Rapid, sensitive procedure to determine buspirone levels in rat brains using gas chromatography with nitrogen-phosphorus detection

Reported here is a rapid, sensitive and relatively inexpensive procedure using gas chromatography with nitrogen-phosphorus detection (GC-NPD) to quantify buspirone levels in brains of rats. The analyte was directly extracted from brain homogenate with toluene after basification and then subjected to GC-NPD analysis using a capillary column. The calibration curves were linear over the range of 10 to 320 ng per 2 ml of brain homogenate, with typical r2 values >0.99. The assay was highly reproducible and gave peaks with excellent chromatographic properties.     

中国南海北部二氧化碳气成因

中国南海北部海域在油气勘探中发现了CO_2气,对油气有无影响?在借鉴世界、中国东部陆地CO_2气成因研究成果的基础上,从分析研究区CO_2,气的分布入手,采用多种方法,在综合分析基础上,对本区CO_2气藏进行分类.认为研究区CO_2;气藏为无机成因,其特征受各盆地或地区具体地质条件的控制.哈萨克斯坦引起了国际石油界的很大关注,但它并不是前苏联唯一的具吸引力之地.美国地质调查调查所(丹佛)和AAPG今年著名讲演者Gregory F Ulmishek说.前苏联有20多个沉积盆地生产油气或具有尚未开采的发现.Ulmishek 1980年到美国前,获得过莫斯科石油学院地质学硕士学位,作为一个地质学家在前苏联工作过23年.他的1993—1994年题为“前苏联主要油气地质学及勘探潜力”的著名演讲在1993年12月6日至17日和1994年3月28日至4月8日的巡回活动中推出.     

苗尾水电站砾质土心墙堆石坝抗震复核分析

根据NB 35047-2015《水电工程水工建筑物抗震设计规范》和《澜沧江苗尾水电站枢纽工程竣工安全鉴定工作大纲》规定要求,应按照设定地震法确定场地相关设计反应谱,作为人工合成地震波的目标谱,对苗尾水电站砾质土心墙堆石坝进行抗震复核计算.根据地震动参数复核结果,结合坝料动力试验成果,采用非线性动力有限元法对苗尾水电站砾质土心墙堆石坝进行抗震复核分析.复核结果表明,大坝抗震安全性能良好,满足规范要求.     

基于分区缓存区重放与多线程交互的多智能体深度强化学习算法

近些年,深度强化学习(Deep Reinforcement Learning,DRL)已成为人工智能领域一个新的机器学习范式与方法论,它在许多高维度大状态的复杂空间任务中能够取得显著的成功.然而,传统的深度强化学习仍然存在着学习效率低、训练时间长的问题,在多智能体的行为决策研究中难以达到理想的效果.针对这些问题,本文提出了一种基于分区缓存区重放与多线程交互的多智能体深度强化学习算法(Partitioned Buffer Replay and Multiple Process Interaction,PBR-MPI).首先,该算法使用分区缓存区的经验重放形式,通过划分奖励空间来区分正面经验、负面经验与中性经验,并在训练时使用分层随机的采样方式抽取这些经验数据.其次,算法运用多线程的交互方式促进了智能体与环境的试错过程,通过智能体的多个克隆体并行的学习并整合它们的学习经验来训练网络模型的参数.然后,为了构建PBR-MPI算法的适用场景,本文根据目前多智能体系统(Multi-Agent System,MAS)的最新研究进展,将多智能体的信息交互方式归纳总结为集中式信息交互、全信息交互和欠信息交互三大类.最后,将新算法与其它的多智能体DRL算法分别在三种不同的信息交互场景中进行对比实验,用于验证和评价PBR-MPI的有效性及整体性能.实验结果表明,在智能体个数为5的多智能体目标追踪任务中,缓存区数为3、线程数为5的PBR-MPI算法的学习收敛速度平均提高了21％,训练效率平均提升了34％,并且在综合性能的评估中新算法的整体性能改善了50％.     

Identifying epidemic spreading dynamics of COVID-19 by pseudocoevolutionary simulated annealing optimizers

Neural Computing and Applications - At the end of 2019, a new coronavirus (COVID-19) epidemic has triggered global public health concern. Here, a model integrating the daily intercity migration...     

A note on octonionic support vector regression

This note presents an analysis of the octonionic form of the division algebraic support vector regressor (SVR) first introduced by Shilton A detailed derivation of the dual form is given, and three conditions under which it is analogous to the quaternionic case are exhibited. It is shown that, in the general case of an octonionic-valued feature map, the usual "kernel trick" breaks down. The cause of this (and its interpretation) is discussed in some detail, along with potential ways of extending kernel methods to take advantage of the distinct features present in the general case. Finally, the octonionic SVR is applied to an example gait analysis problem, and its performance is compared to that of the least squares SVR, the Clifford SVR, and the multidimensional SVR.     

A scalable multi-attribute hybrid overlay for range queries on the cloud

Cloud computing has become a promising paradigm as next generation computing model, by providing computation, software, data access, and storage services that do not need to know the location of physical resources interconnected across the globe providing such services. In such an environment, important issues as information sharing and resource/service discovery arise. In order to overcome critical limitations in centralized approaches for information sharing and resource/service discovery, this paper proposes a framework of a scalable multi-attribute hybrid overlay featured with decentralized information sharing, flexible resource/service discovery, fault tolerance and load balancing. Additionally, the proposed hybrid overlay integrates a structured P2P system with an unstructured one to support complex queries. Mechanisms such as load balancing and fault tolerance implemented in our proposed system to improve the overall system performance are also discussed. Experimental results show that the performance of the proposed approach is feasible and stable, as the proposed hybrid overlay improves system performance by reducing the number of routing hops and balancing the load by migrating requests.     

Fuzzy local maximal marginal embedding for feature extraction

In graph-based linear dimensionality reduction algorithms, it is crucial to construct a neighbor graph that can correctly reflect the relationship between samples. This paper presents an improved algorithm called fuzzy local maximal marginal embedding (FLMME) for linear dimensionality reduction. Significantly differing from the existing graph-based algorithms is that two novel fuzzy gradual graphs are constructed in FLMME, which help to pull the near neighbor samples in same class nearer and nearer and repel the far neighbor samples of margin between different classes farther and farther when they are projected to feature subspace. Through the fuzzy gradual graphs, FLMME algorithm has lower sensitivities to the sample variations caused by varying illumination, expression, viewing conditions and shapes. The proposed FLMME algorithm is evaluated through experiments by using the WINE database, the Yale and ORL face image databases and the USPS handwriting digital databases. The results show that the FLMME outperforms PCA, LDA, LPP and local maximal marginal embedding.     

An SVD-based image watermarking in wavelet domain using SVR and PSO

The paper presents a novel blind watermarking scheme for image copyright protection, which is developed in the discrete wavelet transform (DWT) and is based on the singular value decomposition (SVD) and the support vector regression (SVR). Its embedding algorithm hides a watermark bit in the low–low (LL) subband of a target non-overlap block of the host image by modifying a coefficient of U component on SVD version of the block. A blind watermark-extraction is designed using a trained SVR to estimate original coefficients. Subsequently, the watermark bit can be computed using the watermarked coefficient and its corresponding estimate coefficient. Additionally, the particle swarm optimization (PSO) is further utilized to optimize the proposed scheme. Experimental results show the proposed scheme possesses significant improvements in both transparency and robustness, and is superior to existing methods under consideration here.     

基于物联网的教育与教学设备监控的研究与应用

新的现代化技术服务于教育是高职院校现代教育发展的必然趋势,文章系统地叙述了高职院校为进一步推动物联网在教育与教学设备监控的应用,做了不断的探索和实践,以期对高职院校的教育与教学设备监控建设工作提供一些借鉴。