EDITORIAL by Ivan Čuk

“…From last October issue we have to apologize to the authors Luísa Amaral, José Ferreirinha, Paulo Santos and Albrecht Claessens as we did some errors in article design; you can find corrected article on our web pages. …”
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Maintenance Time Prediction for Predictive Maintenance of Ship Engines by Seunghun Lim, Jungmo Oh, Jinkyu Park

“…However, due to the nature of ship operation, data collection is difficult, and most studies focus on fault detection, hindering the application of predictive maintenance to ships. …”
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Implementation of automation tools for test analysis in the Radiotherapy Quality Assurance Programs by João Guilherme Rivera Santiago, Laura Furnari, Marcus Vinicius Saad de Paula Rodrigues, Victor Augusto Bertotti Ribeiro

“…To implement such tools, it is essential to assess factors such as accuracy, error detection sensitivity, adaptability to institution needs, and ease of access and use for those involved in quality control processes. …”
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Classification of melanoma skin Cancer based on Image Data Set using different neural networks by Rukhsar Sabir, Tahir Mehmood

“…Across these metrics, EfficientNet-B0 consistently outperformed ResNet-18 and basic CNN. The findings from this research suggest that neural network models, particularly EfficientNet-B0, hold significant promise for precise and efficient melanoma skin cancer detection.…”
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A comparative study on quantitative precipitation estimation based on GPM satellite and X-band phased-array weather radar by Yongyan Su, Yongyan Su, Yongyan Su, Di Wang, Wenyu Kong, Bo Zhao, Yan Liu, Xuejiao Chen, Debin Su

“…Furthermore, the root mean square error (RMSE) and mean absolute error (MAE) for XPAR against ground observations were 1.2g mm and 0.64 mm, respectively, while for GPM, these values were significantly higher at 6.98 mm and 1.91 mm. findings highlight the superior capability of XPAR in accurately estimating precipitation, which is crucial for enhancing the detection and early warning of heavy rainfall events.…”
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Machine learning–based feature prediction of convergence zones in ocean front environments by Weishuai Xu, Lei Zhang, Hua Wang

“…The model achieved an accuracy of 82.43% in predicting the convergence zone’s distance with an error of less than 1 km. Additionally, it attained a 77.1% accuracy in predicting the convergence zone’s width within a similar error range. …”
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Thermoluminescence Properties of Plagioclase Mineral and Modelling of TL Glow Curves with Artificial Neural Networks by Mehmet Yüksel, Emre Ünsal

“…Among these, the BR algorithm demonstrated the best performance with an accuracy value of 0.99915, a Mean Absolute Error (MAE) of 2.34 × 10⁻³, and a Mean Squared Error (MSE) of 3.82 × 10⁻⁵, outperforming LM and SCG in in terms of generalization and accuracy. …”
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SentiTSMixer: A Specific Model for Sales Forecasting Using Sentiment Analysis of Customer by Partha Ghosh, Subhashis Das, Subhankar Roy, Ankur Bhattacharjee, Agostino Cortesi, Soumya Sen

“…Experimental results on various types of Amazon data show that, depending on the dataset and the specific error detection techniques used, the proposed model delivers a reduction in error ranging from 65% to 99% compared to established models.…”
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AdapTree: Data-Driven Approach to Assessing Plant Stress Through the AI-Sensor Synergy by Divisha Garg, Harpreet Singh, Yosi Shacham-Diamand

“…These findings validate the reliability and effectiveness of the proposed AI-driven framework in accurately interpreting sensor data for plant stress detection. …”
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An Analysis of Variance of the Pantheon+ Dataset: Systematics in the Covariance Matrix? by Ryan E. Keeley, Arman Shafieloo, Benjamin L’Huillier

“…One simple interpretation of these results is a ∼7% overestimation of errors on SN distance moduli in Pantheon+ data. When the covariance matrix is reduced by subtracting an intrinsic scatter term from the diagonal terms of the covariance matrix, the best-fit <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>χ</mi><mn>2</mn></msup></semantics></math></inline-formula> for the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>Λ</mo></semantics></math></inline-formula>CDM model achieves a normal value of 1580 and no deviation from <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mo>Λ</mo></semantics></math></inline-formula>CDM is detected. …”
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Raman‐Enabled Predictions of Protein Content and Metabolites in Biopharmaceutical Saccharomyces cerevisiae Fermentations by Jeppe Hagedorn, Guilherme Ramos, Miguel Ressurreição, Ernst Broberg Hansen, Michael Sokolov, Carlos Casado Vázquez, Christos Panos

“…Its ability to provide detailed information about molecular vibrations makes it ideal for the detection and quantification of therapeutic proteins and critical control parameters in complex biopharmaceutical mixtures. …”
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High-Frequency Passive Acoustic Recognition in Underwater Environments: Echo-Based Coding for Layered Elastic Shells by Zixuan Dai, Zilong Peng, Suchen Xu

“…Results demonstrate that optimizing material impedance contrasts achieves 99% detection success at a 3 dB signal-to-noise ratio. …”
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Tower Frequency Monitoring Solution and Implementation for Wind Turbine Generator Systems by LIU Jinrui, GUO Yanyifu

“…The research results showed the effectiveness of the proposed frequency monitoring solution in accurately identifying natural frequency changes, with an error of merely 0.012 2 Hz in the shutdown state, and in detecting potential structural damages and faults with high sensitivity and accuracy. …”
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Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index by Dania AL-Najjar, Hazem AL-Najjar, Nadia Al-Rousan, Hamzeh F. Assous

“…In order to test the performance of two prediction models, R2 and various error functions are used. The linear regression model results found that PM10, NO2, CO, month, day, and year are significant, whereas O3, SO2, and AQI indices are insignificant. …”
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Predicting diabetes using supervised machine learning algorithms on E-health records by Sulaiman Afolabi, Nurudeen Ajadi, Afeez Jimoh, Ibrahim Adenekan

“…Methods: This study investigates the early detection and management of diabetes by applying machine learning techniques to electronic health records. …”
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Uncertainty monitoring in Eurasian jays (Garrulus glandarius) by M. Loconsole, A. K. Schnell, E. Garcia-Pelegrin, N. S. Clayton

“…Metacognition abilities encompass enhanced decision-making in uncertain situations, more efficient resource management, error detection and correction, and improved problem-solving skills. …”
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CRAFTS for H i Cosmology. I. Data Processing Pipeline and Validation Tests by Wenxiu Yang, Laura Wolz, Yichao Li, Wenkai Hu, Steven Cunnington, Keith Grainge, Furen Deng, Shifan Zuo, Shuanghao Shu, Xinyang Zhao, Di Li, Zheng Zheng, Marko Krčo, Yinghui Zheng, Linjing Feng, Pei Zuo, Hao Chen, Xue-Jian Jiang, Chen Wang, Pei Wang, Chen-Chen Miao, Yougang Wang, Xuelei Chen

“…We also measure the H i emission of 90 galaxies with redshift z  < 0.07 and compare them with H i -MaNGA spectra, yielding an overall relative H i integral flux error of 16.7%. These results provide an important first step in assessing the feasibility of conducting cosmological H i detection with CRAFTS.…”
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Risk Factors for Multidrug-resistant Tuberculosis by Cleopas Martin Rumende

“…Many new cases of MDR-TB are created by physician’s errors related to drugs regimen, dosing interval and duration of treatment. …”
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Risk Factors for Multidrug-resistant Tuberculosis by Cleopas Martin Rumende

“…Many new cases of MDR-TB are created by physician’s errors related to drugs regimen, dosing interval and duration of treatment. …”
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Data-Driven Analysis of Ocean Fronts’ Impact on Acoustic Propagation: Process Understanding and Machine Learning Applications, Focusing on the Kuroshio Extension Front by Weishuai Xu, Lei Zhang, Ming Li, Xiaodong Ma, Maolin Li

“…Utilizing marine big data statistics and machine learning evaluation metrics such as out-of-bag (OOB) error and Shapley values, this study quantitatively assesses the variations in sound speed structures across the KEF and their effects on acoustic propagation shifts. …”
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