The aim of this study is to forecast the daily average temperature of Mus province in Turkey using time series methods. The performance of three time series forecasting models is compared: LSTM, PROPHET, and ARIMA. The behavior of these models in temperature data is also investigated. It is found that these methods give accurate results according to the MAE, MSE, and RMSE error metrics. However, LSTM produces slightly better results. The temperature data used in this study was obtained from the Mus Meteorology Provincial Directorate. Accurate temperature forecasting is important for many different areas, from energy, agriculture to water resource management. This study is an important research step in temperature analysis and forecasting, and it will contribute to relevant decision-making processes.

This paper presents a comprehensive user study aimed as assessing and differentiating operator expertise within bilateral teleoperation systems. The primary objective is to identify key performance metrics that effectively distinguish novice from expert users. Unlike prior approaches that focus primarily on psychological evaluations, this study emphasizes direct performance analysis across a range of telerobotic tasks. Ten participants (six novices and four experts) were assessed based on task completion time and difficulty, error rates, manipulator motion characteristics, gaze behaviour, and subjective feedback via questionnaires. The results show that experienced operators outperformed novices by completing tasks faster, making fewer errors, and demonstrating smoother manipulator control, as reflected by reduced jerks and higher spatial precision. Also, experts maintained consistent performance even as task complexity increased, whereas novices experienced a sharp decline, particularly at higher difficulty levels. Questionnaire responses further revealed that novices experienced higher mental and physical demands, especially in unfamiliar tasks, while experts demonstrated higher concentration and arousal levels. Additionally, the study introduces gaze transition entropy (GTE) and stationary gaze entropy (SGE) metrics to quantify visual attention strategies, with experts exhibiting more focused, goal-oriented gaze patterns, while novices showed more erratic and inefficient behaviour. These findings highlight both quantitative and qualitative measures as critical for evaluating operator performance and informing future teleoperation training programs.

Hypergraphs and simplicial complexes can be used to model higher-order interactions. Graphs are limited to model and describe pairwise interactions. In this study, the issue of centrality in hypergraphs was studied. We introduce centrality measures based on the entropy of nodes and hyperedges in the hypergraphs. Until now, a lot of measures from various perspectives have been proposed to identify influential nodes, yet non provides a complete solution to the centrality problem. Because there are different perspectives on centrality. It is important to try different models to reach a solution in centrality problems. Entropy, which is a measure of uncertainty, is a guide in centrality measurements. It can produce ideal solutions for centrality. In complex systems, the entropy can be measured by different methods. In this study, the entropy calculation was made according to the union, intersection, and jaccard similarity values for nodes. The way that similarity is measured indicates the type of centrality. Local centralities were detected more precisely when the degree and union similarity values were used. The intersection and jaccard similarities showed us the global centralities. Traditional methods of centrality were also compared with the results of the proposed method. The accuracy of the method was tested with different hypergraph datasets. It has been shown that we can produce efficient results with different similarity parameters according to our wishes in hypergraphs.

This study assesses the effectiveness of five distinct Long Short-Term Memory (LSTM) architectures for forecasting wind speed in Muş, Turkey. The models include Vanilla LSTM, Stacked LSTM, Bidirectional LSTM, Attention LSTM, and Residual LSTM. The data, obtained from the Muş Meteorological Office, underwent preprocessing to handle missing values by averaging the same day and month values between 1969 and 2023. The dataset, containing 20,088 daily wind speed measurements, was split into training and test sets, with 80% allocated for training and 20% for testing. Each model was trained over 100 epochs with a batch size of 32, and performance was assessed using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The Vanilla LSTM model showed the lowest MSE and MAE values, indicating superior overall performance, while the Attention LSTM model achieved the lowest MAPE, demonstrating better percentage accuracy. These findings indicate that the Vanilla and Attention LSTM models are the most effective for wind speed forecasting, with the choice between them depending on the prioritization of total error versus percentage error.