Search

Machine learning has shown its great potential in the accelerated discovery of advanced materials in the field of computational molecular design. High-temperature polymer dielectrics are urgently required with the emerging applications of energy-storage dielectric film capacitors under high-temperature conditions. Here, we demonstrate the successful prediction of polymers with a high dielectric constant (ɛ) and high glass transition temperature (Tg) using a Bayesian molecular design model. The model is trained on a joint data set containing 382 computed ɛ values using density functional perturbation theory and experimentally measured Tg values of ∼7000 polymers to build relative quantitative structure–property relationships and identify the promising polymers with specific desired range of dielectric constant and glass transition temperature. From the hypothetical polymer candidates, ten promising polymers are proposed based on their predicted properties and synthetic accessibility score for high-temperature dielectric film capacitors' application. Moreover, 250k novel polymer structures are generated with the model to support future polymer informatics research. This work contributes to the successful prediction of high-temperature polymer dielectrics using machine learning models. [ABSTRACT FROM AUTHOR]

Early identification of cerebrovascular atherosclerotic ischemic stroke is necessary for accurate treatment and clinical research. To identify novel predictors and build a predictive model of ischemic strokes due to cerebrovascular atherosclerosis. MIMIC-IV database was used to search for clinical data of patients with ischemic stroke. Included patients were divided into two groups according to their etiologies. Univariate and multivariate logistic regressions were used to build the predictive model, and the model reliability parameters were calculated. The cut-off value for the model was selected according to the Youden index. Clinical data from the Neurovascular Center of Changhai Hospital were used to verify the predictive model. Logistical regressions showed a positive correlation between advanced age, peripheral atherosclerosis, history of transient ischemia, and the diagnosis of ischemic strokes due to cerebrovascular atherosclerosis. The history of atrial fibrillation, levels of the National Institutes of Health Stroke Scale, serum potassium, and activated partial thromboplastin time were negatively correlated to the diagnosis of cerebrovascular atherosclerotic ischemic stroke. The predictive model was constructed from logistic regression results, and the area under the curve was 0.764. The cut-off value for the model was set at 0.089 to achieve the highest Youden index, with sensitivity and specificity of 75.9% and 64.1%. Clinical verification of the model revealed that the sensitivity and specificity of the model were 52.5% and 93.0% respectively. The efficacy of the predictive model was acceptable as an aid in predicting cerebrovascular atherosclerotic ischemic stroke. [ABSTRACT FROM AUTHOR]

Aiming at the problem of safety behavior in a power construction site, this paper studies the method of power engineering site safety behavior identification based on KanBIM and Internet of things technology. The traditional fast r-cnn model is improved to enhance the accuracy of video surveillance recognition. The video surveillance effect is strengthened by increasing the number of model anchors, improving the loss function, and refining feature image interpolation prediction. Experiments show that the algorithm has obvious advantages in terms of accuracy and precision in helmet management and unsafe behaviors identification, and can be used to identify safe behaviors in power construction sites. [ABSTRACT FROM AUTHOR]

Background and purpose: DIRECT‐MT showed that endovascular thrombectomy was noninferior to thrombectomy preceded by intravenous alteplase with regard to functional outcome in patients with acute ischemic stroke. In this post hoc analysis, we examined whether infarct size modified the effect of alteplase. Methods: All patients with baseline Alberta Stroke Program Early Computed Tomography Score (ASPECTS) grades were included. The primary outcome was the 90‐day modified Rankin Scale (mRS) score. Multivariate ordinal logistic regression analysis was used to calculate the adjusted common odds ratio (OR) for better functional outcome based on the mRS for thrombectomy alone versus combination therapy. An interaction term was entered to test for an interaction with baseline ASPECTS subgroups: 0–4 versus 5–7 versus 8–10. Results: Of 649 patients, 323 (49.8%) were in the thrombectomy‐alone group and 326 (50.2%) in the combination‐therapy group. There was no significant treatment‐by‐trichotomized ASPECTS interaction with alteplase prior to endovascular treatment for the primary endpoint of ordinal mRS (p‐value interaction term relative to ASPECTS 8–10: ASPECTS 0–4, p = 0.386; ASPECTS 5–7, p = 0.936). Adjusted common ORs for improvement in the 90‐day mRS with thrombectomy alone compared with combination therapy were 1.99 (95% confidence interval = 0.72–5.46) for ASPECTS 0–4, 1.07 (0.62–1.86) for ASPECTS 5–7, and 1.03 (0.74–1.45) for ASPECTS 8–10. There was no significant difference in the safety outcomes between the two groups. Conclusions: Baseline infarct size may not modify the effect of alteplase prior to endovascular thrombectomy with regard to favorable functional outcomes and adverse events. [ABSTRACT FROM AUTHOR]