The environment and the economy are negatively impacted by conventional energy sources, such as coal, gasoline, and other fossil fuels. Pakistan’s reliance on these resources has resulted in a catastrophic energy crisis. This has driven the government to make critical decisions such as early retail closures, power outages for the industrial sector, and an increase to two days a week vacations. Wind energy, accessible and affordable, will become a viable option for meeting Pakistan’s present and future energy demands. Approximately 3% of Pakistan’s land can produce nearly 132 GW of power with an installed capacity of 5 MW per km2. In this study, four zones (Karachi, Thatta, Badin, and Jamshoro) in Sindh province are assessed for the feasibility of wind energy generation. The installed capacity, generator types, and detailed specifications are provided for each zone. Moreover, the wind mapping of Pakistan is presented considering the four potential zones. The zones are analyzed using annual wind speed and power output considering wind data measured at 50 m height over one year. The higher mean speed is recorded at Jamshoro compared to other zones. The analysis indicates that all four sites are suitable for large-scale wind power generation due to their energy potential.

Nowadays, electric load forecasting through a data analytic approach has become one of the most active and emerging research areas. It provides future consumption patterns of electric load. Since there are large fluctuations in both electricity production and use, it is a difficult task to achieve a balance between electric load and demand. By analyzing past electric consumption records to estimate the upcoming electricity load, the issue of fluctuating behavior can be resolved. In this study, a framework for feature selection, extraction, and regression is put forward to carry out the electric load prediction. The feature selection phase uses a combination of extreme gradient boosting (XGB) and random forest (RF) to determine the significance of each feature. Redundant features in the feature extraction approach are removed by applying recursive feature elimination (RFE). We propose an enhanced support vector machine (ESVM) and an enhanced convolutional neural network (ECNN) for the regression component. Hyperparameters of both the proposed approaches are set using the random search (RS) technique. To illustrate the effectiveness of our proposed strategies, a comparison is also performed between the state-of-the-art approaches and our proposed techniques. In addition, we perform statistical analyses to prove the significance of our proposed approaches. Simulation findings illustrate that our proposed approaches ECNN and ESVM achieve higher accuracies of 98.83% and 98.7%, respectively.

The exponential growth of the edge-based Internet-of-Things (IoT) services and its ecosystems has recently led to a new type of communication network, the Low Power Wide Area Network (LPWAN). This standard enables low-power, long-range, and low-data-rate communications. Long Range Wide Area Network (LoRaWAN) is a recent standard of LPWAN that incorporates LoRa wireless into a networked infrastructure. Consequently, the consumption of smart End Devices (EDs) is a major challenge due to the highly dense network environment characterised by limited battery life, spectrum coverage, and data collisions. Intelligent and efficient service provisioning is an urgent need of a network to streamline the networks and solve these problems. This paper proposes a Dynamic Reinforcement Learning Resource Allocation (DRLRA) approach to allocate efficient resources such as channel, Spreading Factor (SF), and Transmit Power (Tp) to EDs that ultimately improve the performance in terms of consumption and reliability. The proposed model is extensively simulated and evaluated with the currently implemented algorithms such as Adaptive Data Rate (ADR) and Adaptive Priority-aware Resource Allocation (APRA) using standard and advanced evaluation metrics. The proposed work is properly cross validated to show completely unbiased results.

Persistent hypokalemia is frequently seen in Distal Renal Tubular acidosis, which is rarely described in children. We report a case of hypokalemic hyperchloremic metabolic acidosis due to distal RTA who was also found to have renal medullary nephrocalcinosis changes. This case report highlights the importance of considering hypokalemia and renal tubular acidosis in the differential diagnosis, which can prevent costly investigations and enable rapid clinical recovery in the affected child.

This paper presents a design for a control system that will ensure the stability and proper operation of a mobile four-wheeled robot. As a result of the nonlinear dynamics, structural and parametric uncertainty of this robot, various control approaches are used in order to achieve stability, proper performance, and minimize modeling errors and uncertainties. There are two types of control approaches applied to ensure this robot is stable, that its performance is appropriate, and that modeling errors and uncertainties are minimized. In the presence of external disturbances and parametric uncertainty, this algorithm uses the signals provided by the sensor from the trajectory to follow the predetermined trajectory. It was assumed in previous articles that the upper bound of uncertainty was known. In this paper, we assume the upper bound of uncertainty and disturbance in robotic system is unknown, since, in many cases, we cannot know the extent of these uncertainties. In this paper, we generalized the sliding mode control law and proved its effectiveness, so that by including an adaptive part to the control law, we make it into a robust-adaptive sliding mode control, and we could estimate the upper bound uncertainties online based on these adaptive laws. This typology can be expressed as a distinct theorem with stable results. Simulations with MATLAB software demonstrate that the controller ensures optimal performance under external disturbances and parametric uncertainty with fewer fluctuations.