Automated evaluation of parapapillary choroidal microvasculature in crowded optic discs:a controlled,optical coherence tomography angiography study

AIM:To compare superficial and deep vascular properties of optic discs between crowded discs and controls using optical coherence tomography angiography(OCT-A).METHODS:Thirty patients with crowded discs,and 47 control subjects were enrolled in the study.One eye of each individual was included and OCT-A scans of optic discs were obtained in a 4.5×4.5 mm^(2) rectangular area.Radial peripapillary capillary(RPC)density,peripapillary retinal nerve fiber layer(pRNFL)thickness,cup volume,rim area,disc area,cup-to-disc(c/d)area ratio,and vertical c/d ratio were obtained automatically using device software.Automated parapapillary choroidal microvasculature(PPCMv)density was calculated using MATLAB software.When the vertical c/d ratio of the optic disc was absent or small cup,it was considered as a crowded disc.RESULTS:The mean signal strength index of OCT-A images was similar between the crowded discs and control eyes(P=0.740).There was no difference in pRNFL between the two groups(P=0.102).There were no differences in RPC density in whole image(P=0.826)and peripapillary region(P=0.923),but inside disc RPC density was higher in crowded optic discs(P=0.003).The PPCMv density in the inner-hemisuperior region was also lower in crowded discs(P=0.026).The pRNFL thickness was positively correlated with peripapillary RPC density(r=0.498,P<0.001).The inside disc RPC density was negatively correlated with c/d area ratio(r=-0.341,P=0.002).CONCLUSION:The higher inside disc RPC density and lower inner-hemisuperior PPCMv density are found in eyes with crowded optic discs.

An attending physician float shift for the improvement of physician productivity in a crowded emergency department

BACKGROUND:Patients backlogged in the emergency department(ED) waiting for an inpatient bed(boarders) continue to require the attention of ED physicians,exacerbating crowding in the ED.To address this problem,we added a "float shift" to our winter schedule solely to care for boarders.We sought to quantify the effect of this float shift,hypothesizing greater physician productivity.METHODS:We performed a retrospective observational study in our community hospital ED,measuring the number of new patients seen in each 10-hour shift in the presence or absence of a float shift physician.We calculated the number of new patients seen per shift for each of the 7 daily shifts,during February(float shift scheduled) and May(float shift unscheduled) of 2008.We then compared the mean number of patients seen per shift in February with May.RESULTS:Total monthly patient volume was 6 656 for February and 6 775 for May,with the mean daily census being 230 and 219 patients,respectively.The number of new patients seen during each shift was greater in February than in May,with a mean increase of 1.1 patients per shift(with the float shift).Surveying participants about intervention effectiveness showed 92%of residents,but only 65%of attending physicians,in favor of maintaining the float shift.CONCLUSION:The presence of a "float shift" physician caring only for boarding patients allows other physicians to maintain and even increase their productivity in our ED,despite the presence of longer throughput times and increased time on diversion.

Identification method of crowded passenger flow based on automatic fare collection data of Nanjing Metro

To relieve traffic congestion in urban rail transit stations,a new identification method of crowded passenger flow based on automatic fare collection data is proposed.First,passenger travel characteristics are analyzed by observing the temporal distribution of inflow passengers each hour and the spatial distribution concerning cross-section passenger flow.Secondly,the identification method of crowded passenger flow is proposed to calculate the threshold via the probability density function fitted by Matlab and classify the early-warning situation based on the threshold obtained.Finally,a case study of Xinjiekou station is conducted to prove the validity and practicability of the proposed method.Compared to the traditional methods,the proposed comprehensive method can remove defects such as efficiency and delay.Furthermore,the proposed method is suitable for other rail transit companies equipped with automatic fare collection systems.

Analyzing Motion Patterns in Crowded Scenes via Automatic Tracklets Clustering

Crowded scene analysis is currently a hot and challenging topic in computer vision field. The ability to analyze motion patterns from videos is a difficult, but critical part of this problem. In this paper, we propose a novel approach for the analysis of motion patterns by clustering the tracklets using an unsupervised hierarchical clustering algorithm, where the similarity between tracklets is measured by the Longest Common Subsequences. The tracklets are obtained by tracking dense points under three effective rules, therefore enabling it to capture the motion patterns in crowded scenes. The analysis of motion patterns is implemented in a completely unsupervised way, and the tracklets are clustered automatically through hierarchical clustering algorithm based on a graphic model. To validate the performance of our approach, we conducted experimental evaluations on two datasets. The results reveal the precise distributions of motion patterns in current crowded videos and demonstrate the effectiveness of our approach.

Design and Availability Research of a Flammable and Explosive Volatiles Monitoring and Early Warning System (FEVMEW) for the Bus Crowded Places

In order to reduce the arson or accidental fire losses, we developed a gas sensitive detector used for the rapid detection and early warning of flammables in crowded places such as buses. A MEMS (Micro-Electro-Mechanical System) based thin film semiconductor was fabricated as the gas sensor. To obtain the target gas selective response, the surface of the sensitive film was modified with highly active metal catalytic nano-particles. Thus the anti-interference ability was improved and the false alarm rate was effectively reduced. Furthermore, the modular embedded system for information acquisition and transmission was developed. Supported by the Airflow Precision control system (APs), the rapid warning of volatile gas of flammable substances was realized. Experiments showed that RAs has satisfied selectivity to volatiles of usual flammable liquid, such as the output voltage reaches 3 V (0 - 3.3 V). With simulation about the actual installation state in bus, MWs sounds an alarm at 2 minutes after splashing 50 mL 92# petrol to the floor. For the last two years, FEVMEW has been integrated into more than 4000 buses in Hefei. This design has been proved feasible according to the actual operation.

Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary actions.The crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test data.In this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene.The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples.The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive field.Five benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

Using NMR-detected hydrogen-deuterium exchange to quantify protein stability in cosolutes,under crowded conditions in vitro and in cells

We review the use of nuclear magnetic resonance(NMR)spectroscopy to assess the exchange of amide protons for deuterons(HDX)in efforts to understand how high concentration of cosolutes,especially macromolecules,affect the equilibrium thermodynamics of protein stability.HDX NMR is the only method that can routinely provide such data at the level of individual amino acids.We begin by discussing the properties of the protein systems required to yield equilibrium thermodynamic data and then review publications using osmolytes,sugars,denaturants,synthetic polymers,proteins,cytoplasm and in cells.

The Mild-Moderate Case Presentation of COVID-19 in Adult Male Patient, Al Wakra Study

Background: COVID-19 is currently one of the most infectious diseases worldwide. In this study, we focused on the mild and moderate cases of COVID-19 that can present with mild respiratory symptoms or non-respiratory symptoms. Many of that cases got miss diagnoses. We aim to help emergency physicians in reaching a proper and faster diagnosis of COVID-19 cases. Method: In this retrospective cross-sectional qualitative study, we collected 100 confirmed cases of COVID-19 that were presented in April 2020 in Al Wakra Hospital, Qatar. All that cases were mild-moderate cases without severe respiratory symptoms. We reviewed the electronic files on patient presentation, emergency department physician’s note, temperature data, and chest X-ray findings. Result: Our result showed about 49% of the total COVID-19 confirmed cases had respiratory symptoms, while the remaining 51% had no respiratory symptoms. The respiratory symptoms, such as cough and sore throat, and non-respiratory symptoms like headache, vomiting, abdominal pain, and skin rash. Regarding fever presentation, we found that 66% of cases had a fever, while 34% had no fever complaints. The most frequently observed body temperature of patients was 37+ °C, followed by 38+ °C, 36+ °C, and 39+ °C. About 41% of cases had non-significant X-ray findings, and 40% cases had significant X-ray findings. The remaining 19% of cases did not undergo any X-ray examination due to mild and stable presentation. Conclusion: The presentations and symptoms of a mild-moderate case of COVID-19 are not respiratory only, there are extra-pulmonary symptoms and presentations should be considered. The most common presentation for mild-moderate COVID-19 was found to be fever. Chest X-ray may be performed depending on the patient’s condition, red flags, and abnormal findings in clinical examination, and should not be routine in cases with the mild presentation of COVID-19 suspicion in the emergency department.

Lightweight Res-Connection Multi-Branch Network for Highly Accurate Crowd Counting and Localization

Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges including crowd multi-scale variations and high network complexity,etc.To tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is proposed.Specifically,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual concatenation.In addition,a compound loss function is introduced for training themethod to improve global context information correlation.The proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public datasets.The accuracy is better than those of many advanced approaches,while the number of parameters is smaller.The experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.

CCM-FL:Covert communication mechanisms for federated learning in crowd sensing IoT

The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized services.Meanwhile,how to protect the private information of users in federated learning has become an important research topic.Compared with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning models.In this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things networks.Different from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal solution.Secondly,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning agents.Theoretical analysis and nu-merical simulations are presented to show the performance of our covert communication mechanisms.We hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.

A Game-Theoretic Approach to Safe Crowd Evacuation in Emergencies

Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route selection.The proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.

A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation metrics.In this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised information.For this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the network.Specifically,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd images.In addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density estimation.The experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

Deep Learning Based Efficient Crowd Counting System

Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an image of the crowd’s density.Therefore in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd.We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance.To analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd counting.The results show the superiority of the proposed system.

Design of Barrier-free Evacuation for Diverse Crowd on Campus Based on Evacuation Simulation: A Case Study of North China University of Technology

By using evacuation simulation technology and taking North China University of Technology as an example,the barrier-free evacuation design scheme for groups with different needs in campus environment was deeply discussed.Based on the data of building layout,population composition,road system and distribution of shelters in the school,a detailed evacuation model was constructed in the Pathfinder emergency evacuation simulation system.By the simulation during the daytime and at night,the total evacuation time of the whole school,evacuation completion time of each building,selection of evacuation paths and shelter utilization were analyzed in detail.The simulation results show that the distribution of shelters on campus is uneven,and their capacity is limited.As a result,the evacuation paths of the disabled,the elderly and children need to be adjusted frequently,which affects the overall evacuation efficiency.In view of this,the optimization strategies of road renovation and entrances of shelters and buildings were put forward from the perspective of space planning.From the perspective of emergency management,it is suggested to improve the campus evacuation infrastructure and strengthen the evacuation drill for teachers and students.These results provide a solid theoretical support for enhancing the construction of campus barrier-free environment and improving the level of emergency management.

基于时延的Flash Crowd控制模型

提出了一种session级别的flash crowd控制策略SGAC(session-granularity admission control),将session控制粒度和request控制粒度相结合,采用请求平均返回时延作为检测和控制的依据.对session采取一旦接受就完成的策略,在实现对服务器过载控制的同时,保护用户session的完整性,并能自动调节新session的准入速率,以提高服务器利用率.采用真实HTTP Log进行模拟,结果表明,SGAC方法能够有效控制服务器过载,保护session的完整性,提高服务器利用率,降低接入端路由器计算开销,保护有价值的交易session.

CrowdTracker:一种基于移动群智感知的目标跟踪方法

面向目标跟踪问题提出一种基于移动群智感知的解决方案CrowdTracker.不同于基于视频监控的目标跟踪方法,通过基于群智的多人协作拍照方式实现对移动目标的轨迹预测和跟踪,其优化目标为在保证准确实时地对目标进行跟踪的同时尽可能地减少用户激励的成本(假设激励与完成任务的参与者人数和参与者完成任务所移动的距离成正比).为实现该目标,提出了目标移动性预测的方法MPRE和任务分配的方法T-centric,P-centric.T-centric是以任务为中心的参与者选择方法,而P-centric是以人为中心的任务选择方法.MPRE通过分析大量的车辆历史轨迹建立城市里车辆的移动模型以预测目标下一步的位置.在预测的区域内通过T-centric或P-centric方法进行跟踪任务分配.通过一个大规模的真实数据集对移动性预测方法MPRE和2种任务分配算法进行实验评估,实验结果表明:CrowdTracker能有效地在实现目标实时跟踪的同时降低激励成本.

转发概率递减的改进Crowds系统

Crowds匿名浏览系统中可以在不影响匿名度水平的前提下,通过递减转发概率减小重路由路径长度,提高系统性能。提出利用路径长度期望值递减规律确定转发概率递减比例系数的方法,仿真实验表明,新方案可以在保持原有匿名度的基础上,有效减小重路由路径长度,提高Crowds系统的通信性能。

Crowds系统中新的前驱攻击模型研究

对前驱攻击模型攻击成功的概率和攻击轮数的原理和机制进行了详细的分析,在此基础上,提出了新的统计前驱攻击模型,该模型在每一轮重路由路径建立完毕后,对路径上所有泄密节点的直接前驱进行统计,减少了攻击的重建路径次数.仿真实验表明,与前驱攻击相比,新的统计前驱攻击可以有效地提高攻击的成功概率.

一种基于Crowds的改进匿名通信系统

提出一种基于Crowds的改进匿名通信系统,通过结合mix匿名通信技术和填充流量技术,使改进的匿名通信系统能抵御两种攻击.此外,对改进的匿名系统在前驱攻击下的匿名性能进行了分析,推导出改进系统在前驱攻击下能提供更好的匿名保护.

Translation and Comments on Bulrush in the Crowd

Nida’s functional equivalence enjoys a great popularity among translation theories,which plays an indispensable role in the practices of translation.Bulrush in the Crowds is a lyric prose cloaked in melancholy atmosphere.This prose is written in simple but lively,vivid language.It is also highly readable,with flexible structures and various writing techniques.Short and condensed casual sentences are widely employed in this prose.Furthermore,it is good at using figure of speech.Thus,when translation is conducted,mood,structure,style and rhetorical devices should be taken into consideration.