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The existing literature contains few references on the better adaptors of online distance education amongst STEM (read as science, technology, engineering and mathematics) and non-STEM (composed of humanities, social science and commerce) study groups in an Indian peri-urban context. The study's objective is to determine the better adaptor amongst these two study groups in online distance learning in higher education systems in an Indian peri-urban context.

The investigation was carried out prior to COVID-19 and during the pandemic. The inquiry is triangulated in nature with a disproportionate stratified random sampling approach used to pick 312 post-graduate students (STEM = 135 and non-STEM = 177) from a peri-urban higher education institute in West Bengal, India, using the “Raosoft” scale. Given the prevailing social distance norms, 235 samples of respondents from 312 students were evaluated via telephonic/online interviews during the COVID-19 period. The data were analysed using SPSS 22.

This study's investigations reveal that the STEM respondents have better digital profiles, better basic computing and Internet knowledge and greater digital usage for academic purposes before the pandemic times than the non-STEM group. This prior digital exposure has enabled the STEM group to cope with regular online distance education during the pandemic more quickly than the non-STEM group, as evidenced by their regular attendance in online classes and their greater awareness of its utilitarian role than the other group.

The study offers a way forward direction to evolve with more inclusive online distance learning in peri-urban Indian regions.

The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view.

The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status.

The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design.

The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.