Wind power generation highly depends on the determination of wind power potential, which drives the design and feasibility of the wind energy production investment. This gives an important role to wind power estimation, which creates the need for an accurate wind data analysis and wind energy potential assessments for a given location. Such assessments require the implementation of an accurate and suitable wind distribution model. Therefore, in the quest for a well-fitted model, eight methods for estimating the Weibull parameters are investigated in this paper. The methods were then investigated by employing statistical tools, and their performances have been discussed in terms of various error indicators such as root mean squared error (RMSE), regression error (R2), chi-square (X2), and mean absolute error (MAE). Meteorological data for diverse terrain from 14 provinces with 30 sites scattered across Iran were employed to examine the performance of the investigated methods. The results demonstrated that the empirical method has superiority over the investigated technique in terms of errors.

In recent times, machine learning algorithms have gained significant traction in addressing aerodynamic challenges. These algorithms prove invaluable for predicting the aerodynamic performance, specifically the Lift-to-Drag ratio of airfoil datasets, when the dataset is sufficiently large and diverse. In this paper, we delve into an exploration of five machine learning algorithms: Random Forest, Gradient Boosting Regression, Decision Tree Regressor, AdaBoost Algorithm, and Linear Regression. These algorithms are scrutinized within the context of various train/test ratios to predict a crucial aerodynamic performance metric—the lift-to-drag ratio—for different angle of attack values. Our evaluation encompasses an array of metrics including R², Mean Square Error, Training time, and Evaluation time. Upon analysis, the Random Forest Method, with a train/test ratio of 0.2, emerges as the frontrunner, showcasing superior predictive performance when compared to its counterparts. Conversely, the Linear Regression algorithm distinguishes itself by excelling in training and evaluation times among the algorithms under scrutiny.

Path planning of an unmanned aerial vehicle (UAV) or its variant supported with weapon systems, also called unmanned combat aerial vehicle (UCAV), is one of the most crucial steps for the autonomous flight and requires compelling decisions being made by considering the existence of the enemy defence, obstacles, and battery or fuel capacity of the vehicle optimally before starting the flight. Immune plasma algorithm (IP algorithm or IPA), inspired by the convalescent plasma treatment which is a medical method being popularized again with the rise of new coronavirus or COVID-19, has been introduced recently as a new optimization technique and the promising performance of the mentioned algorithm validated through a set of numerical and engineering problems. In this study, the IP algorithm was specialized as a three-dimensional UCAV path planner for the first time. Its performance was investigated particularly by using three different battlefield environments and assigning various constants to the population size. Moreover, the effect of two IPA-specific control parameters that are responsible for determining the number of donors and number of receivers on the qualities of the calculated paths were analyzed in detail. The results obtained by the IPA were compared with the results of other three-dimensional path planners guiding optimization algorithms such as simulated annealing (SA), gray wolf optimizer (GWO) and symbiotic organism search (SOS). Experimental studies showed that the main idea lying behind the usage of qualified solutions as donors and transferring information directly from them to the receivers representing the poor solutions of the problem gives a positive contribution to the exploitation-dominant operations of IPA and fully complies with the challenging requirements of the path planning problem. Especially in the battlefields for which an optimal or near optimal path contains complex maneuvers, the advantages from the subtly balanced operational steps of the IPA become more apparent by setting the number of donors and receivers appropriately and IPA is found to be more stable and successful compared to the other tested algorithms.

A retrospective review was carried out to determine the incidence of various causes and outcome of management in patients with acute scrotum. Fifty children had a diagnosis of acute scrotum between 1st January 2007 and 15th May 2012. Age, mode of presentation, associated anomalies, and results of treatment were studied. Diagnosis of acute scrotum was confirmed by physical examination, Doppler ultrasound and biochemical investigations. Clinical presentation consisted of sudden swelling and pain in the inguinoscrotal region. The average age was 7.5 years (2 months-14 years). Causes of acute scrotum were orchitis/epididymo-orchitis (O /EO) in 22, strangulated inguinal hernia (SIH) in 16, testicular torsion (TT) in 11, and torsion of testicular appendage (TTA) in 1. Associated urological anomalies were found in 5 patients with O /EO. Medical treatment was applied to patients with O /EO, and surgical treatment was performed in patients with SIH, TT and TTA. In this series, O /EO was found to rank first as the cause of acute scrotum. Immediate surgical treatment in acute scrotum patients, except those with O /EO, is necessary. Associated urological anomalies should be investigated in patients with O /EO.