Background The COVID-19 pandemic has disrupted the educational system and led to a drastic shift of professional undergraduate teaching for medical and nursing students into online mode. Methods This was a cross-sectional, observational questionnaire-based study to assess the satisfaction level of the students. The questionnaire had 25 items of which 23 were questions with responses on the Likert scale and two items on views and suggestions were open-ended. The online questionnaire was shared through various messaging/mailing platforms. Overall satisfaction was assessed, and a satisfaction index was calculated for each item. Data are presented in frequencies and percentages, and SPSS was used to analyze the data. Results A total of 1068 students participated in the study. The majority were from the age group 21–23 years (54%) and there was almost the same number of participants from both genders. The majority of the students were medical undergraduates (n=919), were in their second year (n=669), belonged to a government institution (n=897) and used a mobile phone for their online classes (n = 871). The majority of the students were dissatisfied (42%) with no significant difference between medical and nursing students (p = 0.192). First-year students were significantly dissatisfied compared with other senior students (p = 0.005). The maximum satisfaction index (78.23%) was observed with faculties being supportive and responsive in resolving the queries and the minimum (46.39%) was observed with issues related to communication and discussion with peer students. There were 662 responses as views which mostly contained negative comments regarding interaction and focus, practical learning, teaching content, and technological/infrastructural flaws. There was major dissatisfaction regarding the practical and clinical learning. Conclusion Online learning is essential at current times but is not an effective alternative for medical and nursing education. Face-to-face classes and practical sessions along with online learning can be a viable option.

No abstract

No abstract

No abstract

Forecasting of prices of commodities, especially those of agricultural commodities, is very difficult because they are not only governed by demand and supply but also by so many other factors which are beyond control, such as weather vagaries, storage capacity, transportation, etc. In this paper time series models namely ARIMA (Autoregressive Integrated Moving Average) methodology given by Box and Jenkins has been used for forecasting prices of Groundnut oil in Mumbai. This approach has been compared with ANN (Artificial Neural Network) methodology. The results showed that ANN performed better than the ARIMA models in forecasting the prices.