The constraints on a general form of the power-law potential and the dissipation coefficient in the framework of warm single field inflation imposed by Planck data will be investigated. By considering a quasi-static Universe, besides a slow-roll condition, the suitable regions in which a pair of theoretical free parameters are in good agreement with Planck results will be estimated. In this method, instead of a set of free parameters, we can visualize a region of free parameters that can satisfy the precision limits on theoretical results. On the other side, when we consider the preformed quantity for the amplitude of scalar perturbations, the conflict between obtained results for free parameters in different steps will be dramatically decreased. As done in prominent literature, based on the friction of the environment, we can divide the primordial Universe into two different epochs, namely weak and strong dissipative regimes. For the aforementioned eras, the free parameters of the model will be constrained and the best regions will be obtained. To do so, the main inflationary observables such as tensor-to-scalar ratio, power-spectra of density perturbations and gravitational waves, scalar and tensor spectral indices, running spectral index and the number of e-folds in both weak and strong regimes will be obtained. Ultimately, it can be visualized, this model can make concord between theoretical results and data originated from cosmic microwave background and Planck 2013, 2015 and 2018.

Over the past decade, fake news and misinformation have turned into a major problem that has impacted different aspects of our lives, including politics and public health. Inspired by natural human behavior, we present an approach that automates the detection of fake news. Natural human behavior is to cross-check new information with reliable sources. We use Natural Language Processing (NLP) and build a machine learning (ML) model that automates the process of cross-checking new information with a set of predefined reliable sources. We implement this for Twitter and build a model that flags fake tweets. Specifically, for a given tweet, we use its text to find relevant news from reliable news agencies. We then train a Random Forest model that checks if the textual content of the tweet is aligned with the trusted news. If it is not, the tweet is classified as fake. This approach can be generally applied to any kind of information and is not limited to a specific news story or a category of information. Our implementation of this approach gives a 70% accuracy which outperforms other generic fake-news classification models. These results pave the way towards a more sensible and natural approach to fake news detection.

The constraints on a general form for powerlaw potential in the framework of warm single field inflation by means of Planck 2013 and 2015 will be investigated. By considering much less dynamic universe, quasi-static evolutions, and also the well-known slowroll conditions the regions which a binary of our free parameters are in good agreement with Planck results will be obtained. The advantage of this likelihood method is that instead of a set of free parameters we can visualize a region of free parameters that can satisfy the precision limits on theoretical results. On the other side, when we consider the preformed quantity for the amplitude of scalar perturbations the conflict between obtained results for free parameters in different steps dramatically decreased. As done in flagship literature, based on the friction of the environment, we can divide the primordial universe into two different epochs namely weak and strong dissipative regimes. For the aforementioned eras, the free parameters of the model will be constrained and the best regions will be obtained. To do so the main inflationary observables such as tensor-to-scalar ratio, power-spectra of density perturbations and gravitational waves, scalar and tensor

Scientists have always been looking for ways to create an effective relationship between humans and the machine, so that this relationship is as close as possible to human relationships, since even the most sophisticated machines do not have any particular effect without human intervention. This association results from brain-generated neural responses due to motor activity or cognitive activity. Communication methods include muscle and non-muscle activities that create brain activity or brainwaves and lead to a hardware device to perform a specific task. BCI was originally designed as a communication tool for patients with neuromuscular disorders, but due to recent advances in BCI devices such as passive electrodes, wireless headset, adaptive software, and cost reduction, it has been used to link the rest of the body. The BCI is a bridge between the signals generated by thoughts in our brain and the machines. BCI has been a successful invention in the field of brain imaging, which can be used in a variety of areas, including helping motor activity, vision, hearing, and any damage that the body sustains. The BCI device records brain responses using invasive, semi-invasive and non-invasive methods including Electroencephalography (EEG), Magnetizhenophyllography (MEG), and Magnetic Resonance Imaging (MRI). Brain response using pattern recognition methods to control any translation application. In this article, a review of various techniques for extracting features and classification algorithms has been presented on brain data. A significant comparative analysis of existing BCI techniques is provided.