DWSI: AN APPROACH TO SOLVING THE POLYGON INTERSECTION-SPREADING PROBLEM WITH A PARALLEL UNION ALGORITHM AT THE FEATURE LAYER LEVEL

A dual-way seeds indexing (DWSI) method based on R-tree and the OpenGeospatial Consortium (OGC) simple feature model was proposed to solve the polygon intersection-spreading problem. The parallel polygon union algorithm based on the improved DWSI and the OpenMP parallel programming model was developed to validate the usability of the data partition method. The experimental results reveal that the improved DWSI method can implement a robust parallel task partition by overcoming the polygon intersection-spreading problem. The parallel union algorithm applied DWSI not only scaled up the data processing but alsospeeded up the computation compared with the serial proposal, and it showed ahigher computational efficiency with higher speedup benchmarks in the treatment of larger-scale dataset. Therefore, the improved DWSI can be a potential approach to parallelizing the vector data overlay algorithms based on the OGC simple data model at the feature layer level

DWSI: AN APPROACH TO SOLVING THE POLYGON INTERSECTION-SPREADING PROBLEM WITH A PARALLEL UNION ALGORITHM AT THE FEATURE LAYER LEVEL

A dual-way seeds indexing (DWSI) method based on R-tree and the OpenGeospatial Consortium (OGC) simple feature model was proposed to solve the polygon intersection-spreading problem. The parallel polygon union algorithm based on the improved DWSI and the OpenMP parallel programming model was developed to validate the usability of the data partition method. The experimental results reveal that the improved DWSI method can implement a robust parallel task partition by overcoming the polygon intersection-spreading problem. The parallel union algorithm applied DWSI not only scaled up the data processing but alsospeeded up the computation compared with the serial proposal, and it showed ahigher computational efficiency with higher speedup benchmarks in the treatment of larger-scale dataset. Therefore, the improved DWSI can be a potential approach to parallelizing the vector data overlay algorithms based on the OGC simple data model at the feature layer level

Research progress in the relationship between mitochondrial dysfunction and osteoporosis

Osteoporosis (OP) is a chronic senile bone disease characterized by decreased bone mass and increased bone fragility. There are many inducing factors and the pathogenesis is complex. To explore the mechanism of OP and improve clinical efficacy has always been a hot topic in life science. In recent years, it has been found that mitochondria play an important role in the pathogenesis of OP. Functional abnormalities such as mitochondrial energy metabolism, mitochondrial oxidative stress, mitochondrial autophagy, mitochondrial-mediated apoptosis and mitochondrial dynamics can interfere with the differentiation of bone marrow mesenchymal stem cells through different signal pathways, cytokines and protein expression to regulate osteoblast activity, proliferation and differentiation, and start the process of osteoclast apoptosis. Therefore, taking mitochondria as the target, regulating the functions of mitochondrial energy metabolism, oxidative stress, autophagy and kinetics, inducing osteogenic differentiation of bone marrow mesenchymal stem cells, promoting osteoblast differentiation and mineralization, and inducing osteoclast apoptosis are potential strategies for the prevention and treatment of OP. In this article, the mechanism of mitochondrial dysfunction in OP was reviewed by referring to relevant literature at home and abroad, in order to lay a foundation for further research

A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data

An overview of the commonly applied evapotranspiration (ET) models using remotely sensed data is given to provide insight into the estimation of ET on a regional scale from satellite data. Generally, these models vary greatly in inputs, main assumptions and accuracy of results, etc. Besides the generally used remotely sensed multi-spectral data from visible to thermal infrared bands, most remotely sensed ET models, from simplified equations models to the more complex physically based two-source energy balance models, must rely to a certain degree on ground-based auxiliary measurements in order to derive the turbulent heat fluxes on a regional scale. We discuss the main inputs, assumptions, theories, advantages and drawbacks of each model. Moreover, approaches to the extrapolation of instantaneous ET to the daily values are also briefly presented. In the final part, both associated problems and future trends regarding these remotely sensed ET models were analyzed to objectively show the limitations and promising aspects of the estimation of regional ET based on remotely sensed data and ground-based measurements

Examining ecosystem deterioration using a total socioenvironmental system approach

We are faced with many challenges such as climate change, environmental pollution, ecosystem deterioration, water scarcity, and deepened socioeconomic inequality. However, there is no consistent framework to explain the interactions between environmental changes and human activities. Therefore, we propose a total socioenvironmental analytical framework (TSEAF) based on the society–nature coevolution theory. TSEAF unifies all components concerning the society–nature coevolution into one system, assimilates biophysical and socioeconomic datasets into a unified database, and unifies analytical methods with assimilated datasets for an integrated analysis. We illustrate TSEAF through a case study on grassland productivity in Inner Mongolia, China. The results of the case study suggested that socioeconomic development covariated with eco-environmental changes. The directions and strengths of covariation decided the interaction dynamics between humans and natural systems. Climatic change and socioeconomic transformation equally affected the productivity of the grassland. Precipitation and temperature remarkably increased (decreased) the grassland productivity when their long-term trends of change were similar (dissimilar). The socioeconomic goals often contradicted each other and displayed mixed impact on the grassland production, thereby showing obvious spatial disparities. The results indicated an urgent need to balance the conflicting socioeconomic targets for sustainable development. In brief, the case study illustrated how to assimilate a unified socioenvironmental database and integrate appropriate analytical methods with the available datasets. It successfully demonstrated the applicability of TSEAF. The proposed framework can be used to examine various other coupled socioenvironmental systems or other geographic areas

The augmented lagrange multipliers method for matrix completion from corrupted samplings with application to mixed Gaussian-impulse noise removal.

This paper studies the problem of the restoration of images corrupted by mixed Gaussian-impulse noise. In recent years, low-rank matrix reconstruction has become a research hotspot in many scientific and engineering domains such as machine learning, image processing, computer vision and bioinformatics, which mainly involves the problem of matrix completion and robust principal component analysis, namely recovering a low-rank matrix from an incomplete but accurate sampling subset of its entries and from an observed data matrix with an unknown fraction of its entries being arbitrarily corrupted, respectively. Inspired by these ideas, we consider the problem of recovering a low-rank matrix from an incomplete sampling subset of its entries with an unknown fraction of the samplings contaminated by arbitrary errors, which is defined as the problem of matrix completion from corrupted samplings and modeled as a convex optimization problem that minimizes a combination of the nuclear norm and the l(1)-norm in this paper. Meanwhile, we put forward a novel and effective algorithm called augmented Lagrange multipliers to exactly solve the problem. For mixed Gaussian-impulse noise removal, we regard it as the problem of matrix completion from corrupted samplings, and restore the noisy image following an impulse-detecting procedure. Compared with some existing methods for mixed noise removal, the recovery quality performance of our method is dominant if images possess low-rank features such as geometrically regular textures and similar structured contents; especially when the density of impulse noise is relatively high and the variance of Gaussian noise is small, our method can outperform the traditional methods significantly not only in the simultaneous removal of Gaussian noise and impulse noise, and the restoration ability for a low-rank image matrix, but also in the preservation of textures and details in the image

A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery

Cloud cover is inevitable in optical remote sensing (RS) imagery on account of the influence of observation conditions, which limits the availability of RS data. Therefore, it is of great significance to be able to reconstruct the cloud-contaminated ground information. This paper presents a sparse dictionary learning-based image inpainting method for adaptively recovering the missing information corrupted by thick clouds patch-by-patch. A feature dictionary was learned from exemplars in the cloud-free regions, which was later utilized to infer the missing patches via sparse representation. To maintain the coherence of structures, structure sparsity was brought in to encourage first filling-in of missing patches on image structures. The optimization model of patch inpainting was formulated under the adaptive neighborhood-consistency constraint, which was solved by a modified orthogonal matching pursuit (OMP) algorithm. In light of these ideas, the thick-cloud removal scheme was designed and applied to images with simulated and true clouds. Comparisons and experiments show that our method can not only keep structures and textures consistent with the surrounding ground information, but also yield rare smoothing effect and block effect, which is more suitable for the removal of clouds from high-spatial resolution RS imagery with salient structures and abundant textured features

Phase transition with regard to

<p><b> of the proposed ALM algorithm.</b> The curves colored red, green and blue define “phase transition” bounds for the case of , respectively.</p