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Currently, research on the small-signal stability of grid-forming converters under two control modes, grid-following and grid-forming, mainly focuses on low-level converters. However, studies on the differences between grid-following modular multilevel converters (MMC) and grid-forming MMC are limited. A comprehensive analysis of the wideband oscillation risk differences in MMC-HVDC systems under these two control modes from the perspective of impedance characteristics is necessary. Therefore, based on the harmonic state-space (HSS) modeling theory, this article establishes wideband sequence impedance models for grid-forming and grid-following MMC. Subsequently, this article identifies key control parameters affecting the impedance characteristics of MMC under two control modes. The wideband oscillation risks of MMC-HVDC systems under both control modes are compared. The study indicates that when the grid strength weakens, grid-following MMC faces instability risks, while grid-forming MMC can maintain stability. In instances where high-frequency resonance peaks exist in the impedance of the grid, both control modes of MMC may face instability risks. Furthermore, by adjusting the obtained key control parameters of MMC, this article effectively suppresses the oscillation risk discussed above. Finally, the analysis results are verified through an electromagnetic transient simulation. [ABSTRACT FROM AUTHOR]

Maize is one of the most successful crops with regard to the utilization of heterosis. The haploid induction technique is one of the fastest methods to obtain pure maize material at the present stage. However, the molecular mechanism of haploid doubling is rarely reported. In this study, we treated B73 and ZNC442 haploid young shoots with colchicine for 0 h, 6.2 h, and 10 h, and analyzed the differentially expressed genes (DEGs). We found that colchicine treatment for 6.2 h and 10 h compared to 0 h resulted in a total of 4868 co-DEGs. GO enrichment analysis and KEGG metabolic pathway analysis found significantly enriched 282 GO terms and 31 significantly pathways, respectively. Additionally, The GO term and KEGG pathway genes of spindle, cytoskeleton, microtubules and nuclear division were selected for analysis, and three candidate genes were screened by taking intersections. Zm00001d033112, Zm00001d010525, and Zm00001d043386 were annotated as kinesin-associated protein 13, kinesin-like protein KIN-10C, and kinesin light-chain LC6, respectively. The real-time fluorescence quantification (RT-PCR) results revealed that Zm00001d033112, Zm00001d010525, and Zm00001d043386 had the same trends as RNA-seq. Interestingly, Zm00001d033112 is homologous gene AT3G20150 in Arabidopsis, which was involved in the regulation of chromosome movement and mitotic spindle assembly. Our study suggests that kinesin genes may play an important role in doubling chromosomes, thus providing valuable information for future studies on the molecular mechanisms of chromosome doubling in maize. [ABSTRACT FROM AUTHOR]

Developing production technology pathways of sustainable aviation fuel (SAF) that align with China's national conditions and aviation transportation needs is crucial for promoting the SAF industry and achieving China's carbon peak and carbon neutrality goals. This article first projects the future SAF demand in China for the coming decades. Using SAF demand data as an input, this article employs the TOPSIS analysis method to comprehensively evaluate the suitability of four SAF production technology pathways at different stages of development in China, which are Hydroprocessed Esters and Fatty Acids (HEFA), Alcohol-to-Jet (AtJ), Natural Gas + Fischer–Tropsch Synthesis (G + FT), and Power-to-Liquid (PtL). The research results reveal the following trends: HEFA-based processes are the most suitable technology pathways for China in the near term; the G + FT route, based on energy crops, appears the most likely to support civil aviation needs in the medium to long term. In the long run, the PtL route holds significant potential, especially with the decreasing costs of green electricity, advancements in carbon capture, utilization, and storage (CCUS) technology, and improvements in SAF synthesis methods. In the final section of this article, we provide recommendations to drive the development of the SAF industry in China. [ABSTRACT FROM AUTHOR]

The pollution of heavy metals is extremely serious in China, including zinc (Zn), copper (Cu), lead (Pb), and cadmium (Cd). Heavy-metal-transporting ATPase (HMA) belongs to a subfamily of the P-ATPase family, which absorbs and transports Zn, Cu, Pb, and Cd in plants. Here, we describe a ZmHMA-encoding HMA family protein that positively regulates Cd and Zn tolerance. The real-time fluorescence quantification (RT-PCR) results revealed that ZmHMA3 had a high expression in B73, and the expression of ZmHMA3 was sensitive to Cd in yeast cells, which was related to Cd accumulation in yeast. Additionally, the Arabidopsis thaliana homologous mutants of AtHMA2 showed Cd sensitivity compared with WT. The overexpressing ZmHMA3 plants showed higher tolerance under Cd and Zn stresses than the wild type. The overexpression of ZmHMA3 led to higher Cd and Zn accumulation in tissues based on the subcellular distribution analysis. We propose that ZmHMA3 improves maize tolerance to Cd and Zn stresses by absorbing and transporting Cd and Zn ions. This study elucidates the gene function of the ZmHMA3 response to Cd and Zn stress and provides a reference for improving the characteristics of heavy metals enrichment in existing maize varieties and the plant remediation technology of heavy-metal-contaminated soil. [ABSTRACT FROM AUTHOR]

Semantic segmentation of Polarimetric SAR (PolSAR) images is an important research topic in remote sensing. Many deep neural network-based semantic segmentation methods have been applied to PolSAR image segmentation tasks. However, a lack of effective means to deal with the similarity of object features and speckle noise in PolSAR images exists. Thisstudy aims to improve the discriminative capability of neural networks for various intensities of backscattering coefficients while reducing the effects of noise in PolSAR semantic segmentation tasks. Firstly, we propose pre-processing methods for PolSAR image data, which consist of the fusion of multi-source data and false color mapping. Then, we propose a Multi-axis Sequence Attention Segmentation Network (MASA-SegNet) for semantic segmentation of PolSAR data, which is an encoder–decoder framework. Specifically, within the encoder, a feature extractor is designed and implemented by stacking Multi-axis Sequence Attention blocks to efficiently extract PolSAR features at multiple scales while mitigating inter-class similarities and intra-class differences from speckle noise. Moreover, the process of serialized residual connection design enables the propagation of spatial information throughout the network, thereby improving the overall spatial awareness of MASA-SegNet. Within the decoder, it is used to accomplish the semantic segmentation task. The superiority of this algorithm for semantic segmentation will be explored through feature visualization. The experiments show that our proposed spatial sequence attention mechanism can effectively extract features and reduce noise interference and is thus able to obtain the best results on two large-scale public datasets (the AIR-POlSAR-Seg and FUSAR-Map datasets). [ABSTRACT FROM AUTHOR]

A more scientific, objective, and reasonable renewal orientation is gradually becoming a research hotspot in the field of industrial heritage conservation and renewal. This study selected five samples to carry out field investigation and face-to-face interviews. POI data were collected and analyzed, which revealed the relationship between environmental resources and the five samples based on the kernel density estimation method. Sequentially, we unprecedentedly created a complete BN-POI-AHP hybrid Bayesian network model that was used to implement simulation analysis of the industrial heritage of the Former Site Museum of Changchun Film Studio. A renewal orientation and a strategy for the community comprehensive sports ground were determined based on the results of the simulation data through the previous model. We eventually achieved a sustainable renewal strategy and innovative research method for industrial heritage, from objective data collection and simulation model creation to generation of a final reasonable plan. [ABSTRACT FROM AUTHOR]

To further improve the accuracy and speed of UAV inspection of transmission line insulator defects, this paper proposes an insulator detection and defect identification algorithm based on YOLOv4, which is called DSMH-YOLOv4. In the feature extraction network of the YOLOv4 model, the improved algorithm improves the residual edges of the residual structure based on feature reuse and designs the backbone network D-CSPDarknet53, which greatly reduces the number of parameters and computation of the model. The SA-Net (Shuffle Attention Neural Networks) attention model is embedded in the feature fusion network to strengthen the attention of target features and improve the weight of the target. Multi-head output is added to the output layer to improve the ability of the model to recognize the small target of insulator damage. The experimental results show that the number of parameters of the improved algorithm model is only 25.98% of that of the original model, and the mAP (mean Average Precision) of the insulator and defect is increased from 92.44% to 96.14%, which provides an effective way for the implementation of edge end algorithm deployment. [ABSTRACT FROM AUTHOR]

The accuracy of insulators and their defect identification by UAVs (unmanned aerial vehicles) in transmission-line inspection needs to be further improved, and the model size of the detection algorithm is significantly reduced to make it more suitable for edge-end deployment. In this paper, the algorithm uses a lightweight GhostNet module to reconstruct the backbone feature extraction network of the YOLOv4 model and employs depthwise separable convolution in the feature fusion layer. The model is lighter on the premise of ensuring the effect of image information extraction. Meanwhile, the ECA-Net channel attention mechanism is embedded into the feature extraction layer and PANet (Path Aggregation Network) to improve the recognition accuracy of the model for small targets. The experimental results show that the size of the improved model is reduced from 244 MB to 42 MB, which is only 17.3% of the original model. At the same time, the mAp of the improved model is 0.77% higher than that of the original model, reaching 95.4%. Moreover, the mAP compared with YOLOv5-s and YOLOX-s, respectively, is improved by 1.98% and 1.29%. Finally, the improved model is deployed into Jetson Xavier NX and run at a speed of 8.8 FPS, which is 4.3 FPS faster than the original model. [ABSTRACT FROM AUTHOR]

To further improve the accuracy of bird nest model detection on transmission towers in aerial images without significantly increasing the model size and to make detection more suitable for edge-end applications, the lightweight model YOLOv5s is improved in this paper. First, the original backbone network is reconfigured using the OSA (One-Shot Aggregation) module in the VOVNet and the CBAM (Convolution Block Attention Module) is embedded into the feature extraction network, which improves the accuracy of the model for small target recognition. Then, the atrous rates and the number of atrous convolutions of the ASPP (Atrous Spatial Pyramid Pooling) module are reduced to effectively decrease the parameters of the ASPP. The ASPP is then embedded into the feature fusion network to enhance the detection of the targets in complex backgrounds, improving the model accuracy. The experiments show that the mAP (mean-Average Precision) of the fusion-improved YOLOv5s model improves from 91.84% to 95.18%, with only a 27.4% increase in model size. Finally, the improved YOLOv5s model is deployed into the Jeston Xavier NX, resulting in a model that runs well and has a substantial increase in accuracy and a speed of 10.2 FPS, which is only 0.7 FPS slower than the original YOLOv5s model. [ABSTRACT FROM AUTHOR]

Water use efficiency (WUE) has garnered considerable attention at global and regional levels. However, spatio-temporal variations of WUE and related influencing factors in the complex karst landforms of southwest China require further elucidation. Herein, the ratio of gross primary productivity (GPP) to evapotranspiration (ET) obtained through the PML-V2 product was used to characterize ecosystem WUE, the spatio-temporal variations to ecosystem WUE, and responses to temperature, precipitation, and the enhanced vegetation index (EVI) in southwest China. The results showed that: (1) The ecosystem WUE in southwest China decreased with increasing latitude and altitude. Spatially, the ecosystem WUE fluctuates in a "W" pattern with increasing longitude because of the karst landforms' distribution patterns. (2) The non-significant trend in increased ecosystem WUE during 2003–2017 may be associated with significant increases in the ET offsetting part of the GPP contribution to ecosystem WUE. Spatial distribution of changes in WUE is similar to GPP owing to the dominant role of GPP in changes to ecosystem WUE. (3) The multi-year average ecosystem WUE was lower in karst than in non-karst landforms; however, vegetation restoration projects have contributed in significantly increasing variation rate of ecosystem WUE in karst than that in non-karst landforms. (4) Temperature, precipitation, and EVI were generally positively correlated with ecosystem WUE and were important factors for the increase in ecosystem WUE. EVI characterized vegetation restoration indicators showed that the ecological engineering construction in the study area was effective and was the dominant factor of ecosystem WUE change in 59.59% of the study area. The results of this study are important for further understanding carbon and water cycling processes in karst regions. [ABSTRACT FROM AUTHOR]

Baijiu is a unique and traditional distilled liquor in China. Flavor plays a crucial rule in baijiu. Up to now, the research on the flavor of baijiu has progressed from the identification of volatile compounds to the research on key aroma compounds, but the release mechanism of these characteristic compounds is still unclear. Meanwhile, volatile compounds account for only a tiny fraction, whereas ethanol and water account for more than 98% of the content in baijiu. By summarizing the ethanol–water hydrogen bond structure in different alcoholic beverages, it was found that flavor compounds can affect the association strength of the ethanol–water hydrogen bond, and ethanol–water can also affect the interface distribution of flavor compounds. Therefore, the research on ethanol–water microstructure in baijiu is helpful to realize the simple visualization of adulteration detection, aging determination and flavor release mechanism analysis of baijiu, and further uncover the mystery of baijiu. [ABSTRACT FROM AUTHOR]

Daqu is not only a crucial starter in the production of baijiu, but it is also an important source of flavoring substances, so maintaining a stable quality is an important part of improving the quality of baijiu. Nonetheless, since the production of daqu is still a natural fermentation process, which is influenced by seasonal factors, the rapid testing of daqu quality is a problem that must be solved. In this study, headspace solid-phase microextraction technology (HS-SPME) was used to explore the volatile components in daqu, and a total of 115 volatile components were extracted. By constructing an untargeted statistical model, the variation in volatile compounds in dissimilar production processes of daqu was studied, and the differences between different maturation stages and the correlations between volatile compounds were analyzed. Subsequently, six compounds, including ethyl acetate, ethanol, phenylethanol, (R,R)-2,3-butanediol, ethyl caproate, and 2,3-butanediol, were further screened out by partial least squares discrimination analysis (PLS-DA), and the symbolic combination of daqu's maturity was speedily judged in accordance with the changes in marker compound concentrations to lay the foundation for the mechanization of baijiu production. [ABSTRACT FROM AUTHOR]

Sauce-aroma Baijiu is a distilled Baijiu well-known in China, and features a unique sauce-aroma style formed by a complex producing process in a specific geographical environment. However, there are few comprehensive reviews of the factors influencing the formation of its flavor. To this end, reviews are hereby carried out over factors including different components in brewing raw materials, geographical environment of Baijiu production, brewing technology including the production of high-temperature Daqu and the brewing process, storage technology including the type of storage containers, storage time and storage temperature involved in the production of Sauce-aroma Baijiu. In addition, the effects of these factors on the flavor formation of Sauce-aroma Baijiu are also revealed, providing references and forging a foundation for stabilizing and improving the quality of Sauce-aroma Baijiu. [ABSTRACT FROM AUTHOR]

Under the influence of space weather, abnormal disturbances in the ionosphere will distort the ionosphere model seriously and affect the global navigation satellite system negatively. This study analyzes the ionospheric activity characteristics and the ionospheric model performance in low latitude during a strong geomagnetic storm from 7 to 9 September 2017. The research goals are to determine the abnormal behavior of the ionosphere during the geomagnetic storm and to refine the ionosphere model in the low latitude. In the experiment, the vertical total electron content (VTEC) peak value at low latitudes caused by this geomagnetic storm was significantly higher than that on the geomagnetic quiet day, and the VTEC peak value increased by approximately 75%. In the main phase of the geomagnetic storm, the degree of VTEC variation with longitude is significantly higher than that of the geomagnetic quiet day. The VTEC variation trend in the northern hemisphere is more severe than that in the southern hemisphere. In the region where VTEC decreases with longitude, the VTEC in the northern hemisphere is higher than that in the southern hemisphere on the same longitude at low latitudes, and this phenomenon is not significantly affected by the geomagnetic disturbance of the recovery phase. During the geomagnetic storm, the daily minimum value of VTEC at different latitudes was basically the same, approximately 5 TECU, indicating that the nighttime VTEC of the ionosphere in low latitudes was weakly affected by latitude and geomagnetic storms. Geomagnetic disturbances during geomagnetic storms will lead to anomalous features of the "Fountain effect" in the ionosphere at low latitudes. In addition, this geomagnetic storm event caused the accuracy of spherical harmonics (SH), polynomial, and ICE models to decrease by 7.12%, 27.87%, and 48.56%, respectively, and caused serious distortion, which is negative VTEC values fitted by the polynomial model. [ABSTRACT FROM AUTHOR]

The partial replacement of chemical fertilizer with straw return is considered an effective method for improving the accumulation of organic matter and soil fertility, but the characteristics of soil nitrogen fixation and mineralization in a double-cropped rice paddy system are unclear. Based on a 12-year field experiment, we conducted a waterlogged incubation experiment for 49 days to determine the effect of long-term straw return combined with reducing chemical fertilizer application on the dynamic changes of mineralized soil nitrogen (N) content and mineralized N rate under the treatments, including NPK (chemical fertilizers application with straw removal), SBR (straw burned return), and SR (straw return). Results showed that, compared with SBR and NPK, SR significantly increased available nitrogen by 7.4% and 16.5%, respectively, due to the higher ammonium nitrogen and nitrate nitrogen, as well as the total carbon, available phosphorus, and slowly available potassium, suggesting that it could stock a sufficient nitrogen source. During the incubation period, the amount of N mineralization was relatively higher under SR than under SBR and NPK treatments, especially during the later mineralization time, whereas there was no difference in the N mineralization rate. In addition, SR significantly increased soil cumulative N mineralization and N mineralization potential. However, SBR significantly decreased the soil mineralizable N ratio compared with SR and NPK, which may result in a worsening of the N mineralization potential. The results indicated that long-term straw return combined with reducing chemical fertilizer application could significantly improve the N supply capacity of paddy rice field soil to better coordinate the soil N supply and immobilization. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) is an important land-surface parameter. Although microwave remote sensing is recognized as one of the most appropriate methods for retrieving SM, such retrievals often cannot meet the requirements of specific applications because of their coarse spatial resolution and spatiotemporal data gaps. A range of general models (GMs) for SM analysis topics (e.g., gap-filling, forecasting, and downscaling) have been introduced to address these shortcomings. This work presents a novel strategy (i.e., optimized wavelet-coupled fitting method (OWCM)) to enhance the fitting accuracy of GMs by introducing a wavelet transform (WT) technique. Four separate GMs are selected, i.e., elastic network regression, area-to-area regression kriging, random forest regression, and neural network regression. The fitting procedures are then tested within a downscaling analysis implemented between aggregated Global Land Surface Satellite products (i.e., LAI, FVC, albedo), Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST, and Random Forest Soil Moisture (RFSM) datasets in both the WT space and the regular space. Then, eight fine-resolution SM datasets mapped from the trained GMs and OWCMs are analyzed using direct comparisons with in situ SM measurements and indirect intercomparisons between the aggregated OWCM-/GM-derived SM and RFSM. The results demonstrate that OWCM-derived SM products are generally closer to the in situ SM observations, and better capture in situ SM dynamics during the unfrozen season, compared to the corresponding GM-derived SM product, which shows fewer time changes and more stable trends. Moreover, OWCM-derived SM products represent a significant improvement over corresponding GM-derived SM products in terms of their ability to spatially and temporally match RFSM. Although spatial heterogeneity still substantially impacts the fitting accuracies of both GM and OWCM SM products, the improvements of OWCMs over GMs are significant. This improvement can likely be attributed to the fitting procedure of OWCMs implemented in the WT space, which better captures high- and low-frequency image features than in the regular space. [ABSTRACT FROM AUTHOR]

Rapid urbanization has brought about many problems in urban environments, including the deterioration of the thermal environment, which greatly affects the sustainable development of cities and the health of urban residents. Therefore, a comprehensive and thorough study of the thermal environment is necessary. In this study, quantitative and qualitative analyses of the interannual and seasonal variation of the thermal environment in the urban area of Chongqing were conducted using a combination of equal sector analysis, mathematical statistics, and principal component analysis. In addition, the mechanism by which multiple integrated human and natural factors affect the urban thermal environment in different seasons was explored. The thermal environment of the land surface has a clear spatial correlation with built-up areas. From 2010 to 2020, the Liangjiang New Area and the western high-tech zone were the main areas of thermal environment area expansion. From 2010 to 2020, a decrease in the area of low-temperature zones and an increase in the area of high-temperature zones were the main trends. In particular, during the summer of 2020, the low-temperature zones almost disappeared, and the area of high-temperature zones was nearly twice as large as in the other seasons. The combined influence of multiple factors on surface temperature has certain seasonal characteristics. The slope, elevation, normalized water body index, fractional vegetation cover, normalized imperviousness index, and nighttime light intensity were the main factors affecting the surface temperature, whereas landscape pattern indicators, as well as the population and points of interest (POI) density, had a low influence. The strength of the combined influence of these multiple factors of the different seasons exhibited the following order: winter > spring > summer > autumn. [ABSTRACT FROM AUTHOR]

Urban impervious surfaces area (ISA) and green space (GS), two primary components of urban environment, are pivotal in detecting urban environmental quality and addressing global environmental change issues. However, the current global mapping of ISA and GS is not effective enough to accurately delineate in urban areas due to the mosaicked and complex structure. To address the issue, the hierarchical architecture principle and subpixel metric method were applied to map 30 m global urban ISA and GS fractions for the years 2015 and circa 2020. We use random forest algorithms for retrieval of the Normalized Settlement Density Index and Normalized Green Space Index from Landsat images using Google Earth Engine. The correlation coefficients of global urban ISA and GS fractions were all higher than 0.9 for 2015 and circa 2020. Our results show global urban ISA and GS areas in circa 2020 were 31.19 × 104 km2 and 17.16 × 104 km2, respectively. The novel ISA and GS fractions product can show potential applications in assessing the effects of urbanization on climate, ecology, and urban sustainability. [ABSTRACT FROM AUTHOR]

In recent years, global forest fires have occurred more frequently, seriously destroying the structural functions of forest ecosystem. Mapping the burn severity after forest fires is of great significance for quantifying fire's effects on landscapes and establishing restoration measures. Generally, intensive field surveys across burned areas are required for the effective application of traditional methods. Unfortunately, this requirement could not be satisfied in most cases, since the field work demands a lot of personnel and funding. For mapping severity levels across burned areas without field survey data, a semi-supervised transfer component analysis-based support vector regression model (SSTCA-SVR) was proposed in this study to transfer knowledge trained from other burned areas with field survey data. Its performance was further evaluated in various eco-type regions of southwestern United States. Results show that SSTCA-SVR which was trained on source domain areas could effectively be transferred to a target domain area. Meanwhile, the SSTCA-SVR could maintain as much spectral information as possible to map burn severity. Its mapped results are more accurate (RMSE values were between 0.4833 and 0.6659) and finer, compared to those mapped by ∆NDVI-, ∆LST-, ∆NBR- (RMSE values ranged from 0.7362 to 1.1187) and SVR-based models (RMSE values varied from 1.7658 to 2.0055). This study has introduced a potentially efficient mechanism to map burn severity, which will speed up the response of post-fire management. [ABSTRACT FROM AUTHOR]

Distilled spent grain (DSG), as the major by-product of baijiu making, contains melanoidins generated via the Maillard reaction. In this study, four melanoidin fractions (RF1‒RF4) were isolated successively from dried DSG (DDSG) using sodium hydroxide solution and water as extractants, and the content, preliminary structure, and ACE-inhibitory activities in vitro of melanoidins were first investigated. The antioxidant activity was also evaluated. The results indicated that the total content of melanoidins was 268.60 mg/g DDSG dry weight (dw) using a model system of glucose and serine as standard, and RF4 showed the highest content of melanoidins (174.30 mg/g DDSG dw). Functional groups like C=O, N‒H, C‒N, O‒H, C‒H, C‒O, C-C, and ‒C‒CO‒C‒ were present in the structure of melanoidins from RF4, as determined by Fourier transform infrared (FT-IR) assay. The highest antioxidant activities, as assessed by 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS•+), ferric-reducing antioxidant power (FRAP), and 1,1-diphenyl-2-picrylhydrazyl radical (DPPH) assays, and the highest angiotensin-converting enzyme (ACE) inhibitory activity (95.92% at 2 mg RF4/mL) were also exhibited by RF4. The RF4 was further fractionated by ultrafiltration based on molecular weight (MW). The more than 100 kDa melanoidins (RF4-6) exhibited the highest yield and antioxidant activity. The 3‒10 kDa melanoidins (RF4-2) were more efficient in ACE-inhibitory activity. Our study could raise awareness of the DDSG as a value-added resource. [ABSTRACT FROM AUTHOR]