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.

This study aims to build a framework called Tucker Reasoning Learning Method to train the upper layer knowledge graph (KG) making explainable and reasonable decisions. The numerical experiments show that the accuracy is 84%. The contributions are as follows: (i) It overcomes in-explainable problems of using deep learning method; (ii) It has more feedback rings and reasonable paths than decision tree method; (iii) Compared with RESCAL’s application in reasoning domain, it enhances 22 percentage points. It is suitable for application scenarios like financial, justice, and medical decision-making, which require explainable and reasoning paths. This study builds a framework called Tucker Reasoning Learning Method to train the upper layer knowledge graph to make explainable and reasonable decisions. The method has the accuracy of 84%, which enhances 22 percentage points compared to the SOTA methods.

Like money or gold, data has emerged as a new class of economic commodity. Big data is now a factor of production on par with other material resources, having permeated every aspect of today’s economy and society. Social development inevitably leads to population aging, which affects all facets of social life, particularly social and economic development. Nevertheless, systematic and thorough study on how population aging affects economic development is still lacking. The economic and fiscal policy trade-offs of aging on economic growth are the main emphasis of this article, which is based on big data techniques. This study examines the effects of population aging on economic development from the perspectives of economic growth, social security, and financial pension expenses, based on an analysis of the current state of population aging and its drivers. It was designed to address the aging of province A’s population and discovered that it not only caused the share of the working-age population to decrease, but also decreased the resources available to the labor force. The proportion of tax revenue in total fiscal revenue will continue to be over 82% by 2021, with 73% of the population being between the ages of 15 and 64. The scale of fiscal pension expenditures in Province A has shown a clear upward trend.

In recent years, tourism has rapidly developed into a key economic sector, with travel route recommendation algorithms playing a vital role in enhancing tourists’ experiences. These algorithms often utilize large amounts of travel data, social networks, and interest hotspots maps to recommend optimal routes. Social networks, as online platforms for communication and content sharing, help connect people, while interest hotspots maps visualize popular topics on social media. This paper proposes a tourism route recommendation algorithm based on social networks and interest hotspots maps, combining tourist preferences and scenic spot data. By analyzing tourist needs and scenic spot conditions, the algorithm improves route recommendations, reducing analysis time and increasing accuracy. Research results show that, before using this algorithm, tourists rated travel time, routes, and attractions at 87.25, 86.84, and 88.62 points, respectively. After using the travel route recommendation algorithm, tourists’ satisfaction was 95.76 points, 96.48 points and 92.89 points respectively. These results can showed that the travel route recommendation algorithm can improve the satisfaction of tourists, and that the research of travel route recommendation algorithm based on social networks and interest hotspots map was of practical value. This also provided a new research path for tourism route recommendation technology.