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The fundamental difference between quantum and traditional plasmas is the electron and ion composition, the former has a much higher density and extremely lower temperature, and it can be modelled by Zakharov-Kuznetsov (ZK) equation. In this paper, the Hirota bilinear method is used to study its solution properties. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is an illness that involves a gradual and irreversible degeneration of the brain. It is crucial to establish a precise diagnosis of AD early on in order to enable prompt therapies and prevent further deterioration. Researchers are currently focusing increasing attention on investigating the potential of machine learning techniques to simplify the automated diagnosis of AD using neuroimaging. The present study involved a comparison of models for the detection of AD through the utilization of 2D image slices obtained from magnetic resonance imaging brain scans. Five models, namely ResNet, ConvNeXt, CaiT, Swin Transformer, and CVT, were implemented to learn features and classify AD based on various perspectives of 2D image slices. A series of experiments were conducted using the dataset from the Alzheimer's Disease Neuroimaging Initiative. The results showed that ConvNeXt outperformed ResNet, CaiT, Swin Transformer, and CVT. ConvNeXt exhibited an average accuracy, precision, recall, and F1 score of 95.74%, 96.71%, 95.74%, and 96.14%, respectively, when applied to a 3-way classification task involving AD, mild cognitive impairment, and normal control subjects. The results suggest that the utilization of ConvNeXt may have potential in the identification of AD using 2D slice images. [ABSTRACT FROM AUTHOR]

Accurate regional distributed PV power forecasting provides data support for power grid management and optimal operation. Distributed PV has the characteristics of large quantity, small capacity and difficulty in obtaining meteorological data. Statistical upscaling method is commonly used to forecast regional power. However, the current research ignores how to reasonably divide the sub-regions with similar output characteristics and mine the spatial and temporal correlation between different sub-regions. Therefore, this paper proposes a short-term regional distributed PV power forecasting method based on sub-region division considering spatio-temporal correlation. Firstly, the representative power plant is selected after dividing the sub-region by the AP clustering algorithm. Then, the GCN is used to extract spatial correlation features, and the LSTM is used to extract the evolution features of dynamic spatial correlation features, and the power forecasting models of representative plants in different weather types are established. Finally, the data integrity and similarity of the sub-region are scored, and the upscaling weight is determined to realize the power forecasting of the whole region. The distributed PV power generation data of Pingshan County, Hebei Province, China is used for simulation test. The results show that the forecasting method proposed has higher forecasting accuracy than the traditional model. • The spatio-temporal correlation between distributed PV power plants is studied. • Introduce how to divide appropriate distributed PV sub-regions. • A power forecasting method considering spatio-temporal correlation is proposed. • Sub-regional data evaluation improves the forecasting accuracy of regional PV. [ABSTRACT FROM AUTHOR]

Yolk-shell SiO2 particles (YP) with center-radial meso-channels were fabricated through a simple and effective method. Al-containing YP-supported NiMo catalysts with different Al amounts (NiMo/AYP-x, x = Si/Al molar proportion) were prepared and dibenzothiophene (DBT) and 4,6-dimethyl-dibenzothiophene (4,6-DMDBT) were employed as the probes to evaluate the hydrodesulfurization (HDS) catalytic performance. The as-prepared AYP-x carriers and corresponding catalysts were characterized by some advanced characterizations to obtain deeper correlations between physicochemical properties and the HDS performance. The average pore sizes of series AYP-x supports are above 6.0 nm, which favors the mass transfer of organic sulfides. The cavity between the yolk and the shell is beneficial for the enrichment of S-containing compounds and the accessibility between reactants and active metals. Aluminum embedded into the silica framework could facilitate the formation of Lewis (L) and Brønsted (B) acid sites and adjust the metal-support interaction (MSI). Among all the as-synthesized catalysts, NiMo/AYP-20 catalyst shows the highest HDS activities. The improved HDS activity of NiMo/AYP-20 catalyst is attributed to the perfect combination of excellent structural properties of the yolk-shell mesoporous silica, enhanced acidity, moderate MSI, and good accessibility/dispersion of active components. [ABSTRACT FROM AUTHOR]

The micro-electro-mechanical system is widely used for energy harvesting and thermal wind sensor, its efficiency and reliability depend upon the pull-in instability. This paper studies a micro-electro-mechanical system using He-Liu [34] formulation for finding its frequency-amplitude relationship. The system periodic motion, pull-in instability and pseudo-periodic motion are discussed. This paper offers a new window for security monitoring of the system reliable operation. [ABSTRACT FROM AUTHOR]

It is important to improve the comprehensive performance of the ductile iron bars (DIBs) for the cylinder block of the extra high pressure hydraulic plunger pump and accelerate the industrial application. In this work, the LZQT600-3 DIBs with the diameter of 145 mm were prepared by the horizontal continuous casting (HCC) process, that is, LZQT600-3 HCCDIBs. The microstructure and room temperature tensile properties of different sections [left-edge (surface layer), left-1/2R (left half of the radius), and the center of the HCCDIBs] were studied. The results show that the spheroidization of LZQT600-3 HCCDIBs matrix from the left-edge, left1/2R to the center is at nodulizing grade II and above. As the cooling rate gradually decreases from surface to the center of the HCCIBs, the number of spheroidized graphite is gradually reduced, the size is gradually increased, the shape factor is decreased, and the pearlite content and lamellate spacing are increased. Along the horizontal direction of the section, the hardness of the material is distributed symmetrically around the center of the HCCDIBs. In the vertical direction, the hardness distribution in the center of the HCCDIBs is asymmetrical due to the gravity during the solidification process. Therefore, the microstructure in the lower part of the section solidifies relatively quickly. The left-edge has the best tensile mechanical properties, and the ultimate tensile strength, yield tensile strength and elongation are 597.3 MPa, 418.5 MPa and 9.6%, respectively. The tensile fracture belongs to the ductile-brittle hybrid fracture. The comprehensive performances of LZQT600-3 HCCDIBs meet the actual application requirements of ultra-high pressure hydraulic plunger pump cylinder. [ABSTRACT FROM AUTHOR]

Accurately forecasting regional distributed photovoltaic (DPV) power is crucial in mitigating the negative impact of high DPV penetration on the reliability and resilience of the distribution network. However, most of the current photovoltaic power forecasting methods suffer from two key problems: (1) ignoring the asymmetric influence relationship among DPV sites; (2) lack of consideration of dynamic spatiotemporal correlation among DPV sites. As a result, these methods are unable to fully adapt to the characteristics of DPV, making it challenging to directly apply the existing forecasting methods to improve the accuracy of DPV power forecasting. To conquer this limitation, a dynamic directed Graph Convolution Neural Network (DDGCN) is applied to regional DPV ultra‐short‐term power forecasting. Unlike the conventional Graph Convolution Neural Network (GCN) based forecasting methods, the proposed method improves GCN to process the directed graph. On this basis, to capture the dynamic and directed adjacency relationship among graph nodes, a temporal attention mechanism is proposed and combined with the directed GCN model. In this way, the dynamic and asymmetric/directed relationships among DPV sites can be taken into account. It is worth noting that the DPVs' adjacency relationship can be constructed without any prior knowledge by end‐to‐end model training. The simulation experiment proves that the prediction accuracy can be further improved by taking into account the dynamic directed relationship among the sites via a real DPV power dataset. [ABSTRACT FROM AUTHOR]