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Evaluation of composites interphase mechanical properties considering CZM and FGM behaviors using single-fiber composite tensile tests
Central and peripheral glutamatergic biomarkers delineate heterogeneous clinical phenotypes in olanzapine-treated schizophrenia
Effects of some surface active ionic liquids on the aqueous solubility, thermodynamic properties and fluorescence behavior of drug diclofenac sodium
Abstract Bio-based surface-active ionic liquids (SAILs) hold considerable promise for pharmaceutical applications, particularly in enhancing the aqueous solubility and bioavailability of poorly water-soluble drugs, such as diclofenac sodium (DFS), a nonsteroidal anti-inflammatory agent classified as BCS Class II. Three distinct (2-hydroxyethyl) amine-based surface-active ionic liquids (SAILs) were synthesized. Subsequently, the interactions between DFS and these synthesized SAILs were investigated using fluorescence spectroscopy at a temperature of 298.15 K. Fluorescence spectroscopy revealed a strong interaction between DFS and SAILs. This was evident from the significant quenching of DFS’s intrinsic fluorescence upon SAIL addition. The association constant and binding sites were determined. Among the tested SAILs, the [2-HEA][Ole] exhibited the strongest interaction with DFS. Furthermore, the solubility of DFS in aqueous SAILs solutions studied at temperature range of (298.15 to 313.15) K was found to increase with increasing SAIL concentration. The solubility data were accurately fitted using the e -NRTL and Wilson models. To gain deeper insights, conductor-like screening model (COSMO) calculations were performed on the studied chemicals. The obtained surface cavity volume ( V ) and dielectric solvation energy from the COSMO calculations provided valuable information about the intermolecular interactions. Finally, thermodynamic analysis using Gibbs and van’t Hoff equations indicated that the dissolution of DFS in these systems is an endothermic process.
Multi-objective optimization of corrugated microperforated panels for broadband sound absorption using adaptive sampling method
Exploring tribo-mechanical behavior of B₄C Reinforced LM25 aluminum matrix composites for structural applications
Hypoxia-induced upregulation of lncRNA SLC9A3-AS1 promotes lung adenocarcinoma progression through the miR-506-5p/ASPH/NOTCH1 pathway
Automated measurement of left ventricular ejection time via contactless suprasternal notch laser vibrometry
Spatiotemporal trends in WBGT and affected population in the MENA region (1951–2021)
Abstract Rising temperatures and changes in other meteorological factors have significantly impacted human thermal comfort worldwide. Among global climate change hotspots, the Middle East and North Africa (MENA) have experienced a particularly rapid temperature rise compared to many other regions. Despite its vulnerability, long-term assessments of spatiotemporal heat stress trends and the affected population in MENA remain limited. This study evaluates changes in thermal stress using the wet-bulb globe temperature (WBGT) index derived from ERA5 meteorological data (0.25° resolution) from 1951 to 2020. The findings revealed that the average WBGT in the MENA region increased by 0.75–2.20 °C when comparing 2011–2020 with the 1951–1960 baseline. The most substantial increase occurred in the Arabian Peninsula, specifically Saudi Arabia, where the rise exceeded 0.4 °C per decade in most regions. Consequently, the annual frequency of “Normal conditions” days decreased by approximately 50 days, while high-risk and extreme heat-stress days increased by a corresponding 40 days across most of the region, particularly in the east. Significant trend analysis reveals a 10-day increase per decade across eastern and western MENA under extreme conditions. This rise has led to an additional 1.23 million people experiencing extreme heat stress for at least one day each year. The findings of this study can be useful to policymakers and researchers concerned with extreme heat in the MENA region.
Robust automated detection of small-scale rainfall-induced landslides in Italy Using SegFormer and high-resolution satellite imagery
The prognostic value of different HPV infection statuses in cervical cancer
Abstract To compare oncological outcomes between HPV-positive and HPV-negative cervical cancer patients in a large Chinese multicenter cohort. This retrospective study included cervical squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma. Propensity score matching (PSM, 2:1) was used to balance baseline characteristics. Kaplan–Meier method and Cox proportional hazards regression were applied to compare 5-year overall survival (OS) and disease-free survival (DFS). The proportional hazards assumption was tested. Multicenter heterogeneity was adjusted using center stratification. Missing data were managed by complete-case analysis and multiple imputation sensitivity analysis. A total of 11,215 eligible patients were included. Before PSM, the HPV-positive group showed significantly better 5-year OS (92.3% vs. 84.3%, P < 0.001) and DFS (88.0% vs. 79.6%, P < 0.001) than the HPV-negative group. Multivariable Cox analysis confirmed HPV negativity as an independent risk factor for OS (HR = 1.545, 95%CI 1.091–2.188, P = 0.014) and DFS (HR = 1.556, 95%CI 1.212–1.997, P < 0.001). After 2:1PSM, HPV-positive patients still had superior 5-year OS (88.4% vs. 84.3%, P = 0.004) and DFS (85.3% vs. 79.6%, P < 0.001). Among 7,998 patients who underwent radical surgery, similar results were observed before and after PSM. HPV-negative status is associated with inferior 5-year OS and DFS and serves as an independent adverse prognostic factor in cervical cancer, including patients treated with radical surgery.
Comparative machinability and neural network-driven optimization of AISI D2 steel using chamfered and conventional tooling
A machine-learning-derived online screening tool for depressive symptoms in chronic digestive system diseases patients: a cross-sectional study with temporal validation from CHARLS
Molecular and biological characterization of a newly discovered Kayfunavirus bacteriophage targeting multidrug-resistant Escherichia coli from swine feces
High-performance absorption and regeneration of water vapor by choline chloride-based hydrophilic deep eutectic solvents
Joint optimization secure and energy-efficient computation offloading framework IoT-enabled edge networks
Digital twin–driven multiscale modelling for real-time defect prediction in metal additive manufacturing
Abstract This paper presents a multiscale modelling system based on a digital twin that can predict defects in metal additive manufacturing in real time, with primary validation scoped to Laser Powder Bed Fusion (LPBF). While the framework is architected to generalise across powder-bed and directed-energy deposition (DED) processes, all experimental evaluations are conducted on LPBF using the publicly available NIST AM-Bench benchmark dataset of IN625 and Ti-6Al-4 V specimens. It combines microscale melt pool dynamics with mesoscale thermal fields and macroscale structural deformation via hierarchical physics-informed neural network (PINN) surrogates, with a real-time Internet of Things (IoT) backbone of sensors. It has a three-tier architecture of edge, fog, and cloud, with inference distributed across latency-sensitive edge nodes, fog-level surrogate aggregation, and cloud-based digital twin calibration. Thermal imaging, acoustic emission, and optical monitoring provide sensor-based feedback to the numerical model, which can be used to adaptively control the process in real time during Laser powder bed fusion (LPBF) and directed energy deposition (DED). The hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) is an extractor of spatio-temporal defect signatures used to classify six defect classification categories, including porosity, lack-of-fusion, cracking, balling, keyholing, and delamination, with a macro-averaged F1-score of 0.9841. Formation of the multiscale coupling, Bayesian calibration and surrogate training formalised in twenty-five governing equations. It was experimentally verified on the publicly available NIST AM-Bench dataset that the proposed DT-MSM structure achieves a mean defect-detection rate of 98.72% and an inference latency of 11.3ms per frame on edge hardware, outperforming seven other baselines. The framework achieves reductions in scrap rate (34.6 per cent) and predictive maintenance lead time (41.2 per cent) in a simulated LPBF production scenario, with improved predictive ability for porosity and cracking compared with offline simulations. Mean plus standard deviation of the results is reported across five random seeds, over which the statistical significance is verified using a paired t-test ( p < 0.01). The proposed methodology supports smart manufacturing and quality control in the new-generation production systems for metal additive manufacturing.
Hazard-portfolio patterns in US food recall severity reveal transferable pathogen signals and firm-specific compliance signals
Characterization of liver disease regression in murine models of liver fibrosis and portal hypertension
Variable selection for estimating optimal treatment regimes with multiple treatments
Abstract We propose a penalized classification method for estimating optimal treatment regimes (OTRs) with multiple treatments when the number of covariates is large. Our approach reformulates the OTR estimation problem as a weighted multiclass classification problem and integrates variable selection with doubly robust estimation into a unified framework that simultaneously performs variable selection and regime estimation. By employing a data expansion technique and incorporating $$L_1$$ -type penalization along with augmented inverse probability weighting (AIPW) estimators, the method effectively identifies the sparse subset of covariates that genuinely drive treatment effect heterogeneity. Extensive simulation studies demonstrate the superior performance of the proposed method in terms of accuracy and double robustness for estimating the optimal treatment regimes. The method’s practical utility is further illustrated through an application to a clinical trial for chronic depression.