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. Second, at the fusion stage, the affine mapping function of multi-dimensional features is established to maintain a constant geometric relationship between the features, which can effectively utilize structural information from a feature map to achieve a better reconstruction effect. Furthermore, our MdAFuse comprises a key feature visualization enhancement algorithm designed to observe the dynamic growth of brain lesions, which can facilitate the early diagnosis and treatment of brain tumors. Extensive experimental results demonstrate that our method is superior to existing fusion methods in terms of visual perception and nine kinds of objective image fusion metrics. Specifically, in the results of MR-PET fusion, the SSIM (Structural Similarity) and VIF (Visual Information Fidelity) metrics show improvements of 5.61% and 13.76%, respectively, compared to the current state-of-the-art algorithm. Our project is publicly available at: https://github.com/22385wjy/MdAFuse.

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 CO2 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. Results indicate that grid integration barriers exist currently at the provincial level. For 2030, optimized offshore wind investment levels should be doubled compared with current government plans, and provincial allocations should be significantly improved considering both resource quality and grid conditions. For 2050, offshore wind capacity in China could reach as high as 1500 GW, prompting a paradigm shift in national transmission structure, favoring long-term storage in the energy portfolio, enabling green hydrogen production in coastal demand centers, resulting in the world's largest wind power market.

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. Then, we fuse all these cross-scale features to improve the problem of inaccurate localization of singular points. Given the novel wavelet convolution unit, we further design a network based on it for fine-grained Alzheimer's disease classifications (i.e., Alzheimer's disease, Normal controls, early mild cognitive impairment, late mild cognitive impairment). Up to now, only a few methods have studied one or several fine-grained classifications, and even fewer methods can achieve both fine-grained and multi-class classifications. We adopt the novel network and diffuse tensor images to achieve fine-grained classifications, which achieved state-of-the-art accuracy for all eight kinds of fine-grained classifications, up to 97.30%, 95.78%, 95.00%, 94.00%, 97.89%, 95.71%, 95.07%, 93.79%. In order to build a reference standard for Alzheimer's disease classifications, we actually implemented all twelve coarse-grained and fine-grained classifications. The results show that the proposed method achieves solidly high accuracy for them. Its classification ability greatly exceeds any kind of existing Alzheimer's disease classification method.

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. However, DIM identification has always relied on human expertise. This article proposes an intelligent DIM identification framework that can discriminate among stable status, rotor angle instability, and voltage instability based on active deep learning (ADL). To reduce human expert efforts required to label the DIM dataset when building DL models, a two-stage batch-mode integrated ADL query strategy (preselection and clustering) is designed for the framework. It samples only the most helpful samples to label in each iteration and considers both information contents and diversity in them to improve query efficiency, significantly reducing the required number of labeled samples. Case studies conducted on a benchmark power system (China Electric Power Research Institute (CEPRI) 36-bus system) and a practical large-area power system (Northeast China Power System) reveal that the proposed approach outperforms conventional methods in terms of accuracy, label efficiency, scalability, and adaptability to operational variability.

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. In this study, a pot experiment was used to evaluate the effects of maize co-exposure to PE MPs (0.5 % and 5 % w/w) and ZnO NPs (500 mg/kg) on growth, element distribution, speciation, and adsorption mechanism. The results demonstrate that individual exposure to PE MPs posed no significant toxicity; however, it significantly decreased maize grain yield (essentially 0). ZnO NP-exposure treatments significantly increased the Zn concentration and distribution intensity in maize tissues. Among them, the Zn concentration in the maize root exceeded 200 mg/kg, compared with 40 mg/kg in the grain. Moreover, the Zn concentrations in various tissues decreased in the following order: stem, leaf, cob, bract, and grain. Reassuringly, ZnO NPs still could not be transported to the maize stem under co-exposure to PE MPs. ZnO NPs had been biotransformed (64 % of the Zn was associated with histidine, with the remainder being associated with P [phytate] and cysteine) in maize stem. This study provides new insights into the plant physiological risks of PE MP and ZnO NP co-exposure in the soil-plant system and assesses the fate of ZnO NPs.

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. A novel user clustering method is proposed together with Benders decomposition to accelerate the solving process. Simulation results show that the proposed method is computationally efficient and can ensure near-optimal performance, effectively reducing the energy consumption and electricity costs compared with the conventional dispatching scheme.

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. The mechanism is to observe the system states and their derivatives and then provides supplementary control to the plant according to the utility function. With the GrHDP design, the controller can adaptively develop an internal goal representation signal according to the observed power system states, therefore, to achieve more effective learning and modulating. Our control approach is validated on a wind power integrated benchmark system with two areas connected by HVDC transmission lines. Compared with the classical direct HDP and proportional integral control, our GrHDP approach demonstrates the improved transient stability under system faults. Moreover, experiments under different system operating conditions with signal transmission delays are also carried out to further verify the effectiveness and robustness of the proposed approach.

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. We evaluate our proposed GrHDP algorithm on two 2-D maze navigation problems, and later on one 3-D maze navigation problem. Compared to the traditional HDP approach, the learning performance of the agent is improved with our proposed GrHDP approach. In addition, we also include the learning performance with two other reinforcement learning algorithms, namely Sarsa(λ) and Q-learning, on the same benchmarks for comparison. Furthermore, in order to demonstrate the theoretical guarantee of our proposed method, we provide the characteristics analysis toward the convergence of weights in neural networks in our GrHDP approach.

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. In this way, we provide an alternative choice rather than crafting the reinforcement signal manually from prior knowledge. In this paper, we adopt the online action-dependent heuristic dynamic programming (ADHDP) design and provide the detailed design of the dual critic network structure. Detailed Lyapunov stability analysis for our proposed approach is presented to support the proposed structure from a theoretical point of view. Furthermore, we also develop a virtual reality platform to demonstrate the real-time simulation of our approach under different disturbance situations. The overall adaptive learning performance has been tested on two tracking control benchmarks with a tracking filter. For comparative studies, we also present the tracking performance with the typical ADHDP, and the simulation results justify the improved performance with our approach.