Taking Xi’an metro station as an example, we analyze the artistic methods of metro public space to explain and spread the ancient rhyme culture, modern industrial civilization, modern revolutionary culture and other regional cultures, and put forward the concept of further innovative design in terms of increasing the occupancy, enlarging the pattern, highlighting the cultural characteristics of the ancient capital, and strengthening the comprehensive utilization of the metro public space in multilevel and multi-carrier. The study first establishes a multi-objective metro public space configuration model based on genetic algorithm to realize the reasonable layout of metro public space. Then it proposes a style migration method of regional cultural elements based on the improved circular consistency generating adversarial network, and realizes the color migration under multiple reference objects to realize the interface and color design of the entrances and exits of the metro public space, the station hall level and the platform level. The results of user experience show that the overall public space of Xi’an Metro Line 4 has an intermediate centrality of 8.8216 and an intermediate centrality potential of 0.5859, and its overall suitability is relatively balanced.

In order to promote the development of medical rehabilitation industry, the study deeply analyzes flexible wearable devices and utilizes joint moment estimation based on skeletal muscle model in order to calculate the joint moments of elbow and wrist joints, so as to carry out the design of flexible pneumatic wrist joint system. And a fuzzy-PI dual-mode control strategy is used in the position control of the flexible pneumatic wrist joint to construct an intelligent flexible rehabilitation device for the wrist joint. The wrist joint rehabilitation equipment is systematically tested to analyze its practical application effect. The response speed of the fuzzy-PI dual-mode control method is faster than that of the traditional PID control strategy, and it can effectively reduce the vibration noise. The accuracy of the hybrid recognition method in this paper is 97%, which is better than the single recognition model. The average time taken by the wrist rehabilitation device on the seven tasks of lifting, grasping, undertaking, pulling, pushing, probing down and probing up is between 2.06 and 2.67 seconds. The output moments of the wrist and elbow positions were 17.1 and 11.6 N.m respectively for the human body-worn wrist joint rehabilitation device with 50N driving force output, and the joint output moments decreased significantly, and the joint comfort of the human body was improved greatly.

Currently, visible and infrared image fusion (VIF) technology has a wide range of applications in road safety monitoring, anti-surveillance, etc. However, the traditional image fusion algorithms in the feature fusion process will have limitations such as part of the information is lost, etc. For this reason, this paper proposes an infrared visible image fusion algorithm based on the double-branching and decomposition of the results. The algorithm firstly adopts the dense block method, extracts visible image features, and uses a feature pyramid network to extract infrared features. The algorithm firstly adopts the dense block method to extract the visible image features, and uses the feature pyramid network to extract the infrared features, then, based on the deep learning network structure to extract the image information of different modalities, and designs the fusion network constrained by the three loss functions of the gradient loss, intensity loss and decomposition loss, so as to obtain a good fusion effect of the image. The experimental results show that the proposed algorithm achieves the optimal value in five indexes, and reaches sub-optimal value in one index, indicating that the proposed algorithm fuses the images with the optimal value and sub-optimal value. At the same time, the proposed algorithm retains the main thermal radiation information of infrared images better than other algorithms such as DenseFuse and IFCNN, which is superior to some extent.

Aiming at the RRT* algorithm in unmanned aerial vehicle (UAV) path planning, there are problems such as poor target bias, slow convergence speed, and tortuous path. This paper introduces an improved Bi-Informed-RRT* algorithm (BPD-APF-Informed-FARRT*), integrating a dual-path balancing operation strategy, a partition-biased sampling strategy, an artificial potential field guidance approach, and a fuzzy adaptive step-size strategy. To begin, the third point between the start and target points is chosen as the middle point, allowing four random trees to be generated at the same time at the start, target, and middle points, hence resolving the delayed convergence issue. Second, the artificial potential field method and the partition-biased sampling strategies are employed in both path generation and optimization phases to guide the placement of new nodes, tackling issues with poor target bias. Then, to address the intricacies of global environments, a fuzzy adaptive step size adjustment strategy is incorporated to boost the exploration efficiency of the growing tree in complex obstacle scenarios. Finally, leveraging the principle of triangular inequality, redundant nodes are removed, and the path is refined using the B-spline curve. Path planning simulation experiments were performed using MATLAB software. The results show that BPD-APF-Informed-FARRT* has more significant advantages in many ways compared with the Bi-RRT*, Informed-RRT*, and Bi-InformedRRT* algorithms. This improved algorithm is a practical and feasible method for solving similar problems.

Globally, tackling climate change and promoting low-carbon development has become a universally accepted course of action. self20century90Since the 1990s, human understanding of climate change has gradually deepened, and a scientific theoretical system and systematic governance framework have been constructed. In order to deeply understand the research status and development stage of carbon emission reduction path, this paper systematically combs and analyzes the relevant literature. This study was selected based on the Web of Science core collection database 2006 Year to 2023 Published by global scholars 8679 the English documents were used as research samples, and CiteSpace software was used to conduct an in-depth visual analysis of the number of published papers, the group of core authors, the distribution of research institutions, published journals, keywords and key areas of research. The results reveal that research in the field of carbon emissions in1991Before 200 years, it was still in its infancy, and then the number of literature increased year by year. The main research institutions are distributed in China, the United States and the United Kingdom, and the research in this field involves environmental science, engineering, environmental science, economics and other disciplines. In addition, climate change and energy development and rational utilization are the two major research hotspots in this field. The study results indicate that: (1) During the study period, the research of carbon emission reduction path received high attention, and the publication volume of relevant literature continued to increase and the growth rate increased significantly. (2) Through the analysis of the author group, it is found that a clear core author group has not been formed in the field of carbon emission reduction path. (3) The main research forces are concentrated in China, the United Kingdom, the United States and other countries, among which 13 research institutions including the Chinese Academy of Sciences and Tsinghua University have shown significant research ability and influence, and the cooperation between the research institutions and the authors is very close.(4) In terms of published journals, yes10The international journals show high attention to the field of carbon emission reduction, covering environmental science and ecology, energy and fuel, environmental engineering, economics and public management and other fields. Through the visual analysis of keywords, it is found that energy transition, microstructure research and carbon emission are the key objects of current research. Based on the analysis results and the actual situation, this paper puts forward the focus and direction of carbon emission reduction path research, aiming to provide theoretical and practical reference for the realization of carbon emission reduction targets.

The rapid development of artificial intelligence technology provides new tools to optimize the design and application of site-specific integrases and drive innovation in this field. In this study, a site-specific integrase generation model based on artificial intelligence was designed. The learning effect of the model to generate site-specific integrase is improved by mining sequence data of site-specific integrase with feature selection and discretization, and then using generative adversarial network as a framework to extract the detail information of protein sequences by using convolutional layer, and extracting the global features of sequences by using self-attention layer. In addition, to address the degradation problem during training, a residual structure module is constructed and spectral normalization is used to ensure training stability. Meanwhile, Gumbel Softmax Trick is used to solve the problem of non-returnable gradient of discrete data generated by the model. The sequence of the site-specific integrase generated by the model showed 92% identity with the training set, which has better sequence quality. In terms of amino acid composition, the Pearson value with the natural amino acid composition was greater than 0.8, and the two were highly correlated. The site-specific integrase can increase the expression of bax protein and decrease the expression of bax-2 protein and Ki67 protein in lung cancer patients, which is favorable for patient treatment. It can up-regulate the expression of ovarian STAR, CYP11A1, CTP19A1, and 3β-HSD genes and promote steroidogenesis in ewes. The alkane content of the group of strains incorporating site-specific integrase was 57.25%~63.00% lower than that of those without the enzyme in a high concentration of petroleum pollution environment.

Based on the excellent achievements of deep learning technology in image recognition in several fields, then the convolutional neural network is expected to play its superior performance in the recognition of micro features of Chinese herbal medicines. The object of analysis in this paper is the microscopic feature images of Chinese herbal medicines, and the residual network will be improved in order to improve the recognition accuracy of the network model on the microscopic images of herbs. On the basis of the traditional CNN network model, CBAM based on mixed domain attention mechanism is added, and residual connection is introduced to increase the transfer of gradient and information flow, preserve image feature data and reduce feature loss. Improved from the traditional residual structure to moving inverted bottleneck convolution (MBConv), the SE module and SAM module are added to the MBConv stage respectively to optimize the feature extraction performance and improve the accuracy of the classification of microscopic features of traditional Chinese medicine. The effect of the addition of the attention mechanism on the network model is analyzed, and the network model is examined in conjunction with the constructed dataset of powdered microscopic images of commonly used Chinese medicinal herbs.The average accuracy of the Attention-TCM-Net network model on the test set reaches 96.47%, which is an improvement of 0.85 percentage points compared with that of the ResNet34 network, and meanwhile, the convergence of the model is significantly better than other models.

With the gradual standardization, professionalization and standardization of the senior care service industry, the construction of the talent team to grasp the senior care service has become an important part of the high-quality development in the senior care service. It is worth noting that at the present stage, there is a serious shortage of nursing staff in China’s elderly care institutions, unreasonable distribution of age structure, disproportionate gender ratio, low cultural level, lagging comprehensive quality, and weak professional ability and skills. To address the above problems, based on the study of relevant information and using the questionnaire survey method, survey interviews were conducted on the nursing staff of the all-weather work mode in the nursing institutions of Hospital C and Hospital F, as well as the nursing staff of the shift work mode in the nursing center of Hospital J and the nursing institution of Hospital L, in order to grasp the personnel arrangements, the nursing staff team, and other professional trainings of the four nursing institutions. Further, comparing the 24/7 care work pattern of the nursing institutions with the shift system work pattern and the difference in burnout found that the shift system work pattern is relatively better and can reduce the burnout of caregivers in their caregiving work. Elderly caregiver work is labor-intensive content, and the burnout resulting from a heavy workload will increase the instability and uncertainty of the caregiver’s career. In order to avoid this phenomenon, we should strengthen the detection and prevention of burnout among nursing staff in nursing institutions, and actively carry out heart health counseling, goal planning and professional knowledge training for nursing staff in the shift system, so as to comprehensively improve the comprehensive quality of nursing staff in the shift system, and thus contribute to the high-quality development of nursing institutions.

Control techniques of Smart windows using Multi-parameter neural feedforward systems as a control strategy shows great potential in improving not only the energy efficiency geometrically but also the building’s indoor environmental quality. In this study, a new smart window control is developed that is based on neural networks which are able to implement multi control strategies in various conditions with regard to temperature, humidity, light and air quality. This allows for a further development of the system: firstly, it thoroughly presents the model, which facilitates the understanding of the mathematic modeling of windows’ dynamic position and, at the same time, shows how the neural network works. The structure comprises a perception layer, which provides perception of the environment, processing layer for analysis and decision making on the input data, and the last action layer that performs windows’ actuation and gives feedback on the action implemented. In terms of the system’s control efficiency, timing, energy consumption and seeking users’ satisfaction, the performance of this control system outperforms other existing systems in empirical application. The control accuracy attained in the proposed system is 97.8%. What is more interesting about this approach is the energy efficiency which stands at 94.3%, this is only the bare minimum, estimation says it surpasses the rest by a great deal. The successful realization of this control system is an important step toward the development of smart buildings that can be relied on for excellent results.

As the digital economy develops, the use of digital methods for the storage of financial documents is being commonly adopted. To protect the data security of financial documents and ensure the traceability of data, this article designed a secure storage and traceability system for financial documents based on blockchain. Firstly, a blockchain platform with a private chain that had better security and scalability was selected, and the data structure and smart contract design were then carried out. Secondly, an identity authentication and permission management mechanism was established, and data storage and transaction processing modules were designed. Asymmetric encryption was used to secure data, and the digital signatures were combined to ensure the integrity of financial documents. Finally, the traceability function was achieved through the immutability of blockchain technology. The task of storage and traceability was accomplished by designing a secure storage and traceability system for financial documents in conjunction with the blockchain technology. In the experiment, it only took about 50 seconds to process traceability tasks with an interval of 50 people, which was shorter than the traditional system’s 180 seconds; it also maintained an accuracy rate of over 90% in traceability tasks with an interval of 100 people; in the face of 1000 network attacks in a short period of time, the financial management system based on blockchain technology was only invaded 20 times, while the traditional financial system was invaded 200 times. This system, in terms of time, traceability accuracy, and data security, were all improved over the traditional system. The design of a secure storage and traceability system for financial documents based on blockchain technology is conducive to strengthening the security of data and the accuracy of traceability.