Millennial-Scale Asian Monsoon Influenced Longjie Lake Evolution during Marine Isotope Stage 3, Upper Stream of Changjiang (Yangtze) River, China

Millennial-scale climate change in Asian monsoon region during MIS 3 has been studied using stalagmite, loess, and peat sediments. However, records from more materials are essential to further illustrate dynamics of these events. In the present study, a time-series of grain size covering 60–30 ka was reconstructed from lake sediments in the Yunnan Province, southwestern China. The time-series contains 14 obvious millennial-scale events during the period. On millennial-scale, the grain size record is generally consistent with mean stalagmite δ18O from Hulu Cave, grain size of Gulang loess sequence, Chinese Loess Plateau, and Greenland ice core δ18O. The results show that the millennial-scale variation was well compared with the Dansgaard-Oeschger (DO) events, indicating that those global events were well documented in lake sediments in the Asian monsoon region. Because the grain size can be used as a proxy for water discharge, we suggest that signal of the DO events might be transmitted to lake evolution by Asian monsoon

The Relationship Between Stroke Patients Characteristics and Family Support with Compliance Rehabilitation

Stroke is a cerebrovascular disease, it is brain function disorders associated with the disease of the blood vessels that supply the brain. The impact of stroke is paralysis. Family support is things that are needed to be considered in the treatment of stroke patients. It is very involved in the compliance rehabilitation of patients to prevent the re-occurrence of stroke. Characteristics of stroke patients may also affect the compliance rehabilitation. The purpose of this research is to determine the relationship between stroke patients characteristics and family support to compliance rehabilitation at the Medical Rehabilitation Unit RSU Haji Surabaya. This research was an analytic observational research with cross sectional design. The subjects of this research are taken using total population technique. The independent variables in this research is family support. The dependent variable is compliance rehabilitation. The results of this research are presented in the form of frequency distributions and calculate the strength of the relationship with Phi coefficient. The result of this research shows that there is a strong relationship between family support and compliance rehabilitation (r=0.582). There are weak relationship between ages (r=-0,027), gender (r=0,092), level of education (r= -0,295), work (r=0,098), and marital status (r=0,319). The conclusion is family support may affect compliance rehabilitation of stroke patients. It is recommended for health workers to provide counseling to improve family support in curing stroke patients

Maximum Principle for Near-Optimality of Mean-Field FBSDEs

The present paper concerns with a near-optimal control problem for systems governed by mean-field forward-backward stochastic differential equations (FBSDEs) with mixed initial-terminal conditions. Utilizing Ekeland’s variational principle as well as the reduction method, the necessary and sufficient near-optimality conditions are established in the form of Pontryagin’s type. The results are obtained under restriction on the convexity of the control domain. As an application, a linear-quadratic stochastic control problem is solved explicitly

Non-Farm Employment Experience, Risk Preferences, and Low-Carbon Agricultural Technology Adoption: Evidence from 1843 Grain Farmers in 14 Provinces in China

Guiding and encouraging farmers to adopt low-carbon agricultural technologies is highly significant for reducing greenhouse gas emissions, mitigating climate change, and achieving agricultural production development and food security. This study used survey data from 1843 grain farmers in 14 provinces of China to empirically analyze the impact of non-farm employment experience and risk preferences on grain farmers’ low-carbon agricultural technology (LCAT) adoption. The results show that for grain farmers: (1) non-farm employment experience significantly promoted the adopting of LCAT. The probability of adopting LCAT by those with non-farm experience is 23.5% higher than those without. (2) Non-farm employment experience reinforced their risk preferences and promoted the adoption of LCAT. The adoption probability of LCAT of those with high-risk preferences was 6.1% higher than those with low-risk preferences. (3) The impact of non-farm employment experience on adopting LCAT was more significant for those with high education and training. Non-farm employment experience outside the province and employment experience in the tertiary sector while working outside significantly affect grain farmers’ LCAT adoption

How Does Vertical Fiscal Imbalance Affect CO₂ Emissions? The Role of Capital Mismatch

Climate warming caused by greenhouse gases is an important practical issue. This study aims to explore the impact of the vertical fiscal imbalance (VFI) on CO2 emissions from the perspective of theoretical analysis and empirical research. This study uses panel data from 30 provinces in China from 2004 to 2018 in order to test this issue. The results show that the VFI has a significant positive impact on CO2 emissions and that the capital misallocation exacerbates the positive impact of the VFI on CO2 emissions. These study results also have a significant temporal heterogeneity. The sample results dating after 2008 were more significant. These conclusions provide economic and political references for local governments in order to develop CO2 neutrality and CO2 peaking policy goals and to promote an in-depth reform of the fiscal system

Assessment of Ecological Environment Quality in Rare Earth Mining Areas Based on Improved RSEI

In past decades, the reckless exploitation of rare earth mines devastated the ecological environment. Under strict regulation and governance, the exploitation has gradually gotten back on track in recent years. In this regard, timely and accurate assessment of the ecological environment quality of rare earth management areas is indispensable for regional mine development planning, ecological protection, and sustainable development. Being relatively objective and providing instant results, the Remote Sensing Ecological Index (RSEI) is widely used in evaluating ecological environment quality. This paper combined Landsat 8 OLI multispectral imagery with meteorological, land type, and other data to set the Net Primary Productivity (NPP). The NPP reflects detailed regional vegetation destruction and climate variation, the greenness index of RSEI. We also used kernel principal component analysis (KPCA) to obtain the improved ecological index K-RSEINPP while evaluating the ecological environment quality of rare earth mining areas in southern Jiangxi and compared this with the traditional RSEI results. The results indicate that: (1) PC1 accounts for 88.51% of the results obtained based on K-RSEINPP, and the average correlation coefficient with each index reaches 0.757, which integrates the characteristics of the four indicators; (2) Compared with other indexes, the K-RSEINPP proposed in this paper can better display the detailed information of the ecological environment in the rare earth mining areas to differentiate mining areas under various statuses and cities with different vegetation coverage, and its results were consistent with the actual verification. Therefore, our K-RSEINPP can provide an effective basis for monitoring and evaluating the ecological environment of the mining area

Design and Implementation of Hybrid CORDIC Algorithm Based on Phase Rotation Estimation for NCO

The numerical controlled oscillator has wide application in radar, digital receiver, and software radio system. Firstly, this paper introduces the traditional CORDIC algorithm. Then in order to improve computing speed and save resources, this paper proposes a kind of hybrid CORDIC algorithm based on phase rotation estimation applied in numerical controlled oscillator (NCO). Through estimating the direction of part phase rotation, the algorithm reduces part phase rotation and add-subtract unit, so that it decreases delay. Furthermore, the paper simulates and implements the numerical controlled oscillator by Quartus II software and Modelsim software. Finally, simulation results indicate that the improvement over traditional CORDIC algorithm is achieved in terms of ease of computation, resource utilization, and computing speed/delay while maintaining the precision. It is suitable for high speed and precision digital modulation and demodulation

A Deep Learning Approach with Extensive Sentiment Analysis for Quantitative Investment

Recently, deep-learning-based quantitative investment is playing an increasingly important role in the field of finance. However, due to the complexity of the stock market, establishing effective quantitative investment methods is facing challenges from various aspects because of the complexity of the stock market. Existing research has inadequately utilized stock news information, overlooking significant details within news content. By constructing a deep hybrid model for comprehensive analysis of historical trading data and news information, complemented by momentum trading strategies, this paper introduces a novel quantitative investment approach. For the first time, we fully consider two dimensions of news, including headlines and contents, and further explore their combined impact on modeling stock price. Our approach initially employs fundamental analysis to screen valuable stocks. Subsequently, we built technical factors based on historical trading data. We then integrated news headlines and content summarized through language models to extract semantic information and representations. Lastly, we constructed a deep neural model to capture global features by combining technical factors with semantic representations, enabling stock prediction and trading decisions. Empirical results conducted on over 4000 stocks from the Chinese stock market demonstrated that incorporating news content enriched semantic information and enhanced objectivity in sentiment analysis. Our proposed method achieved an annualized return rate of 32.06% with a maximum drawdown rate of 5.14%. It significantly outperformed the CSI 300 index, indicating its applicability to guiding investors in making more effective investment strategies and realizing considerable returns

A Particle Filter Track-Before-Detect Algorithm Based on Hybrid Differential Evolution

In this paper, we address the problem of detecting and tracking targets with a low signal-to-noise ratio (SNR) by exploiting hybrid differential evolution (HDE) in the particle filter track-before-detect (PF-TBD) context. Firstly, we introduce the Bayesian PF-TBD method and its weaknesses. Secondly, the HDE algorithm is regarded as a novel particle updating strategy, which is proposed to optimize the performance of the PF-TBD algorithm. Thirdly, we combine the systematic resampling approach to enhance the performance of the proposed algorithm. Then, an improved PF-TBD algorithm based on the HDE method is proposed. Experiment results indicate that the proposed method has better performance in detecting and tracking than previous algorithms when the targets have a low SNR

Assessment of Ecological Environment Quality in Rare Earth Mining Areas Based on Improved RSEI

In past decades, the reckless exploitation of rare earth mines devastated the ecological environment. Under strict regulation and governance, the exploitation has gradually gotten back on track in recent years. In this regard, timely and accurate assessment of the ecological environment quality of rare earth management areas is indispensable for regional mine development planning, ecological protection, and sustainable development. Being relatively objective and providing instant results, the Remote Sensing Ecological Index (RSEI) is widely used in evaluating ecological environment quality. This paper combined Landsat 8 OLI multispectral imagery with meteorological, land type, and other data to set the Net Primary Productivity (NPP). The NPP reflects detailed regional vegetation destruction and climate variation, the greenness index of RSEI. We also used kernel principal component analysis (KPCA) to obtain the improved ecological index K-RSEINPP while evaluating the ecological environment quality of rare earth mining areas in southern Jiangxi and compared this with the traditional RSEI results. The results indicate that: (1) PC1 accounts for 88.51% of the results obtained based on K-RSEINPP, and the average correlation coefficient with each index reaches 0.757, which integrates the characteristics of the four indicators; (2) Compared with other indexes, the K-RSEINPP proposed in this paper can better display the detailed information of the ecological environment in the rare earth mining areas to differentiate mining areas under various statuses and cities with different vegetation coverage, and its results were consistent with the actual verification. Therefore, our K-RSEINPP can provide an effective basis for monitoring and evaluating the ecological environment of the mining area