Advances in the study of ARR3 in myopia by Yi-Ming Guo, Junhan Wei, Jiaqi Wang, Guoyun Zhang, Jiejing Bi, Lu Ye

“…This review summarizes current advancements in elucidating the relationship between ARR3 and myopia, emphasizing genetic variations associated with refractive errors and their implications for myopia research and clinical management. …”
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Good governance and corruption in local governments: The role of internal control and audit by Farhan Shidqi, Zef Arfiansyah

“…Using a panel dataset of 519 local governments over the 2018–2022 period (2,595 observations), the study employs a fixed-effect regression with robust standard errors. The results indicate that internal controls and a more mature internal audit function significantly reduce corruption, while capital expenditure is positively associated with corruption levels. …”
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REINFORCEMENT LEARNING-DRIVEN LANGUAGE AGENTS FOR MULTI-DOMAIN FACT-CHECKING AND COHERENT NEWS SYNTHESIS by Dan Valeriu VOINEA

“…Case studies from news organizations illustrate that these tools can support human fact-checkers by flagging potential errors and synthesizing information across domains. …”
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A Conceptual Framework for User Trust in AI Biosensors: Integrating Cognition, Context, and Contrast by Andrew Prahl

“…Second, we integrate task context by situating sensor applications along an intellective-to-judgmental continuum and showing how demonstrability predicts tolerance for sensor uncertainty and/or errors. Third, we analyze contrast effects that arise when automated sensing displaces familiar human routines, heightening scrutiny and accelerating rejection if roll-out is abrupt. …”
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Voting and Social Media-Based Political Participation by Sascha Göbel

“…Surveys face systematic sampling and measurement errors in the domain of political participation, however. …”
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The effect of research and development personnel on innovation activities of firms: Evidence from small and medium-sized enterprises from the Visegrad Group countries by Aleksandra Zygmunt

“…Research Design & Methods: Fixed effects panel regression with robust standard errors was used for hypothesis testing over the period 2009-2017. …”
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Heuristics Influence on Investment Decision Making at Pakistan Stock Exchange: Mediation of Digital Financial Literacy and Moderation of AI Adoption by Akbar Saeed, Azam Anwar Khan

“…The study also tries to estimate how digital financial literacy works as a mediator since those who have higher digital skills will be able to interpret and evaluate the financial information more effectively while mitigating cognitive errors. Additionally, the paper considers how the increasing applicability of AI in investment frameworks can diminish the role of cognitive heuristics based on analysis by Susskind and Susskind (2015). …”
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Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead by Haytham Elmousalami, Aljawharah A. Alnaser, Felix Kin Peng Hui

“…Predictive accuracy was evaluated using mean absolute percentage error (MAPE) and mean square error (MSE). Additionally, WSPFS advances the smart wind energy deep decarbonization (SWEDD) framework by calculating the carbon city index (CCI) to define the carbon-city transformation curve (CCTC). …”
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An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields by Yi-Fan Wang, Max Yue-Feng Wang, Li-Ying Tu

“…Using historical data from the Federal Reserve Economic Data (FRED), this study finds that the RF model offers the most accurate short-term predictions, achieving the lowest mean squared error (MSE) and mean absolute error (MAE), with an R² value of 0.5760. …”
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The Role of Organisational Culture and Situational Factors in Predicting Workplace Deviation Among Public Employees by Benjamin Adegboyega OLABIMITAN, Sunday Samson BABALOLA

“…Limitations of the study – The study outcome may not be free of sampling error and non-response error due to self-reported surveys that could be subject to bias. …”
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The Impact of Velocity Update Frequency on Time Accuracy for Mantle Convection Particle Methods by S. J. Trim, S. L. Butler, R. J. Spiteri

“…Accordingly, they may be used to efficiently compute solutions to within modest error tolerances. For small error tolerances, however, ERK4 was the most efficient.…”
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Construction and application of a TCN-LSTM-SVM-based time series prediction model for water inflow in coal seam roofs by Xuan LIU, Yadong JI, Kaipeng ZHU, Chunhu ZHAO, Kai LI, Chaofeng LI, Chenhan YUAN, Panpan LI, Pengzhen YAN

“…ResultsThe training and prediction results indicate that the TCN-LSTM-SVM model yielded mean absolute errors (\begin{document}$ {E}_{{\mathrm{MA}}} $\end{document}) ranging from 56.02 m3/h to 129.89 m3/h, mean absolute percentage errors (\begin{document}$ {E}_{{\mathrm{MAP}}} $\end{document}) from 3 % to 7 %, root mean square errors (\begin{document}$ {E}_{{\mathrm{RMS}}} $\end{document}) from 82.60 m3/h to 162.61 m3/h, and coefficients of determination (\begin{document}$ {R}^{2} $\end{document}) from 0.81 to 0.98 based on the training, validation, and test sets. …”
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Deep-learning-based sub-seasonal precipitation and streamflow ensemble forecasting over the source region of the Yangtze River by N. Dong, H. Hao, H. Hao, M. Yang, J. Wei, S. Xu, H. Kunstmann, H. Kunstmann, H. Kunstmann

“…Using these precipitation forecasts as meteorological forcing for the hybrid XAJ-LSTM hydrologic model, we found that forecasted streamflow and flood peaks driven by CNN-based precipitation forecasts have 16 %–33 % lower relative errors and 20 %–31 % lower RMSE compared to those driven by raw forecasts. …”
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A Comprehensive Evaluation of Monocular Depth Estimation Methods in Low-Altitude Forest Environment by Jiwen Jia, Junhua Kang, Lin Chen, Xiang Gao, Borui Zhang, Guijun Yang

“…On the Mid-Air dataset, the Transformer-based DepthAnything demonstrates a 54.2% improvement in RMSE for the global error metric compared to the CNN-based Adabins. On the LOBDM dataset, the CNN-based MiDas has the depth edge completeness error of 93.361, while the Transformer-based Metric3D demonstrates the significantly lower error of only 5.494. …”
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Forecasting temperature and rainfall using deep learning for the challenging climates of Northern India by Syed Nisar Hussain Bukhari, Kingsley A. Ogudo

“…The RNN model in MIMO configuration achieved significantly lower mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) for Jammu, Srinagar, and Ladakh, with respective values of [0.0636, 0.1011, 0.0401] for Jammu, [0.1048, 0.1555, 0.0455] for Srinagar, and [0.0854, 0.1344, 0.0411] for Ladakh. …”
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De-noising method of pulse signal based on double-tree complex wavelet transform and morphological filtering by Dan LI, Huiqian WANG, Tong BAI, Jinzhao LIN, Yu PANG, Xiaoming JIANG, Yuhao JIANG

“…The simulation results show that this algorithm can remove the power line interference and EMG interference, and the quantitative index of SNR and mean square error is superior to the traditional threshold de-noising algorithm. …”
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Affective Reactions When Learning That Our Answer Is Biased: The Role of Negative Feedback in the Arousal of Epistemic Emotions by Katerina Nerantzaki, Paraskevi Stergiadou, Panayiota Metallidou

“…This study investigated how different types of feedback influence emotional reactions in decision-making tasks involving high-confidence errors. The sample consisted of 596 undergraduate and postgraduate university students. …”
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The Flush Model: A Novel Framework to Manage Surgeons’ Mental Fatigue and Cognitive Load by Pierrick Laulan, PhD, Matthieu L. G. Fernandez, MSc, Emeric Abet, MD, Jérôme Dimet, MD, Ulrike Rimmele, PhD

“…Its implementation promises to enhance cognitive resilience, reduce surgical errors, and improve both patient outcomes and surgeon well-being.…”
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Automatic Body Measurement for Straight-Tube Mountain Bike Size Selection Using Closest Size Classification by Patsama Charoenpong, Kunyada Kongtanee, Patee Chaiprasittigul, Kitti Sathapornprasath, Theekapun Charoenpong

“…The average relative absolute errors of the top tube length and seat tube length are 2.00% and 1.93%, respectively. …”
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CHINESE YUAN EXCHANGE RATE AGAINST THE INDONESIAN RUPIAH PREDICTION USING SUPPORT VECTOR REGRESSION by Steven Soewignjo, Ni Wayan Widya Septia Sari, Andini Putri Mediani, M. Aqil Zaidan Kamil, Dita Amelia, Nur Chamidah

“…The model achieves high accuracy, with a Mean Absolute Percentage Error (MAPE) of 1.738%, a Root Mean Squared Error (RMSE) of 50.661 for the training data and a MAPE of 2.516%, and an RMSE of 64.735 for the testing data. …”
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