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The real challenge in the solution of contact problems is the lack of an optimal adaptive scheme. As the contact zone is a priori unknown, successive refinement and iterative method are necessary to obtain a high-accuracy solution. The purpose of this paper is to provide an optimal adaptive scheme based on second-generation finite element wavelets for the solution of non-linear variational inequality of the contact problem.

To generate an elementary multi-resolution mesh, the authors used hierarchical bases (HB) composed of Lagrange finite element interpolation functions. These HB functions are customized using second-generation wavelet techniques for a fast convergence rate. At each step of the algorithm, the active set method along with mesh adaptation is used for solving the constrained minimization problem of contact case. Wavelet coefficients-based error indicators are used, and computation is focused on mesh zones with a high error indication. The authors take advantage of the wavelet transform to develop a parameter-free adaptive scheme to generate an appropriate and optimal mesh.

Adaptive wavelet Galerkin scheme (AWGS), a newly developed method for multi-scale mesh adaptivity in this work, is a combination of the second-generation wavelet transform and finite element method and significantly improves the accuracy of the results without approximating an additional problem of error estimation equations. A comparative study is performed taking a solution on a highly refined mesh and results are generated using AWGS.

The proposed adaptive technique can be utilized in the simulation of mechanical and biomechanical structures where multiple bodies come into contact with each other. The algorithm of the method is easy to implement and found to be successful in producing a sufficiently accurate solution with relatively less number of mesh nodes.

Although many error estimation techniques have been developed over the past several years to solve contact problems adaptively, because of boundary non-linearity development, a reliable error estimator needs further investigation. The present study attempts to resolve this problem without having to recompute the entire solution on a new mesh.

The real challenges in online crack detection testing based on guided waves are random noise as well as narrow-band coherent noise; and to achieve efficient structural health assessment methodology, magnificent extraction of noise and analysis of the signals are essential. The purpose of this paper is to provide optimal noise filtering technique for Lamb waves in the diagnosis of structural singularities.

Filtration of time-frequency information of guided elastic waves through the noisy signal is investigated in the present analysis using matched filtering technique which “sniffs” the signal buried in noise and most favorable mother wavelet based denoising methods. The optimal wavelet function is selected using Shannon’s entropy criterion and verified by the analysis of root mean square error of the filtered signal.

Wavelet matched filter method, a newly developed filtering technique in this work and which is a combination of the wavelet transform and matched filtering method, significantly improves the accuracy of the filtered signal and identifies relatively small damage, especially in enormously noisy data. A comparative study is also performed using the statistical tool to know acceptability and practicability of filtered signals for guided wave application.

The proposed filtering techniques can be utilized in online monitoring of civil and mechanical structures. The algorithm of the method is easy to implement and found to be successful in accurately detecting damage.

Although many techniques have been developed over the past several years to suppress random noise in Lamb wave signal but filtration of interferences of wave modes and boundary reflection is not in a much matured stage and thus needs further investigation. The present study contains detailed information about various noise filtering methods, newly developed filtration technique and their efficacy in handling the above mentioned issues.

The presence of random noise as well as narrow band coherent noise makes the structural health monitoring a really challenging issue and to achieve efficient structural health assessment methodology, very good extraction of noise and analysis of the signals are essential. The purpose of this paper is to provide optimal noise filtering technique for Lamb waves in the diagnosis of structural singularities.

Filtration of time-frequency information of multimode Lamb waves through the noisy signal is investigated in the present analysis using matched filtering technique and wavelet denoising methods. Using Shannon’s entropy criterion, the optimal wavelet function is selected and verification is made via the analysis of root mean square error of filtered signal.

The authors propose wavelet matched filter method, a combination of the wavelet transform and matched filtering method, which can significantly improve the accuracy of the filtered signal and identify relatively small damage, especially in enormously noisy data. Correlation coefficient and root mean square error are additionally computed for performance evaluation of the filters.

The present study provides detailed information about various noise filtering methods and a first attempt to apply the combination of the different techniques in signal processing for the structural health monitoring application. A comparative study is performed using the statistical tool to know whether filtered signals obtained through three different methods are acceptable and practicable for guided wave application or not.

The development of a new class of engineering materials is the current demand for aircraft and automobile companies. In this context metal, composite materials have a widespread application in different areas of manufacturing sectors.

In this paper, an attempt is made to develop the aluminium-based nano metal matrix composite reinforced with graphene nanoparticles (GNP) by using the stir casting method. Different weight percentage (0.4%, 0.8% and 1.2% by weight) of GNPs are used to fabricate metal matrix composites (MMCs). The developed nanocomposites were further validated by density calculation and optical microstructures to discuss the distribution of GNPs. The tensile test was conducted to determine the strength of the developed MMCs and also supported by fractographic analysis. In addition to it, the Rockwell hardness test and impact test (toughness) with fracture analysis were also conducted to strengthen the present work.

The results reveal the uniform distribution of GNPs into the matrix material. The yield strength and ultimate tensile strength obtained a maximum value of 155.67 MPa and 170.28 MPa, respectively. The hardness value (HRB) is significantly increased and 84 HRB was obtained for the sample with AA1100/0.4% GNP, while maximum hardness value (94 HRB) was obtained for the sample AA1100/1.2% GNP. The maximum value of toughness 14.3 Jules/cm² is recorded for base alloy AA1100 while increasing the reinforcement percentage, it decreases up to 9.7 Jules/cm² for AA1100/1.2% GNP.

Graphene nanoparticles are used to develop nanocomposites, which is one of the suitable alternatives for heavy engineering materials such as steels and cast irons. It has improved microstructural and mechanical properties which makes it preferable for many engineering and structural applications.

Novel and emerging technologies such as cognitive analytics attract a lot of hype among academic researchers and practitioners. However, returns on investments in these technologies are often poor. So, identifying mechanisms through which cognitive analytics can add value to firms is a critical research gap. The purpose of this paper is to theorize how cognitive analytics technologies can enable the dynamic capabilities of sensing, seizing and reconfiguring for an organization.

This conceptual paper draws on the extant academic literature on cognitive analytics and related technologies, the business value of analytics and artificial intelligence and the dynamic capabilities perspective, to establish the role of cognitive analytics technologies in enabling the sensing, seizing and reconfiguring capabilities of an organization.

Through arguments grounded in existing conceptual and empirical academic literature, this paper develops propositions and a theoretical framework linking cognitive analytics technologies with organizations’ dynamic capabilities (sensing, seizing and reconfiguring).

This paper has critical implications for both academic research and managerial practice. First, the authors develop a framework using the dynamic capabilities theoretical perspective to establish a novel pathway for the business value of cognitive analytics technology. Second, cognitive analytics is proposed as a novel antecedent of the dynamic organizational capabilities of sensing, seizing and reconfiguring.

To the best of the authors’ knowledge, this is the first paper to theorize how cognitive analytics technologies can enable dynamic organizational capabilities, and thus add business value to an organization.

The purpose of this conceptual paper is to explicate the role played by information technology (IT) in enabling managerial dynamic capabilities. By doing so, this paper seeks to address a critical theoretical gap regarding IT’s role in enabling dynamic capabilities (DCs). DCs are knowledge-intensive and information-intensive processes and play a crucial role in facilitating strategic renewal of firms operating in volatile, uncertain, complex and ambiguous business environments. Although managers play a central role in the DCs framework, extant research has only focused on the role of IT in enabling firm-level and process-level DCs.

This conceptual paper uses the literatures on dynamic managerial capabilities, individual-level information system use, social capital, human capital, managerial cognition and technology-enabled learning to build propositions that link managerial IT use with the enablement of dynamic managerial capabilities.

This paper introduces a new construct called individual IT leveraging capability (IILC) and provides theoretically grounded arguments that link IILC with managerial social capital, managerial cognition and managerial human capital. It also explicates the relationships between managerial social capital, managerial cognition and managerial human capital and the dynamic managerial capabilities of sensing, seizing and reconfiguring.

The establishment of the linkage between IT and dynamic managerial capabilities extends the literature on the business value of IT. This work also adds to the literature on dynamic managerial capabilities by providing a theoretically grounded argument that IT can act as an antecedent of such capabilities.

To the best of the authors’ knowledge, this paper is arguably the first to theorize the role of IT in enabling managerial DC and thus addresses a critical gap in academic research literature.

The study aims to facilitate the medical tourists visiting emerging countries for various kinds of ailments by ranking the possible destinations to avail medical treatments.

A Fuzzy Analytical Hierarchical Process (FAHP) with a mixed-method approach is applied to analyze data collected from patients and substantiate it with medical tour operators in India to gain managerial insights on the choice-making patterns of the patients.

India is a preferred emerging market location due to the low cost and high medical staff quality. India offers value for money, whereas Singapore and Thailand are preferred destinations for quality and technology.

The study will facilitate the emerging markets' governments, hospitals and medical tourists to understand the importance of various determinants responsible for availing medical treatment outside their country.

The study recommends that cost and quality care are the patients' prime focus; government policies must provide clear guidelines on what the hospitals and country environment can offer and accordingly align the marketing strategies.

This study is the first attempt to rank various factors affecting medical tourism using the FAHP approach.

The generic nature of business excellence models and arbitrary approaches followed by organizations to achieve excellence has triggered new approaches to attain excellence; one such approach is to have country- and industry-specific model. World Energy Council has developed an Energy Trilemma Index, which gives relative energy performances of almost 125 countries based on comprehensive energy matrix (affordability, availability and environment impact). Presently, India ranks at 91 as per this index thereby making this sector prudent case for specific excellence model for sector’s overall excellence. The purpose of this paper is to propose European Foundation for Quality Management (EFQM) model with Indian thermal power generating sector specific factors.

The study followed a research approach that combines literature review, qualitative and quantitative techniques. This includes analysis of the literature related to the subject, qualitative analysis to validate gap areas around EFQM model and identify factors critical to Indian thermal power sector. Quantitative analysis was done using SmartPLS 3.2.7 software for structural equation modeling–partial least square.

The research proposes inclusion of Indian thermal power generating sector specific factors in EFQM model, which in turn portrays balanced set of results and corresponding enablers for achieving excellence in the sector. The biggest take away will be sustained and effective contribution toward society and environment by this sector.

This is first of its kind study in India and globally for thermal power generating sector. It will trigger quality consciousness among power generating companies. For India, such excellence mission will definitely help nation to improve efforts toward 24×7 electricity and electricity to all.