Search Results (7,430)

To meet the stringent reliability requirements of rotor blades in turbomachines, greater effort should be devoted to improving both aerodynamic and structural performance in blade design. This paper introduces an aero-structural multi-disciplinary design optimization (MDO) method for compressor rotor blades using a discrete adjoint method and an equivalent stress model (ESM). The principles of the ESM are firstly introduced, and its accuracy in calculating equivalent stress is validated through comparison with a commercial program. Both the aerodynamic performance and the maximum equivalent stress (MES) are selected as optimization objectives. To modify the blade profile, the steepest descent optimization method is utilized, in which the necessary sensitivities of the cost function to the design parameters are calculated by solving the adjoint equations. Finally, the aero-structural MDO of a transonic compressor rotor, NASA Rotor 67, is conducted, and the Pareto solutions are obtained. The optimization results demonstrate that the adiabatic efficiency and the MES are competitive in improving multi-disciplinary performance. For most of the Pareto solutions, the MES can be considerably reduced with increased adiabatic efficiency. Full article

This paper reports a novel effort to estimate and evaluate the coefficients of Hg transfer across the water/air interface in lakes such as Cane Creek Lake (CCL, Cookeville, TN, USA). This was accomplished by calculating the coefficients (k_w) using the Two-Thin Film (TTF) Model for Hg transfer together with the field-measured data of Hg emission flux (F), dissolved gaseous mercury concentration (DGM), air Hg concentration (C_a), and water temperature for Henry’s coefficient (K_H) obtained from a separate field study at the CCL. The daily mean k_w values range from 0.045 to 0.21 m h⁻¹, with the min. at 0.0025–0.14 and the max. at 0.079–0.41 m h⁻¹, generally higher for the summer, and from 0.0092 to 0.15, with the min. at 0.0032–0.033 and the max. at 0.017–0.31 m h⁻¹, generally lower for the fall and winter, exhibiting an apparent seasonal trend. The highest k_w values occur in August (mean: 0.21, max.: 0.41 m h⁻¹). Our k_w results add to and enrich the aquatic interfacial Hg transfer coefficient database and provide an alternative avenue to evaluate and select the coefficients for the TTF Model’s application. The k_w results are of value in gaining insights into the Hg transfer actually occurring across the water/air interface under environmental influences (e.g., wind/wave, solar radiation). Our k_w results do not show a clear, consistent correlation of k_w with wind/wave effect, nor sunlight effect, in spite of some correlations in sporadic cases. Generally, the k_w values do not exbibit the trends prescribed by the model sensitivity study. The comparisons of our k_w results with those obtained using wind-based transfer models (the Liss/Merlivat Model, the Wanninkhof Model, and the modified linear model) show that they depart from each other. The findings of this study indicate that the TTF Model has limitations and weaknesses. One major assumption of the TTF Model is the equilibrium of the Hg distribution between the air and water films across the water/air interface. The predominant oversaturation of DGM shown by our DGM data evidently challenges this assumption. This study suggests that aquatic interfacial Hg transfer is considerably more complicated, involving a group of factors, more than just wind and wave. Full article

Background/Objectives: Malnutrition, systemic inflammation, and immune dysfunction are key determinants of adverse outcomes in older adults following acute illness. Composite biomarkers integrating these domains could enhance early risk stratification. This study investigates, for the first time in acute geriatric care, the prognostic value of the C-reactive protein–albumin–lymphocyte (CALLY) index—a composite marker of nutritional, inflammatory, and immune status—in predicting short-term survival. Methods: We retrospectively analyzed 264 patients admitted to the acute geriatrics ward of Santa Maria della Misericordia Hospital in Perugia. The CALLY index was calculated as: (Albumin × Lymphocytes)/(CRP × 10⁴). The optimal prognostic cut-off was determined using receiver operating characteristic (ROC) curve analysis. Three-month survival was assessed by Kaplan–Meier analysis. Results: The cohort included 167 women (63.3%) and 97 men (36.7%), with a mean age of 88.0 ± 6.4 years. At 3-month follow-up, 80 patients (30.3%) had died. The CALLY index showed an area under the ROC curve of 0.647 (95% CI: 0.576–0.718; p < 0.001), with a cut-off of 0.055 (sensitivity: 68.5%, specificity: 46.3%). Among deceased patients, 42.5% had a CALLY index <0.055. After multivariable adjustment, a lower CALLY index remained independently associated with increased mortality (B = −0.805; OR = 0.45; 95% CI: 0.215–0.930; p = 0.031). Kaplan–Meier analysis demonstrated significantly higher survival in patients with a CALLY index ≥ 0.055 (Log-rank test: 13.71; p < 0.001). Conclusions: The CALLY index shows a modest but statistically significant discriminative ability for predicting short-term mortality in acutely ill older adults. As a simple, low-cost marker derived from routine laboratory tests, it holds potential for integration into clinical workflows to guide nutritional, metabolic, and prognostic management strategies in geriatric acute care. Full article

YKL-40 is a glycoprotein that may be present at elevated levels in many cancers and neurodegenerative diseases. It has been investigated in numerous studies as a potential biomarker for several conditions, including Alzheimer’s Disease (AD). In this study, a biosensor with Surface Plasmon Resonance imaging (SPRi) detection, sensitive to YKL-40, was constructed for the detection of this analyte in the blood plasma of AD patients. Extensive validation of the biosensor was performed. This included the determination of analytical parameters such as the biosensor’s response characteristics, detection and quantification limits, precision, accuracy, repeatability, selectivity, stability, and performance in natural samples. Validation parameters were primarily tested using standard solutions, while natural samples were employed to evaluate repeatability, stability, and assay accuracy in three groups of samples from different patients. A YKL-40-specific antibody was used as the receptor layer, immobilized on a gold plate using the EDC/NHS protocol on thiol 11-MUA. The biosensor exhibited a wide operating range (1–200 ng/mL), a low detection limit (LOD) of 2 pg/mL, and a quantification limit (LOQ) of 7 pg/mL. High precision and accuracy were confirmed by the calculated standard deviations (SD) and coefficients of variation (CV), which ranged from 0.0009 to 7.02 ng/mL and from 0.12% to 9.24%, respectively. The sensor also demonstrated good repeatability (CV = 4.995%) and was capable of detecting the analyte of interest in complex biological matrices. Its applicability was confirmed in a study using plasma from AD patients and two selected control groups: plasma from smokers and patients with prostatitis. This allowed the assessment of YKL-40 levels across different groups. The results were consistent with literature values, and statistical analysis confirmed the significance of concentration differences between groups. Furthermore, ROC curve analysis confirmed the diagnostic usefulness of the constructed YKL-40 test in the context of Alzheimer’s disease. Full article

Accessibility is a key dimension for sustainable transport network management and planning. However, conventional location-based accessibility measures typically rely on average travel times as the sole temporal metric, neglecting detailed travel time distributions. Consequently, these methods yield identical accessibility values for study zones with the same mean travel time but different travel time variations. To overcome this limitation, we developed a novel approach that explicitly integrates the probability density distributions of travel times, modelling the impact of travel time variability on accessibility. We applied the proposed method using GPS data collected from taxis in Harbin, China, and compared its outcomes with those from existing potential accessibility calculations. Across all 103 study zones in Harbin, the existing method underestimated the accessibility by 6–28%, with an average underestimation of 17% when benchmarked against the new method. These inaccuracies also impaired the identification of urban areas with the lowest accessibility levels, leading to the misclassification of 20% of problematic zones. The findings highlight the limitations of existing methods, which produce biassed accessibility estimations and misleading results. In contrast, the proposed travel time variability-integrated accessibility measure demonstrates greater sensitivity to actual traffic conditions, providing a more accurate and objective assessment of network performance. Full article

The failure to accurately identify abnormal vibrations in protected metro areas is a serious threat to the operational safety of metro tunnels and trains, and there is currently no suitable method for effectively improving the accuracy of abnormal vibration identification. To address this issue, an accurate method for identifying abnormal vibrations in a metro reserve based on spatially correlated dense measurement points is proposed. First, by arranging distributed optical fibers along the longitudinal length of a tunnel, dynamic strain vibration signals are extracted via phase-sensitive optical time-domain reflectometry analysis, and analysis of variance (ANOVA) and Pearson correlation analysis are used to jointly downscale the dynamic strain features. On this basis, a spatial correlation between the calculated values of the features of the target measurement points to be updated and its adjacent measurement points is constructed, and the spatial correlation credibility of the dynamic strain features between the dense measurement points and the target measurement points to be updated is calculated via quadratic function weighting and kernel density estimation methods. The weights are calculated, and the eigenvalues of the target measurement points are updated on the basis of the correlation credibility weights between the adjacent measurement points. Finally, a support vector machine (SVM) and back propagation (BP) identification model for the eigenvalues of the target measurement points are constructed to identify the dynamic strain eigenvalues of the abnormal vibrations in the underground tunnel. Numerical simulations and an experiment in an actual tunnel verify the effectiveness of the proposed method. Full article

Background: Papilledema, an ophthalmic finding associated with increased intracranial pressure, is often induced by dermatological medications, including corticosteroids, isotretinoin, and tetracyclines. Early detection is crucial for preventing irreversible optic nerve damage, but access to ophthalmologic expertise is often limited in rural settings. Artificial intelligence (AI) may enable the automated and accurate detection of papilledema from fundus images, thereby supporting timely diagnosis and management. Objective: The primary objective of this study was to explore the diagnostic capability of ChatGPT-4o, a general large language model with multimodal input, in identifying papilledema from fundus photographs. For context, its performance was compared with a ResNet-based convolutional neural network (CNN) specifically fine-tuned for ophthalmic imaging, as well as with the assessments of two human ophthalmologists. The focus was on applications relevant to dermatological care in resource-limited environments. Methods: A dataset of 1094 fundus images (295 papilledema, 799 normal) was preprocessed and partitioned into a training set and a test set. The ResNet model was fine-tuned using discriminative learning rates and a one-cycle learning rate policy. GPT-4o and two human evaluators (a senior ophthalmologist and an ophthalmology resident) independently assessed the test images. Diagnostic metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and Cohen’s Kappa, were calculated for each evaluator. Results: GPT-4o, when applied to papilledema detection, achieved an overall accuracy of 85.9% with substantial agreement beyond chance (Cohen’s Kappa = 0.72), but lower specificity (78.9%) and positive predictive value (73.7%) compared to benchmark models. For context, the ResNet model, fine-tuned for ophthalmic imaging, reached near-perfect accuracy (99.5%, Kappa = 0.99), while two human ophthalmologists achieved accuracies of 96.0% (Kappa ≈ 0.92). Conclusions: This study explored the capability of GPT-4o, a large language model with multimodal input, for detecting papilledema from fundus photographs. GPT-4o achieved moderate diagnostic accuracy and substantial agreement with the ground truth, but it underperformed compared to both a domain-specific ResNet model and human ophthalmologists. These findings underscore the distinction between generalist large language models and specialized diagnostic AI: while GPT-4o is not optimized for ophthalmic imaging, its accessibility, adaptability, and rapid evolution highlight its potential as a future adjunct in clinical screening, particularly in underserved settings. These findings also underscore the need for validation on external datasets and real-world clinical environments before such tools can be broadly implemented. Full article

Objective: Non-contrast computed tomography (CT) remains the gold standard for diagnosing ureteral stones, with excellent sensitivity and specificity. However, reliance on CT alone raises concerns regarding cumulative radiation exposure, particularly in recurrent stone formers. Clinical scoring systems such as CHOKAI, STONE, and modified STONE have been developed to provide practical bedside tools for diagnostic decision-making. This study prospectively compared these three clinical scores for their ability to predict urinary-stone disease in the emergency department. Study Design: Prospective study. Methods and Duration of the Study: Between 6 August 2024 and 15 February 2025, 130 consecutively enrolled adults with flank pain underwent bedside scoring and reference-standard non-contrast CT. Associations were analysed with Chi-Square Tests and multivariable logistic regression. Model calibration was assessed with the Hosmer–Lemeshow test; overall accuracy was calculated. Results: When the variables used in different stone scoring formulas were compared according to the computer tomography results, there was a statistically significant difference (p < 0.01) between patients with and without a history of stone and hydronephrosis. Patients with nausea, history of stone, and hydronephrosis were 11, 4.2, and 5 times more highly to have a stone on computer tomography than those without, respectively. Conclusions: In this Turkish cohort, CHOKAI and modified STONE demonstrated superior predictive performance compared to the original STONE score. These findings suggest that clinical scoring systems, when incorporating predictors such as nausea, prior stone history, and hydronephrosis, may serve as practical alternatives to CT-first diagnostic approaches. Multicenter validation studies are required before routine clinical adoption. Full article

The severe ozone depletion over the Southern polar region, known as the “ozone hole,” is a stark example of global ozone depletion caused by human-made chemicals. This has implications for climate change and increased harmful surface solar UV. Several Chemistry–Climate models (CCMs) tend to underestimate total column ozone (TCO) against satellite measurements over the Southern polar region. This underestimation can reach up to 50% in monthly mean zonally averaged biases during cold seasons. The most significant discrepancies were found in the CCM SOlar Climate Ozone Links version 3 (SOCOLv3). We use SOCOLv3 to study the sensitivity of Antarctic TCO to three key factors: (1) stratospheric heterogeneous reaction efficiency, (2) meridional flux intensity into polar regions from sub-grid scale mixing, and (3) photodissociation rate calculation accuracy. We compared the model results with satellite data from Infrared Fourier Spectrometer-2 (IKFS-2), Microwave Limb Sounder (MLS), and Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). The most effective processes for improving polar ozone simulation are photolysis and horizontal mixing. Increasing horizontal mixing improves the simulated TCO seasonal cycle but negatively impacts CH₄ and N₂O distributions. Using the Cloud-J v.8.0 photolysis module has improved photolysis rate calculations and the seasonal ozone cycle representation over the Southern polar region. This paper outlines how different processes impact chemistry–climate model performance in the southern polar stratosphere, with potential implications for future advancements. Full article

Background: The training impulse (TRIMP) method integrates exercise duration and intensity, yet its application in school-based health programs is limited. This study examined dose–response relationships between TRIMP and changes in body fat percentage (%BF), blood pressure, and cardiorespiratory fitness (CRF) in adolescents. Methods: 145 adolescents (69 males, 76 females; aged 15.01 ± 0.54 years) completed eight-week interval-based physical education programs: high-intensity interval training (HIIT) or high-intensity plyometric training (HIPT). Heart rate was continuously monitored, and TRIMP values were calculated across 16 sessions. Outcomes included %BF, systolic (SBP) and diastolic blood pressure (DBP), and maximal oxygen consumption (VO_2max), assessed pre- and post-intervention. Results: HIIT elicited higher TRIMP than HIPT in females (η²p = 0.043). Linear dose–response patterns were identified (r = 0.18–0.36) (e.g., SBP in males, r = −0.38, p = 0.001; %BF in males, r = −0.36, p = 0.002). In males, one TRIMP unit reduced SBP by 1.8 mmHg (HIIT) and DBP by 1.6 mmHg (HIPT). In females, HIPT-derived TRIMP predicted reductions of ~0.6% in %BF and~0.9 mmHg in DBP. No significant associations were found for VO_2max. Conclusions: TRIMP provides a feasible and sensitive tool for quantifying internal training load in adolescent interventions, linking exercise dose to measurable health benefits and supporting its application in preventive contexts. Full article

Background: Microwave ablation (MWA) is an effective, minimally invasive therapy for benign thyroid nodules; however, the treatment response varies considerably. Identifying imaging biomarkers that can predict volumetric outcomes may optimize patient selection. Diffusion-weighted MRI (DW-MRI) offers a noninvasive assessment of tissue microstructure through apparent diffusion coefficient (ADC) measurements, which may correlate with ablation efficacy. Methods: In this prospective study, 48 patients with 50 cytologically confirmed benign thyroid nodules underwent diffusion-weighted magnetic resonance imaging (DW-MRI) before minimally invasive ablation (MWA). Baseline ADC values were measured, and nodule volumes were assessed by ultrasound at baseline and 1, 3, and 6 months postprocedure. The volume reduction ratio (VRR) was calculated, and associations with baseline variables were analyzed via Pearson correlation and multivariable linear regression. ROC curve analysis was used to evaluate the diagnostic performance of ADC in predicting significant volume reduction (VRR ≥ 50%). Results: Lower baseline ADC values were strongly correlated with greater VRR at 3 months (r = −0.525, p < 0.001) and 6 months (r = −0.564, p < 0.001). Multivariable regression revealed that the baseline ADC was the sole independent predictor of the 6-month VRR (β = −19.52, p = 0.0004). ROC analysis demonstrated excellent discriminative performance (AUC = 0.915; 95% CI: 0.847–0.971), with an ADC cutoff of 2.20 × 10⁻³ mm²/s yielding 90.9% sensitivity and 83.3% specificity for predicting a favorable volumetric response. Conclusions: Baseline ADC values derived from DW-MRI strongly predict volumetric response following microwave ablation of benign thyroid nodules. Incorporating ADC assessment into preprocedural evaluation may enhance patient selection and improve therapeutic outcomes. Full article

Floating vertical−axis wind turbines present unique advantages for deep−water offshore deployments, but their basin model testing encounters significant challenges in aerodynamic load simulation due to Reynolds scaling effects. While Froude−scaled experiments accurately replicate hydrodynamic behaviors, the drastic reduction in Reynolds numbers at the model scale leads to substantial discrepancies in aerodynamic forces compared to full−scale conditions. This study proposed two methodologies to address these challenges. Fully physical model tests adopt a “physical wind field + rotor model + floating foundation” approach, realistically simulating aerodynamic loads during rotor rotation. Semi−physical model tests employ a “numerical wind field + rotor model + physical floating foundation” configuration, where theoretical aerodynamic loads are obtained through numerical calculations and then reproduced using controllable actuator structures. For fully physical model tests, a blade reconstruction framework integrated airfoil optimization, chord length adjustments, and twist angle modifications through Taylor expansion−based sensitivity analysis. The method achieved thrust coefficient similarity across the operational tip−speed ratio range. For semi−physical tests, a cruciform−arranged rotor system with eight dynamically controlled rotors and constrained thrust allocation algorithms enabled the simultaneous reproduction of periodic streamwise/crosswind thrusts and vertical−axis torque. Numerical case studies demonstrated that the system effectively simulates six−degree−of−freedom aerodynamic loads under turbulent conditions while maintaining thrust variation rates below 9.3% between adjacent time steps. These solutions addressed VAWTs’ distinct aerodynamic complexities, including azimuth−dependent Reynolds number fluctuations and multidirectional force coupling, which conventional methods fail to accommodate. The developed techniques enhanced the fidelity of floating VAWT basin tests, providing critical experimental validation tools for emerging offshore wind technologies. Full article

Aromaticity tuning of biaryl monophosphines can significantly impact their catalytic performance. Biaryl monophosphines constitute a crucial class of compounds due to their potential as ligand precursors in asymmetric Pd-catalyzed cross-coupling and some other catalytic reactions. In this study, we investigate the tuning of aromaticity within a series of selected biaryl monophosphine derivatives exhibiting diverse steric and electronic properties. XRD structures and Hirshfeld surface analyses were complemented by DFT calculations. Aromaticity indices, such as geometric HOMA, HOMER, and magnetic NICS, were evaluated and correlated with ligand properties. NICS(1)zz was the most sensitive to aromaticity changes. The results showed that among the ring-activating substituents, methoxy groups were more beneficial than hydroxy ones. The hydroxy groups not only modulated the aromaticity but also induced unfavorable conformational changes of the catalyst precursors through strong inter- and intramolecular hydrogen bonding. The spatial arrangement of the P atom adjacent to the aryl ring system confers catalytic advantages by promoting the assembly of coordination compounds (catalysts) in which Pd—C bond formation occurs, yielding C,P-chelated five-membered palladacyclic structures. The hydroxy substituents blocked access to the P atom, thereby hindering catalytic performance. The studies show that even subtle changes in the monophosphine biaryl scaffold, especially aromaticity tuning should be carefully evaluated during the rational design of new efficient catalysts. The studied compounds were evaluated for their biological activity against three Gram-positive and four Gram-negative bacteria as model microorganisms. The research was supplemented by in vitro cytotoxicity evaluation. Full article

Wild potato relatives are vital for breeding programs to tackle rising temperatures. This study proposes a methodological approach based on the examination of genetic variation among 19 accessions belonging to Solanum chacoense and Solanum commersonii from the Embrapa Potato Genebank under heat stress (HS). Heat tolerance coefficient (HTC) was calculated using genotypic values predicted through mixed models. After 15 days of heat stress (DHS), a significant variation in gas exchange and chlorophyll fluorescence indicates strong breeding potential and photosystem resilience. By 35 DHS, increased pigment variation suggests acclimation. Based on predicted genotypic values, S. chacoense outperforms S. commersonii in tuber production and gas exchange under HS, and principal component analysis (PCA) performed using the HTC shows early resistance driven by photosynthesis, mid-term by tuber yield, and long-term by gas exchange and tuber production. Genotypes BRA00167017-3, BRA00167023-1, BRA00167025-6, and BRA00167028-0 excel in heat comprehensive evaluation values (HCEVs)/comprehensive principal component value (F) rankings, demonstrating robust photosynthesis, thermoregulation, and tuber yield. Cluster analysis identifies these as highly tolerant, ideal for breeding heat-resilient potatoes. These PCA-derived weights and genotype clustering system provide a precise tool for selecting heat-tolerant wild potato germplasm, categorizing them into highly tolerant, moderately tolerant, sensitive with late recovery, and highly sensitive groups acquired for specific objectives of the breeding programs to climate change. Full article

Background and Objectives: Albuminuria is a key clinical marker for early detection of diabetic kidney disease (DKD) in individuals with type 2 diabetes mellitus (T2DM). The triglyceride-glucose (TyG) index, a simple surrogate of insulin resistance, has been increasingly investigated for its potential association with renal complications. This study aimed to evaluate the relationship between the TyG index and albuminuria in patients with T2DM and assess its clinical utility as an accessible metabolic marker reflecting early renal involvement. Materials and Methods: This retrospective cross-sectional study included 570 adult patients with confirmed T2DM who were followed at a tertiary internal medicine outpatient clinic between January and December 2024. Participants were classified as albuminuric or non-albuminuric based on spot urine albumin-to-creatinine ratio (ACR) values. Clinical and biochemical parameters were collected from medical records, and the TyG index was calculated as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Logistic regression models were used to identify independent factors associated with albuminuria. ROC analysis was performed to evaluate the discriminatory accuracy of the TyG index. Results: The median TyG index was significantly higher in the albuminuric group compared to the non-albuminuric group (10.0 vs. 9.1; p < 0.001) and increased progressively with albuminuria severity (p < 0.001). In multivariate logistic regression analysis, elevated TyG index, hyperlipidemia, and reduced estimated glomerular filtration rate were independently associated with albuminuria. When evaluated as a continuous variable, the TyG index showed strong discriminatory ability (area under curve (AUC) = 0.949; 95% confidence interval (CI): 0.933–0.964). Using the optimal cut-off threshold of 9.6, the TyG index maintained high diagnostic performance (AUC = 0.870; 95% CI: 0.839–0.902; sensitivity 87.7%, specificity 86.3%). Subgroup analyses confirmed the robustness of this association across clinical and demographic variables. Conclusions: In this study, higher TyG index values were significantly associated with the presence and severity of albuminuria in individuals with T2DM. While causality cannot be inferred, the findings suggest that the TyG index may serve as a practical, cost-effective tool for identifying patients at increased risk for early diabetic kidney involvement. Prospective longitudinal studies are needed to confirm its predictive value and clinical applicability. Full article