Corrigendum: Bobinac M, Šušić N, Šijačić-Nikolić M, Kerkez Janković I, Veljović-Jovanović S., Photosynthetic insights into winter-green leaves in Quercus pubescens Willd. seedlings... by Editoral

“…Arch Biol Sci. 2024;76(2):223-32. have notified the Editorial Office of an error in the Funding section. The name “Ministry of Education, Science and Technological Development of the Republic of Serbia” was incorrectly stated and should instead read “Ministry of Science, Technological Development and Innovation of the Republic of Serbia.” …”
Get full text

Development of regional mixed-effects height–diameter models for natural black pine stands by Ramazan Ozçelik, Onur Alkan

“…Compared to the fixed-effects model, the mixed-effects model achieved a 32% reduction in the root mean square error (RMSE). The findings suggest that the proposed model is highly suitable for forest inventory studies to predict tree heights in black pine stands.…”
Get full text

A reversible database watermarking method non-redundancy shifting-based histogram gaps by Yan Li, Junwei Wang, Xiangyang Luo

“…First, an integer data histogram is constructed with the absolute value of the prediction error of the data as a variable. Second, the positional relationship between each column and the gap in the histogram is analyzed to find out all the columns adjacent to the gap. …”
Get full text

Augmented Reality (AR) for Precision Farming: Enhancing Farmer Decision-Making in Pest Control by Tandi Moti Ranjan, Kumar Yalakala Dinesh

“…Pest control in modern agriculture is a huge challenge where traditional ways are often labor intensive, error-prone, and require heavy use of pesticides that damage the environment. …”
Get full text

A comparative study of four deep learning algorithms for predicting tree stem radius measured by dendrometer: A case study by Guilherme Cassales, Serajis Salekin, Nick Lim, Dean Meason, Albert Bifet, Bernhard Pfahringer, Eibe Frank

“…As a dominant terrestrial ecosystem, forests play a pivotal role, which is substantially challenged by climate extremes. …”
Get full text

Missing value interpolation algorithm for long-term temperature observation data based on data augmentation multiple interpolation method by Xiaolin Liu, Bo Wang, Shuanglong Jin, Zongpeng Song

“…The experimental results show that the mean square error of the interpolated data is below 0.2, the coefficient of determination is between 0.91 and 0.95, which is closer to 1, and the interpolation time is <5 s. …”
Get full text

Scalable AI-driven air quality forecasting and classification for public health applications by Mohammad Wasil Jalali, Bahir Saidi, Habibullah Farahmand, Mohammad Aref Rezvan Panah, Eda Nur Saruhan

“…Results The TSMixer model stood out in regression tasks, achieving a high R² score of 0.9861 and a low mean squared error (MSE) of 0.0278. In classification tasks, the Random Forest model performed best with an accuracy of 99.96%, slightly outperforming XGBoost at 99.48%. …”
Get full text

Assessing Heterogeneity of Surface Water Temperature Following Stream Restoration and a High-Intensity Fire from Thermal Imagery by Matthew I. Barker, Jonathan D. Burnett, Ivan Arismendi, Michael G. Wing

Get full text

Regionalization Analysis of Environmental Drivers of CONUS Grazing Land Biomass by Jisung Geba Chang, Feng Gao, Martha C. Anderson, Richard Cirone, Haoteng Zhao

“…Investigating the performance of several machine learning approaches in reproducing RAP biomass, the random forest model performed best, with a mean absolute error of 373 lb/acre and a coefficient of determination (<italic>R</italic>²) of 0.66. …”
Get full text

Spatial analysis of drought severity, duration and frequency using different drought indices (Case study: Fars Province, Iran) by Mona Massoudi, Massoud Goodarzi, Abolfazl Moeini, Baharak Motamedvaziri

Get full text

Machine Learning-Based Morphological Classification and Diversity Analysis of Ornamental Pumpkin Seeds by Sıtkı Ermiş, Uğur Ercan, Aylin Kabaş, Önder Kabaş, Georgiana Moiceanu

“…These outcomes reflect the potential of machine learning-based approaches for seed classification automation, error minimization in seed classification, and maximization of efficiency in the seed industry. …”
Get full text

Estimation of grassland use intensity based on the generated high-resolution daily land surface reflectance dataset with GF-6 WFV and HLS by Hanwen Cui, Xiaosong Li, Bin Sun, Guangbin Yang, Ziyu Yang, Chaochao Chen, Licheng Zhao

Get full text

Effective Machine Learning Techniques for Dealing with Poor Credit Data by Dumisani Selby Nkambule, Bhekisipho Twala, Jan Harm Christiaan Pretorius

“…Additionally, the average error rate for these classifiers is over 9%, which implies that the estimates are not far from the actual values. …”
Get full text

Aligning Evapotranspiration from MOD16A2.061 Product to Ground Estimates in Piemonte (NW Italy): an analysis of temporal and spatial biases by E. Ronco, E. C. Borgogno Mondino

Get full text

Linking land value to indicators of soil quality and land use pressure by John J. Drewry, Stephen J. McNeill, Richard W. McDowell, Richard Law, Bryan A. Stevenson

“…The most important explanatory variable in predicting land valuation per hectare in the random forest model was catchment elevation (mean decrease in the mean square error; 0.92), followed by catchment potential evapotranspiration (0.78). …”
Get full text

A Comparative Study of Downscaling Methods for Groundwater Based on GRACE Data Using RFR and GWR Models in Jiangsu Province, China by Rihui Yang, Yuqing Zhong, Xiaoxiang Zhang, Aizemaitijiang Maimaitituersun, Xiaohan Ju

“…The results indicate that among the established 66 × 158 local GWR models, the coefficient of determination (R²) ranges from 0.39 to 0.88, with a root mean squared error (RMSE) of approximately 2.60 cm. The proportion of downscaling models with an R² below 0.5 was 18.52%. …”
Get full text

Spaceborne remote sensing effectively maps species richness across taxonomic groups in a mountain landscape by Cornelius Senf, Lisa Geres, Tobias Richter, Kristin Braziunas, Felix Glasmann, Rupert Seidl, Sebastian Seibold

“…However, validating models by habitat type revealed higher errors within habitat types (i.e., forest or open habitat), especially for immobile species (fungi and plants) that likely vary at smaller spatial scales than the resolution of the spaceborne systems used in this study. …”
Get full text

Evaluating key predictors of breast cancer through survival: a comparison of AFT frailty models with LASSO, ridge, and elastic net regularization by Senyefia Bosson-Amedenu, Emmanuel Ayitey, Francis Ayiah-Mensah, Luyton Asare

“…Model performance was evaluated using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Absolute Error (MAE), and Mean Squared Error (MSE) metrics across three sample sizes (25%, 50%, and 75%). …”
Get full text

Ensemble Machine Learning Model Prediction and Metaheuristic Optimisation of Oil Spills Using Organic Absorbents: Supporting Sustainable Maritime by Le Quang Dung, Pham Duc, Bui Thi Anh Em, Nguyen Lan Huong, Nguyen Phuoc Quy Phong, Dang Thanh Nam

“…To close this gap, our work combines metaheuristic algorithms with ensemble machine learning and suggests a hybrid technique for the precise prediction and improvement of oil removal efficiency. Using Random Forest (RF) and XGBoost models, high R² values (RF: 0.9517–0.9559; XGBoost: 0.9760), minimal errors, and strong generalisation were obtained by predictive modelling. …”
Get full text

Trust in the machine: How contextual factors and personality traits shape algorithm aversion and collaboration by Vinícius Ferraz, Leon Houf, Thomas Pitz, Christiane Schwieren, Jörn Sickmann

“…Additionally, female participants reacted more strongly to algorithm errors. Increased delegation rates improved algorithm accuracy. …”
Get full text