The operation of energy transmission lines with high efficiency without failure has great importance in today's electricity-dependent world. Problems that may occur in electricity transmission lines are failure cause of many operations not only industrial but also daily life. One of the most important causes of the problems encountered in power lines is the change in the amount of sagging. The change of sagging amount causes line breaks and losing energy efficiency. This problem, which is frequently encountered due to seasonal and climatic changes, is one of the major problems of continuity in the power line. The calculation of sag contains uncertain and variable parameters that can change seasonally, climatically and/or structurally such as weight per unit length of the conductor, the horizontal component of tension, total tension, etc. In this case, it is difficult to calculate a precise and reliable sag amount. The sagging of power lines is generally calculated theoretically or measured on-site by the personnel in charge. In this study, a new approach is presented to measure the sag amount by using sensor data of a power line inspection robot, precisely and reliably. The inspection robot moving on the power line can be remotely controlled and send sensor data. The sagging is measured with an error of less than 2 percent in the laboratory test field by using this technique.

Background COVID‐19 infection is severe in the presence of older age, male gender and risk factors. The aim of this study was to examine the relationship between the level of anxiety created by immensely spreading COVID‐19‐related information and age, gender and the presence of risk factors. Material and Methods The data used in this study were obtained by collecting a 25‐question questionnaire created through Google forms with various communication tools. Results The data of 929 people who answered the questionnaire were used. The level of anxiety increased with age significantly, upon hearing that a person from their age group was harmed by the virus (P < .001). The feelings of being depressed and hopeless significantly increased as the age increased (P < .001). There was no significant difference between the genders in terms of feeling depressed and feeling of lack of joy in life (P = .066, P = .308, respectively). Participants with chronic diseases stated that they felt more depressed and hopeless and a lack of joy in life more frequently (P < .001). Conclusion Our results indicated that individuals with older age and having risk factors were more vulnerable to the stress caused by the pandemic. It is necessary for healthcare providers to identify high‐risk groups by considering these situations, in order to make early psychological interventions.

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It is very important to accurately detect wind direction and speed for wind energy that is one of the essential sustainable energy sources. Studies on the wind speed forecasting are generally carried out for long-term predictions. One of the main reasons for the long-term forecasts is the correct planning of the area where the wind turbine will be built due to the high investment costs and long-term returns. Besides that, short-term forecasting is another important point for the efficient use of wind turbines. In addition to estimating only average values, making instant and dynamic short-term forecasts are necessary to control wind turbines. In this study, short-term forecasting of the changes in wind speed between 1-20 minutes using deep learning was performed. Wind speed data was obtained instantaneously from the feedback of the emulated wind turbine's generator. These dynamically changing data was used as an input of the deep learning algorithm. Each new data from the generator was used as both test and training input in the proposed approach. In this way, the model accuracy and enhancement were provided simultaneously. The proposed approach was turned into a modular independent integrated system to work in various wind turbine applications. It was observed that the system can predict wind speed dynamically with around 3% error in the applications in the test setup applications.

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