This paper emphasises the analysing sentiment of Indian citizens based on Twitter data using machine learning (ML) based approaches. The sentiment of about 1,51,798 tweets extracted from Twitter social networking and analysed based on tweets divided into six different segments, i.e., before lockdown, first lockdown, lockdown 2.0, lockdown 3.0, lockdown 4.0 and after lockdown (Unlock 1.0). Empirical results show that ML-based approach is efficient for sentiment analysis (SA) and producing better results, out of 10 ML-based models developed using N-Gram (N = 1,2,3,1-2,1-3) features for SA, linear regression model with term frequency - inverse term frequency (Tf-Idf) and 1-3 Gram features is outperforming with 81.35% of accuracy. Comparative study of the sentiment of the above six periods indicates that negative sentiment of Indians due to COVID-19 is increasing (About 4%) during first lockdown by 4.0% and then decreasing during lockdown 2.0 (34.10%) and 3.0 (34.12%) by 2% and suddenly increased again by 4% (36%) during 4.0 and finally reached to its highest value of 38.57% during unlock 1.0.

In this paper, a nonstandard finite difference technique is considered with semi-uniform nodes for the numerical solution of the two-point boundary value problem subject to Dirichlet boundary conditions in the semi-open domain. To overcome the discretisation issue at the right open end of the domain, i.e., at infinity-semi-uniform nodes were used. The boundary condition at infinity is taken into account precisely by the so-generated semi uniform nodes. The numerical experiment with a proposed method carried out by considering model problems, including singular problems. The method proposed in the paper produces a decent approximate solution of the considered problems and totally conquers the issue of boundary conditions at infinity and singularity.

This study combines an artificial bee colony algorithm with a fuzzy wavelet neural network to establish a new traffic accident prediction model. It introduces the self-adaptive mutation operation of differential evolution algorithms and the selection operator, crossover operator, and mutation operator of a genetic algorithm into the traditional artificial bee colony algorithm. These actions aim to improve the shortcomings of the slow convergence rate and the weak local search ability of the artificial bee colony algorithm in the later period. This study also uses the improved artificial bee colony algorithm to optimise the weights and thresholds of the fuzzy wavelet neural network and to make up for the randomness of the weights and threshold selection of the wavelet neural network. It combines the good nonlinear fitting ability of the self-adaptive fuzzy wavelet neural network with the strong robustness of the artificial bee colony algorithm to build a model for predicting traffic death tolls. Computer simulation shows that the prediction accuracy of the proposed method is higher.

In this paper, a nonstandard finite difference technique is considered with semi-uniform nodes for the numerical solution of the two-point boundary value problem subject to Dirichlet boundary conditions in the semi-open domain. To overcome the discretisation issue at the right open end of the domain, i.e., at infinity-semi-uniform nodes were used. The boundary condition at infinity is taken into account precisely by the so-generated semi uniform nodes. The numerical experiment with a proposed method carried out by considering model problems, including singular problems. The method proposed in the paper produces a decent approximate solution of the considered problems and totally conquers the issue of boundary conditions at infinity and singularity.

The purpose of this paper is to propose a new immersed boundary method for heat transfer calculations in gas-solid flows. In the proposed method, solid particles are fixed in the computational domain due to longer response times of particles (to mimic the gas-solid systems) and treated as sources of velocity and temperature. For calculations of fluid velocity and temperature, Navier-Stokes and energy equations are solved for fixed Cartesian grid. For the validation of the proposed method, number of benchmarking studies are done by comparing the simulation results with the studied problems in literature. Simulations showed good agreement with the literature results which verifies the accuracy and reliability of the immersed boundary method proposed in this paper.

The emergency rescue work in a confined space is not only difficult, but also has a high risk of danger. According to the pothole and small space operating environment, a pothole and small space emergency rescue program and design of the mechanical structure of the relevant equipment, the equipment through the lifting way into the pothole or small space, and approach the unconscious trapped persons, telescopic carriage to hold up part of the human body, and then use the arm tied to the wrist or ankle, after the walking mechanism will rescue objects transported to the hole, relying on lifting equipment to equipment Hang out with the rescue object.

In this paper, we establish the three-point newly half-sweep linear rational finite difference-quadrature discretisation scheme, which is the combination of the three-point half-sweep linear rational finite difference (3HSLRFD) scheme alone with the first-order quadrature scheme especially half-sweep composite-trapezoidal (HSCT) in discretising the first-order linear Fredholm integro-differential equation (FIDE). Based on this established discretisation scheme, the corresponding 3HSLRFD-HSCT approximation equation can be derived and then generate the large-scale and dense linear system. Furthermore, the numerical solution of the first-order linear FIDE can be obtained by implementing the half-sweep refinement of successive over-relaxation (HSRSOR) iterative method to solve the linear system. For the sake of comparison, the formulation of the full-sweep Gauss-Seidel (FSGS) and full-sweep refinement of successive over-relaxation (FSRSOR) methods are also presented as the control method. Finally, several numerical examples of the proposed problem are shown to demonstrate that the HSRSOR iterative approach gives the highest degree of supremacy in terms of number of iterations and execution time as compared to the other two existing methods.

In the process of evolution, the multi-objective firefly algorithm (MOFA) has low optimisation accuracy and is prone to premature convergence, resulting in poor distribution and convergence of the population. To solve this problem, a multi-objective firefly algorithm (MOFA-LC) combining logistic mapping and cross-mutation was proposed. To improve the distribution of the population, the initial population with good ergodicity and uniformity was generated by logistic mapping. To improve population convergence, Levy flights and non-dominated sorting are used to improve the position updating formula. After the individual position updating, the cross-mutation method in the genetic algorithm can be used to improve the optimisation accuracy of the algorithm and make it jump out of the local optimal, overcome the intelligent convergence of the algorithm, and maintain the convergence of the population. In the experimental part, two typical test functions are selected to plot the IGD convergence curves of MOFA-LC and 11 recent multi-objective optimisation algorithms. The results show that MOFA-LC has obvious advantages over other algorithms.

Ready-to-eat food (RTE) are foods that are intended by the producers for direct human consumption without the need for further preparation. In the present study, a deterministic model of Listeriosis disease transmission dynamics with control measures is analysed. Equilibrium points of the model in the absence of control measures were determined, and their local stabilities established. We formulate an optimal control problem and analytically give sufficient conditions for the optimality. The transversality conditions for the model with controls are also given. Numerical simulations of the optimal control strategies were performed to illustrate the results. The numerical findings suggest that the constant implementation of joint optimal control measures throughout the modelling time will be more efficacious in controlling or reducing the Listeriosis disease. The results of this study can be used as baseline measures in controlling Listeriosis from RTE food products.