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This paper aims to comprise the new ideas for the efficient implementation to autonomous mobile robot navigation over the complex environment in presence of static obstacles.

The fuzzy decision via probability and its distribution is applied for the generating objective function subject to the robotics path and obstacle avoidance. The present objective function is formed to achieve high level of significance for the real-time obstacle avoidance and the efficiency.

The proposed controller makes a robot take its decision effectively in complex environment in a feasible time. In comparison with other AI approaches the proposed controller reflects that the proposed method outperforms in terms of optimal path and quality of solution. The experimental and simulation results are nearly same.

It has been tested in a complex crowded environment to find shorter path than existed approaches. For the validation, the experimental and simulation result using Matlab Software (R2008) has provided at the end of the paper.

The paper aims to develop an efficient and compact hybrid S-curve-PSO (particle swarm optimization) controller for the optimal trajectory planning of industrial robots in the presence of obstacles, especially those used in pick-and-place operations.

The proposed methodology comprises a monotonic trajectory through bounded entropy of speed, velocity, acceleration and jerk. Thus, the robot’s trajectory planning corresponds with S-curve-PSO duality. This is achieved by dual navigation with minimal computational complexity. The matrix algebra-based computational complexity transforms the trajectory from random to compact. The linear programming problem represents the proposed robot in Euclidean space, and its optimal solution sets the corresponding optimal trajectory.

The proposed work ensures the efficient trajectory planning of the industrial robot in the presence of obstacles with optimized path length and time. The real-time and simulation analysis of the robot is presented for performance measurement, and their outcomes demonstrate a good correlation. Compared with the existing controller, it gives a noteworthy improvement in performance.

The novel S-curve-PSO hybrid approach is presented here, along with the LIDAR sensors, which generate the environment map and detect obstacles for autonomous trajectory planning. Based on the sensory information, the proposed approach generates the optimal trajectory by avoiding obstacles and minimizing the travel time, jerk, velocity and acceleration. The hybrid S-curve-PSO approach for optimal trajectory planning of the industrial robot in the presence of obstacles has not been presented by any researchers. This method considers the robot’s kinematics as well as its dynamics. The implementation of the PSO makes it computationally superior and faster. The selection of best-fit parameters by PSO assures the optimized trajectory in the presence of obstacles and uncertainty.

With the increasing demand for surveillance and smart transportation, drone technology has become the center of attraction for robotics researchers. This study aims to introduce a new path planning approach to drone navigation based on topology in an uncertain environment. The main objective of this study is to use the Ricci flow evolution equation of metric and curvature tensor over angular Riemannian metric, and manifold for achieving navigational goals such as path length optimization at the minimum required time, collision-free obstacle avoidance in static and dynamic environments and reaching to the static and dynamic goals. The proposed navigational controller performs linearly and nonlinearly both with reduced error-based objective function by Riemannian metric and scalar curvature, respectively.

Topology and manifolds application-based methodology establishes the resultant drone. The trajectory planning and its optimization are controlled by the system of evolution equation over Ricci flow entropy. The navigation follows the Riemannian metric-based optimal path with an angular trajectory in the range from 0° to 360°. The obstacle avoidance in static and dynamic environments is controlled by the metric tensor and curvature tensor, respectively. The in-house drone is developed and coded using C++. For comparison of the real-time results and simulation results in static and dynamic environments, the simulation study has been conducted using MATLAB software. The proposed controller follows the topological programming constituted with manifold-based objective function and Riemannian metric, and scalar curvature-based constraints for linear and nonlinear navigation, respectively.

This proposed study demonstrates the possibility to develop the new topology-based efficient path planning approach for navigation of drone and provides a unique way to develop an innovative system having characteristics of static and dynamic obstacle avoidance and moving goal chasing in an uncertain environment. From the results obtained in the simulation and real-time environments, satisfactory agreements have been seen in terms of navigational parameters with the minimum error that justifies the significant working of the proposed controller. Additionally, the comparison of the proposed navigational controller with the other artificial intelligent controllers reveals performance improvement.

In this study, a new topological controller has been proposed for drone navigation. The topological drone navigation comprises the effective speed control and collision-free decisions corresponding to the Ricci flow equation and Ricci curvature over the Riemannian metric, respectively.

This paper aims to propose an optimized overview of firefly algorithm (FA) over physical-natural impression of fireflies and its application in mobile robot navigation under the natural intelligence mechanism.

The brightness and luminosity are the decision variables in proposed study. The paper achieves the two major goals of robot navigation; first, the optimum path generation and, second, as an obstacle avoidance by co-in-centric sphere-based geometrical technique. This technique comprises the optimum path decision to objective function and constraints to paths and obstacles as the function of algebraic-geometry co-relation. Co-in-centric sphere is the proposed technique to correlate the constraints.

It is found that the present FA based on concentric sphere is suitable for efficient navigation of mobile robots at the level of optimum significance when compared with other approaches.

The paper introduces a novel approach to implement the FA for unknown and uncertain environment.

This paper aims to study the relationship between servant leadership (SL), employee turnover intention (TI) and organizational identification (OI) in hospitals.

The study uses a quantitative approach to investigate the relationships between SL, OI and TI, using data collected from a sample of 266 front-facing employees in a private Indian hospital setup. Structural equation modeling is used to analyze the data and test the hypotheses.

The findings reveal that servant leadership has a positive relationship with organizational identification and negatively impacts turnover intentions of the front-facing employee. Further, the study also reveals, contrary to expectations, organizational identification has no significant mediating effect between servant leadership and turnover intentions.

This research is limited to front-facing employees in hospitals and the study may be extended to other industries in the service sector. Future studies may consider other mediating and moderating variables to fully understand the mechanism of impact of servant leadership on turnover intention. Multi-level studies can also be carried out.

With the ever-increasing expectations for better patient care, robust leadership models have required that address front-facing employee’s well-being, enabling their attention toward patients. This paper provides the impetus for the development and adoption of servant leadership specifically within hospitals and the service sector.

This study is one of the few studies that empirically examines servant leadership in the health-care domain. The study also contributes to the extant literature on servant leadership by empirically examining the mediation effect of organizational identification between SL and TI. To the authors’ best of knowledge, this study may be the first of its kind, providing evidence of servant leadership’s impact on turnover intention and organizational identification in hospitals using data from the Indian context.

This study demonstrates the synthesis of a knowledge organization framework from tourist reviews and an ontological model with its implementation in graph database, which is based on this framework. The aim is to influence place-making outcomes at tourist destinations.

The faceted classification approach has been used for generating and validating the framework based on online reviews about urban tourism parks. The framework was used to develop an ontology using Protégé ontology editor that was implemented using GraphDB.

Three fundamental facet categories, namely Component, Aspect and Outcome, each consisting of several sub-facets, were synthesized from the analyses of the reviews. Besides helping in constructing the ontology, the analysis also helped in calculating an importance-score for the reviews that helped in ranked information retrieval.

The analyses of the reviews were done manually and may carry human bias. But it is robust as it is based on a canonical faceted methodology.

It is envisaged that this study will help tourist destination planners in decision-making by easing the utilization of tourist generated reviews by the knowledge management systems they use. Opinions of tourists will be induced in destination planning thereby helping in the production of quality “places.”

The presented faceted framework aims to specifically aid knowledge organization pertaining to online reviews related to tourist destinations. The focus is on organizing knowledge to facilitate tourism development for better place-making outcomes, which is an important area of research though it has little contributions.

The paper has intended to advance the research on the relationship between COVID-adjusted HR practices (CAHRP) and employees' stress (ES) by exploring the mediating role of employees' satisfaction from remote working (ESRW). Further, a consolidative moderated mediation model with HR pandemic readiness (HRPR) as moderator on the above-stated relationship has also been proposed.

A data set of 2266 respondents from 84 countries across six continents of the world was analysed using techniques like factor analyses, correlations and bootstrapping through PROCESS Macro in SPSS 23.

The results highlighted that ESRW significantly mediated the relationship of CAHRP and ES. Further, HRPR moderated significantly the indirect relationship between CAHRP and ES via employees' satisfaction. This relationship was found stronger at highest level as against lower level of HRPR.

This study has augmented empirical evidences to the existing literature of COVID-19 by presenting HRPR as a new dimension of HR and its role in handling the challenging situations of pandemics.

This study aims to identify the most profitable segment of construction firms amongst real estate, industrial construction and infrastructure. This paper also examines the determinants of profitability of real estate, industrial construction and infrastructure firms.

The data of 67 firms (20 real estate, 21 industrial construction and 26 infrastructure) is collected for a 15-year period (2003–2017). Two models are created using total return on assets (ROA) and return on invested capital (ROIC) as dependent variables.. Leverage, liquidity, age, growth, size and efficiency of the firm are identified as firm-specific independent variables. Two economic variables, i.e. growth in GDP and inflation, are also used as independent variables. Initially, the models are tested for stationarity, multicollinearity and heteroscedasticity, and finally, the coefficients are estimated using Arellano–Bond dynamic panel data estimation to account for heteroscedasticity and endogeneity.

The results suggest that industrial construction is the most profitable segment of construction, followed by real estate and infrastructure. Their profitability is positively driven by liquidity, efficiency and leverage. The real estate firms are somewhat less profitable compared to industrial construction firms, and their profitability is positively driven by liquidity. The infrastructure firms have low ROA and ROIC.

The real estate, infrastructure and industrial construction drastically differ from each other. The challenges involved in real estate, infrastructure and industrial construction are altogether different. Therefore, authors present a comparative analysis of the profitability of real estate, infrastructure and industrial construction segments of the construction and compare their determinants of profitability. The results provided in the study are robust and reliable because of the use of a superior econometric model, i.e. Arellano–Bond dynamic panel data estimation with robust estimates, which accounts for heteroscedasticity and endogeneity in the model.