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Energy is an important basic resource for the development of human society. This paper develops a decision support system for collaborative optimization of biomass energy and intelligence using biomass energy and smart energy management technology. Explain the various methods by which biomass can be transformed into usable energy for power plants. A smart energy management model is constructed using Internet of Things technology, and its performance prediction model is predicted using the gray prediction model. To study the optimal allocation of energy in the system constructed in this paper, the objective function and constraints are built using carbon capture technology, and the optimal allocation conditions of energy are calculated. After experimental analysis, it was found that in 2020, biomass energy power generation reached 29 million kilowatts, which was 020.1 million kilowatts higher than in 2015. The co-optimization strategy of biomass energy and smart energy constructed in this paper supports the energy utilization efficiency of the system to reach more than 60%. Compared to the traditional energy division system, the constructed system saves an annual cost of 52.71%. The payback period was shortened to 3.31 years, resulting in a net present value increase of 2.8991 million yuan. The energy synergy system’s power supply produces the least amount of carbon dioxide from an environmental perspective and has been stabilized at 50 kg.

The search for sustainable energy solutions has become urgent against the current energy crisis and increasing environmental pollution. Biomass energy conversion and utilization technology, a clean and renewable energy source, constantly evolves, particularly in biomass power generation technology. This technology can effectively reduce greenhouse gas emissions and dependence on fossil fuels. Biomass conversion and utilization techniques include combustion, gasification, and biochemical methods that generate different energy forms, including heat, electricity, and fuel. Developing these technologies provides strong technical support for optimizing energy structures and protecting the environment. The article explores the application of biopower technology in intelligent energy management of electricity and improves its efficiency. The efficiency of biopower technology and its influencing factors were investigated using the Data Envelopment Analysis (DEA) model and Tobit regression analysis. A study was conducted on ten biopower energy companies. The energy management efficiency score of 1 was present in six of these companies, suggesting a relatively high operational efficiency level. Company C has a super-efficiency value of 2.654, indicating its outstanding energy management efficiency. Factors such as human resource input, total investment, and annual production costs significantly affect energy management efficiency. By optimizing resource allocation and improving management efficiency, biopower technology can be applied more effectively to intelligent energy management, thus promoting sustainable development.

In general, there are no long-term meteorological or hydrological data available for karst river basins. The lack of rainfall data is a great challenge that hinders the development of hydrological models. Quantitative precipitation estimates (QPEs) based on weather satellites offer a potential method by which rainfall data in karst areas could be obtained. Furthermore, coupling QPEs with a distributed hydrological model has the potential to improve the precision of flood predictions in large karst watersheds. Estimating precipitation from remotely sensed information using an artificial neural network-cloud classification system (PERSIANN-CCS) is a type of QPE technology based on satellites that has achieved broad research results worldwide. However, only a few studies on PERSIANN-CCS QPEs have occurred in large karst basins, and the accuracy is generally poor in terms of practical applications. This paper studied the feasibility of coupling a fully physically based distributed hydrological model, i.e., the Liuxihe model, with PERSIANN-CCS QPEs for predicting floods in a large river basin, i.e., the Liujiang karst river basin, which has a watershed area of 58 270 km-2, in southern China. The model structure and function require further refinement to suit the karst basins. For instance, the sub-basins in this paper are divided into many karst hydrology response units (KHRUs) to ensure that the model structure is adequately refined for karst areas. In addition, the convergence of the underground runoff calculation method within the original Liuxihe model is changed to suit the karst water-bearing media, and the Muskingum routing method is used in the model to calculate the underground runoff in this study. Additionally, the epikarst zone, as a distinctive structure of the KHRU, is carefully considered in the model. The result of the QPEs shows that compared with the observed precipitation measured by a rain gauge, the distribution of precipitation predicted by the PERSIANN-CCS QPEs was very similar. However, the quantity of precipitation predicted by the PERSIANN-CCS QPEs was smaller. A post-processing method is proposed to revise the products of the PERSIANN-CCS QPEs. The karst flood simulation results show that coupling the post-processed PERSIANN-CCS QPEs with the Liuxihe model has a better performance relative to the result based on the initial PERSIANN-CCS QPEs. Moreover, the performance of the coupled model largely improves with parameter re-optimization via the post-processed PERSIANN-CCS QPEs. The average values of the six evaluation indices change as follows: the Nash-Sutcliffe coefficient increases by 14 %, the correlation coefficient increases by 15 %, the process relative error decreases by 8 %, the peak flow relative error decreases by 18 %, the water balance coefficient increases by 8 %, and the peak flow time error displays a 5 h decrease. Among these parameters, the peak flow relative error shows the greatest improvement; thus, these parameters are of page1506 the greatest concern for flood prediction. The rational flood simulation results from the coupled model provide a great practical application prospect for flood prediction in large karst river basins.

Land use and cover change (LUCC) is directly linked to the sustainability of ecosystems and the long-term well-being of human society. The Helong Region in the Loess Plateau has become one of the areas most severely affected by soil and water erosion in China due to its unique geographical location and ecological environment. The long-term construction of terraces and orchards is one of the important measures for this region to combat soil erosion. Despite the important role that terraces and orchards play in this region, current studies on their extraction and understanding remain limited. For this reason, this study designed a land use classification system, including terraces and orchards, to reveal the patterns of LUCC and the effectiveness of ecological restoration projects in the area. Based on this system, this study utilized the Random Forest classification algorithm to create an annual land use and cover (LUC) dataset for the Helong Region that covers eight periods from 1986 to 2020, with a spatial resolution of 30 m. The validation results showed that the maps achieved an average overall accuracy of 87.54% and an average Kappa coefficient of 76.94%. This demonstrates the feasibility of the proposed design and land coverage mapping method in the study area. This study found that, from 1986 to 2020, there was a continuous increase in forest and grassland areas, a significant reduction in cropland and bare land areas, and a notable rise in impervious surface areas. We emphasized that the continuous growth of terraces and orchards was an important LUCC trend in the region. This growth was primarily attributed to the conversion of grasslands, croplands, and forests. This transformation not only reduced soil erosion but also enhanced economic efficiency. The products and insights provided in this study help us better understand the complexities of ecological recovery and land management. [ABSTRACT FROM AUTHOR]

Remote sensing image change detection is crucial for urban planning, environmental monitoring, and disaster assessment, as it identifies temporal variations of specific targets, such as surface buildings, by analyzing differences between images from different time periods. Current research faces challenges, including the accurate extraction of change features and the handling of complex and varied image contexts. To address these issues, this study proposes an innovative model named the Segment Anything Model-UNet Change Detection Model (SCDM), which incorporates the proposed center expansion and reduction method (CERM), Segment Anything Model (SAM), UNet, and fine-grained loss function. The global feature map of the environment is extracted, the difference measurement features are extracted, and then the global feature map and the difference measurement features are fused. Finally, a global decoder is constructed to predict the changes of the same region in different periods. Detailed ablation experiments and comparative experiments are conducted on the WHU-CD and LEVIR-CD public datasets to evaluate the performance of the proposed method. At the same time, validation on more complex DTX datasets for scenarios is supplemented. The experimental results demonstrate that compared to traditional fixed-size partitioning methods, the CERM proposed in this study significantly improves the accuracy of SOTA models, including ChangeFormer, ChangerEx, Tiny-CD, BIT, DTCDSCN, and STANet. Additionally, compared with other methods, the SCDM demonstrates superior performance and generalization, showcasing its effectiveness in overcoming the limitations of existing methods. [ABSTRACT FROM AUTHOR]

Studying spatial and temporal characteristics of land use changes and the driving factors in the Yellow River Basin as well as simulating and predicting future land use is crucial for resource management, ecological protection, and regional sustainable development in the Yellow River Basin. Based on the China Land Cover Dataset (CLCD) of the Yellow River Basin from 1990 to 2021, this study employs various methods such as the Mann–Kendall test and sliding t-test, land use dynamics, the land use transfer matrix, the standard deviation ellipse, the center of gravity migration model, and a geographic detector to explore the spatial and temporal characteristics of land use changes and driving forces in the Yellow River Basin over the past 30 years. Additionally, the study predicts land use types in the study area for the year of 2030 by using the Future Land Use Simulation (FLUS) model. The results show the following: (1) From 1990 to 2021, the area of forest, grassland, water, and impervious surfaces increased significantly, while the area of cropland, shrub, barren land, and wetlands decreased significantly. The most actively changing land use types are cropland, grassland, barren land, and impervious surfaces. (2) The center of gravity for shrub and impervious surfaces shifted westward, while wetlands showed a trend of obvious concentrated distribution, and the remaining land use types exhibited stable directional distributions. (3) Economic factors had a stronger driving effect on land use changes than topographic and climatic factors. The land use changes in the Yellow River Basin are influenced by the coordinated driving forces of multiple factors. (4) In 2030, the main land use types in the Yellow River Basin are still expected to be cropland, grassland, and forest. However, there will be a significant expansion of impervious surfaces and forest land, with substantial encroachment on cropland and grassland. [ABSTRACT FROM AUTHOR]

Watershed hydrological modeling methods are currently the predominant approach for flood forecasting. Digital elevation model (DEM) data, a critical input variable, significantly influence the accuracy of flood simulations, primarily due to their resolution. However, there is a paucity of research exploring the relationship between DEM resolution and flood simulation accuracy. This study aims to investigate this relationship by examining three watersheds of varying scales in southern Jiangxi Province, China. Utilizing the Liuxihe model, a new-generation physically based distributed hydrological model (PBDHM), we collected and collated data, including DEM, land use, soil type, and hourly flow and rainfall data from monitoring stations, covering 22 flood events over the last decade, to conduct model calibration and flood simulation. DEM data were processed into seven resolutions, ranging from 30 m to 500 m, to analyze the impact of DEM resolution on flood simulation accuracy. The results are as follows. (1) The Nash–Sutcliffe efficiency coefficients for the entire set of flood events were above 0.75, demonstrating the Liuxihe model's strong applicability in this region. (2) The DEM resolution of the Anhe and Dutou watersheds lost an average of 7.9% and 0.8% accuracy when increasing from 30 m to 200 m, with further losses of 37.9% and 10.7% from 200 m to 300 m. Similarly, the Mazhou watershed showed an average of 8.4% accuracy loss from 30 m to 400 m and 20.4% from 400 m to 500 m. These results suggest a threshold where accuracy sharply declines as DEM resolution increases, and this threshold rises with watershed scale. (3) Parameter optimization in the Liuxihe model significantly enhanced flood simulation accuracy, effectively compensating for the reduction in accuracy caused by increased DEM resolution. (4) The optimal parameters for flood simulation varied with different DEM resolutions, with significant changes observed in riverbed slope and river roughness, which are highly sensitive to DEM resolution. (5) Changes in DEM resolution did not significantly impact surface flow production. However, the extraction of the water system and the reduction in slope were major factors contributing to the decline in flood simulation accuracy. Overall, this study elucidates that there is a threshold range of DEM resolution that balances data acquisition efficiency and computational speed while satisfying the basic requirements for flood simulation accuracy. This finding provides crucial decision-making support for selecting appropriate DEM resolutions in hydrological forecasting. [ABSTRACT FROM AUTHOR]

Monolayer molybdenum disulfide (MoS2), a typical transition metal dichalcogenide (TMD), is a promising functional material for next-generation electronics. However, growing large-domain-sized MoS2 single crystals on SiO2 remains considerably challenging. The amorphous structure and lack of catalytic activity of SiO2 prevent the deposition of a high-quality monolayer MoS2. Herein, a Na-assisted space-confining method is deployed to directly synthesize a MoS2 single-crystal on SiO2. The domain size of MoS2 reaches 450 μm within 3 min, the fastest growth rate reported so far. This highly efficient growth is probably due to two factors: the narrow space between the sodium–calcium glass, and the SiO2 substrate controls the nucleus density; meanwhile, the release of Na from sodium–calcium glass facilitates the rapid formation of large single crystals. These results provide insights into the rapid growth of large-domain-sized TMD single crystals on amorphous substrates. [ABSTRACT FROM AUTHOR]

Twisted 2D transition metal dichalcogenides (TMDCs) play a significant role in the development of twistronics. However, it is still challenging to prepare high-quality twisted TMDCs by current stacking or folding techniques. Herein, we propose a cross-domain chemical vapor deposition method to synthesize twisted bilayer MoS2 through precisely controlling the supply of molybdenum precursor. It is found that the top layer of a bilayer MoS2 grain maintains its original orientation even when it crosses over to neighboring monolayer MoS2 grain. This suggests that the van der Waals epitaxy can be prevented with the assistance of covalent bonds. Furthermore, the interlayer coupling strength reaches a maximum value at the twisted angle (θ) of 0° or 60° and a minimum at θ = 30°. Moreover, the evolution of in-plane shear mode and out-of-plane breathing mode obtained from low-frequency Raman spectroscopy reveals atomic reconstructions of the moiré pattern. Meanwhile, the shift of the indirect bandgap exhibits an angle dependence consistent with the interlayer coupling strength, which likely comes from the mixing of pz orbitals. The change in A−/A intensity ratio is not mainly originated from the trion binding energy, but the excess electron concentration. Our results offer a feasible approach to prepare high-quality twisted TMDCs and provide a good platform for studying twistronics and related phenomena. [ABSTRACT FROM AUTHOR]

Floods occur frequently in China, and watershed floods are caused mainly by intensive rainfall, but the spatial distribution of this rainfall is often very uneven. Thus, a watershed hydrological model that enables a consideration of a heterogeneous spatial distribution of rainfall is needed. In this study, a flood forecasting scheme based on the Liuxihe model is established for the Zaoshi Reservoir. The particle swarm optimization (PSO) algorithm is used to optimize the model parameters for flood simulation, and the model's performance is assessed by a comparison with measured flood data. The spatial distributions of rainfall selected for this study are non-uniform, with much greater rainfall in some areas than in others in some cases. Rainfall may be concentrated in the middle of the basin, in the reservoir area, or in the upstream portion of the basin. The Liuxihe-model-based flood inflow forecasting scheme for the Zaoshi Reservoir demonstrates an excellent simulation effect, with an average peak simulation accuracy of 96.3%, an average peak time of 1.042 h early, and an average Nash–Sutcliffe coefficient of 0.799. Under the condition of an uneven spatial distribution of rainfall, the Liuxihe model simulates floods well. The PSO algorithm significantly improves the model's simulation accuracy, and its practical application requires only the selection of a typical flood for parameter optimization. Thus, the flood simulation effect of the Liuxihe model is ideal for the watershed above the Zaoshi Reservoir, and the scheme developed in this study can be applied for operational flood forecasting. [ABSTRACT FROM AUTHOR]