Network Restoration Research Articles

Decision-making optimization for post-disaster restoration of multimodal transport networks in terms of resilience

IntroductionPublic health emergencies can have a ripple effect on the resilience of multimodal transport network, which will lead to problems such as route disruptions or blockages, route selection change and information transmission delay spreading to the whole network, further hindering the transportation planning and operational efficiency of the network. MethodsThis study constructs a multimodal transport route optimization model under uncertainty with the objective of the sum of transportation cost, transshipment cost, penalty cost and carbon emission cost. To enhance the computational efficiency of the model, a novel invasive weed optimization with memory and encoding value clustering capabilities is proposed. In addition, by fusing the Q-learning algorithm in reinforcement learning with the novel invasive weed algorithm, the action-value function table obtained from the training facilitates the selection of optimal routes. Based on empirical data, explore the sensitivity analysis of node disruptions, time windows, and fuzzy demand on route decision-making under public health emergencies. ResultsThe transport network is affected by public health emergencies, which makes the optimal route deviate from the expected goal, resulting in an increase in the total cost. The proportion of total cost is determined by the position of nodes in the network, with critical nodes suffering more losses than ordinary nodes. Reasonable setting of time windows and fuzzy demand intervals is an effective way to improve the resilience and transportation efficiency of multimodal transport network. ConclusionsThis study provides more applicable decision-making references for enterprises to prevent the risk of supply chain disruptions caused by public health emergencies.

Bifurcation Instability Modulated by a Connecting Channel Leads to Periodic Water Partitioning in a Simple Channel Network

AbstractWater and mass transport in distributary channel networks play an important role in nourishing fluvial and coastal wetlands, and are largely determined by the morphological configurations of channel bifurcations. While the morphological equilibrium of a single channel bifurcation has been extensively studied, the equilibrium configurations of channel networks with connecting channels linking the bifurcating branches, that is, the “bifurcation‐connecting channel” units that are commonly found in rivers, deltas and estuaries, remain elusive. In this simple yet representative channel network of the “bifurcation‐connecting channel” unit, we observed through numerical simulations an oscillatory water partitioning under moderate Shields stress and channel aspect ratio, in addition to the steady‐state solutions reported in previous studies. The oscillatory water partitioning indicates a newly discovered periodic solution, which is an emergent behavior under constant boundary conditions. We found that the periodic solution is primarily due to the dynamic interactions between bifurcation instability and water surface slope advantage in the two branches modulated by the reversable discharges through the connecting channel, under moderate Shields stress and channel aspect ratio. In such cases, the developed slope advantage in the subordinate branch can suppress the deepening of the dominant branch and eventually lead to the shifting of the dominant branch. In contrast, the channel network attains a steady‐state solution when the slope advantage or the bifurcation instability is dominant with relatively low and high Shields stress (or channel aspect ratio). Our results improve the understanding on the evolution and restoration of channel networks under increasing human interventions in global deltas.

Discourses of Poverty: The Narratives of the Poor in Samar

The study aimed at exploring, through the view of the future for themselves and their children, the perceptions of the poor about the temporal dimensions of poverty. The study adopted a discursive perspective, to illustrate the hopes, dreams and expectations of the poor regarding their future and, ultimately, how they view their status and how the LGU officials would address the different faces and current status of poverty in their respective locality. The researchers used Critical Discourse Analysis (CDA). The following are the themes obtained from the responses of the participants: limited knowledge and lower health status, lack of ambition and motivation to improve the situation, inadequate basic needs like food due to large family size, and poverty are transmitted to the next generation. Eradicating poverty is a challenge that sees no means of receding. LGUs do whatever they can to improve people’s lives. Despite those programs and initiatives of the local government units, there are still municipalities in Samar that are experiencing poverty. The poorest of the poor in Samar suffer from a lack of jobs in rural areas, low motivation to pursue education, and limited resources for large families. This has led to a widening gap between the haves and have-nots. Add to that frequent natural disasters being experienced in Samar at least fifteen typhoons in a year which takes away what little the poor have. The fundamental solution for reducing poverty is to tackle poverty and to pursue equity in income, and education, provide more livelihood programs or interventions, and social participation. Community projects that focus on the restoration and development of social networks and social cohesion and the improvement of the social, communication, and coping skills of the deprived, should be encouraged.

A novel variable neighborhood descent algorithm for service restoration in radial electrical distribution networks

Abstract This paper presents a variable neighborhood descent algorithm for the service restoration problem (SRP) in electrical distribution systems. The restoration problem appears when a permanent fault occurs in the system. The fault must be localized and isolated from the rest of the system. As a consequence of the isolation, the downstream area is de-energized and requires restoration. The variable neighborhood descent algorithm features its ability to solve the SRP for radial distribution systems and introduces a strategy that helps to deal with the system’s radiality. Tests are performed using a 53-node test system. The results show that the proposed algorithm can efficiently solve the SRP in a distribution system, and as a deterministic algorithm, the results can be used to help nondeterministic algorithms.

A two-layer economic resilience model for distribution network restoration after natural disasters

Improving the resilience of energy networks and minimizing outages is crucial, making the development of self-healing strategies essential for ensuring a continuous power supply and boosting resilience. As decentralized prosumers become more integrated into smart grids, innovative coordination methods are required to fully leverage their potential and improve the system's self-healing capabilities. Hence, in this paper, we present a decentralized two-layer model for active distribution networks (ADNs) incorporating commercial energy hubs, Parking lots (PLs), hydrogen refueling stations, and mobile units (MUs). This model aims to maximize the flexible capacities of flexible prosumers under emergencies and includes a dynamic mechanism for transporting hydrogen to refueling stations via trucks. In the first layer of the model, the AND operator provides the required services to flexible prosumers, including commercial energy hubs, PLs and hydrogen refueling stations. During this layer, the movement paths of mobile units, such as battery-based mobile storage units (MSUs) and mobile hydrogen units (MHUs), are determined. In the second layer, prosumers independently plan and decide the extent of their service contributions. Subsequently, with the actual service capacities established, the AND operator re-optimizes the initial planning to finalize the system schedule and precise movement paths of the mobile units. The proposed model was tested on a modified 83-bus distribution network using the CPLEX solver in GAMS. Simulation results demonstrate a 54.9 % reduction in forced load shedding (FLS) due to the dispatch of mobile units and a further 84.4 %% reduction in FLS from leveraging the flexible capacities of commercial energy hubs and PLs.

Power distribution system restoration based on soft open points and islanding by distributed generations

Abstract A powerful and efficient program for restoring the electrical distribution system by effectively utilizing the maximum capabilities within the system, including soft open points, distributed generation resources, and network configuration changes, can significantly reduce both the quantity and duration of lost loads caused by permanent faults. In this research study, we address the issue of distribution network restoration with a focus on soft open points (SOPs) and intentional islanding using distributed generations. This problem is formulated as a constrained optimization problem in order to determine the optimal distribution network configuration, quality of intentional islanding through DGs, control function of SOPs, and amount of load shedding. The objective is to minimize lost load while minimizing switching operations and preferably minimal islanding. Given that this problem involves multiple complexities such as combinatorial nature, mixed-integer variables, non-linearity, non-convexity along with numerous variables and constraints; we employ an evolutionary method known as simulated annealing (SA) algorithm to solve it without any simplifications or assumptions about convexity. To enhance efficiency during implementation of SA algorithm, Kruskal’s algorithm is utilized for generating radial solutions which restricts search space to feasible solutions resulting in quicker attainment of high-quality optimal solutions. Finally, the outcomes of implementing the suggested approach on the 69-bus IEEE distribution system are presented and examined. It is demonstrated that by leveraging the potential of soft open points in load transfer control and network voltage regulation, along with utilizing intentional islanding capability provided by distributed generation resources, the restoration capability of the distribution system can be greatly enhanced.

Increasing resilience and selection of a strategy for restoring transport networks in extreme natural processes

Aim. The development of an approach to increasing resilience by selecting strategies for restoring transport networks affected by extreme natural processes. Methods. This study evaluates the dynamics of extreme natural processes, specifically exogenous geological processes that can that can disrupt transport networks. It includes as framework for assessing the sustainability and restoration of transport networks under climate risk factors. Strategies for restoring the transport network were formulated. Results. The formulated strategies enable network modeling of transport network topology, which can be represented as an undirected weighted graph with a set of nodes and edges. The proposed model allows determining the most effective strategy for quickly restoring the connectivity of the transport network by determining the optimal sequence of restoration for repairing road sections, considering restoration time. The efficiency of restoring damaged sections of the transport network is expected to decrease as the share of the restored network increases. Therefore, it is crucial to estimate the necessary extend of network restoration to perform the necessary extent of network restoration to support emergency and urgent tasks by RSChS formations in specific areas. Conclusion. The analysis and assessment of alternative solutions for restoring the sustainability of transport networks considers the complexity of tasks under climate risk factors, such as extreme natural processes. In some cases, the RSChS problems do not require complete network restoration, unlike the tasks solved by the transport industry. This work aims to develop a framework for assessing restoration strategies, identifying the features of each of the considered strategies under uncertainty, and increasing operational sustainability. The proposed approach is flexible, allowing decision makers to assess various priorities during a specific natural emergency in a certain area, such as average recovery time, process efficiency, and uncertainty levels, when choosing the most desirable strategy. It is assumed that the average recovery time does not differ significantly among strategies for full network restoration. However, for partial restorations necessary for RSChS tasks, the average restoration time depends on the chosen strategy.

IT Failures in Hospitals - Structures and Processes in Hospitals for Coping Cyber Attacks

Clinics are, by definition, part of a country's critical infrastructure. In recent years, hospitals have increasingly become the target of cyber attacks, resulting in disruptions to their functionality lasting weeks to even months. According to the "National Strategy for the Protection of Critical Infrastructures (CRITIS Strategy)", clinics are legally obligated to take preventive measures against such incidents. This involves evaluating, defining, and developing failure concepts for IT-dependent processes within a clinic to be prepared for a cyber attack. Specifically tailored emergency plans for computer system failures should be created and maintained in all IT-dependent areas of a clinic.Additionally, paper-based alternative solutions, such as request forms for diagnostic or consultation services, department-specific emergency documents, and patient documentation charts, should be kept in a readily accessible location known to staff in the respective areas. The complete restoration of a clinic's network after a cyber attack often requires extensive recovery of numerous IT systems, which may take weeks to months in some cases.If the hospital has robust plans for cyber emergency preparedness, including regular scans and real-time backups, stabilization and a quicker resumption of operations may be possible.

Optimization of composite ecological network patterns in Anhui Province based on multi-functional coupling of ecology-climate-economy

Amidst global climate warming and rapid urbanization, ecological and environmental issues are becoming increasingly prominent. Constructing a multi-functional coupled ecological network pattern is essential for promoting the synergy among ecological protection, climate regulation, and economic development. This study focuses on Anhui Province, characterized by diverse topographical features. Utilizing “source and sink” theory, the study employs a combination of Morphological Spatial Pattern Analysis (MSPA), landscape connectivity indices, local spatial autocorrelation, and circuit theory to construct single-factor networks for biodiversity conservation, thermal environment mitigation, and economic development. The analysis of spatial elements such as “source” areas, corridors, ecological strategic points, and ecological nodes led to the formation of an optimized multi-level, multi-functional composite ecological network pattern based on ecology, climate, and economy. The results indicate that: (1) Anhui Province has 14 ecological “source” landscapes and 31 ecological “sink” landscapes. There are 29 climate “source” landscapes and 21 climate “sink” landscapes. Additionally, there are 30 economic “source” landscapes and 22 economic “sink” landscapes. The biological migration corridors are primarily concentrated in Northern and Central Anhui Province, while climate corridors are distributed throughout the province. Economic corridors are relatively dense and dispersed across various cities in Anhui Province, showing a trend of expansion in all directions; (2) In the multi-level “source and sink” landscape network, the total length of economic corridors surpasses climate corridors, which in turn surpass biological migration corridors. In terms of total area, climate corridors exceed biological migration corridors, which surpass economic corridors. The optimal widths for first class and secondary corridors are outlined as follows: biological migration corridors at 1000 m and 6000 m, climate corridors at 2000 m and 6000 m, and economic corridors at 1000 m and 2000 m; (3) Overlaying the multi-objective corridors identified 228 ecological key points and 501 disturbance points. Approximately 7.52 % of the area in economic “source” locations should be allocated to ecological and climate corridors. (4) The proposed multi-functional composite ecological network optimization scheme identifies critical areas for the restoration of the multi-functional composite network. The lengths of the main corridors for biological migration, climate, and economy are 2130.20 km, 2606.14 km, and 1134.63 km respectively. The total area for core and critical restoration zones is 1166.42 km2 for ecology, 2999.95 km2 for climate, and 2902.11 km2 for economy. Constructing a multi-functional composite ecological network can effectively enhance regional ecosystem services, alleviate urban heat island effects, and promote sustainable regional economic development. This study addresses existing gaps in ecological network research and provides scientific basis and technical support for the coordinated optimization of ecological protection, climate regulation, and economic development in Anhui Province and globally.

Riparian Landscape Change: A Spatial Approach for Quantifying Change and Development of a River Network Restoration Model

Natural river landscapes can be biodiversity hotspots but are one of the most human altered ecosystems with habitats significantly damaged around the world, and a third of fish populations threatened with extinction. While riparian ecosystems have been negatively altered by anthropogenic activities, effective planning and restoration strategies can reverse negative impacts by improving habitat quality. However, restoring rivers requires appropriate data on current riparian health while also considering priorities for different stakeholders. To address this, a Geographic Information System (GIS) was used to create a new and transferable restoration priority model based on a section of the river Linth in Switzerland as a case study. The restoration priority model is founded on connectivity, river condition, national priority species and species hotspots. Landscape change of the riparian zone was analyzed using aerial imagery and landscape metrics. Almost a quarter of rivers within the study area were considered high or very high restoration priority, with many aquatic species set to benefit from restoration. From 1946 to 2019, the riparian landscape became highly fragmented due to significant growth in impervious surfaces and a concomitant loss of agricultural land. The GIS model provides a tool by which environmental agencies can manage natural features over large scales, while also planning priorities and targeting conservation strategies to the areas of greatest need.

Coordinated method for islanding division and component restoration in active distribution networks aimed at enhanced resilience

Coordinated method for islanding division and component restoration in active distribution networks aimed at enhanced resilience

A digital twin framework for efficient electric power restoration and resilient recovery in the aftermath of hurricanes considering the interdependencies with road network and essential facilities

The community's resilience in the face of natural hazards relies heavily on the rapid and efficient restoration of electric power networks, which plays a critical role in emergency response, economic recovery, and the functionality of essential lifeline and social infrastructure systems. Leveraging the recent data revolution, the digital twin (DT) concept emerges as a promising tool to enhance the effectiveness of post-disaster recovery efforts. This paper introduces a novel framework for post-hurricane electric power restoration using a hybrid DT approach that combines physics-based and data-driven models by utilizing a dynamic Bayesian network. By capturing the complexities of power system dynamics and incorporating the road network's influence, the framework offers a comprehensive methodology to guide real-time power restoration efforts in post-disaster scenarios. A discrete event simulation is conducted to demonstrate the proposed framework's efficacy. The study showcases how the electric power restoration DT can be monitored and updated in real-time, reflecting changing conditions and facilitating adaptive decision-making. Furthermore, it demonstrates the framework's flexibility to allow decision-makers to prioritize essential, residential, and business facilities and compare different restoration plans and their potential effect on the community.

Distribution Network Restoration Strategy Considering Mobile Energy Storage Systems

Abstract The increasing occurrence frequency of disruptive extreme weather events and the growing share of distributed resources further enhance the importance of restoration of distributed resource-assisted distribution networks. However, most distributed sources are not spatially flexible in scheduling, severely impacting the restoration performance of distribution networks. The emergence of mobile energy storage systems (MESS) provides temporally and spatially flexible resources. Based on the mobility of mobile energy storage, this paper proposes a distribution network restoration strategy considering MESS and prioritized restoration of critical loads. The restoration strategy is established aiming at maximizing weighted loads while satisfying multiple constraints. Specific operating characteristics of MESS are modeled and incorporated into the restoration model. The established restoration model is then reformulated into a mixed-integer second-order cone program. Case studies on a modified IEEE 33-node system verify the proposed restoration strategy.

Optimal Load Shedding During Service Restoration in Electrical Secondary Distribution Network Based on Reinforcement Learning

Optimal Load Shedding During Service Restoration in Electrical Secondary Distribution Network Based on Reinforcement Learning

The four misses of agricultural water husbandry in the Maghreb

AbstractRecurrent droughts plaguing the Maghreb geography have impacted dramatically on the yet scarce renewable water resources. This situation intensified tensions between water users, namely urban, industrial and agricultural sectors. At this point, agricultural water management in large‐scale irrigated schemes is under scrutiny since more than two‐thirds of the Maghreb mobilized water resources are used for agricultural purposes. However, this sector's performance is undermined by mismanagement, misuse, mispricing and misgovernance of agricultural water. From low irrigation water transport efficiencies, low water use efficiency at field level and low valuation of irrigation water by farmers, to nonreasoned pricing policies and lack of good governance, agricultural water husbandry is suffering a number of setbacks. This situation endangers the sustainable development of large‐scale irrigation schemes and could deprive Maghreb countries of the contribution of irrigated agriculture to countries' GDP and food security. These constraints were thoroughly analyzed, and a set of remedies was discussed that included restoration of water transport canals and water distribution networks, improvement of cropping systems, crop water use efficiency, extension services, reasoned subsidies and irrigation water pricing. Only by redirecting water husbandry in the large‐scale irrigation schemes, Maghreb countries can achieve sustainable development.

Multilayer restoration in IP-Optical networks by adjustable robust optimization and deep reinforcement learning

Today, IP-Optical networks apply IP restoration as the default strategy to recover IP traffic from optical failures. This strategy has been preferred over optical restoration as it circumvents the lengthy delays involved in the reconfiguration of the optical layer. Although fast, IP restoration requires the overprovisioning of costly capacity to cope with optical failures. The advent of software-defined optical networking enables a changeover towards more efficient methods that integrate IP-Optical restoration. These methods should not only restore traffic from failures considered in the planning phase, but they should also efficiently restore traffic from unforeseen failures. This paper studies this problem by investigating optimization algorithms for capacity planning and multilayer restoration based on the theory of adjustable robust optimization (ARO). The approach performs offline optimization of the capacities of IP links as well as the routing and capacities of IP tunnels in both failure-free mode of operation and in a foreseen set of optical failures. Besides, the approach optimizes an affine policy that is applied online to recover IP traffic from unforeseen failures, thereby providing robustness to optical failures not regarded in the planning phase. To overcome the limitations of the affine policy, an alternative robust algorithm is formulated based on deep reinforcement learning (DRL) and graph neural networks (GNNs). By training a DRL-GNN agent, the performance of the restoration process is improved by further minimizing the traffic losses when unforeseen optical failures occur. Results in selected scenarios show that the algorithms outperform IP restoration in terms of capacity requirements, while minimizing the traffic losses in the case of failures. Moreover, the DRL-GNN method significantly improves the ARO-based affine algorithm, which shows the capability of learning the complex relationship between the capacity impairments caused by optical failures and the routing strategy required to restore IP traffic.

Urolithin A Inhibits Anterior Basolateral Amygdala to Ventral Hippocampal CA1 Circuit to Ameliorate Amyloid-β-Impaired Social Ability.

Anxiety and social withdrawal are highly prevalent among patients with Alzheimer's disease (AD). However, the neural circuit mechanisms underlying these symptoms remain elusive, and there is a need for effective prevention strategies. This study aims to elucidate the neural circuitry mechanisms underlying social anxiety in AD. We utilized 5xFAD mice and conducted a series of experiments including optogenetic manipulation, Tandem Mass Tag-labeled proteome analysis, behavioral assessments, and immunofluorescence staining. In 5xFAD mice, we observed significant amyloid-β (Aβ) accumulation in the anterior part of basolateral amygdala (aBLA). Behaviorally, 6-month-old 5xFAD mice displayed excessive social avoidance during social interaction. Concurrently, the pathway from aBLA to ventral hippocampal CA1 (vCA1) was significantly activated and exhibited a disorganized firing patterns during social interaction. By optogenetically inhibiting the aBLA-vCA1 pathway, we effectively improved the social ability of 5xFAD mice. In the presence of Aβ accumulation, we identified distinct changes in the protein network within the aBLA. Following one month of administration of Urolithin A (UA), we observed significant restoration of the abnormal protein network within the aBLA. UA treatment also attenuated the disorganized firings of the aBLA-vCA1 pathway, leading to an improvement in social ability. The aBLA-vCA1 circuit is a vulnerable pathway in response to Aβ accumulation during the progression of AD and plays a crucial role in Aβ-induced social anxiety. Targeting the aBLA-vCA1 circuit and UA administration are both effective strategies for improving the Aβ-impaired social ability.

Research on distribution network restoration based on ant colony algorithm

Research on distribution network restoration based on ant colony algorithm

Tough, Waterproofing, and Sustainable Bio‐Adhesive Inspired by the Dragonfly Wing

AbstractThe development of multifunctional bio‐adhesive plays a critical role in achieving a sustainable society, where the intrinsic sensitivity to water and poor dynamics severely bottlenecks its scale‐up application. Inspired by the microstructure of dragonfly wings, a strong and tough adhesive with excellent reprocessability is designed and developed by creating a dynamic network consisting of a lignin polyurea (LPU) framework with soybean protein (SP). The LPU framework act as the rigid nervures to slow crack propagation and transfer stress, while the SP dissipate the strain energy through the interplay from the graded hydrogen and imine bonds generated between LPU and SP. The bio‐adhesive achieves significant enhancements in fracture toughness and water resistance by ≈7 and 23 folds, respectively, compared with SP. Furthermore, the capacity for diffusion and restoration of dynamic network endows the adhesive with superior reprocessability, enabling recycled particleboard to achieve high retention of modules (over 80%). This sustainable bio‐adhesive approach offers a promising eco‐friendly alternative to the conventional petrochemical adhesive.

IT Failures in Hospitals - Structures and Processes in Hospitals for Coping Cyber Attacks

Clinics are, by definition, part of a country's critical infrastructure. In recent years, hospitals have increasingly become the target of cyber attacks, resulting in disruptions to their functionality lasting weeks to even months. According to the "National Strategy for the Protection of Critical Infrastructures (CRITIS Strategy)", clinics are legally obligated to take preventive measures against such incidents. This involves evaluating, defining, and developing failure concepts for IT-dependent processes within a clinic to be prepared for a cyber attack. Specifically tailored emergency plans for computer system failures should be created and maintained in all IT-dependent areas of a clinic.Additionally, paper-based alternative solutions, such as request forms for diagnostic or consultation services, department-specific emergency documents, and patient documentation charts, should be kept in a readily accessible location known to staff in the respective areas. The complete restoration of a clinic's network after a cyber attack often requires extensive recovery of numerous IT systems, which may take weeks to months in some cases.If the hospital has robust plans for cyber emergency preparedness, including regular scans and real-time backups, stabilization and a quicker resumption of operations may be possible.