Numerical Physical Mechanism and Model of Turbulent Cascades in a Barotropic Atmosphere

In a barotropic atmosphere, new Reynolds mean momentum equations including turbulent viscosity, dispersion, and instability are used not only to derive the KdV-Burgers-Kuramoto equation but also to analyze the physical mechanism of the cascades of energy and enstrophy. It shows that it is the effects of dispersion and instability that result in the inverse cascade. Then based on the conservation laws of the energy and enstrophy, a cascade model is put forward and the processes of the cascades are described.

A Study on Small Pest Detection Based on a CascadeR-CNN-Swin Model

This study aims to detect and prevent greening disease in citrus trees using a deep neural network.The process of collecting data on citrus greening disease is very difficult because the vector pests are too small.In this paper,since the amount of data collected for deep learning is insufficient,we intend to use the efficient feature extraction function of the neural network based on the Transformer algorithm.We want to use the Cascade Region-based Convolutional Neural Networks(Cascade R-CNN)Swin model,which is a mixture of the transformer model and Cascade R-CNN model to detect greening disease occurring in citrus.In this paper,we try to improve model safety by establishing a linear relationship between samples using Mixup and Cutmix algorithms,which are image processing-based data augmentation techniques.In addition,by using the ImageNet dataset,transfer learning,and stochastic weight averaging(SWA)methods,more accuracy can be obtained.This study compared the Faster Region-based Convolutional Neural Networks Residual Network101(Faster R-CNN ResNet101)model,Cascade Regionbased Convolutional Neural Networks Residual Network101(Cascade RCNN-ResNet101)model,and Cascade R-CNN Swin Model.As a result,the Faster R-CNN ResNet101 model came out as Average Precision(AP)(Intersection over Union(IoU)=0.5):88.2%,AP(IoU=0.75):62.8%,Recall:68.2%,and the Cascade R-CNN ResNet101 model was AP(IoU=0.5):91.5%,AP(IoU=0.75):67.2%,Recall:73.1%.Alternatively,the Cascade R-CNN Swin Model showed AP(IoU=0.5):94.9%,AP(IoU=0.75):79.8%and Recall:76.5%.Thus,the Cascade R-CNN Swin Model showed the best results for detecting citrus greening disease.

Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of lesions.Thus,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images.The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases.In addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion boundaries.This study uses two databases in training and testing the proposed segmentation approach.The breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq.Results of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound images.The segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data set.For the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.

An on-line free handwritten Chinese character recognition method based on component cascaded HMMs

This paper presents a cascaded Hidden Markov Model （HMM）, which allows state＇s transition, skip and duration. The cascaded HMM extends the way of HMM pattern description of Handwritten Chinese Character （HCC） and depicts the behavior of handwritten curve more reliably in terms of the statistic probability. Hence character segmentation and labeling are unnecessary. Viterbi algorithm is integrated in the cascaded HMM after the whole sample sequence of a HCC is input. More than 26,000 component samples are used tor training 407 handwritten component HMMs. At the improved training stage 94 models of 94 Chinese characters are gained by 32,000 samples, Compared with the Segment HMMs approach, the recognition rate of this model tier the tirst candidate is 87.89% and the error rate could be reduced by 12.4%.

A Study on Cascade R-CNN-Based Dangerous Goods Detection Using X-Ray Image

X-ray inspection equipment is divided into small baggage inspection equipment and large cargo inspection equipment.In the case of inspection using X-ray scanning equipment,it is possible to identify the contents of goods,unauthorized transport,or hidden goods in real-time by-passing cargo through X-rays without opening it.In this paper,we propose a system for detecting dangerous objects in X-ray images using the Cascade Region-based Convolutional Neural Network(Cascade R-CNN)model,and the data used for learning consists of dangerous goods,storage media,firearms,and knives.In addition,to minimize the overfitting problem caused by the lack of data to be used for artificial intelligence(AI)training,data samples are increased by using the CP(copy-paste)algorithm on the existing data.It also solves the data labeling problem by mixing supervised and semi-supervised learning.The four comparative models to be used in this study are Faster Regionbased Convolutional Neural Networks Residual2 Network-101(Faster R-CNN_Res2Net-101)supervised learning,Cascade R-CNN_Res2Net-101_supervised learning,Cascade Region-based Convolutional Neural Networks Composite Backbone Network V2(CBNetV2)Network-101(Cascade R-CNN_CBNetV2Net-101)_supervised learning,and Cascade RCNN_CBNetV2-101_semi-supervised learning which are then compared and evaluated.As a result of comparing the performance of the four models in this paper,in case of Cascade R-CNN_CBNetV2-101_semi-supervised learning,Average Precision(AP)(Intersection over Union(IoU)=0.5):0.7%,AP(IoU=0.75):1.0%than supervised learning,Recall:0.8%higher.

Robustness of community networks against cascading failures with heterogeneous redistribution strategies

Network robustness is one of the core contents of complex network security research.This paper focuses on the robustness of community networks with respect to cascading failures,considering the nodes influence and community heterogeneity.A novel node influence ranking method,community-based Clustering-LeaderRank(CCL)algorithm,is first proposed to identify influential nodes in community networks.Simulation results show that the CCL method can effectively identify the influence of nodes.Based on node influence,a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks.Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process.The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities.When the initial load distribution and the load redistribution strategy based on the node influence are the same,the network shows better robustness against node failure.

Dynamical Analysis of Nonautonomous Trophic Cascade Chemostat Model with Regime Switching and Nonlinear Perturbations in a Polluted Environment

This paper investigates the stochastic dynamics of trophic cascade chemostat model perturbed by regime switching, Gaussian white noise and impulsive toxicant input. For the system with only white noise interference, sufficient conditions for stochastically ultimate boundedness and stochastically permanence are obtained, and we demonstrate that the stochastic system has at least one nontrivial positive periodic solution. For the system with Markov regime switching, sufficient conditions for extinction of the microorganisms are established. Then we prove the system is ergodic and has a stationary distribution. The results show that both impulsive toxins input and stochastic noise have great effects on the survival and extinction of the microorganisms. Finally, a series of numerical simulations are presented to illustrate the theoretical analysis.

Stability Investigation and Improvement for DC Cascade Systems with Simplified Impedance-based Stability Criterion

In DC distributed power systems(DPSs),the complex impedance interactions possibly lead to DC bus voltage oscillation or collapse.In previous research,the stability analysis of DPSs is implemented based on mathematical analysis in control theory.The specific mechanisms of the instability of the cascade system have not been intuitively clarified.In this paper,the stability analysis of DPSs based on the traditional Nyquist criterion is simplified to the resonance analysis of the seriesconnected port impedance(Z=R+jX)of source and load converters.It reveals that the essential reason for impedance instability of a DC cascade system is that the negative damping characteristic(R<0)of the port the overall impedance amplifies the internal resonance source at reactance zero-crossing frequency.The simplified stability criterion for DC cascade systems can be concluded as:in the negative damping frequency ranges(R<0),there exists no zero-crossing point of the reactance component(i.e.,X=0).According to the proposed stability criterion,the oscillation modes of cascade systems are classified.A typical one is the internal impedance instability excited by the negative damping,and the other one is that the external disturbance amplified by negativity in a low stability margin.Thus,the impedance reshaping method for stability improvement of the system can be further specified.The validity of the simplified criterion is verified theoretically and experimentally by a positive damping reshaping method.

Mineral resource analysis by parabolic fractals

Elemental concentration distributions in space have been analyzed using different approaches. These analyses are of great significance for the quantitative characterization of various kinds of distribution patterns. Fractal and multi-fractal methods have been extensively applied to this topic. Traditionally, approximately linear-fractal laws have been regarded as useful tools for characterizing the self-similarities of element concentrations. But, in nature, it is not always easy to find perfect linear fractal laws. In this paper the parabolic fractal model is used. First a two dimensional multiplicative multi-fractal cascade model is used to study the concentration patterns. The results show the parabolic fractal （PF） properties of the concentrations and the validity of non-linear fractal analysis. By dividing the studied area into four sub-areas it was possible to show that each part follows a non-linear parabolic fractal law and that the dispersion within each part varies. The ratio of the polynomial coefficients of the fitted parabolic curves can reflect, to some degree, the relative concentration and dispersal distribution patterns. This can provide new insight into the ore-forming potential in space. The parabofic fractal evaluations of ore-forming potential for the four suhareas are in good agreement with field investigation work and geochemical mapping results based on analysis of the original data.

Recent Research and Development Status of Relaxed Optics and Laser Technology: A Review

It is shown that for laser technologies it was necessary to create a new branch of physics: Relaxed Optics (synthesis of methods of the physical optics, quantum electronics, physical chemistry, physics of irreversible phenomena in unitary system). It is allowed to explain complex chain processes of interaction light and matter. Possible applications of Relaxed Optical methods for the modeling of the laser-induced processes phenomena, including laser implantation (surface and subsurface processes), laser-induced optical breakdown (volume processes) and laser annealing of radiation and other defects in solid, are discussed. Perspectives of using these methods for the creation of new laser technologies, including creation new types of optoelectronic devices (heterostructures, diffraction lattices, etc.), resolution the problems of metallurgy, material science, painting, architecture and a building, are analyzed.

Experimental Investigation of Flow Separation Control Using Dielectric Barrier Discharge Plasma Actuators

Influence of plasma actuators as a flow separation control device was investigated experimentally. Hump model was used to demonstrate the effect of plasma actuators on external flow separation, while for internal flow separation a set of compressor cascade was adopted. In order to investigate the modification of the flow structure by the plasma actuator, the flow field was examined non-intrusively by particle image velocimetry measurements in the hump model experiment and by a hot film probe in the compressor cascade experiment. The results showed that the plasma actuator could be effective in controlling the flow separation both over the hump and in the compressor cascade when the incoming velocity was low. As the incoming velocity increased, the plasma actuator was less effective. It is urgent to enhance the intensity of the plasma actuator for its better application. Methods to increase the intensity of plasma actuator were also studied.

Eureka Webair： Web-Based Air Quality Management, Real-Time Forecasting and Emission Control Optimization

For the assessment and management of regional to local air quality, an integrated environmental management information system was built within the multi national Eureka project 3266 Webair, http：//www.ess.co.at/WEBAIR. The system combines data bases and GIS and a range of coupled models and analytical tools that address a range of typical management problems and cover several levels of nesting from regional to city level and street canyons. The main functions are to support regulatory tasks, compliance monitoring, operational forecasting and reporting, impact assessment EIA （environmental impact assessment）, SEA （strategic environmental assessment） and public information within one consistent framework. A major objective is the improvement of air quality through emission control. The integrated model system together with its shared data bases provides a reliable, consistent basis for the non-linear techno-economic and multi-criteria optimization of emission control strategies （including greenhouse gases and energy efficiency）. A real-time expert system drives, supports and monitors the autonomous and interactive operations, and provides embedded QA/QC （quality assurance/quality control） functions for reliable operations and ease of use.

Risk analysis for a cascade reservoir system using the brittle risk entropy method

The principal means of conserving water and utilizing hydropower in China is to exploit the use of a series of reservoirs in a cascade. This method and its inherent engineering safety problems are receiving increasing attention nowadays. In the field of engineering safety analysis, much work has focused on single reservoir projects in the past few years, but there is little research available on the safety risk analysis of cascade reservoirs, either within China or internationally. Therefore, a framework for risk analysis on the cascade reservoir system based on the theory of system engineering is constructed in this article. A cascading failure model is established and the connection degree factor discussed. In addition, the importance degree of the subsystem, which can be calculated by combining the analytical hierarchy process and the entropy weight method, is explained. According to brittleness theory of a complex system, brittle risk entropy is proposed as a performance index for measuring the collapse uncertainty of the cascade reservoir system. In addition, the brittle risk of the cascade reservoir system is predicted, which provides a reference for safety analysis in water conservation and hydropower construction projects in China.

A Spacetime Odyssey of Neural Progenitors to Generate Neuronal Diversity

To understand how the nervous system develops from a small pool of progenitors during early embryonic development,it is fundamentally important to identify the diversity of neuronal subtypes,decode the origin of neuronal diversity,and uncover the principles governing neuronal specification across different regions.Recent single-cell analyses have systematically identified neuronal diversity at unprecedented scale and speed,leaving the deconstruction of spatiotemporal mechanisms for generating neuronal diversity an imperative and paramount challenge.In this review,we highlight three distinct strategies deployed by neural progenitors to produce diverse neuronal subtypes,including predetermined,stochastic,and cascade diversifying models,and elaborate how these strategies are implemented in distinct regions such as the neocortex,spinal cord,retina,and hypothalamus.Importantly,the identity of neural progenitors is defined by their spatial position and temporal patterning factors,and each type of progenitor cell gives rise to distinguishable cohorts of neuronal subtypes.Microenvironmental cues,spontaneous activity,and connectional pattern further reshape and diversify the fate of unspecialized neurons in particular regions.The illumination of how neuronal diversity is generated will pave the way for producing specific brain organoids to model human disease and desired neuronal subtypes for cell therapy,as well as understanding the organization of functional neural circuits and the evolution of the nervous system.

COVID-19: Analytics of contagion on inhomogeneous random social networks

Motivated by the need for robust models of the Covid-19 epidemic that adequately reflect the extreme heterogeneity of humans and society,this paper presents a novel framework that treats a population of N individuals as an inhomogeneous random social network(IRSN).The nodes of the network represent individuals of different types and the edges represent significant social relationships.An epidemic is pictured as a contagion process that develops day by day,triggered by a seed infection introduced into the population on day 0.Individuals’social behaviour and health status are assumed to vary randomly within each type,with probability distributions that vary with their type.A formulation and analysis is given for a SEIR(susceptible-exposed-infective-removed)network contagion model,considered as an agent based model,which focusses on the number of people of each type in each compartment each day.The main result is an analytical formula valid in the large N limit for the stochastic state of the system on day t in terms of the initial conditions.The formula involves only one-dimensional integration.The model can be implemented numerically for any number of types by a deterministic algorithm that efficiently incorporates the discrete Fourier transform.While the paper focusses on fundamental properties rather than far ranging applications,a concluding discussion addresses a number of domains,notably public awareness,infectious disease research and public health policy,where the IRSN framework may provide unique insights.

Central nucleus-nucleus collisions at relativistic energies with a new method based on Random Matrix Theory

Using the method based on Random Matrix Theory （RMT）, the results for the nearest-neighbor distributions obtained from the experimental data on ^12C-C collisions at 4.2 AGeV/c have been discussed and compared with the simulated data on ^12C-C collisions at 4.2 AGeV/c produced with the aid of the Dubna Cascade Model. The results show that the correlation of secondary particles decreases with an increasing number of charged particles Nch. These observed changes in the nearest-neighbor distributions of charged particles could be associated with the centrality variation of the collisions.

Cyber-Physical Power System(CPPS):A review on measures and optimization methods of system resilience

The Cyber-Physical Power System(CPPS)is one of the most critical infrastructure systems in a country because a stable and secure power supply is a key foundation for national and social development.In recent years,resilience has become a major topic in preventing and mitigating the risks caused by large-scale blackouts of CPPSs.Accordingly,the concept and significance of CPPS resilience are at first explained from the engineering perspective in this study.Then,a review of representative quantitative assessment measures of CPPS resilience applied in the existing literature is provided.On the basis of these assessment measures,the optimization methods of CPPS resilience are reviewed from three perspectives,which are mainly focused on the current research,namely,optimizing the recovery sequence of components,identifying and protecting critical nodes,and enhancing the coupling patterns between physical and cyber networks.The recent advances in modeling methods for cascading failures within the CPPS,which is the theoretical foundation for the resilience assessment and optimization research of CPPSs,are also presented.Lastly,the challenges and future research directions for resilience optimizing of CPPSs are discussed.

Efficient Algorithms for Maximizing Group Influence in Social Networks

In social network applications,individual opinion is often influenced by groups,and most decisions usually reflect the majority’s opinions.This imposes the group influence maximization(GIM) problem that selects k initial nodes,where each node belongs to multiple groups for a given social network and each group has a weight,to maximize the weight of the eventually activated groups.The GIM problem is apparently NP-hard,given the NP-hardness of the influence maximization(IM) problem that does not consider groups.Focusing on activating groups rather than individuals,this paper proposes the complementary maximum coverage(CMC) algorithm,which greedily and iteratively removes the node with the approximate least group influence until at most k nodes remain.Although the evaluation of the current group influence against each node is only approximate,it nevertheless ensures the success of activating an approximate maximum number of groups.Moreover,we also propose the improved reverse influence sampling(IRIS) algorithm through fine-tuning of the renowned reverse influence sampling algorithm for GIM.Finally,we carry out experiments to evaluate CMC and IRIS,demonstrating that they both outperform the baseline algorithms respective of their average number of activated groups under the independent cascade(IC)model.