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Advanced hybrid nanocomposites for infrastructure renewal: enhanced shear and flexural performance in load-bearing applications

Factors associated with postpartum depression in mothers of the kangaroo mother care program in Southern Colombia: a cross-sectional study

Challenges of conventional iterative all-atom and coarse-grained multiscale molecular dynamics

Degree-based Inverse Prodeg indices linking graph theory and cheminformatics with sharp results and QSPR modeling

Diastereoselective and green synthesis of novel Hexahydro Imidazo [1,2-a] pyrimidine, from Azlactones, Brimonidine and Clonidine

Correction: Optimization and experimental analysis of a cleaning device for super rice with high impurity rates based on airflow field enhancement

Effect of changing kick frequency on inter-joint coordination during undulatory underwater swimming

Abstract Undulatory underwater swimming (UUS) contributes to race performance in competitive swimming, yet how inter-joint coordination changes with kick frequency remains unclear. This study examined the effects of kick frequency on trunk–hip and hip–knee inter-joint coordination during UUS using the Modified Vector Coding Technique. Eight male swimmers performed UUS trials at 80–120% of their optimal kick frequency, with kinematics captured using three-dimensional underwater motion analysis. Swimming velocity increased with kick frequency but plateaued beyond 100%. The proportion of the hip–knee anti-phase with distal dominance pattern ––hip and knee rotating in opposite directions with greater angular displacement at the knee–– increased with kick frequency, whereas the absolute durations of this pattern per cycle changed little, indicating that the time required for kick reversal remained nearly constant regardless of kick frequency. Under high-frequency conditions (110% and 120%), preserving the time for kick reversal within a shortened kick cycle was accompanied by constrained trunk undulation during the late downward kick. These findings suggest that when kick frequency is acutely increased beyond the optimal value, hip–knee reversal time is maintained but trunk undulation is impaired, likely contributing to the plateau in swimming velocity.

Optimization algorithm for improving the prediction accuracy of API solubility in green solvent

Abstract Supercritical carbon dioxide (SC-CO₂) is widely used as an environmentally friendly solvent in pharmaceutical processing, where accurate prediction of drug solubility is essential for efficient formulation design, extraction processes, and process optimization. However, predicting solubility behavior in supercritical systems remains challenging due to the nonlinear interactions between thermodynamic conditions and molecular properties. In this study, a hybrid artificial intelligence framework is developed to predict the solubility of active pharmaceutical ingredients (APIs) in SC-CO₂ using a curated dataset of more than 350 experimentally reported measurements. The proposed framework integrates interpretable deep learning (TabNet) and histogram-based gradient boosting (HGB) with three metaheuristic optimization algorithms, namely the Attack-Leave Optimizer (ALO), Energy Valley Optimizer (EVO), and Botox Optimization Algorithm (BOA), to improve hyperparameter tuning and predictive performance. Model evaluation was conducted using multiple statistical indicators, five-fold cross-validation, prediction interval bootstrapping, and multi-objective Pareto front analysis to assess accuracy and robustness. Among the evaluated configurations, the EVO-tuned TabNet model demonstrated the best predictive performance, achieving a coefficient of determination of $$\:{R}^{2}=0.982$$ along with narrow prediction intervals, indicating strong generalization capability within the studied thermodynamic domain. Statistical analysis using the Kruskal–Wallis test confirmed significant differences between optimizer performances ( $$\:p<0.05$$ ). These findings demonstrate that the proposed hybrid pipeline enhances predictive accuracy and interpretability within the thermodynamic domain represented by the compiled dataset. The framework therefore provides a statistically supported computational tool for assisting solvent selection and formulation analysis in supercritical systems, while broader generalization would benefit from future expansion of experimental solubility datasets.

Pan-genome cloning and expression analysis of S-RNase homologous gene in SI and SC of different tomato species

Two-dimensional kinematic analysis of futsal instep shot performance in trained and novice players

COVID-19 awareness and preparedness in secondary schools, Egypt

Glucose-to-potassium ratio has a non-linear J-shaped association with acute kidney injury risk in traumatic brain injury

Abstract Acute kidney injury (AKI) is a severe complication of traumatic brain injury (TBI) associated with poor prognosis. The glucose-to-potassium ratio (GPR), an emerging marker of metabolic stress, may play a role in post-TBI AKI, but its precise relationship is unclear. This retrospective cohort study investigated the association between early-admission GPR and AKI in 2,388 TBI patients from the MIMIC-IV database. Using multivariable logistic regression and restricted cubic spline (RCS) models, we explored both linear and non-linear associations. Of the patients, 56.7% (1,355/2,388) developed AKI. While a simple linear association between GPR and AKI was not significant after adjusting for confounders ( P = 0.20), we discovered a significant non-linear "J-shaped" relationship (P for non-linearity = 0.012). The risk of AKI increased steeply once GPR surpassed an inflection point of approximately 30. A random forest model incorporating multiple clinical variables demonstrated significantly superior predictive performance (AUC = 0.808) compared to traditional logistic regression (AUC = 0.785; DeLong test P = 0.025). These findings reveal that a high GPR (> 30) acts as an independent non-linear risk indicator for post-TBI AKI, Rather than a direct driver of injury, an elevated GPR represents a valuable early warning signal for metabolic decompensation, suggesting clinicians should closely monitor high-risk patients to facilitate early recognition of AKI.

Adaptive quantum kernel selection via leakage-free stacking for clinical diagnostics on NISQ hardware

Experimental and numerical investigation on basaltic soil under RC square footings

Correction: A comparative evaluation of sustainable asphalt binder modifiers for enhanced performance

Spatially context aware multilevel color image segmentation using a Hybrid Artificial Hummingbird and Great Deluge mechanism

Abstract Multilevel image thresholding is an important segmentation technique that partitions an image into meaningful regions in applications such as object recognition, medical imaging, and satellite image analysis. However, conventional techniques are limited by their poor sensitivity to initial conditions, lack of sufficient spatial contextual information, and slow convergence. The proposed method uses a novel hybrid model based on the Artificial Hummingbird Algorithm (AHA) to address the limitations of existing approaches. Here, Latin Hypercube, Sobol, Halton, and Sierpinski strategies are used during the population initialization phase to improve population diversity and search space coverage. This improves the exploration capability of the algorithm and supports better search space. The proposed methodology also uses spatial contextual information to improve the quality of segmentation. It also incorporates a relationship between neighboring pixels in order to retain more structure and enhance the visual performance of the output. For the exploitation phase, the Great Deluge Algorithm (GDA) is utilized as the optimization algorithm. Furthermore, the use of GDA serves as an adaptive acceptance function which reduces the chance of getting stuck during the search process. Minimum Cross Entropy Measure (MCEM) is used as the objective function to obtain optimal threshold values. Different evaluation metrics have been used to compare the results of the proposed method with other existing metaheuristic algorithms. The code is available at https://github.com/suprajatirumalasetti/AHA_GDA_Image_Segmentation_Code .

Pv-aCO2 and Pv-aCO2/Ca-vO2 as surrogates for cardiac output and oxygen metabolism in septic shock patients

Method for determining the flowback rate of fracturing fluid considering gel degradation during hydraulic fracturing

Deep feature extraction and explanation consistency analysis for fine-grained aircraft type classification in remote sensing imagery

Anbalcabtagene autoleucel (PD-1 and TIGIT knockdown CD19 CAR-T) for relapsed/refractory large B-cell lymphoma (CRC01-01)

Anbalcabtagene autoleucel (Anbal-cel) is a CD19-directed CAR T-cell therapy incorporating dual PD-1 and TIGIT knockdown to enhance antitumor function and durability. We report the results of a Phase 1/2 study in patients with relapsed or refractory large B-cell lymphoma (LBCL). In Phase 2, 79 patients received Anbal-cel, and efficacy was evaluated in 73 patients. The complete response (CR) and partial response (PR) rates were 67.1% and 8.2%, respectively. Median progression-free survival (PFS) was 6.04 months (95% CI, 4.34-16.46), and the 6-, 12-, and 18-month PFS rates were 50.9%, 41.1%, and 35.2%, respectively. Median overall survival (OS) was not reached, with 12- and 18-month OS rates of 66.6% and 57.3%. CAR T-cell expansion was significantly greater in responders than in non-responders (median Cmax: 20,403 vs. 8,580 copies/μg). Patients were categorized into long-term response (LR) group or non-LR group based on sustained CR at 6 months. Reduced PD-1 and TIGIT expression on CAR-positive T cells was observed in LR group. Most patients (97.5%) experienced grade ≥3 adverse events, most commonly neutropenia (93.7%), followed by thrombocytopenia (41.8%) and anemia (30.4%). Cytokine release syndrome and neurologic events occurred in 57.0% and 13.9% of patients, respectively. Grade 3 CRS occurred in 8.9% of patients, with no grade 4 events reported, and grade ≥3 neurologic events occurred in 3.8%. Serious infections occurred in 25.3% of patients, and grade 5 infection was reported in 3 patients. This trial was registered at Clinicaltrials.gov (NCT04836507).