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With the increasing penetration of new energy sources in large synchronous grids， renewable generator units that do not have inertial response capability may undermine the inertia of the system. In response to this problem， scholars have proposed a variety of inertia support methods for wind turbines and verified the effect of wind turbine inertia support in the IEEE standard example system. However， there are few research concerning the effect of wind turbine inertia support on the transient frequency stability of synchronous large grids. For this reason， the paper firstly analyzes the frequency drop characteristics in the case of power deficit in large power grid after the renewable generator penetration increase by fault time sorting， typical example simulation and mathematical model analysis. Moreover， the influence of wind turbine inertia support on the frequency drop characteristics of large power grids is studied. Finally， the influence of the inertia response on the primary frequency modulation is analyzed when the wind turbines simultaneously perform inertia and primary frequency modulation support. The results show that different wind turbine inertia support methods have different effects on the system frequency drop， and that some inertia support methods may lead to drastic frequency drop.

The decline of community publicity in the post-unit era is the social feature of economic system transformation. Exploring the spatial evolution mechanism of post-unit community is of great significance to sustainable community governance. By bridging Lefebvre's ternary spatial structure and Bourdieu's four capital types, a research framework of space production is established based on field theory and practice. Taking Xukuang community as a typical case, the qualitative research methods of participatory observation and in-depth interview are used to analyze the multidimensional spatial production process from "unitary continuous network structure" to "multiple discrete group structure". The research shows that: 1) The spatial production difference mechanism of unit to post-unit community transformation is shaped by the institutional change from unit system to community system, on the other hand, it is affected by the factors such as the free flow of modernity and the adjustment of regulatory structure. 2) Post-unit community actors adjust their habits in the reorganization of residents' composition and power relations. The process of competing for community capital is accompanied by the reconstruction of institutional space, the transformation of material space, the alienation of social space and the awakening of subject consciousness. 3) The government discourse represented by the neighborhood committee adopts the discipline strategy to build an ideal community space in the space practice, and forms a connected but not integrated overall relationship with potential residents' resistance. Especially when the production of capital and power touches the boundary of the subject or the emergence of individual critical subject, it often leads to new space struggle, habit adjustment and the direction change of space practice. 4) The space production of post-unit community is jointly driven by the subject of right discourse and the subject of daily life. The promulgation and response of the old community transformation policy from the state to the local construct the expected vision of community governance from the macro level. The power game and inclination of actors mean the sinking and implimentation of the production strategy of the government power subject at the meso level. Under the background of the accumulation of unit path dependence, modernization and aging, the post-unit community as the undertaker encounters the lack of kinetic energy at the micro level, the deconstruction of acquaintance society and the reconstruction of stranger society, and the realistic context contrary to the concept of multiple governance produced by the top-down strong power transmission and the bottom-up weak residents' feedback. The research integrates the concepts of field capital and habitus into the ternary dialectical structure of space production, and the in-depth theoretical integration makes up for the cognitive limitations of the single theoretical perspective. It expands new ideas for the in-depth understanding of the spatial evolution and subject relationship of the old community represented by the post-unit community. And the consideration of people's subjective initiative and social culture to a certain extent broadens the logic of space production in the general sense, which is also a supplement to the existing literature on community governance. Finally, it is an important direction for future community governance to formulate governance goals and countermeasures with residents as the core and promote the common enjoyment of community power.

While newly built urban green spaces aim to address environmental concerns, the resulting green gentrification and social inequality caused by escalating property values have become critical topics of urban socio-spatial research. To prevent green initiatives from becoming unaffordable for their intended beneficiaries in rapidly urbanizing cities, it is essential to examine the spatial and temporal relationships between the construction of new green spaces and rising housing prices. This study employs a difference-in-differences methodology to analyze regional disparities, impact periodicity, and the influence of park size on housing prices, using Nanjing, China as a case study. This result reveals that the introduction of new-built parks in Nanjing significantly impacts housing prices within an 800 m radius. The premium effect of these parks is substantially higher in urban core areas compared to suburban locales, demonstrating spatial differentials. Suburban parks temporally exhibit a prolonged lag and a shorter premium impact duration. Moreover, among various park areas, medium-sized parks demonstrate the most pronounced leverage effect, approximately double that of large parks, while small parks do not significantly affect housing prices. To mitigate the exacerbation of premium effects and enhance social justice in green strategies, we advocate prioritizing the development of small parks, particularly in urban core areas, and leveraging the temporal delay in new-built park impacts for urban policy interventions. [ABSTRACT FROM AUTHOR]

Age has been found to be one of the main risk factors for the severity and outcome of COVID-19. However, differences in SARS-CoV-2 specific antibody responses among COVID-19 patients of different age groups remain largely unknown. In this study, we analyzed the IgG/IgM responses to 21 SARS-CoV-2 proteins and 197 peptides that fully cover the spike protein against 731 sera collected from 731 COVID-19 patients aged from 1 to 92 years. We show that there is no overall difference in SARS-CoV-2 antibody responses in COVID-19 patients in the 4 age groups. By antibody response landscape maps, we find that the IgG response profiles of SARS-CoV-2 proteins are positively correlated with age. The S protein linear epitope map shows that the immunogenicity of the S-protein peptides is related to peptide sequence, disease severity and age of the COVID-19 patients. Furthermore, the enrichment analysis indicates that low S1 IgG responses are enriched in patients aged 60. In addition, high responses of non-structural/accessory proteins are enriched in severe COVID-19 patients aged >70. These results suggest the distinct immune response of IgG/IgM to each SARS-CoV-2 protein in patients of different age, which may facilitate a deeper understanding of the immune responses in COVID-19 patients.

The tracking of carbon and aerosol co-emissions is essential for environmental management. Satellite-based atmospheric synoptic observation networks provide large-scale and multifaceted data to help resolve emission behaviors. This study employs a comprehensive analysis of atmospheric dynamics, combustion byproducts, and aerosol characteristics around power plants. Strong correlations between Aerosol Optical Depth (AOD) at 500 nm and the column-averaged dry-air mole fraction of carbon dioxide (XCO2) were observed, revealing synchronous peaks in their emission patterns. The investigation into combustion completeness utilized metrics such as the ratio of carbon monoxide (CO)/XCO2 and Black Carbon Extinction (BCEXT)/Total Aerosol Extinction (TOTEXT). Discrepancies in these ratios across cases suggest variations in combustion efficiency and aerosol characteristics. Nitrogen dioxide (NO2) distributions closely mirrored XCO2, indicating consistent emission patterns, while variations in sulfur dioxide (SO2) distributions implied differences in sulfide content in the coal used. The influence of coal composition on AOD/XCO2 ratios was evident, with sulfide content contributing to variations besides combustion efficiency. This multifactorial analysis underscores the complex interplay of combustion completeness, aerosol composition, and coal components in shaping the air quality around power stations. The findings highlight the need for a nuanced understanding of these factors for effective air quality management. [ABSTRACT FROM AUTHOR]

Simple Summary: Machine learning methods have shown promise in accurately identifying small lung nodules. However, further exploration is needed to fully harness the potential of machine learning in distinguishing between benign and malignant nodules. This study aimed to develop and evaluate a ResNet50-Ensemble Voting model for detecting the nature (benign or malignant) of small pulmonary nodules (less than 20 mm) based on CT images. This study involved 834 CT imaging data from 396 patients with small pulmonary nodules. CT image features were extracted using ResNet50 and VGG16 algorithms, and classification was performed using XGBoost, SVM, and Ensemble Voting techniques, incorporating ten different combinations of machine learning classifiers. Among the models tested, the ResNet50-Ensemble Voting algorithm demonstrated the highest performance in the test set, achieving an accuracy of 0.943 (0.938, 0.948), with sensitivity and specificity values of 0.964 and 0.911, respectively. The implementation of machine learning models, particularly the ResNet50-Ensemble Voting approach, showed excellent performance in accurately identifying benign and malignant small pulmonary nodules (less than 20 mm) from diverse sources. These models have the potential to assist doctors in accurately diagnosing the nature of early-stage lung nodules in clinical practice. Background: The early detection of benign and malignant lung tumors enabled patients to diagnose lesions and implement appropriate health measures earlier, dramatically improving lung cancer patients' quality of living. Machine learning methods performed admirably when recognizing small benign and malignant lung nodules. However, exploration and investigation are required to fully leverage the potential of machine learning in distinguishing between benign and malignant small lung nodules. Objective: The aim of this study was to develop and evaluate the ResNet50-Ensemble Voting model for detecting the benign and malignant nature of small pulmonary nodules (<20 mm) based on CT images. Methods: In this study, 834 CT imaging data from 396 patients with small pulmonary nodules were gathered and randomly assigned to the training and validation sets in an 8:2 ratio. ResNet50 and VGG16 algorithms were utilized to extract CT image features, followed by XGBoost, SVM, and Ensemble Voting techniques for classification, for a total of ten different classes of machine learning combinatorial classifiers. Indicators such as accuracy, sensitivity, and specificity were used to assess the models. The collected features are also shown to investigate the contrasts between them. Results: The algorithm we presented, ResNet50-Ensemble Voting, performed best in the test set, with an accuracy of 0.943 (0.938, 0.948) and sensitivity and specificity of 0.964 and 0.911, respectively. VGG16-Ensemble Voting had an accuracy of 0.887 (0.880, 0.894), with a sensitivity and specificity of 0.952 and 0.784, respectively. Conclusion: Machine learning models that were implemented and integrated ResNet50-Ensemble Voting performed exceptionally well in identifying benign and malignant small pulmonary nodules (<20 mm) from various sites, which might help doctors in accurately diagnosing the nature of early-stage lung nodules in clinical practice. [ABSTRACT FROM AUTHOR]

Water Aerosol Raman Lidar-II is an active detection instrument with high temporal and spatial resolution at Nanjiao observation station, and that could continuous water vapor mixing ratio (WVMR) measurements. WVMR profiles inversion from lidar data and water ratio retrieved from radiosonde data are in good agreement. The statistical results of the vertical distribution of WVMR indicate that WVMR seasonal mean distribution is consistent with precipitation. In addition, WVMR in Nanjiao station is related to total cloud cover.