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.

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.

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.

Crowdedness estimation approach based on stereovision for bus passengers

An estimation approach is proposed in this paper based on the binocular stereovision to collect the degree of crowdedness in public transports. The proposed method combines the disparity with frame differences to extract the foreground object. An adaptive window normalized cross correlation （NCC） matching and interpolated method is applied to get the sub-pixel image disparity value. Then, the foreground object is projected to the horizontal plane to eliminate the influence of the occlusion and perspective effect. Finally the degree of crowdedness is calculated from the area and the perimeter of the foreground objects. Experimental results show that the proposed method can obtain good estimation results in the simulated scenes in the laboratory and on parking or moving buses. This approach is effective to illumination changes, shadows and occlusion of passengers.

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.

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.

Nanozyme catalysis in a crowded environment:The impact of diffusion and surface shielding

Nanozymes are widely used in various applications as nanosized catalysts for replacing enzymes.An accurate estimation of the catalytic activity of nanozymes in real conditions is critical.In this article,for the first time,we systematically studied the effect of macromolecular molecules co-existing in the real system on the oxidoreductase(peroxidase,oxidase,and catalase)-mimicking nanozymes made of a gold nanoparticle core and a platinum shell,Prussian Blue,Mn_(2)O_(3) and CoO nanoparticles.Comparisons were made with horseradish peroxidase.We distinguished two main mechanisms of the negative impact of macromolecules on nanozyme catalysis—slowed diffusion and surface shielding of nanoparticles.While the first mechanism is typical for enzymes,the second one is specific only for nanozymes.Understanding the mechanisms is essential for developing approaches to reduce the unavoidable impact of macromolecules for various analytical and biomedical applications.

主题公园游客拥挤感知影响因素研究——以清明上河园为例

人民生活水平日益提高,国内旅游人数逐年递增,造成旅游景区拥挤现象频繁发生,拥挤逐渐演变为干扰众多景区发展、影响游客满意度和旅游体验的重要因素之一。以清明上河园为例,运用IPA分析法,对主题公园游客拥挤感知影响因素进行研究。结果显示,景区交通便利度、其他游客的行为、游客数量是影响受访者拥挤感知的关键因素,景区可以从这三个方面重点改进,提升游客满意度。

The Arabidopsis CROWDED NUCLEI genes regulate seed germination by modulating degradation of ABI5 protein

In Arabidopsis, the phytohormone abscisic acid （ABA） plays a vital role in inhibiting seed germination and in postgermination seedling establishment. In the ABA signaling pathway, ABI5, a basic Leu zipper transcription factor, has important functions in the regulation of seed germination. ABI5 protein localizes in nuclear bodies, along with AFP, COP1, and SIZ1, and was degraded through the 26S proteasome pathway. However, the mechanisms of ABI5 nuclear body formation and ABI5 protein degradation remain obscure. In this study, we found that the Arabidopsis CROWDED NUCLEI （CRWN） proteins, predicted nuclear matrix proteins essential for maintenance of nuclear morphology, also participate in ABA-controlled seed germination by regulating the degradation of ABI5 protein. During seed germination, the crwn mutants are hypersensitive to ABA and have higher levels of ABI5 protein compared to wild type. Genetic analysis suggested that CRWNs act upstream of ABIS. The observation that CRWN3 colocalizes with ABI5 in nuclear bodies indicates that CRWNs might participate in ABI5 protein degrada- tion in nuclear bodies. Moreover, we revealed that the extreme C-terminal of CRWN3 protein is necessary for its function in the response to ABA in germination. Our results suggested important roles of CRWNs in ABI5 nuclear body organization and ABI5 protein degradation during seed germination.

Are China＇s Exports Crowding Out or Being Crowded Out？ Evidence from Japan＇s Imports

Previous studies have investigated whether Chinese exports have crowaea oul mose from other countries. However, what has yet to be considered is the evidence based on different quality varieties. Using the most detailed Harmonized System 9-digit product- level data, the present paper provides evidence of crowding-out and crowded-out effects across different product quality segments and across manufacturing sectors by quality segments. The empirical evidence presented in this paper shows that the crowding-out effects of Chinese exports have been greatest at the lower end of the quality spectrum but less significant at the higher quality spectrum. Moreover, since 2007, China＇s own exports of lower quality manufactured goods have been increasingly crowded out. The key policy implication is that China＇s export path is in line with that taken by other Asian economies in previous decades; the crowded-out effect could achieve win-win outcomes for countries involved; and lower income countries would do well to be open to receive those relocated low value-added industries from China. However, the relocation policy in China is best implemented gradually as climbing up the product quality ladder takes time.

Non-monotonic behavior of jam probability and stretchedexponential distribution in pedestrian counterflow

We adopt a floor field cellular automata model to study the statistical properties of bidirectional pedestrian flow movingin a straight corridor. We introduce a game-theoretic framework to deal with the conflict of multiple pedestrians tryingto move to the same target location. By means of computer simulations, we show that the complementary cumulative distributionof the time interval between two consecutive pedestrians leaving the corridor can be fitted by a stretched exponentialdistribution, and surprisingly, the statistical properties of the two types of pedestrian flows are affected differently by theflow ratio, i.e., the ratio of the pedestrians walking toward different directions. We also find that the jam probability exhibitsa non-monotonic behavior with the flow ratio, where the worst performance arises at an intermediate flow ratio of around0.2. Our simulation results are consistent with some empirical observations, which suggest that the peculiar characteristicsof the pedestrians may attributed to the anticipation mechanism of collision avoidance.

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.

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.

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 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.

Investigation on Breakdown Characteristics of Various Surface Terminal Structures for GaN-Based Vertical P-i-N Diodes

GaN-based vertical P-i-N diode with mesa edge terminal structure due to electric field crowding effect, the breakdown voltage of the device is significantly reduced. This work investigates three terminal structures, including deeply etched, bevel, and stepped-mesas terminal structures, to suppress electric field crowding effects at the device and junction edges. Deeply-etched mesa terminal yields a breakdown voltage of 1205 V, i.e., 89% of the ideal voltage. The bevel-mesa terminal achieves about 89% of the ideal breakdown voltage, while the step-mesa terminal is less effective in mitigating electric field crowding, at about 32% of the ideal voltage. This work can provide an important reference for the design of high-power, high-voltage GaN-based P-i-N power devices, finding a terminal protection structure suitable for GaNPiN diodes to further enhance the breakdown performance of the device and to unleash the full potential of GaN semiconductor materials.

A New Multiobjective Particle Swarm Optimization Using Local Displacement and Local Guides

This paper introduces a novel variant of particle swarm optimization that leverages local displacements through attractors for addressing multiobjective optimization problems. The method incorporates a square root distance mechanism into the external archives to enhance the diversity. We evaluate the performance of the proposed approach on a set of constrained and unconstrained multiobjective test functions, establishing a benchmark for comparison. In order to gauge its effectiveness relative to established techniques, we conduct a comprehensive comparison with well-known approaches such as SMPSO, NSGA2 and SPEA2. The numerical results demonstrate that our method not only achieves efficiency but also exhibits competitiveness when compared to evolutionary algorithms. Particularly noteworthy is its superior performance in terms of convergence and diversification, surpassing the capabilities of its predecessors.