Morphological and morphometric analysis of the inferior alveolar canal and mental foramen in black South Africans: A panoramic radiographic study by Ikeoluwa A. Oluwafemi, Nicolene Jooste, Pamela Pillay, Sundika Ishwarkumar-Govender

“…Clinicians should expect to find the MF symmetrically in line with the root tip of the second premolars: however, the position of the MF moves posteriorly with advancing age.…”
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A statistical and machine learning approach for monthly precipitation forecasting in an Amazon city by Ewerton Cristhian Lima de Oliveira, Eduardo Costa de Carvalho, Edmir dos Santos Jesus, Rafael de Lima Rocha, Rafael de Lima Rocha, Helder Moreira Arruda, Ronnie Cley de Oliveira Alves, Ronnie Cley de Oliveira Alves, Renata Gonçalves Tedeschi

“…Besides the use of algorithms, another evaluation was conducted on Feature Composition based on statistical methods to investigate the impact of variables on the prediction.ResultsThe results obtained in our investigation indicate that the vector autoregressive moving average with exogenous regressors (VARMAX) model achieved the best performance in rainfall forecasting, with an average root mean square error (RMSE) of 9.1833 in time series cross-validation, outperforming the other models.DiscussionThe climate-driven patterns directly influenced the performance of the rainfall forecasting models evaluated in this study. …”
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Hybrid Long Short-Term Memory Wavelet Transform Models for Short-Term Electricity Load Forecasting by Agbassou Guenoukpati, Akuété Pierre Agbessi, Adekunlé Akim Salami, Yawo Amen Bakpo

“…The results indicate that the ConvLSTM model outperforms its counterparts based on Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and correlation coefficient (R²) metrics. …”
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Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks by Pramit Pandit, Atish Sagar, Bikramjeet Ghose, Moumita Paul, Ozgur Kisi, Dinesh Kumar Vishwakarma, Lamjed Mansour, Krishna Kumar Yadav

“…For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. …”
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Modeling Time Series with SARIMAX and Skew-Normal and Zero-Inflated Skew-Normal Errors by M. Alejandro Dinamarca, Fernando Rojas, Claudia Ibacache-Quiroga, Karoll González-Pizarro

“…This study proposes an extension of Seasonal Autoregressive Integrated Moving Average models with exogenous regressors (SARIMAX) by incorporating skew-normal and zero-inflated skew-normal error structures to better accommodate asymmetry and excess zeros in time series data. …”
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Enhancing Drought Forecast Accuracy Through Informer Model Optimization by Jieru Wei, Wensheng Tang, Pakorn Ditthakit, Jiandong Shang, Hengliang Guo, Bei Zhao, Gang Wu, Yang Guo

“…This study employed the Informer model to forecast drought and conducted a comparative analysis with Autoregressive Integrated Moving Average (ARIMA), long short-term memory (LSTM), and Convolutional Neural Network (CNN) models. …”
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Epidemiological characteristics and prediction model construction of hand, foot and mouth disease in Quzhou City, China, 2005–2023 by Wenjie Xu, Canjie Zheng, Canya Fu, Canya Fu, Xiaoying Gong, Quanjun Fang, Zhiying Yin

“…Then, root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the fitting and prediction performances of the model.ResultsFrom 2010 to 2023, Quzhou City reported a total of 66,601 cases of hand, foot, and mouth disease (HFMD), with the annual number of reported cases fluctuating between 2,265 and 7,964. …”
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Forecasting invasive mosquito abundance in the Basque Country, Spain using machine learning techniques by Vanessa Steindorf, Hamna Mariyam K. B., Nico Stollenwerk, Aitor Cevidanes, Jesús F. Barandika, Patricia Vazquez, Ana L. García-Pérez, Maíra Aguiar

“…Forecasting models, including random forest (RF) and seasonal autoregressive integrated moving average (SARIMAX), were evaluated using root mean squared error (RMSE) and mean absolute error (MAE) metrics. …”
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Multiresolution comparison of fetal real-time and cine magnetic resonance imaging at 0.55T by Datta Singh Goolaub, Ye Tian, Joshua F.P. van Amerom, John Wood, Jon Detterich, Krishna S. Nayak, Christopher K. Macgowan

“…ABSTRACT: Background: Dynamic fetal cardiovascular MRI (CMR) enables visualization of moving structures to assess congenital heart disease and plan treatment. …”
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The Effect of Fiber Orientation and Crack on Dynamic Characteristics of a Unidirectional Composite Cantilevered Wing Plate by Faisal Ramadhan, Rianto Adhy Sasongko, Leonardo Gunawan, Mahesa Akbar, Djarot Widagdo

“…For the fundamental modes, first bending and torsion, the natural frequencies decrease by approximately 20% as crack moves closer to the root. The changes in natural mode order and frequencies influenced the occurrence of couplings between modes capable of affecting the stability boundary in aeroelastic case.…”
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Establishment of in vitro regeneration system for leaves and petioles of Oxalis triangularis 'Purpurea' by Siyu Chen, Yun Xiao, Xuexuan Wang, Kefei Wu, Siyuan Shen, Junwen Zhai, Shasha Wu

“…The optimal subculture medium of embryonic callus was MS + 1.50 mg·L−1 6-BA + 0.20 mg·L−1 NAA. When moving the adventitious buds to rooting medium, the optimal medium was 1/2 MS + 0.30 mg·L−1 NAA + 0.30 mg·L−1 IBA. …”
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Assessing groundwater drought in Iran using GRACE data and machine learning by Ali Kashani, Hamid R. Safavi

“…Following the application of the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to fill GWSA time series gaps, this study models and forecasts GWSA trends through 2030 using historical data and SSP2 scenario projections of the canESM5 climate model. …”
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A Nonrandomized Trial of the Effects of Passive Simulated Jogging on Short-Term Heart Rate Variability in Type 2 Diabetic Subjects by Jose A. Adams, Jose R. Lopez, Veronica Banderas, Marvin A. Sackner

“…Time domain variables were computed, viz., standard deviation of all normal RR intervals (SDNN), standard deviation of the delta of all RR intervals (SDΔNN), and the square root of the mean of the sum of the squares of differences between adjacent RR intervals (RMSSD). …”
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LT-1 SAR Satellite Constellation for Rock Glacier Topography Mapping and Deformation Monitoring Over the Tibetan Plateau Periglacial Environment by Xuefei Zhang, Xinming Tang, Tao Li, Min Feng, Jing Wang, Xiang Zhang, Xiaoqing Zhou, Jing Lu

“…Then, the deformation velocities of the rock glaciers acquired from the Stacking-InSAR method and the moving area of the recognized rock glaciers are determined. …”
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Effective dose and persistence of Rhodamine‐B in wild pig Vibrissae by Sarah C. Webster, Fred L. Cunningham, John C. Kilgo, Mark Vukovich, Olin E. Rhodes Jr., James C. Beasley

“…Additionally, we measured distance moved by the RB mark away from the vibrissae root and used a GLMM to assess movement rates of RB bands along growing vibrissae. …”
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Prediction of Carbon Dioxide Concentrations in Strawberry Greenhouse by Using Time Series Models by Seung Hyun Shin, Nibas Chandra Deb, Elanchezhian Arulmozhi, Niraj Tamrakar, Oluwasegun Moses Ogundele, Junghoo Kook, Dae Hyun Kim, Hyeon Tae Kim

“…The time-series data were analyzed using the autoregressive integrated moving average (ARIMA) and the Prophet Forecasting Model (PFM), with performance assessed through root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). …”
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NDVI estimation using Sentinel-1 data over wheat fields in a semiarid Mediterranean region by Emna Ayari, Zeineb Kassouk, Zohra Lili-Chabaane, Nadia Ouaadi, Nicolas Baghdadi, Mehrez Zribi

“…In this study, we reconstruct the NDVI time series of wheat fields using the moving averages of the Sentinel-1 normalized VH/VV cross-polarization ratio (IN) and the interferometric coherence in VV polarization over wheat selected fields in a semiarid site in Tunisia during two seasons, from 2018 to 2020. …”
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Solar Energy Forecasting Framework Using Prophet Based Machine Learning Model: An Opportunity to Explore Solar Energy Potential in Muscat Oman by Mazhar Baloch, Mohamed Shaik Honnurvali, Adnan Kabbani, Touqeer Ahmed, Sohaib Tahir Chauhdary, Muhammad Salman Saeed

“…The model evaluation metrics used in this study include the mean absolute error (MAE), the root mean squared error (RMSE), R², and mean bias deviation (MBD). …”
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Comparison of IMU-Based Knee Kinematics with and without Harness Fixation against an Optical Marker-Based System by Jana G. Weber, Ariana Ortigas-Vásquez, Adrian Sauer, Ingrid Dupraz, Michael Utz, Allan Maas, Thomas M. Grupp

“…Prior to the implementation of REFRAME, in comparison to optical estimates, skin-mounted IMU-based angles displayed mean root-mean-square errors (RMSEs) up to 6.5°, while mean RMSEs for angles based on harness-mounted IMUs peaked at 5.1°. …”
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In-Memory Versus Disk-Based Computing with Random Forest for Stock Analysis: A Comparative Study by Chitra Joshi, Chitrakant Banchorr, Omkaresh Kulkarni, Kirti Wanjale

“…As data become increasingly large, diverse and fast-moving, conventional processing systems often fall short of the performance required for modern analytics.Objective: This research seeks to thoroughly assess the performance of two prominent big data processing frameworks-Apache Spark (in-memory computing) and MapReduce (disk-based computing)-with a focus on applying random forest algorithms to predict stock prices. …”
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