Pyrimidinoceptor-mediated activation of phospholipase C and phospholipase A2 in RAW 264.7 macrophages

Electron microscopy of toad (Bufo marinus) muscle fixed without relaxing after a single eccentric contraction at muscle lengths greater than optimum showed over-stretched half-sarcomeres in sufficient numbers to account for more than half of the imposed stretch. Such sarcomeres were absent in another muscle fixed without relaxing after an isometric contraction at the same length and largely absent in a third muscle that underwent eccentric contraction at muscle lengths less than optimum. This provides direct evidence in support of the hypothesis that lengthening of muscles at long length involves lengthening of a few half sarcomeres to beyond filament overlap, while most half sarcomeres are extended much less than in proportion to muscle extension.     

城市生活性街道的空间营造——以天津市鞍山道北段为例

生活性街道是城市居民在城市生活中不可缺少的场所。文章以天津市和平区鞍山道北段为例,通过实地详细调研,运用街道空间理论分析其空间结构、细部要素及市民活动,探讨如何才能营造出宜人的城市生活性街道环境。     

基于模型设计的数字集群分组数据业务的实现

为解决数字集群通信系统中图像、视频等大数据业务的传输问题,深入分析数字集群空中接口协议栈的结构,重点研究分组数据业务提供者子网相关汇聚协议的结构和原理。基于模型设计方法设计该协议的相关功能模块,并进行编码实现。开发中使用IBM Rational Rhapsody这一基于UML建模的开发工具,使整个开发过程简洁而清晰。最后,通过对协议进行一致性测试,证明协议参数及流程符合数字集群通信系统协议标准。     

基于单片机的无线遥控智能小车的设计与实现

从控制的角度论述基于单片机的无线遥控智能小车的工作原理。该智能小车采用STC8C52单片机作为核心控制器,通过无线通信模块遥控小车,实现前进、后退、转弯、刹车等基本功能,并利用红外传感器探测障碍物,实现小车的自动避障。整个系统电路结构简单,且可靠性高。     

信息中心设备管理系统设计与实现

根据高校设备管理的现状,考察并研究了学院的实际情况,介绍以.NET系统作为开发平台和SQL Server2000为数据库技术的B/S三层结构分布式设备管理信息系统。阐述了信息中心设备管理的基本流程,对系统进行了需求分析,得出设备管理所需的基本模块,并对数据库进行了分析,设计了可在网络上运行的设备管理信息系统,完成了设备的入库、浏览、统计、申请、报废、等功能。     

基于ArcEngine的江宁区国土资源业务办公系统的设计与实现

本文针对国土资源管理的业务需求,设计出结构合理、功能全面的国土资源业务办公系统,并利用ArcEngine和C#加以实现,系统的建成使用极大地提高了国土资源管理工作效率。     

考虑疫情扩散传播风险的多目标拼车应急管理优化调度模型

为了解决在疫情背景下传统拼车出行模式面临的交互传染风险,对疫情背景下的拼车管理进行分析,提出了将低风险及潜在风险乘客分开服务的拼车策略。在进一步考虑拼车系统应兼顾的社会效益及服务质量的基础上,构建了在满足基本额定盈利及最小化等待时长的双目标拼车应急管理优化调度模型,并给出了求解该模型的组合模拟退火算法。通过具体算例对模型的有效性进行验证,结果表明模型能够较快收敛到一个稳定值,且计算结果能够反映实际场景,说明了模型及算法的有效性。最后,通过一个仿真实验对所构建模型在病毒传播中的作用进行了分析,结果显示所构建的模型可以有效抑制拼车导致的病毒传播速度。     

Defects restoration during cooling and annealing in PTC type barium titanate ceramics

The PTC-type BaTiO₃ ceramics of similar microstructure are obtained by careful control of a sintering scheme. The defect chemistry of them is modified by varying the cooling rate and annealing conditons. In addition to applying Heywang and Jonker models for explaining the resistivity anomaly of these samples, the outward diffusion of oxygen vacancies (V_O), which left excess barium vacancies (V_Ba) behind, is proposed to be the formation mechanism of surface states. The formation of defect complex consisting of a V _O ^.. - _Ba pair is assumed to be the cause of small diffusivity of V_O in these materials. The phenomena, in which the slower cooling rate raises the resistivity of samples, is ascribed to the higher concentration of excess barium vacancies (V_Ba) contained in these samples. However, the maximum potential barrier height (_max) of the samples is the same, irrespective of the amount of surface state concentration (N _s) and is estimated to be _max = 0.66eV, from the In (_max)-1/T _max plot.     

Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

Speech Enhancement via Residual Dense Generative Adversarial Network

Generative adversarial networks (GANs) are paid more attention to dealing with the end-to-end speech enhancement in recent years. Various GAN-based enhancement methods are presented to improve the quality of reconstructed speech. However, the performance of these GAN-based methods is worse than those of masking-based methods. To tackle this problem, we propose speech enhancement method with a residual dense generative adversarial network (RDGAN) contributing to map the log-power spectrum (LPS) of degraded speech to the clean one. In detail, a residual dense block (RDB) architecture is designed to better estimate the LPS of clean speech, which can extract rich local features of LPS through densely connected convolution layers. Meanwhile, sequential RDB connections are incorporated on various scales of LPS. It significantly increases the feature learning flexibility and robustness in the time-frequency domain. Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments. Specifically, in the untrained acoustic test with limited priors, e.g., unmatched signal-to-noise ratio (SNR) and unmatched noise category, RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes. It indicates that our method is more generalized in untrained conditions.