Foreign direct investment (FDI) has been viewed as a power affecting economic growth (EG) directly and indirectly during the past few decades. This paper reviewed an amount of researches examining the relationships between FDI and EG, especially the effects of FDI on EG, from 1994 up to 2012. The results show that the main finding of the FDI-EG relation is significantly positive, but in some cases it is negative or even null. And within the relation, there exist several influencing factors such as the adequate levels of human capital, the well-developed financial markets, the complementarity between domestic and foreign investment and the open trade regimes, etc.

The increased use of hybrid PET /CT scanners combining detailed anatomical information along withfunctional data has benefits for both diagnostic and therapeutic purposes. This presented study is to makecomparison of cross sections to produce 18F , 82Sr and68Ge via different reactions with particle incident energy up to 60 MeV as a part of systematic studies on particle-induced activations on enriched natNe, natRb, natGa 18O,85Rb, and 69Ga targets, theoretical calculation of production yield, calculation of requiredtarget and suggestion of optimum reaction to produce: Fluorine-18 , Strontium-82 andGermanium-68 touse in Hybrid Machines PET/CT Scanners.

In the field of data security, the critical challenge of preserving sensitive information during its transmission through public channels takes centre stage. Steganography, a method employed to conceal data within various carrier objects such as text, can be proposed to address these security challenges. Text, owing to its extensive usage and constrained bandwidth, stands out as an optimal medium for this purpose. Despite the richness of the Arabic language in its linguistic features, only a small number of studies have explored Arabic text steganography. Arabic text, characterized by its distinctive script and linguistic features, has gained notable attention as a promising domain for steganographic ventures. Arabic text steganography harn

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

The design, synthesis, and characterization of a star shaped 2,4,6-tris-(4`-carboxyphenoxy)-1,3,5-triazine liquid crystalline with columnar discotic mesophase properties establish H-bond interactions with 3,5-dialkoxypyidine were reported. The structures of the synthesized compounds were actually determined by elementary analysis, and FT-IR, ¹HNMR, ¹³CNMR, and mass spectroscopy. The mesomorphic properties of these mesogens were examined using differential scanning calorimetry (DSC) and optical polarizing microscopy (OPM). The synthesized molecules exhibited enantiotropic hexagonal columnar liquid crystal, which depends for the H- bond complex in a 1:3 ratio.

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c