A realistic model for complex networks with local interaction, self-organization and order

In this paper, a new mechanism for the emergence of scale-free distribution is proposed. It is more realistic than the existing mechanism. Based on our mechanism, a model responsible for the scale-free distribution with an exponent in a range of 3-to-5 is given. Moreover, this model could also reproduce the exponential distribution that is discovered in some real networks. Finally, the analytical result of the model is given and the simulation shows the validity of our result,

Analysis of New Model of "Rule by Three Committees" and Discussion on Construction of Villagers' Self-governing Organizations in China

This paper introduces the operation model of "rule by three committees", including the members of "three committees", functional constitution of "three committees", and relationship between restriction and coordination of "three committees". By referring to the villagers' self-governing system in China, and the relevant provisions in Constitution and Village Committee Organization Law, we take into account the legitimacy of "rule by three committees". In terms of the details of system design, we perfect the new model of "rule by three committees" as follows: make the conditions of holding office stringent and ensure the quality requirements of members of "three committees"; standardize the procedures of election, and guarantee the equitable election of members of "three committees"; perfect dismissal procedure, and strengthen the supervision on members of "three committees".

COUPLING MODEL OF EXTENDED MANUFACTURING ORGANIZATION AND ITS APPLICATION

For the feature of complex weapon manufacturing on internet,a coupling model is proposed.By using the model,the correlation between manufacturing cells in an extended manufacturing organization can be evaluated quantitatively,so an appropriate control plan is determined.A strategy to improve and reduce the coupling relationship of the organization is studied.A correlation matrix of extended tasks is built to analyze the relationship between sub-tasks and manufacturing resources.An optimization method for manufacturing resource configuration is presented based on the coupling model.Finally,a software system for analyzing coupling model about manufacturing organization on internet is developed,and the result shows that the coupling model is effective.

Organization and storage model of marine information and its application in the "China Digital Ocean"

Based on the experience and achievement of the＂China Digital Ocean＂, the classification plan for Marine data elements is made, which can be classified into five, including marine point elements, marine line elements, marine polygon elements, marine grid elements and marine dynamic elements. In this paper, the technology of features and object-oriented method, a spatial-temporal data model is proposed, which can be applied in the large information system engineering like the ＂Digital Ocean＂, and this paper discusses the application of spatial data model, marine three-dimensional raster data model and relation data model in the building of Data Warehouse in ＂China Digital Ocean＂, and concludes the merits of these models.

Self Organization Map for Clustering and Classification in the Ecology of Agent Organizations

Development of computational agent organizations or “societies” has become the domiant computing paradigm in the arena of Distributed Artificial Intelligence, and many foreseeable future applications need agent organizations, in which diversified agents cooperate in a distributed manner, forming teams. In such scenarios, the agents would need to know each other in order to facilitate the interactions. Moreover, agents in such an environment are not statically defined in advance but they can adaptively enter and leave an organization. This begs the question of how agents locate each other in order to cooperate in achieving organizational goals. Locating agents is a quite challenging task, especially in organizations that involve a large number of agents and where the resource avaiability is intermittent. The authors explore here an approach based on self organization map (SOM) which will serve as a clustering method in the light of the knowledge gathered about various agents. The approach begins by categorizing agents using a selected set of agent properties. These categories are used to derive various ranks and a distance matrix. The SOM algorithm uses this matrix as input to obtain clusters of agents. These clusters reduce the search space, resulting in a relatively short agent search time.

Considerations for the use of porcine organ donation models in preclinical organ donor intervention research

Use of animal models in preclinical transplant research is essential to the optimization of human allografts for clinical transplantation.Animal models of organ donation and preservation help to advance and improve technical elements of solid organ recovery and facilitate research of ischemia-reperfusion injury,organ preservation strategies,and future donor-based interventions.Important considerations include cost,public opinion regarding the conduct of animal research,translational value,and relevance of the animal model for clinical practice.We present an overview of two porcine models of organ donation:donation following brain death(DBD)and donation following circulatory death(DCD).The cardiovascular anatomy and physiology of pigs closely resembles those of humans,making this species the most appropriate for pre-clinical research.Pigs are also considered a potential source of organs for human heart and kidney xenotransplantation.It is imperative to minimize animal loss during procedures that are surgically complex.We present our experience with these models and describe in detail the use cases,procedural approach,challenges,alternatives,and limitations of each model.

Improving model performance in mapping cropland soil organic matter using time-series remote sensing data

Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(<200),whereas Boruta is recommended when there are many environmental covariates(>500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.

Hierarchical Self-organization of Complex Systems

Researches on organization and structure in complex systems are academic and industrial fronts in modern sciences. Though many theories are tentatively proposed to analyze complex systems, we still lack a rigorous theory on them. Complex systems possess various degrees of freedom, which means that they should exhibit all kinds of structures. However, complex systems often show similar patterns and structures. Then the question arises why such similar structures appear in all kinds of complex systems. The paper outlines a theory on freedom degree compression and the existence of hierarchical self-organization for all complex systems is found. It is freedom degree compression and hierarchical self-organization that are responsible for the existence of these similar patterns or structures observed in the complex systems.

Adaptive Surrogate Model Based Optimization (ASMBO) for Unknown Groundwater Contaminant Source Characterizations Using Self-Organizing Maps

Characterization of unknown groundwater contaminant sources in terms of location, magnitude and duration of source activity is a complex problem. In this study, to increase the efficiency and accuracy of source characterization an alternative methodology to the methodologies proposed earlier is developed. This methodology, Adaptive Surrogate Modeling Based Optimization (ASMBO) uses the capabilities of Self Organizing Map (SOM) algorithm to design the surrogate models and adaptive surrogate models for source characterization. The most important advantage of this methodology is its direct utilization for groundwater contaminant characterization without the necessity of utilizing a linked simulation optimization model. The validation of the SOM based surrogate models and SOM based adaptive surrogate models demonstrates that the quantity and quality of initial sample sizes have crucial role on the accuracy of solutions as the designed monitoring locations. The performance evaluation results of the proposed methodology are obtained using error free and erroneous concentration measurement data. These results demonstrate that the developed methodology could approximate groundwater flow and transport simulation models, and substitute the optimization model for characterization of unknown groundwater contaminant sources in terms of location, magnitude and duration of source activity.

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 Semantic-Sensitive Approach to Indoor and Outdoor 3D Data Organization

Building model data organization is often programmed to solve a specific problem,resulting in the inability to organize indoor and outdoor 3D scenes in an integrated manner.In this paper,existing building spatial data models are studied,and the characteristics of building information modeling standards(IFC),city geographic modeling language(CityGML),indoor modeling language(IndoorGML),and other models are compared and analyzed.CityGML and IndoorGML models face challenges in satisfying diverse application scenarios and requirements due to limitations in their expression capabilities.It is proposed to combine the semantic information of the model objects to effectively partition and organize the indoor and outdoor spatial 3D model data and to construct the indoor and outdoor data organization mechanism of“chunk-layer-subobject-entrances-area-detail object.”This method is verified by proposing a 3D data organization method for indoor and outdoor space and constructing a 3D visualization system based on it.

Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.

Formation of Self-organization InAs Quantum Dots on (001) InP Substrate by As/P Exchange Reaction in MOCVD

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SELF-ORGANIZING ASSEMBLY MODELING BASED ON RELATIONAL CONSTRAINTS

On the research of assembly modeling of mechanical products,current CAD systems can only support the design Process of component-to-assembly. It is difficult to realize the design process of assembly-to -component.The theory of self-organizing assembly modeling based on relational constraints is proposed, which implements the product design of assembly-to-component commencing with conceptual design and supporting abstract design and step-nice refinement design.

A New Algorithm of Self Organization in Wireless Sensor Network

Self organization is one of the most important characteristics in an Ad-hoc Sensor Network. Thousands of Sensors are deployed in a geographical area randomly without considering the location factor. After deployment, sensors are to self organize themselves to form a network of their own. How well the network is formed determines the life of the whole network as well as the quality of data transmission. Self organization based on clustering has proven to be very useful in this regard. Since hierarchical clustering reduces energy consumption by routing data from one node to another. In this paper, we discuss a new algorithm for self organization of sensors deployed in a geographical area. The algorithm forms clusters of sensors by ordering them using a unique triangulation method. This algorithm not only considers all sensors but also groups them so that their inherent clustering property is preserved.

Discrete decision model and multi-agent simulation of the Liang Zong two-chain hierarchical organization in a complex project

Different from the organization structure of complex projects in Western countries, the Liang Zong hierarchical organization structure of complex projects in China has two different chains, the chief-engineer chain and the general-director chain,to handle the trade-off between technical and management decisions. However, previous works on organization search have mainly focused on the single-chain hierarchical organization in which all decisions are regarded as homogeneous. The heterogeneity and the interdependency between technical decisions and management decisions have been neglected. A two-chain hierarchical organization structure mapped from a real complex project is constructed. Then, a discrete decision model for a Liang Zong two-chain hierarchical organization in an NK model framework is proposed. This model proves that this kind of organization structure can reduce the search space by a large amount and that the search process should reach a final stable state more quickly. For a more complicated decision mechanism, a multi-agent simulation based on the above NK model is used to explore the effect of the two-chain organization structure on the speed, stability, and performance of the search process. The results provide three insights into how, compared with the single-chain hierarchical organization, the two-chain organization can improve the search process: it can reduce the number of iterations efficiently; the search is more stable because the search space is a smoother hill-like fitness landscape; in general, the search performance can be improved.However, when the organization structure is very complicated, the performance of a two-chain organization is inferior to that of a single-chain organization. These findings about the efficiency of the unique Chinese-style organization structure can be used to guide organization design for complex projects.

The Dynamic Modeling Algorithm of Information Organization and Allocation Problem on Internet Communications

With the frequent information accesses from users to the Internet, it is important to organize and allocate information resources properly on different web servers. This paper considers the following problem; Due to the capacity limitation of each single web server, it is impossible to put all information resources on one web server. Hence it is an important problem to put them on several different servers such as: (1) the amount of information resources assigned on any server is less than its capacity; (2) the access bottleneck can be avoided. In order to solve the problem in which the access frequency is variable, this paper proposes a dynamic optimal modeling. Based on the computational complexity results, the paper further focuses on the genetic algorithm for solving the dynamic problem. Finally we give the simulation results and conclusions.

Self-organization of Reconfigurable Protocol Stack for Networked Control Systems

In networked control systems （NCS）,the control performance depends on not only the control algorithm but also the communication protocol stack.The performance degradation introduced by the heterogeneous and dynamic communication environment has intensified the need for the reconfigurable protocol stack.In this paper,a novel architecture for the reconfigurable protocol stack is proposed,which is a unified specification of the protocol components and service interfaces supporting both static and dynamic reconfiguration for existing industrial communication standards.Within the architecture,a triple-level self-organization structure is designed to manage the dynamic reconfiguration procedure based on information exchanges inside and outside the protocol stack.Especially,the protocol stack can be self-adaptive to various environment and system requirements through the reconfiguration of working mode,routing and scheduling table.Finally,the study on the protocol of dynamic address management is conducted for the system of controller area network （CAN）.The results show the efficiency of our self-organizing architecture for the implementation of a reconfigurable protocol stack.

MLP training in a self-organizing state space model using unscented Kalman particle filter

Many Bayesian learning approaches to the multi-layer perceptron （MLP） parameter optimization have been proposed such as the extended Kalman filter （EKF）. This paper uses the unscented Kalman particle filter （UPF） to train the MLP in a self- organizing state space （SOSS） model. This involves forming augmented state vectors consisting of all parameters （the weights of the MLP） and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS moder for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.

Crude Extract of Detergent⁃Like Gleditsia sinensis lam Exhibiting Self⁃Organization for Protection of Mild Steel in Harsh Hydrochloric Acid Solution:How to Seek Crude Natural Plant Extracts as Green Corrosion Inhibitors

This study proposes a thought to employ detergent⁃like renewable low⁃cost crude extract of Gleditsia sinensis lam(GSL)as green corrosion inhibitor for mild steel in HCl solution.Crude Gleditsia sinensis lam extract(GSLE)was gained at mild conditions by simply refluxing in ethanol with a Soxhlet extractor.The target GSLE extract exhibited regular self⁃organization in mixed solvents of organic solvents/H2O such as ethanol/H2 O(v/v,50/50)at room temperature,which was evidenced by different means including scanning electron microscopy,transmission electron microscopy,and dynamic light scattering.The study demonstrates that the yielded assemblies of the crude extract of GSLE displayed chemical adsorption on the studied mild steel sample surfaces.Furthermore,the formed stable crude extract assemblies of GSL presented outstanding anti⁃corrosion capability in 1.0 mol/L HCl aqueous solution based on electrochemical measurements including polarization curves and impedance spectroscopy.It is discovered that the maximal corrosion inhibition efficiency could reach approximate 95%.The molecular modeling was performed to acquire the nature of frontier orbitals of the main representative chemical components of crude GSLE for deep understanding of chemical interactions with iron.The results presented herein would guide us to seek sustainable environmentally friendly low⁃cost detergent⁃like plant crude extracts for corrosion inhibition of various metals in aggressive acid environments.