Embedding carbon dots in Eu3+-doped metal-organic framework for label-free ratiometric fluorescence detection of Fe3+ ions

Fluorescent detection is a new spectroscopic measurement for ions sensing due to the advantages of real-time determination with high selectivity, accuracy, and low cost. However, chemosensors based on fluorescent detection are usually determined by absolute intensity from a monochromatic emission signal, which is easy to be fluctuated by the external environment, especially for Fe³⁺ detection in complex fluids. Herein, we rationally design a dual-emission Eu³⁺: CDs@ZIF-8 to construct a ratiometric fluorescent sensor with self-calibrating ability for Fe³⁺ determination. High efficient carbon dots (CDs) are embedded in europium ions (Eu³⁺)-doped MOF by simple stirring preparation at room temperature. The label-free ratiometric fluorescent probe (I_CDs@ZIF-8/I_Eu) exhibits simultaneous blue and red emission under the same excitation at 365 nm. Remarkably, Eu³⁺: CDs@ZIF-8 displays the superiority of high selectivity to Fe³⁺, which shows ratiometric fluorescence characteristics (I₀/I) in a range of 0-6 μmol\L with a low limit of detection (LOD) of 0.897 μmol\L. Besides, the CDs-MOF nanocomposite holds good aqueous dispersibility and low cytotoxicity, which shows great potential applications in medical aid including biological detection and clinical diagnosis.     

Droplet Entrainment Analysis in a Flash Evaporator with an Image-Based Measurement Technique

Local droplet sizes and volumes of entrained liquid are captured with an image-based measurement technique for comparison with a conventional, integral method for entrainment analysis. Experiments in a forced circulation flash evaporation were performed for different operating conditions and with two different chemical systems. Droplet size and frequency rise with an increase in thermal energy input. The local readings confirm the trends found by the integral measurement method. The modification of the image-based probe enables the detection of small (≈ 10 µm) and at the same time fast droplets under challenging operating conditions, such as vacuum and superheated feed similar to industrial process conditions.     

Feedback-based object detection for multi-person pose estimation

In this paper, a novel method is proposed for increasing the performance through coupling of top-down models adjusting the object detector based on a new loss function. Generally, object detectors and keypoint estimators are sequentially used in real-time multi-person pose estimations; however, these two models are separately trained. Therefore, the results of the object detector are not optimized for the keypoint estimator. To solve this problem, we analyze the relationship between the two models and propose a feedback-based loss optimization in the object detector, based on the estimation results of the keypoint estimator. In addition, the resulting bounding box of the object detector is readjusted to improve the accuracy of the keypoint estimation model. The experimental results demonstrate that the proposed approach can perform real-time operations with a high frame rate similar to that of the baseline model. Moreover, it achieved an accuracy of 74.2 average precision (AP), which is higher than the state-of-the-arts model including the human detector used in the experiment.     

The P-graph approach for systematic synthesis of wastewater treatment networks

Wastewater treatment consists of three or four sequential stages: preliminary, primary, secondary, and tertiary. Each stage can comprise multiple alternative technologies that can perform the same tasks with different efficiencies, operating times, and costs. Thus, we propose a systematic approach for designing wastewater treatment networks by utilizing principles of mathematical modeling and generating an exhaustive enumeration of all the possible technologies and their connections during the early stages of designing a treatment facility. Some of these structures are nonintuitive and include recycling, reprocessing, bypasses, and multiple technologies in parallel or series to remove the same contaminant. The nonintuitive structures with multiple technologies may provide a measure of resilience compared to typical heuristic designs. Thus, the combination of P-graph methodology and the sequence of treatment technologies predicted via the optimization algorithm from the maximal structure is based on holistic considerations and does not lead to suboptimal solutions.     

5G技术在医院智能服务中的应用

纵观当今的发展格局,是科学技术高速发展的时代。随着5G时代的来临,网络技术在各个领域中扮演着越来越重要的角色。特别是在医疗方面,为医院的日常工作奠定了坚实的基础。今天就5G网络技术在医院只能服务中的应用展开谈论。     

基于GA-BP的汽车风振噪声声品质预测模型

目前对于汽车风振噪声的优化研究主要以声压级（Sound pressure level，SPL）作为单一评价指标，既不能全面反映噪声的物理属性，也无法考虑人耳对噪声的主观认知过程。为准确评价风振噪声，引入声品质，运用大涡模拟（Large eddy simulation，LES）对风振噪声进行数值仿真，根据实车道路试验判断仿真的准确性；对仿真结果进行声品质客观评价与主观评价，综合声品质客观评价参数与声品质主观评价试验结果建立BP神经网络预测模型；利用遗传算法（Genetic algorithm，GA），进一步对BP神经网络的结构参数进行优化，建立GA-BP声品质预测模型。研究结果表明，GA-BP声品质预测模型在训练速度和预测精度上都优于BP神经网络预测模型。预测模型基于声品质主客观评价结果，其预测值可以代替传统的声压级评价指标，为风振噪声提供更为准确合理的评价。     

基于PMU的电力系统监控体系结构优化设计

文中提出了一种利用有限数量的相量测量单元(PMU)和相量数据集中器(PDC)设计最优监控结构的方法。通过在大量的设定值场景下,使电力系统可观测性曲线的期望值最大化,同时使通信基础设施成本最小化,最终确定PMU和PDC的最佳位置。提出了一种非线性动态扩展卡尔曼滤波(EFK)状态观测器。这种状态观测器可以将暂态行为转换为由代数微分方程描述的广域电力系统,而无需非线性反演技术。最后以IEEE-5电力系统为例,说明了该方法的有效性。     

建筑工业化发展的多群体演化博弈与仿真研究

新型建筑工业化具有高质量、低消耗、可循环发展等特征，其推广已上升到国家战略层面。利用演化博弈方法，建立“政府-开发商-银行” 的三方动态演化博弈模型，进行各博弈主体策略的演化稳定性分析，并针对初始状态、奖惩力度、借贷风险和开发成本等对演化结果的影响进行动态仿真。在此基础上， 考虑开发商群体的网络拓扑特征对演化真实性的影响，引入复杂网络理论， 以无标度网络为载体描述开发商个体的连接偏好和决策机制，构建政府监管下的建筑工业化扩散模型，并通过仿真深入研究相关因素对扩散深度的影响作用，最后结合仿真结果给出相应对策建议。     

基于NR-Transformer的集群作业运行时间预测

高性能集群的作业调度通常使用作业调度系统来实现，准确填写作业运行时间能在很大程度上提升作业调度效率。现有的研究通常使用机器学习的预测方式，在预测精度和实用性上还存在一定的提升空间。为了进一步提高集群作业运行时间预测的准确率，考虑先对集群作业日志进行聚类，将作业类别信息添加到作业特征中，再使用基于注意力机制的NR-Transformer网络对作业日志数据建模和预测。在数据处理上，根据与预测目标的相关性、特征的完整性和数据的有效性，从历史日志数据集中筛选出7维特征，并按作业运行时间的长度将其划分为多个作业集，再对各作业集分别进行训练和预测。实验结果表明，相比于传统机器学习和BP神经网络，时序神经网络结构有更好的预测性能，其中NR-Transformer在各作业集上都有较好的性能。     

基于双时间点检测的毫米波雷达人流量监测方法

针对现有基于视频监控的人流量统计方案成本高、算法复杂且不利于个人隐私保护的局限性，利用毫米波雷达体积小、成本低、分辨率高的特点，提出了一种基于双时间点检测的人流量监测方法。该方法先获取人体目标散射点位置和多普勒频移信息来构成点云数据，然后根据多普勒频移正负来判断人体的运动方向，并筛选具有高多普勒频移值的点云数据以降低干扰点对聚类结果的影响；在双时间点对特定区域内人员数量进行统计，并根据双时间点之间所获取的点云数据聚类结果对所统计人员数据进行修正。实验结果表明，该方法能够用匿名的方式以较高的正确率统计人员进出。