Effective removal of Cr(VI) and methyl orange from the aqueous environment using two-dimensional (2D) Ti3C2Tx MXene nanosheets

Industrial use of heavy metals and dyes critically depends on the effective handling of industrial effluents. Effective remediation of industrial effluents using various adsorbent materials has thus become critical. In this paper, we study two-dimensional MXenes as an adsorbent for removing Cr(VI) and methyl orange (MO) in waters. The physico-chemical performance of MXenes was studied using X-ray diffraction, Fourier transform infrared spectroscopy, Brunauer?Emmett?Teller, scanning electron microscopy, high resolution-transmission electron microscopy, thermogravimetric analysis, and X-ray photoelectron spectroscopy techniques. The adsorption system, including influence of contact time, pH of solutions, co-ions, and desorption experiments were performed for effective Cr(VI) and MO removal. The Cr(VI) and MO removal rate of the MXenes was very fast, and the kinetic system was driven by pseudo-second-order kinetics. The sorption isotherm closely well-tailored with the Langmuir isotherm, and the maximum removal efficiencies were 104 and 94.8 mg/g for Cr(VI) and MO, respectively. The MXenes was successfully regenerated by 0.1 M NaOH aqueous solution and can be repeatedly recycled. The uptake of Cr(VI) and MO by the MXenes was mainly due to chemical adsorption, namely electrostatic adsorption, complexation, surface interactions, and ion exchange mechanisms. This investigation demonstrates the selectivity and feasibility of the MXenes as a real adsorbent for eliminating Cr(VI) and MO from the aqueous environment.     

Understanding the Language of ISIS: An Empirical Approach to Detect Radical Content on Twitter Using Machine Learning

The internet, particularly online social networking platforms have revolutionized the way extremist groups are influencing and radicalizing individuals. Recent research reveals that the process initiates by exposing vast audiences to extremist content and then migrating potential victims to confined platforms for intensive radicalization. Consequently, social networks have evolved as a persuasive tool for extremism aiding as recruitment platform and psychological warfare. Thus, recognizing potential radical text or material is vital to restrict the circulation of the extremist chronicle. The aim of this research work is to identify radical text in social media. Our contributions are as follows: (i) A new dataset to be employed in radicalization detection; (ii) In depth analysis of new and previous datasets so that the variation in extremist group narrative could be identified; (iii) An approach to train classifier employing religious features along with radical features to detect radicalization; (iv) Observing the use of violent and bad words in radical, neutral and random groups by employing violent, terrorism and bad words dictionaries. Our research results clearly indicate that incorporating religious text in model training improves the accuracy, precision, recall, and F1-score of the classifiers. Secondly a variation in extremist narrative has been observed implying that usage of new dataset can have substantial effect on classifier performance. In addition to this, violence and bad words are creating a differentiating factor between radical and random users but for neutral (anti-ISIS) group it needs further investigation.     

An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used to quickly learn the features by recording a small number of training images from vehicle frontal view images. The proposed system employs extensive data-augmentation techniques for effective training while avoiding the problem of data shortage. In order to capture rich and discriminative information of vehicles, the convolutional neural network is fine-tuned for the classification of vehicle types using the augmented data. The network extracts the feature maps from the entire dataset and generates a label for each object (vehicle) in an image, which can help in vehicle-type detection and classification. Experimental results on a public dataset and our own dataset demonstrated that the proposed method is quite effective in detection and classification of different types of vehicles. The experimental results show that the proposed model achieves 96.04% accuracy on vehicle type classification.     

Half-Metallic Ferromagnetism in New Half-Heusler Compounds: an Ab Initio Study of CrTiX (X = Si,Ge, Sn,Pb)

In this work, half-metallic (HM) properties of new Half-Heusler (HH) ferromagnetic compounds CrTiX (X = Si, Ge, Sn, Pb) are studied by means of first principle band structure calculation within the framework of density functional theory (DFT). From the spin-polarized calculations using full-potential linearized augmented plane-wave (FP-LAPW)method, we found that all of these compounds are stable in ferromagnetic MgAgAs-type crystal structure. The latticeparameters of CrTiX compounds increase with increasing atomic radius of X atom and ranges from 5.76 to 6.38 Å. The calculated electronic structure of these compounds in MgAgAs-type structure shows that they are HM materials with an integer magnetic moment of 4 μ _B. Densities of states, electronic band structure, and origin of ferromagnetism have been discussed, and robust HM nature of these compounds is analyzed which makes them fascinating compounds for spintronic devices.     

System Dynamics Approach to Exploring Performance Enhancement in a Construction Organization

Governments and firms in developing economies are concerned about ways to improve performance of local firms. A wealth of research has already discussed the reasons for decline in performance and suggested a number of possible improvement strategies. Among these documented strategies, there are many options for developing economies. The selection among these options, however, depends upon the unique organizational set-up and many other local factors. Moreover an implementation scenario of the selected options requires an experimentation period either by actually implementing the option as a policy or simulating the complete process in a model and thus creating a laboratory for learning. The former option is costly while the latter requires dynamic modeling of engineering processes integrated with the local influencing factors on overall performance. The latter approach is adopted in the present research using system dynamics techniques to model the problem. The effects of three strategic policies on the performance of a construction firm have been simulated. A case study is conducted to validate the utility of some selected policies for performance enhancement.     

Biochemical changes induced by fenpropathrin in the sixth instar larvae of Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae)

Sixth instar larvae of Tribolium castaneum were exposed to one of four sublethal concentrations i.e. 10, 20, 200 or 400 mg/L, of a synthetic pyrethroid, fenpropathrin, for 48 hr. The lactate dehydrogenase activity decreased 44, 15 and 10% after exposure to fenpropathrin at concentrations of 20, 200 and 400 mg/L respectively, while a significant increase was recorded in glutamate oxaloacetate transaminase (15, 16, 34 and 37%) and glutamate pyruvate transaminase (6, 20, 13 and 29%) activities respectively after exposure to 10, 20, 200 and 400 mg/L. The amylase and acid phosphatase activities remained unaffected. The trehalase activity increased 42, 72 and 149%, after 20, 200 and 400 mg/L, the isocitrate dehydrogenase activity increased 28 and 67% after 10 and 20 mg/L, white alkaline phosphatase activity increased 13 and 12% after 10 and 400 mg/L respectively. The weaker (10 and 20 mg/L) and stronger (200 and 400 mg/L) doses elicited two different types of responses. A dose of 20 mg/L resulted in increased soluble proteins (14%), lipids (49%), cholesterol (57%), RNA (18%), and DNA (32%) content per larva, while the stronger dose of 400 mg/L resulted in their decrease except for lipids. The total proteins, lipids and free amino acids content per larva were not affected by either concentration, while the glucose content per insect decreased with increasing concentration of fenpropathrin.     

Fourth-order central compact scheme for the numerical solution of incompressible Navier–Stokes equations

This paper provides an implicit central compact scheme for the numerical solution of incompressible Navier–Stokes equations. The solution procedure is based on the artificial compressibility method that transforms the governing equations into a hyperbolic-parabolic form. A fourth-order central compact scheme with a sixth-order numerical filtering is used for the discretization of convective terms and fourth-order central compact scheme for the viscous terms. Dual-time stepping approach is applied to time discretization with backward Euler difference scheme to the pseudo-time derivative, and three point second-order backward difference scheme to the physical time derivative. An approximate factorization-based alternating direction implicit scheme is used to solve the resulting block tridiagonal system of equations. The accuracy and efficiency of the proposed numerical method is verified by simulating several two-dimensional steady and unsteady benchmark problems.     

An improved surveillance video forgery detection technique using sensor pattern noise and correlation of noise residues

Multimedia Tools and Applications - Digital videos have become an important aspect of our lives lately, from a personal memorable to surveillance videos which can be presented in a court as an...