Equilibrium reconstruction method for self-organized plasmas on reversed field pinches with polarimeter-interferometer

In the reversed field pinch(RFP),plasmas exhibit various self-organized states.Among these,the three-dimensional(3D)helical state known as the“quasi-single-helical”(QSH)state enhances RFP confinement.However,accurately describing the equilibrium is challenging due to the presence of 3D structures,magnetic islands,and chaotic regions.It is difficult to obtain a balance between the available diagnostic and the real equilibrium structure.To address this issue,we introduce KTX3DFit,a new 3D equilibrium reconstruction code specifically designed for the Keda Torus eXperiment(KTX)RFP.KTX3DFit utilizes the stepped-pressure equilibrium code(SPEC)to compute 3D equilibria and uses polarimetric interferometer signals from experiments.KTX3DFit is able to reconstruct equilibria in various states,including axisymmetric,doubleaxis helical(DAx),and single-helical-axis(SHAx)states.Notably,this study marks the first integration of the SPEC code with internal magnetic field data for equilibrium reconstruction and could be used for other 3D configurations.

Dimensionality Reduction Using Optimized Self-Organized Map Technique for Hyperspectral Image Classification

The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature vectors.The high correlation between these features and the noises greatly affects the classification performances.To overcome this,dimensionality reduction techniques are widely used.Traditional image processing applications recently propose numerous deep learning models.However,in hyperspectral image classification,the features of deep learning models are less explored.Thus,for efficient hyperspectral image classification,a depth-wise convolutional neural network is presented in this research work.To handle the dimensionality issue in the classification process,an optimized self-organized map model is employed using a water strider optimization algorithm.The network parameters of the self-organized map are optimized by the water strider optimization which reduces the dimensionality issues and enhances the classification performances.Standard datasets such as Indian Pines and the University of Pavia(UP)are considered for experimental analysis.Existing dimensionality reduction methods like Enhanced Hybrid-Graph Discriminant Learning(EHGDL),local geometric structure Fisher analysis(LGSFA),Discriminant Hyper-Laplacian projection(DHLP),Group-based tensor model(GBTM),and Lower rank tensor approximation(LRTA)methods are compared with proposed optimized SOM model.Results confirm the superior performance of the proposed model of 98.22%accuracy for the Indian pines dataset and 98.21%accuracy for the University of Pavia dataset over the existing maximum likelihood classifier,and Support vector machine(SVM).

Spatial Protection and Development of Traditional Mountain Settlements from the Perspective of Self-organization : A Case Study of Anju Ancient Town in Tongliang District, Chongqing

Through literature research, field research and urban design analysis methods, based on the self-organization theory, the evolution characteristics of the settlement space of Anju Ancient Town with significant characteristics of traditional mountain settlements were analyzed, and then its internal and external influencing factors were summarized. Finally, appropriate protection and development strategies were proposed, such as reasonably planning functional zones, breaking through the single structure, and creating a good living cultural environment. The innovation of this study is that it integrates the self-organization theory into the cognition of traditional mountain settlement space, and discusses the connection between the mountain settlement space and the traditional regional context under the modern development concept, so as to seek the benign protection and reasonable development of traditional mountain settlement space.

SELF-ORGANIZED SEMANTIC FEATURE EVOLUTION FOR AXIOMATIC DESIGN

Aiming at the problem existing in the computer aided design process that how to express the design intents with high-level engineering terminologies, a mechanical product self-organized semantic feature evolution technology for axiomatic design is proposed, so that the constraint relations between mechanical parts could be expressed in a semantic form which is more suitable for designers. By describing the evolution rules for semantic constraint information, the abstract expression of design semantics in mechanical product evolution process is realized and the constraint relations between parts are mapped to the geometric level from the semantic level; With semantic feature relation graph, the abstract semantic description, the semantic relative structure and the semantic constraint information are linked together; And the methods of semantic feature self-organized evolution are classified. Finally, combining a design example of domestic high-speed elevator, how to apply the theory to practical product development is illustrated and this method and its validity is described and verified. According to the study results, the designers are able to represent the design intents at an advanced semantic level in a more intuitional and natural way and the automation, recursion and visualization for mechanical product axiomatic design are also realized.

Self-Organized Public-Key Management for Mobile Ad Hoc Networks Based on a Bidirectional Trust Model

In traditional networks , the authentication is performed by certificate authoritys（CA）,which can＇t be built in distributed mobile Ad Hoc Networks however. In this pa per, we propose a fully self-organized public key management based on bidirectional trust model without any centralized authority that allows users to generate their public-private key pairs, to issue certificates, and the trust relation spreads rationally according to the truly human relations. In contrast with the traditional self-organized public-key management, the average certificates paths get more short, the authentication passing rate gets more high and the most important is that the bidirectional trust based model satisfys the trust re quirement of hosts better.

A Fairness Resource Allocation Algorithm for Coverage and Capacity Optimization in Wireless Self-Organized Network

To achieve the higher resource efficiency, Coverage and Capacity Optimization(CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network(SON). In this paper, a novel CCO scheme is proposed to maximize utility function of the integrated coverage and capacity. It starts with the analysis on the throughput proportional fairness(PF) algorithm and then proposes the novel Coverage and Capacity Proportional Fairness(CCPF) allocation algorithm along with a proof of the algorithms convergence. This proposed algorithm is applied in a coverage capacity optimization scheme which can guarantee the reasonable network capacity by the coverage range accommodation. Next, we simulate the proposed CCO scheme based on telecom operators' real network data and compare with three typical resource allocation algorithms: round robin(RR), proportional fairness(PF) and max C/I. In comparison of the PF algorithm, the numerical results show that our algorithm increases the average throughput by 1.54 and 1.96 times with constructed theoretical data and derived real network data respectively.

Self-Organized Optimization of Transport on Complex Networks

We propose a self-organized optimization mechanism to improve the transport capacity of complex gradient networks. We find that, regardless of network topology, the congestion pressure can be strongly reduced by the self-organized optimization mechanism. Furthermore, the random scale-free topology is more efficient to reduce congestion compared with the random Poisson topology under the optimization mechanism. The reason is that the optimization mechanism introduces the correlations between the gradient field and the local topology of the substrate network. Due to the correlations, the cutoff degree of the gradient network is strongly reduced and the number of the nodes exerting their maximal transport capacity consumedly increases. Our work presents evidence supporting the idea that scale-free networks can efficiently improve their transport capacity by self- organized mechanism under gradient-driven transport mode.

Controllable fabrication of self-organized nano-multilayers in copper–carbon films

In order to clarify the influence of methane concentration and deposition time on self-organized nano-multilayers,three serial copper-carbon films have been prepared at various methane concentrations with different deposition times using a facile magnetron sputtering deposition system. The ratios of methane concentration(CH4/Ar+CH4) used in the experiments are 20%, 40%, and 60%, and the deposition times are 5 minutes, 20 minutes, and 40 minutes, respectively.Despite the difference in the growth conditions, self-organizing multilayered copper-carbon films are prepared at different deposition times by changing methane concentration. The film composition and microstructure are investigated by x-ray photoelectron spectroscopy(XPS), x-ray diffraction(XRD), field emission scanning electron microscopy(FESEM), and high-resolution transmission electron microscopy(HRTEM). By comparing the composition and microstructure of three serial films, the optimal growth conditions and compositions for self-organizing nano-multilayers in copper-carbon film are acquired. The results demonstrate that the self-organized nano-multilayered structure prefers to form in two conditions during the deposition process. One is that the methane should be curbed at low concentration for long deposition time,and the other condition is that the methane should be controlled at high concentration for short deposition time. In particular, nano-multilayered structure is self-organized in the copper-carbon film with copper concentration of 10-25 at.%.Furthermore, an interesting microstructure transition phenomenon is observed in copper-carbon films, that is, the nanomultilayered structure is gradually replaced by a nano-composite structure with deposition time and finally covered by amorphous carbon.

A Modified Earthquake Model of Self-Organized Criticality on Small World Networks

A modified Olami Feder-Christensen model of self-organized criticality on a square lattice with the properties of small world networks has been studied.We find that our model displays power-law behavior and the exponent τ of the model depends on φ,the density of long-range connections in our network.

Self-Organized Criticality Analysis of Earthquake Model Based on Heterogeneous Networks

The original Olami-Feder-Christensen （OFC） model, which displays a robust power-law behavior, is a quasistatic two-dimensional version of the Burridge-Knopoff spring-block model of earthquakes. In this paper, we introduce a modified OFC model based on heterogeneous network, improving the redistribution rule of the original model. It can be seen as a generalization of the originM OFC model We numerically investigate the influence of the parameters θandβ, which respectively control the intensity of the evolutive mechanism of the topological growth and the inner selection dynamics in our networks, and find that there are two distinct phases in the parameter space （θ,β）. Meanwhile, we study the influence of the control parameter a either. Increasing a, the earthquake behavior of the model transfers from local to global.

Self-organized Criticality Model for Ocean Internal Waves

In this paper, we present a simple spring-block model for ocean internal waves based on the self-organized criticality （SOC）. The oscillations of the water blocks in the model display power-law behavior with an exponent of -2 in the frequency domain, which is similar to the current and sea water temperature spectra in the actual ocean and the universal Garrett and Munk deep ocean internal wave model [Geophysical Fluid Dynamics 2 （1972） 225; J. Geophys. Res. 80 （1975） 291]. The influence of the ratio of the driving force to the spring coefficient to SOC behaviors in the model is also discussed.

Influence of Selective Edge Removal and Refractory Period in a Self-Organized Critical Neuron Model

A simple model for a set of integrate-and-fire neurons based on the weighted network is introduced. By considering the neurobiological phenomenon in brain development and the difference of the synaptic strength, we construct weighted networks develop with link additions and followed by selective edge removal. The network exhibits the small-world and scale-free properties with high network efficiency. The model displays an avalanche activity on a power-law distribution. We investigate the effect of selective edge removal and the neuron refractory period on the self-organized criticality of the system.

High-performance self-organized Si nanocomposite anode for lithium-ion batteries

Silicon is being investigated extensively as an anodic material for next-generation lithium ion batteries for portable energy storage and electric vehicles.However,the large changes in volume during cycling lead to the breakdown of the conductive network in Si anodes and the formation of an unstable solid-electrolyte interface,resulting in capacity fading.Here,we demonstrate nanoparticles with a Si@Mn22.6Si5.4C4@C double-shell structure and the formation of self-organized Si-Mn-C nanocomposite anodes during the lithiation/delithiation process.The anode consists of amorphous Si particles less than 10 nm in diameter and separated by an interconnected conductive/buffer network,which exhibits excellent charge transfer kinetics and charge/discharge performances.A stable specific capacity of 1100 mAh·g-1 at 100 mA·g-1 and a coulombic efficiency of 99.2%after 30 cycles are achieved.Additionally,a rate capacity of 343 mAh·g-1 and a coulombic efficiency of 99.4%at 12000 mA·g-1 are also attainable.Owing to its simplicity and applicability,this strategy for improving electrode performance paves a way for the development of high-performance Si-based anodic materials for lithium ion batteries.

Self-organized evolution of mechanism kinetic scheme based on axiomatic design

The self-organized evolution technology of the mechanism kinetic scheme based on axiomatic design is presented. This technology tries to express the constraints between kinetic mechanisms briefly in a semantic form which is more familiar to the designers. Through the mapping process between the kinetic chain unit and the unit instance, the evolution from abstract unit to concrete engineering instance is achieved. The subdivision of unit coupling semantics is studied, and the evolution of semantics is finished. Also, the semantic constraints evolution of unit coupling semantics is described. The product structure models with function and assembly meanings are constructed based on the kinematic chain unit and unit coupling. It provides a basis to realize the inheritance and transfer of constraint information from conceptual design to design for assembly （DFA）. As the engineering practice result shows, the method can help the engineers express their And the automation, recursion and design intension more clearly and naturally in a high semantic level. visualization of the mechanism kinetic scheme design are realized

Always-optimally-coordinated candidate selection algorithm for peer-to-peer files sharing system in mobile self-organized networks

In order to improve the performance of peer-to-peer files sharing system under mobile distributed en- vironments, a novel always-optimally-coordinated （AOC） criterion and corresponding candidate selection algorithm are proposed in this paper. Compared with the traditional min-hops criterion, the new approach introduces a fuzzy knowledge combination theory to investigate several important factors that influence files transfer success rate and efficiency. Whereas the min-hops based protocols only ask the nearest candidate peer for desired files, the selection algorithm based on AOC comprehensively considers users＇ preferences and network requirements with flexible balancing rules. Furthermore, its advantage also expresses in the independence of specified resource discovering protocols, allowing for scalability. The simulation results show that when using the AOC based peer selection algorithm, system performance is much better than the rain-hops scheme, with files successful transfer rate improved more than 50% and transfer time re- duced at least 20%.

Self-Organized Criticality Theory Model of Thermal Sandpile

A self-organized criticality model of a thermal sandpile is formulated for the first time to simulate the dynamic process with interaction between avalanche events on the fast time scale and diffusive transports on the slow time scale. The main characteristics of the model are that both particle and energy avalanches of sand grains are considered simultaneously. Properties of intermittent transport and improved confinement are analyzed in detail. The results imply that the intermittent phenomenon such as blobs in the low confinement mode as well as edge localized modes in the high confinement mode observed in tokamak experiments are not only determined by the edge plasma physics, but also affected by the core plasma dynamics.

Self-organized ordering of earthquakes(M≥8) in China's Mainland

The paper presented a new regular pattern （network structure ） of great earthquakes occurred in China's Mainland during the past 700 years, which may be helpful to improve the understanding of great earthquakes and can serve as a base for the study of prediction of future great earthquakes. It can be done because there are quite complete and confident records of historical and recent earthquakes in a wide extent in China.

Self-organized Criticality in an Integrate-and-Fire Neuron Model Based on Modified Aging Networks

Based on an integrate-and-fire mechanism, we investigate self-organized criticality of a simple neuron model on a modified BA scale-free network with aging nodes. In our model, we find that the distribution of avalanche size follows power-law behavior. The critical exponent τ depends on the aging exponent α. The structures of the network with aging of nodes change with an increase of α. The different topological structures lead to different behaviors in models of integrate-and-fire neurons.

Energy Efficiency in ARQ-Based Multi-Hop Systems and the Tradeoff with Throughput

The minimum energy per bit(EPB)as the energy efficiency(EE)metric in an automatic retransmission request(ARQ)based multi-hop system is analyzed under power and throughput constraints.Two ARQ protocols including type-I(ARQ-I)and repetition redundancy(ARQ-RR)are considered and expressions for the optimal power allocation(PA)are obtained.Using the obtained optimal powers,the EE-throughput tradeoff(EETT)is analyzed and the EETT closed-form expressions for both ARQ protocols and in arbitrary average channel gain values are obtained.It is shown that how different throughput requirements,especially the high levels,affect the EE performance.Additionally,asymptotic analysis is made in the feasible high throughput values and lower and upper EETT bounds are derived for ARQ-I protocol.To evaluate the EE a distributed PA scenario,as a benchmark,is presented and the energy savinggain obtained from the optimal PA in comparison with the distributed PA for ARQ-I and ARQ-RR protocols is discussed in different throughput values and node locations.

The strain relaxation of InAs/GaAs self-organized quantum dot

This paper presents a detailed analysis of the dependence of degree of strain relaxation of the self-organized InAs/GaAs quantum dot on the geometrical parameters. Differently shaped quantum dots arranged with different transverse periods are simulated in this analysis. It investigates the total residual strain energy that stored in the quantum dot and the substrate for all kinds of quantum dots with the same volume, as well as the dependence on both the aspect ratio and transverse period. The calculated results show that when the transverse period is larger than two times the base of the quantum dots, the influence of transverse periods can be ignored. The larger aspect ratio will lead more efficient strain relaxation. The larger angle between the faces and the substrate will lead more efficient strain relaxation. The obtained results can help to understand the shape transition mechanism during the epitaxial growth from the viewpoint of energy, because the strain relaxation is the main driving force of the quantum dot＇s self-organization.