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This paper develops a parsimonious process-level theory that connects organizational structure to exploration and exploitation. Toward this end, it develops a mathematical model of organizational decision making that combines an information processing approach in the spirit of Sah and Stiglitz [Sah RK, Stiglitz JE (1986) The architecture of economic systems: Hierarchies and polyarchies. Amer. Econom. Rev. 76(4):716–727] with elements from signal detection theory. The model is first used to explore a “design space” of organizations and identify trade-offs and dominance relationships among alternative organization designs. The paper then studies open questions in the organization design literature, such as the extent to which exploration and exploitation can be produced by one organization and what is the effect of organization size on exploration. More broadly, this research speaks to calls for the introduction of more process-level explanations in the organizations literature. The paper concludes with testable hypotheses and managerially relevant insights.

Information Service is the heart of the entire Grid software infrastructure. It maintains various information in a Grid environment, and provides this information to users on request. We present an Information Service architecture for information capturing, aggregation, and provisioning in a Grid Virtual Organization (VO). This Information Service is a hierarchical structure which consists of VO layer, site layer and resource layer. The architecture defines downloadable and pluggable information sensors for portability and flexibility of information capturing. The meta-data mechanism is incorporated into our Information Service for information organization and management. Experiments were conducted to evaluate the performance of the Information Service, and the results show that the Information Service presents satisfactory scalability with number of users and number of information amount. Our Information Service has been implemented based on the Globus Toolkit as a Grid service compliant to the Open Grid Services Architecture (OGSA) specifications.

Simulation of wireless networks has recently drawn considerable attention in the area of abstraction. Simulation is a precious tool used to model complex systems where the desired network size is large in scale. For large mobile ad hoc networks, the most computation intensive tasks in simulation are computing interference and determining which receivers are in range of a transmitter. In both cases, O(N2) physical layer calculations are required for a wireless system of N nodes, which scales poorly. In this paper, a geometric model, minimal rectangular coverage area is devised for the optimization of the complexity of interference computations in simulations of wireless mobile ad hoc networks. This method lessens the number of unaffected nodes by considering less area as affected by transmission range, which exists outside the transmission range of a transmitter. However, the experimental results suggest that this geometric model reduce the affected coverage area 12.5% - 78.15% than existing grid based algorithm used in current version of NS2. This paper also discusses about the efficiency considerations of the algorithm in detail. Index Terms-MANET, Receiving Threshold (RT), Carrier-Sense Threshold (CST), Grid based node organization, List based node organization, Network Simulator NS2, Minimal rectangle

This chapter adopts a generic approach that consists of covering the algorithmic elements that are common to most multiple‐point geostatistics (MPS) simulation methods. Concepts such as the grid organization of the sequential simulation, are not unique to MPS but are also used with semivariogram‐based methods. Most MPS algorithms are based on the definition of neighbourhoods. In principle, the use of MPS is not restricted to fixed templates, and it is found that more flexibility in the neighborhood is needed to model spatial features at different scales. In practice, even methods that use fixed templates do it by nesting several templates representing different scales, a process that is known as multiple grids. The chapter reviews the possible approaches for both the storage and restitution steps. It discusses the most common types of data that are available in spatial models, and how conditioning in MPS to such data is approached.

Bernardin and Alvares have reported that self-assessment of managerial styles without training is a poor predictor of perceived managerial effectiveness or conflict resolution methods employed. Their assumptions are unjustified rendering their conclusions meaningless. Although there are several aspects of the problem, the primary one is self-deception.

Energy access remains a challenge for many countries, as recognized by sustainable development goal 7 of the United Nations Development Programme. Although the Myanmar government has set a target of 100% electrification by 2030, less than half of the households are currently connected to the national grid. To expedite electrification, decentralized approaches should be considered. Mini-grids are an effective alternative that can fill the gap between a solar home system and the national grid; however, many of the existing mini-grids in Myanmar are powered by diesel generators. Diesel fuel is significantly more expensive in rural areas than in urban areas due to high transportation costs. Although mini-grids powered by solar photovoltaics and batteries are cost-competitive with diesel generators, the deployment of renewable energy-based mini-grids is slow. In this study, we analyzed the barriers to mini-grid deployment and prioritized the barriers. We conducted a questionnaire survey with stakeholders using the analytic hierarchy process to identify the prioritization of each barrier factor. The K-means clustering method was used to determine tendencies and showed that there was no single, dominant solution. Our results confirm the difficulty of mini-grid deployment and suggest multi-pronged approaches that go beyond economic considerations.

The coronavirus disease 2019 (COVID-19) pandemic has spread worldwide and caused negative economic and health effects. China is one of the most seriously affected countries, and it has adopted grid governance measures at the basic level of society, which include city lockdown, household survey and resident quarantine. By the end of April, China had basically brought the pandemic under control within its own borders, and residents’ lives and factory production gradually began to return to normal. In referring to the specific cases of different communities, schools, and enterprises in the four cities of Anhui, Beijing, Shenzhen and Zibo, we analyze grid-based governance measures and we summarize the effectiveness and shortcomings of these measures and discuss foundations and future challenges of grid governance. We do so in the expectation (and hope) that the world will gain a comprehensive understanding of China’s situation and introduce effective measures that enable the prevention and control of COVID-19.

Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process-including how to verify the quality of the underlying clusters-through supervision, human intervention, or the automated generation of alternative clusters.

Virtual power plants (VPPs) are promising solutions to address the decarbonization and energy efficiency goals in the smart energy grid. They assume the coordination of local energy resources such as energy generation, storage, and consumption. They are used to tackle problems brought by the stochastic nature of renewable energy, lack of energy storage devices, or insufficient local energy flexibility on the demand side. VPP modeling, management, and optimization are open to research problems that should consider, on one side, the local constraints in the operation of the energy resources and power flows and the energy grid’s sustainability objectives on the other side. There are multiple goals to create a VPP, such as to deliver energy services on a market or to the grid operator, to operate a microgrid in autonomy decoupled from the main grid, or to sustain local energy communities. In this paper, we present the results of a narrative review carried out on the domain of VPP optimization for the local energy grid integration. We have defined a search strategy that considers highly rated international databases (i.e., Elsevier, IEEE, and MDPI) in a six-year timeframe and applied objective inclusion/exclusion criteria for selecting articles and publications for the review; 95 articles have been analyzed and classified according to their objectives and solutions proposed for optimizing VPP integration in smart grids. The results of the study show that VPP concepts and applications are well addressed in the research literature, however, there is still work to be done on: engaging prosumers and citizens in such a virtual organization, developing heuristics to consider a wider range of local and global constraints and non-energy vectors, and to decentralize and make transparent the services delivery and financial settlement towards community members. This study can help researchers to understand the current directions for VPP integration in smart grids. As a next step we plan to further analyze the open research directions related to this problem and target the development of innovative solutions to allow the integration of multi-energy assets and management of cross energy sector services in energy communities.

Grid cells in the medial entorhinal cortex (MEC) respond when an animal occupies a periodic lattice of ‘grid fields’ in the environment. The grids are organized in modules with spatial periods, or scales, clustered around discrete values separated on average by ratios in the range 1.4–1.7. We propose a mechanism that produces this modular structure through dynamical self-organization in the MEC. In attractor network models of grid formation, the grid scale of a single module is set by the distance of recurrent inhibition between neurons. We show that the MEC forms a hierarchy of discrete modules if a smooth increase in inhibition distance along its dorso-ventral axis is accompanied by excitatory interactions along this axis. Moreover, constant scale ratios between successive modules arise through geometric relationships between triangular grids and have values that fall within the observed range. We discuss how interactions required by our model might be tested experimentally. In a room, we have a sense of our location relative to the doors and to objects within the room. This is because the brain constructs a mental map of our current environment. As we move around the room, neurons called grid cells fire whenever we are in specific locations. But these locations are not random. They correspond to the corners of a grid of tessellating triangles on the floor, a little like the dots in a regular polka-dot pattern. Grid cells fire whenever we stand on one of the dots. This enables the brain to keep track of where we are and where we are heading. But the brain does not use just a single grid cell map to represent a room. Instead, it uses multiple maps with different spatial scales. These maps differ in the distance between the points at which each grid cell fires, that is, the distance between the polka dots. Some maps have many small triangles, providing high resolution spatial information. Others have fewer, larger triangles. This is similar to how we use maps with different spatial scales when driving between cities versus walking around a single neighborhood. A set of grid cell maps with the same spatial scale—and the same orientation—is known as a grid cell module. Animal experiments suggest that different individuals use a similar combination of grid cell modules that can efficiently map rooms. But how can the brain reliably produce this particular combination? Using a computer model to simulate networks of grid cells, Kang and Balasubramanian identify a mechanism that enables the brain to spontaneously organize into the previously observed combination. The interactions between networks—in particular the balance of inhibitory and excitatory activity—determine the arrangement of grid cell modules. This process still works even with random fluctuations in network activity. Grid cells occupy a brain region that degenerates early in the course of Alzheimer's disease. This may explain why some patients experience difficulty finding their way around as one of their first symptoms. To develop effective treatments, scientists need to understand how neural circuits within this brain region work, and how the disease process disrupts them. The computer model of Kang and Balasubramanian brings the research community a step closer to achieving this. It also provides insights into how neuronal networks self-organize, which is relevant to other brain functions too.