Stability Of Networks Research Articles

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.

"Multimodal Sleep Signal Tensor Decomposition and Hidden Markov Modeling for Temazepam-Induced Anomalies Across Age Groups".

Recent advances in multimodal signal analysis enable the identification of subtle drug-induced anomalies in sleep that traditional methods often miss. We develop and introduce the Dynamic Representation of Multimodal Activity and Markov States (DREAMS) framework, which embeds explainable artificial intelligence (XAI) techniques to model hidden state transitions during sleep using tensorized EEG, EMG, and EOG signals from 22 subjects across three age groups (18-29, 30-49, and 50-66 years). By combining Tucker decomposition with probabilistic Hidden Markov Modeling, we quantified age-specific, temazepam-induced hidden states and significant differences in transition probabilities. Jensen-Shannon Divergence (JSD) was employed to assess variability in hidden state transitions, with older subjects (50-66 years) under temazepam displaying heightened transition variability and network instability as indicated by a 48.57% increase in JSD (from 0.35 to 0.52) and reductions in network density by 12.5% (from 0.48 to 0.42) and modularity by 21.88% (from 0.32 to 0.25). These changes reflect temazepam's disruptive impact on sleep architecture in older adults, aligning with known age-related declines in sleep stability and pharmacological sensitivity. In contrast, younger subjects exhibited lower divergence and retained relatively stable, cyclical transition patterns. Anomaly scores further quantified deviations in state transitions, with older subjects showing increased transition uncertainty and marked deviations in REM-like to NREM state transitions. This XAI-driven framework provides transparent, age-specific insights into temazepam's impact on sleep dynamics, going beyond traditional methods by identifying subtle, pharmacologically induced changes in sleep stage transitions that would otherwise be missed. DREAMS supports the development of personalized interventions based on sleep transition variability across age groups, offering a powerful tool to understand temazepam's age-dependent effects on sleep architecture.

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.

Mixed Grazing Enhances Bacterial Diversity and Network Stability More Than Single Grazing in Both Nondegraded and Moderately Degraded Grasslands

Mixed Grazing Enhances Bacterial Diversity and Network Stability More Than Single Grazing in Both Nondegraded and Moderately Degraded Grasslands

Factor XIII-A Transglutaminase Contributes to Neutrophil Extracellular Trap (NET)-mediated Fibrin(ogen) Network Formation and Crosslinking.

Neutrophil extracellular traps can contribute to thrombosis via stabilization of fibrin network, which is normally conducted by plasma transglutaminase, Factor XIII-A as part of coagulation cascade. The possible presence and activity of FXIII-A in neutrophils or during NETosis are unknown. Here, we investigated potential presence of FXIII-A in neutrophils and participation in NET-fibrin(ogen) interaction in vitro. METHODS: Data mining of human and mouse F13A1/F13a1 mRNA expression in whole-body scRNA sequence atlases was conducted. F13a1 mRNA and protein expression was assessed in isolated mouse bone marrow neutrophils. NETosis was induced using 12-phorbol 13-myristate acetate (PMA), and the transglutaminase activity was assessed with 5-(biotinamido)pentylamine incorporation to plasma fibronectin and a fluorescence-fibrin(ogen)-based activity assay using ATTO488-Cadaverine. Externalization of FXIII-A and its interaction with neutrophil extracellular trap (NET) markers, namely, decondensed DNA, CitH3, and MPO, were examined with immunofluorescence microscopy. NET-fibrin(ogen) interaction was investigated with and without serum and/or transglutaminase inhibitor, NC9. Effect of soluble fibrinogen and fibrin(ogen) network on NETosis was also assessed. Data mining of RNAseq atlases showed F13A1/F13a1 expression in adipose tissue, blood, and bone marrow neutrophils. mRNA expression and protein production were confirmed in isolated neutrophils where expression was comparable to that of macrophages and monocytes. FXIII-A was externalized and active as a transglutaminase and colocalized with NET markers during NETosis. FXIII-A transglutaminase activity promoted NET-fibrin(ogen) interaction and entrapment of neutrophils within fibrin(ogen) matrix. Soluble fibrinogen or fibrin(ogen) network did not induce NETosis. This study identifies neutrophils as a source of FXIII-A and suggests its role in stabilizing NET-fibrin(ogen) matrix structures.

Dynamics of wheat rhizosphere microbiome and its impact on grain production across growth stages.

Crop plant microbiomes are increasingly seen as important in plant nutrition and health, and a key to maintaining food productivity. Currently, little is known of the temporal changes that occur in the wheat rhizosphere microbiome as the plant develops, and how this varies among different sites. We used a pot-based mesocosm experiment with the same modern wheat cultivar grown in eight soils from across the North China Plain, a major wheat producing area. DNA from rhizosphere soil was taken from wheat plants, from seedling up to grain harvesting stage, and amplicon sequenced for prokaryotes and microeukaryotes, followed by community analysis. Our results showed that rhizosphere diversity of prokaryotes and microeukaryotes increased over time in most sites. While there was turnover between earlier- and later-arriving species, the predominant successional model was accumulation, with early arrivals remaining in place as others colonized the rhizosphere. Rhizosphere community network modularity and stability increased during the development and maturation of the wheat plant. The abundances of certain stage-specific keystone species were correlated with eventual grain yield - suggesting a potentially important role in wheat production. Some keystone species belonged to groups previously implicated in various functions. This study provides a basis for further experimental investigation of the wheat rhizosphere microbiome, its role in determining crop yields, and the potential for microbiome engineering to promote yields. The sequential arrival and accumulation of microbiota suggests that deliberate inoculation might accelerate this process.

Effect of K2CO3 on konjac glucomannan/κ-carrageenan-based camellia oil Pickering emulsion gel for the development of fat analogs.

Konjac glucomannan (KGM) undergoes deacetylation in alkaline conditions, while κ-carrageenan (CRG) is sensitive to potassium ions. This study examines the influence of K2CO3 on the mechanical properties of KGM/CRG-based camellia oil Pickering emulsion gels. Texture analysis and rheological testing revealed that the addition of K2CO3 significantly enhanced the mechanical properties of emulsion gels. Texture parameters, such as the hardness, gumminess, and chewiness of the gel, were closer to those of pork back fat when the K2CO3 concentration was 0.2M. The addition of K2CO3 enhance the intermolecular crosslinking within the gel and formed a more uniform and dense gel network, promoting gelation and structural stability, which were confirmed by Fourier transform infrared spectroscopy, X-ray diffraction, and microstructural analysis. Besides, low-field nuclear magnetic resonance and thermogravimetric analyses indicated that the addition of K2CO3 reduced the water mobility of the gel and improved its thermal stability. Physicochemical and color analysis results showed that the energy value of the camellia oil Pickering emulsion gel was only 15.9% that of pork back fat, and its appearance was closer to that of pork back fat than hydrogels. This study provides technical support for the use of KGM/CRG-based camellia oil Pickering emulsion gels as fat analogs.

Recruitment of specific rhizosphere microorganisms in saline-alkali tolerant rice improves adaptation to saline-alkali stress.

Increasing annual soil salinization poses a major threat to global ecological security. Soil microorganisms play an important role in improving plant adaptability to stress tolerance, however, the mechanism of saline-alkali tolerance to plants associated with rhizosphere microbiome is unclear. We investigated the composition and structure of the rhizospheric bacteria and fungi communities of the saline-alkali tolerant (Oryza sativa var. Changbai-9) and sensitive (Oryza sativa var. Kitaake) rice grown in saline-alkali and non-saline-alkali soils. The results demonstrated that the saline-alkali tolerant rice enriched the rhizosphere bacteria taxa, including Hydrogenophaga, Pseudomonas, and Aeromonas, and fungi taxa, such as Chaetomium, Cladosporium and Tausonia, which may facilitate rice growth and enhance rice saline-alkali tolerance. Saline-alkali tolerant rice reduced the Na+/K+ ratio and improved rice yield by enhancing the stability of co-occurrence network associated with recruiting bacterial and fungal keystone species. The rhizosphere bacteria of the saline-alkali tolerant rice exhibited a markedly elevated expression of functions related to the saline-alkali tolerance, including the ABC transporter and the two-component system, compared to sensitive rice under saline-alkali stress. Overall, the saline-alkali tolerant rice responds to saline-alkali stress by recruiting keystone rhizosphere microorganisms to enhance rice saline-alkali tolerance. This study provides a theoretical basis for using specific microorganisms to improve plant tolerance in saline-alkali soils.

Modification speed alters stability of ecological higher-order interaction networks

Modification speed alters stability of ecological higher-order interaction networks

Coordinated Volt/VAR Control in Distribution Networks Considering Demand Response via Safe Deep Reinforcement Learning

Volt–VAR control (VVC) is essential in maintaining voltage stability and operational efficiency in distribution networks, particularly with the increasing integration of distributed energy resources. Traditional methods often struggle to manage real-time fluctuations in demand and generation. First, various resources such as static VAR compensators, photovoltaic systems, and demand response strategies are incorporated into the VVC scheme to enhance voltage regulation. Then, the VVC scheme is formulated as a constrained Markov decision process. Next, a safe deep reinforcement learning (SDRL) algorithm is proposed, incorporating a novel Lagrange multiplier update mechanism to ensure that the control policies adhere to safety constraints during the learning process. Finally, extensive simulations with the IEEE-33 test feeder demonstrate that the proposed SDRL-based VVC approach effectively improves voltage regulation and reduces power losses.

GTD‐CER: Game‐Theoretic Dynamic Clustering for Enhanced Efficiency and Reliability in Wireless Sensor Networks

ABSTRACTEfficient cluster formation is a significant issue in wireless sensor networks (WSNs) for optimizing energy consumption, enhancing data reliability, and prolonging network lifetime. This paper proposes a dynamic cluster formation scheme modeled as a noncooperative game, where each sensor node acts as a player making strategic decisions on whether to become a cluster head (CH) or remain a member node (MN). In this role selection decision‐making phase, nodes evaluate their potential utility function, which incorporates energy consumption, data reliability, and data forwarding efficiency. Nodes dynamically switch roles based on their utility to achieve an optimal network configuration. Simulations conducted using the NS‐3 network simulator demonstrate that the proposed game theory‐based clustering scheme significantly improves network performance. We analyze key measures like energy consumption, packet delivery ratio (PDR), and data forwarding, and the results shows that the proposed scheme reduces energy consumption by 25%, improves PDR by 15%, and extends the network lifetime by 20%, compared to traditional methods. The dynamic role‐switching mechanism prolongs network lifetime by preventing the rapid depletion of node energy, ensuring stable and efficient network operations. It significantly enhances network performance, stability, and longevity.

Bacterial wilt disease alters the structure and function of fungal communities around plant roots

BackgroundFungal communities around plant roots play crucial roles in maintaining plant health. Nonetheless, the responses of fungal communities to bacterial wilt disease remain poorly understood. Here, the structure and function of fungal communities across four consecutive compartments (bulk soil, rhizosphere, rhizoplane and root endosphere) were investigated under the influence of bacterial wilt disease.ResultsThe results showed that bacterial wilt disease caused different assembly patterns of fungal communities in the bulk soil, rhizosphere, rhizoplane and endosphere. Under the influence of bacterial wilt disease, a decreased fungal diversity was observed in the rhizoplane and endosphere, and completely different kinds of fungal genera were enriched in the four compartments. The complexity and stability of fungal networks were less affected, but the number of key fungal members in networks were significantly reduced in diseased samples. Functional predictions based on FUNGuild suggested that with the pathogen infection, saprotrophic fungi were increased in the bulk soil, but pathotrophic fungi (potential plant and animal pathogens) were increased in the rhizosphere, rhizoplane and endosphere.ConclusionThis work provides a deep insight into the effects of bacterial wilt disease on fungal communities along the soil-root continuum, and is helpful to identify plant-associated beneficial fungi to resist plant disease.Clinical trial numberNot applicable.

Distribution patterns and community assembly processes of bacterial communities across different sediment habitats of subsidence lakes.

Subsidence lakes, formed due to extensive underground coal mining activities, present both ecological challenges and opportunities for alternative land use practices, such as photovoltaic power generation and aquaculture. However, the ecological consequences of these anthropogenic activities on bacterial communities within subsidence lakes remain largely unexplored. To address this knowledge gap, we conducted a comprehensive investigation of bacterial communities in two typical subsidence lake districts located in Huainan, Anhui Province, China. A total of 44 sediment samples were collected across four distinct habitats: photovoltaic zones (PV), aquaculture zones (AC), photovoltaic-aquaculture zones (PV_AC), and unimpacted zones (Natural). Bacterial communities were investigated using 16S rRNA gene sequencing and various statistical methods. Our results revealed that the α-diversity and network complexity of bacterial communities in PV, PV_AC, and AC habitats are significantly higher than habitats of Natural (P<0.01). Specifically, the α-diversity is the highest in PV habitats, while the network complexity is the highest in AC habitats. The network stability is the highest in PV and PV_AC habitats, while it is the lowest in AC. There were also significant differences in community structure among different habitats, Nitrospirota in PV (31.9%) was higher than that in the other three habitats, the percentage of Firmicute in Natural (21.96%) and PV_AC (13.70%) was higher than other two habitats, Actinobacteriota has the highest proportion in AC (20.93%). Furthermore, stochastic processes dominate community assembly in PV, PV_AC, and AC habitats, it has the highest proportion in PV, particularly on dispersal limitation (DL). However, deterministic processes prevail in Natural habitats, particularly on heterogeneous selection (HeS). Collectively, these findings highlight the significant differences in sediment bacterial communities across various habitats in subsidence lakes. Our study provides valuable insights into understanding the ecological implications of different habitats in subsidence lake ecosystems.

A Study on Zone-Based Secure Multicast Protocol Technique to Improve Security Performance and Stability in Mobile Ad-Hoc Network

Since MANET consists of only nodes and is in wireless communication with limited resources, cooperation between nodes is very important. Multicasting technology supports various applications that allow data to be immediately transmitted to target nodes. In environments such as MANET, where nodes are constantly moving, finding an efficient path from the source to the destination is a critical challenge. Providing integrity for the data transmitted from the source to the target node set is also an important part. Maintaining the state of neighbor nodes not only increases the communication and processing overhead but also requires more memory. In this paper, we propose a zone-based secure routing algorithm to improve the performance of routing protocols. We will also prove efficient management of node groups in an area and better scalability, performance, and security despite frequent topology changes by using group keys. To evaluate the proposed technique, detailed and extensive simulation performance with PAST-DM and MSZRP are evaluated. The simulation results show that the proposed technique, compared to other routing protocols, can achieve scalability by maintaining the routing load even if the speed of nodes, the number of sources, the number of group members, and the size of the network increase.