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Internet of things (IoT) is a promising technology which provides efficient and reliable solutions towards the modernization of several domains. IoT based solutions are being developed to automatically maintain and monitor agricultural farms with minimal human involvement. The article presents many aspects of technologies involved in the domain of IoT in agriculture. It explains the major components of IoT based smart farming. A rigorous discussion on network technologies used in IoT based agriculture has been presented, that involves network architecture and layers, network topologies used, and protocols. Furthermore, the connection of IoT based agriculture systems with relevant technologies including cloud computing, big data storage and analytics has also been presented. In addition, security issues in IoT agriculture have been highlighted. A list of smart phone based and sensor based applications developed for different aspects of farm management has also been presented. Lastly, the regulations and policies made by several countries to standardize IoT based agriculture have been presented along with few available success stories. In the end, some open research issues and challenges in IoT agriculture field have been presented.

PurposeDuring COVID-19 pandemic, the use of social media enhances information exchange at a global level; therefore, customers are more aware and make backup plans to take optimal decisions. This study explores the customer psychology of impulse buying during COVID-19 pandemic.Design/methodology/approachThe researcher, being a social constructionist, aims at understanding social patterns in impulsive buying strategies during COVID-19 pandemic. Forty UK consumers were participated using the telephonic interview method with the purpose to maintain social distancing practices.FindingsResults revealed that vulnerable group of people, fear of illness, fear of empty shelves, fear of price increase and social inclination to buy extra for staying at home, increased panic impulsive buying behaviour among customers. Many people socially interpreted the evidence of death rate and empty shelves, which led to more disinformation, rumours and sensationalism, which increased customers' impulsive buying behaviour. Finally, risk of going outside, COVID-19 outbreak among employees of local retail stores, and health professionals' recommendations to stay at home, led to impulsive buying behaviour.Originality/valueThis study has constructed a research framework of customer psychology of impulse buying based on the results of this study and fear and perceived risk theories. The study also explains how the fear of fear, risk perception and conformist tendency enhanced impulsive buying during COVID-19 pandemic. This study has discussed specific implications for retailers.

This paper compares different ICA preprocessing algorithms on cross-validated training data as well as on unseen test data. The EEG data were recorded from 22 electrodes placed over the whole scalp during motor imagery tasks consisting of four different classes, namely the imagination of right hand, left hand, foot and tongue movements. Two sessions on different days were recorded for eight subjects. Three different independent components analysis (ICA) algorithms (Infomax, FastICA and SOBI) were studied and compared to common spatial patterns (CSP), Laplacian derivations and standard bipolar derivations, which are other well-known preprocessing methods. Among the ICA algorithms, the best performance was achieved by Infomax when using all 22 components as well as for the selected 6 components. However, the performance of Laplacian derivations was comparable with Infomax for both cross-validated and unseen data. The overall best four-class classification accuracies (between 33% and 84%) were obtained with CSP. For the cross-validated training data, CSP performed slightly better than Infomax, whereas for unseen test data, CSP yielded significantly better classification results than Infomax in one of the sessions.

Abstract:We report the crystal structure and magnetic properties of Zn 1-x Co x O (0 ≤ x ≤ 0.10) nanoparticles synthesized by heating metal acetates in organic solvent. The nanoparticles were crystallized in wurtzite ZnO structure after annealing in air and in a forming gas (Ar95%+H5%). The X-ray diffraction and Xray photoemission spectroscopy (XPS) data for different Co content show clear evidence for the Co +2 ions in tetrahedral symmetry, indicating the substitution of Co +2 in ZnO lattice. However samples with x=0.08 and higher cobalt content also indicate the presence of Co metal clusters. Only those samples annealed in the reducing atmosphere of the forming gas, and that showed the presence of oxygen vacancies, exhibited ferromagnetism at room temperature. The air annealed samples remained nonmagnetic down to 77K. The essential ingredient in achieving room temperature ferromagnetism in these Zn 1-x Co x O nanoparticles was found to be the presence of additional carriers generated by the presence of the oxygen vacancies.