A general and practical protocol for the construction of isoxazolidine-fused isoquinolin-1(2
H
)-ones has been described by electrochemical-oxidation-induced intramolecular annulation
via
amidyl ...radicals. In an undivided cell, isoquinolinones could be easily generated from various available amides bearing CONHOR groups under metal-free, additive-free and external oxidant-free conditions. Moreover, this transformation proceeded smoothly by using cheap 95% ethanol as the green solvent and could be extended to the gram scale.
A general and practical protocol for the construction of isoxazolidine-fused isoquinolin-1(2
H
)-ones has been described by electrochemical-oxidation-induced intramolecular annulation
via
amidyl radicals.
Artificial retina perception system is significant to pattern recognition and visual function emulation. However, the recent artificial retina system is mainly reported on the integration of ...three-terminal transistors, whose structural limitations may result in low processing speeds and high energy consumption due to a low array density and complex line design. Furthermore, the external power source is required to drive devices so that the power consumption of the system would increase. Here we present a self-powered artificial retina perception system by utilizing two-terminal solar cells as artificial neurons and perovskite-based memristors as artificial synapses, ensuring the bio-inspired retina system with extendable crossbar array structure for high-density and low power consumption neural networks. By a light stimulation with various wavelengths and intensities, the electrical pre-synaptic signal is generated in the solar cell and subsequently transferred to the perovskite-based memristor to perform further information preprocessing. Especially, the applicability of the artificial retina system to neuromorphic computing is demonstrated for contrast enhancement and noise reduction. The retina perception system is capable of feature extraction by to implement partial functions of convolutional neural networks (CNNs) on the hardware level with improved recognition rate, boosted recognition speed, and reduced energy consumption.
A self-powered artificial retina perception system is proposed by integrating two-terminal solar cells and perovskite-based memristors. The STP-LTP functionality of perovskite-based memristors is achieved via the passivation. This self-driven artificial optic-neural system can execute both the emulation of biological synapses and partial functions (eg. the feature extraction for external signals) of convolutional neural networks. Display omitted
•We design a self-powered artificial retina perception system composed of solar cells and perovskite-based memristors.•Synaptic plasticity can be implemented by solar cells due to the low operating voltage of ~ 0.4 V.•The system has capabilities of image preprocessing, such as contrast enhancement and noise reduction.•High recognition rate of ~ 86% can be implemented within 180 training epochs.
This study addresses the problem of predicting convergence outcomes in the Duffing equation, a nonlinear second-order differential equation. The Duffing equation exhibits intriguing behavior in both ...undamped free vibration and forced vibration with damping, making it a subject of significant interest. In undamped free vibration, the convergence result oscillates randomly between 1 and −1, contingent upon initial conditions. For forced vibration with damping, multiple variables, including initial conditions and external forces, influence the vibration patterns, leading to diverse outcomes. To tackle this complex problem, we employ the fourth-order Runge–Kutta method to gather convergence results for both scenarios. Our approach leverages machine learning techniques, specifically the Long Short-Term Memory (LSTM) model and the LSTM-Neural Network (LSTM-NN) hybrid model. The LSTM-NN model, featuring additional hidden layers of neurons, offers enhanced predictive capabilities, achieving an impressive 98% accuracy on binary datasets. However, when predicting multiple solutions, the traditional LSTM method excels. The research encompasses three critical stages: data preprocessing, model training, and verification. Our findings demonstrate that while the LSTM-NN model performs exceptionally well in predicting binary outcomes, the LSTM model surpasses it in predicting multiple solutions.
With the gradual integration of internet technology and the industrial control field, industrial control systems (ICSs) have begun to access public networks on a large scale. Attackers use these ...public network interfaces to launch frequent invasions of industrial control systems, thus resulting in equipment failure and downtime, production data leakage, and other serious harm. To ensure security, ICSs urgently need a mature intrusion detection mechanism. Most of the existing research on intrusion detection in ICSs focuses on improving the accuracy of intrusion detection, thereby ignoring the problem of limited equipment resources in industrial control environments, which makes it difficult to apply excellent intrusion detection algorithms in practice. In this study, we first use the spectral residual (SR) algorithm to process the data; we then propose the improved lightweight variational autoencoder (LVA) with autoregression to reconstruct the data, and we finally perform anomaly determination based on the permutation entropy (PE) algorithm. We construct a lightweight unsupervised intrusion detection model named LVA-SP. The model as a whole adopts a lightweight design with a simpler network structure and fewer parameters, which achieves a balance between the detection accuracy and the system resource overhead. Experimental results on the ICSs dataset show that our proposed LVA-SP model achieved an F1-score of 84.81% and has advantages in terms of time and memory overhead.
The HBV capsid protein (CP) plays a vital role in the multiple life cycles of HBV and represents a novel anti-HBV target. Recently, a novel series of heteroaryldihydropyrimidine (HAP) derivatives ...were reported as potent inhibitors of HBV capsid assembly, and they also exhibit antiviral activities. In this study, the structure and activity relationship of 36 heteroaryldihydropyrimidine-based compounds was explored by conducting structure-activity relationship (3D-QSAR) studies using CoMFA and CoMSIA models. The results showed that CoMFA (
q
2
= 0.610,
r
2
= 0.998, and
r
pred
2
= 0.837) and CoMSIA (
q
2
= 0.586,
r
2
= 0.998, and
r
pred
2
= 0.698) have excellent estimation stability and prediction capability. The contour maps of the steric field, electrostaticfield, and hydrogen bond acceptor field revealed the modified regions of these compounds. Subsequently, molecular docking was carried out to investigate the docking mode of the template molecule and receptors, thereby further verifying the results of the 3D-QSAR model. Molecular dynamics (MD) simulations were performed to validate the accuracy of the docking results. Based on these results, we designed 4 new heteroaryldihydropyrimidine-based compounds and predicted their activity. These results may provide important reference for the design and development of potent and new HBV capsid assembly inhibitors.
In silico
design of heteroaryldihydropyrimidine-based selective HBV capsid assembly inhibitors.
Neuroinflammation is tightly associated with onset of depression. The nuclear receptor related 1 protein (Nurr1, also called Nr4a2), its roles in dopaminergic neurons is well understood, which can ...alleviate inflammation. Nevertheless, potential effects of Nr4a2 on neuroinflammation associated with depression still remains unclear. Chronic lipopolysaccharides (LPS) stress induced depressive-behaviors were confirmed via behavioral tests. Differentially expressed genes were detected by using RNA-sequencing. The anterior cingulate cortex (ACC) tissues were collected for biochemical experiments. The Golgi-Cox staining and virus labeling were used to evaluate the dendritic spines. We applied fluoxetine (FLX) and amodiaquine dihydrochloride (AQ, a highly selective agonist of Nr4a2) in mice. Overexpression experiments were performed by injecting with AAV-Nr4a2-EGFP into ACC. Chemogenetic activation of CamkII neurons via injecting the hM3Dq virus. Mice treated with LPS displayed depressive- and anxiety-like behaviors. The reduction of Nr4a2 and FosB induced by LPS were rescued by pretreatment with FLX or AQ. More importantly, LPS-induced behavior deficits in mice were also alleviated via fluoxetine treatment and pharmacological activation the expression of Nr4a2. Meanwhile, enhancing the level of Nr4a2 could improve dendritic spines loss of neuron and morphological changes in microglia. Overexpression of Nr4a2 in ACC reversed the depressive- and anxiety-like behaviors caused by LPS administration. Activation of CamkII neurons in ACC could robustly increase the expression of Nr4a2 and improve LPS-induced behavior deficits. Our findings demonstrate that the Nr4a2 may regulate depressive-like behaviors via alleviating the impairment of morphology and function on microglia and CamkII neurons induced by chronic neuroinflammation.
Display omitted
This study proposes an object detection algorithm based on the improved YOLOv5 network for the uncivilized behavior of reclining public chair, which often occurs in cities. The current object ...detection field is studied by a single object. For the behavior of a lying public chair, the object to be measured is composed of two parts: the chair and the human posture jointly. Furthermore, the features of the object will show a large variability under different shooting angles, so the model’s ability to extract features of the object is extremely important. This paper incorporates the Ghost module based on the YOLOv5 network to enable the model to learn more object features. The Ghost makes the neural network lighter by using linear convolution instead of nonlinear convolution, and its generated redundant features can help the model learn more object features and improve the model performance. In addition, this paper uses a new loss function EIoU to replace the original loss function CIoU. By comparison, EIoU solves the problem that CIoU fails in penalty terms under specific conditions. EIoU enables the model to converge faster and better. After experimental validation on the test set, it is shown that the improved YOLO network improves F1 by 3.5% and mAP by 4.2% compared to the original algorithm.
Abstract
Mass loss is a crucial process that affects the observational properties, evolution path, and fate of highly evolved stars. However, the mechanism of mass loss is still unclear, and the ...mass-loss rate (MLR) of red supergiant stars (RSGs) requires further research and precise evaluation. To address this, we utilized an updated and complete sample of RSGs in the Large Magellanic Cloud (LMC) and employed the 2-DUST radiation transfer model and spectral energy distribution fitting approach to determine the dust-production rates (DPRs) and dust properties of the RSGs. We have fitted 4714 selected RSGs with over 100,000 theoretical templates of evolved stars. Our results show that the DPR range of RSGs in the LMC is 10
−11
M
⊙
yr
−1
–10
−7
M
⊙
yr
−1
, and the total DPR of all RSGs is 1.14 ×10
−6
M
⊙
yr
−1
. We find that 63.3% RSGs are oxygen-rich, and they account for 97.2% of the total DPR. The optically thin RSG, which comprise 30.6% of our sample, contribute only 0.1% of the total DPR, while carbon-rich RSGs (6.1%) produce 2.7% of the total DPR. Overall, 208 RSGs contributed 76.6% of the total DPR. We have established a new relationship between the MLR and luminosity of RSGs in the LMC, which exhibits a positive trend and a clear turning point at
log
L
/
L
⊙
≈
4.4
.
