Enhance System Resilience Research Articles

Climate Change Impacts on Agricultural Systems Mitigation and Adaptation Strategies: A Review

Climate change poses a substantial threat to global agricultural systems, impacting crop yields, livestock productivity, and resource availability through rising temperatures, altered precipitation patterns, and increased frequency of extreme weather events. This review synthesizes current understanding of climate change impacts on agriculture and explores strategies for mitigating greenhouse gas (GHG) emissions while enhancing system resilience through adaptation. Mitigation approaches, such as conservation agriculture, agroforestry, and improved nutrient and livestock management, show significant potential to reduce emissions and increase carbon sequestration. However, economic, technological, and institutional barriers such as high implementation costs, limited access to finance, and inadequate policy support restrict their adoption. Adaptation strategies, including crop diversification, advanced water management, and climate-resilient technologies, are critical for sustaining agricultural productivity in variable climates. Synergies between mitigation and adaptation, such as integrating trees into croplands, can provide co-benefits, but trade-offs arise when resource competition occurs or land is diverted from food production to carbon sequestration. The review highlights the need for interdisciplinary research, robust data infrastructure, and tailored policies to bridge knowledge and resource gaps, particularly in vulnerable regions. Successful implementation requires coordinated efforts across stakeholders, supported by enabling policy frameworks that incentivize sustainable practices and align with global climate goals. Future research should prioritize understanding the long-term impacts of integrated strategies, enhancing monitoring systems, and fostering inclusive approaches that consider local contexts and socio-economic conditions. Addressing these challenges holistically will be crucial to building resilient agricultural systems capable of maintaining food security and supporting livelihoods in the face of accelerating climate change.

Knowledge Spillovers and Integrated Circuit Innovation Ecosystem Resilience: Evidence from China

A resilient innovation ecosystem is an important guarantee for enhancing industrial competitiveness. Knowledge spillover is the key driving force to enhance system resilience. Firstly, we use the MEREC-CoCoSo method to calculate the resilience level of the integrated circuit (IC) innovation ecosystem and analyze the evolution trajectory of the resilience before and after the emergence of the “stuck-neck” problem. Secondly, based on the panel data of 30 provinces (autonomous regions and municipalities directly under the central government) in China from 2011 to 2021, this paper analyzes the mechanism of the impact of intra-regional knowledge spillovers on the resilience of IC innovation ecosystems using the fixed-effect model and analyzes the spatial effect of inter-regional knowledge spillovers on the resilience of innovation ecosystems using the spatial Durbin model under the human capital matrix. Finally, we analyze the regulating role of contractual and relational governance mechanisms and try to open the “black box” of governance. The result shows the following: (1) The polarization of innovative ecosystem resilience of integrated circuits is gradually increasing, with strong spatial agglomeration, high–high agglomeration, low–low agglomeration, and low–high dispersion, and there is an obvious “matthew effect” and “siphon effect”. (2) Both intra- and inter-regional knowledge spillovers contribute significantly to the resilience of IC innovation ecosystems. The contractual governance mechanism can effectively enhance the impact of knowledge spillovers on the resilience of innovation ecosystems in the region, and the relational governance mechanism has a positive impact on the resilience of innovation ecosystems in neighboring regions. (3) Heterogeneity results show that knowledge spillovers within the Pan-PRD region have a significant positive impact on innovation ecosystem resilience. Knowledge spillovers between regions with low innovation capacity have a double effect on innovation ecosystem resilience, and knowledge spillovers between regions with “talent highlands” have a facilitating effect on innovation ecosystem resilience. Accordingly, policy recommendations are put forward to open up channels for innovation knowledge spillover, realize effective allocation of innovation resources, and optimize the system of innovation talents.

Three-stage resilience enhancement via optimal dispatch and reconfiguration for a microgrid

Extreme weather causes an increase in power system failure. Previous studies on system resilience have often overlooked the user-side of a microgrid. This study proposes composite resilience indices (RI) based on the power supply of a large-scale manufacturing campus microgrid to quantify its ability to withstand extreme events. The proposed RI consider load priority, minimum supply load, total energy supplied, and the performance recovery-to-degradation slope ratio in an islanding microgrid. Accordingly, this study presents a three-stage resilience optimal dispatch and reconfiguration strategy, including energy-level scheduling, grid-level reconfiguration, and dynamic-level verification. A multi-objective optimization approach is used for energy scheduling, followed by system reconfiguration via DIgSILENT system modeling to meet the grid code and maximize load supply. Value at Risk methods are used to verify microgrid stability and load shedding requirements, supported by the virtual synchronous generator control of the energy storage system. The test results from a practical large-scale manufacturing campus microgrid validate the proposed approaches for enhancing system resilience considering load values.

Optimal power flow in hybrid AC-DC systems considering N-k security constraints in the preventive-corrective control stage

The optimal power flow methods for AC-DC systems containing VSC-HVDC generally only consider the economy during normal operation, overlooking the distribution of line transmission power in fault conditions. As a result, lines that continue to operate after a fault may experience overloading or operate at full capacity. Thus, a method for optimal power flow calculation is proposed that incorporates N-k security constraints in the preventive-corrective control stage for secure and economic operation of hybrid AC-DC systems. This method ensures that the line transmission power in the system meets the limits in the normal, short-term fault, and long-term fault states. In addition to the optimal power flow in the normal state, the method incorporates the system's imbalance as an indicator to evaluate system resilience. It combines this indicator with the economic, network loss, and performance metrics of the system, forming a two-stage bi-level multi-objective optimization model. Furthermore, to address the curse of dimensionality in anticipating system fault sets, a method for generating the anticipated fault set using non-sequential Monte Carlo simulation is proposed, along with a fault scenario search approach based on robust thinking to identify the most severe faults. Finally, the traditional IEEE 30-bus system was improved, and simulation verification was conducted using examples of an AC/DC system with a three-terminal DC network and a wind-solar-storage hybrid AC/DC system with a three-terminal DC network. The simulation results indicate that the proposed optimal power flow method considering the preventive-corrective control stage with N-k security constraints can effectively enhance system resilience. Furthermore, it improves the economic efficiency while ensuring the secure operation of the system.

Secure impulsive tracking of multi-agent systems with directed hypergraph topologies against hybrid deception attacks

This research delves into the challenges of achieving secure consensus tracking within multi-agent systems characterized by directed hypergraph topologies, in the face of hybrid deception attacks. The hybrid discrete and continuous deception attacks are targeted at the controller communication channels and the hyperedges, respectively. To overcome these threats, an impulsive control mechanism based on hypergraph theory are introduced, and sufficient conditions are established, under which consensus can be maintained in a mean-square bounded sense, supported by rigorous mathematical proofs. Furthermore, the investigation quantifies the relationship between the mean-square bounded consensus of the multi-agent system and the intensity of the deception attacks, delineating a specific range for this error metric. The robustness and effectiveness of the proposed control method are verified through comprehensive simulation experiments, demonstrating its applicability in varied scenarios influenced by these sophisticated attacks. This study underscores the potential of hypergraph-based strategies in enhancing system resilience against complex hybrid attacks.

Resilience optimization of bus-metro double-layer network against extreme weather events

The resilience of bus and metro systems to extreme weather events is a critical concern in urban planning, given their growing complexity and interconnectivity. Traditional studies often simplify or overlook the interdependency between different transportation modes, focusing on recovery strategies for a single mode to enhance system resilience. This study proposes an integrated resilience assessment framework for bus and metro systems, conceptualized as a Bus-Metro Double-Layer Network (B-M DLN). The framework considers both network structure and system function to accurately evaluate the B-M DLN resilience. A resilience optimization model for B-M DLN based on Genetic Algorithm (GA) is established to suggest the optimal recovery sequence of damaged stations, emphasizing the importance of station repair time, node strength, and node degree in recovery prioritization. Through a case analysis of Xi’an City, China, the B-M DLN shows significantly enhanced resilience when applying the optimal recovery strategy, especially in large-scale failure scenarios.

Optimized Feature Selection for DDoS Attack Recognition and Mitigation in SD-VANETs

Vehicular Ad-Hoc Networks (VANETs) are pivotal to the advancement of intelligent transportation systems (ITS), enhancing safety and efficiency on the road through secure communication networks. However, the integrity of these systems is severely threatened by Distributed Denial-of-Service (DDoS) attacks, which can disrupt the transmission of safety-critical messages and put lives at risk. This research paper focuses on developing robust detection methods and countermeasures to mitigate the impact of DDoS attacks in VANETs. Utilizing a combination of statistical analysis and machine learning techniques (i.e., Autoencoder with Long Short-Term Memory (LSTM), and Clustering with Classification), the study introduces innovative approaches for real-time anomaly detection and system resilience enhancement. Emulation results confirm the effectiveness of the proposed methods in identifying and countering DDoS threats, significantly improving (i.e., 94 percent anomaly detection rate) the security posture of a high mobility-aware ad hoc network. This research not only contributes to the ongoing efforts to secure VANETs against DDoS attacks but also lays the groundwork for more resilient intelligent transportation systems architectures.

Advancements in Cooperative Mobile Robots Control Strategies for Large-Scale Material Transport

This paper explores groundbreaking advancements in control strategies for cooperative mobile robots used in large-scale material transport, a critical aspect of modern industrial, manufacturing, logistics, and construction sectors. It delves into the development of sophisticated systems that enable seamless coordination among multiple mobile robot systems. The research presents a novel hierarchical finite state automaton for dynamic mission adaptation and a null space-based control scheme for precise task execution and enhanced system resilience. The introduction of Mecanum wheels facilitates flexible movement and manipulation of materials, thereby increasing operational efficiency and safety. Cutting-edge sensory technology, including LiDAR, and the implementation of the Robot Operating System are highlighted for their roles in enhancing autonomous navigation and intelligent operation. Additionally, the paper discusses the impact of centralized and decentralized control methods in ensuring safe, cooperative object transport. The findings contribute to the vision of Industry 4.0 by promoting the integration of automation and robotic cooperation in complex environments and present a foundational blueprint for further research. Challenges for future work such as scalability, communication efficiency, collision avoidance, and energy efficiency are also considered, underscoring the need for ongoing development of robust and scalable robotic systems to address modern transport challenges.

A Cyber Risk Assessment Approach to Federated Identity Management Framework-Based Digital Healthcare System.

This paper presents a comprehensive and evidence-based cyber-risk assessment approach specifically designed for Medical Cyber Physical Systems (MCPS)- and Internet-of-Medical Devices (IoMT)-based collaborative digital healthcare systems, which leverage Federated Identity Management (FIM) solutions to manage user identities within this complex environment. While these systems offer advantages like easy data collection and improved collaboration, they also introduce new security challenges due to the interconnected nature of devices and data, as well as vulnerabilities within the FIM and the lack of robust security in IoMT devices. To proactively safeguard the digital healthcare system from cyber attacks with potentially life-threatening consequences, a comprehensive and evidence-based cyber-risk assessment is crucial for mitigating these risks. To this end, this paper proposes a novel cyber-risk assessment approach that leverages a three-dimensional attack landscape analysis, encompassing existing IT infrastructure, medical devices, and Federated Identity Management protocols. By considering their interconnected vulnerabilities, the approach recommends tailored security controls to prioritize and mitigate critical risks, ultimately enhancing system resilience. The proposed approach combines established industry standards like Cyber Resilience Review (CRR) asset management and NIST SP 800-30 for a comprehensive assessment. We have validated our approach using threat modeling with attack trees and detailed attack sequence diagrams on a diverse range of IoMT and MCPS devices from various vendors. The resulting evidence-based cyber-risk assessments and corresponding security control recommendations will significantly support healthcare professionals and providers in improving both patient and medical device safety management within the FIM-enabled healthcare ecosystem.

Lag in pollutant removal efficiency relative to microbial community dynamics in a denitrifying phosphorus removal bioreactor

Determining the system status in advance is critical for implementing timely control strategies, preventing wastewater treatment instability due to overload stress, and enhancing system resilience to impact loads. Unlike traditional instability monitoring methods, identifying temporal relationships between pollutant removal efficiency and microbial community dynamics can offer novel insights into identifying optimal intervention windows. Here, we chose nitrite, a vital intermediate of nitrogen reaction in wastewater treatment, as a stress factor. Then, we investigated the effects of nitrite stress on denitrifying phosphorus removal system performance and microbiome. By monitoring carbon, nitrogen, and phosphorus removal efficiency and analyzing microbiome information, we found that variations in bacterial ecology preceded pollutant removal efficiency. The lag in pollutant removal efficiency relative to microbial community dynamics reflects the complex dynamics of species traits, metabolic functions, and functional redundancy. Species in clusters may drove specific pollutant removal; e.g., Betaproteobacteria are more closely associated with nitrogen removal, while Gammaproteobacteria are more linked to phosphorus removal. The cellular secretion function displayed greater susceptibility to overload stress compared to the substrate conversion function, resulting in decreased stability observed after 30 cycles, preceding the decline in substrate conversion efficiency. The lag in performance relative to community stability can be attributed to functional redundancy. Under overload stress conditions, functional redundancy within the microbiome decreased by 30.77% from 60 cycles to 120 cycles. Our results support a novel strategy for system collapse prediction via changes in the microbiome,enabling proactive regulation and ensuring system stability before irreversible deterioration occurs.

A two-stage optimization control strategy of private EVs and public EBs for system resilience enhancement

A two-stage optimization control strategy of private EVs and public EBs for system resilience enhancement

Optimizing Maintenance Resource Scheduling and Site Selection for Urban Metro Systems: A Multi-Objective Approach to Enhance System Resilience

This study developed an optimization model for the strategic location of maintenance resource supply sites and the scheduling of multiple resources following failures in urban metro systems, with the objective of enhancing system resilience. The model employs a multi-objective optimization framework, focusing primarily on minimizing resource scheduling time and reducing costs. It incorporates critical factors such as spatial location, network topology, station size, and passenger flow. A hybrid method, combining the non-dominated sorting genetic algorithm III and the technique for order of preference by similarity to ideal solution, is used to solve the model, with its effectiveness confirmed through a case study of the Nanjing Metro system. The simulation results yielded an optimal number of 21 maintenance resource supply stations and provided their placement. In the event of large-scale failures, the optimal resource scheduling strategy ensures demand satisfaction rates exceed 90% at critical stations, maintaining an overall rate of 87.09%, therefore significantly improving resource scheduling efficiency and the system’s emergency response capabilities and enhancing the physical resilience and recovery capabilities of the urban metro system. Moreover, the model accounts for economic factors, striving to balance emergency response capabilities with production continuity and cost efficiency through effective maintenance strategies and resource utilization. This approach provides a systematic framework for urban metro systems to manage sudden failures, ensuring rapid recovery to normal operations and minimizing operational disruptions in scenarios of limited resources.

Transformer Differential Protection Method for Recognition between Power Transformer Internal Defects and Inrush Current: A Comprehensive Review of Detection Techniques

The cornerstone of any electric power system lies in its power transformers, as their seamless operation is crucial for network reliability. Instant disconnection from the grid is imperative upon detecting any faults to prevent cascading issues. However, distinguishing between fault conditions, like inrush current, which necessitates caution rather than immediate action, poses a challenge for effective protection schemes. This dilemma can lead to relay malfunctions, further jeopardizing system integrity. This paper delves into a thorough analysis and comparison of various methods employed in differential protection to discern between internal faults and inrush currents, aiming to enhance system resilience. This comprehensive review explores the efficacy of intelligent techniques, hybrid approaches, and traditional methods in fault detection. By shedding light on the strengths and limitations of each method, researchers in this domain can glean insights to innovate and address the deficiencies of existing strategies in tackling internal faults and inrush currents detection. Ultimately, this endeavor seeks to fortify the reliability and stability of power systems in the face of dynamic operational challenges.

Sensors-Aided Distribution System Resilience Enhancement by Using Meteorological Data

Sensors-Aided Distribution System Resilience Enhancement by Using Meteorological Data

Assessing the effectiveness of reservoir operation and identifying reallocation opportunities under climate change

Reservoirs are designed and operated to mitigate hydroclimatic variability and extremes to fulfill various beneficial purposes. Existing reservoir infrastructure capacity and operation policies derived from historical records are challenged by hydrologic regime change and storage reduction from sedimentation. Furthermore, climate change could amplify the water footprint of reservoir operation (i.e. non-beneficial evaporative loss), further influencing the complex interactions among hydrologic variability, reservoir characteristics, and operation decisions. Disentangling and quantifying these impacts is essential to assess the effectiveness of reservoir operation under future climate and identify the opportunities for adaptive reservoir management (e.g. storage reallocation). Using reservoirs in Texas as a testing case, this study develops data-driven models to represent the current reservoir operation policies and assesses the challenges and opportunities in flood control and water supply under dynamically downscaled climate projections from the Coupled Model Intercomparison Project Phase 6. We find that current policies are robust in reducing future flood risks by eliminating small floods, reducing peak magnitude, and extending the duration for large floods. Current operation strategies can effectively reduce the risk of storage shortage for many reservoirs investigated, but reservoir evaporation and sedimentation pose urgent needs for revisions in the current guidelines to enhance system resilience. We also identify the opportunities for reservoir storage reallocation through seasonal-varying conservation pool levels to improve water supply reliability with negligible flood risk increase. This study provides a framework for stakeholders to evaluate the effectiveness of the current reservoir operation policy under future climate through the interactions among hydroclimatology, reservoir infrastructure, and operation policy.

From a false sense of safety to resilience under uncertainty.

Understanding and acting upon risk is notably challenging, and navigating complexity with understandings developed for stable environments may inadvertently build a false sense of safety. Neglecting the potential for non-linear change or "black swan" events - highly impactful but uncommon occurrences - may lead to naive optimisation under assumed stability, exposing systems to extreme risks. For instance, loss aversion is seen as a cognitive bias in stable environments, but it can be an evolutionarily advantageous heuristic when complete destruction is possible. This paper advocates for better accounting of non-linear change in decision-making by leveraging insights from complex systems and psychological sciences, which help to identify blindspots in conventional decision-making and to develop risk mitigation plans that are interpreted contextually. In particular, we propose a framework using attractor landscapes to visualize and interpret complex system dynamics. In this context, attractors are states toward which systems naturally evolve, while tipping points - critical thresholds between attractors - can lead to profound, unexpected changes impacting a system's resilience and well-being. We present four generic attractor landscape types that provide a novel lens for viewing risks and opportunities, and serve as decision-making contexts. The main practical contribution is clarifying when to emphasize particular strategies - optimisation, risk mitigation, exploration, or stabilization - within this framework. Context-appropriate decision making should enhance system resilience and mitigate extreme risks.

Dual Functionalization of Hyaluronan Dermal Fillers with Vitamin B3: Efficient Combination of Bio-Stimulation Properties with Hydrogel System Resilience Enhancement.

Hyaluronic acid (HA) hydrogels are commonly used for facial dermal filling and for alternative medical aesthetic purposes. High diversity exists in commercial formulations, notably for the optimization of finished product stability, functionality, and performance. Polyvalent ingredients such as calcium hydroxylapatite (CaHA) or vitamin B3 (niacinamide) are notably used as bio-stimulants to improve skin quality attributes at the administration site. The aim of the present study was to perform multi-parametric characterization of two novel cross-linked dermal filler formulas (HAR-1 "Instant Refine" and HAR-3 "Maxi Lift") for elucidation of the various functional impacts of vitamin B3 incorporation. Therefore, the HAR products were firstly comparatively characterized in terms of in vitro rheology, cohesivity, injectability, and resistance to chemical or enzymatic degradation (exposition to H2O2, AAPH, hyaluronidases, or xanthine oxidase). Then, the HAR products were assessed for cytocompatibility and in vitro bio-stimulation attributes in a primary dermal fibroblast model. The results showed enhanced resilience of the cohesive HAR hydrogels as compared to JUVÉDERM® VOLBELLA® and VOLUMA® reference products in a controlled degradation assay panel. Furthermore, significant induction of total collagen synthesis in primary dermal fibroblast cultures was recorded for HAR-1 and HAR-3, denoting intrinsic bio-stimulatory effects comparable or superior to those of the Radiesse® and Sculptra™ reference products. Original results of high translational relevance were generated herein using robust and orthogonal experimental methodologies (hydrogel degradation, functional benchmarking) and study designs. Overall, the reported results confirmed the dual functionalization role of vitamin B3 in cross-linked HA dermal fillers, with a significant enhancement of hydrogel system stability attributes and the deployment of potent bio-stimulatory capacities.

Three-layer and robust planning models to evaluate the strategies of defense layer, attack layer, and operation layer for optimal protection in natural gas pipeline network

Using game theory techniques, a three-layer planning model (THLPM) for the defense, attack, and operational layers has been established to address malicious damage to pipelines. A robust planning model was developed for situations with uncertain attack resources. In this model, the defense layer protects critical infrastructure within the network to minimize losses. The attack layer targets unprotected facilities based on the defensive strategy. The operational layer formulates the optimal pipeline transmission strategy based on the decisions of the defense and attack layers. By utilizing the duality theorem, the THLPM is transformed into a two-layer planning problem, which is then linearized and solved using a decomposition algorithm. Taking a specific ring-shaped pipeline network as an example, the study analyzes the significant role of backup pipelines in enhancing system resilience and evaluates the offensive and defensive strategies of THLPM. The effectiveness of THLPM in protecting the network was assessed, showing that when defense and attack resources are invested at levels 3 and 10, respectively, operational costs of the network are reduced by 54.2 % compared to a scenario without any defenses. The methods proposed in this paper are applicable to other pipeline systems.

Exploring Fault Tolerance Consensus for Wireless Sensor Networks: A Comprehensive Detailed Study

The integration of wireless networks into modern communication systems has revolutionized information exchange across various applications. However, achieving reliable agreement in these networks is significantly impeded by their unique properties, such as blurring, interruption, and transparency. A fundamental component of distributed systems, fault-tolerant consensus ensures that nodes in the network can agree on a consistent value even in the presence of malfunctioning or corrupted elements. This study explores both non-Byzantine and Byzantine fault-tolerant consensus approaches, with a focus on achieving agreement in the presence of benign faults. The importance of fault-tolerant consensus in wireless connections is underscored, particularly in applications like wireless blockchain, IoT, and vehicular networks. The research delves into wireless network-specific fault-tolerant consensus algorithms to address the specific challenges posed by networking environments. Furthermore, a comparative analysis of Byzantine Fault Tolerance mechanisms in distributed systems is provided to shed light on their features and benefits. It highlights the importance of ensuring system reliability and consistency in wireless networks to maintain seamless communication and data integrity. Moreover, the paper emphasizes the critical role of fault-tolerant consensus in enhancing system resilience and performance. It underscores the need for robust algorithms and protocols to detect and mitigate errors, ensuring reliable communication and coordination despite potential node failures. By providing insights into fault-tolerant consensus mechanisms tailored to dynamic conditions, the study aims to optimize system adaptation and effectively mitigate both Byzantine and non-Byzantine failures in wireless network environments.

Risk assessment and resilience enhancement strategies for urban power supply-demand imbalance affected by extreme weather: A case study of Beijing

Extreme weather and climate events (EWCE) risk assessment and system resilience enhancement are important supports for urban power systems to cope with global climate change and realize green and low-carbon transformation. However, the existing risk assessment methods tend to ignore the impact of cumulative damage events, and lack the systematic analysis of the power supply process. Moreover, there is a deficiency in researches on resilience enhancement strategies with application analysis in practical scenarios. This study established a risk assessment methodology with the occurrence probability of extreme weather, the deviation state of power supply-demand and the state duration as the first-level indexes. The methodology was applied to the assessment of three EWCE scenarios using the urban power structure of Beijing as a case study. Finally, the power supply resilience of EWCE was defined, and the power supply resilience curves with severe impacts and cumulative damages were characterized, and then resilience enhancement strategies were proposed to guide the case applications. The results of the study suggest that coupled extreme weather scenarios and an increase in the percentage of electricity supplied from renewable sources would increase the level of supply imbalance risk. System resilience could be improved by extending the time for supply level minimums to occur and by increasing the level of supply capacity minimums. Beijing would need to focus on the resilience enhancement for extreme low temperature and heavy rainfall superimposed on heat wave. This study could provide theoretical reference for the construction of resilient cities under climate change.