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Scattered data interpolation is an essential technique used in environmental, geospatial and meteorological analysis. It enables the estimation of unknown values within a dataset at the domain by utilising known data points. The utilisation of this method is crucial to generate smooth and continuous surfaces from discrete data, facilitating the creation of more precise models and visualisations of environmental events. The Malaysian rainfall and terrain data introduced in this paper, are essential datasets for these analyses. This paper provides detailed rainfall data from various meteorological stations across Malaysia, sourced from the Department of Irrigation and Drainage Malaysia. Additionally, terrain data for Kalumpang was obtained, which was based on the Agricultural Station in Selangor. These primary datasets are essential for understanding local climatic and topographic variations. However, challenges such as the size of the dataset, time constraints, weather conditions, and difficulties in obtaining data due to sensitivity and confidentiality issues limit the scope of the data collection. Although there are certain limits, the datasets have been carefully processed and prepared to be easily accessible in Microsoft Excel, MATLAB, and Python. This allows for additional research and applications in machine learning. This paper presents raw and pre-processed datasets, enabling comprehensive analyses and advancing scattered data interpolation techniques.

RESUMEN

The extraversion personality trait has a positive correlation with social interaction. In neuroimaging studies, investigations on extraversion in face-to-face verbal interactions are still scarce. This study presents an electroencephalography (EEG)-based investigation of the extraversion personality trait in relation to eye contact during face-to-face interactions, as this is a vital signal in social interactions. A sample of healthy male participants were selected (consisting of sixteen more extraverted and sixteen less extraverted individuals) and evaluated with the Eysenck's Personality Inventory (EPI) and Big Five Inventory (BFI) tools. EEG alpha oscillations in the occipital region were measured to investigate extraversion personality trait correlates of eye contact during a face-to-face interaction task and an eyes-open condition. The results revealed that the extraversion personality trait has a significant positive correlation with EEG alpha coherence in the occipital region, presumably due to its relationship with eye contact during the interaction task. Furthermore, the decrease in EEG alpha power during the interaction task compared to the eyes-open condition was found to be greater in the less extraverted participants; however, no significant difference was observed between the less and more extraverted participants. Overall, these findings encourage further research towards the understanding of neural mechanism correlates of the extraversion personality trait-particularly in social interaction.

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RESUMEN

In this paper, we applied a fractional multi-step differential transformed method, which is a generalization of the multi-step differential transformed method, to find approximate solutions to one of the most important epidemiology and mathematical ecology, fractional stochastic SIS epidemic model with imperfect vaccination, subject to appropriate initial conditions. The fractional derivatives are described in the Caputo sense. Numerical results coupled with graphical representations indicate that the proposed method is robust and precise which can give new interpretations for various types of dynamical systems.

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