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Mesoporous semiconducting metal oxides (SMOs) heterojunctions are appealing sensors for gas detecting. However, due to the different hydrolysis and condensation mechanism of every metal precursor and the contradiction between high crystallinity and high surface area, the synthesis of mesoporous SMOs heterojunctions with highly ordered mesostructures, highly crystallized frameworks, and high surface area remains a huge challenge. In this work, we develop a novel “acid-base pair” adjusted solvent evaporation induced self-assembly (EISA) strategy to prepare highly crystallized ordered mesoporous TiO2/WO3 (OM-TiO2/WO3) heterojunctions. The WCl6 and titanium isopropoxide (TIPO) are used as the precursors, respectively, which function as the “acid-base pair”, enabling the co-assembly with the structure directing agent (PEO-b-PS) into highly ordered mesostructures. In addition, PEO-b-PS can be converted to rigid carbon which can protect the mesostructures from collapse during the crystallization process. The resultant OM-TiO2/WO3 heterojunctions possess primitive cubic mesostructures, large pore size (∼21.1 nm), highly crystalline frameworks and surface area (∼98 m2/g). As a sensor for acetone, the obtained OM-TiO2/WO3 show excellent response/recovery performance (3 s/5 s), good linear dependence, repeatability, selectivity, and long-term stability (35 days).

Summary Single-gas-phase chemical detections have recently attracted much attention in diverse fields; however, the pathways of composite sensing systems for detection of multiple gases could provoke better substantial potentials, which have not been fully explored due to the deficiency of feasible integrated sensing platforms. Here, we demonstrate a Janus mesoporous sensor device based on multi-response mechanisms, which can simultaneously detect multiple gases. The sensor devices based on Janus mesoporous carbon/silica films with asymmetric mesostructures and disparate active sites (–NH2 and –COOH groups) can be prepared via a solvent evaporation-induced self-assembly and selective functionalization strategy. Such Janus devices have exhibited distinct ultrafast response time under ultralow limit of detection and great selectivity and stability for NH3 and H2S sensing, and each gas concentration can be quantified individually. Theoretical calculation reveals that the distinction of response time is ascribed to the interaction difference to target gases, allowing for distinguished multiple signals.

High capacity transition metal oxides have attracted much attention as potential sodium-ion batteries (SIBs) anodes. However, the fast capacity fading greatly limits their practical applications. In this study, highly crystallized mesoporous Fe3O4 nanoparticles encapsulated in the hollow nitrogen-doped carbon nanospheres (denoted as HCM-Fe3O4@void@N-C) have been synthesized and then explored as anode materials for SIBs. The resultant HCM-Fe3O4@void@N-C nanospheres possess a uniform particle size of ~ 180 nm with highly crystallized mesoporous Fe3O4 cores (~ 100 nm in diameter), a large surface area of ~ 250 m2 g−1 and a nitrogen-doped carbon shell (~ 7.6 wt%). Notably, a high discharge capacity of 372 mA h g−1 is obtained after the first five cycles at 160 mA g−1, which can gradually increase and be maintained at an ultrahigh specific capacity of 522 mA h g−1 even after 800 cycles. Besides, remarkable rate performance with a capacity of 196 mA h g−1 at a current density of 1200 mA g–1 and a high Coulombic efficiency (~ 100%) are obtained. Such good performance can be attributed to the unique yolk-shell nanostructure with a high crystallized mesoporous Fe3O4 core, a high conductive N-doped carbon shell, and a suitable void space, paving a new way to design and synthesize high-performance anode materials for SIBs.

Periarticular osteopenia is the most specific hallmark of rheumatoid arthritis (RA). The renin-angiotensin system (RAS) in the synovium has been found to participate in the pathogenic process of RA. This study examined whether and how RAS regulates periarticular osteopenia in RA. The synovial tissues from patients with RA and osteoarthritis (OA) were prepared. Female Sprague-Dawley rats were treated with either saline, bovine type II collagen (CII) to induce arthritis (CIA), or CII combined with perindopril, an inhibitor of angiotensin-converting enzyme (ACE). Expressions of RAS components, including AT1R, AT2R and ACE, in human and rat synovial tissues were detected. Bone mass of rat joints was examined. Levels of RANKL, OPG and DKK-1 in rat synovium and expressions of TRAF6 and β-catenin in rat bone were examined. The results showed that AT1R, AT2R and ACE in human and rat synovium were up-regulated, but the increased ACE in rat synovial tissues was abrogated by perindopril. While CIA rats displayed increased bone resorption and decreased bone formation, perindopril treatment almost completely abrogated the RAS-mediated osteopenia, indicating that inhibition of ACE reduced the joint damages in rats. The expressions of RANKL and DKK-1 increased in CIA rats as compared with those in the control; TRAF6 was up-regulated and β-catenin was down-regulated in the bone tissues of CIA rats. The changes were then reversed by the use of perindopril. Our findings demonstrate that RAS in the synovium promotes periarticular osteopenia by increasing bone resorption and decreasing bone formation through modulating the RANKL/RANK/TRAF6 and Wnt/β-catenin pathways.

Correlation analysis has been widely used to validate the accuracy of synthetic medium-resolution data generated by spatial and temporal fusion methods. However, as the temporal resolution of Landsat data is 16 days, this method only can be used for the validation of multi-temporal Landsat data. This means that the fusion accuracy of most images in a synthetic daily time series have not been validated. Furthermore, the fusion accuracy of each image in a synthetic Landsat time series is different, because there is a negative correlation with the time interval length between the fusion date and the base image date. Therefore, there is a need for temporal validation of synthetic daily Landsat time series data. We propose a suitable validation method in this paper. The improved spatial and temporal data fusion approach (ISTDFA) was applied to generate synthetic daily Landsat Normalized Difference Vegetation Index (NDVI) time series, which were then validated for both spatial and temporal dimensions using actual MODIS NDVI time series. For temporal validation, the correlation coefficients ( R ) between the actual and synthetic 500 m NDVI time series were calculated by pixel-by-pixel to generate imagery with an R value for each pixel. For spatial validation, R between MODIS NDVI imagery and synthetic Landsat NDVI imagery was calculated day by day to generate an R time series. This method was tested and validated in two locations (Bole and Luntai) in Xinjiang Province, China. The results show that, for temporal validation, the R values of 86.08% pixels in Bole and 94.71% pixels in Luntai are higher than 0.9, and in spatial validation, R values are higher than 0.8 on most days. Synthetic daily Landsat NDVI data was used to monitor the phenology of vegetation at a spatial resolution of 30 m successfully, while the MODIS product is limited to 500 m.

In complex heterogeneous areas, it is difficult to map crops with high accuracy using only high spatial resolution or only high spectral resolution remote sensing data. Because the spectral resolution of high spatial resolution data is too low, the spectral differentiations of different vegetation types are very small in high spatial resolution data. It is hard to distinguish between different vegetation types using high spatial resolution data. For high spectral resolution remote sensing data, it is hard to exclude linear objects like roads, bridges and drains from crops due to the low spatial resolution of these data. To address this problem, a novel object-based fine crop mapping method by combining high spatial and high spectral resolution remote sensing data for heterogeneous areas was proposed and validated in Suzhou city, Jiangsu province, China. First, pure crop polygons were derived from a 0.5 m aerial data. Due to the high spatial resolution, non-cultivated land could be easily isolated from arable land. Then, a Hyperion data was used to classify crops for each of the pure crop polygons. The results show that this method can map crops in complex heterogeneous areas with an overall accuracy higher than 95%, which is much higher than the accuracy of maps classified using only high spatial resolution data or only high spectral resolution data, which have an overall accuracy of 58.78% and 77.54%, respectively.

Two key weaknesses of STDFA including sensor difference and spatial variability were adjusted.Three wildly used spatial and temporal fusion methods were compared.The correlation coefficient r had a negative exponential relationship with ratio of land cover change pixels.The accuracy of ISTDFA method had a logarithmic relationship with the size of applied area. Because of low temporal resolution and cloud influence, many remote-sensing applications lack high spatial resolution remote-sensing data. To address this problem, this study introduced an improved spatial and temporal data fusion approach (ISTDFA) to generate daily synthetic Landsat imagery. This algorithm was designed to avoid the weaknesses of the spatial and temporal data fusion approach (STDFA) method, including the sensor difference and spatial variability. A weighted linear mixed model was used to adjust the spatial variability of surface reflectance. A linear-regression method was used to remove the influence of differences in sensor systems. This method was tested and validated in three study areas located in Xinjiang and Anhui province, China. The other two methods, the STDFA and the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), were also applied and compared in those three study areas. The results showed that the ISTDFA algorithm can generate daily synthetic Landsat imagery accurately, with correlation coefficient r equal to 0.9857 and root mean square error (RMSE) equal to 0.0195, which is superior to the STDFA method. The ISTDFA method had higher accuracy than ESTARFM in areas greater than 200?× 200 MODIS pixels while the ESTARFM method had higher accuracy than the ISTDFA method in small areas. The correlation coefficient r had a negative power relation with ratio of land-cover change pixels. A land-cover change of 20.25% pixels can lead to a reduced correlation coefficient r of 0.295 in the blue band. The accuracy of the ISTDFA method indicated a logarithmic relationship with the size of the applied area, so it is recommended for use in large-scale areas.

The renin-angiotensin system contributes to the pathogenesis of rheumatoid arthritis, but that the mechanism is unclear. This study aims to investigate the effect of angiotensin II (Ang II) on osteogenic differentiation of synoviocytes and the underlying mechanism. Ang II was showed to inhibite osteogenic differentiation of synoviocytes, which was mitigated by a Dickkopf-1 (DKK-1) inhibitor. DKK-1 was upregulated by Ang II, which was weakened by the Ang II type 1 receptor (AT1R) blocker, reactive oxygen species (ROS) scavenger, and p38 inhibitor. Ang II increased the levels of AT1R, ROS, and NADPH oxidase (NOX), and the upregulations were mitigated by the AT1R blocker or NOX inhibitor. Furthermore, Ang II activated the p38 pathway, which was blocked by the AT1R blocker, ROS scavenger, or siRNA-MKK3. In brief, these results indicate that Ang II upregulates NOX expression and ROS production via AT1R, activates the MKK3/p38 signaling, and in turn upregulates DKK-1 expression, participating in the inhibition of osteogenic differentiation of synoviocytes.

Amorphous Fe-Y-B thin films have been successfully fabricated by direct-current magnetron sputtering, and the effects of annealing treatment on the microstructure and magnetic properties, especially coercivity and magneto-impedance ratio, were investigated systematically. After low-temperature heat treatments (below 673 K), films still remain amorphous structure and their magnetic properties were largely enhanced due to stress release, e. g., the maximum magneto-impedance ratio increases from about 5.6-46.6% and the coercivity decreases from about 7.7-2.2 Oe for as-deposited film and the film annealed at 573 K for 1 h, respectively. After high-temperature heat treatment at 673 K, crystallization occurs, deteriorating magnetic properties (magnetic softness and magneto-impedance features) of the film. The Fe-Y-B thin film annealed at 573 K for 1 h, possessing good magneto-impedance effect and soft magnetic properties, might have potential application in micro-sized magnetic actuators. (C) 2014 Elsevier B.V. All rights reserved.

The hydriding/dehydriding properties of the La1.8Ca0.2Mg14Ni3 alloy modified by mechanical ball milling for 20 h in different environment media, namely under argon, in toluene and in THF, respectively, have been investigated. XRD and SEM results show that the environment media affect the microstructure and surface morphology of the alloy noticeably. For the three environment media used, THF achieved the highest enhancement in hydriding/dehydriding properties. The effect of argon ranked the second and that of toluene the third. But the difference between the second and the third was very small. For the samples milled in THF, the amount of hydrogen absorbed reached 5.1 wt.% within 300 s at 600 K and 4.2 wt.% within 600 s at room temperature. The amount of hydrogen desorbed reached 5.05 wt.% at 613 K. The remarkable improvement of the hydrogen storage properties of the ball-milled alloy in different environment media experimented is ascribed to the physical and chemical change of the alloy surface. Most probably are due to the appearance of the active sites and the electron donor–acceptor (EDA) complexes produced by the ball-milling process.

In this paper, we deduced an empirical equation for predicting the stabilities of the hydrides of AB 5 -type compounds with the consideration of the effects of geometrical factor and electric factor. The theoretical curves derived from this equation are in good agreement with the test results of La 1− x Ca x Ni 5 , Ml 1− x Ca x Ni 5 and La 1− x Ce x Ni 5 compounds. With this model, we can easily interpret the phenomenon that the initial partial substitution of R by Ca in R 1− x Ca x Ni 5 (R is rare earth metal) compounds cause an increase of hydrogen desorption pressure to a maximum value, whereas in the range of larger Ca content x the hydrogen desorption pressure decreases with increasing x .

This paper investigates the effectiveness of the genetic algorithm (GA) evolved neural network classifier and its application to the land cover classification of remotely sensed multispectral imagery. First, the key issues of the algorithm and the general procedures are described in detail. Our methodology adopts a real coded GA strategy and hybrid with a back propagation (BP) algorithm. The genetic operators are carefully designed to optimize the neural network, avoiding premature convergence and permutation problems. Second, a SPOT-4 XS imagery is employed to evaluate its accuracy. Traditional classification algorithms, such as maximum likelihood classifier, back propagation neural network classifier, are also involved for a comparison purpose. Based on an evaluation of the user's accuracy and kappa statistic of different classifiers, the superiority of applying the discussed genetic algorithm-based classifier for simple land cover classification using multispectral imagery data is established. Thirdly, a more complicate experiment on CBERS (China-Brazil Earth Resources Satellite) data and discussion also demonstrates that carefully designed genetic algorithm-based neural network outperforms than gradient descent-based neural network. This has been supported by the analysis of the changes of connection weights and biases of the neural network. Finally, some concluding remarks and suggestions are also presented.

This paper describes the development of a 1-km landcover dataset of China by using monthly NDVI data spanning April 1992 through March 1993. The method used combined unsupervised and supervised classification of NDVI data from AVHRR. It is composed of five steps: (a) unsupervised clustering of monthly AVHRR NDVI maximum value composites is performed using the ISOCLASS algorithm; (b) preliminary identification is carried out with the addition of digital elevation models, eco-region data and a collection of other landcover/vegetation reference data to identify the clusters with single landcover classes; (c) re-clustering is performed of clusters with size greater than a given threshold value and containing two or more disparate landcover classes; (d) cluster combining is performed to combine all clusters with a single landcover class in one cluster, and all other clusters into one mixed cluster; and (e) supervised classification is used to carry out post-classification of the mixed cluster generated in the previous step by using the maximum likelihood algorithm and the identified single landcover classes of the previous step as training data. The classification is based on extensive use of computer-assisted image processing and tools, as well as the skills of the human interpreter to take the final decisions regarding the relationship between spectral classes defined using unsupervised methods and landscape characteristics that are used to define landcover classes.