Container Terminal Berth-Quay Crane Capacity Planning Based on Markov Chain

This paper constructs a berth-quay crane capacity planning model with the lowest average daily cost in the container terminal, and analyzes the influence of the number of berths and quay cranes on the terminal operation. The object of berth-quay crane capacity planning is to optimize the number of berths and quay cranes to maximize the benefits of the container terminal. A steady state probability transfer model based on Markov chain for container terminal is constructed by the historical time series of the queuing process. The current minimum time operation principle (MTOP) strategy is proposed to correct the state transition probability of the Markov chain due to the characteristics of the quay crane movement to change the service capacity of a single berth. The solution error is reduced from 7.03% to 0.65% compared to the queuing theory without considering the quay crane movement, which provides a basis for the accurate solution of the berth-quay crane capacity planning model. The proposed berth-quay crane capacity planning model is validated by two container terminal examples, and the results show that the model can greatly guide the container terminal berth-quay crane planning

Investigation and Research on Physical Education and Health Curriculum of K-12 School in Guizhou Province

The purpose of this study is to investigate the current situation of physical education and health curriculum in primary and secondary schools in Guizhou Province, and to provide reference for promoting the better implementation of physical education and health curriculum in Guizhou Province. In the form of questionnaires, 1549 parents\u27 questionnaires and 254 teachers\u27 questionnaires were collected and statistically analyzed in Guizhou Province, China. Use Excel to summarize and analyze the collected questionnaires. The results found the teaching content could basically meet the needs of students. The satisfaction of primary school students, junior high school students and senior high school students with physical education and health curriculum evaluation was 71.6%, 68.4% and 63.6%, respectively. Students\u27 satisfaction with the content of physical education and health curriculum in senior high school decreased; both students and teachers believed that all students had the opportunity to participate in sports activities in physical education and health classes, but the time for skill learning and physical training in PE classes in primary and secondary schools was less than 20 minutes. The intensity of classroom exercise in 60% of primary and secondary schools was less than 75%. 94.1% of teachers control exercise load according to experience, and only 3.9% of schools use intelligent monitoring devices to monitor. 50.9% of primary and junior high school physical education classes did not meet the required number of class hours. 69.6% of the students were satisfied with the elective items in the physical education courses offered, but their satisfaction with the senior high school dropped to 61.6%. Primary and secondary schools in Guizhou Province should continue to increase the construction and investment of physical education and health curriculum venues, equipment and facilities, and optimize the use and development of existing physical education curriculum resources. Physical education teachers should constantly update teaching concepts, improve teaching methods and improve course teaching ability. Schools and teachers should carry out physical education and health courses according to the requirements of physical Education and Health Curriculum Standards, and actively promote the Chinese Health physical Education Curriculum Model put forward by JI Liu professor to ensure a certain exercise load and exercise density

Chiral Decomposition of Twisted Graphene Multilayers with Arbitrary Stacking

We formulate the chiral decomposition rules that govern the electronic structure of a broad family of twisted $N+M$ multilayer graphene configurations that combine arbitrary stacking order and a mutual twist. We show that at the magic angle in the chiral limit the low-energy bands of such systems are composed of chiral pseudospin doublets which are energetically entangled with two flat bands per valley induced by the moir\'e superlattice potential. The analytic analysis is supported by explicit numerical calculations based on realistic parameterization. We further show that applying vertical displacement fields can open up energy gaps between the pseudospin doublets and the two flat bands, such that the flat bands may carry nonzero valley Chern numbers. These results provide guidelines for the rational design of various topological and correlated states in generic twisted graphene multilayers.Comment: 6 pages, 4 figure

Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning

Self-supervised learning is an efficient pre-training method for medical image analysis. However, current research is mostly confined to specific-modality data pre-training, consuming considerable time and resources without achieving universality across different modalities. A straightforward solution is combining all modality data for joint self-supervised pre-training, which poses practical challenges. Firstly, our experiments reveal conflicts in representation learning as the number of modalities increases. Secondly, multi-modal data collected in advance cannot cover all real-world scenarios. In this paper, we reconsider versatile self-supervised learning from the perspective of continual learning and propose MedCoSS, a continuous self-supervised learning approach for multi-modal medical data. Unlike joint self-supervised learning, MedCoSS assigns different modality data to different training stages, forming a multi-stage pre-training process. To balance modal conflicts and prevent catastrophic forgetting, we propose a rehearsal-based continual learning method. We introduce the k-means sampling strategy to retain data from previous modalities and rehearse it when learning new modalities. Instead of executing the pretext task on buffer data, a feature distillation strategy and an intra-modal mixup strategy are applied to these data for knowledge retention. We conduct continuous self-supervised pre-training on a large-scale multi-modal unlabeled dataset, including clinical reports, X-rays, CT scans, MRI scans, and pathological images. Experimental results demonstrate MedCoSS's exceptional generalization ability across nine downstream datasets and its significant scalability in integrating new modality data. Code and pre-trained weight are available at https://github.com/yeerwen/MedCoSS

Attention Mechanisms in Medical Image Segmentation: A Survey

Medical image segmentation plays an important role in computer-aided diagnosis. Attention mechanisms that distinguish important parts from irrelevant parts have been widely used in medical image segmentation tasks. This paper systematically reviews the basic principles of attention mechanisms and their applications in medical image segmentation. First, we review the basic concepts of attention mechanism and formulation. Second, we surveyed over 300 articles related to medical image segmentation, and divided them into two groups based on their attention mechanisms, non-Transformer attention and Transformer attention. In each group, we deeply analyze the attention mechanisms from three aspects based on the current literature work, i.e., the principle of the mechanism (what to use), implementation methods (how to use), and application tasks (where to use). We also thoroughly analyzed the advantages and limitations of their applications to different tasks. Finally, we summarize the current state of research and shortcomings in the field, and discuss the potential challenges in the future, including task specificity, robustness, standard evaluation, etc. We hope that this review can showcase the overall research context of traditional and Transformer attention methods, provide a clear reference for subsequent research, and inspire more advanced attention research, not only in medical image segmentation, but also in other image analysis scenarios.Comment: Submitted to Medical Image Analysis, survey paper, 34 pages, over 300 reference

Polymer-stabilized blue phase liquid crystal with a negative Kerr constant

A polymer-stabilized blue-phase liquid crystal (BPLC) with a negative Kerr constant is reported. In a voltage-on state, the double-twist BPLC molecules within the lattice cylinders are reoriented perpendicular to the applied electric field because of their negative dielectric anisotropy. As a result, the induced birefringence has a negative value, which leads to a negative Kerr constant. The negative sign of Kerr constant is experimentally validated by using a quarter-wave plate and a vertical field switching cell. Such a BPLC shows a negligible (similar to 1%) hysteresis and fast response time (similar to 1ms) at the room temperature, although its Kerr constant is relatively small because the employed host has a small Delta epsilon