On the Bayesian generalized extreme value mixture autoregressive model with adjusted SNR in non-standard actuarial data.

This research introduces the Generalized Extreme Value Mixture Autoregressive (GEVMAR) model as an innovative approach for examining non-standard actuarial datasets within general insurance. Information concerning claim reserves often reveals notable volatility and multimodal distributions, attributes that standard models, including previous method such as the Gaussian Mixture Autoregressive (GMAR) model and other autoregressive methodologies, find problematic to manage effectively. The GEVMAR model integrates the Generalized Extreme Value (GEV) distribution alongside Bayesian estimation techniques, augmented by a modified Signal-to-Noise Ratio (SNR) metric to improve predictive accuracy.

Performance of T-based PCA mix control chart with KDE control limit for monitoring variable and attribute characteristics.

In this work, the mixed multivariate T control chart's detailed performance evaluation based on PCA mix is explored. The control limit of the proposed control chart is calculated using the kernel density approach. Through simulation studies, the proposed chart's performance is assessed in terms of its capacity to identify outliers and process shifts.

Bioremediation of micropollutants using living and non-living algae - Current perspectives and challenges.

The emergence and continual accumulation of industrial micropollutants such as dyes, heavy metals, organic matters, and pharmaceutical active compounds (PhACs) in the ecosystem pose an alarming hazard to human health and the general wellbeing of global flora and fauna. To offer eco-friendly solutions, living and non-living algae have lately been identified and broadly practiced as promising agents in the bioremediation of micropollutants. The approach is promoted by recent findings seeing better removal performance, higher efficiency, surface area, and binding affinity of algae in various remediation events compared to bacteria and fungi.

Advances in pretreatment technology for handling the palm oil mill effluent: Challenges and prospects.

The palm oil mill effluent (POME) from palm milling oil activities is discharged into various water bodies which poses several environmental problems including turbidity, increases COD and BOD, adds oil and grease, increases total nitrogen, and other pollutants. Therefore, it requires effective treatment to remove the pollutants before disposal. The objective was to critically discuss the performance of POME pretreatments along with their limitations.