弹体垂直侵彻深度工程计算模型

利用空腔膨胀理论计算各种形状弹头的法向侵彻阻力，并考虑作用在弹体头部的库仑摩擦阻力和作用在弹体侧壁粘滞摩擦阻力的影响，根据牛顿定律确定弹体在靶体介质中的运动微分方程，由运动边界条件确定弹体的侵入深度，最后与试验结果进行了比较，表明结果是可靠的。     

BSF硅太阳电池重离子辐照的深能级瞬态谱

对ＢＳＦ硅太阳电池受碳、氧离子辐照前后的深能级瞬态谱进行了研究，结果表明碳、氧离子在硅太阳电池中引入的缺陷能级非常相似．都观察到了Ｅ１，Ｅ２，Ｅ３，Ｈ１，Ｈ２５个峰，其中氧离子注量超过４×１０１２ｃｍ－２时Ｅ２能级消失．根据能级的性质初步推断了这些能级所对应的缺陷类型．     

UnbiasedDTI: Mitigating Real-World Bias of Drug-Target Interaction Prediction by Using Deep Ensemble-Balanced Learning

Drug-target interaction (DTI) prediction through in vitro methods is expensive and time-consuming. On the other hand, computational methods can save time and money while enhancing drug discovery efficiency. Most of the computational methods frame DTI prediction as a binary classification task. One important challenge is that the number of negative interactions in all DTI-related datasets is far greater than the number of positive interactions, leading to the class imbalance problem. As a result, a classifier is trained biased towards the majority class (negative class), whereas the minority class (interacting pairs) is of interest. This class imbalance problem is not widely taken into account in DTI prediction studies, and the few previous studies considering balancing in DTI do not focus on the imbalance issue itself. Additionally, they do not benefit from deep learning models and experimental validation. In this study, we propose a computational framework along with experimental validations to predict drug-target interaction using an ensemble of deep learning models to address the class imbalance problem in the DTI domain. The objective of this paper is to mitigate the bias in the prediction of DTI by focusing on the impact of balancing and maintaining other involved parameters at a constant value. Our analysis shows that the proposed model outperforms unbalanced models with the same architecture trained on the BindingDB both computationally and experimentally. These findings demonstrate the significance of balancing, which reduces the bias towards the negative class and leads to better performance. It is important to note that leaning on computational results without experimentally validating them and by relying solely on AUROC and AUPRC metrics is not credible, particularly when the testing set remains unbalanced.     

Deep Learning and Structure-Based Virtual Screening for Drug Discovery against NEK7: A Novel Target for the Treatment of Cancer

NIMA-related kinase7 (NEK7) plays a multifunctional role in cell division and NLRP3 inflammasone activation. A typical expression or any mutation in the genetic makeup of NEK7 leads to the development of cancer malignancies and fatal inflammatory disease, i.e., breast cancer, non-small cell lung cancer, gout, rheumatoid arthritis, and liver cirrhosis. Therefore, NEK7 is a promising target for drug development against various cancer malignancies. The combination of drug repurposing and structure-based virtual screening of large libraries of compounds has dramatically improved the development of anticancer drugs. The current study focused on the virtual screening of 1200 benzene sulphonamide derivatives retrieved from the PubChem database by selecting and docking validation of the crystal structure of NEK7 protein (PDB ID: 2WQN). The compounds library was subjected to virtual screening using Auto Dock Vina. The binding energies of screened compounds were compared to standard Dabrafenib. In particular, compound 762 exhibited excellent binding energy of −42.67 kJ/mol, better than Dabrafenib (−33.89 kJ/mol). Selected drug candidates showed a reactive profile that was comparable to standard Dabrafenib. To characterize the stability of protein–ligand complexes, molecular dynamic simulations were performed, providing insight into the molecular interactions. The NEK7–Dabrafenib complex showed stability throughout the simulated trajectory. In addition, binding affinities, pIC50, and ADMET profiles of drug candidates were predicted using deep learning models. Deep learning models predicted the binding affinity of compound 762 best among all derivatives, which supports the findings of virtual screening. These findings suggest that top hits can serve as potential inhibitors of NEK7. Moreover, it is recommended to explore the inhibitory potential of identified hits compounds through in-vitro and in-vivo approaches.     

刚性弹侵彻动力学中的第三无量纲数

采用侵彻阻力的一般表达式,计及速度一次项和减速度效应导致的附加虚拟质量项,利用量纲分析方法,明确定义控制刚性弹侵彻过程的第三无量纲物理量,即阻尼函数ξ.针对一般凸形弹头的侵彻问题开展分析,得到一般化的无量纲侵彻深度的计算公式;同时给出多个常用弹形的形状参数.针对已公开发表的不同弹形、靶材和撞击速度的侵彻实验数据,考察有无阻尼函数的理论预期,分析结果与实验数据一致.     

气体辅助注射成型过程动力学分析

基于气体穿透机理和Hele-Shaw流动模型,对管状模腔中气体冲破熔体前沿形成中空制品气辅成型过程进行了研究,推导出反映充模流动压力梯度比、非牛顿幂率指数等影响因素与计算表层熔体厚度比之间关系的数学公式,建立了熔体前沿和气体前沿速度与位移演化关系的数学模型,分别得出了牛顿流体和非牛顿流体选取不同影响参数时熔体前沿和气体前沿速度、位移的演化曲线.模拟结果表明,所建数学模型能较好的反映熔体前沿和气体前沿速度、位移演化关系.在气体冲破熔体前沿以前,气体接近匀加速运动,前沿位移梯度逐渐增加;熔体前沿的速度几乎保持不变,位移随时间接近线性增长.当气体冲破熔体前沿时,熔体和气体前沿的速度和位移均急速上升.     

Green and Efficient Extraction of Polysaccharide and Ginsenoside from American Ginseng (Panax quinquefolius L.) by Deep Eutectic Solvent Extraction and Aqueous Two-Phase System

In this study, a green and effective extraction method was proposed to extract two main compounds, ginsenosides and polysaccharides, from American ginseng by combining deep eutectic solvents (DESs) with aqueous two-phase systems. The factors of type of DESs, water content in DESs, the solid–liquid ratio, extraction temperature, and extraction time were studied in the solid–liquid extraction. Then, the aqueous two-phase system (DESs-ethylene oxide–propylene oxide (EOPO)) and salty solution exchange (EOPO-salty solution) was applied for the purification of polysaccharides. The content of the polysaccharides and ginsenosides were analyzed by the anthrone–sulfuric acid method and HPLC method, which showed that the extraction efficiency of deep eutectic solvents (DESs) was better than conventional methods. Moreover, the antioxidant activities of ginseng polysaccharides and their cytotoxicity were further assayed. The advantages of the current study are that, throughout the whole extraction process, we avoided the usage of an organic reagent. Furthermore, the separated green solvent DESs and EOPO could be recovered and reused for a next cycle. Thus, this study proposed a new, green and recyclable extraction method for extracting ginsenosides and polysaccharides from American ginseng.     

Electromagnetic Modulation Signal Classification Using Dual-Modal Feature Fusion CNN

AMC (automatic modulation classification) plays a vital role in spectrum monitoring and electromagnetic abnormal signal detection. Up to now, few studies have focused on the complementarity between features of different modalities and the importance of the feature fusion mechanism in the AMC method. This paper proposes a dual-modal feature fusion convolutional neural network (DMFF-CNN) for AMC to use the complementarity between different modal features fully. DMFF-CNN uses the gram angular field (GAF) image coding and intelligence quotient (IQ) data combined with CNN. Firstly, the original signal is converted into images by GAF, and the GAF images are used as the input of ResNet50. Secondly, it is converted into IQ data and as the complex value network (CV-CNN) input to extract features. Furthermore, a dual-modal feature fusion mechanism (DMFF) is proposed to fuse the dual-modal features extracted by GAF-ResNet50 and CV-CNN. The fusion feature is used as the input of DMFF-CNN for model training to achieve AMC of multi-type signals. In the evaluation stage, the advantages of the DMFF mechanism proposed in this paper and the accuracy improvement compared with other feature fusion algorithms are discussed. The experiment shows that our method performs better than others, including some state-of-the-art methods, and has superior robustness at a low signal-to-noise ratio (SNR), and the average classification accuracy of the dataset signals reaches 92.1%. The DMFF-CNN proposed in this paper provides a new path for the AMC field.     

A Hybrid Deep Learning Model for Brain Tumour Classification

A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%.     

对称点模式-深度卷积神经网络的红外光谱识别方法

红外光谱分析在自然科学、工程技术等诸多领域发挥着重要作用.随着计算机和人工智能技术的不断发展,对红外/近红外光谱分析提出了更高的要求.深度学习以人工神经网络为架构,通过对数据进行分层特征提取完成特征/表征学习,在解析数据细节特征方面具有独特的优势,在计算机视觉、语音识别、疾病诊断等多领域得到成功应用.尽管深度学习在图像...