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The evolution of land use/land cover (LULC) patterns significantly influences the dynamics of carbon storage (CS) in terrestrial ecosystems. In response to future environmental changes, however, most studies fail to synthesize the effects of policy pathways and evolving core driving factors on LULC projections. This article presents a systematic framework to assess the dynamic response of the terrestrial ecosystem CS to future LULC changes. After investigating spatiotemporal characteristics and driving forces, policy effects and future core driving factors are integrated into the improved Markov–future land use simulation model to project LULC across diverse scenarios. Then the Integrated Valuation of Ecosystem Service and Tradeoff model is coupled to explore CS dynamics with LULC changes. This framework was applied to the Weihe River Basin. The finding reveals that the overall proportion of cultivated land, forestland and grassland is above 85% and is significantly influenced by policy effects. Precipitation, temperature, population density and gross domestic product are core driving factors of LULC changes. Equal-interval projection is a viable approach to mitigate policy impacts by avoiding error propagation while coupling future core driving factors to improve LULC projection accuracy. Ecological protection should be emphasized in the future. The rate of increase in CS is 1.25 and 1.63 times higher than the historical trend and economic development scenario, respectively, which alleviates carbon loss from the expansion of built-up land. This research provides a valuable reference for future insight and optimization of ecological conservation strategies. [ABSTRACT FROM AUTHOR]

The Hedgehog pathway is thought to be closely associated with the progression of GC; however, a specific link between the Hedgehog pathway on the prognosis and immune infiltration of gastric cancer is still lacking. This study collected Hedgehog pathway-related genes. The Hedgehog pathway-related pattern were identified by consensus cluster analysis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were used to identify the biological functions which were significantly altered between predefined Cluster1 and Cluster2 in consensus clustering. The risk model of gastric cancer based on Hedgehog signaling pathway was constructed by univariate and multivariate COX regression, and the nomogram was constructed. The results showed that there were significant differences in the expression of Hedgehog pathway-related genes between the two groups. In addition, the constructed risk model was significantly correlated with the clinical prognosis and immune cell infiltration level of patients with gastric cancer. The model effectively predicted the efficacy of chemotherapy in GC patients and the sensitivity of drug treatment between groups. We systematically revealed the mechanism of Hedgehog pathway in gastric cancer and selected biomarkers with biological significance from a new perspective, providing potential direction for the treatment of gastric cancer. [ABSTRACT FROM AUTHOR]

The calculation of water supply is affected by many factors, such as water requirements, water-supply capacity, etc., which change with time and the environment. The traditional calculation methods are generally used under several fixed operating situations. The scenarios are too few to cover the various situations encountered in actual operations and take insufficient account of boundary conditions, such as reservoir inflow, water requirements, etc. The water-supply schemes facilitated by these methods are finite and not adaptable to the ever-changing, producing environment. Therefore, based on a traditional water supply-demand balance analysis, this study established a dynamic calculation model that takes into account different variables, such as reservoir inflow, ecological flow, and water demand. This study also constructed a dynamic simulation system based on the comprehensive, integrated platform and dynamic calculation model, which shortens the calculation period from a month to a day. The results show that the water-supply assurance rates calculated under different conditions reach higher than 93.9% for domestic and production, and higher than 50.9% for agriculture, which all exceed the design assurance rates. Additionally, the dynamic calculation model can significantly improve the calculation efficiency, playing an important role in the formulation of the project-operation plan and project-benefit evaluation. [ABSTRACT FROM AUTHOR]

Low flow simulation by hydrological models is a common solution in water research and application. However, knowledge about the influence of the objective functions is limited in relatively arid regions. This study aims to increase insight into the difference between the calibrated objective functions by evaluating eight objectives in three different classes (single objectives: KGE(log(Q)) and KGE(1/Q); multi objectives: KGE(Q)+KGE(log(Q)), KGE(Q)+KGE(1/Q), KGE(Qsort)+KGE(log(Qsort)) and KGE(Qsort)+KGE(1/Qsort); Split objectives: split KGE(Q) and split (KGE(Q)+KGE(1/Q))) in Bahe, a semi-arid basin in China. The calibrated model is Xin An Jiang, and the evaluation is repeated under varied climates. The results show a clear difference between objective functions for low flows, and the mean of KGE and logarithmic transformed-based KGE in time series (KGE(Q)+KGE(log(Q))) presents the best compromise between the estimation for low flows and general simulation. In addition, the applications of the inverse transformed-based KGE (KGE(1/Q)) and the Flow Duration Curve-based series (Qsort) in objectives are not suggested. [ABSTRACT FROM AUTHOR]

Streamflow forecasting is a crucial component in the management and control of water resources. Decomposition-based approaches have particularly demonstrated improved forecasting performance. However, direct decomposition of entire streamflow data with calibration and validation subsets is not practical for signal component prediction. This impracticality is due to the fact that the calibration process uses some validation information that is not available in practical streamflow forecasting. Unfortunately, independent decomposition of calibration and validation sets leads to undesirable boundary effects and less accurate forecasting. To alleviate such boundary effects and improve the forecasting performance in basins lacking meteorological observations, we propose a two-stage decomposition prediction (TSDP) framework. We realize this framework using variational mode decomposition (VMD) and support vector regression (SVR) and refer to this realization as VMD-SVR. We demonstrate experimentally the effectiveness, efficiency and accuracy of the TSDP framework and its VMD-SVR realization in terms of the boundary effect reduction, computational cost, and overfitting, in addition to decomposition and forecasting outcomes for different lead times. Specifically, four comparative experiments were conducted based on the ensemble empirical mode decomposition (EEMD), singular spectrum analysis (SSA), discrete wavelet transform (DWT), boundary-corrected maximal overlap discrete wavelet transform (BCMODWT), autoregressive integrated moving average (ARIMA), SVR, backpropagation neural network (BPNN) and long short-term memory (LSTM). The TSDP framework was also compared with the wavelet data-driven forecasting framework (WDDFF). Results of experiments on monthly runoff data collected from three stations at the Wei River show the superiority of the VMD-SVR model compared to benchmark models. [ABSTRACT FROM AUTHOR]

There are noteworthy problems in current strategies to calculate river water environmental capacity (WEC), including the generalization of tributaries and water intakes, which results in inaccurate calculation results of the WEC, and the difficulty in adapting to dynamic changes in demands and hydrological conditions in terms of practical application. To address these flaws, the subsection summation model (SSM) was built for river WEC calculation. The SSM increases the number of control sections according to drain outlets, water intakes, and tributaries and acquires the WEC of the functional area section by section. The Wei River was taken as the study area for verification and application of the SSM. Supported by a comprehensive integration platform, the WEC simulation system of the Wei River was constructed. The results show that the SSM enhances the accuracy of the WEC calculation, and the results are closer to the actual situation. The simulation system could obtain the WEC according to the demands and changes in the hydrological conditions, thus providing technical means for policymakers. Practitioner points: The subsection summation model provides a more accurate water environmental capacity (WEC) calculation method considering tributaries and water intakes avoiding generalization.The simulation system should be established to make the WEC calculation adapt to the demands or changes in the hydrological conditions.The model and system could supply the basis and technical means for decision‐making. [ABSTRACT FROM AUTHOR]

Reservoir flood control operation (RFCO) is a challenging optimization problem with interdependent decision variables and multiple conflicting criteria. By considering safety both upstream and downstream of the dam, a multi-objective optimization model is built for RFCO. To solve this problem, a multi-objective optimizer, the multi-objective evolutionary algorithm based on decomposition-differential evolution (MOEA/D-DE), is developed by introducing a differential evolution-inspired recombination into the algorithmic framework of the decomposition-based multi-objective optimization algorithm, which has been proven to be effective for solving complex multi-objective optimization problems. Experimental results on four typical floods at the Ankang reservoir illustrated that the suggested algorithm outperforms or performs as well as the comparison algorithms. It can significantly reduce the flood peak and also guarantee the dam’s safety. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is the second leading cause of cancer-associated mortality worldwide. Although the mortality rate of patients with GC has improved, it remains a significant health issue. The MYC proto-oncogene protein serves key roles in cellular proliferation, differentiation, transformation and apoptosis. Previous studies have identified the abnormal expression of MYC-binding protein (MYCBP) during tumorigenesis in multiple types of cancer. Furthermore, evidence demonstrates that the abnormal expression of MYCBP contributes to the invasion and migration of human cancer types, including colon cancer and glioma; however, its influence on GC remains unclear. In the present study, the expression of MYCBP in GC cells and tissues was analyzed by reverse transcription-quantitative polymerase chain reaction. Additionally, GC cell lines were transfected with small interfering RNAs against MYCBP or lymphoid enhancer-binding factor 1 (LEF-1) and assessed by in vitro transwell migration and invasion assays. The results indicated that the expression of MYCBP in GC cells and tissues was markedly increased compared with a normal gastric epithelial cell line and adjacent normal gastric mucosal tissues, respectively. Furthermore, MYCBP downregulation notably inhibited the metastatic capacity of GC cells, and LEF-1 knockdown was found to downregulate the expression of MYCBP. On the basis of the findings of the present study, MYCBP may be a direct target of the β-catenin/LEF-1 pathway via binding LEF-1, and could potentially be used as a biomarker for the diagnosis and prognosis of GC. [ABSTRACT FROM AUTHOR]

Parameter estimation of the nonlinear Muskingum model is a highly nonlinear optimization problem. Although various techniques have been applied to estimate the parameter of the nonlinear Muskingum flood routing model, an efficient method for parameter estimation in the calibration process is still lacking. In this paper, a novel approach of parameter estimation for the nonlinear Muskingum model based on the immune clonal selection algorithm (ICSA) is proposed. ICSA is a new intelligent algorithm, which can effectively overcome the prematurity and slow convergence speed of the traditional evolution algorithm. The ICSA method does not demand any initial estimate of values of any of the parameters. It determines the best parameter values in terms of the sum of square residual between the observed and routed outflows. The performance of this method was compared with other reported parameter estimation approaches. The results indicate that the ICSA method had higher precision than the other techniques and thus provided an efficient way for parameter estimation of the nonlinear Muskingum model. [ABSTRACT FROM AUTHOR]

Due to the uneven distribution of water resources in time and space, the problem of water shortage has become increasingly serious in some areas. To optimize use of water resources, it is urgent to establish multi-objective models and apply effective optimization algorithms to guide reservoir management. This study proposed a model of multi-objective optimization for reservoir operation (MORO) with the objectives of maximizing water diversion and power generation. The multi-objective evolutionary algorithm based on decomposition with adaptive weight vector adjustment (MOEA/D-AWA) was applied to solve the MORO problem. In addition, the performance of the MOEA/D-AWA was compared with two other algorithms based on the hyper-volume index. Huangjinxia reservoir, which is located in Shaanxi, China, was selected as the case study. The results show that: (1) the proposed model is effective and reasonable in theory; (2) the optimization results obtained by MOEA/D-AWA demonstrate this algorithm can be applied to the MORO problem, providing a set of evenly distributed non-dominated solutions; and (3) water diversion and power generation are indeed contradictory objectives. The MORO strategy can be used to efficiently utilize water resources, improve the comprehensive benefits of reservoirs, and provide decision support for actual reservoir operation. [ABSTRACT FROM AUTHOR]

In order to not only solve the technical problems of quantifying the degree and range of the effect that is caused by the water quality of upstream on that of downstream portions of a river, and of dividing the responsibility of transboundary water pollution, but also to tackle the difficulty in adapting to dynamic changes of the traditional water quality model in terms of practical application, pollutant discharge and water consumption were taken as the main influence factors to build the transboundary water quality transfer effect model. Supported by a comprehensive integration platform, the transboundary water quality transfer effect simulation system of the Yellow River mainstream was constructed. The simulation results show that the concentration decreases exponentially along the range. Gansu, Ningxia, and Inner Mongolia had a more significant effect of exceeding standard water consumption on pollution, while Ningxia, Inner Mongolia, Shaanxi, and Shanxi had a more distinct contribution to the over standard pollution discharge effect. The proposed model and simulation system can provide new methods and instruction for quantifying the degree and range of transboundary water pollution, as well as dividing the responsibility for water environment compensation. [ABSTRACT FROM AUTHOR]