A multi-objective strategy in genetic algorithms for gene selection of gene expression data

A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification. It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach that used a single-objective strategy in a genetic algorithm. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Selecting informative genes from microarray data by using hybrid methods for cancer classification

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

IP address allocation protocols in vehicular ad hoc networks

In vehicular ad hoc networks (VANETs), communication takes place between vehicles to vehicles, the vehicles to the road side units, and vice-versa. The basic purpose of these communications is to share and exchange tremendous amount of data and information. For efficient information sharing, a systematic and structured connection establishment algorithm is needed. In VANETs, each connected node of the network need to be assigned a unique address. Hence, an algorithm is needed for the proper assignment of unique address to all nodes in the network. This paper explains different types of IP address protocols in VANETs. We have also explained advantage and disadvantage of existing IP address allocation protocols in VANETs.     

Phase Distribution and Magnetic Properties of Mechanically Alloyed Hard/Soft Ferrite Nanocomposites

Improvement in the magnetic properties of hard/ soft ferrite nanocomposites was studied by varying the composition of the soft phase in SrFe₁₂O₁₉/Ni_0.5Zn_0.5Fe₂O₄ nanocomposites. The SrFe₁₂O₁₉/Ni_0.5Zn_0.5Fe₂O₄ nanocomposites were prepared using the mechanical alloying method. The samples were prepared by varying the amount of the soft phase from 10 to 50 wt% while the amount of the hard phase remained 100 wt% in the ferrite nanocomposites. X-ray diffraction (XRD), a vibrating sample magnetometer (VSM), and a transmission electron microscope (TEM) were used to characterize the samples. From the result, it was found that the nanocomposite magnet with 10 wt% of soft phase content had the highest remanence ratio, M _r / M _s , which was 0.61, while the values of the coercivity, H _c , and magnetization, M _s , measured were 4482.4 G and 9.71 emu/g, respectively, and the average particle size of the ferrite nanocomposites was < 50 nm for all the samples. It was also shown that H _c decreased as the weight percent of the soft ferrite increased, which resulted from the dipolar interaction that occurred in the ferrite nanocomposites, showing the effect of phase distribution on the magnetic properties.     

Lateral Magnetically Modulated Multilayers by Combining Ion Implantation and Lithography

The combination of lithography and ion implantation is demonstrated to be a suitable method to prepare lateral multilayers. A laterally, compositionally, and magnetically modulated microscale pattern consisting of alternating Co (1.6 µm wide) and Co‐CoO (2.4 µm wide) lines has been obtained by oxygen ion implantation into a lithographically masked Au‐sandwiched Co thin film. Magnetoresistance along the lines (i.e., current and applied magnetic field are parallel to the lines) reveals an effective positive giant magnetoresistance (GMR) behavior at room temperature. Conversely, anisotropic magnetoresistance and GMR contributions are distinguished at low temperature (i.e., 10 K) since the O‐implanted areas become exchange coupled. This planar GMR is principally ascribed to the spatial modulation of coercivity in a spring‐magnet‐type configuration, which results in 180° Néel extrinsic domain walls at the Co/Co‐CoO interfaces. The versatility, in terms of pattern size, morphology, and composition adjustment, of this method offers a unique route to fabricate planar systems for, among others, spintronic research and applications.     

Self-reinforcing mechanisms for cellularisation: a longitudinal case study

Firms that adopt cellular manufacturing (CM) often need to restructure their products, personnel, equipment, factory management and organisation culture. Despite voluminous literature on CM, only a few case studies related to cellularisation exist. Therefore, to enhance understanding in this area, this paper presents a longitudinal case study on cellularisation in an electronic assembly plant. Six years of data were collected from the observations and discussions of an industrialist and an academician. Events instrumental to the persistence of CM were highlighted, with reference to path dependence analysis. Cellularisation is not entirely evolutionary or teleological but is an interplay of the two; it is significantly influenced by the positive feedback of self-reinforcing mechanisms after departing from critical junctures. The observed self-reinforcing mechanisms were expounded and divided into six categories.     

Nutratherapeutics approach against cancer: tomato‐mediated synthesised gold nanoparticles

In this study, an eco‐friendly biosynthesis of stable gold nanoparticles (T‐GNPs) was carried out using different concentrations of tomato juice (nutraceuticals) as a reducing agent and tetrachloroauric acid as a metal precursor to explore their potential application in cancer therapeutics. The synthesis of T‐GNPs was monitored by UV‐visible absorption spectroscopy, which unveiled their formation by exhibiting the typical surface plasmon absorption maxima at 522 nm. The size of T‐GNPs was found to be 10.86 ± 0.6 nm. T‐GNPs were characterised by dynamic light scattering, zeta potential, transmission electron microscopy analysis and Fourier transform infrared spectroscopy. T‐GNPs were further investigated for their anti‐cancer activity against human lung carcinoma cell line (A ₅₄₉) and human cervical cancer cell line wherein the IC₅₀ values were found to be 0.286 and 0.200 mM, respectively. T‐GNPs inhibited the growth of cancer cells by generating ROS and inducing apoptosis. T‐GNPs were found highly effective by virtue of their size, metallic property and capping molecules. Thus, this study opens up the prospects of using nutraceutical (tomato juice) as nutratherapeutic agent (T‐GNPs) against critical diseases like lung cancer and cervical cancer.Inspec keywords: gold, nanoparticles, particle size, cancer, ultraviolet spectra, visible spectra, electrokinetic effects, transmission electron microscopy, Fourier transform infrared spectra, cellular biophysics, spectrochemical analysis, nanomedicine, nanofabricationOther keywords: tomato‐mediated synthesised gold nanoparticles, tomato juice, reducing agent, tetrachloroauric acid, cancer therapeutics, UV‐visible absorption spectroscopy, surface plasmon absorption, dynamic light scattering, zeta potential, transmission electron microscopy analysis, Fourier transform infrared spectroscopy, human lung carcinoma cell line, anticancer activity, human cervical cancer cell line, nutratherapeutic agent, lung cancer, Au     

An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received Signal Strength information from available Wireless Local Area Network access points, together with the Wireless Sensor Networks technology. Six signals were recorded on a regular grid of anchor points, covering the research space. An optimization was performed by relative signal weighting, to minimize the average positioning error over the research space. The optimization process was conducted using a standard Quantum Particle Swarm Optimization, while the position error estimate for all given sets of weighted signals was performed using a Multilayer Perceptron (MLP) neural network. Compared to our previous research works, the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned. These experimental results led to the 20% reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process. Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.