Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive act...Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities.However,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction.Therefore,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed framework.The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model.The related experimental data were employed to verify the numerical simulation results.The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.展开更多
Controlled laboratory experiments are proved to be a valuable tool for investigating changes in underground physical properties and the related response of surface geophysical signals.The self-potential(SP)method is w...Controlled laboratory experiments are proved to be a valuable tool for investigating changes in underground physical properties and the related response of surface geophysical signals.The self-potential(SP)method is widely used in mineral resource exploration due to its direct correlation with underground electrochemical gradients.This paper presented the design and construction of an experimental platform based on a multi-channel SP monitoring system.The proposed platform was used to monitor the anodizing corrosion process of different metal blocks from a laboratory perspective,record the real-time SP signal generated by the redox reaction,as well as investigate the geobattery mechanism associated with the natural polarization process of metal mineral resources.The experimental results demonstrate that the constructed SP monitoring platform effectively captures time-series SP signals and provides direct laboratory evidence for the geobattery model.The measured SP data were quantitatively interpreted using the simulated annealing algorithm,and the inversion results closely match the real model.This finding highlights the potential of the SP method as a promising tool for determining the location and spatial distribution of underground polarizers.The study holds reference value for the exploration and exploitation of mineral resources in both terrestrial and marine environments.展开更多
产气荚膜梭菌是革兰氏阳性杆菌,通过释放多种毒素和酶引发畜禽严重疾病,降低畜牧业生产。文献分析可把握学科发展方向,明确当前研究现状和发展趋势。然而,缺乏对产气荚膜梭菌文献的系统梳理,基于此,研究旨在利用Web of science核心数据...产气荚膜梭菌是革兰氏阳性杆菌,通过释放多种毒素和酶引发畜禽严重疾病,降低畜牧业生产。文献分析可把握学科发展方向,明确当前研究现状和发展趋势。然而,缺乏对产气荚膜梭菌文献的系统梳理,基于此,研究旨在利用Web of science核心数据库,以产气荚膜梭菌为关键词归纳,总结文献。结果表明:(1)1980-2022年,发表产气荚膜梭菌相关文献共计9093篇,其中“Foodborne Illness Acquired in the United States-Major Pathogens”,被引频次最多(1401次);(2)发文量较多的国家依次为美国(2688)>日本(850)>德国(644)>英国(561)>加拿大(501)>中国(500);(3)产气荚膜梭菌的关键词最大权重为toxin(1980-2022年),且以微生物,农业,食品科学技术,兽医学以及生物工程与应用生物学等学科交叉。产气荚膜梭菌的有效遏制是核心,快速根除是关键,该文分析产气荚膜梭菌的研究趋势,提出噬菌体治疗、疫苗免疫等今后研究新思路,为保障畜牧业生产提供理论支撑。展开更多
[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been propo...[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.展开更多
Inspired by nature's self-similar designs,novel honeycomb-spiderweb based self-similar hybrid cellular structures are proposed here for efficient energy absorption in impact applications.The energy absorption is e...Inspired by nature's self-similar designs,novel honeycomb-spiderweb based self-similar hybrid cellular structures are proposed here for efficient energy absorption in impact applications.The energy absorption is enhanced by optimizing the geometry and topology for a given mass.The proposed hybrid cellular structure is arrived after a thorough analysis of topologically enhanced self-similar structures.The optimized cell designs are rigorously tested considering dynamic loads involving crush and high-velocity bullet impact.Furthermore,the influence of thickness,radial connectivity,and order of patterning at the unit cell level are also investigated.The maximum crushing efficiency attained is found to be more than 95%,which is significantly higher than most existing traditional designs.Later on,the first and second-order hierarchical self-similar unit cell designs developed during crush analysis are used to prepare the cores for sandwich structures.Impact tests are performed on the developed sandwich structures using the standard 9-mm parabellum.The influence of multistaging on impact resistance is also investigated by maintaining a constant total thickness and mass of the sandwich structure.Moreover,in order to avoid layer-wise weak zones and hence,attain a uniform out-of-plane impact strength,off-setting the designs in each stage is proposed.The sandwich structures with first and second-order self-similar hybrid cores are observed to withstand impact velocities as high as 170 m/s and 270 m/s,respectively.展开更多
文摘Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations.Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities.However,the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction.Therefore,a real-time framework based on the Long-Short Term Memory(LSTM)neural network model is proposed to predict ship motions in regular and irregular head waves.A 15000 TEU container ship model is employed to illustrate the proposed framework.The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model.The related experimental data were employed to verify the numerical simulation results.The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.
基金Project(42174170)supported by the National Natural Science Foundation of China。
文摘Controlled laboratory experiments are proved to be a valuable tool for investigating changes in underground physical properties and the related response of surface geophysical signals.The self-potential(SP)method is widely used in mineral resource exploration due to its direct correlation with underground electrochemical gradients.This paper presented the design and construction of an experimental platform based on a multi-channel SP monitoring system.The proposed platform was used to monitor the anodizing corrosion process of different metal blocks from a laboratory perspective,record the real-time SP signal generated by the redox reaction,as well as investigate the geobattery mechanism associated with the natural polarization process of metal mineral resources.The experimental results demonstrate that the constructed SP monitoring platform effectively captures time-series SP signals and provides direct laboratory evidence for the geobattery model.The measured SP data were quantitatively interpreted using the simulated annealing algorithm,and the inversion results closely match the real model.This finding highlights the potential of the SP method as a promising tool for determining the location and spatial distribution of underground polarizers.The study holds reference value for the exploration and exploitation of mineral resources in both terrestrial and marine environments.
文摘产气荚膜梭菌是革兰氏阳性杆菌,通过释放多种毒素和酶引发畜禽严重疾病,降低畜牧业生产。文献分析可把握学科发展方向,明确当前研究现状和发展趋势。然而,缺乏对产气荚膜梭菌文献的系统梳理,基于此,研究旨在利用Web of science核心数据库,以产气荚膜梭菌为关键词归纳,总结文献。结果表明:(1)1980-2022年,发表产气荚膜梭菌相关文献共计9093篇,其中“Foodborne Illness Acquired in the United States-Major Pathogens”,被引频次最多(1401次);(2)发文量较多的国家依次为美国(2688)>日本(850)>德国(644)>英国(561)>加拿大(501)>中国(500);(3)产气荚膜梭菌的关键词最大权重为toxin(1980-2022年),且以微生物,农业,食品科学技术,兽医学以及生物工程与应用生物学等学科交叉。产气荚膜梭菌的有效遏制是核心,快速根除是关键,该文分析产气荚膜梭菌的研究趋势,提出噬菌体治疗、疫苗免疫等今后研究新思路,为保障畜牧业生产提供理论支撑。
文摘[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.
基金the Science and Engineering Research Board(SERB),Department of Science and Technology,India,for funding this research through grant number SRG/2019/001581。
文摘Inspired by nature's self-similar designs,novel honeycomb-spiderweb based self-similar hybrid cellular structures are proposed here for efficient energy absorption in impact applications.The energy absorption is enhanced by optimizing the geometry and topology for a given mass.The proposed hybrid cellular structure is arrived after a thorough analysis of topologically enhanced self-similar structures.The optimized cell designs are rigorously tested considering dynamic loads involving crush and high-velocity bullet impact.Furthermore,the influence of thickness,radial connectivity,and order of patterning at the unit cell level are also investigated.The maximum crushing efficiency attained is found to be more than 95%,which is significantly higher than most existing traditional designs.Later on,the first and second-order hierarchical self-similar unit cell designs developed during crush analysis are used to prepare the cores for sandwich structures.Impact tests are performed on the developed sandwich structures using the standard 9-mm parabellum.The influence of multistaging on impact resistance is also investigated by maintaining a constant total thickness and mass of the sandwich structure.Moreover,in order to avoid layer-wise weak zones and hence,attain a uniform out-of-plane impact strength,off-setting the designs in each stage is proposed.The sandwich structures with first and second-order self-similar hybrid cores are observed to withstand impact velocities as high as 170 m/s and 270 m/s,respectively.
