Search Results (1,729)

In many Latin American cities, rapid densification, shrinking public land reserves, and growing spatial, social and biophysical fragmentation have heightened the urban significance of large, private, closed open spaces (COSs). COS, marked by restricted access and social homogeneity, operate as capsular urban models that limit socio-environmental integration, urban continuity and resilience. Far from being mere enclaves, the reconfiguration of COS emerges as a critical response to contemporary urban challenges with the capacity to reshape urban structures by generating new social and spatial connectivities. This article examines the transformation of COSs in urban contexts, such as golf clubs, into accessible public landscapes as hybrid open spaces (HOSs), a topic that remains underexplored internationally. For that, this research proposes a design-oriented, multiscalar framework (city and zonal/local) that integrates open and closed spatial programs within the wider urban open space system. Considering urban, biophysical, and sociocultural dynamics, and drawing on the concepts of accessibility, connectivity, diversity, and flexibility, the study develops guidelines and design strategies for hybridising private and public recreational and environmental uses to strengthen urban integration. Using El Rodeo Gold Club in Medellín as a case study, the work contributes to landscape architecture by advancing the transformation of underutilised COS into inclusive, multifunctional HOS, positioning COS as a strategic asset for sustainable urban environments. The framework can be replicable in other similar contexts. Full article

Rising global electricity demand and the expansion of distribution networks require a critical assessment of component-level greenhouse gas contributions. Distribution transformers, although indispensable, have significant life-cycle carbon impacts due to the use of materials, manufacturing, and in-service losses. This study conducts a life-cycle assessment of a single-phase, 75 kVA oil-immersed distribution transformer manufactured in Newfoundland, one of the provinces with the cleanest, hydro-dominated grids in Canada, and evaluates it over a 40-year lifespan. Using a cradle-to-use boundary, the analysis quantifies embodied emissions from raw material extraction, manufacturing, and transportation, alongside operational emissions derived from empirically measured no-load and load losses. All the data are collected directly during the manufacturing process, ensuring high analytical fidelity. The energy efficiency of the transformer is analyzed in MATLAB version R2023b using measured no-load and load losses to generate efficiency, load characteristics under various operating conditions. Under varying load factor scenarios and based on Newfoundland’s 2025 grid intensity of 18 g CO₂e/kWh, the lifetime operational emissions are estimated to range from 0.19 t CO₂e under no-load operation to 4.4 t CO₂e under full-load conditions. A linear regression-based decarbonization model using Microsoft Excel projects grid intensity to reach net-zero around 2037, two years beyond the provincial target, indicating that post-2037 transformer losses will remain energetically relevant but carbon-neutral. Sensitivity analysis reveals that temporary overloading can substantially elevate lifetime emissions, emphasizing the value of smart-grid-enabled load management and optimal transformer sizing. Comparative assessment with fossil fuel-intensive provinces across Canada demonstrates the dominant influence of grid generation mix on life-cycle emissions. Additionally, refurbishment scenarios indicate up to 50% reduction in cradle-to-gate emissions through material reuse and oil reclamation. The findings establish a scalable framework for integrating grid decarbonization trajectories, life-cycle carbon modelling, and circular-economy strategies into sustainable distribution network planning and transformer asset management. Full article

The increasing penetration of photovoltaic (PV) generation, energy storage systems, and flexible loads within modern buildings demands advanced control strategies capable of harnessing dynamic assets while maintaining grid reliability. This Perspective article presents a comprehensive overview of reinforcement learning-driven (RL-driven) control methods for DC flexible microgrids—focusing in particular on building-integrated systems that shift from AC microgrid architectures to true PV–Energy storage–DC flexible (PEDF) systems. We examine the structural evolution from traditional AC microgrids through DC microgrids to PEDF architectures, highlight core system components (PV arrays, battery storage, DC bus networks, and flexible demand interfaces), and elucidate their coupling within building clusters and urban energy networks. We then identify key challenges for RL applications in this domain—including high-dimensional state and action spaces, safety-critical constraints, sample efficiency, and real-time deployment in building energy systems—and propose future research directions, such as multi-agent deep RL, transfer learning across building portfolios, and real-time safety assurance frameworks. By synthesizing recent developments and mapping open research avenues, this work aims to guide researchers and practitioners toward robust, scalable control solutions for next-generation DC flexible microgrids. Full article

Folk culture is an important asset for rural tourism and is crucial for maintaining the vitality of traditional villages. However, many traditional villages face underutilized folk cultural resources, inadequate systematic analysis, and mismatches between resources and markets, which impede the sustainability of rural tourism. To address this gap, this study applies the Resource–Market–Product (RMP) framework to systematically analyze the development of folk culture tourism. The aims are to identify the gaps among resources, markets, and products in folk culture tourism in Xidi Village and propose effective development strategies. This study integrates multiple data sources, including a local chronicle, a pilot survey, and online content analysis. The results reveal that the three core dimensions are generally consistent, but significant gaps exist. Participants identify key strategies to promote folk culture tourism in Xidi Village, including developing a material product system that highlights local characteristics, innovating diversified nonmaterial folk cultural tourism experiences, designing attractive folk culture tourism routes and scenic spot tour lines, and addressing the importance of differentiated tourist demands. This study systematically identifies the challenges and opportunities associated with folk culture tourism in traditional villages in rural areas. It provides feasible insights for promoting sustainable rural tourism and revitalizing traditional culture. Full article

Understanding the economic performance of heritage-rich towns requires a systematic evaluation of how heritage-related components collectively contribute to revenue generation. Existing studies often examine heritage assets, socio-cultural factors, physical infrastructure, and local economic conditions independently, resulting in fragmented insights that limit comprehensive planning for local economic development. This study develops and validates an integrated Cultural Heritage Economy Model that quantifies the influence of heritage resources, social, physical, and economic aspects on revenue generation in heritage contexts. The model is conceptualized through a structured synthesis of theoretical literature and domain-specific indicators, followed by construct operationalization, expert validation, and pilot-level assessment. Using Structural Equation Modelling (SEM-PLS), the study demonstrates strong reliability, convergent validity, discriminant validity, and significant structural relationships. The predictive relevance of the final model is further evaluated through PLSpredict, confirming its suitability for future estimation. The findings confirm that revenue generation is a product of the combined and mutually reinforcing effects of heritage, socio-cultural, physical, and economic dimensions, rather than just by the influence of heritage resources. By offering this novel, empirically grounded, multidimensional framework to estimate heritage-driven economic outcomes, this research establishes a foundational model that can guide evidence-based resource allocation, policy formulation, and long-term sustainable urban development planning. Full article

The application of Generative Artificial Intelligence (GenAI)—defined as a class of AI systems capable of autonomously generating new content such as images, texts, and design solutions based on learned data patterns—has become increasingly widespread in creative design. By supporting ideation, rapid trial-and-error, and data-driven decision-making, GenAI enables designers to explore design alternatives more efficiently and enhances human–computer interaction experiences. In design practice, GenAI functions not only as a productivity-enhancing tool but also as a collaborative partner that assists users in visual exploration, concept refinement, and iterative development. However, users still face a certain learning curve before effectively adopting these technologies. Within the framework of human-centered artificial intelligence, contemporary design practices place greater emphasis on inclusivity across diverse user groups and on enabling intuitive “what-you-think-is-what-you-get” interaction experiences. From a sustainable design perspective, GenAI’s capabilities in digital simulation, rapid iteration, and automated feedback contribute to more efficient design workflows, reduced collaboration costs, and broader access to creative participation for users with varying levels of expertise. These characteristics play a crucial role in enhancing the accessibility of design resources and supporting the long-term sustainability of creative processes. Focusing on the context of China’s digital design industry, this study investigates the application of GenAI in design workflows through an empirical case study of Zhitu AI, a generative design tool developed by Beijing Didi Infinity Technology Development Co., Ltd. The study conducts a literature review to outline the role of GenAI in visual design processes and employs observation-based experiments and semi-structured interviews with users of varying levels of design expertise. The findings reveal key pain points across stages such as prompt formulation, secondary editing, and asset generation. Drawing on the Kano model, the study further identifies potential design opportunities and discusses their value in improving efficiency, supporting non-expert users, and promoting more sustainable and inclusive design practices. Full article

This paper examines a stochastic Stackelberg differential game between an insurer and a pool of homogeneous policyholders. Policyholders dynamically optimize insurance coverage and risky asset allocations to minimize the probability of wealth shortfall, while the insurer, acting as the leader, sets the premium loading to maximize the expected exponential utility of terminal surplus. Employing dynamic programming techniques, we derive closed-form equilibrium strategies for both parties. The analysis reveals that a strong positive correlation between insurance claims and financial market returns incentivizes full coverage with modest premiums, whereas a strong negative correlation may induce market collapse as insurers exit underwriting to exploit natural hedging opportunities. Furthermore, larger policyholder pools generate diversification benefits that reduce equilibrium premiums and stimulate insurance demand. Full article

This paper analyses the factors that affect CO₂ emissions in the BF-BOF steelmaking process using a dynamic econometric approach based on annual data from the Polish steel industry. The analysis commences with the estimation of a baseline dynamic model that describes the relationship between CO₂ emissions in the industry and investment allocations, crude steel production, and lagged CO₂ emissions. The baseline analysis illustrates the dominant feature of strong emission level persistence and poor tracking of selected conventional production-related factors. The analysis proceeds by extending the baseline results through additional consideration of technological factors, material composition factors, and resource use factors in the generation of CO₂ emissions. The additional factors include the use of coke, electricity consumption, fixed asset value, and the scrap ratio. The analysis indicates that these additional factors are essential in improving the accuracy of the modeling process and in clarifying the significance of material composition in CO₂ emissions in particular. The analysis further illustrates the critical result that increased use of electricity leads to high CO₂ emissions in the BF-BOF process. Further analysis indicates that increasing the use of steel scrap leads to substantial CO₂ reductions in the BF-BOF route and other steelmaking technologies. The results also show that CO₂ emissions in the BF-BOF process depend not only on production volume, but also on material composition and the technological structure of the process. In the context of the WFESF project, these findings provide evidence-based guidance for metal industry research by identifying priority levers for mitigation, particularly through improvements in process technology and scrap-based material substitution. Full article

The renewable energy industry is rapidly growing and the need to retire the systems and their associated components at their end of life is fast approaching. The general field of research into recycling materials in construction aims to provide effective and efficient methods for breaking down components into their raw materials; however, there is limited literature on introducing these materials back into a circular economy. This paper follows previous work by quantifying the volumes of raw materials in solar and wind generation systems, as well as the corresponding network connections to the transmission systems. Using three different-sized systems for solar and wind generation systems in NSW, Australia, an analytical trend has been established for quantifying each material that exists within the system. The transmission systems, however, are isolated cases, dependent on variables that are listed within the paper. Ultimately, the amounts of each material on farms within the ranges presented can be extrapolated from the trends using simple polynomial models provided. The wind generation and solar generation assets produce differing types of materials due to their vastly different technologies; however, many useful materials are available for recycling in future renewable energy systems. Solar and wind farms have differing materials that have potential uses in a circular economy, and the masses that have been presented in the paper are considerably monumental that it would be detrimental to our environment if they were not reintroduced into more sustainable forms of generation. By summarising the mass of materials, further research can be developed to understand the opportunities that exist in recycling materials, rather than further damaging the environment through mining new and raw materials. Full article

Condition-based maintenance strategies using AI-driven health monitoring have emerged as valuable tools for industrial reliability, yet their implementation remains challenging in industries with limited operational data. Class I induction motors (≤15 kW), which power critical equipment in industries such as grain handling facilities, represent a significant portion of industrial assets but lack established healthy vibration baselines for effective monitoring. A fundamental challenge exists in deploying AI-based health monitoring systems when no historical performance data is available, creating a ’cold-start’ problem that prevents industries from adopting predictive maintenance strategies without costly pilot programs or prolonged data collection periods. This study developed a data-driven health monitoring framework for Class I induction motors that eliminates the dependency on long-term historical trends. Through extensive experimental testing of 98 configurations on new motors, a correlation between vibration amplitude at rotational frequency and motor power rating was established, enabling the creation of a synthetic signal generation algorithm. A robust Health Index (HI) model with integrated diagnostic capabilities was developed using the JPCCED-HI framework, trained on both experimental and synthetically generated healthy vibration data to detect degradation and diagnose common failure modes. The regression analysis revealed a statistically significant relationship between motor power rating and healthy vibration signatures, enabling synthetic generation of baseline data for any Class I motor within the rated range. When implemented at an operational grain silo facility, the HI model successfully detected faulty behavior and accurately diagnosed probable failure modes in equipment with no prior monitoring history, demonstrating that maintenance decisions could be made based on condition data rather than reactive responses to failures. This framework enables immediate deployment of AI-based condition monitoring in industries lacking historical data, eliminating a major barrier to adopting predictive maintenance strategies. The synthetic data generation approach provides a cost-effective solution to the data scarcity problem identified as a critical challenge in industrial AI applications, while the successful industrial implementation validates the feasibility of this approach for small-to-medium industrial facilities. Full article

Security design for large-scale infrastructure systems requires substantial effort and often causes development delays. In line with NIST guidance, such systems should consider security design throughout a system development lifecycle. Nevertheless, performing security design in early phases of the lifecycle is difficult due to frequent specification changes and variability in analyst expertise, which causes repeated rework. The workload is particularly critical in threat analysis, the key activity of security design, because rework can inflate the workload. To address this challenge, we propose an automated threat-analysis method. Specifically, (i) we systematize past security design cases and develop “templates” that organize the system-configuration and security information required for threat analysis into a reusable 5W-based format (When, Where, Who, Why, What); (ii) we define dependencies among the templates and design an algorithm that automatically generates threat-analysis results; and (iii) observing that threat analysis of large-scale systems often yield overlaps, we introduce “business operations” as an analytical asset, which includes encompassing information, function, and physical resources. We apply our method to an actual large-scale operational system and confirm that it reduces the workload by up to 84% relative to conventional manual analysis, while maintaining both the coverage and the accuracy of the analysis. Full article

This paper presents a novel hypersector-based method with Fuzzy Learning Vector Quantization (FLVQ) for the real-time classification of wind turbine blade defects using data acquired by unmanned aerial vehicles (UAVs). Unlike conventional prototype-based FLVQ approaches that rely on Euclidean distance in the feature space, the proposed method models each defect class as a hypersector on an n-dimensional hypersphere, where class boundaries are defined by angular similarity and fuzzy membership transitions. This geometric reinterpretation of FLVQ constitutes the core innovation of the study, enabling improved class separability, robustness to noise, and enhanced interpretability under uncertain operating conditions. Feature vectors extracted via the pre-trained SqueezeNet convolutional network are normalized onto the hypersphere, forming compact directional clusters that serve as the geometric foundation of the FLVQ classifier. A fuzzy softmax membership function and an adaptive prototype-updating mechanism are introduced to handle class overlap and improve learning stability. Experimental validation on a custom dataset of 900 UAV-acquired images achieved 95% classification accuracy on test data and 98.3% on an independent dataset, with an average F1-score of 0.91. Comparative analysis with the classical FLVQ prototype demonstrated superior performance and noise robustness. Owing to its low computational complexity and transparent geometric decision structure, the developed model is well-suited for real-time deployment on UAV embedded systems. Furthermore, the proposed hypersector FLVQ framework is generic and can be extended to other renewable-energy diagnostic tasks, including solar and hydropower asset monitoring, contributing to enhanced energy security and sustainability. Full article

We investigate the profitability of systematic sector rotation strategies in the Canadian equity market using TSX 60 constituents (2000–2025). Testing 72 distinct strategies across three theoretical frameworks—momentum, mean-reversion, and balanced approaches—with varying rebalancing frequencies, we identify that median-performer selection combined with quarterly rebalancing generates statistically significant risk-adjusted returns (Sharpe ratio 0.922 versus 0.624 for equal-weighted buy-and-hold). Our primary contributions include rigorous out-of-sample validation, demonstrating performance persistence from 2020 to 2025, machine learning regime classification with 72.7% accuracy, and a comprehensive transaction cost analysis. Results support intermediate-horizon mean reversion in sector returns and challenge strict efficient market hypothesis interpretations in concentrated markets. Findings inform tactical asset allocation practices and contribute to the momentum-reversal literature by documenting conditions under which rotation strategies generate economically meaningful alpha. Full article

Artificial Intelligence (AI) is increasingly reshaping how cultural heritage institutions design and deliver digital visitor experiences, particularly through adaptive Augmented Reality (AR) applications. However, most existing AR deployments in museums and galleries remain static, rule-based, and insufficiently responsive to visitors’ contextual, behavioral, and emotional diversity. This paper presents ARIA (Augmented Reality for Interpreting Artefacts), a conceptual and architectural framework for AI-supported, adaptive AR experiences in cultural heritage settings. ARIA is designed to address current limitations in personalization, affect-awareness, and ethical governance by integrating multimodal context sensing, lightweight affect recognition, and AI-driven content personalization within a unified system architecture. The framework combines Retrieval-Augmented Generation (RAG) for controlled, knowledge-grounded narrative adaptation, continuous user modeling, and interoperable Digital Asset Management (DAM), while embedding Human-Centered Design (HCD) and Fairness, Accountability, Transparency, and Ethics (FATE) principles at its core. Emphasis is placed on accountable personalization, privacy-preserving data handling, and curatorial oversight of narrative variation. ARIA is positioned as a design-oriented contribution rather than a fully implemented system. Its architecture, data flows, and adaptive logic are articulated through representative museum use-case scenarios and a structured formative validation process including expert walkthrough evaluation and feasibility analysis, providing a foundation for future prototyping and empirical evaluation. The framework aims to support the development of scalable, ethically grounded, and emotionally responsive AR experiences for next-generation digital museology. Full article

Interventions on Heritage Buildings (HBs) involve significant challenges due to their tangible (embodied in the material, architectural, physical and technical integrity of the cultural asset), and intangible values (linked to socio-historical–cultural and collective identity, memory, customs and symbols meanings), which must be preserved while also adapting to current sustainability and circular economy goals. However, current conservation and management practices often lack systematic tools to trace, assess, and organise material and component information, hindering the implementation of circular strategies. In line with the European Union’s objectives for climate neutrality and resource efficiency and sufficiency, Material and Product Passports (MPPs) have emerged as digital tools that enhance data traceability, interoperability and transparency throughout a building’s lifecycle. This paper examines the potential of MPPs to support circular management of HBs by analysing the structure of MPPs and outlining the information flows generated by rehabilitation, maintenance and adaptive reuse strategies. A mixed methods approach, combining literature review and data structure analysis, is adopted to identify how the different categories of data produced during maintenance, rehabilitation and adaptive reuse processes can be integrated into MPP modules. The research highlights the conceptual opportunities of MPPs to document and interlink historical, cultural, and technical data, thereby improving decision-making and transparency across intervention stages. The analysis suggests that adapting MPPs to the specificities of historic contexts, such as authenticity preservation, reversibility, and contextual sensitivity, can foster innovative, sustainable, and circular practices in the conservation and management of HBs. Full article