Variability of Rainfall Erosivity and Erosivity Density in the Ganjiang River Catchment, China: Characteristics and Influences of Climate Change

Soil erosion is one of the most critical environmental hazards in the world. Understanding the changes in rainfall erosivity (RE) and erosivity density (ED), as well as their affecting factors, at local and catchment scales in the context of climate warming is an important prerequisite of soil erosion prevention and soil loss risk assessment. The present study identified the variability and trends of RE and ED in terms of both time and space in the Ganjiang River catchment over the period of 1960–2012, and also analyzed and discussed the impact of climate change. The results show that RE and ED in the catchment had great monthly variations and high year-to-year variability. Both presented long-term increasing trends over the entire study period. The highest RE and ED were observed in June and in the eastern and northeast parts of the catchment, which indicated that June was the most susceptible month for soil erosion in this area and the lower reaches of the Ganjiang River was the riskiest area for soil erosion. Finally, the East Asian summer monsoon and climate change were highly correlated with changes in RE and ED

Dynamic Property and Magnetic Nonpotentiality of Two Types of Confined Solar Flares

We analyze 152 large confined flares (GOES class $\geq$M1.0 and $\leq$$45^{\circ}$ from disk center) during 2010$-$2019, and classify them into two types according to the criterion taken from the work of Li et al. (2019). "Type I" flares are characterized by slipping motions of flare loops and ribbons and a stable filament underlying the flare loops. "Type II" flares are associated with the failed eruptions of the filaments, which can be explained by the classical 2D flare model. A total of 59 flares are "Type I" flares (about 40\%) and 93 events are "Type II" flares (about 60\%). There are significant differences in distributions of the total unsigned magnetic flux ($\Phi$$_\mathrm{AR}$) of active regions (ARs) producing the two types of confined flares, with "Type I" confined flares from ARs with a larger $\Phi$$_{AR}$ than "Type II". We calculate the mean shear angle $\Psi$$_\mathrm{HFED}$ within the core of an AR prior to the flare onset, and find that it is slightly smaller for "Type I" flares than that for "Type II" events. The relative non-potentiality parameter $\Psi$$_\mathrm{HFED}$/$\Phi$$_\mathrm{AR}$ has the best performance in distinguishing the two types of flares. About 73\% of "Type I" confined flares have $\Psi$$_\mathrm{HFED}$/$\Phi$$_\mathrm{AR}$$<$1.0$\times$$10^{-21}$ degree Mx$^{-1}$, and about 66\% of "Type II" confined events have $\Psi$$_\mathrm{HFED}$/$\Phi$$_\mathrm{AR}$$\geq$1.0$\times$$10^{-21}$ degree Mx$^{-1}$. We suggest that "Type I" confined flares cannot be explained by the standard flare model in 2D/3D, and the occurrence of multiple slipping magnetic reconnections within the complex magnetic systems probably leads to the observed flare.Comment: 11 pages, 8 figure

VAMP - a vision based sensor network for health care hygiene

Adequate hand-washing has been shown to be a critical activity in preventing the transmission of infections such as MRSA in health-care environments. Hand-washing guidelines published by various health-care related institutions recommend a technique incorporating six hand-washing poses that ensure all areas of the hands are thoroughly cleaned. In this paper, an embedded wireless vision system (VAMP) capable of accurately monitoring hand-washing quality is presented. The VAMP system hardware consists of a low resolution CMOS image sensor and FPGA processor which are integrated with a microcontroller and ZigBee standard wireless transceiver to create a wireless sensor network (WSN) based vision system that can be retargeted at a variety of health care applications. The device captures and processes images locally in real-time, determines if hand-washing procedures have been correctly undertaken and then passes the resulting high-level data over a low-bandwidth wireless link. The paper outlines the hardware and software mechanisms of the VAMP system and illustrates that it offers an easy to integrate sensor solution to adequately monitor and improve hand hygiene quality. Future work to develop a miniaturized, low cost system capable of being integrated into everyday products is also discussed

Complete chloroplast genome sequence of Rhododendron mariesii and comparative genomics of related species in the family Ericaeae

Rhododendron mariesii Hemsley et Wilson, 1907, a typical member of the family Ericaeae, possesses valuable medicinal and horticultural properties. In this research, the complete chloroplast (cp) genome of R. mariesii was sequenced and assembled, which proved to be a typical quadripartite structure with the length of 203,480 bp. In particular, the lengths of the large single copy region (LSC), small single copy region (SSC), and inverted repeat regions (IR) were 113,715 bp, 7,953 bp, and 40,918 bp, respectively. Among the 151 unique genes, 98 were protein-coding genes, 8 were tRNA genes, and 45 were rRNA genes. The structural characteristics of the R. mariesii cp genome was similar to other angiosperms. Leucine was the most representative amino acid, while cysteine was the lowest representative. Totally, 30 codons showed obvious codon usage bias, and most were A/U-ending codons. Six highly variable regions were observed, such as trnK-pafI and atpE-rpoB, which could serve as potential markers for future barcoding and phylogenetic research of R. mariesii species. Coding regions were more conserved than non-coding regions. Expansion and contraction in the IR region might be the main length variation in R. mariesii and related Ericaeae species. Maximum-likelihood (ML) phylogenetic analysis revealed that R. mariesii was relatively closed to the R. simsii Planchon, 1853 and R. pulchrum Sweet,1831. This research will supply rich genetic resource for R. mariesii and related species of the Ericaeae

HIPK1 Inhibition Protects against Pathological Cardiac Hypertrophy by Inhibiting the CREB-C/EBPβ Axis

Inhibition of pathological cardiac hypertrophy is recognized as an important therapeutic strategy for heart failure, although effective targets are still lacking in clinical practice. Homeodomain interacting protein kinase 1 (HIPK1) is a conserved serine/threonine kinase that can respond to different stress signals, however, whether and how HIPK1 regulates myocardial function is not reported. Here, it is observed that HIPK1 is increased during pathological cardiac hypertrophy. Both genetic ablation and gene therapy targeting HIPK1 are protective against pathological hypertrophy and heart failure in vivo. Hypertrophic stress-induced HIPK1 is present in the nucleus of cardiomyocytes, while HIPK1 inhibition prevents phenylephrine-induced cardiomyocyte hypertrophy through inhibiting cAMP-response element binding protein (CREB) phosphorylation at Ser271 and inactivating CCAAT/enhancer-binding protein β (C/EBPβ)-mediated transcription of pathological response genes. Inhibition of HIPK1 and CREB forms a synergistic pathway in preventing pathological cardiac hypertrophy. In conclusion, HIPK1 inhibition may serve as a promising novel therapeutic strategy to attenuate pathological cardiac hypertrophy and heart failure

The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations

Variability of Rainfall Erosivity and Erosivity Density in the Ganjiang River Catchment, China: Characteristics and Influences of Climate Change

Soil erosion is one of the most critical environmental hazards in the world. Understanding the changes in rainfall erosivity (RE) and erosivity density (ED), as well as their affecting factors, at local and catchment scales in the context of climate warming is an important prerequisite of soil erosion prevention and soil loss risk assessment. The present study identified the variability and trends of RE and ED in terms of both time and space in the Ganjiang River catchment over the period of 1960–2012, and also analyzed and discussed the impact of climate change. The results show that RE and ED in the catchment had great monthly variations and high year-to-year variability. Both presented long-term increasing trends over the entire study period. The highest RE and ED were observed in June and in the eastern and northeast parts of the catchment, which indicated that June was the most susceptible month for soil erosion in this area and the lower reaches of the Ganjiang River was the riskiest area for soil erosion. Finally, the East Asian summer monsoon and climate change were highly correlated with changes in RE and ED

Improving adaboost for classification on small training sample sets with active learning

Recently, AdaBoost has been widely used in many computer vision applications and has shown promising results. However, it is also observed that its classification performance is often poor when the size of the training sample set is small. In certain situations, there may be many unlabelled samples available and labelling them is costly and time-consuming. Thus it is desirable to pick a few good samples to be labelled. The key is how. In this paper, we integrate active learning with AdaBoost to attack this problem. The principle idea is to select the next unlabelled sample base on it being at the minimum distance from the optimal AdaBoost hyperplane derived from the current set of labelled samples. We prove via version space concept that this selection strategy yields the fastest expected learning rate. Experimental results on both artificial and standard databases demonstrate the effectiveness of our proposed method

Large margin classifications and their applications in computer vision

The concept of large margin classification is used as a principle for analyzing many different algorithms, such as Support Vector Machine (SVM) and Boosting. Here it is the margin instead of raw training error that is used in designing classifiers.DOCTOR OF PHILOSOPHY (EEE