This work was conducted to study limestone rock wettability alteration to enhance oil recovery by flooding using different Nano silica (NS) sizes suspended in saline water and compared with flooding solution of polyethylene glycol (PEG) polymer and sodium dodecyl sulfate (SDS) surfactant, the stability of nanofluids measured by zeta potential. In the flooding system, the secondary recovery by silica nanofluids (0.01 wt. % NS concentration) achieved an oil recovery of 35vol. % and 26.08 vol. % for 10, 52 nm after primary recovery respectively, while PEG polymer and SDS surfactant achieved oil recovery of 5 vol. % and 10 vol. % only respectively. The stability of pressure difference approved that silica nanoparticle never causes any p

The drug promethazine hydrochloride (PRZH) forms with rhodium (II) a colored chelate (?max = 472 nm) complex at (pH = 2.1) which is extractable with benzyl alcohol as organic solvent. Under the appropriate experimental conditions a calibration plot was set up from which some analytical parameter were derived and deduced by regression. Standard addition procedure was also adopted. It has been estimated that the concentration of the drug PRZH to be 24.89 mg per unit and 24.19 mg per unit for both calibrations. Under optimal conditions, the developed method has been achieved the following characteristics: LDR (30 – 150 µg ml-1 ) PRZH , RSD % ( 0.6 – 2.47 ) , sandell sensitivity( 0.0844 µg. cm -2 ) , LOD ( 1.66 µgml-1 ) , recovery

Aluminum oxide (ALO) was grafted by acrylic acid monomer (AlO-AM) and then, it was polymerized to produce alumina grafted poly(acrylic acid) (AlO-AP). The prepared AlO-AM and AlO-AP were characterized by Fourier-transform infrared, differential scanning calorimetry , thermogravemetric analyzer and particle size distribution. Adsorption equilibrium isotherms, adsorption kinetics and thermodynamic studies of the batch adsorption process were used to examine the fundamental adsorption properties of phenol (P) and p-chlorophenol (PCP). The experimental equilibrium adsorption data were analyzed by three widely used two-parameters Langmuir, Freundlich and DubininRadushkevich isotherms. The maximum P and PCP adsorption capacities based on t

An aromatic ester containing two azo groups namely p-nitro phenyl azo-β-naphthyl-(4'-azobenzoic acid)-4-benzoate was synthesized by esterfiaction of 4,4'-azo dibenzoic acid with p-nitro phenyl azo-β-naphthol. Synthesized ester was characterized by CHN-Elemental analysis, FTIR, 1H NMR and 13C NMR. A modified PVA polymer was obtained by grafting 10 g of PVA-polymer via partial esterification with (2, 3, 4 g) p-nitro phenyl azo-1-naphthyl-4-azobenzoic acid)-4-azo benzoate. Grafting PVA-polymer behaviours was studied, by physical measurements (solubility, swelling), thermal properties (DSC) and tensile.

Leap Motion Controller (LMC) is a gesture sensor consists of three infrared light emitters and two infrared stereo cameras as tracking sensors. LMC translates hand movements into graphical data that are used in a variety of applications such as virtual/augmented reality and object movements control. In this work, we intend to control the movements of a prosthetic hand via (LMC) in which fingers are flexed or extended in response to hand movements. This will be carried out by passing in the data from the Leap Motion to a processing unit that processes the raw data by an open-source package (Processing i3) in order to control five servo motors using a micro-controller board. In addition, haptic setup is proposed using force sensors (F

Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

Aspect categorisation and its utmost importance in the eld of Aspectbased Sentiment Analysis (ABSA) has encouraged researchers to improve topic model performance for modelling the aspects into categories. In general, a majority of its current methods implement parametric models requiring a pre-determined number of topics beforehand. However, this is not e ciently undertaken with unannotated text data as they lack any class label. Therefore, the current work presented a novel non-parametric model drawing a number of topics based on the semantic association present between opinion-targets (i.e., aspects) and their respective expressed sentiments. The model incorporated the Semantic Association Rules (SAR) into the Hierarchical Dirichlet Proce

In this paper, RBF-based multistage auto-encoders are used to detect IDS attacks. RBF has numerous applications in various actual life settings. The planned technique involves a two-part multistage auto-encoder and RBF. The multistage auto-encoder is applied to select top and sensitive features from input data. The selected features from the multistage auto-encoder is wired as input to the RBF and the RBF is trained to categorize the input data into two labels: attack or no attack. The experiment was realized using MATLAB2018 on a dataset comprising 175,341 case, each of which involves 42 features and is authenticated using 82,332 case. The developed approach here has been applied for the first time, to the knowledge of the authors, to dete

Until recently, researchers have utilized and applied various techniques for intrusion detection system (IDS), including DNA encoding and clustering that are widely used for this purpose. In addition to the other two major techniques for detection are anomaly and misuse detection, where anomaly detection is done based on user behavior, while misuse detection is done based on known attacks signatures. However, both techniques have some drawbacks, such as a high false alarm rate. Therefore, hybrid IDS takes advantage of combining the strength of both techniques to overcome their limitations. In this paper, a hybrid IDS is proposed based on the DNA encoding and clustering method. The proposed DNA encoding is done based on the UNSW-NB15