Evaluation of an artificial olfactory system for grain quality discrimination

A commercially available Cyranose-320™ conducting polymer-based electronic nose system was used to analyze the headspace from stored barley samples. Three types of barley samples were analyzed, namely, clean barley, naturally Fusarium infected barley and Fusarium inoculated clean barley. The barley samples were stored at moisture contents of 13, 18, 20 and 25 g of water/100 g sample. The raw signals obtained from the electronic nose system were pre-processed by various signal-processing techniques to extract area-based features. Principal component analysis was subsequently performed on the processed signals to further reduce the dimensionalities. Classification models using linear (LDA) and quadratic discriminant analyses (QDA) were developed using the extracted features. The performance of the developed models was validated using leave-1-out cross validation and bootstrapping method. The models classified the barley samples stored into two groups based on the ergosterol content, i.e., “acceptable” (ergosterol content <3.0 μg/g) and “unacceptable” (ergosterol content ?3.0 μg/g). Overall, the total maximum classification accuracy obtained was 86.8% by both LDA and QDA when leave-1-out cross-validation was used. By bootstrapping validation the maximum total classification accuracy obtained was 86.4% and 86.1% respectively, by QDA and LDA. The study proves that there is potential in using an electronic nose system for indicating mold spoilage in stored grains, and necessitates future studies in this direction.     

Exergy Analysis of Solar Still with Sand Heat Energy Storage

This paper studies the experimental and exergy analysis of solar still with the sand heat energy storage system. The cumulative yield from solar still with and without energy storage material is found to be 3.3 and 1.89 kg/m², respectively for 8-h operation. Results show that the exergy efficiency of the system is higher with the least water depth of 0.02 m (m_w = 20 kg). Competitive analysis of second law efficiency shows that the exergy efficiency improves the system by 30% than conventional single slope solar still without any heat storage. The maximum exergy efficiency with energy storage material is found as 13.2% and it is less than the conventional solar still without any material inside the basin.     

Bio-accumulation of metals on reef associated organisms of Lakshadweep Archipelago

Tissue samples of marine organisms from the coastal waters of Agatti Island were subjected to analysis of metals (cadmium, cobalt, copper, iron, magnesium, manganese, nickel, lead and zinc), using Inductively Coupled Plasma Optical Emission Spectrometer (ICP-OES) for the assessment of the present condition of the Island ecosystem and compiling the baseline data for future monitoring, with respect of metal accumulation of marine organisms. Tissue samples of fish, shellfish and seaweed revealed that the metals have different levels of accumulation viz. Cd = 0.08-0.14, Co = 0.01-0.02, Cu = 0.16-0.98, Fe = 3.19-5.3, Mg = 86.73-152.45, Mn = 0.17-0.55, Ni = 0.06-0.26, Pb = 0.11-0.46 and Zn = 3.26-14.2 μg g⁻¹ dry wt. Metal concentrations were more in shellfish and less in finfish. Concentrations of toxic metals such as Cd, Co, Ni and Pb were well below the permissible limits proposed by the World Health Organization.     

Irradiation Target Cooling Using Circular/Slot Air Jet

To study the effect of irradiation on materials, sample coupons are irradiated in cyclotron facilities. During the irradiation process, these samples produce significant heat. This heat needs to be continuously removed from the samples in order to avoid melting of the samples as well as to keep the samples at a particular temperature during irradiation. The area available for heat transfer is limited due to small size of the samples. To increase the heat transfer rate, jet cooling is used as it provides large heat transfer co-efficient. To understand the heat transfer characteristics of jet cooling under these conditions, experiments have been carried out. Two inclined jets hitting on both sides of the target plate give maximum cooling and uniform temperature distribution. This paper gives the details of the numerical and experimental studies carried out and the discussions about the results obtained.     

Enumeration of stereo,position and chiral isomers of polysubstituted giant fullerenes: applications to C180 and C240

Abstract

We have developed combinatorial generation function methods that combine M?bius inversion and character cycle indices for the enumeration of stereo, position and chiral isomers of icosahedral giant fullerenes C₁₈₀ and C₂₄₀. Techniques are also developed for the machine perception of symmetries of especially giant fullerenes. The techniques yield, symmetries, position, stereo and chiral isomers of giant fullerenes which we illustrate with applications to icosahedral C₁₈₀(I_h), and C₂₄₀(I_h). We have obtained combinatorial tables for the isomers of C₁₈₀X_k and C₂₄₀X_k. Our results point to errors in previous computations on C₂₄₀ permutations. We have also outlined applications to NMR and ESR spectroscopy.     

MEMS tunable SOI waveguide Bragg grating filter with 1.3 THz tuning range for C-band 100 GHz DWDM optical network

A MEMS tunable integrated waveguide Bragg grating-based filter for C-band optical dense wavelength-division multiplexing (DWDM) network is presented and analyzed in this work. Waveguide Bragg grating being a notch filter in the transmission spectrum is used to realize a tunable filter by varying the applied voltages to the fixed–fixed beam loaded with this grating. The strain across the grating is enhanced by choosing MEMS beam configuration such that the metal electrode is the bottom-most layer of the composite fixed–fixed beam. Device dimensions are chosen to achieve a narrow full width half maximum of 0.77 nm, allowing filtering of adjacent channels of 100 GHz DWDM network. A large Bragg wavelength shift of 10.4 nm (1552.52–1562.92 nm) was achieved at 45.8 V actuation providing tuning for 14 DWDM channels with inter-channel cross talk below ? 21 dB, with tuning range of 1.3 THz.

     

Role of tool pin profiles on wear characteristics of friction stir processed magnesium alloy ZK60/silicon carbide surface composites

Poor friction and wear resistance are the major drawbacks that restrict structural applications of ZK60 magnesium alloys. The surface properties of magnesium alloy can be enhanced by reinforcing particles in the surface using friction stir processing (FSP). Tool pin profile is the significant process parameters which influences the material flow, particle breakups and its distribution in the processed zone. In this study, an attempt was made in order to understand the major effects of tool pin profiles namely, cylindrical thread (CT), plain cylindrical (PC), plain tapered cylindrical (PTC) and square (SA) pin profiles on the microstructure characteristics and particle distribution of friction stir processed/silicon carbide particle surface composites. The surface composites fabricated by plain tapered cylindrical pin profile yield superior properties which is attributed to the higher shear force and balanced state of material flow and heat generation in the processed zone. The formation of smaller grains and hardness enhancement due to dispersion strengthening are the main causes to get better wear resistance of friction stir processed/silicon carbide particle magnesium alloy.     

Analysis and classification of malignancy in pancreatic magnetic resonance images using neural network techniques

Computer-aided diagnosis (CAD) is a computerized way of detecting tumors in MR images. Magnetic resonance imaging (MRI) has been generally used in the diagnosis and detection of pancreatic tumors. In a medical imaging system, soft tissue contrast and noninvasiveness are clear preferences of MRI. Inaccurate detection of tumor and long time consumption are the disadvantages of MRI. Computerized classifiers can greatly renew the diagnosis activity, in terms of both accuracy and time necessity by normal and abnormal images, automatically. This article presents an intelligent, automatic, accurate, and robust method to classify human pancreas MRI images as normal or abnormal in terms of pancreatic tumor. It represents the response of artificial neural network (ANN) and support vector machine (SVM) techniques for pancreatic tumor classification. For this, we extract features from MR images of pancreas using the GLCM method and select the best features using JAFER algorithm. These features are analyzed by five classification techniques: ANN BP, ANN RBF, SVM Linear, SVM Poly, and SVM RBF. We compare the results with benchmark data set of MR brain images. The analytical outcome presents that the two best features used to classify the MR images using ANN BP technique have 98% classification accuracy.