Mn+2 and Cd+2 Removal from Industrial Wastewater Using Phillipsitic Tuff from Jabal Uniza, Southern Jordan

Southern Jordanian natural zeolitic tuffs outcropped in Uniza volcano have been investigated and tested for their heavy metals' removal capacity. The main species identified in Uniza natural zeolitic tuffs are: phillipsite and, subordinately, chabazite. The effects of particle size and stirring time were examined in the removal experiments. Two types of Uniza phillipsitic tuff were used in the removal of Mn+2 and Cd+2 from treated industrial wastewater. The first type is the reddish bulk sample (UZ1), while the second type is the separated size (UZ2) fraction between 1 and 0.3 mm. Batch and column tests were performed to determine the removal capacity of Mn+2 and Cd+2 from treated industrial wastewater. In batch experiments (static regime), the use of UZ2 type shows a higher removal percentage of Mn+2 and Cd+2 compared to UZ1 type for the same time. The results of column experiments indicate that toxic metal ions such as Mn+2 and Cd+2 can be removed with approximately 100% efficiency from industrial wastewater containing similar ions using Jordanian zeolitic tuff. In dynamic regime, by using UZ1 the manganese ions (Mn+2) were completely removed up to 57 BV (1.71 L) and the zeolite exhaustion took place after more than 112 BV (3.36 L), while by using UZ2 the manganese ions were completely removed up to 67 BV (2.01 L) and the zeolite exhaustion took place just around 114 BV (3.42 L). For an efficient cadmium ions removal, the use of UZ1 sample showed a complete removal up to 85 BV (2.55 L) and the zeolite exhaustion took place after more than 129 BV (3.87 L), while the use of UZ2 showed that the Cd+2 ions were completely removed after 151 BV (4.53 L) and the zeolite exhaustion took place after more than 205 BV (6.15 L)

Using Normalized Difference Vegetation Index (NDVI) to Identify Hydrocarbon Seepage in Kifl Oil Field and Adjacent Areas South of Iraq

The study area lies south of Iraq, it covers 4009 km². The data is used in this research comprise Landsat 8 (OLI) data, and Ancillary data such as geological and topographic maps. The study area include the Kifl Oil Field whereas comprise number of important formations for oil production. There are four oil wells drilled in the Kifl Oil Field. Some of them producer of hydrocarbon and others wells have hydrocarbon evidences.The Normalized Difference Vegetation Index (NDVI) is used to identify prospective hydrocarbon seepage areas within the vegetation cover, the magnitude of green vegetation was quantified to levels and separated from other classes. The classification system for the vegetation in the study area is based on four categories: High vegetation density, Moderate vegetation density, Low vegetation density, and no vegetation. The result of classification reveals that low vegetation density areas, and no vegetation areas could be prospective hydrocarbon seepage areas. Supervised classification apply on the gray scale image of NDVI by chosen training areas of dark tones pixels that have values of absorption  close to values of water absorption which are illustrated prospective hydrocarbon seepage areas. Three classes in the study area included hydrocarbon seepage class compared with other three classes collected from another near area, this comparison has been proved that there is identical behave of the spectral signatures for all three classes. According to the conclusions, the NDVI is effective to identify hydrocarbon seepage in the study area particularly in the regions characterized by vegetation cover. Keywords: Landsat 8 (OLI), Hydrocarbon Seepage, NDVI, Threshold, Anaerobic

E-Learner Recommendation Model Based on Level of Learning Outcomes Achievement

Students in any learning environment differ in their level of knowledge, achieved learning outcomes, learning style, preferences, misunderstand and attempts in solving and addressing problems when their expectations are not met. When a student searches the web as an attempt to solve a problem, he suffers from the large number of resources which are, in most cases, not related to his “needs”, or may be related but complex and advance. The result of his search might make him more confused, scattered, depressed and finally result in wasting his time which – in some cases -may have negative effects on his achievements. From here comes the need for an intelligent learning system that can guide studentsbased on their needs. This research attempts to design and build an educational recommender system for a web-based learning environment in order to generate meaningful recommendations of the most interested and relevant learning materials that suit students’ needs based on their profiles1 . This can be achieved by accessing students’ history, exploring their learning navigation patterns and making use of similar students’ experiences and their success stories. The study proposed a design for a hybrid recommender system architecture which consists of two recommendation approaches: the content and collaborative filtering. The study concentrates on the collaborative recommender engine which will recommend learning materials based on students’ level of knowledge, looking at active students' profiles, and achievements in both learning outcomes and learning outcomes levels making use of similar students’ success stories and reflecting their good experience on active student who are in the same level of knowledge. The design of the collaborative recommender engine includes the “learning” module from which the engine learns past students’ access pattern and the “advising” module from which the engine reflects the experience of similar success stories on active students. The content base recommender engine with its suggested stages is considered as future work, the research used the k-mean cluster algorithm to find out similar students where five distance function are used: Euclidean, Correlation. Jaccard,cosine and Manhattan. The cosine function shows to be the most accurate distance function with the minimum SSE but the highest processing time that doesn’t differ a lot when compared the rest functions. The best number of clusters for the selected dataset was determined using three methods Elbow, Gap-statistic and average Silhouette approach where the best number of cluster shows to be three. The research used the two result rating matrices of similar good and good students with Learnings material in order to calculate learning material weights and rank them based on highest weights which results in a final recommendation list

Dielectric Properties and AC conductivity of (Epoxy / Ion exchange) blend

The dielectric behavior of blend materials epoxy resin- Polyvinyl benzyl dimethyl ethanol ammonium chloride (PBDEAC) (Ion exchange) was analysed as a function of Ion exchange  weight content; temperature and frequency. Blends were prepared by mixing the components and pouring them into suitable moulds. The dielectric parameters have been measured using parallel plate capacitor method in the frequency range from 120 Hz to 2 MHz and in the temperature range from 30 ?C to 110?C. Variations of real (??) and imaginary (?") parts of dielectric constants and loss tangent of material with frequency and temperature have been studied. The experimental results showed that (??) and (?") increased with the addition of Ion exchange filler content. The value of (??) decreased with increasing frequency, which indicates that the major contribution to the polarization comes from orientation polarization. Dielectric loss peaks were also observed in the composite materials at high temperature. The value of (??) increased with increasing temperature, and is due to greater freedom of movement of the dipole molecular chains within the polymer at high temperature. Ac. conductivity and impedance of the composites behaviours as function of frequency and temperature have also been investigated. Keywords: Dielectric properties, epoxy, Ion exchange, ac. conductivity

Marker hiding methods: Applications in augmented reality

© 2015 Taylor & Francis Group, LLC.In augmented reality, the markers are noticeable by their simple design of a rectangular image with black and white areas that disturb the reality of the overall view. As the markerless techniques are not usually robust enough, hiding the markers has a valuable usage, which many researchers have focused on. Categorizing the marker hiding methods is the main motivation of this study, which explains each of them in detail and discusses the advantages and shortcomings of each. The main ideas, enhancements, and future works of the well-known techniques are also comprehensively summarized and analyzed in depth. The main goal of this study is to provide researchers who are interested in markerless or hiding-marker methods an easier approach for choosing the method that is best suited to their aims. This work reviews the different methods that hide the augmented reality marker by using information from its surrounding area. These methods have considerable differences in their smooth continuation of the textures that hide the marker area as well as their performance to hide the augmented reality marker in real time. It is also hoped that our analysis helps researchers find solutions to the drawbacks of each method. © 201

The relationship between corporate forward-looking disclosure and stock return volatility

The study assesses corporate forward-looking disclosure by measuring four attributes, namely disclosure quantity, disclosure coverage, disclosure concentration and disclosure quality, through a sample of 34 listed firms in the Bahrain Bourse from 2014 to 2017. The study also investigates the relationship between these attributes and stock return volatility. Regression analysis has been employed with five different models to examine the relationship between the four attributes of corporate forward-looking disclosure and stock return volatility. The main finding of this study agrees with the results of Bravo et al. (2009) who found that the selection of a specific disclosure index could influence crucially the results of the analysis. In addition, stock return volatility has a statistically significant negative association with the three attributes of forward-looking disclosure, namely disclosure quantity, disclosure coverage and disclosure quality. In contrast, it has a non-significant association with the fourth attribute of forward-looking disclosure, disclosure concentration. This study provides a novel contribution to disclosure quality studies by being the first study to examine forward-looking disclosure quality attributes in the Kingdom of Bahrain

The use and trend of emotional language in the banks’ annual reports: the state of the global financial crisis

This study is of an exploratory nature as it seeks to explore the extent to which the language of emotions in the banks’ annual reports is affected by the global financial crisis (GFC). The language of emotions was analyzed using eight categories (trust, anticipation, sadness, anger, fear, disgust, surprise and joy) in annual reports of 12 listed banks from six countries in the Middle East area (namely, Jordan, Kingdom of Bahrain, United Arab Emirates, Sultanate of Oman, Kuwait, Kingdom of Saudi Arabia) from 2002 to 2017. The final data set consists of 192 bank-year observations. The study time was divided into three periods (pre, during and post GFC). In addition, the study enriches accounting literature by being the first study to test Pollyanna hypothesis using emotion analysis. The results of the study show that the percentage of emotional words in banks’ annual reports (2002–2017) represents almost 22% on average. The trust, anticipation and fear categories were the most affected than other emotional categories during GFC. While the trust category decreased, both the fear and anticipation categories increased. Other findings of the study show that regardless of GFC, emotional words of trust and anticipation categories in banks’ annual reports have dominated the emotional words of the disgust and surprise categories. Therefore, Pollyanna hypothesis is supported. In contrast to the emotional words of the joy category in banks’ annual reports which has not dominated the sadness category. In this case, Pollyanna hypothesis is rejected

Synthesis of carbon-supported PdIrNi catalysts and their performance towards ethanol electrooxidation

Direct ethanol fuel cells (DEFCs) have shown a high potential to supply energy and contribute to saving the climate due to their bioethanol sustainability and carbon neutrality. Nonetheless, there is a consistent need to develop new catalyst electrodes that are active for the ethanol oxidation reaction (EOR). In this work, two C-supported PdIrNi catalysts, that have been reported only once, are prepared via a facile NaBH4 co-reduction route. Their physiochemical characterization (X-ray diffraction (XRD), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), and X-ray photoelectron spectroscopy (XPS)) results show alloyed PdIrNi nanoparticles that are well dispersed (< 3 nm) and exist in metallic state that is air-stable apart from Ni and, slightly, Pd. Their electrocatalytic activity towards EOR was evaluated by means of cyclic voltammetry (CV) and chronoamperometry (CA). Even though the physiochemical characterization of PdIrNi/C and Pd4Ir2Ni1/C is promising, their EOR performance has proven them less active than their Pd/C counterpart. Although the oxidation current peak of Pd/C is 1.8 A/mgPd, it is only 0.48 A/mgPd for Pd4Ir2Ni1/C and 0.52 A/mgPd for PdIrNi/C. These results were obtained three times and are reproducible, but since they do not add up with the sound PdIrNi microstructure, more advanced and in situ EOR studies are necessary to better understand the poor EOR performance

Bio-regeneration of activated carbon: A comprehensive review

© 2018 Elsevier B.V. The use of microorganisms to regenerate activated carbon (AC), bio-generation, can avert costly and logistically challenging ex-situ steam regeneration of carbon normally required to recover its adsorptive capacity. Bio-regeneration employs microbial metabolism in which the microbes use the available organic substrates (contaminants) to generate energy. During this process, they generate equivalent protons and electrons, which are transferred to the substrates to finally break them down to simpler molecules or ions, such as CO2, methane and Cl−. The optimal microbial conditions depend on the temperature, available nitrogen and phosphorus levels, dissolved oxygen levels, and microbe/substrate stoichiometric ratios and the residence time of the AC particles within the reactor. In this review, the authors highlight the most recent development in bio-regeneration including the regeneration mechanism, the relationship between the reversibility of adsorption and the efficiency of bio-regeneration, the general aspects affecting bio-regeneration, the principle and target compounds for bio-regeneration, different established methods for quantifying the bio-regeneration and the efficiency of bio-regeneration. Few case studies of bio-regeneration of activated carbon loaded with different contaminants are presented. Research on microbiology regeneration has gained considerable attention in recent years, but it still needs more contribution from other disciplines including process engineering, biochemistry and material sciences for optimizing the process performance

Website Phishing Detection Using Machine Learning Techniques

Phishing is a cybercrime that is constantly increasing in the recent years due to the increased use of the Internet and its applications. It is one of the most common types of social engineering that aims to disclose or steel users sensitive or personal information. In this paper, two main objectives are considered. The first is to identify the best classifier that can detect phishing among twenty-four different classifiers that represent six learning strategies. The second objective aims to identify the best feature selection method for websites phishing datasets. Using two datasets that are related to Phishing with different characteristics and considering eight evaluation metrics, the results revealed the superiority of RandomForest, FilteredClassifier, and J-48 classifiers in detecting phishing websites. Also, InfoGainAttributeEval method showed the best performance among the four considered feature selection methods