Self-organizing Research Articles

Network of cyano-p-aramid nanofibres creates ultrastiff and water-rich hydrospongels.

The structure-property paradox of biological tissues, in which water-rich porous structures efficiently transfer mass while remaining highly mechanically stiff, remains unsolved. Although hydrogel/sponge hybridization is the key to understanding this phenomenon, material incompatibility makes this a challenging task. Here we describe hydrogel/sponge hybrids (hydrospongels) that behave as both ultrastiff water-rich gels and reversibly squeezable sponges. The self-organizing network of cyano-p-aramid nanofibres holds approximately 5,000 times more water than its solid content. Hydrospongels, even at a water concentration exceeding 90 wt%, are hard as cartilage with an elastic modulus of 50-80 MPa, and are 10-1,000 times stiffer than typical hydrogels. They endure a compressive strain above 85% through poroelastic relaxation and hydrothermal pressure at 120 °C. This performance is produced by amphiphilic surfaces, high rigidity and an interfibrillar, interaction-driven percolating network of nanofibres. These features can inspire the development of future biofunctional materials.

Relaxation time as early warning signal of avalanches in self-organizing systems

Avalanches are sudden, destructive, and extremely difficult to forecast natural disasters that can result in numerous fatalities and extensive property damage. Given the immense danger posed by avalanches, there is a significant amount of attention paid to accurately predicting these events. We investigate the predictability of large avalanches in a class of self-organizing systems, which change their internal structure or function in response to external circumstances by manipulating or organizing other elements of the same system. Here, we propose a practical relaxation time to replace a traditional recovery time, and importantly, the relaxation time does not require the removal of part of the resources (perturb state variables) in the environment. This work provides examples of the forest fire model and sandpile model as self-organizing systems in which the relaxation time successfully predicts the onset of large avalanches. Furthermore, the relaxation time can show a consistent with the increasing trend in both oscillatory and nonoscillatory bifurcations, suggesting that the relaxation time is more universal than traditional indirect metrics such as the variance and the lag-1 autocorrelation function. We aim to identify early warning signals before the onset of large avalanches and provide scientific evidence and significant information for managers to formulate mitigation countermeasures and strategic decisions. Published by the American Physical Society 2024

Self-organized BMP signaling dynamics underlie the development and evolution of digit segmentation patterns in birds and mammals.

Repeating patterns of synovial joints are a highly conserved feature of articulated digits, with variations in joint number and location resulting in diverse digit morphologies and limb functions across the tetrapod clade. During the development of the amniote limb, joints form iteratively within the growing digit ray, as a population of distal progenitors alternately specifies joint and phalanx cell fates to segment the digit into distinct elements. While numerous molecular pathways have been implicated in this fate choice, it remains unclear how they give rise to a repeating pattern. Here, using single-cell RNA sequencing and spatial gene expression profiling, we investigate the transcriptional dynamics of interphalangeal joint specification in vivo. Combined with mathematical modeling, we predict that interactions within the BMP signaling pathway-between the ligand GDF5, the inhibitor NOGGIN, and the intracellular effector pSMAD-result in a self-organizing Turing system that forms periodic joint patterns. Our model is able to recapitulate the spatiotemporal gene expression dynamics observed in vivo, as well as phenocopy digit malformations caused by BMP pathway perturbations. By contrasting in silico simulations with in vivo morphometrics of two morphologically distinct digits, we show how changes in signaling parameters and growth dynamics can result in variations in the size and number of phalanges. Together, our results reveal a self-organizing mechanism that underpins amniote digit segmentation and its evolvability and, more broadly, illustrate how Turing systems based on a single molecular pathway may generate complex repetitive patterns in a wide variety of organisms.

An adaptive intelligent routing algorithm based on deep reinforcement learning

A mobile ad hoc network (MANET) is a self-organizing network composed of multiple mobile nodes. These nodes achieve distributed communication and wireless resource sharing through autonomous collaboration. However, typical routing protocols for MANETs suffer from uneven network load, low network throughput, poor anti-destruction performance, and low transmission reliability. In order to meet the routing protocol characteristics of distributed adaptability, reliability and anti-destruction, balance of load and energy consumption and guarantee of high QoS in MANETs, this paper proposes an adaptive intelligent routing algorithm based on the PER-D3QN model of deep reinforcement learning. To achieve better routing strategy selection, the routing problem is first modeled, a distributed multi-agent reinforcement learning routing framework is designed, and a deep reinforcement learning reward function considering multiple factors is used. Next, the implementation process of the adaptive intelligent routing algorithm using the PER-D3QN model is described in detail. Finally, a mobile self-organizing network environment was constructed based on Networkx, and corresponding data was collected to simulate the algorithm. The simulation results show that the adaptive intelligent routing algorithm proposed in this paper can balance the energy consumption of the network and the load of nodes, its average remaining energy has increased by more than 10% and PL is reduced by at least 10% compared to other algorithms, while guaranteeing high QoS performance of routing, that is, reducing the average end-to-end delay of data packet transmission by over 22% compared to other algorithms and maintaining a high packet delivery ratio.

Energy­efficient routing protocol in wireless sensor networks

Recently, self-organizing networks have gained popularity, which are distinguished by the possibility of connecting terminals anywhere and at any time without the need for a centralized network infrastructure. Mobile self-organizing networks are a promising direction for the development of information and communication systems. However, one should take into account the problems arising from the very nature of such networks: mobile nodes lead to an increase in the dynamism of the network topology due to the possibility of disconnection and the probability of movement; the limited supply of power sources of nodes requires careful management of power consumption in the design of hardware and protocols. Energy-saving design in self-organizing networks is more complex and important than in other wireless networks. The lack of infrastructure requires mobile nodes to act as routers, which increases traffic and therefore power consumption. Also, the energy-saving mode must take into account different network performance criteria, which can be caused by different protocols and power limitations. Thanks to the latest advances in technology and the rapid development of the production of small and affordable sensors, it has become possible to use them technically and economically effectively. These electronic sensors, which have high sensitivity, measure environmental parameters and convert the received data into an electronic signal that is transmitted over a wireless channel. After processing this signal, you can get information about the properties of objects or events that occur near the sensor. The application of a large number of such sensors, which can monitor a large area and exchange data wirelessly, forms a single wireless sensor network. Route allocation is a key challenge for resource-constrained networks. With the growth of computing power in wireless sensor technology, these sensor nodes are required to be equipped with more and more capabilities to perform more complex tasks. Each sensor is limited by its energy level, processing power, and ability to perceive information. Thus, the network of these sensors forms a more reliable and accurate structure. The article discusses the issue of using the HABRP protocol for its application in wireless sensor networks. The use of the HABRP protocol allows for improvement in the efficiency of wireless sensor networks, namely their energy efficiency. Therefore, the energy efficiency and simplicity of the HABRP protocol make it an excellent candidate for use in wireless sensor networks.

An AI-Enabled Framework to Defend Ingenious MDT-Based Attacks on the Emerging Zero Touch Cellular Networks

Deep automation provided by self-organizing network (SON) features and their emerging variants such as zero touch automation solutions is a key enabler for increasingly dense wireless networks and pervasive Internet of Things (IoT). To realize their objectives, most automation functionalities rely on the Minimization of Drive Test (MDT) reports. The MDT reports are used to generate inferences about network state and performance, thus dynamically change network parameters accordingly. However, the collection of MDT reports from commodity user devices, particularly low cost IoT devices, make them a vulnerable entry point to launch an adversarial attack on emerging deeply automated wireless networks. This adds a new dimension to the security threats in the IoT and cellular networks. Existing literature on IoT, SON, or zero touch automation does not address this important problem. In this paper, we investigate an impactful, first of its kind adversarial attack that can be launched by exploiting the malicious MDT reports from the compromised user equipment (UE). We highlight the detrimental repercussions of this attack on the performance of common network automation functions. We also propose a novel Malicious MDT Reports Identification framework (MRIF) as a countermeasure to detect and eliminate the malicious MDT reports using Machine Learning and verify it through a use-case. Thus, the defense mechanism can provide the resilience and robustness for zero touch automation SON engines against the adversarial MDT attacks.

An Anti-Collision Algorithm for Self-Organizing Vehicular Ad-Hoc Network Using Deep Learning

The rapid increase of the number of motor vehicles over the last several decades has driven a corresponding increase in the severity of traffic congestion, which has exhibited a considerable human impact. Intelligent anti-collision control via inter-vehicle communication technology can facilitate vehicles adhering to spacing speed standards and improve road utilization and traffic efficiency. In this paper, we apply a deep learning image estimation model based on joint attention mechanism. The network framework uses a deep estimation network Yolov5 and a location based VANET information fast transmission strategy to work together. This paper mainly considers vehicle distance measurement technology as a point of entry to study multi-sensor information fusion for vehicle collision prevention technology based on the self-organizing vehicular ad-hoc network (VANET). This paper also proposes a solution based on the strategy of one-way transmission of shared information and dynamic valuation of a cluster distance threshold with vehicle density. The proposed vehicle anti-collision control algorithm is designed to realize dynamic vehicle control via inter-vehicle communication. In this paper, the two-way coupling of traffic flow and network simulator is used to randomly generate vehicle nodes on the road, and the behavior of the anti-collision system is simulated. The experimental results show that the predetermined control goal is achieved, which demonstrates the effectiveness of the proposed algorithm.

Цифровой радиоприёмник на основе нейронной сети

One of the areas of digital signal processing used in radio receivers and currently being investigated is the use of artificial neural networks. Signal reception is one of the most difficult theoretical and engineering problems in message transmission. The difficulty lies in the fact that messages need to be extracted from modulated signals, which are exposed to various distorting factors and interference in the radio channel. Therefore, it is desirable to have methods of reception that would be the best (optimal) in the given conditions. There are many different neural network topologies. Single–layer and multi-layer direct propagation are known – perceptrons, recurrent networks, self-organizing networks, as well as hybrid networks (radial-basis, hierarchical classifiers). Each of these types of topologies has its own advantages and disadvantages.The article analyzes current research and development in this area. The implementation of a radio receiver (demodulator) based on a multilayer perceptron is shown and the neural network is trained. Using National Instruments PXI equipment, a study was carried out, which showed that in comparison with an optimal receiver, the probability of a bit error in a receiver based on a neural network is higher, but insignificant. The operation of the receiver under the influence of harmonic interference showed that as the power of the interference increases, the probability of a BER error increases, and the closer the interference is to the carrier frequency, the higher the BER also becomes. Nevertheless, the neural network-based digital receiver under study remains operational at significant levels of interference that are close enough to the carrier frequency of the desired signal.

Designing self-organizing systems using surrogate models and the compromise decision support problem construct

In this paper, we address the following question:How can self-organizing system designers steer the system towards expected global behaviors that satisfy multiple conflicting performance indicators?Self-organizing systems (SOS) have advantages in performing tasks in exploratory and hazardous domains that are not suitable for humans. The design of the SOS is however difficult because negative emergence with unwanted behaviors is likely to happen and multiple conflicting performance indicators need to be considered. To address this challenge, in this paper, we propose an SOS design method using surrogate models and the compromise Decision Support Problem (cDSP) construct. Surrogate models are used to capture the relationship between low-level rules or parameters and high-level emerging system performance. And the cDSP construct is used to explore “good enough” solutions (characterized by rule adoption rates) while managing the trade-offs among conflicting performance indicators. The efficacy of the proposed method is illustrated using a multi-agent box-pushing problem in the Webots simulation environment. It is shown in the results that our method leads to a 6.9% improvement in time efficiency, an 8.4% improvement in energy efficiency, and 26.2% in system reliability.

Current computational methods for spatial transcriptomics in cancer biology.

Cells in multicellular organisms constitute a self-organizing society by interacting with their neighbors. Cancer originates from malfunction of cellular behavior in the context of such a self-organizing system. The identities or characteristics of individual tumor cells can be represented by the hallmark of gene expression or transcriptome, which can be addressed using single-cell dissociation followed by RNA sequencing. However, the dissociation process of single cells results in losing the cellular address in tissue or neighbor information of each tumor cell, which is critical to understanding the malfunctioning cellular behavior in the microenvironment. Spatial transcriptomics technology enables measuring the transcriptome which is tagged by the address within a tissue. However, to understand cellular behavior in a self-organizing society, we need to apply mathematical or statistical methods. Here, we provide a review on current computational methods for spatial transcriptomics in cancer biology.

Self-Organization of UAV Networks for Maximizing Minimum Throughput of Ground Users

Self-Organization of UAV Networks for Maximizing Minimum Throughput of Ground Users

Self-Organizing Mapping Neural Network Implementation Based on 3-D NAND Flash for Competitive Learning

Self-Organizing Mapping Neural Network Implementation Based on 3-D NAND Flash for Competitive Learning

Artificial Intelligence Empowers Emotional Expression and Aesthetic Imagery in Modern Chinese Literature

Abstract This paper explores how artificial Intelligence enhances emotional expression and aesthetic imagery in modern Chinese literature. The study focuses on the latent semantic analysis of literary texts, text mining challenges, techniques and algorithms for latent semantic analysis, and the application of artificial Intelligence in literary criticism. Through text representation, analysis of text mining challenges, latent semantic analysis (LSA), and the application of specific algorithms, this study provides an in-depth understanding of the intrinsic semantics of literary texts. The study employs text representation methods, such as the TF-IDF formulation, to deal with high-dimensional sparse text data. The main challenges include high dimensionality and discovering potential semantic relationships between words. Latent semantic analysis (LSA) realizes data dimensionality reduction through SVD singular value decomposition technique to extract the implicit relationships between words in text. The results show that LSA effectively pulls the potential semantic structure of the text, reduces the “noise”, simplifies the text vectors, and realizes the dimensionality reduction. In addition, the text clustering analysis using Kohonen self-organized feature mapping network reveals the semantic relationship and sentiment distribution among literary works. Intelligent technology can effectively enhance literary works’ emotional depth and aesthetic value and provide a new interpretation perspective for modern literature.

A Novel Multi-Head Self-Organized Operational Neural Network Architecture for Chronic Obstructive Pulmonary Disease Detection Using Lung Sounds

A Novel Multi-Head Self-Organized Operational Neural Network Architecture for Chronic Obstructive Pulmonary Disease Detection Using Lung Sounds

How industry integration improves the eco-efficiency of the textile industry: The case of Qun-yi village in China

During the formulation of policies related to China's Rural revitalization strategy in recent decades, industries (e.g., the textile industry) with high environmental restoration costs and low added value of social and economic benefits have been transformed or phased out to achieve eco-efficiency. In the policy implementation, a new self-organized social network of village committees, entrepreneurs, enterprises and villagers has been established to support village-level self-organization. Aiming to explore the nexus of industry integration and eco-efficiency for industry development in rural areas, we conducted a longitudinal single case study Qun-yi Village. In the evolution process from 1995 to 2022, the textile industry integration process of the Village was divided into three periods. We found that first comprehensive industrial integration characterized by increasing complex industry structures and blurred industry boundaries, has greatly improved eco-efficiency by reducing economic and environmental restoration costs and increasing output value. Second, a successful village-level self-organization featured by strong village committee leadership, participation of actors and perceived benefits significantly facilitate and moderate the process of industrial integration aiming for high eco-efficiency. This study provided important theoretical and policy insights into the industrial integration for high eco-efficiency in rural areas in developing countries from the perspective of village-level self-organization.

Self-Organizing Chebyshev Fuzzy Neural Network Integral Terminal Sliding Mode Control of Active Power Filter

Self-Organizing Chebyshev Fuzzy Neural Network Integral Terminal Sliding Mode Control of Active Power Filter

An Identification Model of Sludge Bulking Based on Self-Organized Recurrent Fuzzy Neural Network

An Identification Model of Sludge Bulking Based on Self-Organized Recurrent Fuzzy Neural Network

Introduction to focus issue: Control of self-organizing nonlinear systems.

Introduction to focus issue: Control of self-organizing nonlinear systems.

Оценка уровня развития агломерационного эффекта в пространственной социально-экономической системе Санкт-Петербургской агломерации

A methodological approach to assessing the level of development of the agglomeration effect is proposed, tested on the example of administrative units of the largest urban agglomeration in Russia – St. Petersburg. The basis of the methodological approach was relative indicators that made it possible to assess the economic efficiency of the functioning of production activities (profitability), the efficiency of labor activity of the economically active population (labor productivity), the level of formation and spatial distribution of types of economic activity (sectoral uniformity coefficient), the level of spatial heterogeneity of types of economic activity (industry diversity coefficient). Based on the results of calculations of profitability and labor productivity indicators, they were clustered by administrative units of the studied agglomeration using the Kohonen self-organizing network method, which made it possible to determine three groups of levels (high, medium and low) and display their spatial distribution on a cartographic basis. As a result, the highest level of profitability is characteristic of the peripheral region of the St. Petersburg agglomeration – Gatchina, the average level was found in the core of the St. Petersburg agglomeration, as well as in the peripheral Vsevolozhsk, Lomonosov and Kirov regions. The group with low values included Tosnensky and Volosovsky districts, as well as Sosnovoborsky urban district. In terms of labor productivity, a high level was noted in the Lomonosovsky district, an average level in the Sosnovoborsky urban district, in the Vsevolozhsky and Kirovsky districts, and a low level in St. Petersburg, Volosovsky, Gatchina and Tosnensky districts. In terms of sectoral evenness, all administrative units of the studied agglomeration have an average level, and in terms of sectoral diversity, an average level was noted only in St. Petersburg, with a high level in all peripheral regions

Machine Learning Driven Threat Identification to Enhance FANET Security using Genetic Algorithm

Emerging as a highly promising technology, Flying Ad-hoc Networks represent self-organizing networks of Unmanned Aerial Vehicles (UAVs), garnering attention for their diverse applications spanning environmental monitoring, disaster management, precision agriculture, surveillance, and military operations. However, these networks face challenges to various security threats, including malicious node detection due to their deployment in dynamic environments. To address this issue, we present an improved novel security solution, Machine Learning-based Threat Identification for FANET using a Genetic Algorithm (ML-TIFGA) in this paper. The research includes the detection of abnormal behavior nodes using a basic genetic algorithm and dynamically adapting the changing network conditions by utilizing a reputation system. To enhance our security solution ML-TIFGA, we evaluated two key factors: cooperation and trustworthiness, which act as genetic elements within the chromosome of the flying node in our genetic population. Further, a mechanism is incorporated to reconfigure the trust, addressing the challenge of dynamically extracting threats through the updated weighted reputation system while considering past behavior monitoring. Significant improvements were found in the experimental results using actual sample values from the NSL-KDD dataset, which produced a remarkable 99.829% classification accuracy. Additionally, threat identification rates reached 98.36% for training and 98.86% for testing samples, with a remarkable improvement of 99.3% in network reliability through ML-TIFGA. When benchmarked against state-of-the-art approaches, performance metrics such as delay, throughput, and data delivery rate exhibited notable enhancements of 24.65%, 29.16%, and 31.73%, respectively.