Self-organizing Research Articles

A Novel Approach to Node Coverage Enhancement in Wireless Sensor Networks Using Walrus Optimization Algorithm

Wireless Sensor Networks (WSNs) are crucial components of modern technology, supporting applications like healthcare, industrial automation, and environmental monitoring. This research aims to design intelligent and adaptive sensor networks by integrating metaheuristics with node coverage optimization in WSNs. By incorporating metaheuristics and optimizing node coverage, WSNs can become more resilient and robust, leading to the development of self-adapting, self-organizing networks capable of efficiently covering dynamic and diverse environments. This research introduces the Walrus Optimization Algorithm for Node Coverage Enhancement in WSNs, called the WaOA-NCEWSN technique. The primary goal of this technique is to optimize the coverage of a target region using a limited number of Sensor Nodes (SNs) and by improving their placement. The WaOA is inspired by walrus behaviours like feeding, migrating, breeding, escaping, roosting, and gathering in response to environmental signals. The WaOA-NCEWSN technique uses an objective function that defines the coverage ratio, representing the maximum probability of coverage in a 2D-WSN monitoring area. Comparative analysis with other models using 50, 75, 100, and 200 nodes shows that the WaOA-NCEWSN technique performs better. The compilation times for the WaOA-NCEWSN technique are 5.14s, 6.48s, 6.54s, and 7.47s for 50, 75, 100, and 200 nodes, respectively. Experimental results indicate that the WaOA-NCEWSN technique offers superior coverage performance compared to other models.

"You sit in abubble while the others go about their lives"-perspectives of Ukrainian refugee mothers with children aged 0-3years

Little is known about the situation of Ukrainian parents with children up to the age of three who have fled to Germany since Russia's attack on Ukraine. The aim of the National Center for Early Intervention research project is to explore the perspective of this group on their lives in Germany. This analysis focuses on the burdens and resources of the parents and their knowledge and use of support services. In 17interviews (duration approx. one hour each), parents (16mothers, one father) who had fled from Ukraine to Germany with children up to the age of three were interviewed between October and December 2022, and their answers were qualitatively analyzed using structured content analysis. The interviewees primarily described the psychological stress caused by war and flight, negative emotions such as feelings of loneliness, sadness and lack of motivation, and obstacles to integration as burdens. Resources were primarily networks of Ukrainian- and Russian-speaking people in Germany as well as personal resources such as self-care skills. Supporting services were generally communicated via electronic media. Services for childcare, language courses, and support with health issues were considered particularly useful. Alack of childcare was the most important reason for not taking up other services. The psychological stress of the interviewees indicates aneed for therapy capacities in their native language. Childcare may enable Ukrainians to attend language courses that promote integration. The self-organized networks of the Ukrainian- and Russian-speaking community should be involved in the development of interventions for this target group in aparticipatory manner from the outset.

Self-organizing nervous systems for robot swarms.

We present the self-organizing nervous system (SoNS), a robot swarm architecture based on self-organized hierarchy. The SoNS approach enables robots to autonomously establish, maintain, and reconfigure dynamic multilevel system architectures. For example, a robot swarm consisting of n independent robots could transform into a single n-robot SoNS and then into several independent smaller SoNSs, where each SoNS uses a temporary and dynamic hierarchy. Leveraging the SoNS approach, we showed that sensing, actuation, and decision-making can be coordinated in a locally centralized way without sacrificing the benefits of scalability, flexibility, and fault tolerance, for which swarm robotics is usually studied. In several proof-of-concept robot missions-including binary decision-making and search and rescue-we demonstrated that the capabilities of the SoNS approach greatly advance the state of the art in swarm robotics. The missions were conducted with a real heterogeneous aerial-ground robot swarm, using a custom-developed quadrotor platform. We also demonstrated the scalability of the SoNS approach in swarms of up to 250 robots in a physics-based simulator and demonstrated several types of system fault tolerance in simulation and reality.

Existence and stability criteria for global synchrony and for synchrony in two alternating clusters of pulse-coupled oscillators updated to include conduction delays

Phase Response Curves (PRCs) have been useful in determining and analyzing various phase-locking modes in networks of oscillators under pulse-coupling assumptions, as reviewed in Mathematical Biosciences, 226:77–96, 2010. Here, we update that review to include progress since 2010 on pulse coupled oscillators with conduction delays. We then present original results that extend the derivation of the criteria for stability of global synchrony in networks of pulse-coupled oscillators to include conduction delays. We also incorporate conduction delays to extend previous studies that showed how an alternating firing pattern between two synchronized clusters could enforce within-cluster synchrony, even for clusters unable to synchronize themselves in isolation. To obtain these results, we used self-connected neurons to represent clusters. These results greatly extend the applicability of the stability analyses to networks of pulse-coupled oscillators since conduction delays are ubiquitous and strongly impact the stability of synchrony. Although these analyses only strictly apply to identical oscillators with identical connections to other oscillators, the principles are general and suggest how to promote or impede synchrony in physiological networks of neurons, for example. Heterogeneity can be interpreted as a form of frozen noise, and approximate synchrony can be sustained despite heterogeneity. The pulse-coupled oscillator model can not only be used to describe biological neuronal networks but also cardiac pacemakers, lasers, fireflies, artificial neural networks, social self-organization, and wireless sensor networks.

A new self-organization of complex networks structure generalized by a new class of fractional differential equations generated by 3D-gamma function

In this note, we suggest a generalization of gamma function to be 3D-gamma function. As a consequence, some special functions are generalized including the Mittag-Leffler function. Moreover, we utilize the generalized Mittag-Leffler to extend the AB-fractional calculus. Examples are introduced to cover our theory. The solvability of abstract Riccati equation is considered, and Hyers-Ulam stability is discovered in the sequel. Based on the new study, we design new stable of Self-organization in complex networks (SOCNs).

Boosting the Development and Management of Wind Energy: Self-Organizing Map Neural Networks for Clustering Wind Power Outputs

Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativeness and topological structure of the competition layer, cluster analysis of the wind power output process in four seasons is realized. The output characteristics are evaluated through multiple evaluation indicators. Taking the wind power output of the Hunan power grid as a case study, the results underscore that the 1 × 3-dimensional competition layer structure had the highest representativeness (72.9%), and the wind power output processes of each season were divided into three categories, with a robust and stable topology structure. Summer and winter were the most representative seasons. Summer had strong volatility and small wind power outputs, which required the utilization of other power sources to balance power supply and load demand. Winter featured low volatility and large wind power outputs, necessitating cooperation with peak-shaving power sources to enhance the power grid’s absorbability to wind power. The seasonal clustering analysis of wind power outputs will be helpful to analyze the seasonality of wind power outputs and can provide scientific and technical support for guiding the power grid’s operation and management.

Self-Organization of Micro- and Nanosystems in the Form of Patterns

In this work, the peculiarities of self-organization of patterned micro- and nanosystems under near-equilibrium condensation conditions were consistently considered. The criteria for stationarity of near-equilibrium condensation were introduced, and interrelations between the condensate local growth kinetics and the corresponding local technological parameters were described. Dissipative self-organization of small supersaturations in physically and chemically active medium-condensate systems were compared. The effectiveness of the unification of dissipative self-organization of small supersaturations and conservative self-organization of patterned micro- and nanosystems formation was shown, which forms the basis of a new concept of complete self-organized systems.

Towards Durability of Redundant Composite Beams

The relevance of the study lies in the methodology improvement of assessing the durability of metal span structures with composite beams via the control for the stress-strain state on the basis of the system redundancy and self-organization. The variation in loads on composite beams of bridge spans during a long operation, is considered herein.Purpose: The aim of this work is to develop a methodology for estimation of durability of composite beams as self-organized systems under load.Research findings: It is shown that redundant composite beams increase their durability and technical utilization.Practical implications: Durability of composite beams with regard to the yield strength delay.

Fragmented perspective of self-organized criticality and disorder in log gravity

We use a statistical model to discuss nonequilibrium fragmentation phenomena taking place in the stochastic dynamics of the log sector in log gravity. From the canonical Gibbs model, a combinatorial analysis reveals an important aspect of the n-particle evolution previously shown to generate a collection of random partitions according to the Ewens distribution realized in a disconnected double Hurwitz number in genus zero. By treating each possible partition as a member of an ensemble of fragmentations, and ensemble averaging over all partitions with the Hurwitz number as a special case of the Gibbs distribution, a resulting distribution of cluster sizes appears to fall as a power of the size of the cluster. Dynamical systems that exhibit a distribution of sizes giving rise to a scale-invariant power-law behavior at a critical point possess an important property called self-organized criticality. As a corollary, the log sector of log gravity is a self-organized critical system at the critical point μl = 1. A similarity between self-organized critical systems, spin glass models and the dynamics of the log sector which exhibits aging behavior reminiscent of glassy systems is pointed out by means of the Pòlya distribution, also known to classify various models of (randomly fragmented) disordered systems, and by presenting the cluster distribution in the log sector of log gravity as a distinguished member of this probability distribution. We bring arguments from a probabilistic perspective to discuss the disorder in log gravity, largely anticipated through the conjectured AdS3/LCFT2 correspondence.

Self-Organized Operational Neural Networks for Early Glaucoma Diagnosis in Digital Fundus Images

Glaucoma leads to permanent vision disability by damaging the optical nerve that transmits visual images to the brain. The fact that glaucoma does not show any symptoms as it progresses and cannot be stopped at the later stages, makes it critical to be diagnosed in its early stages. Although various deep learning models have been applied for detecting glaucoma from digital fundus images, due to the scarcity of labeled data, their generalization performance was limited along with high computational complexity and special hardware requirements. In this study, compact Self-Organized Operational Neural Networks (Self-ONNs) are proposed for early detection of glaucoma in fundus images and their performance is compared against the conventional (deep) Convolutional Neural Networks (CNNs) over three benchmark datasets: ACRIMA, RIM-ONE, and ESOGU. The experimental results demonstrate that Self-ONNs not only achieve superior detection performance but can also significantly reduce the computational complexity making it a potentially suitable network model for biomedical datasets especially when the data is scarce.

Muggle, Juggle, Snuggle, and Struggle: profiles of parenting experiences of mothers and fathers in South Korea during the COVID-19 pandemic

ABSTRACT The current study expands the literature on parenting during the COVID-19 pandemic by considering how much time spent on childcare increased and how the time was perceived by mothers and fathers in South Korea. Using the perspective of family systems theory and taking a person-centered approach, this study examined how parental experiences differ according to parental gender and investigated subgroups of mothers and fathers in terms of parental experiences during the pandemic. This study also examined how those groups differ in terms of the factors of family subsystems, adaptive self-organization, and sociodemographic variables. With a sample of 372 mothers and 366 fathers in South Korea, this study employed latent profile analysis and identified four distinct subgroups of mothers and fathers: Muggle, Juggle, Snuggle, and Struggle. Some parents feel like muggles, managing short childcare times without much joy or challenge, whereas others juggle long hours with their children, finding both joy and difficulty. Some snuggle, enjoying their extended childcare time with delight, whereas others struggle, facing the hardships of prolonged childcare without much enjoyment. The overall profile patterns and the ratios of these profiles were similar for both mothers and fathers. Suggestions for future researchers, practitioners, and policy-makers are discussed.

НЕЙРОСЕТЕВОЙ ПОДХОД К КОГНИТИВНОМУ МОДЕЛИРОВАНИЮ

Some features of cognitive modeling are presented, including the prerequisites for a cognitive approach to solving complex problems. Cognitive modeling involves the use of various artificial neural networks, including convolutional neural networks. The classification of artificial neural networks according to various characteristics is given. The features of self-organizing neural networks and networks using deep learning methods are considered. The artificial neural network, which is a three-layer unidirectional direct propagation network, the interface of a computer program used to approximate functions using the specified neural network, as well as the solution of the image recognition problem using an artificial convolutional neural network, in which the neural network parameters are adjusted for each recognizable image fragment in order to adaptively filter the image, are considered in detail. The analysis of images in video surveillance systems in order to detect fires allows them to be detected at an early stage and, thus, prevent the fire propogation.

A self-organizing map clustering adaptive artificial neural network model for analysis critical success factors in different phases of MTO projects case study: electrical equipment manufacturer and supplier in Iran

PurposeThe purpose of this paper is to investigate the benefit of critical success factors (CSFs) clustering in different phases of make-to-order (MTO) projects and develop standards for management.Design/methodology/approachThis study is based on a questionnaire survey. First of all, collecting data by structured interviews, relying on a questionnaire and second from leader contractors who are active in the engineering and steel industry (in Iran). So, the requirements and objective of the research are presented to the top management of MTO projects to gain their support in data collection. Then 20 CSFs were identified by the literature review so a questionnaire survey was prepared for the CSFs assessment and interview with the experts. Finally analyzing the importance and performance of CSFs in project phases and cluster them in different project phases with self-organizing map as one of the artificial neural network (ANN) approaches due to high predictive accuracy. Review the research result with the top management of MTO project and examine the results obtained from neural networks and validation indices.FindingsCluster analysis shows that the implementation phase is the most important stage in MTO organizations and the other phases like feasibility and start-up, design and planning, delivery and end-phase should be also considered as effective phases in determining the level of organization performance. Different industries with additional data at different periodic times will verify the result. Furthermore, testing the other ANN model will improve risk analysis and could shift this classification approach to a regression type.Research limitations/implicationsThe main limitation of the research is related to the sample. Research findings are limited to the time of data collection so validity is limited to the mentioned time. Different industries with additional data will verify the result. Furthermore, testing different ANN models such as K-MEANS, non-negative matrix factorization (NMF) analyses will improve risk analysis and could meet different classification results to find gaps.Practical implicationsIn this paper, CSF and project phase dimensions are viewed together which is necessary to meet better results for simplifying social and economic benefits. Merge the new findings and latest technologies could prepare the best results and enable managers to create a better framework or implement key factors for minimizing waste.Originality/valueThis paper moves the definition of MTO organizations beyond measuring cost, complexity and financial variables by clustering CSFs in different phases of projects. So, the results enable managers to use this concept in their daily production to minimize waste and could be implemented to efficiently choose factors.

Evaluation of Graduate Student Innovation Capability Cultivation Based on Self-Organizing Networks

Evaluation of Graduate Student Innovation Capability Cultivation Based on Self-Organizing Networks

A role for cross-linking proteins in actin filament network organization and force generation

The high turgor pressure across the plasma membrane of yeasts creates a requirement for substantial force production by actin polymerization and myosin motor activity for clathrin-mediated endocytosis (CME). Endocytic internalization is severely impeded in the absence of fimbrin, an actin filament crosslinking protein called Sac6 in budding yeast. Here, we combine live-cell imaging and mathematical modeling to gain insights into the role of actin filament crosslinking proteins in force generation. Genetic manipulation showed that CME sites with more crosslinking proteins are more effective at internalization under high load. Simulations of an experimentally constrained, agent-based mathematical model recapitulate the result that endocytic networks with more double-bound fimbrin molecules internalize the plasma membrane against elevated turgor pressure more effectively. Networks with large numbers of crosslinks also have more growing actin filament barbed ends at the plasma membrane, where the addition of new actin monomers contributes to force generation and vesicle internalization. Our results provide a richer understanding of the crucial role played by actin filament crosslinking proteins during actin network force generation, highlighting the contribution of these proteins to the self-organization of the actin filament network and force generation under increased load.

A review of research on the development of low-orbit Internet constellations

In recent years, with the surge in Internet users, innovative companies such as SpaceX and OneWeb have planned to build low-orbit Internet constellations, triggering a global boom in the development of low-orbit satellite Internet constellations. Therefore, it is of great significance to study the development of low-orbit Internet constellations at home and abroad and the technical difficulties currently faced by low-orbit Internet constellations in their development. This paper first introduces three representative low-orbit Internet constellation plans abroad (Starlink, OneWeb, Lightspeed) and the latest development of domestic constellations. Then, based on the characteristics of low-orbit Internet constellations, the five major technical challenges faced by low-orbit Internet constellations are analyzed in detail: constellation relative motion evolution prediction, constellation autonomous navigation and orbit determination, constellation variable path planning, constellation self-organization collaborative control, constellation communication and network services. Finally, based on the current development trend of low-orbit Internet constellations, suggestions are given for the development of low-orbit Internet constellations in my country based on my country's national conditions.

Deep self-organizing map neural networks improve the segmentation for inadequate plantar pressure imaging data set

ABSTRACT This study introduces a deep self-organizing map neural network based on level-set (LS-SOM) for the customization of a shoe-last defined from plantar pressure imaging data. To alleviate the over-segmentation problem of images, which refers to segmenting images into more subcomponents, a domain-based segmentation model of plantar pressure images was constructed. The domain growth algorithm was subsequently modified by optimizing its parameters. A SOM with 10, 15, 20, and 30 hidden layers was compared and validated according to domain growth characteristics by using merging and splitting algorithms. Furthermore, we incorporated a level set segmentation method into the plantar pressure image algorithm to enhance its efficiency. Compared to the literature, this proposed method has significantly improved pixel accuracy, average cross-combination ratio, frequency-weighted cross-combination ratio, and boundary F1 index comparison. Using the proposed methods, shoe lasts can be designed optimally, and wearing comfort is enhanced, particularly for people with high blood pressure.

Self-Organizing and Routing Approach for Condition Monitoring of Railway Tunnels Based on Linear Wireless Sensor Network.

Real-time status monitoring is crucial in ensuring the safety of railway tunnel traffic. The primary monitoring method currently involves deploying sensors to form a Wireless Sensor Network (WSN). Due to the linear characteristics of railway tunnels, the resulting sensor networks usually have a linear topology known as a thick Linear Wireless Sensor Network (LWSN). In practice, sensors are deployed randomly within the area, and to balance the energy consumption among nodes and extend the network's lifespan, this paper proposes a self-organizing network and routing method based on thick LWSNs. This method can discover the topology, form the network from randomly deployed sensor nodes, establish adjacency relationships, and automatically form clusters using a timing mechanism. In the routing, considering the cluster heads' load, residual energy, and the distance to the sink node, the optimal next-hop cluster head is selected to minimize energy disparity among nodes. Simulation experiments demonstrate that this method has significant advantages in balancing network energy and extending network lifespan for LWSNs.

Forecasting changes in the earth’s climate system by instrumental measurements and paleodata in the phase-time region, consistent with changes in the barycentric motions of the sun. Part 1

The relevance of the research is due to the need to establish the root cause of climate change on Earth and predict changes in heliocosmic, climatic variables, natural disasters by intramental measurements and paleodata, as time series, for long intervals of the time horizon in the phase domain, taking into account their cyclicity and interdependence of changes. Purpose of research: application of the time series analysis method of the time series to establish the strength of the influence of the barycentric movements of the Sun on the variability of heliocosmic, climatic variables, natural disasters and fires, high-precision prediction of variables in the phase-time domain for long horizons of hundreds and thousands of years by instrumental measurements and paleodata. Objects of research: time series of changes in heliocosmic and climatic variables, natural disasters and fires, curves of changes in climatic changes obtained by analysis of ice cores in Antarctica and bottom sediments in Antarctica. Methods of research: wavelet phase-frequency and phase-time analysis of images of heliocosmic and climatic variables, natural disasters and fires; calculation of the consistency of changes in selected groups on a set of phase-time characteristics of variables in a sliding mode. Main results of research: In the images of wavelet phase-time functions of many variables constructed from observations in 1600–2010, Jose periods of ~178 years were found, which are contained in changes in the characteristics of barycentric movements of the Sun, solar activity, solar constant, CO2,N2OEl Nino, solar wind, the level of the Caspian Sea, temperature in Greenland, the speed of rotation of the Earth, snow accumulation rates in the Indian Ocean sector of Antarctica, global temperature; cyclicity of changes in heliocosmic and climatic variables are consistent with changes in Baricentre; there is a significant difference in the consistency of changes in the phase-time characteristics of variables obtained at the southern hot and northern cold latitudes of the planet by about 2.1 times; in the northern part of the planet, changes in variables are more chaotic, due to the different influence of changes in the Baricentre variable, the magnetic fields of the Sun and the Earth on the variability of variables at different latitudes of the planet; significant variability and resonances of the phase characteristics of heliocosmic and climatic variables are observed by changes in Baricentre on the graphs of coordinated changes in groups of variables in the observed and predicted time intervals. The studies reveal multiple oscillatory responses of the Earth’s climate system to the impact of the Baricentre variable due to the heterogeneity of its components and the Earth’s magnetosphere in space. Changes in the set of phase-time characteristics of variables on the same graph in the observed and predicted time intervals in the interval ± π are displayed as changes in autowaves characteristic of self-organizing systems, characterizing climatic changes in environments in combination with the influence of the variable Baricentre.

Performance Evaluation of Routing Algorithm in Satellite Self-Organizing Network on OMNeT++ Platform

Self-organizing networks of small satellites have gradually gained attention in recent years. However, self-organizing networks of small satellites have high topological change frequency, large transmission delay, and complex communication environments, which require appropriate networking and routing methods. Therefore, this paper, considering the characteristics of satellite networks, proposes the shortest queue length-cluster-based routing protocol (SQL-CBRP) and has built a satellite self-organizing network simulation platform based on OMNeT++. In this platform, functions such as the initial establishment of satellite self-organizing networks and cluster maintenance have been implemented. The platform was used to verify the latency and packet loss rate of SQL-CBRP and to compare it with Dijkstra and Greedy Perimeter Stateless Routing (GPSR). The results show that under high load conditions, the delay of SQL-CBRP is reduced by up to 4.1%, and the packet loss rate is reduced by up to 7.1% compared to GPSR. When the communication load is imbalanced among clusters, the delay of SQL-CBRP is reduced by up to 12.7%, and the packet loss rate is reduced by up to 16.7% compared to GPSR. Therefore, SQL-CBRP performs better in networks with high loads and imbalance loads.