Search Results (245)

Cultural ecosystem services (CESs) play a critical role in urban residents’ well-being, yet conventional evaluations rely heavily on green-space area and overlook how facility quality and basic services influence the delivery of actual cultural benefits. To address this methodological gap, this study develops a three-tier evaluation framework—service potential, actual supply capacity, and actual service utility—to quantify multistage attenuation in CES provision across 95 parks in seven central districts of Shenyang, China. The framework integrates 114 quantitative and qualitative indicators from field surveys, national facility standards, and perception-based assessments, enabling a scientifically robust and replicable assessment of how cultural benefits are transformed from ecological structure to human experience. Results reveal that single-index, area-based assessments substantially overestimate CES supply: district-level supply–demand ratios drop from 66 to 195% to only 11–55% once quality and basic services are incorporated. Comprehensive and special parks retain the highest CES potential, whereas community and linear parks undergo significant losses due to aging facilities, insufficient maintenance, and inadequate infrastructure. Education and cultural services exhibit the most severe shortages, with deficits reaching 59–84%, underscoring structural limitations in learning-oriented spaces. By distinguishing structural (quantity), functional (quality), and experiential (basic service) constraints, the framework provides clear diagnostic guidance for targeted planning and management. Its multistage structure also reflects broader principles of sustainable urban development: improving CES requires not only expanding ecological elements but also enhancing service quality, strengthening infrastructure, and promoting equitable access to cultural benefits. The framework’s generalizability makes it applicable to high-density cities worldwide facing land scarcity and green-space inequality, supporting efforts aligned with SDG 11 to build inclusive, resilient, and culturally vibrant urban environments. Full article

Envisaging future metro operations requires a collective and collaborative approach to understand an operator’s requirements. This study aims to gain an understanding of the current status of metro operators, as well as to identify areas of future innovation and further development. A special emphasis was given to the organisational settings—an underexplored aspect of metro operators in existing research—in addressing the following three designated areas of interest: predictive maintenance, cyber-security, and energy consumption. Therefore, to achieve an insight into metro operator’s internal operations, the study sought to engage in dialogue with operators. A literature review was first conducted to provide a foundation for analysis, and based on it, an online self-completed questionnaire survey was designed and administered to gain responses and insights from an extensive range of real-world metro operators. Follow-up face-to-face and group-wide discussions were also undertaken to obtain further detail and more specific information relating to metro operations. Through a three-dimension analysis framework, current practices, areas of consensus, and future innovative strategies of metro operators’ internal operations and organisational settings are highlighted. These insights collectively underscore the importance of adaptable strategies and cross-sector collaboration for advancing resilient, efficient, and secure metro systems. The outcome of the paper aspires to provide a strong foundation for future research as well as for future metro projects, providing an overview of the existing status of metro operators across the world. Full article

Collective intelligence within a quantum-informed cybernetic paradigm presents a transformative perspective to examine adaptability and resilience in Internet of Things (IoT) systems. This paper introduces Cogitor5, a fifth-order cybernetic system that builds upon the foundational principles of the fourth-order COgITOR framework, a liquid computational system designed for complex adaptive processes. The term COgITOR is etymologically linked to the Latin passive verb cogĭtur, translating to “He is gathered,” in contrast to the more commonly recognized active form cogito, meaning “I gather” or “I think,” as famously articulated by Descartes. In contrast to conventional binary systems, Cogitor5 functions as a simulation-based complex adaptive system, inspired by a population of nano agents represented by nanoparticles suspended in a colloidal medium. These agents exhibit autonomous interactions within the solvent, featuring quantum-enabled properties that facilitate advanced self-organization and coevolutionary dynamics. This intricate model captures the complexities of agent interaction, offering a refined representation of their evolving collective intelligence. The study redefines collective intelligence as emergent process intelligence, relevant to the adaptive capacities of both biological and cybernetic systems. By utilizing metacybernetic principles in conjunction with theories of complex adaptive systems, this paper investigates how IoT networks can evolve to enhance agency trajectory formation and increase adaptability. Cogitor5 serves as an innovative computational framework for addressing the inherent complexities of IoT, providing clarity in examining self-organization, self-regulation, self-maintenance, and sustainability, thus elevating system viability. The methodology encompasses the modeling of collective and process intelligence within the scope of Mindset Agency Theory (MAT), an advanced metacybernetic model that allows for evaluable characteristics. Furthermore, this approach integrates theoretical modelling and a practical case study implemented in Matlab^® to illustrate agency functionality within a dynamic system simulating failures in the nodes of an electric grid. Full article

This paper introduces a digital framework that integrates Building Information Modeling (BIM) and Facility Management (FM) to enhance the lifecycle performance of Sustainable Urban Drainage Systems (SuDS). Addressing the limitations of traditional drainage such as poor resilience and fragmented maintenance, the framework consists of the following four layers: BIM-based 3D asset modeling, sensor-driven monitoring, FM-integrated operations, and climate-informed adaptive planning. Grounded in systems engineering and aligned with International Standard ISO 19650 standards, it enables a dynamic digital twin to support continuous feedback and predictive maintenance. Illustrated through diagrams and comparison, the framework promotes adaptability and long-term sustainability in urban water infrastructure. Full article

Industry 5.0 introduces a shift toward human-centric, sustainable, and resilient industrial ecosystems, emphasizing intelligent automation, collaboration, and adaptive operations. Predictive Maintenance (PdM) plays a critical role in this transition, addressing the limitations of traditional maintenance approaches in increasingly complex and data-driven environments. The convergence of Artificial Intelligence and the Industrial Internet of Things, referred to as the Artificial Intelligence of Things (AIoT), enables real-time sensing, learning, and decision-making for advanced fault detection, Remaining Useful Life estimation, and prescriptive maintenance actions. This study provides a systematic and structured review of AIoT-enabled PdM aligned with Industry 5.0 objectives. It presents a unified taxonomy integrating AI models, Industrial Internet of Things (IIoT) infrastructures, and AIoT architectures; reviews AI-driven techniques, sector-specific implementations in manufacturing, transportation, and energy; and analyzes emerging paradigms such as Edge–Cloud collaboration, federated learning, self-supervised learning, and digital twins for autonomous and privacy-preserving maintenance. Furthermore, this paper synthesizes strengths, limitations, and cross-industry challenges, and outlines future research directions centered on explainability, data quality and heterogeneity, resource-constrained intelligence, cybersecurity, and human–AI collaboration. By bridging technological advancements with Industry 5.0 principles, this review contributes a comprehensive foundation for the development of scalable, trustworthy, and next-generation AIoT-based predictive maintenance systems. Full article

Understanding the seasonal behaviour of sewer failures is essential for infrastructure reliability and sustainable asset management. This study presents a seasonality-centred, data-driven analysis of monthly sewer failures over a 15-year period (2010–2024) in a major city in south-eastern Poland. The assessment is based exclusively on operational failure records, allowing intrinsic temporal regularities to be extracted without the use of external meteorological covariates. Seasonal Decomposition of Time Series by LOESS (STL), Autocorrelation Function (ACF), Seasonal Index (SI) and the Winter–Summer Index (WSI) were applied to quantify periodicity, seasonal amplitude and long-term variability. The results confirm a pronounced annual cycle, with failures peaking around March and reaching minima in September, supported by a strong autocorrelation at a 12-month lag (r ≈ 0.45). The mean WSI value (1.05) indicates a nearly balanced but still winter-sensitive pattern, while annual WSI values ranged from 0.71 to 1.51. The STL seasonal amplitude remained structurally stable at ≈61 failures throughout the study period, while annual values showed a modest but statistically significant increasing tendency. Trend analysis showed no significant monotonic trend in the deseasonalized series (Z ≈ 0.89, p = 0.37), whereas the raw series exhibited a weak but significant upward trend (τ ≈ 0.33, p < 0.001), largely attributable to short-term operational variability rather than to changes in intrinsic failure rate. The study demonstrates that long-term operational data alone are sufficient to capture seasonal and long-term dynamics in sewer failures. The presented framework supports utilities in integrating seasonality diagnostics into preventive maintenance, resource allocation and resilience planning, even in the absence of detailed climatic datasets. Full article

Pharmaceutical manufacturing and logistics rely on accurate prediction and decision making to safeguard product quality, delivery reliability, and patient outcomes. Despite rapid advances in artificial intelligence (AI) and machine learning (ML), few studies benchmark model performance across the diverse operational demands of global pharmaceutical supply chains. Predictive setbacks contribute to financial losses, reduced supply chain efficacy, and potential adverse health consequences, yet understanding these failures offers firms opportunities to refine strategy and strengthen resilience. Drawing on 1.2 million shipments spanning 39 countries, we compare traditional statistical models (ARIMA), ensemble methods (random forests, gradient boosting), and deep neural networks (LSTM, GRU, CNN, ANN) across pricing, demand forecasting, vendor management, and shipment planning. Gradient boosting produced the strongest pricing performance, while ARIMA delivered the lowest demand-forecasting errors but with limited explanatory power; neural networks captured nonlinear demand shocks and achieved superior maintenance-risk classification. We also identified three vendor performance clusters—high-performing, cost-efficient, and mixed-reliability vendors—enabling firms to better align shipment criticality with vendor capabilities by prioritizing high performers for urgent deliveries, leveraging cost-efficient vendors for non-urgent volumes, and managing mixed performers through targeted oversight. These insights highlight the value of our evidence-based roadmap for selecting algorithms in high-stakes healthcare logistics, in rapidly evolving, technologically complex global contexts where increasing algorithmic sophistication elevates the standards for safer, smarter pharmaceutical supply chains. Full article

Modelling connectivity in utility networks is essential for operational management, maintenance planning, and resilience analysis. The CityGML Utility Network Application Domain Extension (UNADE) provides a detailed conceptual framework for representing utility networks; however, most existing implementations rely on relational databases, where connectivity must be reconstructed through joins rather than represented as explicit relationships. This creates challenges when managing densely connected network structures. This study introduces the UNADE–Labelled Property Graph (UNADE-LPG) model, a graph-based representation that maps the classes, relationships, and constraints defined in the UNADE Unified Modelling Language (UML) schema into nodes, edges, and properties. A conversion pipeline is developed to generate UNADE-LPG instances directly from CityGML UNADE datasets encoded in GML, enabling the population of graph databases while maintaining semantic alignment with the original schema. The approach is demonstrated through two case studies: a schematic network and a real-world water system from Frankston, Melbourne. Validation procedures, covering structural checks, topological continuity, classification behaviour, and descriptive graph statistics, confirm that the resulting graph preserves the semantic structure of the UNADE schema and accurately represents the physical connectivity of the network. An analytical path-finding query is also implemented to illustrate how the UNADE-LPG structure supports practical network-analysis tasks, such as identifying connected pipeline sequences. Overall, the findings show that the UNADE-LPG model provides a clear, standards-aligned, and operationally practical foundation for representing utility networks within graph environments, supporting future integration into digital-twin and network-analytics applications. Full article

This review explores the deployment of Artificial Intelligence (AI) technologies to augment key industry processes in the new paradigm of Industry 5.0. Based on a handpicked collection of 35 peer-reviewed articles and leading resources, the study integrates the latest breakthroughs in Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), and Federated Learning (FL) with their applications in predictive maintenance, process planning, real-time monitoring, and operational excellence. The results emphasize AI’s central role in making manufacturing smarter, minimizing system downtime, and facilitating decision-making based on information in various industries like aerospace, energy, and intelligent manufacturing. Yet, the review also highlights significant challenges, ranging from data heterogeneity to model interpretability, security risks, and the ethics of automation. Solutions in the making, including Explainable AI (XAI), privacy-enhancing collaborative models, and enhanced cybersecurity protocols, are postulated to be the key drivers for the development of dependable and resilient industrial AI systems. The study concludes by postulating directions for further research and practice to secure the safe, transparent, and human-centered deployment of AI in industrial settings. Full article

Over-the-air (OTA) firmware updating has become a fundamental requirement in modern Internet of Things (IoT) deployments, where thousands of heterogeneous embedded devices operate in remote and distributed environments. Manual firmware maintenance in such systems is impractical, costly, and prone to security risks, making automated update mechanisms essential for long-term reliability and lifecycle management. This paper presents a unified OTA update architecture for ESP32-based IoT devices that integrates centralized version control and multi-protocol communication support (Wi-Fi, BLE, Zigbee, LoRa, and GSM), enabling consistent firmware distribution across heterogeneous networks. The system incorporates version-compatibility checks, rollback capability, and a server-driven release routing mechanism for development and production branches. An analytical model of timing, reliability, and energy consumption is provided, and experimental validation on a fleet of ESP32 devices demonstrates reduced update latency compared to native vendor OTA solutions, together with reliable operation under simultaneous device loads. Overall, the proposed solution provides a scalable and resilient foundation for secure OTA lifecycle management in smart-industry, remote sensing, and autonomous infrastructure applications. Full article

This paper proposes the “return-to-nature” as an operational design framework for integrating spontaneous habitats and informal green areas into contemporary urban landscapes. Using spatial analysis, field observations, and open-access ecological datasets, the study examines three sites in Eastern Rome—Ex Snia Viscosa, Parco della Serenissima, and the ZSC “Travertini Acque Albule”—to evaluate how low-maintenance, process-based landscapes can contribute to biodiversity networks and climate adaptation. The results reveal recurrent patterns, including the ecological value of unmanaged areas, the interaction between cultural heritage and spontaneous vegetation, and inconsistencies between formal protection boundaries and actual habitat distribution. Based on these findings, six operational principles are defined: access by least impact, differential maintenance, succession windows, interpretive minimalism, co-stewardship, and adaptive monitoring. The study also advances the idea of a Rome–Tivoli Greenway as a transferable Mediterranean model capable of applying these principles at a territorial scale. The findings show that spontaneous urban nature can function as ecological infrastructure, support community stewardship, and reduce management costs, while also presenting risks such as invasive species dynamics and potential conflicts over access. The paper concludes with policy mechanisms—adaptive maintenance regimes, stewardship agreements, and updated planning tools—to operationalise the proposed approach and support more resilient and biodiverse urban landscapes. Overall, the “return-to-nature” framework provides a transferable approach for cities seeking to enhance biodiversity, resilience, and socio-ecological integration through lighter and more adaptive design strategies. Full article

Soil salinity severely threatens soybean productivity worldwide. While transcriptional responses to salt stress are well-documented, the role of post-transcriptional regulation, particularly alternative splicing (AS), remains underexplored. This study combines physiological phenotyping, transcriptome-wide analysis, and molecular genetics to uncover the mechanisms behind the differences in salt tolerance between the salt-sensitive variety Huachun 6 (HC6) and the resistant variety Fiskeby III. Under salt stress, Fiskeby III exhibited superior survival rates and maintained ion homeostasis, as evidenced by a lower Na⁺/K⁺ ratio, compared with HC6. Transcriptomic and splicing analysis revealed extensive salt-induced alternative splicing reprogramming. Genes undergoing differential AS were enriched in pathways related to stress response, ion transport, and RNA splicing. Based on the overlap with both differentially expressed genes (DEG) and alternative splicing (DAS) genes under salt stress, a key splicing factor, GmSR34b, was identified as a central regulator of AS under salt stress. Under NaCl stress, the expression of GmSR34b in leaves peaked at 1 h and a salt stress-specific splicing variant was rapidly induced. A comparative analysis showed that the Fiskeby III cultivar prioritized maintenance of the full-length transcript during prolonged stress, whereas the HC6 cultivar accumulated higher levels of the splicing variant. This indicates differences in the regulation of alternative splicing between these two cultivars. Functional validation confirmed that overexpression of GmSR34b in soybean hairy roots inhibited salt tolerance. This study provides novel insights into the molecular mechanisms of salt tolerance in soybean, suggesting potential strategies for breeding resilient crops through the manipulation of splicing regulators. Full article

Ongoing urbanization, biodiversity decline, and intensifying climate change increasingly challenge the sustainability of urban green spaces (UGS) dominated by conventional, intensively maintained lawns. Although widespread across cities worldwide, lawns are criticised for their low biodiversity value and high resource demands. This paper explores nature-based solutions (NBS) as viable alternatives for enhancing resilience and multifunctionality of urban lawns. It conceptualizes lawns as intertwined ecological, design, and socio-cultural systems, and evaluates strategies for their transformation. Building on case studies from ten Eurasian cities, a narrative literature review, and the authors’ inter- and transdisciplinary research experience, this study develops a typology of NBS alternatives, including urban species-rich meadows, semi-natural grasslands, naturalistic herbaceous perennial plantings, mixed-vegetation groundcovers, edible lawns, pictorial (annual) meadows, and rewilded lawns. Key interventions involve reduced mowing, multifunctional green spaces, adaptive management, and community engagement. Findings demonstrate that these approaches enhance biodiversity, ecosystem services, and climate resilience, but their success depends on local ecological conditions, landscape design, and public perceptions of urban nature. Alternative lawn designs and maintenance practices should employ native, drought- and trampling-resistant plants and context-sensitive design configurations while respecting cultural traditions of urban greening and fostering social acceptance. The paper suggests practical recommendations and directions for future research. Full article

With the rapid development of metro–commercial integration, ensuring the safety of building operations has become increasingly critical. This study proposes a comprehensive safety evaluation framework tailored to integrated metro–commercial complexes. The framework establishes a hierarchical indicator system encompassing risk management, human safety management, facility and equipment safety, intelligent information management, and integrated crowd and operational risk. By combining historical records, real-time sensor data, and management logs, secondary indicators are quantified and normalized, while a hybrid weighting method integrating expert judgment and statistical analysis ensures both theoretical validity and empirical robustness. A case study demonstrates the framework’s applicability, yielding an overall operational safety score of 0.601, which corresponds to a “Moderate” level. Detailed analysis identifies deficiencies in flood resilience, intelligent monitoring reliability, and crowd-related fire risks, underscoring the complexity of safety challenges in such facilities. Targeted optimization measures—including enhanced drainage redundancy, condition-based equipment maintenance, improved intelligent monitoring, evacuation corridor expansion, and catering fire safety upgrades—are shown to substantially improve the composite safety index and operational resilience. This study contributes a dynamic, data-driven, and interpretable evaluation methodology that not only supports scientific safety management in metro–commercial buildings but also provides a reference for broader applications in multifunctional urban infrastructure. Full article

Data loss is a recurring and critical issue in Structural Health Monitoring (SHM) systems, often arising from a range of factors including sensor malfunction, communication breakdown, and exposure to adverse environmental conditions. Such interruptions in data availability can significantly compromise the accuracy and reliability of structural performance assessments, thereby hindering effective decision-making in safety evaluation and maintenance planning. In this study, a novel deep learning-based framework is proposed for data reconstruction in SHM, employing a hybrid architecture that integrates one-dimensional convolutional neural networks (1D-CNNs) with recurrent neural networks (RNNs). By combining these complementary strengths, the hybrid 1D-CNN–RNN model demonstrates superior capacity for accurate signal reconstruction. A real-world case study was conducted using vibration data from the Trai Hut Bridge in Vietnam. Five network configurations with varying depths were examined under single- and multi-channel loss scenarios. The results confirm that the method can accurately reconstruct lost signals. For single-channel loss, the best configuration achieved an MAE = 0.019 m/s² and R² = 0.987, while for multi-channel loss, a deeper network yielded an MAE = 0.044 m/s² and R² = 0.974. Furthermore, the model exhibits robust and stable performance even under more demanding multi-channel data loss conditions, highlighting its resilience to practical operational challenges. The results demonstrate that the proposed CNN–RNN framework is accurate, robust, and adaptable for practical SHM data reconstruction applications. Full article