Stability Of Networks Research Articles

A clustering coefficient structural entropy of complex networks

The structural entropy is a quantification of the topological structural complexity of the static complex networks, which is defined based on the structural characteristics and the Shannon entropy. For instance, the ’degree structural entropy’ is based on the network’s ’first-order’ topological properties: the degree of each node. The ’betweenness structural entropy’ is based on the betweenness centrality of nodes, which is a global topological structural property of static complex networks. The two different structural entropy give two completely different views of the network’s topological structural complexity. However, a ’mesoscopic’ structural entropy is still missing in the network theory. In this work, a clustering coefficient structural entropy of complex networks is proposed to quantify the structural complexity of static networks on the mesoscopic scale. The effectivity of the proposed ’mesoscopic’ structural entropy is verified in a series of networks that grow from two different seed networks under the Barabási–Albert and Erdős–Rényi rules. We also find that the quantification of structural entropy effectively reflects the impact of structural heterogeneity on the growth rule in the early stages of seed network growth. Finally, we observe that the structural ratio of the clustering coefficient structural entropy and degree structural entropy remains stable and unchanged when network growth reaches maturity. We also note that the convergence rate of the network’s structural entropy ratio varies under different guiding rules. These findings suggest that the differences in structural entropy can serve as a novel tool for measuring the stability of complex networks and provide fresh insights into achieving a ’balanced’ state in the dynamic evolution of complex networks.

Six Pulse Type Segmented Thyristor Controlled Reactor with Fixed Capacitor for Reactive Power Compensation

This article presents a Six Pulse Type Segmented Thyristor Controlled Reactor (TCR) integrated with a Fixed Capacitor (FC) for reactive power compensation. The primary objective is to improve voltage stability and power factor in electrical networks, addressing issues related to reactive power imbalance and harmonic distortion. The proposed configuration combines the advantages of segmented TCR and FC, providing a flexible and efficient approach to reactive power management. The novelty of this work lies in the segmentation of the TCR, which enhances the dynamic control of reactive power by allowing more precise regulation and reduced harmonics compared to conventional TCR systems. This segmented approach also minimizes switching losses and thermal stress on thyristors, leading to enhanced reliability and longevity of the system. Additionally, the integration of a fixed capacitor optimizes the overall power factor correction, contributing to improved system efficiency. Key findings from simulation and experimental results demonstrate that the Six Pulse Type Segmented TCR with FC significantly reduces reactive power, stabilizes voltage levels, and effectively suppresses harmonics within permissible limits, adhering to IEEE standards. The system shows a marked improvement in power quality, making it a viable solution for industrial applications where reactive power control is critical. This innovative approach not only provides superior compensation characteristics but also offers a scalable and adaptable framework for modern power systems, highlighting its potential to enhance operational performance and energy efficiency in various electrical grids.

Plasma-assisted particle deposition manufacturing: Multi-functional integrated superhigh temperature thermal protection coating on niobium alloy

Multi-functional integrated thermal protection coating is a promising approach for the high-temperature protection of niobium alloy while facing multiple extremely harsh environments, while hard to avoid the complex/multi-step preparation process. Particularly, a simultaneous demonstration of multi-functional features is still challenging. Herein, a novel HfC-HfO2-MoSi2-Yb2O3 multi-functional layer has been fabricated on the NbSi2 layer surface via plasma-assisted particle deposition manufacturing, endowing the modified silicide-based multilayer composite coating with multiple thermal protective characteristics. The composite coating shows excellent hot corrosion resistance with a corrosion gain of 3.56 mg cm−2 after 200 h, the intact coating structure after three thermal cycles of fast rise and fall from 25 °C–1800 °C, and a high thermal emissivity of above 0.9, as well as the good high-temperature oxidation resistance and ablation resistance demonstrated in our previous study. The superior multiple thermal protective characteristics are attributed to the synergistic effects of multi-functional particles. HfC particle provides the anti-ablation skeleton, MoSi2 particle provides more SiO2 glass phase and seals defects, Yb2O3 particle acts as the stabilizer of glass network, and matching vibration absorption of multiphase/multi-chemical bonds endow the high emissivity of coating. Our work paves the new way and provides an inexpensive and environmentally friendly approach for the development of a new class of multi-functional integrated thermal protection materials.

Microbial Inoculants Drive Changes in Soil and Plant Microbiomes and Improve Plant Functions in Abandoned Mine Restoration.

The application of microbial inoculants holds promise for the sustainable restoration of abandoned mine sites by affecting soil nutrients and microbial communities. However, the responses of plant microbial communities to microbial inoculants in mine restoration remain largely unknown. To bridge this knowledge gap, we conducted a 4-year field experiment at an abandoned carbonate mine site to assess the impacts of microbial inoculants on the soil-plant microbiome. Our findings revealed that microbial inoculants significantly changed roots, fine root bacterial and fungal communities. Further, no significant correlations were observed between the soil-plant nutrient content (Z-score) and microbial alpha diversity. However, a significantly positive correlation was found between the relative abundance of the keystone ecological cluster (Module #1) and soil-plant nutrient content. The application of microbial inoculants also increased complexity, albeit decreased stability of plant microbiome networks, alongside a reduction in stochastic assembly. Conversely, they decreased the complexity but increased the stability of soil microbiome networks, accompanied by an increase in stochastic assembly. Notably, the number of specifically enriched microbiome functional traits of roots and root nodules under the microbial inoculant treatments surpassed that of the control. In summary, our findings underscored the potential of microbial inoculants to enhance soil-plant functionality at abandoned mine restoration sites.

Identification and Counting of Pirapitinga Piaractus brachypomus Fingerlings Fish Using Machine Learning.

Identifying and counting fish are crucial for managing stocking, harvesting, and marketing of farmed fish. Researchers have used convolutional networks for these tasks and explored various approaches to enhance network learning. Batch normalization is one technique that improves network stability and accuracy. This study aimed to evaluate machine learning for identifying and counting pirapitinga Piaractus brachypomus fry with different batch sizes. The researchers used one thousand photographic images of Pirapitinga fingerlings, labeled with bounding boxes. They trained the adapted convolutional network model with batch normalization layers added at the end of each convolution block. They set the training to one hundred and fifty epochs and tested batch sizes of 5, 10, and 20. Furthermore, they measured network performance using precision, recall, and mAP@0.5. Models with smaller batch sizes performed less effectively. The training with a batch size of 20 achieved the best performance, with a precision of 96.74%, recall of 95.48%, mAP@0.5 of 97.08%, and accuracy of 98%. This indicates that larger batch sizes improve accuracy in detecting and counting pirapitinga fry across different fish densities.

Biocontrol agents transform the stability and functional characteristics of the grape phyllosphere microenvironment.

The spread of grape leaf diseases has a negative impact on the sustainable development of agriculture. Diseases induced by Uncinula necator significantly affect the quality of grapes. Bacillus biocontrol agents have been proven effective in disease management. However, limited research has been conducted on the impact of biocontrol agents on the assembly and potential functions of plant phyllosphere microbial communities. This study used high-throughput sequencing combined with bioinformatics analysis and culture omics technology for analysis. The results showed that biocontrol bacteria B. subtilis utilized in this study can significantly reduce the disease index of powdery mildew (p<0.05); concurrently, it exhibits a lower disease index compared to traditional fungicides. A comprehensive analysis has revealed that biocontrol bacteria have no significant impact on the diversity of phyllosphere fungi and bacteria, while fungicides can significantly reduce bacterial diversity. Additionally, biocontrol agents can increase the complexity of fungal networks and enhance the degree of modularity and stability of the bacterial network. The results also showed that the biocontrol agents, which contained a high amount of B. subtilis, were able to effectively colonize the grapevine phyllosphere, creating a microenvironment that significantly inhibits pathogenic bacteria on grape leaves while enhancing leaf photosynthetic capacity. In conclusion, biocontrol agents significantly reduce the grape powdery mildew disease index, promote a microenvironment conducive to symbiotic microorganisms and beneficial bacteria, and enhance plant photosynthetic capacity. These findings provide a basis for promoting biocontrol agents and offer valuable insights into sustainable agriculture development.

Increasing tree phylogenetic diversity stimulates microbial functional potential in a subtropical forest

Abstract Soil microbial functions are closely related to ecosystem productivity, carbon sequestration and their responses to global change. Tree phylogenetic diversity has been found to impact microbial community composition, diversity, and functions, but how it modulates the linkage between microbial community facets and functions remains unclear. Here, 45 plots covering a natural gradient of tree phylogenetic diversity (TPD) were selected in a subtropical forest of southwest China to explore how increasing TPD impacts soil microbial community facets and microbial functional potential. The microbial functional potential was evaluated based on the abundances of carbon, nitrogen, and phosphorus cycling-related functional genes. Soil fungal alpha diversity increased significantly, but bacterial alpha diversity did not change as TPD increased. Both soil microbial network complexity and stability improved significantly with increasing TPD. Ultimately, increasing TPD promoted soil microbial functional potential by stimulating soil carbon and nitrogen availability, microbial keystone diversity, and network stability collectively. These findings emphasize the critical roles of keystone taxa and network stability as microbial factors in stimulating soil microbial function in response to increasing TPD. Therefore, it is strongly recommended to increase TPD in order to stimulate soil microbial functions and other ecosystem functions when implementing afforestation or ecological restoration projects.

Enhancing Adversarial Robustness through Stable Adversarial Training

Deep neural network models are vulnerable to attacks from adversarial methods, such as gradient attacks. Evening small perturbations can cause significant differences in their predictions. Adversarial training (AT) aims to improve the model’s adversarial robustness against gradient attacks by generating adversarial samples and optimizing the adversarial training objective function of the model. Existing methods mainly focus on improving robust accuracy, balancing natural and robust accuracy and suppressing robust overfitting. They rarely consider the AT problem from the characteristics of deep neural networks themselves, such as the stability properties under certain conditions. From a mathematical perspective, deep neural networks with stable training processes may have a better ability to suppress overfitting, as their training process is smoother and avoids sudden drops in performance. We provide a proof of the existence of Ulam stability for deep neural networks. Ulam stability not only determines the existence of the solution for an operator inequality, but it also provides an error bound between the exact and approximate solutions. The feature subspace of a deep neural network with Ulam stability can be accurately characterized and constrained by a function with special properties and a controlled error boundary constant. This restricted feature subspace leads to a more stable training process. Based on these properties, we propose an adversarial training framework called Ulam stability adversarial training (US-AT). This framework can incorporate different Ulam stability conditions and benchmark AT models, optimize the construction of the optimal feature subspace, and consistently improve the model’s robustness and training stability. US-AT is simple and easy to use, and it can be easily integrated with existing multi-class AT models, such as GradAlign and TRADES. Experimental results show that US-AT methods can consistently improve the robust accuracy and training stability of benchmark models.

Mittag-Leffler Stability and Synchronization of Multi-delayed Fractional Neural Networks via Halanay Inequality

Mittag-Leffler Stability and Synchronization of Multi-delayed Fractional Neural Networks via Halanay Inequality

Coordination of smart inverter-enabled distributed energy resources for optimal PV-BESS integration and voltage stability in modern power distribution networks: A systematic review and bibliometric analysis

Integrating photovoltaic (PV) and battery energy storage systems (BESS) in modern power distribution networks presents opportunities and challenges, particularly in maintaining voltage stability and optimizing energy resources. This systematic review and bibliometric analysis investigates the coordination of smart inverter-enabled distributed energy resources (DERs) for enhancing PV-BESS integration and ensuring voltage stability. The study synthesizes recent advancements in smart inverter technologies, which provide grid support functions such as Volt/VAr control, and their applications in DER coordination. A comprehensive review of the literature is conducted to identify prevailing trends, research gaps, and emerging techniques in the field. Bibliometric analysis is employed to quantify the research landscape, highlighting key publications, citations, publications per country, and collaborative networks. The findings reveal that smart inverters play a crucial role in mitigating voltage violations and improving the hosting capacity of PV systems in distribution networks. Furthermore, optimal inverter settings, strategic placement of PV-BESS, and advanced control algorithms are identified as critical factors for effective DER integration. The study concludes by proposing future research directions, including the exploration of smart inverter interactions with legacy grid management systems and the development of robust algorithms for dynamic and adaptive DER coordination. This review serves as a valuable resource for researchers and practitioners aiming to enhance the stability and efficiency of power distribution networks through advanced DER management strategies.

The Problem of Power Variations in Wind Turbines Operating under Variable Wind Speeds over Time and the Need for Wind Energy Storage Systems

One of the most important and efficient sources of green electricity is catching air currents through wind turbine technology. Wind power plants are located in areas where the energy potential of the wind is high but it varies. The time variation of the wind generates fluctuations in the power produced by the wind farms that is injected into the grid. This elevates, depending on the intensity, problems of network stability and the need for balancing energy, thus raising both technical and cost issues. The present paper analyzes the behavior of a wind turbine (WT) over time in varying wind speed conditions, highlighting that without automation algorithms, a WT is far from the operation at the maximum power point (MPP). However, even when it is brought to operate at MPP, there are still significant variations in the power injected into the network. These power variations can be compensated if the wind system has energy storage facilities for the captured wind. All of these assumptions are analyzed using improved mathematical models and processed in simulations, with experimental data used as input from a wind turbine with an installed power of 2.5 [MW] in operation on the Romanian Black Sea coastal area. Consequently, the paper demonstrates that during an operation in the optimal area, from an energy perspective, the wind turbine’s maximum power point requires a storage system for the captured wind energy.

A Decision Framework for Enhancing Adhoc Network Stability and Security

A Decision Framework for Enhancing Network Stability and Security. In the modern digital era, the growing complexity of networks and increasing frequency of cyber threats demand robust strategies for ensuring network stability and security. This paper proposes a decision framework that integrates real-time analytics, proactive monitoring, and adaptive response mechanisms to enhance network resilience against failures, attacks, and performance degradation. The framework leverages advanced machine learning algorithms, network flow analysis, and security protocols to dynamically adjust to network conditions and mitigate risks .The decision framework operates in two key dimensions: Stability: It focuses on ensuring uninterrupted network performance by optimizing resource allocation, traffic management, and fault tolerance mechanisms. By analyzing network traffic patterns and identifying potential bottlenecks or vulnerabilities, the framework makes proactive decisions to reroute traffic, adjust bandwidth, and prioritize critical data flows.Security: The framework enhances security by detecting potential threats, such as Distributed Denial of Service (DDoS) attacks, unauthorized access, or malware propagation. Using a combination of intrusion detection systems (IDS), firewalls, and behavioral anomaly detection, it identifies threats in real-time and implements automatic countermeasures, such as isolating affected network segments, patching vulnerabilities, or blocking malicious traffic.A key innovation of this framework is its use of multi-criteria decision-making (MCDM) techniques to balance trade-offs between network performance and security in real time. The model continuously evaluates factors such as latency, throughput, and risk exposure to make informed, optimal decisions that ensure both stability and protection. Furthermore, the framework adapts to evolving network conditions using reinforcement learning, allowing it to learn from past incidents and improve its decision-making over time.Simulation results demonstrate that the proposed framework significantly reduces network downtime, improves threat detection response times, and mitigates the impact of security breaches. This decision framework presents a scalable solution for modern, dynamic networks, offering enhanced protection while maintaining high performance in the face of complex challenges.

The contribution of seasonal variations and Zostera marina presence to the bacterial community assembly of seagrass bed sediments

BackgroundMicroorganisms play pivotal roles in seagrass ecosystems by facilitating material and elemental cycling as well as energy flux. However, our understanding of how seasonal factors and seagrass presence influence the assembly of bacterial communities in seagrass bed sediments is limited. Employing high-throughput sequencing techniques, this study investigates and characterizes bacterial communities in the rhizosphere of eelgrass (Zostera marina) and the bulk sediments across different seasons. The research elucidates information on the significance of seasonal variations and seagrass presence in impacting the microbial communities associated with Zostera marina.ResultsThe results indicate that seasonal variations have a more significant impact on the bacterial community in seagrass bed sediments than the presence of seagrass. We observed that the assembly of bacterial communities in bulk sediments primarily occurs through stochastic processes. However, the presence of seagrass leading to a transition from stochastic to deterministic processes in bacterial community assembly. This shift further impacts the complexity and stability of the bacterial co-occurrence network. Through LEfSe analysis, different candidate biomarkers were identified in the bacterial communities of rhizosphere sediments in different seasons, indicating that seagrass may possess adaptive capabilities to the environment during different stages of growth and development.ConclusionsSeasonal variations play a significant role in shaping these communities, while seagrass presence influences the assembly processes and stability of the bacterial community. These insights will provide valuable information for the ecological conservation of seagrass beds.

Thermal Infrared Orthophoto Geometry Correction Using RGB Orthophoto for Unmanned Aerial Vehicle

The geometric correction of thermal infrared (TIR) orthophotos generated by unmanned aerial vehicles (UAVs) presents significant challenges due to low resolution and the difficulty of identifying ground control points (GCPs). This study addresses the limitations of real-time kinematic (RTK) UAV data acquisition, such as network instability and the inability to detect GCPs in TIR images, by proposing a method that utilizes RGB orthophotos as a reference for geometric correction. The accelerated-KAZE (AKAZE) method was applied to extract feature points between RGB and TIR orthophotos, integrating binary descriptors and absolute coordinate-based matching techniques. Geometric correction results demonstrated a significant improvement in regions with stable and changing environmental conditions. Invariant regions exhibited an accuracy of 0.7~2 px (0.01~0.04), while areas with temporal and spatial changes saw corrections within 5~7 px (0.10~0.14 m). This method reduces reliance on GCP measurements and provides an effective supplementary technique for cases where GCP detection is limited or unavailable. Additionally, this approach enhances time and economic efficiency, offering a reliable alternative for precise orthophoto generation across various sensor data.

The Identification of New Pharmacological Targets for the Treatment of Glaucoma: A Network Pharmacology Approach

Background: Glaucoma is a progressive optic neuropathy characterized by the neurodegeneration and death of retinal ganglion cells (RGCs), leading to blindness. Current glaucoma interventions reduce intraocular pressure but do not address retinal neurodegeneration. In this effort, to identify new pharmacological targets for glaucoma management, we employed a network pharmacology approach. Methods: We first retrieved transcriptomic data from GEO, an NCBI database, and carried out GEO2R (an interactive web tool aimed at comparing two or more groups of samples in a GEO dataset). The GEO2R statistical analysis aimed at identifying the top differentially expressed genes (DEGs) and used these as input of STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) app within Cytoscape software, which builds networks of proteins starting from input DEGs. Analyses of centrality metrics using Cytoscape were carried out to identify nodes (genes or proteins) involved in network stability. We also employed the web-server software MIRNET 2.0 to build miRNA–target interaction networks for a re-analysis of the GSE105269 dataset, which reports analyses of microRNA expressions. Results: The pharmacological targets, identified in silico through analyses of the centrality metrics carried out with Cytoscape, were rescored based on correlations with entries in the PubMed and clinicaltrials.gov databases. When there was no match (82 out of 135 identified central nodes, in 8 analyzed networks), targets were considered “potential innovative” targets for the treatment of glaucoma, after further validation studies. Conclusions: Several druggable targets, such as GPCRs (e.g., 5-hydroxytryptamine 5A (5-HT5A) and adenosine A2B receptors) and enzymes (e.g., lactate dehydrogenase A or monoamine oxidase B), were found to be rescored as “potential innovative” pharmacological targets for glaucoma treatment.

Enhancing the Vehicular Public Key Infrastructure to Develop Privacy-Preserving Authentication Scheme for Vanet by Using PCM and MSS Scheme

This article proposes a security-based authentication as well as efficient certificate management approach for VANET to detect fraudulent nodes with better precision, less latency and overhead. The primary purpose of the developed system is to establish effectual and heftiness of VANET security that lead to the stability of overall network. VANETs are composed of vehicles and Road Side Units (RSUs) assisting with network management and the vehicles connect with one another and RSUs to furnish roadside information and safety solutions. Security is an essential factor in VANETs because the confidentiality of humans (passengers) is paramount; hence, Vehicular Public Key Infrastructure (VPKI) is utilised to offer authentication and safety services in VANETs. The developed structure provides an encrypted VANET transmission infrastructure by utilising the concepts of Merkle Signature Scheme (MSS) and Pseudo-code Certificate Management (PCM) to minimise overhead for communication and latency while ensuring entity authenticity. Messages are authenticated by sender, encoded with a vehicular public key distributed by a PCM-MSS and decrypted by the destination, resulting in every transmission including a certification from a reliable authority. During that verification, the transmitter and receiver of message’s authentication and validation is accomplished. Simulation findings show that the proposed approach improves the reliability of identifying hostile nodes and PDR while reducing authentication delays and overhead.

Optimized wide area backup for distance relay tele-protection with security adjustment

Tele-protection schemes efficiently clear faults at line ends, but failures in the telecommunication link or relay malfunction can cause delayed fault clearance, network instability and significant power loss. Hence, this paper presents a reliable protection method as a backup for tele-protection schemes which is designed based on nearby distance relays zone pickup. The proposed protective method introduces a novel ability to adjust sensitivity to fault events through a security factor index. This method can adapt to network uncertainties or changes in topology, using one-step calculation of optimal values based on the least square error method and provides the fast adaptation to network changes. In addition, it enables the fault detection even if a relay fails at one of the faulted line terminals. Performance evaluation of the proposed method has been done based on the reliability indices through a wide simulation of fault scenarios on the IEEE 9-bus system. Considering the possible uncertainties, the test results have shown that the proposed method is more efficient with 98.5 % dependability compared to the conventional tele-protection schemes. While this index is evaluated at 90 % and 92.5 % in the PUTT and POTT schemes, respectively.

ADAPTIVE METHOD OF FORMING COMPLEX SIGNALS ENSEMBLES BASED ON MULTI-LEVEL RECURRENT TIME-FREQUENCY SEGMENT MODELING

The article investigates the implementation of an adaptive method for forming ensembles of complex signals based on multilevel recurrent time-frequency segmentation. It addresses the key challenges faced by cognitive wireless networks operating in dynamic radio frequency environments with high levels of interference, necessitating rapid adaptation to changes in the spectral characteristics of signals. The study substantiates the need for adaptive filters and specific transformations to enhance signal processing quality, particularly in environments with high variability in frequency characteristics and significant noise interference. The proposed method of multilevel recurrent time-frequency segmentation allows for the modification of time segment durations and the use of segments of varying lengths, providing flexibility in signal processing and adaptation to current conditions. This adaptability enables optimal signal processing for each individual case, taking into account short-term impulses, long-term fluctuations, and various types of noise and distortions. This approach effectively separates frequency components and reduces interference between them, which is crucial for maintaining high signal quality and communication stability in cognitive networks. It has been proven that the use of adaptive filters such as LMS (Least Mean Squares) and RLS (Recursive Least Squares), as well as fast Fourier Transform (STFT), wavelet, and Hilbert transforms at different stages of multilevel time-frequency segmentation, significantly enhances signal interference resistance and energy efficiency. Comparative analysis of signal metrics before and after filtering and transformation shows an increase in signal quality by 14,3–24,5% and a reduction in noise levels by 21,7–29,6%. The wavelet transform, in particular, proved to be highly effective, allowing for precise extraction of useful frequency components from the noise background and improving signal parameters through dynamic adjustment to specific radio environment conditions. Experimental results confirm the effectiveness of the proposed method, demonstrating its ability to ensure consistently high-quality processing of complex signal ensembles even in dynamic cognitive radio environments.

Sulphur Copolymers with Pyrrole Compounds as Crosslinking Agents of Elastomer Composites for High-Performance Tyres.

Driving a car at extreme speeds, road holding, and sustainability do not go together well. Formula 1 racing is exciting but is not an example of sustainability. The aim of this work was to use materials, suitable for the treads of high-performance racing tyres, that can favour both high performance and sustainability. In particular, the objective was to achieve high dynamic rigidity at high temperatures (>100 °C) and a stable crosslinking network. A copolymer from an industrial waste such as sulphur and a comonomer from a circular biosourced material were used as the crosslinking agent of an elastomer composite based on poly(styrene-co-butadiene) from solution anionic polymerization and a carbon black with a high surface area. The biosourced circular material was 1,6-bis(2,5-dimethyl-1H-pyrrol-1-yl)hexane (HMDP), the di-pyrrole derivative of hexamethylenediamine. Two poly(S-co-HMDP) copolymers, with different S/HMDP ratios (6 and 8.9, Copolymer 1 and Copolymer 2) were carefully characterized by means of 1H-, 13C-, 2D1H-1H-COSY and 2D 1H-13C HSQC NMR. The comparison of the spectra highlighted the substitution with sulphur of the β-position of the pyrrole ring: mono-substitution largely prevailed in Copolymer 1 and also bi-substitution in Copolymer 2. The copolymers were used as additives in the vulcanization system. Compared with a reference composite, they allowed us to achieve more efficient vulcanization, a higher density of the crosslinking network, higher dynamic rigidity, better ultimate tensile properties, and better stability of the crosslinking network at high temperatures. Compared with a traditional oil-based crosslinking agent for elastomer composites with high rigidity and a stable structure at high temperatures, such as the perthiocarbamate 6-((dibenzylcarbamothioyl)disulfaneyl)hexyl 1,3-diphenylpropane-2-sulfinodithioate, the poly(S-co-HMDP) copolymers led to higher dynamic rigidity and better ultimate tensile properties. These improvements occurring simultaneously are definitely unusual. This work paves the way for the upcycling of circular materials in a large-scale application such as in tyres.

Cyber-Physical Perception Interface for Co-Simulation Applications.

Co-simulation can bring improvements to the development of cyber-physical perceptive systems (CPPS) in critical fields, allowing uninterrupted system operation and flexibility to use both real-time sensor data and non-real-time data. This paper proposes a co-simulation approach that integrates physical systems and communication systems, including both hardware and software components. This study demonstrates how systems of different natures with discrete or continuous events can be simulated using three methods: time stepped, global event driven, and variable stepped. Through two case studies from the medical and energy fields, CPPS and co-simulation reveal their importance for the future by improving precision and efficiency, which leads to more accurate diagnoses and personalized treatments in the medical field and increases the stability of energy networks.