Yield prediction is crucial for ensuring national food security and informing trade policies. Most deep learning (DL) models employ normalization techniques to preprocess input data, with the goal of improving training stability and accelerating convergence. However, the role of data preprocessing (i.e. input data normalization) in DL-based yield prediction remains underemphasized. Moreover, conventional normalization approaches often struggle to handle distortions in feature scaling caused by extreme values, such as unusually high precipitation, which can lead to increased prediction inaccuracies. In this study, we introduce a Sequential Midrange Normalization (SMN) method and combine it with the newly developed Agricultural-CNN-LSTM-Attention (AgroCLA) model. This integrated framework, referred to as SMN-AgroCLA, is designed to enhance the accuracy of rice yield predictions under extreme weather conditions. To validate the efficacy of the proposed SMN, we compared it against four other widely used normalization techniques. Yield prediction experiments were conducted using six different deep learning models, incorporating multi-source remote sensing data - including Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Precipitation Measurement (GPM) - from Eastern China between 2008 and 2017. The results demonstrated that SMN method consistently delivered superior prediction performance, even in extreme meteorological conditions such as those experienced in 2015. It achieved an R-2 of 0.815, representing a 17.3% improvement over the next best method, Z-Score Normalization (ZSN). Furthermore, when integrated with SMN, all models exhibited enhanced accuracy and generalization capability, with the AgroCLA achieving the highest accuracy (with R-2 = 0.841). Model accuracy peaked around the flowering stage (around mid-August, R-2 = 0.859), approximately two months ahead of harvest. This study demonstrates the critical role of data normalization in deep learning-based yield prediction and offers a practical solution to mitigate the threat of increasing extreme meteorological disasters to food security.

Study region: The upper Jiao River, located in eastern China, is a representative mountainous watershed that experiences frequent flooding caused by saturation-excess runoff. Study focus: Accurate streamflow simulation is essential for effective water resource management in watersheds, necessitating the use of reliable models and precise data for the simulation and prediction of streamflow. This study integrates multi-source soil moisture datasets into both a process-based model and a data-driven model to explore the potential benefits of calibration and streamflow simulation within a mesoscale watershed in eastern China. The physics-based Distributed-Hydrological-Soil-Vegetable Model (DHSVM), coupled with the multi-objective genetic algorithm epsilon-NSGA-II, and data-driven Informer model, are chosen and applied to the study area. Six parameter calibration and streamflow simulation schemes are developed, including one traditional simulation scheme and five multi-hydrological-element schemes based on multi-source soil moisture datasets, for both the DHSVM and Informer models, with the purpose of comparing the impacts of different soil moisture datasets on model calibration, streamflow simulation and hydrological signatures. New hydrological insights for the region: The results show that the incorporation of soil moisture significantly improves the ability of both DHSVM and Informer models to simulate extreme flows, compared to traditional methods relying solely on outlet streamflow and rainfall. Among the datasets, ERA5 and the merged soil moisture datasets provide superior performance in both calibration and simulation. Simulations using the merged datasets help balance the errors of individual datasets, resulting in optimal outcomes for watersheds without observed soil moisture data. These findings offer valuable insights for improving simulations and forecasts of extreme hydrological events.

Global phenological field observations play a crucial role in validating remote sensing products and algorithms. However, due to the spatial mismatch and scale effect between the field observations and the pixels of remote sensing phenology products, a direct comparison often leads to scale errors and increased uncertainty. Therefore, evaluating the spatial representativeness of field observations for remote sensing product validation is essential. This study developed a novel "bottom-up" evaluation framework named MSPT (Main land cover type, Spatial heterogeneity, Point-area consistency and Temporal consistency), which comprehensively assesses the spatial representativeness of forest phenological field observations within the coarse spatial scale. Based on MSPT method, the capability of global forest phenological field observations to support coarse-scale remote sensing validation was evaluated. Compared with the general method, MSPT significantly improved validation performance. For the start of the growing season (SOS), the root mean square error (RMSE) decreased from 49.70 to 33.75 days, and the percent bias (PBIAS) changed from-0.14 to 0.03. For the end of the growing season (EOS), the RMSE was reduced from 83.42 to 42.53 days, and the PBIAS decreased from 0.15 to 0.08. These findings demonstrate that MSPT enhances the reliability of validation datasets and effectively reducing uncertainty in the evaluation of coarse AVHRR-derived forest phenology products. The framework offers new insights into resolving the scale mismatch between field observations and the pixels of remote sensing products.

ABSTRACT In this study, a physics-informed machine learning-based surrogate model (SM) for the variable infiltration capacity (VIC) model was developed to improve simulation efficiency in the Yarlung Tsangpo River basin. The approach combines the empirical orthogonal function decomposition of low-fidelity VIC models to extract spatial and temporal features, with machine learning techniques applied to refine temporal feature series. This allows for accurate reconstruction of high-fidelity spatial simulations from the results of the low-fidelity model. Using the SM built from the 1.0°-resolution VIC model as an example, the study highlights the challenges and solutions associated with low-fidelity simulations. The SM significantly improves accuracy, achieving a Kling–Gupta efficiency of 0.88, an Nash–Sutcliffe efficiency of 0.97, and a PBIAS value of −6.21% with reduced computational demands. Additionally, different machine learning methods impact the performance of the SM, with the support vector machine regression model performing best in these methods. SMs from varying low-fidelity resolutions maintain similar accuracy, but higher resolutions notably enhance computational efficiency, reducing time by 86.31% when compared to the high-fidelity VIC model. These findings demonstrate the potential of the SM to enhance VIC model simulations while reducing computational requirements.

The Yellow River basin (YRB), as a crucial ecological corridor in the northern part of China, has experienced profound changes in multiple eco-hydrological processes. However, there is still lack of a global view on the variations and causal interactions in the complex hydro-ecological system of YRB. In this study, a set of eco-hydrological variables, regarding water resources (surface water, soil water, groundwater) and ecological environment (vegetation growth, productivity, water use efficiency) are used to represent the main characteristics of the eco-hydrological system in different sub-regions of YRB. The objective of this study is three-fold. Firstly, the individual variation of each eco-hydrological variable was unraveled using trend analysis. Secondly, network analysis was used to analyze the synergistic variations among variables. Finally, an advanced causal discovery tool incorporating prior knowledge was used to investigate the potential causal interactions in eco-hydrological system. The results indicate the decrease of terrestrial water storage anomalies (TWSA) in most parts of the YRB, which is mostly due to the substantial depletions in ground water. The vegetation growth and productivity have noticed prominent increasing trends in YRB, and such increase in the source region is largely due to the warmer climate condition and in the middle reaches is mainly because of the large-scale vegetation restoration. However, the ecosystem water use efficiency (WUE) is not very optimistic, especially in the source region. The causal discovery method captures the inhibitory effect of evapotranspiration on WUE in the upper and some parts of the middle reaches of YRB. Our study provides a new perspective to recognize the complicated eco-hydrological conditions and their variations in YRB during 2001-2019, as well as the potential mechanisms driving these variations.

Hydrological processes, as part of a natural system, are highly complex and chaotic. By analyzing long time series of streamflow data from ~ 4800 hydrologic stations, it is interesting to find out that probability density functions in second order difference (SOD) of the streamflow data are fat-tailed and bell-shaped curves, and their cumulative distribution functions (CDFs) follow an S-shaped curve (S-curve). We found that t-distribution is a good approximation for S-curve, which uses the degree of freedom (DF) to control the tail thickness. We also found that DF of more than 80% of stations are gathered in the range between 5 and 8. Analysis of the S-curves in seven large river basins indicated that the S-curves can vary with time and space, which is regarded as a good indicator for identifying natural and anthropogenic changes. This study provides a symmetrical, identical and concise probability distribution to describe global streamflow under changing environment.

Accurate crop yield mapping is critical for ensuring food security and optimizing agricultural management amid increasing climate variability. However, conventional light use efficiency (LUE) models primarily address water deficit impacts while neglecting excessive moisture effects, a critical limitation in humid regions like China's Middle and Lower Yangtze River Basin (MLYR), where waterlogging reduces winter wheat yields by up to 30%. To address this gap, we propose a novel water stress factor (named ${w_{s\_mod}}$ w s _ mod ) based on soil moisture (SM) and Land Surface Water Index (LSWI) to accurately characterize the influence of excessive water on winter wheat yield. County-level winter wheat yield of three provinces (Jiangsu, Anhui, Hubei) from 2000, 2002, 2004, 2006, 2008, 2010, 2012, and 2014 were used as the training dataset, with the other seven years of county-level data and independent city-level data (2015-2021) for validation. The results show that considering the impact of excess water in humid and semi-humid regions can improve the accuracy of crop yield estimation. The combined LUE model, which considered water excess and water deficit, achieved superior performance (RMSE = 69.34 g C m(-2) year(-1), MAE = 52.23 g C m(-2) year(-1), R = 0.60), reducing 3.86 g C m(-2) year(-1), 3.13 g C m(-2) year(-1) compared with LSWI-based water stress (${w_{s\_lswi}}$ w s _ lswi ) alone. Spatial validation using city-level data (2015-2021) confirmed robustness, with northern MLYR regions (e.g. Huang-Huai-Hai Plain) showing stable high yields ( >200 g C m(- 2) year(-1)) and southern areas exhibiting waterlogging-induced reductions (e.g. 2002, Figure S3). This study could provide reference for estimating crop yields in humid to semi-humid regions at a national or even global scale

Study region: The Shifeng Creek, situated within the Jiao River basin in East China. Study focus: To address complicated contradictory relationships in multi-reservoir scheduling system, this study develops a new two-layer hedging robust optimization model (TL-HRO) for multi-reservoir scheduling by combining the hedging strategy with robust optimization. The first hedging layer of the TL-HRO model integrates critical hedging relationship that exists between flood control and power generation benefits. The flood control benefits can be subdivided into upstream and downstream benefits, which also have a hedging relationship. The second layer mainly focuses on the interaction of the scheduling for the current period with the future period. Furthermore, considering the impact of uncertain inflows on scheduling, this study employed the vine copula function to extract the multivariate spatial-temporal relationships and perform stochastic simulations. For comparison, a multi-objective robust optimization model (MORO) is constructed where multiple objectives are optimized in parallel. New hydrological insights for the region: The TL-HRO model, through iterative optimization, yielded an optimal scheduling solution that improved total benefits by roughly 58.26 %, covering both flood control and power generation. The results further demonstrated that the TL-HRO model is closer to the optimal solution than the MORO model, particularly during flood seasons, under uncertain inflow conditions. This study serves as a valuable reference for decision-makers in formulating efficient scheduling schemes during flood seasons.

Rice, as the main food crop for half of the world's population, could directly threaten food supply chain stability if precise yield prediction methods are lacking. However, existing deep learning (DL) models exhibit strong scale dependency: when the input data resolution changes, the scale of spatial features extracted by the model no longer aligns with the actual size of target objects, leading to performance degradation due to mismatched receptive fields. To address the challenge posed by spatial resolution variation on the generalization ability of remote sensing DL models, this study proposes a DL model, U-shaped spatial-temporal attention network (USTAnet), by designing the multiscale convolution (MSC) block and multidimensional feature integrator blocks. These designs contribute to USTAnet's enhanced resolution generalization (the model's adaptability to remote sensing data with different spatial resolutions) and spatiotemporal generalization capabilities. USTAnet outperforms existing models in accuracy and generalization ability, including U-Net, CNN-GRU, LSTM-CNN, SSTNN, and DDCN, with an R-2 of 0.817 at a 4 km x 4 km resolution. In 1 km x 1 km resolution generalization tests, USTAnet consistently shows the best performance across all years (2011-2015), with an average R-2 of 0.758, which is 15% higher on average than other models. In spatial-temporal generalization tests, USTAnet exhibits the highest performance in 2015, with R(2)values of 0.817. This study proved that USTAnet is a promising kilometer-level yield prediction model with great potential for cross-resolution rice yield prediction applications, which can help promote the application of remote sensing technology in agricultural production.

BACKGROUND:Sarcopenia is associated with decreased survival in cervical cancer patients treated with radiotherapy. Cone-beam computed tomography (CBCT) was widely used in image-guided radiotherapy. Sarcopenia is assessed by the skeletal muscle index (SMI) of third lumbar vertebra (L3). Whereas, L3 is usually not included on the cervical cancer radiotherapy CBCT images. PURPOSE:We aimed to explore the usefulness of CBCT for evaluating SMI and deep learning (DL)-based automatic segmentation and sarcopenia diagnosis for cervical cancer radiotherapy patients. We evaluated the SMI through fifth lumbar vertebra (L5). METHODS:First, L3, L5 skeletal muscle area (SMA) were measured on CT and CBCT. The agreement of L5 skeletal muscle segmentation on CBCT was evaluated using the intraclass correlation coefficient (ICC). The relationships between L5-SMICT and L3-SMICT, L5-SMICBCT were established and assessed by Pearson analysis, Bland-Altman plots. Second, the consequent CBCT images of 248 cervical cancer radiotherapy patients with whole L5 were collected as DL-based automatic segmentation. An independent external validation dataset was used. We proposed an end-to-end anatomical distance-guided dual branch feature fusion network to segment L5 skeletal muscle on CBCT images. The automatic segmentation results were used for sarcopenia diagnosis evaluation. RESULTS:The ICC values were greater than 0.95. The Pearson correlation coefficients (PCC) between L5-SMICT and L3-SMICT is 0.894. The PCC between L5-SMICT and L5-SMICBCT is 0.917. The L3-SMICT could be estimated through L5-SMICBCT by a linear regression equation. The adjusted R2 values were greater than 0.7. The dice similarity coefficient of automatic segmentation is 87.09%. Our proposed DL network predicted sarcopenia with 84.38% accuracy and 85.71% F1-score. In external validation dataset, the sarcopenia diagnosis accuracy and F1-score are 80% and 82.61%, respectively. CONCLUSION:The SMI quantitative measurement using CBCT for cervical cancer patients is feasible. And the DL network has the potential to assist in the sarcopenia diagnosis using CBCT images.

Background:Achieving intelligent detection and grading of lesion cells in ThinPrep cytologic test (TCT) pathological images is challenging but may provide considerable clinical value in improving the accuracy of early cervical cancer screening. Therefore, we sought to design a rapid and accurate method for the fine-grained detection of cervical lesions in massive TCT images. Methods:We developed a YoGaNet architecture based on the YOLOv5l network and dropped multibranch Swin Transformer (DMBST) module. This architecture extracts multilevel global and local features from images, whereas the DMBST module directs the network to learn different features and enhances the capability of fine-grained feature extraction of small objects. The performance of YoGaNet was evaluated on the large public Comparison Detector dataset, containing 7,410 cervical microscopy images and 11 lesion cell categories, and a dataset of a clinical study, which retrospectively enrolled 12 patients with 1,514 cervical microscopy images. Results:In the experiments, YoGaNet achieved the highest mean average precision calculated at an intersection over a union threshold of 0.50 (mAP50) (Comparison Detector dataset: 68.6%; clinical dataset: 36.8%). Compared with those for the Comparison Detector dataset provided in the Liang's reported and the baseline model (YOLOv5l), the mAP50 and recall of YoGaNet improved by 22.8% and 3.2% and by 6.2% and 3.3%, respectively. Moreover, YoGaNet provided considerable advantages in detecting atypical squamous cells of undetermined significance (ASC-US), atypical squamous cells, cannot exclude high-grade squamous intraepithelial lesions (ASCHs), low-grade squamous intraepithelial lesions (LSILs), high-grade squamous intraepithelial lesions (HSILs), squamous cell carcinoma (SCC), Candida cells, and flora cells. Conclusions:YoGaNet improved the fine-grained recognition of cervical cells in massive TCT images as compared with the baseline model and models reported in previous studies and may thus aid in improving early cervical cancer diagnosis. The inference code is available online (https://github.com/coding-with-chen/YoGA-Net-DMBST).

The daily mean air temperature (DMAT) is an essential descriptor of climate change. Seamless global DMAT maps will significantly improve our knowledge of terrestrial meteorological and climatic conditions. This study proposes a novel scheme, Seamless Global Mapping of Daily Mean Air Temperatures (SGM_DMAT). The SGM_DMAT scheme comprises two key phases: Estimating DMAT under clear-sky conditions, and reconstructing missing values under cloudy conditions using data from 2020 to 2022 as the calibration dataset and data in 2023 as the validation dataset. The results demonstrate that combining all valid Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA/AQUA daytime and nighttime land surface temperature (LST) observations under clear-sky conditions, and applying spatial temporal analysis techniques with reference images for cloudy days, ensures robust and seamless DMAT estimation. Specifically, the Extreme Gradient Boosting (XGBoost) was selected as the optimal model of DMAT estimation. The optimal feature dataset includes satellite-derived LSTs, latitude, longitude, elevation above sea level, month, and day of year. The optimal calibration dataset comprises all valid calibration data (AVCD). Additionally, the priority order of DMAT clear-sky estimation models was established using different LST combinations. Finally, robust and seamless global maps of DMAT were generated for the period 2020-2023. For globally seamless mapping products, the R2 was 0.956, with an RMSE of 2.825 degrees C and a MAE of 1.985 degrees C. The proposed SGM_DMAT scheme may aid DMAT estimation in regions that lack sufficient meteorological stations. The seamless global DMAT products have broad applicability including in trend analysis, urban heat island research, and assessment of crop stress due to temperature extremes.

Background:As birth policy can affect maternal and infant health, we sought to identify whether and how the introduction of the two-child policy might have affected the prevalence of placenta previa in pregnant women in mainland China. Methods:In this update meta-analysis and systematic review, we searched PubMed, Web of Science, the Cochrane Library, Weipu, Wanfang, and the China National Knowledge Infrastructure (CNKI) databases for studies evaluating the prevalence of placenta previa in China published between the inception of each database and March 2024, with no restrictions. Two investigators independently extracted the data from each included study. We then combined the prevalence of placenta previa using random-effects models. Results:We included 128 studies in our analysis, 48 more than in our previous review. The prevalence of placenta previa among Chinese pregnant women was 1.44% (95% confidence interval (CI) = 1.32, 1.56). After the implementation of the two-child policy, the prevalence increased significantly, from 1.25% (95% CI = 1.16, 1.34) to 4.12% (95% CI = 3.33, 4.91). Conclusions:The prevalence of placenta previa increased significantly from the one-child policy period to the two-child policy period among mainland Chinese pregnant women, with varying trends across regions. This change requires the attention of health officials and timely adjustment of resource allocation policies. Registration:PROSPERO: CRD42021262309.

Providing reservoirs with accurate forecasts is crucial for effective real-time flood control. This research focuses on the key role of forecasts in real-time flood management for reservoirs. A new approach was developed in this study, integrating a forecast-driven methodology to handle uncertainty in reservoir flood control operations. This involves a novel hybrid of two post-processing techniques: the Cloud model and error-based copula functions, together termed as the stochastic errors-based Cloud (SE-Cloud). Additionally, a multi-objective robust optimization model (MRO) was proposed, encompassing risk, resilience, and vulnerability, to address flood control challenges using ensemble forecasts. For comparative purposes, a two-objective stochastic optimization model (TSO) was also created, aiming to reduce both the highest expected reservoir level and peak discharge. The proposed methodology was applied to the Lishimen reservoir in the Shifeng River subbasin, China, aiming to comprehensively verify the relationships among deterministic forecasts, ensemble forecasts, and flood control performance. The main findings of this study are: (1) The SE-Cloud model was proved to be more efficient in predicting peak flow events and in representing uncertainties in forecasts, with an improvement in hypervolume values ranging from 13.14% to 39.65% over the Cloud model. (2) The MRO strategy resulted in a higher inflow release compared to the TSO, leading to a 0.05m reduction in the anticipated highest water level and a 4.29% increase in peak discharge. (3) With the resilience value downstream remaining constant, it was suggested that increasing upstream vulnerability by using the MRO strategy would not lead to a decrease in resilience. The findings highlight the potential of AI-based ensemble forecasts in augmenting flood control robustness.

Study regionThe Yarlung Zangbo River (YZR) basin on the Tibetan Plateau, ChinaStudy focusDue to global climate change, the risk of drought disaster is increasing. Seasonal hydrological forecast can be beneficial for drought early warning and help reduce risks in water resources and drought management. However, the computational burden of distributed hydrological models remains a limitation for their wide use in seasonal streamflow forecast. This study designs a seasonal streamflow forecast framework based on the surrogate model (SM) for VIC. Both the impacts of pre-processing and post-processing on the seasonal streamflow forecast, and the accuracy, reliability and efficiency of the forecast framework based on SM, are carefully evaluated in the YZR basin, China.New hydrological insights for the regionThe results show that the VIC model forced by CFSv2 has a good predictability and reliability in seasonal streamflow forecast in the YZR basin. Both pre-processing and post-processing can improve streamflow forecast accuracy, while post-processing also improves the forecast reliability more significantly. The SM-simulated streamflow is identical with that of VIC, with NSE larger than 0.95 and Pbias smaller than 5% at Nuxia station, in the YZR basin. The proposed SM-based seasonal streamflow forecast framework has been proven to be a good alternative for the VIC-based framework, with similar forecast accuracy and reliability, and higher computational efficiency, reducing up to 97% computation time.

Study region River basins in the Tibetan Plateau, including the Yarlung Zangbo River (YZR) basin, the Salween River (SWR) basin, the Lancang River (LCR) basin, the Yangtze River (YTR) basin, the Yellow River (YLR) basin, and the Tarim River (TRM) basin. Study focus In recent years, the concept of seasonal catchment memory has emerged as a pivotal element in the field of hydrological science. Precipitation memory curve (PMC) is a good method to describe seasonal precipitation memory process, which is the key function connecting precipitation and terrestrial water storage change (TWSC). The model simulated TWSC with precipitation is proposed based on PMC. According to the performance of the model, this study investigates optimal curve functions to describe seasonal catchment memory in the Tibetan Plateau. New hydrological insights for the region It is found that in most basins, the shape of PMC is consistent with the Boussinesq function or Maillet function. In the SWR basin, PMC varies between warm season and cold season. However, due to relatively less precipitation in the cold season compared to the warm season, the segmented-representation PMC has only a slight improvement on the TWSC simulation. In the TRM basin, the temperature-index method can improve the performance of the model. The PMC of the TRM basin during the warm season exhibits a short duration of precipitation memory of approximately three months.

Reliable Tropical Cyclone (TC) rainfall and flood forecasts play an important role in disaster prevention and mitigation. Numerous studies have demonstrated the promising performance of deep learning in hydrometeorological forecasts. However, few studies have investigated the potential enhancement of advanced TC track forecasts in predicting rainfall and induced flood. In this study, a novel rainfall nowcasting model (TCRainNet) is developed by fusing TC track characteristics with antecedent rainfall in a Convolution LSTM to predict hourly rainfall with a lead time of 6 h. The nowcasts are subsequently used to drive an event-based Xin'anjiang hydrological model for real-time flood forecasting. The model performance is interpretated by the occlusion sensitivity approach, and the propagation of errors from TC track forecasts to flood forecasts is quantified. The results underscore the superiority of TC track characteristics as input features for rainfall nowcasts, as indicated by a Mutual Information value of up to 0.51. The generated nowcasts are found to have averaged Probability of Detection (POD) and Critical Success Index (CSI) greater than 0.27 and 0.2 respectively. The Mean Absolute Error (MAE) of the nowcasts falls below 2.6 mm, which is only 46 % of the ECMWF operational high- resolution forecasts. The rainfall-driven flood forecasts have NSE greater than 0.7 and PBIAS smaller than 20 % with lead time up to + 4 h. It is shown that the position error of 0.45 degrees degrees and intensity error of 10 hPa&7.8 &7.8 m/s in TC track forecasts generally result in 0.9 mm degradation in rainfall forecasts and 10% decline in the accuracy of rainfall-driven flood forecasts. The effectiveness of our method presents favorable applicability in advancing disaster mitigation efforts.

Purpose: The main purpose of this study is to obtain a finite element biomechanical model that accurately mimics pelvic organ prolapse in women, to study pelvic floor supporting structures' biomechanical properties and function. We used thin-sectional high-resolution anatomical images (Chinese Visible Human, CVH) to reconstruct a detailed three-dimensional (3D) biomechanical finite element model of the female pelvic floor supporting structure including cardinal ligament, uterosacral ligament, levator ani muscle (LAM) and perianal body. The Valsalva maneuver was simulated by loading the uterus and bladder with a pressure increasing from 0 to 10 kPa. The stress, strain and displacement of supporting structures were calculated. The cardinal ligament, the utero-sacral ligament and the LAM were stressed greatly when the uterus moved downward, and the maximum stress could reach 0.267 MPa, 1.51 MPa and 0.065 MPa respectively, and the maximum strain could reach 0.154, 0.16, 0.265, and the maximum displacement could reach 1.786 cm, 1.946 cm and 0.567 cm. Displacement of the perineal body also occurred, and its stress, strain and displacement were 0.092 MPa, 0.381, 0.73 cm. The stress, strain and displacement of the supporting structure around the urethra were 0.339 MPa, 0.169, 1.491 cm. Our model based on CVH has more detailed anatomical structures, which is superior to that based on MRI. Our simulation results were consistent with previous findings, which verified the unbalance of abdominal pressure and pelvic floor supporting structures will lead to POP, which provide a theoretical basis for pelvic floor anatomy and function as well as obstetrical surgery.

IntroductionAcupuncture has been shown to be effective in restoring gastrointestinal function in tumor patients receiving the enhanced recovery after surgery (ERAS) protocol. The present systematic review and meta-analysis aimed to evaluate the rationality and efficacy of integrating acupuncture in the ERAS strategy to recuperate gastrointestinal function.MethodsWe searched eleven databases for relevant randomized clinical trials (RCTs) of acupuncture for the treatment of gastrointestinal dysfunction in tumor patients treated with the ERAS protocol. The quality of each article was assessed using the Cochrane Collaboration risk of bias criteria and the modified Jadad Scale. As individual symptoms, the primary outcomes were time to postoperative oral food intake, time to first flatus, time to first distension and peristaltic sound recovery time (PSRT). Pain control, adverse events, and acupoint names reported in the included studies were also investigated.ResultsOf the 211 reviewed abstracts, 9 studies (702 patients) met eligibility criteria and were included in the present systematic review and meta‑analysis. Compared to control groups, acupuncture groups showed a significant reduction in time to postoperative oral food intake [standardized mean difference (SMD) = -0.77, 95% confidence interval (CI) -1.18 to -0.35], time to first flatus (SMD=-0.81, 95% CI -1.13 to -0.48), time to first defecation (SMD=-0.91, 95% CI -1.41 to -0.41, PSRT (SMD=-0.92, 95% CI -1.93 to 0.08), and pain intensity (SMD=-0.60, 95% CI -0.83 to -0.37).The Zusanli (ST36) and Shangjuxu (ST37) acupoints were used in eight of the nine included studies. Adverse events related to acupuncture were observed in two studies, and only one case of bruising was reported. DiscussionThe present systematic review and meta‑analysis suggested that acupuncture significantly improves recovery of gastrointestinal function and pain control in tumor patients receiving the ERAS protocol compared to the control group. Moreover, ST36 and ST37 were the most frequently used acupoints. Although the safety of acupuncture was poorly described in the included studies, the available data suggested that acupuncture is a safe treatment with only mild side effects. These findings provide evidence-based recommendations for the inclusion of acupuncture in the ERAS protocol for tumor patients.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/ PROSPERO, identifier CRD42023430211.

Rivers originating in the Tibetan Plateau are crucial to the population in Asia. However, research about quantifying seasonal catchment memory of these rivers is still limited. Here, we propose a model able to accurately estimate terrestrial water storage change (TWSC), and characterize catchment memory processes and durations using the memory curve and the influence/domination time, respectively. By investigating eight representative basins of the region, we find that the seasonal catchment memory in precipitation-dominated basins is mainly controlled by precipitation, and that in non-precipitation-dominated basins is strongly influenced by temperature. We further uncover that in precipitation-dominated basins, longer influence time corresponds to longer domination time, with the influence/domination time of approximately six/four months during monsoon season. In addition, the long-term catchment memory is observed in non-precipitation-dominated basins. Quantifying catchment memory can identify efficient lead times for seasonal streamflow forecasts and water resource management.