An integrated technique using solar and evaporation ponds for effective brine disposal management

Desalination is a process that involves the removal of salts and non-ionic minerals from seawater to produce freshwater that is fit for human consumption. This process produces brine, which is typically redisposed into the sea. The relatively high salt concentration in the disposed brine increases the salinity of water and soil, which adversely affects the environment. However, brine is found to be rich in economically valuable minerals. In order to effectively manage the disposed brine, this study proposes an integrated technique using solar and evaporation ponds to filter valuable minerals from concentrated brine. The results of this study demonstrate that the proposed technique can be effectively employed for this purpose. Furthermore, this helps reduce desalination costs and complies with the notion of renewable energy production and eco-friendliness.     

Radiation modelling and performance evaluations of fixed,single- and double-axis tracking surfaces: a case study for Dhahran city,Saudi Arabia

This work overviews the solar radiation basics and insolation of different surfaces is presented. A complete solar radiation modelling and investigation on the effect of horizontal plate, yearly tilt, monthly tilt, and single-axis and double-axis tracking surface on the insolation are carried out to conduct performance evaluation using the case study in Dhahran city of Saudi Arabia. The increments received by insolation for the yearly tilt, monthly tilt, and single-axis and dual-axis tracking surface with respect to traditional flat-plate collector is estimated. The results show that the yearly optimal tilt angle due to the south is close to the 0.913 time latitude of Dhahran. It is found that the yearly irradiation gains using yearly and monthly optimal tilts relative to flat panel installation are 7% and 14%, respectively. The yearly insulation gains made by single-axis and dual-axis continuous tracking surfaces are 33% and 48%, respectively.     

Time-based DDoS attack detection through hybrid LSTM-CNN model architectures: An investigation of many-to-one and many-to-many approaches

Internet data thefts, intrusions and DDoS attacks are some of the big concerns for the network security today. Detection of these anomalies, is gaining tremendous impetus with the development of machine learning and artificial intelligence. Even now researchers are shifting the base from machine learning to the deep neural architectures with auto-feature selection capabilities. We in this paper propose multiple deep neural network architectures which can select, co-learn and teach the gradients of the neural network by itself with no human intervention. This is what we call as meta-learning. The models are configured in both many to one and many to many design architectures. We combine long short-term memory (LSTM), bi-directional long short-term memory (BiLSTM), convolutional neural network (CNN) layers along with attention mechanism to achieve the higher accuracy values among all the available deep learning model architectures. LSTMs overcomes the vanishing and exploding gradient problem of RNN and attention mechanism mimics the human cognitive attention that screens the network flow to obtain the key features for network traffic classification. In addition, we also add multiple convolutional layers to get the key features for network traffic classification. We get the time series analysis of the traffic done for the possibility of a DDoS attack without using any feature selection techniques and without balancing the dataset. The performance analysis is done based on confusion matrix scores, that is, accuracy, false alarm rate (FAR), sensitivity, specificity, false-positive rate (FPR), F1 score, area under curve (AUC) analysis and loss functions on well-known public benchmark KDD Cup'99 data set. The results of our experiments reveal that our models outperform existing techniques, showing their superiority in performance.     

A qualitative investigation on the importance of boundary layer in pervaporation separation of an aqueous organic reaction

The importance of boundary layer was investigated for the hydrolysis of ethyl acetate using a dense standard poly(vinyl alcohol) (PVA) membrane. A resistance in a series model approach was used to study the effect of a boundary layer for the permeation of aqueous organic mixtures involved in the study. The initial feed mixture consisted of 10% water (H₂O), 40% ethyl acetate (EA), 50% acetic acid (AA), and 0% ethanol (E) (all weight percentage basis). The experiments were conducted at 65°C. The amount of all species in the feed reservoir was found to decrease with time. Selectivity calculations based on a resistance in the series model approach indicates that the boundary layer contributes to selective permeation of aqueous organic compounds. © 1998 John Wiley & Sons, Inc. J Appl Polym Sci 70: 2361–2369, 1998     

CO2 laser cutting of Kevlar laminate: influence of assisting gas pressure

In the present study, laser cutting of Kevlar laminate is considered, and the effect of assisting gas pressure and laser output power on the end product quality is examined. The end product quality is judged via measurement of out-of-flatness and kerf width ratios. Experimental tests are carried out using a CO₂ laser beam with pulse repetition rate of 300 Hz. The cutting model introduced previously is accommodated to predict the kerf size for various laser output power and assisting gas pressures. The predictions are compared with the experimental results. It is found that the predictions of kerf size are in good agreement with the experimental results. The influence of assisting gas pressure is significant on the resulting cut quality, in which case, out-of-flatness and kerf width ratio improve considerably at high assisting gas pressures (500 kPa).     

Hydrological Delineation of Homogeneous Regions in Tunisia

An innovative method for hydrological delineation of homogeneous regions in Tunisia is presented. The approach considered is based on the shape of the empirical Cumulative Distribution Function (CDF) and similarities of physiographic and climatic characteristics. Based on annual flood data of 49 hydrometric stations, with record lengths ranging from 10 to 100 years, spatial trends of the shape of the CDF were employed to identify mixed processes in the flood series. This resulted in dividing the study area into mixture and non-mixture zones. Hydrologic homogeneity of the delineated regions was tested by L (Linear) moment-based statistical tests. The exercise resulted in dividing Tunisia into 10 homogeneous regions, where mixed flood processes haven't been recognized.     

Single-Shot Characterization of Enzymatic Reaction Constants K(m) and k(cat) by an Acoustic-Driven, Bubble-Based Fast Micromixer

In this work we present an acoustofluidic approach for rapid, single-shot characterization of enzymatic reaction constants K(m) and k(cat). The acoustofluidic design involves a bubble anchored in a horseshoe structure which can be stimulated by a piezoelectric transducer to generate vortices in the fluid. The enzyme and substrate can thus be mixed rapidly, within 100 ms, by the vortices to yield the product. Enzymatic reaction constants K(m) and k(cat) can then be obtained from the reaction rate curves for different concentrations of substrate while holding the enzyme concentration constant. We studied the enzymatic reaction for β-galactosidase and its substrate (resorufin-β-D-galactopyranoside) and found K(m) and k(cat) to be 333 ± 130 μM and 64 ± 8 s(-1), respectively, which are in agreement with published data. Our approach is valuable for studying the kinetics of high-speed enzymatic reactions and other chemical reactions.     

A multi‐start threshold accepting algorithm for multiple objective continuous optimization problems

A multi‐start threshold accepting algorithm with an adaptive memory (MS‐TA) is proposed to solve multiple objective continuous optimization problems. The aim of this paper is to find efficiently multiple Pareto‐optimal solutions. Comparisons are carried out with multiple objective taboo search algorithm and genetic algorithm. Experiments on literature problems show that the proposed algorithm is more effective. The presented multi‐start adaptive algorithm improves the best‐known results by a significant margin. Copyright © 2010 John Wiley & Sons, Ltd.