Electro-oxidation of two reactive azo dyes on boron-doped diamond electrode

Electrochemical oxidation (decolorization/degradation) of blue and red commercial reactive azo dyes was carried out on boron-doped diamond (BDD) electrode. The effect of various quantities of FeSO(4) was investigated in the electro-Fenton reaction on BDD. Progress of dyes degradation during the electrolysis and electro-Fenton reaction was monitored by UV-visible absorption and by estimation of the chemical oxygen demand (COD). Direct electrolysis showed a limiting capacity for red and blue dye removal even at high current densities, e.g. maximum red color and COD removal were 70 and 20%, respectively at 30 mA cm(-2) after 300 min. Higher red and blue color removal efficiencies were achieved by electro-Fenton oxidation. Current density of 30 mA cm(-2) in the presence of 0.05 mmol/L of FeSO(4) resulted in the red color and COD removal of 98 and 96%, respectively. The optimum FeSO(4) concentration for the electro-Fenton reaction was determined to be 0.05 mmol/L. Instantaneous current efficiency (ICE) in the presence of FeSO(4) was almost three times higher than for experiments carried out without FeSO(4).     

A proposed approach for setup time reduction through integrating conventional SMED method with multiple criteria decision-making techniques

Short setup time is an essential element for the effective implementation of many lean pillars, i.e., JIT, and Kanban. Most of the current setup reduction methodologies are based on Shingo’s Single Minute Exchange of Dies (SMED) that suggests the conversion of internal setup operations to external operations. However, the conventional SMED approach – as proposed by Shingo – does not possess a systematic approach to accomplish this conversion. Thus, a new approach is proposed in order to aid the process engineers in implementing SMED. The proposed approach is based on the conventional SMED, but also it incorporates Multiple Criteria Decision-Making Techniques (MCDM) to the third implementation phase. The MCDM techniques used in this work are Analytical Hierarchal Process (AHP), Preference Selection Index (PSI) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The proposed approach provides a systematic procedure for selecting the best setup technique among the available alternatives, and takes also into consideration other factors that affect the decision-making process; including: cost, energy, facility layout, safety, life, quality and maintenance. A real example of PVC industry is used to exemplify the approach. The results demonstrate the capability of the proposed approach in setup time reduction, which in turn will improve machines’ utilization, and increase the productivity and flexibility of the whole facility.     

Nickel/Cobalt nanoparticles for electrochemical production of hydrogen

The electrolytic production of hydrogen (POH) from alkaline water electrolysis is at the forefront of technology for alternative energy sources of the future. The present work evaluates the improvement of electro-catalytic activity (ECA) on Ni electrodes for the POH by electrodeposition of cobalt (Co). Tests were conducted in alkaline solution and the ECA of Ni and Ni–Co electrodes for the POH were compared using alternative and direct current techniques. Tafel polarization tests exemplified a significant improvement in the ECA of the bimetallic electrode (Ni–Co) compared with the Ni-electrode. Besides, the bimetallic electrode required less input overpotential energy (η) for the given POH rate under constant current density. Electrochemical impedance spectroscopy (EIS) revealed a significant increase in the number of electrochemical active sites and changed the surface morphology following the electrodeposition of Co over Ni electrodes.     

A Hybrid Model Using Bio-Inspired Metaheuristic Algorithms for Network Intrusion Detection System

Network Intrusion Detection System (IDS) aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by firewalls. The features selection approach plays an important role in constructing effective network IDS. Various bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more accuracy. Therefore, this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic attack. The proposed model has two objectives; The first one is to reduce the number of selected features for Network IDS. This objective was met through the hybridization of bio-inspired metaheuristic algorithms with each other in a hybrid model. The algorithms used in this paper are particle swarm optimization (PSO), multi-verse optimizer (MVO), grey wolf optimizer (GWO), moth-flame optimization (MFO), whale optimization algorithm (WOA), firefly algorithm (FFA), and bat algorithm (BAT). The second objective is to detect the generic attack using machine learning classifiers. This objective was met through employing the support vector machine (SVM), C4.5 (J48) decision tree, and random forest (RF) classifiers. UNSW-NB15 dataset used for assessing the effectiveness of the proposed hybrid model. UNSW-NB15 dataset has nine attacks type. The generic attack is the highest among them. Therefore, the proposed model aims to identify generic attacks. My data showed that J48 is the best classifier compared to SVM and RF for the time needed to build the model. In terms of features reduction for the classification, my data show that the MFO-WOA and FFA-GWO models reduce the features to 15 features with close accuracy, sensitivity and F-measure of all features, whereas MVO-BAT model reduces features to 24 features with the same accuracy, sensitivity and F-measure of all features for all classifiers.     

Distribution of natural and anthropogenic radionuclides in heavy rainfall areas in Jordan

Soil is the main reservoir of both natural and artificial radionuclides, which are transported to the human body through the food chain. Thus, assessment of the level of radioactivity in soil is of crucial importance. Artificial radionuclide concentrations in soil depend heavily on rainfall and weather conditions. In this study, the soil of the Ras Muneef area, which has the highest rainfall in Jordan, was investigated for its natural and anthropogenic radioactive content. The area was divided into four sectors and in each sector three locations were investigated depending on the land use: undisturbed, cultivated or residential. The depth profile of (137)Cs was investigated and found to depend on the land use. In the undisturbed soils, two types of depth profiles were identified: Gaussian and exponentially decreasing. The annual effective dose was found to range from 19.4 to 72.6 μSv, which falls within the worldwide ranges.     

Corrosion investigation of zinc−aluminum alloy matrix (ZA-27) reinforced with alumina (Al2O3) and fly ash

In this study, zinc?aluminum alloy (ZA-27) matrix composites reinforced by different weight fractions of fly ash or alumina (Al₂O₃) were produced using the traditional stir casting technique. The corrosion behaviors of both unreinforced alloy and reinforced composites were examined using direct current polarization (DCP) test in a simulated sea solution (3.5 wt.% NaCl). Scanning electron microscopy (SEM) and energy dispersive x-ray (EDX) were used to examine the morphology of the composites’ surface before and after corrosion tests. The results of corrosion revealed that reinforcing ZA-27 alloy by fly ash or Al₂O₃ particles decreases its tendency to uniform corrosion due to the formation of weak microgalvanic couple between matrix and reinforcement particles. The fly ash and alumina (Al₂O₃) particles have protected the matrix material from pits formation at early stage of polarization. However, once these pits are formed, they grow faster. Positive hysteresis of the polarization curves implies that the salt layer breakdown and matrix dissolution overshadow surface passivation during the reverse scan. The electrochemical results are consistent with the pits’ morphology of the corroded composite. Composites with fly ash reinforcements have autocatalytic pits, whereas composites with alumina (Al₂O₃) reinforcements have shallow pits.     

Applying ANOVA and DOE to study the effect of manganese on the hardness and wear rate of artificially aged Al-4.5wt%Cu alloys

The addition of manganese may constitute an important means for imparting the mechanical properties of Al-4.5wt%Cu alloys that have been artificially aged. The literature, however, comprises conflicting reports on the effect of manganese on these alloys. Thus, this work tries to reach results that are more conclusive by utilizing statistical approaches such as Design of Experiments and the Analysis of Variance. Accordingly, different percentages of manganese additions under different aging times were investigated in this study for their effect on hardness and wear rate. Manganese was added in concentrations of 0.2, 0.4, 0.6, 0.8 and 1wt%. Following the solution treatment, aging was applied for time durations of 1, 2 and 3 h. Aging temperature was held constant at 200 °C. Results showed that Mn concentration and aging time have significant effect on alloy hardness and wear rate.     

Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

Intrusion detection is a serious and complex problem. Undoubtedly due to a large number of attacks around the world, the concept of intrusion detection has become very important. This research proposes a multilayer bio-inspired feature selection model for intrusion detection using an optimized genetic algorithm. Furthermore, the proposed multilayer model consists of two layers (layers 1 and 2). At layer 1, three algorithms are used for the feature selection. The algorithms used are Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Firefly Optimization Algorithm (FFA). At the end of layer 1, a priority value will be assigned for each feature set. At layer 2 of the proposed model, the Optimized Genetic Algorithm (GA) is used to select one feature set based on the priority value. Modifications are done on standard GA to perform optimization and to fit the proposed model. The Optimized GA is used in the training phase to assign a priority value for each feature set. Also, the priority values are categorized into three categories: high, medium, and low. Besides, the Optimized GA is used in the testing phase to select a feature set based on its priority. The feature set with a high priority will be given a high priority to be selected. At the end of phase 2, an update for feature set priority may occur based on the selected features priority and the calculated F-Measures. The proposed model can learn and modify feature sets priority, which will be reflected in selecting features. For evaluation purposes, two well-known datasets are used in these experiments. The first dataset is UNSW-NB15, the other dataset is the NSL-KDD. Several evaluation criteria are used, such as precision, recall, and F-Measure. The experiments in this research suggest that the proposed model has a powerful and promising mechanism for the intrusion detection system.