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Further research is needed on the capability of residential communities to achieve energy self-sufficiency under the constraints of current standards of land use, in particular for the Hot Summer and Cold Winter climate zone (HSCW) of China, where the majority of communities are dominated by high floor-area ratios, thus high-rise dwellings, namely less solar potential per unit floor area, while most residents adopt a "part-time, part-space" pattern of intermittent energy use behavior, thus using relatively low energy per unit floor area. This study examines 150 communities in Changsha to identify morphological indicators and develop a prototype model utilizing the Grasshopper platform. Community morphology is simulated and optimized by taking building location, orientation, and number of floors as independent variables and building energy consumption, solar PV generation, and energy self-sufficiency rate as dependent variables. The results reveal that the morphology optimization can achieve a 4.26% decrease in building energy consumption, a 45% increase in PV generation, and a 13.2% enhancement in energy self-sufficiency, with the optimal being 39%. It highlights that energy self-sufficiency cannot be achieved solely through morphology improvements. Moreover, the study underscores the crucial role of community orientation in maximizing energy self-sufficiency, with the south–north orientation identified as the most beneficial. Additionally, a layout characterized by a horizontally closed and staggered pattern and a vertically scattered arrangement emerges as favorable for enhancing energy self-sufficiency. These findings underscore the importance of considering morphological factors, particularly community orientation, in striving towards energy-self-sufficient high-rise residential communities within the HSCW climate zone of China. [ABSTRACT FROM AUTHOR]

This study investigates the association between prenatal exposure to per- and polyfluoroalkyl substances (PFASs) and the incidence and frequency of respiratory tract infections (RTIs) in preschool children. We selected 527 mother–infant pairs from Wuhan Healthy Baby Cohort (WHBC), China. Ten PFASs were measured in umbilical cord serum, and we collected data on common RTIs in preschool children aged 4 years through a questionnaire. Associations of single PFASs with the incidence and frequency of RTIs were analyzed via Logistic regression and Poisson regression, while the collective effect was assessed by weighted quantile sum (WQS) regression. Furthermore, stratified and interaction analyses were performed to evaluate if there were sex-specific associations. We found a positive correlation between perfluorododecanoic acid (PFDoDA) and the incidence of tonsillitis, with several PFASs also showing positive associations with its frequency. Moreover, perfluorotridecanoic acid (PFTrDA) showed a positive link with the frequency of common cold. The results of WQS regression revealed that after adjusting for other covariates, PFASs mixture showed a positive association with the incidence of tonsillitis, the frequency of common cold, and episodes. In particular, perfluoroundecanoic acid (PFUnDA), PFDoDA, PFTrDA, perfluorodecanoic acid (PFDA) and 8:2 chlorinated polyfluorinated ether sulfonic acid (8:2 Cl-PFESA) had the most significant impact on this combined effect. The results suggest that both single and mixed exposures to PFASs may cause RTIs in preschool children. However, there was no statistically significant interaction between different PFASs and sex. [ABSTRACT FROM AUTHOR]

Occupant behavior has an important impact on building energy consumption, and the accuracy of an occupant behavior model directly affects the reliability of energy consumption simulation results. Ultra-low energy buildings are crucial to achieving building energy conservation and carbon dioxide reduction in China. In order to effectively promote the development of ultra-low energy buildings in Hot Summer and Cold Winter Climate Zones. where most residents adopt a "part-time, part-space" pattern of intermittent energy use behavior, and to solve the problem of poor indoor thermal environments and the high incremental cost of ultra-low energy, the study described in this paper takes Changsha as an example to carry out a multi-objective optimization study on ultra-low energy housing using a probabilistic behavioral model. On the basis of a probability model representing the residents' actual behavior in Changsha, the optimization objective indicators, key variables and the technology benchmarks for ultra-low energy building were determined, then multi-objective optimization was carried out for a range of energy efficient technologies to obtain the Pareto optimal solutions. The results showed that the set of optimal solutions could reduce energy demand by 50.2 to 60.2% and reduce indoor thermal discomfort time by 3.52–11.09% compared with those of a reference base case, which just meets the requirements of the current design standard for energy efficient domestic buildings. An optimum solution for energy savings and indoor thermal comfort, along with economic costs, was identified, which can assist in decision-making by providing different preferences and provide useful reference for the design of ultra-low energy buildings in Hot Summer and Cold Winter Climate regions. [ABSTRACT FROM AUTHOR]

Vegetation optical depth (VOD), as a microwave-based estimate of vegetation water and biomass content, is increasingly used to study the impact of global climate and environmental changes on vegetation. However, current global operational VOD products have a coarse spatial resolution (~25 km), which limits their use for agriculture management and vegetation dynamics monitoring at regional scales (1–5 km). This study aims to retrieve high-resolution VOD from the C-band Sentinel-1 backscatter data over a grassland of the Heihe River Basin in northwestern China. The proposed approach used an analytical solution of a simplified Water Cloud Model (WCM), constrained by given soil moisture estimates, to invert VOD over grassland with 1 km spatial resolution during the 2018–2020 period. Our results showed that the VOD estimates exhibited large spatial variability and strong seasonal variations. Furthermore, the dynamics of VOD estimates agreed well with optical vegetation indices, i.e., the mean temporal correlations with normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI) were 0.76, 0.75, and 0.75, respectively, suggesting that the VOD retrievals could precisely capture the dynamics of grassland. [ABSTRACT FROM AUTHOR]

Coal gangue (CG) represents a huge amount of industrial solid waste in China, and usually is used as a coarse aggregate to produce low-strength coal-gangue-based concrete. In this paper, in order to prove the possibility to obtain a higher-strength concrete with a higher CG utilization rate, reactive powder concrete (RPC) with coal gangue as a sand replacement at different replacement ratios was studied. RPC samples were prepared by replacing natural river sand (RS) with CG sand at different CG/RS weight ratios from 0–100% at intervals of 25%. Mechanical tests were carried out, and the microstructure features of RPC samples at 28 days were characterized. The test results showed that strong back shrinkage of strength existed. On days 7 and 14, the flexural strengths of samples with CG/RS replacement ratios of 0–75% fluctuated around the mean value. Strengths of samples with a CG/RS replacement ratio of 100% dropped off. However, on day 28, the flexural strengths of samples with CG were all lower than the strengths of samples on days 7 and 14. The flexural strengths and compressive strengths of the RPC with a CG/RS replacement ratio of 100% on day 14 were 14.09 MPa and 37.03 MPa, respectively, which decreased to 6.42 MPa and 28.44 MPa, respectively, on day 28. Compared with natural river sand, CG sand reduced the working performance, compressive strength, and flexural strength of RPC. Microscopic analysis showed that on day 28, increasing the CG replacement ratio could inhibit cement hydration, weaken the interface transition zone, and lead to the degradation of the RPC's performance. Modification of CG sand would be helpful to obtain higher-strength concrete. [ABSTRACT FROM AUTHOR]

In this paper, a deep learning long-short-term memory (LSTM) method is applied to the forecasting of the critical frequency of the ionosphere F2 layer (foF2). Hourly values of foF2 from 10 ionospheric stations in China and Australia (based on availability) from 2006 to 2019 are used for training and verifying. While 2015 and 2019 are exclusive for verifying the forecasting accuracy. The inputs of the LSTM model are sequential data for the previous values, which include local time (LT), day number, solar zenith angle, the sunspot number (SSN), the daily F10.7 solar flux, geomagnetic the Ap and Kp indices, geographic coordinates, neutral winds, and the observed value of foF2 at the previous moment. To evaluate the forecasting ability of the deep learning LSTM model, two different neural network forecasting models: a back-propagation neural network (BPNN) and a genetic algorithm optimized backpropagation neural network (GABP) were established for comparative analysis. The foF2 parameters were forecasted under geomagnetic quiet and geomagnetic disturbed conditions during solar activity maximum (2015) and minimum (2019), respectively. The forecasting results of these models are compared with those of the international reference ionosphere model (IRI2016) and the measurements. The diurnal and seasonal variations of foF2 for the 4 models were compared and analyzed from 8 selected verification stations. The forecasting results reveal that the deep learning LSTM model presents the optimal performance of all models in forecasting the time series of foF2, while the IRI2016 model has the poorest forecasting performance, and the BPNN model and GABP model are between two of them. [ABSTRACT FROM AUTHOR]

Cucumber (Cucumis sativus L.) is one of the main vegetable crops in China. The physiological cultivation mechanism and gene function characteristics of cucumber are of great significance to the construction of modern agriculture. Due to the low genetic transformation rate of cucumber, only in situ hybridization, which does not involve the progress of gene modified transformation, is convenient to study mRNA localization, so it is more suitable for determination on mRNA localization in the mature tissue of cucumber. At present, the existing in situ hybridization technology system is more suitable for cucumber meristem than for the mature tissue of cucumber seedlings. Therefore, we optimized the traditional plant in situ hybridization protocol. Taking a known gene CsNPF7.2 (Nitrate Transporter Families protein) as an example, we then optimized the steps of plant tissue culture, gene probe preparation, plant material sampling and fixation, preparation of cross section, hybridization pretreatment, hybridization incubation, chromogenic reaction, microscopy examination, and treatment after reaction termination in order to obtain a new RNA in situ hybridization technique suitable for identification on mRNA localization in mature tissues of cucumber seedlings. This optimized technique will ensure the yield of probes, the integrity of RNA molecules, and the clarity and integrity of plant tissue structure, which is conducive to the study of gene function and screening of key genes in cucumber. [ABSTRACT FROM AUTHOR]