Stability Of Networks Research Articles

Multiscale modelling of chromatin 4D organization in SARS-CoV-2 infected cells

SARS-CoV-2 can re-structure chromatin organization and alter the epigenomic landscape of the host genome, but the mechanisms that produce such changes remain unclear. Here, we use polymer physics to investigate how the chromatin of the host genome is re-organized upon infection with SARS-CoV-2. We show that re-structuring of A/B compartments can be explained by a re-modulation of intra-compartment homo-typic affinities, which leads to the weakening of A-A interactions and the enhancement of A-B mixing. At the TAD level, re-arrangements are physically described by a reduction in the loop extrusion activity coupled with an alteration of chromatin phase-separation properties, resulting in more intermingling between different TADs and a spread in space of the TADs themselves. In addition, the architecture of loci relevant to the antiviral interferon response, such as DDX58 or IFIT, becomes more variable within the 3D single-molecule population of the infected model, suggesting that viral infection leads to a loss of chromatin structural specificity. Analysing the time trajectories of pairwise gene-enhancer and higher-order contacts reveals that this variability derives from increased fluctuations in the chromatin dynamics of infected cells. This suggests that SARS-CoV-2 alters gene regulation by impacting the stability of the contact network in time.

DEVELOPMENT OF NETWORK SIMULATION MODEL FOR EVALUATING THE EFFICIENCY OF DISTRIBUTED CONSENSUS TAKING INTO ACCOUNT THE INSTABILITY OF NETWORK CONNECTIONS

The dynamic and unpredictable nature of network environments poses a significant challenge for distributed systems, particularly those relying on consensus algorithms for state management and fault tolerance. To address this challenge, this article introduces a novel simulation model designed to study the impact of unstable network connections on clusters running consensus algorithms. The model is engineered to mimic varying degrees of network instability, including latency fluctuations and connection disruptions, which are characteristic of real-world distributed systems. Our proposed model represents a significant advancement in the simulation of distributed networks. It employs a sophisticated network emulation layer capable of generating a wide spectrum of unstable network conditions. The core of the model is a highly configurable consensus mechanism simulator that allows for the adjustment of key parameters such as heartbeat intervals, election timeouts, and message loss rates. This level of configurability enables a comprehensive analysis of consensus behaviors under different network scenarios. The article focuses on the methodology behind the development of the model, detailing the theoretical underpinnings and the implementation strategies used to ensure a realistic representation of network instability. We also discuss the potential applications of the model, which extend beyond academic research into practical domains where distributed ledger technologies and distributed databases are prevalent. Through the deployment of this model, researchers and system architects can gain deeper insights into the resilience and adaptability of consensus algorithms. The model serves as a tool for preemptively identifying and addressing potential issues in distributed systems, facilitating the development of more robust and reliable technologies. In summary, the article showcases the design and capabilities of a new model that enables an in-depth understanding of the delicate interplay between network instability and consensus efficiency. By focusing on the model itself, the article aims to lay a foundation for future studies and improvements in the field of distributed systems.

Linking phosphorus fertility to soil microbial diversity and network complexity in citrus orchards: Implications for sustainable agriculture

Continuous input of chemical fertilizers causes soil acidification, land degradation, and water eutrophication, but its impact on soil microbial communities and co-occurrence patterns of microboes is unclear. In this study, we evaluated the impact of chemical fertilizer on bacterial community diversity and ecological network at 75 citrus sites in a watershed where chemical fertilizer is continuously applied. In addition, 25 natural forest sites adjacent to citrus sites were used as references to compare the potential effects of land conversion from natural forests to citrus orchards on soil bacterial diversity and microbial network. The results showed that the cultivation of citrus results in soil phosphorus (P) accumulation, with significantly higher available and mineral-bound phosphate contents than those found in soils of natural forests. Citrus soil average soil available P was 73.2 mg/kg, which was 20 times higher than that in forest soils. As the P content accumulates, soil bacterial Shannon index linearly decreases from 7.06 to 5.93 and is significantly lower than that of adjacent natural forest soils (7.13). Low P fertility soils have higher microbial network complexity and stability, containing more microbial communities and tighter relationships between microbes compared to higher P fertility soils. Soil P content and soil pH regulates soil microbial network complexity and stability. In addition, soil bacterial community structure in soils of natural forests is significantly different from that in citrus soils, and the bacterial community structure in high P soils is different from that in medium and low P soils. Soil P fertility reduces the relative abundance of dominant communities including Proteobacteria, Bacteroidetes, and Gemmatimonadetes, and also changes the relative abundance of some functional microbial communities, such as some phosphorus cycling microorganisms, including Acetobacteraceae and Beijerinckiaceae. In conclusion, soil phosphate accumulation severely alters soil bacterial community diversity, complexity, stability, and functionality. This could have negative impacts on soil functional stability and the sustainability of citrus orchards.

Hot‐spot aware multicost‐based energy‐efficient routing protocol for WBANs

SummaryWireless body area networks (WBANs) are becoming widely considered in remote healthcare monitoring applications. However, WBAN nodes have limited resources; therefore, effective and reliable routing protocols are pivotal research challenges. Furthermore, balancing the traffic load among sensor nodes is also highly required to increase network stability. In recent years, many interesting routing solutions have been proposed for WBANs; however, the significant feature in terms of stability and energy in these solutions has not been sufficiently addressed. Therefore, in this context, the Hot‐Sspot Aaware Multicost‐based Energy‐efficient Routing (H‐SAMER) protocol is proposed in this paper. The suggested protocol used multieffective cost function for next‐hop node selection based on the data type, where the patient's data are categorized into three classes: normal data, on‐demand data, and emergency data. Furthermore, the H‐SAMER protocol adds awareness to the transmission of control packets by adding the cost value to the RREQ packets. Thus, the simulation scenario shows that the H‐SAMER saves energy 8.57%, 88%, 98%, 97%, and 100% greater than E‐HARP, EH‐RCP, CO‐LAEEBA, EECBSR, and ELR‐W, respectively. Moreover, the first node death of the proposed H‐SAMER is delayed to round number 7500, which proves H‐SAMER to be a stable and reliable effective solution for WBANs.

The Emergence and Stability of Political Family Networks (PFNs) in Banten Province, Indonesia

The emergence of political family networks (PFNs) is an anomaly in Indonesian democracy post-New Order authoritarianism. This article discusses the emergence of political family networks in Banten Province, Indonesia, and how they maintained stability and stayed in power for over two decades. The research data, crucial for our comprehensive understanding, were collected through rigorous, in-depth interviews with key informants in Banten. Observation and study of documents were also carried out during the process of collecting data in the field. The result of the study shows that in the two decades since the 1998 democratic transition, as many as five PFNs emerged and existed in Banten Province. However, the role of local strongman Tubagus Chasan Shohib, who leveraged his social and political influence, became the most intriguing aspect of this study. His influence was critical in this region's emergence and consolidation of PFNs. Stability in the political arena was built through the strengthening of social bonds and transactional, as well as the spread of institutions in a mimetic, normative, and coercive manner.

Examining the social and behavioral dynamics of substance use in a longitudinal network study in rural Appalachia

BackgroundPrior studies have shown that individuals and their peers often have similar substance use behaviors, but the mechanisms driving these similarities – particularly in rural settings, are not well understood. The primary objectives of this analysis are to (1) identify factors that contribute to relationship turnover and maintenance within a rural network of persons who use drugs (PWUD), (2) determine whether assimilation and/or homophily shape participants use of injection drugs, heroin, and stimulants (methamphetamine and cocaine), and (3) assess the extent that these mechanisms influence networks ties and/or behaviors and whether these effects vary across time. MethodsSociometric network data were collected from a cohort of PWUD in rural Eastern Kentucky at baseline (2008–2010) and at four follow-up visits conducted approximately semiannually. Stochastic actor-oriented models (SAOMS) were used to model network structure and participant behaviors as jointly dependent variables and to identify characteristics associated with the maintenance, dissolution, and formation of network ties and changes in drug use behaviors. ResultsFindings suggest (1) greater network stability over time for reciprocal and transitive relationships, (2) both homophily and assimilation played a greater role in shaping injection drug use (IDU) initiation and cessation than they did in shaping heroin and stimulant use, and (3) the importance of these mechanisms appeared consistent over time. ConclusionGiven the stability of particular network structures and evidence of both homophily and assimilation with respect to drug-use behaviors, interventions that leverage social networks could be used to motivate health-promoting behaviors.

Surface Wear Monitoring System of Industrial Transformer Tap-Changer Contacts by Using Voice Signal

Surface wear of the tap-changer contacts of industrial transformers (due to frequent switching times) easily leads to operation failure of industrial transformers, which affects the safety and stability of the transmission network. In this paper, an intelligent voice signal monitoring system was proposed for the abnormal condition (surface wear) of tap-changer contacts. This monitoring system was composed of a voice signal acquisition system, voice analysis system and voice processing system. First, the voice signal of the tap-changer contacts was collected, and the collected voice signal was analyzed in the time domain and the frequency domain. Secondly, the characteristic curve of the voice signal was proposed, and the voice curve was compared with that of the normal operation state. In this case, the running state and surface wear abnormal situation of the tap changer could be monitored and determined, and the cause of the abnormal state could also be further analyzed. This method solved the surface wear problem of the tap changer in industrial transformers, which could be not monitored effectively in real time. This method improved the operational reliability of industrial transformers and had high economic and social benefits.

Symmetric Collaborative Fault-Tolerant Control of Multi-Intelligence under Long-Range Transmission in Air–Ground Integrated Wireless High-Mobility Self-Organizing Networks

With the continuous development and progress of wireless self-organizing network communication technology, how to carry out long-distance cooperative control of multiple intelligences under the framework of an air–ground integrated wireless high-mobility self-organizing network has become a hot and difficult topic that needs to be solved urgently. This paper takes the air–ground integrated wireless high-mobility self-organizing network system as the basic framework and focuses on solving the long-distance cooperative fault-tolerant control of multi-intelligent bodies and the topological stability of a wireless mobile self-organizing network. To solve the above problems, a direct neural network with a robust adaptive fault-tolerant controller is designed in this paper. By constructing a symmetric population neural network model and combining it with the Lyapunov stabilization criterion, the system feedback matrix K has the ability of autonomous adaptive learning, and symmetrically distorts, rotates, or scales the training data to instantly adjust the system’s fault-tolerant corrections and adaptive adjusting factors to resist the unknown disturbances and faults, to achieve the goals of multi-intelligent body stable control and the stable operation of a wireless high-mobility self-organizing network topology. Simulation results show that with the feedback adjustment of the multi-system under the designed controller, the multi-system as a whole has good fault-tolerant performance and autonomous learning approximation performance, and the tracking error asymptotically converges to zero. The experimental results show that the multi-flight subsystems fly stably, the air–ground integrated wireless high-mobility self-organizing network topology has good stability performance, and the maximum relative improvement of the topology stability performance is 50%.

Short-term exposure to antibiotics begets long-term disturbance in gut microbial metabolism and molecular ecological networks

BackgroundAntibiotic exposure can occur in medical settings and from environmental sources. Long-term effects of brief antibiotic exposure in early life are largely unknown.ResultsPost a short-term treatment by ceftriaxone to C57BL/6 mice in early life, a 14-month observation was performed using 16S rRNA gene-sequencing technique, metabolomics analysis, and metagenomics analysis on the effects of ceftriaxone exposure. Firstly, the results showed that antibiotic pre-treatment significantly disturbed gut microbial α and β diversities (P < 0.05). Both Chao1 indices and Shannon indices manifested recovery trends over time, but they didn’t entirely recover to the baseline of control throughout the experiment. Secondly, antibiotic pre-treatment reduced the complexity of gut molecular ecological networks (MENs). Various network parameters were affected and manifested recovery trends over time with different degrees, such as nodes (P < 0.001, R2 = 0.6563), links (P < 0.01, R2 = 0.4543), number of modules (P = 0.0672, R2 = 0.2523), relative modularity (P = 0.6714, R2 = 0.0155), number of keystones (P = 0.1003, R2 = 0.2090), robustness_random (P = 0.79, R2 = 0.0063), and vulnerability (P = 0.0528, R2 = 0.28). The network parameters didn't entirely recover. Antibiotic exposure obviously reduced the number of key species in gut MENs. Interestingly, new keystones appeared during the recovery process of network complexity. Changes in network stability might be caused by variations in network complexity, which supports the ecological theory that complexity begets stability. Besides, the metabolism profiles of the antibiotic group and control were significantly different. Correlation analysis showed that antibiotic-induced differences in gut microbial metabolism were related to MEN changes. Antibiotic exposure also caused long-term effects on gut microbial functional networks in mice.ConclusionsThese results suggest that short-term antibiotic exposure in early life will cause long-term negative impacts on gut microbial diversity, MENs, and microbial metabolism. Therefore, great concern should be raised about children’s brief exposure to antibiotics if the results observed in mice are applicable to humans.D3-AZHri_jesG1CHKmDZa1Video

Irregular network of extended defects in graphene and a new criterion of the crystal lattice melting

Investigation of graphite defects has long and rich history. However, it turned out, that most point defects in graphene, such as vacancies or topological Stone-Wales defect, have high formation energies, preventing them from formation in large quantities. Recently, we have proposed a new type of extended defects in graphene, with formation energy per atom only slightly above the melting temperature. This means, that these planar defects may occur near the melting temperature in sufficient quantities to influence the thermodynamic properties of graphene. Still, there is discrepancy between thermodynamic properties of these defects and their topology, which can be successfully resolved by proposition of a new defects' type – irregular network of extended defects, which will be described in this paper. The study of network stability from these defects enables us to formulate a new criterion of crystal lattice melting, which supersedes the Lindemann/Born criteria.

Study of Microstructure, Texture, and Cooking Qualities of Reformulated Whole Wheat Flour Pasta by Substituting Water with Stearic Acid–Candelilla Wax–Groundnut Oil Oleogel

Oleogels, which are traditionally utilized to reduce saturated and trans fats in bakery foods, have recently shown promising applications in non-bakery foods, particularly in the enhancement of their food texture and cooking qualities. This study investigates the impact of incorporating stearic acid-containing candelilla wax–groundnut oil oleogel in various proportions on the production of whole wheat pasta. Five different pasta samples were prepared by replacing water with oleogels in varying concentrations (2.5%, 5%, 10%, and 15%), and their physicochemical attributes were evaluated using a range of analytical methods for both cooked and uncooked pasta (like microscopy, colorimetry, dimensional analysis, texture, cooking qualities, moisture content, and FTIR). Significant differences in width, thickness, and color properties were observed between the control sample (0% oleogel) and those containing oleogel, with notable variations in surface texture and color intensities, particularly with the higher oleogel content (p &lt; 0.05). Cooked pasta exhibited lower L* values and higher a* values than uncooked pasta. Stereo zoom microscope and field emission scanning electron microscope (FESEM) micrographs demonstrated a change in the pasta surface topology and microstructures. Dark spots on the pasta with greater oleogel concentrations (samples with 10% and 15% oleogel replacement) suggest the formation of starch–lipid complexes. Cooking induced pore formation, which was more pronounced when the oleogel content was increased, impacted the water absorption capacity, swelling index, and moisture content. The cooked samples exhibited higher moisture content and improved polymer network stability compared to the uncooked ones, indicating the potential of oleogel incorporation to modulate pasta properties in a concentration-dependent manner. These findings underscore the versatility of oleogels when their applications are diversified in non-bakery foods to enhance food texture and quality.

The planning method of new energy distribution network in plateau area based on local accommodation

Abstract The planning method of new energy distribution network in plateau area based on local accommodation is studied to improve the local accommodation capacity of new energy distribution network in plateau area. The framework of new energy distribution network planning in the plateau area is constructed, and the distribution network equipment suitable for the plateau environment is selected based on the harsh environment in the plateau area; in the framework of new energy distribution network planning in plateau area, a bi-level planning model considering multi-flexible resource coordinated scheduling and energy storage is established. The upper level planning model takes the maximum accommodation of new energy as the objective, and the lower level planning model takes the optimal daily operation benefit of energy storage under the given configuration as the optimization objective; the probabilistic power flow method and C-PSO are used to solve the bi-level model. The experiment shows that this method ensures the stability of the new energy distribution network in the plateau area, reduces the amount of abandoned wind energy and the operating cost, and improves the local accommodation capacity of the core higher than the distribution network.

The risk of cannabis use disorder is mediated by altered brain connectivity: A chronnectome study.

The brain mechanisms underlying the risk of cannabis use disorder (CUD) are poorly understood. Several studies have reported changes in functional connectivity (FC) in CUD, although none have focused on the study of time-varying patterns of FC. To fill this important gap of knowledge, 39 individuals at risk for CUD and 55 controls, stratified by their score on a self-screening questionnaire for cannabis-related problems (CUDIT-R), underwent resting-state functional magnetic resonance imaging. Dynamic functional connectivity (dFNC) was estimated using independent component analysis, sliding-time window correlations, cluster states and meta-state indices of global dynamics and were compared among groups. At-risk individuals stayed longer in a cluster state with higher within and reduced between network dFNC for the subcortical, sensory-motor, visual, cognitive-control and default-mode networks, relative to controls. More globally, at-risk individuals had a greater number of meta-states and transitions between them and a longer state span and total distance between meta-states in the state space. Our findings suggest that the risk of CUD is associated with an increased dynamic fluidity and dynamic range of FC. This may result in altered stability and engagement of the brain networks, which can ultimately translate into altered cortical and subcortical function conveying CUD risk. Identifying these changes in brain function can pave the way for early pharmacological and neurostimulation treatment of CUD, as much as they could facilitate the stratification of high-risk individuals.

Longitudinal Stability of Reward-Related Resting-State Networks in Youth With Bipolar-I/II Disorder

Longitudinal Stability of Reward-Related Resting-State Networks in Youth With Bipolar-I/II Disorder

Semimetallic state transition in Two-Dimensional carbon nitride covalent networks and enhanced electrocatalytic activity for nitrate to ammonia conversion

Single-atom catalysts (SACs), due to their maximum atomic utilization rate, show tremendous potential for application in the electrocatalytic synthesis of ammonia from nitrate. Yet, the development of superior supports that preserve the high selectivity, activity, and stability of SACs remains an imperative challenge. In this work, based on first-principles calculations and tight-binding (TB) model analysis, a new two-dimensional (2D) carbon nitride monolayer, C7N6, is proposed. The C7N6 structure exhibits a strong covalent network, with dynamical, thermal, and mechanical stability. Surprisingly, the structural transition from C9N4 to C7N6 corresponds to a semimetallic state transition. Further symmetry analysis unveils that the Dirac states in C7N6 are protected by space–time inversion symmetry, and the physical origin of the Dirac cone was confirmed using the TB model. Additionally, a non-zero Z2 invariant and significant topological edge states demonstrate its topologically nontrivial nature. Considering the excellent structural and topological properties of C7N6, a three-step screening strategy is designed to identify eligible SACs for electrochemical nitrate reduction reaction (NO3RR), and Ti@C7N6 is identified as possessing the best activity, with the last proton-electron coupling step *NH2→*NH3 being the potential-determining step (PDS), for which the limiting potential is 0.48 V. Moreover, a free energy diagram shows that the *NOH reaction pathway is energetically preferred on Ti@C7N6, and ab initio molecular dynamics (AIMD) calculations at 500 K confirm its good thermal stability. Our study not only provides excellent CN-based support material but also offers theoretical guidance for constructing highly active and selective SACs for nitrate reduction.

Disease-induced changes in bacterial and fungal communities from plant below- and aboveground compartments

The plant microbes are an integral part of the host and play fundamental roles in plant growth and health. There is evidence indicating that plants have the ability to attract beneficial microorganisms through their roots in order to defend against pathogens. However, the mechanisms of plant microbial community assembly from below- to aboveground compartments under pathogen infection remain unclear. In this study, we investigated the bacterial and fungal communities in bulk soil, rhizosphere soil, root, stem, and leaf of both healthy and infected (Potato virus Y disease, PVY) plants. The results indicated that bacterial and fungal communities showed different recruitment strategies in plant organs. The number and abundance of shared bacterial ASVs between bulk and rhizosphere soils decreased with ascending migration from below- to aboveground compartments, while the number and abundance of fungal ASVs showed no obvious changes. Field type, plant compartments, and PVY infection all affected the diversity and structures of microbial community, with stronger effects observed in the bacterial community than the fungal community. Furthermore, PVY infection, rhizosphere soil pH, and water content (WC) contributed more to the assembly of the bacterial community than the fungal community. The analysis of microbial networks revealed that the bacterial communities were more sensitive to PVY infection than the fungal communities, as evidenced by the lower network stability of the bacterial community, which was characterized by a higher proportion of positive edges. PVY infection further increased the bacterial network stability and decreased the fungal network stability. These findings advance our understanding of how microbes respond to pathogen infections and provide a rationale and theoretical basis for biocontrol technology in promoting sustainable agriculture.Key points• Different recruitment strategies between plant bacterial and fungal communities.• Bacterial community was more sensitive to PVY infection than fungal community.• pH and WC drove the microbial community assembly under PVY infection.

Power prediction of regional distributed photovoltaic clusters with incomplete information based on improved weighted fusion and transfer learning

AbstractThe rapid expansion of low‐voltage distributed photovoltaic (PV) systems with decentralized layouts poses significant data collection challenges. The scarcity of data amplifies prediction complexities, affecting the operational security and stability of distribution networks. To enhance predictive accuracy for distributed regional PV power generation, including unmonitored low‐voltage systems, this paper proposes a novel prediction approach that combines weight optimization and transfer learning. Firstly, to more accurately extrapolate from neighbouring monitored PV to estimate unmonitored PV outputs, a novel SSA‐MLP‐XGBoost model is proposed for monitored PV power plants, in which the hyperparameters of multilayer perceptron (MLP) and (extreme gradient boosting) XGBoost models are fine‐tuned using the sparrow search algorithm (SSA), and the optimized models are synergistically integrated to heighten prediction precision for monitored plants. Secondly, based on the predicted power curves of monitored PV power plants, a weight optimization model is presented to optimize the combined weights between unmonitored and monitored power stations. Furthermore, the power generation model from monitored facilities is transplanted to unmonitored plants for power generation estimation. Ultimately, the authors combine the weight optimization and transfer learning model to get more accurate and robust model. Validation across three regional distributed photovoltaic clusters demonstrates a noteworthy improvement in prediction accuracy—20%, 7.5%, and 25% for the respective clusters compared to the existing methods.

Optimal allocation of distributed generation in meshed power networks: A metaheuristic approach

AbstractThis paper introduces a novel metaheuristic technique, a COOT‐based algorithm, to determine the optimal Distributed Generation (DG) allocation within a loop‐configured network. This method significantly narrows the optimization gap by leveraging a COOT‐based algorithm, ensuring accelerated convergence and resultant global optima. The core incentive for employing this technique is to substantially mitigate power loss, curtail voltage deviation, and bolster system stability in a loop distribution network. To attain optimal outcomes, the elaborated COOT and improved grey wolf optimization improved grey wolf optimization (IGWO) algorithms were executed on IEEE bus‐33 and 69 mesh distribution networks (MDNs) under varying power factors. The derived mathematical results effectively underscore accomplishing the stipulated objectives: a marked reduction in voltage deviation and power loss coupled with an augmentation in system stability. Notably, at unity, incorporating DGs resulted in a paramount reduction in power loss, attaining a decrease of 78% and 85% for bus‐33 and 69 MDNs, respectively. Moreover, an impressive decrease in power loss by 94% and 98% was observed at the optimal power factor for both MDNs. A comparative evaluation of the results accentuates that the proposed COOT and IGWO algorithms eclipse other documented research in performance, showcasing superior efficiency on a techno‐economic basis.

Ego–network stability or ego–network expansion? The effects of ego–network dynamics on recombinant innovation under heterogeneous knowledge base

PurposeThis paper aims to investigate the antecedents of recombinant innovation from the perspective of ego–network dynamics, and further disentangle whether ego–network stability or ego–network expansion is more conducive to recombinant innovation under heterogeneous knowledge base.Design/methodology/approachThis paper uses 1,801 patent data in China’s biotechnology field as a sample and adopts fixed effects regression model to examine the effects of ego–network dynamics on recombinant innovation and further uses the Wald tests to discern which ego–network dynamic is more conducive to recombinant innovation under heterogeneous knowledge base.FindingsThe empirical results indicate that ego–network dynamics have a positive impact on recombinant innovation. Specifically, for firms with high knowledge breadth and high knowledge depth as well as high knowledge breadth and low knowledge depth, ego–network stability is more conducive to recombinant innovation. By contrast, for firms with low knowledge breadth and high knowledge depth, recombinant innovation benefits more from ego–network expansion. As for firms with low knowledge breadth and low knowledge depth, both ego–network stability and ego–network expansion can promote recombinant innovation, while the effects are not significant.Practical implicationsThis research may enlighten managers to choose suitable ego–network dynamics strategies for recombinant innovation based on their knowledge base.Originality/valueThis research not only contributes to the literature on recombinant innovation by revealing the impact of different ego–network dynamics on recombinant innovation but also contributes to network dynamics theory by exploring whether ego–network stability or ego–network expansion is more conducive to recombinant innovation under a heterogeneous knowledge base.

Examining electrical equivalent circuit models of insulators in transmission lines under pollution conditions: a comprehensive study and analysis

This research presents a comprehensive study investigating the behavior of insulators in transmission lines under various pollution conditions. Utilizing equivalent electrical circuits, the analysis aims to understand the impact of pollution on insulator performance, which is crucial for maintaining the reliability and efficiency of power transmission systems. By providing valuable insights into the relationship between pollution and insulator behavior, this study contributes to the development of improved strategies for mitigating the effects of pollution on transmission lines, ultimately enhancing the overall safety and stability of electrical power networks. The study focuses on a digital model that enables the visualization of potential and electric field distribution within a series of glass insulators utilized in Algerian electrical grids. Initially, a parallel RC network equivalent circuit was devised and its parameters were determined through finite element analysis using Comsol Multiphysics. This equivalent circuit was subsequently integrated into ATP/EMTP software to simulate leakage currents, yielding satisfactory outcomes. The model was then incorporated into the isolator's equivalent circuit. Results from various simulations indicate that the occurrence of discharge along the dry strip impacts leakage current. Furthermore, the re-initiation of discharge on the contaminated insulator's surface is influenced by the pollutant deposit's conductivity and distribution. Therefore, understanding the pollution level is vital for accurately evaluating and sizing the insulator chain at the installation site.