Small specimen technologies, such as the small punch test, the indentation test, and the in situ Scanning Electron Microscope (SEM)/Electron Back Scatter Diffraction (EBSD) test, provide important ...data support for understanding mechanical properties when the size of testing materials is limited, such as for alloys, 3D printing metals, and welding joints, as illustrated in Figure 1 ...
Multimodality has become a prominent concept in communication and language education research, and pedagogic discourse in second language (L2) classrooms is fundamentally multimodal. While research ...on willingness to communicate (WTC) has been thriving, little is known about how L2 WTC is related to multimodal classroom pedagogies. This article presents findings from the first large-scale survey study of its kind on EFL students' perceptions of multimodal pedagogies, and the interrelationships between multimodal pedagogic effects, classroom environment, and WTC in English. Data were collected from 2058 Chinese EFL university students and analyzed using frequency analysis and structural equation modeling. The results showed that the use of audio/video and teachers' voices/facial expressions were perceived by the participants as the most satisfactory, whereas the visual design of PowerPoint slides was the least satisfactory. Effective use of audio/video significantly predicted classroom environment and WTC, while teachers' voices/facial expressions contributed to classroom environment, and teachers' gestures and spatial positions predicted WTC. In addition, classroom environment was the strongest predictor of WTC. The findings have immediate implications for L2 teaching and will enable L2 teachers to exploit the potential of multimodal pedagogies to promote students’ WTC and to benefit their learning.
This study involves a large‐scale investigation of willingness to communicate (WTC) in Chinese English‐as‐a‐foreign‐language (EFL) classrooms. A hypothesized model integrating WTC in English, ...communication confidence, motivation, learner beliefs, and classroom environment was tested using structural equation modeling. Validation of the measurements involved exploratory factor analyses on the dataset collected in a pilot study and confirmatory factor analyses in the main study. The results show that classroom environment predicts WTC, communication confidence, learner beliefs, and motivation. Motivation influences WTC indirectly through confidence. The direct effect of learner beliefs on motivation and confidence is identified. The model provides an adequate fit to the data, indicating the potential to draw on individual and contextual variables to account for classroom communication.
AIM To investigate the clinical significance of preoperative systemic immune-inflammation index(SII) in patients with colorectal cancer(CRC). METHODS A retrospective analysis of 1383 cases with CRC ...was performed following radical surgery. SII was calculated with the formula SII =(P × N)/L, where P, N, and L refer to peripheral platelet, neutrophil, and lymphocyte counts, respectively. The clinicopathological features and follow-up data were evaluated to compare SII with other systemic inflammation-based prognostic indices such as the neutrophil-lymphocyte ratio(NLR) and platelet-lymphocyte ratio(PLR) in patients with CRC.RESULTS The optimal cut-off point for SII was defined as 340. The overall survival(OS) and disease-free survival(DFS) were better in patients with low NLR, PLR, and SII(P < 0.05). The SII was an independent predictor of OS and DFS in multivariate analysis. The area under the receiver-operating characteristics(ROC) curve for SII(0.707) was larger than those for NLR(0.602) and PLR(0.566). In contrast to NLR and PLR, SII could effectively discriminate between the TNM subgroups. CONCLUSION SII is a more powerful tool for predicting survival outcome in patients with CRC. It might assist the identification of high-risk patients among patients with the same TNM stage.
Urbanization has eco-environmental consequences; among which are effects on the urban thermal environment, which have drawn extensive attention especially in metropolitan regions having intensive ...population and high building density. In this study, the variation of the thermal environment during the urbanization process from 2001 to 2009 in the Beijing metropolitan region was evaluated using the spatial Lorenz curve and distribution index. In addition, the effects of landscape composition and spatial configuration on the thermal environment were investigated using correlation analysis and piecewise linear regression. The urban heat island (UHI) was found to be much more significant in summer than in spring, autumn and winter. Furthermore, the magnitude of the urban thermal environment in Beijing has increased during the process of urbanization. The suburban areas of Beijing, including the new urban development zone and ecological conservation zone, have increased the magnitude of the thermal environment. However, the opposite effect has occurred in the center of Beijing, including the core functional zone and urban function extended zone. Landscape types such as built-up areas and barren land make the most significant contribution to the thermal environment, whereas ecological land plays a significant role in mitigating the UHI. When the coverage of ecological land exceeded 70% (25km2) of the total land area, the cooling efficiency of this landscape type was relatively obvious, and the shape index and fragmentation index of landscape configuration both had a significantly positive correlation (0.594 and 0.510 Pearson's coefficients, respectively) with average land surface temperature. The Pearson's coefficient between the ecological land proportion and the average land surface temperature was 0.614 (P<0.01); this value was higher than that for the effects of the spatial configuration, indicating that landscape composition affects the thermal environment more than does spatial configuration.
•LST dynamic was examined in Beijing during 2001–2009.•LST increased averagely in the whole metropolitan area but decreased in city center.•Built-up areas and barren land contribute most to UHI.•Cooling effects of ecological land is obvious with the proportion above 70%.•LST is determined more by landscape composition than spatial configuration.
Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best ...predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.
This article reports on a multiple-case study designed to investigate factors influencing willingness to communicate (WTC) in the English as a foreign language (EFL) classroom in China. Four ...university students participated in this study; data were collected through semi-structured interviews, learning journals recorded by the students, and classroom observations over seven months. The data were qualitatively content analyzed. Utilizing Bronfenbrenner’s (1979, 1993) nested ecosystems model as an analytical framework, this study identified six factors underlying classroom WTC in the microsystem: learner beliefs, motivation, cognitive factors, linguistic factors, affective factors, and classroom environment. The existence of the meso-, exo-, and macrosystem, and their effect on classroom WTC, were also suggested in the data. The findings contributed empirical evidence to an ecological understanding of Chinese EFL students’ WTC in their language classrooms, which is socioculturally constructed as a function of the interaction of individual and environmental factors.
•Landscape patterns are decentralized, aggregated, and fragmented in city circles.•Urbanization modes can be represented through associated landscape metrics change.•GWR is valid to measure ...heterogeneous urbanization impact on landscape patterns.•Population density, forest landscape, and PD are greatly related to urbanization.
The temporal and spatial characteristics of landscape pattern change can reflect the spatial impact of urbanization on the ecological environment. Studying the relationship between urbanization and landscape patterns can provide supports for urban ecological management. Previous studies have examined the quantitative relationship between the social economy and landscape patterns of an entire region, but have not considered the spatial non-stability of this relationship. In this study, we characterized the landscape patterns in Beijing City, China during 2000 and 2010 using four landscape metrics, i.e. patch density (PD), edge density (ED), Shannon’s diversity index (SHDI) and the aggregation index (AI). Geographically weighted regression (GWR) was employed to identify the spatial heterogeneity and evolution characteristics of the relationship between the urbanization of population density (POP), gross domestic production (GDP) and nighttime lighting (NTL), and landscape patterns. The evolution of urban landscape patterns indicated a decentralized, aggregated, and fragmented change from the downtown to the suburb and outer suburb. During the 10-year period, the average PD in the downtown increased by 100.6%, and the increase of AI in the suburb was the largest. The PD, ED and SHDI increased by different degrees in the outer suburb. The influences of different urbanization modes on landscape patterns were also different. Infilling mode made the landscape patterns more regular and integrated. The landscape was more broken and complicated under the edge-expanding mode, and the leapfrog mode made PD and SHDI increase slightly. In the relationship interpretation, GWR effectively identified the spatial heterogeneity, and improved the explanatory ability compared to ordinary least squares (OLS). The most intense response to urbanization was the forest landscape and the forest-cultivated land ecotone in the northwest of Beijing City, indicating that this region was ecologically fragile. The population density in the urbanization index had a direct effect on landscape patterns, while the PD affected by urbanization was greater than the shape, aggregation and diversity index. Affected by development policy, urban planning and other factors, the explanation degree of social economy to landscape patterns decreased in 2010. GWR is an effective method for quantifying the spatial differentiation characteristics of urbanization impacts on landscape patterns, which can provide more spatial information and decision criteria for the green development of a compact city.
Urban heat island (UHI) has become an urban eco-environmental problem globally. Land surface temperature (LST) is widely used to quantify UHI. This study used Shenzhen, a southern coastal city in ...China, as an example to explore the relationship between spatial variation of LST in different seasons and the influencing factors in five dimensions, integrating the methods of ordinary least-squares regression, stepwise regression, all-subsets regression, and hierarchical partitioning analysis. The results showed that the most important factor affecting spatial heterogeneity of LST in summer was the normalized difference build-up index (53.62%, for contributing rate), whereas in the transition season the most important factor was the normalized difference vegetation index (NDVI) (47.84%). In winter the construction land percentage and NDVI (26.84% and 25.56%, respectively) were the most influential. Artificial surface and green space had a dominant effect on LST spatial differentiation. Landscape configuration and diversity were not the dominant influencing factors in summer or in the transition season. Furthermore, the independent contribution rate of the Shannon diversity index (SHDI) reached 8.79% in the transition season, while in winter, the independent contribution rates of SHDI and the landscape shape index were 8.52% and 3.45%, respectively. The influence of landscape diversity and configuration factors tended to increase as LST reduced, while the contribution rate of the important factors such as artificial surface and green space decreased significantly. These relationships indicate that the influence of landscape configuration and diversity factors on LST is relatively weak, and can be easily concealed by the influence of landscape components, especially when the spatial variation of LST is not strong. These findings can help to develop UHI adaptation strategies based on local conditions.
•Multiple statistical methods are linked to quantify LST driving forces.•Dominant factors for LST seasonal variation are identified through HP analysis.•Mechanism of LST variation becomes more complicated with the decreasing of LST.
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