Search Results (20,139)

High-viscosity sodium alginate solutions (4.5% by mass, apparent viscosity 1 × 10⁴–2 × 10⁴ cP) are widely used in the preparation of hydrogels, wet spinning, and biomedical materials. Residual bubbles can cause internal voids in hydrogels, mechanical heterogeneity, fiber breakage during spinning, and reduced strength, and can severely affect the cell compatibility and clinical safety of biomaterials. Due to the difficulty of bubble migration, coalescence, and rupture in high-viscosity systems, traditional vacuum-standing degassing takes up to 24 h and is extremely inefficient, severely limiting the quality of subsequent processing. To address this issue, this study proposes a novel vacuum-assisted centrifugal recirculating degassing method for highly viscous sodium alginate solutions and aims to establish a kinetic framework for describing its overall degassing behavior. Using the number density of bubbles larger than 0.5 mm in diameter as an evaluation metric, we conducted vacuum-standing control experiments and univariate experiments with different screen mesh apertures (5, 1.5, 0.3, and 0.07 mm). We experimentally verified a continuous kinetic model of bubble number decay based on vacuum bubble expansion, centrifugally enhanced migration, and removal probability during the cycle. The results indicate that the bubble removal effect of 40 min of vacuum–centrifugal cyclic degassing is equivalent to that of 4 h of vacuum static settling, representing a 450% increase in degassing efficiency. There is an optimal range for a screen aperture, with the best degassing effect observed at 0.3 mm, achieving a bubble removal rate of 83.69%. The established kinetic model exhibits good fitting accuracy (RMSE = 0.17, MAPE = 5.9%) and can accurately predict degassing efficiency under different process conditions. This study provides a quantifiable, modelable, and optimizable process scheme for rapid degassing of high-viscosity sodium alginate solutions, and offers a theoretical reference for the development of degassing technologies for high-viscosity polysaccharide fluids. Full article

The expansion of remote work has transformed labour market conditions across the developed world, yet access to home-based work remains unequally distributed along occupational, sectoral, regional, and organisational lines. Post-pandemic evidence on the persistence of these inequalities is particularly scarce in Central and Eastern European economies, where historically low remote work prevalence, manufacturing-intensive industrial structures, and pronounced regional disparities create a distinctive structural context. Drawing on primary survey data collected from 390 employees in Slovakia in 2025, this study pursues two interrelated empirical goals: to identify the factors predicting a mismatch between the structural feasibility of working from home and its actual availability to employees, and to examine the determinants of experienced work discomfort. Binary logistic regression, multiple linear regression, and a battery of group difference tests were employed across the two analytical strands. The results reveal a pronounced capital–periphery gradient in remote work access, with employees outside the capital city facing dramatically higher odds of mismatch, and identify organisational support as the most practically actionable determinant of work discomfort. Notably, experiencing a mismatch between remote work feasibility and access was not associated with higher discomfort, a finding that challenges assumptions common in the Western European literature and points to the moderating role of contextual expectations in post-communist labour markets. The findings offer directly applicable evidence for employers seeking to reduce work-related strain through targeted support measures, and for policymakers designing regulatory frameworks to promote equitable access to flexible work arrangements across regions and sectors. Full article

Artificial intelligence (AI) is reshaping the field of diagnostic radiology; however, its applications in interventional radiology and pediatric interventional radiology (PIR) remain limited despite clear clinical needs and the rich multimodal data environment characteristic of pediatric procedural care. In this narrative review, I summarize the current state of AI technologies relevant to PIR and outline future perspectives for their clinical integration. Peer-reviewed literature and position statements identified through MEDLINE/PubMed, Embase, Scopus, and major society publications up to the first quarter of 2026 are synthesized, focusing on AI applications across the PIR care pathway, including dose-sparing image acquisition and reconstruction, automated image interpretation and computer-aided diagnosis, data-driven procedural planning and navigation, and post-procedural risk prediction and monitoring. After briefly introducing core machine learning and deep learning concepts, pediatric-specific challenges are discussed, including radiation sensitivity, growth-related anatomical variability, regulatory constraints, and the scarcity of large, annotated datasets, as well as existing and emerging applications along the PIR care pathway: AI-assisted dose reduction and image reconstruction, automated image interpretation, segmentation, and computer-aided diagnosis; data-driven procedural planning, including three-dimensional modelling, augmented reality, AI-enabled/AI-adjacent robotics, and AI-directed procedural navigation; and post-procedural risk prediction and outcome monitoring. Finally, emerging paradigms, including explainable AI, federated learning, and multimodal integration, are highlighted, and research priorities, collaborative frameworks, and governance principles required to ensure safe, equitable, and effective AI deployment in PIR are outlined. In doing so, this review delineates the current evidence gaps and priority directions for clinically meaningful AI adoption in PIR. Although AI has the potential to improve patient care, it has not yet been specifically designed, validated, or deployed in children. Existing work demonstrates feasibility across the PIR workflow, but most tools remain weakly linked to pediatric clinical endpoints. Full article

Wildfires are a major ecological disturbance in Mediterranean ecosystems, affecting vegetation dynamics and landscape resilience. However, the relative importance of environmental factors controlling post-fire vegetation recovery remains insufficiently quantified at regional scales. This study investigates the drivers of vegetation regeneration following 45 large wildfires (>1000 ha) that occurred across Greece between 2017 and 2023. Vegetation recovery was assessed using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series, while environmental predictors included burn severity metrics, soil moisture at four depth layers derived from the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) climate reanalysis dataset, terrain characteristics (slope and aspect), land cover, and time since fire. All variables were harmonized at the fire-perimeter scale and analyzed using two complementary modeling approaches: multiple linear regression and artificial neural network (ANN) modeling. The linear regression model explained approximately 38% of the variability in vegetation recovery (R² = 0.38), while the ANN showed improved predictive performance, indicating the presence of complex relationships among predictors. Across the applied modeling approaches, burn severity, topographic conditions, and soil moisture emerged as important drivers of post-fire vegetation recovery. In particular, Soil Moisture Layer 1 (SM₁) showed the strongest positive association with NDVI recovery, followed by Soil Moisture Layer 4 (SM₄), highlighting the importance of water availability for vegetation regeneration under post-fire conditions. Overall, the results confirm that vegetation recovery is strongly controlled by environmental conditions rather than time alone. The findings contribute to a better understanding of post-fire ecosystem dynamics in Mediterranean landscapes and provide a useful framework for supporting wildfire management and restoration planning. Full article

Pharmacogenomics AI offers significant potential for individualized drug therapy; however, its clinical benefits remain unevenly distributed. Models trained predominantly on European-ancestry data consistently underperform in non-European populations, with polygenic risk scores (PRS) showing an estimated 39–73% reduction in predictive accuracy in African-ancestry cohorts across complex traits. These disparities have driven increased interest in moving beyond single-layer genomic approaches. Multi-omics frameworks integrating genomic, transcriptomic, proteomic, and metabolomic data have emerged as a promising strategy to improve prediction across heterogeneous clinical populations, as each molecular layer provides distinct and complementary biological information. Among these layers, metabolomics may represent a particularly transferable component across populations. Metabolite profiles capture the downstream functional output of biological systems influenced by genetic, environmental, dietary, and microbiome-related factors, and may therefore be less reliant on ancestry-stratified allele frequency structures that underlie performance disparities in genomic models. This review synthesizes evidence regarding the mechanistic basis of genomic bias in pharmacogenomics AI, the emerging role of multi-omics integration, especially metabolomics, in improving predictive performance, and the current landscape of computational strategies for bias mitigation, including federated learning, transfer learning, domain adaptation, and synthetic data generation. Collectively, current evidence supports metabolomics-inclusive multi-omics frameworks as a biologically plausible, hypothesis-generating strategy to reduce reliance on ancestry-linked genomic features. However, direct evidence that such frameworks reduce ancestry-related bias in clinical AI outputs remains limited, underscoring the need for globally diverse datasets and prospective multi-population validation. Full article

Accurate prediction of contaminant transport and self-purification processes in rivers remains challenging because pollutant dispersion, biochemical reactions, and hydrodynamic conditions interact across multiple spatial scales. This study aims to develop and compare mathematical models for soluble contaminant transport and biodegradable organic matter removal in channels and rivers. Unsteady advection–diffusion–reaction equations were formulated for one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) transport scenarios and solved through numerical techniques based on the transformation of partial differential equations into systems of ordinary differential or algebraic equations. In parallel, the classical Streeter–Phelps model and an extended formulation incorporating turbulent diffusion were implemented to evaluate organic load degradation and oxygen deficit dynamics. Simulations were performed using a Matlab R2019a-based computational framework under representative hydraulic and reaction conditions obtained from literature data and empirical correlations. The results showed that, under specific conditions, the 3D model reproduced trends comparable to those predicted by the 2D model, while the latter approached the behavior of the 1D formulation. The Streeter–Phelps model predicted an organic load removal efficiency of 97.74%, a purification index of 1.9564, a critical time of 18.43 h, and a critical distance of 6.93 km. These findings provide a useful framework for river water-quality assessment and support future applications involving complex hydrodynamic and pollutant-loading scenarios. Full article

Background: Cutaneotrichosporon jirovecii is an under-characterized basidiomycetous yeast within the family Trichosporonaceae. Its taxonomic placement, ecological distribution, and functional potential remain incompletely understood because genome-scale resources for C. jirovecii and closely related lineages are limited. Methods: We characterized strain H0426_7, a C. jirovecii-related yeast recovered from a human fecal sample, using ITS-based type-strain comparison, ITS phylogenetic analysis, whole-genome sequencing, average nucleotide identity analysis, read-level assessment of public C. jirovecii-labeled datasets, and comparative functional annotation. Antifungal susceptibility was assessed using the Sensititre YeastOne plate. Results: The ITS sequence of H0426_7 closely matched type-strain material of C. jirovecii, including CBS 6864 and its equivalent deposits. The ITS-based tree placed H0426_7 adjacent to CBS 6864 with bootstrap support of 87%. The final draft genome comprised 38.66 Mb in 1974 contigs, with a GC content of 63.76% and BUSCO completeness of 80.0%. ANI analysis showed that H0426_7 was genomically distinct from the recognized Cutaneotrichosporon species included in the ANI analysis but highly similar to two unclassified feces-derived strains, P10-008 and PK4640, with ANI values exceeding 98.8%. Two public datasets labeled as C. jirovecii showed anomalously low ANI values with H0426_7; read-level taxonomic profiling indicated low target-fungal read proportions, suggesting that these datasets are unsuitable as definitive genome-level references. CAZyme annotation identified 285 family assignments in H0426_7, representing 278 non-redundant predicted proteins, including relatively high GH5 and GH31 counts, suggesting candidate carbohydrate-utilization features shared with the H0426_7/P10-008/PK4640 lineage. Conclusions: H0426_7 is best described as a C. jirovecii-related Cutaneotrichosporon isolate pending availability of a high-quality genome assembly from the C. jirovecii type strain. This study expands genome-scale resources for underrepresented basidiomycetous yeasts and provides a comparative framework for future taxonomic, ecological, and functional studies of feces-associated Cutaneotrichosporon lineages. Full article

To address the critical safety challenges of flooding induced by ship–ice collisions in Arctic shipping routes, this study proposes an Observe–Orient–Predict–Decide–Act (OODA-P)-enhanced closed-loop intelligent damage control decision-support framework integrated with predictive simulation. To address the limitations of existing systems—namely, weak polar adaptability and the absence of a decision feedback loop—this research presents three core findings: (1) A fast time-domain floating condition model was developed by coupling topside icing with progressive flooding. Numerical simulations indicate that neglecting ice accretion leads to an underestimation of the long-term heel angle and transverse stability by 4.4% and 4.5%, respectively, validating the necessity of incorporating coupled ice loads. (2) A serial dual-channel prediction and evaluation mechanism, integrating “situation evolution prediction” and “decision efficacy evaluation,” was designed. This mechanism can proactively forecast long-term deterioration trends in the floating condition within 0.3147 s of acquiring damage information, capable of identifying and flagging potentially high-risk emergency plans before their execution, thus preventing adverse outcomes. (3) The proposed framework was validated through typical polar scenarios and 111 damage control training sessions across three batches, with the full-loop logic flow completing in under 3 s. Compared with the traditional OODA loop, the average emergency response time was reduced from 26.9 to 22.7 min (a 15.5% reduction), while the initial response success rate improved from 74.7% to 97.3% in a simulated training environment. By enabling “virtual trial-and-error” prior to execution, this framework demonstrates the potential to augment traditional experience-based damage control with proactive, simulation-driven decision support, marking a step towards more intelligent interventions. Through the explicit coupling of topside icing and progressive flooding into real-time predictions, this work provides a foundation for further development of polar-adaptable intelligent damage control systems. Full article

Accurate apple origin identification and non-destructive internal quality evaluation are important for fruit traceability, quality grading, and post-harvest management. Unlike previous studies mainly focusing on origin classification, this study established a dual-task near-infrared spectroscopy framework integrating geographical origin classification and soluble solid content (SSC, °Brix) prediction for Fuji apples. Samples were collected from three representative production regions in China: Alar in Xinjiang, Yantai in Shandong, and Luochuan in Shaanxi. Near-infrared diffuse reflectance spectra were acquired from 375 apples, generating 3000 spectral samples for origin classification and 750 SSC-calibrated samples for sugar content prediction. For classification, six deep learning models were evaluated using standardized full-spectrum input without chemometric spectral preprocessing, and the Transformer achieved the best performance, with a test accuracy of 96.22%. For SSC regression, spectra were preprocessed using standard normal variate and Savitzky–Golay filtering. The DNN model achieved the best prediction performance, with MAE = 0.5958 °Brix, RMSE = 0.7333 °Brix, R² = 0.8646, and Pearson r = 0.9338. These results indicate that near-infrared spectroscopy combined with deep learning can support both Fuji apple origin authentication and non-destructive local tissue SSC assessment. Full article

In complex urban environments, GNSS satellite signals suffer from severe multipath errors caused by building occlusion and reflection, which significantly degrades the accuracy of precise point positioning (PPP). This paper proposes a deep-learning-based multipath hemispherical grid correction model (DL-MHM) that integrates combined filtering and satellite embedding mechanisms. The model adopts the multi-system interoperable MHM framework to achieve effective multipath error correction. First, pseudorange and carrier phase observation residuals are calculated using the ionosphere-free combination for PPP. Then, a joint median and Kalman filtering scheme is applied to suppress noise in multi-day continuous residual sequences. A transformer-based time-series learning model is constructed, which introduces satellite-specific embedding vectors to characterize the differences between individual satellites and deeply fuse temporal features. This enables the model to adaptively fit the residual variation patterns of different satellites and accurately extract multipath errors. Finally, the multipath components predicted by the deep learning model are incorporated into the multi-system interoperable MHM model to generate the final multipath corrections. Test results show that in heavily obstructed urban scenarios, the root mean square (RMS) values of the east (E), north (N), and up (U) coordinate residuals are improved by 49.27%, 1.80%, and 3.35%, respectively, after DL-MHM correction compared to the uncorrected data. In open-sky environments, the corresponding improvements are 7.70%, 5.48%, and 34.28%. In all experimental scenarios, the proposed method outperforms both the conventional multipath hemispherical map (MHM) model and the convolutional neural network-long short-term memory (CNN-LSTM)-based MHM model in terms of overall multipath correction performance. The experimental results demonstrate that the proposed DL-MHM model can effectively mitigate multipath errors in complex urban scenarios and significantly improve the accuracy of GNSS precise positioning. Full article

Higher-education institutions increasingly use AI-enabled chatbots, personalised communication, recommendation systems, and predictive information services in academic marketing. Adoption of these systems depends not only on technical availability, but also on institutional trust, emotional engagement, and skepticism regarding the reliability, transparency, and autonomy implications of AI. This study examines the Trust-Tech Nexus framework using stakeholder survey data collected at MIT Art, Design and Technology University, Pune, India (N = 300). The analysis combines psychometric validation, WLSMV confirmatory factor analysis for ordered indicators, and cross-validated predictive modelling. Four three-item constructs were measured with five-point Likert indicators, as follows: AI Adoption, Institutional Trust, Emotional Engagement, and AI Skepticism. Reliability and convergent validity were acceptable, and the WLSMV CFA showed strong practical fit (CFI = 0.991, TLI = 0.988, RMSEA = 0.040, SRMR = 0.039). Discriminant validity was supported by HTMT and Fornell–Larcker evidence, while Harman’s single-factor result was treated only as an initial diagnostic. Construct-only ridge regression produced positive out-of-sample predictive evidence (CV R-squared = 0.352; RMSE = 0.642; MAE = 0.501). Exploratory classification results were moderate and are interpreted only as supplementary segmentation evidence because the binary targets were derived from the AI Adoption composite. The study supports a validated four-construct measurement structure and moderate predictive association in one institutional context, while avoiding causal claims. Full article

The early and accurate detection of lung cancer (LC) is one of the primary challenges in the clinical diagnostics process, which plays a vital role in the treatment of the disease. Although various deep learning (DL) techniques have been presented, the existing DL methods are mainly focused on single-modal images, either computed tomography (CT) or histopathological images, which are associated with poor generalization, diversity, and applicability. To mitigate the existing issues, the present work aims to develop a modality-independent ensemble DL framework that is independently evaluated on CT and histopathological image datasets for LC classification. In this work, the proposed framework was developed using the Beta Normalization Aggregation (BNA) technique, where the performance of three state-of-the-art pre-trained convolutional neural network (CNN) architectures was compared on two distinct imaging modalities images. Based on the comparative analysis of the performance metrics, Xception, DenseNet121, and MobileNetV2, are chosen to develop the Ensemble model. Predictions generated by the selected CNN models are aggregated using the proposed BNA strategy to improve classification robustness, which improves the confidence of the prediction results and discriminative capabilities. The experiments using public data sets have confirmed the excellent performance of the model. On the CT dataset, the proposed BNA Ensemble achieved a testing accuracy of 97.45%, with a precision of 97.88%, recall of 97.45%, F1-score of 97.45%, and an AUC of 0.9986. On the histopathological dataset, the framework achieved an accuracy of 99.80%, with precision, recall, and F1-score all reaching 99.80%, and an AUC of 1.0000. These results demonstrate the effectiveness, robustness, and generalizability of the proposed BNA framework. The analysis of the results using t-SNE plots, confusion matrices, ROC curves, and confidence distributions provided additional insights into feature separability, classification performance, and prediction confidence of the proposed framework. Full article

Grape caliber distributions are critical for packing, grading, yield estimation, and post-harvest logistics. However, estimating reliable caliber histograms from single images remains challenging due to occlusion and dense bunch structure. This work presents a two-stage monocular pipeline that integrates instance segmentation, geometry-aware representations, residual quantity correction, and structured histogram prediction. In the first stage, a YOLO-based model detects grape instances and a calibration object, enabling the construction of geometry-aware auxiliary channels and a segmentation-derived counting prior. In the second stage, these representations are used to estimate total grape count and caliber distributions. Results show that RGBDT consistently outperforms RGB, indicating that geometry-aware cues improve both histogram fidelity and counting accuracy. The framework achieves stable performance under realistic conditions while maintaining low runtime, supporting practical deployment in agricultural environments. Full article

Interzeolite transformation (IZT) has emerged as a versatile strategy for accessing zeolite frameworks through controlled framework reorganization under comparatively simplified synthesis conditions. Unlike traditional synthesis approaches that frequently require organic structure-directing agents (OSDAs), highly alkaline media, and prolonged thermal treatment, IZT converts pre-existing zeolite into a new topology, enabling direct reuse of crystalline matter while reducing synthesis complexity. This review examines how structural drivers, including framework density, structural memory, and building-unit compatibility, govern transformation pathways and phase selectivity across five principal transformation approaches: (i) solution-mediated, (ii) assembly–disassembly–organization–reassembly (ADOR), (iii) mechanically assisted, (iv) steam-assisted, and (v) fully solid-state systems. These approaches promote distinct transformation pathways that govern framework reconstruction, structural inheritance, and phase selectivity. Recent advances in solvent-free, mechanochemical, steam-assisted, and microwave-assisted synthesis demonstrate the potential of IZT to reduce solvent consumption, template usage, and crystallization times. Despite these advances, major challenges remain in predicting transformation outcomes, controlling transient intermediates, and establishing scalable and quantitatively validated sustainability metrics. Collectively, these developments position IZT as a promising platform for the rational and sustainable design of next-generation zeolitic materials. Full article

Short-term traffic forecasting is a critical component of intelligent transportation systems. While deep learning architectures for this task have proliferated rapidly, the sensor-level data characteristics—zero-value prevalence, distributional heterogeneity, and cross-sensor correlation structure—that drive architecture-specific failure modes remain insufficiently understood, and their implications for evidence-based model selection in real deployments have not been systematically addressed. This study addresses that question through a sensor-network diagnostic framework applied to the METR-LA dataset (Metropolitan Los Angeles; 207 inductive loop detectors, 5-min resolution). The framework integrates systematic characterization of sensor data properties, a controlled benchmark of four representative architectures—Transformer, Spatio-Temporal Graph Convolutional Network (STGCN), Diffusion Convolutional Recurrent Neural Network (DCRNN), and Gated Temporal Convolutional Network (Gated TCN)—under a unified 12→3 prediction setting, and a novel per-sensor regression analysis that quantitatively links zero-value ratios to model-specific prediction errors across all 207 sensors. Building on these findings, this study further proposes Graph-Enhanced Transformer (GETFormer), a lightweight hybrid architecture that augments the Transformer with a single-hop Graph Convolutional Network (GCN) layer and a gated residual fusion module. The diagnostic findings and condition-dependent model-selection guidelines provide an empirically grounded foundation for principled hybrid architecture development in urban traffic sensing. Full article