Application of entropy measurement technique in grey based Taguchi method for solution of correlated multiple response optimization problems: A case study in welding

In the present work, an attempt has been made to apply an efficient technique, in order to solve correlated multiple response optimization problems, in the field of submerged arc welding. The traditional grey based Taguchi approach has been extended to tackle correlated multi-objective optimization problems. The Taguchi optimization technique is based on the assumption that the quality indices (i.e. responses) are independent or uncorrelated. But, in practical cases, the assumption may not be valid always. However, the common trend in the solution of multi-objective optimization problems is to initially convert these multi-objectives into an equivalent single objective function. While deriving this equivalent objective function, different priority weights are assigned to different responses, according to their relative importance. But, there is no specific guideline for assigning these response weights. In this context, the present study aims to apply the entropy measurement technique in order to calculate the relative response weights from the analysis of entropy of the entire process. Principal Component Analysis (PCA) has been adopted to eliminate correlation that exists among the responses and to convert correlated responses into uncorrelated and independent quality indices, called principal components. These have been accumulated to calculate the overall grey relational grade, using the theory of grey relational analysis. Finally, the grey based Taguchi method has been used to derive an optimal process environment capable of producing the desired weld quality. The previously mentioned method has been applied to optimize bead geometry parameters of submerged arc bead-on-plate weldment. The paper highlights a detailed methodology of the proposed technique and its effectiveness.     

Monolithic fabrication of electro-fluidic polymer microchips

Integration of electronic wiring with microfluidic chips is an important process as it allows electrical interactions with the fluidic media, for example required for resistive and capacitive sensing. It is also necessary in order to implement various actuation and control mechanisms such as pumping, electrophoresis and temperature control. Typically electrical wire traces are added to microfabricated fluidic chips using metal deposition processes that are carried out after the fluidic chip has been fabricated. The process for adding the wiring is complicated and is limited to select metals that can be deposited by evaporation or sputtering. We present a single step method for integrating electrical wires into polymer microfluidic chips that are fabricated by a hot embossing process. This process can flexibly embed any kind of commercially available metal wire with a microfluidic chip and the wiring may be integrated to come into surface contact with the fluid or may be embedded in close proximity to (but insulated from)the fluid paths for example for local heating purposes. This method significantly reduces total processing time and is thus a valuable method for wire integration into polymer chips. We demonstrate two applications—a microelectrolysis chip and a heater chip that were fabricated using this methodology. The design, fabrication process and the initial test results are presented.     

SCS-CN Based Quantification of Potential of Rooftop Catchments and Computation of ASRC for Rainwater Harvesting

Rooftop rainwater harvesting, among other options, play a central role in addressing water security and reducing impacts on the environment. The storm or annual storm runoff coefficient (RC/ASRC) play a significant role in quantification of potential of rooftop catchments for rainwater harvesting, however, these are usually selected from generic lists available in literature. This study explores methodology/procedures based on one of the most popular and versatile hydrological model, Soil Conservation Service Curve Number (SCS-CN) (SCS 1986) and its variants, i.e., Hawkins SCS-CN (HSCS-CN) model (Hawkins et al. 2001), Michel SCS-CN (MSCS-CN) model (Michel et al. Water Resour Res 41:W02011, 2005), and Storm Water Management Model-Annual Storm Runoff Coefficient (SWMM-ASRC) (Heaney et al. 1976) and compares their performance with Central Ground Board (CGWB) (CGWB 2000) approach. It has been found that for the same amount of rainfall and same rooftop catchment area, the MSCS-CN model yields highest rooftop runoff followed by SWMM-ASRC?>?HSCS-CN?>?SCS-CN?>?CGWB. However, the SCS-CN model has close resemblance with CGWB approach followed by HSCS-CN model, SWMM-ASRC, and MSCS-CN model. ASRCs were developed using these models and it was found that MSCS-CN model has the highest value of ASRC (= 0.944) followed by SWMM-ASRC approach (=0.900), HSCS-CN model (=0.830), SCS-CN model (=0.801), and CGWB approach (=0.800). The versatility of these models lies to the fact that CN values (according to rooftop catchment characteristics) would yield rooftop runoff and therefore ASRC values based on sound hydrological perception and not just on the empiricism. The models have inherent capability to incorporate the major factors responsible for runoff production from rooftop/urban, i.e., surface characteristics, initial abstraction, and antecedent dry weather period (ADWP) for the catchments and would be better a tool for quantification rather than just using empirical runoff coefficients for the purpose.     

Energy Efficient Layered Cluster Head Rotation Based Routing Protocol for Underwater Wireless Sensor Networks

Energy efficiency is of paramount concern in underwater sensor networks. The very nature of underwater environment makes it difficult to deploy an energy efficient network that enhances network lifetime. The existing protocols of terrestrial networks cannot be implemented directly to underwater scenarios and as such new protocols have to be designed because of speed of signal propagation under water. Improving the energy efficiency in UWSNs is an active area of research and many protocols to that end have been proposed. The routing protocol that this paper proposes is Energy Efficient Layered Cluster Head Rotation (EE-LCHR) routing protocol. This protocol makes use of the multi sink architecture and creates virtual layers containing a number of sensor nodes such that the hop count from the sensor nodes in a particular layer to the surface sink is the same. Also each layer has a number of clusters with a cluster head that keeps on rotating depending on the fitness value of the sensor nodes. The proposed protocol as compared to other extant protocols like EE-DBR and DBR improves network lifetime. The presence of virtual layers and rotation of cluster heads together ensure that energy balance is better achieved in our proposed protocol which leads to an enhanced network lifetime.

     

LMA: A generic and efficient implementation of the Levenberg–Marquardt Algorithm

This paper presents an open‐source, generic and efficient implementation of a very popular nonlinear optimization method: the Levenberg–Marquardt algorithm (LMA). This minimization algorithm is well known and hundreds of implementations have already been released. However, none of them offer at the same time a high level of genericity, a friendly syntax and a high computational performance. In this paper, we propose a solution to gather all those advantages in one library named LMA. The main challenge is to implement an efficient solver for every encounter problem. To overcome this difficulty, LMA uses compile time algorithms to design a code specific to the given optimization problem. The features of LMA are presented and the performances are compared with the state‐of‐the‐art best alternatives through extensive benchmarks on different kind of problems. Copyright © 2017 John Wiley & Sons, Ltd.     

Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm)

The present paper focuses on machining (turning) aspects of CFRP (epoxy) composites by using single point HSS cutting tool. The optimal setting i.e. the most favourable combination of process parameters (such as spindle speed, feed rate, depth of cut and fibre orientation angle) has been derived in view of multiple and conflicting requirements of machining performance yields viz. material removal rate, surface roughness, SR \((\hbox {R}_{\mathrm{a}})\) (of the turned product) and cutting force. This study initially derives mathematical models (objective functions) by using statistics of nonlinear regression for correlating various process parameters with respect to the output responses. In the next phase, the study utilizes a recently developed advanced optimization algorithm teaching–learning based optimization (TLBO) in order to determine the optimal machining condition for achieving satisfactory machining performances. Application potential of TLBO algorithm has been compared to that of genetic algorithm (GA). It has been observed that exploration of TLBO appears more fruitful in contrast to GA in the context of this case experimental research focused on machining of CFRP composites.     

Fitting marginal accelerated failure time models to clustered survival data with potentially informative cluster size

Methods for analyzing clustered survival data are gaining popularity in biomedical research. Naive attempts to fitting marginal models to such data may lead to biased estimators and misleading inference when the size of a cluster is statistically correlated with some cluster specific latent factors or one or more cluster level covariates. A simple adjustment to correct for potentially informative cluster size is achieved through inverse cluster size reweighting. We give a methodology that incorporates this technique in fitting an accelerated failure time marginal model to clustered survival data. Furthermore, right censoring is handled by inverse probability of censoring reweighting through the use of a flexible model for the censoring hazard. The resulting methodology is examined through a thorough simulation study. Also an illustrative example using a real dataset is provided that examines the effects of age at enrollment and smoking on tooth survival.     

Scanning electron microscopic studies on some chemically modified bast fibres

The surface morphology of dewaxed jute and of dewaxed and scoured flax and ramie fibres, and the effect of chemically modified morphologies were compared using scanning electron microscopy (SEM). Limited oxidation results in the removal of surface impurities of the bast fibres, producing strands which are clearly distinguishable in the SEM. Treatment of the oxy-fibres with excess phenol (P) and formaldehyde (F) at pH 8 leads to permanentin situ deposition of P-F resin moieties, which makes the strands less clearly visible. Modification of the P-F treated oxy-fibres by vinyl grafting leads to further masking of the fibre strands due to measurable vinyl deposition; in the SEM the fibre strands appear closely cemented together by the grafted-on vinyl polymer. On exposure to a standard microbiological degradative environment, damage to the fibre strands takes place in the order jute flat ramie; oxy-jute oxy-flax oxy-ramie. Each fibre system suffers little microbiological degradation, thereby showing high rot resistance, when the respective oxy-fibres are modified by P-F treatment and also by vinyl grafting in a subsequent step. At this stage the difference between the three fibre systems in rot resistance becomes slight. The SEM observations are supported by analysis of tensile strength (TS) and retention of TS after exposure to a standard microbiological degradative environment.     

Crystal structure analysis of Y2Cu2O5

Single crystals of Y₂Cu₂O₅ were obtained in the flux growth process by controlled heating of a mixture of Y₂O₃, BaO, and CuO in a molar ratio of 1820. These crystals were analyzed by a single-crystal X-ray diffraction analysis. The crystal contains polymeric chains of Cu₂O₅ interspersed by yttrium ions surrounded by octahedral arrangements of oxygen atoms. Crystal data: space group=Pna2₁,a=10.799(2) Å,b=3.4990(5) Å,c=12.459(2) Å,Z=4, 380 reflections,R=0.026,R _w=0.030.     

Degenerately Hydrogen Doped Molybdenum Oxide Nanodisks for Ultrasensitive Plasmonic Biosensing

Plasmonic biosensors based on noble metals generally suffer from low sensitivities if the perturbation of refractive‐index in the ambient is not significant. By contrast, the features of degenerately doped semiconductors offer new dimensions for plasmonic biosensing, by allowing charge‐based detection. Here, this concept is demonstrated in plasmonic hydrogen doped molybdenum oxides (H_xMoO₃), with the morphology of 2D nanodisks, using a representative enzymatic glucose sensing model. Based on the ultrahigh capacity of the molybdenum oxide nanodisks for accommodating H⁺, the plasmon resonance wavelengths of H_xMoO₃ are shifted into visible‐near‐infrared wavelengths. These plasmonic features alter significantly as a function of the intercalated H⁺ concentration. The facile H⁺ deintercalation out of H_xMoO₃ provides an exceptional sensitivity and fast kinetics to charge perturbations during enzymatic oxidation. The optimum sensing response is found at H_1.55MoO₃, achieving a detection limit of 2 × 10^?9m at 410 nm, even when the biosensing platform is adapted into a light‐emitting diode‐photodetector setup. The performance is superior in comparison to all previously reported plasmonic enzymatic glucose sensors, providing a great opportunity in developing high performance biosensors.