ortho-Hydroxylation of aromatic compounds by non-heme Fe complexes has been extensively studied in recent years by several research groups. The nature of the proposed oxidant varies from Fe(III)-OOH to high-valent Fe(IV)═O and Fe(V)═O species, and no definitive consensus has emerged. In this comprehensive study, we have investigated the ortho-hydroxylation of aromatic compounds by an iron complex using hybrid density functional theory incorporating dispersion effects. Three different oxidants, Fe(III)-OOH, Fe(IV)═O, and Fe(V)═O, and two different pathways, H-abstraction and electrophilic attack, have been considered to test the oxidative ability of different oxidants and to underpin the exact mechanism of this regiospecific reaction. By mapping the potential energy surface of each oxidant, our calculations categorize Fe(III)-OOH as a sluggish oxidant, as both proximal and distal oxygen atoms of this species have prohibitively high barriers to carry out the aromatic hydroxylation. This is in agreement to the experimental observation where Fe(III)-OOH is found not to directly attack the aromatic ring. A novel mechanism for the explicit generation of non-heme Fe(IV)═O and Fe(V)═O from isomeric forms of Fe(III)-OOH has been proposed where the O···O bond is found to cleave via homolytic (Fe(IV)═O) or heterolytic (Fe(V)═O) fashion exclusively. Apart from having favorable formation energies, the Fe(V)═O species also has a lower barrier height compared to the corresponding Fe(IV)═O species for the aromatic ortho-hydroxylation reaction. The transient Fe(V)═O prefers electrophilic attack on the benzene ring rather than the usual aromatic C-H activation step. A large thermodynamic drive for the formation of a radical intermediate is encountered in the mechanistic scene, and this intermediate substantially diminishes the energy barrier required for C-H activation by the Fe(V)═O species. Further spin density distribution and the frontier orbitals of the computed species suggest that the Fe(IV)═O species has a substantial barrier height for this reaction, as the substrate is coordinated to the metal atoms. This coordination restricts the C-H activation step by Fe(IV)═O species to proceed via the π-type pathway, and thus the usual energy lowering due to the low-lying quintet state is not observed here.

An increase in the use of smartphones has laid to the use of the internet and social media platforms. The most commonly used social media platforms are Twitter, Facebook, WhatsApp and Instagram. People are sharing their personal experiences, reviews, feedbacks on the web. The information which is available on the web is unstructured and enormous. Hence, there is a huge scope of research on understanding the sentiment of the data available on the web. Sentiment Analysis (SA) can be carried out on the reviews, feedbacks, discussions available on the web. There has been extensive research carried out on SA in the English language, but data on the web also contains different other languages which should be analyzed. This paper aims to analyze, review and discuss the approaches, algorithms, challenges faced by the researchers while carrying out the SA on Indigenous languages.

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The primary objective of this research is to find the disparity for slow adoption of Smart Farming Technologies (SFT) in Ireland. The usage of Cloud Computing technology among Irish farmers would help to find out the adoption behaviour barrier and way to enhance from the present system. The research will also help us to indicate the reasons for farmers in adopting and not adopting any technology. The research followed a mixed method where both surveys and interviews were used to collect the data from Irish farmers. A total sample of 32 farmers were selected through snowball sampling with the help of social websites. This study explored the major factors in adopting new technology among Irish farmers. It also helped to find the perception of farmers and ways to improve from the present system. The result shows that Cloud Computing adoption among the young farmers is greater while it is lower among the old farmers in Ireland.

Smart Farming (SF) is an emerging technology in the current agricultural landscape. The aim of Smart Farming is to provide tools for various agricultural and farming operations to improve yield by reducing cost, waste, and required manpower. SF is a data-driven approach that can mitigate losses that occur due to extreme weather conditions and calamities. The influx of data from various sensors, and the introduction of information communication technologies (ICTs) in the field of farming has accelerated the implementation of disruptive technologies (DTs) such as machine learning and big data. Application of these predictive and innovative tools in agriculture is crucial for handling unprecedented conditions such as climate change and the increasing global population. In this study, we review the recent advancements in the field of Smart Farming, which include novel use cases and projects around the globe. An overview of the challenges associated with the adoption of such technologies in their respective regions is also provided. A brief analysis of the general sentiment towards Smart Farming technologies is also performed by manually annotating YouTube comments and making use of the pattern library. Preliminary findings of our study indicate that, though there are several barriers to the implementation of SF tools, further research and innovation can alleviate such risks and ensure sustainability of the food supply. The exploratory sentiment analysis also suggests that most digital users are not well-informed about such technologies.

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The success of Youtube has attracted a lot of users, which results in an increase of the number of comments present on Youtube channels. By analyzing those comments we could provide insight to the Youtubers that would help them to deliver better quality. Youtube is very popular in India. A majority of the population in India speak and write a mixture of two languages known as Hinglish for casual communication on social media. Our study focuses on the sentiment analysis of Hinglish comments on cookery channels. The unsupervised learning technique DBSCAN was employed in our work to find the different patterns in the comments data. We have modelled and evaluated both parametric and non-parametric learning algorithms. Logistic regression with the term frequency vectorizer gave 74.01% accuracy in Nisha Madulika’s dataset and 75.37% accuracy in Kabita’s Kitchen dataset. Each classifier is statistically tested in our study.