Impact behavior of aminosilane functionalized nanosilica based shear thickening fluid impregnated Kevlar fabrics

In the present work, two types of shear thickening fluids have been synthesized by using neat and aminosilane functionalized silica nanoparticles and their viscosity curves have been obtained by the rheometer. Based on the values of peak viscosity of synthesized shear thickening fluids, the surface functionalized nanosilica based shear thickening fluid has been chosen as a best candidate due to the high viscosity for impregnation into the neat Kevlar of different layers viz. four (04) and eight (08) layers for velocity impact study. The experimental investigations reveal high energy absorption of shear thickening fluid impregnated Kevlar as compared to the neat Kevlar. The maximum energy absorption 62 J is achieved corresponding to the initial velocity 154 m∙s⁻¹ for 08 layers shear thickening fluid impregnated Kevlar specimen. The data have also been analytically determined and validated with the experimental data. The experimental data have good agreement with the analytical data within the accuracy of around 15 to 20%. The present findings can have significant inferences towards the fabrication of shear thickening fluids using nanomaterials for numerous applications such as soft armors, dampers, nanofinishing and so forth.     

Fast frequency estimation and tracking using Lagrange interpolation

A computationally efficient and accurate frequency estimation and tracking algorithm is proposed, based on the adaptive frequency estimator (AFE) of Etter and Hush. A Lagrange interpolator (a fractional delay filter) is used to estimate the gradient of the performance surface of the adaptation, which enables highly accurate estimation. The performance of the new algorithm is demonstrated in the context of tracking a chirp signal in noise     

Biodegradation of gelatin–g-poly(ethyl acrylate) copolymers

Gelatin-g-poly(ethyl acrylate) copolymers were prepared in an aqueous medium, using K₂S₂O₈ initiator. The composition of the graft copolymers was dependent upon temperature and duration of the reaction. The number of grafting sites was small and molecular weight of the grafted poly(ethyl acrylate) branches was high. Three copolymer samples with grafting efficiencies of 33.3%, 61.0%, and 84.0%, were tested for their microbial susceptibility in a synthetic medium employing a mixed inoculum of Bacillus subtilis, Pseudomonas aeruginosa, and Serratia marcescens and the percent weight losses were 12%, 10.1%, and 6.0%, respectively, after 6 weeks of incubation. The extent of degradation seems to decrease with increasing grafting efficiency. There was initial rapid weight loss accompanied by the exponential increase in bacterial population and pH of the culture medium during the first week. The nitrogen analysis also showed the utilization of the polymer. A parallel set of experiments, carried out by employing the samples as the only source of both carbon and nitrogen, showed a marginal but definite increase in the utilization of the polymer.     

Development of a Sensor Fusion Strategy for Robotic Application Based on Geometric Optimization

Fusion of multi-sensor information is an important technology, which is growing exponentially due to its tremendous application potential in many areas. Effective fusion of data from sensors is very critical in increasing an intelligent system's capability to accomplish complex tasks. Appropriate fusion technologies are needed to be developed specially when a system requires redundant sensors to be used. More the redundancy in sensors, more is the computational complexity for controlling the system and more is its intelligence level. This research presents a strategy developed for multiple sensor fusion, based on geometric optimization. Each sensor's uncertainty has been modeled using classical Lagrangian optimization techniques. However, the uniqueness and effectiveness of the present technique lies on the fact that starting from the optimized value as initial estimate the accuracy of the sensory information has further been improved up to any pre defined bounded range, by developing two architectures – FFA (fission–fusion architecture) and FDD (fusion in differential domain). Sufficient evidences and analyses have been provided in the paper to show its effectiveness in various applications.     

Design of particle-reinforced polyurethane mould materials for soft tooling process using evolutionary multi-objective optimization algorithms

Polyurethane is used for making mould in soft tooling (ST) process for producing wax/plastic components. These wax components are later used as pattern in investment casting process. Due to low thermal conductivity of polyurethane, cooling time in ST process is long. To reduce the cooling time, thermal conductive fillers are incorporated into polyurethane to make composite mould material. However, addition of fillers affects various properties of the ST process, such as stiffness of the mould box, rendering flow-ability of melt mould material, etc. In the present work, multi-objective optimization of various conflicting objectives (namely maximization of equivalent thermal conductivity, minimization of effective modulus of elasticity, and minimization of equivalent viscosity) of composite material are conducted using evolutionary algorithms (EAs) in order to design particle-reinforced polyurethane composites by finding the optimal values of design parameters. The design parameters include volume fraction of filler content, size and shape factor of filler particle, etc. The Pareto-optimal front is targeted by solving the corresponding multi-objective problem using the NSGA-II procedure. Then, suitable multi-criterion decision-making techniques are employed to select one or a small set of the optimal solution(s) of design parameter(s) based on the higher level information of the ST process for industrial applications. Finally, the experimental study with a typical real industrial application demonstrates that the obtained optimal design parameters significantly reduce the cooling time in soft tooling process keeping other processing advantages.     

Proinflammatory Interleukin-33 Induces Dichotomic Effects on Cell Proliferation in Normal Gastric Epithelium and Gastric Cancer

Interleukin (IL)-33 is a member of the interleukin (IL)-1 family of cytokines linked to the development of inflammatory conditions and cancer in the gastrointestinal tract. This study is designed to investigate whether IL-33 has a direct effect on human gastric epithelial cells (GES-1), the human gastric adenocarcinoma cell line (AGS), and the gastric carcinoma cell line (NCI-N87) by assessing its role in the regulation of cell proliferation, migration, cell cycle, and apoptosis. Cell cycle regulation was also determined in ex vivo gastric cancer samples obtained during endoscopy and surgical procedures. Cell lines and tissue samples underwent stimulation with rhIL-33. Proliferation was assessed by XTT and CFSE assays, migration by wound healing assay, and apoptosis by caspase 3/7 activity assay and annexin V assay. Cell cycle was analyzed by means of propidium iodine assay, and gene expression regulation was assessed by RT-PCR profiling. We found that IL-33 has an antiproliferative and proapoptotic effect on cancer cell lines, and it can stimulate proliferation and reduce apoptosis in normal epithelial cell lines. These effects were also confirmed by the analysis of cell cycle gene expression, which showed a reduced expression of pro-proliferative genes in cancer cells, particularly in genes involved in G0/G1 and G2/M checkpoints. These results were confirmed by gene expression analysis on bioptic and surgical specimens. The aforementioned results indicate that IL-33 may be involved in cell proliferation in an environment- and cell-type-dependent manner.     

Prediction of burning rate of coal in fluidized-bed combustion

A theory has been proposed to evaluate the burning rate of a single carbon particle in a continuously operated coal-fired fluidized bed. Experimental verification was carried out in a laboratory scale 200 mm × 200 mm combustor. The burning rate increases with the increase of the fluidization velocity and the size of the bed material. The predicted data on burning rate agree fairly well with the experimental values. The gas concentration in the bed and freeboard has also been measured and it is seen that the consumption of oxygen mostly takes place in the bed.     

Fuzzy triangulation signature for detection of change in human emotion from face video image sequence

The present article proposes a geometry-based fuzzy relational technique for capturing gradual change in human emotion over time available from relevant face image sequences. As associated features, we make use of fuzzy membership arising out of five triangle signatures such as - (i) Fuzzy Isosceles Triangle Signature (FIS), (ii) Fuzzy Right Triangle Signature (FRS), (iii) Fuzzy Right Isosceles Triangle Signature (FIRS), (iv) Fuzzy Equilateral Triangle Signature (FES), and (v) Other Fuzzy Triangles Signature (OFS) to achieve the task of appropriate classification of facial transition from neutrality to one among the six expressions viz. anger (AN), disgust (DI), fear (FE), happiness (HA), sadness (SA) and surprise (SU). The effectiveness of the Multilayer Perceptron (MLP) classifier is tested and validated through 10 fold cross-validation method on three benchmark image sequence datasets namely Extended Cohn-Kanade (CK+), M&M Initiative (MMI), and Multimedia Understanding Group (MUG). Experimental outcomes are found to have achieved accuracy to the tune of 98.47%, 93.56%, and 99.25% on CK+, MMI, and MUG respectively vindicating the effectiveness by exhibiting the superiority of our proposed technique in comparison to other state-of-the-art methods in this regard.

     

Feature extraction and dimensionality reduction by genetic programming based on the Fisher criterion

Abstract: Feature extraction helps to maximize the useful information within a feature vector, by reducing the dimensionality and making the classification effective and simple. In this paper, a novel feature extraction method is proposed: genetic programming (GP) is used to discover features, while the Fisher criterion is employed to assign fitness values. This produces non‐linear features for both two‐class and multiclass recognition, reflecting the discriminating information between classes. Compared with other GP‐based methods which need to generate c discriminant functions for solving c‐class (c>2) pattern recognition problems, only one single feature, obtained by a single GP run, appears to be highly satisfactory in this approach. The proposed method is experimentally compared with some non‐linear feature extraction methods, such as kernel generalized discriminant analysis and kernel principal component analysis. Results demonstrate the capability of the proposed approach to transform information from the high‐dimensional feature space into a single‐dimensional space by automatically discovering the relationships between data, producing improved performance.