Wind energy is a clean and renewable source that reduces greenhouse gas emissions. To smooth the impact of wind energy fluctuations on the power grid and power supply, much research has predicted the wind speed of wind farms to estimate power generation. However, most studies overlook the nonlinear relationship between wind speed and power generation, and data sources are usually limited to one or two wind farms. This study constructs a wind power density prediction model based on the LightGBM and artificial neural network to solve the above problems. Its data collection process does not require meteorological measurement equipment and has good universality, stability, and robustness. LightGBM is used to extract feature information and then train it using an artificial neural network, which has a high tolerance for data loss. The model performance was validated using data from six terrains from 2020 to 2022. While results showed that the average prediction error was 71.68 % less than 2 % and 82.188 % less than 6 %, with an average R2 of 0.9755 and an average correlation coefficient of 0.9875, proving the practical significance of the model that can be used to guide electricity trade.
Nipah virus (NiV), a zoonotic paramyxovirus belonging to the genus Henipavirus, is classified as a Biosafety Level-4 pathogen based on its high pathogenicity in humans and the lack of available vaccines or therapeutics. Since its initial emergence in 1998 in Malaysia, this virus has become a great threat to domestic animals and humans. Sporadic outbreaks and person-to-person transmission over the past two decades have resulted in hundreds of human fatalities. Epidemiological surveys have shown that NiV is distributed in Asia, Africa, and the South Pacific Ocean, and is transmitted by its natural reservoir, Pteropid bats. Numerous efforts have been made to analyze viral protein function and structure to develop feasible strategies for drug design. Increasing surveillance and preventative measures for the viral infectious disease are urgently needed.
Carbon dioxide emission is a high-profile issue that can affect both the human economy and human existence, but few scholars have studied the relationship between these two. Therefore, this study constructs the panel threshold regression (PTR) based on the National Bureau of Statistics of China's panel data from 2002 to 2019 in 19 regions. One of the advantages of PTR is to leverage segmented functions, allowing for a more detailed analysis of the data. Besides, by introducing the idea of a threshold, PTR can effectively avoid structural changes in the data. The different between this study and other research is that this study divides 19 regions into two parts: Eastern China and Central China. Based on that, this study researches and compares the different influences of the aging population on carbon emissions in these two regions. The results show that although the Environment Kuznets Curve has been confirmed in both Eastern China and Central China, with the development of the economy, the carbon emissions will increase in Eastern China and decrease in Central China, respectively. In addition, the key factors affecting carbon emissions in Eastern China and Central China are trade dependence and urbanization rate separately. Hence, this study suggests that the regional governments in Eastern China may guide and encourage more international trading companies to move to Central China, and at the same time, the regional governments in Central China should issue more policies to attract these companies, such as: reducing land lease fees and building better transportation infrastructure. Apart from that, the governments in Central China should vigorously increase the rate of urbanization to reduce energy consumption and improve energy efficiency.
The aging population is a common problem faced by most countries in the world. This study uses 18 years (from 2002 to 2019) of panel data from 31 regions in China (excluding Hong Kong, Macao, and Taiwan Province), and establishes a panel threshold regression model to study the non-linear impact of the aging population on economic development. It is different from traditional research in that this paper divides 31 regions in China into three regions: Eastern, Central, and Western according to the classification standard of the National Bureau of Statistics of China and compares the different impacts of the aging population on economic development in the three regions. Although this study finds that the aging population promotes the economy of China's eastern, central, and western regions, different threshold variables have dramatically different influences. When the sum of export and import is the threshold variable, the impact of the aging population on the eastern and the central region of China is significantly larger than that of the western region of China. However, when the unemployment rate is the threshold variable, the impact of the aging population on the western region of China is dramatically higher than the other regions' impact. Thus, one of the contributions of this study is that if the local government wants to increase the positive impact of the aging population on the per capita GDP of China, the local governments of different regions should advocate more policies that align with their economic situation rather than always emulating policies from other regions.
Coronavirus Disease 2019 (COVID-19) is a deadly emerging infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Because SARS-CoV-2 is easily transmitted through the air and has a relatively long incubation time, COVID-19 has rapidly developed into a global pandemic. As there are no antiviral agents for the prevention and treatment of this severe pathogen except for remdesivir, development of antiviral therapies to treat infected individuals remains highly urgent. Here, we showed that baicalein and baicalin exhibited significant antiviral activity against SARS-CoV-2, the causative agent of COVID-19 through in vitro studies. Our data through cell-based and biochemical studies showed that both compounds act as SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) inhibitors directly and inhibit the activity of the SARS-CoV-2 RdRp, but baicalein was more potent. We also showed specific binding of baicalein to the SARS-CoV-2 RdRp, making it a potential candidate for further studies towards therapeutic development for COVID-19 as a selective non-nucleoside polymerase inhibitor.
Al0.85Ga0.15As0.56Sb0.44 has recently attracted significant research interest as a material for 1550 nm low-noise short-wave infrared (SWIR) avalanche photodiodes (APDs) due to the very wide ratio between its electron and hole ionization coefficients. This work reports new experimental excess noise data for thick Al0.85Ga0.15As0.56Sb0.44 PIN and NIP structures, measuring low noise at significantly higher multiplication values than previously reported (F = 2.2 at M = 38). These results disagree with the classical McIntyre excess noise theory, which overestimates the expected noise based on the ionization coefficients reported for this alloy. Even the addition of 'dead space' effects cannot account for these discrepancies. The only way to explain the low excess noise observed is to conclude that the spatial probability distributions for impact ionization of electrons and holes in this material follows a Weibull-Fréchet distribution function even at relatively low electric-fields. Knowledge of the ionization coefficients alone is no longer sufficient to predict the excess noise properties of this material system and consequently the electric-field dependent electron and hole ionization probability distributions are extracted for this alloy.