Stability Of Networks Research Articles

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.

Demand response strategies for air-conditioning systems based on direct load control: Considering envelope structures

In recent years, the surge in rural air-conditioning (AC) electricity consumption has led to an overall increase in peak loads in both winter and summer, presenting a major challenge to the reliability and stability of low-voltage distribution networks. Demand response (DR) strategy not only relieves the peak load, but also reduces the energy consumption and achieves the purpose of energy saving and emission reduction. Among the many DR strategies, direct load control (DLC) stands out as an effective way to quickly reduce building AC loads. Meanwhile, the building envelope is also one of the important factors affecting the AC load. A typical rural house in the Xi’an area of Shaanxi Province was used as a case study. A ground source heat pump system was developed, installed and used as an experimental platform in order to aid the study. Therefore, in this study, the combination of external envelope and DLC strategies was investigated to establish a simulation model of energy consumption in rural building complexes. Two DLC load control strategies were implemented under different external envelope conditions for annual AC energy simulation. The results show that the thickness of the external insulation layer has the greatest impact on the AC load of the building compared to the window-wall ratio and orientation. With a thickness of 0 mm–75 mm insulation, the DCCM strategy can effectively reduce AC energy consumption by 16%–18%; With a thickness of 0 mm–75 mm insulation, the TSCM strategy can effectively reduce AC energy consumption by 5%–8%.

Facial length and angle feature recognition for digital libraries.

With the continuous progress of technology, facial recognition technology is widely used in various scenarios as a mature biometric technology. However, the accuracy of facial feature recognition has become a major challenge. This study proposes a face length feature and angle feature recognition method for digital libraries, targeting the recognition of different facial features. Firstly, an in-depth study is conducted on the architecture of facial action networks based on attention mechanisms to provide more accurate and comprehensive facial features. Secondly, a network architecture based on length and angle features of facial expressions, the expression recognition network is explored to improve the recognition rate of different expressions. Finally, an end-to-end network framework based on attention mechanism for facial feature points is constructed to improve the accuracy and stability of facial feature recognition network. To verify the effectiveness of the proposed method, experiments were conducted using the facial expression dataset FER-2013. The experimental results showed that the average recognition rate for the seven common expressions was 97.28% to 99.97%. The highest recognition rate for happiness and surprise was 99.97%, while the relatively low recognition rate for anger, fear, and neutrality was 97.18%. The data has verified that the research method can effectively recognize and distinguish different facial expressions, with high accuracy and robustness. The recognition method based on attention mechanism for facial feature points has effectively optimized the recognition process of facial length and angle features, significantly improving the stability of facial expression recognition, especially in complex environments, providing reliable technical support for digital libraries and other fields. This study aims to promote the development of facial recognition technology in digital libraries, improve the service quality and user experience of digital libraries.

MURE: Multi-layer real-time livestock management architecture with unmanned aerial vehicles using deep reinforcement learning

In recent years, the combination of unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs) has gained popularity in livestock management (LM) due to energy constraints and network instability. Limited energy storage of sensor nodes (SNs) and the possibility of packet loss contribute to fast energy consumption and unstable networks, respectively. UAVs serve as relay nodes and data sinks, addressing these issues by temporarily storing data to reduce SN workload and establishing mobile nodes for network stability. We propose two innovations based on previous work: 1) We introduce a multi-layer wireless network architecture, categorizing UAVs into two layers based on their functions including data collection and data processing. This enhances task parallelization, bridging performance gaps among multiple UAVs; 2) We overcome the mobility limitation of SNs, considering their real-time movement in the network. Through deep reinforcement learning, UAVs learn to cooperatively locate moving SNs. This accounts for the inevitable mobility of livestock in the industry. Additionally, we simulate the environment and compare our approach to traditional methods, evaluating metrics such as collected data per timestep (DCPS), energy consumed per timestep (ECPS), and network stability (NS). Experimental results demonstrate that our method outperforms traditional approaches, achieving a data collecting gain of 4.84% and 8.20% compared to the methods without considering SN mobility or the multi-layer characteristics of WSNs, respectively. Under energy consumption limits, our method yields energy savings of 3.00% and 1.35% respectively. Furthermore, we extensively study and validate our method against other path planning algorithms, including genetic particle swarm optimization (GPSO), modified central force optimization (MCFO), and rapidly-exploring random trees (RRT). Our approach surpasses these methods in terms of data collecting efficiency and network stability.

Elemental evolution characteristics and influencing factors of green infrastructure network in karst mountain cities: a case study of Qianzhong urban agglomeration in Southwest China

BackgroundThe urban green infrastructure (GI) network is an important conduit for ecological flows and plays a crucial role in improving regional habitats, especially in karst areas that are highly ecologically fragile and sensitive. However, the existing research only focuses on the construction of GI network in karst mountain cities, and the evolution characteristics of its elements and driving mechanism are not clear, which is of great significance for guiding urban land use planning and comprehensively improving the quality of the ecological environment. In view of this, this study took Qianzhong urban agglomeration as the study area, based on multi-source data, and identified ecological sources through ecological resilience analysis. Considering the special geographic environment, the rock exposure rate factor was added to correct the resistance surface, and the minimum cumulative resistance (MCR) and gravity model were coupled to extract the GI network. The complex network topology characterization parameter was introduced to assess the spatial and temporal variations of ecological sources and corridors. Finally, the geographical detector was used to identify the dominant influencing factors and interactions of the spatial distribution of the GI network.ResultsThe results showed that from 2000 to 2020, the condition of GI network elements in the study area presented a decreasing and then an increasing trend. The ecological sources or corridors in highly urbanized areas were critical for ecological flow transport and the overall structural stability of the GI network. The influence of natural factors on the spatial distribution of the GI network gradually weakened, and the influence of human factors continuously increased. The spatial distribution of the GI network was influenced by multiple factors, and the interaction between all the factors was enhanced, which gradually changed from the interaction of natural factors to the interaction of human factors during the study period.ConclusionsThe research results will provide scientific references for the construction of an ecologically safe environment and sustainable development of karst mountain cities.

Changes in soil bacterial community diversity and its network stability under different grazing intensities in plateau rangelands

Grassland degradation is a significant ecological challenge, it changes the grassland environment, including the structure of soil microbial communities. Grazing is a critical factor in this process, yet the impact of different grazing intensities on soil bacterial communities, particularly in unique environments like the Qinghai-Tibet Plateau, remains poorly understood. This study utilized a full-length 16 S rRNA gene sequencing technology combined with environmental factors to assess the impacts of the Holba sheep grazing on soil bacterial community diversity in Zhongba County, Shigatse City, Qinghai-Tibet Plateau. The specific aim was to explore the changes in soil bacterial community diversity and stability under contrasting grazing intensities: prohibited grazing, light grazing, moderate grazing, and heavy grazing. The results showed that grazing activities significantly altered the diversity of bacterial communities, particularly under light and heavy grazing. The spatial dissimilarity in soil bacterial communities was mainly driven by species turnover (i.e., species replacement), which was more pronounced under heavy grazing. The number of keystone species increased with the grazing intensity of the bacterial co-occurrence network. Under heavy grazing, bacterial communities showed the highest number of keystone species and the community stability. Environmental factors such as exchangeable magnesium (EM), soil available kalium (SAK), and soil pH were the most important environmental variables impacting changes in bacterial community structure by influencing community α-diversity and stability. This study enhances our understanding of the response mechanisms of soil microbial communities to grazing intensities and other environmental stresses in grassland ecosystems. In addition, this study also provides important references and guidance for microbial diversity research in extreme environments. These findings are crucial for promoting the sustainable management and conservation of grassland ecosystems in climatically harsh areas such as the Qinghai-Tibet Plateau.

Earthworms alleviate microplastics stress on soil microbial and protist communities

Microplastic (MP) pollution can exert significant pressure on soil ecosystems, however, the interactive effects of MPs on soil bacterial, fungal and protist communities remains poorly understood. Soil macrofauna, such as earthworms, can be directly affected by MPs, potentially leading to a range of feedbacks on the soil microbial community. To address this, we conducted a microcosm experiment to examine the effects of conventional (i.e., polyethylene, polystyrene) and biodegradable MPs (i.e. PBAT, polylactic acid) on the structure of the soil bacterial, fungal, and protist communities in the presence or absence of earthworms. We found that MP contamination negatively affected the diversity and composition of soil microbial and protist communities, with smaller-sized conventional MPs having the most pronounced effects. For example, compared with the unamended control, small-sized polyethylene MPs both significantly reduced the Shannon diversity of soil bacteria, fungi, and protist by 4.3 %, 37.0 %, and 9.1 %, respectively. Biodegradable MPs increased negative correlations among bacteria, fungi, and protists. However, earthworms mitigated these effects, enhancing the diversity and altering the composition of these communities. They also increased the niche width and stability of the soil microbial food web network. Our study indicated that earthworms help attenuate the response of soil microorganisms to MPs stress by influencing the diversity and composition of soil microorganisms and soil physicochemical properties and underscores the importance of considering macrofauna in MPs research.

Alleviating Coral Thermal Stress via Inoculation with Quorum Quenching Bacteria.

In the background of global warming, coral bleaching induced by elevated seawater temperature is the primary cause of coral reef degradation. Coral microbiome engineering using the beneficial microorganisms for corals (BMCs) has become a hot spot in the field of coral reef conservation and restoration. Investigating the potential of alleviating thermal stress by quorum quenching (QQ) bacteria may provide more tools for coral microbial engineering remediation. In this study, QQ bacteria strain Pseudoalteromonas piscicida SCSIO 43740 was screened among 75 coral-derived bacterial strains, and its quorum sensing inhibitor (QSI) compound was isolated and identified as 2,4-di-tert-butylphenol (2,4-DTBP). Then, the thermal stress alleviating potential of QQ bacteria on coral Pocillopora damicornis was tested by a 30-day controlled experiment with three different treatments: control group (Con: 29°C), high temperature group (HT: 31°C), and the group of high temperature with QQ bacteria inoculation (HTQQ: 31°C + QQ bacteria). The results showed that QQ bacteria SCSIO 43740 inoculation can significantly mitigate the loss of symbiotic algae and impairment of photosynthesis efficiency of coral P. damicornis under thermal stress. Significant difference in superoxide dismutase (SOD) and catalase (CAT) enzyme activities between HT and HTQQ was not observed. In addition, QQ bacteria inoculation suppressed the coral microbial community beta-dispersion and improved the stability of microbial co-occurrence network under thermal stress. It was suggested that QQ bacteria inoculation can alleviate coral thermal stress via reshaping microbial interaction and maintain community stability of coral microbiome. This study provided new evidence for the probiotic function of QQ bacteria in corals, which shedding light on the development of new microbiological tools for coral reef conservation.

One-Bit Function Perturbation Impact on Robust Set Stability of Boolean Networks with Disturbances

This paper investigates the one-bit function perturbation (OBFP) impact on the robust set stability of Boolean networks with disturbances (DBNs). Firstly, the dynamics of these networks are converted into the algebraic forms utilizing the semi-tensor product (STP) method. Secondly, OBFP’s impact on the robust set stability of DBNs is divided into two situations. Then, by constructing a state set and defining an index vector, several necessary and sufficient conditions to guarantee that a DBN under OBFP can stay robust set stable unchanged are provided. Finally, a biological example is proposed to demonstrate the effectiveness of the obtained theoretical results.

Intelligent Routing Approaches Based on Ant Colony Optimization for Dynamic Internet of Things Network

Introduction: This paper introduces two meta-heuristic approaches utilizing Swarm Intelligence and ant colony optimization techniques. The strategy comprises applying smart routing technology to optimize a dynamic IoT network computed path. Method: The issue of route selection to achieve the target and critical factors such as network energy, left energy in each gadget, run out IoT nodes count has been explored. After rigorous iterations extending up to 1000, the simulation has yielded results for two distinctive routing approaches. The ABED (ACO- Breadth first search- Euclidean- Dynamic) and the ADED (ACODijkstra algorithm -Euclidean- Dynamic) have simulated and compared their network efficiencies using MATLAB. Results: At node 200, ABED exhibits a performance advantage over ADED of 1.6%. This efficiency differential between ABED and ADED expands to 2.9% at 300 nodes and further to 2.6% at 400 nodes. Furthermore, ABED showcases superior network stability in routing techniques compared to ADED. Specifically, ABED's routing technique achieves a more consistent network compared to ADED. Conclusion: In networks comprising 500 nodes, ABED surpasses ADED by 13.33% in the context of HND (Half Node Dead) and by 6.7% in the case of LND (Last Node Dead).