This research aims to predict new COVID-19 cases in Bandung, Indonesia. The system implemented two types of deep learning methods to predict this. They were the recurrent neural networks (RNN) and long-short-term memory (LSTM) algorithms. The data used in this study were the numbers of confirmed COVID-19 cases in Bandung from March 2020 to December 2020. Pre-processing of the data was carried out, namely data splitting and scaling, to get optimal results. During model training, the hyperparameter tuning stage was carried out on the sequence length and the number of layers. The results showed that RNN gave a better performance. The test used the RMSE, MAE, and R2 evaluation methods, with the best numbers being  0.66975075, 0.470

Nigella sativa has various pharmacological properties and has been used throughout history for a variety of reasons. However, there is limited data about the effects of N. sativa (NS) on human cancer cells. This study aimed at observing the roles of methanolic extract of N. sativa on apoptosis and autophagy pathway in the Human PC3 (prostate cancer) cell line. The cell viability was checked by MTT assay. Clonogenic assay was performed to demonstrate clonogenicity and Western blot was used to check caspase-3, TIGAR, p53, and LC3 protein expression. The results demonstrated that PC3 cell proliferation was inhibited, caspase-3 and p53 protein expression was induced, and LC3 protein expression was modulated. The clonogenic assay showed that PC3

In this paper, a method for data encryption was proposed using two secret keys, where the first one is a matrix of XOR's and NOT's gates (XN key), whereas the second key is a binary matrix (KEYB) key. XN and KEYB are (m*n) matrices where m is equal to n. Furthermore this paper proposed a strategy to generate secret keys (KEYBs) using the concept of the LFSR method (Linear Feedback Shift Registers) depending on a secret start point (third secret key s-key). The proposed method will be named as X.K.N. (X.K.N) is a type of symmetric encryption and it will deal with the data as a set of blocks in its preprocessing and then encrypt the binary data in a case of stream cipher.

We study in this paper the composition operator that is induced by ?(z) = sz + t. We give a characterization of the adjoint of composiotion operators generated by self-maps of the unit ball of form ?(z) = sz + t for which |s|?1, |t|<1 and |s|+|t|?1. In fact we prove that the adjoint is a product of toeplitz operators and composition operator. Also, we have studied the compactness of C? and give some other partial results.

High frequency (HF) radio wave propagation depends on the ionosphere status which is changed with the time of day, season, and solar activity conditions. In this research, ionosonde observations were used to calculate the values of maximum usable frequency (MUF) the ionospheric F2- layer during strong geomagnetic storms (Dst ≤ -100 nT) which were compared with the predicted MUF for the same layer by using IRI-16 model. Data from years 2015 and 2017, during which five strong geomagnetic storms occurred, were selected from two Japanese ionosonde stations (Kokubunji and Wakkanai) located at the mid-latitude region. The results of the present work do not show a good correlation between the observed and predicted MUF values for F2- laye

In this paper, a comparison between horizontal and vertical OFET of Poly (3-Hexylthiophene) (P3HT) as an active semiconductor layer (p-type) was studied by using two different gate insulators (ZrO₂ and PVA). The electrical performance output (I_d-V_d) and transfer (I_d-V_g) characteristics were investigated using the gradual-channel approximation model. The device shows a typical output curve of a field-effect transistor (FET). The analysis of electrical characterization was performed in order to investigate the source-drain voltage (V_d) dependent current and the effects of gate dielectric on the electrical performance of the OFET. This work also considered the effects of the

Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date.  A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s