Stability Of Networks Research Articles

The co-occurrence patterns and assembly mechanisms of microeukaryotic communities in geothermal ecosystems of the Qinghai-Tibet Plateau

Geothermal spring ecosystems, as extreme habitats, exert significant environmental pressure on their microeukaryotic communities. However, existing studies on the stability of microeukaryotic communities in geothermal ecosystems across different habitats and temperature gradients are still limited. In this study, we used high-throughput 18S rDNA sequencing in combination with environmental factor analysis to investigate the co-occurrence patterns, assembly mechanisms, and responses to environmental changes of microeukaryotic communities in sediment and water samples from 36 geothermal springs across different temperature gradients in southern Tibet. The results show that with increasing temperature, the network stability of microeukaryotic communities in sediments significantly improved, while the stability in water communities decreased. The assembly mechanisms of microeukaryotic communities in both sediment and water were primarily driven by undominant processes within stochastic processes. Latitude and longitude were the key factors influencing changes in sediment community composition, while water temperature and electrical conductivity were the major environmental factors affecting water community composition. Additionally, the stability of the geothermal community network was closely related to its response to external disturbances: sediment communities, being in relatively stable environments, demonstrated higher resistance to disturbances, whereas water communities, influenced by environmental changes such as water flow and precipitation, exhibited greater dynamic variability. These findings not only enhance our understanding of the ecological adaptability of microeukaryotic communities in geothermal springs but also provide valuable insights into how microorganisms in extreme environments respond to external disturbances. This is especially significant for understanding how microeukaryotic communities maintain ecological stability under highly dynamic and stressful environmental conditions.

Introduction of Panax notoginseng into pine forests significantly enhances the diversity, stochastic processes, and network complexity of nitrogen-fixing bacteria in the soil

IntroductionNitrogen-fixing bacteria (NFB) have a pivotal impact on the nitrogen cycle within agroforestry systems. The organic management of the Panax notoginseng (sanqi)-Pinus armandii agroforestry (SPA) system resulted in nitrogen deficiency because of the lack of application of chemical fertilizers. Therefore, assessing the variability in NFB due to the cultivation of sanqi in the SPA system becomes crucial.MethodsThe seasonal dynamics in the abundance, diversity, and community structure of NFB in the soil of monocropping pine (MP) and SPA systems were assessed using real-time quantitative polymerase chain reaction and high-throughput sequencing technology.Results and discussionSanqi cultivation triggered a decrease in the abundance of NFB but increased α diversity. Additionally, significant differences in the community structure of NFB were noted between the MP and SPA systems. Moreover, the abundance of Bradyrhizobium and Azospirillum increased in the soil after sanqi was cultivated. Furthermore, the cultivation of sanqi broadened the ecological niche breadth of NFB and increased the stochasticity in its community structure assembly (i.e., dispersal limitation). Additionally, the SPA system increased the network complexity but not the stability of NFB. The structural equation model (SEM) revealed that pH directly impacted the network complexity and stability of NFB in the SPA system. Sanqi cultivation positively influences the community characteristics of NFB in the soil in the SPA system. Our study provides new insights into nitrogen cycling and utilization in the SPA system.

Arbuscular mycorrhizal fungi enhance drought resistance and alter microbial communities in maize rhizosphere soil

Although the benefits of inoculation with arbuscular mycorrhizal fungi (AMF) for mitigating drought stress and enhancing plant growth of host are well‐studied, our understanding of their interactions with soil attributes and rhizosphere microbial communities remains limited. In the present study, we conducted a pot experiment to explore how AMF affects maize growth, antioxidant activity, soil properties, and the rhizosphere microbial communities under drought stress. The results showed that AMF inoculation during drought stress resulted in improved maize seedling growth, significantly increasing plant biomass (42.7%), chlorophyll content (13.4%), and antioxidant capacity. It also enhanced soil nutrient availability and microbial biomass. We observed significant shifts in the structure and composition of the rhizosphere microbial community with AMF inoculation under drought conditions when compared to those under well-watered conditions. Significantly, bacterial diversity and composition showed greater sensitivity to drought stress than fungal communities. This was attributed to plants preferentially reducing the supply of carbon sources to bacterial rather than to fungal communities under drought conditions. AMF inoculation also enhanced the complexity (e.g., degree) and stability of the rhizosphere microbial ecological network, suggesting closer ecological interactions within the soil microbial community. Furthermore, bacterial diversity and richness played a more important role in soil function than those of fungal communities under drought stress, particularly with AMF inoculation. Overall, our findings suggest that AMF inoculation alters soil microbial diversity and interactions, highlighting their key roles in enhancing plant stress tolerance and mitigating the negative effects of drought on agricultural ecosystems.

Lagrange stability of quaternion-valued neural networks with mixed delays on time scales

Lagrange stability of quaternion-valued neural networks with mixed delays on time scales

Thermal stability of monolayer fullerene networks: A molecular dynamics study with machine-learning potential

Thermal stability of monolayer fullerene networks: A molecular dynamics study with machine-learning potential

Analytical selection of hidden parameters through expanded enhancement matrix stability for functional-link neural networks and broad learning systems

Analytical selection of hidden parameters through expanded enhancement matrix stability for functional-link neural networks and broad learning systems

The interactive effects of shrub and aridity affect co-occurrence network complexity and stability of soil bacterial communities on the Qinghai–Tibet Plateau

The interactive effects of shrub and aridity affect co-occurrence network complexity and stability of soil bacterial communities on the Qinghai–Tibet Plateau

Multilayer network instability underlying persistent auditory verbal hallucinations in schizophrenia.

Auditory verbal hallucinations (AVHs) in schizophrenia (SCZ) are linked to brain network abnormalities. Resting-state fMRI studies often assume stable networks during scans, yet dynamic changes related to AVHs are not well understood. We analyzed resting-state fMRI data from 60 SCZ patients with persistent AVHs (p-AVHs), 39 SCZ patients without AVHs (n-AVHs), and 59 healthy controls (HCs), matched for demographics. Using graph theory, we constructed a time-varying modular structure of brain networks, focusing on multilayer modularity. Network switching rates at global, subnetwork, and nodal levels were compared across groups and related to AVH severity. SCZ groups had higher switching rates in the subcortical network compared to HCs. Increased switching was found in two thalamic nodes for both patient groups. The p-AVH group showed lower switching rates in the default mode network (DMN) and two superior frontal gyrus nodes compared to HC and n-AVH groups. DMN switching rates negatively correlated with AVH severity in the p-AVH group. Dynamic changes in brain networks, especially lower DMN and frontal region switching rates, may contribute to the development and persistence of AVHs in SCZ.

Energy efficiency potential determination for an oil treatment and stabilization unit at the field

Relevance. The desire to increase the energy efficiency of industrial enterprises and reduce greenhouse gas emissions from their activities. Reducing specific energy consumption at large oil refineries and petrochemical plants is not new and is quite widely used. Dozens of monographs and thousands of articles are devoted to these areas. But it should be taken into account that in the oil refining industry, all crude oil, even that which does not reach the refinery, necessarily passes through oil preparation and stabilization units at the fields. Therefore, in order to create energy-efficient and environmentally friendly processes throughout the entire oil refining chain, it is necessary to increase the energy efficiency of the oil preparation and stabilization units located at the fields. There are very few research works on thermal energy integration of oil preparation and stabilization units. Aim. Determination of target design and energy values for an energy-efficient retrofit project of the heat exchange network for the surveyed oil preparation and stabilization units and of its energy efficiency potential. Methods. Pinch analysis methods are used to determine the target values of an energy-efficient retrofit project. Mathematical modeling of heat exchange processes in the heat exchange network and economic analysis were performed using the Pinch 2.02 software; the Aspen HYSYS software package was used to create a simulation engineering model of the oil preparation and stabilization units. Results. When determining the target values of the heat exchange network retrofit project, the cost of energy and heat exchange equipment, the cost of collectors for splitting process streams, and technical restrictions on the placement of heat exchanger sections were taken into account. The inclusion of fuel gas in the process stream table has allowed the definition of target parameters for the optimal unit heat exchange network design to evolve. In the process of evolution of target values, the energy efficiency potential of the surveyed oil preparation and stabilization units was determined, which shows the possibility of reducing specific energy consumption by 77%. Upon implementation of the heat exchange network retrofit project and achievement of target parameters, the following economic results will be obtained: IRR=42%, NPV=7425780 USD, DPP 4 years. Also, CO2 emissions can be reduced by 30 thousand tons per year. It should be noted that the evolution of target designation and all target values for the reconstruction project were obtained before the implementation of the heat exchange network oil preparation and stabilization units project itself.

A temporal-spatial feature fusion network for emotion recognition with individual differences reduction.

In the context of EEG-based emotion recognition tasks, a conventional strategy involves the extraction of spatial and temporal features, subsequently fused for emotion prediction. However, due to the pronounced individual variability in EEG and the constrained performance of conventional time-series models, cross-subject experiments often yield suboptimal results. To address this limitation, we propose a novel network named Time-Space Emotion Network (TSEN), which capitalizes on the fusion of spatiotemporal information for emotion recognition. Diverging from prior models that integrate temporal and spatial features, our network introduces a Convolutional Block Attention Module (CBAM) during spatial feature extraction to judiciously allocate weights to feature channels and spatial positions. Furthermore, we bolster network stability and improve domain adaptation through the incorporation of a residual block featuring Switchable Whitening (SW). Temporal feature extraction is accomplished using a Temporal Convolutional Network (TCN), ensuring elevated prediction accuracy while maintaining a lightweight network structure. We conduct experiments on the preprocessed DEAP dataset. Ultimately, the average accuracy for arousal prediction is 0.7032 with a variance of 0.0876, and the F1 score is 0.6843. For valence prediction, the accuracy is 0.6792 with a variance of 0.0853, and the F1 score is 0.6826. TSEN exhibits high accuracy and low variance in cross-subject emotion prediction tasks, effectively reducing individual differences among different subjects. Additionally, TSEN has a smaller parameter count, enabling faster execution.

The impact of structured higher-order interactions on ecological network stability

The impact of higher-order interactions, those involving more than two species, is increasingly appreciated as having the potential to strongly influence the dynamics of complex ecological systems. However, although the critical importance of the structure of pairwise interaction networks is well established, studies of higher-order interactions still largely assume random structures. Here, we demonstrate the strong impact of structured higher-order interactions on simulated ecological communities. We focus on effects caused by interaction modifications within food webs, where a consumer resource interaction is modified by a third species, and for which plausible structures can be hypothesised. We show how interaction modifications introduced under a range of non-random distributions may impact the overall network structure. Local stability and the size of the feasibility domain are critically dependent on the inter-relationship between trophic and non-trophic effects. Where interaction modifications are structured into mutual interference motifs (associated with consumers switching between resources) synergistic signs and topological effects have particularly consequential impacts. Furthermore, we show that previous results of the impact of higher-order interactions on diversity-stability relationships can be reversed when higher-order interactions are structured, not random. Empirical data on interaction modifications will be a key part of improving understanding the dynamics of communities, particularly the distribution of interaction modification signs across networks.

Research on the network structure and gender/age differences of psychological safety among urban residents: network analysis based on a large sample

BackgroundPsychological safety as the key to mental health, not only affects individual happiness and quality of life but also relates to social stability and harmony. However, psychological safety is complex and multidimensional, with unclear internal structures and influencing factors and insufficient research on gender and age differences. Urban residents are living in an environment characterized by fast-paced, high-pressure, multicultural integration, and complex social relationships. Therefore, in-depth exploration of its core dimensions and network structure is crucial for formulating effective mental health strategies and enhancing residents’ sense of psychological safety.MethodsA survey was conducted on 9,282 urban residents using the Psychological Safety Scale. Using R version 4.3.2 for network estimation, centrality estimation, accuracy and stability estimation, and network comparison.ResultsThe results found that the strength centrality index of the general sense of safety dimension is always the highest in the total network and networks of different genders and ages. The network comparison results show that there are significant gender and age differences in the dimensions/item networks of psychological safety. There are connections between trust and relaxation, excitement, and calmness in the dimension network of male samples, while there are no such connections in the dimension network of female samples. The general sense of safety and relaxation connection strength on the male dimension network is significantly stronger than that on the female dimension. In the dimension network of the youth sample, the strength of the connection between calmness and relaxation, trust and relaxation were significantly stronger than those of the middle-age sample, while the strength of the connection between relaxation and excitement was significantly weaker than that of the middle-age sample.ConclusionResearchers should fully consider gender and age factors and adopt more personalized and differentiated strategies for promoting individual psychological safety.

Spatiotemporal Complexity in the Psychotic Brain.

Psychotic disorders, such as schizophrenia and bipolar disorder, pose significant diagnostic challenges with major implications on mental health. The measures of resting-state fMRI spatiotemporal complexity offer a powerful tool for identifying irregularities in brain activity. To capture global brain connectivity, we employed information-theoretic metrics, overcoming the limitations of pairwise correlation analysis approaches. This enables a more comprehensive exploration of higher-order interactions and multiscale intrinsic connectivity networks (ICNs) in the psychotic brain. In this study, we provide converging evidence suggesting that the psychotic brain exhibits states of randomness across both spatial and temporal dimensions. To further investigate these disruptions, we estimated brain network connectivity using redundancy and synergy measures, aiming to assess the integration and segregation of topological information in the psychotic brain. Our findings reveal a disruption in the balance between redundant and synergistic information, a phenomenon we term brainquake in this study, which highlights the instability and disorganization of brain networks in psychosis. Moreover, our exploration of higher-order topological functional connectivity reveals profound disruptions in brain information integration. Aberrant information interactions were observed across both cortical and subcortical ICNs. We specifically identified the most easily affected irregularities in the sensorimotor, visual, temporal, default mode, and fronto-parietal networks, as well as in the hippocampal and amygdalar regions, all of which showed disruptions. These findings underscore the severe impact of psychotic states on multiscale critical brain networks, suggesting a profound alteration in the brain's complexity and organizational states.

Prokaryotic and eukaryotic periphyton responses to warming, nutrient enrichment and small omnivorous fish: a shallow lake mesocosms experiment.

Global change stressors, including climate warming, eutrophication, and small-sized omnivorous fish, may exert interactive effects on the food webs and functioning of shallow lakes. Periphyton plays a central role in the primary production and nutrient cycling of shallow lakes but constitutes a complex community composed of eukaryotes and prokaryotes that may exhibit different responses to multiple environmental stressors with implications for the projections of the effects of global change on shallow lakes. We analyzed the effects of warming, nutrient enrichment, small omnivorous fish and their interactions on eukaryotic and prokaryotic periphyton structures in shallow lake mesocosms. We performed 16S and 18S rRNA high-throughput sequencing to elucidate the effect of the abovementioned stressors. We found that warming promoted periphytic alpha diversity and network complexity, with multi-tolerant genera becoming dominating (e.g. Spirosomaceae and Azospirillaceae). Contrastingly, nutrient enrichment led to reduced prokaryotic diversity and network complexity and stability, with weak disruption of the eukaryotic structure. Small omnivorous fish were major drivers of changes eukaryotic periphyton, facilitating diversity and network complexity, and increasing prokaryotic and eukaryotic biomarker diversity. Omnivorous fish reduced the grazing pressure on periphyton mainly through selective grazing on zooplankton, contributing to periphytic structural stability and functional diversity, especially the proliferation of prokaryotic biomarkers. Nutrient enrichment counteracted the positive effects of warming on periphyton, while concerted action with omnivorous fish led to high TN and TP concentrations and accelerated the negative development of periphytic alpha diversity and network structure. The co-occurrence of the three environmental pressures ultimately resulted in a disruption of periphytic biodiversity and community structure and weakened connectivity with the environment. Our study provided new insights into the understanding of the response of prokaryotic and eukaryotic community structure and ecological functions of freshwater periphyton to global environmental change.

Frobenius Norm-Based Global Stability Analysis of Delayed Bidirectional Associative Memory Neural Networks

The present research investigates the global asymptotic stability of bidirectional associative memory (BAM) neural networks using distinct sufficient conditions. The primary objective of this study is to establish new generalized criteria for the global asymptotic robust stability of time-delayed BAM neural networks at the equilibrium point, utilizing the Frobenius norm and the positive symmetrical approach. The new sufficient conditions are derived with the help of the Lyapunov–Krasovskii functional and the Frobenius norm, which are important in deep learning for a variety of reasons. The derived conditions are not influenced by the system parameter delays of the BAM neural network. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed conclusions regarding network parameters.

Optimal network sizes for most robust Turing patterns

Many cellular patterns exhibit a reaction-diffusion component, suggesting that Turing instability may contribute to pattern formation. However, biological gene-regulatory pathways are more complex than simple Turing activator-inhibitor models and generally do not require fine-tuning of parameters as dictated by the Turing conditions. To address these issues, we employ random matrix theory to analyze the Jacobian matrices of larger networks with robust statistical properties. Our analysis reveals that Turing patterns are more likely to occur by chance than previously thought and that the most robust Turing networks have an optimal size, consisting of only a handful of molecular species, thus significantly increasing their identifiability in biological systems. Broadly speaking, this optimal size emerges from a trade-off between the highest stability in small networks and the greatest instability with diffusion in large networks. Furthermore, we find that with multiple immobile nodes, differential diffusion ceases to be important for Turing patterns. Our findings may inform future synthetic biology approaches and provide insights into bridging the gap to complex developmental pathways.

Detecting and mitigating DIS attack in RPL-based network

The Routing Protocol for Low-Power and Lossy Networks (RPL) is among the most used protocol in the Internet of Things. In RPL, the network is established by creating a tree topology called Destination Oriented Directed Acyclic Graphs (DODAG), However, RPL-based networks stability is vulnerable to different attacks such as DODAG Information Solicitation (DIS) flooding attack. In this attack, a malicious node sends large number of DIS messages in the neighborhood, which compels the receiver nodes to exchange more control messages, causing resources deplete. Unfortunately, RPL has no means to detect or mitigate DIS flooding attacks. In this paper, we present a novel Adaptive Threshold Mechanism to mitigate DIS attack called (ATM-RPL). In the proposed approach, the number of exchanged control messages is used to determine the network state which in turn used to calculate a dynamic threshold. ATM-RPL proved its effectiveness and superiority over similar proposed mechanism in terms of false positive, construction time, power consumption, and packet delivery ratio.

Optimizing Biomimetic 3D Disordered Fibrous Network Structures for Lightweight, High-Strength Materials via Deep Reinforcement Learning.

3D disordered fibrous network structures (3D-DFNS), such as cytoskeletons, collagen matrices, and spider webs, exhibit remarkable material efficiency, lightweight properties, and mechanical adaptability. Despite their widespread in nature, the integration into engineered materials is limited by the lack of study on their complex architectures. This study addresses the challenge by investigating the structure-property relationships and stability of biomimetic 3D-DFNS using large datasets generated through procedural modeling, coarse-grained molecular dynamics simulations, and machine learning. Based on these datasets, a network deep reinforcement learning (N-DRL) framework is developed to optimize its stability, effectively balancing weight reduction with the maintenance of structural integrity. The results reveal a pronounced correlation between the total fiber length in 3D-DFNS and its mechanical properties, where longer fibers enhance stress distribution and stability. Additionally, fiber orientation is also considered as a potential factor influencing stress growth values. Furthermore, the N-DRL model demonstrates superior performance compared to traditional approaches in optimizing network stability while minimizing mass and computational cost. Structural integrity is significantly improved through the addition of triple junctions and the reduction of higher-order nodes. In summary, this study leverages machine learning to optimize biomimetic 3D-DFNS, providing novel insights into the design of lightweight, high-strength materials.

Optimizing Reactive Compensation for Enhanced Voltage Stability in Renewable-Integrated Stochastic Distribution Networks

The rapid expansion of renewable energy sources and the increasing electrical load demand are complicating the operational dynamics of power grids, leading to significant voltage fluctuations and elevated line losses. To address these challenges, we propose an information gap decision-theory-based robust optimization method for the siting and operation of reactive compensation equipment, utilizing static var generators (SVGs) to mitigate voltage fluctuations and reduce losses. Our approach begins by projecting the scale of renewable energy integration and load growth, establishing scenarios with varying renewable-to-load growth ratios. We then develop a multi-objective optimization model that incorporates voltage–loss sensitivity, accounting for the uncertainties in renewable energy production. A case study demonstrates that our method reduces grid voltage fluctuations and losses by 29.53% and 7.75%, respectively, compared to non-intervention scenarios, highlighting its effectiveness in stabilizing distribution networks.

Network analysis of interpersonal sensitivity and self-efficacy in nursing students

BackgroundInterpersonal sensitivity and self-efficacy are key psychological traits that critically impact the mental well-being and professional growth of nursing students. However, the complex interplay between interpersonal sensitivity and self-efficacy of nursing students has not been fully explored.PurposeThis study aimed to explore the complex relationship between interpersonal sensitivity and self-efficacy in nursing students using network analysis, identifying key symptoms within this network.MethodA total of 864 nursing students were recruited in this study. The Chinese Version of the Short Form of the Interpersonal Sensitivity Measure (IPSM-CS) and The New General Self-Efficacy Scale (NGSES) were used to assess interpersonal sensitivity and self-efficacy, respectively. Centrality and bridge centrality indices were used to identify central symptoms and bridge symptoms. Network stability was examined using the case-dropping procedure. The Network Comparison Test was used to investigate the network differences by gender in nursing students.ResultsThe strongest direct relation was observed between the symptoms of interpersonal sensitivity “I feel happy when someone compliments me” and “I can make other people feel happy”. “I feel happy when someone compliments me” exhibited the highest node strength within the interpersonal sensitivity and self-efficacy network, followed by “If others knew the real me, they would not like me” and “I worry about being criticized for things that I have said or done”. “Faced with difficult tasks, confident that it can be accomplished” exhibited the highest bridge strength, followed by “Ability to successfully overcome many challenges”. The stability tests of the whole network indicated robustness.ConclusionThis study highlighted the intricate and dynamic association between interpersonal sensitivity and self-efficacy among nursing students. Identifying central and bridge symptoms can provide nursing educators with valuable insights, benefiting them in enhancing nursing students’ mental health by giving positive feedback, fostering self-awareness, and reinforcing coping strategies.Clinical trial numberNot applicable.