Efficient Procedures of Sensitivity Analysis for Structural Vibration Systems with Repeated Frequencies

Derivatives of eigenvectors with respect to structural parameters play an important role in structural design, identification, and optimization. Particularly, calculation of eigenvector sensitivity is considered when the eigenvalues are repeated. A relaxation factor embedded in the combined approximations (CA) method makes it effective to the structural response at various modified designs. The proposed method is feasible after overcoming the defection of irreversibility of the characteristic matrix. Numerical examples show that it is easy to implement the computational procedure, and the method presented in this paper is efficient for the general linear vibration damped systems with repeated frequencies

Sodium aescinate inhibits microglia activation through NF-κB pathway and exerts neuroprotective effect

Background: Microglia are resident immune cells of the central nervous system that sense environmental changes and maintain central nervous system homeostasis. Dysfunctional microglia produce toxic mediators that lead to neuronal death. Recent studies suggest that Sodium Aescinate has a neuroprotective effect. However, it is unclear whether Sodium Aescinate exerts neuroprotective effects by inhibiting activation of microglia.Method: Traumatic brain injury and lipopolysaccharide neuroinflammation model were used to evaluate the microglia activation in vivo. BV2 and primary microglia cells were used to assess the microglia activation in vitro. Molecular docking technique was used to predict the binding energy of Sodium Aescinate to NF-κB signaling pathway proteins.Result: Sodium Aescinate inhibited microglial activation in-vivo and in-vitro. Sodium Aescinate inhibited the activation of microglia in Traumatic brain injury and lipopolysaccharide mouse models. Sodium Aescinate also inhibited the expression of inflammatory proteins in BV2 and primary microglia cells. Western blot experiment showed that SA inhibited the activation of NF-κB pathway in BV2 and primary microglia cells. Molecular docking results also showed that Sodium Aescinate had a better affinity with the core protein of the NF-κB pathway. Western blot identified that SA inhibited activation of NF-κB pathway. In Traumatic brain injury model and conditioned medium experiment, Sodium Aescinate pretreatment inhibited inflammation and protected neuron.Conclusion: Our study confirmed that the protection effects of Sodium Aescinate on neurons by inhibiting microglia activation through NF-κB pathway

The Role of Microtubules in Pancreatic Cancer: Therapeutic Progress

Pancreatic cancer has an extremely low prognosis, which is attributable to its high aggressiveness, invasiveness, late diagnosis, and lack of effective therapies. Among all the drugs joining the fight against this type of cancer, microtubule-targeting agents are considered to be the most promising. They inhibit cancer cells although through different mechanisms such as blocking cell division, apoptosis induction, etc. Hereby, we review the functions of microtubule cytoskeletal proteins in tumor cells and comprehensively examine the effects of microtubule-targeting agents on pancreatic carcinoma

Image Anomaly Detection Using Normal Data Only by Latent Space Resampling

Detecting image anomalies automatically in industrial scenarios can improve economic efficiency, but the scarcity of anomalous samples increases the challenge of the task. Recently, autoencoder has been widely used in image anomaly detection without using anomalous images during training. However, it is hard to determine the proper dimensionality of the latent space, and it often leads to unwanted reconstructions of the anomalous parts. To solve this problem, we propose a novel method based on the autoencoder. In this method, the latent space of the autoencoder is estimated using a discrete probability model. With the estimated probability model, the anomalous components in the latent space can be well excluded and undesirable reconstruction of the anomalous parts can be avoided. Specifically, we first adopt VQ-VAE as the reconstruction model to get a discrete latent space of normal samples. Then, PixelSail, a deep autoregressive model, is used to estimate the probability model of the discrete latent space. In the detection stage, the autoregressive model will determine the parts that deviate from the normal distribution in the input latent space. Then, the deviation code will be resampled from the normal distribution and decoded to yield a restored image, which is closest to the anomaly input. The anomaly is then detected by comparing the difference between the restored image and the anomaly image. Our proposed method is evaluated on the high-resolution industrial inspection image datasets MVTec AD which consist of 15 categories. The results show that the AUROC of the model improves by 15% over autoencoder and also yields competitive performance compared with state-of-the-art methods

An Improved Back-Projection Algorithm for GNSS-R BSAR Imaging Based on CPU and GPU Platform

Global Navigation Satellite System Reflectometry Bistatic Synthetic Aperture Radar (GNSS-R BSAR) is becoming more and more important in remote sensing because of its low power, low mass, low cost, and real-time global coverage capability. The Back Projection Algorithm (BPA) was usually selected as the GNSS-R BSAR imaging algorithm because it can process echo signals of complex geometric configurations. However, the huge computational cost is a challenge for its application in GNSS-R BSAR. Graphics Processing Units (GPU) provides an efficient computing platform for GNSS-R BSAR processing. In this paper, a solution accelerating the BPA of GNSS-R BSAR using GPU is proposed to improve imaging efficiency, and a matching pre-processing program was proposed to synchronize direct and echo signals to improve imaging quality. To process hundreds of gigabytes of data collected by a long-time synthetic aperture in fixed station mode, a stream processing structure was used to process such a large amount of data to solve the problem of limited GPU memory. In the improvement of the imaging efficiency, the imaging task is divided into pre-processing and BPA, which are performed in the Central Processing Unit (CPU) and GPU, respectively, and a pixel-oriented parallel processing method in back projection is adopted to avoid memory access conflicts caused by excessive data volume. The improved BPA with the long synthetic aperture time is verified through the simulation of and experimenting on the GPS-L5 signal. The results show that the proposed accelerating solution is capable of taking approximately 128.04 s, which is 156 times lower than pure CPU framework for producing a size of 600 m × 600 m image with 1800 s synthetic aperture time; in addition, the same imaging quality with the existing processing solution can be retained

Back‐end‐of‐line SiC‐based memristor for resistive memory and artificial synapse

Two‐terminal memristor has emerged as one of the most promising neuromorphic artificial electronic devices for their structural resemblance to biological synapses and ability to emulate many synaptic functions. In this work, a memristor based on the back‐end‐of‐line (BEOL) material silicon carbide (SiC) is developed. The thin film memristors demonstrate excellent binary resistive switching with compliance‐free and self‐rectifying characteristics which are advantageous for the implementation of high‐density 3D crossbar memory architectures. The conductance of this SiC‐based memristor can be modulated gradually through the application of both DC and AC signals. This behavior is demonstrated to further emulate several vital synaptic functions including paired‐pulse facilitation (PPF), post‐tetanic potentiation (PTP), short‐term potentiation (STP), and spike‐rate‐dependent plasticity (SRDP). The synaptic function of learning‐forgetting‐relearning processes is successfully emulated and demonstrated using a 3 × 3 artificial synapse array. This work presents an important advance in SiC‐based memristor and its application in both memory and neuromorphic computing

Dataset for the journal article: "Back-end-of-line SiC based memristor for resistive memory and artificial synapse"

Dataset to support the figures in the paper &quot;Back-end-of-line SiC based memristor for resistive memory and artificial synapse&quot;. Published in Advanced Electronic Materials </span

Reservoir computing using back-end-of-line SiC-based memristors

The increasing demand for intellectual computers that can efficiently process substantial amounts of data has resulted in the development of a wide range of nanoelectronics devices. Reservoir computing offers efficient temporal information processing capability with a low training cost. In this work, we demonstrate a back-end-of-line SiC-based memristor that exhibits short-term memory behaviour and is capable of encoding temporal signals. A physical reservoir computing system using our SiC-based memristor as the reservoir has been implemented. This physical reservoir computing system has been experimentally demonstrated to perform the task of pattern recognition. After training, our RC system has achieved 100% accuracy in classifying number patterns from 0 to 9 and demonstrated good robustness to noisy pixels. The results shown here indicate that our SiC-based memristor devices are strong contenders for potential applications in artificial intelligence, particularly in temporal and sequential data processing.</p

Dataset supporting the publication &quot;Reservoir computing using back-end-of-line SiC-based memristors&quot;

Dataset supporting the publication &quot;Reservoir computing using back-end-of-line SiC-based memristors&quot;. This data demonstrates results of a back-end-of-line SiC-based memristor that exhibits short-term memory behaviour and is capable of encoding temporal signals. A physical reservoir computing system using our SiC-based memristor as the reservoir has been implemented. This physical reservoir computing system has been experimentally demonstrated to perform the task of pattern recognition. The results demonstrated good robustness to noisy pixels. The results indicate that our SiC-based memristor devices are strong contenders for potential applications in artificial intelligence, particularly in temporal and sequential data processing. The data includes 2 files: data.xlsx checkpoint.pt R. H. would like to thank the Royal Society for a Research Grant (RGS/R2/222171). O. K. thanks EPSRC and AWE Ltd for the ICASE studentship No. 16000087.</span