http://ijaers.com/ Open Access international Journal to publish research paper en-us Wetlands in the Atlantique Department play a fundamental role in the functioning of both environmental and socio-economic systems. However, they are increasingly exposed to hydro-climatic extremes, particularly floods and droughts, exacerbated by climate change and anthropogenic pressures. This study aims to analyze the vulnerability of wetlands in response to hydro-climatic extremes in the Atlantique Department. The methodology is based on the analysis of hydro-climatic data (1958–2023), the use of climate anomaly indices, diachronic analysis of land use/land cover change, and the PEIR (Pressure–State–Impact–Response) framework. The results reveal marked climate variability, characterized by alternating deficit and surplus periods, along with a rising trend in temperatures. Annual precipitation ranges between 1100 and 1340 mm, with mean temperatures of 26.5–26.7 °C, indicating a high exposure to flood and drought risks, with more than 35 % of the territory classified as high-risk zones. These dynamics, combined with 99 % of pressures of anthropogenic origin (urbanization, agricultural pollution, fragmentation of lowlands, etc.), lead to rapid ecosystem degradation and biodiversity loss. In light of this situation, the implementation of integrated and sustainable management strategies is essential to enhance the resilience of wetlands to hydro-climatic extremes. http://ijaers.com/detail/vulnerability-of-wetlands-to-hydroclimatic-extremes-in-the-department-of-atlantique-in-southern-benin/ Géo Warren Pédro Dossou Assogba, Rafiatou Bamisso, Ibouraïma Yabi http://ijaers.com/uploads/issue_files/1IJAERS-0820262-Vulnerability.pdf This study evaluated the correlation between the nondestructive index of absorbance difference (IAD), obtained via DA-Meter, and conventional destructive quality attributes to determine the ripening of 'Fuji Suprema' apples in subtropical Brazil. During the 2018/2019 and 2019/2020 growing seasons, trials investigated several harvest parameters—including orchard row, tree face, height of fruit collection, days after full bloom (DAFB), and sunlight exposure—against attributes like skin color, flesh firmness, soluble solids, titratable acidity, starch index, and seed color. The results showed that the DA-Meter effectively monitors ripening evolution and establishes harvest onset. Ripening monitoring should begin between 142 and 147 DAFB, with readings taken strictly on the sunlit side of the fruit. Ultimately, the strong correlation between IAD and both flesh firmness and soluble solids content demonstrates that this nondestructive index can successfully replace traditional destructive evaluations for apple maturation assessment. http://ijaers.com/detail/nondestructive-analyses-in-monitoring-the-ripening-progress-of-apple-fruit-grown-in-subtropical-areas/ Lucimara Rogeria Antoniolli, Elenilson Godoy Alves Filho, Leo Rufato, Luciano Gebler http://ijaers.com/uploads/issue_files/2IJAERS-08202612-Nondestructive.pdf The use of floating columns in reinforced cement concrete (RCC) buildings can significantly influence structural load transfer, stiffness, and durability, particularly under complex loading conditions. This review examines the application of Artificial Intelligence (AI) for predicting the durability and structural performance of RCC buildings with floating columns using STAAD.Pro analysis. The study reviews existing research on AI-based prediction techniques, structural modeling, load analysis, material behavior, and durability assessment. STAAD.Pro is considered as a computational platform for evaluating structural responses, while AI techniques can be used to identify patterns and predict performance-related parameters. The review highlights the potential of integrating AI with structural analysis to improve durability prediction, optimize design decisions, and support safer and more efficient RCC building design. http://ijaers.com/detail/ai-based-durability-prediction-of-rcc-buildings-with-floating-columns-using-staad-pro-analysis/ Krishnapal Singh Solanki, Mohit Kumar Prajapati http://ijaers.com/uploads/issue_files/3IJAERS-0920267-AI-Based.pdf Paper surface roughness is an important quality parameter because it affects coating quality, printability, and the final appearance of paper. Accurate prediction of roughness remains challenging due to the large number of correlated variables monitored throughout the papermaking process. This study compares three feature selection methods for predicting average paper roughness from industrial data: Pearson correlation analysis, Principal Component Analysis (PCA), and a hybrid approach combining the Least Absolute Shrinkage and Selection Operator (Lasso) with Forward Selection. The dataset consisted of 7,145 hourly observations and 198 process variables collected from an industrial paper production process. After data preprocessing and min-max normalization, the selected variables were used to develop Multiple Linear Regression (MLR) models. The Pearson-based model achieved an R² of 0.696, whereas the PCA model reached an R² of 0.728 using seven principal components that explained 92.37% of the data variance. The best performance was obtained with the Lasso + Forward Selection approach, which achieved an R² of 0.789 using only 20 original process variables. Although PCA reduced the dimensionality of the dataset, the hybrid approach produced a more accurate model while preserving variables with direct physical meaning. These results indicate that combining Lasso with Forward Selection is an effective strategy for predicting paper roughness and can support process monitoring and quality improvement in industrial papermaking. http://ijaers.com/detail/feature-selection-and-multivariate-regression-for-paper-surface-roughness-prediction/ Matheus Assis Domingues, Lílian Cristina Côcco, Fillemon Edilyn da Silva Bambirra Alves, Ricardo Henrique Moreton Godoi, Carlos Itsuo Yamamoto http://ijaers.com/uploads/issue_files/4IJAERS-0920265-Feature.pdf