From mapping to modelling: the evolving multidimensional microplastic risks in China's farmlands.

Label-free serum SERS combined with RFE-GBDT algorithm for non-invasive screening of liver cancer.

To develop and externally validate a computed tomography (CT)-based multitask learning model to predict fracture risk. This study was conducted in two parts, using a multitasking learning approach. We developed a cross-sectional vertebral fracture (VF) detection model using abdominal CT scans of 2553 patients aged 50-80 years. Then, we leveraged this detection model within a multitask learning framework to develop a longitudinal VF prediction model over a 5-year follow-up period. External testing was performed on 1506 patients from two independent hospitals. The performance was compared between the single-task and multitask models, bone-only and bone+muscle images, and image-only and clinical models. For the cross-sectional fracture detection model, the mean age of the patients was 76.2 years, and 66.7% were female. In the classification task for detection of VF, the model using both bone and muscle showed an area under the receiver operating characteristic curve (AUROC) of 0.82 in the development set and 0.80 in the external test sets. Using multitask learning, the bone + muscle image model showed a c-index of 0.68 and had superior performance than the bone-only model in the external test set for 2-year, 3-year, and 5-year AUROCs (0.79 vs. 0.75, 0.71 vs. 0.68, and 0.71 vs. 0.68, respectively, all p < 0.01). Also, the multitask model significantly outperformed the Fracture Risk Assessment Tool (FRAX) (c-index: 0.68 vs. 0.66, p < 0.01). The CT-based multitask learning model integrating both bone and muscle data showed superior predictive performance for VFs compared with models using bone images only and traditional clinical models. Question Vertebral fracture risk remains underestimated in many individuals undergoing CT scans for other reasons, highlighting the need for improved opportunistic prediction tools. Findings A multitask deep learning model integrating both bone and muscle features from CT scans demonstrated superior performance compared to bone-only and traditional clinical models, including FRAX. Clinical relevance The proposed model enables accurate vertebral fracture risk prediction using routinely acquired CT scans, facilitating early identification and intervention without the need for additional tests.

Characterizing the swell-dominated wave climate off West Africa: an intercomparison of global wave reanalyses

This study investigates the development of a nomogram for predicting the short-term collapse progression of osteonecrosis of the femoral head by integrating clinical data with radiomics features obtained from hip joint MRI. The study involved 364 patients with osteonecrosis of the femoral head who had not yet severe collapsed(Collapse < 2mm or no collapse) from two medical centers, selected from a major medical center. MRI images of their hip joints were analyzed to extract radiomics features. A clinical model was developed using criteria such as ARCO classification, CJFH classification, JIC classification, and the modified Kerboul angle. The outcome variable was the occurrence of collapse progression within one year post-examination. Features most significantly associated with collapse progression were identified from both radiomics and clinical data, which were then integrated into separate models. A combined Nomogram model was constructed by merging the clinical and radiomics models. The performance of these models was compared to assess their effectiveness in predicting collapse progression. The Nomogram model demonstrated superior predictive performance compared to both the clinical and radiomics models across all cohorts. In the external validation set, the Nomogram achieved an AUC of 0.919 and an accuracy of 0.879, outperforming the clinical (AUC=0.809) and radiomics (AUC=0.875) models. Statistical significance was confirmed between the Nomogram and clinical model in all cohorts. In summary, our study demonstrates that a nomogram combining hip MRI-based radiomics with clinical data shows superior predictive performance compared to clinical-only models for assessing short-term collapse risk in osteonecrosis of the femoral head.

The efficient treatment of landfill leachate using a novel osmotic microbial fuel cell system employing forward osmosis membrane featuring proton-conducting medium.

Multimodal Ultrasound for Evaluating Gross Extrathyroidal Extension in Papillary Thyroid Cancer: A Comparative Study.

Impact of pin-fin structural parameters on flow and heat transfer in embedded hybrid microchannel heat sinks based on a hotspot-oriented comprehensive evaluation framework

High-performance, low-cost electrocatalysts are critical for sustainable energy technologies, including water splitting and supercapacitors. Herein, we demonstrate a novel anion-engineering strategy to enhance the bifunctional activity of thiospinels via a controlled sulfur-phosphorus exchange. Initially, mixed-metal thiospinels, NiCo 2 S 4 and CuCo 2 S 4 , were synthesized from the thermal decomposition of their respective metal (Ni, Cu, Co) xanthate complexes. Subsequent heating of these materials in trioctylphosphine (TOP) resulted in a sulfur-phosphorus exchange, leading to the formation of anion-engineered metal phosphides, specifically NiCoP and Co 2 P. The formed Co 2 P retained some sulfur (with the presence of Cu sulfides), contrary to NiCoP. We show that these (S/P) mixed-anion compounds exhibit superior electrochemical performance compared to their parent thiospinels. The anion-engineered NiCoP demonstrated a significant improvement in energy storage, achieving a specific charge of 637.73 C/g at 1 A/g. Concurrently, the Co 2 P material, with its tailored anionic composition, showed outstanding water splitting activity, requiring only 167 mV to achieve a current density of 10 mA/cm 2 for the hydrogen evolution reaction. This anion substitution strategy also positively influenced reaction kinetics, long-term stability, and overall electrode durability. Our findings introduce a facile and effective anion-engineering approach for creating high-performance catalysts, highlighting the critical role of anionic composition in optimizing materials for advanced energy applications. • We demonstrate anion-engineering strategy to enhance the bifunctional activity of thiospinels. • NiCo 2 S 4 and CuCo 2 S 4 , were synthesized from decomposition of their metal xanthate complexes. • Heating of these materials in TOP resulted in a sulfur-phosphorus exchange. • This led to the formation of anion-engineered metal phosphides with residuel sulfur. • The (S/P) mixed-anion compounds exhibit superior electrochemical performance.

Analytical Quality by Design approach in the development of a green reversed-phase ultra-high performance liquid chromatography/high-resolution time-of-flight mass spectrometry method for the simultaneous analysis of synthetic antimicrobial and hypotensive peptides

• Proposal of Analog2KG , a pipeline for turning textual analogies into knowledge graphs • Knowledge-graph version of 2 long-text analogy datasets, RattermannKG and WhartonKG • Modification of information extraction methods for maintaining analogical structure • Introduction of an LLM-free discovery methodology for higher-order relationships • Comparison to 3 LLM-enabled information extraction algorithms Analogical reasoning is an increasingly popular, lightweight solution to enable large language model (LLM)-level reasoning without computational complexity. Still, it has yet to be adopted due to its reliance on strictly hand-formatted data. Therefore, we propose Analogy2KG (“Analogy to Knowledge Graph’’), as an automatic pipeline that transforms text into a KG format via a fine-tuned version of information extraction (IE) algorithms for long-text analogies. The need to verify that the complex underlying analogical structure of the data is maintained was done via paired samples tests in the creation and validation of this pipeline. Graph density was used to evaluate the structural quality of the resulting KGs. Lastly, causal relationships were optionally detected using a novel, question-and-answer-based method. Analogy2KG was validated on the Rattermann and Wharton long-text datasets, which suggested that the proposed methodology maintains analogical structure when transforming from text to KGs. The resulting RattermannKG and WhartonKG datasets were introduced to the literature, which is the first instance of a the conversion of long-text analogy dataset into a KG format in the literature. Finally, Analogy2KG had superior performance among three LLM-enabled information extraction algorithms: ChatIE, Code4UIE, and InstructUIE for maintaining analogical structure, despite operating without the need for an LLM backend and a pre-defined relation extractor list; thus, making it an ideal lightweight solution.

An investigation into the impact of discharge pulse width on the performance of long Pulse-Width plasma ignition (LPWPI) systems

The reliable evaluation of lubricating oil condition is critical for ensuring the safety and operational efficiency of heavy-duty equipment in both civilian and defense sectors. Conventional laboratory-based physicochemical analyses, although effective, are inherently time-consuming and do not enable real-time diagnostics or on-site decision-making. In this work, we introduce an innovative approach that leverages infrared thermography coupled with deep learning to achieve rapid, non-destructive, and fully automated classification of lubricating oil samples as either “compliant” (fit for use) or “non-compliant” (unfit for use). The study focuses on two reference lubricants (O-1178 (5W30), gearbox oil and O-1236 (15W40), engine oil) widely deployed in military vehicles, with ground-truth class labels established via standardized laboratory protocols. A comprehensive dataset of over 10,000 thermographic images was generated through controlled cooling cycles, providing the foundation for model development. After comparative analysis of several state-of-the-art convolutional neural network architectures, ResNet-34 and ResNet-50 were selected for their superior performance. The models, trained and validated on stratified and balanced datasets, consistently achieved classification accuracies above 99%, with the ResNet-34 model delivering 100% sensitivity and specificity for the detection of non-compliant samples in both oil types. Complementary metrics, including ROC/AUC (≈1.0) and F1-scores near unity, together with stable training–validation loss convergence, confirmed that the classifiers operated in a saturated performance regime with robust generalization. Interpretation with Grad-CAM heatmaps revealed that the model’s decisions are grounded in physically meaningful thermal micropatterns directly linked to lubricant degradation. This strategy not only minimizes unnecessary oil changes and associated environmental impact, but also elevates predictive maintenance capabilities by enabling immediate, reliable diagnostics in dual-use (civil and military) settings. The proposed methodology establishes a robust and versatile framework for advanced lubricant condition monitoring, readily adaptable to other industrial fluids and diverse operational scenarios requiring rapid, on-site assessment. Future work will extend this framework to additional lubricant types and broader real-world conditions to further consolidate these findings.

OT-ADG: Optimal-transport-driven adaptive data generation for WiFi sensing in airborne mobile networks

A machine learning-based lettuce fresh weight estimation framework incorporating agronomic traits and image features

The Effectiveness of the Patient-Reported Outcomes Measurement Information System Global Health Instrument Mental Health T-Score Versus the Brief Resiliency Scale at Identifying the Potential for Poor Outcomes Following Elective Total Knee and Hip Arthroplasty.

Edge feature enhancement: Generating adversarial edge perturbations for preterm infant movement recognition.

Unveiling impact of domain knowledge and data scale on open-source large language model specialization in anaerobic digestion.

Thermal management of electronic chips using microencapsulated phase change material slurry in a taenidia-inspired spiral channel heat exchanger

State-space models are accurate and efficient neural operators for dynamical systems.