Centralized Topology Research Articles

Cost analysis of distributed storage in AC and DC microgrids

Building and microgrid designs with highly-distributed electrical storage have potential advantages over today’s conventional topologies with centralized storage. This paper studies the capital cost benefits of several residential behind-the-meter distributed-storage topologies, including AC and DC versions of systems with load-packaged batteries and resilient sub-networks. The study begins by defining the block configuration of each topology. This work then develops a model for the cost of the power electronics necessary to interface with the storage elements. Finally, the analysis develops a model for the total cost of each storage topology, incorporating the installation and soft costs. The results suggest that while the cost of power electronics is lower in centralized topologies, the total cost is lower for distributed storage due to the avoided costs of installation and permitting. This paper also explores the benefits of load-packaged batteries for savings in electrical infrastructure.

EEG alpha band functional connectivity and network structure mark hub overload in Mild Cognitive Impairment during memory maintenance

IntroductionWhile decreased alpha-band functional connectivity (FC) and changes in network topology have been reported in Alzheimer’s disease, it is not yet entirely known whether these differences mark cognitive decline in the early stages of the disease.ObjectivesOur study aimed to analyze EEG FC and network differences in the alpha frequency band during visuospatial memory maintenance between Mild Cognitive Impairment (MCI) patients and healthy elderly with subjective memory complaints.MethodsFC and network structure of 17 MCI patients and 20 control participants were studied with 128-channel EEG during a visuospatial memory task. FC was measured by amplitude envelope correlation with leakage correction (AEC-c), while network analysis was performed by applying the Minimum Spanning Tree approach.ResultsIncreasing memory load enhanced the mean alpha-band FC in the control group. In contrast to that, after an initial increase, the MCI group showed significantly (p<0.05) diminished FC in the highest memory load condition. Mean alpha AEC-c correlated significantly with the size and mean diffusivity of medial temporal lobe structures in the entire sample. The network analysis revealed a rerouted network in the MCI group with a more centralized topology and a more unequal traffic load distribution compared to the control group.ConclusionsAlpha-band FC correlates with cognitive load-related modulation, with medial temporal lobe atrophy, and with the disruption of hippocampal fiber integrity in the earliest stages of cognitive decline. The more integrated network topology of the MCI group is in line with the “hub overload and failure” framework and might be part of a compensatory mechanism.DisclosureNo significant relationships.

A systematic review of federated learning applications for biomedical data.

Federated learning (FL) allows multiple institutions to collaboratively develop a machine learning algorithm without sharing their data. Organizations instead share model parameters only, allowing them to benefit from a model built with a larger dataset while maintaining the privacy of their own data. We conducted a systematic review to evaluate the current state of FL in healthcare and discuss the limitations and promise of this technology. We conducted a literature search using PRISMA guidelines. At least two reviewers assessed each study for eligibility and extracted a predetermined set of data. The quality of each study was determined using the TRIPOD guideline and PROBAST tool. 13 studies were included in the full systematic review. Most were in the field of oncology (6 of 13; 46.1%), followed by radiology (5 of 13; 38.5%). The majority evaluated imaging results, performed a binary classification prediction task via offline learning (n = 12; 92.3%), and used a centralized topology, aggregation server workflow (n = 10; 76.9%). Most studies were compliant with the major reporting requirements of the TRIPOD guidelines. In all, 6 of 13 (46.2%) of studies were judged at high risk of bias using the PROBAST tool and only 5 studies used publicly available data. Federated learning is a growing field in machine learning with many promising uses in healthcare. Few studies have been published to date. Our evaluation found that investigators can do more to address the risk of bias and increase transparency by adding steps for data homogeneity or sharing required metadata and code.

A Robot–Sensor Network Security Architecture for Monitoring Applications

This paper presents SNSR (Sensor Network Security using Robots), a novel, open, and flexible architecture that improves security in static sensor networks by benefiting from robot-sensor network cooperation. In SNSR, the robot performs sensor node authentication and radio-based localization (enabling centralized topology computation and route establishment) and directly interacts with nodes to send them configurations or receive status and anomaly reports without intermediaries. SNSR operation is divided into stages set in a feedback iterative structure, which enables repeating the execution of stages to adapt to changes, respond to attacks, or detect and correct errors. By exploiting the robot capabilities, SNSR provides high security levels and adaptability without requiring complex mechanisms. This paper presents SNSR, analyzes its security against common attacks, and experimentally validates its performance.

EEG Alpha and Beta Band Functional Connectivity and Network Structure Mark Hub Overload in Mild Cognitive Impairment During Memory Maintenance.

Background: While decreased alpha and beta-band functional connectivity (FC) and changes in network topology have been reported in Alzheimer’s disease, it is not yet entirely known whether these differences can mark cognitive decline in the early stages of the disease. Our study aimed to analyze electroencephalography (EEG) FC and network differences in the alpha and beta frequency band during visuospatial memory maintenance between Mild Cognitive Impairment (MCI) patients and healthy elderly with subjective memory complaints.Methods: Functional connectivity and network structure of 17 MCI patients and 20 control participants were studied with 128-channel EEG during a visuospatial memory task with varying memory load. FC between EEG channels was measured by amplitude envelope correlation with leakage correction (AEC-c), while network analysis was performed by applying the Minimum Spanning Tree (MST) approach, which reconstructs the critical backbone of the original network.Results: Memory load (increasing number of to-be-learned items) enhanced the mean AEC-c in the control group in both frequency bands. In contrast to that, after an initial increase, the MCI group showed significantly (p < 0.05) diminished FC in the alpha band in the highest memory load condition, while in the beta band this modulation was absent. Moreover, mean alpha and beta AEC-c correlated significantly with the size of medial temporal lobe structures in the entire sample. The network analysis revealed increased maximum degree, betweenness centrality, and degree divergence, and decreased diameter and eccentricity in the MCI group compared to the control group in both frequency bands independently of the memory load. This suggests a rerouted network in the MCI group with a more centralized topology and a more unequal traffic load distribution.Conclusion: Alpha- and beta-band FC measured by AEC-c correlates with cognitive load-related modulation, with subtle medial temporal lobe atrophy, and with the disruption of hippocampal fiber integrity in the earliest stages of cognitive decline. The more integrated network topology of the MCI group is in line with the “hub overload and failure” framework and might be part of a compensatory mechanism or a consequence of neural disinhibition.

Consolidating Multi-Criteria Visible-light Personal Network Coordinator Selection Process in Attocells

Visible Light Communication (IEEE 802.15.7) implements a centralized topology using a visible-light personal network coordinator (VPNC) for network access and data sharing. A VPNC is a user-device (UD) that is dynamically selected based on several parameters ranking its ability as the most capable device in the attocell network. Theoretical performance bounds of attocell networks based on the use of a coordinator have not been significantly explored in the literature. We investigate how various UD capabilities such as device lifetime, network stability, optical power receiving ability and coverage area to accommodate the maximum possible number of associated devices, can contribute towards the enhancement of the IEEE Std 802.15.7 VPNC selection criteria, and develop an enhanced process with new information elements (IEs) added to the VPNC selection criteria. The selection process is extended to use a greedy approach. Simulation results, supported by theoretical modelling, verify a 61% reduction in VPNC handovers, a 30% improved throughput and up to 40% reduction in power consumption when compared with the VPAN (Visible-light Personal Area Network) standard. The proposed enhancement features backward compatibility to the current standard.

The 5G communication technology-oriented intelligent building system planning and design

Due to the irreplaceable advantages of 5G communication technology, there is no need to carry out complex bridge and wiring works in the construction of the network structure of smart buildings It is convenient and flexible to install, easy to expand and transform. This paper uses the special advantages of 5G-communication technology to apply 5G communication technology to intelligent buildings. This article first designs different subsystems based on different functions. Then, the local area networks composed of various subsystems distributed in the building are connected together through a 5G network. Finally, the basic centralized topology is adopted, and the building automatic control system installed in the public facilities in the middle of the plant is used as the network centre to communicate with each sub local area network through the bridge of 5G. In this paper, through the centralized monitoring and unified management of the distributed subsystems, and using the same monitoring interface to manage the different subsystems on a platform, the ultimate cost-effective system integration solution is finally achieved with the least investment optimal performance.

Adaptive rewiring evolves brain-like structure in weighted networks

Activity-dependent plasticity refers to a range of mechanisms for adaptively reshaping neuronal connections. We model their common principle in terms of adaptive rewiring of network connectivity, while representing neural activity by diffusion on the network: Where diffusion is intensive, shortcut connections are established, while underused connections are pruned. In binary networks, this process is known to steer initially random networks robustly to high levels of structural complexity, reflecting the global characteristics of brain anatomy: modular or centralized small world topologies. We investigate whether this result extends to more realistic, weighted networks. Both normally- and lognormally-distributed weighted networks evolve either modular or centralized topologies. Which of these prevails depends on a single control parameter, representing global homeostatic or normalizing regulation mechanisms. Intermediate control parameter values exhibit the greatest levels of network complexity, incorporating both modular and centralized tendencies. The simulation results allow us to propose diffusion based adaptive rewiring as a parsimonious model for activity-dependent reshaping of brain connectivity structure.

Computer modelling reveals the optimal development for the organisational structure of business clusters

Science and technology parks (STPs) foster innovation between firms inhabiting the cluster. Networking channels are considered as integral parts of the knowledge exchange process, and therefore the innovation process. We simulated three organisational topologies for STPs; firstly, in the star model all are connected to the cluster initiative (CI), secondly the strongly connected model, when all are connected to each other, and finally the randomly connected model, where the network follows no centralised topology. Analyses used adjacency matrixes and Monte-Carlo simulation, trading transaction (networking) costs against knowledge benefit. Results show that star topology is the most efficient form from the cost perspective, and this is especially the case for start-up STPs. Later, when the cost of knowledge transformation is lowered, then the strongly connected model becomes the most efficient topology, but this transition to high transaction costs is very risky if direct ties do not quickly result in tangible benefits.

Computer modelling reveals the optimal development for the organisational structure of business clusters

Science and Technology Parks (STPs) foster innovation between firms inhabiting the cluster. Networking channels are considered as integral parts of the knowledge exchange process, and therefore the innovation process. We simulated three organizational topologies for STPs; firstly, in the star model all are connected to the cluster initiative (CI), secondly the strongly connected model, when all are connected to each other, and finally the randomly connected model, where the network follows no centralised topology. Analyses used adjacency matrices and Monte-Carlo simulation, trading transaction (networking) costs against knowledge benefit. Results show that star topology is the most efficient form from the cost perspective, and this is especially the case for start-up STPs. Later, when the cost of knowledge transformation is lowered, then the strongly connected model becomes the most efficient topology, but this transition to high transaction costs is very risky if direct ties do not quickly result in tangible benefits.

A hierarchical model of metabolic machinery based on the kcore decomposition of plant metabolic networks.

Modeling the basic structure of metabolic machinery is a challenge for modern biology. Some models based on complex networks have provided important information regarding this machinery. In this paper, we constructed metabolic networks of 17 plants covering unicellular organisms to more complex dicotyledonous plants. The metabolic networks were built based on the substrate-product model and a topological percolation was performed using the kcore decomposition. The distribution of metabolites across the percolation layers showed correlations between the metabolic integration hierarchy and the network topology. We show that metabolites concentrated in the internal network (maximum kcore) only comprise molecules of the primary basal metabolism. Moreover, we found a high proportion of a set of common metabolites, among the 17 plants, centered at the inner kcore layers. Meanwhile, the metabolites recognized as participants in the secondary metabolism of plants are concentrated in the outermost layers of the network. This data suggests that the metabolites in the central layer form a basic molecular module in which the whole plant metabolism is anchored. The elements from this central core participate in almost all plant metabolic reactions, which suggests that plant metabolic networks follows a centralized topology.

Effective link interference model in topology control of wireless Ad hoc and sensor networks

Topology control is an important way of reducing the link interference in wireless Ad hoc and sensor networks with shared channel. Modeling link interference is the primary step for designing interference-aware topology controls, and it is very critical to the efficacy of the topology control schemes in use. This paper presents a new link interference model, which can not only be easily calculated with global or local algorithms, but also more effectively profile the link interference in co-channel wireless Ad hoc and sensor networks than previous models. Based on the proposed model, a centralized topology control algorithm is designed to construct interference-optimal topologies. In fact, our model can also be fed into most of the already-existed topology control algorithms aimed at reducing link interferences. Finally, extensive simulations and analysis are conducted to evaluate our designs in detail. The simulation results show the outstanding effectiveness of interference reduction achieved by our model and algorithm, in comparison with the previous work.

A novel void node recovery paradigm for long-term underwater sensor networks

Aquatic environment corresponds to more than 70% of the Earth’s surface mostly still unknown and unexplored. Underwater wireless sensor networks have recently been proposed as a way to observe and explore these environments. However, the efficient data delivery is still a challenging issue in these networks because of the impairments of the acoustic transmission. To cope with this problem, we present a novel geographic forwarding protocol and two topology control mechanisms for long-term non-time-critical underwater sensor networks. The proposed routing protocol considers the anycast network architecture in the data forwarding process. The proposed centralized topology control (CTC) and the distributed topology control (DTC) mechanisms organize the network via depth adjustment of some nodes. Simulation results show that with these mechanisms, the data packet delivery ratio achieves more than 90% even in hard and difficult scenarios of very sparse or very dense networks, and the end-to-end delay and energy consumption per delivered packet are reduced.

The global network of biofuel sustainability standards-setters

The role of network structure in shaping the regulatory scope and content of sustainability standards for biofuels is examined. A critical review of the literature on hybrid governance networks suggests the need to bring in network theory. Through a specific network analysis of the standards-setters, it is shown that not only does the institutional hybridity of the standards boards influence the regulatory scope of the standards, but the network centrality and specific topology in which standards-setters are embedded are equally important structural features of hybrid governance. These findings foreground the relevance of incorporating in the current attributional conception of hybridity a network element, taking seriously the role of network structure in shaping regulatory fields. Social Network Analysis as an analytical tool holds great potential for further research into the structural features of hybrid governance.

Economical Design of Utility-Scale Photovoltaic Power Plants With Optimum Availability

This paper presents an algorithm for the economical design of a utility-scale photovoltaic (PV) power plant via compromising between the cost of energy and the availability of the plant. The algorithm inputs are the plant peak power and the price of inverters with respect to their power ratings. The outputs are the optimum inverter ratings and the interconnection topology of PV panels. This paper introduces the effective levelized cost of energy (LCOE) (ELCOE) index as the core of the proposed design algorithm. ELCOE is an improved index based on the conventional LCOE that includes the availability of a power plant in economical assessments. The conventional LCOE index determines centralized topology (e.g., 1-MW inverter for a 1-MW PV power plant) for minimizing the energy generation cost, whereas based on ELCOE, a multistring topology (e.g., a 1-MW PV plant consists of fifty 20-kW inverters) despite of higher investment cost becomes the economically winning topology. Given the price of commercially available PV inverters at present, the case studies in this paper show that, for 0.1-100-MW PV power plants, the economical ratings of inverters range from 8 to 100 kW. The recently installed PV power plants confirm the feasibility of the calculations based on the suggested algorithm.

Stock network stability in times of crisis

Despite many efforts crises on financial markets are in large part still scientific black-boxes. In this paper, we use a winner-take-all approach to construct a longitudinal network of S&P 500 companies and their correlations between 2000 and 2012. A comparison to complex ecosystems is drawn, especially whether the May–Wigner theorem can describe real-world economic phenomena. The results confirm the utility of the May–Wigner theorem as a stability indicator for the US stock market, since its development matches with the two major crises of this period, the dot-com bubble and, particularly, the financial crisis. In those times of financial turmoil, the stock network changes its composition, but unlike ecological systems it tightens and the disassortative structure of prosperous markets transforms into a more centralized topology.

Low-Voltage-Stress AC-Linked Charge Equalizing System for Series-Connected VRLA Battery Strings

This paper presents a low voltage-stress AC-linked charge equalizing system for balancing the energy in a serially connected, valve-regulated lead acid battery string using a modular converter that consists of multiple transformers coupled together. Each converter was coupled through an AC-linked bus to increase the overall energy transfer efficiency of the system and to eliminate the problem of the unbalanced charging of batteries. Previous solutions are based on centralized and modularized topologies. A centralized topology requires a redesign of the hardware and related components. It also faces a high voltage stress when the number of batteries is expanded. Modularized solutions use low-voltage-stress, double-stage, DC-linked topologies which leads to poor energy transfer efficiency. The proposed solution uses a low-voltage stress, AC-linked, modularized topology that makes adding more batteries easier. It also has a better energy transfer efficiency. To ensure that the charge equalization system operates smoothly and safely charges batteries, a small intelligent microcontroller was used in the control section. The efficiency of this charge equalization system is 85%, which is 21% better than other low-voltage-stress DC-linked charging techniques. The validity of this approach was confirmed by experimental results.

Evolution of Robust Network Topologies: Emergence of Central Backbones

We model the robustness against random failure or an intentional attack of networks with an arbitrary large-scale structure. We construct a block-based model which incorporates--in a general fashion--both connectivity and interdependence links, as well as arbitrary degree distributions and block correlations. By optimizing the percolation properties of this general class of networks, we identify a simple core-periphery structure as the topology most robust against random failure. In such networks, a distinct and small "core" of nodes with higher degree is responsible for most of the connectivity, functioning as a central "backbone" of the system. This centralized topology remains the optimal structure when other constraints are imposed, such as a given fraction of interdependence links and fixed degree distributions. This distinguishes simple centralized topologies as the most likely to emerge, when robustness against failure is the dominant evolutionary force.

Agents and workflow engines for inter‐organizational workflows in e‐government cases

PurposeThe paper aims to propose a web‐based “intermediation hybrid architecture” for a workflow management system that integrates services by exploiting and combining the advantages of strict centralized topologies that use workflow engines, with totally distributed systems which use agent technologies.Design/methodology/approachThe proposed operational and technical architectures define the combination of a workflow engine and a software agent platform for the appropriate management of inter‐organizational workflow processes. The proposed system is called “hybrid intermediation portal.”FindingsThe hybrid system was used in e‐government cases for the management of cross‐border workflows that span across multiple European chambers of commerce and industry. That demonstrated the usefulness of the specific approach in the inter‐organizational workflow management domain.Research limitations/implicationsThe approach presented in this paper was implemented and validated in real e‐government cases that proved the advantages of the potential commercial use of the system. Several limitations were located due to the lack of a wide use of web services within governmental organizations.Practical implicationsConcerning security issues, it would be necessary to integrate appropriate security mechanisms without constraining the technical and functional characteristics of the software agents involved.Originality/valueThe contribution of the paper concerns the evolvement of the agent‐enhanced workflow management systems in order to tackle the intelligence issues and the credibility problems concerning the overall control of dispersed inter‐organizational processes.

Improved PNC Selection Criteria and Process for IEEE802.15.3 [Ad Hoc and Sensor Networks

IEEE 802.15.3 is a standard that supports high data rate for wireless personal area networks. It implements a centralized topology for network monitoring and control. The centralized piconet coordinator (PNC) needs to be dynamically selected depending on a number of categories that measure its abilities as the most capable device compared to the surrounding devices in the network. This article presents an extension to the PNC selection criteria in IEEE 802.15.3 by modifying the reserved fields readily available in the standard to support user-centricity as well as other higher layer service dependent criteria. This article also develops the implementation process for the message header exchange between devices to support this new selection process. In addition, simulations are carried out that show the proof of concept of the manner in which this new criteria could be implemented in IEEE 802.15.3.