Fishing vessel track-keeping control based on closed-loop gain shaping algorithm under rough sea conditions by Jianghua SUI, Teer GUO, Chunyu SONG

“…At the same time, an integral separation design is used to solve the integral term influence of conventional PID (proportion integration differentiation) controllers on the transient performance of the system.ResultsBased on the results of simulation experiments on a common 32.98 m fiberglass reinforced plastic (FRP) trawler (model SDB8102) sailing under force 7 of the Beaufort wind scale, the steady state trajectory error is less than 3 m, which verifies that the method is safe, feasible, concise and effective.ConclusionThe results of this study can provide useful references for fishing vessel track-keeping control, as well as the intelligent level enhancement of fishing vessel equipment.…”
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Myopic shift of clinical refraction following cataract phacoemulsification with IOL implantation in an avitreal eye by E. N. Panteleev, A. N. Bessarabov, V. V. Malyshev

“…A retrospective analysis was performed of refractive outcomes using the inverse method of calculation.Results: In a retrospective analysis, the error calculated IOL power by an average of 0.8 D, which leads to a shift in clinical refraction towards myopia. …”
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Changes in Stress-Strain Index in School-Aged Children: A 3-Year Longitudinal Study by Zhengfei Yang, Bo Wu, Zhouyue Li, Mengting Yu, Jinyun Jiang, Shuyuan Chen, Shengsong Xu, Junwen Zeng, Mengyi Wang, Xiao Yang

“…There was a significant decrease in corneal biomechanical properties for NDM patients compared with PNM patients (P<0.01). Conclusion. In 7- to 9-year-old schoolchildren, SSI decreased after three years of the longitudinal study, and the change in SSI was correlated with the change in AL and refractive error. …”
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In Vitro Analysis of the Accuracy of Intraoral Scanning in Children by Eloá Cristina Passucci Ambrosio, Aliny Bisaia, Thiago Cruvinel, Natalino Lourenço Neto, Thais Marchini Oliveira, Maria Aparecida Andrade Moreira Machado

“…Paired t-test, independent t-test, intraclass correlation coefficient (r), Mean Absolute Difference (MAD), Technical Error of Measurement (TEM), and Relative Percentage Error (RPE) were used. …”
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Artificial neural network model for predicting water inflow into a reservoir by A. N. Shilin, M. A. Bogale, L. A. Konovalova

“…CONCLUSION. The results obtained show that the models successfully predicted flood runoff over the reservoir.…”
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Inter-examiner repeatability and validity of static retinoscopy by Nabeela Hasrod

“…Conclusion: Clinically, inter-examiner retinoscopy is repeatable and comparable to autorefraction as results differed only by approximately 0.25 D. …”
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Accuracy of 10 Intraocular Lens Power Calculation Formulas by D. F. Belov, V. P. Nikolaenko, D. E. Dmitrieva

“…To compare refractive results of formulas, mean calculation error (ME), mean absolute calculation error (MAE) and formula performance index (FPI) were assessed.Results. …”
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Transfer learning for accelerated failure time model with microarray data by Yan-Bo Pei, Zheng-Yang Yu, Jun-Shan Shen

“…We use a Leave-One-Out cross validation based procedure to evaluate the relative stability of selected genes and overall predictive power. Conclusion In simulation studies, the transfer learning method for the AFT model can correctly identify a small number of genes, its estimation error is smaller than the estimation error obtained without using the source cohorts. …”
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Safety and efficacy of simultaneous photorefractive keratectomy and corneal cross-linking in managing suspected keratoconus by Ali Dal, Mehmet Canleblebici, Murat Erdağ

“…Eligibility criteria included stable refractive error for at least 1 year, spherical equivalent refractive error not exceeding −4.0 D, and central corneal thickness between 470 and 500 µm. …”
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POWER, METALLURGICAL AND CHEMICAL MECHANICAL ENGINEERING DEVICE OF PIECEWISE-LINEAR APPROXIMATION OF TRANSIENT RESPONSE OF CONTROLLED ELECTRONIC COMPONENTS by T. A. Ismailov, Kh. M. Gajiyev, D. A. Chelushkin

“…The minimum value of the systematic error will be obtained with a time constant of the input circuit equal to 0.001 sec.Conclusion. …”
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Dynamic graph structure and spatio-temporal representations in wind power forecasting by Zang Peng, Dong Wenqi, Wang Jing, Fu Jianglong

“…In conclusion, our model provides a promising approach.…”
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PODCAST EDUCATIVO SOBRE LA LEPRA COMO RECURSO DE APRENDIZAJE by Mirthis Cordeiro Ferreira, Raphael Henrique Gomes da Costa, Ricardo Alexandre Amaral Muniz, Carlos Renato dos Santos, Claudia Benedita dos Santos, Eliane Maria Ribeiro de Vasconcelos

“…Resultados: Después de la intervención, hubo un aumento en la cantidad de estudiantes con conocimiento adecuado, con una proporción de cambios de errores en el pre-test a aciertos en el post-test mayor (p < 0,05) que la proporción de cambios de aciertos en el pre-test a errores en el post-test para las preguntas. …”
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Estimation of Ambient Air PM2.5 Concentration Using MLP and RBF by Ali Mohammadi Bardshahi, Nematollah Jaafarzadeh, Tayebeh Tayebeh, Fazel Amiri

“…The dataset was divided into three subsets: 70% for training, 15% for testing, and 15% for validation.Results: The results showed that the average concentration of PM2.5 was 26.5 μg/m3. The root mean square error (RMSE) was estimated as 6.49 μg/m3. Increasing the input data resulted in a slight reduction in network error, with the RBF model, utilizing 1450 inputs and an RMSE of 6.47, achieving the same accuracy as the MLP model with 10 inputs.Conclusion: Given that the PM2.5 concentration estimates from the RBF and MLP models deviated by less than 23 and 25%, respectively, compared to the observed concentrations, both MLP and RBF can be regarded as reliable tools for predicting PM2.5 levels.…”
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Calibrated muscle models improve tracking simulations without enhancing gait predictions. by Filippo Maceratesi, Míriam Febrer-Nafría, Josep M Font-Llagunes

“…Including gait in the calibration problems improved the knee torques accuracy (normalised root mean square error: 0.31 [Formula: see text] 0.11), compared to the baseline calibration (normalised root mean square error: 0.70 [Formula: see text] 0.21). …”
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OPTIMIZED FACEBOOK PROPHET FOR MPOX FORECASTING: ENHANCING PREDICTIVE ACCURACY WITH HYPERPARAMETER TUNING by Nur Alamsyah, Venia Restreva Danestiara, Budiman Budiman, Reni Nursyanti, Elia Setiana, Acep Hendra

“…Facebook Prophet is applied to forecast case trends, with model performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). A baseline Prophet model is first trained using default parameters. …”
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Influence of Wheel-Rail Contact Point Changes on Force-Measuring Wheelset Measurement and the Compensation Method by CHEN Chunjun, DENG Qian

“…The validity of the wheel-rail force measurement compensation scheme considering wheel-rail contact point position changes is verified through simulation analysis. ［Result & Conclusion］After adding the U.S Grade V track irregularity spectrum excitation, the maximum offset of the wheel-rail contact point position is 17.53 mm, and the maximum relative error of the vertical force reaches 22.85%; after adopting the proposed measuring compensation method, the maximum relative error of the wheel-rail vertical force decreases to 8.02%, and the measurement accuracy is significantly improved. …”
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Comparison of Deep Learning and Gradient Boosting: ANN Versus XGBoost for Climate‐Based Dengue Prediction in Bangladesh by Arman Hossain Chowdhury

“…XGBoost outperformed other models, achieving the lowest root mean square error (918.83) and mean absolute error (479.44). …”
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Artificial Neural Network and Ensemble Models for Flood Prediction in North-Central Region of Nigeria by Sikiru Abdulganiyu Siyanbola, Aisha Olabisi Sowemimo, Zaid Habibu, Timothy Ebuka Eberechukwu

“…The metrics used in evaluating the performance of the models were accuracy score, mean absolute error (MAE), and root mean squared error (RMSE). …”
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Arithmetic on Your Phone: A Large Scale Investigation of Simple Additions and Multiplications. by Federico Zimmerman, Diego Shalom, Pablo A Gonzalez, Juan Manuel Garrido, Facundo Alvarez Heduan, Stanislas Dehaene, Mariano Sigman, Andres Rieznik

“…Our results confirm and replicate, on a large sample, six of the main principles derived in a long tradition of investigation: size effect, tie effect, size-tie interaction effect, five-effect, RTs and error rates correlation effect, and most common error effect. …”
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An improved algorithm for prediction of vehicle trajectories using short-term goal-driven network by Abdalla Tawfik, Zaki Nossair, Roaa Mubarak

“…STG demonstrated a substantial improvement of over 6% in average displacement error and more than 8% in final displacement error. …”
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