Corn(Zea Mays L.) leaf spectrum was monitored at typical development stages with different rates of P supply by means of potted plant experiment.Correlation analysis was made between leaf spectral reflectance and leaf P content. The results showed that booting stage was the sensitive stage of spectral response and both 350-730 nm and 1420-1800 nm were the sensitive bands for P nutrition. Single band hyperspectral variables,narrow band spectral variables,broad band spectral variables and ratio vegetation index(RVI) were constructed at booting stage.And linear regression analysis was performed between those variables and leaf P content.The results of regression analysis showed that significant or most significant regression relationships existed between single band hyperspectral variables,narrow band spectral variables,broad band spectral variables and leaf P content.Regression relationship between leaf P content with R725/625 and R1745/1585 reached most significant level,then R625/555 was significant level. Moreover,regression relationships were better for visible band than near-infrared band.It was convinced that leaf reflectance in visible band was suitable for evaluating P nutrition condition compared with near-infrared band.By analyzing spectral variables of different band width,it was obvious that leaf spectral reflectance of 80-100 nm width did not decrease the precision of P content estimation compared with narrower width in the region of sensitive bands.
Financial institutions, investors, central banks and relevant corporations need an efficient and reliable forecasting approach for determining the future of crude oil price in an effort to reach optimal decisions under market volatility. This paper presents an innovative research framework for precisely predicting crude oil price movements and interpreting the predictions. First, it compares six advanced machine learning (ML) models, including two state-of-the-art methods: extreme gradient boosting (XGB) and the light gradient boosting machine (LGBM). Second, it selects novel data, including user search big data, digital currencies and data on the COVID-19 epidemic. The empirical results suggest that LGBM outperforms other alternative ML models. Finally, it proposes an interpretable framework for facilitating decision making to interpret the prediction results of complex ML models and for verifying the importance of various features affecting crude oil price. The results of this paper provide practical guidance for participants in the crude oil market.
Polyaniline (PANI), a promising conducting polymer for supercapacitor, exhibits high specific capacitance and good rate capability. However, it suffers from low cycling stability due to the breakage or scission of polymer chains and loss of contact caused by the volume change during the charge–discharge, as well as the irreversible oxidation and reduction. Here, a strategy for using aniline tetramers loaded on graphene oxide (AT‐GO) is developed to prevent chain breaking and increase the tolerance of volume change. The potential window is also controlled to reduce the irreversible reactions. In a three electrode test, AT‐GO exhibits a good cycling stability with specific capacitance remaining more than 93 to 96% after 2000 cycles. In a two electrode test, the specific capacitance remains 97.7% of its initial specific capacitance after 2000 cycles by suppressing the side reactions. AT‐GO also shows a high specific capacitance of more than 769 F g −1 at 1 A g −1 and it remains 581 F g −1 at 60 A g −1 , suggesting a good rate capability. These results suggest that AT‐GO is a promising electrode material for practical applications.
Named entity recognition of military equipment is an important task in the construction of knowledge graph in the military domain. It is a key technical means to improve the intelligence degree of military intelligence information retrieval, intelligence analysis, command and decision. There are many problems in the task of named entity recognition in the field of military equipment, such as fuzzy entity boundary, complex grammar structure and many professional words, which directly lead to the loss of accuracy in named entity recognition. Aiming at the above challenges, this paper proposes a BERT- BILSTM -CRF neural network model based on type labeling and part-of-speech labeling of entities in the field of military equipment. BERT's pre-trained language model fully considers the correlation between words when constructing word vectors, which is used to supplement the semantic relations embedded in words, and can solve the fuzzy problem in name entity recognition. BILSTM layer is used to carry out bidirectional semantic coding, which can solve the long-distance dependence problem. Finally, the output of BERT-BILSTM layer is decoded by CRF layer, and the optimal tag sequence is obtained. The experimental results show that compared with CRF model and BILSTM-CRF model, the F1 value of the proposed model is increased by 10%.Compared with the BILSTM-Attention-CRF model, the F value of this model increased by 10.48%, and the recall rate increased by 15.02%.Compared with the BERT-IDCNN-CRF model, the F value of this model is increased by 0.62%, and the recall rate is increased by 4.55%.
The paper briefly introduced the network platform for creative education in our country and the design idea of the generic network platform for undergraduate's creative education.The main functional modules have been designed.Finally discussed the key technologies used for the development of the network platform in this paper.The using of generic network platform for undergraduate 's creative education can provide a communication platform for creative activities across time and space,also be benefit to the undergraduate's creative talents.
Objective This systematic review and meta-analysis aimed to evaluate the prevalence and influencing factors of fertility concerns in breast cancer in young women. Methods A literature search on PubMed, Embase, Web of Science, and Cochrane Library databases was conducted up to February 2023 and was analyzed (Revman 5.4 software) in this study. The papers were chosen based on inclusion standards, and two researchers independently extracted the data. The included studies’ quality was evaluated using criteria set out by the Agency for Healthcare Research and Quality. To identify significant variations among the risk factors, odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) were utilized. Results A total of 7 studies that included 1579 breast cancer in young women were enrolled in the study. The results showed that for breast cancer in young women, the incidence of fertility concerns 53%(95% CI [0.45,0.58]). The results showed that education (2.65, 95% CI 1.65–5.63), full-time work (0.12, 95% CI 1.03–1.93), fertility intentions (7.84, 95% CI 1.50–37.4), depression level (1.25, 95% CI 1.03–1.5), and endocrine therapy (1.32, 95% CI 1.08–1.62) were risk factors for fertility concerns in young women with BC. Having a partner (0.41, 95% CI 0.33–0.5), ≥1 child (0.3, 95% CI 0.22–0.4) were identified as protective factors against fertility concerns in young women with BC. Conclusions The incidence of fertility concerns in breast cancer in young women is at a moderately high level. We should pay more attention to the risk factors of fertility concerns to help breast cancer in young women cope with their fertility concerns and promote their psychological well-being.
Fertility concerns are a pervasive issue but very subtle in patients with cancer. Though various studies have focused on fertility concerns, limited research endeavor has been dedicated to bibliometric analysis. Given this, to visually analyze the hot frontier trends of research related to fertility concerns of patients with cancer using CiteSpace and provide new insights for future research in this field using the bibliometric method. We used CiteSpace software to retrieve the literature related to fertility concerns of patients with cancer in the Web of Science core collection database from the year of establishment to 2022 and conducted visual analysis in terms of authors, countries and regions, research institutions, and keywords. The search resulted in 201 valid articles, and the annual publication volume of literature related to fertility concerns in patients with cancer was generally on the rise; the country with the most publications was the United States, which also had the highest influence; the main research institution was Sloan Kettleson Cancer Research Center; the core research scholar was Jessica R. Gorman; the research hotspots mainly centered on quality of survival, women, survivorship, preservation, breast cancer, adolescence, and infertility. The results of this bibliometric study provide the current status and trends in the fertility concerns of patients with cancer and may help researchers identify the hotspots and frontier trends in this field.
In edge computing, the signal of the 5th generation (5G) base station is often required to be covered by edge servers to guarantee a high transmission rate. And the smaller the total distance between the 5G base stations and the edge servers is, the faster the transmission rate is. Therefore, we need to study the optimal deployment of edge servers and the cloud data center. Firstly, the cluster analysis is used to cluster the 5G base stations and obtain the cluster centers, then the edge servers are deployed in the cluster centers. Secondly, the centroid method is used to determine the location of the cloud data center. By adopting the elbow and gap statistic methods, we also study the optimal number of edge servers to be deployed. Computer simulation results show that our methods provide better deployment locations for the edge servers and the cloud data center.
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