When Sparsity Meets Dynamic Convolution

Dynamic convolution achieves a substantial performance boost for efficient CNNs at a cost of increased convolutional weights. Contrastively, mask-based unstructured pruning obtains a lightweight network by removing redundancy in the heavy network at risk of performance drop. In this paper, we propose a new framework to coherently integrate these two paths so that they can complement each other compensate for the disadvantages. We first design a binary mask derived from a learnable threshold to prune static kernels, significantly reducing the parameters and computational cost but achieving higher performance in Imagenet-1K(0.6\% increase in top-1 accuracy with 0.67G fewer FLOPs). Based on this learnable mask, we further propose a novel dynamic sparse network incorporating the dynamic routine mechanism, which exerts much higher accuracy than baselines ($2.63\%$ increase in top-1 accuracy for MobileNetV1 with $90\%$ sparsity). As a result, our method demonstrates a more efficient dynamic convolution with sparsity

Decentralized Energy Management of Networked Microgrid Based on Alternating-Direction Multiplier Method

With the ever-intensive utilization of distributed generators (DGs) and smart devices, distribution networks are evolving from a hierarchal structure to a distributed structure, which imposes significant challenges to network operators in system dispatch. A distributed energy-management method for a networked microgrid (NM) is proposed to coordinate a large number of DGs for maintaining secure and economic operations in the electricity-market environment. A second-order conic programming model is used to formulate the energy-management problem of an NM. Network decomposition was first carried out, and then a distributed solution for the established optimization model through invoking alternating-direction method of multipliers (ADMM). A modified IEEE 33-bus power system was finally utilized to demonstrate the performance of distributed energy management in an NM

A MYB transcription factor, BnMYB2, cloned from ramie (Boehmeria nivea) is involved in cadmium tolerance and accumulation.

MYB-related transcription factors play important roles in plant development and response to various environmental stresses. In the present study, a novel MYB gene, designated as BnMYB2 (GenBank accession number: MF741319.1), was isolated from Boehmeria nivea using rapid amplification of cDNA ends (RACE) and RT-PCR on a sequence fragment from a ramie transcriptome. BnMYB2 has a 945 bp open reading frame encoding a 314 amino acid protein that contains a DNA-binding domain and shares high sequence identity with MYB proteins from other plant species. The BnMYB2 promoter contains several putative cis-acting elements involved in stress or phytohormone responses. A translational fusion of BnMYB2 with enhanced green fluorescent protein (eGFP) showed nuclear and cytosolic subcellular localization. Real-time PCR results indicated that BnMYB2 expression was induced by Cadmium (Cd) stress. Overexpression of BnMYB2 in Arabidopsis thaliana resulted in a significant increase of Cd tolerance and accumulation. Thus, BnMYB2 positively regulated Cd tolerance and accumulation in Arabidopsis, and could be used to enhance the efficiency of Cd removal with plants

Improved Active Disturbance Rejection Control (ADRC) with Extended State Filters

To address time delay and noise problems in control systems, in this study, we integrated an extended state filter for signal filtering into an active disturbance rejection control (ADRC) system and derived an improved ADRC approach. In addition to the active anti-disturbance and active tracking estimation functions of the existing ADRC, the proposed approach also includes active filtering and active advance prediction functions, which can filter out the effect of measurement noise on system state observation while reducing the delay between the system control output and the detection of the sensor input. We verified through an evaluation in a simulation environment that the proposed approach may be expected to achieve improved control accuracy and increase the stability of closed-loop control systems

A polymer lithium-oxygen battery based on semi-polymeric conducting ionomer as polymer electrolyte

status: publishe

Nondestructive Detection of Microcracks in Poultry Eggs Based on the Electrical Characteristics Model

The eggshell is the major source of protection for the inside of poultry eggs from microbial contamination. Timely detection of cracked eggs is the key to improving the edible rate of fresh eggs, hatching rate of breeding eggs and the quality of egg products. Different from traditional detection based on acoustics and vision, this paper proposes a nondestructive method of detection for eggshell cracks based on the egg electrical characteristics model, which combines static and dynamic electrical characteristics and designs a multi-layer flexible electrode that can closely fit the eggshell surface and a rotating mechanism that takes into account different sizes of eggs. The current signals of intact eggs and cracked eggs were collected under 1500 V of DC voltage, and their time domain features (TFs), frequency domain features (FFs) and wavelet features (WFs) were extracted. Machine learning algorithms such as support vector machine (SVM), linear discriminant analysis (LDA), decision tree (DT) and random forest (RF) were used for classification. The relationship between various features and classification algorithms was studied, and the effectiveness of the proposed method was verified. Finally, the method is proven to be universal and generalizable through an experiment on duck eggshell microcrack detection. The experimental results show that the proposed method can realize the detection of eggshell microcracks of less than 3 μm well, and the random forest model combining the three features mentioned above is proven to be the best, with a detection accuracy of cracked eggs and intact eggs over 99%. This nondestructive method can be employed online for egg microcrack inspection in industrial applications

Fatty Acid Desaturation Is Suppressed in Mir-26a/b Knockout Goat Mammary Epithelial Cells by Upregulating <i>INSIG1</i>

MicroRNA-26 (miR-26a and miR-26b) plays a critical role in lipid metabolism, but its endogenous regulatory mechanism in fatty acid metabolism is not clear in goat mammary epithelial cells (GMECs). GMECs with the simultaneous knockout of miR-26a and miR-26b were obtained using the CRISPR/Cas9 system with four sgRNAs. In knockout GMECs, the contents of triglyceride, cholesterol, lipid droplets, and unsaturated fatty acid (UFA) were significantly reduced, and the expression of genes related to fatty acid metabolism was decreased, but the expression level of miR-26 target insulin-induced gene 1 (INSIG1) was significantly increased. Interestingly, the content of UFA in miR-26a and miR-26b simultaneous knockout GMECs was significantly lower than that in wild-type GMECs and miR-26a- and miR-26b-alone knockout cells. After decreasing INSIG1 expression in knockout cells, the contents of triglycerides, cholesterol, lipid droplets, and UFAs were restored, respectively. Our studies demonstrate that the knockout of miR-26a/b suppressed fatty acid desaturation by upregulating the target INSIG1. This provides reference methods and data for studying the functions of miRNA families and using miRNAs to regulate mammary fatty acid synthesis