Search

Global climate change has necessitated the implementation of green infrastructure that is resilient in a manner of sustainable development. The current understanding of green infrastructure resilience is hindered by the divergence of generic properties and performance in adapting to uncertain disturbances. This study develops an operational methodology that integrates structural and functional properties of green infrastructure, and their connections to shape the general resilience. A further empirical study is conducted in the context of Shenzhen City, where the effectiveness of resilient connections is correlated with the distribution of waterlogging. We demonstrate that green infrastructure present different levels of resilience in terms of its structural composition and functional performance. The Shenzhen city shows a high capacity to maintain soil retention stability, but a feeble capacity regarding water yield and gross primary productivity. The resilient connections of green infrastructure are highly centralized, with a few pivotal nodes performing a high degree of connectivity. It shows that a total of 52.2% of resilient lines are identified as belonging to the fourth level but linking the majority of the nodes. Enhancing the general resilience of green infrastructure could facilitate its adaptation to specific disturbances such as waterlogging. When correlated the resilient connections of green infrastructure with the distribution of waterlogging, a distance of 1.6 km from the waterlogging points is significantly identified where the residuals of the entropy index display the lowest variance. As the distance increases, the composite entropy index initially decreases and then increases. We suggest that the alignment of generic properties and specified performance of green infrastructure is essential in the pursuit of sustainable development. • An operational methodology for general resilience assessment. • Emphasize resilient properties of green infrastructure. • Regions with elevated resilience demonstrate a low degree of waterlogging. • The alignment of general and specified resilience. [ABSTRACT FROM AUTHOR]

Most of the news headline generation models that use the sequence-to-sequence model or recurrent network have two shortcomings: the lack of parallel ability of the model and easily repeated generation of words. It is difficult to select the important words in news and reproduce these expressions, resulting in the headline that inaccurately summarizes the news. In this work, we propose a TD-NHG model, which stands for news headline generation based on an improved decoder from the transformer. The TD-NHG uses masked multi-head self-attention to learn the feature information of different representation subspaces of news texts and uses decoding selection strategy of top-k, top-p, and punishment mechanisms (repetition-penalty) in the decoding stage. We conducted a comparative experiment on the LCSTS dataset and CSTS dataset. Rouge-1, Rouge-2, and Rouge-L on the LCSTS dataset and CSTS dataset are 31.28/38.73, 12.68/24.97, and 28.31/37.47, respectively. The experimental results demonstrate that the proposed method can improve the accuracy and diversity of news headlines. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things (IoT) technology, IoT terminal nodes are facing many challenges in data storage, distribution, and data management. In particular, in the IoT terminal nodes considering access cost, the corresponding data distribution and storage are professional, complex, and miscellaneous. Based on the abovementioned current situation, this article innovatively proposes a complex sensor data placement algorithm based on the cloud storage distribution of IoT terminal nodes. Under this algorithm, the accurate division of IoT data I/O methods is realized through reasonable configuration. Through the adaptive sensing algorithm, while fully considering the access cost of the algorithm, the performance of the IoT data storage system is further optimized. In the corresponding terminal node load balancing problem, this article innovatively proposes the terminal node data sorting and distribution algorithm through the node data. The sorting and distribution algorithm realizes the precise segmentation of the IoT data to be processed, thereby realizing the improvement of data reading and processing speed. Based on the proposed algorithm, this article designs a load balancing cloud storage data distribution optimization system of IoT terminal nodes considering access cost and carries out experimental verification in a real environment. The experimental results show that the data pattern division accuracy corresponding to the proposed distribution strategy is improved to 97.13% and the corresponding data access efficiency is improved to 98.3%, compared with the traditional distribution strategy. Therefore, the data distribution strategy proposed in this article has obvious performance advantages and further promotion value. [ABSTRACT FROM AUTHOR]

Since the implementation of the Chinese economic reforms. The habitat quality of coastal has gradually deteriorated with economic development, but the concept of "ecological construction" has slowed the negative trend. For quantitative analysis of the correlation between the Pearl River Delta urban expansion and changes in habitat quality under the influence of the policy, we first analyzed the habitat quality change based on the InVEST model and then measured the impact of construction land expansion on the habitat quality through habitat quality change index (HQCI) and contribution index (CI) indicators. Finally, the correlation between urbanization level and habitat quality was evaluated using geographically weighted regression (GWR) and the Self-organizing feature mapping neural network (SOFM). The results indicated that: (1) during the study period from 2000 to 2020, habitat quality declined due to urban sprawl, indicating a deterioration of ecological structure and function, and the decrease was most significant from 2000 to 2010. (2) The urbanization index had a negative effect on the habitat quality, but the negative effect have improved after 2000, reflecting the positive effect of policies such as "ecological civilization construction" (3) The implementation degree of ecological civilization varies greatly among cities in the study area: Shenzhen, Dongguan, Foshan, and Zhongshan have the best level of green development. These results reflect the positive role of policies in the prevention of damage to habitat quality caused by economic development and provide a reference for the formulation of sustainable urban development policies with spatial differences. [ABSTRACT FROM AUTHOR]

The problem of urban waterlogging has consistently affected areas of southern China, and has generated widespread concerns among the public and professionals. The geographically weighted regression model (GWR) is widely used to reflect the spatial non-stationarity of parameters in different locations, with the relationship between variables able to change with spatial position. In this research, Shenzhen City, which has a serious waterlogging problem, was used as a case study. Several key results were obtained. (1) The spatial autocorrelation of flood spot density in Shenzhen was significant at the 5% level, but because the Z value was not large it was not very obvious. (2) The degree of impact on flood disasters from large to small was: Built up_ DIVISION > SHDI > Built up_ COHESION > CONTAG > Built up_ LPI. (3) The degree of waterlogging disasters in higher altitude regions was less affected by the landscape pattern. The results of this study highlight the important role of the landscape pattern on waterlogging disasters and also indicate the different impacts of different regional landscape patterns on waterlogging disasters, which provides useful information for planning the landscape pattern and controlling waterlogging. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM 2.5) can pose health problems for humans following urbanization. Because urban vegetation has a large surface area to filter PM 2.5 out of the air, it can be an effective long-term way to mitigate air pollution. Various studies have quantified PM 2.5 removal by vegetation in cities, but the spatial variability of removal within cities and future scenarios have not been well documented. To ensure more reasonable and effective urban tree planting regimes, we used the spatiotemporal i-Tree Eco model combined with the vertical distribution of vegetation in a case study in Shenzhen City, China. The results indicated that the PM 2.5 removal by urban vegetation in 2015 was 1000.1 tons, and the average removal rate by vegetation was 1.6 g m−2 year−1. A maximum hourly local air quality improvement of up to 3% could be achieved, with an average of 1%, which differed significantly with elevation. In terms of vegetation type, evergreen shrubs, evergreen broadleaved forests, and evergreen needle-leaved forests had the highest removal efficiency within <100, 100–300, and >300 m, respectively. For five future planting scenarios, by increasing vegetation cover by 5% in different elevation zones (<100, 100–300, and >300 m), an annual amount of 1220.6–1308 tones could be achieved. Specifically, it was estimated that an increase in evergreen shrubs cover in the developed area (<100 m) would have the best removal potential. Unlabelled Image • A modified dry deposition model was used to assess air pollution removal. • PM 2.5 removal rate by urban vegetation differed across the elevation significantly. • PM 2.5 removal mainly depends on LAI, rather than PM 2.5 concentration. • Evergreen shrubs would be the most effective vegetation type in the developed area. [ABSTRACT FROM AUTHOR]

Motivation Accurate prediction and interpretation of ligand bioactivities are essential for virtual screening and drug discovery. Unfortunately, many important drug targets lack experimental data about the ligand bioactivities; this is particularly true for G protein-coupled receptors (GPCRs), which account for the targets of about a third of drugs currently on the market. Computational approaches with the potential of precise assessment of ligand bioactivities and determination of key substructural features which determine ligand bioactivities are needed to address this issue. Results A new method, SED, was proposed to predict ligand bioactivities and to recognize key substructures associated with GPCRs through the coupling of s creening for Lasso of long e xtended-connectivity fingerprints (ECFPs) with deep neural network training. The SED pipeline contains three successive steps: (i) representation of long ECFPs for ligand molecules, (ii) feature selection by screening for Lasso of ECFPs and (iii) bioactivity prediction through a deep neural network regression model. The method was examined on a set of 16 representative GPCRs that cover most subfamilies of human GPCRs, where each has 300–5000 ligand associations. The results show that SED achieves excellent performance in modelling ligand bioactivities, especially for those in the GPCR datasets without sufficient ligand associations, where SED improved the baseline predictors by 12% in correlation coefficient (r2) and 19% in root mean square error. Detail data analyses suggest that the major advantage of SED lies on its ability to detect substructures from long ECFPs which significantly improves the predictive performance. Availability and implementation The source code and datasets of SED are freely available at https://zhanglab.ccmb.med.umich.edu/SED/. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

While the aerosol optical depth (AOD) product from the Visible Infrared Imaging Suite (VIIRS) instrument has proven effective for estimating regional ground-level particle concentrations with aerodynamic diameters less than 2.5 μm (PM 2.5), its performance at larger spatial scales remains unclear. Despite the wide application of statistical models in building ground-level PM 2.5 satellite remote sensing retrieval models, a limited number of studies have considered the spatiotemporal heterogeneities for model structures. Taking China as the study area, we used the VIIRS AOD, together with multi-source auxiliary variables, to develop a spatially structured adaptive two-stage model to estimate ground-level PM 2.5 concentrations at a 6-km spatial resolution. To this end, we first defined and calculated a dual distance from the ground-level PM 2.5 monitoring data. We then applied the unweighted pair-group method with arithmetic means on dual distances and obtained 13 spatial clusters. Subsequently, we combined the time fixed effects regression (TEFR) model and geographically weighted regression (GWR) model to develop the spatially structured adaptive two-stage model. For each spatial cluster, we examined all possible combinations of auxiliary variables and determined the best model structure according to multiple statistical test results. Finally, we obtained the PM 2.5 estimates through regression mapping. At least seven model-fitting data records per day made a good threshold that could best overcome the model overfitting induced by the second-stage GWR model at the minimum price of losing samples. The overall model fitting and ten-fold cross validation (CV) R2 were 0.82 and 0.60, respectively, under that threshold. Model performances among different spatial clusters differed to a certain extent. High-CV R2 values always exceeded 0.6 while low-CV R2 values less than 0.5 also existed. Both the size of the model-fitting data records and the extent of urban-industrial characteristics of spatial clusters accounted for these differences. The PM 2.5 estimates agreed well with the PM 2.5 observations with correlation coefficients all exceeding 0.5 at the monthly, seasonal, and annual scales. East of Hu's line and north of the Yangtze River were characterized by high PM 2.5 concentrations. This study contributes to the understanding of how well VIIRS AOD can retrieve ground-level PM 2.5 concentrations at the national scale and strategies for building ground-level PM 2.5 satellite remote sensing retrieval models. [ABSTRACT FROM AUTHOR]

Highlights: • The relationship integrated individual difference and temporal effect simultaneously. • The SEC and WY showed co-occurrence patterns. • The SEC was positively affected by WY and the year the data were collected in. • The enhancement of co-benefits between SEC and WY will achieve win-win outcomes. Abstract Recent studies on ecosystem services have highlighted the relationship between multiple services. It is widely accepted that the increase in one ecosystem service may alter the provision of another. However, the individual difference and time effect are often difficult to integrate when the relationship is explored. In this study, we took soil erosion control (SEC) and water yield (WY) as examples to analyze relationships between multiple ecosystem services. Firstly, the biophysical values were evaluated using seven series of land use data from 2000 to 2012. Secondly, the spatial relationship was explored using local autocorrelation and the barycenter model on the basis of correlation analysis on regional and watershed scale. Finally, the pooled regression models, fixed effects models, two-way fixed effects models, and random effects models were introduced to explore the relationship of two ecological indicators for considering time effect and individual differences. We concluded that the SEC and WY presented a positive linear correlation on a watershed scale across time, and showed co-occurrence patterns from a spatial perspective. The SEC was positively affected by WY and the year the data were collected in. There were opportunities to enhance co-benefits between SEC and WY to achieve win-win outcomes. [ABSTRACT FROM AUTHOR]

• Construct a spatial flow model of ecosystem services. • The spatiotemporal differentiation was analyzed by GTWR model. • Spatial pattern optimization based on Bayesian belief network. • Provides a reference for spatial flow pattern at the scale of urban agglomeration. Exploring the spatial flow pattern of ecosystem services and clarifying the transmission path between the supply area and the demand area will help to formulate more scientific and reasonable ecological protection policies. This paper takes the rapid urbanization area representing the Pearl River Delta urban agglomeration as an example. Using Invest model to quantitative the assessment of the supply and demand of carbon sequestration services and water ecosystem services from 2000 to 2020, a spatial flow model of ecosystem services is constructed to clarify the spatial flow of regional ecosystem services pattern. In addition, the Geo-detector model is used to explore the driving factors of the supply and demand relationship of ecosystem services, and the spatiotemporal geographic weighted regression model is further used to analyze the temporal and spatial differentiation of the impact degree. Finally, based on the Bayesian belief network, the optimal state factor configuration is selected to optimize the spatial pattern, and the corresponding optimization strategy is given. The main conclusions are as follows: (1) The supply of carbon sequestration services in the Pearl River Delta decreased gradually during 2000–2020, while the demand increased gradually. The supply of water ecosystem services increased first and then decreased, while the demand showed a downward trend. (2) In terms of spatial heterogeneity of supply and demand matching, the main driving factors of carbon sequestration services included night light brightness value, temperature and vegetation index; The main driving factors of water ecosystem services include night light brightness value, land use type and vegetation index. (3) In terms of optimal areas for supply and demand matching, carbon sequestration services were mainly distributed in Zhaoqing, Huizhou and Jiangmen; The optimal matching areas of water ecosystem service supply and demand were mainly distributed in Zhaoqing, Guangzhou, Huizhou and other cities. [ABSTRACT FROM AUTHOR]

Motivation: Precise assessment of ligand bioactivities (including IC50, EC50, Ki, Kd, etc.) is essential for virtual screening and lead compound identification. However, not all ligands have experimentally determined activities. In particular, many G protein-coupled receptors (GPCRs), which are the largest integral membrane protein family and represent targets of nearly 40% drugs on the market, lack published experimental data about ligand interactions. Computational methods with the ability to accurately predict the bioactivity of ligands can help efficiently address this problem. Results: We proposed a new method, WDL-RF, using weighted deep learning and random forest, to model the bioactivity of GPCR-associated ligand molecules. The pipeline of our algorithm consists of two consecutive stages: (i) molecular fingerprint generation through a new weighted deep learning method, and (ii) bioactivity calculations with a random forest model; where one uniqueness of the approach is that the model allows end-to-end learning of prediction pipelines with input ligands being of arbitrary size. The method was tested on a set of twenty-six non-redundant GPCRs that have a high number of active ligands, each with 200-4000 ligand associations. The results from our benchmark show that WDL-RF can generate bioactivity predictions with an average rootmean square error 1.33 and correlation coefficient (r²) 0.80 compared to the experimental measurements, which are significantly more accurate than the control predictors with different molecular fingerprints and descriptors. In particular, data-driven molecular fingerprint features, as extracted from the weighted deep learning models, can help solve deficiencies stemming from the use of traditional hand-crafted features and significantly increase the efficiency of short molecular fingerprints in virtual screening. [ABSTRACT FROM AUTHOR]

Correlation between urbanization and environmental pollution is a major focus of study in geography, environmental science, and economics. Particulate matter is the primary pollutant of air pollution and made up of heavy metal, organic carbon and aromatic hydrocarbon and complicated chemicals. PM 2.5 are fine particulate matter with diameters that are less than 2.5 μm. The aim of this study on the relationship between urbanization and PM 2.5 concentration is to achieve a win-win situation of both economic development and environmental protection, which is of great significance to sustainable development in China. This paper uses PM 2.5 remote sensing data and statistical yearbook data from 2000 to 2011 to build four panel data models within the urbanization-PM 2.5 concentration Environmental Kuznets Curve (EKC) framework. The goal is to find out the correlations between PM 2.5 concentration and economic urbanization, population urbanization, and space urbanization. Furthermore, scenario simulations are set to predict when China will reach inflection point and achieve its target concentration. Results show that the relationship between economic urbanization and PM 2.5 concentration is an inverted N-shaped or inverted U-shaped curve. Most cities in East China have reached the second inflection point of inverted-N curve to step into the win-win stage while many cities in Middle China still need 10–15 years to arrive at the inflection point of the inverted-U curve. Therefore, China is under great pressure to prevent PM 2.5 pollution and pursue more targeted PM 2.5 -reduction policies for air quality improvement. [ABSTRACT FROM AUTHOR]

Satellite-based remote sensing data have been widely used in estimating ground-level PM 2.5 concentrations as it can provide spatially detailed information. Most modern satellite-based PM 2.5 estimates use statistical models that demand dense PM 2.5 monitoring networks. As the national PM 2.5 monitoring networks in China were not finished until the end of 2012, the research related to PM 2.5 is relatively unsubstantial. To further improve the accuracy and application of remote sensing based estimation models for PM 2.5 and take advantage of the newly established networks, we employed a time fixed effects regression model and geographically weighted regression model to develop a spatiotemporal statistical model that estimated ground-level PM 2.5 concentrations in Beijing–Tianjin–Hebei. The time fixed effects regression model used the aerosol optical depth (AOD) data from the VIIRS (Visible Infrared Imaging Radiometer Suite) instrument as the major predictive variable along with several other dependent variables, including some factors uncommonly discussed in previous literature, i.e., the satellite-derived NO 2 concentrations of the previous day (NO 2 _Lag) and four directional wind vectors, and estimated day-by-day ground-level PM 2.5 surfaces. The geographically weighted regression model used the residuals from the time fixed effects regression model as the dependent variable and the AOD value as the independent variable. Through adding the estimated residuals back to previous surfaces, we obtained the final prediction maps of ground-level PM 2.5 concentrations in Beijing–Tianjin–Hebei with a spatial resolution of 6 km × 6 km. The results were as follows. i). The spatiotemporal statistical model performed satisfactorily in that it successfully captured both the temporal and spatial variations in the PM 2.5 –AOD relationships. The coefficient of determination (R 2 ), mean prediction error (MPE), and root-mean-square error (RMSE) were 0.88301, 8.1331 μg/m 3 , and 13.0574 μg/m 3 , respectively, during model fitting and 0.71889, 12.2712 μg/m 3 , and 19.2927 μg/m 3 , respectively, during model validation. ii). Incorporating the NO 2 _Lag in the time fixed effects regression model significantly improved the model's performance and it played a positive role in ground-level PM 2.5 concentrations. Replacing the simple wind speed with four directional wind vectors was helpful for the model's performance. iii). Meteorological factors and land use characteristics significantly affected the PM 2.5 –AOD relationships. The temperature and surface relative humidity (SRH) played a positive role, whereas the rainfall, planet boundary layer height (PBLH), average relative humidity in the PBLH (RH_PBLH), four directional wind vectors, and normalized difference vegetation index (NDVI) played a negative role. iv). The prediction maps revealed that fine particle pollution in Beijing–Tianjin–Hebei is severe and the pollution pattern presents relatively strong seasonal heterogeneity and southeast–northwest spatial heterogeneity. [ABSTRACT FROM AUTHOR]

Rainfall-runoff modeling is very important for Water Resources Management because accurate and timely prediction can avoid accidents, such as the life risk, economic losses, etc. This paper proposed the novel hybrid optimization algorithm to combine Neural Network (NN) for rainfall-runoff modeling, namely HGASA-NN. Firstly, a novel and specialized hybrid optimization strategy by incorporating Simulated Annealing algorithm (SA) into Genetic Algorithm (GA) was used to train the initial connection weights and thresholds of NN. Secondly, the Back Propagation (BP) algorithm was adjusted the final weights and biases. Finally, a numerical example of daily rainfall-runoff data was used to elucidate the forecasting performance of the proposed HGASA-NN model. The HGASA-NN can make use of not only strong global searching ability of the GASA, but also strong local searching ability of the BP algorithm. The forecasting results indicate that the proposed model yields more accurate forecasting results than the BP-NN and pure GA training NN model. Therefore, the HGASA-NN model is a promising alternative for rainfall-runoff forecasting. [ABSTRACT FROM AUTHOR]

In early 2008, forest ecosystems in southern China suffered damage due to a severe ice storm disaster. The area and degree of forest damage caused by the ice storm was assessed using Satellite Pour l’Observation de la Terre (SPOT)-Vegetation images for Guangdong Province acquired between 1999 and 2008. By using the maximum value composition method and image thresholding techniques, the forest vegetation loss, expressed as the change in net primary productivity (NPP) and two indicators (I1,I2), was estimated. The damage threshold was determined by comparing the standard deviation of pixels of the undamaged areas in 2008 and other years without any disaster, which was 10%. The area of damaged forest vegetation was 47,670 km2, with the northern Guangdong Province most seriously affected. The total loss of NPP for forest vegetation was 50,578,055 t (DW) year−1, with 52 counties (43.7%) suffering forest vegetation damage. Evergreen coniferous forest was most widely affected, but evergreen broad-leaved forest was the most severely damaged vegetation type. Terrain topography influenced the damage to forest vegetation, which was found to increase with increasing elevation and slope gradient. The range and degree of damaged forest determined by remote-sensing data is consistent with the extent of the ice storm, indicating that this study provides a new approach for rapid assessment of forest disasters at a regional scale. [ABSTRACT FROM AUTHOR]

The article presents the methods to encourage the Standardised Patient (SP) teaching methodology in education. It mentions that the SP is used to ensure uniformity in the content and evaluation methods and standardisation of replies from different medical students. It mentions that SPs played an important role in supporting the training of professional and high-quality medical students.

• Opportunity and potential determine cultural ecosystem services (CESs). • A Bayesian belief network was created to model the CES budget. • Budget expectation reveals the supply–demand balance of CESs. • The key influencing factors were identified through a sensitivity analysis. • The results provide a basis for Shenzhen to build a livable city. Sufficient research on cultural ecosystem services (CESs) helps decision makers realize developmental and environmental goals in urban planning, budget allocation, and municipal service provision. The Bayesian belief network (BBN), as a semi-quantitative model, can be used to infer the supply and demand match for CESs. Taking Shenzhen as the study area, we evaluated the CES supply and demand. We used a BBN to simulate the CES supply and demand and identify the key influencing factors affecting the CES budget. CES budget expectations were created to indicate the CES supply and demand balance. The main conclusions are as follows. (1) Considering the community as the evaluation unit, the satisfaction rate of CES demand was 62.5% in Shenzhen. (2) Communities with a high possibility of a CES deficit are primarily distributed in Futian, Nanshan, Luohu, and Bao'an Districts, accounting for 7.45% of the area of Shenzhen. (3) The population density, CES supply potential, and CES supply opportunity jointly determined the CES supply and demand balance. (4) The CES budget expectation results demonstrate that communities with a high risk of imbalance are widely distributed, and balanced communities are primarily concentrated in the Guangming, Pingshan, and Dapeng New Areas. (5) It is necessary to enhance the fairness of CES supply in Shenzhen through the promotion of green space, improvement of infrastructure, and interconnection of communities. By combining BBN with CES as an exercise, this study aims to enrich the methods for evaluating the CES supply and demand balance, providing scientific suggestions for Shenzhen to build a livable city and achieve the Sustainable Development Goals. [ABSTRACT FROM AUTHOR]

PM2.5 refers to particulate matter (PM) in air that is less than 2.5μm in aerodynamic diameter, which has negative effects on air quality and human health. PM2.5 is the main pollutant source in haze occurring in Beijing, and it also has caused many problems in other cities. Previous studies have focused mostly on the relationship between land use and air quality, but less research has specifically explored the effects of urban landscape patterns on PM2.5. This study considered the rapidly growing and heavily polluted Beijing, China. To better understand the impact of urban landscape pattern on PM2.5 pollution, five landscape metrics including PLAND, PD, ED, SHEI, and CONTAG were applied in the study. Further, other data, such as street networks, population density, and elevation considered as factors influencing PM2.5, were obtained through RS and GIS. By means of correlation analysis and stepwise multiple regression, the effects of landscape pattern on PM2.5 concentration was explored. The results showed that (1) at class-level, vegetation and water were significant landscape components in reducing PM2.5 concentration, while cropland played a special role in PM2.5 concentration; (2) landscape configuration (ED and PD) features at class-level had obvious effects on particulate matter; and (3) at the landscape-level, the evenness (SHEI) and fragmentation (CONTAG) of the whole landscape related closely with PM2.5 concentration. Results of this study could expand our understanding of the role of urban landscape pattern on PM2.5 and provide useful information for urban planning. [ABSTRACT FROM AUTHOR]

According to the evidence from both theoretical calculations and experimental findings, conjugated ladder polymers containing large π-conjugated structure, a high number of nitrogen heteroatoms, and a multiring aromatic system, could be an ideal organic anode candidate for lithium-ion batteries (LIBs). In this report, we demonstrated that the nanostructured polyazaacene analogue poly(1,6-dihydropyrazino[2,3g]quinoxaline-2,3,8-triyl-7-(2 H)-ylidene-7,8-dimethylidene) (PQL) shows high performance as anode materials in LIBs: high capacity (1750 mAh g−1, 0.05C), good rate performance (303 mAh g−1, 5C), and excellent cycle life (1000 cycles), especially at high temperature of 50 °C. Our results suggest nanostructured conjugated ladder polymers could be alternative electrode materials for the practical application of LIBs. [ABSTRACT FROM AUTHOR]

According to the evidence from both theoretical calculations and experimental findings, conjugated ladder polymers containing large π-conjugated structure, a high number of nitrogen heteroatoms, and a multiring aromatic system, could be an ideal organic anode candidate for lithium-ion batteries (LIBs). In this report, we demonstrated that the nanostructured polyazaacene analogue poly(1,6-dihydropyrazino[2,3g]quinoxaline-2,3,8-triyl-7-(2 H)-ylidene-7,8-dimethylidene) (PQL) shows high performance as anode materials in LIBs: high capacity (1750 mAh g−1, 0.05C), good rate performance (303 mAh g−1, 5C), and excellent cycle life (1000 cycles), especially at high temperature of 50 °C. Our results suggest nanostructured conjugated ladder polymers could be alternative electrode materials for the practical application of LIBs. [ABSTRACT FROM AUTHOR]

The article presents a study which discusses the use of poly(benzobisimidazobenzophenanthroline) (BBL) and its analogue SBBL nanoparticles as anode materials for rechargeable lithium-ion batteries (LIBs). Topics include the use of various organic materials, including thioethers, organodisulfides, and conducting polymers as electrodes for LIBs, the challenges of developing organic electrodes, and the importance of organic compounds in overcoming the hindrance of low electrical conductivity.

In this paper, an effective hybrid optimization strategy by incorporating the adaptive optimization of particle swarm optimization (PSO) into genetic algorithm (GA), namely HPSOGA, is used for determining the parameters of radial basis function neural networks (number of neurons, their respective centers and radii) automatically. While this task depends upon operator׳s experience with trial and error due to lack of prior knowledge, or based on gradient algorithms which are highly dependent on initial values. In this paper, hybrid evolutionary algorithms are used to automatically build a radial basis function neural networks (RBF-NN) that solves a specified problem, related to rainfall forecasting in this case. In HPSOGA, individuals in a new generation are created through three approaches to improve the global optimization performance, which are elitist strategy, PSO strategy and GA strategy. The upper-half of the best-performing individuals in a population are regarded as elites, whereas the half of the worst-performing individuals are regarded as a swarm. The group constituted by the elites are enhanced by selection, crossover and mutation operation on these enhanced elites. HPSOGA is applied to RBF-NN design for rainfall prediction. The performance of HPSOGA is compared to pure GA in these basis function neural networks design problems, showing that the hybrid strategy is of more effective global exploration ability and to avoid premature convergence. Our findings reveal that the hybrid optimization strategy proposed here may be used as a promising alternative forecasting tool for higher forecasting accuracy and better generalization ability. [ABSTRACT FROM AUTHOR]

There are many new or potential drug targets in G protein-coupled receptors (GPCRs) without sufficient ligand associations, and it is essential and urgent to implement drug discovery targeting these GPCRs. Precise modeling and representing ligand bioactivities are essential for screening and optimizing these GPCR targeted drugs, yet insufficient samples made it difficult to achieve. Transfer learning intends to solve this by introducing rich information from related source domains with sufficient ligand training samples. In addition, ligand molecules naturally constitute a graph structure, which can be utilized by molecular graph convolutional networks to form an end-to-end multiple-level representation learning. This study proposed a novel method, TL-MGCN, using transfer learning with molecular graph convolutional networks to precisely model and represent bioactivities of ligands targeting GPCRs without sufficient data. The study tested TL-MGCN on a series of 54 representative target domain datasets which cover most human subfamilies, and 44 out of them have less than 600 ligand associations. TL-MGCN obtained an average improvement of 28.74%, 17.28%, 10.05%, 77.83%, 43.65% and 14.65% on correlation coefficient (r 2) and 11.90%, 7.43%, 14.86%, 41.46%, 31.02% and 22.94% on root-mean-square error (RMSE) compared with the WDL-RF, transfer learning version of WDL-RF (TR-WDL-RF), attentive FP, GIN, Weave and MPNN predictors, respectively. Users have free access to the web server of TL-MGCN, along with the source codes and datasets, at http://www.noveldelta.com/TL%5fMGCN for academic purposes. [Display omitted] • Using transfer learning to solve the problem of insufficient data. • Utilizing graph convolutional networks to form an end-to-end multiple-level representation. • Free access to the web server of the proposed method. [ABSTRACT FROM AUTHOR]

The conducting polypyrrole (PPy) nanofilms have been in-situ polymerized on quartz substrates under various high reaction pressures (0.1–600 MPa). The effects of the applied pressure on films have been carefully investigated through UV-Vis, FT-IR, SEM, AFM and conductivity measurement. The effect of the applied pressure on the thickness and the electrical conductivity of the PPy films have been discussed. A three-dimensional relationship between film thickness, reaction pressure, and the conductivity of the films has also been described in text. Our results may provide a guide for studying in-situ polymerization of pyrrole and its derivatives under high reaction pressure. [ABSTRACT FROM AUTHOR]

Flexible and lightweight organic thermoelectric (TE) devices have potential applications in self-powered health monitoring for humans and wearable self-powered gas/chemical detection. Two flexible and free-standing polypyrrole (PPy) nanotube films (PPy-1 and PPy-2) have been fabricated and the structures, morphologies and thermoelectric properties of the as-prepared PPy films have been carefully investigated. Our results show that the smaller size and longer length of PPy nanotubes are helpful to enhance the electrical conductivity and Seebeck coefficient while size and length have less effect on thermal conductivity. [ABSTRACT FROM AUTHOR]

Highlight: [•] Different polyaniline (PANI) nanostructures doped with different acids have been prepared. [•] p-Toluenesulfonic acid (p-TSA) doped nanowires have ZT value as high as 2.75×10−5. [•] The performance of nanowires is better than that of nanorods. [ABSTRACT FROM AUTHOR]

DMSP-OLS (Defense Meteorological Satellite Program Operational Linescan System) night-time light data can accurately reflect the scope and intensity of human activities. However, the raw data cannot be used directly for temporal analyses due to the lack of inflight calibration. There are three problems that should be addressed in intercalibration. First, because of differences between sensors, the data are not identical even when obtained in the same year. Second, different acquisition times may lead to random or systematic fluctuations in the data obtained by satellites in different orbits. Third, a pixel saturation phenomenon also exists in the urban centres of the image. Therefore, an invariant region method was used in this article, and the relative radiometric calibration and saturation correction achieved the desired results. In the meantime, intercalibration models for each satellite year of DMSP-OLS night-time light data were produced. Finally, intercalibration accuracy was evaluated, and the intercalibration results were tested with the corresponding gross domestic product (GDP) data. [ABSTRACT FROM PUBLISHER]

AKT is a serine–threonine protein kinase that plays important roles in cell growth, proliferation and apoptosis. It is activated after binding to phosphatidylinositol phosphates (PIPs) with phosphate groups at positions 3,4 and 3,4,5 on the inositol ring. In spite of extensive research on AKT, one aspect has been largely overlooked, namely the role of the fatty acid chains on PIPs. PIPs are phospholipids composed of a glycerol backbone with fatty acids at the sn-1 and sn-2 position and inositol at the sn-3 position. Here, we show that polyunsaturated fatty acids (PUFAs) modify phospholipid content. Docosahexaenoic acid (DHA), an ω3 PUFA, can replace the fatty acid at the sn-2 position of the glycerol backbone, thereby changing the species of phospholipids. DHA also inhibits AKTT308 but not AKTS473 phosphorylation, alters PI(3,4,5)P3 (PIP3) and phospho-AKTS473 protein localization, decreases pPDPK1S241-AKT and AKT–BAD interaction and suppresses prostate tumor growth. Our study highlights a potential novel mechanism of cancer inhibition by ω3 PUFA through alteration of PIP3 and AKT localization and affecting the AKT signaling pathway. [ABSTRACT FROM AUTHOR]

Abstract: We consider night light as a type of consumer goods and propose a model for factors affecting the relationship between night lights and GDP. It is then decomposed into agricultural and non-agricultural productions. Further, the model is modified to determine how the factors affect residents' propensity to consume lights. Models are tested with time-fixed regression on a set of 15-year panel data of 169 countries globally and regionally. We find that light consumption propensity is affected by GDP per capita, latitude, spatial distribution of human activities and gross saving rate, and that light consumption per capita has an inverted-U relationship with GDP per capita. [Copyright &y& Elsevier]

Abstract: The identification of the relationships between different landscape services is important in social–ecological complex systems, especially in areas with intensive human activities. In this paper, Beijing and its peripheral regions are taken as a case study to calculate and map the intensities of five classic landscape services including material production, carbon storage, soil retention, habitat conservation, and population support based on grid maps. Overlap and correlation analyses were used to identify multiple service hotspots and the relationships between landscape services. The results show that (1) landscape services have spatial heterogeneity: high-intensity area of material production and population support is on the southeast plains of this region and high-intensity area of soil retention and habitat conservation is on the northwest; (2) approximately 96.03% of the region can provide at least one type of landscape services, whereas approximately three-quarters of the area provide multiple services, with the multiple service hotspots surrounding Beijing and Tianjin; (3) correlations exist between all pairs of landscape services, but strong correlations (correlation coefficient >0.5 or <−0.5) exist between four pairs, which are soil retention and habitat conservation (0.672), soil retention and population support (−0.613), habitat conservation and population support (−0.540), and material production and population support (0.529); (4) the services can be divided into two trade-off service bundles: the “natural” bundle, which contains carbon storage, soil retention, and habitat conservation, and the “artificial” bundle, which contains material production and population support. Only 4.19% of the area in this region contains these service bundles simultaneously. Finally, an improved understanding of the relationships between services was illustrated, and the importance of such services was highlighted for decision-makers and stakeholders. [Copyright &y& Elsevier]

Accurate forecasting of rainfall has been one of the most important issues in hydrological research such as river training works and design of flood warning systems. Support vector regression (SVR) is a popular regression method in rainfall forecasting. Type of kernel function and kernel parameter setting in the SVR traing procedure, along with the input feature subset selection, significantly influence regression accuracy. In this paper, an effective hybrid optimization strategy by combining the strengths of genetic algorithm (GA) and simulated annealing (SA), is employed to simultaneously optimize the input feature subset selection, the type of kernel function and the kernel parameter setting of SVR, namely GASA-SVR. The developed GASA-SVR model is being applied for monthly rainfall forecasting in Guilin of Guangxi. The GA is carried out as a main frame of this hybrid algorithm while SA is used as a local search strategy to help GA jump out of local optima and avoid sinking into the local optimal solution early. Compared with SVR, pure GA-SVR and HGA-SVR, results show that the hybrid GASA-SVR model can correctly select the discriminating input features subset, successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in rainfall forecasting, can also significantly improve the rainfall forecasting accuracy. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements. Those results show that the proposed GASA-SVR model provides a promising alternative to monthly rainfall prediction. [ABSTRACT FROM AUTHOR]

Recent experiments implicate spins on the surface of metals as the source of flux noise in superconducting quantum interference devices and indicate that these spins are able to relax without conserving total magnetization. We present a model of 1/f flux noise in which electron spins on the surface of metals can relax via hyperfine interactions. Our results indicate that flux noise would be significantly reduced in superconducting materials where the most abundant isotopes do not have nuclear moments, such as zinc and lead. [ABSTRACT FROM AUTHOR]

A common treatment of advanced prostate cancer involves the deprivation of androgens. Despite the initial response to hormonal therapy, eventually all the patients relapse. In the present study, we sought to determine whether dietary polyunsaturated fatty acid (PUFA) affects the development of castration-resistant prostate cancer. Cell culture, patient tissue microarray, allograft, xenograft, prostate-specific Pten knockout and omega-3 desaturase transgenic mouse models in conjunction with dietary manipulation, gene knockdown and knockout approaches were used to determine the effect of dietary PUFA on castration-resistant Pten-null prostate cancer. We found that deletion of Pten increased androgen receptor (AR) expression and Pten-null prostate cells were castration resistant. Omega-3 PUFA slowed down the growth of castration-resistant tumors as compared with omega-6 PUFA. Omega-3 PUFA decreased AR protein to a similar extent in tumor cell cytosolic and nuclear fractions but had no effect on AR messenger RNA level. Omega-3 PUFA treatment appeared to accelerate AR protein degradation, which could be blocked by proteasome inhibitor MG132. Knockdown of AR significantly slowed down prostate cancer cell proliferation in the absence of androgens. Our data suggest that omega-3 PUFA inhibits castration-resistant prostate cancer in part by accelerating proteasome-dependent degradation of the AR protein. Dietary omega-3 PUFA supplementation in conjunction with androgen ablation may significantly delay the development of castration-resistant prostate cancer in patients compared with androgen ablation alone. [ABSTRACT FROM PUBLISHER]

Abstract: Biochemical interactions between proteins and biological macromolecules are dominated by non-covalent interactions. A novel method is presented for quantitatively predicting the number of two most dominant non-covalent interactions, i.e., hydrogen bonds and van der Waals contacts, potentially forming in a hypothetical protein–nucleic acid complex from sequences using support vector machine regression models in conjunction with a hybrid feature. The hybrid feature consists of the sequence-length fraction information, conjoint triad for protein sequences and the gapped dinucleotide composition. The SVR-based models achieved excellent performance. The polarity of amino acids was also found to play a vital role in the formation of hydrogen bonds and van der Waals contacts. We have constructed a web server H-VDW (http://www.cbi.seu.edu.cn/H-VDW/H-VDW.htm) for public access to the SVR models. [Copyright &y& Elsevier]

Abstract: We develop an improved incremental SVM algorithm, named RS-ISVM, to deal with network intrusion detection. To reduce the noise generated by feature differences, we propose a modified kernel function U-RBF, with the mean and mean square difference values of feature attributes embedded in kernel function RBF. Then, given the oscillation problem that usually occurs in traditional incremental SVM’s follow-up learning process, we present a reserved set strategy which can keep those samples that are more likely to be the support vectors in the following computation process. Moreover, in order to shorten the training time, a concentric circle method is suggested to be used in selecting samples to form the reserved set. Academic researches and data experiments show that RS-ISVM can ease the oscillation phenomenon in the learning process and achieve pretty good performance, meanwhile, its reliability is relative high. [Copyright &y& Elsevier]

In this paper, a hybrid rainfall-forecasting approach is proposed which is based on support vector regression, particle swarm optimization and projection pursuit technology. The projection pursuit technology is used to reduce dimensions of parameter spaces in rainfall forecasting. The particle swarm optimization algorithm is for searching the parameters for support vector regression model and to construct the support vector regression model. The observed data of daily rainfall values in Guangxi (China) is used as a case study for the proposed model. The computing results show that the present model yields better forecasting performance in this case study, compared to other rainfall-forecasting models. Our model may provide a promising alternative for forecasting rainfall application. [ABSTRACT FROM AUTHOR]

Abstract: Silicon-doped TiO2 nanofilm was fabricated by chemical vapor deposition which can control the Si-doping amount well and obtain the rapid deposition of film. The results of X–ray diffraction (XRD) and X–ray photoelectron spectroscopy (XPS) analyses revealed that the introduced silicon might be incorporated into titania matrix. This incorporation helped to increase the thermal stability of titania, which was in favor of suppressing phase transformation from anatase to rutile and also inhibiting the growth of anatase crystallite at high calcination temperature. A significant blue-shift with enhanced absorption intensities was observed in the spectrum of UV absorption for Si-doped sample. The Si-doped sample with 5mol % of silicon exhibited the best photoelectrochemical property, and its photocurrent density under UV light was 1.7 times that of TiO2 nanofilm. The photocatalytic (PC) and electro-assisted photocatalytic (EAPC) activities were investigated using phenol as a probe substance. Compared with TiO2 nanofilm, the Si-doped TiO2 showed better PC and EAPC capabilities in the degradation of phenol under UV-light irradiation. The kinetic constants of phenol degradation for the 5mol%-Si-doped TiO2 in PC and EAPC processes under UV-light irradiation were 1.3 and 1.8 times as great as the values for the undoped TiO2 nanofilm. [Copyright &y& Elsevier]

[Display omitted] • Evaluation of dust retention effect of green space at street scale. • PM 2.5 dry deposition rate parameters of native vegetation. • Simulation of the greening vegetation types by microclimate model ENVI-met. • Comprehensive consideration of the effects of plants on the diffusion and settlement of pollutants. The establishment of the road green belt (RGB) is an effective means to reduce particle matter (PM 2.5) emissions from road traffic. This study tested the ability of 23 common tree species in Shenzhen to reduce PM 2.5 concentrations using field investigations and wind tunnel tests. The association between leaf microstructure and individual reduction ability was also analyzed. Finally, the impact of three RGB configurations (i.e., arbor, shrub, arbor + shrub) on road PM 2.5 dispersion and deposition was simulated using the ENVI-met three-dimensional aerodynamic model, based on which an optimal RGB configuration was proposed. There were three key findings of the tests. First, the wind speed was the main factor affecting the PM 2.5 concentration (54.2%), followed by vehicle flow (27.7%), temperature (14.2%), and time factor (7.6%). Second, the range of dry deposition velocity (V d) was 0.04–6.4 m/s, and the dominant dust-retaining plant species were the evergreen trees, Ficus microcarpa and Ficus altissima, and the evergreen shrubs, Codiaeum variegatum and Fagraea ceilanica. A higher proportion of grooves or larger stomata would increase the probability that the blade would capture PM 2.5. Third, the shrub RGB demonstrated the best performance in terms of pollutant dispersion; its PM 2.5 concentration at the respiratory height (RH, 1.5 m) on the pedestrian crossing was 15–20% lower than the other RGB configurations. In terms of pollutant deposition, the arbor + shrub composite RGB was two-fold better than the other RGB configurations. Moreover, it was more advantageous to plant shrub RGBs in street canyons to achieve a balance between the lowest concentration and the largest deposition of PM 2.5 pollutants. The findings of this study will facilitate the RGB configurations with good dust retention ability. [ABSTRACT FROM AUTHOR]

Exploring the relationship between ecosystem health and urbanization is the foundation for solving the ecological and environmental problems of urban ecosystems and has both theoretical and practical significance. This paper investigates the relationship between ecosystem health and urbanization in 260 Chinese cities in the years 2000, 2005 and 2010. For each city, four aspects of ecosystem health are evaluated and three types of urbanization are measured. The paper uses piecewise linear regression to explore the spatiotemporal dynamics of the relationship between urbanization and ecosystem health at both national and regional levels. Finally, the threshold levels of urbanization that cause changes in ecosystem health responses were estimated. The results show significant negative correlations between different dimensions of urbanization and ecosystem health at the national level, with spatial urbanization having a greater effect than economic and population urbanization. The results also reveal regional and temporal differences in the correlations at both national and regional levels, along with threshold levels for the effects of urbanization. This paper aims to explore the dynamic impacts of different urbanization characteristics and stages on ecosystem health, and help in reconciling the contradictions between economy, population, land expansion, and ecosystem health in different urbanization stages by identifying the thresholds of change trends. [Display omitted] • Ecosystem health (EH) is evaluated at 260 Chinese prefecture level cities. • At national scale, correlation thresholds of economic and spatial urbanization with EH are 92.8 and 12.5% respectively. • Spatial urbanization has strongest correlation to ecosystem health. • Significant thresholds can be defined as start, end or change point of different urbanization effects. • Policy making should differ in growth speed in medium level cities. [ABSTRACT FROM AUTHOR]

During the COVID-19 lockdown in Wuhan, transportation, industrial production and other human activities declined significantly, as did the NO2 concentration. In order to assess the relative contributions of different factors to reductions in air pollutants, we implemented sensitivity experiments by Random Forest (RF) models, with the comparison of the contributions of meteorological conditions, human mobility, and emissions from industry and households between different periods. In addition, we conducted scenario analyses to suggest an appropriate limit for control of human mobility. Different mechanisms for air pollutants were shown in the pre-pandemic, pre-lockdown, lockdown, and post-pandemic periods. Wind speed and the Within-city Migration index, representing intra-city mobility intensity, were excluded from stepwise multiple linear models in the pre-lockdown and lockdown periods. The results of sensitivity experiments show that, in the COVID-19 lockdown period, 73.3% of the reduction can be attributed to decreased human mobility. In the post-pandemic period, meteorological conditions control about 42.2% of the decrease, and emissions from industry and households control 40.0%, while human mobility only contributes 17.8%. The results of the scenario analysis suggest that the priority of restriction should be given to human mobility within the city than other kinds of human mobility. The reduction in the NO2 concentration tends to be smaller when human mobility within the city decreases by more than 70%. A limit of less than 40% on the control of the human mobility can achieve a better effect, especially in cities with severe traffic pollution. [ABSTRACT FROM AUTHOR]

Abstract: The mechanical properties including bending strength (σ bb) and fracture toughness (K IC) of sintered Nd–Fe–B magnets have been examined in this paper. Alloyed magnet with Dy, Co, Nb additions shows the higher magnetic properties and bending strength but the lower fracture toughness than the ternary magnet. The zero plastic energy in bending procedure presents the purely brittle nature of Nd–Fe–B magnet. Fracture mechanism has been discussed on the base of the in situ SEM observation of micro-crack nucleation and FE-SEM (field emission scanning electron microscope) observation of indentation cracks. The fracture mechanism of sintered Nd–Fe–B alloy is concluded as two stages: the damage-accumulating stage and the crack-propagating stage. Some suggestions for improving toughness of sintered Nd–Fe–B magnets are mentioned. [Copyright &y& Elsevier]

Abstract: The internal friction and the modulus have been measured on Ti51.5Ni26.0Pd22.5 films under tension load, at three frequencies 0.5, 1.0, 5.0 Hz. in a wide range of temperatures covering the orthorhombic martensite and the reverse transformations. The effect of heat treatment and frequency on the internal friction has also been investigated. Only a transition around the orthorhombic transformation took place and a very sharp step-type internal friction was observed for all films with three kinds of heat treatments. The elastic modulus evolution reversed to internal friction change and a remarkable depression of the modulus corresponded to the internal friction peak. The internal friction of the films was enhanced by crystallization at 750 °C and the highest internal friction obtained at frequency 0.5 Hz. Ageing treatment at 450 °C after crystallizing at 750 °C promoted the peak of internal friction appearing and accelerated the microstructure variation. A high damping capacity of Q −1 =0.05 or 0.06 was reached in the films crystallizing at 750 °C with or without ageing at 450 °C respectively and sustained from room temperature to 100 °C. [Copyright &y& Elsevier]

The impact of elevated CO2 on terrestrial ecosystem C balance, both in sign or magnitude, is not clear because the resulting alterations in C input, plant nutrient demand and water use efficiency often have contrasting impacts on microbial decomposition processes. One major source of uncertainty stems from the impact of elevated CO2 on N availability to plants and microbes. We examined the effects of atmospheric CO2 enrichment (ambient+370 μmol mol−1) on plant and microbial N acquisition in two different mesocosm experiments, using model plant species of annual grasses ofAvena barbataandA. fatua, respectively. TheA. barbataexperiment was conducted in a N-poor sandy loam and theA. fatuaexperiment was on a N-rich clayey loam. Plant–microbial N partitioning was examined through determining the distribution of a15N tracer. In theA. barbataexperiment,15N tracer was introduced to a field labeling experiment in the previous year so that15N predominantly existed in nonextractable soil pools. In theA. fatuaexperiment,15N was introduced in a mineral solution [(15NH4)2SO4 solution] during the growing season ofA. fatua. Results of both N budget and15N tracer analyses indicated that elevated CO2 increased plant N acquisition from the soil. In theA. barbataexperiment, elevated CO2 increased plant biomass N by ca. 10% but there was no corresponding decrease in soil extractable N, suggesting that plants might have obtained N from the nonextractable organic N pool because of enhanced microbial activity. In theA. fatuaexperiment, however, the CO2-led increase in plant biomass N was statistically equal to the reduction in soil extractable N. Although atmospheric CO2 enrichment enhanced microbial biomass C underA. barbataor microbial activity (respiration) underA. fatua, it had no significant effect on microbial biomass N in either experiment. Elevated CO2 increased the colonization ofA. fatuaroots by arbuscular mycorrhizal fungi, which coincided with the enhancement of plant competitiveness for soluble soil N. Together, these results suggest that elevated CO2 may tighten N cycling through facilitating plant N acquisition. However, it is unknown to what degree results from these short-term microcosm experiments can be extrapolated to field conditions. Long-term studies in less-disturbed soils are needed to determine whether CO2-enhancement of plant N acquisition can significantly relieve N limitation over plant growth in an elevated CO2 environment. [ABSTRACT FROM AUTHOR]

Isothermal compression tests were carried out on a spray-formed ultrahigh carbon steel (UHCS-3.0Si) by Gleeble 1500 system. The true stress increases with the decrease of temperature and increase of strain rate. The deformation activation energies, Q, of UHCS-3.0Si are not only temperature dependent but also strain rate dependent. The value of Q increases as the deformation temperature decreases and it is in proportion to the weight percent of undissolved carbides. At high deformation strain rates (5–30 s-1), dislocation slip and cross slip are the main deformation mechanism and at low deformation strain rates (0.1–1 s-1), climb-controlled dislocation creep is the main deformation mechanism. Microstructural evolution was analyzed during deformation at 800 °C, cementite lamellas have fair formability and some are broken up which leads to great softening. [Copyright &y& Elsevier]

The microstructure and electrical properties of annealed Cr–Si–Al and Cr–Si–Al–Zr films were investigated. When sputtered amorphous Cr–Si–Al and Cr–Si–Al–Zr films were heated up to a temperature of 700 °C, both were found to crystallize into two phases: the nanocrystalline Cr(Al,Si)2 and Si phase. The addition of Zr into amorphous Cr–Si–Al films inhibited the nucleation and growth of the crystallization phase, resulting in the higher annealing temperatures for Cr–Si–Al–Zr films in comparison with Cr–Si–Al films to obtain a small temperature coefficient of resistance (TCR). As a result, the Cr–Si–Al–Zr films had higher electrical stability. [Copyright &y& Elsevier]

We estimate the relative radial and angular correlation energies in the ground state of two-electron ions, such as in the hydrogen negative ion, the helium atom and the lithium positive ion, and find they are roughly equal. [ABSTRACT FROM AUTHOR]

The microstructure of annealed Cr–Si–Ni–Al films was investigated by transmission electron microscopy (TEM). Sputtered amorphous Cr–Si–Ni–Al films crystallized into two phases: the intermetallic phase Cr(Al,Si)2 and the pure silicon phase. The formation of the crystallization phases resulted in a significant increase in both the electrical resistivity and the temperature coefficient of resistance (TCR) for the annealed Cr–Si–Ni–Al films. Particularly, the intermetallic Cr(Al,Si)2 particles, which were embedded in the amorphous matrix, were responsible for achieving a zero TCR. In addition, the growth rate of the crystallized phases at the initial stage of reaction was faster than that at the final stage, resulting from the combined redistribution of Cr, Si, Al and Ni elements between the initial crystallization phases and the parent amorphous phase. [Copyright &y& Elsevier]

In the high temperature shape memory alloy (HTSMA) Ti51Pd30Ni19, superelasticity is found for the first time and the superelasticity is quite different from that of NiTi or CuZnAl. The recoverable superelastic strain is 7% without failure of the specimen. Shape memory effect (SME) at room temperature is evaluated as 7.2% with the recovery rate of 100%. The phase transformation temperatures were determined using differential scanning calorimeter (DSC). It has been found that the temperatures are changed during the process of alloy preparation. XRD analyses indicate that the macro- residual stress is eliminated and precipitation occurred after recovery treatment. The characteristics of both mechanical and transformation behavior are associated with the changes in the recovery process. Accordingly, an effective way to obtain the superelasticity is pointed out. [Copyright &y& Elsevier]

When sputtered amorphous Cr–Si–Ni–Al films with an atomic ratio [Si]/[Cr]>2 were heated up to temperature of 600 °C, they were found to crystallize into two phases: the intermetallic phase Cr(Al,Si)2 and the pure silicon phase; the growth rate of the crystalline phases at the initial stage of reaction was faster than that at the final stage, resulting from the combined redistribution of Cr, Si, Al and Ni elements between the initial crystalline phases and the parent amorphous phase. The formation of the crystalline phases resulted in a significant increase of both the electrical resistivity and the temperature coefficient of resistance (TCR) for the annealed Cr–Si–Ni–Al films. In particular, the intermetallic Cr(Al,Si)2 particles, which were embedded in the amorphous matrix, were responsible for achieving a zero TCR. Moreover, the deviation between the TCR25125 (TCR averaged between 25 °C and 125 °C) and the TCR−5525 (TCR averaged between −55 °C and 25 °C) in the annealed Cr–Si–Ni–Al films with small TCR was diminished. [Copyright &y& Elsevier]

The microstructure and electrical properties of both annealed Cr–Si–Ni and Cr–Si–Ni–Al films with an atomic ratio [Si]/[Cr]>2 were investigated. When sputtered amorphous Cr–Si–Ni and Cr–Si–Ni–Al films were heated up to temperature of 600 °C, they all were found to crystallize through two distinct stages. The first stage was due to the primary crystallization of nanocrystalline CrSi2 and Si phase for Cr–Si–Ni films (or Cr(Si,Al)2 and Si phase for Cr–Si–Ni–Al films), while the second stage was due to crystallization of the residual solute-enriched amorphous phase into CrSi2 (or Cr(Si,Al)2) and Si phase. The addition of Al into amorphous Cr–Si–Ni films caused the coarsening of the primary crystallization phase, resulting in a significant increase of both the electrical resistivity and the temperature coefficient of resistance (TCR) for the annealed films. Meanwhile, the subsequent crystallization of the residual solute-enriched amorphous phase was affected, resulting from the combined redistribution of Cr, Si, Al and Ni elements between the primary and parent amorphous phases. [Copyright &y& Elsevier]