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This paper aims to review for the first time existing research literature about the role of gender in creating, sharing and using knowledge in organizations and proposes a conceptual framework to guide future research directions.

Based on the systematic literature review method this study collects, synthesizes and analyses articles related to knowledge management (KM) and gender published in online databases by following a pre-defined review protocol. The paper analyses 41 papers published in peer-reviewed journals.

The role of gender in KM has been rarely addressed in KM journals and journals with specific emphasis on gender. The existing literature is fragmented, but existing research suggests that knowledge sharing might be influenced by gender. Based on the analysis and synthesis, a conceptual framework is proposed to guide further research on determining if gender matters in KM.

Academic researchers should aim to include gender-related variables into their KM research to further explore if gender matters in KM.

The practical implication suggests that managers and knowledge managers should raise awareness about how stereotypes and gendered expectations about role behaviour affect how knowledge and experiences are created and shared within the organization.

The authors believe that a better understanding of knowledge handling and gendered role expectations at the workplace could also have an impact beyond organizational boundaries.

The paper presents the first comprehensive systematic literature review of the article published on knowledge creation, sharing and usage and gender and provides a conceptual framework for future research.

This chapter explores how resilience in the face of difficult and crisis-ridden circumstances influences innovation. By examining Qantas and the critical role played by the CEO and Managing Director Alan Joyce, we discuss how innovation leadership amid a crisis requires resilience with a balanced approach. With a lens of self-level innovation leadership, we showcase Alan Joyce’s resilience and how that flowed onto his team and the organisation to action required changes in a creative and novel way to revitalise. The chapter contributes to the literature by further detailing about how resilience from a business leadership perspective influences the organisation’s ability to encourage innovation in a difficult and crisis-ridden environment. We believe that the lessons learned from the Qantas case can inspire companies and industries that face similar challenges to understand what it means to demonstrate resilience as a leader.

In this chapter, to explore the past and understand the present scenario in financial market, a comprehensive literature review (LR) is performed, in which 809 articles from the database of Scopus for the last 10 years are extracted and analyzed using VOSviewer software for bibliometric analysis. Citation analysis of the popular identified factors is highlighted that will help the future researchers to focus on the identified popular factors for research in the financial market. The chapter also presents a conceptual model of financial market, to uncover the future of financial markets.

It is worth mentioning that in supply chain management (SCM), managerial decisions are often based on optimization of resources. Till the early 2000s, supply chain optimization problems were being addressed by conventional programming approaches such as Linear Programming, Mixed-Integer Linear Programming and Branch-and-Bound methods. However, the solution convergence in such approaches was slow. But with the advent of Swarm Intelligence (SI)-based algorithms like particle swarm optimization and ant colony optimization, a significant improvement in solution of these problems has been observed. The purpose of this paper is to present and analyze the application of SI algorithms in SCM. The analysis will eventually lead to development of a generalized SI implementation framework for optimization problems in SCM.

A structured state-of-the-art literature review is presented, which explores the applications of SI algorithms in SCM. It reviews 56 articles published in peer-reviewed journals since 1999 and uses several classification schemes which are critical in designing and solving a supply chain optimization problem using SI algorithms.

The paper revels growth of swarm-based algorithms and seems to be dominant among all nature-inspired algorithms. The SI algorithms have been used extensively in most of the realms of supply chain network design because of the flexibility in their design and rapid convergence. Large size problems, difficult to manage using exact algorithms could be efficiently handled using SI algorithms. A generalized framework for SI implementation in SCM is proposed which is beneficial to industry practitioners and researchers.

The paper proposes a generic formulation of optimization problems in distribution network design, vehicle routing, resource allocation, inventory management and supplier management areas of SCM which could be solved using SI algorithms. This review also provides a generic framework for SI implementation in supply chain network design and identifies promising emerging issues for further study in this area.

In a wireless sensor network (WSN), the sensor nodes are distributed in the network, and in general, they are linked through wireless intermediate to assemble physical data. The nodes drop their energy after a specific duration because they are battery-powered, which also reduces network lifetime. In addition, the routing process and cluster head (CH) selection process is the most significant one in WSN. Enhancing network lifetime through balancing path reliability is more challenging in WSN. This paper aims to devise a multihop routing technique with developed IIWEHO technique.

In this method, WSN nodes are simulated originally, and it is fed to the clustering process. Meanwhile, the CH is selected with low energy-based adaptive clustering model with hierarchy (LEACH) model. After CH selection, multipath routing is performed by developed improved invasive weed-based elephant herd optimization (IIWEHO) algorithm. In addition, the multipath routing is selected based on certain fitness functions like delay, energy, link quality and distance. However, the developed IIWEHO technique is the combination of IIWO method and EHO algorithm.

The performance of developed optimization method is estimated with different metrics, like distance, energy, delay and throughput and achieved improved performance for the proposed method.

This paper presents an effectual multihop routing method, named IIWEHO technique in WSN. The developed IIWEHO algorithm is newly devised by incorporating EHO and IIWO approaches. The fitness measures, which include intra- and inter-distance, delay, link quality, delay and consumption of energy, are considered in this model. The proposed model simulates the WSN nodes, and CH selection is done by the LEACH protocol. The suitable CH is chosen for transmitting data through base station from the source to destination. Here, the routing system is devised by a developed optimization technique. The selection of multipath routing is carried out using the developed IIWEHO technique. The developed optimization approach selects the multipath depending on various multi-objective functions.

The principal intention behind the activity is to regulate the speed, current and commutation of the brushless DC (BLDC) motor. Thereby, the authors can control the torque.

In order to regulate the current and speed of the motor, the Multi-resolution PID (MRPID) controller is proposed. The altered Landsman converter is utilized in this proposed suppression circuit, and the obligation cycle is acclimated to acquire the ideal DC-bus voltage dependent on the speed of the BLDC motor. The adaptive neuro-fuzzy inference system-elephant herding optimization (ANFIS-EHO) calculation mirrors the conduct of the procreant framework in families.

Brushless DC motor's dynamic properties are created, noticed and examined by MATLAB/Simulink model. The performance will be compared with existing genetic algorithms.

The presented approach and performance will be compared with existing genetic algorithms and optimization of different structure of BLDC motor.

Inspired optimization algorithms respond to numerous scientific and engineering difficulties based on its flexibility and simplicity. Such algorithms are valid for optimization difficulties devoid of structural alterations.

This paper presents a nature-inspired optimization algorithm, named Sailfish optimizer (SFO) stimulated using sailfish group. Monetary custom of energy is a dangerous problem on wireless sensor network (WSN).

Network cluster is an effective method of reducing node power consumption and increasing network life. An algorithm for selecting cluster head (CHs) based on enhanced cuckoo search was proposed. But this algorithm uses a novel encoding system and wellness work. It integrates a few problems. To overthrow this method many metaheuristic-based CH selection algorithms are presented. To avoid this problem, this paper proposed the SFO algorithm based energy-efficient CH selection of WSN.

The proposed SFO algorithm based energy-efficient algorithm is used for discovering the CHs ideal situation. The simulations under delay, delratio, drop, energy, network lifetime, overhead and throughput are carried out.