(1) Background: The literature discusses the inverse Maxwell distribution theoretically without application. Control charting is promising, but needs development for inverse Maxwell processes. (2) Methods: Thus, we develop the $V_{IM}$ control chart for monitoring the inverse Maxwell scale parameter and studied its statistical properties. The chart’s performance is evaluated using power curves and run length properties. (3) Results: Further, we use simulated data to compare the shift detection capability of our chart with Weibull, gamma, and lognormal charts. (4) Conclusion: The analysis demonstrates our chart’s efficiency for monitoring skewed processes. Finally, we apply our chart for monitoring real world lifetimes of car brake pads.

Natural and anthropogenic aerosols over the Kingdom of Saudi Arabia (KSA) play a major role in affecting the regional radiation budget. The long-term variability of these aerosols’ physical and optical parameters, including aerosol optical depth (AOD) and Ångström exponent (α), were measured at a location near central KSA using the Solar Village (SV) AERONET (Aerosol Robotic Network) station during the period December 1999–January 2013. The AERONET measurements show an overall increase in AOD on an annual basis. This upward trend is mainly attributed to a prolonged increase in the monthly/seasonal mean AOD during March–June and during August–September. In contrast, lower AOD values were observed during November–December. This can be attributed to a low frequency of dust outbreaks and higher precipitation rates. An overall, weak declining trend in α was observed, except during the summer. The spring and summer seasons experienced a pronounced increase in the number of coarse particles (~2 µm) during April 2006–January 2013 as compared to December 1999–March 2006, suggesting an increase in natural aerosol loadings. Using the HYSPLIT model, it was found that the March 2009 dust storm contributed to the mixing of long-transported dust with anthropogenic local emissions near the SV. The results suggest that extensive industrial activity contributed to the increase of anthropogenic emissions over KSA during the period April 2006–January 2013.

The special causes of variations, which is also known as a shift, can occur in a single or more than one related process characteristics. Statistical process control tools such as control charts are useful to monitor shifts in the process parameters (location and/or dispersion). In real-life situation, the shift is emerging in different sizes, and it is hard to identify it with classical control charts. Moreover, more than one process of characteristics required special attention because they must monitor jointly due to the association among them. This study offers two adaptive control charts to monitor the different sizes of a shift in the process mean vector. The novelty behind this study is to use dimensionally reduction techniques such as principal component analysis (PCA) and an adaptive method such as Huber and Bi-square functions. In brief, the multivariate cumulative sum control chart based on PCA is designed, and its plotting statistic is utilized as an input in the classical exponentially weighted moving average (EWMA) control chart. The run length (RL) properties of the proposed and other control charts are obtained by designing algorithms in MATLAB through a Monte Carlo simulation. For a single shift, the performance of the control charts is assessed through an average of RL, standard deviation of RL, and standard error of RL. Likewise, overall performance measures such as extra quadratic loss, relative ARL, and the performance comparison index are also used. The comparison reveals the superiority over other control charts. Furthermore, to emphasize the application process and benefits of the proposed control charts, a real-life example of the wind turbine process is included.

Mortality modelling is a practical method for the government and various fields to obtain a picture of mortality up to a specific age for a particular year. However, some information on the phenomenon may remain in the residual vector and be unrevealed from the models. We handle this issue by employing a multivariate control chart to discover substantial cohort changes in mortality behavior that the models still need to address. The Hotelling T² control chart is applied to the externally studentized deviance model, which is already optimized using a machine-learning decision tree. This study shows a mortality model with the lowest MSE, MAPE, and deviance, by accomplishing simulations in various countries. In addition, the model that is more sensitive in detecting signals on the control chart is singled out so that we can perform a decomposition to determine the attributes of death in the specific outlying age group in a particular year. The case study in the decomposition uses data from the country Saudi Arabia. The overall results demonstrate that our method of processing and producing mortality models with machine learning can be a solution for developing countries or countries with limited mortality data to produce accurate predictions through monitoring control charts.

Accurate and real time flood prediction in coastal cities remains challenging due to the nonlinear and dynamic nature of the hydrological processes, coupled with limitations in long-term historical data availability. This study introduced a hybrid deep learning model, CNN-Transformer-SKANs, that combines convolutional neural networks (CNNs), Transformer layers, and Swallow Kolmogorov Arnold Networks (SKANs) to forecast the hourly water level in Venice, Italy. The model is trained on two years of high resolution meteorological and hydrological data, including variables such as wind speed, tide level, humidity, atmospheric pressure and prior water level. Comparative results with baseline models such as LSTM, CNN-LSTM, Transformer and their KANs enhanced variants demonstrate that proposed CNN-Transformer-SKANs achieves superior accuracy (Nash-Sutcliffe Efficiency, NSE ≈ 0.99; Root Mean Square Error, RMSE <0.03 m) and robustness even under reduced training data scenarios and synthetic extreme value simulations, achieving RMSE as low as 0.02-0.03 m and NSE up to 0.99, outperforming traditional LSTM and CNN-LSTM baselines (RMSE 0.04-0.07 m; NSE 0.90-0.97). The proposed architecture exhibits strong generalization performance, making it a reliable tool for an operational flood early warning system in climate sensitive regions.