Graphene and derivatives have been frequently used to form advanced nanocomposites. A very significant utilization of polymer/graphene nanocomposite was found in the membrane sector. The up-to-date overview essentially highlights the design, features, and advanced functions of graphene nanocomposite membranes towards gas separations. In this concern, pristine thin layer graphene as well as graphene nanocomposites with poly(dimethyl siloxane), polysulfone, poly(methyl methacrylate), polyimide, and other matrices have been perceived as gas separation membranes. In these membranes, the graphene dispersion and interaction with polymers through applying the appropriate processing techniques have led to optimum porosity, pore sizes, and pore distribution, i.e., suitable for selective separation of gaseous molecules. Consequently, the graphene-derived nanocomposites brought about numerous revolutions in high-performance gas separation membranes. The structural diversity of polymer/graphene nanocomposites has facilitated the membrane selective separation, permeation, and barrier processes, especially in the separation of desired gaseous molecules, ions, and contaminants. Future research on the innovative nanoporous graphene-based membrane can overcome design/performance-related challenging factors for technical utilizations.

Magnetic graphene oxide nanocomposites (M-GO) were successfully synthesized by partial reduction co-precipitation method and used for removal of Sr(II) and Cs(I) ions from aqueous solutions. The structures and properties of the M-GO was investigated by X-ray diffraction, Fourier transformed infrared spectroscopy, X-ray photoelectron spectroscopy, transmission electron microscopy, scanning electron microscopy, vibrating sample magnetometer (VSM) and N₂-BET measurements. It is found that M-GO has 2.103 mg/g and 142.070 mg/g adsorption capacities for Sr(II) and Cs(I) ions, respectively. The adsorption isotherm matches well with the Freundlich for Sr(II) and Dubinin–Radushkevich model for Cs(I) and kinetic analysis suggests that the adsorption process is pseudo-second-ordered.

Research indicates a strong correlation between sociodemographic factors and success in learning to read. This study examines the sociodemographic characteristics of 1131 preschool and 1st-grade children in Portuguese public schools and explores the relationship between these characteristics and key competencies for reading acquisition. The collection included a sociodemographic questionnaire and pre-reading skills, such as letter-sound knowledge. To assess the relationship between the sociodemographic variables and the letter-sound knowledge, inter-subjects (parametric and non-parametric) difference tests were conducted, as well as correlation analyses. To understand whether letter-sound knowledge is predicted by sociodemographic variables, a multiple linear regression analysis was performed using the Enter method. The results suggest that the mother’s education is the variable that most strongly contributes to success in reading acquisition. Socioeconomic status and the type of school also play a role in reading achievement. Identifying the sociodemographic factors that most strongly correlate with reading acquisition success is crucial for a more accurate identification of at-risk children and to provide targeted support/inclusion in reading skills promotion projects.

Breast cancer was a prevalent form of cancer worldwide. Thermography, a method for diagnosing breast cancer, involves recording the thermal patterns of the breast. This article explores the use of a convolutional neural network (CNN) algorithm to extract features from a dataset of thermographic images. Initially, the CNN network was used to extract a feature vector from the images. Subsequently, machine learning techniques can be used for image classification. This study utilizes four classification methods, namely Fully connected neural network (FCnet), support vector machine (SVM), classification linear model (CLINEAR), and KNN, to classify breast cancer from thermographic images. The accuracy rates achieved by the FCnet, SVM, CLINEAR, and k-nearest neighbors (KNN) algorithms were 94.2%, 95.0%, 95.0%, and 94.1%, respectively. Furthermore, the reliability parameters for these classifiers were computed as 92.1%, 97.5%, 96.5%, and 91.2%, while their respective sensitivities were calculated as 95.5%, 94.1%, 90.4%, and 93.2%. These findings can assist experts in developing an expert system for breast cancer diagnosis.

Cartography includes two major tasks: map making and map application, which is inextricably linked to artificial intelligence technology. The cartographic expert system experienced the intelligent expression of symbolism. After the spatial optimization decision of behaviorism intelligent expression, cartography faces the combination of deep learning under connectionism to improve the intelligent level of cartography. This paper discusses three problems about the proposition of “deep learning + cartography”. One is the consistency between the deep learning method and the map space problem solving strategy, based on gradient descent, local correlation, feature reduction and non-linear nature that answer the feasibility of the combination of “deep learning + cartography”; the second is to analyze the challenges faced by the combination of cartography from its unique disciplinary characteristics and technical environment, involving the non-standard organization of map data, professional requirements for sample establishment, the integration of geometric and geographical features, as well as the inherent spatial scale of the map; thirdly, the entry points and specific methods for integrating map making and map application into deep learning are discussed respectively.