Search Results (755)

Artificial intelligence (AI) is rapidly transforming rheumatology, particularly in imaging and laboratory diagnostics where data complexity challenges traditional interpretation. This narrative review summarizes current evidence on AI-driven tools across musculoskeletal ultrasound, radiography, MRI, CT, capillaroscopy, and laboratory analytics. A structured literature search (PubMed, Scopus, Web of Science; 2020–2025) identified 90 relevant publications addressing AI applications in diagnostic imaging and biomarker analysis in rheumatic diseases, while twelve supplementary articles were incorporated to provide contextual depth and support conceptual framing. Deep learning models, notably convolutional neural networks and vision transformers, have demonstrated expert-level accuracy in detecting synovitis, bone marrow edema, erosions, and interstitial lung disease, as well as in quantifying microvascular and structural damage. In laboratory diagnostics, AI enhances the integration of traditional biomarkers with high-throughput omics, automates serologic interpretation, and supports molecular and proteomic biomarker discovery. Multi-omics and explainable AI platforms increasingly enable precision diagnostics and personalized risk stratification. Despite promising performance, widespread implementation is constrained by significant domain-specific validation gaps, data heterogeneity, lack of external validation, ethical concerns, and limited workflow integration. Clinically meaningful progress will depend on transparent, validated, and interoperable AI systems supported by robust data governance and clinician education. The transition from concept to clinic is under way—AI will likely serve as an augmenting rather than replacing partner, standardizing interpretation, accelerating decision-making, and ultimately facilitating precision, data-driven rheumatologic care. Full article

Background: Despite advancements in pharmacological therapy, lung transplantation (LuTX) remains the only life-prolonging treatment in end-stage idiopathic pulmonary fibrosis (IPF). However, real-world referral patterns in Central and Eastern European (CEE) countries remain poorly characterized. We aimed to comprehensively review factors influencing referral and identify systemic barriers to LuTX access. Methods: Baseline characteristics of IPF patients potentially eligible for LuTX, enrolled in the European MultiPartner IPF Registry between 2012 and 2022 (n = 1256), were retrospectively analyzed. LuTX (n = 94) and potentially eligible but not transplanted (n = 1162) subgroups were compared. National experts also completed a questionnaire assessing transplant referral and listing practices across different healthcare systems. Results: Only 7.5% of potentially eligible subjects were transplanted, revealing substantial geographic disparities, with Israel having the highest rates (43.1%), followed by Austria (9.5%), Hungary (7.8%), and the Czech Republic (4.6%). LuTX patients were younger (60.2 ± 7.4 vs. 62.6 ± 6.2 years, p < 0.001), had worse lung function (FVC 60 ± 15 vs. 74 ± 21% predicted; p < 0.001, TLCO 41 ± 15 vs. 49 ± 19% predicted; p < 0.001), and were more likely to receive antifibrotic and oxygen therapies. The most frequent reasons for exclusion from referral/listing were age > 70 years and concomitant heart/renal failure. Conclusions: This first comprehensive CEE analysis demonstrates low IPF transplant rates with high inter-country variability. Patients presenting early with functionally advanced disease are more likely transplanted, while advanced age remains the primary exclusion factor, highlighting critical access gaps potentially contributing to regional outcome differences. Full article

Recent advancements in deep learning have significantly improved fault diagnosis methods. However, challenges such as insufficient feature extraction, limited long-range dependency modeling, and environmental noise continue to hinder their effectiveness. This paper presents a novel mixture of multi-view convolutional (MOM-Conv) layers integrating the Mixture of Experts (MOE) mechanism. This design effectively captures and fuses both local and contextual information, thereby enhancing feature extraction and representation. This proposed approach aims to improve prediction accuracy under varying noise conditions, particularly in rolling ball bearing systems characterized by noisy signals. Additionally, we propose the Mamba-enhanced adaptive self-attention long short-term memory (MASA-LSTM) model, which effectively captures both global and local dependencies in ultra-long time series data. This model addresses the limitations of traditional models in extracting long-range dependencies from such signals. The architecture also integrates a multi-step temporal state fusion mechanism to optimize information flow and incorporates adaptive parameter tuning, thereby improving dynamic adaptability within the LSTM framework. To further mitigate the impact of noise, we transform vibration signals into denoised multi-channel representations, enhancing model stability in noisy environments. Experimental results show that our proposed model outperforms existing state-of-the-art approaches on both the Paderborn and Case Western Reserve University bearing datasets, demonstrating remarkable robustness and effectiveness across various noise levels. Full article

Digital mining has been evolving in recent years under the Industry 4.0 paradigm. In this sense, technological tools such as sensors aid the management and operation of mining projects, reducing the risk of accidents, increasing productivity, and promoting business sustainability. DT is a technological tool that enables the integration of various Industry 4.0 technologies to create a virtual model of a real, physical entity, allowing for the study and analysis of the model’s behavior through real-time data collection. A digital twin of an underground mine is a real-time, virtual replica of an actual mine. It is like an extremely detailed “simulator” that uses data from sensors, machines, and personnel to accurately reflect what is happening in the mine at that very moment. Some of the functionalities of an underground mining DT include (i) accurate geometry of the real physical asset, (ii) real-time monitoring capability, (iii) anomaly prediction capability, (iv) scenario simulation, (v) lifecycle management to reduce costs, and (vi) a support system for smart and proactive decision-making. A digital twin of an underground mine offers transformative benefits, such as real-time operational optimization, improved safety through risk simulation, strategic planning with predictive scenarios, and cost reduction through predictive maintenance. However, its implementation faces significant challenges, including the high technical complexity of integrating diverse data, the high initial cost, organizational resistance to change, a shortage of skilled personnel, and the lack of a comprehensive, multi-layered conceptual framework for an underground mine digital twin. To overcome these barriers and gaps, this paper proposes a strategy that includes defining an advanced, multi-layered conceptual framework for the digital twin. Simultaneously, it advocates for fostering a culture of change through continuous training, establishing partnerships with specialized experts, and investing in robust sensor and connectivity infrastructure to ensure reliable, real-time data flow that feeds the digital twin. Finally, validation of the advanced multi-layered conceptual framework for digital twins of underground mines is carried out through a questionnaire administered to a panel of experts. Full article

Forest fires have become increasingly frequent and severe due to climate change and intensified human activities, posing critical challenges to ecological security and emergency management. Despite the availability of abundant environmental, spatial, and operational data, these resources remain fragmented and heterogeneous, limiting the efficiency and accuracy of fire prediction and response. To address this challenge, this study proposes a Semantic Digital Twin-Driven Framework for integrating multi-source data and supporting forest fire prediction and response. The framework constructs a multi-ontology network that combines the Semantic Sensor Network (SSN) and Sensor, Observation, Sample, and Actuator (SOSA) ontologies for sensor and observation data, the GeoSPARQL ontology for geospatial representation, and two domain-specific ontologies for fire prevention and emergency response. Through systematic data mapping, instantiation, and rule-based reasoning, heterogeneous information is transformed into an interconnected knowledge graph. The framework supports both semantic querying (SPARQL) and rule-based reasoning (SWRL) to enable early risk alerts, resource allocation suggestions, and knowledge-based decision support. A case study in Sichuan Province demonstrates the framework’s effectiveness in integrating historical and live data streams, achieving consistent reasoning outcomes aligned with expert assessments, and improving decision timeliness by enhancing data interoperability and inference efficiency. This research contributes a foundational step toward building intelligent, interoperable, and reasoning-enabled digital forest systems for sustainable fire management and ecological resilience. Full article

The heavy environmental impact of the construction industry—responsible for 39% of world CO₂ emissions and consuming over 40% of natural resources—supports the need for evidence-based decision-making tools for sustainable material selection balancing environmental, economic, and social considerations. This research develops and evaluates an integrated decision support system that couples cradle-to-grave lifecycle assessment (LCA) with various multi-criteria decision-making (MCDM) methods to optimize climate-resilient material selection for schools. The methodology is an integration of hybrid Analytic Hierarchy Process–Technique for Order of Preference by Similarity to Ideal Solution (AHP-TOPSIS) and VIKOR techniques validated with eight case studies in hot-arid, hot-humid, and temperate climates. Environmental, economic, social, and technical performance indices were evaluated from primary experimental data and with the input from 22 international experts with climate change assessment expertise. Ten material options were examined, from traditional, recycled, and bio-based to advanced composite systems throughout full building lifecycles. The results indicate geopolymer–biofiber composite systems achieve 42% reduced lifecycle carbon emissions, 28% lower cost of ownership, and 35% improved overall sustainability performance compared to traditional equivalents. Three MCDM techniques’ cross-validation demonstrated a satisfactory ranking correlation (Kendall’s τ = 0.87), while Monte Carlo uncertainty analysis ensured framework stability across 95% confidence ranges. Climate-adaptive weighting detected dramatic regional optimization contrasts: thermal performance maximization in tropical climates and embodied impact emphasis in temperate climates. Three case studies on educational building projects demonstrated 95.8% accuracy in validation of environmental performance and economic payback periods between 4.2 and 6.8 years in real-world practice. Full article

Background/Objectives: Family members who provide care play a central but often underestimated role in the healthcare system and are frequently exposed to considerable physical, emotional, and social stress. To better understand and support their needs, a structured self-assessment tool (SSA-PA) was developed. This development addresses the current lack of practical, validated instruments that enable caregivers to systematically reflect on their own stress levels and resources, which is becoming increasingly important in view of the growing demand for care and the risk of caregiver burnout. This tool aims to promote self-reflection, identify individual stresses and resources, and enable more targeted support for family caregivers. Methods: The development process (September–December 2024) followed a multi-phase design that integrated theoretical foundations from nursing, health, and psychology, in particular Orem’s theory of self-care deficit, Lazarus and Folkman’s stress and coping model, and Engel’s biopsychosocial model. Four core dimensions were defined: (1) health and self-care, (2) burden and stress, (3) support and resources, and (4) satisfaction and quality of life. The final tool comprises 37 items (mostly 5-point Likert scales), supplemented by multiple-choice and open-ended questions. Content validity was ensured through expert review and testing with nine family caregivers. Internal consistency was assessed using Cronbach’s alpha (α = 0.998), indicating very high reliability, although possible item redundancies were identified. The evaluation took place in January 2025 with 33 family caregivers to assess user-friendliness, relevance, and perceived usefulness. Results: The majority of participants rated the tool as user-friendly and clearly structured. Around 80% reported a high level of comprehensibility, and over half stated that the tool provided new insights into their own health and care burden. Qualitative feedback highlighted the value of the tool for self-reflection and motivation to seek external support. Suggestions for improvement included shorter item formulations, improved visual feedback (e.g., progress bars or charts), and expanded question areas on financial burdens and digital support options. Conclusions: The SSA-PA is a theoretically grounded and user-centered tool for assessing and reflecting on the situation of family caregivers. It not only enables systematic self-assessments but also promotes awareness and proactive coping strategies. Future research should focus on conducting factor analyses to further validate the construct, testing the tool in larger samples, and exploring its integration into structured care consultations to improve the quality of home care. Full article

To address the limitation that single-index evaluation fails to fully reflect the development performance of reservoirs of different types and at various development stages, a multi-index comprehensive evaluation system featuring the workflow of “index screening–weight determination–model evaluation–strategy guidance” was established. Firstly, the grey correlation analysis method (with a correlation degree threshold set at 0.65) was employed to screen 12 key evaluation indicators, including reservoir physical properties (porosity, permeability) and development dynamics (recovery factor, water cut, well activation rate). Subsequently, the fuzzy analytic hierarchy process (FAHP, for subjective weighting, with the consistency ratio (CR) of expert judgments < 0.1) was coupled with the attribute measurement method (for objective weighting, with information entropy redundancy < 5%) to determine the indicator weights, thereby balancing the influences of subjective experience and objective data. Finally, two evaluation models, namely the fuzzy comprehensive decision-making method and the unascertained measurement method, were constructed to conduct evaluations on 308 reservoirs in the Liaohe Oilfield (covering five major categories: integral medium–high-permeability reservoirs, complex fault-block reservoirs, low-permeability reservoirs, special lithology reservoirs, and thermal recovery heavy oil reservoirs). The results indicate that there are 147 high-efficiency reservoirs categorized as Class I and Class II in total. Although these reservoirs account for 47.7% of the total number, they control 71% of the geological reserves (154,548 × 10⁴ t) and 78% of the annual oil production (738.2 × 10⁴ t) in the oilfield, with an average well activation rate of 65.4% and an average recovery factor of 28.9. Significant quantitative differences are observed in the development characteristics of different reservoir types: Integral medium–high-permeability reservoirs achieve an average recovery factor of 37.6% and an average well activation rate of 74.1% by virtue of their excellent physical properties (permeability mostly > 100 mD), with Block Jin 16 (recovery factor: 56.9%, well activation rate: 86.1%) serving as a typical example. Complex fault-block reservoirs exhibit optimal performance at the stage of “recovery degree > 70%, water cut ≥ 90%”, where 65.6% of the blocks are classified as Class I, and the recovery factor of blocks with a “good” rating (42.3%) is 1.8 times that of blocks with a “poor” rating (23.5%). For low-permeability reservoirs, blocks with a rating below medium grade account for 68% of the geological reserves (8403.2 × 10⁴ t), with an average well activation rate of 64.9%. Specifically, Block Le 208 (permeability < 10 mD) has an annual oil production of only 0.83 × 10⁴ t. Special lithology reservoirs show polarized development performance, as Block Shugu 1 (recovery factor: 32.0%) and Biantai Buried Hill (recovery factor: 20.4%) exhibit significantly different development effects due to variations in fracture–vug development. Among thermal recovery heavy oil reservoirs, ultra-heavy oil reservoirs (e.g., Block Du 84 Guantao, with a recovery factor of 63.1% and a well activation rate of 92%) are developed efficiently via steam flooding, while extra-heavy oil reservoirs (e.g., Block Leng 42, with a recovery factor of 19.6% and a well activation rate of 30%) are constrained by reservoir heterogeneity. This system refines the quantitative classification boundaries for four development levels of water-flooded reservoirs (e.g., for Class I reservoirs in the high water cut stage, the recovery factor is ≥35% and the water cut is ≥90%), as well as the evaluation criteria for different stages (steam huff and puff, steam flooding) of thermal recovery heavy oil reservoirs. It realizes the transition from traditional single-index qualitative evaluation to multi-index quantitative evaluation, and the consistency between the evaluation results and the on-site development adjustment plans reaches 88%, which provides a scientific basis for formulating development strategies for the Liaohe Oilfield and other similar oilfields. Full article

The real estate industry, known for its complexity and exposure to systemic and idiosyncratic risks, requires increasingly sophisticated investment risk assessment tools. In this study, we present the Real Estate Construction Investment Risk (RECIR) model, a machine learning-based framework designed to quantify and manage multi-dimensional investment risks in construction projects. The model integrates diverse data sources, including macroeconomic indicators, property characteristics, market dynamics, and regulatory variables, to generate a composite risk metric called the total risk score. Unlike previous artificial intelligence (AI)-based approaches that primarily focus on forecasting prices, we incorporate regulatory compliance, forensic risk assessment, and explainable AI to provide a transparent and accountable decision support system. We train and validate the RECIR model using structured datasets such as the American Housing Survey and World Development Indicators, along with survey data from domain experts. The empirical results show the relatively high predictive accuracy of the RECIR model, particularly in highly volatile environments. Location score, legal context, and economic indicators are the dominant contributors to investment risk, which affirms the interpretability and strategic relevance of the model. By integrating AI with ethical oversight, we provide a scalable, governance-aware methodology for analyzing risks in the real estate sector. Full article

Rolling bearings constitute critical rotating components within rolling mill equipment. Production efficiency and the operational safety of the whole mechanical system are directly governed by their operational health state. To address the dual challenges of the over-reliance of conventional diagnostic methods on expert experience and the scarcity of fault samples in industrial scenarios, we propose a virtual–physical data fusion-optimized intelligent fault diagnosis framework. Initially, a dynamics-based digital twin model for rolling bearings is developed by leveraging their geometric symmetry. It is capable of generating comprehensive fault datasets through parametric adjustments of bearing dimensions and operational environments in virtual space. Subsequently, a symmetry-informed architecture is constructed, which integrates multi-scale convolutional neural networks with attention mechanisms and bidirectional gated recurrent units (MCNN-AT-BiGRU). This architecture enables spatiotemporal feature extraction and enhances critical fault characteristics. The experimental results demonstrate 99.5% fault identification accuracy under single operating conditions. It maintains stable performance under low SNR conditions. Furthermore, the framework exhibits superior generalization capability and transferability across the different bearing types. Full article

This white paper, prepared by a consortium of Canadian national and provincial organizations and experts, outlines urgent strategies to curb the rising incidence of HPV-related cancers, of which, cervical cancer is currently the fastest-growing cancer in Canada. Despite school-based vaccination programs, the national HPV vaccine uptake remains suboptimal at about 64%, far below the 90% coverage target by 2025 necessary to eliminate cervical cancer by 2040. The report emphasizes a multi-pronged approach: support access to HPV vaccination with expanded funding policies and education around school-based programs while addressing inequities in underserved populations. HPV testing is highlighted as the preferred method for cervical cancer screening, offering higher sensitivity than Pap smears. Self-collection is presented as an innovative strategy to reduce barriers, particularly for marginalized groups, with promising evidence from Canadian pilots and international models. Crucially, we call for investment in comprehensive, population-based databases to track vaccination, screening participation, and follow-up care. Robust registries would allow targeted outreach to under- or never-screened individuals, ensure timely follow-up of abnormal results, and measure the impact of prevention programs across Canada. With vaccination, equitable access to HPV testing, integration of self-collection, and strong data systems, Canada can achieve its goal of eliminating cervical cancer within two decades. Full article

The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) has enabled personalized sports coaching, yet a significant gap remains: translating low-level sensor data into high-level, contextualized feedback. Large Language Models (LLMs) excel at reasoning and instruction but lack a native understanding of physical kinematics. This paper introduces BoxingPro, a novel framework that bridges this semantic gap by fusing wearable sensor data with LLMs for automated boxing coaching. Our core contribution is a dedicated translation methodology that converts multi-modal time-series data (IMU) and visual data (video) into structured linguistic prompts, enabling off-the-shelf LLMs to perform sophisticated biomechanical reasoning without extensive retraining. Our evaluation with professional boxers showed that the generated feedback achieved an average expert rating of over 4.0/5.0 on key criteria like biomechanical correctness and actionability. This work establishes a new paradigm for integrating sensor-based systems with LLMs, with potential applications extending far beyond boxing to any domain requiring physical skill assessment. Full article

This paper proposes a stepwise expert-teaching reinforcement learning framework for intelligent frequency control in hydro–thermal–wind–solar–compressed air energy storage (CAES) integrated systems under high renewable energy penetration. The proposed method addresses the frequency stability challenge in low-inertia, high-volatility power systems, particularly in Southwest China, where large-scale renewable-energy-based energy bases are rapidly emerging. A load frequency control (LFC) model is constructed to serve as the training and validation environment, reflecting the dynamic characteristics of the hybrid system. The stepwise expert-teaching PPO (SETP) framework introduces a stepwise training mechanism in which expert knowledge is embedded to guide the policy learning process and training parameters are dynamically adjusted based on observed performance. Comparative simulations under multiple disturbance scenarios are conducted on benchmark systems. Results show that the proposed method outperforms standard proximal policy optimization (PPO) and traditional PI control in both transient response and coordination performance. Full article

In Turkey, flexible pavement containing bituminous material is widely preferred on highways. Rigid pavement, which is based on concrete, is generally used in small-scale, specific projects. This situation, which has arisen due to historical and technical reasons, has also brought with it certain prejudices against rigid pavement applications. A review of the literature reveals that many factors influence the choice of highway pavement type, but decision-makers tend to make their selection based on the most important factors, disregarding other parameters. The lack of a systematic factor analysis is a shortcoming in this regard. In this research, a combined multi-criteria decision-making study was conducted, including the neglected factors, to address this technical deficiency in the pavement type selection process. Through detailed analysis, parameters likely to influence pavement type selection were identified and analyzed using the hybrid AHP-TOPSIS approach, guided by the opinions of experts in the field. The analysis shows that comfort (user ride quality), financial, and environmental factors are the most effective main criteria, while maintenance and repair costs, eco-friendliness, and initial construction costs were identified as the most critical sub-criteria influencing the choice of pavement type. Based on the analysis results, a detailed decision support system was presented to decision-makers according to the characteristics of the alternatives obtained. The results highlight the need for decision-making frameworks that prioritize both long-term cost efficiency and user safety, contributing to more sustainable and resilient pavement applications. Full article

Financial sustainability in higher education is increasingly fragile due to policy shifts, rising costs, and funding volatility. Legacy early-warning systems based on static thresholds or rules struggle to adapt to these dynamics and often overlook fairness and interpretability—two essentials in public-sector governance. We propose a university financial risk early-warning framework that couples a causal-attention Transformer with Multi-Objective Bayesian Optimization (MBO). The optimizer searches a constrained Pareto frontier to jointly improve predictive accuracy (AUC↑), fairness (demographic parity gap, DP_Gap↓), and computational efficiency (time↓). A sparse kernel surrogate (SKO) accelerates convergence in high-dimensional tuning; a dual-head output (risk probability and health score) and SHAP-based attribution enhance transparency and regulatory alignment. On multi-year, multi-institution data, the approach surpasses mainstream baselines in AUC, reduces DP_Gap, and yields expert-consistent explanations. Methodologically, the design aligns with LLM-style time-series forecasting by exploiting causal masking and long-range dependencies while providing governance-oriented explainability. The framework delivers earlier, data-driven signals of financial stress, supporting proactive resource allocation, funding restructuring, and long-term planning in higher education finance. Full article