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Accurate PV power prediction is crucial for enhancing grid planning, optimizing dispatch operations, and advancing management strategies. In pursuit of this objective, this study proposes a short-term distributed PV power prediction method that incorporates temporal and spatial feature extraction as well as similar day analysis. Firstly, to address the poor adaptability of traditional clustering methods to time-series data, the K-shape clustering algorithm is employed to categorize the time series into different weather types. Secondly, to overcome the challenges posed by varying time resolutions in similar day analysis, a novel method based on Dynamic Time Warping (DTW) is proposed. This method calculates the similarity between the target days and the days to be collected, considering both the time of day and the day of the week. Subsequently, a PV power generation prediction model based on a convolutional long short-term memory (CNN-LSTM) network is developed to enhance prediction accuracy. To tackle the difficulty of manual hyperparameter tuning, the chaos reverse sparrow search algorithm (CRSSA) is introduced. Finally, a case study is conducted on the measured data of a distributed photovoltaic power station in a certain region of China. By comparing RMSE and MAPE, compared with other prediction models, the proposed prediction model and solving algorithm effectively reduced the relative error by more than 1%, verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Simple Summary: Most MAGE family genes have been identified on the mammalian genome and are located on the X chromosome. These genes are classified into type I and type II based on their sequence and function. DreNDN was the root of this evolutionary tree. In pigs, type I MAGE genes are predominantly expressed in the testis tissue, and type II MAGE genes are primarily expressed in the brain tissue. These findings are a valuable resource for acquiring insight into the evolution and expression of the MAGE family genes. The melanoma-associated antigen (MAGE) family found in eukaryotes plays a crucial role in cell proliferation and differentiation, spermatogenesis, neural development, etc. This study explored the validation and evolution of MAGE genes in eukaryotic genomes and their distribution and expression patterns in pigs. In total, 249 MAGE genes were found on 13 eukaryotic species. In total, 33, 25, and 18 genes were located on human, mouse, and pig genomes, respectively. We found eight, four, and three tandemly duplicated gene clusters on the human, mouse, and pig genomes, respectively. The majority of MAGE genes in mammals are located on the X chromosome. According to the phylogenetic analysis, the MAGE family genes were classified into 11 subfamilies. The NDN gene in zebrafish (DreNDN) was the root of this evolutionary tree. In total, 10 and 11 MAGE genes on human and mouse genomes, respectively, exhibited a collinearity relationship with the MAGE genes on pig genomes. Taking the MAGE family genes in pigs, the MAGE subfamilies had similar gene structures, protein motifs, and biochemical attributes. Using the RNA-seq data of Duroc pigs and Rongchang pigs, we detected that the expression of type I MAGE genes was higher in reproductive tissues, but type II MAGE genes were predominantly expressed in the brain tissue. These findings are a valuable resource for gaining insight into the evolution and expression of the MAGE family genes. [ABSTRACT FROM AUTHOR]

Pancreatic cancer is an aggressive and highly fatal malignant tumor. Recent studies have shown that cancer stem cells (CSCs) play an important role in resisting current therapeutic modalities. Furthermore, CD133 is highly expressed in CSCs. High-intensity focused ultrasound (HIFU) is a promising non-invasive therapeutic strategy for unresectable pancreatic cancers. In our study, we synthesized targeted CD133 organosilane nanomicelles by encapsulating perfluorohexane (PFH). The CD133 antibody on the surface could specifically bind to CD133-positive pancreatic cancer cells and selectively concentrate in pancreatic cancer tumor tissues. PFH was introduced to improve the ablation effect of HIFU due to its liquid–gas phase transition properties. By combining with the dorsal skinfold window chamber model (DSWC) of pancreatic cancer in nude mice, multiphoton fluorescence microscopy was used to evaluate the targeting effect of nanomicelles on pancreatic cancer tumor tissue. These multifunctional nanomicelles synergistically affected HIFU treatment of pancreatic cancer, providing an integrated research platform for diagnosing and treating pancreatic cancer with HIFU. [ABSTRACT FROM AUTHOR]

In order to investigate the effect of rheological parameter of blends on mixing performance of dynamic mixers, the flow of virtual material (VM)/thermoplastic polyurethanes (TPU) with high and low viscosities in it are simulated. The effect of rheological parameter ratios, including zero shear viscosity ratio (η0VM/η0TPU), relaxation time ratio (λVM/λTPU) and non‐Newtonian index ratio (NVM/NTPU) on pressure drop (Δp), segregation scale (S), and power consumption (P) are analyzed using Taguchi Orthogonal Method, and the effects of rotation speed (n) of the rotor and flow rate ratio (QVM/QTPU) are studied using single factor method. The results indicate η0VM/η0TPU is the most significant factor affecting Δp, S, and P. When η0VM/η0TPU = 1, λVM/λTPU = 1, NVM/NTPU = 1, S of blends reach the minimum value. With n increasing, the influences of QVM/QTPU and viscosity of TPU on S are reduced. [ABSTRACT FROM AUTHOR]

Accurately assessing and predicting the impacts of land use changes on ecosystem carbon stocks in the Yellow River Basin (YRB) and exploring the optimization of land use structure to increase ecosystem carbon stocks are of great practical significance for China to achieve the goal of "double carbon". In this study, we used multi-year remote sensing data, meteorological data and statistical data to measure the ecosystem carbon stock in the YRB from 2000 to 2020 based on the InVEST model, and then simulated and measured the ecosystem carbon stock under four different land use scenarios coupled with the FLUS model in 2030. The results show that, from 2000 to 2020, urban expansion in the YRB continued, but woodland and grassland grew more slowly. Carbon stock showed an increasing trend during the first 20 years, with an overall increase of 7.2 megatons, or 0.23%. Simulating the four land use scenarios in 2030, carbon stock will decrease the most under the cropland protection scenario, with a decrease of 17.7 megatons compared with 2020. However, carbon stock increases the most under the ecological protection scenario, with a maximum increase of 9.1 megatons. Furthermore, distinct trends in carbon storage were observed across different regions, with significant increases in the upstream under the natural development scenario, in the midstream under the ecological protection scenario and in the downstream under the cropland protection scenario. We suggest that the upstream should maintain the existing development mode, with ecological protection prioritized in the middle reaches and farmland protection prioritized in the lower reaches. This study provides a scientific basis for the carbon balance, land use structure adjustment and land management decision-making in the YRB. [ABSTRACT FROM AUTHOR]