Critical Infrastructure Components Research Articles

Urbanization inequality: evidence from vehicle ownership in Chinese cities

Unequal outcomes resulting from urbanization can pose a significant challenge to sustainable development. Vehicles are an important urbanization dimension as a critical component of urban infrastructure by providing mobility and accessibility to social services. China’s vehicle ownership (referred to as in-use vehicle stocks) has been growing quickly since 2000, but its per capita stocks are still much lower than that in developed economies. This raises the question of whether and when China’s vehicle stocks will reach a peak level close to that in the developed countries. By analyzing vehicle stocks in 283 Chinese cities during 2001–2018, we have the following findings: (1) vehicle stocks are predominantly distributed in northern and eastern coastal cities and provincial capital cities; (2) inequality in vehicle ownership rates between cities shows a declining trend at both national and region scales; (3) the growth of vehicle ownership rates follows an S-shape curve and most cities are still at the early stage of motorization; (4) China is likely to have a lower saturation level of vehicle ownership rate. These results could help to accurately forecast future vehicle demand in China, estimate the resulting environmental impacts, and explore strategies to achieve carbon neutrality in transportation.

Social value accumulation through Australian aquatic facilities

ABSTRACT Research question Despite being a critical component of public infrastructure, academic research has rarely delved into aquatic facilities as key for acquiring social value. Therefore, this study analyzed how social value is accumulated through public aquatic facilities and commercial swim schools in Australia, specifically exploring the social capital created for local communities. Research methods Twenty-eight interviews with users and managers of aquatic facilities were conducted, representing over 110 facilities across 5 states or territories in Australia. Results and findings Guided by Putnam’s conceptualization of social capital, our data demonstrated the critical importance of aquatic facilities in communities across Australia. The analysis identified that aquatic facilities create a range of benefits to community members, including health and well-being, social connection, safety and education, and employment opportunities. Implications While arguably more difficult to operationalize and quantify, this research provides a foundation to understand the social value, as well as provides avenues to improve the social value achieved by aquatic facilities.

Optimizing Safety Practices and Culture: A Comprehensive Examination through Perception Surveys in Malaysia's Logistics Industry

This study investigates safety practices within Malaysia's logistics industry, a critical component of the nation's economic infrastructure. Employing a quantitative method, specifically a descriptive analysis approach, the research conducts surveys with industry experts and professionals, alongside a quantitative analysis of safety practices. The goal is to comprehensively understand existing safety challenges, identify potential hazards, and assess compliance with safety regulations. Drawing on international best practices, the study benchmarks successful safety models implemented in global logistics hubs, providing valuable insights into the Malaysian context. The outcomes of this research aim to offer a holistic understanding of safety practices and propose actionable recommendations for stakeholders, policymakers, and regulatory bodies. By addressing safety concerns and fostering improvements in protocols, training, and technology integration, the research contributes to the development of a safer and more resilient logistics ecosystem in Malaysia. Ultimately, the study aims to promote sustainable growth in the industry, ensuring the well-being of workers and the public involved in logistics operations while aligning with global standards for safety and security in the logistics sector.

Multi-Scale Adaptive Feature Network Drainage Pipe Image Dehazing Method Based on Multiple Attention

Drainage pipes are a critical component of urban infrastructure, and their safety and proper functioning are vital. However, haze problems caused by humid environments and temperature differences seriously affect the quality and detection accuracy of drainage pipe images. Traditional repair methods are difficult to meet the requirements when dealing with complex underground environments. To solve this problem, we researched and proposed a dehazing method for drainage pipe images based on multi-attention multi-scale adaptive feature networks. By designing multiple attention and adaptive modules, the network is able to capture global features with multi-scale resolution in complex underground environments, thereby achieving end-to-end dehazing processing. In addition, we also constructed a large drainage pipe dataset containing tens of thousands of clear/hazy image pairs of drainage pipes for network training and testing. Experimental results show that our network exhibits excellent dehazing performance in various complex underground environments, especially in the real scene of urban underground drainage pipes. The contributions of this paper are mainly reflected in the following aspects: first, a novel multi-scale adaptive feature network based on multiple attention is proposed to effectively solve the problem of dehazing drainage pipe images; second, a large-scale drainage pipe data is constructed. The collection provides valuable resources for related research work; finally, the effectiveness and superiority of the proposed method are verified through experiments, and it provides an efficient solution for dehazing work in scenes such as urban underground drainage pipes.

Road Damage Assessment

In this research, the emphasis is placed on the vital function of roadway systems in spurring economic growth, connecting different communities, and ensuring access to crucial services like education and healthcare. The study identifies several key factors contributing to the wear and tear of road infrastructures, such as extreme weather conditions, high traffic volumes, suboptimal construction techniques, and lack of regular upkeep. Specifically, the occurrence of potholes is highlighted as a major concern, leading to not only discomfort for commuters but also potential vehicle damage and increased risk of traffic incidents. To address this, the research proposes a novel approach that involves monitoring the flow and categorizing vehicles by their weight to gauge their impact on road surfaces. For the accurate identification and mapping of potholes, the study utilizes the advanced capabilities of the YoloV8 algorithm. This technique is further enhanced by integrating it with a mobile application and Google Maps, enabling a comprehensive and city-wide application of the pothole detection system. Roads are a critical component of a nation's infrastructure, but they are subject to deterioration over the time due to various factors. Road damage assessment software has emerged as an innovative solution to monitor, evaluate, and prioritize road maintenance needs. This report provides a comprehensive overview of road damage assessment software, starting with an introduction, followed by a literature survey, limitations of the existing systems, problem statement, proposed system, framework, design details, methodology, experimental setup, details of the database, performance evaluation, software and hardware setup, future work, and references. Key Words: Road , Potholes , Damage , deep learning, Accidents, YOLOv8

EMERGING TRENDS IN CYBERSECURITY FOR CRITICAL INFRASTRUCTURE PROTECTION: A COMPREHENSIVE REVIEW

As critical infrastructure becomes increasingly interconnected and digitized, the need for robust cybersecurity measures to safeguard essential systems is more pressing than ever. This review article explores the dynamic landscape of cybersecurity for critical infrastructure, focusing on emerging trends, current challenges, and future prospects. The historical overview delves into the evolution of cyber threats, emphasizing the need for adaptive security measures. Key components of critical infrastructure are examined, elucidating the specific challenges each sector faces. The current state of critical infrastructure cybersecurity is analyzed, with a spotlight on frameworks that guide organizations in bolstering their defenses. The heart of the review explores emerging trends in cybersecurity, covering artificial intelligence and machine learning for threat detection, IoT security, blockchain applications, and advancements in cloud computing security. Challenges and threats on the horizon, including advanced persistent threats and quantum computing implications, are scrutinized to provide insights into potential vulnerabilities.
 Keywords: Cybersecurity; Critical Infrastructure; Artificial Intelligence; Internet-of-Things; Blockchain.

Condition Rating Prediction for Highway Bridge Based on Elman Neural Networks and Markov Chains

Bridges are a critical component of transportation infrastructure, playing a vital role in connectivity. The safe operation of bridges demands significant resource and capital investment, particularly as the operation phase is the most extended period in a bridge’s life cycle. Therefore, the efficient allocation of resources and funds is crucial for the maintenance and repair of bridges. This study addresses the need to predict changes in bridge condition over time. The commonly used state-based Markov chain method for bridge condition rating prediction is straightforward but limited by its assumptions of homogeneity and memorylessness. To improve upon this, we propose a novel method that integrates an Elman neural network with a Markov chain to predict the bridge condition rating. Initially, the ReliefF algorithm conducts a sensitivity analysis on bridge features to obtain the importance ranking of these features that affect the bridge condition. Next, six significant features are selected for data classification: bridge age, average daily truck traffic volume, material type, skew angle between bridges and roads, bridge deck structure type, and bridge type. The Elman neural network is then trained to train a prediction model for bridge condition ratings using the classified data, which can predict the condition levels of bridges. The Markov chain’s transition probability matrix is derived using a genetic algorithm to match the deterioration curve predicted by the Elman neural network. This proposed method, when applied to actual bridge data, demonstrates its effectiveness as evidenced by the condition rating of an actual bridge.

Cross trust: A decentralized MA-ABE mechanism for cross-border identity authentication

With an increasing demand for authenticated data exchange between jurisdictions, ensuring the privacy and security of data interactions is crucial for national security, public health, and economic vitality, becoming a fundamental national infrastructure. Current solutions can be categorized into two types: fully decentralized autonomous systems based on blockchains or centralized solutions that rely on authoritative centers such as certification authorities (CAs). In reality, a balance needs to be struck between guaranteed authority and privacy independence. A certain authority is needed as an authorization guarantee, and decentralization is required to ensure privacy and the independence of the authority. This paper proposes a novel scheme, CT-MA-ABE (Cross-Trust Multiple Authorization Attribute-Based Encryption), to address these issues by implementing MA-ABE for cross-border institutional authorization interactions, utilize blockchain certification authority (BCA) for credibility and encryption-based authorization to protect attribute data privacy. This solution integrates the role of 'notary' in cross-border interactions, addressing the supervision problem in fully decentralized approaches while also considering the trust issue in centralized systems. This paper also introduces the Universal Certificate Authority Pool (UCAP), an innovative hybrid federated authorization method, creatively utilizing the implied authorization conditions of attributes to create a flexible and transitive authorization mechanism based on attribute relationships and extensions, enhancing privacy protection and improving the speed of authorization matrix calculation. The successful deployment of the system between the legal jurisdictions in South China, Zhuhai and Macau as a critical infrastructure component for securing data interactions further demonstrates its effectiveness as a reliable and secure solution.

Modeling fatigue-assisted pitting in pipeline steel with PCA-XFEM

We present a localized corrosion and cracking damage mechanism (initiation of the pit, propagation of the pit, pit-to-crack transition, and stable cracking) in a pipeline steel under the application of fatigue loading by using sequential coupling of Probabilistic Cellular Automata (PCA) and eXtended Finite Element Method (XFEM). PCA is utilized for describing the localized corrosion damage mechanism. XFEM is used to simulate arbitrary inhomogeneities (cracks, voids, and soft/hard material interfaces (inclusions)) with the help of Level Set Methods (LSM). In the XFEM, the standard finite element is enriched by additional degrees of freedom by invoking the notion of the partition of unity, which alleviates the burden of mesh conformity to the internal boundaries and avoids re-meshing. LSM is used to locate the voids and material interfaces and track the crack propagation location. A localized strain criterion initiates cracks from the pit surface when the pit reaches a critical strain value.The coupling of PCA and XFEM demonstrates good agreement with experimental data regarding stress-assisted pitting corrosion and pit-induced fatigue cracking in the pipeline steel. The study also investigates the effects of boundaries, and initial pit size on fatigue crack initiation and growth. Boundary effects significantly influence crack initiation life and growth behavior, highlighting the importance of considering both defect characteristics and boundary conditions in assessing fatigue performance and structural integrity. Various initial pit sizes and geometric features further affect crack initiation location and transition time. Furthermore, the PCA-XFEM model effectively captures crack behavior in both short and long crack regimes, demonstrating good agreement with experimental data for a selected pit width of 140μm. By incorporating crack driving parameters derived from experiments, the model accurately predicts crack propagation, further enhancing its reliability and predictive capabilities in assessing fatigue crack behavior. Overall, this approach provides valuable insights for predicting the service life and integrity of pipelines subjected to similar loading conditions, thus aiding in designing and maintaining these critical infrastructure components.

Heat supply pipeline detection based on temperature gradient data of shallow ground

Heat supply pipelines are a critical component of urban infrastructure, with numerous underground projects relying on this crucial and often concealed system. In cases where blueprints are unavailable, it is crucial to determine the precise location and depth of heat supply pipelines. One of the most straightforward methods for detecting these pipelines is measuring underground temperature. Temperature gradient data is less sensitive to environmental temperature changes for detecting shallow heat sources than temperature data. This paper presents an extension of the correlation imaging method to handle temperature gradient data. This method can accurately determine the horizontal center position and the depth of the heat source. This temperature tomographic approach is reliable and has a high resolution, as evidenced by both synthetic and field examples.

A Real-Time Strand Breakage Detection Method for Power Line Inspection with UAVs

Power lines are critical infrastructure components in power grid systems. Strand breakage is a kind of serious defect of power lines that can directly impact the reliability and safety of power supply. Due to the slender morphology of power lines and the difficulty in acquiring sufficient sample data, strand breakage detection remains a challenging task. Moreover, power grid corporations prefer to detect these defects on-site during power line inspection using unmanned aerial vehicles (UAVs), rather than transmitting all of the inspection data to the central server for offline processing which causes sluggish response and huge communication burden. According to the above challenges and requirements, this paper proposes a novel method for detecting broken strands on power lines in images captured by UAVs. The method features a multi-stage light-weight pipeline that includes power line segmentation, power line local image patch cropping, and patch classification. A power line segmentation network is designed to segment power lines from the background; thus, local image patches can be cropped along the power lines which preserve the detailed features of power lines. Subsequently, the patch classification network recognizes broken strands in the image patches. Both the power line segmentation network and the patch classification network are designed to be light-weight, enabling efficient online processing. Since the power line segmentation network can be trained with normal power line images that are easy to obtain and the compact patch classification network can be trained with relatively few positive samples using a multi-task learning strategy, the proposed method is relatively data efficient. Experimental results show that, trained on limited sample data, the proposed method can achieve an F1-score of 0.8, which is superior to current state-of-the-art object detectors. The average inference speed on an embedded computer is about 11.5 images per second. Therefore, the proposed method offers a promising solution for conducting real-time on-site power line defect detection with computing sources carried by UAVs.

Power Station Engine Failure Early Warning System Using Thermal Camera

Power station engines are critical infrastructure components that require constant monitoring to prevent failures and ensure an uninterrupted power supply. This paper proposes a failure early warning system based on a thermal camera using a computer vision approach. The system uses a thermal camera to generate thermal images in a video format, which is then processed by an automated fire detection engine and temperature detection engine. The results of these two subsystems are then used as input for an anomaly detection engine, which predicts the likelihood of engine failure. Based on the results of the experiments, it can be concluded that the YOLOv7 model outperforms Faster R-CNN in detecting fires, achieving a higher mAP score on the one-class dataset. The proposed temperature and anomaly detection system also accurately detected temperature levels and anomalies in thermal images. Furthermore, in the failure time prediction experiment, the Holt-Winters additive method with additive errors, additive trend, and additive seasonality model was identified as the best fit among the models evaluated. In contrast, the Decision Tree model showed good performance and a short training time, making it a good choice for applications where training time is critical. These results highlight the importance of selecting the most suitable method for a given application. Moreover, it demonstrates the effectiveness of different models and approaches for engine failure early warning systems in a power station using a thermal camera.

FAIR Island: real-world examples of place-based open science.

The relationship between people, place, and data presents challenges and opportunities for science and society. While there has been general enthusiasm for and work toward Findable, Accessible, Interoperable, and Reusable (FAIR) data for open science, only more recently have these data-centric principles been extended into dimensions important to people and place-notably, the CARE Principles for Indigenous Data Governance, which affect collective benefit, authority to control, responsibility, and ethics. The FAIR Island project seeks to translate these ideals into practice, leveraging the institutional infrastructure provided by scientific field stations. Starting with field stations in French Polynesia as key use cases that are exceptionally well connected to international research networks, FAIR Island builds interoperability between different components of critical research infrastructure, helping connect these to societal benefit areas. The goal is not only to increase reuse of scientific data and the awareness of work happening at the field stations but more generally to accelerate place-based research for sustainable development. FAIR Island works reflexively, aiming to scale horizontally through networks of field stations and to serve as a model for other sites of intensive long-term scientific study.

Survey on Intrusion Detection Systems Based on Machine Learning Techniques for the Protection of Critical Infrastructure.

Industrial control systems (ICSs), supervisory control and data acquisition (SCADA) systems, and distributed control systems (DCSs) are fundamental components of critical infrastructure (CI). CI supports the operation of transportation and health systems, electric and thermal plants, and water treatment facilities, among others. These infrastructures are not insulated anymore, and their connection to fourth industrial revolution technologies has expanded the attack surface. Thus, their protection has become a priority for national security. Cyber-attacks have become more sophisticated and criminals are able to surpass conventional security systems; therefore, attack detection has become a challenging area. Defensive technologies such as intrusion detection systems (IDSs) are a fundamental part of security systems to protect CI. IDSs have incorporated machine learning (ML) techniques that can deal with broader kinds of threats. Nevertheless, the detection of zero-day attacks and having technological resources to implement purposed solutions in the real world are concerns for CI operators. This survey aims to provide a compilation of the state of the art of IDSs that have used ML algorithms to protect CI. It also analyzes the security dataset used to train ML models. Finally, it presents some of the most relevant pieces of research on these topics that have been developed in the last five years.

Urban biotope classification incorporates urban forest and green infrastructure for improved environmental land-use planning in Mexico City

Urban forests are recognized worldwide as the most critical component of green infrastructure due to their capacity to provide various environmental goods and services. As cities continue to expand and their environmental problems intensify, there is a growing need for urban forests and green infrastructure to be better incorporated into strategic land-use planning, especially in developing cities. The first step in building an urban forest management plan is to capture characteristics of the urban forest and how these change across the built environment. Here, we used an urban biotope approach to classify urban forests and environmental characteristics in Mexico City. We sampled 500 fixed-area randomly stratified plots across the city to characterize urban forest structural and compositional variables. PCA and the broken-stick method were used to reduce the number of 25 urban forest variables down to five significant principal components that accounted for 78% of the data's cumulative variation. Ward's method helped classify biotopes into a hierarchical system with seven finer-level biotopes defined by urban forest characteristics (Dunn = 0.09, AC = 0.98), nested within two broader-level biotopes defined by forest canopy conditions (Silhouette = 0.59, AC = 0.99). A no-tree canopy biotope was extracted from sampling locations with no trees. The biotopes derived here can fundament biotope mapping, and support decision-making in urban forest planning, including the identification of available planting spaces, tree diversity targets, and canopy protection. Our work in Mexico City demonstrates how the biotope approach can be adapted and used to better incorporate urban forests and green infrastructure into future management planning for any city.

Measuring accessibility to public services and infrastructure criticality for disasters risk management

Component criticality analysis of infrastructure systems has traditionally focused on physical networks rather than infrastructure services. As an example, a key objective of transport infrastructure is to ensure mobility and resilient access to public services, including for the population, service providers, and associated supply chains. We introduce a new user-centric measure for estimating infrastructure criticality and urban accessibility to critical public services - particularly healthcare facilities without loss of generality - and the effects of disaster-induced infrastructure disruptions. Accessibility measures include individuals’ choices of all services in each sector. The approach is scalable and modular while preserving detailed features necessary for local planning decisions. It relies on open data to simulate various disaster scenarios, including floods, seismic, and compound shocks. We present results for Lima, Peru, and Manila, Philippines, to illustrate how the approach identifies the most affected areas by shocks, underserved populations, and changes in accessibility and critical infrastructure components. We capture the changes in people’s choices of health service providers under each scenario. For Lima, we show that the floods of 2020 caused an increase in average access times to all health services from 33 minutes to 48 minutes. We identify specific critical road segments for ensuring access under each scenario. For Manila, we locate the 22% of the population who lost complete access to all higher health services due to flooding of over 15 cm. The approach is used to identify and prioritize targeted measures to strengthen the resilience of critical public services and their supporting infrastructure systems, while putting the population at the center of decision-making.

Exploring the elements of effective public cycle parking: A literature review

In the field of cycle policy and planning, alongside ambitions to expand segregated cycle networks, there is a push across many countries for increasing the quality and quantity of cycle parking as a critical component of cycling infrastructure. In order to support these measures, planning guides have been developed to guide the expansion and improvement of cycle parking facilities. A common feature of these policies and guides for cycle parking interventions is an absence of dialogue with, or reference to, peer-reviewed research investigating cycle parking and the potential effectiveness of different approaches to cycle parking planning. The use of such research could help to create cycle parking that may be more effective in attracting and providing for people cycling. On this basis, we engage in a literature review of a select body of cycling research whose findings could contribute to more effective cycle parking planning practice. Drawing on our review, we propose a number of tentative ‘elements’ for effective public cycle parking planning practice: visibility, protection, accessibility, proximity, integration, and diversification. These elements could be used in conjunction with local knowledge and context-specific assessment measures to maximise the potential effectiveness of cycle parking planning in different regions, and can be situated as part of a wider struggle to acquire public space for cycling within car-dominant contexts.

Unsupervised Learning for Feature Selection: A Proposed Solution for Botnet Detection in 5G Networks

The world has seen exponential growth in deploying Internet of Things (IoT) devices. In recent years, connected IoT devices have surpassed the number of connected non-IoT devices. The number of IoT devices continues to grow and they are becoming a critical component of the national infrastructure. IoT devices' characteristics and inherent limitations make them attractive targets for hackers and cyber criminals. Botnet attack is one of the serious threats on the Internet today. This article proposes pattern-based feature selection methods as part of a machine learning (ML)-based botnet detection system. Specifically, two methods are proposed: the first is based on the most dominant pattern feature values and the second is based on maximal frequent itemset mining. The proposed feature selection method uses Gini impurity and an unsupervised clustering method to select the most influential features automatically. The evaluation results show that the proposed methods have improved the performance of the detection system. The developed system has a true positive rate of 100% and a false positive rate of 0% for best performing models. In addition, the proposed methods reduce the computational cost of the system as evidenced by the detection speed of the system.

Effect of quenching in aqueous polyvinylpyrrolidone solutions on the microstructure and pitting corrosion resistance of AISI 1045 carbon steel

AbstractCarbon steels are widely used as infrastructure components owing to their high strength, ductility, and weldability. However, as corrosion degrades critical infrastructure components, exploring the relationship between the microstructure and corrosion resistance of carbon steel is of particular interest in engineering. To analyze this relationship, it is first essential to evaluate the corrosion resistance of each microstructure in a carbon steel specimen with a single chemical composition to eliminate the effects of alloying elements. In this study, we compared the pitting corrosion resistance of four microstructures (water‐quenched martensite, tempered martensite, bainite and degenerate pearlite, and ferrite–pearlite) of an AISI 1045 medium‐carbon steel. The pitting corrosion resistances of the microstructures in near‐neutral pH solutions containing NaCl were ordered as follows: (high) water‐quenched martensite > bainite and degenerate pearlite > tempered martensite > ferrite–pearlite (low). Elucidating the effect of the microstructure on the pitting corrosion resistance may contribute to the development of corrosion‐resistant carbon steels with excellent mechanical properties.

A Novel Model for Enhancing the Resilience of Smart MicroGrids’ Critical Infrastructures with Multi-Criteria Decision Techniques

Microgrids have the potential to provide reliable electricity to key components of a smart city’s critical infrastructure after a disaster, hence boosting the microgrid power system’s resilience. Policymakers and electrical grid operators are increasingly concerned about the appropriate configuration and location of microgrids to sustain post-disaster critical infrastructure operations in smart cities. In this context, this paper presents a novel method for the microgrid allocation problem that considers several technical and economic infrastructure factors such as critical infrastructure components, geospatial positioning of infrastructures, power requirements, and microgrid cost. In particular, the geographic allocation of a microgrid is presented as an optimization problem to optimize a weighted combination of the relative importance of nodes across all key infrastructures and the associated costs. Furthermore, the simulation results of the formulated optimization problem are compared with a modified version of the heuristic method based on the critical node identification of an interdependent infrastructure for positioning microgrids in terms of the resilience of multiple smart critical infrastructures. Numerical results using infrastructure in the city of Pittsburgh in the USA are given as a practical case study to illustrate the methodology and trade-offs. The proposed method provides an effective method for localizing renewable energy resources based on infrastructural requirements.