Self-organizing Model Research Articles

2D Self-organized ONN model for Handwritten Text Recognition

Deep Convolutional Neural Networks (CNNs) have recently reached state-of-the-art Handwritten Text Recognition (HTR) performance. However, recent research has shown that typical CNNs’ learning performance is limited since they are homogeneous networks with a simple (linear) neuron model. With their heterogeneous network structure incorporating non-linear neurons, Operational Neural Networks (ONNs) have recently been proposed to address this drawback. Self-ONNs are self-organized variations of ONNs with the generative neuron model that can generate any non-linear function using the Taylor approximation. In this study, in order to improve the state-of-the-art performance level in HTR, the 2D Self-organized ONNs (Self-ONNs) in the core of a novel network model are proposed. Moreover, deformable convolutions, which have recently been demonstrated to tackle variations in the writing styles better, are utilized in this study. The results over the IAM English dataset and HADARA80P Arabic dataset show that the proposed model with the operational layers of Self-ONNs significantly improves Character Error Rate (CER) and Word Error Rate (WER). Compared with its counterpart CNNs, Self-ONNs reduce CER and WER by 1.2% and 3.4 % in the HADARA80P and 0.199% and 1.244% in the IAM dataset. The results over the benchmark IAM demonstrate that the proposed model with the operational layers of Self-ONNs outperforms recent deep CNN models by a significant margin while the use of Self-ONNs with deformable convolutions demonstrates exceptional results.

CODED SEMANTICAL VOLUME OF FEMALE NOUNS WITH INITIAL LETTERS A, B, C, G, H, I IN MODERN GERMAN: A SYNERGETIC APPROACH (based on DUDEN explanatory dictionary)

The presented work is one of the first linguosynergetically oriented stages of the research on coding semantic potential of nouns in relation to category of gender and initial letters of their alphabetical order in explanatory dictionaries of German, namely with initial letters A; B, C, G, H, I in the Duden explanatory dictionary of German. Deutsches Universalwörterbuch. The statement that the driving factor of language development is the synergetic law of making the least effort and conservation of language energy is methodologically important in the study. In a synergistic sense, the author draws parallels between the dictionary as a representation of linguistic generalization about the structured amount of knowledge of learned extraverbal reality and mental lexicon, which is not an arbitrary accumulation of contributions, but is a structured hierarchical system of such contributions. In the synergetic sense, lexical units and their meanings at the linguistic level contain encoded structured information. The linguosynergetic approach of the research applied in this article assumes that language as a self-regulating system under the influence of external energy and information has developed a mechanism for encoding information-semantic volume of nouns in correlation with their grammatical gender and initial letters of their alphabetical order in German. The results obtained on the basis of synergetic-quantitative approach are extrapolated to the linguosynergetic model of self-organization of noun naming according to the principle of minimization of effort, which directs coding of semantic volume of polysemic or monosemic nouns to language economy and minimization of lexical effort of a person. It is proven that since the closest distances in the lexical structure of a word at the language level are the closest in comparison with the knowledge structures of the mental lexicon of a person, then the self-regulating language system under the influence of external energy and information launches mechanisms to optimize the volume of the monosemic model of nouns with one meaning and the semantic volume of the polysemic model with meanings that are closest to the main meaning in the hierarchically structured chain of a polysemous word at the dictionary level.

Reconciling Power-law Slopes in Solar Flare and Nanoflare Size Distributions

We unify the power laws of size distributions of solar flare and nanoflare energies. We present three models that predict the power-law slopes α E of flare energies defined in terms of the 2D and 3D fractal dimensions (D A , D V ): (i) the spatiotemporal standard self-organized criticality model, defined by the power-law slope α E1 =1 + 2/(D V + 2) = (13/9) ≈ 1.44; (ii) the 2D thermal energy model, α E2 = 1 + 2/D A = (7/3) ≈ 2.33; and (iii) the 3D thermal energy model, α E3 = 1 + 2/D V = (9/5) ≈ 1.80. The theoretical predictions of energies are consistent with the observational values of these three groups, i.e., α E1 = 1.47 ± 0.07, α E2 = 2.38 ± 0.09, and α E3 = 1.80 ± 0.18. These results corroborate that the energy of nanoflares does not diverge at small energies, since (α E1 < 2) and (α E3 < 2), except for the 2D model (α E2 > 2). Thus, while this conclusion does not support nanoflare scenarios of coronal heating from a dimensionality point of view, magnetic reconnection processes with quasi-1D or quasi-2D current sheets cannot be ruled out.

Effect of the self-organized learning model (Pintrich) in improving lateral thinking in tennis for female students

Preparing educational units with the Pintrich self-organized learning model to improve lateral thinking in tennis for female students, Recognizing the effect of the self-organized learning model (Pintrich) in improving lateral thinking in tennis for female students, The research community was identified with female students of the second academic year in the College of Education for Girls (Department of Physical Education and Sports Sciences) at the University of Kufa for the academic year 2021-2022, and their number is 40. The method of a lottery was used in selecting the sample. At first, fifteen female students were selected for the experimental group, then fifteen female students for the control group, and ten female students for the exploratory experiment. Thus, the percentage of the research sample is (72.72%) of the research community, Conclusions: The experimental group that implemented the educational units according to the Pintrich self-organizer in improving the lateral thinking in tennis for female students outperformed the control group, There are significant differences for the Pintrich self-organized learning model in improving the lateral thinking of female students between the control and experimental groups in favour of the post-test.

Momentum lost or creating new constellations? Insights from an exercise-at-work project during the COVID-19 pandemic - a mixed methods approach.

Exercise-at-work programmes have been identified as venues to decrease inequalities in physical activity and exercise between socioeconomic groups and to improve employees' health and wellbeing. Drawing on a multiple institutional logics perspective and adopting a mixed-methods approach, this paper investigates how employees, exercise-ambassadors and managers at five Danish workplaces experience Covid-19 induced changes to a 1-year exercise-at-work project, and how these changes impacted upon the workplace. Our results suggest that Covid-19 and the altered format of exercise and delivery polarized employees' opportunities for exercise at work. However, the generally positive experiences of exercise-at-work activities and their influence on social environment and collaboration (identified prior to Covid-19 lockdown) remained among those employees who continued with activities. Self-organized adaptions and models of employee exercise which emerged suggest that community logic endured despite the crisis. We show how Covid-19 induced organizational changes led to interplays between institutional logics, with family and state logics becoming more prominent. Specifically, the exercise-at-work programme changed from an aligned model, with complementary logics and minimal conflict, to a model where logics of profession and corporation became dominant at the expense of community logic (exercise-ambassadors activities), but constrained by a state and a family logic.

Developed and developing countries: towards the common target with different speeds

Objective: to analyze the expected economic transformations in the 21st century in various countries of the world. Methods: abstract-logical method, methods of modeling of socio-economic self-organization, methods of long-term forecasting of world dynamics, theory of institutional matrices.Results: based on the performed analysis, the following conclusions are made: 1) in the coming decades, a complex continuum of types, transitions, combinations and forms of economic structures will be observed in the world (with an increase in the proportion of post-industrial forms). At the same time, there will be a shift towards strengthening distributive institutions in developed countries and a shift towards strengthening market institutions in developing countries that continue the processes of economic and socio-political modernization; 2) the process of internationalization of the labor force will be intensified (due to the development of remote employment) along with the internationalization of capital; 3) a new technological wave (2030s – 2070s) will bring new resources and expand the sphere for production and services, which will support economic growth in both developed and developing countries; 4) at the same time in some areas, in particular in medicine, a very significant regulation of economic activity is inevitable, while in other areas, in particular in robotics, economic mechanisms of a market-corporate type may strengthen; 5) thus, the most important task in the coming decades is to increase the flexibility of socio-economic development of all countries of the world, namely, gaining the ability to regulate the ratio of market and distribution mechanisms depending on the spheres of activity and tasks to be solved without the pressure of ideological dogmas.Scientific novelty: based on the historical trends analysis using the theory of institutional matrices, a forecast of economic transformations in the 21st century is made for the countries of various types and the expected features of these transformations are substantiated.Practical significance: the proposed methods of long-term forecasting can be used in strategic planning systems in the Russian Federation.

A self-organized critical model and multifractal analysis for earthquakes in Central Alborz, Iran

This paper is devoted to a phenomenological study of the earthquakes in central Alborz, Iran. Using three observational quantities, namely the weight function, the quality factor, and the velocity model in this region, we develop a modified dissipative sandpile model which captures the main features of the system, especially the average activity field over the region of study. The model is based on external stimuli, the location of which is chosen (I) randomly, (II) on the faults, (III) on the low active points, (IV) on the moderately active points, and (V) on the highly active points in the region. We uncover some universal behaviors depending slightly on the method of external stimuli. A multi-fractal detrended fluctuation analysis is exploited to extract the spectrum of the Hurst exponent of the time series obtained by each of these schemes. Although the average Hurst exponent depends slightly on the method of stimuli, we numerically show that in all cases it is lower than 0.5, reflecting the anti-correlated nature of the system. The lowest average Hurst exponent is found to be associated with the case (V), in such a way that the more active the stimulated sites are, the lower the average Hurst exponent is obtained, i.e. the large earthquakes are more anticorrelated. Moreover, we find that the activity field achieved in this study provide information about the depth and topography of the basement, and also the area that can potentially be the location of the future large events. We successfully determine a high activity zone on the Mosha Fault, where the mainshock occurred on May 7th, 2020 (M_W 4.9).

Online Monitoring of Ecoenvironmental Pollution Data in Internet Environment

In view of the problems of poor air quality and serious soil pollution in the current ecological environment, an online monitoring of ecological environment pollution data in the Internet environment is proposed. The concentration of pollution sources that may appear in the atmospheric environment is analyzed through the analysis coefficient of ecological environment pollutants, and the specific grade data corresponding to the air quality index is determined. Observation and mapping are carried out for the area to be monitored, and then, the monitoring points are determined, the monitoring equipment is arranged, and the soil profile of the area is sampled in the monitoring area. The self-organizing neural network model is used to conduct spatial cluster analysis on the pollution of ecological environment pollution areas, evaluate the sensitivity of soil pollution, and build a land ecological pollution monitoring information model. Based on the Internet of Things technology and the comprehensive use of GPS, sensors, and other technologies and equipment, the framework of monitoring technology is constructed, and the online monitoring of ecological environment pollution data is completed. The experimental results show that the proposed method has high data accuracy in different locations and different seasons, which is consistent with the actual situation; because of the maturity of the Internet of Things technology, compared with the literature method, the method in this paper can provide a cost-effective data foundation, which is more cost-effective, and save time and energy.

A novel proactive soft load balancing framework for ultra dense network

A major challenge for the future wireless network is to design the self-organizing architecture. The reactive self-organizing model of traditional networks needs to be transformed into an active self-organizing network. Due to the user mobility and the coverage of small cells, the network load often becomes unbalanced, resulting in poor network performance. Mobility management has become an important issue to ensure seamless communication when users move between cells, and proactive mobility management is one of the important functions of the active Self-Organizing Network (SON). This paper proposes a proactive mobility management framework for active SON, which transforms the original reactive load balancing into a forward-aware and proactive load balancing. The proposed framework firstly uses the BART model to predict the users’ temporal and spatial mobility based on a weekly cycle and then formulate the MLB optimization problem based on the soft load. Two solutions are proposed to solve the above MLB problem. The simulation results show that the proposed method can better optimize the network performance and realize intelligent mobile management for the future network.

Integration of Critical and Creative Thinking Skills in Solving HOTS Questions through the SOLE Model on Counting Rules

One of the provisions of student to be able to answer the challenges of the 21st century is to be able to integrate critical and creative thinking skills in solving problems. The purpose of study was to determine the increase in integration of students’ critical and creative thinking through HOTS questions using the Self-Organized Learning Environments model in the Counting Rules learning topic at SMA Negeri 1 Yogyakarta. This classroom action research was carried out in two cycles, in which cycle consisted of planning, action, observation, and reflection. This study uses three stages in the SOLE model, namely Question, Investigate, and Review. Giving inquiry questions followed by the application of students’ knowledge in interesting and directed tasks in the student worksheets to increase curiosity and provoke students to start using their critical thinking skills. Solving LOTS, MOTS, and HOTS problem solving gradually train students to integrate critical and creative thinking skills. Each group presents the results of their exploration and investigation to improve the integration of critical and creative thinking skills of students. The result of observations on integration of critical thinking show that 15,63% of students are in very high criteria and 84,73% in high criteria. The same result is shown in the integration of creative thinking skills. The achievement test of student learning outcomes in the aspect of knowledge and skills is 87,50%. In this study also obtained the result of the implementation of learning is 93,33%. This research concludes that there is an increase in the integration of students’ critical and creative thinking skills in the Counting Rules.

Insight into structural composition of dissolved organic matter in saline-alkali soil by fluorescence spectroscopy coupled with self-organizing map and structural equation modeling

Soil salinization has been occurring all over the world, which severely affected crop production and threatened the life of mankind. It is necessary to take serious steps to improve soil fertility for the sustainability and productive capacity of agriculture. Soil samples of different depths were collected from native vegetation communities (Comm. Phragmites communis (CPC) and Comm. Populus alba (CPA)) and irrigated crops (corn fields (CFD) and seed melon fields (SMF)) in Hetao irrigation area of China. Three dimensional excitation-emission matrix (EEM) fluorescence technology combined with self-organizing map were used to analyze the dissolved organic matter (DOM) composition and structural characteristics in saline-alkali soils and its spatial distribution under different vegetation covers. Critical factors were recognized by classification and regression tree (CART) for distinguishing soil samples, and latent factors were revealed with structural equation modeling (SEM) for improving the humification degree of DOM from saline soils in Hetao irrigation area. Five components were obtained in the DOM substances, i.e., tyrosine-like (C1), tryptophan-like (C2), UV fulvic-like (C3), visible fulvic-like (C4) and humic-like (C5). The protein-like peaks were all obvious, and the fulvic-like peaks (600-735 a.u.) were conspicuous in the CPC soil than in others, except CFD1 and SMF1. C1 was the critical factor to distinguish native vegetation from irrigated crops, and C1 and C2 were the critical factors to distinguish CFD from SMF. Contrary to the HA/FA (0.20) and A/C (0.25), the path coefficient (-0.15) of sources with T/H was negative, indicating that the incremental contents of fluorenscense substances were in the sequences of protein-like > visible fulvic-like > UV fulvic-like > humic-like, affecting by the allochthonous. C1 (1.00) and C4 (1.00) were the primary components for improving the humification degree of DOM, which were principally originated from plant debris. EEM combined with self-organizing map, CART and SEM is an efficient way to distinguish different salinized soils and reveal the latent factors for improving the soil fertility.

A model for self-organization of sensorimotor function: spinal interneuronal integration.

Control of musculoskeletal systems depends on integration of voluntary commands and somatosensory feedback in the complex neural circuits of the spinal cord. It has been suggested that the various connectivity patterns that have been identified experimentally may result from the many transcriptional types that have been observed in spinal interneurons. We ask instead whether the muscle-specific details of observed connectivity patterns can arise as a consequence of Hebbian adaptation during early development, rather than being genetically ordained. We constructed an anatomically simplified model musculoskeletal system with realistic muscles and sensors and connected it to a recurrent, random neuronal network consisting of both excitatory and inhibitory neurons endowed with Hebbian learning rules. We then generated a wide set of randomized muscle twitches typical of those described during fetal development and allowed the network to learn. Multiple simulations consistently resulted in diverse and stable patterns of activity and connectivity that included subsets of the interneurons that were similar to "archetypical" interneurons described in the literature. We also found that such learning led to an increased degree of cooperativity between interneurons when performing larger limb movements on which it had not been trained. Hebbian learning gives rise to rich sets of diverse interneurons whose connectivity reflects the mechanical properties of the system. At least some of the transcriptomic diversity may reflect the effects of this process rather than the cause of the connectivity. Such a learning process seems better suited to respond to the musculoskeletal mutations that underlie the evolution of new species.NEW & NOTEWORTHY We present a model of a self-organizing early spinal cord circuitry, which is attached to a biologically realistic sensorized musculoskeletal system. Without any a priori-defined connectivity or organization, learning induced by spontaneous, fetal-like motor activity results in the emergence of a well-functioning spinal interneuronal circuit whose connectivity patterns resemble in many respects those observed in the adult mammalian spinal cord. Hence, our result questions the importance of genetically controlled wiring for spinal cord function.

Optimal path selection algorithm for UAV formation guided by a UV virtual fence.

This paper studies the optimal flight path selection problem of unmanned aerial vehicle (UAV) formations in multi-obstacle areas. According to the characteristics of wireless ultraviolet (UV) light communication conforming to the probability coverage model, a wireless UV light self-organizing network coverage model is proposed; the UV light of different wavelengths is divided into a detection area, communication area, and early warning area. A consensus algorithm with leaders and followers is designed, and an artificial potential field method is introduced to ensure that the UAV formation successfully avoids obstacles. Simulation results show that the UAV formation can choose a reasonable flight route under the improved algorithm, and the average obstacle avoidance time is reduced by 6.75 s compared with the original algorithm. Comparing the flight paths of formations in barrier-free and multi-obstacle environments, the obstacle avoidance range of UAVs is not more than 1 m. The UV light self-organizing coverage model can be applied to military communications and disaster relief.

The interconnection between independent reactive control policies drives the stringency of local containment

The lack of medical treatments and vaccines upon the arrival of the SARS-CoV-2 virus has made non-pharmaceutical interventions the best allies in safeguarding human lives in the face of the COVID-19 pandemic. Here we propose a self-organized epidemic model with multi-scale control policies that are relaxed or strengthened depending on the extent of the epidemic outbreak. We show that optimizing the balance between the effects of epidemic control and the associated socio-economic cost is strongly linked to the stringency of control measures. We also show that non-pharmaceutical interventions acting at different spatial scales, from creating social bubbles at the household level to constraining mobility between different cities, are strongly interrelated. We find that policy functionality changes for better or worse depending on network connectivity, meaning that some populations may allow for less restrictive measures than others if both have the same resources to respond to the evolving epidemic.

A model for self-organization of sensorimotor function: the spinal monosynaptic loop.

Recent spinal cord literature abounds with descriptions of genetic preprogramming and the molecular control of circuit formation. In this paper, we explore to what extent circuit formation based on learning rather than preprogramming could explain the selective formation of the monosynaptic projections between muscle spindle primary afferents and homonymous motoneurons. We adjusted the initially randomized gains in the neural network according to a Hebbian plasticity rule while exercising the model system with spontaneous muscle activity patterns similar to those observed during early fetal development. Normal connectivity patterns developed only when we modeled β motoneurons, which are known to innervate both intrafusal and extrafusal muscle fibers in vertebrate muscles but were not considered in previous literature regarding selective formation of these synapses in animals with paralyzed muscles. It was also helpful to correctly model the greatly reduced contractility of extrafusal muscle fibers during early development. Stronger and more coordinated muscle activity patterns such as observed later during neonatal locomotion impaired projection selectivity. These findings imply a generic functionality of a musculoskeletal system to imprint important aspects of its mechanical dynamics onto a neural network, without specific preprogramming other than setting a critical period for the formation and maturation of this general pattern of connectivity. Such functionality would facilitate the successful evolution of new species with altered musculoskeletal anatomy, and it may help to explain patterns of connectivity and associated reflexes that appear during abnormal development.NEW & NOTEWORTHY A novel model of self-organization of early spinal circuitry based on a biologically realistic plant, sensors, and neuronal plasticity in conjunction with empirical observations of fetal development. Without explicit need for guiding genetic rules, connection matrices emerge that support functional self-organization of the mature pattern of Ia to motoneuron connectivity in the spinal circuitry.

Evolution model of high quality of service for spatial heterogeneous wireless sensor networks

The complex network theory is helpful to design the self-organizing evolution mechanism of the network and generate wireless sensor networks (WSNs) with high quality of service. Focusing on the heterogeneous WSNs, we firstly propose an evolution model based on the fitness model in complex network and then propose two self-organizing evolution models based on the topology hops for heterogeneous WSNs. By considering the topology hops in the self-organizing evolution mechanism, the two models can effectively reduce the reverse connection and balance the load of nodes around the sink in network evolution process. Simulation results show that compared with three classical models, the proposed models provide a higher quality of service, such as longer network lifetime, better energy-balanced factor, shorter shortest path length and stronger robustness against malicious attacks. Our self-organizing evolution models provide a new perspective and reference for the design of heterogeneous WSNs with high quality of service.

Self-Assembling Toroidal Cell Constructs for Tissue Engineering Applications.

Developing tissues have intricate, three-dimensional (3D) organizations of cells and extracellular matrix (ECM) that provide the framework necessary to meet morphogenic and necessary demands. Migrating cells, in vivo, are exposed to numerous conflicting signals: chemokines, ECM, growth factors, and physical forces. While most of these have been studied individually in vivo or in vitro, our understanding of how cells integrate these various signals is lacking. We previously developed a novel self-organizing cellularized collagen hydrogel model that is adaptable, tunable, reproducible, and capable of mimicking the multitude of stimuli that cells experience. Our model produced self-assembled toroids of cells that were formed by 24 h. Data we present here show toroids initially form as early as 3 h after seeding. Additionally, toroids formed when cells were seeded on various collagen subtypes and were sensitive to the composition of the hydrogel. Moreover, we found differences in remodeling in toroid gels compared to gels with cells embedded in them using both a collagen binding peptide and rheology. Using scanning electron microscopy, we observed toroids forming a crater-like structure compared to whole gel contractions in mixed in gels. Finally, when multiple cells were mixed prior to seeding, heterogeneous toroids formed with some containing clusters of cells.

A Unified Software/Hardware Scalable Architecture for Brain-Inspired Computing Based on Self-Organizing Neural Models.

The field of artificial intelligence has significantly advanced over the past decades, inspired by discoveries from the fields of biology and neuroscience. The idea of this work is inspired by the process of self-organization of cortical areas in the human brain from both afferent and lateral/internal connections. In this work, we develop a brain-inspired neural model associating Self-Organizing Maps (SOM) and Hebbian learning in the Reentrant SOM (ReSOM) model. The framework is applied to multimodal classification problems. Compared to existing methods based on unsupervised learning with post-labeling, the model enhances the state-of-the-art results. This work also demonstrates the distributed and scalable nature of the model through both simulation results and hardware execution on a dedicated FPGA-based platform named SCALP (Self-configurable 3D Cellular Adaptive Platform). SCALP boards can be interconnected in a modular way to support the structure of the neural model. Such a unified software and hardware approach enables the processing to be scaled and allows information from several modalities to be merged dynamically. The deployment on hardware boards provides performance results of parallel execution on several devices, with the communication between each board through dedicated serial links. The proposed unified architecture, composed of the ReSOM model and the SCALP hardware platform, demonstrates a significant increase in accuracy thanks to multimodal association, and a good trade-off between latency and power consumption compared to a centralized GPU implementation.

Solution of the Self-Organized Critical Manna Model in Space Dimensions 2 to 4

Solution of the Self-Organized Critical Manna Model in Space Dimensions 2 to 4

Automation of aeronautical information processing based on multi-agent technologies

Progress in the development of computer engineering provides an opportunity to address a wider variety of challenges using computer software systems. The task of automatic aeronautical navigation information processing is referred to the number of such issues. This stipulates the necessity to adopt new approaches to design and develop similar systems. One of these approaches is based on the application of the collective activity idea of a set of agents – multi-agent technologies. In this regard, the purpose of the article is to consider the features of the automated aeronautical navigation information processing implementation on the basis of multi-agent technologies. To achieve this goal, the problem-structural methodology of hybrid systems synthesis, which allows us to create self-organizing models, was selected. Each element of which develops, obtaining data and knowledge from other elements. In the research process, a formal definition of the multi-agent system of automatic aeronautical information processing is presented, which involves a set of agents, environment of agent functioning, a set of permissible relations between agents, description of rules for forming a network of agents, a set of individual and joint actions, communication interactions, behavior and actions strategies, a possibility of system evolution. Furthermore, an emphasis is placed on the description of each agent. For this purpose, the authors propose to use four elements: a set of variables, inputs and outputs, an autonomous technique that performs appropriate changes over a set of variables. As agents, the paper comes up with the idea to use the following: Pilots Notification Agent, Preflight Information Bulletin Agent, Data Generation Agent, Aviation Processes Agent, Aviation Database Generation Agent, Aeronautical Maps Creation Agent, Aeronautical Data Set Export/Import Agent, Publications and References Agent. In addition, the article presents the multi-agent system diagram of automated aeronautical information processing and describes in detail processing an application in the agent using the mathematical expression. The results, obtained in the course of investigations, can be used to improve the effectiveness of the analytical component in the structure of the system to form the direct and reverse coordination relationship while solving aerial navigation problems.