Connection Topology Research Articles

Effect of cell-to-cell variation and module configuration on the performance of lithium-ion battery systems

The performance of lithium-ion battery modules significantly depends on cell-to-cell variations and connection topology. In particular, inhomogeneous distribution across the parallel battery module results in performance degradation and potential safety problems. This study evaluates the overall performance of battery modules, including parallel-connected cell groups with different system terminal positions, and examines the effect of cross-connectors. First, a battery module model with electrochemical, thermal, and aging properties is introduced. An LMN structure that allows all battery module structures was proposed for the first time. Next, modules with different topologies are simulated to analyze the cell-to-cell variations in terms of current, temperature, and aging. Finally, six performance indices are extracted from the simulation results to represent the module performance. The module with the cross-contacting system terminal reduces the effect of the connection resistance on the cell-to-cell variation, showing clear improvement over other topologies of up to 61.5%. Additionally, the use of cross-connectors reduces inconsistencies between cells and improves the safety of the battery modules in the event of potential problems. The number of cycles increases by over four times compared with the general case and no significant overdischarge state is observed in a particular cell. The proposed method can be used to solve system-level problems and assist engineers in designing battery management systems to provide longer-lasting and more efficient energy storage systems.

Connectivity of Urban Spaces Typology and Urban Mobility Topology

In the study, the connectivity of the ontological and epistemological knowledge of the space in intervening in urban transportation and mobility problems is questioned through the sample area. The purpose of the questioning; is to understand whether potential urban mobility analysis and original application principles can be obtained by trying to reflect the epistemological knowledge of the space to the ontological knowledge. Urban mobility studies; it is seen as sustainable and up-to-date approaches that are handled with original preparation, plan, implementation and monitoring processes, that bring together land use regulations, development plans and transportation plans in the solution of urban transportation problems, and combine spatial and other relational dimensions that develop accordingly. As a result of the inquiry, it was determined that the connectivity between epistemological and ontological knowledge of the space effectively solved urban transportation and mobility problems. It has been seen that it is effective in directing planning and design, especially in urban mobility planning, at the lower scale, during plan implementations, or before plan making. The first stage of the study consists of understanding the ontological and epistemological layers of urban space. In the second stage, the connectivities of these layers in intervening in urban transportation and mobility problems are examined. In the study, while producing urban mobility resolutions, improvements were made to the ontological and epistemological layers of the space (topographic, typological, topological, and hodological), and these were tested on the sample area. Thus, it will be understood whether the method to be followed will be valid in other cities.

Swin-ResUNet+: An edge enhancement module for road extraction from remote sensing images

Road extraction from remote sensing images is very important in navigation, urban planning, traffic management and other fields. Deep learning methods have achieved great success in computer vision tasks. Therefore, road extraction from remote sensing images using deep learning methods can significantly improve the road extraction accuracy. However, these methods generally have problems such as low road extraction accuracy, slow training speed, high computational complexity, and poor road topology connectivity. In order to solve the above issues, we propose a Swin-ResUNet+ structure and use the new paradigm Swin-Transformer to extract roads in remote sensing images. Specifically, we construct an Edge Enhancement module based on residual connection and add this module to each stage of the encoder, which can obtain the edge information in remote sensing images. Based on the Edge Enhancement module, we propose a Swin-ResUNet+ structure in order to better capture the topology of roads. On the Massachusetts road dataset, our model has the least computational cost with only less than one percent accuracy decrease. On the DeepGlobe2018 road dataset, our model not only has the least computational complexity but also achieves the highest values of mIOU, mDC, mPA and F1-score. In a word, Swin-ResUNet+ obtains a much better trade-off between accuracy and efficiency than previous CNN-based and Transformer-based methods.

An impedance-multi-method-based fault location methodology for transmission lines connected to inverter-based resources

This paper is motivated by the growing penetration of Inverter-Based Resources (IBRs) in power systems and their impacts on fault diagnosis systems. Also, a motivating factor is the scarcity of works in the literature that evaluate the effects and provide solutions to impedance-based fault location in lines interconnecting IBRs to the grid. In this context, the performance of ten traditional impedance-based (one- and two-terminal) fault location methods is evaluated, and a multi-method methodology that minimizes the obtained fault location errors is proposed. To conduct the analysis and propose the methodology, the PSCAD software is used to carry out massive fault simulations on two systems with different voltage levels and connection topologies of IBRs to the grid. The results demonstrate that the proposed methodology significantly minimizes the fault location percentage errors, being a very promising proposal, especially for conditions in which communication channels between the line terminals are unavailable.

Optimization of Substation Siting and Connection Topology in Offshore Wind Farm Based on Modified Firefly Algorithm

Optimization of Substation Siting and Connection Topology in Offshore Wind Farm Based on Modified Firefly Algorithm

Disrupted resting-state functional connectivity and network topology in mild traumatic brain injury: an arterial spin labelling study.

Mild traumatic brain injury can cause different degrees of cognitive impairment and abnormal brain structure and functional connectivity, but there is still a lack of research on the functional connectivity and topological organization of cerebral blood flow fluctuations. This study explored the cerebral blood flow, functional connectivity and topological organization of the cerebral blood flow network in acute mild traumatic brain injury patients. In total, 48 mild traumatic brain injury patients and 46 well-matched healthy controls underwent resting-state arterial spin labelling perfusion MRI and neuropsychological assessments. The functional connectivity and topological organization of the cerebral blood flow network were analysed. Then, the correlation between the changes in cerebral blood flow network characteristics and cognitive function was explored. Acute mild traumatic brain injury patients showed decreased cerebral blood flow in the right insula and increased cerebral blood flow in the right inferior temporal gyrus and left superior temporal gyrus. Abnormal cerebral blood flow network connection patterns mainly occur in sensorimotor network, default mode network, cingulo-opercular network and occipital network-related regions. Furthermore, mild traumatic brain injury disrupted the topological organization of the whole brain, which manifested as (i) reduced global efficiency; (ii) abnormal degree centrality, betweenness centrality, nodal clustering coefficient and nodal efficiency; and (iii) decreased intermodular connectivity between the occipital network and sensorimotor network. Finally, the change in network topology was correlated with the cognitive score of the mild traumatic brain injury. This study provided evidence of abnormal functional connectivity and network topology based on cerebral blood flow in acute mild traumatic brain injury patients, revealing their potential use as early markers for mild traumatic brain injury, which may contribute to both disease diagnosis and assessment.

ABLA: Application-Based Load-Balanced Approach for Adaptive Mapping of Datacenter Networks

Cloud-based services are growing more rapidly than ever, and so does the management challenge on their providers’ side. Cloud-based datacenter networks are built with nodes of huge processing power, connected by high bandwidth capacities to carry their interior traffic requirements. However, such cloud networks still have limits that are imposed not necessarily by their physical components, but by the schemes of resource management being deployed. Traditionally, for an institute to provide services, it needs to have its own datacenter facility that interconnects its servers through a topology that matches its desired administrative policies and scaling objectives. With the theme of cloud-based IaaS, such datacenter topologies can be created virtually over the cloud. Nowadays, a significant part of those institutes who provide us with our daily services have their infrastructures hosted over cloud ones. Therefore, resources of such cloud networks need to be efficiently utilized, in order to keep their performance and hosting prices competitive. A typical datacenter network mainly consists of server nodes and network links. Besides the resources of the server nodes, the network bandwidth resources are considered a crucial key determinant for the whole datacenter performance. Indeed, a server without sufficient bandwidth capacities is almost useless. Proposals in the literature present schemes for resource utilization on either side of the problem at a time: the nodes or the links. Working in isolation can never deliver efficient mapping solutions. ABLA is an Application-Based, and Load balancing Approach for adaptive mapping proposal. ABLA’s methodology tackles both sides of the datacenter, the nodes and links. It starts by (1) breaking down the node’s resource requirement for the requested applications to be hosted over the virtual server machines besides (2) reading the topological connectivity and bandwidth requirements for each virtual node to all other nodes in the virtual datacenter topology. Compared to other models in the literature, the simulation results show that our proposed ABLA model provides for complete mapping services via load-balanced hosting networks. This allows for competitive hosting prices, with higher performance and service satisfaction rates.

Observation of Termination-Dependent Topological Connectivity in a Magnetic Weyl Kagome Lattice.

Engineering surfaces and interfaces of materials promises great potential in the field of heterostructures and quantum matter designers, with the opportunity to drive new many-body phases that are absent in the bulk compounds. Here, we focus on the magnetic Weyl kagome system Co3Sn2S2 and show how for the terminations of different samples the Weyl points connect differently, still preserving the bulk-boundary correspondence. Scanning tunneling microscopy has suggested such a scenario indirectly, and here, we probe the Fermiology of Co3Sn2S2 directly, by linking it to its real space surface distribution. By combining micro-ARPES and first-principles calculations, we measure the energy-momentum spectra and the Fermi surfaces of Co3Sn2S2 for different surface terminations and show the existence of topological features depending on the top-layer electronic environment. Our work helps to define a route for controlling bulk-derived topological properties by means of surface electrostatic potentials, offering a methodology for using Weyl kagome metals in responsive magnetic spintronics.

Design of a class of reconfigurable hybrid mechanisms for large complex curved surface machining based on topological graph theory

A design method for hybrid mechanism configuration synthesis based on topological graph theory is proposed to address the challenge of machining large and complex curved surfaces in the aerospace domain. The method leads to the generation of a new class of reconfigurable large extension hybrid mechanisms. Firstly, the spatial mechanism topological graph (SMTG) is obtained by evolving the structure of chemical molecules, which is used to express the spatial mechanism topological relationships. Then, combining graph theory methods with the SMTG, the topological relationships between motion modules of the hybrid mechanism are expressed, and the mechanisms of each motion module are designed. Finally, mechanisms of different functional modules are topologically connected and combined using the proposed topological connection relationship for the motion modules of hybrid mechanisms, thus resulting in a class of reconfigurable hybrid mechanisms (RHMs) with large extensibility. The RHMs exhibit strong structural stability, a large working space, and a small volume of occupation. It can be installed on the ends of industrial robots or in large guideways in the future to achieve high-precision machining of large complex curved structures.

Consensus robustness of multi-agent systems against heterogeneous asymmetric input saturations and asynchronous time-varying communication delays

This paper investigates the combined effect of input saturations and communication delays on the multi-agent consensus over a switching topology of weak connectivity. The input saturations are in a general form such that the saturation level of each agent is asymmetric and those of distinct agents are heterogeneous. Besides, the communication delays are time-varying and those in different channels are asynchronous. Both continuous- and discrete-time multi-agent systems are studied. For both the multi-agent systems over a switching digraph of uniform joint strong connectivity, it is demonstrated that the conventional distributed protocols, despite the simultaneous existence of the input saturations and communication delays, still guarantee the consensus results. It turns out that the consensus results are of great robustness against arbitrary heterogeneous asymmetric input saturations and arbitrary bounded communication delays. Simulations are carried out to verify this finding.

Nonsmooth Control Barrier Function design of continuous constraints for network connectivity maintenance

This paper considers the problem of maintaining global connectivity of a multi-robot system while executing a desired coordination task. Our approach builds on optimization-based feedback design formulations, where the nominal cost function and constraints encode desirable control objectives for the resulting input. Our solution uses the algebraic connectivity of the multi-robot interconnection topology as a control barrier function and critically embraces its nonsmooth nature. We take advantage of the understanding of how Laplacian eigenvalues behave as their multiplicities change, in combination with the flexibility provided by the concept of control barrier function, to carefully design additional constraints that guarantee the resulting optimization-based controller is continuous and maintains network connectivity. The technical treatment combines elements from set-valued theory, nonsmooth analysis, and algebraic graph theory to imbue the proposed constraints with regularity properties so that they can be smoothly combined with other control constraints. We provide simulations and experimental results illustrating the effectiveness and continuity of the proposed approach in a resource gathering problem.

Vulnerability assessment of critical infrastructures for cascading failures: An application to water distribution networks

Critical infrastructures form the backbone of any nation, and the failures of these infrastructures could lead to economic disruptions and fatal consequences. Assessing the vulnerability of such critical infrastructures has now evolved into a necessary and imperative task for researchers around the world. Water distribution networks form an essential class of critical infrastructures of modern society. The uninterrupted functioning of such networks is crucial for ensuring a nation’s economic development and welfare of society. The objective of this article is to evaluate the vulnerability of nodes for Water Distribution Networks considering cascading failures. The system dynamics of a water network are included in the simulation process to estimate the impacts of cascading failures. Vulnerability metrics based on loss of topological connectivity and supply capability are formulated in this article. The applicability and significance of the proposed methodology are demonstrated using a sample network case study. Identification of different vulnerability zones is done by employing k-means clustering to perform a methodical comparison between the proposed and existing metrics of vulnerability assessment.

A Novel Physical Unclonable Function Based on Silver Nanowire Networks

AbstractAs digital security demands continue to rise, physical unclonable functions (PUFs) have emerged as a potential solution for providing unique identifiers and cryptographic keys. However, conventional memory‐based PUFs have limited encoding capacity, which can compromise their security. Herein, an Ag nanowire‐based PUF that offers a high encoding capacity is introduced. The prototype PUF leverages the intrinsic topology of the nanowire network for encoding, which enables to achieve encoding capacities that are on the order of nn−2. The encoding method for a 4‐node PUF is validated and its encoding capacity distribution at binary and ternary bit levels is assessed. The ternary bit encoding method allows for more detailed differentiation of the internal topological connection structure of the PUF, resulting in a larger encoding capacity. Additionally, an authentication system that is proposed. For a 7 × 7 array PUF, the estimated decryption time is ≈1.5*1058 years, demonstrating the PUF's resistance to attacks. The Ag nanowire‐based PUF design offers a promising approach to enhancing authentication system security and overcoming limitations of existing PUFs. Further exploration of this innovative PUF technology in the realm of hardware security can have significant implications for digital security in modern devices.

Permeability model of fracture network based on branch length distribution and topological connectivity

Fracture networks are of significance in the production of coalbed methane from unconventional reservoirs. However, the complex distribution and geometry of fracture networks make effective predictions of their permeability difficult. This study obtains the shape of a natural fracture network in coal based on a stereomicroscopy experiment and analyzes the structural characteristics of the fracture network using graph theory. The fractal scaling law for the branch length distribution of the fractures and the relationships among the fractal dimensions of the branch length distribution, fracture area, porosity, connectivity, and ratio of maximum to minimum branch length are established. A new permeability model for a complex fracture network is developed based on fractal theory, and two important fracture characteristics, namely, tortuosity and connectivity, are considered. The model is verified using the results of previous studies and seepage tests, and the influence of the fracture network characteristic parameters on the permeability is analyzed. The results show that the permeability increases with increasing porosity, fractal dimension, proportionality coefficient, maximum fracture branch length, and connectivity and decreases with increasing tortuosity fractal dimension and dip angle.

Embedding Kalker's variational theory into railway vehicle system dynamics and its efficiency improvement

The wheel–rail contact plays a vital role in railway transportation, hence an accurate wheel–rail contact model is the key to the simulation of railway vehicle dynamics. Though Kalker's variational theory is the canon, fewer works try to embed it into railway vehicle dynamics, mainly due to the computational efficiency problem and the complex wheel–rail geometry processing. This paper proposes a wheel–rail geometry processing method to embed KVT into railway vehicle multi-body dynamics based on the trajectory coordinate formulation, which conveniently introduces the wheel–rail contact surface pair for tracking the potential contact areas. After this prior detection, coarse detection is performed to narrow the range of penetrated elements. Multiple contact patches can also be identified based on the connection topology of discrete elements of wheel–rail surfaces. Thus, Kalker's variational theory can be used on each separate patch for local contact evaluation. An initial prediction method for the contact status is embedded in the multi-body dynamics simulation to accelerate the solution of Kalker's variational problem, in which the tractions with contact divisions obtained from the neighbouring system states are selected as the iterative initial value. The importance of predicting the contact tractions and demarcation for the convergence of the iteration is revealed. The proposed method is fully demonstrated by the Manchester contact benchmark and simulations of a railway vehicle passing through a curved track. As an advantage of embedding Kalker's variational theory, the precise traction pattern is obtained simultaneously along with the vehicle dynamics simulation, and the influencing mechanism of the track geometry on the vehicle system is analysed. The improvement of solution efficiency is also verified in simulations.

The influence of very preterm birth on adolescent EEG connectivity, network organization and long-term outcome

ObjectiveThe aim of this study was to explore differences in functional connectivity and network organization between very preterm born adolescents and term born controls and to investigate if these differences might explain the relation between preterm birth and adverse long-term outcome. MethodsForty-seven very preterm born adolescents (53% males) and 54 controls (54% males) with matching age, sex and parental educational levels underwent high-density electroencephalography (EEG) at 13 years of age. Long-term outcome was assessed by Intelligence Quotient (IQ), motor, attentional functioning and academic performance. Two minutes of EEG data were analysed within delta, theta, lower alpha, upper alpha and beta frequency bands. Within each frequency band, connectivity was assessed using the Phase Lag Index (PLI) and Amplitude Envelope Correlation, corrected for volume conduction (AEC-c). Brain networks were constructed using the minimum spanning tree method. ResultsVery preterm born adolescents had stronger beta PLI connectivity and less differentiated network organization. Beta AEC-c and differentiation of AEC-c based networks were negatively associated with long-term outcomes. EEG measures did not mediate the relation between preterm birth and outcomes. ConclusionsThis study shows that very preterm born adolescents may have altered functional connectivity and brain network organization in the beta frequency band. Alterations in measures of functional connectivity and network topologies, especially its differentiating characteristics, were associated with neurodevelopmental functioning. SignificanceThe findings indicate that EEG connectivity and network analysis is a promising tool for investigating underlying mechanisms of impaired functioning.

Specific topology and topological connection sensitivity enhanced graph learning for lncRNA–disease association prediction

Predicting disease-related candidate long noncoding RNAs (lncRNAs) is beneficial for exploring disease pathogenesis due to the close relations between lncRNAs and the occurrence and development of human diseases. It is a long-term and challenging task to adequately extract specific and local topologies in individual lncRNA network and individual disease network, and integrate the information of the connection relationships. We propose a new graph learning-based prediction method to encode specific and local topologies from each individual network, neighbor topologies with different connection relationships, and pairwise attributes. We first construct a lncRNA network composed of all the lncRNA nodes and their similarities, and a single disease network that contains all the disease nodes and disease similarities. Then, a network-aware graph convolutional autoencoder is constructed to encode the specific and local topologies of each network. Secondly, a heterogeneous network is established to embed all lncRNA, disease, and miRNA nodes and their various connections. Afterwards, a connection-sensitive graph neural network is designed to deeply integrate the neighbor node attributes and connection characteristics in the heterogeneous network and learn neighbor topological representations. We also construct both connection-level and topology representation-level attention mechanisms to extract informative connections and topological representations. Finally, we build a multi-layer convolutional neural networks with weighted residuals to adaptively complement the detailed features to pairwise attribute encoding. Comprehensive experiments and comparison results demonstrated that NCPred outperforms seven state-of-the-art prediction methods. The ablation studies demonstrated the importance of local topology learning, neighbor topology learning, and pairwise attribute encoding. Case studies on prostate, lung, and breast cancers further revealed NCPred’s capacity to screen potential candidate disease-related lncRNAs.

Brain network characteristics between subacute and chronic stroke survivors in active, imagery, passive movement task: a pilot study.

The activation patterns and functional network characteristics between stroke survivors and healthy individuals based on resting-or task-state neuroimaging and neurophysiological techniques have been extensively explored. However, the discrepancy between stroke patients at different recovery stages remains unclear. To investigate the changes in brain connectivity and network topology between subacute and chronic patients, and hope to provide a basis for rehabilitation strategies at different stages after stroke. Fifteen stroke survivors were assigned to the subacute group (SG, N = 9) and chronic group (CG, N = 6). They were asked to perform hand grasping under active, passive, and MI conditions when recording EEG. The Fugl-Meyer Assessment Upper Extremity subscale (FMA_UE), modified Ashworth Scale (MAS), Manual Muscle Test (MMT), grip and pinch strength, modified Barthel Index (MBI), and Berg Balance Scale (BBS) were measured. Functional connectivity analyses showed significant interactions on frontal, parietal and occipital lobes connections in each frequency band, particularly in the delta band. The coupling strength of premotor cortex, M1, S1 and several connections linked to frontal, parietal, and occipital lobes in subacute subjects were lower than in chronic subjects in low alpha, high alpha, low beta, and high beta bands. Nodal clustering coefficient (CC) analyses revealed that the CC in chronic subjects was higher than in subacute subjects in the ipsilesional S1 and occipital area, contralesional dorsolateral prefrontal cortex and parietal area. Characteristic path length (CPL) analyses showed that CPL in subacute subjects was lower than in chronic subjects in low beta, high beta, and gamma bands. There were no significant differences between subacute and chronic subjects for small-world property. Subacute stroke survivors were characterized by higher transfer efficiency of the entire brain network and weak local nodal effects. Transfer efficiency was reduced, the local nodal role was strengthened, and more neural resources needed to be mobilized to perform motor tasks for chronic survivors. Overall, these results may help to understand the remodeling pattern of the brain network for different post-stroke stages on task conditions and the mechanism of spontaneous recovery.

Faster quantum concentration via Grover's search

ABSTRACT One of the most important challenges in information networks is to gather data from a larger set of nodes to a smaller set of nodes. This can be done via the use of a concentrator architecture in the connection topology. This paper is a proof-of-concept that demonstrates a quantum-based controller in large interconnection networks can asymptotically perform this task faster. We specifically present quantum algorithms for routing concentration assignments on full capacity fat-and-slim concentrators, bounded fat-and-slim concentrators, and regular fat-and-slim concentrators. Classically, the concentration assignment takes time on all these concentrators, where n is the number of inputs. Powered by Grover's quantum search algorithm, our algorithms take time, where c is the capacity of the concentrator. Thus, our quantum algorithms are asymptotically faster than their classical counterparts, when . In general, satisfies implying a time complexity of for any .

M@C78 (M = U, Th): Inherent Topological Connectivity Existed in Thermodynamically Stable Isomers and the Possibility of an Endohedral Fullerene Containing One Heptagon Ring.

The density functional theory combined with statistical thermodynamic analyses of M@C78 (M = U and Th) demonstrated that four isomers, M@D3h(24109)-C78, M@C2v(24107)-C78, M@C1(22595)-C78, and M@C1(23349)-C78, and a nonclassical isomer, M@C1(id7)-C78, containing one heptagon ring possess outstanding thermodynamic stabilities in the two M@C78 series. Especially, the M@C1(id7)-C78 isomer is the first nonclassical C78 fullerene that can exist stably. Importantly, these five fullerene cages are found to be related in the form of Stone-Wales (SW) transformations. Geometric analyses disclosed that, unlike lanthanide metals, actinide metals are more likely to bond with sumanene-type hexagonal rings when they are encapsulated in IPR C78 cages. Frontier molecular orbital analysis showed that both U and Th atoms donate four electrons to the C78 carbon cages.