Search Results (6,323)

(1) Background: Pain assessment still relies primarily on subjective self-report. To address these limitations, we developed Piq, an EEG-based index derived from beta-band brain activity (Piq_β) aimed at providing objective pain identification and quantification. (2) Methods: The study combined cross-sectional and longitudinal designs. Resting-state brain activity was recorded for five minutes, and EEG signals were preprocessed using a dedicated algorithm. Piq_β performance was assessed by identifying an optimal cutoff to discriminate pain from no pain, evaluating its association with VNRS, and estimating agreement using a modified concordance criterion (exact match or ±1 category). A graded scale was also established to classify pain into distinct categories, according to intensity. (3) Results: An optimal cutoff of 10% for Piq_β yielded 97.8% sensitivity and 88.2% specificity. Piq_β correlated with self-reported scores (ρ = 0.60, p < 0.0001) with acceptable agreement (mean bias −1.02), accounting for clinically acceptable discrepancies. Five levels of pain were proposed, and Piq_β demonstrated the ability to track intra-individual fluctuations over time, accounting for clinically acceptable discrepancies. (4) Conclusions: These findings provide strong evidence to support the Piq_β index as a valuable complement to subjective pain ratings. Full article

Political trust is essential for implementing the United Nations 2030 Agenda, particularly Sustainable Development Goal (SDG) 16 on building effective, accountable and inclusive institutions. At the same time, there has been a long-standing decline in political trust within democratic countries, which presents a considerable obstacle to the enactment of sustainable development policies. Although prior research has explored the relationship between economic conditions and political trust, evidence on how different dimensions of inequality jointly shape trust remains limited. This study addresses this gap by analysing how economic inequality, regional economic disparities, and subjective income perceptions affect political trust. Using data from the European Social Survey (Round 9), we estimate multilevel models that account for both individual- and country-level factors. The results demonstrate a negative relationship between individual income and political trust, while lower economic inequality strengthens this negative relationship. Our findings highlight that reducing economic inequality is crucial for enhancing political trust, suggesting that governments should prioritize equitable resource distribution and address regional disparities to foster trust in institutions. By integrating subjective well-being with objective economic indicators, this research offers a comprehensive view of how inequality affects political trust across the EU countries and outlines institutional and distributive conditions that support progress toward the SDGs. Full article

When determining subgroups with heterogeneous treatment effects in cancer clinical trials, the threshold of a variable that defines subgroups is often pre-determined by physicians based on their experience, and the optimality of the threshold is not well studied, particularly when the mixture cure rate model is considered. We propose a mixture cure model that allows optimal subgroups to be estimated for both the time to event for uncured subjects and the cure status. We develop a smoothed maximum likelihood method for the estimation of model parameters. An extensive simulation study shows that the proposed smoothed maximum likelihood method provides accurate estimates. Finally, the proposed mixture cure model is applied to a colon cancer study to evaluate the potential differences in the treatment effect of levamisole plus fluorouracil therapy versus levamisole alone therapy between younger and older patients. The model suggests that the difference in the treatment effect on the time to cancer recurrence for uncured patients is significant between patients younger than 67 and patients older than 67, and the younger patient group benefits more from the combined therapy than the older patient group. Full article

Wellbore instability is a critical challenge in deep coalbed methane (CBM) development, especially for extended-reach horizontal wells subjected to pronounced horizontal in situ stress anisotropy. This study integrates uniaxial and triaxial laboratory testing of deep coal samples with an analytical Mohr–Coulomb-based model to quantify how horizontal stress contrast redistributes near-wellbore stresses and controls collapse pressure. Mechanical parameters from core experiments and log-derived stresses are embedded into the model and applied to six representative horizontal wells in the Ordos Basin. At 2000 m depth, circumferential stress perpendicular to the minimum horizontal stress direction exceeds orthogonal directions by 20 MPa (wells 1–3) and 40–50 MPa (wells 4–6). As the horizontal stress ratio n = ${\sigma }_H$/${\sigma }_h$ (where ${\sigma }_H$ and ${\sigma }_h$ are the maximum and minimum horizontal in situ stresses, respectively) increases from 1.07 to 1.28, the equivalent mud density required to prevent collapse rises from 1.53 to 1.77–1.81 g/cm³, representing a 15–18% increase. These results demonstrate that explicitly accounting for horizontal stress anisotropy—calibrated by uniaxial and triaxial tests—is essential for reliable collapse-pressure estimation in extended-reach wells drilled in deep coal seams, without invoking additional trajectory-optimization assumptions. Full article

Background/Objectives: The pharyngeal microbiota plays a critical role in respiratory health by supporting immune modulation, colonization resistance, and metabolic functions. Disruptions in this microbial ecosystem are associated with respiratory diseases; however, standard diagnostics often target individual pathogens, overlooking overall microbial dynamics. This study investigates the composition and diversity of the pharyngeal microbiota in three populations: individuals with pre-COPD (with and without concurrent acute respiratory infection [ARI]) and those with stable COPD. Methods: Pharyngeal swabs were analyzed using 16S rDNA sequencing on the Illumina MiSeq platform. Taxonomic and functional profiles were generated with MicrobAT^®, while microbial diversity was evaluated using the Shannon index and PERMANOVA. Group differences in microbiota composition were assessed via Kruskal–Wallis tests and robust PCA. The sample size was estimated at 8 subjects per group to detect significant differences (α = 0.05, 80% power, SD ≈ 20). Results: Twenty-nine swabs were collected: 11 from pre-COPD subjects (PC), 9 from ARI patients receiving antibiotics, and 9 from individuals with stable severe COPD. Microbial diversity was preserved in the PC group (100%) but markedly reduced in ARI (25%) and COPD (15%). Microbiota composition differed significantly across groups (R² = 0.371, p = 0.001), particularly at the phylum level. Functional analysis revealed minimal deficits in PC (<10%) but major impairments in ARI (81%) and COPD (56%), indicating reduced microbial functional capacity. Conclusions: Broad-spectrum microbial analysis highlights the importance of assessing pharyngeal microbiota beyond traditional pathogen detection, offering potential for innovative diagnostic and therapeutic approaches. Full article

Background: Glioblastoma (GBM) is among the most aggressive and morphologically heterogeneous brain tumors. Beyond static imaging biomarkers, its structural organization can be viewed as a nonlinear dynamical system. Characterizing morphodynamic attractors within such a system may reveal latent stability patterns of morphological change and potential indicators of morphodynamic organization. Methods: We analyzed 494 subjects from the multi-institutional BraTS 2020 dataset using a fully automated computational pipeline. Each multimodal MRI volume was encoded into a 16-dimensional latent space using a 3D convolutional autoencoder. Synthetic morphological trajectories, generated through bidirectional growth–shrinkage transformations of tumor masks, enabled training of a contraction-regularized Neural Ordinary Differential Equation (Neural ODE) to model continuous-time latent morphodynamics. Morphological complexity was quantified using fractal dimension (DF), and local dynamical stability was measured via a Lyapunov-like exponent (λ). Robustness analyses assessed the stability of DF–λ regimes under multi-scale perturbations, synthetic-order reversal (directionality; sign-aware comparison) and stochastic noise, including cross-generator generalization against a time-shuffled negative control. Results: The DF–λ morphodynamic phase map revealed three characteristic regimes: (1) stable morphodynamics (λ < 0), associated with compact, smoother boundaries; (2) metastable dynamics (λ ≈ 0), reflecting weakly stable or transitional behavior; and (3) unstable or chaotic dynamics (λ > 0), associated with divergent latent trajectories. Latent-space flow fields exhibited contraction-induced attractor-like basins and smoothly diverging directions. Kernel-density estimation of DF–λ distributions revealed a prominent population cluster within the metastable regime, characterized by moderate-to-high geometric irregularity (DF ≈ 1.85–2.00) and near-neutral dynamical stability (λ ≈ −0.02 to +0.01). Exploratory clinical overlays showed that fractal dimension exhibited a modest negative association with survival, whereas λ did not correlate with clinical outcome, suggesting that the two descriptors capture complementary and clinically distinct aspects of tumor morphology. Conclusions: Glioblastoma morphology can be represented as a continuous dynamical process within a learned latent manifold. Combining Neural ODE–based dynamics, fractal morphometry, and Lyapunov stability provides a principled framework for dynamic radiomics, offering interpretable morphodynamic descriptors that bridge fractal geometry, nonlinear dynamics, and deep learning. Because BraTS is cross-sectional and the synthetic step index does not represent biological time, any clinical interpretation is hypothesis-generating; validation in longitudinal and covariate-rich cohorts is required before prognostic or treatment-monitoring use. The resulting DF–λ morphodynamic map provides a hypothesis-generating morphodynamic representation that should be evaluated in covariate-rich and longitudinal cohorts before any prognostic or treatment-monitoring use. Full article

Humans tend to rely on heuristic strategies for intuitive judgment during decision-making. Existing research proposes an availability heuristic, suggesting that individuals are inclined to use highly available information as a basis for judgment. To explore the behavioral and electrophysiological characteristics of the availability heuristic in information visualization, 24 right-handed participants were recruited for the experiment. Using behavioral and event-related potentials (ERPs) analysis techniques, within-subject behavioral and electroencephalogram (EEG) experiments were conducted under four conditions: polar coordinate system with higher number, polar coordinate system with lower number, Cartesian coordinate system with higher number, and Cartesian coordinate system with lower number. The behavioral results revealed that in the angle estimation task, the polar coordinate condition induced a more significant availability heuristic effect compared to the Cartesian coordinate condition, exhibiting smaller numerical estimation deviations. This indicates that the degree of semantic relevance between the available information and the target task is a critical factor determining the facilitative effect of such information on judgment. The ERPs results showed that the polar coordinate condition elicited smaller N2 and P2 amplitudes than the Cartesian coordinate condition during angle judgment, suggesting reduced semantic conflict and lower attentional demand in task processing under the polar coordinate condition. By providing behavioral and electrophysiological evidence of intuitive decision-making processes, this study lays a theoretical foundation for the rational application of intuitive effects in information visualization design. Furthermore, the findings imply that using available information semantically aligned with the target task can significantly enhance the effectiveness of the availability heuristic, thereby mitigating availability bias. Full article

The rising global burden of chronic diseases highlights the limitations of traditional dietary guidelines. Precision Nutrition (PN) aims to deliver personalized dietary advice to optimize individual health, and the effective implementation of PN fundamentally relies on comprehensive and accurate dietary data. However, conventional dietary assessment methods often suffer from quantification errors and poor adaptability to dynamic changes, leading to inaccurate data and ineffective guidance. Machine learning (ML) offers a powerful suite of tools to address these limitations, enabling a paradigm shift across the nutritional management pipeline. Using dietary data as a thematic thread, this article outlines this transformation and synthesizes recent advances across dietary assessment, in-depth mining, and nutritional intervention. Additionally, current challenges and future trends in this domain are also further discussed. ML is driving a critical shift from a subjective, static mode to an objective, dynamic, and personalized paradigm, enabling a loop nutrition management framework. Precise food recognition and nutrient estimation can be implemented automatically with ML techniques like computer vision (CV) and natural language processing (NLP). Integrating with multiple data sources, ML is conducive to uncovering dietary patterns, assessing nutritional status, and deciphering intricate nutritional mechanisms. It also facilitates the development of personalized dietary intervention strategies tailored to individual needs, while enabling adaptive optimization based on users’ feedback and intervention effectiveness. Although challenges regarding data privacy and model interpretability persist, ML undeniably constitutes the vital technical support for advancing PN into practical reality. Full article

Since skeletal development is largely completed by adulthood, it is difficult for traditional methods to capture subtle age-related structural changes in bones and surrounding tissues. Recent advances in deep learning have demonstrated remarkable potential in medical image-based age estimation. The cervical vertebrae, as captured in lateral cephalometric radiographs (LCR), have shown particular value in such tasks. To systematically investigate the contribution of different vertebral representations to age estimation, we developed four distinct input modes: (1) Contour (C); (2) Mask (M); (3) Cervical Vertebrae (CV) and (4) Cervical vertebrae region (SR). Using a large-scale LCR dataset of 20,174 subjects aged 4–40 years, grouped into 5-year intervals, we evaluated these modes with deep learning models. The Mean Absolute Error (MAE) was used to evaluate performance. Results indicated that the SR mode achieved the lowest overall MAE, particularly for the C1–C4 combination, followed by CV, while C and M modes showed similar and poorer performance. For subjects younger than 25 years, MAEs for individual vertebrae (C1–2, C3, C4) were less than 5 years across all modes; however, in the 26–40 years group, MAEs for C and M modes exceeded 10 years, whereas CV and SR modes remained below 10 years for most combinations. Combining vertebrae consistently improved accuracy over individual ones, with continuous combinations (e.g., C1–2 + C3) outperforming discontinuous ones (e.g., C1–2 + C4). Visualization of age-related salience revealed that salient regions varied by input mode and expanded with increased information content. These findings underscore the critical importance of incorporating peripheral soft tissue and comprehensive vertebral context for accurate age estimation across a wide age spectrum. Full article

The ICESat-2 satellite collects LiDAR data along linear orbital tracks using a photon-counting green wavelength (532.27 nm) instrument. The utility of combining ICESat-2 data with satellite imagery for training and subsequently applying satellite-derived bathymetry models to provide estimates of shallow water depth is well-established. However, automating and improving the accuracy of the identification of ICESat-2 photon events (PEs) representing bathymetry remains a challenge. This article presents an algorithm for automated extraction of PEs reflected from the ocean floor (rather than the ocean surface or noise in the water column). The algorithm is unique in examining both the density of PEs surrounding a subject PE and their position relative to the subject PE. This is accomplished by establishing three concentric ellipses around the subject PE, dividing them into radial “sectors” in 2D space (along-track vs. PE depth/height), recording the number of neighboring PEs in each sector and using this information to fit a LightGBM model. Agreement with PEs identified by an image interpreter is approximately 98%. Testing suggests that the accuracy of the algorithm is relatively insensitive to the size and shape of the ellipses used to define a PE’s neighborhood and to the number of radial sectors used. The model produced also appears to be robust across different geographic areas and data densities. Full article

This study investigates the seismic retrofit of historic single-nave churches through the optimization of roof diaphragms designed to enhance energy dissipation. The proposed strategy introduces a deformable box-type diaphragm above the existing roof, composed of timber panels and steel connectors with a cover of steel stripes, where energy dissipation is concentrated in the connections. The retrofit design is guided by the estimation of Equivalent Damping Ratio (EDR) instead of the usually adopted resistance criterion, considering an energy-based approach to improve global seismic performance while preserving architectural integrity. In this way, the retrofitted configuration of the roof can be considered a damper. Three numerical phases are presented to assess the effectiveness of the equivalent damping-based intervention. In the first one, the seismic response of the initial non-retrofitted configuration is implemented using a 3D linear finite element model subjected to a response spectrum. Subsequently, nonlinear equivalent models subjected to spectrum-compatible accelerograms are implemented, simulating the possible retrofitted configurations of the roofs to detect the optimum damping and finding the corresponding roof diaphragm configuration. In the third one, the response of the detected retrofitted configuration is also evaluated by nonlinear 3D model subjected to accelerograms. The three phases with the relative numerical approaches are here applied to a case study, located in a high seismic hazard area. The results demonstrate that the EDR-based methodology can optimize the retrofitted roof diaphragm configuration; the nave transverse response is improved in comparison with that designed with the traditional approach, considering only the over-strength of the interventions. Comparisons about the approaches based on the EDR and the strength criteria are presented in terms of lateral displacements, in-plane shear acting on the roof diaphragm, and in-plane stresses on the façade. Full article

In Kampinoski National Park (KNP), located in central Poland near the Warsaw agglomeration, the ASF epidemic lasted five years (2017–2021). The virus likely entered the park via wild boar migrating along a natural ecological corridor or through unintentional transmission by residents and tourists. Between 2014 and 2021, intensive monitoring and wild boar population reduction were implemented. The wild boar population, estimated at 11 individuals/km² in 2013, began to be reduced from 2014 onward, when ASF was first detected in Poland. In 2018, at the peak of the epidemic, the density of wild boar dropped to about 0.51 individuals/km²—the lowest in the park’s history. Between 2017 and 2021, 408 ASF cases were recorded, mainly in dead wild boar, although age and sex structure analysis suggests that the actual mortality rate could have been up to about 50% higher. The early intervention—culling—which was controversial from the National Park’s perspective, appears to have played a key role in controlling the situation and likely in limiting the epidemic. At the same time, the limited organizational resources of KNP highlighted the need for proactive management and close institutional cooperation in protected areas subject to high anthropogenic pressure. Full article

Accurate and efficient tomato ripeness estimation is crucial for robotic harvesting and supply chain grading in smart agriculture. However, manual visual inspection is subjective, slow and difficult to scale, while existing vision models often struggle with cluttered field backgrounds, small targets and limited throughput. To overcome these limitations, we introduce TSE-YOLO, an improved real-time detector tailored for tomato ripeness estimation with joint detection and segmentation. In the TSE-YOLO model, three key enhancements are introduced. The C2PSA module is improved with ConvGLU, adapted from TransNeXt, to strengthen feature extraction within tomato regions. A novel segmentation head is designed to accelerate ripeness-aware segmentation and improve recall. Additionally, the C3k2 module is augmented with partial and frequency-dynamic convolutions, enhancing feature representation under complex planting conditions. These components enable precise instance-level localization and pixel-wise segmentation of tomatoes at three ripeness stages: verde, semi-ripe (semi-maduro), and ripe. Experiments on a self-constructed tomato ripeness dataset demonstrate that TSE-YOLO achieves 92.5% mAP@0.5 for detection and 92.2% mAP@0.5 for segmentation with only 9.8 GFLOPs. Deployed on Android via Ncnn Convolutional Neural Network (NCNN), the model runs at 30 fps on Dimensity 9300, offering a practical solution for automated tomato harvesting and grading that accelerates smart agriculture’s industrial adoption. Full article

Rapid urban expansion has heightened the demand for accurate, scalable, and real-time methods to assess tree health and the provision of ecosystem services. Urban trees are the major contributors to air-quality improvement and climate change mitigation; however, their monitoring is mostly constrained to inherently subjective and inefficient manual inspections. In order to break this barrier, we put forward a lightweight multimodal deep-learning framework that fuses RGB imagery with environmental and biometric sensor data for a combined evaluation of tree-health condition as well as the estimation of the daily oxygen production and CO₂ absorption. The proposed architecture features an EfficientNet-B0 vision encoder upgraded with Mobile Inverted Bottleneck Convolutions (MBConv) and a squeeze-and-excitation attention mechanism, along with a small multilayer perceptron for sensor processing. A common multimodal representation facilitates a three-task learning set-up, thus allowing simultaneous classification and regression within a single model. Our experiments with a carefully curated dataset of segmented tree images accompanied by synchronized sensor measurements show that our method attains a health-classification accuracy of 92.03% while also lowering the regression error for O₂ (MAE = 1.28) and CO₂ (MAE = 1.70) in comparison with unimodal and multimodal baselines. The proposed architecture, with its 5.4 million parameters and an inference latency of 38 ms, can be readily deployed on edge devices and real-time monitoring platforms. Full article

Biased or weighted sampling frequently arises in reliability testing, biomedical survival analysis, and quality-control studies, where the observed data deviate systematically from the target population. This paper develops a unified framework for nonparametric estimation of probability density distribution, hazard rate, and regression functions when the data are subject to biased sampling. The proposed weighted kernel estimators adjust for biasing functions $w(x)$, enabling asymptotically unbiased estimation under general sampling distortions. Comprehensive theoretical results are provided, including bias-variance decompositions, optimal bandwidth orders, and mean-squared error properties. Extensive numerical simulations and a real-data application to the Channing House dataset demonstrate the practical advantages and robustness of the proposed estimators compared with naïve approaches. The results confirm the method’s theoretical validity and its broad applicability in survival and reliability studies involving biased data. Full article