Reliable Infrastructure Research Articles

Enhancing Security and Accessibility of Personal Records in Nigeria using Blockchain Technology: A Case Study of Modibbo Adama University, Yola

The security and accessibility of personal records have become critical challenges in Nigeria, given the country's reliance on outdated and vulnerable systems for managing sensitive data. This research explores the role of blockchain technology in enhancing the security and accessibility of personal records in Nigeria. Blockchain, known for its decentralised, transparent, and tamper-resistant nature, offers a promising solution to issues such as data breaches, unauthorized access, and inefficient record-keeping. The study investigates how blockchain can provide a more secure and reliable infrastructure for managing personal records, focusing on key attributes such as encryption, immutability, and data transparency. It also examines the potential for improving accessibility, especially in areas where record retrieval is often delayed or compromised by bureaucratic bottlenecks. By evaluating case studies and existing applications of blockchain technology in record management, this research identifies opportunities and challenges for blockchain adoption in Nigeria's public and private sectors. Through a mixed-method approach that includes surveys and interviews with key stakeholders in data management, the study highlights the feasibility of integrating blockchain into Nigeria’s personal records system. The findings suggest that blockchain technology, despite certain challenges such as cost and technological infrastructure, can significantly enhance the security and accessibility of personal records in Nigeria. Recommendations for policy development, technological investment, and further research are also provided.

Multiple Indicator Vulnerability to Energy Poverty: Assessing Spatial Variability Across Chile

This study investigates the multifaceted issue of energy poverty, focusing on its spatial and socioeconomic dimensions, with a particular emphasis on the case of Chile. Despite global initiatives like Sustainable Development Goal 7, which advocates for universal access to affordable and reliable energy, millions remain vulnerable to energy poverty. In Chile, this phenomenon is exacerbated by geographic and climatic variability, resulting in significant disparities in energy access, affordability, and efficiency. Using a multidimensional framework adapted from Bouzarovski and Petrova, we assessed energy poverty through factors including household income, fuel costs, energy infrastructure reliability, and regional climate needs. This analysis integrated composite indicators to map vulnerability at the regional level, highlighting high-risk areas primarily in the central and southern regions, where low incomes and dependency on biomass for heating amplified exposure occur. The findings reveal that approximately 4 million Chileans face a high risk of energy poverty, underscoring the need for regionally tailored policies that address both immediate economic constraints and structural energy inequalities. This research contributes to a comprehensive understanding of energy poverty in Chile and offers policy recommendations aimed at reducing socioeconomic disparities and achieving sustainable, equitable energy access.

AI-Driven Predictive Maintenance in Data Infrastructure: A Multi-Modal Framework for Enhanced System Reliability

This article presents a comprehensive framework for implementing artificial intelligence-driven predictive maintenance in modern data infrastructure environments. While traditional maintenance approaches have relied on reactive or scheduled interventions, the proposed framework leverages multiple AI technologies, including machine learning, natural language processing, and reinforcement learning, to create a proactive maintenance ecosystem. The methodology integrates diverse data streams from infrastructure components, including sensor data, system logs, and historical maintenance records, to predict potential failures and optimize maintenance schedules. The approach combines time series analysis for trend identification, natural language processing for unstructured data analysis, and reinforcement learning for dynamic schedule optimization. Implementation across multiple case studies, including cloud service providers and manufacturing environments, demonstrates significant improvements in system reliability, reduction in unplanned downtime, and optimization of maintenance resource allocation. The results indicate that AI-driven predictive maintenance substantially outperforms traditional approaches in both accuracy and cost-effectiveness. This article contributes to the growing field of intelligent infrastructure management and provides practical guidelines for organizations seeking to enhance their data infrastructure reliability through advanced predictive maintenance strategies.

Advancing Data Center Operations Through AI and Machine Learning : A Comprehensive Analysis of Predictive Maintenance and Resource Optimization

This article systematically analyzes the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) technologies on modern data center operations. Through an extensive review of implemented cases and empirical data from multiple data centers, the article demonstrates how AI-driven solutions significantly enhance operational efficiency, reduce maintenance costs, and improve infrastructure reliability. The findings indicate that predictive maintenance algorithms achieve a 47% reduction in unexpected equipment failures, while ML-based resource optimization leads to a 31% improvement in resource utilization rates. The article examines integrating deep learning models for real-time energy management, resulting in an average 23% reduction in cooling costs and a 0.15 improvement in Power Usage Effectiveness (PUE). Additionally, the article analyzes the implementation of AI-powered security frameworks, which demonstrated a 92% accuracy rate in anomaly detection and reduced false positives by 76% compared to traditional rule-based systems. The article also presents a novel framework for capacity planning using neural networks, achieving an 89% accuracy in demand forecasting over 12 months. These findings provide valuable insights for data center operators and establish best practices for implementing AI/ML solutions in mission-critical infrastructure environments. The article concludes with recommendations for overcoming integration challenges and a roadmap for future technological adoption.

Cybersecurity Challenges in Net Energy Metering: Legal Frameworks and Practical Solutions

Net Energy Metering (NEM) has emerged as a key factor in Modern Energy Management, empowering consumers to produce their own electricity and return Net Excess Generation (NEG) to the grid. However, the increased usage of NEM has introduced significant network security challenges. As millions of electronic devices are interconnected through communication networks across critical power facilities cybersecurity has become a concerning factor, directly affecting the reliability of such extensive infrastructure. This paper gives an insight into the rising cyber offences associated with NEM systems, stipulates the legal framework, e-governance, and liabilities under the Information Technology (IT) Act, 2000. Categorization of potential cyber threats like Access control, database security and encryption, intrusion detection, malicious software, operating system vulnerabilities and security, application security has been highlighted. Evaluation of Time of Day (ToD) pricing mechanism focusing on real time monitoring and energy efficiency during peak and off-peak consumption. Incorporating Virtual Private Networks (VPNs) and other cybersecurity measures to safeguard the integrity and reliability of NEM systems. The paper illustrates the impact of cybercrimes on NEM, by exploring existing applications and case studies. It underscores the necessity of implementing robust cybersecurity practices to protect these systems. The objective of the paper is to raise awareness about the cybersecurity challenges encountered in NEM systems and to propose practical solutions to mitigate these risks, ensuring a safe and regulated environment for the functioning of smart grids in a progressively growing digital world.

Towards Reliable Infrastructures with Compressed Computation

Towards Reliable Infrastructures with Compressed Computation

A hybrid machine learning and simulation framework for modeling and understanding disinformation-induced disruptions in public transit systems

Transportation infrastructure networks are prone to disruptions, most of which are beyond control. However, the spread of disinformation can worsen downtime in these systems by indirectly causing disruptions, such as station closures or rerouting of services based on false reports. The relationship between disinformation and the service disruptions is very important with reference to enhancing the resilience of transportation systems. This paper contributes to the field by applying artificial intelligence techniques to analyze how disinformation impacts service disruptions, particularly focusing on the Port Authority Trans-Hudson (PATH) system in New Jersey and New York, providing insights for improving operational responsiveness. The disruption operational impacts of disinformation are analyzed using several data sources, including schedules, ridership reports, and real-time alerts. A machine learning-based K-means algorithm framework is applied to cluster disruption alerts from social media. Disruption scenarios dominated by disinformation are identified using advanced natural language processing (NLP) methods, specifically BERTopic and Latent Dirichlet Allocation (LDA) topic modeling techniques. A Monte Carlo simulation is applied to quantify the effects of this dominant disinformation-induced disruption scenario on the commuter time and costs. This study reveals that disinformation significantly influences infrastructure reliability and points out the necessity for effective strategies to combat its impacts. The findings reveal the importance of transportation disruptions to the functioning of the transportation system and emphasize the need for robust measures to reduce the adverse effects, hence making the system to be more resilient and secure in the public’s perception.

Corrosion resistance in LNG plant design: Engineering lessons for future energy projects

Corrosion resistance is a critical consideration in the design and operation of liquefied natural gas (LNG) plants, where harsh environmental conditions and aggressive chemicals can significantly impact asset integrity and safety. This paper explores engineering lessons learned from past LNG projects, emphasizing the importance of corrosion management strategies to enhance the longevity and reliability of energy infrastructure. The study highlights key factors contributing to corrosion in LNG facilities, including materials selection, environmental exposure, and operational practices. By employing advanced materials such as corrosion-resistant alloys and coatings, engineers can mitigate corrosion risks, leading to reduced maintenance costs and extended equipment lifespan. The paper examines successful case studies where proactive corrosion management has been implemented, showcasing innovative engineering solutions such as cathodic protection, corrosion monitoring systems, and design modifications that enhance resistance to corrosion-related failures. Furthermore, it addresses the role of regular inspections, predictive maintenance, and risk-based approaches in identifying potential corrosion issues before they escalate into significant problems. As LNG plants continue to proliferate globally, the integration of corrosion resistance into design and operational frameworks becomes increasingly vital. This paper also discusses emerging technologies and materials that hold promise for improving corrosion resistance, including nanotechnology and smart coatings that provide real-time monitoring capabilities. By learning from past projects and embracing a proactive approach to corrosion management, future energy projects can enhance safety, reduce operational risks, and achieve greater sustainability in their operations. Ultimately, the lessons derived from this research will contribute to developing best practices for designing and operating LNG plants and other energy facilities, ensuring that they meet the challenges of a rapidly evolving energy landscape.

The formation of a sustainable structured infrastructure for Peer-Reviewed Medical Journals in Russia: A perspective on the issue

The article provides an overview of the current infrastructure of medical peer-reviewed journals in Russia, and lists the main problems in a historical perspective. The directions of journal development are considered in the context of consolidating the joint efforts of the editorial and publishing community to improve and promote the publications of Russian-speaking authors. The article concludes that the creation of a reliable infrastructure of medical publications will become the basis for the development of equal and fair relations with the editorial and publishing communities of other countries.

Mechanics-informed autoencoder enables automated detection and localization of unforeseen structural damage

Structural health monitoring ensures the safety and longevity of structures like buildings and bridges. As the volume and scale of structures and the impact of their failure continue to grow, there is a dire need for SHM techniques that are scalable, inexpensive, can operate passively without human intervention, and are customized for each mechanical structure without the need for complex baseline models. We present a novel “deploy-and-forget” approach for automated detection and localization of damage in structures. It is a synergistic integration of entirely passive measurements from inexpensive sensors, data compression, and a mechanics-informed autoencoder. Once deployed, the model continuously learns and adapts a bespoke baseline model for each structure, learning from its undamaged state’s response characteristics. After learning from just 3 hours of data, it can autonomously detect and localize different types of unforeseen damage. Results from numerical simulations and experiments indicate that incorporating the mechanical characteristics into the autoencoder allows for up to a 35% improvement in the detection and localization of minor damage over a standard autoencoder. Our approach holds significant promise for reducing human intervention and inspection costs while enabling proactive and preventive maintenance strategies. This will extend the lifespan, reliability, and sustainability of civil infrastructures.

Optimizing on-site testing for post-tensioned concrete bridges

The decreasing structural reliability of key infrastructure due to aging and degradation represents a significant challenge for engineers, infrastructure managers, and society in general. The concealed nature of tendons in post-tensioned concrete bridges makes the assessment of their structural health a crucial point of several national bridge inspection guidelines. This assessment is commonly performed by means of direct and/or indirect tendon testing. A widely spread approach to the design and quantification of the number of tests is the American FHWA method, also adopted by the Italian guidelines. Unfortunately, in light of its frequentist nature and the consequent negligence of the prior knowledge on the health state of the structure, this one can lead to test campaigns with redundant and non-optimized allocation of resources and time. The present article proposes a novel Bayesian perspective of the FHWA test quantification methodology that accounts for: (i) the prior knowledge in an explicit manner by means of Bayes’ theorem; (ii) the test uncertainties, as in the case of Nondestructive Testing. By means of a demonstrative application, this article displays how this novel methodology leads to an optimized number of tests whilst guaranteeing the required structural reliability and user safety levels.

Assessment of Cybersecurity Risks in Digital Twin Deployments in Smart Cities

Digital Twin (DT) technology has become a cornerstone of modern smart city infrastructure, providing real-time insights and operational efficiencies by creating a virtual replica of physical systems such as traffic networks, energy grids, and public services. While these advancements enable optimized urban management and improved decision-making, they also present new cybersecurity challenges that can potentially jeopardize the safety and reliability of critical infrastructures. This study addresses the cybersecurity risks associated with Digital Twin deployments in smart cities, focusing on threats such as unauthorized access, data manipulation, and hijacking of the DT models, which could result in service disruptions and compromise public safety. The research employs a comprehensive risk assessment methodology based on the NIST Cybersecurity Framework, where potential risks are identified, evaluated, and prioritized according to their severity and likelihood of occurrence. To mitigate these risks, a multi-layered security framework was developed, incorporating encryption mechanisms, robust access control, and an Artificial Immune System (AIS)-based anomaly detection model. The framework was tested through a simulated case study on a smart transportation system within a smart city environment, demonstrating its effectiveness in preventing data tampering and detecting unauthorized access. The results indicate that the proposed security model reduced data manipulation incidents by 35%, decreased response times for threat detection by 25%, and improved overall system resilience by 40%. These findings underscore the critical need for proactive cybersecurity strategies in ensuring the secure and resilient deployment of Digital Twin technologies in smart cities. The study concludes by emphasizing the importance of continuous security monitoring and adaptive threat management to safeguard smart city ecosystems from evolving cyber threats

Lightning damage assessment on solar panels using in-house portable infrared thermography camera with Convolutional Neural Network (CNN) algorithm

ABSTRACT As the demand for renewable energy sources increases, the vulnerability of solar panels to lightning strikes becomes a critical concern. This research explores the correlation between lightning-induced voltage fluctuations and the resultant damage intensity on solar panels. The study adopts a systematic approach, first investigating the correlation between lightning-induced voltage assessment using 30kV, 60kV and 90 kV impulse voltage with multi-stage Marx impulse generator and the damage intensity on monocrystalline and polycrystalline solar panels. A portable active infrared thermography equipment with tCam-Mini was employed to conduct this research with wireless streaming. Furthermore, the study integrates Convolutional Neural Network (CNN)-based image classification techniques to enhance the efficiency of damage assessment. The results highlight the potential of neural networks in improving the accuracy and speed of image classification for damaged and undamaged samples. The findings contribute valuable insights into enhancing the resilience of solar panel systems against lightning strikes, ultimately advancing the reliability and sustainability of solar energy infrastructure. A new CNN model was developed to classify the images obtained from thermography with 90.21% accuracy for greyscale and 85.31% accuracy on thermal images.

Adaptation of digital gamification in professional education amid martial law challenges

Introduction: The growing interest within the scientific community in the potential of gamification in professional education during wartime drives the relevance of this research. Objective: The main aim of this article is to identify the significance of positive and negative factors in gamification in professional education and analyse their impact on the educational environment. Methods: The research used scientific literature analysis, statistical data analysis, and generalisation and systematisation to substantiate the main features and positive and negative aspects of gamification in professional education. To justify the feasibility of applying gamification elements in the Ukrainian context, an expert survey of lecturers was conducted, which allowed for the formation of weighted average indicators by criteria using Excel tools and the performance of a correlation SWOT analysis in the JASP program (Classical Correlation tool). Results: The analysis revealed that lecturers rate the advantages and opportunities of gamification higher than the challenges and threats. Positive effect include of gamification on the development of students' collaborative skills, engagement, and motivation, however, negative consequences the creation of unhealthy competition, reduced learning effectiveness, and the need for enhanced control over game elements. Conclusions: The research identified that the main aspects that will contribute to the effective integration of game elements into the learning process are an integrated approach, infrastructure reliability, flexibility, and adaptability of educational programmes, which will ensure the further development of approaches to student engagement and the enhancement of their motivation in the process of acquiring professional skills.

Report on the 5th International Workshop on Engineering and Cybersecurity of Critical Systems and 2nd International Workshop on Software Vulnerability Management (EnCyCriS/SVM - 2024)

Increasing system interconnectivity, decentralization, and introduction of new, more intelligent technologies, result in critical infrastructures becoming exposed to increased risk of cyber, physical, and combine cyber-physical attacks. Cyber-attacks on critical systems can inflict severe consequences on to people, society, economy, and national security, and can have adverse effects on safety and reliability of critical infrastructures. The joint EnCyCriS-SVM workshop facilitates discourse and discussion among researchers, practitioner, and students who are working on challenges and solutions related to the industrial revolution. Focus is given on sharing industry experience and project results pertaining to cyber threats on critical systems; secure software engineering; and attack detection and response mechanisms.

Research on Protective Effect of Lead Buffer Device under Explosion Load

In modern society, the frequency of explosion accidents is gradually increasing, which brings a great threat to the safety of people's lives and property and social stability. Explosive loads are extremely destructive, with huge energy and powerful impact force released in an instant, capable of causing serious damage to buildings and infrastructure. Therefore, it is of great practical significance to study explosion protection measures in depth and improve the damage resistance of buildings and infrastructure under explosion loads. The traditional underground dispersed layer can reduce the impact of blast load on the structure to a certain extent, but with the continuous improvement of protection requirements, its limitations are gradually revealed. For example, the protection of underground dispersed layers may be less than ideal in some complex explosion situations, and their performance is susceptible to environmental factors such as salinity, consolidation, precipitation and ice, resulting in reduced protection effectiveness. In order to find a more effective solution for explosion protection, the idea of replacing the underground dispersion layer with lead dampers was proposed. As a new type of protective device, lead damper has unique advantages. Lead has good plastic deformation ability, and can absorb a large amount of energy through its own deformation under the action of explosion load, so as to effectively reduce the impact of explosion on the structure. In addition, lead dampers can be customized to meet specific engineering needs, allowing them to better adapt to different explosion scenarios and structural types. The purpose of this study is to further investigate the protective effect of lead dampers instead of underground dispersed layers under blast loads. Through a combination of theoretical analysis, numerical simulation and experimental research, the performance of the lead damper is comprehensively evaluated, and its response mechanism under blast load is analyzed, as well as the advantages and disadvantages compared with the underground dispersed layer. At the same time, the influence of the design parameters of the lead damper on the protection effect will be studied, so as to provide scientific basis and guidance for its application in practical engineering. Through this study, it is expected to provide a new and more effective means of protection for explosion protection engineering, and improve the safety and reliability of buildings and infrastructure under explosion loads. This will not only help reduce the losses caused by explosion accidents and ensure the safety of people's lives and property, but also contribute to the stability and sustainable development of society.

Sustainable Development of the China Railway Express under the Belt and Road Initiative: Focusing on Infrastructure Reliability and Trade Facilitation

The China Railway Express (CR Express) is an important component of the Belt and Road Initiative (BRI). The sustainable development of CR Express provides critical support for regional economic integration and promotes a balanced development of the supply chain. Infrastructure reliability and trade facilitation greatly impact the operation of CR Express and are crucial for improving the competitiveness of transnational trade and cross-border efficiency. Inconsistent transportation infrastructure standards and low-efficient transportation service procedures affect the cross-border movement of cargo among countries. This paper integrates quantified metrics of infrastructure reliability and trade facilitation into a spatial friction model based on the electrical resistance theory, estimating the impact of these factors on the transportation flow of CR Express. Additionally, three scenarios of infrastructure reliability and trade facilitation are established for the four trade routes from Zhengzhou to Hamburg. Numerical experiments show that compared with inland river routes and traditional ocean routes, infrastructure reliability and trade facilitation significantly influence the transport flow of CR Express. These research results can provide a reference for the improvements of CR Express transportation efficiency and the simplification of customs clearance processes, potentially promoting the sustainable development of the CR Express supply chain to some extent.

Tensile testing in high-pressure gaseous hydrogen using the hollow specimen method

Metallic materials, predominantly steels, are the most common structural materials in the various components along the hydrogen supply chain. Ensuring their sustainable and safe use in hydrogen technologies is a key factor in the ramp-up of the hydrogen economy. This requires extensive materials qualification, however, most of the accepted; and standardized test methods for determining the influence of gaseous hydrogen on metallic materials describe complex and costly procedures that are only available to a very limited extent worldwide. The hollow specimen technique is a simple, rapid, and economical method designed to overcome the limitations of the current methods for the qualification of metallic materials under high-pressure hydrogen gas. However, this technique is not yet standardized. The TransHyDE-H2Hohlzug project is presented in this article, along with the main steps required to optimize the hollow specimen technique. This includes closing knowledge gaps related to the specimen geometry, surface quality, and gas purity in dedicated working packages, thus contributing to a comprehensive standardization of the technique for tests in high-pressure hydrogen gas.Impact statementThe hydrogen economy is considered a key solution for achieving climate neutrality in Europe, as it plays a crucial role in the decarbonization of sectors such as transport, industry, power, etc. Ensuring the safety and reliability of infrastructure is crucial for the ramp-up of the hydrogen economy. Therefore, it is necessary to meticulously study the materials and components used for infrastructure under conditions that closely resemble in-service conditions. The currently standardized methods are limited as they do not precisely replicate in-service conditions, and when they do, they are often complex, costly, and not easily accessible. This article presents the hollow specimen technique, a simple, and economical method developed to address the limitations of current standardized methods. The results from this work will contribute to the standardization of this technique for tests in high-pressure hydrogen gas. This will enable a faster evaluation of materials for hydrogen applications by industry and academia, thereby contributing to the growth of the hydrogen economy.Graphical abstract

ERIRMS Evaluation of the Reliability of IoT-Aided Remote Monitoring Systems of Low-Voltage Overhead Transmission Lines.

Researchers have studied instances of power line technical failures, the significant rise in the energy loss index in the line connecting the distribution transformer and consumer meters, and the inability to control unauthorized line connections. New, innovative, and scientific approaches are required to address these issues while enhancing the reliability and efficiency of electricity supply. This study evaluates the reliability of Internet of Things (IoT)-aided remote monitoring systems specifically designed for a low-voltage overhead transmission line. Many methods of analysis and comparison have been employed to examine the reliability of wireless sensor devices used in real-time remote monitoring. A reliability model was developed to evaluate the reliability of the monitoring system in various situations. Based on the developed models, it was found that the reliability indicators of the proposed monitoring system were 98% in 1 month. In addition, it has been proven that the reliability of the system remains high even when an optional sensor in the network fails. This study investigates various IoT technologies, their integration into monitoring systems, and their effectiveness in enhancing the reliability and efficiency of electrical transmission infrastructure. The analysis includes data from field deployments, case studies, and simulations to assess performance metrics, such as accuracy, latency, and fault detection capabilities.

Railway Infrastructure Management: Selection of Overhead Contact Line Ampacity Considering Operational and Design Factors

This paper presents a novel approach to determining the ampacity of DC and AC railway overhead contact lines, considering both operational and design factors. The ampacity of contact lines is influenced by several design parameters, including materials, cross section and the number of catenary and contact wires, etc. Additionally, the permissible long-term and short-term current values, which correspond to the permissible temperatures defined by EN 50119:2020, are affected by operational factors such as ambient temperature, solar radiation, heat exchange conditions, wind speed, wear of contact wires. Existing methods for determining the ampacity of overhead contact lines often fail to account adequately for these critical factors and lack the necessary procedures to calculate the 30 min ampacity required by EN 50119:2020. In response, the method proposed in this study adapts the procedures of IEEE 738-2012 to the specific needs of railway contact lines, taking into account the effect of contact wire wear on current distribution and overall ampacity. The proposed method offers a more realistic and practical approach to determining the ampacity of contact lines, thereby enhancing the reliability and efficiency of the traction power supply infrastructure. This method could be useful for railway infrastructure managers to ensure the safe and efficient operation under varying environmental and operational conditions.