Evaporation rate and separation factor of molecular distillation in a falling film apparatus

During molecular distillation in a falling film apparatus, temperature and concentration of the more volatile components undergo a marked decrease in axial and radial directions, due to the distillation in vacuo. The gradients produce changes in the rate of surface evaporation and separation efficiency along the flow path. A theoretical study of this problem is presented for binary mixtures. The respective differential equations for heat and mass transfer in the liquid film are solved numerically. As the results demonstrate, the aspect ratio of an apparatus should be large when a high separation is required and small when a high distillation rate is more important.     

Artificial neural network-driven federated learning for heart stroke prediction in healthcare 4.0 underlying 5G

In recent years, smart healthcare, artificial intelligence (AI)-aided diagnostics, and automated surgical robots are just a few of the innovations that have emerged and gained popularity with the advent of Healthcare 4.0. Such technologies are powered by machine learning (ML) and deep learning (DL), which are preferable for disease diagnosis, identifying patterns, prescribing treatments, and forecasting diseases like stroke prediction, cancer prediction and so forth. Nevertheless, much data is needed for AI, ML, and DL-based systems to train effectively and provide the desired outcomes. Further, it raises concerns about data privacy, security, communication overhead, regulatory compliance and so forth. Federated learning (FL) is a technology that protects data security and privacy by limiting data sharing and utilizing model information of distributed systems to enhance performance. However, existing approaches are traditionally verified on pre-established datasets that fail to capture real-life applicability. Therefore, this study proposes an AI-enabled stroke prediction architecture consisting of FL based on the artificial neural network (ANN) model using data from actual stroke cases. This architecture can be implemented on healthcare-based wearable devices (WD) for real-time use as it is effective, precise, and computationally affordable. In order to continuously enhance the performance of the global model, the proposed FL-based architecture aggregates the optimizer weights of many clients using a fifth-generation (5G) communication channel. Then, the performance of the proposed FL-based architecture is studied based on multiple parameters such as accuracy, precision, recall, bit error rate, and spectral noise. It outperforms the traditional approaches regarding accuracy, which is 5% to 10% higher.     

Congenital arteriovenous shunts (author''s transl)

In 95 women taking reduced-level-oral contraceptives and in 24 same-aged women controls, the measure of erythrocyte and serum folate led to the following conclusions: 1) Folate levels in women consuming the pill were generally found to be of the same range as those of controls. 2) No significant difference could be shown between results for different kinds of contraceptives.     

Effect of endothelin-1 on some hemostatic parameters in normotensive rats

Some parameters of hemostasis and fibrinolysis were investigated in rats administered with endothelin-1 (ET-1). ET-1 (0.5, 1.0, 5.0 nmol/kg) dose-dependently shortened the bleeding time (BT). Concomitantly significant shortening of the clotting time (CT) was observed. ET-1 produced prolongation of the activated partial thromboplastin time (APTT), whereas prothrombin time (PT) remained unchanged. ET-1 did not influence in vitro platelet aggregation induced by ADP and collagen. The euglobulin clot lysis time (ECLT) was significantly shortened after ET-1 administration. Our results suggest that ET-1 modulates the process of hemostasis and fibrinolysis in the rat.     

Almost sure convergence of classification procedures using Hermite series density estimates

A multidimensional classification procedure is examined derived from the multiple Hermite series estimate of probability density functions. Conditions for the almost sure convergence of the integrated square error for the estimate are presented and the rate of the convergence is studied. The probability of misclassification, conditioned on a learning sequence of length n, is shown to converge to the Bayes risk almost surely as rapidly as $\text{O}\text{(n}{}^{\mathrm{<mtext>?1</mtext><mtext>2+\delta </mtext>}}\text{)}$, δ positive.     

Effect of cleavage of ether linkages on physicochemical properties of coals

Coals of different rank were treated with naphthalene anion in tetrahydrofuran. It was found that, as in the case of coal of 87.8% C (daf), large amounts of unreactive oxygen bonds, possibly of the ether type, were cleaved. Cleavage of these ether linkages led to a lowering of the melting point and, in some types of coals, to a large increase in dilatation and a substantial increase in Gieseler fluidity.     

Kernel classification rules from missing data

Nonparametric kernel classification rules derived from incomplete (missing) data are studied. A number of techniques of handling missing observation in the training set are taken into account. In particular, the straightforward approach of designing a classifier only from available data (deleting missing values) is considered. The class of imputation techniques is also taken into consideration. In the latter case, one estimates missing values and then calculates classification rules from such a completed training set. Consistency and speed of convergence of proposed classification rules are established. Results of simulation studies are presented