended to include both water storage anomalies and water demand anomalies in hydrologic models [ABSTRACT FROM AUTHOR]

Aim: To estimate the causal associations of type 2 diabetes and glycaemic traits with cognitive function, and to determine the potential mediating role of various brain imaging‐derived phenotypes (IDPs) using Mendelian randomization (MR) analysis. Methods: Using publicly available summary data, we performed a series of univariable and multivariable MR analysis to infer causality. Two‐step MR analysis was then conducted in turn to evaluate the potential mediating role of each brain IDP. Results: There was no evidence of causal associations between type 2 diabetes and cognitive function outcomes. Each 1‐SD unit higher genetically predicted fasting proinsulin was associated with worse cognitive performance, as evidenced by both univariable (beta: −0.022; 95% confidence interval [CI] −0.038, −0.007) and multivariable MR analysis (beta: −0.027; 95% CI −0.048, −0.005). In addition, the univariable MR analysis identified several causal associations between fasting proinsulin and brain IDPs, and between brain IDPs and cognitive performance. The inverse association of genetically predicted fasting proinsulin with cognitive performance did not attenuate after adjusting for each of the brain IDPs in multivariable MR analysis. Conclusions: The present MR study provided credible evidence for the causal association between genetically predicted fasting proinsulin and cognitive function, informing a potential diagnosis and intervention target for patients with cognitive impairment. No significant brain IDP included in this study was identified as lying on the causal pathway from fasting proinsulin to cognitive performance. Future research involving more specific and granular brain IDP or other brain process is warranted to explore the potential biological mechanism underlying the association. [ABSTRACT FROM AUTHOR]

Hydrometeorological forecasting is crucial for managing water resources and mitigating the impacts of extreme hydrologic events. At sub-seasonal scales, readily available hydrometeorological forecast products often exhibit large uncertainties and insufficient accuracies to support decision making. We propose a deep learning based modelling framework for sub-seasonal joint precipitation and streamflow forecasts for a lead time of up to 30 days. This is achieved by coupling (1) a convolutional neural network (CNN) architecture with ResNet blocks for statistically downscaling of the ECMWF raw precipitation forecasts to (2) a hybrid hydrologic model integrating the conceptual Xin'anjiang model (XAJ) and the long-short term memory network (LSTM) for streamflow forecasting. The CNN incorporates a specialized loss function that combines the continuous form of threat score and mean absolute error. Applying the modeling framework to the source region of the Yangtze River Basin, results indicate that the CNN-based downscaling model exhibits ~13 % and ~10 % less RMSE than the raw ECMWF forecasts and the quantile mapping (QM) forecasts, respectively, averaged over the 30-day lead time. Similarly, the CNN achieves a ~2 % and ~5 % lower RMSE than raw forecasts and QM for precipitation events above the 90th percentile of historic daily precipitation. Using these precipitation forecasts as meteorological drivers for the hybrid XAJ-LSTM hydrologic model, we found that forecasted streamflow and flood peaks driven by CNN-based precipitation forecasts have 18 %–32 % lower relative errors and 13 %–22 % lower RMSE compared to those driven by raw forecasts. However, the standalone XAJ model shows marginal improvements, or in some cases, no improvement at all, with the same enhanced precipitation forecasts. This highlights the importance of understanding the effectiveness of the hydrologic model as part of the sub-seasonal hydrometeorological modeling chain. Our study is expected to provide implications for leveraging advanced AI techniques to enhance sub-seasonal hydrometeorological forecasting accuracy and operational efficiency for effective water resources management and disaster preparedness. [ABSTRACT FROM AUTHOR]