Modeling and Analysis of UAV-Assisted Mobile Network with Imperfect Beam Alignment

With the rapid development of emerging 5G and beyond (B5G), Unmanned Aerial Vehicles (UAVs) are increasingly important to improve the performance of dense cellular networks. As a conventional metric, coverage probability has been widely studied in communication systems due to the increasing density of users and complexity of the heterogeneous environment. In recent years, stochastic geometry has attracted more attention as a mathematical tool for modeling mobile network systems. In this paper, an analytical approach to the coverage probability analysis of UAV-assisted cellular networks with imperfect beam alignment has been proposed. An assumption was considered that all users are distributed according to Poisson Cluster Process (PCP) around base stations, in particular, Thomas Cluster Process (TCP). Using this model, the impact of beam alignment errors on the coverage probability was investigated. Initially, the Probability Density Function (PDF) of directional antenna gain between the user and its serving base station was obtained. Then, association probability with each tier was achieved. A tractable expression was derived for coverage probability in both Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) condition links. Numerical results demonstrated that at low UAVs altitude, beam alignment errors significantly degrade coverage performance. Moreover, for a small cluster size, alignment errors do not necessarily affect the coverage performance.     

Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions. From this perspective, an automated AI technique with a digital processing method can be used to improve these signals. This paper proposes two classifiers: long short-term memory (LSTM) and support vector machine (SVM) for the classification of seizure and non-seizure EEG signals. These classifiers are applied to a public dataset, namely the University of Bonn, which consists of 2 classes –seizure and non-seizure. In addition, a fast Walsh-Hadamard Transform (FWHT) technique is implemented to analyze the EEG signals within the recurrence space of the brain. Thus, Hadamard coefficients of the EEG signals are obtained via the FWHT. Moreover, the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings. Also, a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers. The LSTM classifier provides the best performance, with a testing accuracy of 99.00%. The training and testing loss rates for the LSTM are 0.0029 and 0.0602, respectively, while the weighted average precision, recall, and F1-score for the LSTM are 99.00%. The results of the SVM classifier in terms of accuracy, sensitivity, and specificity reached 91%, 93.52%, and 91.3%, respectively. The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s, respectively. The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals. Eventually, the proposed classifiers provide high classification accuracy compared to previously published classifiers.     

Preparation and characterization of sol–gel derived glasses in the ternary Na2O–Al2O3–P2O5 system

A new sol–gel synthesis route to organic free Na₂O–Al₂O₃–P₂O₅ xerogels was developed. These xerogels contain P₂O₅ up to 67.5 mole % and Al₂O₃ up to 20 mole % and could be melted to homogeneous glasses in air at significantly lower temperatures than those applied using the conventionally melting method. Aluminum isopropoxide dissolved in isopropanol, orthophosphoric acid dissolved in isopropanol and sodium nitrate were used as precursors for Al₂O₃, P₂O₅ and Na₂O respectively. Clear stable alcoholic solutions were prepared in the presence of HNO₃. Addition of HNO₃ in the concentration used, enabled homogeneous mixing of the precursors, controlled over the gelation step and prevented the fast precipitation of the AlPO₄ crystalline phase which breaks down the glass formation ability. The sol–gel route developed was investigated using different analytical tools. Differential thermal analysis (DTA) coupled with thermo-gravimetric (TG) analysis indicated that neither organic residues nor sodium nitrate residues were present in the dried gels after gelation. Fourier transform infrared spectroscopy (FTIR) showed the presence of all characteristic phosphate groups and bonds in the dried gels, especially the P=O and the P–O–P bonds. Solid state ³¹P Magic angle spinning nuclear magnetic resonance (MAS NMR) revealed the formation of Q² chain-like polyphosphates of variable polymerization degrees in the dried gels. Solid state ²⁷Al MAS NMR revealed that octahedrally coordinated Al is the preferred moiety in the dried gels. However, powder X-ray diffraction (XRD) investigation indicated the presence of crystalline Q¹ oligomeric pyrophosphate species in the dried gels. Isotropic chemical shifts belonging to these pyrophosphate species were also recorded in the ³¹P MAS NMR spectra. Characterization of the prepared glasses showed that their properties are in very good agreement with those glasses prepared by the conventionally melting method.     

Enterprise Resource Planning Systems Introduction and Internal Auditing Legitimacy: An Institutional Analysis

In this article, the authors examine how the internal audit function maintains its legitimacy when enterprise resource planning systems are introduced. This work centers on an in-depth case study of a multinational bank and finds that enterprise resource planning systems impose an institutional logic of control based on interlinked assumptions. These assumptions motivate changes in the practice and structure of the internal audit function to become an integrated and comprehensive function to maintain its legitimacy.     

Theranostic Interpolation of Genomic Instability in Breast Cancer

Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. “Omics” technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer.     

Electrochemical and Theoretical Investigation for the Protection of Aluminum Corrosion in Hydrochloric Acid using Some Azole Derivatives

Protection of Metals and Physical Chemistry of Surfaces - Three compounds of azole derivatives were investigated as inhibitors for the dissolution of aluminum (Al) in 0.5 M HCl solution using...     

Sintering and microstructure of ultrafine yttriazirconia compacts

Sintering behaviour of ultrafine yttria-zirconia solid solution compacts, with yttria contents ranging between 4 and 10 mol % have been investigated and the microstructural developments during sintering were followed. The 4 mol % yttria-zirconia compacts sinter at 1200 ° C to a high density with a relatively fine uniform microstructure. Firing at 1400 ° C produces no improvement in densification and discontinuous grain growth develops. The lowest density values are obtained with the 10 mol % yttria-zirconia compacts, with clustering in the grains and greater porosity in the microstructure. The activation energies were determined for both 4 and 10 mol % Y₂O₃ -containing bodies in the temperature range 800 to 1000 ° C.     

Infrared spectra and composition dependence investigations of the vitreous V2O5/P2O5 system

A series of glasses in the V₂O₅-P₂O₅ system was prepared and their compositions analysed. The glass densities and molar volumes were determined. The results obtained revealed three compositional regions. In addition, the infrared absorption spectra of these glasses were measured at room temperature in the frequency range 1600–200 cm^–1. The compositional dependence of the bands present, attributed to a given band and mode of vibration, was investigated. However, the infrared data confirmed the results obtained from the density and molar volume measurements.