Search Results (250)

Nudging remains a cost-effective data assimilation technique in coupled climate models, yet conventional schemes with fixed spatial strengths struggle to represent heterogeneous ocean processes. This study introduces an adaptive nudging framework in which a spatially varying gain matrix dynamically balances model and observational errors, providing a more physically consistent determination of nudging coefficients. Implemented in the SPEEDY-NEMO coupled model, the method is systematically evaluated against a traditional latitude-dependent scheme. Results show substantial improvements in subsurface temperature assimilation across key regions, including the Niño3.4, tropical Indian Ocean, North Pacific, North Atlantic, and northeastern Pacific. The most pronounced gains occur above and within the thermocline, where strong stratification renders fixed nudging strengths inadequate, yielding a 20–30% reduction in RMSE and a 30–50% increase in correlation. In mid- to high-latitude regions, improvements extend to greater depths, consistent with deeper thermocline structures. The adaptive framework corrects both systematic bias and variance, enhancing not only the mean state but also variability representation. Additional benefits are found in salinity, currents, and sea surface height, demonstrating that spatially adaptive nudging provides a more effective and practical alternative for improving ocean state estimation in coupled models. Full article

The development of foundation models for brain MRI depends critically on the scale, diversity, and consistency of available data, yet systematic assessments of these factors remain scarce. In this study, we analyze 54 publicly accessible brain MRI datasets encompassing over 538,031 scans to provide a structured, multi-level overview tailored to foundation model development. At the dataset level, we characterize modality composition, disease coverage, and dataset scale, revealing strong imbalances between large healthy cohorts and smaller clinical populations. At the image level, we quantify voxel spacing, orientation, and intensity distributions across 14 representative datasets, demonstrating substantial heterogeneity that can influence representation learning. We then perform a quantitative evaluation of preprocessing variability, examining how intensity normalization, bias field correction, skull stripping, spatial registration, and interpolation alter voxel statistics and geometry. While these steps improve within-dataset consistency, residual differences persist between datasets. Finally, a feature-space case study using a 3D DenseNet121 shows measurable residual covariate shift after standardized preprocessing, confirming that harmonization alone cannot eliminate inter-dataset bias. Together, these analyses provide a unified characterization of variability in public brain MRI resources and emphasize the need for preprocessing-aware and domain-adaptive strategies in the design of generalizable brain MRI foundation models. Full article

Stereo cameras, also known as depth cameras or RGB-D cameras, are increasingly employed in a large variety of machinery for obstacle detection purposes and navigation planning. This also represents an opportunity in agricultural machinery for safety purposes to detect the presence of workers on foot and avoid collisions. However, their outdoor performance at medium and long range under operational light conditions remains weakly quantified: the authors then fit a field protocol and a model to characterize the pipeline of stereo cameras, taking the Intel RealSense D455 as benchmark, across various distances from 4 m to 16 m in realistic farm settings. Tests have been conducted using a 1 square meter planar target in outdoor environments, under diverse illumination conditions and with the panel being located at 0°, 10°, 20° and 35° from the center of the camera’s field of view (FoV). Built-in presets were also adjusted during tests, to generate a total of 128 samples. The authors then fit disparity surfaces to predict and correct systematic bias as a function of distance and radial FoV position, allowing them to compute mean depth and estimate a model of systematic error that takes depth bias as a function of distance, light conditions and FoV position. The results showed that the model can predict depth errors achieving a good degree of precision in every tested scenario (RMSE: 0.46–0.64 m, MAE: 0.40–0.51 m), enabling the possibility of replication and benchmarking on other sensors and field contexts while supporting safety-critical perception systems in agriculture. Full article

To meet the growing demand for quantitative remote sensing applications in GIIRS radiometric calibration, this paper proposes a temperature field-driven adaptive scan mirror thermal radiation interference correction method. Based on the on-orbit deep space observation data from the Fengyun-4B satellite, this paper systematically analyzes the thermal radiation interference characteristics caused by scan mirror deflection and constructs the first scan mirror thermal radiation response model suitable for GIIRS. On the basis of this model, this paper further introduces the dynamic variation characteristics of the internal thermal environment of the instrument, enabling adaptive response and compensation for radiation disturbances. This method overcomes the limitations of relying on static calibration parameters and improves the generality and robustness of the model. Independent validation results show that this method effectively suppresses the interference of scan mirror deflection on instrument background radiation and enhances the consistency of the deep space and blackbody spectral diurnal variation time series. After correction, the average system bias of the interference-sensitive channel decreased by 94%, and the standard deviation of radiance bias from 2.5 mW/m²·sr·cm⁻¹ to below 0.5 mW/m²·sr·cm⁻¹. In the O-B test, the maximum improvement in relative standard deviation reached 0.15 K. Full article

Objective: Accurate knowledge of apical morphology is crucial for determining the correct working length and achieving an optimal seal, both of which are vital for long-term endodontic success. This review summarizes and evaluates the current literature on the physiological foramen, focusing on its diameter and the distance between the anatomical apex and the physiological foramen. Materials and Methods: A systematic literature search was conducted using the databases PubMed (via Medline), Embase, LILACS, and Scopus. Studies addressing the anatomy of the physiological foramen were selected based on predefined inclusion criteria. A total of 743 records were identified. After removing 103 duplicates, the titles and abstract of 640 records were screened, with 625 being excluded as irrelevant. Fifteen full texts were assessed and six excluded for not meeting inclusion criteria. Five additional articles were found through manual search. In total, 14 studies were included in the review. The risk of bias was assessed using the AQUA tool. Results: Considerable variation in the diameter of the physiological foramen was observed across the included studies, ranging from 0.15 mm to 0.43 mm depending on tooth type and location. Additionally, the distance between the anatomical apex and the physiological foramen varied from 0.1 mm to 1.2 mm. Conclusions: The results demonstrate considerable heterogeneity in the dimensions and position of the physiological foramen, with oval shapes occurring more frequently than round or irregular ones. Standardized definitions and consistent terminology are essential to improve comparability across studies and to enhance the clinical applicability of research findings. Recognizing these anatomical variations optimizes endodontic treatment outcomes and minimizes procedural errors. Full article

The Ordovician No. 6 fault zone reservoir in the Shunbei Oilfield exhibits ultra-deep-burial, high-pressure, and high-temperature conditions. Its pronounced tectonic control and significant heterogeneity render traditional in situ stress prediction methods—based on linear elasticity and anisotropy assumptions—inadequate for accurately characterizing the evolution and uncertainty of carbonate reservoir stiffness. Therefore, quantitatively predicting the development patterns and distribution characteristics of the Shunbei No. 6 structural fault zone is crucial for the exploration and development of Ordovician carbonate reservoirs in the Shunbei region. This study integrates wave impedance inversion with high-confining-pressure PFC particle flow biaxial test results to establish a constitutive calibration system consistent with seismic and experimental data. It introduces a nonlinear weakening function incorporating higher-order derivative constraints to fuse structural fracture and effective stress weakening effects, enabling dynamic correction of elastic parameters. This approach establishes a novel in situ stress prediction model. Simulation results indicate a predicted range for maximum horizontal principal stress between 201 and 261 MPa, with minimum horizontal principal stress ranging from 124 to 173 MPa. Predicted stress values for three key wells exhibit measurement errors within 6.92% compared to actual logging data, displaying a zoned spatial distribution consistent with regional tectonic stress evolution patterns. Simultaneously, sensitivity analysis reveals that the Young’s modulus fitting accuracy improved from 0.89 to 0.95, with a 43% reduction in mean square error, with the proportion of outliers reduced to below 1%. This significantly enhances response continuity and numerical stability in high-gradient disturbance zones and stiffness drop regions. The new model explicitly incorporates the nonlinear coupling between fracture geometry and pore pressure disturbance into the parameter field, eliminating systematic bias along fracture zones. Higher-order derivative constraints suppress numerical oscillations in high-gradient areas, stabilizing variance and preventing anomaly propagation. Residual distributions exhibit enhanced symmetry and reduced spatial autocorrelation, effectively suppressing numerical oscillations and divergence in complex fracture zones while significantly improving stress prediction accuracy for the study area. Overall, this research provides novel methodologies for predicting in situ stresses in ultra-deep carbonate reservoirs, offering engineering guidance and parameterization references for scheme deployment in complex fractured karst systems. Full article

Background/Objectives: Chronic diseases are among the leading causes of morbidity and healthcare costs worldwide. Diabetes mellitus is one of the most prevalent and costly chronic conditions in the United States, with a disproportionate burden in Puerto Rico. Surveillance of diabetes relies mainly on infrequent cohort studies and self-report surveys, which are limited in accuracy, segmentation, and timeliness. This study aimed to develop a generalizable methodology for monitoring chronic disease prevalence using routinely collected laboratory data, while correcting for systematic biases and diagnostic errors. Methods: We analyzed more than five years of de-identified laboratory test results (2020–2024) from a large, island-wide network of clinical laboratories in Puerto Rico. To produce unbiased prevalence estimates, we applied a mathematical correction framework that accounted for two main sources of distortion: (1) classification errors from treatment effects and test limitations, quantified through confusion matrices derived from longitudinal records; and (2) self-selection bias from differential testing rates, estimated empirically by demographic segment. Demographic reweighting ensured representativeness with respect to census data. Results: Using diabetes as a test case, corrected estimates for 2024 showed an adult prevalence of 18.0%, compared to 14.1% based on raw laboratory frequencies. The large amount of data provided high-resolution estimates by age, sex, and location, enabling fine-grained detection of demographic and geographic disparities. Conclusions: Bias-corrected laboratory surveillance provides accurate, timely, and demographically representative estimates of chronic disease prevalence. The methodology is scalable, cost-effective, and broadly applicable to other multi-stage chronic conditions, offering a foundation for next-generation public health monitoring and targeted interventions. Full article

Developing a high-precision regional geoid model is a key element in modernizing Kazakhstan’s vertical reference framework and ensuring consistent GNSS-based height determination. However, the mountainous terrain of southeastern Kazakhstan, characterized by strong topographic gradients and sparse terrestrial gravity coverage, poses significant modelling challenges. This study presents the first AI-enhanced hybrid geoid model developed for the Almaty region, integrating classical gravimetric modelling with modern machine-learning simulation. The baseline solution was computed using the Least-Squares Modification of Stokes’ Formula with Additive Corrections, combining digitized Soviet-era terrestrial gravity data, the global geopotential model XGM2019e_2159, and the FABDEM 30 m digital elevation model. Validation using GNSS/levelling benchmarks revealed a systematic bias of −0.06 m and an RMS of 0.08 m. To improve the fit between modelled and observed undulations, three machine-learning regressors—Gaussian Process Regression (GPR), Support Vector Regression (SVR), and LSBoost—were applied to model the residual correction surface. Among them, SVR provided the best held-out performance (RMSE = 0.04 m), while LOOCV, 10-fold and spatial CV confirmed stable generalization across terrain regimes. The resulting hybrid model, designated N_ALM2025, achieved centimetre-level consistency with GNSS/levelling data. The results demonstrate that integrating classical geoid computation with AI-based residual modelling provides an efficient computational framework for high-precision geoid determination in complex mountainous environments. Full article

Background: The relative merits of the Henderson–Hasselbalch (HH) versus Stewart frameworks for interpreting dialysis-associated acid–base shifts remain debated. Dialysis alters systemic pH through exogenous bicarbonate delivery, chloride displacement, and removal of organic anions. We compared these approaches across hemodialysis (HD) and peritoneal dialysis (PD). Methods: We studied 53 HD patients with paired pre/post-HD blood gas and chemistry (106 observations) and 41 PD patients cross-sectionally, totaling 147 datasets. Derived variables followed the Figge/Stewart implementation [apparent SID (SIDa), effective SID (SIDe), strong ion gap (SIG), albumin-corrected anion gap (AGc)]. For HD, changes in pH (ΔpH) were modeled using HH predictors (ΔHCO₃⁻, ΔPCO₂) and Stewart predictors (ΔSIDa, ΔATOT, ΔPCO₂). For cross-sectional data (pre-HD, post-HD, and PD), HH- and Stewart-based level models were fitted. Stewart-predicted pH was also computed using the Figge and the simplified Constable electroneutrality equation. Results: HD increased pH by 0.11, driven by ΔHCO₃⁻ = +5.7 mΕq/L, ΔCl⁻ = −2.3 mEq/L, and declines in unmeasured anions (ΔSIG = −3.9; ΔAGc = −3.3). SIDa increased only marginally (+1.3 mEq/L), whereas SIDe rose by +5.3 mEq/L and fully tracked the alkalinization. In Δ-models, HH explained 90% of variance in ΔpH (R² = 0.903) compared with 51% for Stewart (R² = 0.514). In level models, HH explained 96% of pH variance versus 36% for Stewart. Bland–Altman analysis showed systematic overestimation of pH by the Figge and Constable approach (bias + 0.111), most pronounced pre-HD. PD patients had consistently higher AGc and SIG values than HD patients, indicating a greater burden of unmeasured anions. Conclusions: Alkalinization during HD is primarily attributable to bicarbonate gain, chloride displacement, and organic-anion clearance. The HH framework provides superior predictive performance for ΔpH, while closed-system Stewart formulations based on SIDa underestimate alkalinization. However, a broader physicochemical interpretation using SIDe and SIG, which incorporate bicarbonate and unmeasured anions, coherently describes the observed physiology. Future applications of the Stewart approach in dialysis should emphasize SIDe and SIG to better reflect the open-system physiology of both HD and PD. Our findings suggest that the HH model remains more predictive of alkalinization, while SIDe and SIG refine the physicochemical understanding. Full article

Epiretinal membrane (ERM) is a common retinal pathology associated with progressive visual impairment, requiring timely and accurate assessment. Recent advances in artificial intelligence (AI) have enabled automated approaches for ERM detection, segmentation, and postoperative best corrected visual acuity (BCVA) prediction, offering promising avenues to enhance clinical efficiency and diagnostic precision. We conducted a comprehensive literature search across MEDLINE (via PubMed), Scopus, CENTRAL, ClinicalTrials.gov, and Google Scholar from the inception to 31 December 2023. A total of 42 studies were included in the systematic review, with 16 eligible for meta-analysis. Risk of bias and reporting quality were assessed using the QUADAS-2 and CLAIM tools. Meta-analysis of 16 studies (533,674 images) showed that deep learning (DL) models achieved high diagnostic accuracy (AUC = 0.97), with pooled sensitivity and specificity of 0.93 and 0.97, respectively. Optical coherence tomography (OCT)-based models outperformed fundus-based ones, and although performance remained high under external validation, the positive predictive value (PPV) declined—highlighting the importance of testing model generalizability. To the best of our knowledge, this is the first systematic review and meta-analysis to critically evaluate the role of AI in the detection, segmentation, and postoperative BCVA prediction of ERM across various ophthalmic imaging modalities. Our findings provide a clear overview of current evidence supporting the continued development and clinical adoption of AI tools for ERM diagnosis and management. Full article

Background: The development of the curve of Spee (CoS) is influenced by skeletal morphology, orofacial growth, tooth eruption timing, mandibular relationships, overbite, and neuromuscular development. This systematic review aims to determine the most effective orthodontic methods in correcting the curve of Spee. Methods: The systematic review protocol was registered on the PROSPERO platform and conducted according to the Cochrane and PRISMA guidelines. For its development, a standardized search was performed across different databases (MEDLINE, Cochrane Library, Embase and Web of Science) and grey literature. The risk of bias was assessed using Faggion, Jr.’s guidelines for in vitro and in silico studies of dental materials, and the Rob-2 and ROBINS-1 tools for clinical studies. Results: The initial search found 748 studies, with 44 selected after full-text review. Of these, 22 were included in the quantitative analysis, assessing the effectiveness of braces (with or without extractions) and invisible aligners. Key methods for correcting the curve of Spee include various orthodontic archwires (nickel–titanium (NiTi), stainless steel, beta-titanium), continuous and segmented techniques, reverse curve archwires, aligners, and treatment modalities including extraction protocols. Most in vitro studies and randomized studies had a high risk of bias, and non-randomized studies showed moderate to high bias risk. Conclusions: The results suggest that conventional techniques, particularly non-extraction approaches, may be more effective than aligners in correcting the curve of Spee, although the available evidence remains limited. Full article

Restrictive cardiomyopathy (RCM) poses a significant challenge in diagnosis, is frequently identified in advanced stages, and has limited therapeutic options, which may lead to adverse cardiovascular outcomes. This narrative review examines the application of artificial intelligence (AI) across key diagnostic modalities and delineates priorities for translational advancement. The discussed diagnostic tools include echocardiography, cardiac magnetic resonance (CMR), electrocardiography (ECG), and electronic health records (EHR). A targeted, non-systematic search of PubMed and Scopus was performed to identify studies focused on model development, validation, or diagnostic accuracy concerning RCM and related infiltrative disorders. The findings suggest that AI can enable earlier detection, standardize imaging protocols, and enhance phenotype-driven management of RCM. Nonetheless, several challenges exist, including limited data access, the absence of external validation, variability across imaging devices and locations, and the imperative for transparent, explainable systems. Key priorities for successful implementation encompass establishing multi-center collaborations, detecting and correcting bias, clinician involvement in deployment, and integrating multimodal data, including imaging, signal data, and -omics. If effectively integrated into clinical practice, AI has the potential to redefine the management of RCM from a condition recognized primarily in its later stages to one characterized by early detection, dynamic risk assessment, and personalized treatment strategies. Full article

Atomic force microscopy (AFM) is widely used to quantify mechanical properties, typically Young’s modulus, by fitting force–indentation data with various mathematical contact models. However, results across laboratories often diverge, and the causes remain unresolved. Here, we interrogate the methodology by which mechanical properties are defined in AFM indentation and identify key limitations of the Hertz model, the standard model for determining mechanical properties, notably that the contact radius is not directly determined, which limits the accuracy of the estimated Young’s modulus. We hypothesize that this inference systematically overestimates the true tip–sample contact, which inflates the contact area and thereby underestimates Young’s modulus. This bias is amplified under large indentation conditions, which are frequently used in soft-material studies. To isolate and clarify the issue, we focus on a well-characterized polymer, polyvinyl chloride (PVC), using it as a controlled testbed for contact radius overestimation. Our analysis is focused on the contact radius and Hertz-based extraction of Young’s modulus. We determined the contact radius and Young’s modulus using AFM with two different probes: a sphere with a 20 nm radius (SPHERE20) and a sphere with a 2 µm radius (SPHERE2000). The results were compared to macroscopic data obtained using a standard measurement (ISO 527-1:2019) of Young’s modulus. The contact was modeled using finite element analysis (FEA). The dependence of the contact radius on the indentation was compared to the Hertz model. The results from FEA fit corrected contact radius values, and it is smaller by 15.46% (SPHERE20) and 57.9% (SPHERE2000) than those calculated by the Hertz model. Full article

Background/Objectives: The optimal timing for orthodontic treatment in pediatric patients with malocclusion, particularly Class II discrepancies, remains a topic of ongoing clinical debate. Early treatment during the mixed dentition stage harnesses craniofacial growth potential, whereas later intervention may capitalize on pubertal growth for greater skeletal correction, especially for skeletal and airway improvements. This systematic review aimed to compare the outcomes of early versus late orthodontic treatment to assess their relative effectiveness. Methods: A systematic review was conducted in accordance with PRISMA guidelines, including randomized controlled trials and observational studies published between 2015 and 2025. Eleven studies comparing early and late treatment were analyzed, and the risk of bias was evaluated using standardized assessment tools. Results: Of the eleven studies, eight reported statistically significant improvements favoring early orthodontic intervention. Early treatment was associated with greater enhancement of maxillary and mandibular arch development, improved jaw relationships, and expanded airway dimensions. Studies utilizing headgear or other growth-modifying appliances also showed more favorable eruption patterns and alignment, underscoring the clinical relevance of early-phase management. Conclusions: Early orthodontic treatment can provide meaningful benefits in guiding skeletal growth, improving dental arch form, and enhancing treatment efficiency. These benefits were most consistently supported in skeletal and airway outcome domains. While late treatment may be suitable for some cases, personalized planning remains essential. Further large-scale, standardized longitudinal studies are needed to refine treatment-timing protocols in pediatric orthodontics. Full article

This study systematically investigated the effects of mix design conditions (water–cement ratio) and rubber properties (particle size, surface area, and mixing ratio) on the flowability and mechanical characteristics of rubberized concrete, in which rubber particles were incorporated as part of the fine aggregate. The fresh properties (slump and air content) and hardened properties (compressive strength and Young’s modulus) were measured, and their correlations with rubber surface area and mixing ratio were analyzed. The results showed that slump and air content converged to constant values with increasing rubber surface area, exhibiting symmetric behavior. These characteristics were accurately approximated using logistic and exponential functions. In contrast, compressive strength did not decrease monotonically with increasing rubber content but could be divided into three distinct regions: a low-substitution region (Region I), an intermediate transition region (Region II), and a high-substitution region (Region III). Particularly in Region II, where the rate of strength reduction increased sharply, the logistic function was found to describe the asymmetric behavior more appropriately than the conventional exponential function. Furthermore, an estimation formula incorporating a correction term into the logistic function was proposed to account for the influence of the W/C ratio on compressive strength. This two-stage estimation model achieved higher predictive accuracy than conventional equations, eliminating the 0.88 bias observed in previous models. Finally, a practical design methodology based on this two-stage model was presented, demonstrating its applicability to concrete with various mixture ratios and water–cement ratios. Full article