Preparation of PVC/PVP composite polymer membranes via phase inversion process for water treatment purposes

In this work, new composite membranes were successfully prepared via phase inversion technique using polyvinyl chloride(PVC) and polyvinylpyrrolidone(PVP) as polymers and tetrahydrofuran(THF) and N-methyl-2-pyrrolidone(NMP) as solvents. The prepared membranes have been characterized by scanning electron microscope(SEM), and fourier transforms infrared spectroscopy(FTIR). The scanning electron microscope results prove that the prepared membranes are smooth and their pores are distributed throughout the whole surface and bulk body of the membrane without any visible cracks. The stress–strain mechanical test showed an excellent mechanical behavior enhanced by the presence of PVP in the prepared membranes. The membranes performance results showed that the salt rejection reached 98% with a high flux. This, in turn, makes the prepared membranes can be applied for sea and brackish water treatment through membrane distillation technology.     

Development,characterization, and in vitro bioactivity studies of sol–gel bioactive glass/poly(l-lactide) nanocomposite scaffolds

Developing materials combining the advantages of synthetic polymers and bioactive glass nanoparticles can provide an efficient bone engineering scaffold. In this study, sol–gel bioactive glass (SG) nanoparticles were synthesized by quick alkali-mediation; sol–gel derived bioactive glass/poly(l-lactide) nanocomposite scaffolds were then developed. The influence of the glass content on the porosity of nanocomposite scaffolds was evaluated by SEM. The results showed that the neat polymer scaffold (PLA) has a highly interconnected porous structure with a maximum pore size of about 250 μm. For the composite scaffold containing 25 wt.% glass (SGP25), the decrease in the maximum pore size, (to about 200 μm) was not significant while for the SGP50 composite scaffold containing 50 wt.% glass it was a significant decrease (to about 100 μm). The apparent porosity of the scaffolds was 56.56% ± 7.15, 54.14% ± 3.84, and 53.11% ± 3.99 for PLA, SGP25, and, SGP50 respectively. FT-IR, TGA, and XRD results revealed some interaction of the glass filler with the polymeric matrix in the scaffolds. The degradation study showed that, by increasing the glass content in the scaffolds, the water absorption decreased, the weight loss increased, and the cumulative ion concentrations released from them also increased. This indicates the possibility of modulating the degradation rate by varying the glass/polymer ratio. At the end of the incubation period, the weight losses were around 5.44% ± 0.96, 32.50% ± 2.73, and 41.47% ± 3.02 for the PLA, SGP25, and SGP50, respectively. Moreover, the water uptake reached 119.65% ± 18.88 and 93.39% ± 13.01 for SGP25 and SGP50, respectively. The addition of the SG to the scaffolds was found to enhance their in vitro bioactivity. Therefore, these nanocomposite scaffolds have a potential to be applied in bone engineering. All data are expressed as mean ± standard deviation (n = 3).     

Poly(2-hydroxyethylmethacrylate-graft-folic acid), synthesis,solubility enhancement,and release dynamic of folic acid

A series of poly (2-hydroxyethylmethacrylate-graft-folic acid) (FAHEMA) systems are synthesized by grafting of folic acid (FA) into poly (2–hydroxyethylmethacrylate) via an esterification reaction. The structure of these copolymers is confirmed by NMR and CHN analyses. The thermal behavior of these materials is characterized by DSC and TGA analyses. The surface morphology of FAHEMA films before and after the release process is examined by the SEM method. The cumulative FA released in different pH media from FAHEMA materials occurred via a retro-esterification reaction at body temperature during 72 h in which the influence of the swelling degree of PHEMA, the FA content and pH media on the dynamic release is widely investigated. The results obtained revealed that the solubility of FA in water deduced from the release process is widely improved compared with literature reports. It is also revealed that the diffusion of water in different pH media through the PHEMA matrix and that of FA through FAHEMA materials perfectly obeyed the Fickian models. It was deduced from the kinetic study that the release performance is obtained with the copolymers containing initially 10 and 20 wt% of FA contents.     

Hyperspectral Images-Based Crop Classification Scheme for Agricultural Remote Sensing

Hyperspectral imaging is gaining a significant role in agricultural remote sensing applications. Its data unit is the hyperspectral cube which holds spatial information in two dimensions while spectral band information of each pixel in the third dimension. The classification accuracy of hyperspectral images (HSI) increases significantly by employing both spatial and spectral features. For this work, the data was acquired using an airborne hyperspectral imager system which collected HSI in the visible and near-infrared (VNIR) range of 400 to 1000 nm wavelength within 180 spectral bands. The dataset is collected for nine different crops on agricultural land with a spectral resolution of 3.3 nm wavelength for each pixel. The data was cleaned from geometric distortions and stored with the class labels and annotations of global localization using the inertial navigation system. In this study, a unique pixel-based approach was designed to improve the crops' classification accuracy by using the edge-preserving features (EPF) and principal component analysis (PCA) in conjunction. The preliminary processing generated the high-dimensional EPF stack by applying the edge-preserving filters on acquired HSI. In the second step, this high dimensional stack was treated with the PCA for dimensionality reduction without losing significant spectral information. The resultant feature space (PCA-EPF) demonstrated enhanced class separability for improved crop classification with reduced dimensionality and computational cost. The support vector machines classifier was employed for multiclass classification of target crops using PCA-EPF. The classification performance evaluation was measured in terms of individual class accuracy, overall accuracy, average accuracy, and Cohen kappa factor. The proposed scheme achieved greater than 90 % results for all the performance evaluation metrics. The PCA-EPF proved to be an effective attribute for crop classification using hyperspectral imaging in the VNIR range. The proposed scheme is well-suited for practical applications of crops and landfill estimations using agricultural remote sensing methods.     

Degradation of azo dye with dirhodium(II) caprolactamate as heterogeneous catalyst

The kinetics of the oxidative degradation of an azo dye Metanil Yellow (MY) was investigated in aqueous solution using dirhodium(II) caprolactamate, Rh(2)(cap)(4), as a catalyst in the presence of H(2)O(2) as oxidizing agent. The reaction process was followed by UV/Vis spectrophotometer. The decolorization and degradation kinetics were investigated and both followed a pseudo-first-order kinetic with respect to the [MY]. The effects of various parameters such as H(2)O(2) and dye concentrations, the amount of catalyst and temperature have been studied. The studies show that Rh(2)(cap)(4) is a very effective catalyst for the formation of hydroxyl radicals HO(?) which oxidized and degraded about 92% of MY into CO(2) and H(2)O after 24 h as measured by total carbon analyzer.     

A Novel Enhanced Naïve Bayes Posterior Probability (ENBPP) Using Machine Learning: Cyber Threat Analysis

Neural Processing Letters - Machine learning techniques, that are based on semantic analysis of behavioural attack patterns, have not been successfully implemented in cyber threat intelligence....     

Generative adversarial network (GAN) and enhanced root mean square error (ERMSE): deep learning for stock price movement prediction

The prediction of stock price movement direction is significant in financial circles and academic. Stock price contains complex, incomplete, and fuzzy information which makes it an extremely difficult task to predict its development trend. Predicting and analysing financial data is a nonlinear, time-dependent problem. With rapid development in machine learning and deep learning, this task can be performed more effectively by a purposely designed network. This paper aims to improve prediction accuracy and minimizing forecasting error loss through deep learning architecture by using Generative Adversarial Networks. It was proposed a generic model consisting of Phase-space Reconstruction (PSR) method for reconstructing price series and Generative Adversarial Network (GAN) which is a combination of two neural networks which are Long Short-Term Memory (LSTM) as Generative model and Convolutional Neural Network (CNN) as Discriminative model for adversarial training to forecast the stock market. LSTM will generate new instances based on historical basic indicators information and then CNN will estimate whether the data is predicted by LSTM or is real. It was found that the Generative Adversarial Network (GAN) has performed well on the enhanced root mean square error to LSTM, as it was 4.35% more accurate in predicting the direction and reduced processing time and RMSE by 78 s and 0.029, respectively. This study provides a better result in the accuracy of the stock index. It seems that the proposed system concentrates on minimizing the root mean square error and processing time and improving the direction prediction accuracy, and provides a better result in the accuracy of the stock index.

     

An enhanced algorithm for improving real-time video transmission for tele-training education

Tele-training in surgical education has not been effectively implemented. There is a stringent need for a high transmission rate, reliability, throughput, and reduced distortion for high-quality video transmission in the real-time network. This work aims to propose a system that improves video quality during real-time surgical tele-training. The proposed approach aims to minimise the video frame’s total distortion, ensuring better flow rate allocation and enhancing the video frames’ reliability. The proposed system consists of a proposed algorithm for Enhancing Video Quality, Distorting Minimization, Bandwidth efficiency, and Reliability Maximization called (EVQDMBRM) algorithm. The proposed algorithm reduces the video frame’s total distortion. In addition, it enhances the video quality in a real-time network by dynamically allocating the flow rate at the video source and maximizing the transmission reliability of the video frames. The result shows that the proposed EVQDMBRM algorithm improves the video quality with the minimized total distortion. Therefore, it improves the Peak Signal to Noise Ratio (PSNR) average by 51.13 dB against 47.28 dB in the existing systems. Furthermore, it reduces the video frames processing time average by 58.2 milliseconds (ms) against 76.1, and the end-to-end delay average by 114.57 ms against 133.58 ms comparing to the traditional methods. The proposed system concentrates on minimizing video distortion and improving the surgical video transmission quality by using an EVQDMBRM algorithm. It provides the mechanism to allocate the video rate at the source dynamically. Besides that, it minimizes the packet loss ratio and probing status, which estimates the available bandwidth.

     

Refurbishment of public housing villas in the United Arab Emirates (UAE): energy and economic impact

This study aims at assessing the technical and economic benefits of refurbishing existing public housing villas in the UAE. Four representative federal public housing villas built between 1980s and 2010s were modeled and analyzed. The Integrated Environmental Solutions-Virtual Environment (IES-VE) energy modeling software was used to estimate the energy consumption and savings due to different refurbishment configurations applied to the villas. The refurbishment technical configurations were based on the UAE’s Estidama green buildings sustainability assessment system. The refurbishment configurations include upgrading three elements: the wall and roof insulation as well as replacing the glazing. The annual electricity savings results indicated that the most cost-efficient refurbishment strategy is upgrading of wall insulation (savings up to 20.8 %) followed by upgrading the roof’s insulation (savings up to 11.6 %) and lastly replacing the glazing (savings up to 3.2 %). When all three elements were refurbished simultaneously, savings up to 36.7 % were achieved (villa model 670). The savings translated to CO₂ emission reduction of 22.6 t/year. The simple and discounted payback periods for the different configurations tested ranged between 8 and 28 and 10 and 50 years, respectively.