Search Results (5,328)

Traditional methods of asset management in electric power systems rely upon fixed schedules and reactive measurements, leading to challenges in the transparent prioritization of maintenance under evolving operating conditions and incomplete data. In this paper, we introduce a new, fully integrated artificial intelligence (AI)-driven approach for enhancing the resilience and reliability of open-source asset management tools to support improved performance and decisions in electric power system operations. This methodology addresses and overcomes several significant challenges, including data heterogeneity, algorithmic limitations, and inflexible decision-making, through a three-module workflow. The data fidelity module provides a domain-aware pipeline for identifying structural (missing) values from explicit missingness using sophisticated imputation methods, including Multiple Imputation Chain Equations (MICE) and Generative Adversarial Network (GAN)-based hybrids. The characterization module employs seven complementary weighting strategies, including PCA, Autoencoder, GA-based optimization, SHAP, Decision-Tree Importance, and Entropy Weighting, to achieve objective feature weight assignment, thereby eliminating the need for subjective manual rules. The optimization module enhanced the action space through multi-objective optimization, balancing reliability maximization and cost minimization. A synthetic dataset of 100 power transformers was used to validate that the MICE achieved better imputation than other methods. The optimized weighting framework successfully categorizes Health Index values into five condition levels, while the multi-objective maintenance policy optimization generates decisions that align with real-world asset management practices. The proposed framework provides the Transmission and Distribution System Operators (TSOs/DSOs) with an adaptable, industry-oriented decision-support workflow system for enhancing reliability, optimizing maintenance expenses, and improving asset management policies for critical power infrastructure. Full article

The ambiance-based approach to old urban fabrics has emerged as a response to the evolution of heritage, focusing on the spirit of place and the relationship between people and their environment. It aims to preserve the identity of architectural and urban spaces, incorporating intangible elements beyond their physical character. In Algeria, colonial-era urban fabrics continue to structure cities. Skikda, a city in eastern Algeria was created ex-nihilo during this era. In this context, Didouche Mourad Street—the main thoroughfare and structuring element of the city—constitutes the core of the analysis. This study focuses on the French colonial period (1838–1962), considered a foundational phase in the spatial and sensory formation of the street. It aims to restitute the sensory urban ambiences of this period and to analyse their evolution in order to identify sensory permanences contributing to the heritage identity of the place. A thematic content analysis was used to identify sensory ambiences, supported by NVivo software to quantify their recurrences and analyse their spatio-temporal dynamics. The findings show that some ambiences have persisted, others have disappeared, and new ones have emerged through successive transformations. By documenting the sensory history of the street, this research proposes a conceptual and methodological framework for the interpretation of heritage urban ambiences and for informing contemporary rehabilitation approaches, considering permanent ambiences as interpretative tools and reference points for understanding heritage dynamics. Full article

Refugee-serving primary health centres/clinics (PHCs) provide culturally safe, integrated care for refugee children, yet little is known about how their health conditions and outcomes are assessed. This scoping review examines the current literature on the health conditions and outcomes of refugee children aged 0–5 years and how they are measured in refugee-serving PHCs in Canada. In partnership with the New Canadians Health Centre and guided by Joanna Briggs Institute methodological guidelines, we systematically searched Medline, CINAHL, Scopus, and Embase. Included studies focused on refugee children in Canada and reported health conditions, outcomes, and their measurements within PHCs. Twenty-five studies (2008–2024) met the inclusion criteria, most from Ontario (n = 11), followed by Alberta and Saskatchewan (n = 4 each). Reported health conditions or outcomes (n = 24) spanned the physical (n = 19), developmental, and mental health domains (n = 5). Communicable (e.g., gastrointestinal infections, hepatitis) and non-communicable conditions (e.g., malnutrition, vitamin D deficiency) were mostly reported. Although some standardized approaches were used, substantial variability exists across provinces and conditions or outcomes measured. Findings reveal a disproportionate focus on physical health and notable variability and gaps in child health measures, limited cultural adaptation, and lack of longitudinal data. Standardized, culturally responsive, and age-appropriate measurement approaches are needed to enhance health equity and inform evidence-based policy for refugee children in Canada. Full article

Land-use allocations—such as housing density, parcel size, housing typologies, parks, and other green areas—constitute key spatial planning (zoning) parameters that significantly shape how resources and opportunities are distributed within cities. As such, they play a central role in producing or constraining social justice across urban areas and communities, functioning as mechanisms through which planning and development processes deliver—or withhold—critical resources. Yet the literature remains limited in explaining how the allocation of specific zoning parameters contributes to social justice outcomes, which parameters matter most, and which dimensions of social justice they affect. This paper addresses this gap by examining and conceptualizing how spatial planning (zoning) parameters shape social justice in cities. A conceptual review approach, guided by Jabareen’s methodology, is employed to analyze and categorize planning parameters according to their specific contributions to social justice in cities. Accordingly, the study identifies three dimensions of social justice shaped by these parameters—inclusion, accessibility, and recognition—each addressing a key aspect of urban justice. Building on these concepts, we develop a new conceptual framework, referred to as the Conceptual Framework for Just Urbanism. At its core is the logic of difference, which explains how planning parameters are allocated unevenly across geographies, demographic groups, and socioeconomic conditions, producing spatially differentiated inequalities. The study concludes that planning parameters and zoning are powerful carriers of urban justice through their distribution of resources and opportunities. Full article

This paper presents the results of an integrated research and design process developed within the Master’s study programme in Architecture at the University of Belgrade—Faculty of Architecture, aimed at exploring architectural agency in conditions of ecological degradation, declining biodiversity, and the urgent need for regenerative transformation of the built environment. Moving beyond technologically driven notions of “green design,” the study investigates architectural approaches that support ecosystem restoration, biodiversity enhancement, and multispecies coexistence while strengthening health and well-being. Grounded in a three-phase methodological framework, the research (1) formulates conceptual models of therapeutic landscapes through typo-morphological, place-based, and adventure-based analytical approaches; (2) evaluates these models using the New European Bauhaus (NEB) Checklist to assess their alignment with the core values of sustainability, beauty, and togetherness; and (3) synthesizes the findings into regenerative design scenarios that integrate ecological processes, multisensory experience, and community participation. The results position therapeutic landscapes as a spatial practice in which architecture functions as ecological infrastructure, a metabolic system where natural cycles, cultural meanings, bodily experiences, and more-than-human agencies interact. In this sense, architectural design becomes the basis for re-naturalization, regeneration, ecological care, multisensory experience, and resilience in urban, peri-urban, and rural communities. Full article

The increasing availability of satellite data at different spatial resolutions offers new opportunities for environmental monitoring, highlighting the limitations of medium-resolution products for fine-scale territorial analysis. However, it also raises the need to enhance the resolution of low-quality imagery to enable more detailed spatial assessments. This study investigates the effectiveness of different super-resolution techniques applied to low-resolution (LR) multispectral Sentinel-2 satellite imagery to generate high-resolution (HR) data capable of supporting advanced knowledge extraction. Three main methodologies are compared: traditional bicubic interpolation, a generic Artificial Neural Network (ANN) approach, and a Convolutional Neural Network (CNN) model specifically designed for super-resolution tasks. Model performances are evaluated in terms of their ability to reconstruct fine spatial details, while the implications of these methods for subsequent visualization and environmental analysis are critically discussed. The evaluation protocol relies on RMSE, PSNR, SSIM, and spectral-faithfulness metrics (SAM, ERGAS), showing that the CNN consistently outperforms ANN and bicubic interpolation in reconstructing geometrically coherent structures. The results confirm that super-resolution improves the apparent spatial detail of existing spectral information, thus clarifying both the practical advantages and inherent limitations of learning-based super-resolution in Earth observation workflows. Full article

Every year, the scientific community continues to drive advances in healthcare, opening up new perspectives in the treatment and management of various diseases. Despite vast strides being made in the quality of life and longevity, we still face an equally significant growth in the burden of oncological pathologies. Although current trends lean towards preventive and personalized medicine, numerous hurdles remain to be cleared to develop robust strategies in the field of oncology. Among all types of tumors, one of the prominent positions is occupied by hepatocellular carcinoma (HCC), which is one of the most widespread primary cancers with a high mortality rate. Conventional approaches to HCC therapy, such as surgery or chemotherapy, rarely provide steady performance due to the highly polymorphous nature of the cancerous process. In this study, we suggest an alternative methodological framework for designing potent siRNAs targeting genes implicated in hepatocellular carcinoma, implementing RNA interference mediated by synthetic small interfering RNAs (siRNAs) against mRNAs of ITGB1 and CD47 genes. Products of these genes are renowned drivers of tumor progression. We have developed a software algorithm for the design of unmodified and modified siRNAs, carried out solid-phase synthesis of the most promising molecules, and proved their capability to perform a more than 50-fold suppression of expression of the target genes in vitro. Full article

As an important factor affecting the yield and quality of grain crops and threatening grain security, traditional pest and disease monitoring can no longer meet the needs of accurate and efficient agricultural production. The development of remote sensing technology provides a new monitoring method, which is specific, accurate and efficient, and provides real-time, rapid and non-destructive spectral data information for the identification of the occurrence and severity of pests and diseases and can realize large-scale monitoring of grain crop pests and diseases. In this paper, through the statistics and analysis of the published literature on remote sensing monitoring of grain crop diseases and pests, the research hotspots and directions of remote sensing monitoring of grain crop diseases and pests are clarified. Based on this foundation, this paper systematically elaborates the mechanism underlying remote sensing-based monitoring and prediction of diseases and insect pests in grain crops. It reviews various remote sensing monitoring approaches for such diseases and pests by leveraging multi-source remote sensing data. Furthermore, it summarizes methodologies for constructing monitoring and prediction models for grain crop diseases and insect pests. Finally, the paper discusses current challenges and future development trends in this field. Full article

With the advancement of the “dual carbon” goals, power transmission and transformation projects face complex challenges arising from the construction of new power systems. Traditional cost management models struggle to meet dynamic management demands, necessitating the establishment of analytical methods that systematically reflect the relationship between cost management levels and cost dynamics. This paper introduces system dynamics theory and methodology to construct a cost management model applicable to all phases of transmission and transformation projects. It aims to deeply analyze the relationship between project cost levels and expenses from the perspectives of system structure, feedback mechanisms, and dynamic behavior. Research indicates that pathways such as controlling cost deviations and optimizing resource allocation significantly impact total project costs. Specifically, enhancing design accuracy can effectively mitigate cost shocks caused by carbon price fluctuations, while timely implementation of cost control measures can significantly improve cost management levels. The system dynamics approach effectively reveals the dynamic interaction mechanism between cost management levels and costs in power transmission and transformation projects, providing theoretical foundations and methodological support for enhancing project cost control efficiency. Full article

The development of sustainable smart cities is closely linked to the implementation of artificial intelligence in urban services, which opens up new possibilities for efficient resource management, improving the quality of life and strengthening the participation of citizens. At the same time, the question arises as to how legal and strategic frameworks can support the use of artificial intelligence in a way that contributes to environmental, social and economic sustainability in line with the objectives of the European Union. The aim of this scientific study is to examine the interdisciplinary use of artificial intelligence, data management and sustainability at the European Union level, including support instruments such as regulatory initiatives and funding programs, and to assess their implementation in relation to smart cities. Methodologically, the research is based on a legal analysis of key European and national documents, supplemented by descriptive statistics and visualizations of indicators of digitalization and urban sustainability. In the scientific study, we use the methods of synthesis, comparison and abstraction. The results suggest that the legislative and support framework of the European Union can be a significant impetus for the transformation of individual smart cities, but requires effective coordination and strategic management at the level of local governments. The research highlights the need for an integrated legal-managerial approach that will enable the full use of the potential of artificial intelligence in supporting sustainable urban development of cities. Full article

The parameter extraction of proton exchange membrane fuel cells (PEMFCs) has been an active area of study over the past few years, relying on metaheuristic optimizers and experimental datasets to achieve accurate current/voltage (I/V) curves. This work develops a mirage search optimizer (MSO) to precisely estimate the PEMFC model parameters. The MSO employs two search techniques based on the physical phenomena of light bending caused by atmospheric refractive index gradients: a superior mirage for global exploration and an inferior mirage for local exploitation. The MSO employs optical physics to direct search behavior, in contrast to conventional optimization approaches, allowing for a dynamic balance between exploration and exploitation. Convergence efficiency is increased by its iteration-dependent control and fitness-based influence. Using two common PEMFC modules, a comparison study with previously published methodologies and new, recently developed optimizers—the Educational Competition Optimizer (ECO), basketball team optimization (BTO), the fungal growth optimizer (FGO), and the naked mole rat optimizer (NMRO)—was conducted to evaluate the proposed MSO for parameter identification. Furthermore, the two models were tested under various temperatures and pressures. For the three examples studied, the MSO achieved the best sum of squared errors (SSE) values with an intriguing overall standard deviation (STD). It is undeniable that the STD and cropped SSE values, among other difficult techniques, are quite competitive and display the fastest convergence. According to the MSO, the BCS 500W, Ballard Mark V, and Modular SR-12 each have MSO values of 0.011697781, 0.852056, and 1.42098181379214 × 10⁻⁴, respectively. Additionally, the comparison results demonstrate that the proposed MSO can be successfully used to quickly and accurately define the PEMFC model. Full article

High-entropy alloys (HEAs) have attracted significant attention due to their exceptional physical, chemical, and mechanical properties. The current development of HEAs primarily depends on time-consuming and costly trial-and-error approaches, which not only hinder the efficient exploration of new compositions but also result in unnecessary resource and energy consumption, thereby negatively affecting sustainable development and production. To address this challenge, this study introduces a machine learning-based methodology for predicting the yield strengths of various HEA compositions. The model was trained using 181 data points and achieved an R² performance score of 0.85. To further assess its reliability and generalization capability, the model was validated using external data not included in the collected dataset. The validation was performed across four categories: modified Cantor alloys, refractory HEAs, eutectic HEAs, and other HEAs. The predicted yield strength trends were found to align with the actual experimental trends, demonstrating the model’s robust performance across various categories of HEAs. The proposed machine learning approach is expected to facilitate the combinatorial design of HEAs, thereby enabling efficient optimization of compositions and accelerating the development of novel alloys. Moreover, it has the potential to serve as a guideline for sustainable alloy design and environmentally conscious production in future HEA development. Full article

Deindustrialization has left many industrial buildings inactive, raising questions about their role in contemporary urban life. This article explores how semiotics and psychogeography can reframe such structures as dynamic architectural happenings, shifting emphasis from preservation toward social value and collective experience. This research focuses on Komotini, Greece, where the Technical Chamber Square is reinterpreted through references to the adjacent Tobacco Warehouse. By integrating architectural traces of the past into new recreational and sporting functions, this study demonstrates how heritage can be embedded into everyday practices. Methodologically, this research employs qualitative approaches, including demographic and historical analysis of Komotini’s urban and industrial development, alongside psychogeographic drifting walks. Twenty interviews were conducted with local business owners, residents, and visitors, as well as psychogeographic walks, generating insights into how communities interact with industrial heritage. The findings indicate that semiotics and psychogeography are effective tools for activating public spaces near former industrial sites, enabling the built environment to be understood as a layered record of successive interventions. The study concludes that adaptive redesign offers designers a methodology that can embed industrial fragments into vibrant public realms that sustain diverse communities, catalyze local economies, and honor historical identity through lived practices. Full article

The impressive advancements in semi-supervised learning have driven researchers to explore its potential in object detection tasks within the field of computer vision. Semi-Supervised Object Detection (SSOD) leverages a combination of a small labeled dataset and a larger, unlabeled dataset. This approach effectively reduces the dependence on large labeled datasets, which are often expensive and time-consuming to obtain. Initially, SSOD models encountered challenges in effectively leveraging unlabeled data and managing noise in generated pseudo-labels for unlabeled data. However, numerous recent advancements have addressed these issues, resulting in substantial improvements in SSOD performance. This paper presents a comprehensive review of 28 cutting-edge developments in SSOD methodologies, from Convolutional Neural Networks (CNNs) to Transformers. We delve into the core components of semi-supervised learning and its integration into object detection frameworks, covering data augmentation techniques, pseudo-labeling strategies, consistency regularization, and adversarial training methods. Furthermore, we conduct a comparative analysis of various SSOD models, evaluating their performance and architectural differences. We aim to ignite further research interest in overcoming existing challenges and exploring new directions in semi-supervised learning for object detection. Full article

Variable precision fuzzy rough sets (VPFRSs) and multi-granulation fuzzy rough sets (MGFRSs) are both significant extensions of rough sets. However, existing variable precision models generally lack the inclusion property, which poses potential risks in applications. Meanwhile, multi-granulation models tend to emphasize either optimistic or pessimistic scenarios but overlook compromise situations. A generalized fuzzy remote neighborhood system is a symmetric union-fuzzified form of the neighborhood system, which can extend the fuzzy rough set model to a more general framework. Moreover, semi-grouping functions eliminate the left-continuity required for grouping functions and the associativity in t-conorms, making them more suitable for information aggregation. Therefore, to overcome the limitations of existing models, we propose an optimistic (OP), pessimistic (PE), and compromise (CO) variable precision fuzzy rough set (OPCAPFRS) based on generalized fuzzy remote neighborhood systems. The semi-grouping function and its residual minus are employed in the OPCAPFRS. We discuss the basic properties of the OPCAPFRS and prove that it satisfies the generalized inclusion property (GIP). This partially addresses the issue that a VPFRS cannot fulfill the inclusion property. A novel methodology for addressing multi-attribute decision-making (MADM) problems is developed through the fusion of the proposed OPCAPFRS framework and the VIKOR technique. The proposed method is applied to the problem of selecting an optimal CPU. Subsequently, comparative experiments and a parameter analysis are conducted to validate the effectiveness and stability of the proposed method. Finally, three sets of experiments are performed to verify the reliability and robustness of the new approach. It should be noted that the new method performed ranking on a dataset containing nearly ten thousand samples, obtaining both the optimal solution and a complete ranking, thereby validating its scalability. Full article