Spatial Modeling of Residential Crowding in Alexandria Governorate, Egypt: A Geographically Weighted Regression (GWR) Technique

Despite growing research for residential crowding effects on housing market and public health perspectives, relatively little attention has been paid to explore and model spatial patterns of residential crowding over space. This paper focuses upon analyzing the spatial relationships between residential crowding and socio-demographic variables in Alexandria neighborhoods, Egypt. Global and local geo-statistical techniques were employed within GIS-based platform to identify spatial?variations of residential crowding determinates. The global ordinary least squares (OLS) model?assumes homogeneity of relationships between response variable and explanatory variables?across the study area. Consequently, it fails to account for heterogeneity of spatial relationships. Local model known as a geographically weighted regression (GWR) was also employed using the same?response variable and explanatory variables to capture spatial non-stationary of residential?crowding. A comparison of the outputs of both models indicated that OLS explained 74 percent of?residential crowding variations while GWR model explained 79 percent. The GWR improvedstrength of the model and provided a better goodness of fit than OLS. In addition, the findings of this analysis revealed that residential crowding was significantly associated with different structural measures particularly social characteristics of household such as higher education and illiteracy. Similarly, population size of neighborhood and number of dwelling rooms were found to have direct impacts on residential crowding rate. The spatial relationship of these measures distinctly varies over the study area.

Effect of Crowding in Metro Carriages on Travelers’ Disutility: An Evidence from Guangzhou Metro

Overcrowding has become a non negligible issue for metros in China.This paper analyzes the impact of crowding in metro carriages on people’s travel behaviors based on the stated preference survey data from college students and full time workers in Guangzhou.The data were analyzed by using the constant value per trip model and the travel time multiplier model with the multinomial logit modeling technique.Results show that there is a significant impact of crowding in metro carriages on passengers’welfare.The relationship between the disutility of crowding and the standee density is not linear.Commuters do not care much about crowding in the metro,whereas non commuters and college students pay special attention to crowding in metro carriages,especially when the carriage is crowded.This paper presents new findings of the impact of demographic characteristics on the evaluation of crowding in metro carriages in developing countries.

The transcriptomic signature of responses to larval crowding in Drosophila melanogaster

Intraspecific competition at the larval stage is an important ecological factor affecting life-history,adaptation and evolutionary trajectory in holometabolous insects.However,the molecular pathways underpinning these ecological processes are poorly characterized.We reared Drosophila melanogaster at three egg densities(5,60,and 300 eggs/mL)and sequenced the transcriptomes of pooled third-instar larvae.We also examined emergence time,egg-to-adult viability,adult mass,and adult sex-ratio at each density.Medium crowding had minor detrimental effects on adult phenotypes compared to low density and yielded 24 differentially expressed genes(DEGs),including several chitinase enzymes.In contrast,high crowding had substantial detrimental effects on adult phenotypes and yielded 2107 DEGs.Among these,upregulated gene sets were enriched in sugar,steroid and amino acid metabolism as well as DNA replication pathways,whereas downregulated gene sets were enriched in ABC transporters,taurine,Toll/Imd signaling,and P450 xenobiotics metabolism pathways.Overall,our findings show that larval crowding has a large consistent effect on several molecular pathways(i.e.,core responses)with few pathways displaying density-specific regulation(i.e.,idiosyncratic responses).This provides important insights into how holometabolous insects respond to intraspecific competition during development.

Priority-based Residential Demand Response for Alleviating Crowding in Distribution Systems

The dynamic pricing environment offers flexibility to the consumers to reschedule their switching appliances.Though the dynamic pricing environment results in several benefits to the utilities and consumers,it also poses some challenges.The crowding among residential customers is one of such challenges.The scheduling of loads at low-cost intervals causes crowding among residential customers,which leads to a fall in voltage of the distribution system below its prescribed limits.In order to prevent crowding phenomena,this paper proposes a priority-based demand response program for local energy communities.In the program,past contributions made by residential houses and demand are considered as essential parameters while calculating the priority factor.The non-linear programming(NLP)model proposed in this study seeks to reschedule loads at low-cost intervals to alleviate crowding phenomena.Since the NLP model does not guarantee global optima due to its non-convex nature,a second-order cone programming model is proposed,which captures power flow characteristics and guarantees global optimum.The proposed formulation is solved using General Algebraic Modeling System(GAMS)software and is tested on a 12.66 kV IEEE 33-bus distribution system,which demonstrates its applicability and efficacy.

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.

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.

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 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.

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.

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.

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.

Covalent Assembly of Penicillin Acylase in Mesoporous Silica Based on Macromolecular Crowding Theory

To improve the covalent immobilization of penicillin acylase （PA）, macromolecular crowding theory was applied to its immobilization. Influence of mass ratio of enzyme to the silica, as well as, activation time with glutaraldehyde on the activity of assembled PA, was studied. In the mesopores, the effect of fl-cyclodextrin （β-CD） on the immobilization of the enzyme was also investigated. It was remarkable that the coupled yield and relative activity reached 99.5% and 92.3%, respectively, when penicillin acylase assembled covalently in the mesopores. The results here indicate that mimicked macromolecule crowding could significantly ameliorate the performance of covalently immobilized PA.

GLOBAL OPTIMIZATION OF PUMP CONFIGURATION PROBLEM USING EXTENDED CROWDING GENETIC ALGORITHM

An extended crowding genetic algorithm (ECGA) is introduced for solvingoptimal pump configuration problem, which was presented by T. Westerlund in 1994. This problem hasbeen found to be non-convex, and the objective function contained several local optima and globaloptimality could not be ensured by all the traditional MINLP optimization method. The concepts ofspecies conserving and composite encoding are introduced to crowding genetic algorithm (CGA) formaintain the diversity of population more effectively and coping with the continuous and/or discretevariables in MINLP problem. The solution of three-levels pump configuration got from DICOPT++software (OA algorithm) is also given. By comparing with the solutions obtained from DICOPT++, ECPmethod, and MIN-MIN method, the ECGA algorithm proved to be very effective in finding the globaloptimal solution of multi-levels pump configuration via using the problem-specific information.

PERSISTENCE AND THE GLOBAL DYNAMICS OF THE POSITIVE SOLUTIONS FOR A RATIODEPENDENT PREDATOR-PREY SYSTEM WITH A CROWDING TERM IN THE PREY EQUATION

This paper deals with the global dynamical behaviors of the positive solutions for a parabolic type ratio-dependent predator-prey system with a crowding term in the prey equation, where it is assumed that the coefficient of the functional response is less than the coefficient of the intrinsic growth rates of the prey species. We demonstrated some special dynamical behaviors of the positive solutions of this system which the persistence of the coexistence of two species can be obtained when the crowding region in the prey equation only is designed suitably. Furthermore, we can obtain that under some conditions, the unique positive steady state solution of the system is globally asymptotically stable.

Transcriptomic Profiling of the Immune Response to Crowding Stress in Juvenile Turbot (Scophthalmus maximus)

In this study,juvenile turbot Scophthalmus maximus were vaccinated with attenuated Edwardsiella tarda(EIBAV1)and reared at two different densities,low density(LD),(5.25±0.02)kg m−2,as control group and high density(HD),(20.53±0.05)kg m−2,as experimental group.Only density was considered as the variable.Five weeks after vaccination,the transcriptomes of spleen and head kidney from the turbot in two groups were analyzed with RNA-Seq technology.A total of 447 million reads were assembled into 41136 genes with an average length of 1274 bp and a N50 size of 2295 bp.A comparison of gene expression between HD and LD groups revealed 1155 differentially expressed genes(DEGs).Enrichment and pathway analysis of the 10 immune-related DEGs showed the centrality of toll-like receptor signaling pathway,cytosolic DNA-sensing pathway and platelet activation in the host immune responses.The 5 overexpressed inflammatory cytokines and 5 downregulated signal-regulated cytokines genes are covered by these immune-related DEGs.It was inferred that cells suffer damage and the immune response is restrained in turbot under crowding stress.

EXISTENCE AND UNIQUENESS OF THE POSITIVE STEADY STATE SOLUTION FOR A LOTKA-VOLTERRA PREDATOR-PREY MODEL WITH A CROWDING TERM

This paper deals with a Lotka-Volterra predator-prey model with a crowding term in the predator equation.We obtain a critical value λ1^D(Ω0),and demonstrate that the existence of the predator inΩ0 only depends on the relationship of the growth rateμof the predator and λ1^D(Ω0),not on the prey.Furthermore,whenμ<λ1^D(Ω0),we obtain the existence and uniqueness of its positive steady state solution,while whenμ≥λ1^D(Ω0),the predator and the prey cannot coexist inΩ0.Our results show that the coexistence of the prey and the predator is sensitive to the size of the crowding regionΩ0,which is different from that of the classical Lotka-Volterra predator-prey model.

Niching method using clustering crowding

This study analyzes drift phenomena of deterministic crowding and probabilistic crowding by using equivalence class model and expectation proportion equations. It is proved that the replacement errors of deterministic crowding cause the population converging to a single individual, thus resulting in premature stagnation or losing optional optima. And probabilistic crowding can maintain equilibrium multiple subpopulations as the population size is adequate large. An improved niching method using clustering crowding is proposed. By analyzing topology of fitness landscape using hill valley function and extending the search space for similarity analysis, clustering crowding determines the locality of search space more accurately, thus greatly decreasing replacement errors of crowding. The integration of deterministic and probabilistic replacement increases the capacity of both parallel local hill climbing and maintaining multiple subpopulations. The experimental results optimizing various multimodal functions show that,the performances of clustering crowding, such as the number of effective peaks maintained, average peak ratio and global optimum ratio are uniformly superior to those of the evolutionary algorithms using fitness sharing, simple deterministic crowding and probabilistic crowding.

Changes in the crown angulation and dental arch widths after nonextraction orthodontic treatment: Model analysis of mild crowding with high canines

This study was undertaken to examine which factors contributed to the correction of crowding in two patients who underwent nonextraction orthodontic treatment. A study model analysis was conducted to determine the effects of the orthodontic treatment for crowding with high canines on crown angulation and dental arch width in two patients. The results showed that the crown angulation was significantly increased, indicating distal tipping in the maxillary dental arch. This tendency was most commonly observed in the premolars among the lateral teeth. With respect to the dental arch width, the largest change was evident in the first molar and first premolar regions in cases 1 and 2, respectively. On the basis of these results, up-righting of mesially tipped lateral teeth and expansion of narrow dental arches could prove to be the keys to the success of space regaining or correction of high canines and mild crowding.