Search Results (2,139)

Background: atrial fibrillation (AF) patients with obesity and high thromboembolic risk need oral anticoagulant therapy. Limited data are available on direct oral anticoagulants (DOACs) in this population, and a point-of-care method has been validated to support rapid clinical decisions and to identify on-off plasma concentration thresholds. Methods: This is a monocentric, cross-sectional diagnostic accuracy study on obese AF outpatients referred to Policlinico Umberto I of Rome. Urinary Dipsticks were assessed with separate pads for factor Xa (FXA-i) and thrombin inhibitor (THR-i) and compared to the reference standard of trough and peak plasma concentrations with chromogenic assays/dTT and prespecified plasma thresholds for each DOAC. Study endpoints were the sensitivity, specificity, positive and negative predictive values (PPV and NPV) of DOACs Dipstick compared to plasma concentrations. Sub-analyses according to obesity severity and type of DOAC were performed. Results: 320 paired plasma and urine samples were available from 160 enrolled patients (mean age 73.2 ± 9.1 years). Compared to trough plasma concentrations, DOACs Dipstick showed a sensitivity of 99.24% (mean, 95% confidence interval, CI 95.82–99.98), specificity of 6.89% (0.85–22.76), PPV 82.80% (81.32–84.18), NPV 66.67% (15.79–95.52). On the other hand, compared to peak plasma concentrations, DOACs Dipstick showed a sensitivity of 97.8% (93.7–99.5), specificity of 0% (0.0–15.4), and PPV of 85.9% (85.6–86.2). Urinary Dipstick showed a sensitivity of 99.10% (95.4–100.0), specificity of 4.70% (0.60–16.20) and a PPV and NPV of 74.50% (73.2–75.8) and 66.70 (15.7–95.6), compared to plasma thresholds > 30 ng/mL of FXA-I and THR-I. Sub-analyses showed similar results between FXA-i and THR-i. Conclusions: The urine point-of-care has high sensitivity, acceptable PPV, but low specificity and NPV in AF obese patients and may be useful only in selected clinical scenarios. Full article

Using panel data from 273 prefecture-level cities in China from 2008 to 2020, this study employs the Entropy Weight Method -Technique for Order Performance by Similarity to Ideal Solution (EWM-TOPSIS) model to measure people’s well-being and applies a staggered Difference-in-Differences (DID) model to evaluate the impact of the carbon emissions trading system on people’s well-being. The findings indicate that the carbon emissions trading system generally improves people’s well-being. The mechanism analysis reveals that the primary channel through which the carbon emissions trading system improves people’s well-being is the stimulation of green technology innovation. Additionally, fiscal expenditure decentralization negatively moderates the carbon emissions trading system’s impact on people’s well-being, whereas marketization degree does not exert a moderating effect. Further research reveals that fiscal expenditure decentralization exhibits a double threshold effect, while the degree of marketization displays a single threshold effect. The carbon emissions trading system exhibits heterogeneous impacts on people’s well-being. From a regional perspective, the carbon emissions trading system enhances people’s well-being in non-Yangtze River Economic Belt (YREB) regions, whereas it dampens people’s well-being in YREB cities. Regarding resource endowment, the carbon emissions trading system positively influences people’s well-being in non-resource-based cities, but its impact remains statistically insignificant in resource-based cities. Full article

Background/Objectives: To construct and compare multivariable prediction models for the early prediction of large-for-gestational-age (LGA) neonates, using ultrasound biometry and maternal characteristics. Methods: This retrospective cohort study analyzed data from singleton pregnancies that underwent routine ultrasound examinations at 30⁺⁰–34⁺⁰ weeks of gestation. Ultrasound parameters included fetal abdominal circumference (AC), head circumference (HC), femur length (FL), HC-to-AC ratio, mean uterine artery pulsatility index (mUtA-PI), and presence of polyhydramnios. LGA neonates were defined as those having a birthweight > 90th percentile. Logistic regression was used to evaluate associations between ultrasound markers and LGA after adjusting for the following maternal and pregnancy-related covariates: maternal age, body mass index, parity, gestational diabetes mellitus (GDM), pre-existing diabetes, previous cesarean section (PCS), assisted reproductive technology (ART) use, smoking, hypothyroidism, and chronic hypertension. Associations were expressed as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). Three prognostic models were developed utilizing the following predictors: (i) biometric ultrasound measurements including AC, HC-to-AC ratio, FL, UtA-PI, and polyhydramnios (Model 1), (ii) a combination of biometric ultrasound measurements and clinical–maternal data (Model 2), and (iii) only the estimated fetal weight (EFW) (Model 3). Results: In total, 3808 singleton pregnancies were included in the analyses. The multivariable analysis revealed that AC (aOR 1.07, 95% CI [1.06, 1.08]), HC to AC (aOR 1.01, 95% CI [1.006, 1.01]), FL (aOR 1.01, 95% CI [1.009, 1.01]), and the presence of polyhydramnios (aOR 4.97, 95% CI [0.7, 58.8]) were associated with an increased risk of LGA, while a higher mUtA-PI was associated with a reduced risk (aOR 0.98, 95% CI [0.98, 0.99]). Maternal parameters, such as GDM, pre-existing diabetes, elevated pre-pregnancy BMI, absence of uterine artery notching, mUtA-PI, and multiparity, were significantly higher in the LGA group. Both models 1 and 2 showed similar performance (AUCs: 84.7% and 85.3%, respectively) and outperformed model 3 (AUC: 77.5%). Bootstrap and temporal validation indicated minimal overfitting and stable model performance, while decision curve analysis supported potential clinical utility. Conclusions: Models using biometric and Doppler ultrasound at 30–34 weeks demonstrated good discriminative ability for predicting LGA neonates, with an AUC up to 84.7%. Adding maternal characteristics did not significantly improve performance, while the biometric model performed better than EFW alone. Sensitivity at conventional thresholds was low but increased substantially when lower probability cut-offs were applied, illustrating the model’s threshold-dependent flexibility for early risk stratification in different clinical screening needs. Although decision curve analysis was performed to explore potential clinical utility, external validation and prospective assessment in clinical settings are still needed to confirm generalizability and to determine optimal decision thresholds for clinical application. Full article

This study investigated the impact of two yeast strains (SP665 and CGC62) and glucanase enzyme treatments (A-D) on the secondary fermentation kinetics and aroma profile of sparkling Prosecco wines. The strains exhibited markedly different fermentation behaviors: SP665 induced rapid refermentation, reaching 8.5 bar in 46 days, while CGC62 showed a slower fermentation rate, reaching 6.5 bar in 64 days. Despite these kinetic differences, basic enological parameters after refermentation and following three months of lees aging were similar for both strains. A total of 66 volatile compounds across various chemical families were identified and quantified. Principal component analysis (PCA) revealed that aging time (T1 vs. T2) was the main driver of variability (50.74% of total variance), with SP665 and CGC62 wines showing distinct profiles. At T1, SP665 wines had higher levels of acetate esters and norisoprenoids, while CGC62 wines were richer in volatile sulfur compounds (VSCs) and monoterpenoids. At T2, SP665 wines showed increased levels of carbon disulfide, higher alcohols, and ethyl butanoate, whereas CGC62 wines retained higher concentrations of varietal compounds and certain esters. The effect of glucanase enzymes varied depending on yeast strain and aging stage. Enzyme treatments, especially C (β-glucanase) and D, influenced the concentration of several aroma compounds, particularly in CGC62 wines, enhancing varietal aromas and esters. However, the impact on SP665 wines was more limited and emerged primarily after aging. Although differences in aroma composition were statistically significant, most changes were below olfactory perception thresholds. Overall, glucanase enzymes and yeast selection influenced aroma development, though their effects may have limited sensory relevance. Full article

Plastic streams dominated by polyethylene (PE) including PE HD/MD (High Density/Medium Density) and PE LD/LLD (Low Density/Linear Low Density), polypropylene (PP), and polyvinyl chloride (PVC) across Europe demand a design framework that links synthesis with end of life reactivity, supporting circular economic goals and European Union waste management targets. This work integrates polymerization derived chain architecture and depolymerization mechanisms to guide selective valorization of commercial plastic wastes in the European context. Catalytic topologies such as Bronsted or Lewis acidity, framework aluminum siting, micro and mesoporosity, initiators, and strategies for process termination are evaluated under relevant variables including temperature, heating rate, vapor residence time, and pressure as encountered in industrial practice throughout Europe. The analysis demonstrates that polymer chain architecture constrains reaction pathways and attainable product profiles, while additives, catalyst residues, and contaminants in real waste streams can shift radical populations and observed selectivity under otherwise similar operating windows. For example, strong Bronsted acidity and shape selective micropores favor the formation of C₂ to C₄ olefins and Benzene, Toluene, and Xylene (BTX) aromatics, while weaker acidity and hierarchical porosity help preserve chain length, resulting in paraffinic oils and waxes. Increasing mesopore content shortens contact times and limits undesired secondary cracking. The use of suitable initiators lowers the energy threshold and broadens processing options, whereas diffusion management and surface passivation help reduce catalyst deactivation. In the case of PVC, continuous hydrogen chloride removal and the use of basic or redox co catalysts or ionic liquids reduce the dehydrochlorination temperature and improve fraction purity. Staged dechlorination followed by subsequent residue cracking is essential to obtain high quality output and prevent the release of harmful by products within European Union approved processes. Framing process design as a sequence that connects chain architecture, degradation chemistry, and operating windows supports mechanistically informed selection of catalysts, severity, and residence time, while recognizing that reported selectivity varies strongly with reactor configuration and feed heterogeneity and that focused comparative studies are required to validate quantitative structure to selectivity links. In European post consumer sorting chains, PS and PC are frequently handled as separate fractions or appear in residues with distinct processing routes, therefore they are not included in the polymer set analyzed here. Polystyrene and polycarbonate are outside the scope of this review because they are commonly handled as separate fractions and are typically optimized toward different product slates than the gas, oil, and wax focused pathways emphasized here. Full article

Background/Objectives: Individuals with type 2 diabetes (T2D) achieve less total body weight loss (TBWL) with obesity medications compared to those without T2D. The individualized metabolic surgery (IMS) score, originally developed to predict T2D remission after bariatric surgery, inversely correlates with TBWL response to semaglutide. IMS reflects T2D severity, incorporating HbA1c and T2D duration and medication use. This study aims to evaluate TBWL with tirzepatide across IMS severity categories and identify predictors of response in a real-world cohort. Methods: This retrospective analysis included 717 adults with T2D using tirzepatide for overweight or obesity within the Mayo Clinic Health System. Patients were stratified by IMS severity (mild, moderate, severe) and quartiles. Primary endpoint: TBWL% at 15 months. Secondary endpoints: categorical thresholds (≥5%, ≥10%, ≥15%, ≥20%) and predictors of TBWL%. Linear mixed-effects models and regression models were employed. Results: At 15 months, TBWL was greater in mild versus severe IMS groups (14.8% vs. 11.0%, p = 0.015), with similar trends across quartiles. The proportion achieving ≥ 20% TBWL was nearly two-fold higher in mild versus severe IMS (27% vs. 14%, p = 0.03). Female sex independently predicted greater TBWL, whereas insulin use, higher T2D medication burden (particularly weight-promoting agents), and HbA1c > 7% were associated with less TBWL. Conclusions: Tirzepatide produced clinically meaningful TBWL across all IMS categories, although TBWL declined with increasing IMS severity. Glycemic control and T2D medication use emerged as strong predictors of TBWL. The IMS score may serve as a practical tool to anticipate weight-loss trajectories, guide personalized treatment decisions, and inform patient counseling. Full article

University rankings based on scientometric indicators frequently rely on compensatory aggregation models that allow extreme values to dominate the evaluation, while also remaining sensitive to outliers and unstable weighting procedures. These issues reduce the reliability and interpretability of the resulting rankings. This study proposes a hybrid BWM–GRA–PROMETHEE (BGP) framework that combines judgement-based weighting Best-Worst Method (BWM), outlier-resistant normalization Grey Relational Analysis (GRA), and a non-compensatory outranking method Preference Ranking Organization Methods for Enrichment Evaluation (PROMETHEE II). The framework is applied to an expert-validated set of scientometric indicators to generate more stable and behaviorally grounded rankings. The results show that the proposed method maintains stability under weight and threshold variations and preserves ranking consistency even under outlier-contaminated scenarios. Comparative experiments further demonstrate that BGP is more robust than Additive Ratio Assesment (ARAS), Multi-Attributive Border Approximation Area Comparison (MABAC), and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), achieving the highest Spearman. This study contributes a unified evaluation framework that jointly addresses three major methodological challenges in scientometric ranking, outlier sensitivity, compensatory effects, and instability from data-dependent weighting. By resolving these issues within a single integrated model, the proposed BGP approach offers a more reliable and methodologically rigorous foundation for researchers and policymakers seeking to evaluate and enhance research performance. Full article

Smart cities increasingly reuse existing Wi-Fi infrastructure to sense crowding, but many smart-campus tools still fail to support routine, day-to-day decisions. A short-horizon field feasibility study was conducted to prototype a low-maintenance, prefix-based early-warning rule that turns anonymized campus Wi-Fi access-point counts into an interpretable lunchtime crowd signal. Daily 7-min access-point profiles from five university canteens (11:00–14:00) were aggregated, winsorized, smoothed, and row-z-scored, then clustered into demand-shape typologies using k-means++. Two typologies were obtained (Early Peak and Late Shift), and a cosine-similarity atlas was frozen. At 11:28, the five-bin occupancy prefix was compared to typology centroids, and an Early Peak badge was issued when similarity to the Early Peak centroid exceeded a preset threshold. On held-out days, the Early Peak typology could be identified at 11:28 with coverage of 0.73 and agreement of 0.86 relative to end-of-day labels. In 20 matched canteen-weekday pairs, badge days were associated with a Hodges–Lehmann median reduction of 0.193 standard-deviation units in peak crowding (≈9% lower). Given the short (3-week) horizon and limited hold-out window, results are presented as feasibility evidence and motivate a larger controlled evaluation. Simple, interpretable rules built on existing Wi-Fi telemetry were shown to be deployable as a feasibility-level decision aid on a smart campus, while broader smart-city transferability should be validated through longer-horizon controlled evaluations. Full article

Rotating machinery plays a critical role in industrial systems, and effective anomaly detection and assessment are indispensable for ensuring operational safety and reliability. However, the performance of existing methods is often constrained by the difficulty in acquiring fault samples—such samples are typically scarce during the initial operational phase of equipment. To address this challenge, this paper proposes a novel anomaly detection and evaluation framework based on Data-Accumulation-Aware Generative Adversarial Networks (DAA-GANs) and similarity estimation. The core innovation of this framework lies in its adaptability across different data accumulation stages. During the early operational phase dominated by normal samples, only normal data is used to train the DAA-GAN to establish a baseline detector. As fault data gradually accumulates, the detection threshold undergoes adaptive adjustment through collaborative optimization of normal and abnormal samples, thereby enhancing the detector’s generalization capability. Upon amassing annotated fault samples of varying severity, the framework assesses anomaly severity by analyzing the similarity between test outputs of unknown samples and known fault samples. The framework is validated through two case studies: a fault simulation model for a torque-splitting transmission system and the publicly available Case Western Reserve University (CWRU) bearing dataset. In the simulation case, the detection accuracy reaches 100% for the gear tooth breakage levels. On the CWRU dataset, the proposed method achieves an overall average detection accuracy of 99.83% across three operating speeds (1730/1750/1772 rpm), and the similarity-based assessment provides consistent severity identification. These results demonstrate that the proposed framework can support reliable anomaly detection and severity assessments under progressive data accumulation. Full article

With the rapid growth of the marine economy and the increasing demand for maritime security, ship target detection has become critically important in both military and civilian applications. However, in complex remote sensing scenarios, challenges such as visual similarity among ships, subtle inter-class differences, and the continual emergence of new categories make traditional closed-world detection methods inadequate. To address these issues, this paper proposes an open-world detection framework for remote sensing ships. The framework integrates two key modules: (1) a Fine-Grained Feature and Extreme Value-based Unknown Recognition (FEUR) module, which leverages tail distribution modeling and adaptive thresholding to achieve precise detection and effective differentiation of unknown ship targets; and (2) a Joint Optimization-based Incremental Learning (JOIL) module, which employs hierarchical elastic weight constraints to differentially update the backbone and detection head, thereby alleviating catastrophic forgetting while incorporating new categories with only a few labeled samples. Extensive experiments on the FGSRCS dataset demonstrate that the proposed method not only maintains high accuracy on known categories but also significantly outperforms mainstream open-world detection approaches in unknown recognition and incremental learning. This work provides both theoretical value and practical potential for continuous ship detection and recognition in complex open environments. Full article

Spoofing attacks remain among the most critical security threats in Connected and Autonomous Vehicles (CAVs). This work introduces a lightweight, two-level spoofing detection framework based on Kalman filtering, designed for real-time deployment in vehicular platoons that communicate over Dedicated Short-Range Communications (DSRC). At the first level, a heuristic residual-based detector identifies abnormal measurement deviations using adaptive statistical thresholds. At the second level, a Mahalanobis distance test assesses model consistency using covariance-aware anomaly scoring at a 95% confidence level. The combination of these complementary mechanisms enables both rapid alerting and robust statistical verification without the need for machine-learning training or centralized processing. Simulation results from 20 independent nodes demonstrate that the proposed approach achieves an average F1-score of 0.92 and Area Under the ROC Curve (AUC) of 0.72, outperforming standalone detectors while maintaining low computational cost. Compared with deep learning and adaptive Extended Kalman Filter (EKF) approaches, the proposed framework achieves similar detection performance while substantially reducing computational complexity and enabling full real-time operation, making it suitable for embedded in-vehicle security modules. Full article

Background: Machine learning models have been shown to be a time-saving and cost-effective tool for peptide-based drug discovery. In this regard, different graph learning-driven frameworks have been introduced to exploit graph representations derived from predicted peptide structures. Such graphs are always derived by applying a Euclidean distance threshold between amino acid pairs, despite the fact that there is no evidence other than intuitive reasoning that supports the Euclidean distance as the most suitable. Objective: In this work, we examined the use of different distance functions to derive graph representations from predicted peptide structures to train deep graph learning-based models to predict antiviral peptides. Methods: To this end, we first analyzed how differently the closeness of the amino acids is characterized by different distance functions. Then, we studied the similarity between the graphs derived with several distance functions, as well as between them and random graphs. Finally, we trained several models with the best graph representations and analyzed how different they are regarding their predictions. Comparisons regarding state-of-the-art models were also performed. Results and Conclusion: We demonstrated that only using Euclidean distance thresholds is not sufficient criterion to build graphs representing structural features of predicted peptide structures, since other distance functions enabled building dissimilar graphs codifying different chemical spaces, which were useful in the construction of better discriminative models. Full article

To achieve China’s carbon peak and carbon neutrality goals, it is essential to increase the market penetration of New Energy Vehicles (NEVs) and understand consumer attitudes. Based on a big data set of over 20,000 online user reviews, this study employs the Latent Dirichlet Allocation (LDA) model to extract themes, popular brands, and focal points across different time windows. The research constructs a data-driven threshold filtering mechanism that integrates topic probability, frequency, keyword weight, and cross-temporal topic similarity to quantify consumer reviews, enabling an in-depth analysis of the dynamic evolution of attitudes in the NEV market. The findings reveal a dual shift in consumer sentiment: first, a transition in focus from basic configurations and aesthetics toward quality experience; and second, a shift in purchasing decisions toward a socially driven model dominated by word-of-mouth and family collaboration. Full article

Efficient water recycling in the hydrometallurgical industry requires the dewatering of hypersaline Na₂SO₄ or similar brines via non-evaporative methods. Unfortunately, many non-evaporative methods require the use of specific solutes and are not compatible with complex hydrometallurgical effluents. Forward Osmosis (FO) uses a draw solution to link known non-evaporative water recycling methods with feed solutions that are otherwise incompatible. There is minimal experimental data on the dewatering performance of today’s available commercial FO membranes, especially with hypersaline concentrations (>70,000 mg/L total dissolved solids). This study tests the commercially available Aquaporin HFFO2 hollow fibre FO membrane module with hypersaline Na₂SO₄ or NaCl feed solutions versus a MgCl₂ draw solution. It identifies a key requirement to maintain water flux above a certain threshold to prevent a decrease in Na Rejection or an increase in Mg reverse flux. It also defines a minimum osmotic differential that can be used to parameterize water flux, similar to the temperature of approach in heat exchangers, but to determine the extent of water removal in FO. We demonstrate that even under mildly acidic conditions, existing FO membranes can concentrate Na₂SO₄ to saturation, paving the way for their use in the hydrometallurgical industry. Full article

Background: Total hip arthroplasty (THA) is a well-tolerated and effective procedure that can improve a patient’s mobility and quality of life. A main concern, however, is the release of metal ions into the body due to wear and corrosion. Commonly reported ions are Co and Cr, while others, such as Ti, Mo, and Ni, are less frequently studied. The objective of this study was to characterize compartmentalization and time-dependent ion behaviors across serum, whole blood, and urine after hip prosthetic implantation. The goal of using Random Forest (RF) was to determine whether machine learning modeling could support temporal trends across data. Methods: Data was gathered from the literature of clinical studies, and we conducted a pooled analysis of the temporal kinetics from cohorts of patients who received hip prosthetics. Mean ion concentrations were normalized to µg/L across each fluid and weighted by cohort sample size. RF was used as a study-level test of predictive accuracy across ions. Results: For serum and whole blood, Co and Cr displayed one-phase association models, while Ti showed an exponential rise and decay. Ions typically rose quickly within the first 24 months postoperatively. Serum Co and whole blood had similar patterns, tapering off just under 2 µg/L, but serum Cr (~2.02 µg/L) was generally higher than that of whole blood (~0.99 µg/L). Mean urinary Co levels were greater than those of Cr, suggesting a larger, freely filterable fraction for Co. RF was implemented to determine predictive accuracy for each ion, showing a stronger fit for Co (R² = 0.86, RMSE = 0.57) compared to Cr (R² = 0.52, RMSE = 0.50). Conclusions: Sub-threshold exposure was prevalent across cohorts. Serum and whole blood Co and Cr displayed distinct kinetic profiles and, if validated, could support fluid-specific monitoring strategies. We present a methodology for interpreting ion kinetics and show potential for machine learning applications in postoperative monitoring. Full article