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In this work we have studied the spreading of militantism in 1D and 2D square lattice by operating the q-XY opinion and its utility optimization as control mechanism for the update process. The average utility and opinion value for a pair of opinion nodes are calculated in the framework of standard statistical mechanics using the q-opinion utility U = − J 2 ( O → 2 − 2 ) − F O → + q ( J 2 ( O → 2 − 2 ) ∗ F O → ) . The agreement with an incoming zealot opinion is modelled as one by one dialogue starting from a random opinion configuration. Each time step one node updates its opinion value according to a set of rules and by specific probabilities based on local utility improvement. New opinion values are accepted with a Metropolis-like probability calculated by employing q-utilities of the nodes under updating process. For the chain opinion structure in the interaction type preserving regimes where 0$?> 1 − q J 2 ( O → 2 − 2 ) > 0 given 0 ≤ O ≤ 1 it resulted that that both 1D and 2D structures accept the militant offer only partially. For bounded chain the agreement level is at average values for short chain and low values for bounded long opinion chain. Bounded chain structures are found resistant toward militantism accommodation. For square opinion lattices we obtained also that the militant attitude would never cover all the community. Homogenous-nodes group resulted somewhat deterring in accepting the militant opinion with an agreement level of around 0.6. For heterogonous communities the edge agreement level closed up to around 0.7-0.8. The model reproduces the fact that there would be always a fraction of opposition whatsoever regime is applied. It produces various outcomes imitating the complexity of the social behaviour.

Log-periodic (LP) functions of the general form y = y 0 + A(t − t c ) m (1 + Bcos(ω ∗ log(t − t c ) + ϕ 1)) have been demonstrated effective in the analysis of the processes characterized by the discrete scale of invariance (DSI) structure and also self-organization behaviour. If other self-organization processes opposing or supporting the principal activity would be present, a multilevel DSI structure is expected to develop and the resulting dynamics would depart from the log-periodic shape. When discussing the processes characterising the daily new positive records for the COVID-19 cases in the country (Albania) we have identified the elements that are responsible to generate modified LP behaviour. The new records that initially represented simply the findings of the state laboratories, are modified each successive days by the pressure for more tests form anxious individuals and other effects which produce a herding behaviour which emergence a LP dynamics. Meanwhile, the reactive behaviour aiming to oppose undesired occurrence would generate additional LP sub-processes that can be trapped by a modified LP function of the form y = y 0 + A(t − t c ) m + B(t − t c ) m cos (ω ∗ log(t − t c ) + ϕ 1) + C cos( (ω − ω 1 ) log(t − t c ) + ϕ 2)+D(t − t c ) m cos ((ω + ω 1) log(t − t c ) + ϕ 3). During the period of the self-organization behaviour the process is highly nonlinear and therefore the classical models (SIR) based on the mean field assumption and the corresponding ODE equations are not effective to represent the system dynamics. In our case the LP fit to the Covid-19 new cases data series, has challenged the ODE models for the time interval of initial appearance of the positive cases in the country (as of 2 March 2020) up to 3 month later. Also, the LP function has predicated the multiphase waving behaviour and we have forecasted two peak, each of them weeks before recurrence, respectively at 28 April and 10-12 June. Those peaks have been confirmed later within 2 days uncertainty. It resulted that the first regime have been succeeded by another new self-organization regime due to drastic condition changes as result of the socio-economic opening which started in the end of the May 2020. As result, the new regime is juxtaposed over the old one and the LP dynamics remained characteristic and another critical time has appeared. The new critical time has been reproduced with good certainty (1 August) and also the magnitude of the new cases. We observed that our empirical LP function is effective in the describing long term dynamics whereas local techniques involving neural networks approaches have reproduced very well the new cases after the first regime. We concluded that after identification of the LP regimes, we can adopt successfully short time forecasting for new occurrence when working away from the critical time identified by the first method.