Data-driven condition monitoring reduces downtime of wind turbines and increases reliability. Wind turbine operation and maintenance (O\&M) cost is a significant factor that calls for automated fault detection systems in wind turbines. In this manuscript, the anomaly detection problem for wind turbine gearbox is formulated based on adaptive threshold and twin support vector machine (TWSVM). In this work, SCADA data from wind farms located in the UK is considered with samples from thirteen months before failure. Gearbox oil and bearing temperatures are used as two univariate time-series for analyzing adaptive threshold. The effectiveness of the proposed method is compared with standard classifiers like support vector machines (SVM), k-nearest neighbors (KNN), multi-layer perceptron neural network (MLPNN), and decision tree (DT). Anomaly detection of wind turbine gearbox using TWSVM and adaptive threshold results in an accurate performance, thus increasing the reliability. The missed failure and false positive rate that indicate the proposed methodology's ability is also investigated to discriminate between false alarms, and comparison with previous studies shows superior performance.

People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.

The intelligent condition monitoring of wind turbines reduces their downtime and increases reliability. In this manuscript, a feature selection-based methodology that essentially works on regression models is used for identifying faulty scenarios. Supervisory control and data acquisition (SCADA) data with 1009 samples from one year and one month before failure are considered. Gearbox oil and bearing temperatures are treated as target variables with all the other variables used for the prediction model. Neighborhood component analysis (NCA) as a feature selection technique is employed to select the best features and prediction performance for several machine learning regression models is assessed. The results reveal that twin support vector regression (99.91%) and decision trees (98.74%) yield the highest accuracy for gearbox oil and bearing temperatures respectively. It is observed that NCA increases the accuracy and thus reliability of the condition monitoring system. Furthermore, the residuals from the class of support vector regression (SVR) models are tested from a statistical point of view. Diebold–Mariano and Durbin–Watson tests are carried out to establish the robustness of the tested models.

A microbial fuel cell (MFC) is a potentially viable renewable energy option which promises effective and commercial harvesting of electrical power by bacterial movement and at the same time also treats wastewater. Microbial fuel cells are complicated devices and therefore research in this field needs interdisciplinary knowledge and involves diverse areas such as biological, chemical, electrical, etc. In recent decades, rapid strides have taken place in fuel cell research and this technology has become more efficient. For effective usage, such devices need advanced control techniques for maintaining a balance between substrate supply, mass, charge, and external load. Most of the research work in this area focuses on experimental work and have been described from the design perspective. Recently, the development in mathematical modeling of such cells has taken place which has provided a few mathematical models. Mathematical modeling provides a better understanding of the operations and the dynamics of MFCs, which will help to develop control and optimization strategies. Control-oriented bio-electrochemical models with mass and charge balance of MFCs facilitate the development of advanced nonlinear controllers. This work reviews the different mathematical models of such cells available in the literature and then presents suitable parametrization to develop control-oriented bio-electrochemical models of three different types of cells with their uncertain parameters.

Autonomous unmanned aerial vehicles work seamlessly within the GPS signal range, but their performance deteriorates in GPS-denied regions. This paper presents a unique collaborative computer vision-based approach for target tracking as per the image’s specific location of interest. The proposed method tracks any object without considering its properties like shape, color, size, or pattern. It is required to keep the target visible and line of sight during the tracking. The method gives freedom of selection to a user to track any target from the image and form a formation around it. We calculate the parameters like distance and angle from the image center to the object for the individual drones. Among all the drones, the one with a significant GPS signal strength or nearer to the target is chosen as the master drone to calculate the relative angle and distance between an object and other drones considering approximate Geo-location. Compared to actual measurements, the results of tests done on a quadrotor UAV frame achieve $99\%$ location accuracy in a robust environment inside the exact GPS longitude and latitude block as GPS-only navigation methods. The individual drones communicate to the ground station through a telemetry link. The master drone calculates the parameters using data collected at ground stations. Various formation flying methods help escort other drones to meet the desired objective with a single high-resolution first-person view (FPV) camera. The proposed method is tested for Airborne Object Target Tracking (AOT) aerial vehicle model and achieves higher tracking accuracy.

Performance of the UAVs for a particular application can be enhanced by hybrid design, where take‐off, hover, and landing happen like rotary‐wing UAVs, and flies like fixed‐wing UAVs. A backstepping controller and an adaptive backstepping controller are designed for trajectory tracking and payload delivery in a medical emergency or medical substance delivery like vaccine delivery in the presence of wind gust. Simulation results show that the backstepping controller effectively tracks the trajectory during the entire flight envelope, including take‐off, hovering, the transition phase, level flight mode, and landing. A comparison between Backstepping, Integral Terminal Sliding Mode (ITSMC) and Adaptive Backstepping controllers for payload delivery show that the adaptive backstepping controller effectively tracks the altitude and attitude. ITSMC is capable of tracking the desired trajectory for a change in the mass but has sluggish response. The backstepping controller generates a steady‐state error in altitude during the mass change in biplane quadrotor.