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This paper provides an alternative narrative for organizing food systems. It introduces meshwork as a novel theoretical lens to examine the ontological assumptions underlying the shadow and informal dynamics of organizing food. Through a longitudinal qualitative case study, we place relationality and becoming at the centre of organizing food and food systems, demonstrating how entangled relationships can create a complex ontology through the meshwork knots, threads and weave. We show how issues of collective concern come together to form dynamic knots of interactions, how the threads within the meshwork indicate processes of movement, and how the weave suggests degrees of food system resilience—but always in flow. This theoretical approach thus provides a platform for addressing thoughtful concerns about “food matters” including the integrity of our global food system, the negative health and environmental impacts of industrialized food production, and food safety issues.

Network densification has emerged as a powerful paradigm to boost spectral efficiency and accommodate the continual rise in demand for wireless capacity. In dense cell deployments however, overlapping coverage areas may cause highly varying interference conditions among different cells. Moreover, denser networks experience more temporal load fluctuations due to daily and hourly changing usage patterns. The currently applied universal reuse frequency allocation is not suitable to deal with these issues, and needs to be tailored to dense cell deployments to ensure adequate performance in such scenarios. In this paper we present a dynamic, load aware and self-adapting frequency allocation scheme designed for dense cellular networks: the DyCRA scheme (Dynamic Cost/Reward based Allocation). The scheme makes decisions based on cost-reward trade-offs: rewards arise in the form of capacity, and costs arise in the form of interference (under spatial reuse). We quantify these costs and rewards based on SINR, and use periodic load estimates to determine if access points are in need of extra frequencies, or can spare them, and the cost/reward structure is used to determine which frequencies are allocated or released. Extensive simulation results show that the DyCRA scheme provides efficient resource allocations that adapt to changing traffic conditions and yields significant performance gains in scenarios with nonstationary traffic demands.

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.

The research discusses experiential outcome in the application of crowd simulation technology to analyze the pedestrian circulation in public spaces to facilitate design and planning decisions. The paper describes how to connect spatial design with agent-based simulation (ABS) for various design and planning scenarios. It describes the process of visualizing and representing pedestrian movement, as well as pathfinding and crowd behavior study. An ABS consists of a large number of agents, which are controlled by simple localized rules to interact with each other within a virtual environment, thereby formulating a bottom-up system. The concept of the ABS has been widely used in computer science, biology, and social science to simulate swarm intelligence, dynamic social behavior, and fire evacuation. The simulation consists of interacting agents which can create various complexities. This paper describes research on using local interactions to generate passenger flow analysis. An ABS is used to optimize the pedestrian flow and construct the micro-level complexity within a simulated environment. We focus on how agent-driven emergent patterns can evolve during the simulation in response to various design iterations. The research extends to the agents’ interactions driven by a set of rules and external environment. Our research method includes data collection, quantitative analysis, and crowd simulation on two train stations and surrounding areas in Sihui train station in Beijing, and Xuzhou, China. By proposing a mix-use program with the local public transportation system, the new development is integrated with the existing urban infrastructure and public space. Through the multi-agent simulation, we evaluate the crowd flow, total travel time, density, and public accessibility. Based on the result of ABS, we discussed whether various space design methods can improve pedestrian flow efficiency and passenger experience, as well as shortening transfer time, and reducing congestion.

The bureaucratic organizational structure has been recently challenged by a number of organizations that claim to offer employee emancipation and autonomy through self-management, self-organizing, or “holacracy.” To facilitate theorizing about such organizational-level self-management, I examine it as an ideal type of organizational form, comparing it to two more established organizational forms, Weberian bureaucracy and Mintzberg’s adhocracy. More particularly, building on the four universal problems every organization needs to solve—two of which I divide into two sub-problems—I utilize a framework of six fundamental problems of organizing—task division, task allocation, rewarding desired behavior, eliminating freeriding, providing direction, and ensuring coordination—to demonstrate how these three forms of organizing have found different solutions to them. The radically decentralized model of authority at the heart of self-managing organizations is shown to lead to solutions to these problems that are based on peer-based accountability and rewarding, transparency of key information, and bottom-up emergent processes where employees have the authority and responsibility to identify necessary tasks and ensure that they get done. It is concluded that the self-managing organization indeed is a novel form of organizing that can better explain certain real-life organizational outliers than the existing paradigms of organizing. It is argued to be especially viable in industries where interdependence between units is low, outputs are highly tailored, and employee expertise and motivation are high. Accordingly, research on such organizations can offer several new insights relevant to both the practice and theory of organization design.

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.

International audience; Due to the recorded success by Internet of Things (IoT) technology, more and more domains use it as a communications and exchange network such as e-health, smart cities, vehicles, etc. IoT don’t stop integrating an important number of components and objects that are characterized by their complexity and heterogeneity. Such constraints make the existing routings protocols unsuitable for IoT communications. To accomplish all the expected tasks and satisfy the user services, it is important to guarantee a quality of communication that answers to the requirements of the various applications in terms of data and processing (availability, integrity, efficiency, etc.). The objective of our work in this paper is to proposes a smart and efficient routing algorithm to improve the IoT communications performance. The proposed method is called SERA (Smart and Efficient Routing Algorithm) which is based on self-organization of the communications between devices according to their new parameters. The evaluation of our proposed method has shown that SERA improves considerably the IoT communications performance in terms of some QoS and energy efficiency parameters.

According to the principles articulated in the agile manifesto, motivated and empowered software developers---relying on technical excellence and simple designs---create business value by delivering working software to users at regular short intervals. These principles have spawned many practices. At the core of these practices is the idea of autonomous, self-managing, or self-organizing teams whose members work at a pace that sustains their creativity and productivity. This article summarizes the main challenges faced when implementing autonomous teams and the topics and research questions that future research should address.

As the number of elderly persons with chronic illnesses increases, a new public infrastructure for their care is becoming increasingly necessary. In particular, technologies that can monitoring bio-signals in real-time have been receiving significant attention. Currently, most healthcare monitoring services are implemented by wireless carrier through centralized servers. These services are vulnerable to data concentration because all data are sent to a remote server. To solve these problems, we propose self-organizing P2P middleware for healthcare monitoring that enables a real-time multi bio-signal streaming without any central server by connecting the caregiver and care recipient. To verify the performance of the proposed middleware, we evaluated the monitoring service matching time based on a monitoring request. We also confirmed that it is possible to provide an effective monitoring service by evaluating the connectivity between Peer-to-Peer and average jitter.

Long term evolution (LTE)-advanced was introduced as real fourth generation (4G) with its new features and additional functions, satisfying the growing demands of quality and network coverage for the network operators’ subscribers. The term muti-tier has also been recently used with respect to the heterogeneity of the network by applying the various sub-network cooperative systems and functionalities with self-organising capabilities. Using indoor short-range low-power cellular base stations, for example, femtocells, in cooperation with existing long-range macrocells are considered as the key technical challenge of this multi-tier configuration. Furthermore, shortage of network spectrum is a major concern for network operators which forces them to spend additional attentions to overcome the degradation in performance and quality of services in 4G HetNets. This study investigates handover between the different layers of a heterogeneous LTE-advanced system, as a critical attribute to plan the best way of interactive coordination within the network for the proposed HetNet. The proposed comprehensive handover algorithm takes multiple factors in both handover sensing and decision stages, based on signal power reception, resource availability and handover optimisation, as well as prioritisation among macro and femto stations, to obtain maximum signal quality while avoiding unnecessary handovers.

A new approach for the production of oriented films and fibers with angular-dependent reflective colors is presented. The process consists of spray coating a solution of cholesteric liquid-crystalline monomers onto a melt-processed and oriented polyamide-6 substrate followed by UV curing. Reflectivity measurements and optical microscopy show that a well-defined liquid-crystalline and planar alignment is obtained. It is further demonstrated that a reflection up to 80% is obtained by coating oriented films on both sides of the oriented substrate with a single-handedness cholesteric liquid-crystal coating. The high reflectivity is attributed to the close to half-wave retardation induced by the anisotropic polymer substrate. Also, polyamide-6 filaments are successfully coated and fibers are obtained with an angular-dependent color in a single dimension along the fiber direction, which originates from the planar cholesteric alignment on a curved surface.

A remarkable accomplishment of self organizing models is their ability to simulate the development of feature maps in the cortex. Additionally, these models have been trained to tease out the differential causes of multiple feature maps, mapped on to the same output space. Recently, a Laterally Interconnected Synergetically Self Organizing Map (LISSOM) model has been used to simulate the mapping of eccentricity and meridional angle onto orthogonal axes in the primary visual cortex (V1). This model is further probed to simulate the development of the radial bias in V1, using a training set that consists of both radial (rectangular bars of random size and orientation) as well as non-radial (sinusoidal gratings of random phase, frequency and orientation) stimuli. The radial bias describes the preference of the visual system towards orientations that match the angular position (meridional angle) of that orientation with respect to the point of fixation. Recent fMRI results have shown that there exists a coarse scale orientation map in V1, which resembles the meridional angle map, thereby providing a plausible neural basis for the radial bias. The LISSOM model, trained for the development of the retinotopic map, on probing for orientation preference, exhibits a coarse scale orientation map, consistent with these experimental results, quantified using the circular cross correlation ($r_c$). The $r_c$ between the orientation map developed on probing with a thin annular ring containing sinusoidal gratings with a spatial frequency of 0.5 cycles per degree (cpd) and the corresponding meridional map for the same annular ring, has a value of 0.8894. While the $r_c$ between the two maps reduces on increasing the spatial frequency of the gratings and the width of the annulus, robust orientation decoding is still possible, again consistent with experimental findings. These results suggest that the radial bias goes beyond the current understanding of a node to node correlation between the two maps.

Elasticity is one of the most known capabilities related to cloud computing, being largely deployed reactively using thresholds. In this way, maximum and minimum limits are used to drive resource allocation and deallocation actions, leading to the following problem statements: How can cloud users set the threshold values to enable elasticity in their cloud applications? And what is the impact of the application’s load pattern in the elasticity? This article tries to answer these questions for iterative high performance computing applications, showing the impact of both thresholds and load patterns on application performance and resource consumption. To accomplish this, we developed a reactive and PaaS-based elasticity model called AutoElastic and employed it over a private cloud to execute a numerical integration application. Here, we are presenting an analysis of best practices and possible optimizations regarding the elasticity and HPC pair. Considering the results, we observed that the maximum threshold influences the application time more than the minimum one. We concluded that threshold values close to 100% of CPU load are directly related to a weaker reactivity, postponing resource reconfiguration when its activation in advance could be pertinent for reducing the application runtime. Elasticidade é uma das capacidades mais conhecidas da computação em nuvem, sendo amplamente implantada de forma reativa usando thresholds. Desta forma, os limites máximos e mínimos são usados ??para conduzir ações de alocação de recursos e desalocação eles, levando às seguintes sentenças-problema: Como podem os usuários definir os valores de limite para permitir a elasticidade em suas aplicações em nuvem? E qual é o impacto do padrão de carga do aplicativo na elasticidade? Este artigo tenta responder a estas perguntas para aplicações iterativas de computação de alto desempenho, mostrando o impacto de ambos os lthresholds e padrões de carga no desempenho do aplicativo e consumo de recursos. Para isso, foi desenvolvido um modelo de elasticidade baseado em PaaS chamado autoplastic, empregando-o sobre uma nuvem privada para executar um aplicativo de integração numérica. Apresenta-se uma análise das melhores práticas e possíveis otimizações no que diz respeito ao par elasticidade e HPC. Considerando os resultados, observou-se que o limite máximo influencia o tempo de aplicação mais do que o mínimo. Concluiu-se que os limiares próximos a 100% da carga de CPU estão diretamente relacionados com a reactividade mais fraca, adiando reconfiguração de recursos quando sua ativação com antecedência pode ser pertinente para reduzir o tempo de execução do aplicativo.

The socio-technical systems research paradigm is about the complexity of real situations. It confronts us with the quest for variables that could provide us with insight into the behavior of such systems. Their behavior emerges according to internal system properties and adaptation of the system to external conditions. In our view, behavioral patterns are one of those particular variables since machines can recognize them and their dynamics. Based on the synthesis of three different theoretical frameworks, this paper proposes a concept of patterns- based information system organization. The authors built the concept on the Deacon discussion of theory of information, Hofkirchner’s unified information theory and related system behavior, and Kelso’s explanation of pattern creation processes in self-organizing systems. All three researchers have included patterns in their theoretical proposal. According to this analysis of the existing theories and their synthesis, we conclude that in order to design machines that can automatically support new behavior, we have to analyze humans and machines as a complex whole with dynamic relationships and emerging patterns as a dependent variable of behavior. By developing this theoretical concept, we establish a departure point for future research and search for different variables that correlate with pattern formation.

Based on liquid crystal networks we developed 'smart' coatings with responsive surface topographies. Either by prepatterning or by the formation of self-organized structures they can be switched on and off in a pre-designed manner. Here we provide an overview of our methods to generate coatings that form surface structures upon the actuation by light. The coating oscillates between a flat surface and a surface with pre-designed 3D micro-patterns by modulating a light source. With recent developments in solid state lighting, light is an attractive trigger medium as it can be integrated in a device for local control or can be used remotely for flood or localized exposure. The basic principle of formation of surface topographies is based on the change of molecular organization in ordered liquid crystal polymer networks. The change in order leads to anisotropic dimensional changes with contraction along the director and expansion to the two perpendicular directions and an increase in volume by the formation of free volume. These two effects work in concert to provide local expansion and contraction in the coating steered by the local direction of molecular orientation. The surface deformation, expressed as the height difference between the activated regions and the non-activated regions divided by the initial film thickness, is of the order of 20%. Switching occurs immediately when the light is switched 'on' and 'off' and takes several tens of seconds.

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. � 2015 Philips and Chakravarthy.

International audience; This paper presents the study of the relationship between Autonomic Network Management (ANM) and Software-Defined Networking (SDN) under the prism of Long Term Evolution (LTE) Self-Organizing Networks (SONs). The ANM and SDN paradigms, besides sharing a few common goals, have shown to be complementary to each other in terms of level of abstractions and expectations. In particular, we consider the Unified Management Framework (UMF) introduced in the FP7 UNIVERSELF project, which focuses on higher level self-* functionality, often assuming as given a fictional adaptation layer between such autonomics and the managed infrastructure. On the other hand, SDN architectures provide by design a uniform control substrate for the programmatic management of network resources through vendor-agnostic APIs. However, despite the popularity of flow-based control in core networks and data centers, a similar widely-adopted abstraction to that of the "flow" have yet to be defined for radio access and SONs. In this work we propose such a novel abstraction layer designed to realize SON programmability. The prototype Autonomic SDN (AutoSDN) controller has been integrated with UMF to self-optimize an LTE-Advanced heterogeneous network, enabling SON functions to be provided by 3rd parties and to be hot-plugged into the network.

In this paper we present two methods for non-uniformity correction of imaging array detectors based on neural networks, both of them exploit image properties to supply lack of calibrations while maximizing the entropy of the output. The first method uses a self-organizing net that produces a linear correction of the raw data with coefficients that adapt continuously. The second method employs a kind of contrast equalization curve to match pixel distributions. Our work originates from Silicon detectors but the treatment is general enough to be applicable to many kinds of array detectors like those used in Infrared imaging or in high energy physics.

Self-organizing neural network which is an unsupervised learning algorithm is to discover the inherent relationships of data. Such technique has become an important tool for data mining, machine learning and pattern recognition. Most self-organizing neural networks have a difficulty in reflecting data distributions precisely if data distributions are very complex. And meanwhile, it is also hard for these algorithms to learn new data incrementally without destroying the previous learnt data. In this paper, we propose a robust energy artificial neuron based incremental self-organizing neural network with a dynamic structure (REISOD). It can adjust the scale of network automatically to adapt the scale of the data set and learn new data incrementally with preserving the former learnt results. Moreover, several experiments show that our algorithm can reflect data distributions precisely.

Uncoordinated deployment of eNBs in local area environments will benefit from having support for dynamic frequency re-use mechanisms also known as "autonomous com- ponent carrier selection", where each eNB dynamically selects to use only a subset of the available component carriers (i.e. using from one component carrier up to the maximum num- ber of available component carriers). Component carriers are selected autonomously by each eNB depending on the offered traffic, interference coupling with neighboring cells, etc. In this contribution we propose a simple and distributed scheme based on so-called background interference matrices (BIMs) for autonomous component carrier selection. Extensive numerical simulation campaigns indicate that the proposed scheme effec- tively allows each eNB to select the most attractive frequency configuration; improving average user capacity and boosting cell edge performance considerably. Index Terms—Spectrum-sharing, LTE-Advanced, Local Area, Self-organizing, Inter-cell Interference Coordination (ICIC).

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 The Authors. Peer-to-peer (P2P) systems provide a new solution to distributed information and resource sharing because of its outstanding properties in decentralization, dynamics, flexibility, autonomy, and cooperation, summarized as DDFAC in this paper. After a detailed analysis of the current P2P literature, this paper suggests to better exploit peer social relationships and peer autonomy to achieve efficient P2P structure design. Accordingly, this paper proposes Self-organizing peer-to-peer social networks (SoPPSoNs) to self-organize distributed peers in a decentralized way, in which neuron-like agents following extended Hebbian rules found in the brain activity represent peers to discover useful peer connections. The self-organized networks capture social associations of peers in resource sharing, and hence are called P2P social networks. SoPPSoNs have improved search speed and success rate as peer social networks are correctly formed. This has been verified through tests on real data collected from the Gnutella system. Analysis on the Gnutella data has verified that social associations of peers in reality are directed, asymmetric and weighted, validating the design of SoPPSoN. The tests presented in this paper have also evaluated the scalability of SoPPSoN, its performance under varied initial network connectivity and the effects of different learning rules. National Natural Science of Foundation of China

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

Despite its long tradition and well-known contributions, corporate strategy research is yet far from being mature. This paper proposes an innovative framework that approaches the field from the theoretical perspective provided by complexity theory. We propose to see the corporate level of the organization as the driver, pacer and framer of the overall firm's evolution process. Drive is provided by the cognitive representation of the corporate fitness landscape that is implicit in the firm's corporate plan. Pacing is a consequence of the kind of strategic initiatives (‘search strategy’) developed by the company. Framing is achieved through the architectural design that the corporate level implements for the firm.

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)