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The paper, titled "Déjà vu All Over Again: What the Past Can Teach Us About the Future" by William Mateer, Paul Tolchinsky, PhD, and Peter Turgoose, explores the contemporary challenges faced by today's organizations as they respond to the increasing turbulence in the world. The authors advocate for a response capability that is rooted in the foundational principles that make each organization successful yet unique, applying the lessons that the past can teach us about the future. The paper draws inspiration from three seminal books published in the late 1970s and early 1980s, reflecting on the enduring relevance of their insights in the present. The authors focus on Classics in Organization Behavior, Classics in Organization Theory, and Classics of Industrial and Organizational Psychology. The central narrative centers around the case study of GE Aviation (formerly GE Aircraft Engines) facility in Bromont, Quebec, Canada, a maverick organization which has thrived for over 40 years as a self-organizing workplace. The socio-technical perspective applied at Bromont originated from the work at the Tavistock Institute in the 1950s, challenging the prevailing technological imperative in favor of a more holistic approach that considers people as integral to the system, rather than expendable spare parts. The paper details Bromont's evolution, success factors, and challenges, emphasizing the importance of continuous adaptation, employee involvement, and a flat organizational structure. The authors derive key lessons from Bromont, including the significance of leadership support, rigorous hiring processes, the benefits of smaller organizational sizes, the role of managers as enablers, and the need for a unique, contextualized approach rather than blindly copying models. The paper concludes by advocating for the ongoing optimization of socio-technical systems, aligning growth and learning within a flat organizational structure, and maintaining a balance between new business strategies and foundational principles. [ABSTRACT FROM AUTHOR]

In this philosophical-theoretical study of Lev Tolstoy's pedagogical legacy of his Yasnaya Polyana school in the Russian Empire (1859–1862), I raised three major questions: (1) was Lev Tolstoy a democratic educator, and if so, why can one claim that, (2) if so, what kind of a democratic educator was he, and (3) what kind of limitations to his democratic education have I observe and what were the sources of these limitations? My answer to the first question was unequivocally positive. I argue that Tolstoy was the conceptual founder of democratic non-coercive education and the first known practitioner of democratic education for children. In my view, his democratic education was based on educational offerings provided by the teachers. His democratic educational philosophy was based on non-coercion, naturalism, anarchism, liveliness, pragmatism, pedagogical experimentation, student responses, pedagogical self-reflection, and dialogism. At the same time, his democratic education was limited to his uncritical acceptance of conventionalism. Tolstoy's attraction to Progressive Education was facilitated by ignoring his enormous powers, both explicit and implicit, that he manifested exercised in the school and enacted through his "pervasive informality." In my judgment, Tolstoy overemphasized pedagogy over self-education and did not distinguish learning from education. Still, Tolstoy's pioneering work in democratic education, both in theory and practice, remains mostly unacknowledged and unanalyzed while continuing to be highly relevant and potentially influential. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sustainable urban development districts have become an answer to the challenge of increasing urbanization while decreasing human impact on the environment. Like other domains of public administration, urban development has in recent decades moved towards heterogeneous governance. Urban development becomes project ecologies, where several construction projects are carried out in parallel and in sequence. This paper sheds light on public and private developers' coordinating between their construction projects and the influence this has on the built environment of urban development districts. The space between projects in project ecologies is relevant to explore further to understand how the long-term goals of urban policy are achieved in practice. Through the theoretical lens of self-organizing, the discussion is informed by a qualitative study of two cases where developers built together in sustainable profiled urban development districts. The paper contributes to construction management research by illustrating how developers play a key role in finalizing the design and construction of new districts through self-organizing. In effect, new urban districts can only be realized through joint efforts and coordination amongst developers. The paper also provides policymakers with insights into how developers become key players in organizing new neighborhoods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Open organizations are structures in which members of the public engage in work for the organization. Examples include open-source software, Amnesty International, Wikipedia, and Lego communities. Much research focuses on structural design characteristics of open organizations, such as pre-specified task divisions and integration teams. These practices require the organization to a priori structure in response to its mission. Increasingly, however, open organizations like CrowdDoing and Hyperloop Transportation Technologies (HyperloopTT) require public involvement across volatile, uncertain, complex, ambiguous (VUCA) contexts. These open organizations must respond to changing political, competitive, and socio-economic events. Structural clarity is more difficult, and contributors may participate in the creative development of new technologies, new policies, and new sources of funding. Working from practices supporting participant engagement in more stable environments, we qualitatively observe HyperloopTT to understand internal practices for open organizing in more VUCA contexts. We observe four practices allowing for the flexibility, versatility, and accommodations needed for open organizing in such settings. The HyperloopTT practices allow more porosity and self-determination—not simply in how people divide and integrate tasks, but also in the exploration and experimentation of the work itself. More than task workers, we see a new class of open organizing participants: creative work designers. [ABSTRACT FROM AUTHOR]

This paper proposes a new trajectory tracking control scheme for the four mecanums wheel omnidirectional mobile robot (FM-OMR). Considering the influence of uncertainty on tracking accuracy, a self-organizing fuzzy neural network approximator (SOT1FNNA) is proposed to estimate the uncertainty. In particular, since the structure of traditional approximation network is preset, it will cause problems such as input constraints and rule redundancy, resulting in low adaptability of the controller. Therefore, a self-organizing algorithm including rule growth and local access is designed according to the tracking control requirements of omnidirectional mobile robots. In addition, a preview strategy (PS) based on Bezier curve trajectory re-planning is proposed to solve the problem of tracking curve instability caused by the lag of tracking starting point. Finally, the simulation verifies the effectiveness of this method in tracking and trajectory starting point optimization. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been one of the most significant public health events in human history. Domestic violence cases surged globally during the COVID-19 pandemic. In Taiwan, this trend was particularly evident, with a year-over-year increase in reported cases. This study aims to investigate the complex dynamics of domestic violence in Taiwan during the COVID-19 pandemic through the lens of Complexity Theory. The researcher employed a qualitative case study method and conducted semistructured interviews with four participants. A cross-case analysis revealed four themes illustrating polymorphic correlations of factors and five themes related to self-organizing behaviors. This study amplifies the voices of domestic violence victims during the COVID-19 pandemic and offers insights into the intricate dynamics between domestic violence incidents and disasters, individuals, and systems. Implications for theory and practice are discussed., (© 2024 Wiley Periodicals LLC.)

Traffic emission is one of the most severe issues in our modern societies. A large part of emissions occurs in cities and especially at intersections due to the high interactions between vehicles. In this paper, we proposed a cellular automata model to investigate the different traffic emissions (CO 2 , PM, VOC, and NO x ) and speeds at a two-lane signalized intersection. The model is designed to analyze the effects of signalization by isolating the parameters involved in vehicle-vehicle interactions (lane changing, speed, density, and traffic heterogeneity). It was found that the traffic emission increases (decreases) with the increasing of green lights duration ( T g ) at low (high) values of vehicles injection rate (α). Moreover, by taking CO 2 as the order parameter, the phase diagram shows that the system can be in four different phases (I, II, III, and IV) depending on α and T g . The transition from phase II (I) to phase III (II) is second order, while the transition from phase II to phase IV is first order. To reduce the traffic emission and enhance the speed, two strategies were proposed. Simulation results show a maximum reduction of 13.6% in vehicles' emissions and an increase of 9.5% in the mean speed when adopting self-organizing intersection (second strategy) at low and intermediate α. However, the first strategy enhances the mean speed up to 28.8% and reduces the traffic emissions by 3.6% at high α. Therefore, the combination of both strategies is recommended to promote the traffic efficiency in all traffic states. Finally, the model results illustrate that the system shows low traffic emission adopting symmetric lane-changing rules than asymmetric rules. [ABSTRACT FROM AUTHOR]

The purpose of this work is to study the peculiarities of external influence, namely osmotic pressure, on cancer cells. Methods. For this purpose, spatially distributed systems describing the dynamics of a three-dimensional cell lattice, a cell spheroid, and a cell surface have been considered. The studied models are based on the basic principles of hydrodynamics, and their numerical simulation has been performed using a modified Euler method. Results. The paper proposes three-dimensional models to study the dynamics of cancer cells in the epidermal layer of the skin; the models include the possibility of adding additional pressure and changing it with a single parameter. It is shown that it is possible to achieve a slowdown of cancer cell growth in all considered models at certain values of osmotic pressure.

Robotic manipulators have extensive applications in academic and industrial fields such as grabbing, welding, and machining. Nowadays, better tracking performance and faster transient response have been the fundamental demands in the field of manipulator control. However, robotic manipulators suffer from uncertainties due to various factors in real-world environments. Therefore, achieving satisfactory tracking performance for the manipulator in the presence of uncertainties is a pressing problem. Moreover, most existing control schemes struggle to determine the convergence time of the whole system and rely on specifically designed parameters via experience and prior knowledge, which leads to poor transferability and generalization. To address the abovementioned issues, a predefined-time controller based on a self-organizing interval type-2 function-link fuzzy neural network (IT2FLFNN) is proposed in this paper. Different from most existing fuzzy neural network (FNN) based control schemes, the proposed network starts from an empty rule base and it is constructed by a self-organizing mechanism during the online control process, which provides greater flexibility in terms of network structure and input partition. Also, a function-link network (FLN) is integrated into IT2FLFNN to enhance its convergence speed and rejection of uncertainties. Furthermore, to ensure the predefined-time convergence, a predefined-time sliding-mode control part is merged into the control framework and online learning laws for IT2FLFNN are established via the Lyapunov stability analysis. Finally, a comparative simulation and an experiment demonstrate the superiority of the proposed IT2FLFNN control scheme. • The proposed scheme guarantees the predefined-time stability of the manipulator system. • A novel fuzzy neural network is utilized to handle the nonlinear uncertainties effectively. • A self-organizing mechanism is introduced to optimize the network structure online. • Both simulation and experiment results validate the superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

• In MANET, self-organizing mobile nodes collaborate to build a flexible network architecture. With a MANET, data must transit through numerous intermediate nodes even before reaches its destination node. Local link connectivity is essential for the establishment and preservation of routes. • To decrease energy usage, these findings suggest the Aggregation-based Archive Population Optimization (AAPO) along with Ad-hoc On-demand Distance Vector (AODV) Routing Protocol. For both local and global optimal routes, the proposed optimized model gives top priority. There exist two different types of rules used by AAPO to evaluate the quality of a route before using it for data transmission. • Instead of relying on just one path for transmitting data, AAPO leverages many paths. Also, it minimizes the issue of the network nodes being suddenly inaccessible, which eliminates interruptions in transmissions. • However, the suggested optimal routing system considers mobility and energy availability when responding to queries, resulting in significant performance improvements, utilizing 42.69 % of the scenario's power and functioning at 208.6 kbps with an average PDR of 87.1 %. Also, detailed simulations are conducted using the Matlab (R2021b) software to assess the suggested approach. Furthermore, the suggested protocol outperforms the current methods in terms of effectiveness. In today's 6G era, Mobile ad hoc networks (MANETs) have been enhanced by rapid technological advancement in the wireless communication industry to better support a wide range of domains, including military operations, emergency operations, urban settings, and natural crises management. In MANET, self-organizing mobile nodes collaborate to build a flexible network architecture. With a MANET, data must transit through numerous intermediate nodes even before reaches its destination node. Local link connectivity is essential for the establishment and preservation of routes. To decrease energy usage, these findings suggest the Aggregation-based Archive Population Optimization (AAPO) along with Ad-hoc On-demand Distance Vector (AODV) Routing Protocol. For both local and global optimal routes, the proposed optimized model gives top priority. There exist two different types of rules used by AAPO to evaluate the quality of a route before using it for data transmission. Instead of relying on just one path for transmitting data, AAPO leverages many paths. Also, it minimizes the issue of the network nodes being suddenly inaccessible, which eliminates interruptions in transmissions. However, the suggested optimal routing system considers mobility and energy availability when responding to queries, resulting in significant performance improvements, utilizing 42.69 % of the scenario's power and functioning at 208.6 kbps with an average PDR of 87.1 %. Also, detailed simulations are conducted using the Matlab (R2021b) software to assess the suggested approach. Furthermore, the suggested protocol outperforms the current methods in terms of effectiveness. [ABSTRACT FROM AUTHOR]

The present communication revisits the almost century-and-a-half-old problem of some identical small magnets floating freely on the water's surface under the action of a superimposing magnetic field created by a stronger magnet placed above them. Originally introduced and performed by Alfred Marshall Mayer and reported in a series of articles starting from 1878 onward, the proposed experiments were intended to provide a model (theoretical and educational) for the building block of matter that, at a microscopic level, is the atom. The self-organizing patterns formed by the repelling small magnets under the influence of a single attractive central force are presented in a slightly different reenactment of the original experiments. Although the set-up is characterized by an axially symmetric magnetostatic structure, and the floated magnets are all identical, the resulting equilibrium patterns are not necessarily symmetrical, as one would expect. To the authors' best knowledge, the present communication proposes for the first time a quantitative approach to that extremely complex conceptual problem by providing a methodology for computing the equilibrium point coordinates in the case of n = 1...20 floating magnets, as proposed by the original A.M. Mayer experiments. A good agreement between the experiments and computed data was demonstrated for n = 2...15 (1st variant), but it was less accurate while still preserving the experimental set-up configurations for n = 15 (2nd variant)...20. Finally, this study draws the conclusions from the performed experiments and their corresponding computer simulations, identifies some open issues, and outlines possible solutions to address them, as well as future developments concerning the subject in general. [ABSTRACT FROM AUTHOR]

Staphylococcus aureus (S. aureus), especially methicillin-resistant S. aureus (MRSA), causes wound infections, whose treatment remains a clinical challenge. Bacterium-infected wounds often create acidic niches with a pH 4.5-6.5. Endolysin LysSYL, which is derived from phage SYL, shows promise as an antistaphylococcal agent. However, endolysins generally exhibit instability and possess low bioavailability in acidic microenvironments. Here, an array of self-assembling peptides is designed, and peptide L5 is screened out based on its gel formation property and bioavailability. L5 exerted a pH-switchable antimicrobial effect (pH 5.5) and formed biocompatible hydrogels at neutral pH (pH 7.4). The LysSYL-loaded L5 can assemble L5@LysSYL hydrogels, increase thermal stability, and exhibit the slow-release effect of LysSYL. Effective elimination of S. aureus is achieved by L5@LysSYL through bacterial membrane disruption and cell separation inhibition. Moreover, L5@LysSYL hydrogels exhibit great potential in promoting wound healing in a mouse wound model infected by MRSA. Furthermore, L5@LysSYL hydrogels are safe and can decrease the cytokine levels and increase the number of key factors for vessel formation, which contribute to wound healing. Overall, the self-assembling L5@LysSYL can effectively clean MRSA and promote wound healing, which suggests its potential as a pH-sensitive wound dressing for the management of wound infections., (© 2024 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.)

This article aims to propose a more efficient control algorithm for uncertain nonlinear systems. An intelligent self-organizing double function-link fuzzy brain emotional control system is proposed which comprises a self-organizing double function-link fuzzy brain emotional controller (SDFLFBEC) and a compensation controller. The proposed SDFLFBEC consists of four substructures and a fuzzy inference system. The substructures are the prefrontal cortex, the amygdala, a double function-link network (FLN) and a self-organizing structure. The prefrontal cortex and the amygdala networks work as a mathematical form that presumes the judgment and emotion of a brain. Specifically, a new double FLN is designed to support the above networks for updating their weights. Next, a self-organizing structure can automatically add or prune the layers to achieve efficient network structure. In addition, the fuzzy inference rules are presented to explain the inference processes of the amygdala and orbitofrontal networks. From the above factors, the proposed SDFLFBEC can effectively reduce the tracking error and achieve favorable control performance. The parameters of the control system are adjusted online using the derived adaptation laws that are taken from a Lyapunov function so that the stability of the system is ensured. Simulation studies of a biped robot and the experimental results of a magnetic levitation system are employed to validate the effectiveness and superiority of the proposed SDFLFBEC. [ABSTRACT FROM AUTHOR]

With continuously rising trends in applications of information and communication technologies in diverse sectors of life, the networks are challenged to meet the stringent performance requirements. Increasing the bandwidth is one of the most common solutions to ensure that suitable resources are available to meet performance objectives such as sustained high data rates, minimal delays, and restricted delay variations. Guaranteed throughput, minimal latency, and the lowest probability of loss of the packets can ensure the quality of services over the networks. However, the traffic volumes that networks need to handle are not fixed and it changes with time, origin, and other factors. The traffic distributions generally follow some peak intervals and most of the time traffic remains on moderate levels. The network capacity determined by peak interval demands often requires higher capacities in comparison to the capacities required during the moderate intervals. Such an approach increases the cost of the network infrastructure and results in underutilized networks in moderate intervals. Suitable methods that can increase the network utilization in peak and moderate intervals can help the operators to contain the cost of network intrastate. This article proposes a novel technique to improve the network utilization and quality of services over networks by exploiting the packet scheduling-based erlang distribution of different serving areas. The experimental results show that significant improvement can be achieved in congested networks during the peak intervals with the proposed approach both in terms of utilization and quality of service in comparison to the traditional approaches of packet scheduling in the networks. Extensive experiments have been conducted to study the effects of the erlang-based packet scheduling in terms of packet-loss, end-to-end latency, delay variance and network utilization.

Copyright of Transactions of Nanjing University of Aeronautics & Astronautics is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• A dynamic cooperative decision-making among self-organizing CAVs is developed • The decision-making problem is modeled based on a coalitional game approach. • The proposed model allows transferring utilities among CAVs to achieve a cooperative decision. • A novel concept of universal, collectible, and tradable credit scheme (UCTCS) is introduced as an incentivization and reward mechanism for cooperative decisions made by CAVs. This research introduces a novel approach to cooperative decision-making among self-organizing connected and autonomous vehicles (CAVs). In this approach, a coalitional game is played by a group of players who form alliances of different sizes based on the collective payoff they receive. The players continuously evaluate the potential benefits of different coalition formations and adjust their decisions accordingly. The proposed approach utilizes the V2V communication feature of CAVs, which enables CAVs to participate in a cooperative game, thereby resolving conflicting situations that often arise during lane-changing decisions. By working together within the same coalition, CAVs on a hypothetical three-lane freeway segment can collectively determine their target lanes, rather than engaging in individual decision-making that could result in a win-lose situation. The proposed approach considers up to nine CAVs interacting with each other and aims to find Pareto-optimal coalitions in lane-changing decisions. The approach considers lead CAVs that cooperate via acceleration to enlarge the gap between the subject and lead CAVs. The game is modelled as a dynamic transferable utility problem, allowing the utilities obtained from the coalition agreement to be expressed as real numbers and distributed among coalition members. The framework is generalizable to other traffic and demand management problems while the cooperative CAVs can be compensated for reaching an agreement in a universal, collectible, and tradable credit scheme (UCTCS) that can be used in a wide spectrum of traffic and demand management applications. The effects of the proposed coalitional lane-changing decision-making on traffic efficiency are compared to a non-cooperative decision-making model on a simulated road segment. Overall, our analysis suggests that the proposed coalitional approach can positively impact macroscopic traffic characteristics, leading to potentially improved traffic flow, reduced congestion, and enhanced travel time efficiency. [ABSTRACT FROM AUTHOR]

Pulsed-Coupling Oscillators (PCOs) are recently considered as the best energy efficient source of unregulated syncs in Wireless Sensor Network (WSN). PCO utilizes firefly-sync to draw complices. In any case, for sensor networks, a PCO is not feasible, as synchronous transmission costs cannot be borne by WSNs and the processing of information. For certain situations, the exhaustion of a node's battery energy policy (due to the packet collision) is a nonsensical substitute of batteries. To prevent this, A novel process named this analysis the Desynchronization-Traveling-Wave-Pulse-Coupled-Oscillator (DTWPCO) algorithm, an energy-efficient WSN self-organizer that uses Travelling-Wave-Pulse-Coupled-Oscillator (TWPCO) phases locking and the PCO antiphase desynchronization process. The plan aims to reduce the high-power consumption within the network in order to display signs of improved data collection during data transmission for the sensor nodes (SNs). The results of the computer simulation showed that the proposed DTWPCO mechanism was able to achieve 50% and 58% reduction in energy by increasing the amount of transmitted data by SNs, in contrast to TWPCO and PCO methods. The method also increases the data processing ratio by up to 73% and 70% in comparison to the TWPCO and PCO methods, respectively.

Multiagent Systems (MASs) have multiple different characteristics, such as autonomy, and asynchronous and social features, which make these systems difficult to understand. Thus, there is a lack of procedures guaranteeing that multiagent systems once implemented would behave as desired. Determining the reliability of such systems is further complicated by the fact that current agent-based approaches may also involve non-deterministic characteristics, such as learning, self-adaptation and self-organization (SASO). Nonetheless, there is a gap in the literature regarding the testing of systems with these features. This paper presents an approach based on metadata and the publish-subscribe paradigm to develop test applications that address the process of failure diagnosis in a self-organizing MAS. The novelty of the proposed approach involves its ability to test self-organizing MAS systems in the context of local and global behavior. To illustrate the use of this approach, we developed a self-organizing MAS system based on the Internet of Things (IoT), which simulates a set of smart street lights, and we performed functional ad-hoc tests. The street lights need to interact with each other in order to achieve the global goals of reducing energy consumption and maintaining the maximum value of visual comfort in illuminated areas. To achieve these global behaviors, the street lights develop local behaviors automatically through a self-organizing process based on machine-learning algorithms.

Swarm control has become a challenging topic for the current unmanned aerial vehicle (UAV) swarm due to its conflicting individual behaviors and high external interference. However, in contrast to static obstacles, limited attention has been paid to the fission–fusion behavior of the swarm against dynamic obstacles. In this paper, inspired by the interaction mechanism and fission–fusion motion of starlings, we propose a Bio-inspired Self-organized Fission–fusion Control (BiSoFC) algorithm for the UAV swarm, where the number of UAVs in the sub-swarm is controllable. It solves the problem of swarm control under dynamic obstacle interference with the tracking function. Firstly, we establish the kinematic equations of the individual UAV and swarm controllers and introduce a fission–fusion control framework to achieve the fission–fusion movement of the UAV swarm with a lower communication load. Afterward, a sub-swarm selection algorithm is built upon the topological interaction structure. When a swarm is faced with different tasks, the swarm that can control the number of agents in a sub-swarm can accomplish the corresponding task with a more reasonable number of agents. Finally, we design a sub-swarm trapping algorithm with a tracking function for the dynamic obstacles. The simulation results show that the UAV swarm can self-organize fission sub-swarms to cope with dynamic obstacles under different disturbance situations, and successfully achieve the goal of protecting the parent swarm from dynamic obstacles. The experimental results prove the feasibility and effectiveness of our proposed control algorithm.

Decades of progress in the semiconductor industry has led to lithographically printed dimensions that are small enough that the positions of individual molecules and the stochastic variation in the number of photons have a significant effect on the quality of photoresist patterns. These effects scale badly and will be more important as feature sizes continue to shrink. Self-organizing materials can provide regular patterns of molecules that have the potential to minimize stochastic effects. Some such reported materials are block copolymers, bottle brush polymers and DNA, all of which have been used as part of lithographic patterning. A key challenge for self-organizing materials is defect levels. The energy to rearrange has to be high enough that random defects aren’t created thermally but low enough that rearrangement into preferred domains can occur. All of the methods can generate accurate CDs based on the chemical composition of the material, but they all need some way to control the positions of the feature edges. There are methods for guiding the self-organization, but the final position is the sum of the guide pattern misalignment and the intrinsic alignment error of the self-organizing materials. Thus it can be worse than the positioning of the guide structures. Alignment and defect levels are thus two big challenges for manufacturing introduction of self-organizing materials.

Cooperative control is currently a challenging topic of crowded unmanned underwater vehicle (UUV) swarm. However, individual behavior conflict and chain-avalanche collision involved in this swarm are easily triggered due to the fluctuations and disturbances. In order to address the two problems, a bio-inspired self-organized cooperative control consensus derived from adaptive dynamic interaction topology is investigated in this paper. Firstly, a novel following-interaction framework incorporating the topological interaction and visual interaction is devised to ensure the minimum number and optimal distribution for neighborhoods. Then, an adaptive dynamic computing model inspired by single-nearest-neighbor following and weighted- multiple-nearest-neighbors following is proposed to steer a sensitive following behavior, in which the influence of each individual on this following behavior is described by a nonlinear weight. Finally, a distributed control protocol is put forward by using the proposed following model and mathematics-based potential fields to achieve the cohesive flocking and avoiding collision, and its sufficient conditions is proven by Laypunov and LaSalle invariance principle to accomplish a self- organized cooperative control. Simulation results are presented for illustrating the feasibility and effectiveness of our proposed control approach. [ABSTRACT FROM AUTHOR]

Predicting the stock price movements based on quantitative market data modeling is an open problem ever. In stock price prediction, simultaneous achievement of higher accuracy and the fastest prediction becomes a challenging problem due to the hidden information found in raw data. Various prediction models based on machine learning algorithms have been proposed in the literature. In general, these models start with the training phase followed by the testing phase. In the training phase, the past stock market data are used to learn the patterns toward building a model that would then use to predict future stock prices. The performance of such learning algorithms heavily depends on the quality of the data as well as optimal learning parameters. Among the conventional prediction methods, the use of neural network has greatest research interest because of their advantages of self-organizing, distributed processing, and self-learning behaviors. In this work, dynamic nature of the data is mainly focused. In conventional models the retraining has to be carried out for two cases: the data used for training has higher noise and outliers or model trained without preprocessing; the learned data has to update dynamically for recent changes. In this sense, propose a self-repairing dynamic model called repairing artificial neural network (RANN) that correct such errors effectively. The repairing includes adjusting the prediction model from noise, outliers, removing a data sample, and adjusting an attribute value. Hence, the total reconstruction of the prediction model could be avoided while saving training time. The proposed model is validated with five different real-time stock market data and the results are quantified to analyze its performance.

This article argues that while creative spaces are believed to instigate creative production, their strongest value is in producing new possibilities for self-organizing. By zooming in on short snapshots of resistance against gentrification in creative spaces in Amsterdam, I investigate whether small-scale and grass-roots forms of resistance and self-organizing between independent workers in the creative industries can be understood as examples of the autonomist notions of 'the common' and 'the multitude'. By placing observations of creative workers' self-organizing practices alongside autonomist theory, I suggest that autonomist thought is a promising philosophy for a politicized view of creative production, because it celebrates multiplicity and uniqueness. This is a timely topic in a society with growing numbers of freelancers and increasing flexibilization of labour. This article contributes to research on self-organizing among creative workers and to the literature on work conditions in the creative industries. [ABSTRACT FROM AUTHOR]

This writing is a set of thoughts provoked by the challenge of living in semi-captivity while having a need to practice a live art. Such circumstances require us not to be aseptic, but to keep coping with dirtiness, which is an integral part of performing our creativity, and to keep our bodies in virulently live socializing mode in order to empower the collective body. The virus-related panic may force us to rethink the quality of socializing within the art field as a part of the culture industry, and beyond it. The current devitalization of theatre triggers plenty of questions that may be condensed into a more radical one: can we, at all, practice socialization within the art (as a) field?

The responses of many cortical neurons to visual stimuli are modulated by the position of the eye. This form of gain modulation by eye position does not change the retinotopic selectivity of the responses, but only changes the amplitude of the responses. Particularly in the case of cortical responses, this form of eye position gain modulation has been observed to be multiplicative. Multiplicative gain modulated responses are crucial to encode information that is relevant to high-level visual functions, such as stable spatial awareness, eye movement planning, visual-motor behaviors, and coordinate transformation. Here we first present a hardwired model of different functional forms of gain modulation, including peaked and monotonic modulation by eye position. We use a biologically realistic Gaussian function to model the influence of the position of the eye on the internal activation of visual neurons. Next we show how different functional forms of gain modulation by eye position may develop in a self-organizing neural network model of visual neurons. A further contribution of our work is the investigation of the influence of the width of the eye position tuning curve on the development of a variety of forms of eye position gain modulation. Our simulation results show how the width of the eye position tuning curve affects the development of different forms of gain modulation of visual responses by the position of the eye.

Perceptions of lifelike marvels are viewed as the best data source of unconstrained synchronization. Such synchronization is imperative for the best possible coordination of intensity cycles for wireless sensor network (WSN) energy conservation. Fireflies, which have a comparative structure to WSN, utilize the guideline of pulse-Coupled Oscillators (PCOs) for light blaze outflow to pull in mating accomplices. This conduct can be impersonated for the improvement of WSNs and have decentralized energy efficiency conduct. In any case, a fascinating component of WSNs is that the PCO is utilized by the firefly synchronization to pull in mating accomplices; however, it cannot be utilized in genuine sensor networks. This is because of the failure of the sensor nodes to get data packets utilized by the first PCO model because of deafness. Subsequently, energy utilization turns out to be high and a large portion of the data is lost. For most situations, the PCO model is not appropriate for sensor networks because of high packet collision since WSNs cannot bear the cost of transmission and gathering data concurrently. It likewise expands energy utilization because the battery substitution is unthinkable upon the fatigue of a node battery energy strategy. Accordingly, this paper broadly surveys and talks about the algorithms developed to address the difficulties and the systems of incorporating energy-efficient firefly inspired time synchronization over WSNs and the properties of transmission state inside the deafness and packet collision. In particular, it is an exhaustive audit incorporating instrument, points of interest and detriments of past related work inside the transmission state. The paper helps scientists to (1) keep away from deafness that happens in the transmit state in WSNs, (2) prevent packet collision for the time of transmission in WSNs, and (3) increment the data gathering all through the transmission states in WSNs. It additionally features the recommendation of a few appropriate open issues as proposals for future research.