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Primate vision research has shown that in the retinotopic map of the primary visual cortex, eccentricity and meridional angle are mapped onto two orthogonal axes: whereas the eccentricity is mapped onto the nasotemporal axis, the meridional angle is mapped onto the dorsoventral axis. Theoretically such a map has been approximated by a complex log map. Neural models with correlational learning have explained the development of other visual maps like orientation maps and ocular-dominance maps. In this paper it is demonstrated that activity based mechanisms can drive a self-organizing map(SOM) into such a configuration that dilations and rotations of a particular image (in this case a rectangular bar) are mapped onto orthogonal axes. We further demonstrate using the LISSOM model, with an appropriate boundary and realistic initial conditions, that a retinotopic map which maps eccentricity and meridional angle to the horizontal and vertical axes respectively can be developed. This developed map bears a strong resemblance to the complex log map. We also simulated lesion studies which indicate that the lateral excitatory connections play a crucial role in development of the retinotopic map.

The Earth is a self-organizing system liking a living organism. Lovelock proposed Gaia as a metaphor to designate the check and balance ofterrestrial temperatures: the Earth is never too hot so that the ocean could boil, and the Earth is never too cold that the ocean could freeze from top to bottom. Hsü proposed that Gaia is endothermic because the life on Earth has been alternate successions of air-conditioners and heaters which evolved and deactivate or reinforce the terrestial greenhouse of carbon dioxide in atmosphere. When Earth was heating up too much, the air-conditioneers, such as anaerobic bacteria, cyanobacteria, skeletal organisms and trees, and finally calcareous plankton, went to work to bring the terrestrial temperature down. When the Earth was freezing at times of continental glaciation, heaters went to work, such as methanogenic bacteria, Ediacaran faunas, tundra and desert plants, and now Homo sapiens. Gaia has to have other organs to keep the self-organizing system vital. This paper presents a postulate that the Miocene Mediterranean Sea acted as Gaia´s kidney. The steady influx of dissolved ions and debris into the ocean causes inevitable increase of ocean´s salinity. The fossil and geochemicl records indicate that the ocean has never been too saline nor too brackish for the survival of normal marine organisms: the salinity ranged from about 32 to 36 pro mil during the last billion years. Ocean-drilling cruises to the Mediterranean discovered a very large salt formation, deposited during some 5 million years ago when the Mediterranean dried up. A study of the geochemical balance of the oceans indicates that the deposition of very large salt bodies in isolated basins such as the Miocene Mediterranean every 100 million years or so. The saline giants have the function of Gaia´s kidney. With periodical removals of the salt ions and the heavy metals from seawater, the world´s ocean have been rendered forever habitable. Gaia has to have a kidney. The desiccation of the Mediterranean is the evidence of a functioning kidney. Earlier kidney functions were performed during the deposition of the Cretaceous (South Atlantic), Jurassic (Gulf of Mexico), Permo-Triassic (Europe), Devonian (Canada),.Cambrian/Precambrian (Gondwana) saline giants.

A novel scheme for diagnosing sensor failures in a flight control system is presented. In the proposed scheme, a set of self-organizing fuzzy systems named as extended sequential adaptive fuzzy inference systems (ESAFISs) are applied as the online approximators for recognizing the sensor outputs. ESAFIS is an online learning fuzzy system with concurrent structure and parameter learning. The rules of the ESAFIS are added or deleted based on the input data without predefining them by trial and error. From an analysis of the residual signals between the estimated states and the measurements of the sensors, the failure diagnosis by determining the failure detection, identification and accommodation can be achieved. The efficiency of the proposed scheme is demonstrated by simulation examples where hard and soft failures in the angular rate gyros are successfully diagnosed. [ABSTRACT FROM AUTHOR]

The article compares migrant structures established in two trade unions in Italy and the Netherlands. The study relies on qualitative data to analyse the extent to and circumstances under which these structures promote the organizational inclusion of migrant workers. The analysis suggests that union procedures based on participatory democracy have a positive effect on organizational inclusion as compared to top-down and centrally driven procedures. The findings underline the importance of trade union support in the functioning of special structures and the relevance of union identity and organizational structure in explaining the differences across these two national cases. The findings also highlight the significance of internally generated resources in balancing the legitimacy and recognition of migrant structures. [ABSTRACT FROM AUTHOR]

Traffic in urban areas is generally regulated by traffic lights, which represent a traffic flow bottleneck if they are not effectively configured. The dynamic nature of traffic flows makes reliable prediction possible only over a limited time horizon, and hence necessitates continual re-computation solutions. As intersections network size increases, the exponential growth in joint signal timing and traffic states exhibit a computational barrier to determining effective traffic lights timing. So, real-time traffic signal control optimization becomes a challenging problem. The present work investigates the issue of adaptive traffic control using real-time traffic data in multiple intersections. Each network intersection is controlled by an autonomous agent operating with a view of incoming traffic. The generated model presents ability to identifying dynamic changes in traffic flow conditions as well as adjusting green lights sequences and offering a good coordination between each intersections neighboring. The main contribution of this model is a real-time adaptive control of the traffic lights, according to discontinuity and heterogeneity of traffic flow. Simulation results confirm the system effectiveness compared to fixed time control, actuated time control and green wave method. And that by mitigating the congestion in terms of maximum throughput traffic flow, minimum waiting time and stops while maintaining fairness among all the network traffic lights. [ABSTRACT FROM AUTHOR]

In this paper, we examine do-it-yourself (DIY) urbanism through place-based and technology initiatives that support collective networked action to foster civic engagement in neighborhood contexts. The goal is to engage informal and ad-hoc networks of individual and collective actors in productively addressing critical conditions within their urban localities. Based on participatory research and co-design with residents and urban activist groups, Delai Sam and Partizaning, in districts of Moscow, Russia, we devised a series of interventions using a network of mailboxes, prototype online collaboration platforms (SynchroniCITY and MicroAct) and community-based workshops to engage participants in revealing issues, designing solutions and coordinating urban actions. Through these experiences, we consider the challenges of DIY urbanism as an inclusive, sustained and meaningful form of cooperative engagement and urban activism. [ABSTRACT FROM PUBLISHER]

A self-organizing radial basis function (RBF) neural network (SODM-RBFNN) was presented for predicting the production yields and operating optimization. Gradient descent algorithm was used to optimize the widths of RBF neural network with the initial parameters obtained by k-means learning method. During the iteration procedure of the algorithm, the centers of the neural network were optimized by using the gradient method with these optimized width values. The computational efficiency was maintained by using the multi-threading technique. SODM-RBFNN consists of two RBF neural network models: one is a running model used to predict the product yields of fluid catalytic cracking unit (FCCU) and optimize its operating parameters; the other is a learning model applied to construct or correct a RBF neural network. The running model can be updated by the learning model according to an accuracy criterion. The simulation results of a five-lump kinetic model exhibit its accuracy and generalization capabilities, and practical application in FCCU illustrates its effectiveness. [ABSTRACT FROM AUTHOR]

Broad network (BN) is commonly used as an efficient and computationally inexpensive machine learning method. The flat structure of BN ensures the efficiency of model learning, but in the mean time the structural characteristics also affect the network performances, such as generalization. However, it is challenging to improve the BN's generalization performance via adjusting its structure based on certain specific criteria. In this paper, to get the required network structure, a self-organizing BN based on the information evaluation method (IE-SOBN) is designed. First, a capability evaluation metric (CEM) is introduced into BN, which can evaluate the ability of hidden neurons to express and transmit data information. Then, the evaluation metric obtained by CEM is used as the basis for structure adjustment. Second, a structure adjustment mechanism (SAM) based on CEM is proposed to dynamically optimize the BN structure, leading to an improved generalization performance. Third, the convergence of the suggested IE-SOBN is theoretically analyzed to ensure its effectiveness. Finally, the advantages of IE-SOBN are verified by three real applications. The generalization performance and test accuracy of the proposed IE-SOBN are satisfactory in the experiments. [ABSTRACT FROM AUTHOR]

Primate vision research has shown that in the retinotopic map of the primary visual cortex, eccentricity and meridional angle are mapped onto two orthogonal axes: whereas the eccentricity is mapped onto the nasotemporal axis, the meridional angle is mapped onto the dorsoventral axis. Theoretically such a map has been approximated by a complex log map. Neural models with correlational learning have explained the development of other visual maps like orientation maps and ocular-dominance maps. In this paper it is demonstrated that activity based mechanisms can drive a self-organizing map (SOM) into such a configuration that dilations and rotations of a particular image (in this case a rectangular bar) are mapped onto orthogonal axes. We further demonstrate using the Laterally Interconnected Synergetically Self Organizing Map (LISSOM) model, with an appropriate boundary and realistic initial conditions, that a retinotopic map which maps eccentricity and meridional angle to the horizontal and vertical axes respectively can be developed. This developed map bears a strong resemblance to the complex log map. We also simulated lesion studies which indicate that the lateral excitatory connections play a crucial role in development of the retinotopic map. [ABSTRACT FROM AUTHOR]

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This letter presents a rule-based solution search (RBSS) algorithm for self-optimization in a cellular network. It improves the solution search process of classical optimization methodologies by including a set of rules that capture the knowledge of how a given problem can be solved. The results obtained in a scenario using measurements from a real network reveal that including the RBSS algorithm achieves reductions of up to 60% and 90% in the convergence time of Particle Swarm and Genetic Algorithms, respectively. [ABSTRACT FROM PUBLISHER]

This paper presents a case study in which multi-age students (aged 6-12, N = 32) in small groups made autonomous inquiries about the phenomenon of winter fishing within the framework of design-oriented pedagogy. The research analyzed storytelling videos that the students produced as learning objects. These videos revealed a picture of the developed learning process and influence afforded and emerged learning ecosystem on it. The research question was as follow: What do the digital stories reveal about the emerged learning ecosystem and learning processes? The qualitative content analysis of the videos' structure, students' commentary, and descriptions of the implemented activities revealed that each small group constructed its own niche in the learning ecosystem. By self-organizing and utilizing the afforded community, technology, and information resources, the students constructed their own interpretations of their chosen research tasks and related inquiries. Based on the findings, we argue that such inquiry-driven learning tasks and afforded learning resources guide students to search for strategic types of knowledge to understand the given phenomena and communicate their complex study processes. The study confirms that students' agency in design-oriented pedagogy will resemble that expected of 21st-century learners. Moreover, young children (from the age of 6) can participate and become active community members in the co-developed learning process and in the creation of local knowledge in the form of storytelling. [ABSTRACT FROM AUTHOR]

We evaluated the extent to which the externalizing behavior construct is self-organizing in the first 2 years of life. Based on dynamic systems theory, we hypothesized that changes in physical aggression, defiance, activity level, and distress to limitations would each he predicted by earlier manifestations of one another. These hypotheses were evaluated via mothers' and fathers' reports of 274 infants' externalizing behaviors at 8, 15, and 24 months of child age. Eight-month measures of physical aggression, activity level, and/or distress to limitations explained increases in physical aggression, defiance, activity level, and distress to limitations from 8 to 15 months. Increases in defiance and activity level from 15 to 24 months were predicted by 15-month physical aggression and/or distress to limitations. These findings suggest that the externalizing behavior construct is formed by dynamic interplay among its individual elements, particularly between 8 and 15 months. [ABSTRACT FROM AUTHOR]

This paper uses narrative and storying to retell how two girls ages 5 and 7 continue to make use of an iPod touch to expand their literacy and numeracy. The paper explores the stopwatch and alarm features within the clock application, as well as the weather application, and the Internet browsing capability of the iPod touch. It also explores some of the challenges and inequities inherent in using this type of technology. The paper concludes with the author’s belief that it is important to allow learners freedom to explore and freely play with the technology and that emergent, self-directed, and self-organized learning is a natural and gentle approach to lifelong learning. Becoming literate and numerate is infinitely complicated, yet very simple. It is infinitely complicated because there are so many variables within and without the individual who is learning these skills that understanding the process fully is nearly impossible. Yet, it is simple because so many successfully become literate and numerate so naturally. Relating to this point Schuerwegen (2011) writes, Ergo, when a child grows up in a reading, writing, counting environment, especially an electronically driven society such as ours, he will one day find the need to pick up all these skills, at least as much as he needs them. (p. 22)

In recent years Wireless Sensor Networks (WSNs) have been deployed for Building Monitoring (BM) as they provide a low cost and reconfigurable alternative to centralized cable based sensor systems. Using WSNs gives rise to unique issues in its practical usage. Lifetime of a WSN is one such crucial issue to be addressed during deployment. Clustering is an effective way of extending the lifetime of a WSN. In this article we propose a distributed and energy driven clustering algorithm where the selection of the cluster heads (CHs) are based on relative residual energy level of sensors. Furthermore, the CHs are rotated only when their energy drops below a dynamic threshold computed by the algorithm. As a result, the overheads in the inter sensor communications will be reduced and thereby the proposed algorithm will favor more powerful nodes over the weaker ones to prolong the lifetime of the entire WSN. This will effectively prolong the usability of the monitoring system and thus the underlying safety of the building. The results will show that the proposed algorithm performs better when compared to existing clustering algorithms. Further we present theoretical analysis of the performance of the proposed algorithm in terms of correctness and complexity and explain how to identify the optimal values for key parameters such as transmission range and re-clustering trigger threshold function value in order to maximize the network lifetime.

Although there is no substantial change on this side, that leaders achieve results through the others work, there are still differences in the way, how leader achieves this. Based on empirical data from Cross Cultural CEO project this paper aims to classify leaders of Estonian organizations. The empirical study involved interviews with CEOs of 63 companies and questionnaires completed by the CEOs and their subordinates. Two types of top managers were received: directive type and team player. Self‐organizing theory has been applied for interpreting results. Implications are developed for leadership development in countries in transition. First Published Online: 14 Oct 2010

Globalization, the process by which local settlements and ecosystems are becoming linked in a global network, presents policy scientists and planners with difficult design challenges. Coping with either natural or built environments in isolation is extremely challenging in its own right (e.g.built environments at different scales: a single building, a collection of buildings, a neighbourhood or a city are in themselves very complex). In the Anthropocene era, where human activities shape the planetary system in which built and natural environments are becoming more tightly linked across scales, these complex systems need to be considered as elements in a global network,i.e.as a coupled social–ecological system (SES) at the global scale. In the context of this spiralling complexity, multi-scale and multilevel processes become more important and design/management problems become extraordinarily difficult. Preliminary ideas are explored for how research on this multilevel design problem might proceed. Specifically, based on combining insights from a collection of theories and models based on resilience and robustness concepts, the basic elements of a new approach are presented that recognizes the importance of self-organizing processes at multiple scales and emphasizes the use of feedbacks to link these processes across scales. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes an adaptive self-organizing Hermite-polynomial-based neural control (ASHNC) system which is composed of a neural controller and a supervisor compensator. The neural controller uses a self-organizing Hermite-polynomial-based neural network (SHNN) to approximate an ideal feedback controller. For the SHNN, the developed self-organizing approach is clearly and easily used for real-time systems and the parameter learning ability is effective with high convergence precision and fast convergence time. The supervisor compensator is designed to eliminate the approximation error between the neural controller and ideal feedback controller without chattering phenomena. Moreover, a proportional–integral (PI) type adaptation law is derived based on the Lyapunov stability theory; thus not only the system stability of the control system can be guaranteed but also the convergence of the tracking error can be speeded up. Finally, the proposed ASHNC system is applied to a chaotic system. Simulation results demonstrate that the proposed ASHNC system can achieve favorable control performance. [Copyright &y& Elsevier]

An antilock braking system (ABS) is designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining an adequate ability to steer the vehicle. However, the performance of ABS is often degraded under harsh road conditions. In this paper, a self-organizing function-link fuzzy cerebellar model articulation controller (SOFFC) is proposed and is used as the uncertainty observer of the ABS. The self-organizing approach automatically generates and prunes the fuzzy rules for the SOFFC, without the need for preliminary knowledge. The learning algorithms not only extract the fuzzy rules for the SOFFC, but adjust the parameters of the SOFFC as well. A hybrid control system, composing a computational controller and a hyperbolic tangent compensator (HTC), is then proposed for the ABS. The computational controller, which contains an SOFFC uncertainty observer, forms the principal controller, and the HTC is used to compensate for the estimation uncertainty, in order to achieve ultimately bounded stability in the system. Finally, simulations are performed that demonstrate the effectiveness of the proposed hybrid control system in an ABS under various road conditions. [ABSTRACT FROM AUTHOR]

Abstract: To relieve the burdens of network controller design and approximation error bound determination, a self-structuring fuzzy-neural backstepping control system (SSFNBS) with a B-spline-based compensator is proposed. In this paper, a network-identification-based control is represented where the self-structuring fuzzy neural network-based (SSFNN) is used as the observer to approximate the controlled system dynamics. To balance the tradeoff between the structure efficiency and the identification accuracy, a structure learning mechanism of the node-adding process and the node-pruning process is introduced. On the other hand, the B-spline-based compensator is introduced to dispel the effect of approximation error. With the adoption of B-spline functions, the compensation controller can be given in a recurrent way based on the introduction of knot vector and the drawbacks of the conventional compensation controllers can be freed. With the introduction of the B-spline function, the proposed SSFNBS features the following advantages: (1) the capability of network-based controller is improved, (2) the design of the compensation controller can be easily established based on the characteristics of the B-spline function, (3) the stability of closed-loop control system is guaranteed by the means of Lyapunov function with the tuning law of the B-spline-based compensator. To investigate the capabilities of the proposed approach, the SSFNBS is applied to the nonlinear system, chaotic system, and wing rock motion control problems. Through the simulation results the advantages of the proposed SSFNBS can be observed. [Copyright &y& Elsevier]