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Ecological management zoning is crucial for maintaining regional ecological security and realizing differentiated urban ecological governance. However, the existing zoning methods are overly focused on ecological functional attributes and fail to adequately consider the impacts of human activities, resulting in an insufficiently rational allocation of resources. Taking Guizhou Province as an example, using multi-source data and spatial analysis tools, this study proposed an ecological management zoning framework based on the coupling analysis of the blue-green infrastructure (BGI) network and gray infrastructure (GI) network. The results indicated that (1) the BGI network in the study area included 179 sources, with a total area of 54,228.80 km2, and 232 corridors. (2) There were 53 sources in the GI network, totaling 709.19 km2, and the corridors of the first, second, and third levels were 11,469.31 km, 6703.54 km, and 5341.30 km, respectively. (3) There were 606 barrier points identified, mainly distributed in the central part of the study area, and the total area of the disturbance zone was 1132.50 km2, which had the largest distribution in Qiandongnan, followed by Qiannan. (4) At the county scale, five ecological management zones were identified in the study area based on four indicators, namely, the source area ratio of BGI network, corridor density of BGI network disturbance zone area ratio, and density of barrier point. Then, we proposed targeted optimizations and restorations for each zone. This study organically linked ecological functional attributes and anthropogenic impacts to identify ecological management zones, which will provide new perspectives on synergies between ecological protection and economic development.

In this work, the authors are focusing on various aspects to support the reliability and security of a self-powered disaster recovery network (DRN) infrastructure. Different methods and algorithms were suggested to resolve the different logistic requirements to build a Green DRN Infrastructure. First of all the architecture of a DRN node is enhanced to support its robustness and availability against the different failure events which may occur due to many reasons such as power supply shortage or Denial of Service attacks. Secondly, the DRN Infrastructure Clustering algorithm is suggested to limit the size of the ad-hoc network and to isolate the problems in each cluster which offers a more efficient, robust and secured system. Finally, several fault tolerance procedures are described which provide different solutions against nodes or DRN server failure. This study also discussed the need to ensure the successful combination of different security solutions (using an Embedded Cooperative Hybrid Intrusion Detection System) and reliability methods (using the DRN clustering algorithm) with a probably managed solar energy powered. The different factors governing the behaviour of the system, its evaluation metrics are determined and its performance measurement is tested using an experimental platform customised for this purpose.

This study is part of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world's knowledge of physical infrastructure in Africa. The AICD will provide a baseline against which future improvements in infrastructure services can be measured, making it possible to monitor the results achieved from donor support. It should also provide a more solid empirical foundation for prioritizing investments and designing policy reforms in the infrastructure sectors in Africa. The AICD is based on an unprecedented effort to collect detailed economic and technical data on the infrastructure sectors in Africa. The project has produced a series of original reports on public expenditure, spending needs, and sector performance in each of the main infrastructure sectors, including energy, information and communication technologies, irrigation, transport, and water and sanitation. The first phase of the AICD focused on 24 countries that together account for 85 percent of the gross domestic product, population, and infrastructure aid flows of Sub-Saharan Africa. Under a second phase of the project, coverage is expanding to include as many of the additional African countries as possible.

Purpose: This article focuses on the problem of deciding the annual investment that a company should allocate to the rehabilitation of its water distribution and sanitation networks. The objective is to find the investment amount necessary to maintain an adequate quality and sustainability of the infrastructure. It is not a simple decision, as there are different criteria that may be of interest to the managing company. In this paper, we consider four criteria related to the reliability of individual pipes and the complete network. These indicators are the infrastructure value index, the average age of network pipes, the risk index and the probability of failure.Design/methodology/approach: A methodology is proposed to estimate the best annual investment by analysing the evolution of these indicators. Concretely, two strategies are tested. The first one is a minimax-based approach that seeks a balanced solution for all the indicators. The second one is named as minimal deviation strategy and seeks to minimise the deviation of all the indicators in the last year of the time horizon compared to the initial year.Findings: In order to obtain a realistic sample of the performance of both strategies, 201 scenarios, i.e. 201 different annual investments have been tested. According to the first strategy, an annual investment of 55.5 M€ is the best option, while the minimal deviation strategy presents an annual investment of 39.5 M€ as the best decision. The study reveals that different evaluation functions lead to completely different annual investment. Concretely, the minimax evaluation function is more conservative than the minimal deviation strategy.Originality/value: This study proposes an original approach to address the decision problem of investments in asset management. To the best of the authors’ knowledge, it is the first attempt to treat that problem using this kind of evaluation functions. However, it is still a simple proposal and there are many options to continue this line of research.

This paper discusses the effect of network infrastructure on environmental pollution reduction and the realization mechanism behind it. Based on the panel data of 285 cities in China from 2005 to 2019, this study regards the “Broadband China” pilot policy as a quasi-natural experiment to clarify the pollution emission reduction effect of network infrastructure construction through differences-in-differences method and other methods. The research results show the following: (1) The Broadband China pilot policy has reduced environmental pollution, that is, the construction of network infrastructure has the effect of environmental pollution reduction. The conclusion is still established after a series of robustness tests such as parallel trend test, placebo test, and instrumental variable method. Through the heterogeneity test, it is found that the pollution reduction effect of network infrastructure construction is more obvious in non-resource-based cities, first and second tier cities, and cities in the eastern region (2). The construction of network infrastructure plays a restraining role on local environmental pollution. Due to the insufficient level of regional linkage and the siphon effect of pilot cities, the spatial spillover characteristics of the pollution reduction effect are not obvious (3). The mechanism of action shows that green innovation is an important mediating effect mechanism for network infrastructure construction to reduce environmental pollution. Cities in regions with high degree of marketization and environmental regulation can strengthen the effect of network infrastructure construction on environmental pollution reduction. The research conclusions are conducive to accelerating the development of the digital economy represented by the construction of network infrastructure and provide a useful reference for promoting the level of environmental pollution reduction and achieving high-quality development.

Green infrastructure networks enhance the protection and improvement of urban ecological environments, augment the efficiency and quality of ecosystem services, and furnish residents with healthier and more comfortable living conditions. Although previous research has investigated the construction or optimization methods of green infrastructure networks, these studies have been relatively isolated and lacking in case studies for mountainous cities. In the development of green infrastructure, mountainous cities must specifically consider the impact of terrain on network construction. Taking Fuzhou, a mountainous city in China, as an example, this study constructs and optimizes the green infrastructure network by employing morphological spatial pattern analysis, connectivity analysis, the Minimum Cumulative Resistance model, and circuit theory. These methodologies increase the connectivity of the Green Infrastructure within the study area, thereby promoting the health of the local ecosystem and creating conducive circumstances for the city’s sustainable development. The findings reveal that: (1) Green infrastructure in Fuzhou takes up 5366.38 ha, constituting 21.76% of the study area, primarily situated in the northwest and south; (2) Fuzhou’s Green Infrastructure network comprises 10 hubs and 17 corridors with a hub area of 1306.98 ha, predominantly distributed in the mountains encircling the city, including Meifeng Mountain, Gaogai Mountain, and Qingliang Mountain; (3) Based on optimization, the circuit centrality index categorizes hub importance into three protection levels, pinpointing nine crucial protected areas in the corridors and 680 areas requiring enhancement, including 68 areas for first-level improvement, 149 areas for second-level improvement, and 463 areas for third-level improvement. This research offers a methodological reference for constructing and optimizing green infrastructure networks in mountainous cities, providing both theoretical and practical foundations for optimizing green infrastructure networks in Fuzhou City.

OptiDRL casts MEC resource allocation as a multiobjective decision-making problem, balancing latency, energy usage, and resource utilization. A DRL agent is learned in this context with a well-crafted reward function that balances these objectives. The optical integration provides ultra-low latency and high-throughput communication, further improving system efficiency. We deploy and test OptiDRL on simulation environments mimicking real-world MEC environments. Experimental results show that OptiDRL outperforms current state-of-the-art benchmark algorithms with a significant latency reduction of up to 35 %, resource saving of 25 %, and scalability improvement under changing workload scenarios. This paper proves the potential in integrating optical networking with DRL to advance intelligent MEC resource management.

Rapid progress of computing and info-communication technologies (ICT) has changed the ecosystem of power production and delivery. Today, an energy network is a complex set of interrelated devices and information systems covering all areas of electric power operations and applying ICT based on open standards, such as IEC 60870, IEC 61850, and IEC 61970. According to IEC 62351, the energy networks are faced with high cybersecurity risks caused by open communications, security requirements rarely considered in the energy facilities, partial and difficult upgrades, and incompatibility of secure tools with industrial solutions. This situation results in new security challenges, e.g., denial of service attacks on the connected controllers, dispatching centers, process control systems, and terminals. IEC 62351 describes possible ways to comprehensive security in the energy networks. Most of them used in traditional networks (e.g., firewalls, intrusion detection systems) can be adapted to the energy networks. Honeypot systems as a protection measure help us to mitigate the attacks and maintain necessary security in the networks. Due to the large scale of an energy network and heterogeneity of its components, a new design, deployment, and management strategy for the honeypot systems are required. The paper suggests a new method for organizing a virtual network infrastructure of a hybrid honeypot system and a dynamic management method that adapts the network topology to the attacker’s actions according to the development graph of potential attacks. This technique allows us to dynamically build virtual networks of arbitrary scale. Because of the similarity of the virtual network to the virtualized origin and providing the level of interactivity of its nodes corresponding to real devices, this technique deploys an energy network indistinguishable from the real one for the attackers. A prototype of our honeypot system has been implemented, and experiments on it have demonstrated the more efficient use of the computing resources, the faster reaction to the attacker’s actions, and the deployment of different sizes of virtual networks for the given limits of the computing resources.

Against the backdrop of rapid urbanization, there is a passive adaptation state shown in urban and rural ecological spaces. Due to the shrinking of ecological patches and the fracture of corridors, problems such as the obstruction of ecological processes, the decline of ecosystem services, and the loss of biodiversity occur. Considering that county ecological space is the key level to undertake provincial ecological security patterns and implement ecological demonstration projects, the construction of a county ecological infrastructure (EI) network is beneficial to the protection of regional ecological security, the improvement of the structures and functions of farmland ecosystems, and the enhancement of the quality of human settlements. In this study, taking Langzhong County in Sichuan Province as an example, a method path for an EI network construction was explored, and an optimization strategy for ecological patterns was proposed. Firstly, morphological spatial pattern analysis (MSPA) and a patch importance index were employed to identify ecological sources. Secondly, by constructing a landscape resistance surface and adopting a minimum cumulative resistance (MCR) model, potential EI corridor paths were extracted. Thirdly, the interaction force values between ecological sources were calculated with a gravity model and important ecological corridors were identified for priority protection and restoration. Finally, an EI corridor network was optimized by combining network structure indexes (α, β, and γ) with the field situation, and stepping stone patches and ecological breakpoints were identified. Based on the analysis results, an ecological protection pattern, which involved three vertical and two horizontal ecological belts, four ecological control zones, and six clusters of EI networks in Langzhong County, was put forward, aiming at protecting 50 ecological sources, repairing 105 ecological corridors of different grades, adding 9 stepping stone patches near long-distance corridors and 15 at the intersection of ecological corridors, and repairing 18 ecological breakpoints. This study has guiding significance for the optimization of county ecological patterns, the construction of farmland forest belts, and site selection of ecological restoration projects.

Rapid urbanization results in habitat fragmentation. GIN analysis provides an objective basis for the complete construction of ecological networks. Although there is currently no uniform standard for GIN analysis, this study found that the core region and the connectivity between the core became two key elements of GIN analysis. By analyzing the morphological recognition of GIN, Morphological Spatial Pattern Analysis (MSPA) is targeted in the core area and connectivity analysis, so it has become the mainstream of GIN analysis methods. At the same time, this study finds that there are commonalities in MSPA analysis methods of different scholars. However, it should be noted that GIN analysis by MSPA method in China has two main research gaps: First, it lacks attention to the space scale of urban administrative district level; Second, there is a lack of research on GIN that considers time as a factor. In order to fill these two research gaps, the Shijingshan District of Beijing was selected as the research site to conduct GIN analysis based on Morphological Spatial-Temporal Pattern Analysis (MSTPA). At the same time, the analysis method and data of this study can provide a reference for future scholars to carry out the same type of research.