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• An energy-efficient oriented multi-objective urban block form design workflow is proposed. • The relationship between urban form factors and building energy consumption, as well as solar energy potential, is explored. • Reducing energy demand, increasing energy generation, and improving environmental performance should be considered together in the design of energy-efficient blocks. • BD, OSR, SC, and PAR have significant correlations with the total EUI of the block. • FAR, BD, AF, OSR, SC, and PAR are highly correlated with rooftop PV energy generation. Urban form can significantly affect building energy consumption and solar energy potential, which is essential to sustainable urban development. However, few studies have proposed a comprehensive framework to discuss this. In addition, how to bridge the gap between theory and practice, and how to combine quantitative research results with urban form design is also a matter of concern. Based on this background, this study proposes a multi-objective urban form design optimization framework based on the Grasshopper platform. Three optimization objectives were identified: minimum building energy consumption, maximum solar potential and maximum sunlight hours. Then, taking Jianhu in China as an example, the building types are extracted from the actual urban context to create blocks. The Ladybug Tools plugin is used for performance simulation, and multi-objective optimization is run in Wallacei X. Furthermore, based on the optimization process data, a correlation analysis of urban form factors with building energy consumption and solar energy potential is conducted. Finally, the research findings are integrated, and energy-efficient urban form design strategies are proposed. The research results show that a block form with excellent energy performance in Jianhu has the following characteristics: (i) The open space is often located in the center of the block. High-rise buildings are located on the west side. (ii) The block presents a layout consisting of a low south side and high north side. (iii) The orientation of the block is 15° south by westor due south. (iv) Avoid building types with large shape coefficient such as P-4, C-1, S-3 and C-2. In addition, building density (BD), open space ratio (OSR), shape coefficient (SC) and average perimeter-area ratio (PAR) have significant correlations with the total building energy use intensity of the block. For solar potential, floor area ratio (FAR), BD, the average number of floors (AF), OSR, SC, and PAR are highly correlated with rooftop PV energy generation, and sky view factor (SVF) is significantly correlated with rooftop PV energy generation. [ABSTRACT FROM AUTHOR]

• GF-1 and GF-6 remote sensing data have potential for wetland change detection. • Spatial-temporal attention method can identify information changes in cultivated land to wetland areas. • Sanjiang National Nature Reserve has large wetland restoration areas located near water sources. • The results of the study have important implications for the restoration and sustainable development of wetlands. Wetlands are important ecosystems, and detecting changes in wetlands plays a very important role in the management of wetland resources. Therefore, we selected Sanjiang National Nature Reserve as the study area and proposed a method in which bi-temporal GaoFen (GF) remote sensing data was combined with a spatial–temporal attention neural network (STANet) model, coupled with the selection of spectral variables. We used the bi-temporal images from GF-1 and GF-6 and compared three band selection methods, Relief F, principal component analysis, and RGB combination, and two STANet-based change detection methods (BAM: basic spatial–temporal attention module and PAM: pyramid spatial–temporal attention module) to improve the change detection of the wetland restoration. The results show that the accuracy of wetland change detection is related to different combinations of spectral variable selection and STANet models. The combination of Relief F feature selection and the BAM model led to the highest accuracy of the wetland change detection, 71.72 % for F1-Score and 76.92 % for MIou. The vector results of the change detection statistics show that the area of the Sanjiang National Nature Reserve cultivated lands converted to wetlands is 46.05 km2. This study implies that the STANet method has the capacity of detecting complex wetland restoration. Red, Bule, NIR and Red Edge1 bands of GaoFen data can provide effective information in the detection of changes in wetlands. The results of the study can be used as a reference for rational planning of wetland restoration, seeking a balance between cultivated lands and wetlands, which is important for the sustainable development of wetland. [ABSTRACT FROM AUTHOR]

Circadian clocks are endogenous timekeeping mechanisms that coordinate internal physiological responses with the external environment. EARLY FLOWERING3 (ELF3), PSEUDO RESPONSE REGULATOR (PRR9), and PRR7 are essential components of the plant circadian clock and facilitate entrainment of the clock to internal and external stimuli. Previous studies have highlighted a critical role for ELF3 in repressing the expression of PRR9 and PRR7. However, the functional significance of activity in regulating circadian clock dynamics and plant development is unknown. To explore this regulatory dynamic further, we first employed mathematical modeling to simulate the effect of the prr9/prr7 mutation on the elf3 circadian phenotype. These simulations suggested that simultaneous mutations in prr9/prr7 could rescue the elf3 circadian arrhythmia. Following these simulations, we generated all Arabidopsis elf3/prr9/prr7 mutant combinations and investigated their circadian and developmental phenotypes. Although these assays could not replicate the results from the mathematical modeling, our results have revealed a complex epistatic relationship between ELF3 and PRR9/7 in regulating different aspects of plant development. ELF3 was essential for hypocotyl development under ambient and warm temperatures, while PRR9 was critical for root thermomorphogenesis. Finally, mutations in prr9 and prr7 rescued the photoperiod-insensitive flowering phenotype of the elf3 mutant. Together, our results highlight the importance of investigating the genetic relationship among plant circadian genes. [ABSTRACT FROM AUTHOR]

Conservation of crop wild relatives is critical for plant breeding and food security. The lack of clarity on the genetic factors that lead to endangered status or extinction create difficulties when attempting to develop concrete recommendations for conserving a citrus wild relative: the wild relatives of crops. Here, we evaluate the conservation of wild kumquat (Fortunella hindsii) using genomic, geographical, environmental, and phenotypic data, and forward simulations. Genome resequencing data from 73 accessions from the Fortunella genus were combined to investigate population structure, demography, inbreeding, introgression, and genetic load. Population structure was correlated with reproductive type (i.e., sexual and apomictic) and with a significant differentiation within the sexually reproducing population. The effective population size for one of the sexually reproducing subpopulations has recently declined to ~1,000, resulting in high levels of inbreeding. In particular, we found that 58% of the ecological niche overlapped between wild and cultivated populations and that there was extensive introgression into wild samples from cultivated populations. Interestingly, the introgression pattern and accumulation of genetic load may be influenced by the type of reproduction. In wild apomictic samples, the introgressed regions were primarily heterozygous, and genome-wide deleterious variants were hidden in the heterozygous state. In contrast, wild sexually reproducing samples carried a higher recessive deleterious burden. Furthermore, we also found that sexually reproducing samples were self-incompatible, which prevented the reduction of genetic diversity by selfing. Our population genomic analyses provide specific recommendations for distinct reproductive types and monitoring during conservation. This study highlights the genomic landscape of a wild relative of citrus and provides recommendations for the conservation of crop wild relatives. Author summary: Conservation genomics offers a comprehensive approach to understand the underlying genetic and environmental factors affecting the conservation of species. Despite its importance, the conservation genomics of most crop wild relatives remains poorly understood. In this study, we investigated the population fragmentation, inbreeding, gene flow, and genetic load of a citrus wild relative, Fortunella hindsii, using a combination of genomic, geographical, environmental, and phenotypic data, as well as forward simulations. Fortunella hindsii, listed on the registry of National Key Protected Wild Plants in China, has two types of reproduction, sexual and apomictic. Conservation genomics provided insights into the genetic diversity and structure, which are critical for developing effective conservation strategies. Our analysis also helped to assess the risks of hybridization and introgression from cultivated to wild populations. We found that different patterns of introgression and genetic load may be influenced by reproductive type; for example, deleterious variants may hide in the heterozygous state in apomictic populations. Sexually reproducing samples with the self-incompatibility mechanism can prevent rapid loss of genetic diversity caused by selfing. This study serves as an example of conservation genomics and the importance of utilizing important wild relatives of crops to inform broader conservation efforts. [ABSTRACT FROM AUTHOR]

• Dongting Lake has experienced land covers changes due to extreme hydrologic events. • We use qualitative and quantitative methods for change detection in Dongting Lake. • A new change detection method Bi-SRUNet++ is proposed for change detection. • This method has the great potential to improve the accuracy of wetland change detection. • The obtained information is important for wetland management and sustainable development. The utility of semantic change detection in myriad change scenarios has garnered considerable attention in contemporary research; however, its applicability in monitoring alterations in wetland ecosystems remains incompletely elucidated. To surmount the constraints associated with binary change detection methodologies—chiefly their insufficiency in the extraction of bi-temporal attributes—we introduced the Bi-Temporal Semantic Reasoning UNet++ (Bi-SRUNet++) algorithm. This algorithm leverages the architectural strengths of UNet++ as its foundational network to precisely delineate features pertinent to multi-class change detection. As a preliminary step, the study focused on the Dongting Lake wetland in China and conducted an analysis of feature trends predicated upon the monthly Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI), as derived from Landsat 8 data spanning 2021–2022. Subsequently, the optimal temporal phases for change detection were ascertained through differential analyses between NDWI and NDVI metrics. Implementing the Bi-SRUNet++ algorithm on a pair of Sentinel-2 images, captured during the optimal phases, yielded augmented change information. Comparative evaluations reveal that the Bi-SRUNet++ algorithm, conceptualized on the framework of Bi-Temporal Semantic Reasoning Network (Bi-SRNet), surpasses the performance indices of its counterpart Semantic Segmentation and Change Detection Late Fusion (SSCD-l). Furthermore, the incorporation of the UNet++ backbone network amplifies the algorithm's capacity for semantic feature extraction, thereby enhancing the efficacy of Bi-SRUNet++ in wetland change detection. The analysis divulges that the total altered area of Dongting Lake during the 2021–2022 period amounts to 1187.97 km2, comprising a water loss of 1186.16 km2, a 715.34 km2 transformation into vegetation, and a conversion of 469.96 km2 into mudflats. The codes and partial dataset in this paper are available at: https://github.com/vivianmiumiu/Bi-SRUNetplusplus-for-SCD. [ABSTRACT FROM AUTHOR]

Buildings are one of the most important energy use sectors in cities, and forecasting the abnormal increase in building energy demand in certain climatic conditions is necessary to adjust building energy operations and implement energy policy. Accordingly, this research proposes a data-driven abnormally high energy demand detection framework in urban buildings based on their design parameters and local weather data, with the support of machine learning techniques. In this study, 71 public buildings with energy records in Jianhu city, Jiangsu province, China, were selected to abstract urban morphologies at community scale. The weather profile for the city was obtained from year 2015–2018 to create weather characteristics. Three machine learning algorithms—random forest, support vector machine, and artificial neural network—were applied to identify the months of abnormally high electricity consumption in different building types. This framework also explores key variables in the data and provides the basis for a system that prioritizes the acquisition of variables when complete data is unavailable. The results show that, with complete data, the accuracy score of the system in this study can reach 0.854 with the SVM algorithm, and the model returned an accuracy of 0.865 with the RF model after the key variable selection. Based on those results, the framework in this study can generate preemptive warnings for months with an expected abnormally high energy consumption in target buildings as a prerequisite of energy policy. • A data-driven abnormally high electricity use detection system is proposed. • Different morphologies under different weather conditions were tested. • Key factors of urban morphologies and weather conditions were identified. • Three machine learning techniques were applied for the prediction in the system. • This study can be applied for energy management in building and urban scales. [ABSTRACT FROM AUTHOR]

Abstract: Using 10-year lightning localization data observed by the TRMM (Tropical Rainfall Measuring Mission) Lightning Imaging Sensor (LIS), the relationship between lightning activity and a series of convective indices was investigated over nine monsoon-prone areas of China in which high-impact weather (HIW) events are frequently observed. Two methods were used to verify and reconstruct LIS lightning data. First, LIS lightning flash data were verified by both surface thunderstorm reports and ground-based lightning detection data. Seasonal, monthly, and 5-day distributions of LIS observed lightning activity agree well with the surface reports and ground-based lightning observations. Second, due to LIS''s low sampling frequency, a data reconstruction and compensation scheme for LIS lightning observations was designed using both LIS lightning seasonal diurnal cycles and surface thunderstorm reports. After data reconstruction, five lightning products were derived: daily mean and maximum LIS flash rate, daily mean and maximum LIS lightning cell rate, and number of lightning days per five day period. Then, a series of convective indices describing convection conditions were derived from radiosonde data according to atmospheric instability and convective potential analysis. Correlation analysis for each study region was done between 10-year lightning derived products and corresponding convective indices by 5-day periods. The correlation analysis results show that higher lightning flash rate and lightning probability are associated with more unstable air and smaller vertical wind shear in a nearly saturated lower layer in most of the study regions. But the correlation varies from region to region. The best correlation between lightning activity and convective indices was found in eastern and southern China, whereas the correlation is lowest in some inland or basin topography regions in which topographic effects are more significant. Moreover, ambient moisture plays a much more important role in the convective development of thunderstorms in southern China than other regions. Thunderstorm development mechanism differences among regions were also discussed. Based on the close relationship between lightning activity and convective indices, some regression equations for forecasting 5-day mean or maximum LIS lightning flash rate and lightning area (a thunderstorm cell) rate, and 5-day lightning days for the study regions were developed using convective indices as predictors. The verifications show that the convective index-based lightning forecast methods can provide a reasonable lightning outlook including probability and lightning flash rate forecasts for a 5-day period. [Copyright &y& Elsevier]

Accurate measurement of forest growing stem volume (GSV) is important for forest resource management and ecosystem dynamics monitoring. Optical remote sensing imagery has great application prospects in forest GSV estimation on regional and global scales as it is easily accessible, has a wide coverage, and mature technology. However, their application is limited by cloud coverage, data stripes, atmospheric effects, and satellite sensor errors. Combining multi-sensor data can reduce such limitations as it increases the data availability, but also causes the multi-dimensional problem that increases the difficulty of feature selection. In this study, GaoFen-2 (GF-2) and Sentinel-2 images were integrated, and feature variables and data scenarios were derived by a proposed adaptive feature variable combination optimization (AFCO) program for estimating the GSV of coniferous plantations. The AFCO algorithm was compared to four traditional feature variable selection methods, namely, random forest (RF), stepwise random forest (SRF), fast iterative feature selection method for k-nearest neighbors (KNN-FIFS), and the feature variable screening and combination optimization procedure based on the distance correlation coefficient and k-nearest neighbors (DC-FSCK). The comparison indicated that the AFCO program not only considered the combination effect of feature variables, but also optimized the selection of the first feature variable, error threshold, and selection of the estimation model. Furthermore, we selected feature variables from three datasets (GF-2, Sentinel-2, and the integrated data) following the AFCO and four other feature selection methods and used the k-nearest neighbors (KNN) and random forest regression (RFR) to estimate the GSV of coniferous plantations in northern China. The results indicated that the integrated data improved the GSV estimation accuracy of coniferous plantations, with relative root mean square errors (RMSErs) of 15.0% and 19.6%, which were lower than those of GF-2 and Sentinel-2 data, respectively. In particular, the texture feature variables derived from GF-2 red band image have a significant impact on GSV estimation performance of the integrated dataset. For most data scenarios, the AFCO algorithm gained more accurate GSV estimates, as the RMSErs were 30.0%, 23.7%, 17.7%, and 17.5% lower than those of RF, SRF, KNN-FIFS, and DC-FSCK, respectively. The GSV distribution map obtained by the AFCO method and RFR model matched the field observations well. This study provides some insight into the application of optical images, optimization of the feature variable combination, and modeling algorithm selection for estimating the GSV of coniferous plantations. [ABSTRACT FROM AUTHOR]