Functional finishing in cotton fabrics using zinc oxide nanoparticles

Nanotechnology, according to the National Nanotechnology Initiative (NNI), is defined as utilization of structure with at least one dimension of nanometer size for the construction of materials, devices or systems with novel or significantly improved properties due to their nano-size. The nanostructures are capable of enhancing the physical properties of conventional textiles, in areas such as anti-microbial properties, water repellence, soil-resistance, anti-static, anti-infrared and flame-retardant properties, dyeability, colour fastness and strength of textile materials. In the present work, zinc oxide nanoparticles were prepared by wet chemical method using zinc nitrate and sodium hydroxide as precursors and soluble starch as stabilizing agent. These nanoparticles, which have an average size of 40 nm, were coated on the bleached cotton fabrics (plain weave, 30 s count) using acrylic binder and functional properties of coated fabrics were studied. On an average of 75%, UV blocking was recorded for the cotton fabrics treated with 2% ZnO nanoparticles. Air permeability of the nano-ZnO coated fabrics was significantly higher than the control, hence the increased breathability. In case of nano-ZnO coated fabric, due to its nano-size and uniform distribution, friction was significantly lower than the bulk-ZnO coated fabric as studied by Instron® Automated Materials Testing System. Further studies are under way to evaluate wash fastness, antimicrobial properties, abrasion properties and fabric handle properties.     

Structural parameters identification of uncertain multi-storey shear buildings using fuzzy neural network modelling

Present paper proposes a fuzzy neural network (FNN)-based modelling for the identification of structural parameters of uncertain multi-storey shear buildings. Here, the method is developed to identify uncertain structural mass, stiffness and damping matrices from the dynamic responses of the structure without any optimization processes that are generally used to solve inverse vibration problems. Uncertainty has been taken in term of fuzzy numbers. The governing equations of motion are first solved by the classical method to get responses of the consecutive stories. Further the governing equations of motion are modified based on relative responses of consecutive stories in such a way that the new set of equations can be implemented in a cluster of FNNs. As such the model starts solving the nth floor by FNN modelling to estimate the structural parameters. Subsequently, series of FNN models are used to estimate the parameters for (n ? 1)th storey to the first storey. One may note that single layer FNNs have been used for training for each cluster of the FNN such that the converged weights give the uncertain structural parameters. The initial weights in the FNN architecture are taken as the design parameters in uncertain (fuzzy) form. In order to validate the present model, various example problems of different multi-storey shear structures have been considered. Related results are incorporated in term of tables and graphs. Comparisons between theoretical and identified results are carried out and are found to be in good agreement.     

Connectivity opportunity selection in heterogeneous wireless multi-hop networks

This paper addresses the problem of selecting the best connectivity alternative for the user in a generic Heterogeneous Wireless Multi-hop Network (HWMN), integrating distinct wireless technologies and multi-mode cooperating stations. We propose a Connectivity opportunity Selection Algorithm (CSA) that uses network state information and mobility profile information to select the best connectivity based on the applications’ requirements. We provide a simulation-based performance evaluation of the CSA and compare it with a greedy network selection scheme. Furthermore, we propose an extended reference model that allows the integration of the concept of connectivity opportunity and our proposed CSA with the framework being defined by the upcoming IEEE 802.21 standard for Media Independent Handover services.     

Investigation of Anti-Cancer Drug Nimustine Interaction with Calf Thymus DNA

Nimustine, a chloroethyl nitrosourea derivative (CENU), is an antineoplastic agent, which is used for the treatment of various types of cancer. The present study focuses on the prediction and investigation of binding properties of nimustine with DNA using molecular modeling and UV–Visible spectroscopic technique. The docking study show that nimustine plausibly binds within the major groove of DNA. Further analysis of docking suggests direct interaction of nimustine with the moieties of heterocyclic nitrogenous bases of DNA. The free binding energy value of the best nimustine-DNA docked conformer is predicted as ?4.31 kcal/mol using docking results.The molecular modeling study also reveals that the interaction between nimustine and DNA is majorly governed by van der Waals forces, hydrogen bonding and hydrophobic interactions, whereas the contribution of electrostatic forces stands negligible. Further, UV–Visible spectra of free calf thymus DNA and its complexes with varying concentration of nimustine indicate the binding constant (K _a ) value as 3.27 × 10³ M^?1, which suggests moderate interaction of nimustine with DNA. The spectroscopic results are further used to calculate the binding free energy of the complex using the relation ΔG = ?RT ln (K _a ). This accounts for a value of ?4.79 kcal/mol. It corroborates well with the docking outcomes. The results of present study may help in designing and synthesis of new chloroethyl nitrosourea derivatives with improved efficacy and specificity for the target molecules.     

Fatigue during prolonged intermittent overhead work: reliability of measures and effects of working height

Shoulder pain is prevalent among industrial workers and existing evidence supports that overhead work is an important specific risk factor. Existing guidelines are limited, with overhead work typically recommended to be avoided, and research on overhead work has been mixed in terms of the effects of increasing arm reach. A laboratory-based simulation of overhead work was conducted, at three working heights, in order to facilitate improved guidelines and to identify potential non-linear effects of overhead work height. Several indicators of shoulder fatigue served as outcome measures and a preliminary study was performed to assess the reliability of several of these measures. Fatigue measures based on electromyography (EMG) generally had low reliability, whereas excellent reliability was exhibited for ratings of perceived discomfort (RPD). Consistent with this, no effects of overhead work height were found on EMG-based measures, yet clear non-linear effects were found on RPD and task performance. The source of the effects of work height appeared to be related to a combination of muscle activation levels and demands on precision/control at the highest location. These results support the utility of subjective measures for relatively low-level intermittent exertions and demonstrate increasingly detrimental fatigue and performance effects at extremes in reach during overhead work.     

Subthreshold Modeling of Graded Channel Double Gate Junctionless FETs

Silicon - In this paper, 2-D analytical models of channel central potential, threshold voltage, subthreshold current and subthreshold swing for graded channel double gate (GC-DG) Junctionless FETs...     

Feature Extraction Methods in Language Identification: A Survey

Wireless Personal Communications - Language Identification (LI) is one of the widely emerging field in the areas of speech processing to accurately identify the language from the data base based on...     

N-PSO: endmember extraction using advance particle swarm optimization for NLMM

This paper presents a fully unsupervised endmember extraction technique for hyperspectral image unmixing using nonlinear mixing model. The underlying idea of the model is that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive noise. These nonlinear functions are approximated using polynomial functions, leading to a polynomial post-nonlinear mixing model (PPNM). In an unknown environment, the evaluation of the parameters involved in PPNM model is a tedious task, which is categorized as an NP hard problem. A method based on the combination of swarm intelligence, least-square (LS) and sub-gradient-based optimization (SO) is proposed to estimate the parameters involved in the model. The particle swarm optimization (PSO) is used to search the optimal endmember combination in the feasible solution space. The nonlinearity and respective abundances are evaluated using the LS and SO method, respectively. The proposed method is equipped with an adaptive tuning parameter-free mechanism and modified updating strategy. This strategy not only improves the result in terms of overall accuracy but also maintains physical constraints on the value of the resultant endmember set. The proposed method has been evaluated using simulated and real hyperspectral scenes. The experimental results on the hyperspectral scenes show that the proposed method obtains a higher extraction precision than those of the existing endmember extraction algorithms. Statistical analysis on a real hyperspectral image shows that the results obtain using N-PSO are 20–40% better than those from the existing approaches.