Large-scale Networks Research Articles

Research on PCI Sequence Optimization in Large-scale Mobile Communication Networks based on Genetic Algorithm

Research on PCI Sequence Optimization in Large-scale Mobile Communication Networks based on Genetic Algorithm

Retraction Note: An enhanced soft computing-based formulation for secure data aggregation and efficient data processing in large-scale wireless sensor network

Retraction Note: An enhanced soft computing-based formulation for secure data aggregation and efficient data processing in large-scale wireless sensor network

Optimal allocation of autonomous PV-powered fast charging stations for electrified transportation considering traffic dynamics

In response to the imperative to reduce greenhouse gas emissions, global efforts are focusing on electrifying transportation and adopting renewable energy sources. This necessitates establishing reliable fast-charging stations (FCSs) for the widespread integration of electric vehicles (EVs). Indeed, the arbitrary distribution of FCSs can detrimentally affect the efficiency of traffic movement. Thus, this paper presents a planning methodology aimed at optimizing the allocation of FCSs within large-scale transportation networks. The energy system proposed includes an autonomous photovoltaic (PV)-powered FCS based on a DC microgrid, supplemented by a stationary battery energy storage system for backup power. The behavior of EVs within the traffic flow is modeled using a realistic traffic simulation software called Dynamic Network Assignment Simulation for Advanced Road Telematics (DYNASMAT). The conflicting objectives of minimizing total annual costs and EV average waiting time at stations are addressed through multi-objective optimization. To solve this optimization problem, the GAMS software is employed. A transportation network with 92 nodes and 284 links is utilized to test the proposed approach. The proposed autonomous PV-powered FCS is compared with Natural Gas-fired Distributed Generators (NGDG)-powered FCS. The simulation results show that while the NGDG-powered FCS exhibits higher emissions, the autonomous PV-powered equivalent operates emission-free. However, the annual cost of the autonomous PV-powered FCS is higher than that of the NGDG-powered FCS.

Network-level crash risk analysis using large-scale geometry features

Road traffic crashes are common occurrences that create substantial losses and hazards to society. A complex interaction of components, including drivers, vehicles, roads, and the environment, can impact the causes of these crashes. Due to its complexity, crash identification, and prediction research over large-scale areas faces several obstacles, including high costs and challenging data collecting. This study offers a method for large-scale road network crash risk identification based on open-source data, given that roadways’ horizontal and vertical geometric alignment is crucial in highway traffic crashes. This methodology includes a comprehensive technique for feature extraction from horizontal curves (H-curves) and vertical curves (V-curves) and a novel way of combining the XGBoost model’s attributes with the Harris Hawks Optimization (HHO) algorithm—referred to as the HHO-XGBoost model. Using this model on the road geometry-crash risk dataset developed specifically for this study, the HHO approach adaptively identifies the optimal set of XGBoost hyperparameters and yields favorable outcomes. This study creates a three-dimensional road geometry database that may be utilized for various road infrastructure management, operation, and safety in addition to completing a tiered risk analysis of “region-road-segment” for large-scale road networks. It also offers direction on using swarm intelligence algorithms in integrated learning models.

Quantum multicast based on joint remote state preparation

Effective propagation of information among multiple users is the purpose of realizing large-scale quantum communication networks. In this paper, multicast protocols for any single, two and three qubits with real amplitude and complex phase information are presented. They were realized using a composite of Greenberger–Horne–Zeilinger states as shared channels. Joint remote state preparation was the main method for completing quantum multicast. At the same time, quantum state tomography of the schemes was carried out on the IBM Quantum platform. The obtained states were compared with the target states by fidelity. The analysis of communication efficiency and noise effects shows that our protocol has advantages in the case of complex coefficients.

Effects of voltage schemes on the conductance modulation of artificial synaptic device based on 2D hBN memristor: Its applications for pattern classifications

Artificial synaptic devices utilizing nonvolatile memristors with 2D materials show potential for neuromorphic computing systems due to their intriguing electrical properties, simple structure, and capability to emulate biological synaptic behaviors. Here, we fabricated nonvolatile memristors based on 2D multilayer hBN film and demonstrated their analog memory performance along with biological synaptic characteristics, including paired-pulse facilitation and depression (PPF and PPD), and synaptic plasticity. We utilized different voltage scheme algorithms to analyze the impact of synaptic functions on image classification tasks across various pattern datasets through simulations. Our findings reveal that pattern recognition outcomes varied due to the modulation of synaptic plasticity by the applied voltage schemes. We also investigated the relationship between conductance update variations and classification accuracy to highlight the significance of optimized synaptic plasticity in improving image recognition capabilities in large-scale neural networks. Our experimental results contribute to the development of high-performance neuromorphic computing for diverse pattern classification tasks using artificial synaptic devices based on 2D hBN.

Cascaded partitioned phase modulation for cross-connection of orbital angular momentum mode and polarization multiplexing channels.

Multi-dimensional orbital angular momentum (OAM) mode multiplexing provides a promising route for enlarging communication capacity and establishing comprehensive networks. While multi-dimensional multiplexing has gained advancements, the cross-connection of these multiplexed channels, especially involving modes and polarizations, remains challenging due to the needs for multi-mode interconversion and on-demand polarization control. Herein, we propose an OAM mode-polarization cross-transformation solution via cascaded partitioned phase modulation, which enables the divergently separated OAM modes to be independently phase-imposed within distinct spatial regions, leading to the synergistic conversion operation of mode and polarization channels. In demonstrations, we implemented the cross-connection of three OAM modes and two polarization multiplexed channels, achieving the mode purity that exceeds 0.951 and polarization contrast up to 0.947. The measured mode insertion losses and polarization conversion losses are below 3.42 and 3.54 dB, respectively. Consequently, 1.2 Tbit/s quadrature phase shift keying signals were successfully exchanged, yielding the bit-error-rates close to 10-6. Incorporating with increased partitioned phase treatments, this approach shows promise in accommodating massive mode-polarization multiplexed channels, which hold the potential to augment networking capability of large-scale OAM mode multiplexing communication networks.

Effective resistance, random walk and algorithms

Efficiently determining the effective resistance of large-scale electrical networks is crucial for optimizing energy distribution and assessing network robustness. In this paper, we propose a novel approach that combines random walk simulations and advanced linear algebra techniques to compute the effective resistance of complex electrical networks. Our method leverages the concept of random walks to simulate the flow of current through the network, capturing its behavior under various conditions. Subsequently, we employ graph Laplacian-based linear algebra algorithms to analyze the resulting data, enabling accurate computation of the effective resistance. In our model, we aim to deploy these combined algorithm to develop a novel method to deal with such methods.

Utilising activity patterns of a complex biophysical network model to optimise intra-striatal deep brain stimulation

A large-scale biophysical network model for the isolated striatal body is developed to optimise potential intrastriatal deep brain stimulation applied to, e.g. obsessive-compulsive disorder. The model is based on modified Hodgkin–Huxley equations with small-world connectivity, while the spatial information about the positions of the neurons is taken from a detailed human atlas. The model produces neuronal spatiotemporal activity patterns segregating healthy from pathological conditions. Three biomarkers were used for the optimisation of stimulation protocols regarding stimulation frequency, amplitude and localisation: the mean activity of the entire network, the frequency spectrum of the entire network (rhythmicity) and a combination of the above two. By minimising the deviation of the aforementioned biomarkers from the normal state, we compute the optimal deep brain stimulation parameters, regarding position, amplitude and frequency. Our results suggest that in the DBS optimisation process, there is a clear trade-off between frequency synchronisation and overall network activity, which has also been observed during in vivo studies.

Exploring the mediating role of the ventral attention network and somatosensory motor network in the association between childhood trauma and depressive symptoms in major depressive disorders

BackgroundChildhood trauma is closely tied to adult depression, but the neurobiological mechanisms remain unclear. Previous studies suggested associations between depression and large-scale brain networks such as the Ventral Attention Network (VAN) and Somatosensory Motor Network (SMN). This study hypothesized that functional connectivity (FC) within and between these networks mediates the link between childhood trauma and adult depression. MethodsThe Childhood Trauma Questionnaire (CTQ) assessed developmental experiences, and the Hamilton Rating Scale for Depression (HAMD-17) gauged depressive symptoms. Resting-state functional magnetic resonance imaging (fMRI) analyzed FC within and between the VAN and SMN. ResultsDepression group exhibited significantly higher HAMD and CTQ scores, as well as elevated FC within the VAN and between the VAN and SMN (P < 0.05). Positive correlations were found between HAMD total score and FC within the VAN (P < 0.05, r = 0.35) and between the VAN and SMN (P < 0.05, r = 0.34), as well as with CTQ total score (P < 0.05, r = 0.27). Positive correlations were also observed between CTQ total score and FC within the VAN (P < 0.05, r = 0.31) and between the VAN and SMN (P < 0.05, r = 0.29). In the mediation model, FC within and between the VAN and SMN significantly mediated childhood trauma and depression. LimitationsThe cross-sectional design limits causal inference. The sample size for different trauma types is relatively small, urging caution in generalizing findings. ConclusionsThe study underscores the association between depression severity, VAN dysfunction, abnormal VAN-SMN FC, and childhood trauma. These findings contribute to understanding the neurobiological mechanisms underlying childhood trauma and depression.

Hippocampal temporal dynamics and spatial heterogeneity unveil vulnerability markers in the offspring of bipolar patients.

Bipolar disorder (BD) is a highly heritable disorder characterized by emotion dysregulation and recurrent oscillations between mood states. Despite the proven efficacy of early interventions, vulnerability markers in high-risk individuals are still lacking. BD patients present structural alterations of the hippocampus, a pivotal hub of emotion regulation networks composed of multiple subregions with different projections. However, the hippocampal dynamic functional connectivity (dFC) in BD remains unclear. We aim to investigate whether the dFC of hippocampal subdivisions differentiates BD patients, offspring of BD patients (BDoff), and healthy controls (HC); and whether it correlates with symptoms differently between these groups. We studied for the first time the dFC of the hippocampus through a cutting-edge micro-co-activation patterns (μCAPs) analysis of resting-state functional MRI data of 97 subjects (26 BD, 18 BDoff, 53 HC). μCAPs allow a data-driven differentiation within the seed region. dFC between the hippocampal body and a somatomotor-μCAP was lower both in BD patients (p-valueFDR:0.00015) and in BDoff (p-valueFDR:0.020) than in HC. Inversely, dFC between the hippocampal head and a limbic-μCAP was higher in BD patients than in HC (p-valueFDR: 0.005). Furthermore, the correlations between a frontoparietal-μCAP and both depression and emotion dysregulation symptoms were significantly higher in BD than HC (p-valueFDR <0.02). Overall, we observed alterations of large-scale functional brain networks associated with decreased cognitive control flexibility and disrupted somatomotor, saliency, and emotion processing in BD. Interestingly, BDoff presented an intermediate phenotype between BD and HC, suggesting that dFC of hippocampal subregions might represent a marker of vulnerability to BD.

Proactive blockchain deployment mechanism in resource-constrained rate-splitting multiple access IoT networks

In this paper, we propose an innovative blockchain deployment mechanism tailored for resource-constrained rate-splitting multiple access (RSMA) Internet of Things (IoT) networks. To address the storage limitations and security concerns inherent in IoT environments, our approach includes several advanced techniques. First, we utilize a Block Allocation Strategy Contract (BASC) to manage storage efficiently. Second, we employ a deep reinforcement learning (DRL) model to dynamically perform block assignment, ensuring optimal storage utilization. To accommodate the resource constraints of IoT devices, we adopt the Smart Byzantine Fault Tolerance (SBFT) consensus mechanism, which offers low latency and energy efficiency. Our framework demonstrates superior performance in storage optimization and reduced running time compared to existing methods, making it well-suited for large-scale IoT networks. Through extensive simulations, we validate the effectiveness of our proposed solution in enhancing security and operational efficiency in RSMA IoT networks.

Automated Distribution of Polarization-Entangled Photons Using Deployed New York City Fibers

The distribution of high-fidelity high-rate entanglement over telecommunication infrastructure is one of the main paths toward large-scale quantum networks, enabling applications such as quantum encryption and network protection, blind quantum computing, distributed quantum computing, and distributed quantum sensing. However, the fragile nature of entangled photons operating in real-world fiber infrastructure has historically limited continuous operation of such networks. Here, we present a fully automated system capable of distributing polarization-entangled photons over a 34-km deployed fiber in New York City, achieving high rates of nearly 5×105 pairs/s. Separately, we demonstrate a high fidelity of approximately 99% for rates up to 2×104 pairs/s. Lastly, we achieve 15 days of continuous distribution, with a network up-time of 99.84%. Our work paves the way for practical deployment of always-on entanglement-based networks with rates and fidelity adequate for many current and future use cases. Published by the American Physical Society 2024

Exploring the Embodied Mind: Functional Connectome Fingerprinting of Meditation Expertise

BackgroundShort mindfulness-based interventions have gained traction in research due to their positive impact on well-being, cognition, and clinical symptoms across various settings. However, these short-term trainings are viewed as preliminary steps within a more extensive transformative path, presumably leading to long-lasting trait changes. Despite this, little is still known about the brain correlates of these meditation traits. MethodsTo address this gap, we investigated the neural correlates of meditation expertise in long-term Buddhist practitioners, comparing the large-scale brain functional connectivity of 28 expert meditators with 47 matched novices. Our hypothesis posited that meditation expertise would be associated with specific and enduring patterns of functional connectivity present during both meditative (open monitoring/open presence and loving-kindness and compassion meditations) and nonmeditative resting states, as measured by connectivity gradients. ResultsApplying a support vector classifier to states not included in training, we successfully decoded expertise as a trait, demonstrating its non–state-dependent nature. The signature of expertise was further characterized by an increased integration of large-scale brain networks, including the dorsal and ventral attention, limbic, frontoparietal, and somatomotor networks. The latter correlated with a higher ability to create psychological distance from thoughts and emotions. ConclusionsSuch heightened integration of bodily maps with affective and attentional networks in meditation experts could point toward a signature of the embodied cognition cultivated in these contemplative practices.

MEG Microstates: An Investigation of Underlying Brain Sources and Potential Neurophysiological Processes

Microstates are transient scalp configurations of brain activity measured by electroencephalography (EEG). The application of microstate analysis in magnetoencephalography (MEG) data remains challenging. In one MEG dataset (N = 113), we aimed to identify MEG microstates at rest, explore their brain sources, and relate them to changes in brain activity during open-eyes (ROE) or closed-eyes resting state (RCE) and an auditory Mismatch Negativity (MMN) task. In another dataset of simultaneously recorded EEG-MEG data (N = 21), we investigated the association between MEG and EEG microstates. Six MEG microstates (mMS) provided the best clustering of resting-state activity, each linked to different brain sources: mMS 1–2: left/right occipito-parietal; mMS 3: fronto-temporal; mMS 4: centro-medial; mMS 5–6: left/right fronto-parietal. Increases in occipital alpha power in RCE relative to ROE correlated with greater mMS 1–2 time coverage (τbs < 0.20, ps > .002), while the lateralization of deviance detection in MMN was associated with mMS 5–6 time coverage (τbs < 0.16, ps > .012). No temporal correlation was found between EEG and MEG microstates (ps > .05), despite some overlap in brain sources and global explained variance between mMS 2–3 and EEG microstates B-C (rs > 0.60, ps < .002). Hence, the MEG signal can be decomposed into microstates, but mMS brain activity clustering captures phenomena different from EEG microstates. Source reconstruction and task-related modulations link mMS to large-scale networks and localized activities. Thus, mMSs offer insights into brain dynamics and task-specific processes, complementing EEG microstates in studying physiological and dysfunctional brain activity.

Data-driven and equation-free methods for neurological disorders: analysis and control of the striatum network.

The striatum as part of the basal ganglia is central to both motor, and cognitive functions. Here, we propose a large-scale biophysical network for this part of the brain, using modified Hodgkin-Huxley dynamics to model neurons, and a connectivity informed by a detailed human atlas. The model shows different spatio-temporal activity patterns corresponding to lower (presumably normal) and increased cortico-striatal activation (as found in, e.g., obsessive-compulsive disorder), depending on the intensity of the cortical inputs. By applying equation-free methods, we are able to perform a macroscopic network analysis directly from microscale simulations. We identify the mean synaptic activity as the macroscopic variable of the system, which shows similarity with local field potentials. The equation-free approach results in a numerical bifurcation and stability analysis of the macroscopic dynamics of the striatal network. The different macroscopic states can be assigned to normal/healthy and pathological conditions, as known from neurological disorders. Finally, guided by the equation-free bifurcation analysis, we propose a therapeutic close loop control scheme for the striatal network.

Load-balanced Routing Heuristics for Bandwidth Allocation of AVB Flow in TSN

Time-Sensitive Networking (TSN) is a new technology developed from Ethernet that guarantees deterministic transmission of various types of flows, such as Time-triggered (TT) flows and Audio-video-bridging (AVB) flows, in the same network. Currently, Time-aware Shaping (TAS) and Credit-based Shaping (CBS) are widely used for scheduling hybrid flows, where the credit value in CBS is directly linked to the logical bandwidth value idleSlope , which affects the real-time performance of AVB flows. However, as the network scale increases, existing bandwidth allocation methods result in higher computation times and lower allocation success rates. In this paper, the aforementioned problem is addressed by two-step: first, we design a load-balanced routing heuristic (LBRH) to improve the allocation success rate; second, we accelerate the CBS bandwidth allocation by using unified bandwidth allocation scheme, which integrates LBRH and further improves the allocation success rate. Performance evaluation in multiple test cases shows that LBRH can improve the success rate of bandwidth allocation, and the unified bandwidth allocation scheme integrating LBRH significantly reduces the overall execution time while improving the success rate of bandwidth allocation, which is more suitable for large-scale TSN networks with complex routing.

Multiscale heterogeneity of white matter morphometry in psychiatric disorders.

Inter-individual variability in neurobiological and clinical characteristics in mental illness is often overlooked by classical group-mean case-control studies. Studies using normative modelling to infer person-specific deviations of grey matter volume have indicated that group means are not representative of most individuals. The extent to which this variability is present in white matter morphometry, which is integral to brain function, remains unclear. We applied Warped Bayesian Linear Regression normative models to T1-weighted magnetic resonance imaging data and mapped inter-individual variability in person-specific white matter volume deviations in 1,294 cases (58% male) diagnosed with one of six disorders (attention-deficit/hyperactivity, autism, bipolar, major depressive, obsessive-compulsive and schizophrenia) and 1,465 matched controls (54% male) recruited across 25 scan sites. We developed a framework to characterize deviation heterogeneity at multiple spatial scales, from individual voxels, through inter-regional connections, specific brain regions, and spatially extended brain networks. The specific locations of white matter volume deviations were highly heterogeneous across participants, affecting the same voxel in fewer than 8% of individuals with the same diagnosis. For autism and schizophrenia, negative deviations (i.e., areas where volume is lower than normative expectations) aggregated into common tracts, regions and large-scale networks in up to 35% of individuals. The prevalence of white matter volume deviations was lower than previously observed in grey matter, and the specific location of these deviations was highly heterogeneous when considering voxel-wise spatial resolution. Evidence of aggregation within common pathways and networks was apparent in schizophrenia and autism but not other disorders.

Variation in the development of Neolithic societies atop the Central Anatolian Plateau: recent results from Balıklı

Regional variation in the historic development of agricultural societies in South-west Asia is increasingly apparent. Recent investigations at the wetland site of Balıklı (c. 8300–7900 BC) provide new insights into the initial processes of sedentism in Central Anatolia and the interaction of early communities within local and larger-scale networks. Located near major obsidian sources, excellent architectural preservation and faunal and botanical records at Balıklı suggest cultural connections to the upper Middle Euphrates region, yet inhabitants of the site do not appear to have participated in the wider South-west Asian obsidian-exchange networks and largely relied on wild resources.

Salience network resting state functional connectivity during airway inflammation in asthma: A feature of mental health resilience?

BackgroundInflammation is an established contributor to the pathophysiology of depression and the prevalence of depression in those with chronic inflammatory disease is two- to four-fold higher than the general population. Yet little is known about the neurobiological changes that confer depression or resilience to depression, that occur when episodes of heightened inflammation are frequent or span many years. MethodsWe used an innovative combination of longitudinal resting state functional magnetic resonance imaging coupled to segmental bronchial provocation with allergen (SBP-Ag) to assess changes in resting state functional connectivity (rsFC) of the salience network (SN) caused by an acute inflammatory exacerbation in twenty-six adults (15 female) with asthma and varying levels of depressive symptoms. Eosinophils measured in bronchoalveolar lavage fluid and blood provided an index of allergic inflammation and the Beck Depression Inventory provided an index of depressive symptoms. ResultsWe found that in those with the highest symptoms of depression at baseline, SN rsFC declined most from pre- to post-SBP-Ag in the context of a robust eosinophilic response to challenge, but in those with low depressive symptoms SN rsFC was maintained or increased, even in those with the most pronounced SBP-Ag response. ConclusionsThus, the maintenance of SN rsFC during inflammation may be a biomarker of resilience to depression, perhaps via more effective orchestration of large-scale brain network dynamics by the SN. These findings advance our understanding of the functional role of the SN during inflammation and inform treatment recommendations for those with comorbid inflammatory disease and depression.