Predicting Natural Rubber Crystallinity by a Novel Machine Learning Algorithm Based on Molecular Dynamics Simulation Data.

Natural rubber (NR) with excellent mechanical properties, mainly attributed to its strain-induced crystallization (SIC), has garnered significant scientific and technological interest. With the aid of molecular dynamics (MD) simulations, we can investigate the impacts of crucial structural elements on SIC on the molecular scale. Nonetheless, the computational complexity and time-consuming nature of this high-precision method constrain its widespread application. The integration of machine learning with MD represents a promising avenue for enhancing the speed of simulations while maintaining accuracy. Herein, we developed a crystallinity algorithm tailored to the SIC properties of natural rubber materials. With the data enhancement algorithm, the high evaluation value of the prediction model ensures the accuracy of the computational simulation results. In contrast to the direct utilization of small sample prediction algorithms, we propose a novel concept grounded in feature engineering. The proposed machine learning (ML) methodology consists of (1) An eXtreme Gradient Boosting (XGB) model to predict the crystallinity of NR; (2) a generative adversarial network (GAN) data augmentation algorithm to optimize the utilization of the limited training data, which is utilized to construct the XGB prediction model; (3) an elaboration of the effects induced by phospholipid and protein percentage (ω), hydrogen bond strength (εH), and non-hydrogen bond strength (εNH) of natural rubber materials with crystallinity prediction under dynamic conditions are analyzed by employing weight integration with feature importance analysis. Eventually, we succeeded in concluding that εH has the most significant effect on the strain-induced crystallinity, followed by ω and finally εNH.

Efficacy Evaluation of Zoledronic Acid Combined with Chemotherapy in the Treatment of Lung Cancer Spinal Metastases on Computed Tomography Images on Intelligent Algorithms.

To analyze the effectiveness and safety of zoledronic acid combined with chemotherapy for lung cancer spinal metastases, 96 patients with lung cancer spinal metastases were averagely classified into the experimental group (gemcitabine, cisplatin, and zoledronic acid) and the control group (gemcitabine and cisplatin). An optimized noise variance estimation algorithm (OMAPB) was proposed based on the maximum a posteriori Bayesian method (MAPB), and the algorithm was applied to the patient's computed tomography (CT) scan. The results indicated that in terms of curative effect, the number of complete remission (CR), partial remission (PR) cases, effective rate, and clinical benefit rate of the test group was significantly higher than those of the control group. The number of progress disease (PD) cases was significantly lower than that of the control group (P < 0.05). The disease progression time of the test group patients was 6.2 months, and the disease progression time of the control group patients was 3.7 months (P < 0.05). The test group patients had 8 cases of bone marrow suppression and gastrointestinal reactions after treatment. In the test group, there were 8 cases of bone marrow suppression, 9 cases of gastrointestinal reaction, 3 cases of fever, 4 cases of pain, and 2 cases of hair loss. The patients in the control group were complicated with bone marrow suppression in 14 cases, gastrointestinal reaction in 17 cases, fever in 5 cases, pain in 4 cases, and hair loss in 6 cases. The difference was statistically significant (P < 0.05). It showed that zoledronic acid combined with chemotherapy could effectively improve the treatment efficiency and clinical benefit rate of patients with lung cancer spinal metastases, prolong the progression of the disease, reduce the degree of bone tissue damage, and would not increase chemotherapy adverse events.

A Machine Learning Framework to Predict the Tensile Stress of Natural Rubber: Based on Molecular Dynamics Simulation Data.

Natural rubber (NR), with its excellent mechanical properties, has been attracting considerable scientific and technological attention. Through molecular dynamics (MD) simulations, the effects of key structural factors on tensile stress at the molecular level can be examined. However, this high-precision method is computationally inefficient and time-consuming, which limits its application. The combination of machine learning and MD is one of the most promising directions to speed up simulations and ensure the accuracy of results. In this work, a surrogate machine learning method trained with MD data is developed to predict not only the tensile stress of NR but also other mechanical behaviors. We propose a novel idea based on feature processing by combining our previous experience in performing predictions of small samples. The proposed ML method consists of (i) an extreme gradient boosting (XGB) model to predict the tensile stress of NR, and (ii) a data augmentation algorithm based on nearest-neighbor interpolation (NNI) and the synthetic minority oversampling technique (SMOTE) to maximize the use of limited training data. Among the data enhancement algorithms that we design, the NNI algorithm finally achieves the effect of approaching the original data sample distribution by interpolating at the neighborhood of the original sample, and the SMOTE algorithm is used to solve the problem of sample imbalance by interpolating at the clustering boundaries of minority samples. The augmented samples are used to establish the XGB prediction model. Finally, the robustness of the proposed models and their predictive ability are guaranteed by high performance values, which indicate that the obtained regression models have good internal and external predictive capacities.