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ObjectiveTo examine temporal and spatial distribution patterns and influencing factors of human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) epidemic among adolescents in Hubei province, and to predict prevalence trend of the epidemic for providing reference to HIV/AIDS epidemic prevention and control in the adolescents. MethodsThe data on registered HIV/AIDS incident cases aged 15 – 24 years in Hubei province from 2009 through 2019 were collected from Hubei Provincial Center for Disease Control and Prevention and analyzed statistically with spatial autocorrelation, geographically weighted regression, geographic detector, and autoregressive integrated moving average (ARIMA) model. Results The HIV/AIDS incidence among the adolescents in the province increased rapidly during the period, with an average annual growth rate of 16.2%. The HIV/AIDS epidemic was the most serious among adolescents living in the central and eastern part of Hubei province, especially in Wuhan city and its surrounding counties and the epidemic spread gradually across the province. Regions with stable high-high clustering of the epidemic were observed in urban Wuhan and its surrounding areas. The regions with high clustering of the epidemic distributed generally from northwest to southeast of the province during the period. The incidence of HIV/AIDS in the adolescents was affected by natural and social factors and there were differences in the intensity and spatial distribution of the factors including traffic network density, population density, the number of schools, number of medical and health institutions, urbanization rate, altitude, and gross domestic product per capita ranking in downward order of the effect intensity of the factor. Both the population density and the traffic network density have a positive effect on the epidemic. The interaction between influencing factors plays an important role in promoting the spread of the epidemic. When any two influencing factors interact with each other, their influence will be enhanced interactively or nonlinearly. Based on the result of ARIMA analysis, the HIV/AIDS epidemic could be more serious among the adolescents in the province during 2020 – 2023, especially in urban Wuhan and some other prefectures. ConclusionThe HIV/AIDS epidemic was spreading among adolescents in Hubei province during 2009 – 2019, suggesting that relevant education and intervention should be strengthened in the population.

Analyzing the future changes in runoff is crucial for efficient water resources management and planning in arid regions with large river systems. This paper investigates the future runoffs of the headwaters of the Tarim River Basin under different emission scenarios by forcing the hydrological model SWAT-Glacier using six regional climate models from the Coordinated Regional Downscaling Experiment (CORDEX) project. Results indicate that compared to the period of 1976~2005, temperatures are projected to increase by 1.22 ± 0.72 °C during 2036~2065 under RCP8.5 scenarios, with a larger increment in the south Tianshan mountains and a lower increment in the north Kunlun Mountains. Precipitation is expected to increase by 3.81 ± 14.72 mm and 20.53 ± 27.65 mm during 2036–2065 and 2066–2095, respectively, under the RCP8.5 scenario. The mountainous runoffs of the four headwaters that directly recharge the mainstream of the Tarim River demonstrate an overall increasing trend in the 21st century. Under the RCP4.5 and RCP8.5 scenarios, the runoff is projected to increase by 3.2% and 3.9% (amounting to 7.84 × 108 m3 and 9.56 × 108 m3) in 2006–2035. Among them, the runoff of the Kaidu River, which is dominated by rainfall and snowmelt, is projected to present slightly decreasing trends of 3~8% under RCP4.5 and RCP8.5 scenarios. For catchments located in the north Kunlun Mountains (e.g., the Yarkant and Hotan Rivers which are mix-recharged by glacier melt, snowmelt, and rainfall), the runoff will increase significantly, especially in summer due to increased glacier melt and precipitation. Seasonally, the Kaidu River shows a forward shift in peak flow. The summer streamflow in the Yarkant and Hotan rivers is expected to increase significantly, which poses challenges in flood risk management. [ABSTRACT FROM AUTHOR]

It is of great significance to study and analyze the surface water resources and their change trend in the groundwater overexploitation area of the North China Plain, which is of great significance to solve the shortage of water resources in the groundwater overexploitation area of the North China Plain, promote the exploitation of groundwater, and realize the sustainable development of water resources. This paper takes Minyou Irrigation District of Handan City, a typical overexploitation area in the North China Plain, as an example. Based on the measured rainfall and runoff data from 1957 to 2020, the Mann–Kendall trend test, cumulative anomaly method, double cumulative curve method, and Morlet wavelet transform were used to analyze and predict the trend of water resources in the irrigation area and the individual contribution of climate change and human activities to runoff change. The results show that the annual rainfall and annual runoff in the irrigation area have a significant downward trend and significant cyclical changes throughout the study period. In 1977, the annual runoff showed a sudden change, and the average contribution rates of climate change and human activities to its change were 40.55% and 59.46%, respectively. In the future (2020–2035), runoff will remain stable and rainfall will show an increasing trend. The research results can provide scientific reference for the development, utilization, and rational allocation of surface water resources in the groundwater overexploitation area of the North China Plain. [ABSTRACT FROM AUTHOR]

Aiming at the portfolio problem of gold and bitcoin with a given linear trading commission, this paper puts forward the stage implementation forecast and optimal portfolio model. In the aspect of data prediction, SMA is used to predict the initial data, LSTM is used to predict the price trend of long-term data, and daily updated real-time price data is predicted. Considering the risk aversion of investors, the heuristic algorithm is used to solve the daily trading strategy of maximizing utility from September 12th, 2016 to September 12th, 2021. The simulation analysis of the sliding window shows that the algorithm can realize reasonable prediction, which verifies the effectiveness of the algorithm.

The temporal and spatial evolution pattern of ecosystem sensitivity has been one of the most important components to restore and improve ecosystem stability, together with the driving factors and future development trends. The Loess Plateau is one of the typical areas with the most severe soil erosion in the world. Alternatively, the implementation of the "Grain for Green" project in 1999 has significantly improved the ecological environment. It is a high demand to examine the suitability of ecological governance measures using the temporal and spatial evolution characteristics of ecological sensitivity in the Loess Plateau over the past 20 years. In this study, an ecological sensitivity index system was constructed to couple the spatial distance index and the entire array-polygon indicator under the Driving-Pressure-State-Impact-Response framework. The temporal and spatial patterns and their driving forces of ecological sensitivity were investigated at five-time points from 2000 to 2020 in the Loess Plateau using a geographic information evolution map and a geographic detector. The CA-Markov model was used to simulate the ecological sensitivity for ecological zoning in 2030. The results showed that: 1) Spatially, the ecological sensitivity of the Loess Plateau exhibited a distribution pattern of high in the northwest and low in the southeast, with the high sensitivity types decreasing towards the southeast direction and the aggregation weakening, while the low sensitivity types increased towards the northwest direction, but the aggregation strengthened at the original position. Temporally, there was a varying trend of first increasing and then decreasing. 2) The ecological sensitivity transfer types were mainly fluctuating stability. In individual sensitivity types, the insensitive transfer type was mainly stability, while the low, moderate, and severe sensitive transfer types were mainly fluctuating stability, and the extremely sensitive transfer type was mainly reduction. The largest area proportion of the ecological improvement zone (35.17%) was mainly distributed in the western part of the Loess Plateau. The areas with 0 and 1 changes accounted for 62.06%, indicating a stable and improving trend in the ecological environment. 3) The most significant effect of the natural factors on the ecological sensitivity among the driving factors. The NDVI, precipitation, and their interaction posed a significant driving effect on ecological sensitivity. 4) The ecological sensitivity can be expected as the distribution pattern of high in the northwest and low in the southeast in 2030, where the low sensitivity will be the main sensitive type. The key areas of ecological protection were obtained as the sandy land, agricultural irrigation region, and the mid-west of the loess sorghum gully region. Overall, the ecological environment of the Loess Plateau has improved after the ecological governance measures. This study can provide a strong reference for the ecological environment and high-quality development in the Loess Plateau. [ABSTRACT FROM AUTHOR]

Time series forecasting covers a wide range of topics, such as predicting stock prices, estimating solar wind, estimating the number of scientific papers to be published, etc. Among the machine learning models, in particular, deep learning algorithms are the most used and successful ones. This is why we only focus on deep learning models. Even though it is a hot topic, there are only a few comprehensive studies, and in many studies, there is not much detail about the tested models, which makes it impossible to constitute a comparison chart. Thus, one of the main motivations for this work is to present comprehensive research by providing details about the tested models. In this study, a corpus of the asked questions and their metadata were extracted from the software development and troubleshooting website. Then, univariate time series data were created from the frequency of the questions that included the word "python" as the tag information. In the experiments, deep learning models were trained on the extracted time series, and their prediction performances are presented. Among the tested models, the model using convolutional neural network (CNN) layers in the form of wavenet architecture achieved the best result. [ABSTRACT FROM AUTHOR]

The Yellow River has long been known for having low water and abundant sediment. The amount of sediment transported in the upper and middle reaches of the Yellow River (UMRYR) has changed significantly in recent years, resulting in an obvious imbalance in the spatiotemporal distribution of the water resources in the Yellow River Basin (YRB). The changes in the sediment transport in the Yellow River significantly affect ecological security and socioeconomic development in the YRB. In this study, the Google Earth Engine (GEE) platform was used to obtain the potential driving factors influencing the five main gauge stations in the UMRYR: vegetation, soil moisture, population, precipitation, land types, etc. The data on the annual sediment transport (AST) were from the River Sediment Bulletin of China (2001~2020). Linear regression and the Mann–Kendall test were used to study the temporal variation in the AST. The first-order difference was determined from the original data to remove the autocorrelation, and it met the requirement of sample independence. The factors without collinearity were used for the driving force analysis using linear regression (linear model) and random forest regression (nonlinear model). We used the selected driving factors to establish the linear regression, the random forest model for predicting the AST, and cross-validation for verifying the prediction accuracy. Furthermore, the prediction outcomes were compared with the simplest ARIMA time-series model (control model). Our findings showed that the changing trend and the mutation of the AST were different in the UMRYR during the past 20 years. However, after the first-order difference of the AST, the amount of interannual variation in the annual sediment transport (ΔAST) was almost unchanged in the UMRYR. The five driving factors were chosen to establish the prediction models of linear regression and random forest regression, respectively. Compared with the control model, ARIMA, the prediction accuracy of the random forest model was the highest. [ABSTRACT FROM AUTHOR]

This study aims to clarify the vulnerability characteristics of the ecological–economic–social system of oasis city clusters in arid zones, promote the deepening of research on the sustainable development of urban clusters, and provide crucial practical reference significance for solving the series of problems brought about by urbanization. This article takes the arid zone oasis city cluster, the Urumqi–Changji–Shihezi urban agglomeration, as the research object and constructs an indicator system from three dimensions of ecological environment, regional economic, and social development, and adopts the comprehensive index method, GeoDetector, the GM(1, 1) gray prediction model, and other methods to study the vulnerability pattern and spatial and temporal changes of the urban cluster from 2009 to 2018. The results show that (1) from 2009 to 2018, the change in the integrated ecological–economic–social system vulnerability index of the Urumqi–Changji–Shihezi urban agglomeration shows a general downward trend, followed by significant differences in the vulnerability of each dimension, with an average vulnerability index of 1.8846, 1.6377, and 0.9831 for the social vulnerability, regional economic, and ecological environment dimensions, respectively; (2) the evolution of the spatial pattern of changes in the vulnerability index of different systems in each region of the Urumqi–Changji–Shihezi urban agglomeration tends to change from large to slight spatial differences, in which the social and ecological environmental vulnerability changes are more prominent in addition to the vulnerability changes of the regional economy; (3) parkland area per capita, arable land area per capita, GDP per capita, social fixed asset investment, population density, and urban road area per capita are the main drivers of decreasing vulnerability of ecological–economic–social systems in urban agglomerations; (4) by predicting and calculating the vulnerability index of each region of the ecological–economic–social system of urban agglomerations, it is found that the vulnerability index of urban agglomerations will show a decreasing trend from 2009 to 2018, and the difference of the vulnerability index between systems will narrow; (5) finally, targeted countermeasures and suggestions to reduce the vulnerability of ecological–economic–social systems are proposed to provide scientific references for the sustainable development of arid oasis cities. [ABSTRACT FROM AUTHOR]

Disruptive technologies are related to a country's competitiveness and international status. Accurately identifying and predicting the trends in disruptive technologies through scientific methods can effectively grasp the dynamics of technological development, adjust the national science and technology strategic layout, and better seize the high ground in international competition. Based on patent text data, this paper uses the improved LDA2Vec model combined with relevant indicators to identify the main topics in disruptive technologies, and predicts and analyzes the development trend through the establishment of an ARIMA model. Taking the energy technology field as an example, the main topics and development trends concerning disruptive technologies in this field are obtained. The study found that ten technologies, including energy storage technology, energy internet management technology, and offshore wind energy technology, are disruptive technologies in the energy technology field, and the development speed of energy storage technology is the fastest. To verify the correctness of the conclusion, this paper compares the results with artificial verification methods such as expert interviews and document verification, and finds that the two are basically consistent, thus verifying the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Financial time series have been extensively studied within the past decades; however, the advent of machine learning and deep neural networks opened new horizons to apply supercomputing techniques to extract more insights from the underlying patterns of price data. This paper presents a tri-state labeling approach to classify the underlying patterns in price data into up, down and no-action classes. The introduction of a no-action state in our novel approach alleviates the burden of denoising the dataset as a preprocessing task. The performance of our labeling algorithm is experimented with using machine learning and deep learning models. The framework is augmented by applying the Bayesian optimization technique for the selection of the best tuning values of the hyperparameters. The price trend prediction module generates the required trading signals. The results show that the average annualized Sharpe ratio as the trading performance metric is about 2.823, indicating the framework produces excellent cumulative returns. [ABSTRACT FROM AUTHOR]

Stock markets are a popular kind of financial markets because of the possibility of bringing high revenues to their investors. To reduce risk factors for investors, intelligent and automated stock market forecast tools are developed by using computational intelligence techniques. This study presents a hyperparameter optimal genetic programming-based forecast model generation algorithm for a-day-ahead prediction of stock market index trends. To obtain an optimal forecast model from the modeling dataset, a differential evolution (DE) algorithm is employed to optimize hyperparameters of the genetic programming orthogonal least square (GpOls) algorithm. Thus, evolution of GpOls agents within the hyperparameter search space enables adaptation of the GpOls algorithm for the modeling dataset. This evolutionary hyperparameter optimization technique can enhance the data-driven modeling performance of the GpOls algorithm and allow the optimal autotuning of user-defined parameters. In the current study, the proposed DE-based hyper-GpOls (DEHypGpOls) algorithm is used to generate forecaster models for prediction of a-day-ahead trend prediction for the Istanbul Stock Exchange 100 (ISE100) and the Borsa Istanbul 100 (BIST100) indexes. In this experimental study, daily trend data from ISE100 and BIST100 and seven other international stock markets are used to generate a-day-ahead trend forecaster models. Experimental studies on 4 different time slots of stock market index datasets demonstrated that the forecast models of the DEHypGpOls algorithm could provide 57.87% average accuracy in buy–sell recommendations. The market investment simulations with these datasets showed that daily investments to the ISE100 and BIST100 indexes according to buy or sell signals of the forecast model of DEHypGpOls could provide 4.8% more average income compared to the average income of a long-term investment strategy. [ABSTRACT FROM AUTHOR]

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Agro-Environment Science is the property of Journal of Agro-Environment Science Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Water quality plays an important role in river habitats. This study revealed the annual and seasonal variations and trend prediction of water quality in the middle Yangtze River after the third impoundment stage of the Three Gorges Reservoir. Multivariate statistical methods including principal component analysis/factor analysis (PCA/FA), Mann–Kendall (M–K) tests, discriminant analysis (DA), rescaled range (R/S) analysis, and the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) were used. Herein, eight water quality constituents including pH, electrical conductivity (EC), chloride (Cl), dissolved oxygen (DO), ammonia nitrogen (NH3N), total phosphorus (TP), water temperature (T), and permanganate index (CODmn) were monthly monitored in the Jiujiang hydrological transaction during 2010–2019. The information of eight water quality constituents, related to salinity, nutrient status, and oxidation reactions efficiency, was extracted. Water quality status remained as fair-good during 2010–2019 based on the results of CCME-WQI, with the seasonal significance ranked as T > DO > Cl > pH > EC > TP > NH3N > CODmn. In the future decade, annual average T was predicted to continue to increase although it might decrease in the wet season. EC was predicted to continue increasing annually especially in the wet season while Cl might decrease. NH3N and TP might maintain a significant decreasing trend in the future wet and dry seasons. DO maintained significantly increasing especially in the future dry seasons, whereas CODmn will continue to decrease annually and seasonally. The continued alkalization trend of waterbody was predicted, which is more significant in the wet season. The results provide helpful references for the ecological protection of the middle Yangtze River. [ABSTRACT FROM AUTHOR]

As an important equipment in the power system, the operating state of the power transformers is related to the safe operation of the entire system. It is of great importance to determine the transformer state and take countermeasures in advance. Dissolved gas analysis (DGA) is one of the most common methods for state evaluation of the oil-immersed transformer. Therefore, a time series forecasting model based on Seasonal Autoregressive Integrated Moving Average (SARIMA) model is presented to forecast the dissolved gas concentration in transformer oil in this paper. Firstly, the influence of the period parameter on the prediction effect and the correlation between the ambient temperature and the gas concentration are analyzed. Then, comparing the prediction results of the autoregressive (AR) model, the long short-term memory (LSTM) model and the SARIMA model on the trend component of the gas, it is found that the SARIMA model has the most accurate and stable performance. And the accuracy of the prediction results is higher when the external factors related to the prediction data are considered in the SARIMA model. Finally, the reliability of the SARIMA model for the recent prediction on actual data is verified. The case studies show that the SARIMA model can be the suitable method for predicting the future trend of the dissolved gas concentration in power transformer oil.

Abstract Background With the accelerated global integration and the impact of climatic, ecological and social environmental changes, China will continue to face the challenge of the outbreak and spread of emerging infectious diseases and traditional ones. This study aims to explore the spatial and temporal evolutionary characteristics of the incidence of Class B notifiable infectious diseases in China from 2007 to 2020, and to forecast the trend of it as well. Hopefully, it will provide a reference for the formulation of infectious disease prevention and control strategies. Methods Data on the incidence rates of Class B notifiable infectious diseases in 31 provinces, municipalities and autonomous regions of China from 2007 to 2020 were collected for the prediction of the spatio-temporal evolution and spatial correlation as well as the incidence of Class B notifiable infectious diseases in China based on global spatial autocorrelation and Autoregressive Integrated Moving Average (ARIMA). Results From 2007 to 2020, the national incidence rate of Class B notifiable infectious diseases (from 272.37 per 100,000 in 2007 to 190.35 per 100,000 in 2020) decreases year by year, and the spatial distribution shows an “east-central-west” stepwise increase. From 2007 to 2020, the spatial clustering of the incidence of Class B notifiable infectious diseases is significant and increasing year by year (Moran’s I index values range from 0.189 to 0.332, p

In this paper, we classify scientific articles in the domain of natural language processing (NLP) and machine learning (ML), as core subfields of artificial intelligence (AI), into whether (i) they extend the current state-of-the-art by the introduction of novel techniques which beat existing models or whether (ii) they mainly criticize the existing state-of-the-art, i.e. that it is deficient with respect to some property (e.g. wrong evaluation, wrong datasets, misleading task specification). We refer to contributions under (i) as having a ‘positive stance’ and contributions under (ii) as having a ‘negative stance’ (to related work). We annotate over 1.5 k papers from NLP and ML to train a SciBERT-based model to automatically predict the stance of a paper based on its title and abstract. We then analyse large-scale trends on over 41 k papers from the last approximately 35 years in NLP and ML, finding that papers have become substantially more positive over time, but negative papers also got more negative and we observe considerably more negative papers in recent years. Negative papers are also more influential in terms of citations they receive.

Landslides along the Three Gorges Reservoir in China pose a threat to coastal residents and waterway safety. To reduce false positive misjudgments caused by a sudden local change in the landslide deformation curve, in this paper, we propose an effective method for predicting the deformation trend of reservoir bank landslides. We take reservoir bank landslides in the Wanzhou District of the Three Gorges Reservoir area as the research object. The Time-Series Interferometric Synthetic Aperture Radar (InSAR) method and 62 Sentinel-1A images from 2018 to 2022 were selected for landslide deformation monitoring, and the Hurst index was calculated to characterize the deformation trend. Furthermore, we propose a method for predicting the deformation trend based on the statistical distribution of deformation rates and the physical significance of the Hurst index. After the field survey and Global Positioning System (GPS) verification, the Time-Series InSAR results are shown to be reliable. We take the Sifangbei landslide as a representative case to analyze the validation results. It is found that the determined Sifangbei landslide deformation trend is consistent with the conclusions for the region. In addition, the deformation trend of a reservoir bank slope has obvious spatial and temporal differences. Changes in the reservoir water level and concentrated rainfall play roles similar to those of catalysts. The proposed method, involving the combination of Time-Series InSAR and the Hurst index, can effectively monitor deformation and predict the stability trend of reservoir bank landslides. The presented research results provide new ideas and solutions for landslide prevention and risk mitigation in reservoir areas. [ABSTRACT FROM AUTHOR]

Aiming at the portfolio problem of gold and bitcoin with a given linear trading commission, this paper puts forward the stage implementation forecast and optimal portfolio model. In the aspect of data prediction, SMA is used to predict the initial data, LSTM is used to predict the price trend of long-term data, and daily updated real-time price data is predicted. Considering the risk aversion of investors, the heuristic algorithm is used to solve the daily trading strategy of maximizing utility from September 12th, 2016 to September 12th, 2021. The simulation analysis of the sliding window shows that the algorithm can realize reasonable prediction, which verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

The negative public opinions and views on overseas direct investment (ODI) of a multinational enterprise (MNE) will damage the image of its brand and are likely to bring it serious economic and social losses. So, it is important for the MNE to understand the formation and spread mechanism of public opinion risk (POR) in order to effectively respond to and guide the public opinion. This research proposed a multifractal-based situation optimization method to explore the POR evolution based on the media-based negative sentiment on China’s ODI. The sentiment measurement is obtained by a directed crawler for gathering the text of media reports corresponding to a certain ODI event using a URL knowledge base from the GDELT Event Database. Taking the public opinion crisis of the tax evasion incident of the local arm of China’s MNE in India as an example, the experiments show that this method could dynamically monitor the POR event in real-time and help MNE guide the effective control and benign evolution of public opinion of the event.

Tracking the trends and taking early steps accordingly is important in academia, as well as in other domains such as technology and finance. In this work, we focus on the problem of predicting the trending research topics from a collection of academic papers. Previous efforts model the problem in different ways and mostly apply classical approaches such as correlation analysis and clustering. There are also several recent neural model based solutions, however they rely on feature vectors and additional information for the trend prediction. In this work, given a collection of publications within the observation time window, we predict whether the use of a keyword will increase, decrease or be steady for the future time window (prediction window). As the solution, we propose a family of deep neural architectures that focus on generating summary representations for paper collections under the query keyword. Due to the sequence based nature of the data, Long Short-Term Memory (LSTM) module plays a core role, but it is combined with different layers in a novel way. The first group of proposed neural architectures consider each paper as a sequence of keywords and use word embeddings to construct paper collection representations. In this group, the proposed architectures differ from each other in the way year based and overall summary representations are constructed. In the second group, each paper is directly represented as a vector and the use of different paper embedding techniques are explored. The analyses of the models are performed on a variety of paper collections belonging to different academic venues, obtained from Microsoft Academic Graph data set. The experiments conducted against baseline methods show that proposed deep neural based models achieve higher trend prediction performance than the baseline models on the overall. Among the proposed models, paper embedding based models provide better results for most of the cases.

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: In the context of rapid economic and social development, there has been a continuous intensification of population aging, transformation of disease patterns, and wide application of new medical technologies. As a result, health expenditures in various countries have sharply soared. How to utilize limited medical resources to maximize the improvement of health levels has become a hot and challenging issue related to the well-being of all humanity. The relevant indicators of total health expenditure play a crucial role in monitoring and evaluating the fairness of health financing and health security in the region., Objective: This study explores the changes in the main expenses that constitute China's total health expenditure and uses indicators related to health expenditure to observe the changes and future development trends of China's health expenditure. Based on this, the utilization of China's health expenditure is monitored to identify possible problems, and thereby targeted suggestions for promoting the development of China's health and wellness cause are put forward., Methods: Based on the comparison of previous literature, this paper analyzes the changes and future development trends in China's health expenditure by using the relevant indicators of China's health expenditure through the structural variation analysis method and the gray prediction model., Results: The results show that the scale of government, social, and out-of-pocket health expenditures has continuously expanded, with social health expenditures becoming the main funding source for total health expenditures. The burden of medical expenditures on individuals has been further reduced. In the institutional method of total health expenditures, hospital expenditures account for about 60% of the total and are the main component. The expenditures of health administration and medical insurance management institutions are the main driving force behind the growth of total health expenditures. However, the proportion of health expenditures in China's GDP is relatively low, so more investment is needed in the healthcare sector, and the burden of individual medical expenses also needs to be continuously reduced., Discussion: In the future, China should further increase its investment in the medical and health sector. Specifically, the government should persist in investing in fundamental medical and health services. Simultaneously, efforts should be made to establish a scientific cost control mechanism for pharmaceuticals and broaden financing channels for healthcare, such as accelerating the development of commercial health insurance., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li and Zhang.)

We investigate the dynamics of music creation style distributions to understand cultural evolution involving intelligence to create complex artefacts. Previous work suggested that a music creation style can be quantified as statistics describing a generative process of music data, and that the distribution of music creation styles in a society has cluster structure related to the presence of different musical genres. To find patterns in the dynamics of the cluster structure, we analysed statistics of melodies in Japanese popular music data and statistics of audio features in American popular music data. Using statistical modelling methods, we found that intra-cluster dynamics, such as the contraction and the shift of a cluster, as well as inter-cluster dynamics represented by clusters’ relative frequencies, often exhibit notable dynamical modes. Additionally, to compare the individual contributions of these different dynamical aspects for predicting future creation style distributions, we constructed a fitness-based evolutionary model and found that the predictions of cluster frequencies and cluster variances often have comparable contributions. Our results highlight the relevance of intra-cluster dynamics in music style evolution, which have often been overlooked in previous studies. The present methodology can be applied to cultural artefacts whose generative process can be characterized by a discrete probability distribution.

Analyzing the future changes in runoff is crucial for efficient water resources management and planning in arid regions with large river systems. This paper investigates the future runoffs of the headwaters of the Tarim River Basin under different emission scenarios by forcing the hydrological model SWAT-Glacier using six regional climate models from the Coordinated Regional Downscaling Experiment (CORDEX) project. Results indicate that compared to the period of 1976~2005, temperatures are projected to increase by 1.22 ± 0.72 °C during 2036~2065 under RCP8.5 scenarios, with a larger increment in the south Tianshan mountains and a lower increment in the north Kunlun Mountains. Precipitation is expected to increase by 3.81 ± 14.72 mm and 20.53 ± 27.65 mm during 2036–2065 and 2066–2095, respectively, under the RCP8.5 scenario. The mountainous runoffs of the four headwaters that directly recharge the mainstream of the Tarim River demonstrate an overall increasing trend in the 21st century. Under the RCP4.5 and RCP8.5 scenarios, the runoff is projected to increase by 3.2% and 3.9% (amounting to 7.84 × 108 m3 and 9.56 × 108 m3) in 2006–2035. Among them, the runoff of the Kaidu River, which is dominated by rainfall and snowmelt, is projected to present slightly decreasing trends of 3~8% under RCP4.5 and RCP8.5 scenarios. For catchments located in the north Kunlun Mountains (e.g., the Yarkant and Hotan Rivers which are mix-recharged by glacier melt, snowmelt, and rainfall), the runoff will increase significantly, especially in summer due to increased glacier melt and precipitation. Seasonally, the Kaidu River shows a forward shift in peak flow. The summer streamflow in the Yarkant and Hotan rivers is expected to increase significantly, which poses challenges in flood risk management.

It is of great significance to study and analyze the surface water resources and their change trend in the groundwater overexploitation area of the North China Plain, which is of great significance to solve the shortage of water resources in the groundwater overexploitation area of the North China Plain, promote the exploitation of groundwater, and realize the sustainable development of water resources. This paper takes Minyou Irrigation District of Handan City, a typical overexploitation area in the North China Plain, as an example. Based on the measured rainfall and runoff data from 1957 to 2020, the Mann–Kendall trend test, cumulative anomaly method, double cumulative curve method, and Morlet wavelet transform were used to analyze and predict the trend of water resources in the irrigation area and the individual contribution of climate change and human activities to runoff change. The results show that the annual rainfall and annual runoff in the irrigation area have a significant downward trend and significant cyclical changes throughout the study period. In 1977, the annual runoff showed a sudden change, and the average contribution rates of climate change and human activities to its change were 40.55% and 59.46%, respectively. In the future (2020–2035), runoff will remain stable and rainfall will show an increasing trend. The research results can provide scientific reference for the development, utilization, and rational allocation of surface water resources in the groundwater overexploitation area of the North China Plain.

Background: With the accelerated global integration and the impact of climatic, ecological and social environmental changes, China will continue to face the challenge of the outbreak and spread of emerging infectious diseases and traditional ones. This study aims to explore the spatial and temporal evolutionary characteristics of the incidence of Class B notifiable infectious diseases in China from 2007 to 2020, and to forecast the trend of it as well. Hopefully, it will provide a reference for the formulation of infectious disease prevention and control strategies.Methods: Data on the incidence rates of Class B notifiable infectious diseases in 31 provinces, municipalities and autonomous regions of China from 2007 to 2020 were collected for the prediction of the spatio-temporal evolution and spatial correlation as well as the incidence of Class B notifiable infectious diseases in China based on global spatial autocorrelation and Autoregressive Integrated Moving Average (ARIMA).Results: From 2007 to 2020, the national incidence rate of Class B notifiable infectious diseases (from 272.37 per 100,000 in 2007 to 190.35 per 100,000 in 2020) decreases year by year, and the spatial distribution shows an "east-central-west" stepwise increase. From 2007 to 2020, the spatial clustering of the incidence of Class B notifiable infectious diseases is significant and increasing year by year (Moran's I index values range from 0.189 to 0.332, p < 0.05). The forecasted incidence rates of Class B notifiable infectious diseases nationwide from 2021 to 2024 (205.26/100,000, 199.95/100,000, 194.74/100,000 and 189.62/100,000) as well as the forecasted values for most regions show a downward trend, with only some regions (Guangdong, Hunan, Hainan, Tibet, Guangxi and Guizhou) showing an increasing trend year by year.Conclusions: The current study found that since there were significant regional disparities in the prevention and control of infectious diseases in China between 2007 and 2020, the reduction of the incidence of Class B notifiable infectious diseases requires the joint efforts of the surrounding provinces. Besides, special attention should be paid to provinces with an increasing trend in the incidence of Class B notifiable infectious diseases to prevent the re-emergence of certain traditional infectious diseases in a particular province or even the whole country, as well as the outbreak and spread of emerging infectious diseases. [ABSTRACT FROM AUTHOR]

Price trend prediction is critical across sectors, including finance, energy, and supply chains, guiding strategic decision-making. Machine Learning's rise, fueled by abundant data and computational power, has transformed trend prediction. Advanced algorithms enable precise forecasting. The Brazilian Energy Commercialization Platform shapes the nation's energy sector, influencing electricity contract trading. Despite opportunities, the Brazilian energy market faces liquidity challenges. This research introduces ensemble strategies utilizing diverse data sources and machine learning to enhance price trend prediction accuracy. One of the main contributions of this study is to investigate how the use of different data sources can improve the prediction of energy prices. The study considers Close-to-Close, Intraday, and Day Ahead trends, incorporating technical indicators, water flow rates, energy storage, precipitation, and load data. Employing stacking and performance-weighted averages, the proposed strategies aim to synergistically improve prediction accuracy and financial returns in the Brazilian energy market. [ABSTRACT FROM AUTHOR]

Predicting the price of Bitcoin poses a challenge for researchers, merchants, traders and investors alike. This paper delves into the analysis of a Bitcoin price and volume dataset, spanning from September 2014 to July 2023. The objective is to extract multiple features related to price volatility and employ them to forecast the Bitcoin price for the subsequent 7 days. To achieve this, an Ensemble Learning Method (ELM) is proposed, able to estimate prices in both bullish and bearish markets. For price prediction, we consider three categories of predictors: 1) Bitcoin historical data; 2) volatility indicators; 3) trend prediction (price up or down) obtained through binary classification. Further, we employ a combination of ensemble models (regressors and classifiers) to predict the price at the daily level. The predictions of these models are stacked and weighted by the proposed ELM to improve the forecast accuracy. The ELM is rigorously tested under various market scenarios, yielding results that demonstrate a noteworthy level of forecast accuracy. The period of 2021 stands out as particularly interesting for prediction due to several dramatic price swings. The ELM achieves a substantial 26% improvement in overall accuracy compared to the best-performing individual ensemble model. Throughout the entire year-2021, the Mean Absolute Error (MAE) stood at 319 USD, indicating a notably low MAE. [ABSTRACT FROM AUTHOR]

Time series forecasting covers a wide range of topics, such as predicting stock prices, estimating solar wind, estimating the number of scientific papers to be published, etc. Among the machine learning models, in particular, deep learning algorithms are the most used and successful ones. This is why we only focus on deep learning models. Even though it is a hot topic, there are only a few comprehensive studies, and in many studies, there is not much detail about the tested models, which makes it impossible to constitute a comparison chart. Thus, one of the main motivations for this work is to present comprehensive research by providing details about the tested models. In this study, a corpus of the asked questions and their metadata were extracted from the software development and troubleshooting website. Then, univariate time series data were created from the frequency of the questions that included the word “python” as the tag information. In the experiments, deep learning models were trained on the extracted time series, and their prediction performances are presented. Among the tested models, the model using convolutional neural network (CNN) layers in the form of wavenet architecture achieved the best result.

The Yellow River has long been known for having low water and abundant sediment. The amount of sediment transported in the upper and middle reaches of the Yellow River (UMRYR) has changed significantly in recent years, resulting in an obvious imbalance in the spatiotemporal distribution of the water resources in the Yellow River Basin (YRB). The changes in the sediment transport in the Yellow River significantly affect ecological security and socioeconomic development in the YRB. In this study, the Google Earth Engine (GEE) platform was used to obtain the potential driving factors influencing the five main gauge stations in the UMRYR: vegetation, soil moisture, population, precipitation, land types, etc. The data on the annual sediment transport (AST) were from the River Sediment Bulletin of China (2001~2020). Linear regression and the Mann–Kendall test were used to study the temporal variation in the AST. The first-order difference was determined from the original data to remove the autocorrelation, and it met the requirement of sample independence. The factors without collinearity were used for the driving force analysis using linear regression (linear model) and random forest regression (nonlinear model). We used the selected driving factors to establish the linear regression, the random forest model for predicting the AST, and cross-validation for verifying the prediction accuracy. Furthermore, the prediction outcomes were compared with the simplest ARIMA time-series model (control model). Our findings showed that the changing trend and the mutation of the AST were different in the UMRYR during the past 20 years. However, after the first-order difference of the AST, the amount of interannual variation in the annual sediment transport (ΔAST) was almost unchanged in the UMRYR. The five driving factors were chosen to establish the prediction models of linear regression and random forest regression, respectively. Compared with the control model, ARIMA, the prediction accuracy of the random forest model was the highest.

Aiming at the problem of insufficient stability of outlet moisture content in tobacco cut stem airflow drying, an integrated model combining trend prediction adjustment control and zoning feedback adjustment control was designed based on the original PID control model. The gap between the actual value and the set value of outlet moisture content was divided into several different intervals in this model, and a real-time control strategy was determined by the interval of gap lied in and the change trend in the interval. At the same time, according to the change trend of inlet moisture content, the change of outlet moisture content was predicted and necessary adjustments were taken. The practical application research results showed that the standard deviation of outlet moisture content in tobacco cut stem airflow drying was reduced by 27. 9% after applying the integrated control model, CPK increased by 45. 4%. The integrated control model could effectively improve the stability of outlet moisture content in tobacco cut stem airflow drying. [ABSTRACT FROM AUTHOR]

Since the Ming Dynasty, the flood disaster has been one of the important natural disasters affecting our country. However, relatively few studies on the evolution law of flood disaster at large spatial–temporal scales have been reported. Based on the datasets of flood stations from 1470 to 2000, this paper quantitatively analyzes and discusses the spatial and temporal evolution pattrens of floods in China in the past 500 years by using regional gravity center model, wavelet analysis, Daniel index, and M–K test, and predicts the trend of flood disasters in the future. The results show that: (1) Since 1470, there has been an increasing trend of flood intensity index in China;(2) From 1470 to 2000, there were two high centers of flood disaster in 1560 and 1925, respectively, and one low center appeared in 1745; (3) Flood risk changes from easy occurrence in the eastern coastal area to uniform spatial distribution over the whole study region; (4) In the past 500 years, the graver center of flood disaster is located in Baokang County, Hubei Province, and the gravity center of flood disaster shows a trend of moving to northwest both at time scales of 50-year and 100 year; (5) The flood disaster in the studied area will still show an overall upward trend in the future.The research can provide decision support for the precise prevention and control of flood disasters in China. [ABSTRACT FROM AUTHOR]

The existing research on teaching development of teachers fails to effectively quantify the teaching development trend. This paper deeply mines the evaluation data on the teaching quality of college teachers, before analyzing and predicting the trend features for teaching development of college teachers based on knowledge discovery. Firstly, the knowledge features of the teaching development trend of college teachers were examined. Next, the fluctuation features of the time series on the teaching quality development of college teachers were described based on chaotic time series. In addition, a prediction model for teaching development of college teachers was established for weighted first-order chaotic time series, and used to simulate the nonlinear features of the time series on the teaching quality development of college teachers. The prediction model was proved effective through experiments. [ABSTRACT FROM AUTHOR]

We propose a Recurrent Trend Predictive Neural Network (rTPNN) for multi-sensor fire detection based on the trend as well as level prediction and fusion of sensor readings. The rTPNN model significantly differs from the existing methods due to recurrent sensor data processing employed in its architecture. rTPNN performs trend prediction and level prediction for the time series of each sensor reading and captures trends on multivariate time series data produced by multi-sensor detector. We compare the performance of the rTPNN model with that of each of the Linear Regression (LR), Nonlinear Perceptron (NP), Multi-Layer Perceptron (MLP), Kendall- $\tau $ combined with MLP, Probabilistic Bayesian Neural Network (PBNN), Long-Short Term Memory (LSTM), and Support Vector Machine (SVM) on a publicly available fire data set. Our results show that rTPNN model significantly outperforms all of the other models (with 96% accuracy) while it is the only model that achieves high True Positive and True Negative rates (both above 92%) at the same time. rTPNN also triggers an alarm in only 11 s from the start of the fire, where this duration is 22 s for the second-best model. Moreover, we present that the execution time of rTPNN is acceptable for real-time applications.

The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. In this paper, the features are selected by various feature selection algorithms, and the parameters of the machine learning-based stock price trend prediction models are set through time-sliding window cross-validation based on 8-year data of Chinese A-share market. Through the analysis of different integrated models, the model performs best when the random forest algorithm is used for both feature selection and stock price trend prediction. Based on the random forest algorithm, a long-short portfolio is constructed to validate the effectiveness of the best model.

The behavior of peoples' request for a post on online social media is a stochastic process that makes post's ranking highly skewed in nature. We mean peoples interest for a post can grow/decay exponentially or linearly. Considering this nature of the evolutionary peoples' interest, this paper presents a Growth-based Popularity Predictor (GPP) model for predicting and ranking the web-contents. Three different kinds of web-based real datasets namely Movielens, Facebook-wall-post and Digg are used to evaluate the performance of the proposed model. This performance is measured based on four information-retrieval metrics Area Under receiving operating Characteristic (AUC), Novelty, Precision, and Kendal's Tau. The obtained results show that the prediction performance can be further improved if the score is mapped onto a cumulative predicted item's ranking.

Landslides along the Three Gorges Reservoir in China pose a threat to coastal residents and waterway safety. To reduce false positive misjudgments caused by a sudden local change in the landslide deformation curve, in this paper, we propose an effective method for predicting the deformation trend of reservoir bank landslides. We take reservoir bank landslides in the Wanzhou District of the Three Gorges Reservoir area as the research object. The Time-Series Interferometric Synthetic Aperture Radar (InSAR) method and 62 Sentinel-1A images from 2018 to 2022 were selected for landslide deformation monitoring, and the Hurst index was calculated to characterize the deformation trend. Furthermore, we propose a method for predicting the deformation trend based on the statistical distribution of deformation rates and the physical significance of the Hurst index. After the field survey and Global Positioning System (GPS) verification, the Time-Series InSAR results are shown to be reliable. We take the Sifangbei landslide as a representative case to analyze the validation results. It is found that the determined Sifangbei landslide deformation trend is consistent with the conclusions for the region. In addition, the deformation trend of a reservoir bank slope has obvious spatial and temporal differences. Changes in the reservoir water level and concentrated rainfall play roles similar to those of catalysts. The proposed method, involving the combination of Time-Series InSAR and the Hurst index, can effectively monitor deformation and predict the stability trend of reservoir bank landslides. The presented research results provide new ideas and solutions for landslide prevention and risk mitigation in reservoir areas.

China has pledged to reach peak carbon dioxide emission by 2030, and carbon neutrality by 2060 at the Seventy-Fifth Session of the United Nations General Assembly in 2020. As the largest energy consumer and emitter of greenhouse gases, carbon peak and carbon neutrality targets have posed a high requirement for China's sustainable development of energy and economy. In recent years, even though the growth rate of China's energy consumption has decreased, and carbon dioxide emissions gradually enter the plateau, fossil fuels still supply more than 80% of the energy consumption in China. Besides learning from the adjustment of energy consumption structure in developed countries, it is worth discussing in-depth about the change of the strategic path of energy transition and how to advance energy transition under carbon peak and carbon neutrality targets, the strategic goals in China's economic and social developments. It is also an important action in China's energy revolution. Furthermore, it is an important step to achieve the development of civilization. China needs to keep working on how to reduce carbon emission, to find the optimal strategic path, and to reach the equilibrium between carbon dioxide emission reduction and economic development in the fields of electrical engineering, industry, civil engineering, and agriculture along the path towards carbon neutrality. [ABSTRACT FROM AUTHOR]

The forecast of sales volume trend of fresh vegetables has significant referential function for government dominant departments, producers and consumers. In order to evaluate the e-commerce sales information of fresh vegetables scientifically and accurately, the sales volume information of such four common vegetables as baby cabbage, potatoes, bok choy and tomatoes, from Anhui Jinghui Vegetable E-commerce Co., Ltd. was selected as the research object to establish the sales trend prediction system. Taking the improved SVR as an example, we introduced the overall architecture, detailed design and function realization of the system. The system can reflect the short-term sales volume trend of fresh vegetables, and also can provide guidance for the realization of e-commerce order-oriented management and scientific production. [ABSTRACT FROM AUTHOR]