1-[4-(2-Chloro­eth­oxy)-2-hy­droxy­phen­yl]ethanone

In the title compound, C10H11ClO3, obtained by the reaction of 2,4-dihy­droxy­acetophenone, potassium carbonate and 1-bromo-2-chloro­ethane, an intra­molecular O—H⋯O hydrogen bond occurs

Fuzzy Sparse Autoencoder Framework for Single Image Per Person Face Recognition

The issue of single sample per person (SSPP) face recognition has attracted more and more attention in recent years. Patch/local-based algorithm is one of the most popular categories to address the issue, as patch/local features are robust to face image variations. However, the global discriminative information is ignored in patch/local-based algorithm, which is crucial to recognize the nondiscriminative region of face images. To make the best of the advantage of both local information and global information, a novel two-layer local-to-global feature learning framework is proposed to address SSPP face recognition. In the first layer, the objective-oriented local features are learned by a patch-based fuzzy rough set feature selection strategy. The obtained local features are not only robust to the image variations, but also usable to preserve the discrimination ability of original patches. Global structural information is extracted from local features by a sparse autoencoder in the second layer, which reduces the negative effect of nondiscriminative regions. Besides, the proposed framework is a shallow network, which avoids the over-fitting caused by using multilayer network to address SSPP problem. The experimental results have shown that the proposed local-to-global feature learning framework can achieve superior performance than other state-of-the-art feature learning algorithms for SSPP face recognition

CPMLHO:Hyperparameter Tuning via Cutting Plane and Mixed-Level Optimization

The hyperparameter optimization of neural network can be expressed as a bilevel optimization problem. The bilevel optimization is used to automatically update the hyperparameter, and the gradient of the hyperparameter is the approximate gradient based on the best response function. Finding the best response function is very time consuming. In this paper we propose CPMLHO, a new hyperparameter optimization method using cutting plane method and mixed-level objective function.The cutting plane is added to the inner layer to constrain the space of the response function. To obtain more accurate hypergradient,the mixed-level can flexibly adjust the loss function by using the loss of the training set and the verification set. Compared to existing methods, the experimental results show that our method can automatically update the hyperparameters in the training process, and can find more superior hyperparameters with higher accuracy and faster convergence

Fuzzy superpixels for polarimetric SAR images classification

Superpixels technique has drawn much attention in computer vision applications. Each superpixels algorithm has its own advantages. Selecting a more appropriate superpixels algorithm for a specific application can improve the performance of the application. In the last few years, superpixels are widely used in polarimetric synthetic aperture radar (PolSAR) image classification. However, no superpixel algorithm is especially designed for image classification. It is believed that both mixed superpixels and pure superpixels exist in an image.Nevertheless, mixed superpixels have negative effects on classification accuracy. Thus, it is necessary to generate superpixels containing as few mixed superpixels as possible for image classification. In this paper, first, a novel superpixels concept, named fuzzy superpixels, is proposed for reducing the generation of mixed superpixels.In fuzzy superpixels ,not al lpixels are assigned to a corresponding superpixel. We would rather ignore the pixels than assigning them to improper superpixels. Second,a new algorithm, named FuzzyS(FS),is proposed to generate fuzzy superpixels for PolSAR image classification. Three PolSAR images are used to verify the effect of the proposed FS algorithm. Experimental results demonstrate the superiority of the proposed FS algorithm over several state-of-the-art superpixels algorithms

Thermodynamics shapes the biogeography of propionate‐oxidizing syntrophs in paddy field soils

Soil biogeochemical processes are not only gauged by the dominant taxa in the microbiome but also depend on the critical functions of its ‘rare biosphere’ members. Here, we evaluated the biogeographical pattern of ‘rare biosphere’ propionate-oxidizing syntrophs in 113 paddy soil samples collected across China. The relative abundance, activity and growth potential of propionate-oxidizing syntrophs were analysed to provide a panoramic view of syntroph biogeographical distribution at the continental scale. The relative abundances of four syntroph genera, Syntrophobacter, Pelotomaculum, Smithella and Syntrophomonas were significantly greater at the warm low latitudes than at the cool high latitudes. Correspondingly, propionate degradation was faster in the low latitude soils compared with the high latitude soils. The low rate of propionate degradation in the high latitude soils resulted in a greater increase of the total syntroph relative abundance, probably due to their initial low relative abundances and the longer incubation time for propionate consumption. The mean annual temperature (MAT) is the most important factor shaping the biogeographical pattern of propionate-oxidizing syntrophs, with the next factor being the soil's total sulfur content (TS). We suggest that the effect of MAT is related to the thermodynamic conditions, in which the endergonic constraint of propionate oxidation is leveraged with the increase of MAT. The TS effect is likely due to the ability of some propionate syntrophs to facultatively perform sulfate respiration

Number 2 Feibi Recipe Reduces PM2.5-Induced Lung Injury in Rats

Air pollution is the main cause of respiratory diseases. Fine particulates with the diameter below 2.5 μm can get into the alveoli and then enter the blood circulation through the lung tissue ventilation function and cause multiple systemic diseases especially the respiratory diseases. This study investigated the pathological mechanism of the lungs injury in rats induced by PM2.5 and the effect and mechanism of the Chinese herbal medicine number 2 Feibi Recipe (number 2 FBR) on lungs injury. In this experiment, Wistar rats were used. Lungs injury was induced by PM2.5. Number 2 FBR was used to treat the rats. The result showed that number 2 FBR could improve the lung injury in the rats. Meanwhile, it significantly reduced pathological response and inflammatory mediators including interleukin-6 (IL-6), interleukin-13 (IL-13), interleukin-17 (IL17), monocyte chemotactic protein-1 (MCP-1), and transforming growth factor-α (TNF-α) and upregulated glutathione peroxidase (GSH-Px) in the PM2.5 induced lung injury in the rats. Collectively, number 2 FBR appears to attenuate the lungs injury in rats induced by PM2.5

Development of a Wireless MEMS Multifunction Sensor System and Field Demonstration of Embedded Sensors for Monitoring Concrete Pavements, Volume II

This two-pronged study evaluated the performance of commercial off-the-shelf (COTS) micro-electromechanical sensors and systems (MEMS) embedded in concrete pavement (Final Report Volume I) and developed a wireless MEMS multifunctional sensor system for health monitoring of pavement systems (Final Report Volume II). The Volume I report focused on the evaluation of COTS MEMS sensors embedded in concrete pavement sections. The Volume II report covers the set of MEMS sensors that were developed as single-sensing units for measuring moisture, temperature, strain, and pressure. These included the following sensors: (1) nanofiber-based moisture sensors, (2) graphene oxide (GO)–based moisture sensors, (3) flexible graphene strain sensors with liquid metal, (4) graphene strain and pressure sensors, (5) three-dimensional (3D) planar and helical structured graphene strain sensors, (6) temperature sensors, and (7) water content sensors. In addition, the MEMS temperature sensors and the MEMS water content sensors were integrated into one sensing unit as a multifunctional sensor. A wireless signal transmission system was built for MEMS sensor signal readings. Characterization of the sensors was conducted and sensor responses were analyzed using different applications. The sensors developed were installed and tested inside concrete. The results demonstrated the capability to detect sensor response changes at the installed locations

Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification

The increasing applications of polarimetric synthetic aperture radar (PolSAR) image classification demand for effective superpixels’ algorithms. Fuzzy superpixels’ algorithms reduce the misclassification rate by dividing pixels into superpixels, which are groups of pixels of homogenous appearance and undetermined pixels. However, two key issues remain to be addressed in designing a fuzzy superpixel algorithm for PolSAR image classification. First, the polarimetric scattering information, which is unique in PolSAR images, is not effectively used. Such information can be utilized to generate superpixels more suitable for PolSAR images. Second, the ratio of undetermined pixels is fixed for each image in the existing techniques, ignoring the fact that the difficulty of classifying different objects varies in an image. To address these two issues, we propose a polarimetric scattering information-based adaptive fuzzy superpixel (AFS) algorithm for PolSAR images classification. In AFS, the correlation between pixels’ polarimetric scattering information, for the first time, is considered through fuzzy rough set theory to generate superpixels. This correlation is further used to dynamically and adaptively update the ratio of undetermined pixels. AFS is evaluated extensively against different evaluation metrics and compared with the state-of-the-art superpixels’ algorithms on three PolSAR images. The experimental results demonstrate the superiority of AFS on PolSAR image classification problems