The marital and fertility sentiment orientation of Chinese women and its influencing factors - An analysis based on natural language processing.

BACKGROUND: With the evolution of China's social structure and values, there has been a shift in attitudes towards marriage and fertility, with an increasing number of women holding diverse perspectives on these matters. In order to better comprehend the fundamental reasons behind these attitude changes and to provide a basis for targeted policymaking, this study employs natural language processing techniques to analyze the discourse of Chinese women. METHODS: The study focused on analyzing 3,200 comments from Weibo, concentrating on six prominent topics linked to women's marriage and fertility. These topics were treated as research cases. The research employed natural language processing techniques, such as sentiment orientation analysis, Word2Vec, and TextRank. RESULTS: Firstly, the overall sentiment orientation of Chinese women toward marriage and fertility was largely pessimistic. Secondly, the factors contributing to this negative sentiment were categorized into four dimensions: social policies and rights protection, concerns related to parenting, values and beliefs associated with marriage and fertility, and family and societal culture. CONCLUSION: Based on these outcomes, the study proposed a range of mechanisms and pathways to enhance women's sentiment orientation towards marriage and fertility. These mechanisms encompass safeguarding women and children's rights, promoting parenting education, providing positive guidance on social media, and cultivating a diverse and inclusive social and cultural environment. The objective is to offer precise and comprehensive reference points for the formulation of policies that align more effectively with practical needs.

Information Cocoons on Short Video Platforms and Its Influence on Depression Among the Elderly: A Moderated Mediation Model.

Background: As the elderly increasingly engage with new media, particularly short video platforms, concerns are arising about the formation of "information cocoons" that limit exposure to diverse perspectives. While the impact of these cocoons on society has been investigated, their effects on the mental well-being of the elderly remain understudied. Given the prevalence of depression among the elderly, it is crucial to understand the potential link between information cocoons and depression among older adults. Methods: The study examined the relationships between information cocoons and depression, loneliness, and family emotional support among 400 Chinese elderly people. The statistical software package SPSS was used to establish a moderated mediation model between information cocoons and depression. Results: Information cocoons directly predicted depression among the elderly participants. Family emotional support moderated the first half and the second half of the mediation process, whereby information cocoons affected the depression of the elderly through loneliness. Specifically, in the first half of the mediation process, when the level of information cocoons was lower, the role of family emotional support was more prominent. In the second half of the process, when the level of family emotional support was higher, such support played a more protective role in the impact of loneliness on depression. Discussion: The findings of this study have practical implications for addressing depression among the elderly population. Understanding the influence of information cocoons on depression can inform interventions aimed at promoting diverse information access and reducing social isolation. These results will contribute to the development of targeted strategies to improve the mental well-being of older adults in the context of evolving media landscapes.

An innovative coupling technique for integrating oil spill prediction model with finite volume method-based ocean model.

Marine oil spill pollution have increased recent years, threatening the safety of the marine environment. This paper proposes a coupling technique: Fast Mapping & Addressing(FMA) that integrates the oil spill model with oceanographic model. The FMA technique is based on hash function and spatial quadtree algorithm to achieve efficient addressing from an unstructured to a structured grid. The oil spill model simulates the oil spill process, while the ocean model simulates the ocean currents. The efficiency is improved about ten times compared to the interpolation algorithm. Results reveal the difference between the simulation results of the ocean model and the measured data is minimal, with an MAE(Mean Absolute Error) and RMSE(Root Mean Square Error) of about 0.06 m. Moreover, two oil spill events in China's nearshore were selected to simulate and verify the results. Indeed, our model's results agree with the observed data, demonstrating that our model can achieve a satisfied simulation of oil spill.

Research on sunken & submerged oil detection and its behavior process under the action of breaking waves based on YOLO v4 algorithm.

Marine oil spill pollution is one of the most serious marine pollution issues. Aiming at the problems of low accuracy and slow speed in the process of detecting the behavior of sunken and submerged oil by traditional methods, a technology of sunken & submerged oil tracking based on YOLO v4 (YOLO refers to 'you look only once') algorithm is proposed in this paper. The image data used in this study are pictures of real oil pollution moving under breaking waves, and they are collected in the laboratory. First, the YOLO v4 model under CSPDarknet53 framework was established. Then, in order to simplify the oil detection model and ensure the efficiency of the model, this research used Mosaic data enhancement, random flipping, and Gaussian noise fuzzy data enhancement, as well as Cosine Annealing Learning Rate, and Label Smoothing to improve the effect of deep learning model. After data enhancement, the final data set was divided into a training set and a test set proportionally. The training set had 878 pictures, and the test set had 1945 pictures. The test set contained the situation where oil droplets were completely occluded by waves, so that the detection accuracy was closer to the real situation. The results show that the oil droplet is hit and then sunk, forming 'sunken and submerged oil' under the action of breaking waves of wave heights of 10 cm, 15 cm, 20 cm, 25 cm and 30 cm. The submergence time enhances with the increase of wave height of breaking wave, that is, the residence time of oil droplet for 10 cm, 15 cm, 20 cm, 25 cm and 30 cm breaking waves is 2.32 s, 2.52 s, 2.62 s, 3.20s, 7.12 s, respectively. The deepest position of oil droplet under the water for 10 cm, 15 cm, 20 cm, 25 cm and 30 cm breaking waves is 0.165 m, 0.179 m, 0.226 m, 0.297 m, 0.428 m, respectively. However, the drift velocity and sinking velocity of oil droplet show nonlinear variation. The speed of sinking to the deepest is 0.208 m/s, 0.222 m/s, 0.212 m/s, 0.359 m/s, 0.303 m/s, respectively.

Prediction of Yangtze River streamflow based on deep learning neural network with El Niño-Southern Oscillation.

Accurate long-term streamflow and flood forecasting have always been an important research direction in hydrology research. Nowadays, climate change, floods, and other anomalies occurring more and more frequently and bringing great losses to society. The prediction of streamflow, especially flood prediction, is important for disaster prevention. Current hydrological models based on physical mechanisms can give accurate predictions of streamflow, but the effective prediction period is only about 1 month in advance, which is too short for decision making. The artificial neural network (ANN) has great potential for predicting runoff and is not only good at handling non-linear data but can also make long-period forecasts. However, most of ANN models are unstable in their predictions when faced with raw flow data, and have excessive errors in predicting extreme flows. Previous studies have shown a link between the El Niño-Southern Oscillation (ENSO) and the streamflow of the Yangtze River. In this paper, we use ENSO and the monthly streamflow data of the Yangtze River from 1952 to 2018 to predict the monthly streamflow of the Yangtze River in two extreme flood years and a small flood year by using deep neural networks. In this paper, three deep neural network frameworks are used: stacked long short-term memory, Conv long short-term memory encoder-decoder long short-term memory and Conv long short-term memory encoder-decoder gate recurrent unit. The results show that the use of ConvLSTM improves the stability of the model and increases the accuracy of the flood prediction. Besides, the introduction of ENSO to the experimental data resulted in a more accurate prediction of the time of the occurrence of flood peaks and flood flows. Furthermore, the best results were obtained on the convolutional long short-term memory + encoder-decoder gate recurrent unit model.

Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model.

The 2019 novel coronavirus (2019-nCoV) outbreak has been treated as a Public Health Emergency of International Concern by the World Health Organization. This work made an early prediction of the 2019-nCoV outbreak in China based on a simple mathematical model and limited epidemiological data. Combing characteristics of the historical epidemic, we found part of the released data is unreasonable. Through ruling out the unreasonable data, the model predictions exhibit that the number of the cumulative 2019-nCoV cases may reach 76,000 to 230,000, with a peak of the unrecovered infectives (22,000-74,000) occurring in late February to early March. After that, the infected cases will rapidly monotonically decrease until early May to late June, when the 2019-nCoV outbreak will fade out. Strong anti-epidemic measures may reduce the cumulative infected cases by 40%-49%. The improvement of medical care can also lead to about one-half transmission decrease and effectively shorten the duration of the 2019-nCoV.