Numerical investigation on heat transfer characterization of liquid lithium metal in pipe

Liquid Li metal is a promising nuclear reactor coolant; however, relevant research regarding its heat transfer characteristics remains insufficient. In this study, a steady-state two-dimensional mathematical model is established to describe the heat transfer process of liquid Li in a straight pipe. A numerical analysis is conducted to investigate the effects of inlet velocity, inlet temperature, and wall heat flux on heat transfer in liquid Li. The results indicate the advantage of using liquid Li for improving heat transfer at high inlet temperatures (> 1000 K) compared with using liquid sodium and lead–bismuth eutectic. Considering the mechanism of the outlet radial heat flow model, the ratio of turbulent to molecular diffusion coefficients presents a parabolic distribution along the radius of the pipe. Increasing the inlet velocity, decreasing the inlet temperature, and decreasing the wall heat flux can effectively weaken the dominant role of molecular heat transfer owing to the low Prandtl number of liquid Li. The heat transfer of liquid Li is investigated comprehensively in this study, and the results provide a basis for the practical application of liquid Li as a promising coolant.     

Steganalysis of neural networks based on parameter statistical bias

Many pretrained deep learning models have been released to help engineers and researchers develop deep learning-based systems or conduct research with minimall effort. Previous work has shown that at secret message can be embedded in neural network parameters without compromising the accuracy of the model. Malicious developers can, therefore, hide malware or other baneful information in pretrained models, causing harm to society. Hence, reliable detection of these vicious pretrained models is urgently needed. We analyze existing approaches for hiding messages and find that they will ineluctably cause biases in the parameter statistics. Therefore, we propose steganalysis methods for steganography on neural network parameters that extract statistics from benign and malicious models and build classifiers based on the extracted statistics. To the best of our knowledge, this is the first study on neural network steganalysis. The experimental results reveal that our proposed algorithm can effectively detect a model with an embedded message. Notably, our detection methods are still valid in cases where the payload of the stego model is low.     

基于无人机实时数据多波次任务规划模型分析

针对导弹部队多波次作战任务规划问题, 依据无人机的实时数据, 构建了基于路径的多层规划模型, 并设计了模型的算法求解流程。使用遗传算法与禁忌搜索混合算法, 得出了任务规划中的最优路径规划, 并在此基础上进行了冲突的消除。通过仿真案例表明, 用无人机协同配合导弹部队作战, 实时传输作战数据, 能够解决战场信息模糊不确定的问题; 使用多层规划模型能够为导弹多波次规划作战的路径进行科学的决策和选择。利用遗传算法和禁忌搜索混合算法, 能够避免局部最优导致无法输出结果的现象。     

The Effectiveness of Structural Monetary Policy and Macro-Prudential Policies — Based on the DSGE Model That Includes Bank Heterogeneous Credit

Journal of Systems Science and Complexity - Structural monetary policy and macro-prudential policies are important parts of the policy system of the People’s Bank of China. By constructing a...     

基于反应位移法的某地铁车站抗震性能研究

以沈阳某地铁车站为计算模型,考虑地层与车站结构间相互作用,按照反应位移法原理计算地层相对位移、结构周围剪力和结构惯性力.结果表明,车站层间位移满足规范限值要求,抗震组合为非控制工况,结构受力合理,对类似工程有一定参考意义.     

分布式声传感器井中背景噪声统计特性分析

分布式声传感器技术(DAS: Distributed Acoustic Sensor)是一种新近提出的高精度勘探数据采集方法,受制于复杂背景噪声的影响,DAS记录处理水平有待进一步提高.针对上述问题,采用基于替代序列的时间序列平稳性检验方法和Lilliefors检验法对DAS记录中复杂背景噪声的平稳性和高斯性进行研究.用以分析的实际地震数据是按照勘探工业要求采集的井中DAS记录.研究结果表明,DAS背景噪声是非平稳、非高斯随机过程且噪声特性随记录时长发生变化.相关研究成果实现了针对DAS背景噪声特性的可靠认知,对提高DAS记录处理水平具有重要参考价值.     

CAPM模型在加密货币市场的适用性检验

为解决DCEP(央行数字货币)发行后可能遇到的风险,加密货币市场的系统风险和超额收益之间的关系,利用资产定价模型(CAPM)对加密货币市场的适用性进行检验,采用定义式和回归方程两种方法估计加密货币i的beta系数,对beta系数进行时间序列回归,横截面回归,发现参数估计不显著,得到CAPM模型不能很好的解释加密货币市场风险和收益之间的关系,适用性不强的结果.     

多维度深海鱼类识别算法

针对深海光线分布不均匀导致鱼类识别检测困难的问题,提出了符合视觉认知的多维度深海鱼类识别算法.该方法从时间维度优化传统的高斯混合模型(GMM)初步确定变化区域,从空间维度构建目标特征,完整提取运动目标,从时空关联维度建立深度学习的鱼类识别框架,试验结果表明:本算法可在多种复杂条件下准确提取运动目标,面积交迭度(AOM)达到80％以上,优于当前主流算法.     

化学实验教学创新设计的调查报告

为了了解化学实验教学创新设计的现状,对常德市普通高中化学教师进行问卷调查,调查结果显示高中化学实验教学创新设计有弱化的趋势.在此基础上,从学科教学的角度对弱化的原因进行了分析,并提出化学实验教学创新设计的OCPP评价模式,尽可能建构、揭示或阐述实验教学创新设计的基本原理.OCPP模式包括目标评价(Objective Evaluation)、内容评价(Content Evaluation)、过程评价(Process Evaluation)和成果评价(Product Evaluation)4个部分.     

科技成果转化的探索与思考 ——基于湖南省科技成果转化的实证研究

科技成果转化是推动科技与经济结合的直接动力,是促进经济社会高质量发展的必要途径.文章通过对科技成果转化的重要性、现状和障碍的分析,提出科技行政管理部门应采取的管理举措.