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This study investigated the effects of various load conditions on the wear characteristics of Si3N4 full-ceramic ball bearings in response to 6007 Si3N4 full-ceramic ball bearing operation-related wear problems. Using an ABLT-1A tester, the wear behavior of 6007 Si3N4 ceramic ball bearings was investigated under loads of 100, 500, and 800 N, with a rotation speed of 6000 r/min and a duration of 12,000 min. The test load, and the 100 and 500 N radial loads had an equilibrium temperature difference of about 1.91°C, while the 500 and 800 N radial loads exhibited an equilibrium temperature difference of about 2.35°C. Each group's temperature rate difference is evident. Bearing internal clearance increased by 35.7%, 50.34%, and 56.1%, respectively, and the fault frequencies of the bearing elements appeared within the vibration signal. The surface roughness of each component of the bearing increased with the increase in the test load, while a large number of plow grooves and spalling appeared on the surface, and severe cage wear was observed. Results show that when load, temperature, clearance, runout, and frequency domain amplitude increased, the following were observed: development of rolling body surface spalling and a large area of wear spot; raceway surface crater along the direction of movement of the fur-row; obvious scratches were observed on the cage surface in the pocket hole edge melting phenomenon; the cage became the bearing system's "weak link." The test results are a reference value for the wear behavior of Si3N4 full-ceramic ball bearings under this test load. [ABSTRACT FROM AUTHOR]

Soil moisture (SM), as a crucial input variable of land surface processes, plays a pivotal role in the global hydrological cycle. The aim of this paper is to examine the spatiotemporal variability in SM in the Heihe River Basin using all-weather land surface temperature (LST) and reanalysis land surface data. Initially, we downscaled and generated daily 1 km all-weather SM data (2020) for the Heihe River Basin. Subsequently, we investigated the spatial and temporal patterns of SM using geostatistical and time stability methods. The driving forces of the monthly SM were studied using the optimal parameter-based geographical detector (OPGD) model. The results indicate that the monthly mean values of the downscaled SM data range from 0.115 to 0.146, with a consistently lower SM content and suitable temporal stability throughout the year. Geostatistical analysis revealed that months with a higher SM level exhibit larger random errors and higher variability. Driving analysis based on the factor detector demonstrated that in months with a lower SM level, the q values of each driving factor are relatively small, and the primary driving factors are land cover and elevation. Conversely, in months with a higher SM level, the q values for each driving factor are larger, and the primary driving factors are the normalized difference vegetation index and LST. Furthermore, interaction detector analysis suggested that the spatiotemporal variation in SM is not influenced by a single driving factor but is the result of the interaction among multiple driving factors, with most interactions enhancing the combined effect of two factors. [ABSTRACT FROM AUTHOR]

Our current understanding of the cognitive processes underlying human learning enables cognitive psychology to offer valuable guides to the design of curricula in school subjects. This article summarizes some principles […]

Spatiotemporal fusion methods provide an effective way to generate both high temporal and high spatial resolution data for monitoring dynamic changes of land surface. But existing fusion methods face two main challenges of monitoring the abrupt change events and accurately preserving the spatial details of objects. The Flexible Spatiotemporal DAta Fusion method (FSDAF) was proposed, which can monitor the abrupt change events, but its predicted images lacked intra-class variability and spatial details. To overcome the above limitations, this study proposed a comprehensive and automated fusion method, the Enhanced FSDAF (EFSDAF) method and tested it for Landsat–MODIS image fusion. Compared with FSDAF, the EFSDAF has the following strengths: (1) it considers the mixed pixels phenomenon of a Landsat image, and the predicted images by EFSDAF have more intra-class variability and spatial details; (2) it adjusts the differences between Landsat images and MODIS images; and (3) it improves the fusion accuracy in the abrupt change area by introducing a new residual index (RI). Vegetation phenology and flood events were selected to evaluate the performance of EFSDAF. Its performance was compared with the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Spatial and Temporal Reflectance Unmixing Model (STRUM), and FSDAF. Results show that EFSDAF can monitor the changes of vegetation (gradual change) and flood (abrupt change), and the fusion images by EFSDAF are the best from both visual and quantitative evaluations. More importantly, EFSDAF can accurately generate the spatial details of the object and has strong robustness. Due to the above advantages of EFSDAF, it has great potential to monitor long-term dynamic changes of land surface. [ABSTRACT FROM AUTHOR]

The spatio-temporal pattern of surface ecological status affects urban thermal environment distribution significantly. Urban thermal pattern, however, is a complicated physical phenomenon involving a series of terrestrial environmental parameters. Thus, it is insufficient to employ only one ecological parameter for depicting the variation of land surface temperature (LST). This paper begins with the analysis of four ecological parameters' influence on LST using regression analysis, based on 24 Landsat images which cover Xi'an of China from 1992 to 2014. These four parameters include greenness degree (i.e. the soil adjusted vegetation index, SAVI), soil moisture degree (i.e. the normalized difference moisture index, NDMI), dryness degree (i.e. the normalized difference soil index, NDSI) and resident aggregation degree (i.e. the normalized difference build-up index, NDBI). Besides, contribution intensity index was introduced to investigate the contribution effect of four ecological parameters on LST, and a new ecological index, integrated ecological index (IEI), was founded using the principal component analysis technique to integratedly represent its spatial and mathematical correlations with LST. Results indicate that four ecological parameters all possessed pronounced performance in impacting LST pattern in all dates: SAVI and NDMI were found to be correlated negatively with LST, whereas NDBI and NDSI correlated positively with LST. Additionally, SAVI had a profound impact on LST distribution compared with the other three parameters, and there was the biggest heating contribution in the lowest SAVI category. Further finding suggests that IEI as a new ecological index can be used to integratedly estimate the spatio-temporal change of LST, manifesting a negative correlation with LST. Our study thinks that the comprehensive characterization of surface ecological status is conducive to benefit us to better understand the spatio-temporal mechanism of thermal environment and ecosystem and to help urban decision-makers to execute effective conservation policies for the ecosystem. [ABSTRACT FROM AUTHOR]

Clear and green water bodies reflect more green light than red light, while brown water bodies reflect more red light than green light. It is thus believed that the normalized difference water indexes using the green band and infrared wavelengths (NDWIgreen) and the normalized difference water indexes using the red band and infrared wavelengths (NDWIred) would act differently in mapping inland water bodies with different colours. This study aims to evaluate the performance of NDWIgreenand NDWIredin mapping water bodies with different colours in the Yellow River Basin using the Landsat-8 Operational Land Imager (OLI) imagery. The results show that NDWIgreenand NDWIredachieve overall accuracy of 95.99–96.82% and 88.32–96.68% in clear water mapping, 95.58–96.35% and 93.50–94.60% in green water mapping, 88.12–92.63% and 90.08–93.10% in brown water mapping, respectively. It is shown that both NDWIgreenand NDWIredcan serve the purpose of inland water mapping. Moreover, the comparative analysis suggests that NDWIgreenis more suitable for clear and green water mapping, while NDWIredis more adequate for brown water mapping. [ABSTRACT FROM AUTHOR]