Investigating the effect of silver nanorods embedded in polydimethylsiloxane matrix using nanoindentation and its use for flexible electronics

The stretchable electrodes with excellent flexibility, electrical conductivity, and mechanical durability are the most fundamental components in the emerging and exciting field of flexible electronics. This article proposes a method for fabrication of such a stretchable electrode by embedding silver nanorods (AgNRs) into a polydimethylsiloxane (PDMS) matrix that is grown by a unique glancing angle deposition technique. The surface, mechanical, and electrical properties of PDMS are significantly changed after embedding the AgNRs in it. The results show that surface roughness and polarity increase after AgNRs are embedded in the PDMS matrix. Elastic modulus (E) and hardness (H) decrease with an increase in the indentation load as a result of the indentation depth effect. Due to strong interfacial adhesion of AgNRs embedded in the PDMS matrix, the E and H of nanocomposite are increased by 167.6 and 93.3% compared with PDMS film, respectively. Furthermore, the AgNRs-PDMS film has an electrical resistivity value in the order of 10⁻⁷ Ωm. It remains conductive during various mechanical strains such as bending, twisting, and stretching, which is demonstrated using a light-emitting diode circuit. Simultaneously, the antimicrobial activity of silver could make it a promising candidate for wearable electronics.     

Escherichia coli,Salmonella spp., and Staphylococcus aureus susceptibility to antimicrobials of human and veterinary importance in poultry sector of India

In India, little attention has been paid on antimicrobial resistance (AMR) in the context of developing “One Health” approach. Hence, utilizing multi-disciplinary approach, we assess the AMR level and dynamics/pattern of multi-drug resistance (MDR) in Escherichia coli, Salmonella spp., and Staphylococcus aureus circulating over the different stages of poultry in India. A total of 342 isolates including E. coli (n = 143), Salmonella spp. (n = 104), and S. aureus (n = 95) were recovered from fecal (n = 80) and cecal (n = 80) samples of chicken, collected across the different poultry-retail shops and poultry-farms located at urban and rural areas of Rajasthan, India, respectively. High rates of AMR to drugs that are critically/highly important both in human and veterinary medicine were observed among all the isolates. Upward trends in AMR prevalence was observed in poultry-retail shops than in poultry-farms. Notably, >90% of all the isolates were MDR, of particular, pattern/prevalence of MDR was substantially varied across the poultry-farms vs. poultry-retail shops. Our results indicate AMR including MDR to be common in E. coli, Salmonella spp., and S. aureus distributed frequently in poultry. The study encourages the formulation of national policy, programmes and further research with a “One Health” approach that can benefits to the human/animal and the environment.     

A Scenario Based Impact Assessment of Trace Metals on Ecosystem of River Ganges Using Multivariate Analysis Coupled with Fuzzy Decision-Making Approach

The growing consciousness about the health risks associated with environmental pollutants has brought a major shift in global concern towards prevention of hazardous/trace metals discharge in water bodies. Majority of these trace metals gets accumulated in the body of aquatic lives, which are considered as potential indicators of hazardous content. This results in an ecological imbalance in the form of poisoning, diseases and even death of fish and other aquatic lives, and ultimately affect humans through food chain. Trace metals such as Cd, Cr, Cu, Mn, Ni, Pb and Zn originated from various industrial operations containing metallic solutions and agricultural practices, have been contributing significantly to cause aquatic pollution. The present study develops a novel approach of expressing sustainability of river’s ecosystem based on health of the fish by coupling fuzzy sensitivity analysis into multivariate analysis. A systematic methodology has been developed by generating monoplot, two dimensional biplot and rotated component matrix (using ‘Analyze it’ and ‘SPSS’ software), which can simultaneously identify critical trace metals and their industrial sources, critical sampling stations, and adversely affected fish species along with their interrelationships. A case study of assessing the impact of trace metals on the aquatic life of river Ganges, India has also been presented to demonstrate effectiveness of the model. The clusters pertaining to various water quality parameters have been identified using Principal Component Analysis (PCA) to determine actual sources of pollutants and their impact on aquatic life. The fuzzy sensitivity analysis reveals the cause-effect relationship of these critical parameters. The study suggests pollution control agencies to enforce appropriate regulations on the wastewater dischargers responsible for polluting river streams with a particular kind of trace metal(s).     

An Experimental Study to Evaluate the Effect of Ambient Temperature during Manual Lifting and Design of Optimal Task Parameters

In the present study, the effect of lifting task parameters on the heart rate and oxygen uptake of workers during manual lifting tasks in different ambient conditions was evaluated. The experiments conducted in two different temperature conditions showed a significantly higher oxygen uptake and heart rate in colder conditions as compared to warmer conditions. Three other factors, namely, load, lifting frequency, and vertical distance were found to significant affect the responses. Various combinations of significant factors were used to calculate oxygen uptake and heart rate. These were then compared with the safe limits as per the maximum aerobic capacity of workers. Based on these comparisons, the safe combinations were identified that can be used to design lifting tasks in varied ambient conditions. The study further concluded that lifting tasks performed in winter should have different relaxation or fatigue allowances built into the cycle time of the task to compensate for higher exertion. © 2011 Wiley Periodicals, Inc.     

Investigation of machining characteristics in rotary ultrasonic machining of alumina ceramic

Alumina ceramic is well documented as a much-demanded advanced ceramic in the present competitive structure of manufacturing and industrial applications owing to its excellent and superior properties. The current article aimed to experimentally investigate the influence of several process variables, namely: spindle speed, feed rate, coolant pressure, and ultrasonic power, on considered machining characteristics of interest, i.e., chipping size and material removal rate in the rotary ultrasonic machining of alumina ceramic. Response surface methodology has been employed in the form of a central composite rotatable design to design the experiments. Variance analysis testing has also been performed with a view to observing the consequence of the considered parameters. The microstructure of machined rod samples was evaluated and analyzed using a scanning electron microscope. This analysis has revealed and confirmed the presence of plastic deformation that caused removal of material along with brittle fractures in rotary ultrasonic machining of alumina ceramic. The validity and competence of the developed mathematical model have been verified with test results. The multi-response optimization of machining responses (material removal rate and chipping size) has also been attempted by employing a desirability approach, and at an optimized parametric setting the obtained experimental values for material removal rate and chipping size were 0.4166?mm³/s and 0.5134?mm, respectively, with a combined desirability index value of 0.849.     

Supplier behavior modeling and winner determination using parallel MDP

Behavioral uncertainty of a supplier is a major challenge to a buyer operating in e-procurement setting. Modeling suppliers’ behavior from past transactions, estimation of possible future performance and integrating this knowledge with the winner determination process can bring a new dimension to procurement process automation. We propose a states-space model to capture the uncertainty involved in long-term supplier behavior. The states represent the performance level of a supplier. This behavioral aspect is then integrated with the winner determination process of a multi-attribute reverse auction for efficient supplier selection using parallel MDP. We also propose an implementation framework to collect the feedback on supplier, generate an aggregate performance score and integrate it with the winner determination process. The performance aggregation and winner determination with help of Markov decision process effectively uses the past performance information. In addition, it updates performance information in regular invervals and allevates the problem of maintaining a long history. We compare the MDP-based selection with that of performance score-based selection through a simulation experiment. It is observed that our scheme gives better buyer utility, selects best suppliers and fetches better quality product. The benefits realized through these attributes to the buyer increases the efficiency of the MDP-based selection process.     

Classification of patterns representing Apples and Oranges in three-qubit system

The study of the classification of Apples and Oranges in a warehouse has been undertaken in a three-qubit system using the method of repeated iterations in Grover’s algorithm and Ventura’s algorithm separately. Operator describing an inversion about average has been constructed as a square matrix of order eight, the phase inversion operators and corresponding iteration operators for patterns separately representing Apples and Oranges have been derived, and various possible superpositions as the choice for search states for the classification of these patterns have been obtained for starting states consisting of two patterns and a single pattern, respectively. It has been demonstrated that on the second iteration of the exclusion superposition by the corresponding iteration operators, the patterns Apples and Oranges, respectively, are most suitably classified using the Grover’s algorithm. The probabilities of classifications of Apples have also been calculated by using Ventura’s algorithm (Ventura and Martinez in Inf Sci 124:273–296, 2000; Found Phys Lett 12:547–559, 1999) for all the possible superpositions as the search states, and the results have been compared with those of Grover’s algorithm and it has been demonstrated that in general for classification of a given pattern (Apples) in three-qubit system, the Grover’s and Ventura’s algorithms are effective in the cases where the number of patterns in the stored database is larger or smaller, respectively.