Lightweight deep neural network for radio frequency interference detection and segmentation in synthetic aperture radar.

Radio frequency interference (RFI) poses challenges in the analysis of synthetic aperture radar (SAR) images. Existing RFI suppression systems rely on prior knowledge of the presence of RFI. This paper proposes a lightweight neural network-based algorithm for detecting and segmenting RFI (LDNet) in the time-frequency domain.

Local lowest-rank dynamic mode decomposition for transient feature extraction of rolling bearings.

The fault diagnosis mainly relies on the detection of periodic pulse components caused by the local damage. However, the impulsive component in bearing vibration signals is usually disturbed by noise and some harmonics due to the harsh working environment, which makes great challenge to fault diagnosis. To enhance the performance in fault diagnosis of rolling bearing, a local lowest-rank dynamic mode decomposition (LLRDMD) is proposed in this paper.

Enzyme-Powered Micro/Nanomotors for Cancer Treatment.

The incidence and lethal rate of cancers are rapidly rising recently, however current treatments of cancers, such as surgical resection, radiotherapy, chemotherapy and targeted therapy, usually require long treatment period and have more side effects and high recurrence rate. Enzyme-powered micro/nanomotors (EMNMs), with powerful self-propulsion, enhanced permeability and good biocompatibility, have shown great potential in crossing biological barrier and targeted drug transportation for cancer treatment; moreover, advanced approaches based on EMNMs such as photothermal therapy and starvation therapy have also been widely explored in cancer treatment. Although there are several review works discussing the progress of micro/nanomotors for biomedical applications, there is not one review paper with the focus on the cancer treatment based on EMNMs.

Improved Dynamic Mode Decomposition and Its Application to Fault Diagnosis of Rolling Bearing.

To solve the intractable problems of optimal rank truncation threshold and dominant modes selection strategy of the standard dynamic mode decomposition (DMD), an improved DMD algorithm is introduced in this paper. Distinct from the conventional methods, a convex optimization framework is introduced by applying a parameterized non-convex penalty function to obtain the optimal rank truncation number. This method is inspirited by the performance that it is more perfectible than other rank truncation methods in inhibiting noise disturbance.

[Effects of different salinity levels on nitrification processes in sediments of Minjiang River Estuary, China].

Two estuary wetlands in Minjiang River, Shanyutan and Daoqingzhou, were selected as the research objects. Wetland sediments were collected to examine the effects of different salinity levels on nitrification processes in the wetland with a culture experiment. The results showed that the nitrification rate of sediment in Minjiang River estuary wetland was generally low.

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy.

Dynamic mode decomposition (DMD) is essentially a hybrid algorithm based on mode decomposition and singular value decomposition, and it inevitably inherits the drawbacks of these two algorithms, including the selection strategy of truncated rank order and wanted mode components. A novel denoising and feature extraction algorithm for multi-component coupled noisy mechanical signals is proposed based on the standard DMD algorithm, which provides a new method solving the two intractable problems above. Firstly, a sparse optimization method of non-convex penalty function is adopted to determine the optimal dimensionality reduction space in the process of DMD, obtaining a series of optimal DMD modes.

Antidiabetic, antihyperlipidemic and antioxidant activities of a novel proteoglycan from ganoderma lucidum fruiting bodies on db/db mice and the possible mechanism.

Previously, we screened a proteoglycan for anti-hyperglycemic, named FYGL, from Ganoderma Lucidum. For further research of the antidiabetic mechanisms of FYGL in vivo, the glucose homeostasis, activities of insulin-sensitive enzymes, glucose transporter expression and pancreatic function were analyzed using db/db mice as diabetic models in the present work. FYGL not only lead to a reduction in glycated hemoglobin level, but also an increase in insulin and C-peptide level, whereas a decrease in glucagons level and showed a potential for the remediation of pancreatic islets.