Network Restoration Research Articles

Expression of HIV-1 Reverse Transcriptase in Murine Cancer Cells Increases Mitochondrial Respiration

Changes in metabolic pathways are often associated with the development of a wide range of pathologies. Increased glycolysis under conditions of sufficient tissue oxygen supply and its dissociation from the Krebs cycle, known as aerobic glycolysis or the Warburg effect, is a hallmark of many malignant neoplasms. Identification of specific metabolic shifts can characterize the metabolic programming of individual types of tumor cells, the stage of their transformation, and predict their metastatic potential. Viral infection can also alter the metabolism of cells to support the process of viral replication. Infection with human immunodeficiency virus type 1 (HIV-1) is associated with an increased incidence of various cancers, and for some viral proteins a direct oncogenic effect was demonstrated. In particular, we showed that the expression of HIV-1 reverse transcriptase (RT) in 4T1 breast adenocarcinoma cells increases the tumorigenic and metastatic potential of cells in vitro and in vivo by a mechanism associated with the ability of RT to induce reactive oxygen species in cells (ROS). The aim of this work was to study the molecular mechanism of this process, namely the effect of HIV-1 RT on the key metabolic pathways associated with tumor progression: glycolysis and mitochondrial respiration. Expression of HIV-1 RT had no effect on the glycolysis process. At the same time, it led to an increase in mitochondrial respiration and the level of ATP synthesis in the cell, while not affecting the availability of the substrates, carbon donors for the Krebs cycle, which excludes the effect of RT on the metabolic enzymes of cells. Increased mitochondrial respiration was associated with restoration of the mitochondrial network despite the RT-induced reduction in mitochondrial mass. Increased mitochondrial respiration may increase cell motility, which explains their increased tumorigenicity and metastatic potential. These data are important for understanding the pathogenesis of HIV-1 infection, including the stimulation of the formation and spread of HIV-1 associated malignancies.

Resilience assessment of railway networks: Combining infrastructure restoration and transport management

During railways operations, unplanned events might occur which can result in rail traffic being heavily impacted. The paper proposes a passenger-centred resilience assessment for disruption scenarios which consist of multiple simultaneous disruptions. It combines train traffic operations, passenger flows and network restoration. To evaluate resilience, an optimization-based approach has been developed for solving the new infrastructure restoration and transport management (IRTM) problem. Additionally, this approach develops mitigation plans for the best infrastructure restoration and traffic recovery and it captures the time-dependent transport network performance during disruptions. The approach is general with respect to types of disruptions, and can be applied for evaluation against short disruptions (1–2 h) as well as more substantial ones (multiple days or weeks). The performance of the proposed approach has been demonstrated on a Dutch railway network. Furthermore, the resilience of the system is assessed against the critical infrastructure disruption scenarios in the network. This optimization-based approach shall enable decision makers to quantify accurately impacts of multiple disruptions by considering the created inconveniences to passengers in the railway operation due to these disruptions.

Geoinformation system approach to the analysis of the impact of armed conflicts on the ecological condition of the environment

The article considers the experience of solving problems in assessing the impact of armed conflict on the environment. An analysis of international approaches implemented in the framework of relevant UN resolutions to prevent critical changes in environmental parameters caused by direct and indirect effects of armed conflict. The analysis showed that in the conditions of the armed conflict on the territory of Donbass unbalanced military natural-technogenic geosystem MNTGS was formed, which is characterized by destruction and pollution of soils, deterioration of biodiversity, destruction of potentially dangerous objects due to fires and active hostilities. In addition, the decommissioning and flooding of a large number of coal mines increases the environmental and man-made impact of MNTGS on the natural environment and safety of life. Taking into account the above, the article presents the main results of the development of a generalized model for assessing the impact of factors of armed conflict on all components of the environment. Suggested to intensify environmental monitoring of the areas of the armed conflict and increase its information content through the use of GIS, remote sensing and mathematical modeling technologies; organize measures to increase the resilience and accelerate the restoration of critical infrastructure networks in Donbass; to begin research of the newest ecological-technogenic and ecological-resource factors of military-technogenic threats in Europe's largest man-made coal region.

Impacts of filler loading and particle size on the transition to linear-nonlinear dichotomy in the rheological responses of particle-filled polymer solutions

In this study, the nonlinear behavior of carbon black-filled polybutadiene solutions under large-amplitude oscillatory shear is investigated. The results show that in the nonlinear regime, the third harmonic intensity, as measured by the ratio of the third to the first harmonics I3/I1, decreases significantly above a critical concentration ϕc of the polymer in the matrix, which results in the amplitude stress deviating strongly from the linear dependence of strain, while the time dependence of stress remains sinusoidal. Increasing the filler particle size significantly decreases the critical ϕc. However, increasing the filler loading basically has no effect on the transition to linear-nonlinear dichotomy. This transition happens when the mesh size ξ of the entangled polymer network in the matrix becomes smaller than the primary filler particle size. Above ϕc, the topological hindrance of the entangled polymer chains apparently considerably slows down the recovery speed of the broken filler network in the material. Hence, the quasisinusoidal response in the system that has a strain-dependent modulus is probably due to the restoration of the broken filler network requiring longer than the time scale of a typical dynamic perturbation.

Rabphilin3A reduces integrin-dependent growth cone signaling to restrict axon regeneration after trauma

Neural repair after traumatic spinal cord injury depends upon the restoration of neural networks via axonal sprouting and regeneration. Our previous genome wide loss-of-function screen identified Rab GTPases as playing a prominent role in preventing successful axon sprouting and regeneration. Here, we searched for Rab27b interactors and identified Rabphilin3A as an effector within regenerating axons. Growth cone Rabphilin3a colocalized and physically associated with integrins at puncta in the proximal body of the axonal growth cone. In regenerating axons, loss of Rabphilin3a increased integrin enrichment in the growth cone periphery, enhanced focal adhesion kinase activation, increased F-actin-rich filopodial density and stimulated axon extension. Compared to wild type, mice lacking Rabphilin3a exhibited greater regeneration of retinal ganglion cell axons after optic nerve crush as well as greater corticospinal axon regeneration after complete thoracic spinal cord crush injury. After moderate spinal cord contusion injury, there was greater corticospinal regrowth in the absence of Rph3a. Thus, an endogenous Rab27b - Raphilin3a pathway limits integrin action in the growth cone, and deletion of this monomeric GTPase pathway permits reparative axon growth in the injured adult mammalian central nervous system.

Model and solution method for mean-risk cost-based post-disruption restoration of interdependent critical infrastructure networks

Critical infrastructure networks (CINs), such as power grids, water distribution systems, and telecommunication networks, are essential for the functioning of society and the economy. As these infrastructure networks are not isolated from each other, their functions are not independent and may be vulnerable to disruptive events (e.g., component failures, terrorist attacks, natural disasters). For decision makers, how to restore the functions of CINs while accounting for interdependencies and various uncertainties becomes a challenging task. In this work, we study the post-disruption restoration problem for a system of interdependent CINs under uncertainty. We propose a two-stage mean-risk stochastic restoration model using mixed-integer linear programming (MILP) with the goal of minimizing the total cost associated with unsatisfied demands, repair tasks, and flow of interdependent infrastructure networks. The restoration model considers the availability of limited time and resources and provides a prioritized list of components to be restored along with assigning and scheduling them to the available network-specific work crews. Additionally, the model features flexible restoration strategies including multicrew assignment for a single component and a multimodal repair setting along with the consideration of full and partial functioning and dependencies between the multi-network components. The proposed model is illustrated using the power and water networks in Shelby County, Tennessee, United States, under two hypothetical earthquake scenarios.

Real-Time UAV Routing Strategy for Monitoring and Inspection for Postdisaster Restoration of Distribution Networks

After a natural disaster, a quick inspection of all damaged components is crucial to recover the functionality of distribution networks. Unmanned aerial vehicles (UAVs) can perform inspection tasks, particularly for damages that are difficult to access for human repair crews. Additionally, UAVs can monitor the transmission lines to find potential dangers and early-stage damages, and to monitor the road infrastructure to provide real-time information about traffic conditions so that repair crews can select the best ways to reach damages. Besides, due to unpredictable events during restoration, the UAV routing strategy (UAVRS) needs to be updated in real time. Thus, the proposed UAVRS in this article determines the optimal routes for the UAVs allocated to inspect damages as well as the optimal routes for the UAVs to monitor transmission lines and roads in real time for distribution networks. To tackle the multi-time-scale characteristic of the proposed UAVRS, a two-layer decision-making architecture is proposed. A bilevel programming problem is solved in the first layer for the large-time-scale problem, and a mixed-integer linear programming problem is solved for the small-time-scale problem in the second layer. A case study based on the distribution network in Zaltbommel and its neighbor areas, in The Netherlands, illustrates the effectiveness of our real-time method compared to the offline methods. Furthermore, different solvers are studied and compared in view of the real-time requirement.

Decentralized restoration of distribution network using a multi-agent system

Decentralized restoration of distribution network using a multi-agent system

A Decentralized Technique for Autonomous Distribution Network Restoration in Presence of Coupling Neighboring Microgrids

A Decentralized Technique for Autonomous Distribution Network Restoration in Presence of Coupling Neighboring Microgrids

UAVs assisted Network Partition Detection and Connectivity Restoration in Wireless Sensor and Actor Networks

Wireless sensor networks have been extensively employed in different data collection and monitoring applications because of their serviceability in harsh environments. The interconnected network is a fundamental requirement for these applications. The simultaneous failure of multiple nodes causes connectivity loss and can partition the network into various segments. It is crucial to identify the partitions in the partitioned network and reconnect them to achieve application-related goals. This paper provides a new Unmanned Aerial Vehicles (UAVs) assisted Network Partition Detection and Connectivity Restoration (UAV-NetRest) solution encompassing all phases from failure detection to network partition recovery. The proposed algorithm is tested according to detection and recovery time, distance traveled by UAVs in detection and recovery phases, the number of messages transmitted, and the number of deployed relay nodes. The performance of the UAV-NetRest's recovery algorithm is compared with the existing recovery techniques proposed in the literature. The simulation results demonstrate that the UAV-NetRest recovers the network with significantly less recovery time and minimum distance traveled by UAVs. The algorithm deploys minimum relay nodes as compared to other techniques. Network partitioning is an unpredictable incident, and simulation results entirely depend on the number of failed nodes, proximity and topology of network partitions, and the number of UAVs employed. The paper provides a new framework that covers all the aspects required for network partition recovery. This proposed work can be a seed source for future researchers to further optimize the detection and recovery phases.

Learning Sequential Distribution System Restoration via Graph-Reinforcement Learning

A distribution service restoration algorithm as a fundamental resilient paradigm for system operators provides an optimally coordinated, resilient solution to enhance the restoration performance. The restoration problem is formulated to coordinate distribution generators and controllable switches optimally. A model-based control scheme is usually designed to solve this problem, relying on a precise model and resulting in low scalability. To tackle these limitations, this work proposes a graph-reinforcement learning framework for the restoration problem. We link the power system topology with a graph convolutional network, which captures the complex mechanism of network restoration in power networks and understands the mutual interactions among controllable devices. Latent features over graphical power networks produced by graph convolutional layers are exploited to learn the control policy for network restoration using deep reinforcement learning. The solution scalability is guaranteed by modeling distributed generators as agents in a multi-agent environment and a proper pre-training paradigm. Comparative studies on IEEE 123-node and 8500-node test systems demonstrate the performance of the proposed solution.

Multi-stage supply restoration of active distribution networks with SOP integration

Supply restoration from outages is essential for improving the reliability of active distribution networks (ADNs) after fault isolation. Soft open point (SOP) can adjust the power flow among feeders and provide voltage support for power outage areas. Considering the sequential coordination of switching operation and SOP control mode selection, a multi-stage supply restoration method with SOPs is proposed for ADNs. First, the sequential energization is formulated, in which the impact of SOP control mode on restoration sequence is analyzed. By providing voltage support, the coordination of SOPs will rapidly energize the outage area and improve the voltage profile. Then, a multi-stage restoration model with SOPs is proposed, in which reconfiguration of switches and control mode selection of SOPs are coordinated in sequence to maximize the load recovery level of ADNs. Through the switching action-time mapping, secure operation is ensured during the entire supply restoration process. Finally, the effectiveness of the proposed method is validated on a modified IEEE 33-node distribution system and practical distribution networks with four-terminal SOP. Results show that the proposed method can fully exploit the potential benefits of SOPs and effectively enhance the load recovery level of ADNs.

Reinforcement learning based connectivity restoration in wireless sensor networks

Reinforcement learning based connectivity restoration in wireless sensor networks

Supply Restoration in Active Distribution Networks Based on Soft Open Points with Embedded DC Microgrids

As disturbances due to natural disasters or man-made attacks intensify awareness regarding power systems’ resilience enhancement, the scientific community concentrates on exploring state-of-the-art technologies for emergency supply restoration strategies. Recent studies are increasingly focusing on the expanded flexibility of soft open points (SOPs) compared to conventional tie-switches to increase the restoration rate of critical loads; however, the potential of this novel technology is not limited to this aspect, with SOPs being used to improve the voltage level and increase the hosting capacity of renewable energy sources (RESs). This paper proposes a deterministic model for the optimal coordination of SOPs and distributed resources in an active distribution network (ADN) aiming at re-establishing the energy supply to critical loads after a prolonged interruption occurrence. At the same time, the support of DC microgrids with integrated RESs, embedded in SOPs, for the restoration process is explored. The efficiency of the proposed optimization model is verified based on a 24-h analysis performed on the modified IEEE 33-bus system, while considering the load and generation uncertainties as well.

A Hierarchical Restoration Mechanism for Distribution Networks Considering Multiple Faults

Service restoration of distribution networks in contingency situations is one of the highly investigated and challenging problems. In the conventional service restoration method, utilities reconfigure the topological structure of the distribution networks to supply the consumer load demands. However, the advancements in renewable distributed generations define a new dimension for developing service restoration methodologies. This paper proposes a hierarchical service restoration mechanism for distribution networks in the presence of distributed generations and multiple faults. The service restoration problem is modeled as a complicated and hierarchical program. The objectives are to achieve the maximization of loads restored with minimization of switch operations while simultaneously satisfying grid operational constraints and ensuring a radial operation configuration. We present the service restoration mechanism, which includes the dynamic topology analysis, matching isolated islands with renewable distributed generations, network reconfiguration, and network optimization. A new code scheme that avoids feasible solutions is applied to generate candidate solutions to reduce the computational burden. We evaluate the proposed mechanism on the IEEE 33 and 69 systems and report on the collected results under multitype fault cases. The results demonstrate the importance of the available renewable distributed generations in the proposed mechanism. Moreover, simulation results verify that the proposed mechanism can obtain reasonable service restoration plans to achieve the maximization of loads restored and minimization of switching operations under different faults.

Лекарственное обеспечение и развитие аптечной сети Курской области в первое послевоенное двадцатилетие (1945–1965 годы)

Relevance. This article examines issues related to the provision of medicines and the development of the pharmacy network of the Kursk region in the first post-war twentieth anniversary (1945-1965), when our country not only restored what was destroyed by the war, but also made a significant step forward in many areas, including in the field of healthcare. The experience gained during this period in the development of such an aspect of healthcare as drug provision and the development of a pharmacy network is of great interest. This is all the more relevant, since at present the world civilization, including our country, is facing such a challenge as the coronavirus pandemic. The purpose – the study of the problems of restoration and development of drug supply and pharmacy network of the Kursk region in the first post-war twentieth anniversary. Objectives: to study the process of restoration and development of the pharmacy network, changes in its material and technical base and personnel equipment; consider the main forms of drug provision for the population; to analyze the directions and ways of supplying the region with medicines and medical equipment, including through the manufacture of medicines at the local level; to study the impact of state policy in the field of healthcare on the development of drug provision and pharmacy network of the region. Methodology. The author of the article, when studying this topic, relies on the principles of historicism, con-sistency and scientific objectivity. All this implies a balanced, comprehensive study of the studied archival documents and published sources using problem-chronological and system-structural methods. In addition, fundamental analytical methods were used, including analysis, synthesis, and comparison. This made it possible to objectively assess the problems, main directions and results of the restoration and development of the drug supply and pharmacy network of the Kursk region in the first post-war twentieth anniversary. Results. On the basis of archival materials, which are being introduced into scientific circulation for the first time, the article reveals the problems faced by the provision of medicines and the development of the pharmacy network in the regions of our country in the first post-war twentieth anniversary, as important aspects of the development of healthcare in general. This made it possible to objectively assess the main directions of this development, including identifying its difficulties and shortcomings. Conclusion. The author concludes that the drug supply and pharmacy network of the Kursk region in the first post-war twentieth anniversary achieved tangible results. It was possible to restore not only what was destroyed during the war, but also to make a significant step forward in this direction. However, many problems in the development of the pharmacy chain still remained unresolved.

Research on Rolling Co-Optimization of Fault Repair and Service Restoration in Distribution Network Based on Combined Drive Methodology

Research on Rolling Co-Optimization of Fault Repair and Service Restoration in Distribution Network Based on Combined Drive Methodology

A Dynamic Facility Location Problem with Random Demand and the Restoration of Transportation Networks in Disaster Relief

A Dynamic Facility Location Problem with Random Demand and the Restoration of Transportation Networks in Disaster Relief

Service Restoration in Distribution Networks Using Available Data Including Uncertainty

The growth in penetration of photovoltaic generation systems (PVs) makes it difficult to reconfigure electrical power distribution networks, including service restorative operations. This is because distribution network operators cannot monitor or control the PVs, and this brings an additional uncertainty in available data that the operators must rely on. The authors focus on the service restorative operations and propose a problem framework and its solution method that finds the optimal restoration configuration under extensive PV installation. The traditional service restoration problems require accurate data on load sections; however, it is not practical in the distribution networks where PVs are extensively installed. Based on this background, the robust optimization and the two-stage stochastic programming are applied in the proposed formulation to address the PV-originated uncertainty using only readily available data. Through numerical simulations and discussion on their results, the validity of the proposal is verified.

Manufacturing Free-Standing Graphene Oxide/Carbon Nanotube Hybrid Papers and Improving Electrical Conductivity by a Mild Annealing Treatment

The present work aims to evaluate the manufacturing processes of free-standing graphene oxide (GO)/reduced graphene oxide(rGO)/carbon nanotube (MWCNT) papers-like and the effect on electrical conductivities after annealing treatment. The pure GO and the hybrid papers were obtained by vacuum filtration and submitted to mild thermal annealing at 400 °C under an argon atmosphere. Analyses comparing the carbon paper-like materials before and after thermal treatment were done by thermogravimetric analysis (TGA), Raman spectroscopy, scanning electron microscopy (SEM), and electrical conductivity measurements. The characterization results show the annealing process effect on the morphology of paper materials. The GO papers and the hybrid (GO/rGO/MWCNT) exhibited good free-standing structure properties. The SEM results suggest the formation of gas void due to the thermal treatment. TGA and Raman results showed the thermal reduction and restoration of the carbon sp2 network of the graphene oxide induced by thermal treatment. The thermal treatment was responsible for changing the almost insulator characteristic of free-standing pure GO paper, achieving an electrical conductivity of 752 S/m, and improving 45% of the free-standing hybrid carbon nanomaterials papers (GO/rGO/MWCNT) electrical conductivity, achieving the value of 1020 S/m.