钾基蒙脱石对镍离子的吸附作用

钾基蒙脱石的特殊结构决定了其具有一定的阳离子交换能力,这正是钾基蒙脱石可以用于吸附重金属离子的前提。本文采用液闪法研究了钾基蒙脱石对水溶液中镍离子的吸附性能,探讨了温度、pH、吸附剂用量、溶液中镍初始浓度等因素对钾基蒙脱石吸附镍离子的影响。结果表明,较高碱性pH值有利于钾基蒙脱石界面羟基络合配位大量镍离子;钾基蒙脱石用量可提高镍吸附率;提高温度也有利于镍的吸附;热力学吸附模型可采用Freundlich方程来描述。     

Social context cognition crowd‐sourcing and semi‐automatic parametrization

This paper presents a semi‐automatic method of parameterizing an existing social context cognition model. It discusses benefits of the social context cognition models for example in personality modeling and their key issue that is parametrization. It briefly introduces social context cognition model and describes a new method of its crowd‐sourcing‐based parametrization. Later, validation is provided, and ability to recreate social context cognition in the provided samples is presented with good generalization for the unknown cases. Finally, model's stability for the continuous stream of dynamic social context input data is shown. Presented system contributes to the believable agent modeling and social simulations by making much needed applications of social context cognition models easier by addressing the so far unsolved troublesome parametrization issues. Copyright © 2016 John Wiley & Sons, Ltd.     

基于人群活力的北京市延庆区城市公园风环境评价

[目的]已有风环境研究多从单一条件对城市空间风环境进行评价并提出优化策略,忽略了人群有效活动空间的影响,以简化体块代替公园物理环境部分要素,一定程度影响了风环境模拟分析结果的准确性。[方法]以占北京市风力资源70%的延庆区4个典型城市公园为对象,从公园人群游憩活力视角,结合场景建模技术、通讯探针和人工智能识别技术,确定公园游客活动范围,以此作为公园风环境评价的研究区域;利用不同季节风速、风压和涡旋分布等评价要素量化评价公园风环境质量,并以公园风环境质量综合评价来表征公园现状风环境质量优劣程度;以公园风环境质量评分方式相关性评价来表征公园进行风环境适应性改造的难易程度。[结果]结果表明：百泉公园现状风环境质量最差,且评分随季节变化呈现出明显差异性和不规律性;集贤城市森林公园风环境质量评分随季节变化有规律地逐渐减小,且进行风环境适应性改造难度最小。[结论]研究建立了全面的公园风环境质量评价体系,对延庆区城市公园建设和管理具有应用价值,为华北地区城市公园风环境质量评价提供创新方法。     

基于 BIM 与 Pathfinder 的楼梯间堆积物对高层住宅建筑人群疏散影响研究

高层住宅建筑发生火灾时,楼梯间作为主要逃生通道,保证其畅通性尤为重要,但楼梯间堆积物却是人群疏散的主要阻碍。利用 BIM 与 Pathfinder 软件相结合,建立应急疏散仿真模型,设定 574 名不同年龄阶段的疏散人员参与模拟,在Steering 行为模式下进行仿真,分别研究堆积物摆放面积、摆放形状及摆放位置对高层住宅建筑人群疏散时间、疏散速度及拥堵情况产生的影响,并以此为高层住宅建筑楼梯间的规范管理提供理论依据。     

基于贝叶斯网络的群体性事件智能视频分析与判定技术

为提高群体性事件智能视频监控的可靠性,提出在传统图像处理算法实现目标检测和特征提取的基础上,采用贝叶斯网络进一步对事件特征进行分析,重点介绍人群聚集事件判定贝叶斯网络建模、参数设置和推理等关键过程,并以具体的实例进行验证。结果表明:该方法具有建模简单、对智能视频分析算法依赖性低、鲁棒性和移植性强等特点,对建立可靠的智能视频监控系统具有参考价值。     

Real-time monitoring of events applied to syndromic surveillance

Abstract

This article focuses on monitor plans aimed at the early detection of the increase in the frequency of events. The literature recommends either monitoring the time between events (TBE) if events are rare or counting the number of events per unit non-overlapping time intervals otherwise. Some authors advocate using the Bernoulli model for rare events, applying presence or absence of events within non-overlapping and exhaustive time intervals. This Bernoulli model does improve the real-time monitoring assessment of these events compared to counting events over a larger interval, making them less rare. However this approach became inefficient if more than one event starts occurring within the intervals. Monitoring TBE is the real-time option for outbreak detection, because outbreak information is accumulated when an event occurs. This is preferred to waiting for the end of a period to count events. If the TBE reduces significantly, then the incidence of these events increases significantly. This article explores this TBE option relative to using the monitoring of counts when the TBEs are either Exponentially, Gamma or Weibull distributed for moderately low count scenarios. The article will discuss and compare the approaches of using an Exponentially Weighted Moving Average (EWMA) statistic for the TBEs to the EWMA of counts. Several robust options will be considered when the future change in event frequency is unknown. Our goal is to have a robust monitoring plan which is able to efficiently detect many different levels of shifts. These robust plans are compared to the more traditional event monitoring plans for both small and large changes in the event frequency.     

基于多尺度多任务卷积神经网络的人群计数

在智能监控领域，实现人群计数具有重要价值，针对人群尺度不一、人群密度分布不均及遮挡等问题，提出一种多尺度多任务卷积神经网络（MMCNN）进行人群计数的方法。首先提出一种新颖的自适应人形核生成密度图描述人群信息，消除人群遮挡影响；其次通过构建多尺度卷积神经网络解决人群尺度不一问题，以多任务学习机制同时估计密度图及人群密度等级，解决人群分布不均问题；最后设计一种加权损失函数，提高人群计数准确率。在UCF_CC_50和World Expo'10数据库上进行了评估，验证了自适应人形核的有效性。实验结果表明：所提算法比Sindagi等的方法（SINDAGI V A，PATEL V M.CNN-based cascaded multi-task learning of high-level prior and density estimation for crowd counting.Proceedings of the 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance.Piscataway，NJ：IEEE，2017：1-6）在UCF_CC_50数据库上平均绝对误差（MAE）数值和均方误差（MSE）数值分别降低约1.7和45；与Zhang等的方法（ZHANG Y，ZHOU D，CHEN S，et al.Single-image crowd counting via multi-column convolutional neural network.Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington，DC：IEEE Computer Society，2016：589-597）相比，在World Expo'10数据库上所提算法的MAE值降低约1.5，且在真实公共汽车数据库上仅0~3人的计数误差，表明其实用性较强。     

Effect of Methylal Quenching Gas for Self-Quenching Streamer (SQS) Tube with Ar-isoC4H10-Methylal Mixture

Abstract

Properties of a self-quenching streamer (SQS) tube with an Ar-isoC₄H₁₀ (75:25) mixture containing 2.5% methylal vapor have been investigated for uniform α-ray irradiation. The effect of electro-negative methylal as the second quenching gas has been studied for avalanche sizes, pulse shapes of current signals, dead zones, and counting-rate characteristics of the SQS tube. The dead zone and counting-rate characteristics are improved very much by the use of the mixture containing methylal.     

Parallel color-coding

We present new parallelization and memory-reducing strategies for the graph-theoretic color-coding approximation technique, with applications to biological network analysis. Color-coding is a technique that gives fixed parameter tractable algorithms for several well-known NP-hard optimization problems. In this work, by efficiently parallelizing steps in color-coding, we create two new biological protein interaction network analysis tools: Fascia for subgraph counting and motif finding and FastPath for signaling pathway detection. We demonstrate considerable speedup over prior work, and the optimizations introduced in this paper can also be used for other problems where color-coding is applicable.     

A mobile crowd sensing ecosystem enabled by CUPUS: Cloud-based publish/subscribe middleware for the Internet of Things

Mobile crowd sensing (MCS) is a novel class of mobile Internet of Things (IoT) applications for community sensing where sensors and mobile devices jointly collect and share data of interest to observe phenomena over a large geographic area. The inherent device mobility and high sensing frequency has the capacity to produce dense and rich spatiotemporal information about our environment, but also creates new challenges due to device dynamicity and energy constraints, as well as large volumes of generated raw sensor data which need to be processed and analyzed to extract useful information for end users. The paper presents an ecosystem for mobile crowd sensing which relies on the CloUd-based PUblish/Subscribe middleware (CUPUS) to acquire sensor data from mobile devices in a flexible and energy-efficient manner and to perform near real-time processing of Big Data streams. CUPUS has unique features compared to other MCS platforms: It enables management of mobile sensor resources within the cloud, supports filtering and aggregation of sensor data on mobile devices prior to its transmission into the cloud based on global data requirements, and can push information of interest from the cloud to user devices in near real-time. We present our experience with implementation and deployment of an MCS application for air quality monitoring built on top of the CUPUS middleware. Our experimental evaluation shows that CUPUS offers scalable processing performance, both on mobile devices and within the cloud, while its data propagation delay is mainly affected by transmission delay on wireless links.