Solar/UV-induced photocatalytic degradation of three commercial textile dyes.

The photocatalytic degradation of three commercial textile dyes with different structure has been investigated using TiO(2) (Degussa P25) photocatalyst in aqueous solution under solar irradiation. Experiments were conducted to optimise various parameters viz. amount of catalyst, concentration of dye, pH and solar light intensity. Degradation of all the dyes were examined by using chemical oxygen demand (COD) method. The degradation efficiency of the three dyes is as follows: Reactive Yellow 17(RY17) > Reactive Red 2(RR2) > Reactive Blue 4 (RB4), respectively. The experimental results indicate that TiO(2) (Degussa P25) is the best catalyst in comparison with other commercial photocatalysts such as, TiO(2) (Merck), ZnO, ZrO(2), WO(3) and CdS. Though the UV irradiation can efficiently degrade the dyes, naturally abundant solar irradiation is also very effective in the mineralisation of dyes. The comparison between thin-film coating and aqueous slurry method reveals that slurry method is more efficient than coating but the problems of leaching and the requirement of separation can be avoided by using coating technique. These observations indicate that all the three dyes could be degraded completely at different time intervals. Hence, it may be a viable technique for the safe disposal of textile wastewater into the water streams.     

The effect of 9-beta-D-arabinofuranosyl-2-fluoroadenine and 1-beta-D-arabinofuranosylcytosine on the cell cycle phase distribution, topoisomerase II level, mitoxantrone cytotoxicity, and DNA strand break production in K562 human leukemia cells

The UvrA and UvrB proteins form part of the UvrABc endonuclease, which is responsible for nucleotide excision repair in Escherichia coli. Using a mobility shift gel assay we have studied the binding of UvrA dimer, UvrB monomer and UvA(2)B trimer complexes with 40, 50 and 136 bp (32)P-end-labelled DNA fragments adducted with aflatoxin B(1). UvrA was shown to re-associate with adduct specific UvrB: DNA complexes, a phenomenon which could be reversed by the addition of 500 mM potassium chloride or anti-UvrA anti-sera. Re-association was shown to be UvrA concentration dependent. Re-association of UvrA(2)B to the UvrB:DNA complex was not seen. We have also shown that the UvrB:DNA complex, in the case of aflatoxin B(1), is extremely stable with a half-life excess of 400 min and that fragment termini are not a specific substrate for UvrA binding.     

Neural networks for signal detection in non-Gaussian noise

We employ neural networks to detect known signals in additive non-Gaussian noise. Training of the neural network for signal detection and its operation at some specified probability of false alarm are discussed. Performance of neural detectors are presented under several non-Gaussian noise environments and are compared with those of matched filter and locally optimum detectors     

Soft computing approach to fault diagnosis of centrifugal pump

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study.     

Improved corrosion protection of aluminum alloys by electrodeposited silanes

Organofunctional silanes recently have emerged as outstanding, environmentally friendly corrosion protectors for metal substrates, compared with conventional chromate treatments. A simple immersion technique is typically used to coat the metal surface with silane films. However, the thickness and uniformity of the films are uncontrolled in this process. This paper proposes a new deposition technique for the silane films on the metal surface, i.e., by electrodeposition. Hydrolyzed silanes are water-soluble, ionized molecules, so they can be deposited on metals by electrodeposition. Various combinations of silane mixtures were tested at different voltages, pH values, bath concentrations, and exposure times on panels of alloy aluminum and mirror-polished ferro-plate. The surface structure was characterized by scanning electron microscopy (SEM) and ellipsometry. The resistance of the film to corrosion was investigated by direct current (DC) polarization and electrochemical impedance spectroscopy (EIS) techniques. Electrodeposition results in a more organized and uniform film with fewer pores, compared with immersed or dipped films. This paper was presented at the 2nd International Surface Engineering Congress sponsored by ASM International, on September 15–17, 2003, in Indianapolis, Indiana, and appears on pp. 320–26 of the Proceedings.     

The influence of sulfur dioxide on propane oxidation activity over supported platinum and palladium

Earlier studies have shown that sulfur dioxide and metal-support interaction can strongly influence propane oxidation over platinum. In particular, oxidation activity is enhanced when platinum is supported on sulfated -alumina or zirconia compared to -alumina. Therefore, it is of interest to compare the performance of palladium under the same experimental conditions. Four model catalysts were examined: Pt/-alumina, Pt/zirconia, Pd/-alumina and Pd/zirconia. The metal loading was kept at or below 0.05 wt% to emphasize changes in activity attributable to metal-support interaction. Reaction rates were measured with and without sulfur dioxide. Surface sulfation was analyzed by measuring acid strength and evaluating spectra obtained by Fourier-transform infrared spectroscopy. In contrast to platinum, sulfation does not promote propane oxidation on Pd/-alumina, and Pd/zirconia is less active than Pd/-alumina.     

Design of high-speed data transfer turbo code for body channel communication transceivers

In this work, a digital differential transmitter based on low-power wireless compensation transceiver for body channel communication (BCC) is proposed. Further, the proposed transceiver is composed of Touch Status Detection Unit (TSDU), Wireless Status Compensation Unit (WSCU), and a reconfigurable preamplifier. Initially, the human body channel environment for wireless communication is investigated based on properties from 1 to 100 MHz. Further, the turbo code-based encoding scheme is used to encode the data before transferring the data on the transmitter side. Also, the proposed error-correcting parallel turbo decoder using a modified step-by-step algorithm is presented. The turbo code-based decoding scheme is used to recover the error-free transmitted data at the receiver side. Results demonstrate that the proposed BCC transceiver is designed using 90 nm CMOS technology and it is observed that the proposed BCC transceiver has utilized an area of 600mm². Also, the maximum data rate achieved by a proposed BCC transceiver was 100 Mbps, and the overall transceiver power consumption is 0.42 mW, and energy for communication is 0.02 nj/b.     

An Optimized Deep-Learning-Based Low Power Approximate Multiplier Design

Approximate computing is a popular field for low power consumption that is used in several applications like image processing, video processing, multimedia and data mining. This Approximate computing is majorly performed with an arithmetic circuit particular with a multiplier. The multiplier is the most essential element used for approximate computing where the power consumption is majorly based on its performance. There are several researchers are worked on the approximate multiplier for power reduction for a few decades, but the design of low power approximate multiplier is not so easy. This seems a bigger challenge for digital industries to design an approximate multiplier with low power and minimum error rate with higher accuracy. To overcome these issues, the digital circuits are applied to the Deep Learning (DL) approaches for higher accuracy. In recent times, DL is the method that is used for higher learning and prediction accuracy in several fields. Therefore, the Long Short-Term Memory (LSTM) is a popular time series DL method is used in this work for approximate computing. To provide an optimal solution, the LSTM is combined with a meta-heuristics Jellyfish search optimisation technique to design an input aware deep learning-based approximate multiplier (DLAM). In this work, the jelly optimised LSTM model is used to enhance the error metrics performance of the Approximate multiplier. The optimal hyperparameters of the LSTM model are identified by jelly search optimisation. This fine-tuning is used to obtain an optimal solution to perform an LSTM with higher accuracy. The proposed pre-trained LSTM model is used to generate approximate design libraries for the different truncation levels as a function of area, delay, power and error metrics. The experimental results on an 8-bit multiplier with an image processing application shows that the proposed approximate computing multiplier achieved a superior area and power reduction with very good results on error rates.     

Non‐fragile reliable control synthesis of the sugarcane borer

This study considers the problem of non‐fragile reliable control synthesis for mathematical model of interaction between the sugarcane borer (Diatraea saccharalis) and its egg parasitoid Trichogramma galloi. In particular, the control could be substituted by periodic releases of a small population of natural enemies and hence it is important to propose the time‐varying controller in sugarcane borer. The main aim of this study is to design a state feedback non‐fragile (time‐varying) reliable controller such that the states of the sugarcane borer system reach the equilibrium point within the desired period. A novel approach is proposed to deal with the uncertain matrices which appear in non‐fragile reliable control. Finally, simulations based on sugarcane borer systems are conducted to illustrate the advantages and effectiveness of the proposed design technique. The result reveals that the proposed non‐fragile control provides good performance in spite of periodic releases of a small population of natural enemies occurs.Inspec keywords: microorganisms, plant diseases, biology computing, state feedback, biocontrol, control system synthesisOther keywords: nonfragile reliable control synthesis, sugarcane borer, mathematical model, Diatraea saccharalis, egg parasitoid, Trichogramma galloi, periodic releases, natural enemies, state feedback nonfragile time‐varying reliable controller, equilibrium point, design technique     

Ultrasound-assisted preparation of novel ibuprofen-loaded excipient with improved compression and dissolution properties

Context: Most of the active pharmaceutical ingredients (APIs) suffer from a drawback of poor aqueous solubility. In addition to the same, some APIs show poor tabletting behavior creating problems in formulation development. Crystal engineering can be an efficient tool in rectification of such problems associated with the APIs. Thus present work deals with crystallization of ibuprofen (a model drug) onto the surface of dicalcium phosphate (DCP) particles using different techniques.

Objective: The objective of the present work was to prepare ibuprofen-loaded DCP particles and further to analyze them for compressibility and dissolution behavior.

Materials and methods: Various crystallization techniques such as solvent evaporation (SE), melt crystallization (MC), melt sonocrystallization (MSC), antisolvent crystallization (AC), and antisolvent sonocrystallization (ASC) were screened for the preparation of ibuprofen-loaded DCP. Products obtained from different techniques were analyzed for physicochemical, micromeritic and compression properties.

Results and discussion: ASC technique was found to be suitable for preparing directly compressible ibuprofen-loaded DCP particles. The change in the crystal habit (needle to plate shape) of ibuprofen and its crystallization in miniscular form onto the surface of DCP particles showed significant improvement in the dissolution rate and compression properties of ibuprofen due to an increase in specific surface area when compared with ibuprofen crystallized by other techniques. Additionally, the tablets prepared from ASC powder did not require binder since ibuprofen acted as melt binder during compression.

Conclusion: Directly compressible ibuprofen-loaded DCP particles can serve as an alternative for conventional ibuprofen tablets prepared by wet granulation technique.