Current development of Pakistan’s economy, transportation and industry with the improvement of urbanization, environmental pollution problems have gradually become prominent, but this is contrary to people’s vision of pursuing a high-quality life. Now the problem of haze, photochemical problems in the air, and global warming is already a key issue of global concern. This is focused on the ambient air quality of Lahore city of Pakistan. The study reveals that the particulate matter in the Lahore season (PM _2.5 , PM ₁₀ ) exceeds Pakistan’s National Environmental Quality Standards (NEQS). Correlation study suggests the positive correlation between the particulate matter and other mass concentration particles like Ozone (O ₃ ), Nitrogen Oxide (NO), Sulphur Dioxide (SO ₂ ). Higher values of CO/NO suggest that mobile sources are one of the major factors of this increase in NO. Further estimation of backward trajectory is done by the Hybrid-Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model which provides the path of those particles in the last year period and the source of origin is from Afghanistan. This study provides in depth analysis of all factors of air pollutants by correlation between those factors. Prediction of future concentration of PM _2.5 is predicted using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model which gives the increasing value of PM _2.5 in next year and provides the lowest and highest predicts (more than $100~\mu \text{g}/\text{m}^{3}$ ).

Background: Chronic Kidney Disease (CKD), i.e., gradual decrease in the renal function spanning over a duration of several months to years without any major symptoms, is a life-threatening disease. It progresses in six stages according to the severity level. It is categorized into various stages based on the Glomerular Filtration Rate (GFR), which in turn utilizes several attributes, like age, sex, race and Serum Creatinine. Among multiple available models for estimating GFR value, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), which is a linear model, has been found to be quite efficient because it allows detecting all CKD stages. Methods: Early detection and cure of CKD is extremely desirable as it can lead to the prevention of unwanted consequences. Machine learning methods are being extensively advocated for early detection of symptoms and diagnosis of several diseases recently. With the same motivation, the aim of this study is to predict the various stages of CKD using machine learning classification algorithms on the dataset obtained from the medical records of affected people. Specifically, we have used the Random Forest and J48 algorithms to obtain a sustainable and practicable model to detect various stages of CKD with comprehensive medical accuracy. Results: Comparative analysis of the results revealed that J48 predicted CKD in all stages better than random forest with an accuracy of 85.5%. The study also showed that J48 shows improved performance over Random Forest. Conclusions: The study concluded that it may be used to build an automated system for the detection of severity of CKD.

Currently, in most countries, the electricity sector is liberalized, and electricity is traded in deregulated electricity markets. In these markets, electricity demand is determined the day before the physical delivery through (semi-)hourly concurrent auctions. Hence, accurate forecasts are essential for efficient and effective management of power systems. The electricity demand and prices, however, exhibit specific features, including non-constant mean and variance, calendar effects, multiple periodicities, high volatility, jumps, and so on, which complicate the forecasting problem. In this work, we compare different modeling techniques able to capture the specific dynamics of the demand time series. To this end, the electricity demand time series is divided into two major components: deterministic and stochastic. Both components are estimated using different regression and time series methods with parametric and nonparametric estimation techniques. Specifically, we use linear regression-based models (local polynomial regression models based on different types of kernel functions; tri-cubic, Gaussian, and Epanechnikov), spline function-based models (smoothing splines, regression splines), and traditional time series models (autoregressive moving average, nonparametric autoregressive, and vector autoregressive). Within the deterministic part, special attention is paid to the estimation of the yearly cycle as it was previously ignored by many authors. This work considers electricity demand data from the Nordic electricity market for the period covering 1 January 2013–31 December 2016. To assess the one-day-ahead out-of-sample forecasting accuracy, Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) are calculated. The results suggest that the proposed component-wise estimation method is extremely effective at forecasting electricity demand. Further, vector autoregressive modeling combined with spline function-based regression gives superior performance compared with the rest.

In recent years, efficient modeling and forecasting of electricity prices became highly important for all the market participants for developing bidding strategies and making investment decisions. However, as electricity prices exhibit specific features, such as periods of high volatility, seasonal patterns, calendar effects, nonlinearity, etc., their accurate forecasting is challenging. This study proposes a functional forecasting method for the accurate forecasting of electricity prices. A functional autoregressive model of order $\mathbb{P}$ is suggested for short-term price forecasting in the electricity markets. The applicability of the model is improved with the help of functional final prediction error (FFPE), through which the model dimensionality and lag structure were selected automatically. An application of the suggested algorithm was evaluated on the Italian electricity market (IPEX). The out-of-sample forecasted results indicate that the proposed method performs relatively better than the nonfunctional forecasting techniques such as autoregressive (AR) and naïve models.