Search Results (21,299)

Traditional Case-Based Reasoning (CBR) methods face significant methodological challenges, including limited information resources in case databases, methodologically inadequate similarity calculation approaches, and a lack of standardized case revision mechanisms. These limitations lead to suboptimal case matching and insufficient solution adaptation, highlighting critical gaps in the development of CBR methodologies. This paper proposes a novel CBR framework enhanced by generative AI, aiming to improve and innovate existing methods in three key stages of traditional CBR, thereby enhancing the accuracy of retrieval and the scientific nature of corrections. First, we develop an ontology model for comprehensive case representation, systematically capturing scenario characteristics, risk typologies, and strategy frameworks through structured knowledge representation. Second, we introduce an advanced similarity calculation method grounded in triangle theory, incorporating three computational dimensions: attribute similarity measurement, requirement similarity assessment, and capability similarity evaluation. This multi-dimensional approach provides more accurate and robust similarity quantification compared to existing methods. Third, we design a generative AI-based case revision mechanism that systematically adjusts solution strategies based on case differences, considering interdependence relationships and mutual influence patterns among risk factors to generate optimized solutions. The methodological framework addresses fundamental limitations in existing CBR approaches through systematic improvements in case representation, similarity computation, and solution adaptation processes. Experimental validation using actual case data demonstrates the effectiveness and scientific validity of the proposed methodological framework, with applications in risk assessment and emergency response scenarios. The results show significant improvements in case-matching accuracy and solution quality compared to traditional CBR approaches. This method provides a robust methodological foundation for CBR-based decision-making systems and offers practical value for risk management applications. Full article

Accurate cerebral vasculature segmentation using digital subtraction angiography (DSA) is critical for diagnosing and treating cerebrovascular diseases. However, conventional single-frame analysis methods often fail to capture fine vascular structures due to background noise, overlapping anatomy, and dynamic contrast flow. In this study, we propose a novel vessel-enhancing preprocessing technique using temporal differencing of DSA sequences to improve cerebrovascular segmentation accuracy. Our method emphasizes contrast flow dynamics while suppressing static background components by computing absolute differences between sequential DSA frames. The enhanced images were input into state-of-the-art deep learning models, U-Net++ and DeepLabv3+, for vascular segmentation. Quantitative evaluation of the publicly available DIAS dataset demonstrated significant segmentation improvements across multiple metrics, including the Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and Vascular Connectivity (VC). Particularly, DeepLabv3+ with the proposed preprocessing achieved a DSC of 0.83 ± 0.05 and VC of 44.65 ± 0.63, outperforming conventional methods. These results suggest that leveraging temporal information via input enhancement substantially improves small and complex vascular structure extraction. Our approach is computationally efficient, model-agnostic, and clinically applicable for DSA. Full article

The optimization of beamforming in multi-base station (multi-BS) reconfigurable intelligent surface (RIS)-aided systems is a challenging task due to its high computational complexity. This paper first demonstrates that an optimized multi-BS system exhibits superior communication performance compared to a centralized large-scale single-BS system. To efficiently solve the complex beamforming problem in the multi-BS environment, this paper proposes a novel optimization solver based on a graph neural network (GNN) that models the physical structure of the system. Experimental results show that the proposed GNN solver finds solutions of higher quality, achieving a 42% performance increase with 45% less total computational complexity compared to a conventional iterative optimization method. Furthermore, when compared to other complex AI models such as transformer and Bi-LSTM, the proposed GNN achieves similar state-of-the-art performance while having less than 1% of the parameters and a fraction of the computational cost. These findings demonstrate that the GNN is a powerful, efficient, and practical solution for beamforming optimization in multi-BS RIS-aided systems, satisfying the demands for performance, computational efficiency, and model compactness. Full article

Many similarities can be observed among the timber structures that were built in ancient Japan, China, and Korea, bearing witness to the exchanges between these countries. Japan and China had technical manuals that contributed to the development of architecture in each country. Korea might also have had such manuals, but none have been found. In this study, the author analyzed a Chinese technical manual, the Yingzao Fashi, to derive its system for determining the proportions of architectural elements. The author then applied this system to the dimensions of historic buildings in Korea and Japan to obtain insights into proportionality in Chinese, Japanese, and Korean architectural elements. Full article

Side-chain engineering is versatile for tuning the chain mobility of graft polymers and governs their thermal stability. However, it remains elusive to predict the graft effect on chain mobility, especially for competitive side-chain types. Here, relying on molecular dynamics simulation and energy landscape theory, we introduce a three-stage virtual pipeline to sequentially refine the screening of graft chain mobility while minimizing computation cost, by taking the example of grafting similar side-chain types (hydroxyethyl methacrylate (HEMA), methyl methacrylate (MMA), and vinyl acetate (VAC)) onto amorphous polypropylene (PP). Ascribed to their structural similarity, these graft systems exhibit a non-evident chain mobility distinction, with the atom displacement—governing the local “roughness” in potential energy landscape (PEL)—exhibiting only weak-to-modest correlation with their initial atomic energy, volume, and stress. This necessitates the subsequent-stage screening for broader PEL navigation, which confirms a stability and roughness rank of VAC ≥ MMA > HEMA > PP, with their chain activation energy revealing that these side chains enhance the PEL roughness through a counterbalance between possibly lowering the overall energy barrier but extensively wrinkling the landscape. Overall, the three-stage screening establishes a state-of-the-art efficient strategy to evaluate thermal stability of graft polymers in stepwise higher precision from local to ergodic roughness inspection. Full article

With the rapid expansion of multi-platform remote sensing applications, cloud contamination significantly impedes cross-platform data utilization. Current cloud detection methods face critical technical challenges in cross-platform settings, including neglect of atmospheric radiative transfer mechanisms, inadequate multi-scale structural decoupling, high intra-class variability coupled with inter-class similarity, cloud boundary ambiguity, cross-modal feature inconsistency, and noise propagation in pseudo-labels within semi-supervised frameworks. To address these issues, we introduce a Physics-Guided Multi-Representation Network (PGMRN) that adopts a student–teacher architecture and fuses tri-modal representations—Pseudo-NDVI, structural, and textural features—via atmospheric priors and intrinsic image decomposition. Specifically, PGMRN first incorporates an InfoNCE contrastive loss to enhance intra-class compactness and inter-class discrimination while preserving physical consistency; subsequently, a boundary-aware regional adaptive weighted cross-entropy loss integrates PA-CAM confidence with distance transforms to refine edge accuracy; furthermore, an Uncertainty-Aware Quadruple Consistency Propagation (UAQCP) enforces alignment across structural, textural, RGB, and physical modalities; and finally, a dynamic confidence-screening mechanism that couples PA-CAM with information entropy and percentile-based thresholding robustly refines pseudo-labels. Extensive experiments on four benchmark datasets demonstrate that PGMRN achieves state-of-the-art performance, with Mean IoU values of 70.8% on TCDD, 79.0% on HRC_WHU, and 83.8% on SWIMSEG, outperforming existing methods. Full article

This study explores the effectiveness of 3D Digital Image Correlation (DIC) for measuring displacement and strain of a propeller undergoing angular motion. Traditional methods, such as strain gauges, face limitations including physical interference, technical difficulties in sensor connections, and restricted measurement points, leading to inaccuracies in capturing true conditions. To overcome these challenges, this research utilizes non-contact 3D DIC technology, enabling measurement of surface displacements and deformations without interfering with the tested component. Experiments were conducted using the model aircraft propellers mounted on a custom-built test stand for partial angular motion. The 1 Mpx high-speed cameras captured strain and displacement data across the propeller blades during motion. The DIC strain measurements were then compared to strain gauge data to evaluate their accuracy and reliability. The results demonstrate that 3D DIC enables precise displacement measurements, while strain measurements are subject to certain limitations. Displacement measurements were achieved with a noise level of ±10 μm, while strain measurement noise ranged from 26 to 174 µm/m depending on direction. Strain gauge measurements were also performed for verification of the DIC measurements and calibration of the filtering procedure. Two types of non-metallic materials were used in the study: Nylon LGF60 PA6 for the propeller and 3D-printed PC ABS for the cantilever beam used in strain measurement validation. This study underscores the potential of DIC for monitoring rotating components, with a particular focus on measuring strains that are often overlooked in publications addressing similar topics. Additionally, it focuses on comparing DIC strain measurements with strain gauge data on rotating components, addressing a critical gap in existing literature, as strain measurement in rotating structures remains underexplored in current research. Full article

Fatigue damage failure is a process where the mechanical properties of different materials continuously degrade under the action of cyclic loads. The cumulative analysis of fatigue damage has a significant impact on the service structure of major equipment. This paper starts from the mechanism of fatigue damage evolution, comprehensively considers the influence of the order of high-low cycle load mixed cyclic loading on the fatigue life performance, and based on the Manson-Halford nonlinear fatigue damage accumulation theory and the mechanism of relative cumulative damage, a new nonlinear damage accumulation fatigue life model is established, and a fatigue damage accumulation influencing factor D_cr is introduced to improve the prediction accuracy of the model. The new model proposed in this paper is verified through multi-level fatigue load data. By comparing the prediction results with other models under the same experimental conditions, the fatigue life prediction error precision of the new model is the best in similar cases, generally with an error precision between 10% and 20%, which proves the effectiveness and accuracy of the nonlinear damage accumulation model proposed in this paper. At the same time, the improved method in this paper has better stability while ensuring prediction accuracy. Full article

(1) Background: Data literacy is becoming increasingly important for healthcare professionals in both outpatient care and research. Since healthcare data and the possibilities for its use and misuse are increasing in these areas, healthcare professionals need diverse knowledge regarding the collection, use and evaluation of data. A core component of this is an understanding of the ethical, legal, and social implications (ELSI) of working with health data. (2) Methods: Within the DIM.RUHR project (Data Competence Center for Interprofessional use of Health Data in the Ruhr Metropolis), the challenge of training in data literacy for different healthcare professionals is addressed. Based on a learning objectives matrix for interprofessional data literacy education, an ELSI concept was developed through collaboration with interprofessional project partners. The study was conducted between December 2024 and April 2025. (3) Results: The foundational structure of the ELSI concept was based on the learning objectives matrix and an unstructured literacy search for ELSI concepts in similar contexts. Using an iterative design-based research approach, a group of experts from different fields (didactics, applied ethics, health sciences, law, sociology, informatics, and psychology) developed an ELSI concept for healthcare professionals. The following categories were identified as crucial: 1. philosophy of science: a basic understanding of science and the hurdles and opportunities; 2. ethics: an overview of the biomedical principles and a technological assessment; 3. law: an overview of the reservation of permission and self-determination; 4. social aspects: an overview of health inequalities and different forms of power relations and imbalances. (4) Conclusions: The ELSI concept can be used in the orientation of healthcare professionals in outpatient care and research—regardless of their profession—to develop data competencies, with the aim of providing a holistic view of the challenges and potential in the collection, use, and evaluation of healthcare data. The DIM.RUHR project’s approach is to develop open educational resources that build on the ELSI concept to teach specific skills at different competence levels. Full article

The development of biomaterials with tailored properties is indispensable for biomedical applications. In this study, amorphous silica/sodium alginate (SiO₂/SA) hybrids were synthesized via the sol–gel method by incorporating 2, 5, and 8% sodium alginate into the silica matrix. The hybrids were characterized to evaluate their structural, surface, thermal, moisture-responsive, and biological properties. FTIR and XRD analyses confirmed the formation of organic–inorganic networks and amorphous structures. BET measurements revealed a specific surface area of 325 m²/g for SiO₂/SA2%, decreasing with higher SA content to 104.3 m²/g for SiO₂/SA8%; the moisture sorption capacity followed a similar trend. Thermal analysis indicated improved stabilization of the polymer within the silica matrix. Cytotoxicity tests on HaCaT (human keratinocyte) cells line revealed moderate toxicity for the SiO₂/SA2% hybrid (~40% cell viability inhibition (CVI)), while increasing the SA content reduced cytotoxicity, with a CVI of 33% for SiO₂/SA5% and ~15% for SiO₂/SA8%, all within non-toxic ranges according to ISO standards. The SiO₂/SA5% hybrid demonstrated the best balance between functional properties and biocompatibility. These preliminary results suggest that further optimization with intermediate SA concentrations (e.g., 6–7%) could further reduce cytotoxicity while maintaining desirable properties, supporting the potential of silica/sodium alginate hybrids in future biomedical applications. Full article

This article presents the development of a device for collecting data from acceleration sensors. The developed device uses a 32-bit ESP32 microcontroller, which offers good computational capabilities and rich communication peripherals. The current work examines the structure of the developed system, as well as its operational algorithm. The text presents the main components of the device and the method used for data acquisition. Vibration data was collected using a digital accelerometer. The configuration and parameterization of the device were carried out via a JSON file, which controlled the number of measurements and the rate at which they were performed. The acquired data can be easily filtered and processed using mathematical software, allowing it to be presented in a format suitable for further use in machine learning algorithms and artificial neural networks. The developed solution represents a low-cost alternative to similar vibration data acquisition systems, enabling condition monitoring of various machine components and predictive maintenance at a low hardware cost. Full article

Background/Objectives: This study aimed to identify the functional adaptations that enable competitive performance in a Paralympic cyclist with optimized bilateral transtibial prostheses compared to a conventional cyclist. Additionally, it describes the development of the prosthesis, designed through a user-centered engineering process incorporating Quality Function Deployment (QFD), Computer-Aided Design (CAD), Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), and topological optimization, with the final design (Design 1.4) achieving optimal structural integrity, aerodynamic efficiency, and anatomical fit. Methods: Both athletes performed a progressive cycling test with 50-watt increments every three minutes until exhaustion. Cardiorespiratory metrics, lactate thresholds, and joint kinematics were assessed. Results: Although the conventional cyclist demonstrated higher Maximal Oxygen Uptake (VO₂max) and anaerobic threshold, the Paralympic cyclist exceeded 120% of his predicted VO₂max, had a higher Respiratory Exchange Ratio (RER) [1.32 vs. 1.11], and displayed greater joint ranges of motion with lower trunk angular variability. Lactate thresholds were similar between athletes. Conclusions: These findings illustrate, in this specific case, that despite lower aerobic capacity, the Paralympic cyclist achieved comparable performance through efficient biomechanical and physiological adaptations. Integrating advanced prosthetic design with individualized evaluation appears essential to optimizing performance in elite adaptive cycling. Full article

Efficient and selective separation of gallium (Ga(III)) from alkaline industrial waste streams remains a significant challenge due to the coexistence of chemically similar ions such as Al(III) and V(V). In this study, a novel ion-imprinted chitosan-based adsorbent (CS/(H-CGCS)-Ga-IIP) was synthesized via a hybrid cross-linking strategy using glutaraldehyde and siloxane-modified chitosan. The optimized material exhibited a high adsorption capacity of 106.31 mg·g⁻¹ for Ga(III) at pH 9, with fast adsorption kinetics reaching equilibrium within 60 min. Adsorption behavior followed the pseudo-second-order kinetic and Langmuir isotherm models, and thermodynamic analysis indicated a spontaneous and endothermic process. In simulated Bayer mother liquor systems, the material demonstrated outstanding selectivity and a distribution coefficient ratio k_d-Ga/k_d-Al = 146.9, highlighting its strong discrimination ability toward Ga(III). Mechanistic insights from SEM-EDS, FTIR, and XPS analyses revealed that Ga(III) adsorption occurs via electrostatic interaction, ligand coordination, and structural stabilization by the siloxane network. The material maintained good adsorption performance over three regeneration cycles, indicating potential for reuse. These findings suggest that CS/(H-CGCS)-Ga-IIP is a promising candidate for the sustainable recovery of gallium from complex alkaline waste streams such as Bayer process residues. Full article

CD44, a structurally diverse cell-surface glycoprotein, plays a multifaceted and indispensable role in neural tissue across both physiological and pathological conditions. It orchestrates complex cell–extracellular matrix interactions and intracellular signaling through its variant isoforms and post-translational modifications and is broadly expressed in neural stem/progenitor cells, microglia, astrocytes, and selected neuronal populations. The interactions of CD44 with ligands such as hyaluronan and osteopontin regulate critical cellular functions, including migration, differentiation, inflammation, and synaptic plasticity. In microglia and macrophages, CD44 mediates immune signaling and phagocytic activity, and it is dynamically upregulated in neuroinflammatory diseases, particularly through pathways involving Toll-like receptor 4. CD44 expression in astrocytes is abundant during central nervous system development and in diseases, contributing to glial differentiation, reactive astrogliosis, and scar formation. Though its expression is less prominent in mature neurons, CD44 supports neural plasticity, circuit organization, and injury-induced repair mechanisms. Additionally, its expression at nervous system barriers, such as the blood–brain barrier, underscores its role in regulating vascular permeability during inflammation and ischemia. Collectively, CD44 emerges as a critical integrator of neural cell function and intercellular communication. Although the roles of CD44 in glial cells appear to be similar to those explored in other tissues, the expression of this molecule and its variants on neurons reveals peculiar functions. Elucidating the cell-type-specific roles and regulation of CD44 variants may offer novel therapeutic strategies for diverse neurological disorders. Full article

Variability in the Laptev Sea–East Siberian Sea circulation system modulates freshwater circulation in the Arctic Ocean, yet details of these wind-driven mechanisms remain poorly understood. Based on in situ observations from the 2018 Sino-Russian joint Arctic expedition, this study investigates the modulatory influence of wind on circulation structures and freshwater transport in the study area and examines the long-term variation characteristics of this circulation and its inherent connection with the Arctic wind. In situ measurements confirm two freshwater transport pathways: a coastal-current route and a geostrophic slope-current route. As the Beaufort High moves toward the Canadian Basin, it shifts wind patterns from anticyclonic to cyclonic, which regulates the transport of shelf water by influencing the prevailing wind direction. Furthermore, our analysis identifies two main modes of long-term changes in summer surface circulation: the first mode characterizes the coastal-current architecture, while the second mode delineates slope-current configurations. Crucially, large-scale modes of the Arctic wind play an important role in regulating circulation. Its first mode corresponds to the summer anticyclonic circulation pattern of the Arctic Ocean Oscillation, which drives the eastward strengthening of the coastal current, while the third mode presents a mechanism similar to the Arctic Dipole, which promotes the development of the slope current by enhancing the convergence of the polar current and wind. This has led to the long-term strengthening of the slope current. Full article