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To address the ambiguity surrounding methods and standards for reducing carbon emissions in industrial processes， it is essential to quantitatively assess the impact of carbon emissions and formulate a scientifically grounded carbon reduction plan. Traditional approaches to carbon reduction in industrial processes are confined to measures such as energy substitution and equipment modification within the factory. Therefore， a carbon handprint method for describing the positive climate impact of products throughout their lifecycle is proposed. Based on the carbon footprint， the paper establishes a more comprehensive evaluation system. With the stator coils for hydroelectric generators manufactured in the industrial processes of a particular company as the research subject， various carbon reduction schemes both inside and outside the boundaries are considered. Boundary delineation， carbon footprint calculation， and carbon handprint analysis are conducted， and efficiency increments， lifespan increments， and carbon handprints generated from different schemes are calculated. The results show that the replacement of insulation materials has the most significant effect， and the carbon handprint method proves to be an effective guide for carbon reduction transformation in the case company.

To improve the current source-grid, load-storage microgrid coordinated optimal scheduling method, which is not ideal in terms of efficiency and effectiveness, the study combines convolutional neural network, variational modal decomposition, and long and short-term memory neural network to realize the short-term prediction of microgrid electric load. Based on this, a mathematical model having source-grid, load-storage coordinated optimal scheduling and an improved particle swarm algorithm are proposed for it. Compared with the particle swarm backpropagation model, the proposed microgrid power load short-term prediction model reduces the average absolute percentage error and root mean square error by 0.38% and 39.5%, respectively. In addition, the economic cost of the proposed power grid coordination and optimization scheduling model based on improved particle swarm optimization algorithm (IPSO) is lower, at $3954.3, and the load fluctuation is less, at 56.6 W. This indicates that the model proposed by the research institute helps to achieve self-sufficiency of electricity within the microgrid and mutual assistance between microgrids, thereby tapping into scheduling potential, and also helps to achieve economic electricity scheduling strategies, avoiding unnecessary thermal power generation and carbon dioxide emissions, and improving reliability. Therefore, the scheme proposed in the study can effectively realize the coordinated and optimal dispatch of source-network load and storage beneficial to the power enterprises. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the image quality of brain MRI sequences accelerated by constellation shuttling imaging (uCS) to provide a basis for clinical application. Methods: Twenty volunteers underwent brain MRI scanning （TSE axial T1WI, T2-FLAIR, sagittal T1WI and GRE sagittal 3D-T1WI） twice using parallel imaging (routine scanning) and corresponding uCS accelerated acquisition. The evaluation of images in each sequence was conducted through combining the subjective (Likert five-point scale method, image artifact and overall quality) and objective (signal-to-noise ratio measurement) methods. Results: The scanning time of using uCS in each MRI sequence was shorter than that of conventional scanning. The total scanning time of uCS in each volunteer was 542 s, while that of conventional scanning was 735 s, which decreased 26.3%. The scanning time of 3D sequence was nearly reduced 39%. There is no statistically difference between uCS and conventional sequence in subjective evaluation (overall image quality and image artifact degree). The SNR of uCS axial T1WI and sagittal T1WI sequences was significantly higher than those of conventional axial T1WI and sagittal T1WI sequences(P

The red edge band is considered as one of the diagnosable characteristics of green plants, but the large-scale remote sensing retrieval of fractional vegetation coverage (FVC) based on the red edge band is still rare. To explore the application of the red edge band in the remote sensing estimation of FVC, this study proposed a new vegetation index (normalized difference red edge index, RENDVI) based on the two red edge bands of Chinese GaoFen-6 satellite (GF-6). The FVC estimated by using three vegetation indices (NDVI, RENDVI1, and RENDVI2) were evaluated based on the field survey FVC obtained in Minqin Basin of Gansu Province. The results showed that there was a good linear correlation between the FVC estimated by GF-6 WFV data and the FVC investigated in the field, and the most reasonable estimation of FVC was obtained based on RENDVI2 model (R 2 = 0.97611 and RMSE = 0.07075). Meanwhile, the impact of three confidence levels (1, 2, and 5%) on FVC was also analyzed in this study. FVC obtained from NDVI and RENDVI2 has the highest accuracy at 2% confidence, while FVC based on RENDVI1 achieved the best accuracy at 5% confidence. It could be concluded that it is feasible and reliable to estimate FVC based on red edge bands, and the GF-6 Wide Field View (WFV) data with high temporal and spatial resolution provide a new data source for remote sensing estimation of FVC.

Aroma, an important quality characteristic of plant fruits, is produced by volatile organic compounds (VOCs), mainly terpenes, aldehydes, alcohols, esters, ketones, and other secondary metabolites, in plant cells. There are significant differences in the VOC profile of various fruits. The main pathways involved in the synthesis of VOCs are the terpenoid, phenylalanine, and fatty acid biosynthesis pathways, which involve several key enzyme-encoding genes, transcription factors (TFs), and epigenetic factors. This paper reviews the main synthetic pathways of the main volatile components in fruit, summarizes studies on the regulation of aroma formation by key genes and TFs, summarizes the factors affecting the fruit aroma formation, describes relevant studies on the improvement of fruit flavor quality, and finally proposes potential challenges and prospects for future research directions. This study provides a theoretical basis for the further precise control of fruit aroma quality and variety improvement. [ABSTRACT FROM AUTHOR]

Objective: Establish an in situ model for investigating HNSCC, focusing on tumor growth, metastasis, and the immune microenvironment. Methods: Generated a monoclonal SCCVII‐ZsGreen cell line through lentiviral transfection. Selected monoclonal lines with growth rates similar to the original SCCVII for in vivo tumorigenesis. Monitored tumor development and metastasis through fluorescence in vivo imaging. Employed immunohistochemistry to assess immune cell distribution in the tumor microenvironment. Results: SCCVII‐ZsGreen exhibited comparable proliferation and in vivo tumorigenicity to SCCVII. In situ tumor formation on day 10, with cervical metastasis in C57BL/6 mice by day 16. No significant fluorescence signals in organs like liver and lungs, while SCCVII‐ZsGreen presence confirmed in cervical lymph node metastases. Immunohistochemistry revealed CD4+ T, CD8+ T, B, and dendritic cells distribution, with minimal macrophages. Conclusion: Our model is a valuable tool for studying HNSCC occurrence, metastasis, and immune microenvironment. It allows dynamic observation of tumor development, aids preclinical drug experiments, and facilitates exploration of the tumor immune contexture. [ABSTRACT FROM AUTHOR]

Under the background of China’s economy from high-speed development to high-quality development, the development of coal industry has entered a critical transition period. Safety, intelligence, innovation and green have become the “four elements” and boosters of high-quality development of coal industry. Based on the safety development, Wanbei Coal and Power Group Company actively responds to the intelligent development trend of coal industry, gives play to the lasting power of :“four-wheel drive” and promotes the transformation and high-quality development of the coal industry through continuous scientific and technological innovation and green development.