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In optimum designs of deep-groove ball bearings (DDGBs), an extended service life is one of the vital criteria. The life of a bearing depends on several factors. The purpose of this paper is to sequentially optimize three prime objectives for DDGB, i.e. the dynamic capacity (C_d), the maximum bearing temperature (T_max) and the elasto-hydrodynamic minimum film thickness (H_min).

For solving constrained non-linear optimization formulations with multitude of objectives, an optimal design methodology has been put forth with the help of artificial bee colony algorithms. A study on the constraint violation has been carried out. By the Monte Carlo simulation method, a sensitivity investigation of diverse design variables has been done to examine variations in three objective functions and violation of constraints.

Excellent improvement in the dynamic capacity (C_d), the maximum bearing temperature (T_max) and the elasto-hydrodynamic minimum film thickness (H_min) have been found in optimized bearing designs.

Ball bearing design has been done based on multi-discipline objectives that are based on strength, tribology and thermal consideration. This type of design is essential in practical scenario where these physical phenomena will be present simultaneously.

This paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from January 2013 to September 2020.

This study employed both the quantile vector autoregression (QVAR) and time-varying parameter VAR (TVP-VAR) technique to examine the magnitude of static and dynamic directional spillovers and dependence of markets.

Results show that the magnitude of connectedness is extremely higher at quantile levels (q = 0.05 and q = 0.95) compared to those in the mean of the conditional distribution. This connotes that connectedness between green bonds and other assets increases with shock size for both negative and positive shocks. This further indicates that return shocks spread at a higher magnitude during extreme market conditions relative to normal periods. Additional analyses show the behavior of return transmission between green bond and other assets is asymmetric.

The findings of this study offer significant implications for portfolio investors, policymakers, regulatory authorities and investment community in terms of carefully assessing the unique characteristics offered by each markets in terms of return spillovers and dependence and diversifying the portfolios.

The study, first, uses a relatively new statistical technique, the QVAR advanced by Ando et al. (2018), to capture upper and lower tails’ quantile price connectedness and directional spillover. Therefore, the results possess adequate power against departure from mean-based conditional connectedness. Second, using a portfolio of green investments, carbon markets, financial markets and commodity markets, the uniqueness of this study lies in the examination of the static and dynamic dependence of the markets examined.

The COVID-19 outbreak disrupted the business environment and severely affected the morale and performance of the employees. Further, the Indian automobile industry witnessed major setbacks and drastically impacted sector in COVID-19. Talent agility is an emerging concept in the field of HRM that will foster innovations and productivity in the automobile industry. Thus, this study aims to explore the barriers to building in-house agile talents in the Indian automobile industry in the new normal.

The barriers of talent agility were identified through a literature review and validated through experts’ opinions. This study used a hybrid approach, which combines Interpretive Structural Modelling-Polarity (ISM-P) and decision-making trial and evaluation laboratory (DEMATEL) to develop a hierarchical structural model of the barriers, followed by classification into cause and effect groups.

The result of the multi-method approach identified that shortage of skills and competencies, lack of IT infrastructure, lack of ambidextrous leaders, lack of smart HRM technologies and practices, lack of attractive reward system/career management, poor advanced T&D, poor industry, institute interface and financial constraints are the critical barriers.

It can provide a strategic roadmap for automobile manufacturers to promote talent agility in the current wave of digitalization (Industry 4.0). This study can help the managers to address and overcome the barrier and hurdles in building talent agility.

This study is unique in that it addresses the contemporary issues related to talent agility in the context of the Indian automobile industry in the current rapidly changing environment. This study developed a holistic integrated ISM(P)-DEMATEL hierarchical framework on the barriers of talent agility indicating inner dependency weights, i.e., the strength of interrelationship between the barriers.

The fatigue, thermal and wear-based bearing lives are the most important factors in the design of spherical roller bearings (SRBs). The main novelty of the present work is consideration of thermal effects as an additional objective function. The dynamic capacity (Cd), the elasto-hydrodynamic minimum film thickness (h_min) and the maximum bearing temperature (T_max) are related directly with overall lives of the bearing, and these constitute the multiple tasking operating requirements for the design of bearings.

These tasks depend upon various bearing design variables and associated constraints to help in formulating the realistic design optimization problem and in the present work these have been used to get optimal designs of SRBs in the form of Pareto-optimum fronts by using genetic algorithms.

The optimized lives show higher values as compared to the standard lives and better design choices as compared to that are available in the literature. The robustness of obtained designs are shown by conducting the sensitivity analysis of optimized operating requirements by perturbing optimum bearing variables.

The robustness of the design could be improved by optimizing tolerances of design variables based on desired variation in multiple tasking operating requirements.

For the aerospace and space applications such critical design of bearings are required based on multiple tasking operating requirements. For example for higher temperature application in gearboxes, turbines and drilling equipment.

In critical applications, multiple tasking operating requirements are essential and often bearings put limit to the life of satellites and aircraft, and bearing design methodology proposed and implemented in the present paper addresses these issues, especially addition of thermal issue in bearing design.

This study aims to identify the key enablers for the adoption of Industry 4.0 (I4.0) in the automobile industry of India, which has been severely impacted by COVID-19. Adopting I4.0 will provide organizations greater flexibility and resilience during the COVID-19 pandemic.

Based on the literature review and experts’ opinions, 21 enablers were identified. Further, contextual relationships among the identified factors and a hierarchical digraph was developed by using the total interpretive structural modelling (TISM) technique. Finally, fuzzy cross-impact matrix multiplication applied to classification (MICMAC) analysis was conducted to classify the enablers into different categories based on their dependence and driving power.

The results indicate that top management support, clarity on government policy, strategic vision on I4.0 and development of new industrial policy are the most influential factors, with the highest driving power placed at the bottom of the TISM hierarchical model. Furthermore, agile workforce, smart HR practices and IT standardization and security are identified as linkage enablers with the most driving and dependency power.

The hierarchical TISM model and fuzzy MICMAC approach provide a comprehensive understanding of the I4.0 implementation process through a visual, logical structure to the managers. It will help the researchers and practitioners understand the contextual relationship among various enablers in fostering the I4.0 adoption process and digital reorganization in the automobile industry during the COVID-19 pandemic.

This study provides a holistic TISM hierarchical framework on I4.0 adoption that will elevate the next maturity level of innovation adoption and may act as a blueprint for automobile industries during the COVID-19 pandemic.

The primary aim of this paper is to present a novel design approach for a ring voltage-controlled oscillator (VCO) suitable for L-band applications, whose oscillation frequency is less sensitive to power supply variations. In a few decades, with the advancement of modern wireless communication equipment, there has been an increasing demand for low-power and robust communication systems for longer battery life. A sudden drop in power significantly affects the performance of the VCO. Supply insensitive circuit design is the backbone of uninterrupted VCO performance. Because of their important roles in a variety of applications, VCOs and phase locked loops (PLLs) have been the subject of significant research for decades. For a few decades, the VCO has been one of the major components used to provide a local frequency signal to the PLL.

First, this paper chose to present recent developments on implemented techniques of ring VCO design for various applications. A complementary metal oxide semiconductor (CMOS)-based supply compensation technique is presented, which aims to reduce the change in oscillation frequency with the supply. The proposed circuit is designed and simulated on Cadence Virtuoso in 0.18 µm CMOS process under 1.8 V power supply. Active differential configuration with a cross-coupled NMOS structure is designed, which eliminates losses and negates supply noise. The proposed VCO is designed for excellent performance in many areas, including the L-band microwave frequency range, supply sensitivity, occupied area, power consumption and phase noise.

This work provides the complete design aspect of a novel ring VCO design for the L-band frequency range, low phase noise, low occupied area and low power applications. The maximum value of the supply sensitivity for the proposed ring VCO is 1.31, which is achieved by changing the VDD by ±0.5%. A tuning frequency range of 1.47–1.81 GHz is achieved, which falls within the L-band frequency range. This frequency range is achieved by varying the control voltage from 0.0 to 0.8 V, which shows that the proposed ring VCO is also suitable for low voltage regions. The total power consumed by the proposed ring VCO is 14.70 mW, a remarkably low value using this large transistor count. The achievable value of phase noise is −88.76 dBc/Hz @ 1 MHz offset frequency, which is a relatively small value. The performance of the proposed ring VCO is also evaluated by the figure of merit, achieving −163.13 dBc/Hz, which assures the specificity of the proposed design. The process and temperature variation simulations also validate the proposed design. The proposed oscillator occupied an extremely small area of only 0.00019 mm² compared to contemporary designs.

The proposed CMOS-based supply compensation method is a unique design with the size and other parameters of the components used. All the data and results obtained show its originality in comparison with other designs. The obtained results are preserved to the fullest extent.

A switching depth controller based on a variable buoyancy system (VBS) is proposed to improve the performance of small autonomous underwater vehicles (AUVs). First, the requirements of VBS for small AUVs are analyzed. Second, a modular VBS with high extensibility and easy integration is proposed based on the concepts of generality and interchangeability. Subsequently, a depth-switching controller is proposed based on the modular VBS, which combines the best features of the linear active disturbance rejection controller and the nonlinear active disturbance rejection controller.

The controller design and endurance of tiny AUVs are challenging because of their low environmental adaptation, limited energy resources and nonlinear dynamics. Traditional and single linear controllers cannot solve these problems efficiently. Although the VBS can improve the endurance of AUVs, the current VBS is not extensible for small AUVs in terms of the differences in individuals and operating environments.

The switching controller’s performance was examined using simulation with water flow and external disturbances, and the controller’s performance was compared in pool experiments. The results show that switching controllers have greater effectiveness, disturbance rejection capability and robustness even in the face of various disturbances.

A high degree of standardization and integration of VBS significantly enhances the performance of small AUVs. This will help expand the market for small AUV applications.

This solution improves the extensibility of the VBS, making it easier to integrate into different models of small AUVs. The device enhances the endurance and maneuverability of the small AUVs by adjusting buoyancy and center of gravity for low-power hovering and pitch angle control.

This study explores the concept of “talent agility” since its inception using bibliometric analysis and paves the way for future research agenda in the field of business and management.

The articles were retrieved from the Scopus database comprising 67 publications from 2001 to September 2020. This study has applied bibliometric techniques such as citation analysis, co-citation analysis, bibliographic coupling, co-authorship analysis, and co-occurrence keyword analysis.

The bibliometric analysis reveals the publication trends, most influential authors, articles, journals, countries, and important keywords. Subsequently, it presents the network connections among them in the field of talent agility. It identified emerging research diversifications in the literature on talent agility.

The limitation of this study is the extraction of research articles from a single database, i.e. Scopus. This study provides insights to the researchers and practitioners on comprehensive understandings of the emerging concept “talent agility”, an overview of the publication trends, emerging research themes identified in the clusters, and suggests possible directions for further study.

This study provides the intellectual structure of talent agility literature and its advancement over the periods.

Although Industry 4.0 has created a digital disruption in the business world, there is a huge demand for competent personnel to adapt to innovation in a highly volatile environment. This study aims to develop a conceptual framework on innovation adoption, thus creating a sustainable business performance in the Indian automobile industry.

A sample of 272 employees working in the Indian automobile industry was empirically tested to examine the hypothesized relationships. The proposed relationships were further tested using structural equation modelling (SEM) with AMOS 26 software.

Results highlight that knowledge management, climate for innovation, learning agility and internal corporate communication positively affect innovation adoption. Perceived innovation characteristics also have a significant relationship with innovation adoption and sustainable business performance. Furthermore, innovation adoption has a positive relationship with sustainable business performance. However, ambidextrous leadership has a non-significant relationship with innovation adoption.

This study was confined to the Indian automobile industry, which restricts its generalizability. Thus, future research can be conducted in other sectors and country contexts.

The paper provides valuable insights to practitioners, HR professionals and managers to develop an agile talent who quickly adapts innovative practices to deliver desired results. Organizations must incorporate digital leadership style, robust knowledge management, climate for innovation, learning agility and corporate communication into their talent development practices that further enhance strategic capabilities and lessen expenditures.

This paper provides a holistic framework of innovation adoption in the Indian automobile industry to attain sustainable business performance.

The purpose of this paper is to explore the role that computer vision can play within new industrial paradigms such as Industry 4.0 and in particular to support production line improvements to achieve flexible manufacturing. As Industry 4.0 requires “big data”, it is accepted that computer vision could be one of the tools for its capture and efficient analysis. RGB-D data gathered from real-time machine vision systems such as Kinect ® can be processed using computer vision techniques.

This research exploits RGB-D cameras such as Kinect® to investigate the feasibility of using computer vision techniques to track the progress of a manual assembly task on a production line. Several techniques to track the progress of a manual assembly task are presented. The use of CAD model files to track the manufacturing tasks is also outlined.

This research has found that RGB-D cameras can be suitable for object recognition within an industrial environment if a number of constraints are considered or different devices/techniques combined. Furthermore, through the use of a HMM inspired state-based workflow, the algorithm presented in this paper is computationally tractable.

Processing of data from robust and cheap real-time machine vision systems could bring increased understanding of production line features. In addition, new techniques that enable the progress tracking of manual assembly sequences may be defined through the further analysis of such visual data. The approaches explored within this paper make a contribution to the utilisation of visual information “big data” sets for more efficient and automated production.