Advancing Open Science

Traditional analytical methods for assessing oil oxidation frequently depend on expensive and intricate equipment or elaborate procedures, thereby hindering their practical use in everyday situations. Sensory evaluation and GC-MS analysis indicated that during storage, the peroxide value (PV) and aldehyde content of pecan oil increased, consistent with progressive oxidation, while the acid value (AV) remained stable. The shelf-life prediction model further underscores its reliability as an oxidation marker. The coefficient of determination (R²) for the first-order kinetic model at temperatures of 20, 40, 50, and 60 °C ranged from 0.9183 to 0.9841. The correlation coefficients between the measured and predicted shelf-life values were 0.9993 for cold-pressed pecan oil (CPO) and 0.9866 for hot-pressed pecan oil (HPO). A filter-paper-based colorimetric aldehyde sensor was developed for the visual assessment of pecan oil shelf-life, which leverages the chemical reaction between hydroxylamine sulfate and aldehydes to generate a distinct naked-eye color shift from red to purple-blue—this enables the qualitative identification of whether cold-pressed (CPO) and hot-pressed (HPO) pecan oil complies with the national peroxide value (PV) limit of 0.25 g/100 g or exceeds it. Specifically, CPO is deemed to be expired when a* ≤ 11 and HPO when a* ≤ 15; consistent red-to-purple-blue color changes for the sensor yielded 100% sensitivity and 100% specificity for both oils at the national PV limit, thereby validating its application as a highly accurate qualitative (pass/fail) indicator for oil oxidation. By contrast, sensory evaluation can also reliably distinguish when pecan oil exceeds the national PV limit qualitatively, yet it lacks quantitative accuracy due to inherent subjective biases.

Oral squamous cell carcinoma (OSCC) is a highly aggressive malignancy characterized by frequent recurrence and metastasis, which poses a significant global health problem. One of the prominent hallmarks of cancer is glucose metabolic reprogramming, wherein glycolysis is preferred over oxidative phosphorylation for macromolecule biosynthesis and energy production, even in the presence of oxygen. Non-coding RNAs (ncRNAs) are defined as a class of RNAs that are not translated into proteins, which include microRNAs, long non-coding RNAs, and circular RNAs. Recent studies have found that ncRNAs are crucial in regulating glycolysis in OSCC, wherein they reshape the metabolic landscape by modulating the expression of glucose transporters, essential enzymes, and transcription factors, ultimately influencing tumorigenesis. This comprehensive review systematically summarizes the regulatory mechanisms of ncRNAs involved in glucose metabolic reprogramming in OSCC, evaluates their potential as diagnostic biomarkers and therapeutic targets, and identifies clinically relevant ncRNAs through an integrative analysis of patient-derived data. These insights provide a mechanistic understanding of the metabolic alterations that drive progression in OSCC, as well as knowledge that can facilitate the development of clinically translatable targeted interventions for this aggressive malignancy.

To investigate the leakage characteristics of damaged double-hull oil tanks in still water, this study conducted both model tests and numerical simulations on the leakage process of a damaged double-hull oil tank model. Based on a 75,000 DWT oil tanker, a scaled model was designed according to similarity criteria. The effects of different damaged locations (side-shell and bottom) and various breach sizes on the tank’s leakage behavior were examined. The evolution of multiphase flow inside the tank and the surrounding flow field was captured, and the leakage pressure under fixed model conditions was measured. The model test results indicate that larger breach sizes lead to a more rapid stabilization of the pressure load during leakage and the liquid exchange process. For side shell breaches, after an initial phase of pressure-difference-driven leakage, a density-driven flow develops at the stable liquid interface. Bottom breaches cause flooding that results in an oil sealing phenomenon at the bottom, leading to a pronounced oil–water stratification. Corresponding numerical simulations of the model tests were performed, and the results were compared and validated against the model test data.

Major depressive disorder requires scalable, low-burden screening tools. We examined whether three-channel resting-state EEG can support reliable discrimination between major depressive disorder and healthy controls using a lightweight model compatible with portable implementations. This work makes three main contributions: (i) a compact hybrid fusion model combining raw-window Conv1D embeddings with per-channel spectral–statistical descriptors for three-channel resting-state EEG, (ii) a leakage-resistant subject-independent (cross-subject) evaluation protocol with subject-level inference via majority voting, and (iii) a preliminary external feasibility test on an independent portable three-channel cohort without fine-tuning. The proposed model fuses a Conv1D encoding of raw ≈15 s eyes-closed windows (3840 samples; 15.36 s at 250 Hz) with per-channel spectral and statistical descriptors. Training uses subject-independent splits to avoid leakage, class weighting, and data augmentation (including MixUp); hyperparameters are selected via randomized search with refinement. The model is trained on a publicly available MDD dataset and subsequently applied, without fine-tuning, on an independent acquisition of 20 subjects recorded with a portable three-channel device; we report both window-level and subject-level (majority-vote) performance. On the held-out test subjects from the public dataset, the hybrid model attains 93.43% window-level accuracy. The independent evaluation is reported as a preliminary external feasibility analysis; given the small cohort, we report subject-level performance with 95% confidence intervals to reflect uncertainty and avoid over-interpreting cross-device generalization. The model occupies approximately 40.19 MB on disk, and the architecture is compatible with post-training int8 (TFLite) quantization for resource-constrained hardware. These results, obtained on limited samples, support the feasibility of three-channel EEG for major depressive disorder detection using a lightweight hybrid architecture and motivate prospective clinical validation, on-device inference and quantization studies, and broader evaluation across centers and devices.

Turbine blades serve as critical components in aircraft engines, yet casting defects inevitably arise during manufacturing. Therefore, accurate pre-service turbine blade defect detection is critical for aircraft engine safety. However, existing deep learning-based detection methods face several challenges: poor image quality, intraclass variance, interclass similarity, and irregular defect geometries. Moreover, most existing defect detection methods rely primarily on spatial-domain features, which are insufficient for capturing fine-grained texture information, limiting their ability to discriminate complex defect patterns. To address these challenges, we propose a novel Spatial-Frequency Complementary Fusion Network (SFCF-Net) that synergistically integrates spatial and frequency-domain features through complementary cross-modal fusion for accurate defect segmentation. First, a Selective Cross-modal Calibration (SCC) module is introduced that selectively calibrates spatial-frequency features through gated cross-modal interactions, effectively preserving fine-grained details under poor image conditions. Next, we propose a Cross-modal Refinement and Complementation (CRC) module that employs dual-stage attention mechanisms to model intra- and inter-modal feature dependencies, enabling robust discrimination between similar defect categories while maintaining consistency within the same defect class. Finally, we propose an Asymmetric Window Attention (AWA) module that employs bidirectional rectangular windows for accurate defect geometric characterization. Comprehensive experiments on the Aero-engine Turbine Blade Casting Defect Segmentation (ATBCD-Seg) dataset and a public benchmark demonstrate that SFCF-Net consistently outperforms state-of-the-art methods across multiple evaluation metrics, meeting practical requirements for automated quality control in blade manufacturing.

As networks expand in size and complexity, coupled with an exponential increase in intrusions on network and IoT systems, this leads to traditional models failing to capture increasingly intricate correlations among network components accurately. Graph Convolution Networks (GCNs) have recently acquired prominence for their capacity to represent nodes, edges, or entire graphs by aggregating information from adjacent nodes. However, the correlations between nodes and their neighbours, as well as related edges, differ. Assigning higher weights to nodes and edges with high similarity improves model accuracy and expressiveness. In this paper, we propose the GCN-DQN model, which integrates GCN with a multi-head attention mechanism and DQN (Deep Q Network) to adaptively adjust attention weights optimizing its performance in intrusion detection tasks. After extensive experiments using the UNSW NB15 and CIC-IDS2017 dataset, the proposed GCN-DQN outperformed the baseline model in classification accuracy. We also applied LIME and SHAP techniques to provide explainability to our proposed intrusion detection model.

Flooding is one of the major natural disasters that have a major impact on urban areas due to the increasing intensity of factors like extreme weather conditions, climate change, and unplanned urbanization. Considering Cook County, Illinois, the rapid development of the region, flat topography, and the induced rainfall extremes from climate change increase the potential risk of flooding when interacting with dense urban exposure and infrastructure. This study employed the Frequency Ratio (FR) model in a GIS environment to create a high-resolution flood susceptibility map of the county. The map was developed using 281 historical flood points collected from several authoritative sources, such as National Oceanic and Atmospheric Administration (NOAA) Storm Events Database records, Federal Emergency Management Agency (FEMA) Flood Insurance Study (FIS) and Flood Insurance Rate Map (FIRM)-based FIRMette products, and U.S. Geological Survey (USGS) flood-inundation studies. Thirteen conditioning factors, including land use, elevation, slope, soil drainage, rainfall, and distance to the stream, were used to calculate FR values and to develop the Flood Susceptibility Index (FSI). The resulting FSI was grouped into four susceptibility zones: low, medium, high, and very high. The findings indicated that more than 64% of Cook County has a high and very high risk of flood susceptibility, particularly in the vicinity of major river corridors. The model was validated using testing data with a 91.4% prediction accuracy, which also demonstrated the reliability and applicability of the FR model in the urban flood susceptibility assessment. The map serves as a valuable tool for risk-based urban planning and design of flood mitigation infrastructure in one of the most populated counties in the United States.

Objective: The performance of chrono-impedance measurement, a novel electrochemical method for determining free fatty acids (FA), was evaluated in a real-world clinical setting. Methods: Patients presenting to the emergency department with chest pain or discomfort were included. Routine diagnostic tests were performed in accredited laboratories. Chrono-impedance was measured using a screen-printed carbon electrode connected to a dedicated potentiostat. Serum total free-FA levels were determined by gas chromatography with flame ionization detection. Results: Among 104 patients, 21 received a specific diagnosis, while the remaining 83 patients were discharged with non-specific pain. Mean free-FA level was 0.9 ± 0.6 mM. Palmitic, linoleic, stearic, oleic, and arachidonic acids accounted for 74.9% of total free FAs. Impedance plots showed a characteristic logarithmic increase over time for all patients. When instantaneous impedance values at four different time points (10, 100, 376.6, and 500 s) were examined, a significantly strong correlation was observed between impedance and FA molarity (r = 0.8312, 0.9897, 0.9947, and 0.9951) and FA weight (r = 0.9572, 0.9878, 0.9996, and 0.9998), respectively. Conclusions: Chrono-impedance demonstrated a very high correlation with total free-FA levels in real patient samples.