Environmental efficacy of polyethylene microplastics: Enhancing the solidification of CuO nanoparticles and reducing the physiological toxicity to peanuts.

Microplastics and metal-based nanoparticles (NPs) are environmental pollutants that have attracted significant attention. However, there have been relatively few studies on the combined pollution of these substances in the soil-plant system. To investigate the environmental impact and interaction mechanisms of these two pollutants, a pot experiment was conducted to examine the effects of soil exposure on peanut growth.

High-Quality Fusion and Visualization for MR-PET Brain Tumor Images via Multi-Dimensional Features.

The fusion of magnetic resonance imaging and positron emission tomography can combine biological anatomical information and physiological metabolic information, which is of great significance for the clinical diagnosis and localization of lesions. In this paper, we propose a novel adaptive linear fusion method for multi-dimensional features of brain magnetic resonance and positron emission tomography images based on a convolutional neural network, termed as MdAFuse. First, in the feature extraction stage, three-dimensional feature extraction modules are constructed to extract coarse, fine, and multi-scale information features from the source image.

Grid integration feasibility and investment planning of offshore wind power under carbon-neutral transition in China.

Offshore wind power, with accelerated declining levelized costs, is emerging as a critical building-block to fully decarbonize the world's largest CO emitter, China. However, system integration barriers as well as system balancing costs have not been quantified yet. Here we develop a bottom-up model to test the grid accommodation capabilities and design the optimal investment plans for offshore wind power considering resource distributions, hourly power system simulations, and transmission/storage/hydrogen investments.

Fine-Grained and Multiple Classification for Alzheimer's Disease With Wavelet Convolution Unit Network.

In this article, we propose a novel wavelet convolution unit for the image-oriented neural network to integrate wavelet analysis with a vanilla convolution operator to extract deep abstract features more efficiently. On one hand, in order to acquire non-local receptive fields and avoid information loss, we define a new convolution operation by composing a traditional convolution function and approximate and detailed representations after single-scale wavelet decomposition of source images. On the other hand, multi-scale wavelet decomposition is introduced to obtain more comprehensive multi-scale feature information.

Intelligent Power System Stability Assessment and Dominant Instability Mode Identification Using Integrated Active Deep Learning.

Simulation analysis is critical for identifying possible hazards and ensuring secure operation of power systems. In practice, large-disturbance rotor angle stability and voltage stability are two frequently intertwined stability problems. Accurately identifying the dominant instability mode (DIM) between them is important for directing power system emergency control action formulation.

Co-exposure of maize to polyethylene microplastics and ZnO nanoparticles: Impact on growth, fate, and interaction.

Microplastics (MPs), especially polyethylene MPs (PE MPs), which are the primary component of mulch, have attracted increasing attention in recent years. ZnO nanoparticles (NPs), which constitute a metal-based nanomaterial commonly used in agricultural production, co-converge with PE MPs in the soil. However, studies revealing the behavior and fate of ZnO NPs in soil-plant systems in the presence of MPs are limited.

Coordination of Macro Base Stations for 5G Network with User Clustering.

With the increasing amounts of terminal equipment with higher requirements of communication quality in the emerging fifth generation mobile communication network (5G), the energy consumption of 5G base stations (BSs) is increasing significantly, which not only raises the operating expenses of telecom operators but also imposes a burden on the environment. To solve this problem, a two-step energy management method that coordinates 5G macro BSs for 5G networks with user clustering is proposed. The coordination among the communication equipment and the standard equipment in 5G macro BSs is developed to reduce both the energy consumption and the electricity costs.

Adaptive Modulation for DFIG and STATCOM With High-Voltage Direct Current Transmission.

This paper develops an adaptive modulation approach for power system control based on the approximate/adaptive dynamic programming method, namely, the goal representation heuristic dynamic programming (GrHDP). In particular, we focus on the fault recovery problem of a doubly fed induction generator (DFIG)-based wind farm and a static synchronous compensator (STATCOM) with high-voltage direct current (HVDC) transmission. In this design, the online GrHDP-based controller provides three adaptive supplementary control signals to the DFIG controller, STATCOM controller, and HVDC rectifier controller, respectively.

Adaptive learning in tracking control based on the dual critic network design.

In this paper, we present a new adaptive dynamic programming approach by integrating a reference network that provides an internal goal representation to help the systems learning and optimization. Specifically, we build the reference network on top of the critic network to form a dual critic network design that contains the detailed internal goal representation to help approximate the value function. This internal goal signal, working as the reinforcement signal for the critic network in our design, is adaptively generated by the reference network and can also be adjusted automatically.

Goal representation heuristic dynamic programming on maze navigation.

Goal representation heuristic dynamic programming (GrHDP) is proposed in this paper to demonstrate online learning in the Markov decision process. In addition to the (external) reinforcement signal in literature, we develop an adaptively internal goal/reward representation for the agent with the proposed goal network. Specifically, we keep the actor-critic design in heuristic dynamic programming (HDP) and include a goal network to represent the internal goal signal, to further help the value function approximation.