Search Results (276)

To support dual-carbon objectives and enhance the accuracy of rooftop distributed photovoltaic (PV) planning, this study proposes a multidimensional coupled evaluation framework that integrates an improved rooftop segmentation network (CESW-TransUNet), a residual-fusion ensemble, and physics-based shading and performance simulations, thereby correcting the bias of conventional 2-D area–based methods. First, CESW-TransUNet, equipped with convolution-enhanced modules, achieves robust multi-scale rooftop extraction and reaches an IoU of 78.50% on the INRIA benchmark, representing a 2.27 percentage point improvement over TransUNet. Second, the proposed residual fusion strategy adaptively integrates multiple models, including DeepLabV3+ and PSPNet, further improving the IoU to 79.85%. Finally, by coupling Ecotect-based shadow analysis with PVsyst performance modeling, the framework systematically quantifies dynamic inter-building shading, rooftop equipment occupancy, and installation suitability. A case study demonstrates that the method reduces the systematic overestimation of annual generation by 27.7% compared with traditional 2-D assessments. The framework thereby offers a quantitative, end-to-end decision tool for urban rooftop PV planning, enabling more reliable evaluation of generation and carbon-mitigation potential. Full article

Bridges play an increasingly indispensable role in endorsing the economic and social development of societies by linking highways and facilitating the mobility of people and goods. Concurrently, they are susceptible to high traffic volumes and an intricate service environment over their lifespans, resulting in undergoing a progressive deterioration process. Hence, efficient measures of maintenance, repair, and rehabilitation planning are critical to boost the performance condition, safety, and structural integrity of bridges while evading less costly interventions. To this end, this research paper furnishes a mixed review method, comprising systematic literature and scientometric reviews, for the meticulous examination and analysis of the existing research work in relation with maintenance fund allocation models of bridges (BriMai_all). With that in mind, Scopus and Web of Science databases are harnessed collectively to retrieve peer-reviewed journal articles on the subject, culminating in 380 indexed journal articles over the study period (1990–2025). In this respect, VOSviewer and Bibliometrix R package are utilized to create a visualization network of the literature database, covering keyword co-occurrence analysis, country co-authorship analysis, institution co-authorship analysis, journal co-citation analysis, journal co-citation, core journal analysis, and temporal trends. Subsequently, a rigorous systematic literature review is rendered to synthesize the adopted tools and prominent trends of the relevant state of the art. Particularly, the conducted multi-dimensional review examines the six dominant methodical paradigms of bridge maintenance management: (1) multi-criteria decision making, (2) life cycle assessment, (3) digital twins, (4) inspection planning, (5) artificial intelligence, and (6) optimization. It can be argued that this research paper could assist asset managers with a practical guide and a protocol to plan maintenance expenditures and implement sustainable practices for bridges under deterioration. Full article

Glaucoma is traditionally defined as an ocular disease characterized by progressive retinal ganglion cell degeneration, in some cases with elevated intraocular pressure (IOP), and optic nerve damage. However, growing evidence indicates that glaucoma shares critical features with neurodegenerative disorders, including Alzheimer’s and Parkinson’s diseases. This study aimed to explore the systemic nature of primary open-angle glaucoma (POAG) by integrating visual function, cognitive performance, and transcriptomic profiling. We conducted a multidimensional assessment of POAG patients and age-matched controls, accounting for demographic factors. Structural parameters included retinal nerve fiber layer (RNFL) thickness, measured using optical coherence tomography (OCT), and visual field indices mean deviation (MD) and pattern standard deviation (PSD). Cognitive function was evaluated across multiple domains, encompassing visual memory, executive function, processing speed, and verbal fluency. Additionally, transcriptomic analysis was performed from conjunctival samples to identify differentially expressed genes (DEGs) and enriched pathways. POAG patients exhibited significant RNFL thinning, which correlated with both visual field loss and cognitive impairments, particularly in terms of visual memory and executive function. Transcriptomic profiling revealed a distinct gene expression signature in POAG, including upregulation of TTBK1 and CCN2 (CTGF), genes associated with tau phosphorylation and extracellular matrix remodeling. Functional enrichment analysis indicated the involvement of neurodegenerative pathways, such as glutamate signaling, calcium signaling, and cell adhesion. Our findings support the reclassification of glaucoma as a neurodegenerative disease with both ocular and cognitive manifestations. Furthermore, biomarkers such as TTBK1 and CCN2 may serve as potential targets for early detection and neuroprotective therapy. Full article

This study investigates the multifaceted factors influencing the success of government-funded construction projects and addresses the challenges posed by climate-induced flooding, proposing integrated solutions encompassing structural vulnerability, system capacity, and organizational preparedness. By examining the challenges faced by coastal infrastructure, such as aging infrastructure, sea-level rise, and extreme weather events, this research seeks to identify strategies that enhance resilience and minimize the impact of flooding on coastal communities. The study presents a systematic review of 80 scholarly articles integrating quantitative and qualitative findings. Utilizing the PRISMA guidelines, the review highlights structural analysis, hydraulic modeling, and organizational surveys, to assess the resilience of coastal infrastructure systems. The results of this study offer actionable insights for policymakers, infrastructure managers, and coastal communities, facilitating informed decision-making and promoting climate-resilient development. Coastal regions around the world are increasingly vulnerable to climate-induced hazards such as sea level rise, storm surges, and intense flooding events. Among the most at-risk assets are transport infrastructure and buildings, which serve as the backbone of urban and regional functionality. This research paper presents a multidimensional assessment framework that integrates structural vulnerability, system capacity, and organizational preparedness to evaluate the resilience of coastal infrastructure. Drawing upon principles of resilience such as robustness, redundancy, safe-to-fail design, and change-readiness, the study critically reviews and synthesizes existing literature, identifies gaps in current assessment models, and proposes a comprehensive methodology for resilience evaluation. By focusing on both transport systems and building infrastructure, the research aims to inform adaptive strategies and policy interventions that enhance infrastructure performance and continuity under future climate stressors. Full article

Background/Objective: Frailty, a geriatric syndrome characterized by decreased reserve and resistance to stressors in older adults, has been established as a robust predictor of health outcomes. Recently, the Neurocognitive Frailty Index (NFI) was introduced, including 42 physical and cognitive elements that collectively assess an individual’s vulnerability to age-related health decline. While this multidimensional approach improves predictive accuracy for cognitive decline, its high dimensionality might be a barrier to widespread adoption. Methods: We employed several machine learning techniques to reduce the dimensions of NFI while maintaining its predictive power. We trained five models: Network Analysis, neural networks, Least Absolute Shrinkage and Selection Operator Regression (LASSO), Random Forest, and eXtreme Gradient Boosting (XGBoost). Each model was calibrated using a dataset from the Canadian Study of Health and Aging, which included various cognitive, health, and functional variables. Results: Results indicated that six variables had minimal impact on outcome. This reduction in dimensionality resulted in a modified NFI scale including 36 elements, while maintaining good predictive performance for cognitive change similar to the original NFI. Conclusions: Our findings support the feasibility of applying machine learning techniques to modify predictive models in neurodegenerative diseases beyond frailty assessment. We recommend exploring the application of this scale using other data. The results also emphasize the potential of machine learning approaches for improving predictive models, highlighting their value as a tool for advancing our understanding of aging and its complexities. Full article

Coastal public spaces in large Euro-Mediterranean cities represent critical zones of negotiation between land and sea, where ecological fragilities, infrastructural pressures and social demands intersect. Grounded in the concept of the urban amphibious, this study explores the spatial-functional complexity of city-sea interfaces through the development of CITI4SEA (City-Sea Interface Typological Indicators for Spatial-Ecological Assessment), an original multidimensional framework for the evaluation of coastal public spaces. The methodology builds on a geo-database of 149 coastal municipalities in eight EU Member States and applies a set of indicators to seven major cities (with populations over 500,000 and comprehensive port infrastructure). Through a structured evaluation grid applied to 23 coastal public spaces, the framework enables a cross-comparative analysis of spatial configurations, ecological qualities, and patterns of public use. Results reveal the emergence of transnational clusters based on shared planning logics and degrees of socio-environmental integration, rather than geographic proximity. The study also identifies asymmetries in accessibility, environmental performance and equipment provision. Beyond mapping spatial disparities, the contribution offers a replicable tool for assessing littoral transformations within the broader framework of Integrated Coastal Zone Management (ICZM) and Maritime Spatial Planning (MSP), supporting context-specific strategies for resilient and inclusive coastal governance. Full article

When considering an urban agglomeration as a unit, promoting the coupling and coordination of the land price gradient and industrial gradient is crucial for achieving regional integrated development. We selected the Beijing–Tianjin–Hebei Urban Agglomeration (BTHUA) as a case study; constructed a three-dimensional analytical framework involving static coupling, dynamic coupling, and spatial matching; theoretically clarified the coupling mechanism between the land price gradient and industrial gradient; and systematically assessed their spatial-temporal patterns and coupling characteristics. The results indicate that from 2012 to 2022, both the land price gradient and industrial gradient within the BTHUA exhibited a “core-periphery” spatial distribution, gradually forming an over-all pattern of “one core, multiple nodes, and multi-level rings.” For the Beijing–Tianjin–Hebei urban agglomeration, overall static coupling and spatial matching exhibit an evolutionary trajectory of “first rising, then declining.” By contrast, dynamic coupling remains relatively weak, exhibiting a corridor-shaped distribution along core and sub-core cities. All three indicators consistently show that core cities outperform peripheral cities. Nonlinear mechanism analysis based on the gradient boosting decision tree method showed that “second-nature” factors like economic development and public utilities significantly promote multidimensional coupling. Conversely, “first-nature” factors, such as geographic conditions, have limited impacts with threshold effects; surpassing these thresholds results in inhibitory effects. Based on the research findings, this study proposes that regional integration should serve as the guiding principle, emphasizing the cultivation of regional development corridors, the implementation of flexible and functionally aligned land supply policies, the strengthening of land use performance audits, and the reorientation of fiscal and financial policies toward structural and qualitative improvements. These measures can provide valuable references for promoting coordinated industrial development and balanced land allocation in urban agglomerations. Full article

Accurate and efficient cryptocurrency price prediction is vital for investors in the volatile crypto market. This study comprehensively evaluates nine models—including baseline, zero-shot, and deep learning architectures—on 21 major cryptocurrencies using daily and hourly data. Our multi-dimensional evaluation assesses models based on prediction accuracy (MAE, RMSE, MAPE), speed, statistical significance (Diebold–Mariano test), and economic value (Sharpe Ratio). Our research found that the optimally fine-tuned TimeGPT model (without variables) demonstrated superior performance across both Daily and Hourly datasets, with its statistical leadership confirmed by the Diebold–Mariano test. Fine-tuned Chronos excelled in daily predictions, while TFT was a close second to TimeGPT for hourly forecasts. Crucially, zero-shot models like TimeGPT and Chronos were tens of times faster than traditional deep learning models, offering high accuracy with superior computational efficiency. A key finding from our economic analysis is that a model’s effectiveness is highly dependent on market characteristics. For instance, TimeGPT with variables showed exceptional profitability in the volatile ETH market, whereas the zero-shot Chronos model was the top performer for the cyclical BTC market. This also highlights that variables have asset-specific effects with TimeGPT: improving predictions for ICP, LTC, OP, and DOT, but hindering UNI, ATOM, BCH, and ARB. Recognizing that prior research has overemphasized prediction accuracy, this study provides a more holistic and practical standard for model evaluation by integrating speed, statistical significance, and economic value. Our findings collectively underscore TimeGPT’s immense potential as a leading solution for cryptocurrency forecasting, offering a top-tier balance of accuracy and efficiency. This multi-dimensional approach provides critical, theoretical, and practical guidance for investment decisions and risk management, proving especially valuable in real-time trading scenarios. Full article

The safety of composite insulators in high-voltage transmission lines is directly related to the stable operation of the power system, which is a fundamental condition for the normal functioning of people’s lives and industrial production. Composite insulators are exposed to outdoor conditions for extended periods of time, and with the increase in service life, they are subjected to aging due to external environmental factors and electrical stresses. This aging leads to a decline in their electrical insulation, mechanical properties, and other performance, which, in severe cases, may result in power system failures. Therefore, accurate assessment and detection of the aging status of composite insulators are particularly important. Traditional detection methods such as visual inspection, hardness testing, and hydrophobicity testing have limitations, including single functionality and susceptibility to environmental interference, which cannot comprehensively and accurately reflect the aging condition of the insulators. In recent years, spectroscopy-based detection technologies have been increasingly applied for the rapid detection of composite insulators due to their advantages, such as high sensitivity, non-contact measurement, and multi-dimensional information extraction. Common spectroscopic detection methods include Ultraviolet Discharge (UV Discharge), Fourier Transform Infrared (FTIR) Spectroscopy, Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Laser-Induced Breakdown Spectroscopy (LIBS), and Terahertz (THz) Spectroscopy. These methods offer non-contact, remote, and rapid capabilities, enabling detailed analysis of the insulator’s surface microstructure, chemical composition, and aging characteristics. This paper introduces = spectroscopy-based methods for detecting the aging status of composite insulators, analyzing the advantages and limitations of these methods, and discussing the challenges of their industrial application. Furthermore, the paper reviews the research progress and practical applications of spectroscopic techniques in the evaluation of insulator aging status, systematically summarizing important achievements in the field and providing an outlook for future developments. Full article

Job satisfaction is widely acknowledged as a complex, multidimensional construct that significantly influences both employee well-being and organizational performance. Within the field of education, empirical research specifically focusing on preschool teachers’ job satisfaction remains scarce. Progress in this area is related to the availability of psychometrically robust measurement instruments. This study examined job satisfaction among Greek preschool teachers using a revised version of the Teachers’ Satisfaction Inventory (TSI), integrating theoretical frameworks emphasizing job satisfaction’s multidimensional nature and its critical role in organizational effectiveness. The present study aimed to (1) validate the TSI’s psychometric properties for preschool teachers and (2) assess satisfaction levels by incorporating two new dimensions: salary and perceived prestige. An extended version of the 30-item TSI, measuring seven dimensions, was administered to 224 Greek preschool teachers. Psychometric properties were assessed using confirmatory factor analysis (χ² = 743.33, df = 384, CFI = 0.992, RMSEA = 0.067, SRMR = 0.078), reliability (Cronbach’s α = 0.77 to 0.94) and convergent analyses (AVE = 0.661 to 0.854). Findings indicated that the revised TSI is a valid and reliable instrument with a strong seven-factor structure (factors’ correlation = 0.143 to 0.787). Results revealed high satisfaction with colleagues and students, but significant dissatisfaction with salary and prestige. The findings underscore the need for policymakers to address financial compensation and societal recognition to enhance retention and well-being in preschool education. This study contributes a validated tool for assessing preschool teachers’ job satisfaction while highlighting context-specific challenges in Greece. Full article

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from reactive to predictive management models in spine surgery. We also highlight multi-omics platforms and federated learning frameworks that support integrative, privacy-preserving analytics at scale. Despite these advances, challenges remain—including algorithmic opacity, regulatory fragmentation, data heterogeneity, and limited generalizability across populations and clinical settings. Through a multidimensional lens, this review outlines not only current capabilities but also future directions to ensure safe, equitable, and high-fidelity AI deployment in spine care delivery. Full article

Purpose: Accurate survival prediction is essential for optimizing the treatment planning in patients with castration-resistant prostate cancer (CRPC). However, the traditional statistical models often underperform due to limited variable inclusion and an inability to account for complex, multidimensional data interactions. Methods: We retrospectively collected 46 clinical, laboratory, and pathological variables from 801 patients with CRPC, covering the disease course from the initial disease diagnosis to CRPC progression. Multiple machine learning (ML) models, including random survival forests (RSFs), XGBoost, LightGBM, and logistic regression, were developed to predict cancer-specific mortality (CSM), overall mortality (OM), and 2- and 3-year survival status. The dataset was split into training and test cohorts (80:20), with 10-fold cross-validation. The performance was assessed using the C-index for regression models and the AUC, accuracy, precision, recall, and F1-score for classification models. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: Over a median follow-up of 24 months, 70.6% of patients experienced CSM. RSFs achieved the highest C-index in the test set for both CSM (0.772) and OM (0.771). For classification tasks, RSFs demonstrated a superior performance in predicting 2-year survival, while XGBoost yielded the highest F1-score for 3-year survival. The SHAP analysis identified time to first-line CRPC treatment and hemoglobin and alkaline phosphatase levels as key predictors of survival outcomes. Conclusion: The RSF and XGBoost ML models demonstrated a superior performance over that of traditional statistical methods in predicting survival in CRPC. These models offer accurate and interpretable prognostic tools that may inform personalized treatment strategies. External validation and the integration of emerging therapies are warranted for broader clinical applicability. Full article

Background/Objectives: Frailty is a multidimensional syndrome characterized by diminished physiological reserves, reduced mobility, and increased fall risk. While clinical assessments are commonly used to screen for frailty, they may not capture minor deficits in postural control. Center-of-pressure (CoP) metrics from force plates provide objective markers of postural control, yet their role in frailty screening remains underexplored. This study aimed to investigate the associations between functional performance measures and CoP-based metrics to identify predictors of frailty among older adults. Methods: Eighty-three adults aged ≥ 55 years with a history of falls were classified as frail or pre-frail based on modified Fried criteria. Functional assessments (Timed Up and Go (TUG), grip strength, Berg Balance Scale [BBS], Falls Efficacy Scale [FES]) and CoP metrics (mean velocity, sway path; eyes open/closed) were evaluated. Both unadjusted and age-adjusted logistic regression models were used to identify independent predictors of frailty. Results: Increased TUG time and number of falls were the strongest risk factors for frailty, while increased sway path and CoP velocity were protective. In particular, sway path under eyes-closed conditions showed the strongest protective association (OR = 0.323, p < 0.001). Additionally, fear of falling (OR = 1.078, p = 0.013) emerged as a significant psychological factor, consistently associated with increased frailty risk regardless of physical performance. Correlation analysis supported these findings, showing that better functional performance was linked to lower frailty risk. Conclusions: CoP sway path and mean velocity independently predict frailty status and offer added value beyond traditional clinical tools. These findings highlight the importance of incorporating instrumented balance assessments into frailty screening to capture nuanced postural control deficits and guide early intervention strategies. Full article

As the deployment of wind energy systems continues to rise globally, ensuring the reliability and efficiency of grid-connected Doubly Fed Induction Generator (DFIG) wind turbines has become increasingly critical. Two core challenges faced by these systems include fault diagnosis in power electronic converters and accurate prediction of wind conditions for adaptive power control. Recent advancements in artificial intelligence (AI) have introduced powerful tools for addressing these challenges. This study presents the first unified comparative performance analysis of two deep learning-based models: (i) a Convolutional Neural Network-Long Short-Term Memory CNN-LSTM with Variational Mode Decomposition for real-time Grid Side Converter (GSC) fault diagnosis, and (ii) an Incremental Generative Adversarial Network (IGAN) for wind attribute prediction and adaptive droop gain control, applied to grid-integrated DFIG wind turbines. Unlike prior studies that address fault diagnosis and wind forecasting separately, both models are evaluated within a common MATLAB/Simulink framework using identical wind profiles, disturbances, and system parameters, ensuring fair and reproducible benchmarking. Beyond accuracy, the analysis incorporates multi-dimensional performance metrics such as inference latency, robustness to disturbances, scalability, and computational efficiency, offering a more holistic assessment than prior work. The results reveal complementary strengths: the CNN-LSTM achieves 88% accuracy with 15 ms detection latency for converter faults, while the IGAN delivers more than 95% prediction accuracy and enhances frequency stability by 18%. Comparative analysis shows that while the CNN-LSTM model is highly suitable for rapid fault localization and maintenance planning, the IGAN model excels in predictive control and grid performance optimization. Unlike prior studies, this work establishes the first direct comparative framework for diagnostic and predictive AI models in DFIG systems, providing novel insights into their complementary strengths and practical deployment trade-offs. This dual evaluation lays the groundwork for hybrid two-tier AI frameworks in smart wind energy systems. By establishing a reproducible methodology and highlighting practical deployment trade-offs, this study offers valuable guidance for researchers and practitioners seeking explainable, adaptive, and computationally efficient AI solutions for next-generation renewable energy integration. Full article

This study develops and operationalizes a multi-dimensional framework for sustainable building retrofit that aligns with national 2050 net-zero objectives. First, we conduct a scoping review of international standards (e.g., ISO), sustainability reporting guidelines (GRI G4), and peer-reviewed studies to define an indicator system spanning three pillars—environmental (carbon neutrality, resource circulation, pollution management), social (habitability, durability/safety, regional impact), and economic (direct support, deregulation). Building on this structure, we propose a transparent 0–3 rubric at the sub-indicator level and introduce the Sustainable Building Retrofit Index (SRI) to enable cross-case comparability and over-time monitoring. We then apply the framework to seven countries (United States, Canada, United Kingdom, France, Germany, Japan, and South Korea), score their retrofit systems/policies, and synthesize results through radar plots and a composite SRI. The analysis shows broad emphasis on carbon neutrality and habitability but persistent gaps in resource circulation, pollution management, regional impacts, and deregulatory mechanisms. For South Korea, policies remain energy-centric, with relatively limited treatment of resource/pollution issues and place-based social outcomes; economic instruments predominantly favor direct financial support. To address these gaps, we propose (i) life-cycle assessment (LCA)–based reporting that covers greenhouse gas and six additional impact categories for retrofit projects; (ii) a support program requiring community and ecosystem-impact reporting with performance-linked incentives; and (iii) targeted deregulation to reduce uptake barriers. The paper’s novelty lies in translating diffuse sustainability principles into a replicable, quantitative index (SRI) that supports benchmarking, policy revision, and longitudinal tracking across jurisdictions. The framework offers actionable guidance for policymakers and a foundation for future extensions (e.g., additional countries, legal/municipal instruments, refined weights). Full article