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China implemented a strict lockdown policy to prevent the spread of COVID-19 in the worst-affected regions, including Wuhan and Shanghai. This study aims to investigate impact of these lockdowns on air quality index (AQI) using a deep learning framework. In addition to historical pollutant concentrations and meteorological factors, we incorporate social and spatio-temporal influences in the framework. In particular, spatial autocorrelation (SAC), which combines temporal autocorrelation with spatial correlation, is adopted to reflect the influence of neighbouring cities and historical data. Our deep learning analysis obtained the estimates of the lockdown effects as − 25.88 in Wuhan and − 20.47 in Shanghai. The corresponding prediction errors are reduced by about 47% for Wuhan and by 67% for Shanghai, which enables much more reliable AQI forecasts for both cities.

The ocean floor is a vast, uncharted territory, rich with geological and sedimentological secrets waiting to be uncovered [...]

The propagation of dam-break waves on different rough beds was observed to be quasi-steady in the range $11.3 < x/h_{dam} < 18.8$, where $x$ is measured from the dam position. These quasi-steady propagation speeds converge with the steady ideal fluids model of Stoker (Water Waves, 1957, Interscience) when the tailwater depth $h_2$ becomes greater than ${\\sim }0.5k_s$, in the range $0.001< k_s/h_{dam}< 0.2$, where $k_s$ is the roughness and $h_{dam}$ the depth behind the dam. Hence, this convergence encourages the use of Stoker's steady, ideal fluid solution to develop more general models, including friction effects due to bed roughness and/or viscosity. The new experimental data support a MacLaurin series for the celerity $c$, in analogy with the series in terms of $\\sqrt [4]{{h_2}/{h_{dam}}}$, derived for Stoker's model, $h_2$ being the tailwater depth. Compared with the retarding effect of the tailwater, 1 mm of roughness is found to be equivalent to $\\sim$13 mm of tailwater, and 1 $\\mathrm {\\mu }$m of viscous length (${\\nu }/{\\sqrt {gh_{dam}}}$, where ${\\nu}$ is the kinematic viscosity and g the acceleration due to gravity) is equivalent to $\\sim$1700 $\\mathrm {\\mu }$m of tailwater. While the MacLaurin series quantifies the similar effects of small roughness and small tailwater depths acting separately, the new data illustrate for the first time the complex interplay between tailwater and roughness on ‘wet beds’ with many details yet to be investigated. In particular, it was shown that a small amount of tailwater on a rough bed acts as a lubricant, so that $c(h_2, h_{dam}, k_s)$ is an increasing function of $h_2$ for $h_2 < 0.5k_s$.

In‐situ observations of hydrodynamics and suspended sediment concentrations (SSCs) were conducted on an abandoned lobe in the northern part of the modern Yellow River Delta, China. The SSC record at the site is found to be the superposition of a general trend (fast increase and slow decrease cycle) caused by storm waves (SubSSC1) and relatively smaller fluctuations caused by tidal currents (SubSSC2). Physically, this indicates that storm waves eroded the bottom sediments while tidal currents then re‐suspended and advected the suspended sediments in the study area. To further obtain the suspended sediment transport parameters, first, SubSSC1 is modeled with significant wave height which incorporates a “memory curve” to consider the remaining impacts of historical waves. It is detected that waves in the past 75 hr still influence the present SSC which is reasonable because 75 hr is roughly the typical duration of a normal storm. Second, SubSSC2 is modeled with tidal excursion and trigonometric functions with measured periodicities. Finally, some sediment transport parameters, for example, the background SSC, the horizontal SSC gradient, the tidal constituents that advect it, and their relative time lags are optimized from the best fits of the measured and modeled SSC time series. The proposed framework for model construction and parameter optimization can be extended to other sea areas for inferring sediment transport parameters from field SSC time series at a specific station. Plain Language Summary The evolution of our coastal zone fundamentally depends on the transport volume and direction of sediment, and understanding them is very beneficial for our coastal engineering construction and long‐term planning. Conducting in situ observations on‐site is one of the most reliable methods, but its observation cost is expensive. Therefore, we hope to extract as much information as possible from as few observation points as possible. This article successfully extracted the above information from the observation data of a station. First, we analyzed the data to preliminarily clarify that the suspended sediment in the area mainly comes from local erosion resuspension and advection transport from other regions. Furthermore, we conducted data modeling (i.e., constructed mathematical expressions of suspended sediment components from different sources, but with undetermined coefficients). Finally, we adjusted the model parameters to approximate the measured results, determined the undetermined coefficients, and explained the practical significance of the undetermined coefficients in physics. The analysis method we proposed can be extended to other sea areas. Key Points Storm wave‐induced suspended sediment concentration (SSC) variation is modeled with a “memory curve” of wave height Tidally‐induced SSC variation is modeled with tidal excursion and trigonometric functions Sediment transport parameters are estimated from the optimal matching of measured and modeled SSC time series

Sunspots play a crucial role in both weather forecasting and the monitoring of solar storms. In this work, we propose a novel combined model for sunspot prediction using improved gated recurrent units (GRU) guided by pinball loss for probabilistic forecasts. Specifically, we optimize the GRU parameters using the slime mould algorithm and employ a seasonal-trend decomposition procedure based on loess to tackle challenges related to sequence prediction, such as self-correlations and non-stationarity. To address prediction uncertainty, we replace the traditional l 2 -norm loss with pinball loss. This modification extends the conventional GRU-based point forecasting to a probabilistic framework expressed as quantiles. We apply our proposed model to analyze a well-established historical sunspot dataset for both single- and multi-step ahead forecasting. The results demonstrate the effectiveness of our combined model in predicting sunspot values, surpassing the performance of other existing methods.

As one of the critical indicators of the lake ecosystem, the lake surface water temperature is an important indicator for measuring lake ecological environment. However, there is a complex nonlinear relationship between lake surface water temperature and climate variables, making it difficult to accurately predict. Fortunately, satellite remote sensing provides a wealth of data to support further improvements in prediction accuracy. In this paper, we construct a new deep learning model for mining the nonlinear dynamics from climate variables to obtain more accurate prediction of lake surface water temperature. The proposed model consists of the variable correlation information module and the temporal correlation information module. The variable correlation information module based on the Self-Attention mechanism extracts key variable features that affect lake surface water temperature. Then, the features are input into the temporal correlation information module based on the Gated Recurrent Unit (GRU) model to learn the temporal variation patterns. The proposed model, called Attention-GRU, is then applied to lake surface water temperature prediction in Qinghai Lake, the largest inland lake located in the Tibetan Plateau region in China. Compared with the seven baseline models, the Attention-GRU model achieved the most accurate prediction results; notably, it significantly outperformed the Air2water model which is the classic model for lake surface water temperature prediction based on the volume-integrated heat balance equation. Finally, we analyzed the factors influencing the surface water temperature of Qinghai Lake. There are different degrees of direct and indirect effects of climatic variables, among which air temperature is the dominant factor.

The transport processes of marine suspended sediments are important to the material cycle and the shaping of seafloor topography. Existing sediment monitoring methods are limited in their use under high concentration conditions, and are not effective in monitoring and capturing sediment in 3D directions, and there is an inability to accurately explain sediment transport processes. To infer the transport process of suspended sediments, this study proposed a time-series vector in situ observation device. An accompanying time-series analytic method was developed for sediment transport fluxes. The correlation between the internal and external flow velocities of the capture tube was established through indoor tests, and then the applicability of the device was verified by the correlation between the theoretical capture quality and the actual capture quality, and the analytic formula of the flux was refined. The proposed observation technique can be used for in situ long-term observation and sampling of marine suspended sediments under conventional and even extreme sea conditions, achieving accurate time-series suspended sediment capture and high-resolution transport flux analysis. The technique thus provides a more effective means for scientific research into the dynamics of seafloor sedimentation, the mechanisms of ocean carbon sinks, and the processes of the carbon cycle.

Wave-induced silty seabed liquefaction is one of the key threats to offshore infrastructure stability. The excess pore pressure (EPP) response is the key parameter for judging seabed liquefaction. Many studies have examined the EPP response to surface waves in initially well-consolidated seabed; few works have explored initially less-consolidated seabed, which is widely distributed in estuaries due to the massive river sediment discharge and, thereafter, rapid accumulation. Here, we investigate the EPP response of silty seabed with various initial consolidation degrees using wave flume experiments. We found that (1) in initially liquefied seabed, the EPP magnitude monotonically increases with wavelength, while in initially consolidated seabed, there is a maximal response wavelength which is inversely related to consolidation degree. (2) Furthermore, we found two opposite EPP responses to cyclic surface wave loading under varying seabed conditions in initially liquefied and consolidated seabeds. That is, under the same waves, the EPP magnitude is inversely related to the consolidation degree in initially liquefied seabed, while the EPP magnitude is positively related to the consolidation degree in initially consolidated seabed. In other words, the influence of initial seabed consolidation degree on EPP magnitude behaves like a “√” shaped curve. Our findings provide some implications for further understandings of wave-induced silty seabed liquefaction.

Marine sediment transport is closely related to seafloor topography, material transport, marine engineering safety, etc. With a developed time-series vector observation device, the sediment capture and transport process can be observed. The structure of the capture tube and the internal filter screen can significantly affect the flow field during the actual observation, further influencing the sediment transport observation and particle capture process. This paper presents a numerical model for investigating the effect of device structure on seawater flow to study the processes of marine sediment transport observation and sediment particle capture. The model is based on the solution of both porous media and the Realizable k-ε turbulence in Fluent software. The flow velocity distribution inside and outside the capture tube with different screen pore sizes (0.300, 0.150, and 0.075 mm) is analyzed. To enhance the reliability of the numerical simulation, the simulation calculation results are compared with the test results and have good coincidence. Finally, by analyzing the motion law of sediment in the capture tube, the accurate capture of sediment particles is achieved, and the optimal capture efficiency of the sediment trap is obtained.

Sepsis after cardiac surgery represents a severe perioperative complication with high incidence and mortality rates. While the cardioprotective benefits of β-blocker following cardiac surgery are widely recognized, their impact on sepsis development remains unclear. This study aims to investigate the association between early postoperative β-blocker use and the incidence of sepsis, as well as clinical outcomes, in patients undergoing cardiac surgery. The analysis incorporated data from the MIMIC-IV database, with confounding factors addressed through propensity score matching (PSM), inverse probability of treatment weighting (IPTW), and overlap weighting (OW). Logistic regression models assessed the risk of sepsis and in-hospital mortality, while Cox proportional hazards models evaluated 28-day and 1-year mortality. Kaplan-Meier survival curves and log-rank tests compared survival between groups. Sensitivity analyses using Fine-Gray competing risk models and cumulative incidence functions were performed. Subgroup analyses explored heterogeneity of treatment effects, and metoprolol was further stratified by dose to assess dose-response relationships. A total of 3,154 patients treated with β-blocker and 5,220 controls were included. Early β-blocker use was associated with a reduced risk of sepsis and lower in-hospital mortality across all methods. For 28-day and 1-year mortality, β-blocker use showed a trend toward risk reduction. Competing risk analyses demonstrated lower cumulative incidence of sepsis in the β-blocker group. Subgroup and dose-response analyses indicated that both low and high doses of metoprolol were associated with reduced postoperative sepsis risk and mortality outcomes. Early use of β-blocker after cardiac surgery was associated with a lower incidence of sepsis, with potential benefits observed in both short-term and long-term prognosis. These findings provide valuable evidence for optimizing perioperative drug management strategies.