Influence of drought stress on phosphorus dynamics and maize growth in tropical ecosystems.

Drought has a significant impact on ecosystem functions, especially on the biogeochemical cycling of phosphorus (P), which is a crucial nutrient for plant growth and productivity. Despite its importance, the effects of different drought scenarios on soil P cycling and availability remain poorly understood in previous studies. This study simulated drought conditions in tropical soils using maize as a test crop under varying field capacity (FC) levels (100%, 80%, 60%, 40%, and 20%) over a 60-day pot experiment.

An optimized decomposition integration framework for carbon price prediction based on multi-factor two-stage feature dimension reduction.

The carbon trading market is an effective tool to combat greenhouse gas emissions, and as the core issue of carbon market, carbon price can stimulate the market for technological innovation and industrial transformation. However, the complex characteristics of carbon price such as nonlinearity and nonstationarity bring great challenges to carbon price prediction research. In this study, potential influencing factors of carbon price are introduced into carbon price forecasting, and a novel hybrid carbon price forecasting framework is developed, which contains data decomposition and reconstruction techniques, two-stage feature dimension reduction methods, intelligent and optimized deep learning forecasting with nonlinear integrated models and interval forecasting.

Two-stage deep learning hybrid framework based on multi-factor multi-scale and intelligent optimization for air pollutant prediction and early warning.

Effective prediction of air pollution concentrations is of great importance to both the physical and mental health of citizens and urban pollution control. As one of the main components of air pollutants, accurate prediction of PM can provide a reference for air pollution control and pollution warning. This study proposes an air pollutant prediction and early warning framework, which innovatively combines feature extraction techniques, feature selection methods and intelligent optimization algorithms.

Carbon price forecasting using multiscale nonlinear integration model coupled optimal feature reconstruction with biphasic deep learning.

Precise carbon price forecasting matters a lot for both regulators and investors. The improvement of carbon price forecasting can not only provide investors with rational advice but also make for energy conservation and emission reduction. But traditional methods do not perform well in prediction because of the nonlinearity and non-stationarity of carbon price.

An innovative random forest-based nonlinear ensemble paradigm of improved feature extraction and deep learning for carbon price forecasting.

Carbon price is the basis of developing a low carbon economy. The accurate carbon price forecast can not only stimulate the actions of enterprises and families, but also encourage the study and development of low carbon technology. However, as the original carbon price series is non-stationary and nonlinear, traditional methods are less robust to predict it.

A hybrid wavelet transform based short-term wind speed forecasting approach.

It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales.