Search Results (12,278)

In the era of digital transformation, transaction-intensive e-business applications—such as high-frequency trading (HFT), e-monetary services and decentralized marketplaces—require infrastructures that are not only fast and secure but also sustainable. Current solutions often prioritize short-term performance over long-term resilience, leading to inefficiencies in energy use and system reliability. This paper introduces a conceptual framework for sustainable transaction processing, leveraging energy-efficient hardware accelerators, real-time communication protocols inspired by industrial automation and lightweight authentication mechanisms. By integrating associative memory-based matching engines and optimized network architectures, the proposed approach ensures predictable latency, robust security and scalability without compromising sustainability. The framework aligns with the United Nations Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure) by reducing resource consumption, enhancing operational resilience and supporting future-ready digital ecosystems. Full article

With the growing heterogeneity of public transport systems, accurate representation of passenger service processes at bus stops shared by multiple operators has become increasingly important. This study develops and validates a microscopic model of passenger boarding and alighting at bus stops characterized by unstructured service patterns, diverse vehicle fleets, and irregular stopping positions. The approach focuses on individual passenger movements, enabling modeling of walking times from different waiting positions and assessing how passenger distribution and bus stopping positions affect total dwell time. Variables describing the boarding and alighting process, including waiting position, vehicle stopping position, individual boarding and alighting times, and passenger walking speed, were modeled as random variables following theoretical distributions (beta, logistic, log-normal, and normal). Bayesian estimation and bootstrap methods were applied to assess parameter stability and model fit. Field studies were conducted in two Polish cities (Kraków and Kielce) at 18 high-interference bus stop locations. Results indicate that the proposed probabilistic modeling approach enhances the accuracy of passenger flow representation and supports analysis of the effects of passenger dispersion and bus stopping position on service efficiency. The developed model can be used in microsimulation of bus stop operations, transport infrastructure design, and decision-making by transport management authorities. Full article

While most prison healthcare litigation seeks individual relief, some cases lead to broader structural reform via consent decrees—court-approved “legally binding performance improvement plans” designed to improve conditions. This study systematically analyzes 121 such settlements from 1970 to 2022 to assess their policy goals and implementation strategies. We identify the substantive areas targeted—general medical care, mental health, dental services, and treatment for specialized conditions like HIV, Hepatitis C, and COVID-19—and trace trends across time and geography. These agreements span 39 states and the federal system, with most states subject to multiple cases. They frequently mandate changes to budgets, staffing, facility infrastructure, training, and patient rights, alongside monitoring for quality improvement. Our findings suggest that consent decrees function not only as judicial remedies but as tools of policy development and institutional reform, shedding light on the role of courts in shaping correctional healthcare delivery. These findings also show how institutional responses to healthcare failures in prisons shape the conditions under which serious harm—and in some cases, preventable death—occur behind bars. Full article

Conventional climate-risk frameworks, ranging from ESG ratings to Integrated Assessment Models (IAMs), systematically underestimate physical risks by overlooking the non-linear physics that govern infrastructure failure. These top-down models perceive climate change as a manageable operational expense, thereby obscuring the substantial capital requirements necessary to sustain system reliability as global temperatures escalate. This study proposes a physics-first framework to quantify the “Adaptation Gap”—a measurable, unaccounted-for capital liability representing the additional cost needed to upgrade assets to maintain fault tolerance. Within this specific geographic and asset context, it has been determined that restoring fault tolerance for new equipment necessitates a 19.7% (95% CI: 16.5–22.9%) increase in capital expenditure, which increases the Adaptation Gap to 28.7% for typical in-service assets, potentially increasing the true cost for aging assets to between 25% and 30%. Although the quantitative findings are specific to the case study, the methodological framework—assessed as superior to traditional risk metrics—is designed for global application in pricing the Adaptation Gap across all infrastructure sectors with thermal constraints. Our methodology provides a blueprint for establishing a new standard of climate-adjusted valuation, transforming abstract physical risks into a tangible, auditable capital liability. Full article

In this paper, we propose a multi-layered hierarchical block tracking (HBT) system for continuous real-time sensing and efficient network management of highly mobile nomadic infrastructure. In order to solve the limitations of the existing high-resolution satellite direct connection method in the fast tracking and management of mobile infrastructure due to the high processing delay and large data processing burden, we introduce event-based data abstraction and Infra Map management in a multi-layered network consisting of a Low Earth Orbit satellite (LEO), high-altitude platform (HAP), and low-altitude platform (LAP). Through this, unnecessary data transmission is minimized and processing speed and Surveillance Resolution (SR) are improved. Experimental results show that the HBT structure achieves the improved SR at low-speed conditions, maintaining high tracking stability even under dynamic movement scenarios, while reducing processing delay when compared to the existing LEO–ground direct communication. As a result, we verify that the HBT structure shows lower processing delay and high tracking stability when compared to the existing LEO–ground direct communication. Full article

Cloud-based platforms form the backbone of smart city ecosystems, powering essential services such as transportation, energy management, and public safety. However, their operational complexity generates vast volumes of system logs, making manual anomaly detection infeasible and raising reliability concerns. This study addresses the challenge of data scarcity in log anomaly detection by leveraging Large Language Models (LLMs) to enhance domain-specific classification tasks. We empirically validate that domain-adapted classifiers preserve strong natural language understanding, and introduce a Proximal Policy Optimization (PPO)-based approach to align semantic patterns between LLM outputs and classifier preferences. Experiments were conducted using three Transformer-based baselines under few-shot conditions across four public datasets. Results indicate that integrating natural language analyses improves anomaly detection F1-Scores by 5–86% over the baselines, while iterative PPO refinement boosts classifier’s “confidence” in label prediction. This research pioneers a novel framework for few-shot log anomaly detection, establishing an innovative paradigm in resource-constrained diagnostic systems in smart city infrastructures. Full article

Global demand for adaptable and rapidly deployable construction solutions in offshore, coastal, and fluvial environments continues to rise, driven by pressing needs to develop energy platforms, improve coastal resilience, and support emergency response in the face of natural disasters. Increased investment in human-made coastal infrastructure, such as piers, support structures for power lines, offshore wind farms, and seawall protection systems, further underscores this trend. This study investigates the development of printable concrete mixtures for underwater environments using seawater as a replacement for freshwater, using a 3D printing syringe-based extrusion system. The effect of seawater addition and the printing medium (in air vs. underwater) was assessed via rheological and mechanical performance characterization. The results indicate rheological properties are favorable for seawater adoption by producing mixtures with higher yield stress and viscosity with the same levels of admixtures used for freshwater. Seawater-based mixtures demonstrated superior dimensional stability compared to freshwater counterparts, maintaining cross-sectional geometry, while compressive strength results showed no statistical differences between in-air and underwater samples. However, flexural strength was significantly influenced by geometry and printing medium. These findings establish critical rheological parameters for printable underwater mixtures and highlight the need for optimized curing strategies and layer bonding techniques to improve interfacial strength in underwater 3D printing applications. Full article

Country parks are an important instrument for implementing China’s strategies on ecological civilization and integrated urban–rural development. This study conducted field surveys in seven country parks of Shanghai. Meanwhile, stakeholder seminars were organized with local residents and park authorities. To assess visitor satisfaction, a questionnaire survey was administered both on-site and online. Through case analysis and a policy review, this study systematically identifies key challenges in leveraging country parks for rural revitalization. The findings indicate that visitors highly value the ecological qualities of the parks, and basic infrastructure like roads and resting facilities generally meets expectations. However, shuttle services and smart guiding systems remain notable shortcomings that hinder the overall visitor experience. Moreover, gaps in service quality, local cultural representation, and the depth of nature education constitute the primary weaknesses affecting visitor satisfaction. Regarding rural revitalization, this study identifies four main limitations in the contribution of country parks: (1) Inadequate functional positioning and weak integration with surrounding resources; (2) Low land use efficiency and an unbalanced provision of supporting facilities; (3) Homogenized industrial formats with limited innovation and integration capacity; and (4) Restricted participation of local farmers and underdeveloped multi-stakeholder governance mechanisms. To address these issues, this study proposes four strategic recommendations: (1) Develop distinctive local brands and strengthen synergies with surrounding resources; (2) Promote mixed land use and enhance supporting service facilities; (3) Foster diversified business formats and facilitate the value realization of ecological products; and (4) Expand income-generation channels for farmers and improve multi-stakeholder governance frameworks. The research demonstrates that optimizing the functions of country parks can improve ecological and recreational services and help establish an integrated “ecology–industry–community” framework through industrial chain extension and community participation, thereby supporting rural revitalization. Full article

Ship lock zones represent bottlenecks and a particular challenge for authorities managing vessel traffic. Traditionally, the control strategy of such systems has relied heavily on the subjective judgment, experience, and tacit knowledge of ship lock operators. To address the inherent uncertainty and imprecision associated with these subjective assessments, fuzzy logic and fuzzy set theory have been adopted as appropriate mathematical frameworks. In this work, the control strategy and the Fuzzy Decision Support System (FDSS) of a single-chamber ship lock designed for two vessels on a two-way waterway are analyzed and modeled. The input data is generated based on a synthesized dataset reflecting the annual schedule of vessel arrivals. The software is based on proposals and suggestions of experienced ship lock operators, and it is further validated through vessel traffic simulations. Moreover, the development of an appropriate Supervisory Control and Data Acquisition (SCADA) system integrated with a Programmable Logic Controller (PLC) is detailed, providing the necessary infrastructure for real-time deployment of the fuzzy control algorithm. The proposed control system represents an original contribution and offers practical applications both as a decision-support tool for real-time lock management and as a training platform for novice or less experienced operators. Full article

The release of PES-Learn version 1.0 as an open-source software package for the automatic construction of machine learning models of semi-global molecular potential energy surfaces (PESs) is presented. Improvements to PES-Learn’s interoperability are stressed with new Python API that simplifies workflows for PES construction via interaction with QCSchema input and output infrastructure. In addition, a new machine learning method is introduced to PES-Learn: kernel ridge regression (KRR). The capabilities of KRR are emphasized with examination of select semi-global PESs. All machine learning methods available in PES-Learn are benchmarked with benzene and ethanol datasets from the rMD17 database to illustrate PES-Learn’s performance ability. Fitting performance and timings are assessed for both systems. Finally, the ability to predict gradients with neural network models is presented and benchmarked with ethanol and benzene. PES-Learn is an active project and welcomes community suggestions and contributions. Full article

The rapid urbanization of coastal regions has intensified the demand for durable underground infrastructure like shield tunnels, where reinforced concrete (RC) structures are critical yet susceptible to long-term degradation in marine environments. This study develops an integrated machine vision-based framework for assessing the long-term durability of RC in marine shield tunnels by synergistically combining point cloud analysis and deep learning-based damage recognition. The methodology involves preprocessing tunnel point clouds to extract the centerline and cross-sections, enabling the quantification of geometric deformations, including segment misalignment and elliptical distortion. Concurrently, an advanced YOLOv8 model is employed to automatically identify and classify surface corrosion damages—specifically water leakage, cracks, and spalling—from images, achieving high detection accuracies (e.g., 95.6% for leakage). By fusing the geometric indicators with damage metrics, a quantitative risk scoring system is established to evaluate structural durability. Experimental results on a real-world tunnel segment demonstrate the framework’s effectiveness in correlating surface defects with underlying geometric irregularities. This integrated approach offers a data-driven solution for the continuous health monitoring and residual life prediction of RC tunnel linings in marine conditions, bridging the gap between visual inspection and structural performance assessment. Full article

Radar interferometry can provide important information for Structural Health Monitoring (SHM) of bridges and other transportation structures. In this article, joint communication and sensing (JCAS) telecommunication infrastructure is tested as a ground-based radar, offering advantages in terms of long-term costs, deployment and maintenance. This work specifically addresses the estimation of the radar support movement (i.e., pylon or mast), which represents a major challenge in this kind of measurements. Movements of the radar system combine with the true target motion and, if not correctly compensated, can compromise the accuracy of the results. A technique for estimating radar movements based on the displacement tracking of multiple permanent scatterers (PSs) in the scenario is presented. True target displacements can then be retrieved by applying linear regression methods to fixed PSs located at different viewing angles, accounting for both radar movements and atmospheric displacement components. The technique was validated using real data acquired during an experimental campaign on a bridge test site. First, results obtained for a target subject to known displacements are shown. A second measurement session was aimed at testing the method for bridge dynamic monitoring. Finally, the same technique was applied antenna mast monitoring in terms of modal analysis and vibration characterization. Full article

Green roofs have emerged as a key nature-based solution for improving environmental quality, strengthening urban resilience, and enhancing human wellbeing. In the hospitality sector—where sustainable design and guest experience increasingly intersect—the incorporation of green roof gardens is particularly significant. Urban hotels face heightened challenges related to elevated temperatures, reduced green space, and the growing need for restorative environments within dense urban settings. This study aims to examine how green roof gardens function as integrated ecological, social, and psychological infrastructures in hotel environments. It evaluates the extent to which rooftop green spaces contribute to environmental sustainability, enhance guest experience, and foster community connections. The research adopts a qualitative design combining a comprehensive literature review conducted at selected five-star hotels in Greece. Data from secondary sources and field-based assessments were thematically analyzed to identify recurring patterns in environmental performance, social use, and psychological benefits. Findings indicate that hotel green roof gardens act as multifunctional systems that deliver significant ecological benefits—such as improved microclimate regulation, stormwater retention, and biodiversity support—while simultaneously enriching social interaction and guest experience through accessible, esthetically appealing spaces. Observations further highlight their contribution to psychological wellbeing by offering restorative environments characterized by greenery, natural light, and panoramic views. The study concludes that green roof gardens represent an effective design strategy that integrates sustainability, hospitality experience, and urban wellbeing. Their application in hotels provides both conceptual insight and practical guidance for the development of more resilient, livable, and guest-centered urban environments. These findings underscore the importance of incorporating green roofs into contemporary tourism and urban planning practices. Full article

The rapid proliferation of Electric Vehicles (EVs) offers a promising pathway toward reducing greenhouse gas emissions and fostering a sustainable environment. However, the large-scale integration of EVs presents significant challenges to distribution networks, potentially increasing stress on grid infrastructure. To address these challenges, this study proposes the integration of a Virtual Power Plant (VPP) framework within EV charging stations as a novel approach to facilitate dynamic power management. The proposed framework integrates electric vehicle (EV) scheduling, battery energy storage (BES) charging, and vehicle-to-grid (V2G) support, while dynamically monitoring energy generation and consumption. This approach aims to enhance voltage regulation and minimize both EV charging durations and waiting periods. A modified IEEE 13-bus test network, equipped with six strategically placed EV charging stations, has been employed to evaluate the performance of the proposed model. Simulation results indicate that the proposed VPP-based method enables dynamic power coordination through EV scheduling, significantly improving the voltage stability margin of the distribution system and efficiently reduces charging times for EV users. Full article

The rapid proliferation of Electric Vehicle (EV) infrastructures has led to the massive generation of high-frequency streaming data uploaded to cloud platforms for real-time analysis, while such data supports intelligent energy management and behavioral analytics, it also encapsulates sensitive user information, the disclosure or misuse of which can lead to significant privacy and security threats. This work addresses these challenges by developing a secure and scalable scheme for protecting and verifying streaming data during storage and collaborative analysis. The proposed scheme ensures end-to-end confidentiality, forward security, and integrity verification while supporting efficient encrypted aggregation and fine-grained, time-based authorization. It introduces a lightweight mechanism that hierarchically organizes cryptographic keys and ciphertexts over time, enabling privacy-preserving queries without decrypting individual data points. Building on this foundation, an electric vehicle key management and query system is further designed to integrate the proposed encryption and verification scheme into practical V2X environments. The system supports privacy-preserving data sharing, verifiable statistical analytics, and flexible access control across heterogeneous cloud and edge infrastructures. Analytical and experimental evidence show that the designed system attains rigorous security guarantees alongside excellent efficiency and scalability, rendering it ideal for large-scale electric vehicle data protection and analysis tasks. Full article