This study was conducted to evaluate the hydrocarbon biodegradation abilities of Enterobacter cloacae, Staphylococcus aureus, Sphingomonas paucimobilis, and Pentoae species which were isolated from different diesel-contaminated soil samples. The isolates were identified by the Vitek 2 system. Fourier-transform spectroscopy (FT-IR) tested the potential of these isolates to biodegrade the diesel according to the peak areas, a significant decrease in the area of the peaks at 2856-2928 cm−1 corresponds to aliphatic hydrocarbons. The appearance of small peaks at 900-1032 cm−1 refers to substituted benzene derivative compounds. An appearance of some new peaks at 3010- 3030 cm−1 which indicate the presence of alcohol (-OH) and ketones (RC=O)

Four mixed ligand complexes were prepared from 1,10-phenanthroline (Phen), 5-chlorosalicylic acid (CSA), and anthranilic acid (Anthra) dissolved in aqueous ethanol at a ratio of (1:1:1:1) M: Phen:CSA: Anthra, M(II)= Cu, Zn, Cd, and Hg. The prepared compounds were analyzed by flame atomic absorption, FT—IR, UV-Vis, and spectroscopic methods, as well as conductivity measurements and magnetic properties. After analyzing the prepared compounds using the acquired data, the complexes formed by mixing ligands were concluded to adopt an octahedral geometry. That study has been conducted to test the inhibitory effectiveness of the complexes (1,10-Phenanthroline (Phen), 5-Chlorosalicylic acid (CSA), Na[Cu(Phen)(CSA)(Anthra), Na[Zn(Phen)(CSA)(Anthr

Drastic threat to the natural system is caused by the uncontrolled release of synthetic pollutants, including azo dyes. This study centered on the decolorization and biodegradation of water soluble azo dye reactive blue (RB) in a batch mode sequential anaerobic-aerobic processes. A local sewage treatment plant was the source where activated sludge was collected to be used as non-adapted mixed culture with both free and the alginate immobilized cells for RB biodegradation. Under anaerobic conditions, the free and immobilized mixed cells were proved to completely decolorize 10 mg/ L of RB within 20 and 30 h, respectively. Alginate- immobilized mixed cells, resulted in 88%, 87%, and 87% maximum COD removals with samples con

Four mixed ligand complexes were prepared from 1,10-phenanthroline (Phen), 5-chlorosalicylic acid (CSA), and anthranilic acid (Anthra) dissolved in aqueous ethanol at a ratio of (1:1:1:1) M: Phen:CSA: Anthra, M(II)= Cu, Zn, Cd, and Hg. The prepared compounds were analyzed by flame atomic absorption, FT-IR, UV-Vis, and spectroscopic methods, as well as conductivity measurements and magnetic properties. After analyzing the prepared compounds using the acquired data, the complexes formed by mixing ligands were concluded to adopt an octahedral geometry. That study has been conducted to test the inhibitory effectiveness of the complexes (1,10-Phenanthroline (Phen),  5-Chlorosalicylic acid (CSA), Na[Cu(Phen)(CSA)(Anthra), Na[Zn(Phen)(CSA

An accurate assessment of the pipes’ conditions is required for effective management of the trunk sewers. In this paper the semi-Markov model was developed and tested using the sewer dataset from the Zublin trunk sewer in Baghdad, Iraq, in order to evaluate the future performance of the sewer. For the development of this model the cumulative waiting time distribution of sewers was used in each condition that was derived directly from the sewer condition class and age data. Results showed that the semi-Markov model was inconsistent with the data by adopting ( 2 test) and also, showed that the error in prediction is due to lack of data on the sewer waiting times at each condition state which can be solved by using successive conditi

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

This paper presents a new algorithm in an important research field which is the semantic word similarity estimation. A new feature-based algorithm is proposed for measuring the word semantic similarity for the Arabic language. It is a highly systematic language where its words exhibit elegant and rigorous logic. The score of sematic similarity between two Arabic words is calculated as a function of their common and total taxonomical features. An Arabic knowledge source is employed for extracting the taxonomical features as a set of all concepts that subsumed the concepts containing the compared words. The previously developed Arabic word benchmark datasets are used for optimizing and evaluating the proposed algorithm. In this paper,

This paper presents the Extended State Observer (ESO) based repetitive control (RC) for piezoelectric actuator (PEA) based nano-positioning systems. The system stability is proved using Linear Matrix Inequalities (LMIs), which guarantees the asymptotic stability of the system. The ESObased RC used in this paper has the ability to eliminate periodic disturbances, aperiodic disturbances and model uncertainties. Moreover, ESO can be tuned using only two parameters and the model free approach of ESO-based RC, makes it an ideal solution to overcome the challenges of nano-positioning system control. Different types of periodic and aperiodic disturbances are used in simulation to demonstrate the effectiveness of the algorithm. The comparison studi

Cancer is in general not a result of an abnormality of a single gene but a consequence of changes in many genes, it is therefore of great importance to understand the roles of different oncogenic and tumor suppressor pathways in tumorigenesis. In recent years, there have been many computational models developed to study the genetic alterations of different pathways in the evolutionary process of cancer. However, most of the methods are knowledge-based enrichment analyses and inflexible to analyze user-defined pathways or gene sets. In this paper, we develop a nonparametric and data-driven approach to testing for the dynamic changes of pathways over the cancer progression. Our method is based on an expansion and refinement of the pathway bei