Machining plan is the core of guiding manufacturing production and is regarded as one of the keys to ensure the quality of product processing. Existing process design methods are inefficient to quickly handle the mach...
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Machining plan is the core of guiding manufacturing production and is regarded as one of the keys to ensure the quality of product processing. Existing process design methods are inefficient to quickly handle the machining plan changed induced by the unpredictable events in real-time production. It inevitably causes time and economic losses for the enterprise. In order to express the evolutionary characteristics of product processing, the construction method of digital twin process model (DTPM) is proposed based on the knowledge-evolution machining features. Three key technologies include correlation structure of process knowledge, expression method of the evolution geometric features and the association mechanism between two are solved. On this basis, the construction framework of DTPM is illustrated. Then, the organisation and management mechanism of multi-source heterogeneous data is discussed in detail. At last, a case study of the complex machined part is researched, the results show that the processing time reduced by about 7% and the processing stability improved by 40%. Meanwhile, the implementation scheme, application process and effect of this case are described in detail to provide reference for enterprises.
Decentralized peer-to-peer Local Energy Markets (LEMs) are gaining popularity as local power production from Renewable Energy Sources (RESs) increases. The study investigates a blockchain-driven LEM in which prosumers...
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The purpose of the study is to optimize power price in smart homes that connect to share energy. A Demand Side Management (DSM) system is used to coordinate P2P energy trading between smart homes using the improved ea...
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The governance of service ecosystem needs to balance efficiency and fairness to promote the sustainable development of the system, making it an important topic. However, service entities in the service ecosystem have ...
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ISBN:
(纸本)9798350368567;9798350368550
The governance of service ecosystem needs to balance efficiency and fairness to promote the sustainable development of the system, making it an important topic. However, service entities in the service ecosystem have autonomy and engage in dynamic game with governance strategies, leading to governance challenges. To address this issue, we model the governance process as a repeated sequential game between the governance algorithm and service entities, and propose a novel two-level learning algorithm. This algorithm considers the learning evolution of service entities and the co-evolutionary of the governance algorithm, using reinforcement learning to learn effective governance strategies while also considering the response function of service entities to balance efficiency and fairness. Combining the ideas of bilevel optimization and online learning, this algorithm effectively balances exploration (understanding the service entities' responses function) and exploitation (choosing efficient actions). We apply the algorithm to a classic service ecosystem, the ride-hailing service system, and empirically demonstrate its effectiveness in the governance task of order dispatching.
Allium chinense *** is an important medicinal and edible homologous plant and indigenous to China and grown in other countries. Phytochemical studies have shown that it is rich in biologically active natural products....
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Allium chinense *** is an important medicinal and edible homologous plant and indigenous to China and grown in other countries. Phytochemical studies have shown that it is rich in biologically active natural products. Herein, five new and twelve known steroidal saponins were isolated from the bulbs of Allium chinense ***. These compounds were structurally determined by analysis of their NMR, IR and HR-ESI-MS data. All isolates were evaluated for inhibition of porcine pancreatic lipase activity. Compounds 4, 7 and 10 exhibited potent inhibition of porcine pancreatic lipase. The relationship between structure and activity of these compounds is also investigated in this paper.
BACKGROUNDIn the present study, we address the quality degradation of corn during post-harvest processing by developing a viscoelastic composite model to predict kernel damage during post-harvest processing. The model...
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BACKGROUNDIn the present study, we address the quality degradation of corn during post-harvest processing by developing a viscoelastic composite model to predict kernel damage during post-harvest processing. The model, based on high-resolution scanning and inverse modeling techniques, provides an accurate representation of the complex internal components of corn kernels, which allows for the analysis of their stress response and damage susceptibility trends under different impact *** results show that the viscoelastic model has a relative error of 2.4% compared to the drop test data, thus demonstrating that the viscoelastic model has a very high accuracy in predicting impact damage and is able to accurately localize the flour-like endosperm as the main region of impact damage. The model showed a high correlation (0.99) between predicted and experimental damage rates by response surface *** damage during processing is reduced by reducing the proportion of flour-like endosperm in maize kernels. A new method for reducing damage during food handling and processing operations is proposed, providing an important reference for improving kernel durability and contributing to the development of gentler processing techniques to improve the quality of maize products. (c) 2025 Society of Chemical Industry.
In this work, red mud-blast furnace slag (RM-BFS) based geopolymers with excellent electromagnetic wave absorption property is synthesized through the incorporation of wave-absorption materials. The electromagnetic ab...
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In this work, red mud-blast furnace slag (RM-BFS) based geopolymers with excellent electromagnetic wave absorption property is synthesized through the incorporation of wave-absorption materials. The electromagnetic absorption property of geopolymer is characterized through the arched test, in which the reflection loss of specimens is tested by vector network analyzer (VNA). The microstructure and conductivity of geopolymers are characterized through scanning electron microscopy (SEM), concrete porous structure analyzer and conductivity tester. The crystalline phase and functional groups of geopolymers are characterized by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). Compared to Portland cement concrete (PCC), wave absorption band of the geopolymer is wider and the absorption peak in high frequency band is more intense. With the optimal addition of hollow glass microsphere (HGM), Fe2O3 and graphite particles, the absorption bandwidth with reflection loss less than -5 dB are 12.4, 7.7 and 8.1 GHz, and the maximum reflection loss are -9.4, -8.4 and -8.7 dB, respectively. This study might give a clue for preparation of geopolymers with high electromagnetic wave absorption properties.
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