This quantitative study proposes and validates the Hybrid force model by analysing the challenges and opportunities in online education during COVID-19 through an online survey from students and teachers of Indian higher education institutions. The proposed model considers the learner as a system of study and talks about the amalgamation of knowledge, human force (teacher), and techno-logical force for better online learning opportunities and experience. The results show Google Meet, Zoom, and Microsoft Team as the main online platforms for imparting the classes while almost 80% of the students prefer a non-traditional teaching method (online and hybrid). The results highlight that more than 88% of the students prefer to have a personal teaching assistant in their learning trajectory.

The electrochemical behaviour and surface changes on AA6061-T6 alloy exposed to Caribbean seawater from the Cozumel Channel for 30 days under laminar flow (0.1 m s⁻¹) were studied, contrasting then with stationary (no flow) conditions. Monitoring of open-circuit potential and current fluctuations, both considered as electrochemical noise (EN), were employed as two nondestructive methods. The calculated corrosion current, based on Rn, was one order higher in laminar flow. The fluctuations of current were transformed in the frequency domain. Their power spectral density (PSD) plots were obtained in order to gain information concerning the dynamic of the spontaneous release of energy during the corrosion process. The value of the exponent β in PSD graphs suggested that the localised corrosion on AA6061-T6 surface occurs as a persistent stationary process, in which dynamic is controlled by oxygen diffusion and its renewal at the metal interface. The changes in the morphology and elemental composition of the formed layers revealed that the localized attacks occurred in the vicinity of intermetallic particles rich in Fe and Cu, which act as cathodes.

Congestive heart failure carries immense importance in the realm of public health. This significance arises from its substantial influence on the number of lives lost, economic burdens, the potential for prevention, and the opportunity to enhance the well-being of both individuals and the broader community through decision-making in healthcare. Several researchers have proposed neural networks for classification of different congestive heart failure categories. However, there is little information about the confidence of the prediction on short-term series. Therefore, evaluating classification models is required for effective decision-making in healthcare. This paper explores the use of three classical variants of neural networks to classify three groups of patients with congestive heart failure. The study considered the iterative method Multilayer Perceptron neural network (MLP), two non-iterative models (Extreme Learning Machine (ELM) and Random Vector Functional Link Network (RVFL)), and the CNN approach. The results showed that the deep feature learning system obtained better classification rates than MLP, ELM, and RVFL. Several scenarios designed by coupling some deep feature maps with the RVFL and MLP models showed very high simulation accuracy. The overall accuracy rate of CNN–MLP and CNN–RVFL varies between 98% and 99%.