Large eddy simulations of turbulent channel flow based on interscale energy transfer

A previously developed modeling procedure for large eddy simulations (LESs) is extended to allow physical space implementations for inhomogeneous flows. The method is inspired by the well-established theoretical analyses and numerical investigations of homogeneous, isotropic turbulence. A general procedure that focuses on recovering the full subgrid scale (SGS) dissipation from resolved fields is formulated, combining the advantages of both the structural and the functional strategy of modeling. The interscale energy transfer is obtained from the test-filtered velocity field, corresponding subfilter scale (SFS) stress or, equivalently, the similarity model is used to compute the total SGS dissipation. The energy transfer is then cast in the form of eddy viscosity, allowing it to retain the desired total SGS dissipation and making the method numerically robust as an automatic step of backscatter control. The method is capable of providing a proper amount of total energy dissipation in actual, low resolution LES runs. The new approach is general and self-contained, working well for different filtering kernels, Reynolds numbers, and grid resolutions

Integrative Analysis of lncRNA-mRNA Profile Reveals Potential Predictors for SAPHO Syndrome

Synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome is known as a rare disease characterized by inflammatory lesions on bones and skin. Polymorphism of clinical manifestation and lack of molecular biomarkers have both limited its diagnosis. Our study performed RNA sequencing (RNA-seq) and integrative bioinformatics analysis of long noncoding RNA (lncRNA)-messenger RNA (mRNA) profile in patients with SAPHO syndrome and healthy controls. A total of 4,419 differentially expressed (DE) mRNAs and 2,713 lncRNAs were identified (p < 0.05, fold change > 2) and a coexpression network was constructed to further investigate their regulatory interactions. The DE lncRNAs were predicted to interact with mRNAs in both cis and trans manners. Functional prediction found that the lncRNA-targeted genes may function in SAPHO syndrome by participating in biological process such as adipocytokine signaling pathway, ErbB signaling pathway, FoxO signaling pathway, as well as production and function of miRNAs. The expression levels of three pairs of coexpressed lncRNA-mRNAs were validated by qRT-PCR, and their relative expression levels were consistent with the RNA-seq data. The deregulated RNAs GAS7 and lnc-CLLU1.1-1:2 may serve as potential diagnostic biomarkers, and the combined receiver operating characteristic (ROC) curve of the two showed more reliable diagnostic ability with an AUC value of 0.871 in distinguishing SAPHO patients from healthy controls. In conclusion, this study provides a first insight into long noncoding RNA transcriptome profile changes associated with SAPHO syndrome and inspiration for further investigation on clinical biomarkers and molecular regulators of this inadequately understood clinical entity

New Insights into the PPAR γ

Diabetic nephropathy (DN) is a severe complication of diabetes and serves as the leading cause of chronic renal failure. In the past decades, angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin II receptor blockers (ARBs) based first-line therapy can slow but cannot stop the progression of DN, which urgently requests the innovation of therapeutic strategies. Thiazolidinediones (TZDs), the synthetic exogenous ligands of nuclear receptor peroxisome proliferator-activated receptor-γ (PPARγ), had been thought to be a promising candidate for strengthening the therapy of DN. However, the severe adverse effects including fluid retention, cardiovascular complications, and bone loss greatly limited their use in clinic. Recently, numerous novel PPARγ agonists involving the endogenous PPARγ ligands and selective PPARγ modulators (SPPARMs) are emerging as the promising candidates of the next generation of antidiabetic drugs instead of TZDs. Due to the higher selectivity of these novel PPARγ agonists on the regulation of the antidiabetes-associated genes than that of the side effect-associated genes, they present fewer adverse effects than TZDs. The present review was undertaken to address the advancements and the therapeutic potential of these newly developed PPARγ agonists in dealing with diabetic kidney disease. At the same time, the new insights into the therapeutic strategies of DN based on the PPARγ agonists were fully addressed

Concept for a Future Super Proton-Proton Collider

Following the discovery of the Higgs boson at LHC, new large colliders are being studied by the international high-energy community to explore Higgs physics in detail and new physics beyond the Standard Model. In China, a two-stage circular collider project CEPC-SPPC is proposed, with the first stage CEPC (Circular Electron Positron Collier, a so-called Higgs factory) focused on Higgs physics, and the second stage SPPC (Super Proton-Proton Collider) focused on new physics beyond the Standard Model. This paper discusses this second stage.Comment: 34 pages, 8 figures, 5 table

An empirical study of customer satisfaction with marine products wholesale market

1470-1476Present study is to build an appropriate model of marine products wholesale markets customer satisfaction theory by studying and improving on some existing international mainstream satisfaction index models. This study also gives some advices to improve the customer satisfaction for marine products wholesale markets. The results show that the customer satisfaction of marine products wholesale markets is decided by the customer perceived service quality and the customer previous expectation. Most reasonable approaches to make the marine wholesale customers satisfied are to provide timely and high efficiency marine products transaction information, construct an excellent infrastructure environment, build fair transaction order and charging rules as well as offer the convenient surrounding services

Patch-Wise Semantic Segmentation for Hyperspectral Images via a Cubic Capsule Network with EMAP Features

In order to overcome the disadvantages of convolution neural network (CNN) in the current hyperspectral image (HSI) classification/segmentation methods, such as the inability to recognize the rotation of spatial objects, the difficulty to capture the fine spatial features and the problem that principal component analysis (PCA) ignores some important information when it retains few components, in this paper, an HSI segmentation model based on extended multi-morphological attribute profile (EMAP) features and cubic capsule network (EMAP–Cubic-Caps) was proposed. EMAP features can effectively extract various attributes profile features of entities in HSI, and the cubic capsule neural network can effectively capture complex spatial features with more details. Firstly, EMAP algorithm is introduced to extract the morphological attribute profile features of the principal components extracted by PCA, and the EMAP feature map is used as the input of the network. Then, the spectral and spatial low-layer information of the HSI is extracted by a cubic convolution network, and the high-layer information of HSI is extracted by the capsule module, which consists of an initial capsule layer and a digital capsule layer. Through the experimental comparison on three well-known HSI datasets, the superiority of the proposed algorithm in semantic segmentation is validated

Spatio-temporal instabilities in viscoelastic channel flows: The centre mode

We study in this work the spatio-temporal characteristics of the centre-mode linear instability in viscoelastic channel flows of Oldroyd-B and FENE-P fluids. The linear complex Ginzburg-Landau equation derived using an amplitude expansion method is adopted to determine whether the flow is convectively unstable or absolutely unstable. Comparison of the obtained results with those from the conventional saddle-point searching method shows a favourable agreement. This demonstrates the good predictability of the linear complex Ginzburg- Landau equation which is more suitable for a parametric study in a large space than the conventional method. We found that the centre-mode instability in the viscoelastic channel flow is of a convective nature, i.e., the trailing edge of the wavepacket travels downstream. This could be attributed to the relatively high phase speed, low spreading rate and low growth rate of the instability, collectively preventing a disturbance from travelling upstream. Our results show that stronger polymer elasticity (a greater elasticity number or larger polymer concentration) marginally affects the trailing-edge velocity of the centre mode. Decreasing the maximum polymer extensibility in the FENE-P model reduces the spreading rate of the wavepacket and makes the centre-mode disturbance more convective. Theoretical derivations at asymptotically high Reynolds numbers confirm the convective nature of the instabilities from numerical observations. Our results may be conducive to a better understanding of the spatio-temporal instability in viscoelastic experiments

Hyperspectral Image Classification Based on Multi-Scale Convolutional Features and Multi-Attention Mechanisms

Convolutional neural network (CNN)-based and Transformer-based methods for hyperspectral image (HSI) classification have rapidly advanced due to their unique characterization capabilities. However, the fixed kernel sizes in convolutional layers limit the comprehensive utilization of multi-scale features in HSI land cover analysis, while the Transformer’s multi-head self-attention (MHSA) mechanism faces challenges in effectively encoding feature information across various dimensions. To tackle this issue, this article introduces an HSI classification method, based on multi-scale convolutional features and multi-attention mechanisms (i.e., MSCF-MAM). Firstly, the model employs a multi-scale convolutional module to capture features across different scales in HSIs. Secondly, to enhance the integration of local and global channel features and establish long-range dependencies, a feature enhancement module based on pyramid squeeze attention (PSA) is employed. Lastly, the model leverages a classical Transformer Encoder (TE) and linear layers to encode and classify the transformed spatial–spectral features. The proposed method is evaluated on three publicly available datasets—Salina Valley (SV), WHU-Hi-HanChuan (HC), and WHU-Hi-HongHu (HH). Extensive experimental results have demonstrated that the MSCF-MAM method outperforms several representative methods in terms of classification performance

Lightweight Apple Detection in Complex Orchards Using YOLOV5-PRE

The detection of apple yield in complex orchards plays an important role in smart agriculture. Due to the large number of fruit trees in the orchard, improving the speed of apple detection has become one of the challenges of apple yield detection. Additional challenges in the detection of apples in complex orchard environments are vision obstruction by leaves, branches and other fruit, and uneven illumination. The YOLOv5 (You Only Look Once version 5) network structure has thus far been increasingly utilized for fruit recognition, but its detection accuracy and real-time detection speed can be improved. Thus, an upgraded lightweight apple detection method YOLOv5-PRE (YOLOv5 Prediction) is proposed for the rapid detection of apple yield in an orchard environment. The ShuffleNet and the GhostNet lightweight structures were introduced into the YOLOv5-PRE model to reduce the size of the model, and the CA (Coordinate Attention) and CBAM (Convolutional Block Attention Module) attention mechanisms were used to improve the detection accuracy of the algorithm. After applying this algorithm on PC with NVIDIA Quadro P620 GPU, and after comparing the results of the YOLOv5s (You Only Look Once version 5 small) and the YOLOv5-PRE models outputs, the following conclusions were obtained: the average precision of the YOLOv5-PRE model was 94.03%, which is 0.58% higher than YOLOv5s. As for the average detection time of a single image on GPU and CPU, it was 27.0 ms and 172.3 ms, respectively, which is 17.93% and 35.23% higher than YOLOV5s. Added to that, the YOLOv5-PRE model had a missed detection rate of 6.54% when being subject to back-light conditions, and a false detection rate of 4.31% when facing front-light conditions, which are 2.8% and 0.86% higher than YOLOv5s, respectively. Finally, the feature extraction process of the YOLOv5-PRE model was presented in the form of a feature map visualization, which enhances the interpretability of the model. Thus, the YOLOv5-PRE model is more suitable for transplanting into embedded devices and adapts well to different lighting conditions in the orchard, which provides an effective method and a theoretical basis for the rapid detection of apples in the process of rapid detection of apple yield

Response Surface Optimization of the Preparation Process of Compound Yogurt with Cistanche deserticola and Jujube and Evaluation of the Antioxidant Activity of Compound Yogurt with Biosensors

The preparation process of compound yogurt with added cistanche and jujube was optimized using single-factor experiments and response surface curve analysis by applying evaluation indicators including taste, texture, odor, and color. Besides, a rapid evaluation of the antioxidant activity of the compound yogurt was performed by employing the DPPH free radical scavenging system and electrochemical biosensors. Results of the single-factor experiments and response surface analysis indicated that the quality of the compound yogurt was optimal when the percentages of jujube juice, cistanche juice, and honey were 18%, 8%, and 5%, respectively. This composition achieved a sensory score of 92.2, which was close to the predicted score of 92.5. Under the same optimized conditions, the evaluation of the antioxidant activity with the preparation of DNA/CS-N-G/GCE biosensor obtained through the layer-by-layer modified glassy carbon electrode assembly of DNA, chitosan, and nitrogen-doped graphene indicated that L-ascorbic acid, compound yogurt, and plain yogurt present peak current values of 6.5×10−5、8.5×10−5 and 1.05×10−4 A, respectively. This represented a decreasing trend in antioxidant activity. Further validation of DPPH free radical scavenging experiments showed that the IC50 value of L-ascorbic acid, compound yogurt and plain yogurt were 4.54, 15.65 and 22.94 mg/mL, respectively. This was consistent with the results of the evaluation of the compound yogurt using the biosensor, and there was no significant difference between the two methods. Therefore, the addition of cistanche and jujube extracts into plain yogurt significantly enhanced the inhibition of damage to DNA by free radicals, thus improving the antioxidant activity in the compound yogurt. The constructed sensor could be used for the rapid evaluation of antioxidant activity of compound yogurt