Self-organized Structures Research Articles

Green innovation ecosystems: Spatial organization mode and associated network renewal under coupling effect

The coupling effect of green innovation participants, elements, and environments directly reflects the operation and evolution of a green innovation ecosystem. This paper studies the spatial organization mode of green innovation ecosystems and the structural evolution and self-organization mode of their associated networks under this coupling effect. By selecting relevant data from 286 cities in China from 2010 to 2021, the green innovation ecosystem coupling coordination index (GIECCI) is calculated using a coupling coordination model. The calculation result is used as a weight to modify a gravity model to construct an innovation ecological network. After analyzing the calculation results, the findings are as follows. (1) The polarization effect of the GIECCI is significantly alleviated, presenting a quantitative structure close to the normal distribution; However, the existence of the Matthew effect trap leads to great resistance to green innovation in some cities. (2) Cities with the same or a similar GIECCI are more likely to form agglomerations, including mature, cross-distribution, and budding agglomeration models. (3) The connectivity status of urban nodes significantly varies, and the mismatch between connectivity symmetry and the total strength of green innovation gravity is widespread. These findings may provide a reference for government departments to plan the layout of green innovation industries and choose sustainable innovation strategies for enterprises.

Self-organized submicron structures in photoresist films by UV-laser irradiation at water-confined conditions

A new kind of self-organized pattern formation process has been found during laser irradiation of polymer films in water confinement just below the laser ablation threshold, resulting in a randomly oriented pattern with a period of about 475 nm. The morphology, orientation, period, and amplitude of these patterns are inconsistent with both laser-induced periodic surface structures that typically consist of linear grooves with periods smaller the laser wavelength and wrinkling patterns that feature a much larger period and appear at layered systems. Excimer laser (λ = 248 nm, tp = 25 ns) exposure of 650 nm thick photoresist films on silicon wafers cause the growth of irregular submicron patterns. The pattern morphology that is examined with imaging techniques is correlated to processing parameters. The amplitude of these laser-induced self-organized (LISE) submicron structures are strengthened with pulse number and laser fluence. The experimental results are discussed together with simulations of laser heating the photoresist film in water confinement. The proposed pattern formation mechanism of such laser-induced self-organized submicron structures at temporal excitation of a confined polymer surface comprises the formation of an oriented roughness based on LIPSS that are developed to wrinkled structures due to the transient formation of a soft subsurface area that provides conditions for wrinkling of the water cooled, stiff polymer surface by laser-induced stress fields. Size, amplitude, and morphology of the LISE pattern provide good properties for applications in such fields as wetting, friction, optics, and bioactivity.

Slanted Structure of Blue Phase II Self-Aligned on One-Dimensional Patterned Surfaces.

Orientation analysis of liquid crystal (LC) devices is important for understanding and controlling light propagation behavior in these devices. Blue phases (BPs)─unique LC phases that self-organize into three-dimensional helical structures─are candidate materials for LC-based reflective diffractors. We realized BPII deflectors with large diffraction angles using one-dimensional surface alignment patterns. We analyzed the BPII lattice arrangement in the deflector based on Bragg reflections and demonstrated that the lattice structure was slanted with respect to the substrate, which was confirmed using transmission electron microscopy. In addition, we calculated the similarity between the alignment pattern on the substrate surface and the director arrangement of BPII near the interface, confirming that the structure observed experimentally matched the most probable calculated structure. Thus, this study contributes to the optical design of BPII deflectors and provides information on the orientation mechanism in high-order self-organizing soft matter structures.

Self-organization of primitive metabolic cycles due to non-reciprocal interactions

One of the greatest mysteries concerning the origin of life is how it has emerged so quickly after the formation of the earth. In particular, it is not understood how metabolic cycles, which power the non-equilibrium activity of cells, have come into existence in the first instances. While it is generally expected that non-equilibrium conditions would have been necessary for the formation of primitive metabolic structures, the focus has so far been on externally imposed non-equilibrium conditions, such as temperature or proton gradients. Here, we propose an alternative paradigm in which naturally occurring non-reciprocal interactions between catalysts that can partner together in a cyclic reaction lead to their recruitment into self-organized functional structures. We uncover different classes of self-organized cycles that form through exponentially rapid coarsening processes, depending on the parity of the cycle and the nature of the interaction motifs, which are all generic but have readily tuneable features.

Gradient self-organized dislocation in expanded austenite layer during low temperature nitriding

Abstract A typical nitrogen expanded austenite layer is formed by plasma-based low-energy nitrogen ion implantation (PBLEII) on AISI 304L austenitic stainless steel at a moderate temperature of 380° C. The dislocation self-organization structure in the nitrogen expanded austenite layer is characterized as an evolution from partial and Lomer-Cottrell dislocations in the inner layer near the interface to multilayer stacking faults in the outer nitrided layer. The self-organized dislocation density and forms are essentially dependent on the plastic deformation, strain-gradient, and nitrogen-related stacking fault energies, respectively, due to the constrained expansion in the nitrided layer. As the nitrogen concentration in the austenitic matrix increases, the stacking fault energy gradually decreases, resulting in the transformation of the defect from Lamer-Cottrell dislocations to a multilayer stacking faults. The appropriate stress, which is associated with orderly stress relief during dislocation self-organization, preserves the integrity of the nitrided layer with a combinedly improved in wear and corrosion resistance. Nitriding-induced dislocation self-organization is basically explored as the formation mechanism of the nitrogen expanded austenite layer, contributing to development of the specific low-temperature nitriding austenitic steel.

Self-organization and evolution of structure and function in cultured neuronal networks

Cultured neuronal networks (CNNs) have recently achieved major relevance as an alternative to in vivo models. While many works investigate the evolution of functional connectivity alone, experimental evidence of the simultaneous change of the structural neuronal network substrate is scarce. In the present study, we monitored the coevolution of structural and functional connectivities of neuronal cultures grown on top of microelectrode arrays, in a setup that allows the simultaneous recording of electrophysiological signals and microphotography detailed to the single-link level. During the observed 3 weeks lifespan, initially isolated invertebrate neurons form an ex novo complex circuitry of neuronal aggregates characterized by a small-world topology, with abundant neuronal loops (high clustering) and short distances. At the same time, the observation of synchronization events among electrodes reaches a maximum, coinciding with the spatial percolation of the neuronal network. At this stage, the correlation between the structural and the corresponding functional network is the largest, with around 15% of the physical links relating electrodes that fire synchronously. As the culture matures, this correlation smoothly declines but becomes more significant. Finally, at the local level, we found that the electrodes supporting the most coherent activity, the functional hubs, are not hubs in the physical circuitry but nodes with an average degree. This study demonstrates that functional networks of self-organized neuronal systems are discreet proxies of the underlying structure. It paves the way for future investigations to elucidate the intricate relationship between structure and function in other scenarios mediated by external stimulation.

AUTOMATED DEFINITION OF THE DISCRETE ELEMENTS INTERACTIONS IN WORKSPACE OF EQUIPMENT

Automated monitoring of the presence of such particles present near the main operational means of production or medical equipment with the determination of their trajectories is necessary to improve the efficiency of this equipment and the quality of operations. When performing measurements of the parameters of abstract objects of different origin and properties, for example, at precise parts production, problems of contamination of the surface of the object with discrete particles of another origin are often encountered. It is now known that every abstract entity forms around the area of the presence of solid particles. These solid particles, under the action of interaction forces, have the property to be ordered in space and on the surface of the object. This paper is a result of research and modelling of the interaction of such particles during their shredding and their structural self-organization. Severally consideration is given to the formation of dust layers under the action of coupling forces is reviewed. Models of behaviour of these layers for some typical surface forms of control object are created.

Low-temperature anomalies and nanosized levels formation of self-organized structures in the non-crystalline solids of As(Ge)-S(Se) systems

The relationship between low-temperature anomalies of physical and chemical properties and the formation of self-organized structures in non-crystalline solids of the As(Ge)-S(Se) systems is discussed. Obtained temperature dependences for the specific heat capacity and coefficient of linear expansion in the temperature domain , detailing the linear and nonlinear contributions. The influence of the obtaining conditions of non-crystalline solids on the low-temperature behavior of physico-chemical properties and the change in the ratios of various contributions and temperature intervals is considered. The correlation and common features of the formation of self-organized structures of non-crystalline materials in the region of low temperatures and softening temperatures, manifested in the presence of nanolevels of structuring, are analyzed.

Enhancing predictions of blast-induced ground vibration in open-pit mines: Comparing swarm-based optimization algorithms to optimize self-organizing neural networks

The objective of this paper is to present a method for predicting blast-induced ground vibration in open-pit mines that is based on the use of self-organizing neural networks (SONIA) and metaheuristic algorithms. In order to improve the accuracy of the SONIA model, several metaheuristic algorithms were employed, including the Manta Ray Foraging Optimization (MRFO), Hunger Games Search (HGS), Aquila Optimization (AO), and Naked Mole-Rat Algorithm (NMRA). Additionally, the k-fold cross-validation technique was used to identify the best parameters for the algorithms, which were then used to retrain the models for predicting blast-induced ground vibration. The effectiveness of the proposed method was evaluated using a case study of an open-pit coal mine in Vietnam, which had 288 blasting events. The study found that SONIA was a suitable neural network for predicting blast-induced ground vibration due to its self-organizing structure, even with a small dataset containing complex relationships. However, the SONIA model could be further optimized using the metaheuristic algorithms to improve its accuracy. The study found that the MRFO-SONIA model was the most reliable and accurate, with the lowest error (MAE = 0.379, RMSE = 0.453, MAPE = 0.08) and the highest reliability (R2 = 0.896). Meanwhile, the HGS-SONIA, AO-SONIA, and NMRA-SONIA models provided lower performance, with MAE values of 0.455, 0.500, and 0.492, RMSE values of 0.552, 0.603, and 0.580, MAPE values of 0.100, 0.112, and 0.111, and R2 values of 0.845, 0.815, and 0.829, respectively. The results of this study demonstrated the potential of using metaheuristic-based SONIA models to enhance predictions of blast-induced ground vibration in open-pit mines. This approach could prove useful in other operations in open-pit mines where there is a need to predict vibrations or other disturbances/negative effects resulting from specific mining activities.

A Self-Organizing Multi-Layer Agent Computing System for Behavioral Clustering Recognition

Video behavior recognition often needs to focus on object motion processes. In this work, a self-organizing computational system oriented toward behavioral clustering recognition is proposed, which achieves the extraction of motion change patterns through binary encoding and completes motion pattern summarization using a similarity comparison algorithm. Furthermore, in the face of unknown behavioral video data, a self-organizing structure with layer-by-layer accuracy progression is used to achieve motion law summarization using a multi-layer agent design approach. Finally, the real-time feasibility is verified in the prototype system using real scenes to provide a new feasible solution for unsupervised behavior recognition and space-time scenes.

Quint points lattice and multistability in a damped-driven curved carbon nanotube oscillator model.

Single-walled carbon nanotubes (SWCNTs) can undergo arbitrarily large nonlinear deformations without permanent damage to the atomic structure and mechanical properties. The dynamic response observed in curved SWCNTs under externally driven forces has fundamental implications in science and technology. Therefore, it is interesting to study the nonlinear dynamics of a damped-driven curved SWCNT oscillator model if two control parameters are varied simultaneously, e.g., the external driven strength and damping parameters. For this purpose, we construct high-resolution two-dimensional stability diagrams and, unexpectedly, we identify (i) the existence of a quint points lattice merged in a domain of periodic dynamics, (ii) the coexistence of different stable states for the same parameter combinations and different initial conditions (multistability), and (iii) the existence of infinite self-organized generic stable periodic structures (SPSs) merged into chaotic dynamics domains. The quint points lattice found here is composed of five distinct stability domains that coalesce and are associated with five different periodic attractors. The multistability is characterized by the coexistence of three different multi-attractors combinations for three exemplary parameter sets: two periodic attractors, two chaotic attractors, or one periodic and one chaotic attractor. This study demonstrates how complex the dynamics of a damped-driven curved SWCNT oscillator model can be when parameters and initial conditions are varied. For this reason, it may have a relevant impact on new theoretical and experimental applications of damped-driven curved SWCNTs.

Toward Trusted Unmanned Aerial Vehicle Swarm Networks: A Blockchain-Based Approach

The unmanned aerial vehicle (UAV) swarm is widely applied in many fields, such as disaster relief and geological exploration. However, it is highly vulnerable when facing an attack from internal malicious UAVs due to the underlying disconnected, intermittent, and limited (DIL) network environment. Recently, building trusted networks has been considered as a strategy to address security issues. Nevertheless, deploying a trusted network in a swarm faces many challenges imposed by the dynamic topology and limited resources of DIL networks. In order to overcome these challenges, we developed a blockchain-based UAV trusted self-organizing networking mechanism (BC-UTSON) to enhance internal security. Three key technologies are proposed for assisting BC-UTSON in achieving trustworthiness. UAV practical Byzantine fault tolerance (U-PBFT) is proposed to guarantee the lightweight consensus and real-time full-node trustworthiness evaluation under the hierarchical self-organizing networking structure of a UAV swarm. A blockchain-based multiweighted subjective logic (BMWSL) scheme has been designed to identify malicious UAV nodes based on the trustworthiness evaluation, which also assists the trusted path quality-aware dynamic routing (TPDR) mechanism to prevent data from routing through malicious nodes. The experimental results show that the proposed BC-UTSON performs much better than nonblockchain-based mechanisms for the UAV swarm.

Corrosion behavior of self-organized TiO2 nanotubular arrays grown on Ti6Al4V for biomedical applications

The electrochemical interactions between a biomaterial and a human fluid are important to understand when developing surfaces on titanium biomedical composites to improve bone regeneration, healing times, and longevity. Electrochemical corrosion is a major problem in implant-tissue interaction in that environment. The purpose of this research is to use different electrochemical methods such as open circuit potential (OCP), electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PDP) to examine the effect of self-organized TiO2 nanotubular surfaces on the corrosion resistance of Ti6Al4V surface. The electrochemical behavior of polished Ti6Al4V (control), and anodized samples at a constant voltage of 30V and various time-periods of anodization, such as 3hr, 4hr, and 5hrs respectively is the main focus of our study. All the anodized samples were annealed at 500 °C for 3hr with preheating rate of 5 °C per minute. The resistance of the nanotubular structure is improved after annealing because the anatase phase is stabilised, and the fluoride ions are removed. The electrochemical behavior of the nanotubular surfaces were investigated by using the simulated body fluid (SBF, 7.4 pH) solution. Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS), X-ray Diffraction (XRD), Atomic Force Microscopy (AFM), and a Drop Shape Analyzer were all used to investigate the nanotubular morphology, chemical compositions, and structure. According to the findings, the anodic oxidation process results in a nanotubular structure that improves the surface area and influences the surface's chemical composition. Surfaces with self-organized TiO2 nanotubular structures had greater corrosion resistance than polished Ti6Al4V because the passive film had a larger surface area and a more dispersed amorphous crystal structure. Finally, the physical condition of the post-electrochemical treated sample was evaluated by the SEM images and compositional analysis by the EDS and XRD. The PDP curves showed the corrosion rate of the samples polished Ti6Al4V, TiO2 30V 3hr, TiO2 30V 4hr, and TiO2 30V 5hrs are 0.39, 0.35, 0.09, and 0.24 mm per year respectively. Similarly, the efficiencies of the anodized samples TiO2 30V 3hr, 4hr, and 5hrs are 9.96, 76.44, and 38.30% respectively. When comparing all of the anodized samples, the overall corrosion resistance is improved by 2 times.

RISK MANAGEMENT OF INNOVATION ACTIVITIES IN THE DIGITAL ECOSYSTEM

The Purpose of the Study is to elaborate the content of the research categories in the field of innovation ecosystem development, to identify the features of creating network ecosystems according to various possible classification criteria, to form directions and models of risk-oriented management and digital adaptation for innovative ecosystems, and to outline the prospects for managing the risks of innovative activities of the digital ecosystem. The Research Methodology.is based on the existing theoretical results in the field of innovation management, risk management, and areas of digital transformation of ecosystems. The study uses a dialectical approach, system analysis, methods of strategic management, and modeling. The scientific Novelty lies in the formation of a model of risk management in the innovation ecosystem, which includes procedures for strategic analysis and strategic management, is the basis for the innovative development of the organization and is aimed at preventing risks and minimizing losses associated with the use of innovations. The Conclusions. Innovation ecosystems are self-organized structures that have stable relationships between individual participants, aimed at forming a flexible, adaptive environment. The innovative development of ecosystems facilitates the process from the birth of an idea to the development, commercialization, implementation, and support of various types of innovations. The study of the architecture of innovation ecosystems and key participants in different network systems confirms species diversity, differences in coverage, and interaction models. Identification of innovation ecosystems using a risk-based approach ensures the continuity of the processes of identifying existing risks, and their mitigation through the involvement of strategic management, and the use of monitoring tools, allowing updating the ecosystem architecture, identifying weaknesses, and implementing updated and improved models. The creation of adapted models for ecosystems in the context of digital adaptation outlines the prospects for the development of innovative digital ecosystems to the basic strategies of market behavior based on risk-oriented management.

Beltrami fields in partially ionized magnetized dusty plasma

The relaxed state of a magnetized four-component partially ionized dusty (PID) plasma is explored. PID plasma is composed of inertial electrons, positive ions, neutrals, and static negatively charged dust particles. When the neutrals are dragged along with the ions, the steady-state solution of vortex dynamic equations for inertial electron and ion species yields a triple Beltrami relaxed state, which is a linear combination of three single force-free fields and is characterized by three self-organized structures. The impact of plasma parameters such as Beltrami parameters and neutral (degree of ionization) and dust species densities on the nature of the relaxed state is investigated. Furthermore, the role of scale separation and its implication in space plasma are also presented. The present research will aid in elucidating the role of neutral and dust species in space, astrophysical, and laboratory plasmas.

Spiral Waves: Linear and Nonlinear Theory

Spiral waves are striking self-organized coherent structures that organize spatio-temporal dynamics in dissipative, spatially extended systems. In this paper, we provide a conceptual approach to various properties of spiral waves. Rather than studying existence in a specific equation, we study properties of spiral waves in general reaction-diffusion systems. We show that many features of spiral waves are robust and to some extent independent of the specific model analyzed. To accomplish this, we present a suitable analytic framework, spatial radial dynamics, that allows us to rigorously characterize features such as the shape of spiral waves and their eigenfunctions, properties of the linearization, and finite-size effects. We believe that our framework can also be used to study spiral waves further and help analyze bifurcations, as well as provide guidance and predictions for experiments and numerical simulations. From a technical point of view, we introduce non-standard function spaces for the well-posedness of the existence problem which allow us to understand properties of spiral waves using dynamical systems techniques, in particular exponential dichotomies. Using these pointwise methods, we are able to bring tools from the analysis of one-dimensional coherent structures such as fronts and pulses to bear on these inherently two-dimensional defects.

The Mind and the Brain: A Multiscale Interpretation of Cognitive Brain Functionality

Welcome to a special issue of Journal of Multiscale Neuroscience focused on The Mind and The Brain: A Multiscale Interpretation of Cognitive Brain Functionality. This special issue contains six articles that come from different disciplinary perspectives and methods that themselves span a range of spatiotemporal scales for analyzing cognition and behavior. At a time when the field of cognitive science is transitioning away from the computer metaphor of the mind and toward complex interactive frameworks (Spivey, 2023), these articles serve as waypoints for how to go about building those new theories. The articles include philosophical reviews of the processes that allow for self-organization to emerge in a multiscale cognitive system (Silberstein, 2023) and how best to model such multiscale processes (Favela, 2023). They include dense-sampling measures of postural movements (Corbin et al., 2023), time series analyses of music perception (Waddington & Balasubramaniam, 2023), and recurrence quantification analysis of spoken sentence comprehension (Nguyen & Spivey, 2023). And it all culminates in a big-picture perspective on how mental activity across any and all life forms may be best understood as emerging from collective action among sub-elements interacting to form self-organized metastable cognitive structures (Falandays et al., 2023).

Spike timing-dependent plasticity under imbalanced excitation and inhibition reduces the complexity of neural activity.

Excitatory and inhibitory neurons are fundamental components of the brain, and healthy neural circuits are well balanced between excitation and inhibition (E/I balance). However, it is not clear how an E/I imbalance affects the self-organization of the network structure and function in general. In this study, we examined how locally altered E/I balance affects neural dynamics such as the connectivity by activity-dependent formation, the complexity (multiscale entropy) of neural activity, and information transmission. In our simulation, a spiking neural network model was used with the spike-timing dependent plasticity rule to explore the above neural dynamics. We controlled the number of inhibitory neurons and the inhibitory synaptic weights in a single neuron group out of multiple neuron groups. The results showed that a locally increased E/I ratio strengthens excitatory connections, reduces the complexity of neural activity, and decreases information transmission between neuron groups in response to an external input. Finally, we argued the relationship between our results and excessive connections and low complexity of brain activity in the neuropsychiatric brain disorders.

Abstract 5917: Development of a medium-to-high throughput organoid-bacteria co-culture platform for the assessment of host pathogen interaction

Abstract Pathogens such as colibactin-producing bacteria strains of the Enterobacteriaceae family are highly prevalent in the gut microbiota of colorectal cancer (CRC) patients. HUB patient derived Organoids, (HUB-OrganoidsTM or PDOs) are self-organized epithelial cell structures with near-physiological features, extensively used to model aspects of cancer initiation and progression. Studies conducted by Pleguezuelos-Manzanos et al. (2020) showed that repetitive cycles of colibactin-producing bacteria into the organoid lumen via microinjection, led to two co-occurring mutational signatures appearance, a single-base substitution signature (SBS-pks) and an indel signature (ID-pks), identified in a subset of CRC patients (~2% and 7%, respectively). These results highlight the relevance of organoids for modelling host-pathogen interactions and the application of these systems as in vitro platforms for investigating long-standing cause-correlation relationship between the presence of genotoxic pathogens and CRC development. The limited scalability of sophisticated co-culture strategies with pathogens such as microinjection has represented a bottleneck in the use of organoids as throughput screening tools in the development of preventive therapies. Thus, there is an unmet need to develop alternative co-culture systems compatible with the process of compound screening. An organoid-bacteria co-culture system compatible with medium-to-high throughput screening readouts has been developed. This co-culture system is currently tailored for the modeling of colibactin genotoxic effects in the gut epithelium, but theoretically could be extended as a discovery platform to identify targetable, novel and complex interactions between host and pathogen. Key words: colibactin, organoids, CRC, screening Citation Format: Eider Valle-Encinas, Roshni Nair, Katerina Pisa, Mayke Doorn, Farzin Pourfarzad, Lani San Mateo, Nicole Desch, Prashanth Gokare, David Pocalyko, Sylvia F. Boj, Carla Verissimo. Development of a medium-to-high throughput organoid-bacteria co-culture platform for the assessment of host pathogen interaction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5917.

Research on Communication Stability of Inter-Cannonball Network Based on OPNET

In modern warfare, achieving strikes against military targets commonly involves utilizing methods such as fire coverage and missile precision guidance. While effective, fire coverage requires significant ammunition support, and missiles can be costly. Therefore, an intelligent strike solution can be an effective way to address these challenges. The inter-cannonball wireless communication network provides a solid foundation for inter-cannonball networking and joint strikes. The synergy improves the strike’s range and precision, ensuring own safety and destroying the enemy’s core with success. In this paper, the reliability of inter-cannonball group communication is studied. The vast array of activities and short striking distance of cannonballs make wireless networking between cannonballs a technological challenge. For this unique communication environment, the network of cannonballs is discussed, the self-organizing structure model of the network is proposed, the corresponding node topology is established, the protocol framework of inter-cannonball communication is presented, and the simulation parameters of the communication network are set. On this premise, discrete event simulation is performed by using the OPNET program to validate the impact of failure rate and node movement speed on the inter-cannonball network communication reliability. And the main performance indices of the inter-cannonball communication system are derived. The simulation results indicate that it can maintain basic communication stability, with wireless LAN delays of less than 100 ms, even when the node failure rate reaches 20%. The simulation verification method, on the other hand, overcomes the limitations of the real environment, optimizes the design phase, reduces research costs, and accelerates the development of intelligent cannonballs.