Search

IntroductionThis study aims to explore the relationship between healthcare and future education among the rural low-income population, using J City in Guangdong Province as the focal area. Addressing both healthcare and educational concerns, this research seeks to provide insights that can guide policy and support for this demographic.MethodsUtilizing big data analysis and deep learning algorithms, a targeted intelligent identification classification model was developed to accurately detect and classify rural low-income individuals. Additionally, a questionnaire survey methodology was employed to separately investigate healthcare and future education dimensions among the identified population.ResultsThe proposed model achieved a population identification accuracy of 91.93%, surpassing other baseline neural network algorithms by at least 2.65%. Survey results indicated low satisfaction levels in healthcare areas, including medical resource distribution, medication costs, and access to basic medical facilities, with satisfaction rates below 50%. Regarding future education, issues such as tuition burdens, educational opportunity disparities, and accessibility challenges highlighted the concerns of rural low-income families.DiscussionThe high accuracy of the model demonstrates its potential for precise identification and classification of low-income populations. Insights derived from healthcare and education surveys reveal systemic issues affecting satisfaction and accessibility. This research thus provides a valuable foundation for future studies and policy development targeting rural low-income populations in healthcare and education.

To address the issue of efficiently reusing the massive amount of unstructured knowledge generated during the handling of track circuit equipment faults and to automate the construction of knowledge graphs in the railway maintenance domain, it is crucial to leverage knowledge extraction techniques to efficiently extract relational triplets from fault maintenance text data. Given the current lag in joint extraction technology within the railway domain and the inefficiency in resource utilization, this paper proposes a joint extraction model for track circuit entities and relations, integrating Global Pointer and tensor learning. Taking into account the associative characteristics of semantic relations, the nesting of domain-specific terms in the railway sector, and semantic diversity, this research views the relation extraction task as a tensor learning process and the entity recognition task as a span-based Global Pointer search process. First, a multi-layer dilate gated convolutional neural network with residual connections is used to extract key features and fuse the weighted information from the 12 different semantic layers of the RoBERTa-wwm-ext model, fully exploiting the performance of each encoding layer. Next, the Tucker decomposition method is utilized to capture the semantic correlations between relations, and an Efficient Global Pointer is employed to globally predict the start and end positions of subject and object entities, incorporating relative position information through rotary position embedding (RoPE). Finally, comparative experiments with existing mainstream joint extraction models were conducted, and the proposed model’s excellent performance was validated on the English public datasets NYT and WebNLG, the Chinese public dataset DuIE, and a private track circuit dataset. The F1 scores on the NYT, WebNLG, and DuIE public datasets reached 92.1%, 92.7%, and 78.2%, respectively.

In order to enhance the operational efficiency of the healthcare industry, this paper investigates a medical information diagnostic platform through the application of swarm and evolutionary algorithms. This paper begins with an analysis of the current development status of medical information diagnostic platforms based on Chat Generative Pre-trained Transformer (ChatGPT) and Internet of Things (IoT) technology. Subsequently, a comprehensive exploration of the advantages and disadvantages of swarm and evolutionary algorithms within the medical information diagnostic platform is presented. Further, the optimization of the swarm algorithm is achieved through reverse learning and Gaussian functions. The rationality and effectiveness of the proposed optimization algorithm are validated through horizontal comparative experiments. Experimental results demonstrate that the optimized model achieves favorable performance at the levels of minimum, average, and maximum algorithm fitness values. Additionally, preprocessing data in a 10 * 10 server configuration enhances the algorithm’s fitness values. The minimum fitness value obtained by the optimized algorithm is 3.56, representing a 3 % improvement compared to the minimum value without sorting. In comparative experiments on algorithm stability, the optimized algorithm exhibits the best stability, with further enhancement observed when using sorting algorithms. Therefore, this paper not only provides a new perspective for the field of medical information diagnostics but also offers effective technical support for practical applications in medical information processing.

As the digital revolution deepens, the digital economy (DE) is reshaping the global industrial structure (IS). This paper utilizes data from 280 Chinese cities between 2007 and 2020 to conduct an in-depth analysis of how DE propels the upgrading of IS and explores the role of human capital (HC) in this process. The research indicates that DE significantly fosters the optimization of IS. Additionally, it was discovered that the growth of HC plays a pivotal mediating role in this evolution, complementing existing research on the relationship between higher education and industrial upgrading. By adopting spatial econometric methods, this study unveils the spillover effects of DE in geographical space, identifying a positive influence on the industrial upgrading of surrounding regions. The paper also confirms the nonlinear characteristics of DE's impact on IS upgrading, which manifests as a pronounced inverted U-shaped trend, marking a novel discovery. Further findings suggest that in regions with more advanced artificial intelligence technologies, the impact of DE on industrial optimization is more significant, highlighting the role of regional disparities in the digital transformation. In conclusion, the paper proposes policy recommendations based on the research findings to facilitate the development of DE and the elevation of IS levels, thereby promoting high-quality economic growth.

Frequency-selective rasorbers (FSRs), which have absorptive and transmissive properties at different frequencies, are crucial in a wide range of applications in communication and radar engineering. However, the FSRs currently available usually exhibit a single high-performance transmission band, which cannot meet the rising demands for modern multiband communication/radar systems. In this article, we propose a dual-band frequency-selective rasorber that utilizes a cascaded metasurface and is designed with an equivalent circuit model and parameter optimization. In addition, a prototype dual-band frequency-selective rasorber is fabricated and characterized. Microwave measurements present two highly transparent peaks located near 11.5 and 17.5 GHz with an insertion loss of around 0.5 dB. Also, there are two absorption bands with absorptivities higher than 80% at 3.9–10.2 GHz and 13–15 GHz. Such low insertion loss for both transparent bands is achieved by optimizing the position of resistors through mode analysis. Our approach facilitates the manufacture of high-performance multi-band frequency-selective rasorbers for use in multi-band communication systems.

[ Objectives ] This study was conducted to investigate the effects of seeding rate and light on the seed yield and active ingredients of Verrwnia anthelmintica. [Methods J With V. anthelmintica L. Willd as the research object, the seed yield was determined by staged harvesting combined with the actual cutting and measuring method; and the caffeoylquinic acids ( CQA) were used as the characterization indexes, and high perlormance liquid chromatography was used to determine the contents of 3,4-CQA, 3,5-CQA and 4,5-CQA of V. anthelmintica seeds under different seeding rate+ light treatments, so as to compare the changes in seed yield and chemical composition of V. anthelmintica under different seeding rates and light factors. [ Results] In Moyu County, Hetian and similar areas, the suitable planting rate of V. anthelmintica was in the range of 13. 5 -15. 0 kglhm2 . For different harvesting purposes, the optimal seeding rate and light conditions of V. Anthelmintica were different. [ Conclusions] The seeding rate and light conditions can be used to interlere with the seed yield and quality of V. anthelmintica. [ABSTRACT FROM AUTHOR]

[Objectives] This study aimed to investigate the effect of sowing rate, fertilizer amount and light on yield of Cichorium glandulosum Boiss.et Huet. [Methods]The Latin square design was adopted to investigate the effect of sowing rate + fertilizer amount, sowing rate +light on root and seed yield of C. glandulosum Boiss.et Huet. The changes in the seed and root yield of C. glandulosum Boiss. et Huet. under the influence of sowing rate,N application rate and light were compared and analyzed.[Results]In Moyu, Hotan and similar areas, the suitable sowing rate of C. glandulosum Boiss.et Huet. was 4.5-15.0 kg/ha. Without applying fertilizer, if the main purpose is to harvest the roots, the sowing rate of C. glandulosum Boiss.et Huet.is recommended to be 7.5-15.0 kg/ha; and if the main purpose is to harvest the seeds, the sowing rate is recommended to be 4.5-7.5 kg/ha. Shading will affect the root and seed yield. At the fertilizer amount of 450 kg/ha, the optimal sowing rate of C. glandulosum Boiss. et Huet.is 4.5 kg/ha.[Conclusions] This study provides a certain reference basis for artificial cultivation of C. glandulosum Boiss.et Huet. [ABSTRACT FROM AUTHOR]

[ Objectives J This study aimed to investigate the effect of sowing rate, fertilizer amount and light on yield of Ciclwriwn glandul~ sum Boiss. et Huet. [Methods] The Latin square design was adopted to investigate the effect of sowing rate + fertilizer amount, sowing rate + light on root and seed yield of C. glandulosum Boiss. et Huet. The changes in the seed and root yield of C. glanduloswn Boiss. et Huet. under the influence of sowing rate, N application rate and light were compared and analyzed. [Results] In Moyn, Hotan and similar areas, the suitable sowing rate of C. glGJ1duloswn Boiss. et Huet. was 4. 5 - 15. 0 kg/ha. Without applying fertilizer, if the main purpose is to harvest the roots, the sowing rate of C. gl.anduwswn Boiss. et Huet. is recommended to be 7. 5 -15. 0 kg/ha; and if the main purpose is to harvest the seeds, the sowing rate is recommended to be 4. 5 - 7. 5 kg/ha. Shading will affect the root and seed yield. At the fertilizer amount of 4 50 kg/ha, the optimal sowing rate of C. glanduloswn Boiss. et Huet. is 4. 5 kg/ha. [Conclusions] This study provides a certain reference basis for artificial cultivation of C. glandulosum Boiss. et Huet [ABSTRACT FROM AUTHOR]