Removal and recovery of nickel(II) from aqueous solution by loofa sponge-immobilized biomass of Chlorella sorokiniana: characterization studies

The biosorption process for the removal of nickel(II) by loofa sponge-immobilized biomass of Chlorella sorokiniana (LIBCS), a newly developed immobilized biosorbent, was characterized. Effects of environmental factors on metal uptake capacity of LIBCS were studied and compared with free biomass of C. sorokiniana (FBCS). Nickel(II) removal by LIBCS was found to be influenced by pH of the solution, initial metal concentration, and biomass concentration. The biosorption of nickel(II) ions by both LIBCS and FBCS increased as the initial concentration of nickel(II) ions increased in the medium. No loss to biosorption capacity of LIBCS for nickel(II) was found due to the presence of loofa sponge, indeed as compared to FBCS an increase of 25.3% was noted in the biosorption capacity of LIBCS. Maximum biosorption capacities for FBCS and LIBCS were found as 48.08 and 60.38 mg nickel(II)/g, respectively, whereas the amount of nickel(II) ions adsorbed on the plain loofa sponge was 6.1mg/g. During these biosorption studies, LIBCS exhibited excellent physical and chemical stability without any significant release/loss of microalgal biomass from loofa sponge matrix. The kinetics of nickel(II) removal was extremely fast reaching at equilibrium in about 15 min for LIBCS and 20 min for FBCS. The biosorption equilibrium was well described by the Langmuir and Freundlich adsorption isotherms. The biosorption capacities were found to be solution pH dependent and the maximum adsorption was found at a solution pH 4-5. The LIBCS could be regenerated using 75 mM HCl, with up to 98% recovery. The LIBCS were shown to be robust and stable with little decrease in the nickel(II) uptake capacity when used in consecutive seven biosorption-desorption cycles. Continuous removal of nickel(II) from electroplating effluent by LIBCS packed in fixed bed column bioreactor confirm the possibility of developing a biological treatment process for the removal of toxic metals from authentic wastewater.     

On Active Noise Control Systems With Online Acoustic Feedback Path Modeling

The presence of strong acoustic feedback degrades the convergence speed of the active noise control (ANC) filter, and in the worst case the ANC system may become unstable. A fixed feedback neutralization filter, obtained offline, can be used to neutralize the acoustic feedback. The feedback path, however, may be time varying, and we may need continual adjustments during online operation of the ANC system. This paper proposes a new method for online modeling of the acoustic feedback path in ANC systems. The proposed method uses three adaptive filters; a noise control filter, a feedback path modeling (FBPM) filter, and an adaptive noise cancelation (ADNC) filter. The objective of ADNC filter is to remove the disturbance from the desired response of FBPM filter. In comparison with the existing method, which works only for predictable noise sources, the proposed method can work, as well, with the broadband noise sources. The computer simulations are carried out for narrowband (predictable) (case I) and broadband (random) noise sources (case II). It is demonstrated that the proposed method performs better than the existing method in both cases     

Image thresholding based on the EM algorithm and the generalized Gaussian distribution

In this paper, a novel parametric and global image histogram thresholding method is presented. It is based on the estimation of the statistical parameters of “object” and “background” classes by the expectation-maximization (EM) algorithm, under the assumption that these two classes follow a generalized Gaussian (GG) distribution. The adoption of such a statistical model as an alternative to the more common Gaussian model is motivated by its attractive capability to approximate a broad variety of statistical behaviors with a small number of parameters. Since the quality of the solution provided by the iterative EM algorithm is strongly affected by initial conditions (which, if inappropriately set, may lead to unreliable estimation), a robust initialization strategy based on genetic algorithms (GAs) is proposed. Experimental results obtained on simulated and real images confirm the effectiveness of the proposed method.     

Microstructure and characterization of phases in TIG welded joints of Zircaloy-4 and stainless steel 304L

Tungsten inert gas (TIG) welded joints between Zircaloy-4 and stainless steel 304L have been studied by scanning electron microscope (SEM) having energy dispersive system (EDS) as an attachment. Intermetallic compound Zr(Cr, Fe)₂ and Zr₂Fe–Zr₂Ni eutectic phase have been observed in the molten zone. The surface area occupied by intermetallic compound Zr(Cr, Fe)₂ is about twice compared to Zr₂Fe–Zr₂Ni eutectic phase. The shape of the intermetallic compound is rod like. The phases were also identified by using X-ray diffraction (XRD) technique. EDS and XRD results are quite in agreement.     

Analysis of deformations and stresses in balanced and unbalanced adhesively bonded single-strap joints

Adhesively bonded joints are increasingly being used in joining various structural components. Adequate understanding of the behaviour of adhesively bonded joints is necessary to ensure efficiency, safety and reliability of such joints. While several joint configurations, such as the single- and double-lap joints have received considerable consideration, the single-strap joint configuration has received little attention, partly because earlier studies have shown it to be the least efficient.

One of the objectives of this paper is to demonstrate that strap joints can be as efficient as lap joints, as long as they are properly designed. This will be done through a detailed analytical investigation into influence of the parameters that govern peak stresses in the adhesive. The next objective is to produce simple equations by which the design of strap joint could be facilitated in an effective manner. For this, the developed analytical expressions are simplified and shown to provide accurate results. The derived solutions provide better insight into understanding the parameters that most influence the edge forces.     

Dissolution-Seepage Coupled Analysis through Formations Containing Soluble Materials

Seepage flow can dissolve particulate soluble materials contained in soil layers and rock formations. The above-mentioned dissolution increases the porosity of the formation and hence seepage flow, which in turn progressively increases the dissolution rate. Due to progressive dissolution, several dams around the world have lost functionality or even failed. Dissolution propagation can be modeled as progress of a solution front, with its progression and resulting excess seepage coupled in the analysis. This is made possible in this paper by simultaneously solving the governing differential equation of seepage and the equation expressing progress of the solution front. The outcome (coupled differential equation) is nonlinear and transient, since both porosity and coefficient of permeability vary with the advancement of the solution front through the medium. The finite-element method is used to solve the resulting nonlinear partial differential equation. Using several examples, influence of material properties and geometry characteristics on the solution front progress and the resulting excess seepage loss is evaluated. Furthermore, effectiveness of different countermeasures (e.g., positive cutoffs and their positions) in dam foundations are studied. Contaminant transport can also be easily modeled and analyzed after applying some modifications into the approach.     

Photo‐induced Leishmania DNA degradation by silver‐doped zinc oxide nanoparticle: an in‐vitro approach

Recently, the authors reported newly synthesised polyethylene glycol (PEG)ylated silver (9%)‐doped zinc oxide nanoparticle (doped semiconductor nanoparticle (DSN)) which has high potency for killing Leishmania tropica by producing reactive oxygen species on exposure to sunlight. The current report is focused on Leishmania DNA interaction and damage caused by the DSN. Here, we showed that the damage to Leishmania DNA was indirect, as the DSN was unable to interact with the DNA in intact Leishmania cell, indicating the incapability of PEGylated DSN to cross the nucleus barrier. The DNA damage was the result of high production of singlet oxygen on exposure to sunlight. The DNA damage was successfully prevented by singlet oxygen scavenger (sodium azide) confirming involvement of the highly energetic singlet oxygen in the DNA degradation process.Inspec keywords: silver, zinc compounds, nanoparticles, nanomedicine, DNA, microorganisms, cellular biophysics, biomedical engineeringOther keywords: photo‐induced Leishmania DNA degradation, PEGylated silver‐doped zinc oxide nanoparticle, Leishmania tropica, reactive oxygen species, sunlight, Leishmania DNA interaction, Leishmania cell, DNA damage, singlet oxygen scavenger, sodium azide, DNA degradation process, ZnO:Ag     

Inverse optimality in the class of Hopfield neural networks with input nonlinearity

This paper presents the chaos suppression problem in the class of Hopfield neural networks (HNNs) with input nonlinearity using inverse optimality approach. Using the inverse optimality technique and based on Lyapunov stability theory, a stabilizing control law, which is optimal with respect to meaningful cost functional, is determined to achieve global asymptotically stability in the closed-loop system. Numerical simulation is performed on a four-dimensional hyper-chaotic HNN to demonstrate the effectiveness of the proposed method.     

Intelligent facial emotion recognition and semantic-based topic detection for a humanoid robot

Automatic perception of human affective behaviour from facial expressions and recognition of intentions and social goals from dialogue contexts would greatly enhance natural human robot interaction. This research concentrates on intelligent neural network based facial emotion recognition and Latent Semantic Analysis based topic detection for a humanoid robot. The work has first of all incorporated Facial Action Coding System describing physical cues and anatomical knowledge of facial behaviour for the detection of neutral and six basic emotions from real-time posed facial expressions. Feedforward neural networks (NN) are used to respectively implement both upper and lower facial Action Units (AU) analysers to recognise six upper and 11 lower facial actions including Inner and Outer Brow Raiser, Lid Tightener, Lip Corner Puller, Upper Lip Raiser, Nose Wrinkler, Mouth Stretch etc. An artificial neural network based facial emotion recogniser is subsequently used to accept the derived 17 Action Units as inputs to decode neutral and six basic emotions from facial expressions. Moreover, in order to advise the robot to make appropriate responses based on the detected affective facial behaviours, Latent Semantic Analysis is used to focus on underlying semantic structures of the data and go beyond linguistic restrictions to identify topics embedded in the users’ conversations. The overall development is integrated with a modern humanoid robot platform under its Linux C++ SDKs. The work presented here shows great potential in developing personalised intelligent agents/robots with emotion and social intelligence.