This research attempts to theoretically establish the factors influencing experiences of users when trading non-fungible tokens (NFT) facilitated by blockchain technology in the metaverse, a place where users conduct real-life activities in a virtual environment. Based on over 204 thousand user reviews obtained from 15 metaverse platforms, themes influencing users’ experiences were identified through natural language processing. Upon mapping these themes through the lens of social exchange theory and value affordance theory, the final set of factors influencing user experience were established and their relevance examined through multiple regression models. We found that interactivity, visibility, verification, value creation, transparency, and efficiency significantly influenced experience of a user in the NFT buying/selling process in the metaverse, whereas decentralized self-reliance, identity protection and token preference were found to be less influential while determining the same. The findings can be used by organizations developing metaverses to enhance the virtual experience of users, adding value to their business.

Unlike adoption, the post-adoption behaviour of metaverse applications has not been thoroughly investigated. The study aims to investigate the active and passive resistance-causing barriers experienced by users of Metaverse applications post adoption. A total of 70631 critical user reviews were obtained from 10 different Metaverse applications and subjected to text analytics procedures to identify relevant themes as barriers to post-adoption metaverse usage. The findings revealed high presence of system-related barriers and low presence of functional and psychological barriers as active resistance with high presence of passive resistance through individual barriers. The findings can be utilized by metaverse-developers to retain users post adoption.

Identified series-based information on boron (B) is not comprehensively available in the Entisol of north-eastern terai region of India. This region is frequently reported to be deficient in available B (av-B) due to intense leaching and low solubility of primary B minerals. The present experiment was conducted in the Cooch Behar district with the aim to assess the surface soil (0-15 cm) status of av-B in four dominant soil series in post-monsoon months of dry winter. Hot water (HW) and 0.01 M hot calcium chloride (HCC) (0.01 M CaCl2) were used to extract av-B, where HW extracted higher amount of B than HCC in all soil series. The mean B concentration was highest in the Rajpur series (HW-B = 1.71 mg kg-1, HCC-B = 1.48 mg kg-1). The four principal components explained 79.58% of the total variance, while pH and SOC contributed maximum variability among all the soil factors under study. Spatial interpolated (Inverse Distance Weighted, IDW) maps and nutrient index value (NIV) based fertility rating showed the soils in the study area were not deficient in av-B, with a majority of portions exceeding the B critical limit of toxicity for sensitive crops. Boron availability also got increased in dry periods with assured irrigation supply to winter crops along with the high depth of water table (DTW) of the terai region. Accordingly, the local farmers need to check excess B fertilizer (borax) application in dry post-monsoon periods considering long-term effects of B fertilizers on soil, cropping system and production economics.

The purpose of this research is to establish the necessary and sufficient conditions for food safety and security during pandemic outbreaks, focusing on the case of COVID-19 to ensure resilience of the food supply chain. The study emphasises on the complexity theory of fuzzy set Qualitative Comparative Analysis (fsQCA), to establish a result-driven definition of Industry 5.0 (I5.0) during and post pandemics. The results of this study revealed that a combination of conditions derived from pandemic policy related reforms and I5.0 enablers will assist manufacturers and suppliers in establishing food safety and security during and post the COVID-19 era in a developing economy. Food safety and security being the goal, based on a survey of 140 food companies, this study provides insights to manufacturers and policymakers to enable selective implementation of I5.0 enabling technologies and pandemic policies.

The series-based information on boron (B) is not comprehensively available in the Fluvisols of north-eastern Terai region of India. This region is frequently reported to be deficient in available B (av-B) due to intense leaching and low solubility of primary B minerals. The present experiment was conducted in the Cooch Behar district with the aim to assess the surface soil (0–15 cm) status of av-B in four dominant soil series (Lotafela, Matiarkuthi, Rajpur and Balarampur) in post-monsoon months of dry winter. Hot water (HW) and 0.01 M hot calcium chloride (HCC) (0.01 M CaCl2) were used to extract av-B, where HW extracted higher amount of B than HCC in all soil series. The mean HW-B concentration was highest in the Rajpur series (1.71 mg kg–1) followed by Balarampur (1.64 mg kg–1), Matiarkuthi (1.58 mg kg–1) and Lotafela (1.57 mg kg–1). Similar result was also notice in HCC-B. The four principal components explained 79.58

Agriculture produces food for the world, supply a nation's revenue and drives technological innovation. Traditional routine-analysis methods for identifying yield limiting factors at field level lacks required precision, mostly non-green and largely non-accessible to the farmers. Precision agriculture, a technology-enabled approach in which biosensors can be utilized as a real-time, sensitive and quantitative analytical tool, is gaining traction with upsurge in global food demand. As a point-of-care (POC) device it can be used for crop protection, soil health monitoring and crop productivity assessment, etc. This chapter marks the application and accessibility of biosensors in precision agriculture for sustainable crop production.

The current research aims to extend the existing literature on the differentiation of Blockchain Technology (BCT) from Distributed Ledger Technology (DLT) and traditional database management systems (DBMS) on grounds of providing additional security to user applications. Through a qualitative hierarchy methodology, it identifies prominent factors contributing to the same. 31 working professionals across a range of industry types were subjected to a semi-structured interview and the interview transcripts were analysed through NVivo and Rstudio. The findings suggested that the use of BCT in various daily use applications will drastically enhance security over traditional DBMS on grounds of traceability, immutability, transparency, accountability and non-hackability of data. The implementation of BCT over DLT in applications pointed towards enhancement of security too, but considering the areas only when the technical novelty of BCT is needed over the already robust DLT. While some industries would benefit straightaway, some need to strategically set their requirements on grounds of application security and think mindfully before implementing BCT considering the enhanced technical novelty of the same for visibly reaping its benefits.

Adoption of a high analysis synthetic fertilizer-based modern agricultural system has aggravated problems of micronutrient deficiencies like boron (B). Elevating organic carbon (OC) content in soil is assumed to be effective to deal with such constraints. This study conducted for two seasons aimed to evaluate interactive effect of farm yard manure (FYM) and B on yield of cauliflower and residual soil B status in inherently B deficient acidic Entisol. The field experiment was laid down in randomized block design with thirteen treatment combinations of FYM and B. According to the results FYM and soil borax application significantly influenced curd diameter (CD), curd yield, curd B uptake, residual soil B content, and soil OC content over control and single-dose foliar spray. FYM at 20 t ha(-1) with borax at 5 kg ha(-1) turned out as the best treatment combination in terms of curd yield (24.19 t ha(-1)), apparent boron recovery (ABR) (10.01%), and boron use efficiency (BUE) (3448 kg kg(-1)). Hot water was found more efficient over hot CaCl2 to extract soil B. The results suggest the feasibility of using organic manure with B fertilizer to improve soil properties and cauliflower productivity.

Urbanization leads to substantial changes in the terrestrial ecosystems. Soils in urban and peri-urban areas are often subject to severe degradation and soil pollution can rise to levels warranting immediate attention. Along with heavy metal contamination and urban waste dumping, occurrence of microplastics in urban soils is a topic of increasing concern in recent years. The present study was conducted in the town of Cooch Behar and its periphery in Eastern India, with the aims to identify the effect of urbanization on soil properties and to quantify the extent of microplastic pollution in a small urban area. Surface soil samples were collected across the town and from its surroundings and analysed for selected soil quality parameters. Quantitative estimation of soil microplastics was performed along with microscopic observation of morphotypes. Assessment of land use-land cover (LULC) status indicated significant increase in urban settlements in 24 years timespan from 1995 to 2019, concurrent with a trend of agricultural intensification around the town during this period. Results showed high soil NO3--N in some locations and high available P content in the majority of study areas. High Zn ((x) over bar 1.91 mu g g(-1)), Cu ((x) over bar 3.36 mu g g(-1)), Cr ((x) over bar 2.31 mu g g(-1)) and Hg ((x) over bar 4.69 mu g g(-1)) concentrations were observed while bio-available Cd was found above the critical limit in some places. Soils of some urban and peri-urban locations showed high presence of microplastics (0.2-0.5 or > 0.5 mg g(-1)). This is one of the first studies of soil contamination and microplastic pollution in small urban settlements (like municipal towns) in India and it indicated the extent of severity due to unmanaged human activities.

Runoff estimation, as well as forecasting, is a challenging hydro-climatological topic since governing physical processes is complex, and in reality, it is hardly represented by a system of the equations. Due to the complex nature and extreme spatial-temporal variability of the processes which control runoff, it is difficult to set up a reliable framework for runoff prediction and forecasting based on available observations only. In this research, two kinds of methods have been approached. The first one is a conceptual method, Soil Conservation Service Curve Number (SCS-CN) method, which combines the climatic factors and watershed parameters in one unit called the  Curve Number (CN). The other method is the Artificial Neural Network (ANN) modeling, where two different kinds of models, Feed Forward Back Propagation (FFBP) and Cascade Forward Back Propagation (CFBP) model have been applied. The runoff-rainfall coefficient has been chosen as the standard parameter of the study result. Among 16 years, the year 2000 has the highest annual, seasonal monthly total runoff (monsoon season, July to Sept.). In artificial neural network models, generated coefficient correlation (R) values varied from 0.96 to 0.99 range, which indicated a good correlation between the rainfall-runoff data set. The models developed for the present study can be utilized for further basin hydrologic analysis.

In a world of ever-increasing microblogs, the opinions, preferences, support, frustration, anger and other emotions of people regarding various events and individuals, surface in varied ways on social media. The purpose of this research is to find those hidden patterns in raw data, which can explain meaningful insights about its creation, the groups of people who created them and their sentiments which led to the generation of such data. Sentiment analysis has always been an effective methodology for discovering emotion and bias towards or against a situation, topic, thought or initiative and finding other meaningful insights from unstructured data. In this research, we attempted a type of document clustering wherein we attempted to classify the sentiments of the citizens of India as they micro-blogged their opinions, thoughts, views and ideas during the implication of the Citizenship Amendment Act (CAA) on the social networking site, Twitter. By analyzing the tweets of 13,000 twitter users during a specific timeline during which the discussion regarding the CAA was at its peak, we analyzed the sentiment of those twitter users by clustering their tweets (documents) into four sentiment groups with the help of Latent Dirichlet Allocation (LDA) which is an important tool for topic modelling in the domain of sentiment analysis. Using political ritual theory, the present paper examines the sentiments of people who tweeted during a protest in India. After the classification, our research also maps the online political behaviour of these 13,000 social media participants to the postulates of political ritual theory which is explained by previous research regarding the behaviour of physically co-existing political participants and also justifies this display of various sentiments regarding the CAA in the footsteps of political rituals.