Xinjiang Uygur Autonomous Region Education Department

To address natural aggregate scarcity and improve the high-value utilization of Reclaimed Asphalt Pavement (RAP), this study proposes a steel slag-RAP hot recycled asphalt mixture (SSRM) as a sustainable alternative to conventional limestone-RAP mixtures (RM). Unlike previous studies mainly focusing on performance verification, an integrated environmental-economic evaluation framework was developed based on real highway expansion project data under a "cradle-to-gate" boundary and incorporating transportation distance thresholds. SSRM containing 50% RAP and 23% steel slag as coarse aggregate replacement was evaluated through rutting, semi-circular bending (SCB), freeze-thaw splitting (TSR), and skid resistance tests. Compared with RM, SSRM exhibited 14-16% higher dynamic stability and 20-25% higher fracture energy at -10 °C, along with improved moisture stability and skid resistance, mainly attributed to the rough and alkaline characteristics of steel slag enhancing adhesion and aggregate interlocking. Life-cycle assessment (GWP100) and cost analysis indicate that SSRM reduces carbon emissions by 10-11% relative to RM and about 40% compared with conventional virgin mixtures, while initial construction costs decrease by 9-10%. Transportation sensitivity analysis identifies equal-emission and equal-cost thresholds of approximately 590 km and 380 km, respectively. Within typical material supply radii (300-400 km), SSRM demonstrates both environmental and economic advantages, providing a practical framework for low-carbon material selection in highway construction.

Individualized postoperative management of colorectal ovarian metastases demands precision medicine tools, yet current approaches lack consideration of prognostic heterogeneity and targeted therapy benefit guidance and suffer from high costs and long turnaround times of genetic testing. In this retrospective, prospective multicohort study, we developed and validated an interpretable transformer-based transfer learning model to predict patient prognosis, targeted therapy benefits and molecular mutations by integrating digital pathology with RNA data. The performance of the model was assessed with the AUC, accuracy, sensitivity, specificity, PPV and NPV. The model accurately predicted peritoneal recurrence, with AUCs of 0.90, 0.74, and 0.83 across patient cohorts. It also achieved precise prognostic stratification for peritoneal recurrence-free survival in the training (HR=107.22, 95% CI 24.18-475.52; p<0.001), external test (HR=4.97, 95% CI 1.16-21.37; p=0.03), and prospective test (HR=10.53, 95% CI 2.02-54.94; p=0.01) sets. The model revealed a significant association between prognostic stratification and tumor microenvironment heterogeneity (p < 0.05), thereby enhancing its biological interpretability. Further analysis revealed that only patients classified as high risk with BRAF/RAS mutations could benefit from the addition of targeted therapy to adjuvant chemotherapy (HR 0.38, 95% CI 0.18-0.79; p=0.007). Moreover, the model predicted BRAF/RAS mutations with AUCs of 0.96/0.94 in the training set, maintaining cross-cohort generalizability with AUCs of 0.64-0.83. This pathobiology-based deep learning model can robustly detect prognosis and mutation and identify targeted therapy beneficiaries, serving as a potential precision tool in clinical decision-making for the management of colorectal ovarian metastases.

Abstract During gas storage injection and production operations, high-speed gas flow continuously impacts the completion string, easily triggering strong vibrations and leading to fatigue failure, which seriously threatens downhole safety and production continuity. To address this issue, this study establishes a transient finite element model of a 30 m section of Φ88.9×6.45 mm P110 tubing under 40 MPa high-pressure conditions based on two-way fluid-structure interaction (FSI) theory, and systematically conducts a detailed finite element simulation analysis of the string’s vibration characteristics. Finite element calculation results indicate that fluid pressure is the dominant factor causing significant radial expansion in the central section of the string, while wellhead and wellbottom constraints lead to high-risk stress concentration at the ends. Under thin-wall conditions, finite element simulations show that deformation and stress increase by 53% and 51%, respectively, compared to standard conditions. Under thin-walled, high-pressure conditions at 60 MPa, finite element method simulations reveal that deformation and stress increase by 53% and 51%, respectively, compared to the 40 MPa condition. Under the same high-pressure environment, two-way FSI consistently results in higher radial deformation and stress values in the tubular string compared to one-way FSI. Based on these finite element numerical results, the study provides critical theoretical support for the safety design and risk mitigation of gas storage tubular strings.

The dynamic recognition technology of abnormal production behavior is based on the statistical analysis method to establish a static model, which cannot adapt to these dynamic changes in time, resulting in a decrease in the accuracy of abnormal recognition. Therefore, a dynamic recognition technology of abnormal production behavior based on fast Projection Onto Convex Sets(POCS) and power big data is designed. The acquisition model is used to aggregate power big data, and the data quality is improved through linear interpolation and Gaussian filtering. A fast POCS model is established. Based on the convex optimization theory, the normal behavior of power production is modeled as multiple convex sets to mine the patterns and laws in power data. Combined with genetic algorithm and preset error threshold, abnormal production behavior is determined. The dynamic recognition technology of abnormal production behavior based on fast POCS and power big data performs well in the experiments of confusion matrix, false recognition rate and loss function iteration process. Its recognition accuracy exceeds 94%, the false recognition rate is stable at 2.8%~3.0%, and the loss function converges to a low value quickly, which is better than other comparison methods, verifying the practicality and superiority of this technology in power anomaly detection.