Infrastructure Components Research Articles

Distributional equality in Ethereum? On-chain analysis of Ether supply distribution and supply dynamics

Blockchain-based emerging technologies such as decentralized finance (DeFi), cryptocurrencies, tokens, and smart contracts have introduced innovative frameworks for resource allocation and economic interactions. Ethereum, as the major technical network foundation of DeFi and tokenized assets, is becoming increasingly pivotal in facilitating an extension and alternative to traditional finance for many stakeholders, including those who are “unbanked”. Moreover, the recent transition of Ethereum from a proof-of-work (PoW) mechanism to a proof-of-stake (PoS) consensus mechanism and the Shanghai upgrade may significantly impact Ether (ETH) distribution. However, the status quo and dynamics of wealth distribution, especially after these changes in governance structure, remain unclear. By utilizing a rich dataset spanning the entire Ethereum history from July 2015 to December 2024, we analyze the balances across address groups of different sizes and the role of key economic activities and infrastructure components within Ethereum, such as exchanges, DeFi platforms, and staking. To provide detailed insights into ETH’s distributional equality, our approach combines descriptive, longitudinal, and causal inference analyses; a complete enumeration of more than 98 million unique wallet addresses; and novel on-chain analysis. Our findings show a substantial concentration of ETH within a small fraction of addresses, with approximately 0.3% of wallets holding nearly 95% of the total supply, despite the majority of wallets holding less than 0.1% ETH. However, the ETH distribution broadly resembles wealth distributions in traditional economies, with a log-normal body and Pareto-like tails. We assert that previous studies have overstated the concentration of ETH. Additionally, our dynamic analysis reveals a nuanced trend toward less concentration over time, driven by market cycles, increasing staking participation, and reinvestment in DeFi. These results challenge the notion of pervasive centralization. This study contributes to a deeper understanding of the current ETH distribution and its evolution over time. Therefore, this work provides an objective, data-driven basis for the ongoing discussion on wealth (in)equality in blockchain-based ecosystems, particularly in DeFi.

‘Are you being served?’ Public service media: audience conceptions of value in UK critical media infrastructure

ABSTRACT Public Service Media (PSM) faces existential challenges to its relevance and sustainability in the digital age. This article reports on audience focus group attitudes to the notion of PSM as a component of national infrastructure. The research finds audiences derive considerable individual and societal value from PSM. This includes as a source of trustworthy news in times of national crisis, and against the threat posed by ‘fake news’, but also extends to positive contributions drama and comedy content make to national life. However, the findings reveal a disconnect between the value articulated by respondents and views on the sustainability of PSM. From this, two main arguments are advanced. At a theoretical level, a new model of public value is required that accounts for the infrastructural role played by PSM. At a practical level, UK PSMs must become more effective in demonstrating and communicating their value and unique contribution to public life.

Logistics Infrastructure as a Driver of Social and Economic Development of the Region

The works of domestic and foreign scientists show that the links of the main and supporting components of the logistics infrastructure can be considered as a driver of the socio-economic development of the subjects of countries. The purpose of this article is to assess the impact of the integral indicator of the maturity level of the regional logistics infrastructure on the corresponding socio-economic indicators of the Southern Federal District. The author conducted a multiple regression analysis, which showed that the relationship between socio-economic indicators and characteristics of the regional logistics infrastructure can be presented in the form of linear multiple regression models for most subjects of the Southern Federal District. The analysis of the maturity level of the logistics infrastructure of the subjects of the Southern Federal District showed that all regions can be divided into three groups with above-average, average, and below-average levels of development, which is due to both geographic and infrastructural factors. The value of the linear Pearson correlation coefficient and the scatter diagram allowed us to conclude that the relationship between GRP and the integral indicator of the maturity level of the regional logistics infrastructure can be presented in the form of a linear dependence. Regions with the highest level of maturity of the logistics infrastructure are characterized by a high value of GRP. Reengineering of the existing logistics infrastructure, as well as the placement of large logistics platforms in the region will contribute to the growth of the gross regional product.

Strategic and Performance Planning in US Local Health Departments: A Comparative Analysis of Strategic, Community Health Improvement, and Quality Improvement Plans.

Local health departments (LHDs) are a critical component of the US public health infrastructure. To guide their work and evaluate progress, LHDs develop and maintain a range of planning documents, including strategic plans (SPs), community health improvement plans (CHIPs), and quality improvement plans (QIPs). The goal of this study was to provide information on current LHD practices related to strategic planning and performance improvement. The study used an environmental scan and document analysis of a sample of 93 plans from 31 LHDs to explore commonalities and differences within the strategies, goals, and priorities of SPs, CHIPs, and QIPs. Framework analysis was used, involving initial document review, development of a coding framework, and applying this framework to all documents iteratively. To ensure reliability, 19% of documents were double-coded, with discrepancies resolved through team discussions. Of the 31 LHDs included in this analysis, most were urban (89.7%) and medium-sized (64.5%), with a large percentage from the Midwest (41.9%). The analysis revealed that most LHDs used established frameworks to develop plans and identify priority areas. Frequently listed priority areas included behavioral health, social determinants of health, access to care, chronic disease, and nutrition and physical activity. Frequently listed strategies to improve priority areas included building collaborations and partnerships, advocating for policy change, increasing access to services and resources, and increasing awareness of issues. While CHIPs frequently incorporated social determinants of health as part of a guiding framework, QIPs focused on fostering continuous quality improvement. Differences were documented based on jurisdiction size, degree of rurality, and region. The study identified a widespread commitment to develop plans using established frameworks, community engagement, and data-driven decision-making, while also highlighting the diverse needs and capacities of LHDs in addressing public health challenges.

Predicting shear strength in reinforced concrete deep beams through finite element modeling of diverse concrete materials

This study investigates the prediction of the strength of reinforced concrete deep beams, critical components in urban infrastructure, by evaluating their load-carrying capacities through finite element modeling and nonlinear inelastic analyses using LS-DYNA software. Four widely used concrete material models were examined: Mat084/085, Mat159, Mat072R3, and Mat016. Analyses were conducted on two single-span and four double-span beams with varying reinforcement configurations and an aspect ratio of 1.0, based on well-documented experimental setups. Comparative analyses of force-displacement behavior and stress distributions revealed significant differences in shear strength predictions across the models, with Mat159 providing the most accurate results. These findings establish a reliable and cost-effective approach for predicting the capacities of deep beams, reducing reliance on extensive experimental testing. The study contributes valuable insights for improving strength predictions in critical infrastructure applications such as bridges and foundations.

The Metaverse Revolution: Transforming the Future of Retail

This comprehensive article explores the transformative impact of the Metaverse on the retail industry, examining how this immersive digital realm is revolutionizing traditional commerce paradigms. It investigates the convergence of cutting-edge technologies with innovative retail strategies, highlighting the evolution from static retail spaces to dynamic, AI-driven environments. It delves into real-time customer intelligence systems, adaptive retail environments, and sophisticated technical infrastructure components, including edge computing, blockchain, and artificial intelligence. The article examines practical applications such as virtual showrooms, AI-powered shopping assistants, hyper-personalized marketing, and supply chain optimization. Through extensive analysis of current industry research and market trends, this article demonstrates how the Metaverse is fundamentally reshaping customer interactions, operational efficiency, and business models in the retail sector. It reveals the critical role of emerging technologies in creating seamless, personalized shopping experiences that bridge physical and digital retail environments, while also addressing the challenges and opportunities presented by this digital transformation.

FaultSeg: A Dataset for Train Wheel Defect Detection

Wheels are a critical component of railway infrastructure and work as the load carrier of the train. However, defective wheels pose a serious risk to safety that can gravely jeopardize people’s safety. There is a significant risk of injury or death from defective wheels, endangering the lives of individuals. In this research, FaultSeg dataset is presented for automatic train wheel defect detection for railway transportation around the world. The FaultSeg consists of 829 manually annotated images of faulty wheels acquired by an indigenously developed wayside data acquisition system. Expert Annotators have manually annotated three classes of potential defects: Cracks/Scratches, Shelling, and Discoloration. To assess the practicality of the FaultSeg dataset for training and testing advanced deep learning (DL) models, the dataset was used to train and evaluate the YOLOv9 instance segmentation algorithm. The model achieves an approximate score of 87% accuracy. These results showcase the usability of the FaultSeg dataset in automatic inspection systems and data driven predictive maintenance strategies to safeguard and ensure the safety of railway transportation.

Surrogate-Assisted Cost Optimization for Post-Tensioned Concrete Slab Bridges

The study uses surrogate modeling techniques to evaluate cost optimization methodologies for post-tensioned concrete slab bridges. These structures are key components in transportation infrastructure, where design efficiency can yield significant economic benefits. The research focuses on a three-span slab bridge, with spans of 24, 34, and 28 m, optimized through the Kriging surrogate model combined with heuristic algorithms such as simulated annealing. Input variables included deck depth, base geometry, and concrete grade, with Latin Hypercube Sampling ensuring diverse design exploration. Results reveal that the optimized design achieves a 6.54% cost reduction compared to conventional approaches, primarily by minimizing material usage—concrete by 14.8% and active steel by 11.25%. Among the predictive models analyzed, the neural network demonstrated the lowest prediction error but required multiple runs for stability, while the Kriging model offered accurate local optimum identification. This work highlights surrogate modeling as a practical and efficient tool for bridge design, reducing costs while adhering to structural and serviceability criteria. The methodology facilitates better-informed decision-making in structural engineering, supporting more economical bridge designs.

Planning Amidst Uncertainty: Identifying Core CCS Infrastructure Robust to Storage Uncertainty

Carbon Capture and Storage (CCS) is a critical technology for reducing anthropogenic CO2 emissions, but its large-scale deployment is complicated by uncertainties in geological storage performance. These uncertainties pose significant financial and operational risks, as underperforming storage sites can lead to costly infrastructure modifications, inefficient pipeline routing, and economic shortfalls. To address this challenge, we propose a novel optimization workflow that is based on mixed-integer linear programming and explicitly integrates probabilistic modeling of storage uncertainty into CCS infrastructure design. This workflow generates multiple infrastructure scenarios by sampling storage capacity distributions, optimally solving each scenario using a mixed-integer linear programming model, and aggregating results into a heatmap to identify core infrastructure components that have a low likelihood of underperforming. A risk index parameter is introduced to balance trade-offs between cost, CO2 processing capacity, and risk of underperformance, allowing stakeholders to quantify and mitigate uncertainty in CCS planning. Applying this workflow to a CCS dataset from the US Department of Energy’s Carbon Utilization and Storage Partnership project reveals key insights into infrastructure resilience. Reducing the risk index from 15% to 0% is observed to lead to an 83.7% reduction in CO2 processing capacity and a 77.1% decrease in project profit, quantifying the trade-off between risk tolerance and project performance. Furthermore, our results highlight critical breakpoints, where small adjustments in the risk index produce disproportionate shifts in infrastructure performance, providing actionable guidance for decision-makers. Unlike prior approaches that aimed to cheaply repair underperforming infrastructure, our workflow constructs robust CCS networks from the ground up, ensuring cost-effective infrastructure under storage uncertainty. These findings demonstrate the practical relevance of incorporating uncertainty-aware optimization into CCS planning, equipping decision-makers with a tool to make informed project planning decisions.

Факторы и тенденции постиндустриальной трансформации экономики проблемных регионов Северо-Кавказского федерального округа

The article presents the results of empirical studies on the identification of the degree of post-industrial orientation of the regional economy, carried out on the factual basis of problematic entities of the North Caucasian Federal District. It is shown that in the context of planning the regional policy of post-industrial transit of the economy, it is necessary to be guided by the synthetic level of its post-industrial orientation, which consists of the current and forecast state of the innovation and infrastructure component and the level of activity of creative capital. Institutional support and management coordination The tasks of operational development of corrective directions, mechanisms and instruments of regional socio-economic policy aimed at changing the technological structure, ensuring the dominance of post-industrial sectors and industries of the economy, accumulating creative capital are impossible in their implementation without a detailed analysis of the accumulated transformation potential. In the article, from the standpoint of diagnosing the degree of post-industrial orientation of the regional economy, parametric and methodological support for calculating aggregate indices of innovative development and the degree of innovativeness of the economy is formed. Using the proposed analytical apparatus, reliable empirical estimates of the degree of post-industrial orientation of the regional economy of the subjects of the North Caucasian Federal District were obtained. The article is an addition to the cycle of researchers' works on the problems of economic transformation of the regions of the South of Russia. The obtained analytical results, empirical estimates and calculation material for the subjects of the North Caucasian Federal District can be used in further scientific research.

The Role of IoT and Analytics in Shaping Smart Cities: A Case Study Approach

The paper titled "The Role of IoT and Analytics in Shaping Smart Cities: A Case Study Approach" explores the transformative impact of the Internet of Things (IoT) and data analytics on urban environments, emphasizing their role in the development of smart cities. As municipalities increasingly adopt technology to enhance quality of life, this research identifies key components of smart city infrastructure, including urban planning, basic services, and policy frameworks. The study highlights how IoT facilitates real-time data collection and analysis, which are crucial for effective urban management and decision-making. By examining various case studies, particularly focusing on Barcelona, the paper illustrates practical applications of IoT in traffic management, waste reduction, and citizen engagement. The findings underscore the necessity for policymakers and urban planners to embrace these technologies to foster sustainable urban development while addressing challenges such as data security and integration. This comprehensive literature review serves as a foundational resource for stakeholders aiming to leverage technological synergies in creating resilient and efficient urban spaces.

Security in Storage Area Networks: A Technical Overview

Storage Area Network (SAN) security has emerged as a critical component of modern enterprise infrastructure, addressing the challenges posed by exponential data growth and evolving cyber threats. This technical article examines the fundamental components and implementation strategies for securing SAN environments, encompassing access control management, data protection mechanisms, network security infrastructure, physical security controls, compliance frameworks, and best practices. The comprehensive article covers advanced technologies including artificial intelligence-driven monitoring systems, quantumresistant encryption protocols, and automated incident response capabilities, while highlighting the importance of maintaining a balance between security measures and operational efficiency. Through the integration of multiple security layers and automated controls, organizations can establish robust defense mechanisms against emerging threats while ensuring optimal performance of their storage infrastructure.

Tube deformation measurement with camera and laser

Tube deformation measurement with camera and laser

Mitigating Voltage Violations in Smart City Microgrids Under Coordinated False Data Injection Cyberattacks: Simulation and Experimental Insights

This article investigates the impacts of coordinated false data injection attacks (FDIAs) on voltage profiles in smart microgrids integrated with renewable-based distributed energy resources (DERs), a critical component of urban energy infrastructure in smart cities. By leveraging simulation and experimental methods, a coordinated framework is developed for understanding and mitigating these threats, ensuring the stability of renewable-based DERs integral to modern urban systems. In the examined framework, a team of attackers independently identify the optimal times of two different cyberattacks leading to undervoltage and overvoltage in a smart microgrid. The objective function of each model is to increase the voltage violation in the form of either overvoltage or undervoltage caused by the corresponding FDIA. In such a framework, the attackers design a multi-objective optimization problem (MOOP) simultaneously resulting in voltage violations in the most vulnerable regions of the targeted microgrid. Considering the conflict between objective functions in the developed MOOP, a Pareto-based solution methodology is utilized to obtain a set of optimal solutions, called non-dominated solutions, as well as the best compromise solution (BCS). The effectiveness of the unified FDIA is verified based on simulation and experimental validations. In this regard, the IEEE 13-node test feeder has been modified as a microgrid for the simulation analysis, whereas the experimental validation has been performed on a lab-scale hybrid PV/wind microgrid containing renewable energy resources.

Regional-scale bridge health monitoring: survey of current methods and roadmap for future opportunities under changing climate

Climate-related extreme events are becoming increasingly frequent, posing significant threats to bridges, which are critical components of transportation infrastructure. This paper offers an overview of recent advancements in methodologies and technologies for conducting structural health monitoring (SHM) of bridges over large areas, where deploying sensors on every structure may be cost-prohibitive for local administrations. It specifically examines two approaches that have garnered interest in the past decade: indirect SHM, which involves instrumenting vehicles and analyzing their dynamic responses to infer information about bridges, and satellite interferometric radar data, which offer static displacement measurements for large regions and has recently been exploited for civil SHM purposes. Additionally, it reviews the recent developments in population-based SHM, which facilitates knowledge-sharing among structures with similar characteristics within a population. Through an analysis of the advantages and limitations of these three rapidly developing research areas, the paper outlines future opportunities and lays the roadmap for a comprehensive “regional-scale SHM” approach based on remote and/or crowdsourced data, supported by population-level analyses. Specific topics addressed include strategies for similarity assessment among monitored structures, available data sources, and feature extraction/integration approaches for different scenarios.

Adoption of 4.0 technologies in MSMEs in the Caribbean: a multiple cases study

By employing the qualitative methodology of multiple cases study, the adoption of 4.0 technologies in MSMEs in Puerto Rico is explored, in terms of the drivers, benefits, problems, and challenges of the incorporation processes. The findings reflect that MSMEs belong mostly to the regulated sector and agree that the adoption of technologies allows them interoperability, data analysis, projections, efficiency and effectiveness in real time and close and frequent linkage with customers. Participants of the study highlight their dependence on power energy system as well as its instability, as the biggest obstacles, in addition to the lack of capital, among others. The resilient infrastructure component (energy as well as technology) evidences the need for future research on the subject from a geospatial approach, which imparts originality to this study and determines its importance in the development of public policies and resilient business models.

What drives potential accessibility decomposition? Temporal and spatial variability of the impact of infrastructure and population components in France, Spain, and Poland in the years 1960–2020

Changes in potential accessibility are the effect of both the expansion of transport infrastructure and shortening travel times, as well as land-use processes, e.g., changes in population size and distribution. The aim of the paper is to indicate the spatial and temporal variability of the impact of the infrastructure and population components on changes in potential accessibility in France, Spain and Poland over six decades in the period 1960-2020. The simulations for various parameters of the impedance function presented indicate that in nationwide conditions the greatest improvement in accessibility as a result of the infrastructure component takes place at a specific value of the so-called half-life, predominantly about 60 minutes. For the population component, the length of the trip is less important in assessing changes in accessibility. It has been shown that periods of very high impact of the development of road infrastructure on improving accessibility are mostly limited to a single decade in each of the countries examined, i.e., in the 1970s in France, the 1990s in Spain and the 2010s in Poland. Three approaches to distinguishing typologies have been proposed depending on the use of three dimensions of the interplay of the impact of accessibility components on changes in accessibility. These three dimensions are: (1) the dominance (of strength) of the components, (2) the combination of influence signs of the components and (3) the ratio of the components.

An Improved iTransformer with RevIN and SSA for Greenhouse Soil Temperature Prediction

In contemporary agricultural practices, greenhouses serve as a critical component of infrastructure, where soil temperature plays a vital role in enhancing pest management and regulating crop growth. However, achieving precise greenhouse environmental control continues to pose a significant challenge. In this context, the present study proposes ReSSA-iTransformer, an advanced predictive model engineered to accurately forecast soil temperatures within greenhouses across diverse temporal scales, encompassing both long-term and short-term horizons. This model capitalizes on the iTransformer time-series forecasting framework and integrates Singular Spectrum Analysis (SSA) to decompose environmental variables, thereby augmenting the extraction of pivotal features, such as soil temperature. Furthermore, to mitigate the prevalent distribution shift issues inherent in time-series data, Reversible Instance Normalization (RevIN) is incorporated within the model architecture. ReSSA-iTransformer is adept at executing multi-step forecasts for both extended and immediate future intervals, thereby offering comprehensive predictive capabilities. Empirical evaluations substantiate that ReSSA-iTransformer surpasses conventional models, including LSTM, Informer, and Autoformer, across all assessed metrics. Specifically, it attained R2 coefficients of 98.51%, 97.03%, 97.26%, and 94.83%, alongside MAE values of 0.271, 0.501, 0.648, and 1.633 for predictions at 3 h, 6 h, 24 h, and 48 h intervals, respectively. These results highlight the model’s superior accuracy and robustness. Ultimately, ReSSA-iTransformer not only provides dependable soil temperature forecasts but also delivers actionable insights, thereby facilitating enhanced greenhouse management practices.

ПРОЕКТУВАННЯ МОДЕЛІ ХМАРНОЇ ІНФРАСТРУКТУРИ ВНЗ НА ОСНОВІ ПЛАТФОРМИ APACHE CLOUDSTACK

This paper describes the problems of using cloud computing in education. In the article it has been investigated the concept of cloud infrastructure in higher educational institutions. The model of cloudy infrastructure is offered. The hybrid model is most recent for higher educational institution. Conceptual provisions (technical and methodical) of designing of cloud are formulated. Described experience of deployment corporate clouds on the basis of the Apache CloudStack platform. The diagram of the organization of physical components of cloudy infrastructure (a hypervisor, physical and virtual area networks, routers, the domain controller, the VPN server) is provided.

Construction and Application Practice of the Index System for Bridge Lifecycle Monitoring

Bridges are crucial components of infrastructure, and comprehensive and effective monitoring is required throughout their entire lifecycle. This paper centers on the construction and application of the index system for bridge lifecycle monitoring. It presents the significance of such monitoring, details the design principles and key components like structural performance, environmental impact, and load-carrying capacity assessment indicators, along with data collection methods and sensor technologies. Conclusions affirm its importance for bridge safety, durability and serviceability. Future efforts should target intelligent, integrated monitoring and index system optimization to widen its use in bridge engineering.