Stability Of Networks Research Articles

Crucial effect of ovomucin on alkali-induced egg white gel formation: Properties, structure and facilitation mechanism

Alkali-induced preserved egg gel formation is a dynamic process that involves complex protein changes. Ovomucin (OVM) is closely associated with the gel properties of egg white. In this study, the effect of OVM in alkali-induced egg white gel (AEWG) formation was investigated. The results suggested that OVM reduced the gel formation time by 15 %. The mechanical properties of the fully formed gel were also improved by OVM. Specifically, OVM increased the storage modulus (G′) of the gel by 1.5-fold, while the hardness significantly increased from 78.90 ± 4.24 g to 99.80 ± 9.23 g. Low-field nuclear magnetic resonance (LF-NMR) demonstrated that OVM significantly shortened T23 relaxation time and reduced the water mobility, thus increasing the water holding capacity (WHC). Meanwhile, the presence of OVM resulted in a more homogeneous and denser microscopic morphology of the gel. Selective solubility experiments revealed that disulfide bonds are the primary force in gel formation. OVM promoted the formation of more disulfide bonds, which increased the strength and stability of the gel network. Overall, this research proved OVM plays a critical role in the performance improvement of AEWG, which provides a new insight into the quality control of preserved egg and protein gel foods.

Experimental and Numerical Study of Air Flow Reversal Induced by Fire in an Inclined Mine Working

Effective fire prevention in mine workings and tunnels requires a thorough theoretical analysis of the heat and mass transfer processes within these structures. This involves using established models to calculate non-isothermal air flow dynamics in long tunnels and mine workings. While the ventilation of tunnels has been extensively studied, significant challenges persist regarding mine ventilation systems, particularly due to their complex and branched topology. This study aimed to address these challenges and gaps in mine ventilation. We designed a laboratory bench to simulate an inclined mine working with a heat source (fire) and validated a mathematical model of heat and mass transfer in such settings. Using experimental measurements, we verified the model’s accuracy. It is important to note that our experimental and theoretical analyses focused solely on the thermal effects of a fire, without considering the release of harmful impurities. Using the validated model, we conducted multiparameter simulations to identify the conditions leading to the formation of a thermal slug in an inclined mine working and the subsequent reversal of air flow. The simulation data enabled us to determine the dependency of the critical heat release rate on the aerodynamic parameters of the mine working. Additionally, we evaluated the changes in average air density within a mine working at the critical heat release rate. These findings are crucial for the further development of a network-based method to analyze air flow stability in mine ventilation networks during fires.

Axo-axonic synaptic input drives homeostatic plasticity by tuning the axon initial segment structurally and functionally.

Homeostatic plasticity maintains the stability of functional brain networks. The axon initial segment (AIS), where action potentials start, undergoes dynamic adjustment to exert powerful control over neuronal firing properties in response to network activity changes. However, it is poorly understood whether this plasticity involves direct synaptic input to the AIS. Here, we show that changes of GABAergic synaptic input from chandelier cells (ChCs) drive homeostatic tuning of the AIS of principal neurons (PNs) in the prelimbic (PL) region, while those from parvalbumin-positive basket cells do not. This tuning is evident in AIS morphology, voltage-gated sodium channel expression, and PN excitability. Moreover, the impact of this homeostatic plasticity can be reflected in animal behavior. Social behavior, inversely linked to PL PN activity, shows time-dependent alterations tightly coupled to changes in AIS plasticity and PN excitability. Thus, AIS-originated homeostatic plasticity in PNs may counteract deficits elicited by imbalanced ChC presynaptic input at cellular and behavioral levels.

Advanced control and energy management algorithm for a multi-source microgrid incorporating renewable energy and electric vehicle integration

This paper presents a comprehensive study on advanced control and energy management in a multi-source/multi-load microgrid. The microgrid integrates solar panels, a wind turbine system, a Li-ion battery-based storage system, an electric vehicle (EV), and a DC load, all inter-connected through power electronic converters to a DC bus and linked to the AC grid via a DC/AC inverter. The study outlines the primary control objectives: DC bus voltage regulation, optimization of photovoltaic and wind energy conversion, and maintaining high-quality energy injection into the grid. Given the intermittent nature of solar and wind energy and the varying energy demands that affect battery life and performance, a novel energy management algorithm is introduced. This algorithm addresses different operating modes, such as Constant Current (CC) and Constant Voltage (CV), to enhance network stability, energy quality, and optimize energy distribution between the various sources and the EV. The proposed fuzzy logic controllers, known for their robustness to uncertainties, flexibility, and ease of implementation, are employed to achieve these objectives. The Particle Swarm Optimization (PSO) is used to fine-tune the fuzzy logic control parameters, ensuring optimal performance. This optimization process included minimizing Integral Absolute Error (IAE), Integral Square Error (ISE), and Integral Time-weighted Square Error (ITSE) as objective functions. Extensive simulations under various operating scenarios demonstrated significant improvements in control accuracy, stability, and system performance.

Effects of humic acid fertilizer on the growth and microbial network stability of Panax notoginseng from the forest understorey

Humic acid (HA) can substantially enhance plant growth and improve soil health. Currently, the impacts of HA concentrations variation on the development and soil quality of Panax notoginseng (Sanqi) from the forest understorey are still unclear. In this study, exogenous HA was administered to the roots of Sanqi at varying concentrations (2, 4, and 6 ml/L). Subsequently, the diversity and community structure of bacteria and fungi were assessed through high-throughput sequencing technology. The investigation further involved analyzing the interplay among the growth of sanqi, soil edaphic factors, and the microbial network stability. Our finding revealed that moderate concentrations (4 ml/L) of HA improved the fresh/dry weight of Sanqi and NO3-−N levels. Compared with control, the moderate concentrations of HA had a notable impact on the bacterial and fungal communities compositions. However, there was no significant difference in the α and β diversity of bacteria and fungi. Moreover, the abundance of beneficial bacteria (Bradyrhizobium) and harmful bacteria (Xanthobacteraceae) increased and decreased at 4 ml/L HA, respectively, while the bacterial and fungal network stability were enhanced. Structural equation model (SEM) revealed that the fresh weight of Sanqi and bacterial and fungal communities were the factors that directly affected the microbial network stability at moderate concentrations of HA. In conclusion, 4 ml/L of HA is beneficial for promoting Sanqi growth and soil quality. Our study provides a reference for increasing the yield of Sanqi and sustainable development of the Sanqi-pine agroforestry system.

Network Stability and Breakthrough Innovations

Network Stability and Breakthrough Innovations

Association between plant microbiota and cadmium uptake under the influence of microplastics with different particle sizes

Plant microbiota are an important factor impacting plant cadmium (Cd) uptake. However, little is known about how plant microbiota affects the Cd uptake by plants under the influence of microplastics (MPs) with different particle sizes. In this study, bacterial structure and assembly in the rhizosphere and endosphere in pakchoi were analyzed by amplicon sequencing of 16S rRNA genes under the influence of different particle sizes of polystyrene microplastics (PS-MPs) combined with Cd treatments. Results showed that there were no significant differences observed in the shoot endophytes among different treatments. However, compared to Cd treatment, larger-sized PS-MPs (2 and 20 μm) significantly increased community diversity and altered the structural composition of rhizosphere bacteria and root endophytes, while smaller-sized PS-MPs (0.2 μm) did not. Under the treatment of larger-sized PS-MPs, the niche breadth of rhizosphere bacteria and root endophytes were significantly increased. And larger-sized PS-MPs also maintained stability and complexity of bacterial co-occurrence networks, while smaller-sized PS-MPs reduced them. Furthermore, compared to Cd treatment, the addition of larger particle size PS-MPs decreased the proportion of homogeneous section, while increased the proportion of drift in root endophytic bacterial community assembly. The role of larger-sized MPs in the community assembly of rhizosphere bacteria was opposite. Using random forest and structural equation models, the study found that larger-sized PS-MPs can promote the colonization of specific bacterial taxa, such as Brevundimonas, AKAU4049, SWB02, Ellin6055, Porphyrobacter, Sphingorhabdus, Rhodobacter, Erythrobacter, Devosia and some other bacteria belonging to Alphaproteobacteria, in the rhizosphere and root endosphere. The colonization of these taxa can may induce the formation of biofilms in the roots, immobilize heavy metals through oxidation processes, and promote plant growth, thereby reducing Cd uptake by pakchoi. The findings of this study provide important insights into the microbial mechanisms underlying the influence of MPs with different particle sizes on plant Cd uptake.

Toward next-generation networks: A blockchain-based approach for core network architecture and roaming identity verification

With the evolution of next-generation communication networks, ensuring robust Core Network (CN) architecture and data security has become paramount. This paper addresses critical vulnerabilities in the architecture of CN and data security by proposing a novel framework based on blockchain technology that is specifically designed for communication networks. Traditional centralized network architectures are vulnerable to Distributed Denial of Service (DDoS) attacks, particularly in roaming scenarios where there is also a risk of private data leakage, which imposes significant operational demands. To address these issues, we introduce the Blockchain-Enhanced Core Network Architecture (BECNA) and the Secure Decentralized Identity Authentication Scheme (SDIDAS). The BECNA utilizes blockchain technology to decentralize data storage, enhancing network security, stability, and reliability by mitigating Single Points of Failure (SPoF). The SDIDAS utilizes Decentralized Identity (DID) technology to secure user identity data and streamline authentication in roaming scenarios, significantly reducing the risk of data breaches during cross-network transmissions. Our framework employs Ethereum, free5GC, Wireshark, and UERANSIM tools to create a robust, tamper-evident system model. A comprehensive security analysis confirms substantial improvements in user privacy and network security. Simulation results indicate that our approach enhances communication CNs security and reliability, while also ensuring data security.

The Behavioral and Positional Drivers of Network Stability: Evidence from High-Growth Software Firms

The Behavioral and Positional Drivers of Network Stability: Evidence from High-Growth Software Firms

Robust hybrid control strategy for active power management in Kabertene wind farm within Algeria’s PIAT grid

The paper introduces a hybrid control strategy for optimised active power management in Algeria's Kabertene wind farm, crucial for the pole insalah-adrar-timimoune (PIAT) grid's stability. This strategy merges simultaneous interconnection and damping assignment (SIDA) passivity theory, passivity-based control (PBC), and multivariable proportional-integral-derivative (PID) controllers. This combined approach ensures frequency and voltage stability within the PIAT grid, which encompasses various elements like wind farms, solar plants, gas turbines, and dynamic impedance (Z), current (I), and active power (P) (D-ZIP), load model. By tailoring controllers for doubly fed induction generators (DFIGs) using SIDA-PBC principles and optimising internal parameters, the strategy achieves precise control of active power output. Additionally, particle swarm optimisation (PSO) refines power scheduling, which is especially beneficial for intermittent renewable sources like DFIGs. This comprehensive strategy offers numerous advantages: improved network stability, minimized voltage deviations, reduced frequency fluctuations, and enhanced integration of renewable energy sources. The paper emphasises practical implementation considerations, providing valuable guidance for efficient Kabertene wind farm operation and integration. This research contributes significantly to fostering cleaner and more reliable energy systems, facilitating the PIAT grid's transition towards sustainable energy generation.

Spartina alterniflora invasion reduces soil microbial diversity and weakens soil microbial inter-species relationships in coastal wetlands.

Soil microorganisms play a crucial role in the plant invasion process, acting as both drivers of and responders to plant invasion. However, the effects of plant invasion on the complexity and stability of co-occurrence networks of soil microbial communities remain unclear. Here, we investigated how the invasion of Spartina alterniflora affected the diversity, composition, and co-occurrence networks of soil bacterial and fungal communities in the Yellow River Delta, China. Compared to the native plant (Suaeda salsa), S. alterniflora invasion decreased the α-diversity of soil bacterial communities but did not affect that of fungal communities. The β-diversity of soil bacterial and fungal communities under S. salsa and S. alterniflora habitats also differed dramatically. S. alterniflora invasion increased the relative abundance of the copiotrophic phylum Bacteroidota, whereas decreased the relative abundances of the oligotrophic phyla Acidobacteriota and Gemmatimonadota. Additionally, the relative abundance of Chytridiomycota, known for its role in degrading recalcitrant organic matter, increased substantially within the soil fungal community. Functional predictions revealed that S. alterniflora invasion increased the relative abundance of certain soil bacteria involved in carbon and nitrogen cycling, including aerobic chemoheterotrophy, nitrate reduction, and nitrate respiration. More importantly, S. alterniflora invasion reduced the complexity and stability of both soil bacterial and fungal community networks. The shifts in soil microbial community structure and diversity were mainly induced by soil available nutrients and soil salinity. Overall, our study highlights the profound impacts of S. alterniflora invasion on soil microbial communities, which could further indicate the modification of ecosystem functioning by invasive species.

Optimizing domestic energy management with a wild Mice colony-inspired algorithm: Enhancing efficiency and coordination in smart grids through dynamic distributed energy storage

Energy management (EM) is a critical strategy that spans the production and consumption of electricity, enhancing the stability of the electricity network. Smart grid technology significantly improves the electrical system's energy efficiency (EE), facilitating a transformation from a conventional power grid (PG) to a smart PG. This paper presents a key strategy for modeling the EE of the smart grid tailored to domestic demand, establishing smart coordination between domestic demand, energy production, and storage to reduce energy waste and costs. Our model integrates various energy sources, including renewable energy (RE), photovoltaic (PV) systems, wind power, and an energy storage system (ESS), interconnected with the PG. The model's structure ensures the coordinated flow of electricity in a residential house through an optimal control method (OCM). To develop a robust closed-loop control model, we employ Demand Response (DR) schemes within the Real-Time Electricity Pricing (RTEP) framework. We construct a dynamic model of the ESS to compute the System Performance Index (SPI), corresponding to energy costs. To enhance our model, we introduce a Dynamic Distributed Energy Storage Strategy (DDESS). Additionally, we introduce a novel optimization algorithm inspired by the behavioral patterns of wild mice, called the Wild Mice Colony (WMC). By analyzing the targeted and advantageous behaviors of wild mice in colonies, we propose that these behaviors can serve as a model for addressing complex, uncertain problems. This strategy is highly advantageous, capable of reducing total energy consumption (EC) from the main grid by over 100 % of the load demand, optimizing the energy system, and ensuring synchronization. The performance of DDESS optimizes energy flow (EF) during the repayment plan, leading to minimized EC costs from the PG.

Nutrient availability contributes to structural and functional diversity of microbiome in Xinjiang oilfield.

Indigenous microbial enhanced oil recovery (IMEOR) is a promising alternative way to promote oil recovery. It activates oil recovery microorganisms in the reservoir by adding nutrients to the injected water, utilizing microbial growth and metabolism to enhance recovery. However, few studies have focused on the impact of injected nutrients on reservoir microbial community composition and potential functions. This limits the further strategic development of IMEOR. In this study, we investigated the effects of nutrition on the composition of the reservoir bacterial community and functions in the Qizhong block of Xinjiang Oilfield, China, by constructing a long core microbial flooding simulation device. The results showed that the microbial community structure of the reservoir changed from aerobic state to anaerobic state after nutrient injection. Reducing the nutrient concentration increased the diversity and network stability of the reservoir bacterial community. At the same time, the nitrogen metabolism function also showed the same change response. Overall, these results indicated that nutrition significantly affected the community structure and function of reservoir microorganisms. Injecting low concentrations of nutrients may be more beneficial to improve oil recovery. This study is of great significance for guiding IMEOR technology and saving costs at the field site.

A month in review: longitudinal dynamics between daily PTSD symptom networks, affect, and drinking behaviors in female college students.

Sexual victimization (SV) is common among college women, with approximately half of those who have experienced SV meeting criteria for posttraumatic stress disorder (PTSD) within a year. Both SV and PTSD are associated with alcohol misuse among college women, often explained by the self-medication hypothesis. Existing literature focuses on overall PTSD severity rather than potential day-to-day fluctuations in specific symptoms, which might play a crucial role in understanding alcohol misuse risk. Studies also examine only same-day or next-day associations between PTSD and drinking, neglecting the potential for longer-term changes. This study explores the short-term longitudinal stability and time-lagged predictive dynamics of PTSD symptoms, affect, and drinking behavior among 174 female college heavy episodic drinkers over four weeks. Participants were categorized into three groups: those with a history of SV and PTSD (n = 77), women with SV but without PTSD (n = 59), and women without prior trauma history (n = 38) to be able to examine differences by trauma exposure, and PTSD. We compared the longitudinal stability of PTSD symptom networks, affect (arousal, positive affect, and negative affect), and drinking behavior across groups. Support vector regression determined which PTSD symptom networks and affect best predict drinking behavior at specific time lags within a 0-7 day range. The PTSD group showed higher longitudinal stability for PTSD symptom networks (adjusted ps <.049) and arousal (adjusted ps <.048), but lower stability for negative affect (adjusted p =.013) and drinking behavior, including alcohol cravings (adjusted p =.019) and consumption (adjusted ps =.012), compared to the comparison groups. This suggests individuals with PTSD have more stable symptoms and arousal levels but greater fluctuations in negative affect and alcohol-related behaviors. Secondary analysis revealed PTSD symptom networks optimally predicted alcohol cravings with a three-day time lag (r=.88, p <.001) and consumption with a four-day time lag (r=.82, p <.001). These findings challenge assumptions regarding immediate effects of PTSD and affect on drinking behavior and underscore the need for therapeutic approaches that consider longer-range effects. Future research should expand on these findings by incorporating longer-range assessments and exploring a broader range of symptom interactions.

Dynamic Silos: Increased Modularity and Decreased Stability in Intraorganizational Communication Networks During the COVID-19 Pandemic

Workplace communications around the world were drastically altered by the COVID-19 pandemic, related work-from-home orders, and the rise of remote work. To understand these shifts, we analyzed aggregated, anonymized metadata from over 360 billion emails within 4,361 organizations worldwide. By comparing month-to-month and year-over-year metrics, we examined changes in network community structures over the 24 months before and after COVID-19. We also examined shifts across multiple communication media (email, instant message, video call, and calendaring software) within a single global organization and compared them with communications shifts that were driven by changes in formal organizational structure. We found that in 2020, organizations around the world became more siloed than in 2019, evidenced by increased modularity. This shift was concurrent with decreased stability within silos. Collectively, our analyses indicate that following the onset of COVID-19, employees’ communications began to shift more dynamically between subcommunities (teams, workgroups, or functional areas). At the same time, once in a subcommunity, they limited their communication to other members of that community. We term these network changes dynamic silos. We provide initial insights into the meaning and implications of dynamic silos for the future of work. This paper was accepted by Lamar Pierce, organizations. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.02797 .

Improving islanded distribution system stability with adaptive decision-making framework

Abstract In an integrated distribution system incorporating distributed generation (DG), various technical challenges must be addressed when the grid becomes disconnected and transforms into an islanded system. The main focus in such circumstances revolves around ensuring the stability of the islanded network. This study presents an advanced decision-making framework for supporting islanded networks by integrating metaheuristic Black Widow Optimization (BWO) and the rate of change of the voltage stability index (RoCVSI). The Rate of Change of the Voltage Stability Index (RoCVSI) detects instability in islanded networks by continuously monitoring rapid changes in the voltage stability margin. Upon identifying potential instability, an advanced decision-making strategy utilizing the Black Widow Optimization (BWO) algorithm is deployed. BWO generates multiple load-shedding scenarios and evaluates their impact on system stability, iteratively refining the solutions through processes similar to selection and cannibalism in black widow spiders. The optimal load-shedding strategy is then implemented to selectively shed specific loads, thereby reducing demand and enhancing island stability. The proposed scheme’s effectiveness for islanded network stability is assessed by extensively analyzing the IEEE 33-bus system. The efficiency of the proposed approach is confirmed through a comparative analysis, with results indicating the better efficiency of the proposed method in the islanded network.

Community metagenomics reveals the processes of cadmium resistance regulated by microbial functions in soils with Oryza sativa root exudate input

Plants exert a profound influence on their rhizosphere microbiome through the secretion of root exudates, thereby imparting critical effects on their growth and overall health. The results unveil that japonica rice showcases a remarkable augmentation in its antioxidative stress mechanisms under Cd stress. This augmentation is characterized by the sequestration of heavy metal ions within the root system and the prodigious secretion of a spectrum of flavonoids, including Quercetin, Luteolin, Apigenin, Kaempferide, and Sakuranetin. These flavonoids operate as formidable guardians, shielding the plant from oxidative damage instigated by Cd-induced stress. Furthermore, the metagenomic analyses divulge the transformative potential of flavonoids, as they induce profound alterations in the composition and structural dynamics of plant rhizosphere microbial communities. These alterations manifest through the recruitment of plant growth-promoting bacteria, effectively engineering a conducive milieu for japonica rice. In addition, our symbiotic network analysis discerns that flavonoid compounds significantly improved the positive correlations among dominant species within the rhizosphere of japonica rice. This, in turn, bolsters the stability and intricacy of the microenvironmental ecological network. KEGG functional analyses reveal a notable upregulation in the expression of flavonoid functional genes, specifically cadA, cznA, nccC, and czrB, alongside an array of transporters, encompassing RND, ABC, MIT, and P-ATPase. These molecular orchestrations distinctly demarcated the rhizosphere microbiome of japonica rice, markedly enhancing its tolerance to Cd-induced stress. These findings not only shed light on the establishment of Cd-resistant bacterial consortia in rice but also herald a promising avenue for the precise modulation of plant rhizosphere microbiomes, thereby fortifying the safety and efficiency of crop production.

Making platform firms’ competitive advantage sustainable: The roles of network orchestration capabilities and collaborative innovation

Currently, platform firms lack a sustainable competitive advantage (SCA). Most studies on platform firms’ SCA have adopted a market, rather than a resource, perspective. However, compared with traditional firms, platform firms are highly dependent on external resources, establish close ties with network members. Thus, this research introduces Network Orchestration Theory, defines platform firms as sponsor–orchestrators, rather than player–orchestrators as in existing studies, and develops a theoretical model in which network orchestration capabilities enhance SCA via collaborative innovation, and environmental complexity moderates the main effect. The regression results from Chinese platform firms show that network orchestration capabilities (knowledge mobility, innovation appropriability and network stability) enhanced platform firms’ SCA, collaborative innovation mediated, and environmental complexity weakened the positive effect of innovation appropriability on SCA. This research provides suggestions for platform firms’ SCA, supports research on the classification of network orchestrators, and advances platform research and Network Orchestration Theory.

Psychopathology symptoms of frontline nurses under sudden public health crisis: A network analysis.

Public health crises can significantly impact the emotional well-being of healthcare workers. Network analysis is a novel approach to exploring interactions between mental disorders at the symptom level. This study aimed to elucidate the characteristics of post-traumatic stress disorder (PTSD), anxiety, depression, and insomnia symptoms network among frontline nurses under sudden public health crisis. A cross-sectional survey was conducted online among 556 frontline nurses through convenience sampling in Hubei Province, China, from 21 February 2020, to 10 March 2020. Symptoms of PTSD, anxiety, depression, and insomnia were assessed by the Post-Traumatic Stress Disorder Checklist (PCL-5), Generalized Anxiety Disorder scale (GAD-7), Patient Health Questionnaire (PHQ-9) and Insomnia Severity Index, respectively. Central symptoms (the most important symptoms, activation has the strongest influence the other nodes) and bridge symptoms (nodes where deactivation can prevent activation from spreading from one disorder to another) were identified via centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. We found that the correlation between PHQ-9 item 9 'suicidal thoughts' and PCL-5 item 16 'reckless or self-destructive behaviour' was the strongest. Moreover, 'reckless or self-destructive behaviour' was the strongest central symptom, and PHQ-9 item 3 'sleep problems' was the most important bridge symptom. Other major symptoms included GAD-7 item 6 'uncontrollable anxiety' and PHQ-9 item 2 'depressed or sad mood'. Timely, systemic targeting interventions on central symptoms and bridge symptoms may effectively alleviate co-occurring experiences of psychopathological symptoms among frontline nurses.

Assessing Voltage Stability in Distribution Networks: A Methodology Considering Correlation among Stochastic Variables

Distributed photovoltaic (PV) output exhibits strong stochasticity and weak adjustability. After being integrated with the network, its interaction with stochastic loads increases the difficulty of assessing the distribution network’s static voltage stability (SVS). In response to this issue, this article presents a probabilistic assessment method for SVS in a distribution network with distributed PV that considers the bilateral uncertainties and correlations on the source and load sides. The probabilistic models for the uncertain variables are established, with the correlation between stochastic variables described using the Copula function. The three-point estimate method (3PEM) based on the Nataf transformation is used to generate correlated samples. Continuous power flow (CPF) calculations are then performed on these samples to obtain the system’s critical voltage stability state. The distribution curves of critical voltage and load margin index (LMI) are fitted using Cornish-Fisher series. Finally, the utility function is introduced to establish the degree of risk of voltage instability under different scenarios, and the SVS assessment of the distribution network is completed. The IEEE 33-node distribution system is utilized to test the method presented, and the results across various scenarios highlight the method’s effectiveness.