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An autonomous mobile robot requires efficient perception of the environment to perform various tasks in a challenging environment. The precise sensory information from the range sensors is required to accomplish prerequisites, such as SLAM, path planning and localization. But the accuracy and precision of the sensors become unreliable in harsh environmental conditions because of the effect of rain, dust, humidity, fog and smoke. The purpose of this paper is to generate robust mapping of the environment in harsh environmental conditions.

This paper presents a novel technique, averaging data with short range selection (ADWSRS), to reduce the effect of harsh environmental (rain, wind, humidity, etc.) conditions on sensory information (range) to generate reliable grid mapping. The sensory information on laser and sonar sensors in terms of probability values (occupied/unoccupied cell) in generating grid maps are fused after passing through two newly designed pre-processing filters: laser averaging filter and short range selection filter. This proposed approach relies on various aspects such as averaging laser data analogous to current pose of the sensor, selection of short range with respect to threshold value to remove the effect of specular reflection/crosstalk of sonar and a newly designed apparatus in which dirt cover (glass cover) and air blower are coupled to remove the influence of dirt, rain and humidity.

This proposed approach is tested in different environmental conditions, and to verify the consistency of the proposed approach, qualitative and quantitative analyses are carried out, which shows 42.5 per cent improvement in the probability value of occupied cells in the generated grid map.

The proposed ADWSRS approach reduced the effect of harsh environmental conditions such as fog, rain and smoke to generate efficient mapping of the environment, which may be implemented in diverse applications such as autonomous navigation, localization, path planning and mapping.

Accurate perception of the environment using range sensors such as laser scanner, SONAR, infrared, vision, etc., for the application, such as path planning, localization, autonomous navigation, simultaneously localization and mapping, is a highly challenging area. The reliability of the perception by range sensors relies on the sensor accuracy, precision, sensor model, sensor registration, resolution, etc. Laser scanner is even though accurate and precise but still the efficient and consistent mapping of the environment is yet to be attained because laser scanner gives error as the extrinsic and intrinsic parameters varied which cause specular reflection, refraction, absorption, etc., of the laser beam. The paper aims to discuss this issue.

This paper presents an error analysis in sensory information of laser scanner due to the effect of varying the scanning angle with respect to the optical axis and surface reflectivity or refractive index of the targets. Uncertainties caused by these parameters are reduced by proposing a new technique, tilt mounting system (TMS) with designed filters of tilting the angular position of a laser scanner with the best possible selection of range and scanning angle for the robust occupancy grid mapping. Various experiments are performed in different indoor environments, and the results are validated after the implementation of the TMS approach with designed filters.

After the implementation of the proposed TMS approach with filters, the errors in the laser grid map are reduced by 15.6 percent, which results in 62.5 percent reduction in the collision of a mobile robot during autonomous navigation in the laser grid map.

The TMS approach with designed filter reduces the effect of variation in intrinsic and extrinsic parameters to generate efficient laser occupancy grid map to achieve collision-free autonomous navigation.

An efficient perception of the complex environment is the foremost requirement in mobile robotics. At present, the utilization of glass as a glass wall and automated transparent door in the modern building has become a highlight feature for interior decoration, which has resulted in the wrong perception of the environment by various range sensors. The perception generated by multi-data sensor fusion (MDSF) of sonar and laser is fairly consistent to detect glass but is still affected by the issues such as sensor inaccuracies, sensor reliability, scan mismatching due to glass, sensor model, probabilistic approaches for sensor fusion, sensor registration, etc. The paper aims to discuss these issues.

This paper presents a modified framework – Advanced Laser and Sonar Framework (ALSF) – to fuse the sensory information of a laser scanner and sonar to reduce the uncertainty caused by glass in an environment by selecting the optimal range information corresponding to a selected threshold value. In the proposed approach, the conventional sonar sensor model is also modified to reduce the wrong perception in sonar as an outcome of the diverse range measurement. The laser scan matching algorithm is also modified by taking out the small cluster of laser point (w.r.t. range information) to get efficient perception.

The probability of the occupied cells w.r.t. the modified sonar sensor model becomes consistent corresponding to diverse sonar range measurement. The scan matching technique is also modified to reduce the uncertainty caused by glass and high computational load for the efficient and fast pose estimation of the laser sensor/mobile robot to generate robust mapping. These stated modifications are linked with the proposed ALSF technique to reduce the uncertainty caused by glass, inconsistent probabilities and high load computation during the generation of occupancy grid mapping with MDSF. Various real-world experiments are performed with the implementation of the proposed approach on a mobile robot fitted with laser and sonar, and the obtained results are qualitatively and quantitatively compared with conventional approaches.

The proposed ASIF approach generates efficient perception of the complex environment contains glass and can be implemented for various robotics applications.

Modern service robots are designed to work in a complex indoor environment, in which the robot has to interact with the objects in different ambient light intensities (day light, tube light, halogen light and dark ambiance). The variations in sudden ambient light intensities often cause an error in the sensory information of optical sensors like laser scanner, which reduce the reliability of the sensor in applications such as mapping, path planning and object detection of a mobile robot. Laser scanner is an optical sensor, so sensory information depends upon parameters like surface reflectivity, ambient light condition, texture of the targets, etc. The purposes of this research are to investigate and remove the effect of variation in ambient light conditions on the laser scanner to achieve robust autonomous mobile robot navigation.

The objective of this study is to analyze the effect of ambient light condition (dark ambiance, tube light and halogen bulb) on the accuracy of the laser scanner for the robust autonomous navigation of mobile robot in diverse illumination environments. A proposed AIFA (Adaptive Intensity Filter Algorithm) approach is designed in robot operating system (ROS) and implemented on a mobile robot fitted with laser scanner to reduce the effect of high-intensity ambiance illumination of the environment.

It has been experimentally found that the variation in the measured distance in dark is more consistent and accurate as compared to the sensory information taken in high-intensity tube light/halogen bulbs and in sunlight. The proposed AIFA approach is implement on a laser scanner fitted on a mobile robot which navigates in the high-intensity ambiance-illuminating complex environment. During autonomous navigation of mobile robot, while implementing the AIFA filter, the proportion of cession with the obstacles is reduce to 23 per cent lesser as compared to conventional approaches.

The proposed AIFA approach reduced the effect of the varying ambient light conditions in the sensory information of laser scanner for the applications such as autonomous navigation, path planning, mapping, etc. in diverse ambiance environment.

Research signifies that well-governed companies exhibit long-run financial results and sustainable growth. In the context of SMEs, this paper aims to review the literature on corporate governance, the implementation challenges of corporate governance and its relationship with performance. Entwined with Indian scenarios, the study can be generalized to other emerging economies, with geographic considerations.

Studies from 1990 to 2020 are included in the literature review. Three databases were used for the extraction of relevant research articles: Scopus, EBSCO and ScienceDirect. To identify the relevant work, keywords along with Boolean operators for literature search were used from the research databases. The selected articles were further refined based on the authors’ keywords, journal type, data analysis methodologies and abstract analysis. Finally, 115 articles were selected and categorized into themes based on inclusion criteria for further study.

Corporate governance provides tangible and intangible benefits to SMEs. The study emphasizes on designing a cost-effective discrete governance mechanism for SMEs than the prevailing corporate governance code for large firms. Furthermore, implementing the corporate governance structure with a great level of discipline and stability is equally essential and related to performance.

Listing of SMEs is a relatively new phenomenon in emerging economies, including India. With listing, corporate governance and financial performance are expected to shift. The inclusion of the changing landscape of SME governance makes this study unique and relevant in the current scenario. The study will benefit the policymakers and firms to adopt optimum governance practices and link it optimally with performance.

Nowadays, informed decision-making is catching up. Technological advancements and computing ability further fuel and facilitate this tilt toward informed decision-making. In such a scenario, data is cynosure. Therefore, the ability to gather data by a nation (incredibly accurate public data) becomes equally important and relevant, as measured by statistical performance indicators (SPI). This study aims to explore the association of financial inclusion (FI); environmental, social and governance (ESG); poverty; and SPI.

The panel data of 140 nations for nine years are gathered to explore the association of FI, ESG and poverty with the SPI. Panel data estimation is conducted to arrive at the results.

The findings of this study highlight mixed outcomes for FI. ESG is positively associated with SPI, but poverty is not associated with SPI. These findings imply that an increase in FI may reduce the statistical capacity of the nations. An increase in ESG increases the capacity. However, change in poverty does not influence the SPI. The recommendation based on this study’s outcome suggests auditing the FI and poverty vis-à-vis SPI to ensure SPI’s veracity and robustness in the long run.

The way in which the individual social, economic and environmental indicators influence the SPI needs to be tested to establish the veracity and robustness of the SPI, which is barely researched as observed in the literature.

The purpose of this study is to look at the multifaceted relationship mechanism between corporate social responsibility (CSR) and organizational performance (OP) via sustainability risk management (SRM) and organizational reputation (OR).

This research connects CSR to OP via SRM and OR. Based on a sample of 325 managers of multinational firms in India, a theoretical model was proposed and analyzed through sequential mediation regressions analysis.

The findings indicate that CSR is positively and appreciably associated with OP. Furthermore, SRM and OR have been found to have a sequentially mediating effect on the interrelationship between CSR and OP. The study recognizes that organizations with a proactive approach to CSR tend to manage sustainability risk more actively, which helps to improve OR and ultimately results in better OP.

The research advances understanding of the triple bottom line and offers a platform for building strategic and successful CSR policies by offering valuable insights on the link between CSR and OP.

The purpose of this research is to provide the necessarily and resourceful information regarding range sensors to select the best fit sensor for robust autonomous navigation. Autonomous navigation is an emerging segment in the field of mobile robot in which the mobile robot navigates in the environment with high level of autonomy by lacking human interactions. Sensor-based perception is a prevailing aspect in the autonomous navigation of mobile robot along with localization and path planning. Various range sensors are used to get the efficient perception of the environment, but selecting the best-fit sensor to solve the navigation problem is still a vital assignment.

Autonomous navigation relies on the sensory information of various sensors, and each sensor relies on various operational parameters/characteristic for the reliable functioning. A simple strategy shown in this proposed study to select the best-fit sensor based on various parameters such as environment, 2 D/3D navigation, accuracy, speed, environmental conditions, etc. for the reliable autonomous navigation of a mobile robot.

This paper provides a comparative analysis for the diverse range sensors used in mobile robotics with respect to various aspects such as accuracy, computational load, 2D/3D navigation, environmental conditions, etc. to opt the best-fit sensors for achieving robust navigation of autonomous mobile robot.

This paper provides a straightforward platform for the researchers to select the best range sensor for the diverse robotics application.

The paper aims to explore the influence of cybersecurity on the semantic orientation of the sports consumers. Focusing on both sport and esports, this study finds the social media factors contributing in the sentiment formation and commenting behavior on Twitter and proposes a scheme for attitude modulation through identification of highly engaged nano-influencers.

Experimental design was used as the research methodology. Data mining from Twitter using RStudio software was conducted using the keyword “cybersecurity” during the time of pandemic. Final corpus of 31,891 tweets were considered for the study. Initial sentiment analysis has been conducted to explore the consumer's emotional inclination towards cybersecurity. Further through generalized equation modeling the impact of social media attributes over the consumer's posting behavior has been analyzed.

The research findings reveal that users are inherently positive towards cybersecurity adoption in sports and the factors such as number of tweets, number of positive words contained in these tweets and the authenticity of the information source boost the pre-established tweeting behavior. However, the influx of information from non-organizational sources such as trending topics and discussions have negative impact over the users.

This study is first to explore the role of nano-influencers as communication moderators over digital social platforms. This study offers a new understanding of key contributing attributes of sentiments formation over social media and offers a scheme of selection of nano-influencers to modulate the pre-established sentiments of the users. Finally, the current study offers valuable insights into social media engagements and selection of nano-influencers for practicing marketing managers.