L.C. Deepa, S. Sathiyanarayanan, C. Marikkannu and D. Mukherjee
A new zinc phosphating bath, which produces coatings at relatively lower temperatures within a reasonable time by using of chemical accelerators has been devised. Improvement of…
Abstract
A new zinc phosphating bath, which produces coatings at relatively lower temperatures within a reasonable time by using of chemical accelerators has been devised. Improvement of the bath performance by the addition of divalent cations like calcium, manganese and magnesium has been studied. Bath formulation and operating conditions have been optimized by coating weight determinations. Corrosion resistance property of the resultant coatings has been evaluated in 1,000 ppm Cl− by electrochemical methods such as potentiodynamic polarization and impedance measurements. Results of the electrochemical techniques have been complemented by salt spray, humidity and immersion tests. Porosity and roughness of the coatings have also been studied. Results show that the phosphating bath with manganese addition gives good coatings within 30 min. Studies have shown that the corrosion resistance of the resultant coatings are much superior than the conventional coatings.
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Asha Shivappa Kotnurkar and Deepa C. Katagi
The current paper investigates the bioconvective third-grade nanofluid flow containing gyrotactic organisms with Copper-blood nanoparticles in permeable walls.
Abstract
Purpose
The current paper investigates the bioconvective third-grade nanofluid flow containing gyrotactic organisms with Copper-blood nanoparticles in permeable walls.
Design/methodology/approach
The equations governing the flow are solved by adopting the Adomian decomposition method.
Findings
The results show that the biconvection Peclet number decreases the density of motile microorganisms, and the Rayleigh number also decreases the velocity profile.
Practical implications
The present study can be applied to design the higher generation microsystems.
Originality/value
To the best of the authors’ knowledge, no such investigation has been carried out in the literature.
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Pavana Kumara Bellairu, Shreeranga Bhat and E.V. Gijo
The aim of this article is to demonstrate the development of environment friendly, low cost natural fibre composites by robust engineering approach. More specifically, the prime…
Abstract
Purpose
The aim of this article is to demonstrate the development of environment friendly, low cost natural fibre composites by robust engineering approach. More specifically, the prime objective of the study is to optimise the composition of natural fibre reinforced polymer nanocomposites using a robust statistical approach.
Design/methodology/approach
In this research, the material is prepared using multi-walled carbon nanotubes (MWCNT), Cantala fibres and Epoxy Resin in accordance with the ASTM (American Society for Testing and Materials) standards. Further, the composition is prepared and optimised using the mixture-design approach for the flexural strength of the material.
Findings
The results of the study indicate that MWCNT plays a vital role in increasing the flexural strength of the composite. Moreover, it is observed that interactions between second order and third order parameters in the composition are statistically significant. This leads to proposing a special cubic model for the novel composite material with residual analysis. Moreover, the methodology assists in optimising the mixture component values to maximise the flexural strength of the novel composite material.
Originality/value
This article attempts to include both MWCNT and Cantala fibres to develop a novel composite material. In addition, it employs the mixture-design technique to optimise the composition and predict the model of the study in a step-by-step manner, which will act as a guideline for academicians and practitioners to optimise the material composition with specific reference to natural fibre reinforced nanocomposites.
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Pawan S. Budhwar, Arup Varma and Anastasia A. Katou
Mergers and acquisitions (M&) are increasingly becoming a strategy of choice for companies attempting to achieve and sustain competitive advantage. However, not all M&As are a…
Abstract
Mergers and acquisitions (M&) are increasingly becoming a strategy of choice for companies attempting to achieve and sustain competitive advantage. However, not all M&As are a success. In this paper, we examine the three main reasons highlighted in the literature as major causes of M&A failure (clashing corporate cultures, absence of clear communication, and employee involvement) in three Indian pharmaceutical companies, and we analyze the role played by the HR function in addressing them. Also, we discuss the importance of gaining the commitment and focus of the workforce during the acquisition process through employee involvement.
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Deepa George and Saurabh Sinha
The demand for higher bandwidth has resulted in the development of mm-wave phased array systems. This paper aims to explore a technique that could be used to feed the individual…
Abstract
Purpose
The demand for higher bandwidth has resulted in the development of mm-wave phased array systems. This paper aims to explore a technique that could be used to feed the individual antennas in a mm-wave phased array system with the appropriate phase shifted signal to achieve the required directivity. It presents differential Colpitts oscillators at 5 and 60 GHz that can provide differential output signals to the quadrature signal generators in the proposed phase shifter system.
Design/methodology/approach
The phase shifter system comprises a differential Colpitts voltage controlled oscillator (VCO) and utilizes the vector-sum technique to generate the phase shifted signal. The differential VCO is connected in the common-collector configuration for the 5-GHz VCO, and is extended using a cascode transistor for the 60-GHz VCO for better stability at mm-wave. The vector sum is achieved using a variable gain amplifier (VGA) that combines the in-phase and quadrature phase signal, generated from oscillator output using hybrid Lange couplers. The devices were fabricated using IBM 130-nm SiGe BiCMOS process, and simulations were performed with a process design kit provided by the foundry.
Findings
The measured results of the 5-GHz and 60-GHz VCOs indicate that differential Colpitts VCO could generate oscillator output with good phase noise performance. The simulation results of the phase shifter system indicate that the generation of signals with phases from 0° to 360° in steps of 22.5° was achieved using the proposed approach. A Gilbert mixer topology was used for the VGA and the linearity was improved by a pre-distortion circuit implemented using an inverse tanh cell.
Originality/value
The measurement results indicate that differential Colpitts oscillator in common-collector configuration could be used to generate differential VCO signals for the vector-sum phase shifter. The simulation results of the proposed phase shifter system at mm-wave show that the phase shift could be realised at a total power consumption of 200 mW.
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Limitations encountered with the models developed in the previous studies had occurrences of global minima; due to which this study developed a new intelligent ubiquitous…
Abstract
Purpose
Limitations encountered with the models developed in the previous studies had occurrences of global minima; due to which this study developed a new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization. Ubiquitous machine learning computational model process performs training in a better way than regular supervised learning or unsupervised learning computational models with deep learning techniques, resulting in better learning and optimization for the considered problem domain of cloud-based internet-of-things (IOTs). This study aims to improve the network quality and improve the data accuracy rate during the network transmission process using the developed ubiquitous deep learning computational model.
Design/methodology/approach
In this research study, a novel intelligent ubiquitous machine learning computational model is designed and modelled to maintain the optimal energy level of cloud IOTs in sensor network domains. A new intelligent ubiquitous computational model that learns with gradient descent learning rule and operates with auto-encoders and decoders to attain better energy optimization is developed. A new unified deterministic sine-cosine algorithm has been developed in this study for parameter optimization of weight factors in the ubiquitous machine learning model.
Findings
The newly developed ubiquitous model is used for finding network energy and performing its optimization in the considered sensor network model. At the time of progressive simulation, residual energy, network overhead, end-to-end delay, network lifetime and a number of live nodes are evaluated. It is elucidated from the results attained, that the ubiquitous deep learning model resulted in better metrics based on its appropriate cluster selection and minimized route selection mechanism.
Research limitations/implications
In this research study, a novel ubiquitous computing model derived from a new optimization algorithm called a unified deterministic sine-cosine algorithm and deep learning technique was derived and applied for maintaining the optimal energy level of cloud IOTs in sensor networks. The deterministic levy flight concept is applied for developing the new optimization technique and this tends to determine the parametric weight values for the deep learning model. The ubiquitous deep learning model is designed with auto-encoders and decoders and their corresponding layers weights are determined for optimal values with the optimization algorithm. The modelled ubiquitous deep learning approach was applied in this study to determine the network energy consumption rate and thereby optimize the energy level by increasing the lifetime of the sensor network model considered. For all the considered network metrics, the ubiquitous computing model has proved to be effective and versatile than previous approaches from early research studies.
Practical implications
The developed ubiquitous computing model with deep learning techniques can be applied for any type of cloud-assisted IOTs in respect of wireless sensor networks, ad hoc networks, radio access technology networks, heterogeneous networks, etc. Practically, the developed model facilitates computing the optimal energy level of the cloud IOTs for any considered network models and this helps in maintaining a better network lifetime and reducing the end-to-end delay of the networks.
Social implications
The social implication of the proposed research study is that it helps in reducing energy consumption and increases the network lifetime of the cloud IOT based sensor network models. This approach helps the people in large to have a better transmission rate with minimized energy consumption and also reduces the delay in transmission.
Originality/value
In this research study, the network optimization of cloud-assisted IOTs of sensor network models is modelled and analysed using machine learning models as a kind of ubiquitous computing system. Ubiquitous computing models with machine learning techniques develop intelligent systems and enhances the users to make better and faster decisions. In the communication domain, the use of predictive and optimization models created with machine learning accelerates new ways to determine solutions to problems. Considering the importance of learning techniques, the ubiquitous computing model is designed based on a deep learning strategy and the learning mechanism adapts itself to attain a better network optimization model.
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Verena Tandrayen-Ragoobur and Deepa Gokulsing
The paper innovates on the existing literature by assessing the gender gap in Science, Technology, Engineering and Maths (STEM) tertiary education enrolment and career choice in a…
Abstract
Purpose
The paper innovates on the existing literature by assessing the gender gap in Science, Technology, Engineering and Maths (STEM) tertiary education enrolment and career choice in a small country setting and by extending on Master and Meltzoff (2016) theoretical framework to provide a holistic explanation of the gender disparity through a mix of personal, environmental and behavioural factors. The study first probes into the existence of potential gender disparity in STEM tertiary enrolment in Mauritius. Second, in contrast with existing studies where selective factors are used to measure the gender gap in STEM education, this paper investigates into a combination of personal, environmental and behavioural factors that may influence participation in STEM education and career.
Design/methodology/approach
The study uses a survey of 209 undergraduates enroled in the main public university and investigates into the existence of a gender gap in STEM tertiary education enrolment and the reasons behind this disparity. Consistent with the theoretical model, the empirical analysis also investigates into the work environment (which cannot be measured from the survey), via semi-structured interviews of 15 women in STEM professions.
Findings
The logit regression results first reveal the existence of a gender disparity in the choice of STEM-related degrees. The probability of a female student to enrol in a STEM degree is lower than that of a male student, after controlling for all the personal, environmental and behavioural factors. The most important set of reasons influencing the student's STEM degree choice are self-efficacy and the student's academic performance in STEM subjects at secondary school level. The findings also demonstrate that young women are relatively more likely to choose STEM degrees than their male counterparts when they are supported by their family, school and teachers. There is further evidence of lower participation of women in STEM professions as well as significant challenges which women in STEM careers face compared to their male colleagues.
Originality/value
This study adopts a holistic framework to assess the factors that hinder women's participation in STEM tertiary education and career in Mauritius.
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Kushagra Sharan, Deepak Dhayanithy and Deepa Sethi
This paper aims to examine the relationship between organizational learning (OL) and technology through the lens of strategic factors and to ascertain future research directions.
Abstract
Purpose
This paper aims to examine the relationship between organizational learning (OL) and technology through the lens of strategic factors and to ascertain future research directions.
Design/methodology/approach
The systematic literature review method was applied in three stages to the 76 articles obtained from Scopus, Web of Science, Google Scholar and EBSCO databases.
Findings
This research revealed the evolution of the role of OL in innovation, performance, knowledge management and technological adoption and showcases a detailed conceptual model relating technology outcomes (technological innovation and capabilities) to OL outcomes (technology absorptive capacity, technological proactivity, as well as information technology [IT] and organization process alignment).
Research limitations/implications
This review includes articles mainly in English and excludes conference proceedings.
Practical implications
This research attempts to guide managers and policymakers to foster an organizational culture conducive to technological adoption and OL. It helps organizations develop strategies for new product development, including strategic alliances and strategic leadership.
Originality/value
This review formalizes the linkages between technological absorptive capacity, technological proactivity and IT with technological innovation and capabilities. It identifies research gaps and elucidates future research directions.
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Shrutika Sharma, Vishal Gupta and Deepa Mudgal
The implications of metallic biomaterials involve stress shielding, bone osteoporosis, release of toxic ions, poor wear and corrosion resistance and patient discomfort due to the…
Abstract
Purpose
The implications of metallic biomaterials involve stress shielding, bone osteoporosis, release of toxic ions, poor wear and corrosion resistance and patient discomfort due to the need of second operation. This study aims to use additive manufacturing (AM) process for fabrication of biodegradable orthopedic small locking bone plates to overcome complications related to metallic biomaterials.
Design/methodology/approach
Fused deposition modeling technique has been used for fabrication of bone plates. The effect of varying printing parameters such as infill density, layer height, wall thickness and print speed has been studied on tensile and flexural properties of bone plates using response surface methodology-based design of experiments.
Findings
The maximum tensile and flexural strengths are mainly dependent on printing parameters used during the fabrication of bone plates. Tensile and flexural strengths increase with increase in infill density and wall thickness and decrease with increase in layer height and wall thickness.
Research limitations/implications
The present work is focused on bone plates. In addition, different AM techniques can be used for fabrication of other biomedical implants.
Originality/value
Studies on application of AM techniques on distal ulna small locking bone plates have been hardly reported. This work involves optimization of printing parameters for development of distal ulna-based bone plate with high mechanical strength. Characterization of microscopic fractures has also been performed for understanding the fracture behavior of bone plates.