Search results

This paper aims to focus on the evaluation of the electrical properties of bio-based polyurethane material derived from cashew nut husk tannin and also the effect of temperature and frequency on the dielectric values and alternate current (AC) conductivity.

Bio-based polyurethane is prepared from cashew nut husk tannin as polyol, their dielectric constant and dielectric loss factor are measured using an inductance capacitance resistance (LCR) metre, and the AC conductivity is determined using dielectric constant and loss values.

The dielectric constant values are high, and the values decrease with an increase in frequency but increase with an increase in temperature. The AC conductivity values are low; hence, the material can be categorized as insulators or semi-conductors. Because the polyurethanes have a good dielectric value and are cost-effective, as they are derived from renewable biomaterial waste, they have promising applications for the future.

The experiment is carried out up to the frequency of 200 KHz because of the limitation in the instrument. But for the institute of printed circuits (IPC) and other specifications, the values of dielectric loss and dielectric constant will be generally coated for 1 MHz.

The high dielectric constant and loss values show that the polyurethane can be opted for use as capacitors in electronic devices, and the values are comparable to the requirements of IPC4101A/24IPC; hence, they are suitable for the application as printed circuit board (PCB) laminate.

The use of biomaterial waste in the production of polyurethane will bring down the dependence of polyurethane industry on fossil fuel reserve, reduce carbon dioxide foot print and reduce the cost of production.

The motivation of the work was its ecological aspect and also aims on the use of an alternative bio-based material in the PCB industry.

This paper aims to examine the influence of leaves of Cucurbita maxima (LCM) extract on the corrosion of mild steel (MS) in 1N H₂SO₄ by gravimetric measurement and electrochemical study. The inhibition efficiency increased with increase in concentration of LCM. The corrosion rate increased with increase in temperature and decreased with increase in concentration of inhibitor compared to blank. The maximum inhibition efficiency of 96 per cent was obtained at 3 per cent v/v LCM at 343K for 1 h. The inhibitory action was explained on the basis of adsorption of secondary metabolites in the plant extract on the mild steel surface. The adsorption process obeys Langmuir, El-Awady, Temkin and Freundlich adsorption isotherms and was best fitted by Langmuir adsorption isotherm. Some kinetic and thermodynamic parameters are also discussed.

Weight loss measurements were conducted under total immersion of already weighed mild steel coupons in 100 ml of the test solution containing 0.05, 0.5, 1, 1.5, 2, 2.5 and 3 per cent of LCM extract and in blank solution at different temperature range (303, 313, 323, 333 and 343 K) for 1h. The electrochemical experiments were conducted at room temperature and at various concentrations of LCM extract. The electrochemical experiments were performed using a potentiostat. A three-electrode cell was used for the impedance measurements conducted at open circuit potential.

The inhibitor (LCM) shows good inhibition performance for the corrosion of mild steel in 1N H₂SO₄ solution. The inhibition efficiency increased and the corrosion rate decreased with increasing concentrations of inhibitor.

The studied plant material is a new one in mild steel corrosion. The influence of the plant material is clearly explained in the submitted paper.

The purpose of this paper is to optimize the performance for complex repairable system of paint manufacturing unit using a new hybrid bacterial foraging and particle swarm optimization (BFO-PSO) evolutionary algorithm. For this, a performance model is developed with an objective to analyze the system availability.

In this paper, a Markov process-based performance model is put forward for system availability estimation. The differential equations associated with the performance model are developed assuming that the failure and repair rate parameters of each sub-system are constant and follow the exponential distribution. The long-run availability expression for the system has been derived using normalizing condition. This mathematical framework is utilized for developing an optimization model in MATLAB 15 and solved through BFO-PSO and basic particle swarm optimization (PSO) evolutionary algorithms coded in the light of applicability. In this analysis, the optimal input parameters are determined for better system performance.

In the present study, the sensitivity analysis for various sub-systems is carried out in a more consistent manner in terms of the effect on system availability. The optimal failure and repair rate parameters are obtained by solving the performance optimization model through the proposed hybrid BFO-PSO algorithm and hence improved system availability. Further, the results obtained through the proposed evolutionary algorithm are compared with the PSO findings in order to verify the solution. It can be clearly observed from the obtained results that the hybrid BFO-PSO algorithm modifies the solution more precisely and consistently.

There is no limitation for implementation of proposed methodology in complex systems, and it can, therefore, be used to analyze the behavior of the other repairable systems in higher sensitivity zone.

The performance model of the paint manufacturing system is formulated by utilizing the available uncertain data of the used manufacturing unit. Using these data information, which affects the performance of the system are parameterized in the input failure and repair rate parameters for each sub-system. Further, these parameters are varied to find the sensitivity of a sub-system for system availability among the various sub-systems in order to predict the repair priorities for different sub-systems. The findings of the present study show their correspondence with the system experience and highlight the various availability measures for the system analyst in maintenance planning.

The purpose of this paper is to understand the integrated impact of the application of protection measures against identity theft on consumers’ synergistic perception of trust, the cost of products/services and operational performance (OP) – all of which in turn is postulated to contribute to purchase intention (PI) when shopping online.

In order to accomplish the specified aim, this study first conducted an experiment by involving the students from a university in Bangladesh. Then a survey was conducted to capture their opinion based on the previous experiment.

The study identified that in e-commerce, OP and trust have potential impact on pursuing consumers’ PI. Traditionally, price is always an issue in marketing; however, for e-commerce, this issue does not have direct impact on PI.

The main limitation of this study is that a less established e-commerce example was utilized to conduct the experiment and survey for validating the model. Also, the study was conducted only in the context of Bangladesh and a student sample was utilized. Future studies can test the model in different contexts (particularly to verify the impact of privacy) by utilizing data from consumers.

This study has resolved a controversial issue by generating clear guidelines that the overall conjoint effect of OP, trust, and price on PI is neither negative nor neutral. Synergistically, the application of these controlling tools of identity theft can substantially enhance consumers’ trust, which is the single most predictor to pursue consumer PI.

This study has provided in-depth insight into the impact of different controlling measures in e-commerce PI. Practitioners have potential learning from this study that if consumers find the application of different controlling mechanisms against cybercrimes, particularly identity theft, enhancing the reliability, authenticity and security of transactions in this virtual medium, they do not mind paying a higher price. Such insights have not been provided by existing studies on this topic. Developing trust on e-commerce purchase is the driving force, not the price.

The purpose of this paper is to empirically examine how different supply chain attributes as determinants of the openness of supply chain affect organizational performance (OP).

Data were collected from 259 supply chain executives in Pakistan. Structural equation modeling was used to test the hypotheses.

The findings indicate that organizations may take the selective view of their supply chains resulting in a varying focus on different SC attributes. The results show that though all identified supply chain attributes positively relate to OP, some attributes such as combined agility and cooperation among supply chain partners have a weak correlation coefficient. This indicates that overall the relative openness of supply chain among selected sample of Pakistani organizations is low.

Supply chain executives may not have a selective focus on some attributes; rather, they may consider to have a broader perspective drawing upon a wider range of supply chain attributes as identified in the current study. In order to remain competitive, Pakistani manufacturing organizations need to learn more about opening up their boundaries and enhance the openness of their supply chain.

The contribution of the current study is two folds. First, drawing upon the current literature, it proposes the instrument to measure the relative openness of supply chain. Second, it empirically tests the selected conceptual model which highlights the relevance of supply chain attributes and their role in the resulting relative degree of supply chain openness. The empirical examination of the selected conceptual model of supply chain openness tends to make contribution to the wider literature on supply chain management.

The purpose of this paper is to identify the criticality of various sub-systems through the behavioral study of a multi-state repairable system with hot redundancy. The availability of the system is optimized to evaluate the optimum combinations of failure and repair rate parameters for various sub-systems.

The behavioral study of the system is conducted through the stochastic model under probabilistic approach, i.e., Markov process. The first-order differential equations associated with the stochastic model are derived with the use of mnemonic rule assuming that the failure and repair rate parameters of all the sub-systems are constant and exponentially distributed. These differential equations are further solved recursively using the normalizing condition to obtain the long-run availability of the system. A particle swarm optimization (PSO) algorithm for evaluating the optimum availability of the system and supporting computational results are presented.

The maintenance priorities for various sub-systems can easily be set up, as it is clearly identified in the behavioral analysis that the sub-system (A) is the most critical component which highly influences the system availability as compared to other sub-systems. The PSO technique modifies input failure and repair rate parameters for each sub-system and evaluates the optimum availability of the system.

A bottom case manufacturing system is under the evaluation, which is the main component of front shock absorber in two-wheelers. The input failure and repair rate parameters were parameterized from the information provided by the plant personnel. The finding of the paper provides the various availability measures and shows the grate congruence with the system behavior.

The purpose of this paper is to make people aware of organic corrosion inhibitors.

As it is a literature review paper, no specific method is used.

It has been found that plant extracts and oils show inhibition efficiency up to 98 percent, so it is certain that plant extracts and oils are effective corrosion inhibitors and can be successfully used at the industrial level.

Plant extracts and oils are also found to be non‐toxic, highly efficient, renewable and cheap. But less effort has been given towards the identification of which compound is active in the extract.

The paper shows detailed account of the inhibitors obtained from plants, which are used as natural corrosion inhibitors.

The purpose of this paper is to study corporate social responsibility (CSR) enablers, with special reference to Indian firms. Further, the CSR enablers have been identified and their ranking obtained, and interactions and interrelationships among the different enablers have also been studied.

The interpretive structural modeling (ISM) approach was used to evaluate the interactions among the various enablers of CSR implementation, from the perspective of Indian companies.

In the course of this paper, based on a detailed literature review, nine enablers were identified and after seeking expert opinion, eight CSR implementation enablers were shortlisted. The present study has identified that top management commitment, organizational culture and formal strategic planning are the main enablers of CSR implementation in Indian companies, whereas corporate reputation, better financial performance and customer satisfaction are the dependent enablers that have high dependence power but weak driving power.

The ISM-based model of CSR enablers presented in this paper will prove to be of interest to academicians and practitioners alike. While academicians will gain a better understanding of enablers of CSR implementation from the perspective of one of the fastest growing economies of the world, practitioners will gain a better understanding of CSR enablers, and their enhanced knowledge will help them to effectively implement CSR in their organizations.

The paper aims to develop a novel method for the classification of different physical activities of a human being, using fabric sensors. This method focuses mainly on classifying the physical activity between normal action and violent attack on a victim and verifies its validity.

The system is realized as a protective jacket that can be worn by the subject. Stretch sensors, pressure sensors and a 9 degree of freedom accelerometer are strategically woven on the jacket. The jacket has an internal bus system made of conductive fabric that connects the sensors to the Flora chip, which acts as the data acquisition unit for the data generated. Different activities such as still, standing up, walking, twist-jump-turn, dancing and violent action are performed. The jacket in this study is worn by a healthy subject. The main phases which describe the activity recognition method undertaken in this study are the placement of sensors, pre-processing of data and deploying machine learning models for classification.

The effectiveness of the method was validated in a controlled environment. Certain challenges are also faced in building the experimental setup for the collection of data from the hardware. The most tedious challenge is to collect the data without noise and error, created by voltage fluctuations when stretched. The results show that the support vector machine classifier can classify different activities and is able to differentiate normal action and violent attacks with an accuracy of 98.8%, which is superior to other methods and algorithms.

This study leads to an understanding of human physical movement under violent activity. The results show that data compared with normal physical motion, which includes even a form of dance is quite different from the data collected during violent physical motion. This jacket construction with woven sensors can capture every dimension of the physical motion adding features to the data on which the machine learning model will be built.

Unlike other studies, where sensors are placed on isolated parts of the body, in this study, the fabric sensors are woven into the fabric itself to collect the data and to achieve maximum accuracy instead of using isolated wearable sensors. This method, together with a fabric pressure and stretch sensors, can provide key data and accurate feedback information when the victim is being attacked or is in a normal state of action.

The purpose of this paper is to explore the relationship between various risk management strategies and risk management practices in order to design and hence enact a suitable supply chain risk mitigation (RM) plan. Additionally, this study proposes a hierarchical framework to explain the mutual relationship between supply chain risk management (SCRM) practices and strategies by considering the underlying dimensions between them.

An amalgamation of systematic literature analysis (SLA) and correspondence analysis (CA) has been performed to develop the conceptual framework. A real-life case of Indian petroleum supply chain has been considered to validate and explain the proposed model.

The results reveal three underlying dimensions, which associate the relationship between RM strategies. They are, risk adaptability of SC managers with a variance of 34.71%, followed by resource capability of the firm and the degree of sophistication of RM practices, with variances of 27.72 and 20.35%, respectively. Risk avoidance strategy comprises of practices such as supplier evaluation, technology adaption, flexible process and information security. On the other extreme, the risk sharing strategy includes revenue sharing, insurance, collaboration, public–private partnership and so on as essential RM practices.

The study not only focuses on the distinction between RM strategies and practices, which were used interchangeably in the prior literature, but also provides an association between the same by exploring the underlying dimensions. These underlying dimensions perform a crucial role while developing a risk management plan. This study explicitly focuses on the RM step of SCRM process. Pre and post risk mitigation phases of SCRM process, such as risk assessment and risk monitoring, are beyond the scope of the current research.

The paper develops a framework for mapping various RM strategies with their corresponding practices by considering the Indian petroleum supply chain as a viable case study. Various theoretical and business implications are derived in the context of the developing country.