The use of natural (Pinus pinaster) resin in the production of printing ink and the printability effect

Alkyd resins are generally used in the production of printing inks. All industries look for alternative raw materials in the production of ink with the growing inclination toward using natural products. Resins forming the vehicle of the ink to be obtained from natural resources will provide benefits for the environment, nature, and living creatures. The aim of the study was to promote the use of natural resin in the ink system. Natural Pinus pinaster resin was added into vegetable and mineral oil-based solvents in pure form with alkyd resin in different amounts and ink varnishes of different combinations were prepared. Then, printing inks were produced from these varnishes in pure and hybrid form. Following the assessment of the rheological properties of the inks prepared, printing tests were conducted to assess the printing quality parameters. Ideal mixing ratios of the natural resins in the ink were determined for printability. The environmental importance and advantages of the use of natural resins were discussed. Recommendations were given in line with the results to encourage widespread use of natural resins in near future.     

Characterization of the Ion Beam Current Density of the RF Ion Source with Flat and Convex Extraction Systems

Silicon - The characterization of ion beam current density distribution and beam uniformity is crucial for improving broad-beam ion source technologies. The design of the broad ion beam extraction...     

Macrophage Polarization in Cardiac Tissue Repair Following Myocardial Infarction

Cardiovascular disease is the leading cause of mortality and morbidity around the globe, creating a substantial socio-economic burden as a result. Myocardial infarction is a significant contributor to the detrimental impact of cardiovascular disease. The death of cardiomyocytes following myocardial infarction causes an immune response which leads to further destruction of tissue, and subsequently, results in the formation of non-contractile scar tissue. Macrophages have been recognized as important regulators and participants of inflammation and fibrosis following myocardial infarction. Macrophages are generally classified into two distinct groups, namely, classically activated, or M1 macrophages, and alternatively activated, or M2 macrophages. The phenotypic profile of cardiac macrophages, however, is much more diverse and should not be reduced to these two subsets. In this review, we describe the phenotypes and functions of macrophages which are present in the healthy, as well as the infarcted heart, and analyze them with respect to M1 and M2 polarization states. Furthermore, we discuss therapeutic strategies which utilize macrophage polarization towards an anti-inflammatory or reparative phenotype for the treatment of myocardial infarction.     

Oxygen reduction activity of Pt and Pt-Mn-Co electrocatalysts sputtered on nano-structured thin film support

We report on extensive measurements of oxygen reduction activity of Pt and Pt-Co-Mn electrocatalysts using the rotating ring-disk electrode (RRDE) method. The electrocatalysts were prepared by sputtering from Pt or Pt, Co and Mn targets onto 3M's nano-structured thin film support (NSTF) structures. The area specific activity of Pt/NSTF, measured in 0.1 M HClO₄ and at room temperature, is similar to that of bulk Pt. The area specific measurements show a 20 mV reduction in the Pt-Co-Mn/NSTF overpotential compared to Pt/NSTF. The corresponding kinetic gain in the area specific activity of the ternary alloy is about a factor of two. This ORR enhancement factor observed in the ternary Pt-Co-Mn/NSTF by RRDE measurements is similar to the results obtained in 50 cm² H₂/air fuel cells.     

Buoyancy effects on gaseous slip flow in a vertical rectangular microchannel

Consideration is given to the buoyancy effects on the fully developed gaseous slip flow in a vertical rectangular microduct. Two different cases of the thermal boundary conditions are considered, namely uniform temperature at two facing duct walls with different temperatures and adiabatic other walls (case A) and uniform heat flux at two walls and uniform temperature at other walls (case B). The rarefaction effects are treated using the first-order slip boundary conditions. By means of finite Fourier transform method, analytical solutions are obtained for the velocity and temperature distributions as well as the Poiseuille number. Furthermore, the threshold value of the mixed convection parameter to start the flow reversal is evaluated. The results show that the Poiseuille number of case A is an increasing function of the mixed convection parameter and a decreasing function of the channel aspect ratio, whereas its functionality on the Knudsen number is not monotonic. For case B, the Poiseuille number is decreased by increasing each of the mixed convection parameter, the Knudsen number, and the channel aspect ratio.     

Propagation of quasi-simple waves in a compressible rotating atmosphere

Summary A class of self-propagating linear and nonlinear travelling wave solutions for compressible rotating fluid is studied using both numerical and analytical techiques. It is shown that, in general, a three dimensional linear wave is not periodic. However, for some range of wave numbers depending on rotation, horizontally propagating waves are periodic. When the rotation is equal to , all horizontal waves are periodic. Here, is the ratio of specific heats. The analytical study is based on phase space analysis. It reveals that the quasi-simple waves are periodic only in some plane, even when the propagation is horizontal, in contrast to the case of non-rotating flows for which there is a single parameter family of periodic solutions provided the waves propagate horizontally. A classification of the singular points of the governing differential equations for quasi-simple waves is also appended.     

Concept learning games

In this paper, we intend to have a game theoretic study on the concept learning problem in a multi-agent system. Concept learning is a very essential and well-studied domain of machine learning when it is studied under the characteristics of a multi-agent system. The most important reasons are the partiality of the environment perception for any agent and also the communication holdbacks, resulting into a deep need for a collaborative protocol in favor of multi-agent transactions. Here we wish to investigate multi-agent concept learning with the help of its components, thoroughly with a game theoretic taste, esp. on the pre-learning processes. Based on two standard notations, we address the non-unanimity of concepts, classification of objects, voting and communicating protocol, and also the learning itself. In such a game of concept learning, we consider a group of agents, communicating and consulting to upgrade their ontologies based on their conceptualizations of the environment. For this purpose, we investigate the problem in two separate and standard distinctions of game theory study, cooperation and competition. Several solution concepts and innovative ideas from the multi-agent realm are used to produce an approach that contains the reasoning process of the agents in this system. Some experimentations come at the end to show the functionality of our approach. These experimentations come distinctly for both cooperative and competitive views.     

Performance Analysis of Machine Learning Algorithms for Classifying Hand Motion-Based EEG Brain Signals

Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals; these signals can be recorded, processed and classified into different hand movements, which can be used to control other IoT devices. Classification of hand movements will be one step closer to applying these algorithms in real-life situations using EEG headsets. This paper uses different feature extraction techniques and sophisticated machine learning algorithms to classify hand movements from EEG brain signals to control prosthetic hands for amputated persons. To achieve good classification accuracy, denoising and feature extraction of EEG signals is a significant step. We saw a considerable increase in all the machine learning models when the moving average filter was applied to the raw EEG data. Feature extraction techniques like a fast fourier transform (FFT) and continuous wave transform (CWT) were used in this study; three types of features were extracted, i.e., FFT Features, CWT Coefficients and CWT scalogram images. We trained and compared different machine learning (ML) models like logistic regression, random forest, k-nearest neighbors (KNN), light gradient boosting machine (GBM) and XG boost on FFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFT features gave the maximum accuracy of 88%.