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Sonar sensor-based mobile robot mapping is an efficient and low cost technique for the application such as localization, autonomous navigation, SLAM and path planning. In multi-robots system, numbers of sonar sensors are used and the sound waves from sonar are interacting with the sound wave of other sonar causes wave interference. Because of wave interference, the generated sonar grid maps get distorted which resulted in decreasing the reliability of mobile robot’s navigation in the generated grid maps. This research study focus in removing the effect of wave interfaces in the sonar mapping to achieve robust navigation of mobile robot.

The wrong perception (occupancy grid map) of the environment due to cross talk/wave interference is eliminated by randomized the triggering time of sonar by varying the delay/sleep time of each sonar sensor. A software-based approach randomized triggering technique (RTT) is design in laboratory virtual instrument engineering workbench (LabVIEW) that randomized the triggering time of the sonar sensor to eliminate the effect of wave interference/cross talk when multiple sonar are placed in face-forward directions.

To check the reliability of the RTT technique, various real-world experiments are perform and it is experimentally obtained that 64.8% improvement in terms of probabilities in the generated occupancy grid map has been attained when compared with the conventional approaches.

This proposed RTT technique maybe implementing for SLAM, reliable autonomous navigation, optimal path planning, efficient robotics vision, consistent multi-robotic system, etc.

This study aims to develop cause-and-effect relationships among the critical success factors (CSFs) of fintech adoption and rank these CSFs based on their importance in the model.

To achieve the objectives of the study, the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) approach was used. The data was collected from 16 experts using a questionnaire.

The findings demonstrated the interrelationships among the CSFs. In total, 16 critical factors were recognized as causal factors, and the remaining eight were considered effect factors. The CSFs were ranked based on their importance in fintech adoption.

This study is novel as it investigates CSFs of fintech adoption using FDEMATEL, and it contributes to understanding the nature of these factors and how they affect fintech adoption. The findings propose a significant basis to deepen fintech adoption and deliver a clue to design a practical framework for fintech adoption.

Modern technologies are seen as an essential component of the fourth industrial revolution (industry 4.0) and their adoption is vital to transform the existing manufacturing system into industry 4.0-based manufacturing system. Therefore, the primary objective of this research explores the barriers of modern technology adoption and their mitigating solutions in order to align with Industry 4.0 objectives.

Barriers to adopting modern technologies and respective mitigating solutions are identified from the available literature. Further, these barriers are ranked with the help of expert opinions by using the BWM method appropriately. The identified solutions are ranked using the combined compromise solution (CoCoSo) method.

Several modern technologies and their capabilities are recognised to support the industry 4.0-based manufacturing systems. This study identifies 22 barriers to the effective adoption of modern technologies in manufacturing and 14 solutions to overcome these barriers. Change management, the high initial cost of technology and appropriate support infrastructure are the most significant barriers. The most prominent solutions to overcome the most considerable barriers are ‘supportive research, development and commercialisation environment’, ‘updated policy and effective implementation’ and ‘capacity building through training’ that are the top three solutions that need to be addressed.

The barriers and solutions of modern technology adoption are obtained through a comprehensive literature review, so there is a chance to ignore some significant barriers and their solutions. Furthermore, ranking barriers and solutions is done with expert opinion, which is not free from biases.

This identification and prioritisation of barriers will help managers to understand the barriers so they can better prepare themselves. Furthermore, the suggested solutions to overcome these barriers are helpful for the managers and could be strategically adopted through optimal resource utilisation.

This study proposes a framework to identify and analyse the significant barriers and solutions to adopting modern technologies in the manufacturing system. It might be helpful for manufacturing organisations that are willing to transform their manufacturing system into industry 4.0.

Throughout history, pandemics have played a significant role in reshaping human civilizations through mortalities, morbidities, economic losses and other catastrophic consequences. The present COVID-19 pandemic has brought the world to its knees resulting in overstretched healthcare systems, increased health inequalities and disruptions to people’s right to health including life-saving routine immunization programs across the world.

This is a commentary paper.

Immunization remains one of the most successful, safe, cost-effective and proven fundamental disease prevention measures in the history of public health. However, the COVID-19 pandemic has effectively thrown the world's immunization practices out of gear, depriving approximately 80 million infants, in rich and poor countries alike, at risk of triggering a resurgence of vaccine-preventable diseases such as diphtheria, measles and polio. It is estimated that each COVID-19 death averted by suspending immunization sessions in Africa could lead to 29-347 future deaths due to other diseases including measles, yellow fever, polio, meningitis, pneumonia and diarrhoea.

The value of implementing robust immunization policies cannot be underestimated. Risks associated with postponing immunization services and the fact that COVID-19 is now an integral part of human civilization have resulted in several countries making special efforts to continue their immunization services. However, critical precautionary measures are warranted to prevent COVID-19 among healthcare service providers, facilitators, caregivers and children during the immunization sessions.

This paper aims to optimize the quantum of aonla pulp that could be mixed with guava pulp to make a nutritional rich fruit bar. The developed fruit bar will not only help in the improvement of processing value of both Guava and underused but highly nutritional Aonla but also serve the purpose of improvement in nutritional status of consumers.

Response surface methodology (RSM) using Box–Behnken design was used with the process variables as aonla and guava pulp ratio, PR (30:70, 40:60, 50:50); pectin concentration, PC (0, 0.15, 0.30%); and drying temperature, DT (50, 60, 70°C) for optimization of process conditions. The prepared mixed fruit leather was evaluated for physico-chemical, textural and sensory properties such as titratable acidity (TA), ascorbic acid content (AA), L value (lightness), cutting force (CF), taste and overall acceptability (OAA).

Second-order regression models fitted for TA, AA, L value (lightness), CF, taste and OAA were highly significant (P = 0.01) with the coefficient of determination (R² = 0.85). The TA and AA of mixed fruit bar increased whereas L value, CF, taste and OAA decreased with increasing level of aonla pulp in the blend formulation. The optimum process conditions for mixed aonla-guava bar with desirable characteristics were 40:60 (PR), 0.02% (PC) and 56°C (DT). The corresponding optimum values of TA, AA, L value, CF, taste and OAA were 1.00%, 164 mg/100 g, 50, 5066 g, 7.83 and 7.92, respectively. The design formulation and data analysis using RSM validated the optimum solution.

This paper demonstrates that optimum blending of aonla and guava pulp has improved the overall nutritional characteristics and acceptability of the final product. This will not only help in reducing the associated post-harvest losses but also encourage the cultivators/local processing industries by stabilizing the price during glut sea.

In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI’s influence on supply chain risk mitigation (SCRM).

This study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses.

This study’s findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence.

This study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.

The objective of this research is to identify and prioritize the critical factors that influence the adoption of blockchain technology within the banking sector.

A well-known theoretical framework, the “Technology Organization Environment (TOE),” was chosen to analyze what criteria and sub-criteria affect blockchain adoption in the banking sector after a thorough assessment of the prior literature. Following that, 3 evaluation criteria and 14 sub-criteria were selected and verified using expert opinion. A survey design was created, and data for the study has been collected from various information technology (IT) managers/officers in the banking sector. A fuzzy analytic hierarchy process (Fuzzy-AHP) was then used to meet the purpose of the research.

The study identified that the organizational dimension is the most significant criteria for blockchain adoption in the banking sector, followed by the environmental dimension. In contrast, the technological dimension is the least influential criterion. Clientele pressure, IT resources, financial resources, pressure from competitors and relative advantage are the most influential sub-criteria for blockchain adoption.

This study provides valuable insights to bank managers, blockchain and IT developers, third-party service providers and policymakers. For instance, adopting the same blockchain platform is easier for both large and small banks for banking operations by using third-party service provider. At the same time, banks should have the banks' own core team to implement the blockchain-based systems or to have control over the third-party service providers during the adoption stage.

To the best of the authors' knowledge, no empirical studies have used a holistic organizational context to understand the factors influencing the adoption of blockchain technology from traditional to blockchain-based banking systems.

This article sheds light on the missing links concerning the study of using integrated enabling technologies toward sustainable and circular agriculture supply chains by examining the available literature and proposing future research possibilities.

The relevant literature was researched through online databases such as Scopus, Web of Science, Academic Search Premier, Emerald, IEEE Xplore, Science Direct, World Scientific Net and Springer-Link Journals, covering a period from 1999 to 2020. A systematic literature review based on 75 papers analyzed the integration of the concepts of enabling technologies, sustainability, circular economy and supply chain performance in agriculture supply chains.

It was identified that enabling technologies and agriculture supply chains alone have been explored further than integrated enabling technologies, sustainability, circular economy, supply chain performance and agriculture supply chains. Enabling technologies and agriculture supply chains' main findings are: enabling technologies have been studied to improve food safety, food quality and traceability in agriculture supply chains. The main results regarding integrated enabling technologies, sustainability, circular economy, supply chain performance and agriculture supply chains are: Internet of Things and information communication technology play an important role in addressing food security, traceability and food quality, which help achieve sustainable development goals.

This review study provides 13 research questions to underpin future trends regarding integrated technologies' application in agriculture supply chains for circular and sustainable growth.

Big Data can be utilised for efficient use of resources and to provide better services to the resident in order to enhance the delivery of urban services and create sustainable build environment. However, the adoption of Big Data faces many challenges at the implementation level. Therefore, the purpose of this paper is to identify the challenges towards the efficient application of Big Data in smart cities development and analyse the inter-relationships.

The 14 Big Data challenges are identified through the literature review and validated with the expert’s feedback. After that the inter-relationships among the identified challenges are developed using an integrated approach of fuzzy Interpretive Structural Modelling (fuzzy-ISM) and fuzzy Decision-Making Trial and Evaluation Laboratory (fuzzy-DEMATEL).

Evaluation of interrelationships among the challenges suggests that diverse population in smart cities and lack of infrastructure are the significant challenges that impede the integration of Big Data in the development of smart cities.

This study will enable practitioners, policy planners involved in smart city projects in tackling the challenges in an optimised manner for the hindrance free and accelerated development of smart cities.

This research is an initial effort to develop an interpretive structural model of Big Data challenges for smart cities development which gives a clearer picture of how the identified challenges interact with each other.