Network Maintenance Research Articles

Siltation of Small Water Reservoir under Climate Change: A Case Study from Forested Mountain Landscape of Western Carpathians, Slovakia

The effectiveness of small reservoirs in a mountain landscape continuously decreases over time due to the gradual siltation. We examined two hypotheses about the enhanced siltation rate and the constant relative contribution of unpaved roads to the siltation of a small water reservoir in the period 1990–2014, with pronounced climate change, compared to the reference period 1970–1989. Analysis was based on deposit volumes extracted from the reservoir in 1989 and detected at the reservoir bottom in 2014. The geographical information systems, image analysis and universal soil loss equation were applied to model the soil erosion according to the two sources—catchment area and roads—to study siltation processes in depth. Despite expectations, rates of siltation were almost unchanged in compared periods. The positive changes in vegetation cover (the forest expansion and changes in forest management practices) offset the enlarged rain erosivity. On the other hand, road erosion increased by 41% from 1990 and became twice as much a contributor to sediments compared to 1970–1989. The intensity of erosion from roads was from 13 to 29 times greater than from other areas. Therefore, proper maintenance of road networks could rapidly decrease reservoir siltation. Moreover, we recommend continuous cover forestry as a critical measure for central Europe’s forested regions to prevent growing erosion pressures.

Building Networks to Promote Knowledge of Brazil’s Biodiversity: The experience of the INCT - Virtual Herbarium

The Botanical Society of Brazil (SBB) for many years discussed the need to improve the nation’s herbarium collections, by providing training and educating botanists as specialists in plant and fungal taxonomy. It was in this context that an audacious project was developed, which envisaged personnel training for studies in plant and fungal diversity (particularly taxonomy and curation of collections) and the organization and online delivery of the label data of plant and fungal specimens from the herbaria of Brazil - the National Institute of Science and Technology (INCT) Virtual Herbarium. The INCT-Virtual Herbarium had the benefit of the expertise from existing initiatives which, when brought together, made its development possible within a short period of time. These initiatives included: the Herbaria Network of SBB; the Herbarium Network of the Northeast; the Brazilian National Research and Education Network (RNP); and the Reference Center for Environmental Information (CRIA), responsible for the development and maintenance of the speciesLink Network. When it began in 2009, the INCT-Virtual Herbarium brought together 25 Brazilian herbaria sharing 48 plant and fungal databases online (one herbarium can share more than one data set, e.g., plants, fungi, carpological collection), and two US herbaria. Today, the network includes 140 Brazilian herbaria and 25 herbaria from other countries, which hold collections originating from Brazil, thus creating a national virtual herbarium with 190 data sets, sharing over 11 million data records and over 4.5 million associated images. The INCT-Virtual Herbarium is not only a data aggregator, but also an active and integrated community with common objectives such as: free sharing of good quality data; fomenting the important role of herbaria in documenting knowledge of the flora and funga, training, and developing tools and systems to improve data quality and in identifying data and knowledge gaps to help plan new collecting expeditions. The INCT-Virtual Herbarium chose to adopt a collegiate management model, led by a Management Committee of six members from different institutions, together with coordinators of the following subject areas: flowering plant taxonomy, taxonomy of cryptogams, personnel training, liaison with herbaria, product research, and online information systems. A variety of indicators and applications were developed to make online monitoring of activities viable and these are all freely available to anyone interested. Not only the Management Committee but also each partner herbarium can follow the evolution of the INCT, as well as that of each herbarium, in qualitative and quantitative terms. Besides the scientific community, the INCT reaches the general public and decision-makers, providing information that has improved the understanding of preservation and conservation of Brazil’s biological heritage, represented by the botanical and mycological collections. The INCT Virtual Herbarium adopted the speciesLink platform as the basis for its information system. This platform began its development in 2001 with support from the São Paulo Research Foundation (FAPESP), and in 2008 was the only network in Brazil with national scope and international recognition. Some initial decisions on the architecture of the network, which would serve the INCT-Virtual Herbarium were fundamental to its success: the use of international standards and protocols; the concept of minimum interference with the collections, where each data provider could retain sensitive data, having full control of its data; the acceptance of the data without a quality filter, as the network has important tools to identify errors; any changes or corrections are made only by those responsible for the curation of the data; recognition and credit awarded to each and every participant. The speciesLink network uses the Darwin Core data standard (Wieczorek et al. 2012) and participated actively with the Global Biodiversity Information Facility (GBIF) and other networks in developing the DiGIR protocol (Distributed Generic Information Retrieval) and its later evolution the TAPIR protocol (TDWG Access Protocol for Information Retrieval). The use of these standards and protocols facilitated the process of repatriating collection data of Brazilian material deposited in herbaria in other countries. In 2015 the INCT Virtual Herbarium started to transfer its data sets to GBIF and SiBBr, the Brazilian Biodiversity Information System and the Brazilian node of GBIF. These data sets are updated every month, but their updating in SiBBr and GBIF depends on indexing actions of these networks. The development of the INCT-Virtual Herbarium network has shown that a large community of widely dispersed scientists can work together around an important objective, providing Brazil's biodiversity data, and benefiting the whole community.

The Application of AI-Based Technology in Computer Network Operation and Maintenance

As an emerging IT technology, artificial intelligence has unique advantages in data extraction and data processing. In this paper, an artificial intelligence search method based on the K-means algorithm is proposed. According to the similarity between data samples, the samples are divided into many different categories. The ARIMA algorithm is used to perform regression analysis on the traffic data, and a Boosting model is established to improve the time series prediction accuracy. And, we use the relative distance between elements to represent the degree of dissimilarity of different types of variables. It can process massive data in a short time and obtain user behavior information. A large number of experiments show that the method proposed in this paper can complete information search more effectively and complete information classification quickly.

Reconstruction and variability of tropical pollination networks in the Brazilian Atlantic Forest

Loss of biodiversity comprehends not only the extinction of individual species, but also the loss of the ecological interactions among them. Survival of species, continuation of ecosystem functioning in nature, and ecosystem services to humans depend on the maintenance of well-functioning networks of species interactions (e.g. plant–pollinator networks and food webs). Analyses of ecological networks often rely on biased and incomplete survey data, especially in species-rich areas, such as the tropics. We used a network inference method to reconstruct pollination data compiled from a large tropical rainforest habitat extent. To gain insight into the characteristics of plant–pollinator interactions across the region, we combined the reconstructed pollination network with species distribution modelling to obtain local pollination networks throughout the area. We explored how global network properties relate to natural forest cover and land cover heterogeneity. We found that some network properties (the sum and evenness of link weights, connectance and nestedness) are positively correlated with forest cover, indicating that networks in sites with more natural habitat have greater diversity of interactions. Modularity was not related to forest cover, but seemed to reflect habitat heterogeneity, due to the broad spatial scale of the study. We believe that the methodology suggested here can facilitate the use of incomplete network data in a reliable way and allow us to better understand and protect networks of species interactions in high biodiversity regions of the world.

Retinal microglia protect against vascular damage in a mouse model of retinopathy of prematurity.

Retinopathy of prematurity (ROP) is a common cause of blindness in preterm babies. As a hypoxia-induced eye disease characterized by neovascularization, its association with retinal microglia has been noted but not well documented. We performed a comprehensive analysis of retinal microglia and retinal vessels in mouse oxygen-induced retinopathy (OIR), an animal model of ROP. In combination with a pharmacological inhibitory strategy, the role of retinal microglia in vascular network maintenance was investigated. Postnatal day (P) 7 C57BL/6J mouse pups with their nursing mother were exposed to 75% oxygen for 5 days to induce OIR. Age-matched room air-treated pups served as controls. On P12, P17, P21, P25, and P30, retinal microglia and vessels were visualized and quantified based on their location and activation status. Their relationship with retinal vessels was also analyzed. On P5 or P12, retinal microglia inhibition was achieved by intravitreal injection of liposomes containing clodronate (CLD); retinal vasculature and microglia were examined in P12 and P17 OIR retinae. The number of retinal microglia was increased in the superficial areas of OIR retinae on P12, P17, P21, P25, and P30, and most of them displayed an amoeboid (activated) morphology. The increased retinal microglia were associated with increased superficial retinal vessels in OIR retinae. The number of retinal microglia in deep retinal areas of OIR retinae also increased from P17 to P30 with a ramified morphology, which was not associated with reduced retinal vessels. Intravitreal injection of liposomes-CLD caused a significant reduction in retinal microglia. Loss of retinal microglia before hyperoxia treatment resulted in increased vessel obliteration on P12 and subsequent neovascularization on P17 in OIR retinae. Meanwhile, loss of retinal microglia immediately after hyperoxia treatment on P12 also led to more neovascularization in P17 OIR retinae. Our data showed that activated microglia were strongly associated with vascular abnormalities upon OIR. Retinal microglial activation continued throughout OIR and lasted until after retinal vessel recovery. Pharmacological inhibition of retinal microglia in either hyperoxic or hypoxic stage of OIR exacerbated retinal vascular consequences. These results suggested that retinal microglia may play a protective role in retinal vasculature maintenance in the OIR process.

Development of Algorithm for Calculating Data Packet Transmission Delay in Software-Defined Networks

The relevance of this type of network is associated with the development and improvement of protocols, methods, and tools to verify routing policies and algorithmic models describing various aspects of SDN, which determined the purpose of this study. The main purpose of this work is to develop specialized methods to estimate the maximum end-to-end delay during packet transmission using SDN infrastructure. The methods of network calculus theory are used to build a model for estimating the maximum transmission delay of a data packet. The basis for this theory is obtaining deterministic evaluations by analyzing the best and worst-case scenarios for individual parts of the network and then optimally combining the best ones. It was found that the developed method of theoretical evaluation demonstrates high accuracy. Consequently, it is shown that the developed algorithm can estimate SND performance. It is possible to conclude the configuration optimality of elements in the network by comparing the different possible configurations. Furthermore, the proposed algorithm for calculating the upper estimate for packet transmission delay can reduce network maintenance costs by detecting inconsistencies between network equipment settings and requirements. The scientific novelty of these results is that it became possible to calculate the achievable upper data delay in polynomial time even in the case of arbitrary tree topologies, but not only when the network handlers are located in tandem. Doi: 10.28991/ESJ-2022-06-05-010 Full Text: PDF

Deep learning technology of Internet of Things Blockchain in distribution network faults

AbstractNowadays, the development of human society and daily life are inseparable from the power supply. Therefore, people also put forward higher requirements for the reliability of distribution network, but power companies can only passively deal with distribution network failures, which is a bottleneck for the improvement of distribution network reliability. The Internet of Things (IoT) is the best solution for online equipment status monitoring and basic data sharing for large, widely distributed, relatively fixed, and large numbers of equipment. The construction of the IoT for power distribution equipment faces many important problems, including the selection of networking, equipment selection, and interaction standards. When researching the implementation plan, research on the distribution of IoT market was carried out. Based on the grid, the idea of optimizing the investment selection plan of the power distribution using IoT was discussed, and a result verification model was established. After the completion of the theoretical part, a case study of medium-voltage distribution grid equipment management and medium-voltage distribution network equipment management based on the grid was carried out by taking a real enterprise application situation as an example. Realizing fault diagnosis of distribution network will not only provide decision support for operation and maintenance of distribution network for power companies, but also have great economic and social benefits. Aiming at the shortcomings of single data mining method in distribution network fault diagnosis, hybrid data mining method is proposed. First, rough set theory is used to reduce the original fault data and form a simplified rule set. Because of the non-linearity of distribution network fault and the strong learning ability, adaptability, and robustness of Bayesian network, Bayesian network can be used to classify distribution network faults. Therefore, a simplified fault diagnosis system is established in this paper, and its correctness is confirmed. Then, the learning and training are carried out by using Bayesian network to call the simplest rule set, which has the characteristics of short learning and training time and high diagnostic accuracy.

Designing of multi-objective optimal virtual power plant model for reliability enhancement in radial network: a case study of Indian power sector

One of the major driving factors in the shifting of the present grid paradigm to an active grid network is the reliability and resiliency of the utility network. With hefty investment in the distribution network protection and maintenance, the reliability of the feeders is considerably enhanced; however, large numbers of outages are still occurring every year which caused major production loss to the manufacturing sector. In this paper, the role of the solar grid-based Virtual Power Plant (VPP) is evaluated in the state power utility for the reliability enhancement and cost minimization using a multi-objective model based on MILP optimization. A 90 bus industrial feeder having automatic reclosers, DER, and DSM is selected on which the MCS method is utilized for computing reliability indices using the utility reliability parameters. The value of reliability indices such as EENS is declined by 68% by utilizing the VPP scenario. These values of this reliability index are fed into the multi-objective model for cost minimization. After running the optimization, the results reveal that the operational and the annual energy cost are reduced by 61% and 55% respectively which advocates the VPP implementation in the utility network. Both modes of the Virtual Power Plant such as grid-connected and autonomous mode have been discussed in detail. Lastly, the results of the developed model with MILP are compared with the proprietary derivative algorithm, and it is found that the proposed MILP is more cost-effective. The overall results advocate the VPP implementation in the utility grid as the economical advantage is provided to both utility and the consumers in terms of reduction in EENS and energy charges respectively.

Novel DNM1L variants impair mitochondrial dynamics through divergent mechanisms.

Imbalances in mitochondrial and peroxisomal dynamics are associated with a spectrum of human neurological disorders. Mitochondrial and peroxisomal fission both involve dynamin-related protein 1 (DRP1) oligomerisation and membrane constriction, although the precise biophysical mechanisms by which distinct DRP1 variants affect the assembly and activity of different DRP1 domains remains largely unexplored. We analysed four unreported de novo heterozygous variants in the dynamin-1-like gene DNM1L affecting different highly conserved DRP1 domains, leading to developmental delay, seizures, hypotonia, and/or rare cardiac complications in infancy. Single-nucleotide DRP1 stalk domain variants were found to correlate with more severe clinical phenotypes, with in vitro recombinant human DRP1 mutants demonstrating greater impairments in protein oligomerisation, DRP1-peroxisomal recruitment, and both mitochondrial and peroxisomal hyperfusion compared to GTPase or GTPase-effector domain variants. Importantly, we identified a novel mechanism of pathogenesis, where a p.Arg710Gly variant uncouples DRP1 assembly from assembly-stimulated GTP hydrolysis, providing mechanistic insight into how assembly-state information is transmitted to the GTPase domain. Together, these data reveal that discrete, pathological DNM1L variants impair mitochondrial network maintenance by divergent mechanisms.

Skills and Capabilities of Public Health Informatics in Health Workers

Objectives:To identify skills and capabilities related to public health informatics in health workers who work in public health service institutions, in order to know the informatics-related aspects of the work carried out by public health workers.Method:This research is a quantitative descriptive study with a cross sectional approach. The sampling technique is simple random sampling. The sample was obtained as many as 133 health workers who have been actively working for at least 1 year at the Primary Health Service/Puskesmas or District/City Health Offices in Banyumas Regency, as well as with the main task/work in public health services. The research instrument used is a questionnaire. Data analysis using univariate analysis.Results :Based on the results of the study, the results of the skills and capabilities of use and analysis of data were good (65.4%); system design skills and capabilities are good (53.4%); media design skills and capabilities are good 69.9%; skills and capabilities of routine use of informatics system (66.9%); network maintenance skills and capabilities (60.2%); database administrator capability skills (62.4%). Based on the percentage of the results of the checklist from the question “Which of the following activities are you able to do or do at/for your health institution”, public health workers in general have a higher percentage than sanitarians, nutrition workers, midwives, nurses and doctors.Conclusions: Based on the results of the study, the results of the skills and capabilities of use and analysis of data, system design, media design, routine use of informatics system, network maintenance, and database administrator were good. Public health workers in general have a higher percentage than another health workers who work related to public health services. Suggestions: It is advisable to hold regular training to upgrade and update the skills and capabilities of health workers related to public health informatics, according to the development of information and communication technology utilized. in public health service institutions.

A Novel, Efficient, Green and Real-Time Load Balancing Algorithm for 5G Network Measurement Report Collecting Clusters

With the wide application of data mining and deep learning in mobile cellular network operation and maintenance, network measurement report (MR) plays an increasingly important role in artificial intelligence for IT operations (AIOps). For the integrity of MR reported by the operation and maintenance (OM) proxy of base station, existing collecting methods are typically based on static distributed clustering. Due to the lack of effective load balancing scheme, nevertheless, these methods typically result in some issues, e.g., low collecting efficiency, poor scalability, and excessive number of servers. Thus, in this work, leveraging the historical law of uploading MR for load forecasting, we propose the weighted least-connection load balancing algorithm (LPWLC) based on load forecasting. First, the historical law of reported MR is utilized to predict the load. Second, using the strategy of static binding and dynamic load adjustment, we bind OM with the assigned server in one cycle, calculate the server load in real-time, and evaluate the server weight by the load of each server. Finally, real-time load adjustment is carried out in line with the number of request connections and the weight of servers. Compared with the existing ones, the proposed algorithm could remove backup servers, thereby effectively reducing the cost and power consumption. Compared with the existing methods, this method has improved the load balancing degree by 28%, and reduced the energy consumption by 104[Formula: see text]W per hour.

CAPRIN1 haploinsufficiency causes a neurodevelopmental disorder with language impairment, ADHD and ASD.

We describe an autosomal dominant disorder associated with loss-of-function variants in the Cell cycle associated protein 1 (CAPRIN1; MIM*601178). CAPRIN1 encodes a ubiquitous protein that regulates the transport and translation of neuronal mRNAs critical for synaptic plasticity, as well as mRNAs encoding proteins important for cell proliferation and migration in multiple cell types. We identified 12 cases with loss-of-function CAPRIN1 variants, and a neurodevelopmental phenotype characterized by language impairment/speech delay (100%), intellectual disability (83%), attention deficit hyperactivity disorder (82%) and autism spectrum disorder (67%). Affected individuals also had respiratory problems (50%), limb/skeletal anomalies (50%), developmental delay (42%) feeding difficulties (33%), seizures (33%) and ophthalmologic problems (33%). In patient-derived lymphoblasts and fibroblasts, we showed a monoallelic expression of the wild-type allele, and a reduction of the transcript and protein compatible with a half dose. To further study pathogenic mechanisms, we generated sCAPRIN1+/- human induced pluripotent stem cells via CRISPR-Cas9 mutagenesis and differentiated them into neuronal progenitor cells and cortical neurons. CAPRIN1 loss caused reduced neuronal processes, overall disruption of the neuronal organization and an increased neuronal degeneration. We also observed an alteration of mRNA translation in CAPRIN1+/- neurons, compatible with its suggested function as translational inhibitor. CAPRIN1+/- neurons also showed an impaired calcium signalling and increased oxidative stress, two mechanisms that may directly affect neuronal networks development, maintenance and function. According to what was previously observed in the mouse model, measurements of activity in CAPRIN1+/- neurons via micro-electrode arrays indicated lower spike rates and bursts, with an overall reduced activity. In conclusion, we demonstrate that CAPRIN1 haploinsufficiency causes a novel autosomal dominant neurodevelopmental disorder and identify morphological and functional alterations associated with this disorder in human neuronal models.

Intelligent failure localization and maintenance of network based on reliability

Intelligent failure localization and maintenance of network based on reliability

Stochastic control of ecological networks.

The paper models the maintenance of ecological networks in forest environments, built from bioreserves, patches and corridors, when these grids are subject to random processes such as extreme natural events. It also outlines a management plan to support the optimized results. After presenting the random graph-theoretic framework, we apply the stochastic optimal control to the graph dynamics. Our results show that the preservation of the network architecture cannot be achieved, under stochastic control, over the entire duration. It can only be accomplished, at the cost of sacrificing the links between the patches, by increasing the usage of the control devices. This would have a negative effect on the species migration by causing congestion among the channels left at their disposal. The optimal scenario, in which the shadow price is at its lowest and all connections are well-preserved, occurs at half of the course, be it the only optimal stopping moment found on the stochastic optimal trajectories. In such a scenario, the optimal forestry management policy has to integrate agility, integrated response, and quicker response time.

Markov Chains for Fault-Tolerance Modeling of Stochastic Networks

Most real-world networks are time-varying, and many are subject to the stochastic functioning of their nodes and edges. Examples can be seen in the human brain undergoing an epileptic seizure, spontaneous infection and recovery in epidemics, and intermittent functioning of devices in the Internet of Things. Moreover, such networks are becoming increasingly large due to rapid technological advances. However, little has been done to study time-varying, large-scale, stochastic networks (SNs) from a reliability engineering perspective. Toward this goal, this article develops a fault-tolerance model for a type of time-varying network in which nodes (and/or edges) stochastically switch between active and inactive states. It considers fault tolerance from a global connectivity point of view, which has applications in many natural and engineered networks. Specifically, this article presents a Markov chain framework that models the dynamic behavior of nodes and allows for the computation of quantitative measures, including availability and time-to-failure metrics. To accommodate large-scale networks and emphasize global connectivity, this framework utilizes percolation theory, which has recently been of interest in the reliability engineering discipline, to characterize network failure. This article makes several contributions: it proposes a Markov chain framework for computing fault-tolerance metrics that is tractable for large-scale networks, it shows the existence of a phase transition in network availability of a time-varying SN, and it accounts for finite-size effects of percolation in the fault-tolerance model. The proposed methodology is applied to Erdös–Rényi random graphs and a real, large-scale power grid. Experimental results provide insights into network design, maintenance, and failure prevention of time-varying SNs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work develops a fault-tolerance model for time-varying stochastic networks in which nodes (and/or edges) randomly switch between active and inactive states. To address increasingly large-scale networks that are being studied, this article appeals to percolation theory. Fault tolerance is, thus, studied from a global connectivity perspective where the existence of a large connected component containing most of the nodes characterizes the functioning of the network. Specifically, this article presents a continuous-time Markov chain (CTMC) framework that models node dynamics and allows for the computation of fault-tolerance metrics, including network availability and mean time to failure. The proposed framework computes metrics efficiently for large networks and allows for studying their asymptotics. The percolation threshold describing the dissolution of the large connected component is used as the failure criterion in the CTMC. The practitioner should note that several assumptions are made in the proposed CTMC framework: nodes possess identical and time-invariant failure and recovery rates, node failure and recovery times are exponentially distributed, and node dynamics are independent of one another. In addition, fault-tolerance metrics computed for finite networks are estimators of the true metric values. The proposed framework is advantageous for quantifying the fault tolerance of large-scale, time-varying networks where the combinatorial explosion and a changing network topology pose challenges to the use of traditional reliability methods. A case study of a power grid network shows how to apply the proposed methodology to real networks.

Optimizing Node Deployment in Rechargeable Camera Sensor Networks for Full-View Coverage

Full-view coverage realized by camera sensor networks (CSNs) is highly demanded for monitoring and recognizing objects appearing at target points. However, it aggravates the energy shortage in CSNs as caused by the need to generate and process much sensed data. Undoubtedly, enabling CSN nodes to be rechargeable and harvest energy from their surroundings is an effective method to overcome the energy limitation of a CSN and ensuring its perpetual operation. Moreover, using rechargeable nodes can avoid the replacement of batteries, and thus can reduce network maintenance cost. In this article, we investigate how to design and deploy a rechargeable CSN with the fewest nodes to achieve full-view coverage of all target points while guaranteeing its connectivity and perpetual operation. We first formulate the problem as an integer linear program and prove its NP-hardness, and then propose a greedy heuristic and a differential evolution algorithm to solve it. Extensive simulation results reveal that the latter is able to achieve a larger success rate and higher solution quality but spends more time than the former.

A hybrid approach for optimization of electric power distributed networks with photovoltaic sources

This paper presents a hybrid approach, which associates a mathematical programming technique with a metaheuristic, to optimize the electricity distribution network with penetration of photovoltaic (PV) generation. This optimization considers the distribution network reconfiguration (DNR) and the adjustment of the power factor of photovoltaic (PV) inverters about the reactive power, controlling the voltage profile and minimizing network losses. The methodology uses particle swarm optimization (PSO) for DNR and an optimal power flow (OPF) to adjust the value of the PV inverters and maintenance of network operating limits, this being a great difference from other methodologies. The objective of the hybrid approach is to obtain the best result for the problem, taking advantage of the best characteristics of both techniques. The hybrid approach was compared with the PSO, highlighting its superiority in terms of the solutions found. The tests were performed using the AMPL Modeling Language in the 33-node system with the addition of PV generation. The approach considers the variations in the powers generated by the PV systems and the variations in the loads over 24 h. The results indicate that the combination of PV inverter control and DNR can reduce the active power losses by more than 37% in 24 h.

Estimating greenhouse gas emissions arising from the maintenance of sewer networks

As the push to reduce global carbon dioxide emissions intensifies, infrastructure managers are rethinking how maintenance is planned and executed to minimise greenhouse gases (GHGs) without sacrificing the performance of their infrastructure networks. This paper addresses this need. The work includes a detailed description of the estimation of GHG emissions that arise from common sewer maintenance interventions. These detailed estimates were subsequently used to project emissions from three maintenance scenarios for a large sewer network. The study found that emissions from maintenance mainly originate from materials and that emissions from transport, traffic congestion and machinery were of secondary importance. The current practice promises the lowest emissions up to 2035 but does not address the ageing pipe network. As a consequence, the network condition and performance deteriorate further as failures rapidly accumulate. Embracing preventive maintenance coupled with circular economy thinking is shown to be an effective solution to improve the network condition and performance while at the same time offering savings of 6222 t carbon dioxide a year. These findings provide infrastructure managers with a detailed example of the potential of circular economy thinking in reducing long-term emissions along with insights into the expected trade-offs that arise with network performance and resource utilisation.

Prediction of Minimum Night Flow for Enhancing Leakage Detection Capabilities in Water Distribution Networks

In South Korea, a water supply enhancement project is being carried out to preemptively respond to drought and water loss by reducing pipeline leakages and supplying stable tap water through the maintenance of an aging water supply network. In order to reduce water leakage, a District Metered Area (DMA) was established to monitor and predict the minimum night flow based on flow data collected from IoT sensors. In this study, a model based on Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) was constructed to predict the MNF (minimum night flow) of County Y. The prediction of MNF results was compared with the MLP networks and the LSTM model. The outcome showed that the LSTM-MNF model proposed in this study performed better than the MLP-MNF model. Therefore, the research methods of this study can contribute to technical support for leakage reductions by preemptively responding to the expected increase in leakage through the prediction of the minimum flow at night.

Spatial assessment of the inland excess water presence on subsurface drained areas in the Körös Interfluve (Hungary)

Due to extreme meteorological and soil hydrological situations the agricultural production security is highly unpredictable. To release the extent and duration of inland excess water (IEW) inundations or two-phase soil conditions during the period intended for cultivation, subsurface drainage (SD) has been used as a best practice in several countries. SD interventions took place between 1960’s and 1990 in Hungary. After 1989, land ownership conditions changed, thus professional operation and the necessary maintenance of the SD networks designed as a complex system became insignificant. In this paper, our aim was to present the IEW hazard in one of the most equipped areas by SD in Hungary. The occurrence frequency of IEW inundations in drained and non-drained (control) areas in different time intervals were compared. According to our results, we could state that the frequency of IEW on the subsurface drained areas was moderately lower in only a few periods compared to the control areas. IEW hazard of the arable areas at the Körös Interfluve was classified as nonhazarded in 52.7% of the area. Another 38.2% were moderately hazarded, 8.26% of the lands were meanly hazarded and less than 1% were highly hazarded area by IEW.