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Substance addiction such as tobacco, alcohol, opioids, drug addiction and so on are increasing day by day. Awareness about the harmful effects of substance addiction on health and strong will power of people to get rid of the addiction have positive impact on controlling substance addiction. The aim of this research is to examine the impact of awareness and strong determination, the two cognitive factors that have greater impact in preventing substance addiction. Addiction adversely affects self‐efficacy of younger generation, thought processing, metabolisms in human body, psychosocial effects and cardiovascular diseases. In this paper, we have developed a mathematical model regarding substance addiction. It consist of six compartments namely, susceptible class, light substance addict class, risk substance addict class, potential substance addict class, rehabilitation class and quitter class. The substance addiction generation number (RA) has been determined, and the model is locally asymptotically stable at substance addictions free equilibrium point (SAFEP) D∘ when RA<1. It is found that, when RA=1, a backward bifurcation can occur and when RA>1, the substance addictions persistent equilibrium point (SAPEP) D⋆ becomes stable. Further analysis gives the global asymptotic stability of SAFEP. An application of this model, smoking model, is explored. Smoking model gives a different comparative study which proves the relevance and the importance of our model. We get a clear idea about smokers' behaviour at different stages of high determination and knowledge and also establish the relationship between RA and these parameters. Finally, we give some recommendations for preventing addiction which can be implemented in society. [ABSTRACT FROM AUTHOR]

Dynamics of discrete‐time neural networks have not been well documented yet in fractional‐order cases, which is the first time documented in this manuscript. This manuscript is mainly considered on the stability criterion of discrete‐time fractional‐order complex‐valued neural networks with time delays. When the fractional‐order β holds 1 < β < 2, sufficient criteria based on a discrete version of generalized Gronwall inequality and rising function property are established for ensuring the finite stability of addressing fractional‐order discrete‐time‐delayed complex‐valued neural networks (FODCVNNs). In the meanwhile, when the fractional‐order β holds 0 < β < 1, a global Mittag–Leffler stability criterion of a class of FODCVNNs is demonstrated with two classes of neuron activation function by means of two different new inequalities, fractional‐order discrete‐time Lyapunov method, discrete version Laplace transforms as well as a discrete version of Mittag–Leffler function. Finally, computer simulations of two numerical examples are illustrated to the correctness and effectiveness of the presented stability results. [ABSTRACT FROM AUTHOR]