Self-organizing Scheme Research Articles

Swirling Due to Misaligned Perception-Dependent Motility.

A system of particles with motility variable in terms of a vision-type of perception is investigated by a combination of Langevin dynamics simulations in two-dimensional systems and an analytical approach based on conservation law principles. Persistent swirling with predetermined direction is here induced by differentiating the self-propulsion direction and the perception cone axis. Clusters can have a fluidlike center with a rotating outer layer or display a solidlike rotation driven by the outer layer activity. Discontinuous motility with misaligned perception might therefore serve as a powerful self-organization strategy in microrobots.

Designing Complex Tapestries with Photography-Inspired Manipulation of Self-Organized Thin-Films.

Thin-films patterned with complex motifs are of fundamental interest because of their advanced optical, mechanical and electronic properties, but fabrication of these materials remains challenging. Self-organization strategies, such as immersion controlled reaction-diffusion patterning, have shown great potential for production of patterned thin-films. However, the autonomous nature of such processes limits controllable pattern customizability and complexity. Here, it is demonstrated that photography inspired manipulation processes can overcome this limitation to create highly-complex tapestries of micropatterned films (MPF's). Inspired by classical photographic processes, MPF's are developed, bleached, exposed, fixed, and contoured into user-defined shapes and photographic toning reactions are used to convert the chemical composition MPF's, while preserving the original stripe patterns. By applying principles of composite photography, highly complex tapestries composed of multiple MPF layers are designed, where each layer can be individually manipulated into a specific shape and composition. By overcoming fundamental limitations, this synergistic approach broadens the design possibilities of reaction-diffusion processes, furthering the potential of self-organization strategies for the development of complexmaterials.

Synthesis mechanism of pelleted heterostructure Ti64–TiB composites via an interdiffusion and self-organization strategy based on powder metallurgy

Titanium matrix composites (TMCs) are the most attractive next-generation structural materials for lightweight design due to their high specific modulus and specific strength. Designing heterogeneous grain structures has been a widespread strategy for overcoming the strength-ductility trade-off in powder metallurgical TMCs. However, achieving a customizable heterogeneous grain structure in TMCs remains a formidable challenge. Here, we propose a novel interdiffusion and self-organization strategy based on the powder metallurgy method to fabricate a pelleted heterostructure Ti6Al4V(Ti64)–TiB composites, which integrates a heterogeneous distribution of reinforcements and heterogeneous grain structures of the matrix. The study reveals that the formation of the pelleted heterostructures is primarily governed by the interdiffusion reaction process between Ti and Al, and V elements, coupled with the growth and distribution changes of TiB induced by the interdiffusion reaction. Moreover, the pellet size is tailored by the spherical Ti–TiB composite powder, allowing precise control over the heterogeneous grain regions. This research contributes valuable insights into the manufacturing process of pelleted heterostructure Ti64–TiB composites and provides a conceptual framework applicable to the design of similar structures in various metal matrix composite systems.

Resilience in Platoons of Cooperative Heterogeneous Vehicles: Self-Organization Strategies and Provably-Correct Design

Resilience in Platoons of Cooperative Heterogeneous Vehicles: Self-Organization Strategies and Provably-Correct Design

Synchronized rotations of active particles on chemical substrates.

Many microorganisms use chemical 'signaling' - a quintessential self-organizing strategy in non-equilibrium - that can induce spontaneous aggregation and coordinated motion. Using synthetic signaling as a design principle, we construct a minimal model of active Brownian particles (ABPs) having soft repulsive interactions on a chemically quenched patterned substrate. The interplay between chemo-phoretic interactions and activity is numerically investigated for a proposed variant of the Keller-Segel model for chemotaxis. Such competition not only results in a chemo-motility-induced phase-separated state, but also results in a new cohesive clustering phase with synchronized rotations. Our results suggest that rotational order can emerge in systems by virtue of activity and repulsive interactions alone without an explicit alignment interaction. These rotations can also be exploited by designing mechanical devices that can generate reorienting torques using active particles.

Patterning Complex Line Motifs in Thin Films Using Immersion Controlled Reaction-Diffusion.

The discovery of self-organization principles that enable scalable routes towards complex functional materials has proven to be a persistent challenge. Here reaction-diffusion driven, immersion controlled patterning (R-DIP) is introduced, a self-organization strategy using immersion controlled reaction-diffusion for targeted line patterning in thin films. By modulating immersion speeds, the movement of a reaction-diffusion front over gel films is controlled, which induces precipitation of highly uniform lines at the reaction front. A balance between the immersion speed and diffusion provides both hands-on tunability of the line spacing (d = 10 - 300 µm) as well as error-correction against defects. This immersion-driven patterning strategy is widely applicable, which is demonstrated by producing line patterns of silver/silver oxide nanoparticles, silver chromate, silver dichromate, and lead carbonate. Through combinatorial stacking of different line patterns, hybrid materials with multi-dimensional patterns such as square-, diamond-, rectangle- and triangle-shaped motifs are fabricated. The functionality potential and scalability is demonstrated by producing both wafer-scale diffraction gratings with user-defined features as well as an opto-mechanical sensor based on Moirépatterning. This article is protected by copyright. All rights reserved.

Loss-free tensile ductility of dual-structure titanium composites via an interdiffusion and self-organization strategy

The deformation-coordination ability between ductile metal and brittle dispersive ceramic particles is poor, which means that an improvement in strength will inevitably sacrifice ductility in dispersion-strengthened metallic materials. Here, we present an inspired strategy for developing dual-structure-based titanium matrix composites (TMCs) that achieve 12.0% elongation comparable to the matrix Ti6Al4V alloys and enhanced strength compared to homostructure composites. The proposed dual-structure comprises a primary structure, namely, a TiB whisker-rich region engendered fine grain Ti6Al4V matrix with a three-dimensional micropellet architecture (3D-MPA), and an overall structure consisting of evenly distributed 3D-MPA "reinforcements" and a TiBw-lean titanium matrix. The dual structure presents a spatially heterogeneous grain distribution with 5.8 μm fine grains and 42.3 μm coarse grains, which exhibits excellent hetero-deformation-induced (HDI) hardening and achieves a 5.8% ductility. Interestingly, the 3D-MPA "reinforcements" show 11.1% isotropic deformability and 66% dislocation storage, which endows the TMCs with good strength and loss-free ductility. Our enlightening method uses an interdiffusion and self-organization strategy based on powder metallurgy to enable metal matrix composites with the heterostructure of the matrix and the configuration of reinforcement to address the strength-ductility trade-off dilemma.

Self-Organizing Interval Type-2 Fuzzy Neural Network With Adaptive Discriminative Strategy

Covariate shift is a critical issue of interval type-2 fuzzy neural networks (IT2FNNs) due to the distribution discrepancy between training and testing samples. In this situation, IT2FNNs usually struggle to identify potential features from samples with explicit inductive biases. To address this problem, a self-organizing IT2FNN with an adaptive discriminative strategy (ADS-SOIT2FNN) is developed to maintain the identification performance in the presence of covariate shift. First, a granularity-based metric (GM), using higher-order statistics of local samples, is designed to distinguish the distribution discrepancy caused by covariate shift. The multiple kernels incorporated into GM are able to cover the sample features of the whole Hilbert space. Second, a self-organizing strategy, associated with GM-based discriminative information, is presented to alleviate the structural bias by growing and pruning fuzzy rules. Then, a compact structure of ADS-SOIT2FNN is achieved to adapt to the covariate shift of samples and further strengthen its inductive ability. Third, an adaptive risk mitigation learning algorithm (RMLA) is introduced to update the parameters of ADS-SOIT2FNN. RMLA can regulate the derivatives of parameters with arbitrary distribution samples, which is beneficial for maintaining the global accuracy by relieving the risk of parameter biases. Finally, the effectiveness of ADS-SOIT2FNN is verified by some experiments for identifying nonlinear systems with covariate shift.

Laser-Induced Transient Self-Organization of TiNx Nano-Filament Percolated Networks for High Performance Surface-Mountable Filter Capacitors.

Filter capacitors (FCs) are substantial for digital circuits and microelectronic devices, and thus more compact FCs are eternally demanded for system miniaturization. Even though microsupercapacitors are broadly regarded as an excellent candidate for future FCs, yet due to the limitation of available electrode materials, the capacitive performance of reported MSCs drops sharply under high-frequency alternating current. Herein, wepresent a unique laser-induced transient self-organization strategy, which synergizes pulsed laser energy and multi-physical field controlled coalescence processes, leading to the rapid and controllable preparation of titanium nitride ultrafine nano-filaments (diameter ≈3-5 nm) networks. Their chaotic fractal nanoporous structure, superior specific surface area, and excellent conductivity render these nanostructures promising candidates for FCs. Surface-mounted filter capacitors based on this electrode material exhibit ultra-long cycle-life (2 000 000 cycles) with record ultrahigh volumetric energy density of 9.17 mWh cm-3 at 120 Hz in aqueous electrolyte, displaying advantages in function, size, and integrability compared with the state-of-the-art aluminum electrolytic capacitors. The method here provides a versatile toolbox for designing novel nanostructures with intriguing characteristics and insights for developing advanced and miniaturized filter and power devices.

Deep awareness as a factor of environmental motivation at sea

There are environmental problems at sea and some of them are specific. The International Maritime Organization is on the way of strict controlling permanently intensification regulations. One of the most important of them is the International Convention for the Prevention of Pollution from Ships (MARPOL). To prevent uncontrolled and undesired actions of seafarers officers are interested in strong methods including motivating others and themselves as leadership skills are requested by practice and the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW). After considering different types of motives, factors in/decreasing their effectiveness existing at sea, the authors developed the practical research, analyzed the revealed data on the nature of the dominating motives among cadets of the Maritime University and the seafarers, and concluded highly developed awareness of the existing pollution at sea. Established forming experiment proved that the deep awareness of environmental problems adds to the inside, the strongest environmental motivation to fallow International regulations, acceptable from both behavioral and self-controlling points of view. Further actualized professional self-organization strategy helped to educational ecological project development, which were involved in the working courses.

Traffic light control design approaches: a systematic literature review

<span>To assess different approaches to traffic light control design, a systematic literature review was conducted, covering publications from 2006 to 2020. The review’s aim was to gather and examine all studies that looked at road traffic and congestion issues. As well, it aims to extract and analyze protruding techniques from selected research articles in order to provide researchers and practitioners with recommendations and solutions. The research approach has placed a strong emphasis on planning, performing the analysis, and reporting the results. According to the results of the study, there has yet to be developed a specific design that senses road traffic and provides intelligent solutions. Dynamic time intervals, learning capability, emergency priority management, and intelligent functionality are all missing from the conventional design approach. While learning skills in the adaptive self-organization strategy were missed. Nonetheless, the vast majority of intelligent design approach papers lacked intelligent fear tires and learning abilities.</span>

The self-management organization as a way for the in situ conservation of native poultry genetic resources

Objective: To identify local organization strategies employed by peasant women to conserve their native hens. Methodology: Informal dialogues were held with five peasant women from the rural community of La Cuchilla, Nopala de Villagrán, Hidalgo, Mexico. The information collected was analyzed based on self-management organizational elements. Likewise, the proposals of three authors were used as a foundation for the analysis of the community organizational points in question, with the aim of strengthening the self-management organization. Results: Peasant women use specific self-organization strategies to conserve their native hens, which are considered of great value as a result of their resistance to diseases and their adaptation to the local feeding and environmental conditions. Based on the findings and the authors consulted, an analysis framework is proposed to design of actions that strengthen the self-management participation of women in the conservation of their native poultry resource. Study Limitations: Further studies about women self-management organization mechanisms in rural communities aimed at the conservation of their resources are recommended. The said studies will enable a more complex description of their functionality, potential, and cooperation mechanisms, among other elements that strengthen their structure. Conclusion: The mechanisms of self-management organization have great potential for the local and community conservation of native poultry resources

Inner-Imaging Networks: Put Lenses Into Convolutional Structure.

Despite the tremendous success in computer vision, deep convolutional networks suffer from serious computation costs and redundancies. Although previous works address that by enhancing the diversities of filters, they have not considered the complementarity and the completeness of the internal convolutional structure. To respond to this problem, we propose a novel inner-imaging (InI) architecture, which allows relationships between channels to meet the above requirement. Specifically, we organize the channel signal points in groups using convolutional kernels to model both the intragroup and intergroup relationships simultaneously. A convolutional filter is a powerful tool for modeling spatial relations and organizing grouped signals, so the proposed methods map the channel signals onto a pseudoimage, like putting a lens into the internal convolution structure. Consequently, not only is the diversity of channels increased but also the complementarity and completeness can be explicitly enhanced. The proposed architecture is lightweight and easy to be implement. It provides an efficient self-organization strategy for convolutional networks to improve their performance. Extensive experiments are conducted on multiple benchmark datasets, including CIFAR, SVHN, and ImageNet. Experimental results verify the effectiveness of the InI mechanism with the most popular convolutional networks as the backbones.

Self-organizing Strategies in Game of Agent Movement

In this paper, a stochastic game model of self-organization of strategies of stochastic game of mobile agents in the form of cyclic behavioral patterns, which consist of coordinated strategies for moving agents in a limited discrete space, is developed. The behavioral pattern of a multi-agent system is a visualized form of orderly movement of agents that arises from their initial chaotic movement during the learning of a stochastic game. The mobility of multi-step stochastic game agents is ensured by the fact that in discrete moments of time they randomly, simultaneously and independently choose their own pure strategy of moving in one of the possible directions. Current player payments are defined as loss functions that depend on the strategies of neighboring players. These functions are formed from the penalty for irregular spacing of agents in a limited discrete space and the penalty for collisions when moving agents. Random selection of players’ strategies aims to minimize their average loss functions. The generation of sequences of pure strategies is performed by a discrete distribution based on the vectors of mixed strategies. The elements of the vectors of mixed strategies are the conditional probabilities of choosing the appropriate pure displacement strategies. Mixed strategies change over time, adaptively taking into account the value of current losses. This provides an increase in the probability of choosing those strategies that lead to a decrease in the functions of average losses. The dynamics of the vectors of mixed strategies is determined by the Markov recurrent method, for the construction of which a stochastic approximation of the modified condition of complementary non- rigidity, which is valid at Nash equilibrium points, is performed, and a projection operator for expandable unit epsilon simplex is applied. The convergence of the recurrent game method is ensured by compliance with the fundamental conditions and limitations of stochastic approximation. The stochastic game begins with untrained mixed strategies that set a chaotic picture of moving agents. During the learning of the stochastic game, the vectors of mixed strategies are purposefully changed so as to ensure an orderly, conflict-free movement of agents. As a result of computer simulation of stochastic game, cyclic patterns of self-organization of mobile agents on the surface of a discrete torus and within a rectangular region on a plane are obtained. The reliability of the experimental studies was confirmed by the similarity of the obtained patterns of moving agents for different sequences of random variables. The results of the study are proposed to be used in practice for the construction of distributed systems with elements of self-organization, solving various flow and transport problems and collective decision-making in conditions of uncertainty.

Soft female leadership (SFL) framework for driving the gender equality change in engineering education: learning outcomes of leader and leadership development

The foundation of gender equality was built some decades ago, but higher education institutions are far from achieving it. Perhaps the leadership needs to integrate new narratives for a greater commitment especially, in engineering and new tools for the existing toolbox. This study aims to share the outcomes of soft female leadership (SFL) development for gender equality at the RMEI network, entailing the commitment of top-managers from engineering schools, creation of new leaders to lead the change at their institutions, students acting as change-agents, and an active community of practice. The SFL toolbox comprises self-awareness, humanistic care, intuition, creativity, and trust. The transformation of mindset, skillset, and culture entails using Sustainable Development Goals (SDGs), ‘Systemic View of Life’ arguments, and drawing knowledge from organizational learning frameworks, scientific phenomena, and mechanisms, such as a) The 4I-Intuiting-Interpreting-Integrating-Institutionalizing organizational learning process starting from intuition to achieve an institutional change; b) The ‘Stigmergy’ scientific mechanism of self-organized collective schemes with coordinated actions and interactions, in which the action performed by an agent leaves a trace in the environment that stimulates subsequent actions; c) The ‘Spillover’ phenomenon advocating that the behavior of an agent can bring the adoption of related behaviors by other agents. RMEI gender equality plan was evaluated by the HORIZON2020 TARGET project consortium. The SFL excelled as successful in setting goals, articulating a policy that integrates systems approach frameworks, insights from science and technology, innovation, ecology, philosophy, self-awareness, ethics, and values. The Covid-19 pandemic disrupted physical meetings, but the process of change was not ceased at the network because we disrupted the disruption by boosting collaborative knowledge consolidation and dissemination processes. The SFL framework integrates context, regional, and temporal characteristics, alongside cognitive, affective, and motivational outcomes over behavioral outcomes, new mindsets beyond organizational skills, and collaborative learning over individual learning.

A Self-Organized Scheduling Algorithm for Embedded Real-Time Systems

In this paper, the authors present a self-organized approach for scheduling tasks on processors in embedded real-time systems. For such a mapping, two conflicting objectives have to be optimized: the reliability and the schedule length. Since the scheduling problem is NP-hard, a heuristic algorithm is used to produce schedules achieving different trade-offs between the two objectives. Moreover, a self-organization strategy based on dynamic crowding distance is adopted. This allows a better exploration of the objective space as well as an enhanced solution diversity. The proposed algorithm, reliability schedule length trad-offs algorithm (RSTA), is tested and compared with the popular SPEA2 algorithm where experimental results are promising on both quality and diversity of solutions.

Self-organization scheme for balanced routing in large-scale multi-hop networks

We propose a self-organization scheme for cost-effective and load-balanced routing in multi-hop networks. To avoid overloading nodes that provide favourable routing conditions, we assign each node with a cost function that penalizes high loads. Thus, finding routes to sink nodes is formulated as an optimization problem in which the global objective function strikes a balance between route costs and node loads. We apply belief propagation (its min-sum version) to solve the network optimization problem and obtain a distributed algorithm whereby the nodes collectively discover globally optimal routes by performing low-complexity computations and exchanging messages with their neighbours. We prove that the proposed method converges to the global optimum after a finite number of local exchanges of messages. Finally, we demonstrate numerically our framework’s efficacy in balancing the node loads and study the trade-off between load reduction and total cost minimization.

A organicidade dos egressos(as) da Licenciatura em Educação do Campo: uma construção em percurso

O presente ensaio tem por objetivo apresentar um relato de experiência em torno das estratégias de auto-organização de professores egressos(as) da Licenciatura em Educação do Campo na Universidade Federal do Recôncavo da Bahia. Para tal, fizemos uma revisão bibliográfica das principais referências da área de Educação do Campo sobre egressos/as articulando com nossas próprias vivências. Assim, emolduramos nosso estudo a partir da abordagem por meio de escrevivências, termo cunhado por Conceição Evaristo. Como resultado, evidenciamos a importância da organicidade em prol da superação dos desafios enfrentados por professores recém-formados em um curso contra-hegemônico. A pesquisa nos levou a compreender a necessidade de articulação permanente, ampliando e fortalecendo a rede de personalidades que constituem a Educação do Campo.
 Palavras-chave: auto-organização, recém-formados, LEdoC.
 
 The organicity of newcomer teachers who have a major in Education for the Countryside: a construction in progress
 ABSTRACT. This essay aims to present an experience report on the self-organization strategies of teachers who have a major in Education for the countryside in the Federal University of Recôncavo da Bahia. Thus, we carried out a bibliographical review of the preeminent academic references in the Rural Education for the countryside that study newcomer teacher. Additionally, we articulate the outcome of this research evidence with our own experiences. So, we framed our study by the living-and-writing (escrevivências) approach, a term coined by Conceição Evaristo. As a result, we highlight the importance to overcome the challenges faced by newcomer teacher in a counter-hegemonic course the search defends the need for permanent articulation, expanding and strengthening the network of people that constitute the Education for the countryside.
 Keywords: self-organization, newly graduate, LEdoC
 
 La organicidad de los egresados de la Grado en Educación de Campo: una construcción en curso
 RESUMEN. Este ensayo tiene como objetivo presentar un informe de experiencia sobre las estrategias de auto organización de los docentes egresados ​​de la Licenciatura en Educación Rural de la Universidad Federal de Recôncavo da Bahia. Para ello, realizamos una revisión bibliográfica de las principales referencias en el campo de la Educación Rural sobre egresados ​​articulando nuestras propias experiencias. Así, enmarcamos nuestro estudio desde el enfoque a través de los escritos, término acuñado por Conceição Evaristo. Como resultado, destacamos la importancia de la organicidad para superar los desafíos que enfrentan los docentes recién graduados en un curso contra hegemónico. La investigación nos llevó a comprender la necesidad de una articulación permanente, ampliando y fortaleciendo la red de personalidades que conforman la Educación Rural.
 Palabras clave: auto organización, recién graduado, LEdoC.

A self-organizing base station sleeping and user association strategy for dense cellular networks

Due to the rising concerns of energy consumption in wireless networks, base station (BS) sleeping strategies were introduced to save energy in low traffic scenarios. In this paper we analyse a weighted trade-off between energy consumption and user-perceived performance in dense cellular networks. We present an optimization problem representing this trade-off and derive properties of its optimal solutions. Using these properties we design a self-organizing strategy that dynamically (online) makes load-aware user association and BS operation decisions. Our strategy is self-organizing in the sense that it does not need any information or optimization beforehand, it simply relies on real-time load measurements at the BSs and user-reported SINR values. We furthermore present extensive simulation results, demonstrating the effectiveness of our self-organizing strategy and the impact of increased energy consumption on the user-perceived performance.

Nonlinear systems modelling based on self-organizing fuzzy neural network with hierarchical pruning scheme

In this paper, a self-organizing fuzzy neural network with hierarchical pruning scheme (SOFNN-HPS) is proposed for nonlinear systems modelling in industrial processes. In SOFNN-HPS, to strike the optimal balance between system accuracy and network complexity, an online self-organizing scheme for identifying the structure and parameters of the network simultaneously is developed. First, to enhance the characterization ability of the fuzzy rules for nonlinear systems, the asymmetric Gaussian functions that can partition the input space more flexibly are introduced as membership functions. Second, a hierarchical pruning scheme, which is designed by rule density and rule significance, is used to delete the redundant fuzzy rules while using the geometric growing criteria to generate fuzzy rules automatically, which can avoid the requirement of pre-setting the pruning threshold and prevent the mistaken deletion of significant rules. Third, an adaptive allocation strategy is adopted to set the antecedent parameters of the fuzzy rules in the learning process, which can not only adjust the region of generalized ellipsoidal basis functions for better local approximation, but also balance the accuracy of the system and the interpretability of the rule base obtained. Finally, to speed up the convergence of the estimation error, a modified recursive least square algorithm is used to update the consequent parameters of the resulting fuzzy rules online. In addition, the convergence proofs of the estimation error and the network linear parameters of SOFNN-HPS are given, and they are helpful in successfully applying the SOFNN-HPS in practical engineering. To verify the effectiveness of SOFNN-HPS, two benchmark test problems and a key water quality parameter prediction experiment in the wastewater treatment process are examined. The simulation results demonstrate that the proposed SOFNN-HPS algorithm can obtain a self-organizing fuzzy neural network with compact structure and powerful generalization performance. The source codes of SOFNN-HPS and other competitors can be downloaded from https://github.com/hyitzhb/SOFNN-HPS.