Network Of Processes Research Articles

Development of Slow Oscillation-Spindle Coupling from Infancy to Toddlerhood

Abstract Sleep has been demonstrated to support memory formation from early life on. The precise temporal coupling of slow oscillations with spindles has been suggested as a mechanism facilitating this consolidation process in thalamocortical networks. Here, we investigated the development of sleep spindles and slow oscillations and their coordinate interplay comparing frontal, central and parietal electroencephalogram recordings during a nap between infants aged 2–3 months (n = 31) and toddlers aged 14–17 months (n = 49). Spindles and slow oscillations showed quite different maturational patterns between age groups, as to topography, amplitude, and density. Notably, spindle-slow oscillation co-occurrence in the infants did not exceed chance levels, and was increased to significant levels only in the toddlers. In the infants, the slow oscillation upstate over frontocortical regions was even associated with a significant decrease in spindles, contrasting with the adult-like increase in spindles seen in toddlers. These results point to an immature processing in thalamo-cortical networks during sleep in early infancy, possibly diminishing efficacy of sleep-dependent memory formation at this age.

Detailed-level modelling of influence spreading on complex networks.

The progress in high-performance computing makes it increasingly possible to build detailed models to investigate spreading processes on complex networks. However, current studies have been lacking detailed computational methods to describe spreading processes in large complex networks. To fill this gap we present a new modelling approach for analysing influence spreading via individual nodes and links on various network structures. The proposed influence-spreading model uses a probability matrix to capture the spreading probability from one node to another in the network. This approach enables analysing network characteristics in a number of applications and spreading processes using metrics that are consistent with the quantities used to model the network structures. In addition, this study combines sub-models and offers a comprehensive look at different applications and metrics previously discussed in cases of social networks, community detection, and epidemic spreading. Here, we also note that the centrality measures based on the probability matrix are used to identify the most significant nodes in the network. Furthermore, the model can be expanded to include additional properties, such as introducing individual breakthrough probabilities for the nodes and specific temporal distributions for the links.

High-resolution multimodal profiling of human epileptic brain activity via explanted depth electrodes.

The availability and integration of electrophysiological and molecular data from the living brain is critical to understand and diagnose complex human disease. Intracranial stereo electroencephalography (SEEG) electrodes used for identifying the seizure focus on epilepsy patients could enable the integration of such multimodal data. Here, we report MoPEDE (Multimodal Profiling of Epileptic Brain Activity via Explanted Depth Electrodes), a method that recovers extensive protein-coding transcripts, including cell-type markers, DNA methylation and short variant profiles from explanted SEEG electrodes matched with electrophysiological and radiological data allowing for high-resolution reconstructions of brain structure and function. We find gene expression gradients that correspond with the neurophysiology-assigned epileptogenicity index but also outlier molecular fingerprints in some electrodes, potentially indicating seizure generation or propagation zones not detected during electroclinical assessments. Additionally, we identify DNA methylation profiles indicative of transcriptionally permissive or restrictive chromatin states and SEEG-adherent differentially expressed and methylated genes not previously associated with epilepsy. Together, these findings validate that RNA profiles and genome-wide epigenetic data from explanted SEEG electrodes offer high-resolution surrogate molecular landscapes of brain activity. The MoPEDE approach has the potential to enhance diagnostic decisions and deepen our understanding of epileptogenic network processes in the human brain.

Propagation and distribution of shear and tensile hydraulic fractures affected by frictional coal beddings.

As a typical sedimentary rock, coal seams are usually rich in bedding planes, which significantly affect the propagation process of hydraulic fracture (HF) network and reservoir permeability. In this study, the profile characteristics and the bedding roughness of the coal seam at depth of 2800m from Daniudi Gas Field in China is investigated. The failure mechanism of shear-expansion effect under low pressure and dynamic shear under high pressure of coal beddings induced by the dissolution-corrosion effect of fracturing liquid is analyzed. The development process of HF network under different bedding angles, joint roughness coefficients (JRCs), stress differences, and confining pressures is numerically simulated by cohesive element method. Results show that the JRC of coal beddings has significant effects on the morphology of HF networks. When the JRC exceeds 15, a HF network structure with tensile main fractures and shear secondary cracks is developed. The shear fractures account for a considerable proportion (33-66%) multi-layer layered coal seams and should be taken seriously during reservoir reconstruction. As confining pressure increases, the total length of tensile HFs sharply decreases, while the change in the total length of shear HFs is not significant. When the confining pressure increases from 5 to 15MPa, the length ratio of shear HFs to tensile hydraulic fractures increases from 0.36 to about 1, confirming that increasing confining pressure can promote the transformation of HF failure types from tension to shear. The ratio of secondary crack to main crack length is between 6.31 and 9.78, indicating the secondary fractures occupy an absolute proportion in the hydraulic fracture network. This study is of significance for understanding the hydraulic fracture network development in coal reservoirs.

A High-Stability Pressure-Sensitive Implantable Memristor for Pulmonary Hypertension Monitoring.

Pulmonary hypertension (PH) significantly affects the quality of life and lifespan of humans and has promoted the development of flexible implantable electronic devices for PH diagnosis and prevention. Traditional implantable devices based on the von Neumann architecture face insurmountable challenges in processing large amounts of biological data due to computational bottlenecks. Memristors, with integrated in-memory sensing and computing capabilities, can effectively eliminate computational bottlenecks and become one of the most promising products in implantable devices for health monitoring. Here, a memristor with the Ag/MnO2/BaTiO3/FTO structure is implemented and implanted into Sprague‒Dawley (SD) rats. With polydimethylsiloxane (PDMS) packaging, the device can be continuously worked in vivo for up to four weeks, demonstrating excellent stability and biocompatibility. Furthermore, a memristive sensor array is designed for pulmonary artery blood pressure monitoring based on the pressure-responsive characteristics of the as-prepared memristive device. The front-end memristive sensor array can collect and feedback pressure signal, while noise reduction is achieved through memristive logic circuits, and ultimately the memristor neural network processes and classifies the information. Therefore, this work demonstrates the potential of implantable memristors for pulmonary artery pressure monitoring and provides new inspiration for the design of efficient, real-time, and reliable implantable pressure monitoring devices in medical health monitoring.

Retraction Note: Quantum optical techniques for quality data transmission process in cognitive networks

Retraction Note: Quantum optical techniques for quality data transmission process in cognitive networks

Multi-source partial discharge pattern recognition in GIS based on Grabcut-MCNN

Partial discharge (PD) surveillance constitutes a pivotal methodology for diagnosing insulation failures in electrical equipment. Enhancing comprehensively the precision of identifying PD anomalies in Gas Insulated Switchgear (GIS) is of paramount significance for ensuring the steady functioning of power grids. This study introduces a novel framework that integrates Phase-Resolved PD Graph Segmentation (PRPD-Grabcut) with a tailored MobileNets-based Convolutional Neural Network (MCNN) to classify GIS-related PD issues. Leveraging image segmentation via PRPD-Grabcut, crucial features are extracted from PRPD diagrams, which then facilitate the construction of the MCNN model. This model employs depth-wise separable convolutions alongside inverted residual architectures to tackle the vanishing gradient dilemma inherent in Deep Convolutional Neural Networks (DCNNs) during GIS PD pattern discernment. Upon the model's subsequent training and validation, empirical evidence illustrates that the PRPD-Grabcut-MCNN hybrid significantly alleviates the computational load and storage requisites of the model, concurrently enhancing the recognition precision and expediting the training process of the neural network. Relative to diverse established lightweight neural network architectures, MCNN manifests superior performance in terms of recognition accuracy, reduced cross-entropy loss, and expedited training duration.

Statistical Properties of SIS Processes with Heterogeneous Nodal Recovery Rates in Networks

The modeling and analysis of epidemic processes in networks have attracted much attention over the past few decades. A major underlying assumption is that the recovery process and infection process are homogeneous, allowing the associated theoretical studies to be conducted in a convenient manner. However, the recovery and infection processes usually exhibit heterogeneous rates in the real world, which makes it difficult to characterize the general relations between the dynamics and the underlying network structure. In this work, we focus on the susceptible–infected–susceptible (SIS) epidemic process with heterogeneous recovery rates in a finite-size complete graph. Specifically, we study the metastable-state statistical properties of SIS epidemic dynamics with two different nodal recovery rates in complete graphs. We propose approximate solutions to the metastable-state expectation and the variance in the number of infected nodes within the framework of the mean-field approximation method. We also derive several upper and lower bounds of the steady-state probability that a node is in the infected state. We verify the proposed approximate solutions of the mean and variance via simulations. This work provides insights into the fluctuations in the statistical properties of epidemic processes with complex dynamical behaviors in networks.

Effects of AlOx Sub‐Oxide Layer on Conductance Training of Passive Memristor for Neuromorphic Computing

AbstractMemristors are recognized as crucial devices for the hardware implementation of neuromorphic computing. The conductance training process of memristors has a direct impact on the performance of neuromorphic computing. However, memristor breakdown and conductance decay still hinder the precise training process of neural networks based on passive memristor. Here, AlOx/LiNbO3 (LN) memristors are designed by inserting a AlOx sub‐oxide layer between the single‐crystalline LN thin film with oxygen vacancies (OVs) and Pt layer. Under the same training conditions, lower conductance and self‐compliance current effects are observed in AlOx/LN memristor. Slight spontaneous decay of conductance is achieved after the removal of the external stimulation. To explore the effects of AlOx sub‐oxide layer on the prevention of device breakdown and suppression of conductance decay, the memristive mechanism of devices with and without AlOx layer is revealed via time‐of‐flight secondary ion mass spectrometer (ToF‐SIMS). It is reasonable to believe that the AlOx inserting layer in memristors can serve as a self‐compliance current layer to inhibit device breakdown and provide the OVs reservoir to suppress conductance decay. These results offer new possibilities and theoretical grounds for achieving more reliable and precise conductance training of passive memristors.

Double standards in status ascriptions? The role of gender, behaviors, and social networks in status orders among adolescents

Abstract We examine the gendered distribution of peer-ascribed status in schools. Using network data from more than 14,000 students in 676 classrooms, we explore gender differences in the ascription of status and the types of behavior rewarded with status. On average, girls receive slightly fewer status ascriptions than boys, and students tend to grant status more frequently within the same gender. Contextual analyses show that classroom demographics can moderate some of these patterns. We also uncover gender-specific differences and similarities in status-related behaviors. Notably, girls engaging in substance use are awarded with slightly more status ascriptions than boys. However, network models reveal that most behaviors affect peer status similarly for both genders, suggesting that previous findings of gender-behavioral differences based on regression analysis may be conflated with network processes. Our study updates long-held notions regarding gendered status orders in schools and highlights the value of a multidimensional approach to status processes. We discuss implications for future social network research on status ascriptions and other relational cognitions and consider how school-based interventions might benefit from our findings.

Achieving view-distance and -angle invariance in motion prediction using a simple network

Recently, human motion prediction has gained significant attention and achieved notable success. However, current methods primarily rely on training and testing with ideal datasets, overlooking the impact of variations in the viewing distance and viewing angle, which are commonly encountered in practical scenarios. In this study, we address the issue of model invariance by ensuring robust performance despite variations in view distances and angles. To achieve this, we employed Riemannian geometry methods to constrain the learning process of neural networks, enabling the prediction of invariances using a simple network. Furthermore, this enhances the application of motion prediction in various scenarios. Our framework uses Riemannian geometry to encode motion into a novel motion space to achieve prediction with an invariant viewing distance and angle using a simple network. Specifically, the specified path transport square-root velocity function is proposed to aid in removing the view-angle equivalence class and encode motion sequences into a flattened space. Motion coding by the geometry method linearizes the optimization problem in a non-flattened space and effectively extracts motion information, allowing the proposed method to achieve competitive performance using a simple network. Experimental results on Human 3.6M and CMU MoCap demonstrate that the proposed framework has competitive performance and invariance to the viewing distance and viewing angle.

Overload cascading failure detection algorithm for multi-layer supply chain network considering delay probability factor

At present, the supply chain is multi-layered and complicated, and its failure detection is based solely on the characteristic threshold, which does not lack the ability to describe the complex supply chain. An overload cascading failure detection algorithm for multi-layer supply chain network considering delay probability is proposed. Based on the multi-layer supply chain network including raw material suppliers, manufacturers, distributors and retailers, the overload cascading failure model of the multi-layer supply chain network is constructed through networking, and the overload cascading failure process of the multi-layer supply chain network is described through three aspects: initial load, node capacity and overload node load distribution. By monitoring and analyzing the status update messages of each node in the supply chain network, and considering the delay probability factor, the node and path that may lead to cascading failure are identified by predicting the delay probability of the next message through the historical message delay probability, and the overload cascading failure detection of the multi-layer supply chain network is realized. The experimental results show that the algorithm can effectively detect the failure of each overloaded enterprise node in the multi-layer supply chain network, which is helpful to enhance the early warning and response ability of the cascade failure of the supply chain network. The algorithm can realize the failure detection of supply chain network under different attack conditions and initial failure ratio of nodes, and measure the anti-risk ability of supply chain network. Under deliberate attack and when the initial attack node is a supplier network layer node, it has a greater impact on the multi-layer supply chain network.

Telemedicine networks for acute stroke: An analysis of global coverage, gaps, and opportunities.

Despite the proven efficacy of telestroke in improving clinical outcomes by providing access to specialized expertise and allowing rapid expert hyperacute stroke management and decision-making, detailed operational evidence is scarce, especially for less developed or lower income regions. We aimed to map the global telestroke landscape and characterize existing networks. We employed a four-tiered approach to comprehensively identify telestroke networks, primarily involving engagement with national stroke experts, stroke societies, and international stroke authorities. A carefully designed questionnaire was then distributed to the leaders of all identified networks to assess these networks' structures, processes, and outcomes. We identified 254 telestroke networks distributed across 67 countries. High-income countries (HICs) concentrated 175 (69%) of the networks. No evidence of telestroke services was found in 58 (30%) countries. From the identified networks, 88 (34%) completed the survey, being 61 (71%) located in HICs. Network setup was highly heterogeneous, ranging from 17 (22%) networks with more than 20 affiliated hospitals, providing thousands of annual consultations using purpose-built highly specialized technology, to 11 (13%) networks with fewer than 120 consultations annually using generic videoconferencing equipment. Real-time video and image transfer was employed in 64 (75%) networks, while 62 (74%) conducting quality monitoring. Most networks established in the past 3 years were located in low- and middle-income countries (LMICs). This comprehensive global survey of telestroke networks found significant variation in network coverage, setup, and technology use. Most services are in HICs, and a few services are in LMICs, although an emerging trend of new networks in these regions marks a pivotal moment in global telestroke care. The wide variation in quality monitoring practices across networks, with many failing to report key performance metrics, underscores the urgent need for standardized, resource-appropriate, quality assurance measures that can be adapted to diverse settings.

Design and preparation of composite bipolar plates with synergistic conductive networks of graphite and metal foam

Graphite composite bipolar plate (BP) have limited conductivity, which makes them less than ideal for extensive application in proton exchange membrane fuel cell (PEMFC). In traditional graphite composite BP, the construction process of the conductive network is entirely random. To enhance conductivity, it is typically necessary to increase the content of conductive fillers or incorporate high aspect ratio carbon nanomaterials to optimize the density of the conductive network. In this work, we manufactured a highly conductive composite bipolar plate with synergistic conductive networks of graphite and metal foam (MF). With a synergistic conductive network, the ASR of CuG80-20ppi decreased to 7.7 mΩ cm2. Additionally, thermal conductivity increased to 24.76 W/(m·K), and in-plane conductivity reached 294.1 S/cm at 80 wt% mass fractions of conductive filler, using 20 ppi copper foam as the metallic conductive network. CuG80-20ppi exhibited a flexural strength of 52.2 MPa. G80 and CuG80-20ppi have the 80 wt% conductive filler and are molded into BP with parallel flow fields. CuG80-20ppi enhances the current density of PEMFC by 0.132 W/cm2 and reduces the ohmic impedance by 5.25%. Therefore, the findings of this study offer valuable insights for the enhancement of composite BP conductivity, while simultaneously ensuring the stability and continuity of the synergistic conductive network. A novel approach is presented to enhance the electrical conductivity of graphite composite bipolar plate (BP) for proton exchange membrane fuel cells (PEMFCs). By embedding metal foam as a supplementary conductive network within the graphite matrix, a synergistic conductive architecture is achieved. This innovation significantly reduces the area specific resistance (ASR) to 7.7 mΩ cm2 and boosts thermal conductivity to 24.76 W/(m·K). The resulting CuG80-20ppi BP, with 80 wt% conductive filler, exhibits improved mechanical strength and conductivity, contributing to a 0.132 W/cm2 increase in PEMFC current density and a 5.25% reduction in ohmic impedance. This work highlights the potential of metal foam integration to enhance BP conductivity and PEMFC performance.

A novel recovery controllability method on temporal networks via temporal lost link prediction

Abstract Temporal networks are essential in representing systems where interactions between elements evolve over time. A crucial aspect of these networks is their controllability the ability to guide the network to a desired state through a set of control inputs. However, as these networks evolve, links between nodes can be lost due to various reasons, such as network failures, disruptions, or attacks. The loss of these links can severely impair the network’s controllability, making it challenging to recover desired network functions. In this paper, while investigating the destructive effects of various attacks on controllability processes in temporal networks, a new controllability recovery method is proposed, in which it prevents disruptions in this type of network processes by predicting lost links. In the proposed method, using network embedding and feature extraction, the dissimilarity of the nodes is calculated and then the missing links are predicted by designing a neural network. The results of the implementation of the proposed method on the datasets have demonstrates that the proposed method performed better than other conventional methods.

Durability of epoxy and vinyl ester polymers in wet, seawater, and seawater sea sand concrete environments: Molecular dynamics simulations

Epoxy and vinyl ester (VE) deteriorate under harsh environmental conditions typical of marine and offshore applications. Enhancing their performance requires a deeper understanding of their molecular-level interactions with these environments. This study uses molecular dynamics simulations to investigate the behavior of epoxy and VE under dry, wet, Na2SO4, NaCl, NaOH, KOH, and simulated seawater sea sand concrete (SWSSC) pore solution environments. The effect of temperatures ranging from 300 K to 368 K was also explored. Key parameters, such as radial distribution function, hydrogen bond dynamics, diffusion coefficients of water molecules and ions, swelling ratio, and glass transition temperature (Tg), were analyzed. Results show that epoxy is more sensitive to environmental effects than VE. In the dry state, three types of hydrogen bonds (H-bonds) were identified, with 64 % of the hydroxyl (OH) group involved in epoxy and 34 % in VE. As temperature increases, polymer chain interactions weaken, leading to a decrease in H-bonds, with epoxy showing a faster decline. Absorbed water molecules accumulate near the polymer polar sites, particularly at the OH groups, breaking some polymer-polymer H-bonds and forming new ones with the polymer polar sites. The hydrogen atoms of most absorbed water molecules orient away from the OH group, creating binding sites for water clustering through water-water H-bonds, leading to plasticization and potential hydrolytic degradation. Ions increase the number of polymer-water H-bonds, significantly decreasing polymer-polymer H-bonds (by 59.22 % for epoxy and 51.52 % for VE in SWSSC), thus accelerating material deterioration. Free volume distribution and dynamics, the transition between bound and free water, hydrogen bond dynamics, as well as ion hydration and dehydration collectively influence the water diffusion process in the polymer networks. Ion species diffuse much slower than water molecules, with epoxy showing higher permeability to Cl- and OH- compared to VE. Glass transition temperature (Tg) of both polymers is sensitive to environmental exposure, with epoxy being more affected. The detrimental effect on Tg is ranked as SWSSC > KOH > NaOH > NaCl > Na2SO4 > Wet. These findings provide new nanoscale insights into the interactions between polymers and SWSSC environments, offering a foundation for accurately predicting the durability of polymer-reinforced SWSSC.

On the Knowledge of Neural Networks: The Genesis, Characterization, and Justification

This paper systematically analyzes the knowledge generation process of neural networks through the lens of Philosophy for Science and Epistemology. This paper first argues that for neural network’s knowledge to be possible, it is necessary that the truth of the world can be represented by mathematics, since neural networks are, by their first nature, a mathematical model, and this paper provides few arguments that support the mathematical nature of truth. For a central characterization of neural network’s knowledge, this paper argues that neural networks bypass the three inherent limits of traditional sciences, such as physics and chemistry, suggested by Eugene P. Wigner (1995). The three limits are: 1) Traditional sciences are inevitably approximations of the transcendental truth; 2) Scientists will never stop pursuing deeper, more profound scientific theories that encompass more information than previous theories, despite every theory being an approximation; 3) The increasing complexity and depth of the successive scientific theories poses a significant challenge to human intellect. Neural networks bypass these limits by adaptively learning any underlying function to the given data space, without creating any fundamental hypothesis, which are inevitably not transcendental, like that of traditional sciences. This is also another distinction that supports the differentiation between “machine knowledge” and “human knowledge”, as first proposed by Wheller (2016). This paper argues that the only way humans can justify machine knowledge is through Reliabilism, the epistemic justification through trust in the knowledge generation process. Although reliabilism may currently seem unacceptable, this paper predicts that scientists will take control over machine knowledge in the future, as testified by scientists exploring the chemical world, which is similarly beyond direct comprehension. In the end, this paper suggests the necessity of discussing how AI can justify its own knowledge as conscious beings.

Different Adaption Strategies of Abundant and Rare Microbial Communities in Sediment and Water of East Dongting Lake.

The dynamics of aquatic microbes is of great importance for comprehending the acclimatisation and evolution of microorganisms in lake ecology. However, little is known about the adaption strategies of microbial communities in East Dongting Lake, which had special and complexity geographical characteristics. A semi-enclosed lake area (A) and a waterway connected to Yangtze River (B) both existed in the lake zone. Here, we investigated bacterial and fungal community diversity, community network and community assembly processes in sediment and water. The results indicated that the proportion of OTU numbers and their relative abundance for rare and abundant taxa were different obviously between sediment and water, but not between bacteria and fungi. However, abundant subcommunities dominated the shifts of bacterial community diversity and structure in A region, while rare subcommunities for fungal community diversity. Compared to fungal community, bacterial network was more compact and more key stones were identified as rare taxa. In addition, stochastic processes (dispersal limitation) drove the community assembly of abundant and rare subcommunities, but the effects of deterministic processes (including variable and heterogeneous selections) affected more on rare rather than abundant taxa. Partial Mantel test further indicated that the effect of environmental factors was a stronger force in shaping abundant bacterial subcommunities (TOC, NH4+-N, TN, and ORP) and rare fungal subcommunities (ORP). Environmental factors explained more of the variation in bacterial community structure than that in fungal community structure, although they had additional effects on fungal community diversity and community assembly. Moreover, bacterial community affected the fungal community as a biotic factor in water. This research provided new insights into better understanding of microbial communities in the complex environment of the East Dongting Lake.

The efficiency of synchronization dynamics and the role of network syncreactivity

Synchronization of coupled oscillators is a fundamental process in both natural and artificial networks. While much work has investigated the asymptotic stability of the synchronous solution, the fundamental question of the transient behavior toward synchronization has received far less attention. In this work, we present the transverse reactivity as a metric to quantify the instantaneous rate of growth or decay of desynchronizing perturbations. We first use the transverse reactivity to design a coupling-efficient and energy-efficient synchronization strategy that involves varying the coupling strength dynamically according to the current state of the system. We find that our synchronization strategy is able to synchronize networks in both simulation and experiment over a significantly larger (often by orders of magnitude) range of coupling strengths than is possible when the coupling strength is constant. Then, we characterize the effects of network topology on the transient dynamics towards synchronization by introducing the concept of network syncreactivity: A network with a larger syncreactivity has a larger transverse reactivity at every point on the synchronization manifold, independent of the oscillator dynamics. We classify real-world examples of complex networks in terms of their syncreactivity.

Insight into a new perspective on the complex propagation processes in networks: dynamic link equations

Insight into the spread of epidemics under different transmission mechanisms in networks has long been an important research question in the field of complex network dynamics. Currently, under simple transmission mechanisms, our analysis of the dynamic processes in networks starts only from the node level, considering the scale of infected nodes in the network. However, the information provided by this lowest-order approach to considering dynamic processes in networks is very limited. Most importantly, it is not applicable to the analysis of dynamic processes in networks under more common complex transmission mechanisms, as it neglects the interactions between nodes. Therefore, in this article, we propose a set of closed link dynamic equations to gain insight into complex propagation processes from a microscopic perspective. Fundamentally, we have developed a set of analytical tools for analyzing complex dynamic behaviors at the link level, enabling us to reexamine the complex dynamic processes on networks from a higher-order perspective. Additionally, we apply the proposed analytical framework to complex SIS epidemiological models on two real and synthetic networks, and extensive numerical simulation results demonstrate the feasibility and effectiveness of the proposed method.