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Near surface air temperature (Tβ) is one of the most critical variables in climatology, hydrology, epidemiology, and environmental health. In situ measurements are not efficient for characterizing spatially heterogeneous Tβ, while remote sensing is a powerful tool to break this limitation. This study proposes a mapping framework for daily mean Ta using an enhanced empirical regression method based on remote sensing data. It differs from previous studies in three aspects. First, nighttime light data is introduced as a predictor (besides land surface temperature, normalized difference vegetation index, impervious surface area, black sky albedo, normalized difference water index, elevation, and duration of daylight) considering the urbanization-induced Tβ increase over a large area. Second, independent components are extracted using principal component analysis considering the correlations among the above predictors. Third, a composite sinusoidal coefficient regression is developed considering the dynamic Tβ-predictor relationship. This method was performed at 333 weather stations in China during 2001-2012. Evaluation shows overall mean error of -0.01 K, root mean square error (RMSE) of 2.53 K, correlation coefficient (R2) of 0.96, and average uncertainty of 0.21 K. Model inter-comparison shows that this method outperforms six additional empirical regressions that have not incorporated nighttime light data or considered predictor independence or coefficient dynamics (by 0.18-2.60 K in RMSE and 0.00-0.15 in R2). [ABSTRACT FROM AUTHOR]

Change detection in multi-temporal remote sensing images has usually been treated as a problem of explicitly detecting land cover transitions. To date, multi-dimensional change vector analysis has been an effective solution to such problems. However, using change vector analysis makes it hard to calculate multiple directions or kinds of change. Through combining multi-feature object-based image analysis and change vector analysis, this paper presents a novel method for object-based change detection of multiple changes. Our technique, named self-adaptive weight-change vector analysis, carries out: (1) change vector analysis to determine magnitude and direction of changes; and (2) self-adaptive weight-based analysis of the standard deviation of image objects. Furthermore, a polar representation has been adopted to acquire visual change information for image objects. This paper proposes an automatic technique that can be applied to the field of multi-feature object-based change detection for very high resolution remotely sensed images. The two-step automatic detection strategy includes extraction of changed objects using an expectation-maximization algorithm to estimate the threshold under a Gaussian assumption, and identification of different kinds of changes using a K-means clustering algorithm. The effectiveness of our approach has been tested on both multispectral and panchromatic fusion images. Results of these two experimental cases confirm that this approach can detect multiple kinds of change. We found that self-adaptive weight-change vector analysis had superior capabilities of object-based change detection compared with standard change vector analysis, yielding Kappa statistics of 0.7976 and 0.7508 for Cases 1 and 2, respectively. [ABSTRACT FROM AUTHOR]

A new algorithm was developed in this research to minimize aerosol effects on the normalized difference vegetation index (NDVI). Simulation results show that in red-NIR reflectance space, variations in red and NIR channels to aerosol optical depth (AOD) follow a specific pattern. Based on this rational, the apparent reflectance in these two bands of neighboring pixels were used to reduce aerosol effects on NDVI values of the central pixel. We call this method the neighboring pixels (NP) algorithm. Validation was performed over vegetated regions in the border area between China and Russia using Landsat 8 Operational Land Imager (OLI) imagery. Results reveal good agreement between the aerosol corrected NDVI using our algorithm and that derived from the Landsat 8 surface reflectance products. The accuracy is related to the gradient of NDVI variation. This algorithm can achieve high accuracy in homogeneous forest or cropland with the root mean square error (RMSE) being equal to 0.046 and 0.049, respectively. This algorithm can also be applied to atmospheric correction and does not require any information about atmospheric conditions. The use of the moving window analysis technique reduces errors caused by the spatial heterogeneity of aerosols. Detections of regions with homogeneous NDVI are the primary sources of biases. This new method is operational and can prove useful at different aerosol concentration levels. In the future, this approach may also be used to examine other indexes composed of bands attenuated by noises in remote sensing. [ABSTRACT FROM AUTHOR]

The environmental monitoring of earth surfaces requires land surface temperatures (LSTs) with high temporal and spatial resolutions. The disaggregation of LST (DLST) is an effective technique to obtain high-quality LSTs by incorporating two subbranches, including thermal sharpening (TSP) and temperature unmixing (TUM). Although great progress has been made on DLST, the further practice requires an in-depth theoretical paradigm designed to generalize DLST and then to guide future research before proceeding further. We thus proposed a generalized paradigm for DLST through a conceptual framework (C-Frame) and a theoretical framework (T-Frame). This was accomplished through a Euclidean paradigm starting from three basic laws summarized from previous DLST methods: the Bayesian theorem, Tobler's first law of geography, and surface energy balance. The C-Frame included a physical explanation of DLST, and the T-Frame was created by construing a series of assumptions from the three basic laws. Two concrete examples were provided to show the advantage of this generalization. We further derived the linear instance of this paradigm based on which two classical DLST methods were analyzed. This study finally discussed the implications of this paradigm to closely related topics in remote sensing. This paradigm develops processes to improve an understanding of DLST, and it could be used for guiding the design of future DLST methods. [ABSTRACT FROM PUBLISHER]

We examine the role of firm IT capability in contributing to internal control and external audit in the post-SOX environment. Specifically, we measure the effectiveness of both the overall internal control and the five components of internal control as defined by the Committee of Sponsoring Organizations of the Treadway Commission's Internal Control--Integrated Framework (COSO 1992). Our findings support the concern that accounting professionals have regarding the impact of the use of IT on business risks and controls relevant to the audit. Specifically, IT capability directly mitigates audit fee increases, but not audit delay increases, indicating high IT capability signals lower business risks associated with the use of IT and reduces the auditor's risk premium. Further, IT capability has pervasive impacts on both the effectiveness of internal control and the components of effective internal control, which in turn further restrain audit fee and audit delay increases. Overall, our results suggest that a firm's IT capability has the additional benefits of supporting the functioning of internal control and the efficiency of the audit process. [ABSTRACT FROM AUTHOR]

The American Jobs Creation Act of 2004 (the Act [U.S. House of Representatives 2004]) created a tax holiday encouraging firms to repatriate foreign earnings and invest that capital in the United States. However, the Act did not require a direct tracing of the spending of repatriated funds; accordingly, repatriating firms could ignore the investment prescriptions of the Act, since tax regulators were provided no legally viable basis to pursue violations of the spending requirements. We use this event to provide evidence on the effect of political scrutiny on firms' tax law compliance in a setting where regulatory enforcement plays essentially no role. Our findings suggest that repatriating firms facing greater levels of policymaker scrutiny, relative to other repatriating firms, exhibited greater compliance with the Act by increasing relative expenditures on permitted uses (R&D investment) and restraining expenditures on nonpermitted uses. We also find that the spending patterns are different only for the group of high-scrutiny firms (i.e., there is a threshold effect). Our estimates imply that the repatriation tax holiday induced, among such high-scrutiny firms alone, $0.41 of additional R&D spending per revenue dollar forgone by the Treasury under the Act. The evidence is generally consistent with the political cost hypothesis. [ABSTRACT FROM AUTHOR]

Carbon capture and storage (CCS) is one of the critical techniques for mitigating global climate warming which can capture and store the CO2 released during industrial processes into deep geological sites. However, there is a possible risk that the CO2 might leak from under the ground. Therefore, it is important that CO2 leakage spots on the surface of sequestration fields be detected at large scale and on a long-term basis. The purpose of this study was to design a new model to identify CO2 leakage spots through monitoring the spectral change of vegetation on the surface of the sequestration fields. A field simulation experiment was performed at the Sutton Bonington campus of the University of Nottingham to collect the leaf and canopy reflectance of beans, which grew in soil with different levels of CO2 concentration. Visible symptoms of bean stress caused by CO2 leakage may include the reducing bean vigor and the bean leaves turning yellow. The canopy spectra and leaf spectra were processed by a smoothing, continuum removal and first derivative method. We found that, while the CO2 concentration of the soil increased, the sum of the first derivative reflectance in the 500-550 nm (SDg) increased and that in the 680-760 nm (SDr) decreased gradually compared with that of the control plots. Moreover, the newly developed ratio index SDr/SDg can be effectively used to distinguish gassed and ungassed beans at both leaf and canopy scales. Significant correlation was found between the SPAD values and the SDr/SDg indices (R2 = 0.5687, n = 197). This study indicates that hyperspectral remote sensing techniques can be used to detect CO2 leaking spots by investigating the spectral change of vegetation reflectance in the sequestration fields. [ABSTRACT FROM AUTHOR]

Abstract: The spatio-temporal distribution of vegetation is an important component of the urban/suburban environment. Therefore, correct estimation of vegetation cover in urban/suburban areas is fundamental in land use studies. In this study, the potential of extracting fractional vegetation cover (FVC) from remotely sensed data and ground measurements is explored. Based on the assumption that pixel has a mosaic structure, sub-pixel models for FVC estimation are first introduced. Then a combined approach of using different sub-pixel models for FVC estimation based on land cover classification is proposed. The experimental result, derived from a case study in Haidian district, Beijing, indicates that the accuracy of FVC estimation using the proposed method can be up to 80.7%. The results suggest that this method may be generally useful for FVC estimation in urban and suburban areas. [Copyright &y& Elsevier]

Remotely sensed, angle-based vegetation indices that measure vegetation amounts by the angle between an approximated soil line and a simulated vegetation isoline in the red-near-infrared reflectance space were developed and evaluated in this paper. Ünsalan and Boyer previously proposed an angle-based vegetation index, θ (denoted as θNDVI in this paper), based on the normalized difference vegetation index (NDVI) with the objective of overcoming the saturation problem in the NDVI. However, θNDVI did not consider strong soil background influences present in the NDVI. To reduce soil background noise, an angle-based vegetation index, θSAVI, based on the soil-adjusted vegetation index (SAVI), was derived using trigonometric analysis. The performance of θNDVI and θSAVI was evaluated and compared with their corresponding vegetation indices, NDVI and SAVI. The soil background influence on θNDVI was found to be as significant as that on the NDVI. θNDVI was found to be more sensitive to vegetation amount than the NDVI at low vegetation density levels, but less sensitive to vegetation fraction at high vegetation density levels. Thus, the saturation effect at high vegetation density levels encountered in the NDVI was not mitigated by θNDVI. By contrast, θSAVI exhibited insignificant soil background effects and weaker saturation, as in SAVI, but also improved upon the dynamic range of SAVI. Analyses and evaluation suggest that θSAVI is an optimal vegetation index to assess and monitor vegetation cover across the entire range of vegetation fraction density levels and over a wide variety of soil backgrounds. [ABSTRACT FROM AUTHOR]

Using optical fiber devices at the terminal is an important means to improve the performance of free space optical communication systems. We proposed a Fraunhofer diffraction integral based method to calculate the coupling efficiency between spatial light and few-mode fiber, and studied the influences of alignment tolerance on coupling efficiency. Numerical simulations confirm that the results obtained by this new method are consistent with the traditional calculation method. What is more, a few-mode fiber has flatter response characteristics to F-number mismatch, fiber lateral offset and defocus. Compared with single mode fiber, few-mode fibers can not only improve the maximum coupling efficiency, but also their coupling efficiency is much less sensitive to deviations from ideal conditions, thereby making them a more practical option for real world applications. [ABSTRACT FROM AUTHOR]

Chronic wounds in diabetes undergo a lifetime risk of developing into diabetic foot ulcers. Oxygen is crucial to wound healing by regulating cell proliferation, migration, and neovascularization. However, current oxygen therapies, including hyperbaric oxygen (HBO) and topical gaseous oxygen (TGO), mainly employ gaseous oxygen delivery, which is much less effective in penetrating the skin. Here, we introduce an oxygen-producing patch, made of living microalgae hydrogel, which can produce dissolved oxygen. The superior performance of the patch that results from its dissolved oxygen delivery is >100-fold much more efficient than TGO penetrating the skin. Further experiments indicate that the patch could promote cell proliferation, migration, and tube formation in vitro, and improve chronic wound healing and the survival of skin grafts in diabetic mice. We believe that the microalgae-gel patch can provide continuous dissolved oxygen to improve chronic wound healing. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] In order to solve the core problems in the digitization process of cultural and creative works in China, taking cultural and creative works as the research object, this paper constructs a metadata hierarchy model of cultural and creative works based on their internal and external information characteristics. This model is standardized, accurately described, easy to operate and adapted to the needs of management and research, and is useful in preservation, transmission, knowledge transformation and innovation of cultural and creative works. This paper aims to provide possible solutions for the digitization process of cultural and creative works and make the maintenance of artistic information resources feasible, which is conducive to the standardized management of the data of literary and creative works and meets the needs of the research, appreciation and dissemination of literary and creative works. [Method/Process] The CDWA data standard is designed to describe artistic works, including the physical form, digital images and the relationship between time and space, characters, history and culture, etc. At the same time, it also has a unique description of elements for the preservation and management, which lays the foundation for data exchange and sharing. The completeness and comprehensiveness of information description of CDWA is quite consistent with the purpose of systematically and comprehensively describing cultural and creative works. Different from AACR2R and RDA to describe paper literature, electronic resources, music works and micro literature, the main purpose of CCO is to promote the standardization of cultural relics such as artworks and their image description. It is the first set of data content standards in the field of cultural assets. The data content standards of CCO focus on descriptive metadata and normative control of artistic works. Based on the above, this paper builds the meta framework of digital resource construction of cultural and creative works, then based on the data content standard CCO, it catalogues and describes the cultural and creative works designed by teachers and students of School of Art and Design of Wuxi Institute of Technology, China, and then promotes the practice of digital construction. [Results/Conclusions] Based on the discussion and analysis of existing metadata models of art and intangible cultural heritage, a metadata model was formed to describe cultural and creative works from three aspects: basic metadata, management metadata and associated metadata. As an indispensable part of revealing, transforming and innovating the cultural and artistic value of artistic works, this paper constructed a modular and extensible metadata standard and model which is much more suitable for cultural and creative works, and provided solutions for the digitization process of cultural and creative works. What's more, this study plays an important role in preservation and inheritance of characteristic resources. It also promoted the formation of new formats and new models of digital cultural and creative industries and provided data support for the vigorous development of China's cultural and creative industries.

Cancer progression is governed by multifaceted interactions of cancer cells with their microenvironment and one of these ways is through secreted compounds. Substances released by gastric cancer cells have not being profiled in a proteome-wide manner. ITRAQ-based tandem mass spectrometry was employed to quantify proteins secreted by HFE145 normal, MKN7 well-differentiated, and MKN45 poorly differentiated gastric cancer cell lines. The expression levels of 237 proteins were found to be significantly different between normal and cancer cells. Further examination of 16 gastric cell lines and 115 clinical samples validated the up-regulation of CTSS expression in gastric cancer. Silencing CTSS expression suppressed the migration and invasion of gastric cancer cells in vitro. Subsequent secretomics revealed that CTSS silencing resulted in changes in expression levels of 197 proteins, one-third of which are implicated in cellular movement. Proteome-wide comparative secretomes of normal and gastric cancer cells were produced that constitute a useful resource for gastric cancer research. CTSS was demonstrated to play novel roles in gastric cancer cell migration and invasion, putatively via a network of proteins associated with cell migration, invasion, or metastasis. Cathepsin S is member of a large group of extracellular proteases, which are attractive drug targets. The implicated role of CTSS in gastric cancer metastasis provides an opportunity to test existing compounds against CTSS for adjuvant therapy and/or treatment of metastatic gastric cancers.