Infrastructure Components Research Articles

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

Recreation and its synergies and trade-offs with other ecosystem services of Alpine and pre-Alpine grasslands

Alpine and pre-Alpine grasslands provide numerous ecosystem services including provisioning services (e.g. fodder production), regulating services (e.g. soil erosion reduction), and cultural services (e.g. recreation). While intensively managed grasslands specifically target the production of fodder, more extensively used grasslands are known for being hotspots of biodiversity. However, there is a need to better understand the relationship among the supply of ecosystem services, specifically regarding the use of grasslands for cultural ecosystem services such as recreation. In this study, we investigated the synergies and trade-offs of ecosystem services and analyzed underlying variables related to the recreational use of grasslands. We investigated the supply of recreation (indicated by Photo-User-Days from geo-tagged photos on grasslands), fodder production (indicated by yield), and regulating and habitat ecosystem services (indicated by agri-environmental payments), and analyzed their relationship to management-related variables with a Redundancy Analysis. To better explain the recreational use of grasslands, we further analyzed how environmental and infrastructural features influence the occurrence of Photo-User-Days with a hurdle regression. Finally, we conducted spatial analyses to understand the distribution of Photo-User-Days in space. We found a weak but significant negative relationship between Photo-User-Days and yield, which implies that people slightly prefer extensive grassland to intensive grassland for recreation. Our results also show that agri-environmental schemes targeted towards extensive grassland management can positively influence the recreational use. Other factors, such as proximity to touristic features (e.g. castles), presence of infrastructural features (e.g. cable cars), and environmental characteristics (e.g. low share of croplands, distance to forests), also influenced the spatial distribution of photos on grasslands. The importance of these factors underscores the value of grasslands as a component of the cultural landscape for recreational purposes. These results also suggest that cultural ecosystem services of grasslands can be considered to be co-produced by natural, social, and infrastructural components. The study further discusses limitations to the explanatory power of geo-tagged photo analysis to determine the wide range of cultural ecosystem services of grasslands. We conclude that grasslands play an important role for recreation in (pre-)Alpine landscapes, which can also be effectively supported through targeted agri-environmental payments.

Spatial data infrastructure components to provide regional climate information services

The assessment of expected climate changes on a regional scale requires appropriate data and information products that must be easily accessible and usable for decision makers. The regional climate information platform ReKIS is a long-established project of three German federal states to address this purpose. However, with increasing content, this web-based data exchange platform lacked in suitable metadata descriptions, search functionalities and interoperability, thus hampering the discovery and access of suitable information. This paper describes the extending and enhancing of ReKIS using state-of-the-art components of Spatial Data Infrastructures (SDI). The components support metadata acquisition, maintenance and publishing using a metadata catalogue. The implementation of a WebGIS server increases interoperability by offering OGC-compliant services and a web framework for knowledge-transfer allows for non-expert access to climate services. The approaches were developed and implemented in a real-world scenario and are suitable for a transfer to other comparable platforms and use cases.

The Usage of Additives on Porous Asphalt

Roads are an important infrastructure component, but often face problems such as changes in temperature, humidity, and standing water. To overcome this problem, a layer of porous asphalt which has air voids to reduce standing water on the road surface can be a solution. However, although porous asphalt has water-related benefits, its structure is less durable and requires increased resistance. Therefore, this study aims to analyze the effect of adding additives on the strength, permeability, and porosity of porous asphalt. Three types of additives studied were crumb rubber (CR), nano silica (NS), and Wetbond-SP. Several laboratory tests were conducted on the prepared specimens, namely the Marshall, Cantabro Loss, and permeability tests. The results showed that adding NS onto the specimen was able to increase the stability value of porous asphalt, while the addition of CR and Wetbond-SP had no significant effect on the stability parameter. However, the addition of Wetbond-SP improved the adhesion between the aggregate and asphalt, resulting in a denser mixture. The three additives also increased moisture resistance and cohesion loss in the mixture, as indicated by a decrease in Cantabro Loss (CL) percentage. Additives also impact the permeability and porosity values, where adding more additives in the mixture will reduce the permeability and porosity values. Based on the results of the study, the optimum concentration of CR added was 1% of the total weight of the mixture, NS was 4% of the weight of asphalt content, and Wetbond-SP was 0.4% of the weight of asphalt content.

Livestock Management System

Abstract: Currently, advancements in farm automation and technological techniques contribute significantly to boosting farm productivity, with a notable focus on cattle health monitoring systems in research. This system encompasses infrastructure, hardware, and software components aimed at enhancing cattle health, crucial for overall farm productivity. Given the challenges posed by managing numerous cattle in dairy farms, continuous monitoring of individual health becomes imperative. Leveraging sensor technology to monitor vital signs such as temperature and heart rate allows for early detection of health issues, reducing the need for frequent health inspections and long-term healthcare costs for the animals.

Examining regulating ecosystem service provision by brownfield and park typologies and their urban distribution

Environmental hazards including air pollution, heat waves, and flooding are pressing issues in cities. In response, many cities are pledging to increase green infrastructure, adopting nature-based solutions for enhancing resilience through ecosystem service provision. At the same time, urban densification is increasingly considered necessary to limit urban sprawl, where redevelopment of brownfield is often promoted as an alternative to development of greenbelt land. Consequently, while the drive to install additional green infrastructure is intensifying, the synchronous loss of brownfield vegetation may result in unintentional net losses. It is therefore essential to evaluate brownfield’s contribution to ecosystem service provision. Here we present a comparative analysis of the regulating ecosystem services provided by brownfield and park trees in Greater Manchester, UK. Parks were selected as a comparator as they represent the most abundant and widely distributed types of open green space, and typically contain a high proportion of trees. Ecosystem service provision was evaluated across statistically defined typologies of twenty-six brownfield and twelve park types (varying in terms of landscape metrics, land, and canopy cover) using i-Tree Canopy. In terms of carbon storage brownfield (3194 ha) provided an estimated 52,765 t of carbon storage and annual carbon sequestration of 2101 t/y, for parks (4128 ha), 143,064 t of carbon storage, carbon sequestration of 5696 t/y. Annual air pollution removal is estimated at 313 t/y of air pollution removal for brownfields, and 849 t/y for parks. Avoided surface water runoff is estimated at 135,018 m3/y for brownfields and 366,280 m3/y for parks. In densely built areas, brownfields provided approximately five times more regulating ecosystem services than parks, and several types of vegetated brownfield provided more regulating ecosystem services than many types of park. We conclude that brownfield is an important component of green infrastructure, where this knowledge is critical to inform planning and management, prevent loss of ecosystem services, and identify where strategic greening of brownfield may address inequities in existing green infrastructure.

The role of stroke care infrastructure on the effectiveness of a hub-and-spoke telestroke model in South Carolina

ObjectiveTo examine the relationship between stroke care infrastructure and stroke quality-of-care outcomes at 29 spoke hospitals participating in the Medical University of South Carolina (MUSC) hub-and-spoke telestroke network. Materials and methodsEncounter-level data from MUSC's telestroke patient registry were filtered to include encounters during 2015-2022 for patients aged 18 and above with a clinical diagnosis of acute ischemic stroke, and who received intravenous tissue plasminogen activator. Unadjusted and adjusted generalized estimating equations assessed associations between time-related stroke quality-of-care metrics captured during the encounter and the existence of the two components of stroke care infrastructure—stroke coordinators and stroke center certifications—across all hospitals and within hospital subgroups defined by size and rurality. ResultsTelestroke encounters at spoke hospitals with stroke coordinators and stroke center certifications were associated with shorter door-to-needle (DTN) times (60.9 min for hospitals with both components and 57.3 min for hospitals with one, vs. 81.2 min for hospitals with neither component, p <.001). Similar patterns were observed for the percentage of encounters with DTN time of ≤60 min (63.8% and 68.9% vs. 32.0%, p <.001) and ≤45 min (34.0% and 38.4% vs. 8.42%, p <.001). Associations were similar for other metrics (e.g., door-to-registration time), and were stronger for smaller (vs. larger) hospitals and rural (vs. urban) hospitals. ConclusionsStroke coordinators or stroke center certifications may be important for stroke quality of care, especially at spoke hospitals with limited resources or in rural areas.

Energy Efficient Business Management System for Improving QoS in Network Model

Assuring the safe and effective operation of a company's technological infrastructure is an essential part of business management. Monitoring, administering, and troubleshooting the many components and systems of the network are all part of the process. Additionally, it is the responsibility of business administrators to find methods to enhance the existing system, as well as to make certain that all resources are distributed in such a manner that their use may be maximized. Management of the business is a key component of the technological infrastructure of any organization, and it is of the utmost importance that it be carried out effectively in order to guarantee a safe and effective operation. It is essential for network operators to discover strategies to improve the energy efficiency of their networks without adversely affecting the quality of service (QoS) for reasons related to both cost and sustainability. In this research, an in-depth analysis of business management techniques for effective resource utilization is presented. This analysis begins with the design of the network and continues all the way through to the delivery of accurate data. An Energy Efficient Network (EEN) must be able to give programmability and flexibility to network infrastructures, as well as the ability to operate networks in a rapid way and provide operators with more control. It is impossible to ignore energy throughout the process of meeting the needs and requirements of network services, especially when taking into consideration the consequences on the long-term viability of both the environment and businesses. Energy efficiency in both current and future networks is the topic of discussion in this research. The findings indicate that energy efficient networks are successful in overcoming the present issues that stand in the way of the application of energy efficiency techniques, also the discussed model are effective in addressing some of the obstacles that are encountered by small and medium-sized businesses.

Manhole Cover Classification Based on Super-Resolution Reconstruction of Unmanned Aerial Vehicle Aerial Imagery

Urban underground pipeline networks are a key component of urban infrastructure, and a large number of older urban areas lack information about their underground pipelines. In addition, survey methods for underground pipelines are often time-consuming and labor-intensive. While the manhole cover serves as the hub connecting the underground pipe network with the ground, the generation of underground pipe network can be realized by obtaining the location and category information of the manhole cover. Therefore, this paper proposed a manhole cover detection method based on UAV aerial photography to obtain ground images, using image super-resolution reconstruction and image positioning and classification. Firstly, the urban image was obtained by UAV aerial photography, and then the YOLOv8 object detection technology was used to accurately locate the manhole cover. Next, the SRGAN network was used to perform super-resolution processing on the manhole cover text to improve the clarity of the recognition image. Finally, the clear manhole cover text image was input into the VGG16_BN network to realize the manhole cover classification. The experimental results showed that the manhole cover classification accuracy of this paper’s method reached 97.62%, which verified its effectiveness in manhole cover detection. The method significantly reduces the time and labor cost and provides a new method for manhole cover information acquisition.

Road Accessibility during Natural Hazards Based on Volunteered Geographic Information Data and Network Analysis

Natural hazards can present a significant risk to road infrastructure. This infrastructure is a fundamental component of the transportation infrastructure, with significant importance. During emergencies, society heavily relies on the functionality of the road infrastructure to facilitate evacuation and access to emergency facilities. This study introduces a versatile, multi-scale framework designed to analyze accessibility within road networks during natural hazard scenarios. The first module of the framework focuses on assessing the influence of natural hazards on road infrastructure to identify damaged or blocked road segments and intersections. It relies on near real-time information, often provided by citizen science through Volunteered Geographic Information (VGI) data and Natural Language Processing (NLP) of VGI texts. The second module conducts network analysis based on freely available Open Street Map (OSM) data, differentiating between intact and degraded road networks. Four accessibility measures are employed: betweenness centrality, closeness centrality, a free-flow assumption index, and a novel alternative routing assumption measure considering congestion scenarios. The study showcases its framework through an exemplary application in California, the United States, considering different hazard scenarios, where degraded roads and connected roads impacted by the hazard can be identified. The road extraction methodology allows the extraction of 75% to 100% of the impacted roads mentioned in VGI text messages for the respective case studies. In addition to the directly extracted impacted roads, constructing the degraded network also involves finding road segments that overlap with hazard impact zones, as these are at risk of being impacted. Conducting the network analysis with the four different measures on the intact and degraded network, changes in network accessibility due to the impacts of hazards can be identified. The results show that using each measure is justified, as each measure could demonstrate the accessibility change. However, their combination and comparison provide valuable insights. In conclusion, this study successfully addresses the challenges of developing a generic, complete framework from impact extraction to network analysis independently of the scale and characteristics of road network types.

Abstract 3568: Managing large-scale cancer research data programs

Abstract Purpose: The National Cancer Institute’s (NCI’s) Cancer Research Data Commons (CRDC) is a data ecosystem for the cancer research community that provides cloud-based, secure storage and analytic tools for many cancer data types, including genomic, proteomic, imaging, and clinical data. CRDC requires a carefully designed, federated data governance framework to enable CRDC’s goals to sustain a collaborative data environment where researchers can access timely, accurate, and relevant cancer data. Methods: In 2023, CRDC chartered a Data Governance Framework of Committees and Working Groups across all CRDC components to: Enable diverse data type sharing; provide secure data access; optimize common infrastructure components and functions; and adhere to FAIR data principles (Findable, Accessible, Interoperable, and Reusable). We considered many designs for the CRDC Data Governance Framework, including fully centralizing or decentralizing decision-making bodies. Implementing a fully centralized data governance framework for a diverse data ecosystem like CRDC would risk top-down decisions that do not meet the unique needs of the CRDC and cancer research community. A fully decentralized data governance framework risks inconsistencies and compliance challenges. Additional governance design considerations included a federated approach that emphasizes clear roles and responsibilities; prioritizing cross-component policies to ensure consistency and promote operational efficiency; defining measurable performance standards; and keeping data owners, stewards, and users informed, trained, and supported. Results: We have determined that long term sustainment of CRDC requires a federated governance approach with broad stakeholder participation through Committees and Working Groups. Currently established CRDC governance committees include the Enterprise Architecture Review Team (EART), Submission Review Team, Data Standards Working Group, and Data Advisory Board consisting of cancer research domain experts, researchers, and skilled cloud engineers and developers. In 2023, the CRDC EART defined data lifecycle stages and identified technical optimization opportunities for submission coordination and indexing. The established governance groups are working to identify optimization opportunities for data quality checks, submission review workflows, study acceptance criteria, data submission reviews, and cross-CRDC emerging data standards requirements. Conclusions: Sustaining large, complex, cancer research data requires a federated governance approach with participation from all relevant stakeholder groups and cannot simply be top-down imposition of policies, standards, and procedures. The newly established CRDC federated governance framework will contribute to improving the long-term sustainability of the CRDC infrastructure and ensuring CRDC data drive meaningful research outcomes. Citation Format: Juergen Klenk, Dina Mikdadi, Chelsea Owens, Angela Maggio, Bhavani Singh, Eric Barner, Tanja Davidsen, Erika Kim. Managing large-scale cancer research data programs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3568.

Technoeconomic analysis of rooftop PV system in elevated metro station for cost-effective operation and clean electrification

Metro system constitutes an essential component of urban transportation infrastructure, simultaneously serving as a significant energy consumer and carbon emitter. Integration of rooftop PV systems in elevated stations can effectively mitigate carbon emissions and enhance the utilization rate of clean energy. Nevertheless, current research rarely explores the application and feasibility analysis of rooftop PV systems in elevated metro stations. To address this research gap, the study investigates potential of rooftop PV systems applied in the elevated stations, and optimizes the PV scale and battery capacity for achieving the maximum net present value. The results show that the optimized photovoltaic-battery (PVB) system can bring about a PV self-sufficiency (SS) of 0.54, in the baseline scenario. For the entire line, SS can achieve 0.26–0.59 with the integration of PVB systems. Furthermore, the impact of skylights on energy consumption for illumination and PV power generation is discussed, and the PV potential of skylights is investigated. The results show that PV skylights lead to an increase a rise in the SS of 8.6%–15.3%. This study contributes reference for the potential of PV systems on elevated stations and for cost-effective operation and clean electrification of the urban rail transit sector.

Ageing of elastomers in air and in hydrogen environment: A comparative study

EPDM, HNBR and FKM materials were exposed at 150 °C to air under atmospheric pressure and to hydrogen at 50 bar for different ageing times. All measurements after hydrogen exposure were conducted on samples in degassed condition to assess irreversible effects resulting from that exposure and to compare them to those after ageing in air. Density, hardness, tensile properties, compression set, and hydrogen permeability of all samples were analysed. In both ageing environments, HNBR exhibited the most significant changes of material properties. However, for both EPDM and HNBR, considerably less severe ageing effects were observed under hydrogen in comparison to ageing in air. On the other hand, FKM showed about the same low level of deterioration in both ageing environments but exhibited poor resistance against damage due to rapid gas decompression in hydrogen environment that can lead to seal failure. The obtained results may serve as a guidance toward a better understanding for design and utilisation of elastomeric materials in future hydrogen infrastructure components.

Comprehensive identification, localization, and validation of innovation measurement indices at the farm level in Iran

IntroductionInnovation is a fundamental component of agricultural infrastructure, fostering the sector’s development and productivity. Innovation indices tailored to the farm level are benchmarks for assessing innovation within this agricultural context. Therefore, this study aims to discover, localize, and validate comprehensive innovation measurement indices at the farm level. A distinguishing feature of this research is its endeavor to discover and validate a comprehensive tool for measuring innovation at the farm level. Notably, there has been no prior research on discovering comprehensive innovation indices at the farm level, marking a novel aspect of this study.Method and resultsThe current research is applied in terms of its purpose and falls into the category of mixed research (quantitative and qualitative), as well as exploratory and descriptive research. This study has been conducted in three stages: The first phase involved reviewing existing literature and extracting indices for measuring innovation. In the second phase, a tool was designed specifically to gauge innovation at the farm level. The third phase entailed face and content validation of the identified indices. This validation process was conducted quantitatively and qualitatively through surveys of subject-matter experts in Iran, and by calculating the content validity ratio (CVR) and content validity index (CVI).To measure innovation at the farm level, 90 items across 7 dimensions (education, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, creative outputs, and innovative outputs) were extracted, localized, and developed. According to the results, dimensions such as innovative outputs (product innovation, process innovation, and marketing innovation), education (farmers’ education status in technology and innovation), and infrastructure (familiarity, access, and application of Information and Communication Technology [ICT]) demonstrated high credibility based on CVR and CVI scores.ConclusionThe utilization of innovation assessment indices can help mitigate the challenges associated with evaluating innovation status on farms, thereby enhancing their condition and increasing farmers’ profitability. Accurate and reliable data on innovation status empower farmers in market competition and improve economic performance. Attention to innovation and the use of assessment tools can facilitate the attraction of new investors to the agricultural industry. Furthermore, improving agricultural processes through innovative technologies can contribute to the preservation of natural resources and the reduction of environmental pollution.

Exploring Smart Cities: Maximizing the Impact of Big Data on Smart Mobility

In the contemporary era, smart cities represent a synergy of technology, urban development, and ecological sustainability, with big data at the forefront, offering profound insights for revolutionizing city living. However, the challenge of transforming this vast data influx into practical wisdom persists. This research investigates the complex relationship between smart cities and the utilization of big data for enhancing urban mobility. Despite extensive research, the conversion of data into a strategic asset sparks debate, driven by advancements in analytics, methodologies, and practices. This study navigates the realm of big data within the framework of smart cities, focusing on smart mobility as a crucial component of urban infrastructure. It addresses the hurdles encountered by both academic and industry sectors in converting data into implementable strategies. Our discussion promotes a collaborative approach among scholars, business leaders, and government entities to convert emerging challenges into opportunities, setting the stage for a smarter, sustainable urban future.

Analyzing the Effect of Sewer Network Size on Optimization Algorithms’ Performance in Sewer System Optimization

Sewer systems are a component of city infrastructure that requires large investment in construction and operation. Metaheuristic optimization methods have been used to solve sewer optimization problems. The aim of this study is to investigate the effects of network size on metaheuristic optimization algorithms. Cuckoo Search (CS) and four versions of Grey Wolf Optimization (GWO) were utilized for the hydraulic optimization of sewer networks. The purpose of using different algorithms is to investigate whether the results obtained differ depending on the algorithm. In addition, to eliminate the parameter effect, the relevant algorithms were run with different parameters, such as population size. These algorithms were performed on three different-sized networks, namely small-sized, medium-sized, and large-sized networks. Friedman and Wilcoxon tests were utilized to statistically analyze the results. The results were also evaluated in terms of the optimality gap criterion. According to the results based on the optimality gap, the performance of each algorithm decreases as the network size increases.

Enhancing 802.1X authentication with identity providers using EAP-OAUTH and OAuth 2.0

EAP-OAUTH is a novel Extensible Authentication Protocol (EAP) method that integrates the OAuth 2.0 framework to provide a secure and flexible authentication mechanism for LANs and WLANs that implement the IEEE 802.1X framework. EAP-OAUTH leverages existing, OAuth 2.0-enabled Identity Providers (IdPs) and their single sign-on (SSO) capabilities, thus offering a streamlined authentication experience for both users and organizations. The advantages of EAP-OAUTH for users include an SSO experience and enhanced privacy, while organizations benefit from simplified identity management, reduced operational costs, consistent security policies, and easier compliance. Furthermore, EAP-OAUTH represents a promising solution for addressing the challenges of authentication in modern wireless networks, such as the deployment of various multi-factor or risk-based, adaptive authentication strategies. This article presents an in-depth analysis of the EAP-OAUTH method, its design, implementation, and use cases in enterprise networks and public hotspots. It explores the OAuth 2.0 Device Authorization Grant flow and allows network clients to perform fast re-authentications without resorting to sessions on IdPs or even their SSO features. The implementation of EAP-OAUTH is demonstrated in real-world scenarios, using two IdPs (Google and Auth0), confirming its effectiveness, suitable performance and compatibility with various components of typical Wi-Fi infrastructures.

The lifeblood of LifeWatch ERIC: national in-kind contributions

A distributed research infrastructure such as LifeWatch ERIC necessitates close cooperation among the various components that constitute the research infrastructure and contribute to the overall services, facilities and resources it offers to the user community. In-kind contributions are non-monetary contributions, consisting of labour, services, facilities and access to resources, typically provided by the distributed components of the research infrastructure. The financing for in-kind contributions is usually provided by the national funding agency on behalf of the LifeWatch member country or by the member directly, with a value being accredited towards the member's funding obligations to LifeWatch ERIC. The management and validation of in-kind contributions towards shared objectives are integral to the legal framework supporting LifeWatch ERIC. This involves a rigorous assessment process to verify each in-kind contribution. This short article outlines that process and explores the function of the In-Kind Contributions Committee (IKCC) at LifeWatch ERIC.

Acoustic tunnel lining cavity detection using cepstral coefficients with optimized filter bank

Tunnels are an essential component of modern transportation infrastructure, and their structural health is critical to traffic safety, which can be seriously affected by tunnel lining cavities. In this paper, an acoustic-based detection approach for assessing the integrity of tunnel linings is studied. By tapping the tunnel lining surface, acoustic signals are sampled and analyzed using a novel feature parameter extraction algorithm-the energy-frequency cepstral coefficient, which uses wavelet packet decomposition to obtain energy distribution statistics in the frequency domain of the signal, and constructs a signal-dependent filter bank to achieve the cepstral coefficient extraction. Compared with the traditional Mel filter bank, this method can adaptively adjust the resolution of the filter bank according to the frequency characteristics of the classified samples. This allows for higher frequency resolution in regions where the energy distribution is concentrated. As a result, the extracted feature parameters achieve both dimensional compression and superior information retention. Experimental results show that the proposed energy-frequency cepstral coefficient feature outperforms the traditional Mel-frequency cepstral coefficient feature, resulting in a higher accuracy of tunnel lining detection. The convolutional neural network model achieves an accuracy of 99.2%, with a 78.9% reduction in error rate compared with the traditional Mel-frequency cepstral coefficient feature parameters. Additionally, a particle swarm optimization support vector machine model is trained to achieve an accuracy rate of 99.6% and an error rate reduction of 76.5%.

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.&#x0D; Keywords: Cybersecurity; Critical Infrastructure; Artificial Intelligence; Internet-of-Things; Blockchain.