No abstract

In this paper we consider, discuss, improve and generalize recent fixed point results for mappings in b−rectangular metric spaces. Thus, all our results are with much shorter proofs. Also, we prove Reich type theorem in the frame of b−metric space. The proofs of all our results are without using Hausdorff assumption. One example is given to support the result.

Of concern is a class of abstract semilinear integrodifferential equations with nonlocal initial conditions. Under some suitable hypotheses, we establish some new theorems about the existence of asymptotically almost automorphic solutions to the integrodifferential equations. Moreover, an example is given to illustrate our results.

No abstract

This paper is concerned with mixed g-monotone mappings in partially ordered metric spaces. We establish several coupled coincidence and coupled common fixed point theorems, which generalize and complement some known results. Especially, our main results complement some recent results due to Lakshmikantham and Ćirić. Two examples are given to illustrate the usability of our results.

No abstract

PurposeMYCN amplification plays a critical role in defining high-risk subgroup of patients with neuroblastoma. We aimed to develop and validate the CT-based machine learning models for predicting MYCN amplification in pediatric abdominal neuroblastoma.MethodsA total of 172 patients with MYCN amplified (n = 47) and non-amplified (n = 125) were enrolled. The cohort was randomly stratified sampling into training and testing groups. Clinicopathological parameters and radiographic features were selected to construct the clinical predictive model. The regions of interest (ROIs) were segmented on three-phrase CT images to extract first-, second- and higher-order radiomics features. The ICCs, mRMR and LASSO methods were used for dimensionality reduction. The selected features from the training group were used to establish radiomics models using Logistic regression, Support Vector Machine (SVM), Bayes and Random Forest methods. The performance of four different radiomics models was evaluated according to the area under the receiver operator characteristic (ROC) curve (AUC), and then compared by Delong test. The nomogram incorporated of clinicopathological parameters, radiographic features and radiomics signature was developed through multivariate logistic regression. Finally, the predictive performance of the clinical model, radiomics models, and nomogram was evaluated in both training and testing groups.ResultsIn total, 1,218 radiomics features were extracted from the ROIs on three-phrase CT images, and then 14 optimal features, including one original first-order feature and eight wavelet-transformed features and five LoG-transformed features, were identified and selected to construct the radiomics models. In the training group, the AUC of the Logistic, SVM, Bayes and Random Forest model was 0.940, 0.940, 0.780 and 0.927, respectively, and the corresponding AUC in the testing group was 0.909, 0.909, 0.729, 0.851, respectively. There was no significant difference among the Logistic, SVM and Random Forest model, but all better than the Bayes model (p <0.005). The predictive performance of the Logistic radiomics model based on three-phrase is similar to nomogram, but both better than the clinical model and radiomics model based on single venous phase.ConclusionThe CT-based radiomics signature is able to predict MYCN amplification of pediatric abdominal NB with high accuracy based on SVM, Logistic and Random Forest classifiers, while Bayes classifier yields lower predictive performance. When combined with clinical and radiographic qualitative features, the clinics-radiomics nomogram can improve the performance of predicting MYCN amplification.

Thermally conductive polymer matrix composite (PMC) foams with effective thermal conductivities (k eff ) higher than their solid counterparts have been developed for the first time. Using a material system consists of low density polyethylene and micron-scale or submicron-scale hexagon boron nitride platelets as a case example, this article demonstrates that foaming-assisted filler networking is a feasible processing strategy to enhance PMC's k eff , especially at a low hBN loading. Parametric studies were conducted to identify the structure-to-property relationships between foam morphology (e.g., cell population density, cell size, and foam expansion) and the PMC foam's k eff . In particular, there exists an optimal cell size to maximize the PMC foam's k eff for foams with up to 50% volume expansion. However, an optimal cell size is absent for PMC foams with higher volume expansion. X-ray diffraction (XRD) analyses reveal that both the presence of hBN platelets and foam expansion promoted the crystallization of LDPE phase. Moreover, the XRD spectra also provide evidence for the effect of foam expansion on the orientation of hBN platelets. Overall, the findings provide new directions to design and fabricate thermally conductive PMC foams with low filler contents for heat management applications.