Search Results (176)

Leaf chlorophyll content (LCC), leaf area index (LAI), and above-ground biomass (AGB) are important growth parameters for characterizing potato growth and predicting yield. While deep learning has demonstrated remarkable advancements in estimating crop growth parameters, the limited availability of field data often compromises model accuracy and generalizability, impeding large-scale regional applications. This study proposes a novel deep learning model that integrates multi-task learning and few-shot learning to address the challenge of low data in growth parameter prediction. Two multi-task learning architectures, MTL-DCNN and MTL-MMOE, were designed based on deep convolutional neural networks (DCNNs) and multi-gate mixture-of-experts (MMOE) for the simultaneous estimation of LCC, LAI, and AGB from Sentinel-2 imagery. Building on this, a few-shot learning framework for growth prediction (FSLGP) was developed by integrating simulated spectral generation, model-agnostic meta-learning (MAML), and meta-transfer learning strategies, enabling accurate prediction of multiple growth parameters under limited data availability. The results demonstrated that the incorporation of calibrated simulated spectral data significantly improved the estimation accuracy of LCC, LAI, and AGB (R² = 0.62~0.73). Under scenarios with limited field measurement data, the multi-task deep learning model based on few-shot learning outperformed traditional mixed inversion methods in predicting potato growth parameters (R² = 0.69~0.73; rRMSE = 16.68%~28.13%). Among the two architectures, the MTL-MMOE model exhibited superior stability and robustness in multi-task learning. Independent spatiotemporal validation further confirmed the potential of MTL-MMOE in estimating LAI and AGB across different years and locations (R² = 0.37~0.52). These results collectively demonstrated that the proposed FSLGP framework could achieve reliable estimation of crop growth parameters using only a very limited number of in-field samples (approximately 80 samples). This study can provide a valuable technical reference for monitoring and predicting growth parameters in other crops. Full article

The accelerating growth of the digital economy (DE) offers fresh momentum towards reaching carbon emissions’ peak and neutrality. Nevertheless, the impact mechanism of the DE on carbon emissions intensity (CEI) is insufficiently characterized. Our study first constructs an expanded comprehensive indicator system to evaluate DE development level from five dimensions containing 17 indicators. Panel data from 30 Chinese provincial regions (2013–2023) were analyzed using fixed effects, mediating effects, and spatial Durbin models to empirically examine the relationship and mechanisms between DE and CEI. Considering the existence of indirect effects of DE on CEs, the mechanism associated with the effect of the DE on CEs from the perspectives of economic growth, industrial structure upgrading, and scientific and technology innovation has been explored. The findings indicate notable regional disparities in the DE level across various provincial regions of China. China’s DE development significantly inhibits CEI. Furthermore, the DE’s development has successfully curtailed CE growth via three mediating mechanisms. And the DE exhibits a critical spatial spillover effect on CEI, and that effect also exhibits regional heterogeneity. Our findings can aid in regional DE development and the creation of policies to reduce CEs. Full article

With the deepening of the green and low-carbon concept in the field of road engineering, the cold construction asphalt pavement technology has developed rapidly due to its advantages such as low energy consumption, low pollution, and convenient construction. The adhesion between emulsified asphalt and aggregates, as a core factor affecting the performance of cold-mixed mixtures and the lifespan of the pavement, has attracted much attention in terms of its mechanism of action and evaluation methods. However, at present, there are still many issues that need to be addressed in terms of the stability control of adhesion between emulsified asphalt and aggregates, the explanation of the microscopic mechanism, and the standardization of testing methods in complex environments. These problems restrict the further promotion and application of the cold construction technology. Based on this, this paper systematically analyzes the current development status, application scenarios, and future trends of the theory and testing methods of the adhesion between emulsified asphalt and aggregates by reviewing a large number of relevant studies. The research aims to provide theoretical support and practical references for the improvement of adhesion in the cold construction asphalt pavement technology. Research shows that in terms of the adhesion mechanism, the existing results have deeply analyzed the infiltration and demulsification adhesion process of emulsified asphalt on the surface of aggregates and clarified the key links of physical and chemical interactions, but the understanding of the microscopic interface behavior and molecular-scale mechanism is still insufficient. In terms of testing methods, although objective and subjective evaluation methods such as mechanical tensile tests, surface energy evaluation, and adhesion fatigue tests have been developed, the standardization of testing, data comparability, and practical engineering applicability still need to be optimized. Comprehensive analysis shows that the research on the adhesion between emulsified asphalt and aggregates is showing a trend from macroscopic to microscopic, from static to dynamic. There are challenges in predicting and controlling the adhesion performance under complex environments, as well as important opportunities for developing advanced characterization techniques and multiscale simulation methods. Full article

Spherical powder materials are essential raw materials for manufacturing processes such as metal additive manufacturing and powder metallurgy. They possess characteristics that are key factors influencing the performance of additive manufacturing. This paper introduces the fundamental principles and characteristics of laser additive manufacturing technology and analyzes the technical principles, advantages, and disadvantages of three alloy powder preparation methods: gas atomization, centrifugal atomization, and plasma atomization. It further elucidates the influence of process parameters of these three powder preparation techniques on the characteristics of alloy powders. Finally, the development trends in alloy powder preparation for laser additive manufacturing are projected. Full article

Nitrile rubber (NBR) is prone to adhesion and hysteresis deformation when in contact with hard materials, leading to wear failure. To mitigate this issue, the deposition of diamond-like carbon (DLC) films onto the rubber surface is a commonly employed method. By utilizing pulsed arc ion plating technology and adjusting the arc voltage of the pulsed arc ion source, tetrahedral amorphous carbon (ta-C) films with varying sp³ content were prepared on the surface of NBR. The effects of arc voltage on the structural composition and friction performance of NBR/ta-C materials were examined. A scanning electron microscopy analysis revealed that the ta-C film applied to the surface of NBR was uniform and dense, exhibiting typical network crack characteristics. The results of Raman spectroscopy and X-ray photoelectron spectroscopy indicated that as the arc voltage increased, the sp³ content in the film initially rose before declining, reaching a maximum of 72.28% at 300 V. Mechanical tests demonstrated that the bonding strength and friction performance of the film are primarily influenced by the percentage of sp³ content. Notably, the ta-C film with lower sp³ content demonstrates enhanced wear resistance. At 200 V, the sp³ content of the film is 58.16%, resulting in optimal friction performance characterized by a stable friction coefficient of 0.38 and minimal wear weight loss. This performance is attributed to the protective qualities of the ta-C film and the formation of a graphitized transfer film. These results provide valuable insights for the design and development of wear-resistant rubber materials. Full article

Under the background of climate change, the risk spillover within the energy system is constantly intensifying. Clarifying the coupling relationship between entities within the energy system can help policymakers propose more reasonable policy measures and strengthen risk prevention. To estimate the risk spillover of energy-related systems, this paper constructs five subsystems: the fossil fuel subsystem, the electricity subsystem, the green bond subsystem, the renewable energy subsystem, and the carbon subsystem. Then, a quantitative risk analysis is conducted on two major energy consumption/carbon emission entities, China and Europe, based on the DCC-GARCH-CoVaR method. The result shows that (1) Markets of the same type often have more significant dynamic correlations. Of these, the average dynamic correlation coefficient of GBI-CABI (the Chinese green bond subsystem) and FR-DE (the European electricity subsystem) are the largest, by 0.8552 and 0.7347. (2) The high correlation between energy markets results in serious risk contagion, and the overall risk spillover effect within the European energy system is about 2.6 times that within the Chinese energy system. Of these, EUA and CABI are the main risk connectors of each energy system. Full article

For cold venting processes frequently employed in oil and gas fields, precisely predicting the instantaneous diffusion process of the vented explosive and/or toxic gases is of great importance, which cannot be captured by the Reynolds-averaged Navier–Stokes (RANS) method. In this paper, the large eddy simulation (LES) method is introduced for gas diffusion in an open space, and the diffusion characteristics of the sulfur-containing natural gas in the cold venting process is analyzed numerically. Firstly, a LES solution procedure of compressible gas diffusion is proposed based on the ANSYS Fluent 2022, and the numerical solution is verified using benchmark experiments. Subsequently, a computational model of the sulfur-containing natural gas diffusion process under the influence of a wind field is established, and the effects of wind speed, sulfur content, the venting rate and a downstream obstacle on the natural gas diffusion process are analyzed in detail. The results show that the proposed LES with the DSM sub-grid model is able to capture the transient diffusion process of heavy and light gases released in turbulent wind flow; the ratio between the venting rate and wind speed has a decisive influence on the gas diffusion process: a large venting rate increases the vertical diffusion distance and makes the gas cloud fluctuate more, while a large wind speed decreases the vertical width and stabilizes the gas cloud; for an obstacle located closely downstream, the venting pipe makes the vented gas gather on the windward side and move toward the ground, increasing the risk of ignition and poisoning near the ground. The LES solution procedure provides a more powerful tool for simulating the cold venting process of natural gas, and the results obtained could provide a theoretical basis for the safety evaluation and process optimization of sulfur-containing natural gas venting. Full article

TiAl intermetallic alloy is a crucial high-performance material, and its microstructure evolution at high temperatures is closely related to the process parameters. Observing the lamellar structure is key to exploring growth kinetics, and the feature extraction of precipitate phases can provide an effective basis for subsequent evolution studies and process parameter settings. Traditional observation methods struggle to promptly grasp the growth state of lamellar structures, and conventional object detection has certain limitations for clustered lamellar structures. This paper introduces a novel method for high-temperature precipitate phase feature extraction based on the YOLOv5-obb rotational object detection network, and a corresponding precipitate phase dataset was created. The improved YOLOv5-obb network was compared with other detection networks. The results show that the proposed YOLOv5-obb network model achieved a precision rate of 93.6% on the validation set for detecting and identifying lamellar structures, with a detection time of 0.02 s per image. It can effectively and accurately identify γ lamellar structures, providing a reference for intelligent morphology detection of alloy precipitate phases under high-temperature conditions. This method achieved good detection performance and high robustness. Additionally, the network can obtain precise positional information for target structures, thus determining the true length of the lamellar structure, which provides strong support for subsequent growth rate calculations. Full article

Purpose—Through the study, we discovered the key factors for decision makers involved in public crisis governance with respect to deciding whether to cooperate or not, and to understanding how they relate to governance performance. Methodology—This study extends the classic NK model, constructs an agent-based model to simulate the decision-making process of collaborative public crisis governance, and analyzes the effects of individual trust level, the level of cooperative relationship between individuals, and the standard deviation of the individual trust level on governance performance. Findings—This study found that when the complexity of a public crisis event is high, a high level of trust in the participants and a slightly lower level of inter-individual partnership would be appropriate in order to improve the responsiveness of governance decisions. If a small number of high-quality governance decisions are pursued, and there are high levels of trust and standard deviations of trust levels of the participating governance agents, as well as a strong level of partnership between individuals, it will result in more in-depth, comprehensive and high-quality governance decisions. Value—Although this study has slight shortcomings concerning the homogeneity of the choice of participating governance agents and the sensitivity of the simulation experiment, it has some theoretical contributions regarding decision making for collaborative public crisis governance. Full article

The gangue mixed in raw coal has small feature differences from coal, in order to solve the existing gangue recognition, methods generally have slow detection speed and are difficult to deploy at the edge end of the problem, a lightweight gangue target detection algorithm is proposed to enhance the research for the field of coal mining. Firstly, a lightweight EfficientViT module is the backbone of the network; secondly is the introduction of the DRBNCSPELAN4 module, which can better capture target information at different scales; finally, the lightweight shared convolutional detection head Detect_LSCD is reconstructed in order to further reduce the model size and improve the detection speed for coal and gangue. The experimental results indicate that in the model compared with the original algorithm, mAP@50–95 is improved by 1.2%, model weight size, the number of parameters, and floating point operations are reduced by 52.34%, 55.35%, and 50.35%, respectively, and inference speed is accelerated by 20.87% on a Raspberry Pi 4B device. In the field of coal gangue sorting, the algorithm not only has high-precision, real-time detection performance, but also achieves significant results in the lightweight model, making it more suitable for deployment on edge equipment to meet the requirements of controlling the robotic arm sorting gangue. Full article

Based on the economic and climate policy uncertainty index and the price data of major carbon emission trading markets from May 2014 to August 2023, this paper uses the generalized autoregressive conditional heteroskedasticity and mixing data sampling (GARCH-MIDAS) model to analyze the impact of policy uncertainty on carbon market price volatility. The results indicate the following: (1) The price volatility in the Hubei carbon market is influenced by both economic and climate policy uncertainties, while the Guangdong market is only affected by climate policy uncertainty, and the Shenzhen carbon market is only affected by economic policy uncertainty. (2) Before the establishment of the national carbon market, the carbon market prices in Hubei were impacted by both policy uncertainties, while Guangdong and Shenzhen carbon markets were only affected by climate policy uncertainties. (3) On the contrary, after the establishment of the national carbon market, only the Shenzhen carbon market was affected by both policy uncertainties, and the price volatility in the Guangdong and Hubei carbon markets was not affected by policy uncertainties. The above research conclusions are helpful for regulatory agencies and policymakers to assess the future direction of the pilot carbon market and provide an empirical basis for preventing and resolving policy risks. At the same time, the proposed GARCH-MIDAS model effectively solves the inconsistent frequency problem of policy uncertainty and carbon price volatility, providing a new perspective for the study of factors affecting carbon market volatility. Full article

The interaction law between soil and tillage components is the basis for designing and selecting soil tillage components. This paper uses the discrete element method to explore the soil penetration performance of the comb teeth of a pneumatic film-stripping tillage residual film recycler under different structural and working state parameters. The soil particle contact model is set up, the virtual prototype of the comb roller is established, and EDEM (Version 2018, DEM Solutions Company, Edinburgh, UK) discrete element software is applied to simulate the interaction between the comb roller and the soil particles during the residual film recycler’s operation. Simulation and test results show that using a spiral arrangement of tooth comb knives (Alar, 843300, China, Zhongyuan Stainless Steel Bending Manufacturing Co.) can reduce the impact load on the machine, improving the soil disturbance and facilitating the penetration of soil mulch. The composite force on the combing roller increases with comb depth in the soil for a combing roller depth of 6–18 cm. Moreover, the rotational speed varies within the range of 60–120 r/min. The forward speed of the recycling machine significantly affects the soil penetration performance of the comb roller; the power it consumes increases with forward speed. This study can provide a reference for the structural design and optimization of working parameters of future deep tillage machines. Full article

Low-carbon development (LCD) in China has become the critical measure to achieve sustainable development and handle climate change. This investigation evaluates 30 provincial regions’ LCD quality from dimensions of low-carbon (LC) economy, resources utilization, LC environment, and LC society. According to the integrated weights combined subjective weights identified through the best–worst method (BWM) and objective weights attained through the anti-entropy weight (AEW) method, the top five sub-criteria in 2021 were coal consumption relative to total primary energy consumption, industrial sulfur dioxide (SO₂) emission, carbon dioxide emissions intensity, industrial dust emission, and forest coverage rate. According to the comprehensive evaluation results obtained through the MARCOS model, Beijing’s comprehensive score is far ahead, and its scores in resource utilization, LC environment, and LC economy are also in a leading position. Moreover, the level of LCD quality shows a gradually reduced pattern from east to west. The obstacle analysis demonstrates that the obstacle factors with high frequency of occurrence include real GDP, energy intensity, coal consumption relative to total primary energy consuming, carbon dioxide emissions intensity, industrial dust emission, industrial SO₂ emission, forest coverage rate, and the number of private vehicles. Suggestions are proposed based on the results, including increase infrastructure construction, optimize energy structure and develop renewable energy, protect the ecological environment with intensify efforts, and accelerate industrial transformation and upgrading to optimize industrial structure. Full article

Chalcogenide perovskites have gained attention as alternative semiconductor materials, yet their experimental investigation remains limited. This study investigates the synthesis and characterization of a series of chalcogenide perovskite powder samples via the sulfurization of oxide precursors at different temperatures. Zr- and Hf-based chalcogenide perovskites adopted a perovskite structure with a Pnma space group, while Ti-based chalcogenides formed hexagonal phases. The minimum synthesis temperature varied among materials and was correlated with the strength of the A cation–oxygen bonds. The synthesized chalcogenide perovskites exhibit bandgaps suitable for solar cell absorption layers, and the photoluminescence (PL) results indicate that SrZrS₃, SrHfS₃, CaZrS₃, and CaHfS₃ are promising candidates for light-emitting semiconductors. Full article

The deep-sea landing vehicle (DSLV) swarm exploration system is a novel platform for the detection of marine mineral resources. A high-precision cooperative localization system with Ultra-Short Baseline (USBL), Doppler Velocity Log (DVL), and electronic compass (EC) plays a vital role in the DSLV swarm exploration system. However, DVL measurements can be seriously interrupted due to the complex operational underwater environment, leading to unstable localization performance. The accuracy of the cooperative localization system could be further degraded by the persistent rubber track slippage during the vehicle’s movement over the soft seabed. In this study, a data-driven cooperative localization algorithm with a velocity prediction model is proposed to improve the positioning accuracy of DSLV under track slippage. First, a velocity prediction model for DVL measurements is constructed using multi-output least squares support vector regression (MLSSVR), and a genetic algorithm (GA) is further employed to optimize the model’s hyperparameters in order to enhance the robustness of the framework. Furthermore, the outputs of MLSSVR are fed into a DSLV position estimation framework based on the Unscented Kalman Filter (UKF) to improve localization accuracy in the presence of DVL failures. To validate the proposed method, the RecurDyn multibody dynamics simulation platform is applied for data synthesis, accounting for both the impact of the soft seabed and real-world motion simulation. The experimental results indicate that during DVL failure, the proposed algorithm can effectively compensate for the cooperative localization errors caused by track slippage, thereby significantly improving the accuracy and reliability of the DSLV cooperative localization system. Full article