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Container technology has gained a widespread application in cloud computing environments due to its low resource overhead and high flexibility. However, as the number of containers grows, it becomes increasingly challenging to achieve the rapid and coordinated optimization of multiple objectives for container scheduling, while maintaining system stability and security. This paper aims to overcome these challenges and provides the optimal allocation for a large number of containers. First, a large-scale multi-objective container scheduling optimization model is constructed, which involves the task completion time, resource cost, and load balancing. Second, a novel optimization algorithm called LSMOF-AD (large-scale multi-objective optimization framework with muti-stage and adaptive differential strategies) is proposed to effectively handle large-scale container scheduling problems. The experimental results show that the proposed algorithm has a better performance in multiple benchmark problems compared to other advanced algorithms and can effectively reduce the task processing delay, while achieving a high resource utilization and load balancing compared to other scheduling strategies. [ABSTRACT FROM AUTHOR]

A Brain–Computer Interface (BCI) is a medium for communication between the human brain and computers, which does not rely on other human neural tissues, but only decodes Electroencephalography (EEG) signals and converts them into commands to control external devices. Motor Imagery (MI) is an important BCI paradigm that generates a spontaneous EEG signal without external stimulation by imagining limb movements to strengthen the brain's compensatory function, and it has a promising future in the field of computer-aided diagnosis and rehabilitation technology for brain diseases. However, there are a series of technical difficulties in the research of motor imagery-based brain–computer interface (MI-BCI) systems, such as: large individual differences in subjects and poor performance of the cross-subject classification model; a low signal-to-noise ratio of EEG signals and poor classification accuracy; and the poor online performance of the MI-BCI system. To address the above problems, this paper proposed a combined virtual electrode-based EEG Source Analysis (ESA) and Convolutional Neural Network (CNN) method for MI-EEG signal feature extraction and classification. The outcomes reveal that the online MI-BCI system developed based on this method can improve the decoding ability of multi-task MI-EEG after training, it can learn generalized features from multiple subjects in cross-subject experiments and has some adaptability to the individual differences of new subjects, and it can decode the EEG intent online and realize the brain control function of the intelligent cart, which provides a new idea for the research of an online MI-BCI system. [ABSTRACT FROM AUTHOR]

For the quick and accurate determination of the cable force under the hybrid boundary between the ear plate and the anchor plate, this paper proposes a method for identifying the boundary conditions and bending stiffness of the cables based on the frequency method. First, the influence of the parameters, namely, cable boundary conditions, bending stiffness, inclination angle, and linear density, on the cable force was analyzed. Next, the actual bending stiffness of the cable under the boundary conditions of the upper hinged support and the lower solid support and the solid support at both ends were determined, and the cable force in the two states was calculated. Finally, the hydraulic jack tension and the cable force in the two states were compared and analyzed to define the actual boundary conditions and bending stiffness of the cable. The results show that the boundary conditions, bending stiffness, and linear density of the cable considerably influence the cable force, whereas the inclination angle has a negligible effect on the cable force. The bending stiffness under the two boundary conditions of the cable were both 0.12Eimax, and the calculated value of the cable force under the solid support boundary condition at both ends was consistent with the value of the hydraulic jack tension. [ABSTRACT FROM AUTHOR]

Land cover information plays an essential role in the study of global surface change. Multiple land cover datasets have been produced to meet various application needs. The FROM-GLC30 (Finer Resolution Observation and Monitoring of Global Land Cover) dataset is one of the latest land cover products with a resolution of 30 m, which is a relatively high resolution among global public datasets, and the accuracy of this dataset is of great concern in many related researches. The objective of this study was to calculate the accuracy of the FROM-GLC30 2017 dataset at the continental scale and to explore the spatial variation differences of each land type accuracy in different regions. In this study, the visual interpretation land cover results at 20,936 small watershed sampling units based on high-resolution remote sensing images were used as the reference data covering 65 countries in Asia, Europe, and Africa. The reference data were verified by field survey in typical watersheds. Based on that, the accuracy assessment of the FROM-GLC30 2017 dataset was carried out. The results showed (1) the area proportion of different land cover types in the FROM-GLC30 2017 dataset was generally consistent with that of the reference data. (2) The overall accuracy of the FROM-GLC30 2017 dataset was 72.78%, and was highest in West Asia–Northeast Africa, and lowest in South Asia. (3) Among all the seven land cover types, the accuracy of bareland and forest was relatively higher than that of others, and the accuracy of shrubland was the lowest. The accuracy for each land cover type differed among regions. The results of this work can provide useful information for land cover accuracy assessment researches at a large scale and promote the further practical applications of the open-source land cover datasets. [ABSTRACT FROM AUTHOR]