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Viscosity modeling of propyl butanoate and 2-alkanol mixtures using modified Cohen-Turnbull and UNIFAC-VISCO approaches
Honey-biosynthesized silver nanoparticles against vancomycin-resistant enterococci: integrated antibacterial, antibiofilm, mechanistic and preliminary safety evaluation
The peptidoglycan endopeptidase MepH of uropathogenic Escherichia coli supports competitive fitness during prolonged urinary tract infections
A robust multi-criteria supplier evaluation in supply chain management using interval-valued bipolar q-fractional fuzzy aggregation operators
A ridge-isolated tuned mass damper for seismic rehabilitation of a 3D structure under bidirectional earthquakes
Fibroblast activation protein-targeted near-infrared labeling of cancer-associated fibroblasts in a non-small-cell lung cancer tumor microenvironment
Development and validation of interpretable machine learning models to predict 30-day mortality in patients with intracerebral hemorrhage
Abstract Intracerebral hemorrhage (ICH) carries high early mortality. To enable personalized decision-making, we developed and validated interpretable machine learning models for 30-day mortality prediction. This retrospective cohort study included patients with ICH extracted from the Medical Information Mart for Intensive Care (MIMIC) clinical database. Model development was performed using the MIMIC-IV (v3.1), while temporal validation was conducted on a distinct, non-overlapping cohort from the MIMIC-III CareVue subset (v1.4). Multiple imaging-free machine learning models were developed using routinely available clinical variables from the first 24 h. Following data preprocessing and feature selection, the model development phase incorporated class weight balancing to address data imbalance and utilized Bayesian hyperparameter optimization. Discrimination, calibration, and decision curve analysis were assessed for all models. The final model was selected based on its superior performance and elucidated using SHapley Additive exPlanations (SHAP) to ensure transparency. Robustness and statistical reliability were evaluated through sensitivity analyses and sample size justification. Nine machine learning models were developed and evaluated in the MIMIC-IV cohort ( n = 1,478; 1,034 for training, 444 for internal test). LightGBM was identified as the optimal model, demonstrating good discrimination (AUC: 0.859), adequate calibration (slope: 1.017; Brier score: 0.132), and potential clinical utility in decision curve analysis. Furthermore, on the temporal validation set ( n = 339), the model maintained robust performance with an AUC of 0.811 and acceptable calibration (slope: 0.873; Brier score: 0.174). SHAP analysis enhanced clinical interpretability, and the model has been deployed as an open-access web tool for the early, individualized prediction. In this study, an interpretable LightGBM model demonstrated strong performance for 30-day mortality prediction in ICH, offering potential for individualized risk assessment and clinical integration. However, the lack of multicenter geographical external validation limits its generalizability, warranting further studies.
Hybrid temporal convolutional network-reservoir computing model for enhanced remaining useful life prediction in aerospace systems
Abstract Accurate prediction of Remaining Useful Life (RUL) is essential for predictive maintenance in the aerospace industry, where unexpected failures pose significant safety risks and increase operational costs. Conventional deep learning models, such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), have demonstrated strong predictive capabilities; however, they often incur high computational costs, are sensitive to noise, and struggle to capture long-term degradation patterns. To overcome these issues, this study presents a new hybrid deep learning model that combines Temporal Convolutional Networks (TCNs) with Reservoir Computing, leveraging the strengths of both architectures. The model is tested using the well-known NASA C-MAPSS dataset, a standard benchmark for RUL estimation. Performance is measured using both Root Mean Squared Error (RMSE) and a penalty-based PHM score that emphasizes timely failure prediction. The model attains test RMSE values of 14.81, 16.26, 15.57, and 17.97 on FD001, FD002, FD003, and FD004, respectively. Correspondingly, the PHM scores are reduced to 57.1, 204.46, 190.95, and 446.73 across the same subsets. Experimental results demonstrate that the hybrid architecture consistently outperforms the standalone TCN and Reservoir components, as well as other benchmark methods, achieving substantially improved PHM scores while retaining competitive RMSE performance. These results suggest that the proposed method provides a practical, real-time solution for predictive maintenance in aeroengine health monitoring, thereby improving reliability and reducing maintenance costs.
Star-patterned FSS-assisted multi-step notched antenna for enhancing circular polarization, gain, and impedance matching
Feature extraction from real-world polysomnography reports of obstructive sleep apnea cohort using large language model
Abstract Electronic health records (EHRs) are a valuable resource for generating real-world evidence. However, their utilization can be challenging as these reports are largely unstructured texts stored in image formats or accumulated scans or images, thereby hindering efficient data feature extraction. With the development of computer vision and large language models (LLMs), there is a growing opportunity to explore their application in overcoming these challenges. This paper explores the potential use of computer vision and LLMs to extract data from text-containing images of polysomnography (PSG) reports obtained from the sleep laboratory center of a tertiary care hospital in Thailand. We utilized a two-phase approach: (1) extracting text from image-based PSG reports using Differential Binarization Network (DBNet) within EasyOCR Python library, and (2) deriving feature values from the extracted text using ChatGPT-3.5 through task-specific prompt strategies. Performance was measured across different stages of the conversion process. Results show that computer vision and LLMs have the potential to substantially enhance the efficiency of feature extraction for evidence synthesis. The most common errors encountered in both phases were numerical, symbol, and character encoding errors. ChatGPT-3.5 reliably extracted features from sleep reports, with further error reduction achieved through improved prompt strategies. Although promising, we emphasize the need for extensive testing across diverse document qualities and conditions to fully understand the challenges and pitfalls of using computer vision and LLMs for feature extraction in real-world scenarios.
KIAA1429 promotes gallbladder cancer progression through m6A-dependent post-transcriptional modification of KIF20A
Biosurfactant-mediated degradation of petroleum hydrocarbons by indigenous bacteria from contaminated soil in Hyderabad, India
A blockchain-based governance layer for verifiable bid integrity in electricity market clearing
A sustainable driven intrusion detection model for green CPS using ISP analysis and energy aware deep ensemble learning
BeMapper: BicNet and evolutionary-based multi-agent path planning with effective reinforcement
Rivaroxaban Then Aspirin vs. Aspirin Alone after Total Hip or Knee Arthroplasty
AgNW emulsion inks for direct printing of high-resolution and stretchable electrodes with semi-embedded network structure
Magnetometry with a space-based differential atom interferometer
Abstract Atom interferometers deployed in space are excellent tools for high precision measurements, navigation, or Earth observation. In particular, differential interferometric setups feature common-mode noise suppression and enable reliable measurements in the presence of ambient platform noise. Here we report on orbital magnetometry campaigns performed with differential single- and double-loop interferometers in NASA’s Cold Atom Lab aboard the International Space Station. By comparing measurements with atoms in magnetically sensitive and insensitive states, we have realized atomic magnetometers mapping magnetic field curvatures. Our results pave the way towards precision quantum sensing missions in space.