Due to the impact of artificial intelligence generated content technologies such as AlphaGo and ChatGPT, the governance of intelligent science and technology becomes a significant concern all over the world. This review addressed related issues by integrating traditional public safety management with new artificial intelligence (AI) technologies and provided an alternative philosophy and approach for safety, security and sustainability in the future society of AI.

Given the limitations of traditional theater production processes, which heavily rely on expert experience and manual labor, the theater industry faces a pressing demand for a transition from a paradigm primarily rooted in individual expertise and traditional competencies to one that harnesses intelligent technology and data-driven approaches. This transition aims to enhance production and operational efficiency while elevating artistic expression to address the diverse preferences of audiences more effectively. Within this context, this paper proposes the concept of "parallel theaters", specifically designed to facilitate collaborative human-machine creativity and intelligent management across the entire theater workflow. By establishing a comprehensive theater model, creating a computational experimentation platform for theatrical art and enabling a virtual-to-real feedback loop, the "parallel theaters" contributes to the intelligent and automated design and execution of critical theater production and management aspects, such as script writing, stage design, performance coordination, audience engagement and marketing. In addition, the paper explores the potential cross-disciplinary applications of "parallel theaters" in education and psychotherapy, analyzing its ability to foster the integration of the theater industry with other fields, thereby expanding the influence of the performing arts. As an avant-garde model for theater creation and management, "parallel theaters" opens new avenues for the industry in the intelligence era, promising enhanced efficiency, enriched artistic expression and a more diverse audience engagement.

Financial budget management is an integral part of corporate governance, critically important for managing, controlling, coordinating and assessing internal resources. However, many enterprises still face challenges in financial budgeting, including unscientific target setting, slow execution control and ineffective budget outcome prediction. Although the advancement of digital and intelligent technologies offers strong support for mitigating these issues, it fail to address them fundamentally. Parallel intelligence theory, by creating artificial systems parallel to the real one and leveraging their virtual-real feedback and parallel execution, can realize description, prediction and prescription of financial budgeting, thereby enhancing the scientific accuracy of budget targets, the controllability of the budget process and the reliability of budget outcomes. This paper combined parallel intelligence theory with financial budgeting to propose a new paradigm of parallel financial budgeting. It provided a viable technological framework and theoretical guidance for the deep integration of business and finance as well as intelligent services supported by it in complex environments, fostering intelligent transformation and innovation in corporate financial budget management. The paper delineated the concept of parallel financial budgeting, presented its fundamental framework, and discussed its key characteristics and supporting technologies.

From its historical origin to present function, education is an inspiration and integral part of our quest for artificial intelligence(AI). We must ensure that teaching and education should be positioned as the first and most important field for research and development of intelligent science and technology. Otherwise increasingly complicated AI technology would not only remain unexplainable but also become ungovernable, and be manipulated easily by some minority of our human beings to harm the majority of us. Considering the current impact to all aspects of our society at the global level by ChatGPT and likewise artificial intelligence-generated content(AIGC)/artificial general intelligence(AGI) technologies, hereby we investigate a new approach to effectively deploy intelligent technology, especially digital teachers and parallel schools, for intelligent teaching and smart education. We hope this would lead to a paradigm shift in teaching and education for the benefits of humanity, and safe, healthy, sustainable development of our shared human community.

The allocation of combat resources on the future battlefield is a multi-stage confrontation problem with total resource budget constraints, which is characterized by high complexity of environment, dynamic uncertainty, and strong game confrontation. Based on the Blotto game model, the research firstly modelled the resource allocation problem in the multi-stage confrontation scenario as a two-level online Blotto game, then transformed the original problem into an online shortest path problem on a directed acyclic graph to realize the intuitive formulation of the resource allocation problem. The resource allocation problem was analyzed and solved by referring to the Lagrange game. In addition, the LagrangeBwK-Exp3-G algorithm was proposed to minimize the high probability regret of the resource allocation problem under the condition of multi-stage antagonism, and the high-probability regret bound of the algorithm on the time range T was obtained by mathematical derivation. Finally, a multi-channel power allocation experiment of satellite communication under the condition of multi-stage confrontation was designed to verify the good performance of LagrangeBwK-Exp3-G algorithm.

Most existing visual SLAM systems assume that the external environment is static, ignoring the influence of dynamic objects on the SLAM system. This assumption largely affects the accuracy and robustness of autonomous navigation. To address this issue, a dynamic SLAM system was proposed, which combined semantic information based on object detection and geometric information from multi-view geometry constraints by defining and discriminating the dynamic feature points in the system based on the moving probability. Experiment results on the public TUM dataset and our robot in real environment showed that, when comparing with ORB-SLAM2, the absolute trajectory error could be reduced larger than 94%, and the average relative position and attitude errors were reduced at least 41% and 40%, respectively, in high dynamic environments. It means that the proposed SLAM system effectively removes dynamic feature points, thus improving the localization accuracy and robustness of the visual SLAM system within high dynamic environments.

A robust control method based on extremum seeking is proposed for the attitude tracking problem of quadrotor UAV with uncertainty. Firstly, a nonlinear attitude model of a four rotor UAV was established, and considering the uncertainty of model parameters, a robust controller was designed to ensure the dynamic boundedness of tracking errors. Secondly, a learning-based controller was designed by combining a robust controller with a model-free learning algorithm, which automatically and iteratively adjusted the feedback gain of the robust controller and optimized the expected performance cost function online. Finally, numerical simulations were performed using MATLAB, which demonstrated a reduction of 0.246 in the steady-state tracking error of the proposed control method compared to the classical robust control method. This result confirms the robustness and superiority of the proposed method.

The 6G network covers the entire space, air, ground, and sea. For diversified and personalized scenarios, the 6G network needs to provide customized services, that is, on-demand services. In order to realize on-demand services in all domains and scenarios, accurate, real-time, and intelligent cognition of the characteristics of the scenarios is an important prerequisite. How to enable the network to autonomously and intelligently recognize different scenarios and services, convert them into scenario-specific network key performance indicator (KPI), and further efficiently schedule network resources is a key problem that urgently needs to be solved. This paper applies the knowledge graph to the cognitive recognition of network scenarios, forms a standardized description of 6G network scenarios, and builds a knowledge graph based on the 6G scenario ontology. At the same time, a scene cognition reasoning method based on knowledge graph embedding is proposed, which realizes the embedding learning of graph nodes and relationships and reasons about scene feature nodes, achieving high accuracy. The method proposed in this paper helps to realize the autonomous control of the service life cycle of scene awareness, cognition, and on-demand services in the 6G full-scenario network, and has important innovation and guiding significance for improving the autonomy and intelligence of the next-generation network.

Breast tumors adversely affect the holistic well-being of women. Histopathological images are a critical substantiation for doctors to diagnose breast tumor types. The structure of various types of tumor cells exhibits significant correlations, thereby posing challenges to the diagnosis using conventional methods. In this work, the enhanced EfficientNet was employed for the diagnosis of breast tumors, which enabled the network model to learn the features of the disease automatically and improve the accuracy of the diagnosis of breast tumor types. Firstly, the convolutional block attention module was used to extract effective features. Secondly, the group convolution and channel shuffle operations were introduced to improve the feature representation ability of the model. Thirdly, the Hard-Swish activation function was applied to improve the convergence speed of the model. Finally, Experiments showed that the improved EfficientNet network achieved 98.4% accuracy in eight classifications on the BreakHis dataset, which was expected to act a decision aid tool in breast tumor diagnostic research.

Due to the problems of traditional material transfer equipment such as single working mode and inflexible adjustment of transfer path, intelligent material transfer system has gradually become a research hotspot in the field of logistics transmission. A path planning algorithm based on A* and dynamic window method was proposed for the modular material transmission platform, in order to improve its obstacle avoidance ability during the transfer process by flexibly adjusting the material transfer path. In the global path planning, the smoothness and static obstacle avoidance of the transmission path were realized by improving the A* weight function and integrating the Bezier curve and matrix interference theory. And by introducing the dynamic window method and extracting the global path key points as transition points for local path guidance of the transmission target, dynamic obstacle avoidance was realized when the path falling into local optimization was avoided. The research results showed that the path planning algorithm based on A* and dynamic window method could reduce the total global path length by 4.6% and the total path turning angle by 42.3%, while the dynamic obstacles could be effectively avoided in the local planning, which verified the rationality of the path planning algorithm.

Aiming at the problems of unbalanced brightness of lung cell images and achieving accurate segmentation of abnormal cell contour difficultly, an abnormal cell segmentation model based on U-Net was proposed, which combined the dense connection mechanism and attention mechanism. Firstly, U-Net with encoder-decoder structure was used to segment abnormal cells. Secondly, the dense block was introduced into U-Net to improve the propagation ability between features and extract more characteristic information of abnormal cells. Finally, the attention mechanism was used to increase the weight of abnormal cell regions and reduce the interference of the imbalance of brightness to the model. The experimental results show that the IoU value and Dice similarity coefficient achieved by this method are 0.6928 and 0.8060, respectively. Compared with other models, this proposed method is able to segment low-contrast regions and abnormal cells with diverse shapes.

YOLOv3 algorithm is characterized by high detection accuracy and high speed in the aspect of target detection and recognition. It is outstanding among similar algorithms, but there are still obvious problems. The loss operation of the wide and high part of the loss function of this algorithm is not obvious to the distinction between the big target and the small target, which leads to the problem that the loss calculation is not accurate enough and the prediction box size of the small target is not accurate. In view of this defect, the loss function of YOLOv3 algorithm was improved, the wide-height coordinate error was modified into the form of proportion, the non-maximum suppression method of the original model was improved and the overlap threshold was changed from the fixed value to the form of attenuation function. Finally, the model was applied to vehicle detection. The experimental results showed that the improved model improved the accuracy rate, recall rate and F1 value to different degrees while basically not affecting the vehicle detection speed, making the overall performance of the model better than the original YOLOv3 model.

Due to varying degrees of overlap in point cloud models, point cloud registration is prone to problems, such as feature matching errors and high difficulty in registration. Therefore, a point cloud registration method based on principal component analysis and feature map matching is proposed. Before registration, the principal component analysis method with spindle correction was used to adjust the initial pose, then the K-dimensional tree was established to search the overlapping area. Secondly, the fast point feature histograms features of the sampling points were calculated according to the overlapping area of the two-point cloud, and the point cloud feature graph matching and trimmed iterative closest point (TrICP) fine registration were performed. Registration experiments were carried out according to the existing datasets and the actual scanning model. The experimental results show that the method has good stability and higher accuracy, and the accuracy can be improved by more than 25% compared with other algorithms.