Predictive Approach Research Articles

Recent advancements in postpartum depression prediction through machine learning approaches: a systematic review

Postpartum depression (PPD) is a significant mental health concern affecting mothers worldwide, irrespective of demographic factors. Detecting and managing PPD at an early stage is crucial for effective intervention. In the context of mental health, intelligent predictive models based on machine learning (ML) have emerged as valuable tools. However, there remains a relative scarcity of research specifically targeting postpartum mental health due to several prominent factors that collectively impede the widespread adoption and practical implementation of ML in the field of PPD. This paper provides an updated overview of ML approaches for PPD prediction. A systematic search across IEEE Xplore, PubMed, Science Direct, and Scopus yielded 1,074 relevant articles. The performance of ML algorithms varies depending on the dataset and the problem being addressed. Notably, the findings reveal that the random forest (RF) algorithm consistently demonstrates the highest predictive accuracy, followed by support vector machine (SVM), logistic regression (LR), XGBoost, and AdaBoost. The development of advanced data techniques in PPD has encouraged interdisciplinary collaboration between researchers in psychiatry and computer science that holds great potential for refining the accuracy and reliability of PPD predictive models, ultimately resulting in improved outcomes for mothers and their families through early detection, intervention, and support.

Current Computational Methods for Protein-peptide Complex Structure Prediction.

Peptide-mediated protein-protein interactions (PPIs) play an important role in various biological processes. The development of peptide-based drugs to modulate PPIs has attracted increasing attention due to the advantages of high specificity and low toxicity. In the development of peptide-based drugs, one of the most important steps is to determine the interaction details between the peptide and the target protein. In addition to experimental methods, recently developed computational methods provide a cost-effective way for studying protein-peptide interactions. In this article, we carefully reviewed recently developed protein-peptide docking methods, which were classified into three groups: template-based docking, template-free docking, and hybrid method. Then, we presented available benchmarking sets and evaluation metrics for assessing protein-peptide docking performance. Furthermore, we discussed the use of molecular dynamics simulations, as well as deep learning approaches in protein-peptide complex prediction.

Corporate Governance and Innovation: A Predictive Modeling Approach with Machine Learning

Corporate Governance and Innovation: A Predictive Modeling Approach with Machine Learning

A Novel Approach to Heart Disease Prediction Using Artificial Intelligence Techniques

INTRODUCTION: Heart disease remains one of the leading causes of mortality worldwide, necessitating the development of accurate and efficient prediction models OBJECTIVES: To research new models for heart disease prediction METHODS: This paper presents a novel approach for predicting heart disease using advanced artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms RESULTS By leveraging patient data and integrating various AI models, this approach aims to enhance prediction accuracy and support early diagnosis and intervention CONCLUSION: This study presents a novel AI-based approach for heart disease prediction, demonstrating the efficacy of ML and DL models in improving diagnostic accuracy

MULTICR: Predicting Merged and Abandoned Code Changes in Modern Code Review Using Multi-Objective Search

Modern Code Review (MCR) is an essential process in software development to ensure high-quality code. However, developers often spend considerable time reviewing code changes before being merged into the main code base. Previous studies attempted to predict whether a code change was going to be merged or abandoned soon after it was submitted to improve the code review process. However, these approaches require complex cost-sensitive learning, which makes their adoption challenging since it is difficult for developers to understand the main factors behind the models’ predictions. To address this issue, we introduce in this paper, MULTICR, a multi-objective search-based approach that uses Multi-Objective Genetic Programming (MOGP) to learn early code review prediction models as IF-THEN rules. MULTICR evolves predictive models while maximizing the accuracy of both merged and abandoned classes, eliminating the need for misclassification cost estimation. To evaluate MULTICR, we conducted an empirical study on 146,612 code reviews from Eclipse, LibreOffice, and Gerrithub. The obtained results show that MULTICR outperforms existing baselines in terms of Mathew correlation coefficient (MCC) and F1 scores while learning less complex models compared to decision trees. Our experiments showed also how MULTICR allows identifying the main factors related to abandoned code reviews as well as their associated thresholds, making it a promising approach for early code review prediction with notable performance and interoperability. Additionally, we qualitatively evaluate MULTICR by conducting a user study through semi-structured interviews involving 10 practitioners from different organizations. The obtained results indicate that 90% of the participants find that MULTICR is useful and can help them to improve the code review process. Additionally, the learned IF-THEN rules of MULTICR are transparent.

Enhanced 3D dose prediction for hypofractionated SRS (gamma knife radiosurgery) in brain tumor using cascaded-deep-supervised convolutional neural network.

Gamma Knife radiosurgery (GKRS) is a well-established technique in radiation therapy (RT) for treating small-size brain tumors. It administers highly concentrated doses during each treatment fraction, with even minor dose errors posing a significant risk of causing severe damage to healthy tissues. It underscores the critical need for precise and meticulous precision in GKRS. However, the planning process for GKRS is complex and time-consuming, heavily reliant on the expertise of medical physicists. Incorporating deep learning approaches for GKRS dose prediction can reduce this dependency, improve planning efficiency and homogeneity, streamline clinical workflows, and reduce patient lagging times. Despite this, precise Gamma Knife plan dose distribution prediction using existing models remains a significant challenge. The complexity stems from the intricate nature of dose distributions, subtle contrasts in CT scans, and the interdependence of dosimetric metrics. To overcome these challenges, we have developed a "Cascaded-Deep-Supervised" Convolutional Neural Network (CDS-CNN) that employs a hybrid-weighted optimization scheme. Our innovative method incorporates multi-level deep supervision and a strategic sequential multi-network training approach. It enables the extraction of intra-slice and inter-slice features, leading to more realistic dose predictions with additional contextual information. CDS-CNN was trained and evaluated using data from 105 brain cancer patients who underwent GKRS treatment, with 85 cases used for training and 20 for testing. Quantitative assessments and statistical analyses demonstrated high consistency between the predicted dose distributions and the reference doses from the treatment planning system (TPS). The 3D overall gamma passing rates (GPRs) reached 97.15% ± 1.36% (3mm/3%, 10% threshold), surpassing the previous best performance by 2.53% using the 3D Dense U-Net model. When evaluated against more stringent criteria (2mm/3%, 10% threshold, and 1mm/3%, 10% threshold), the overall GPRs still achieved 96.53% ± 1.08% and 95.03% ± 1.18%. Furthermore, the average target coverage (TC) was 98.33% ± 1.16%, dose selectivity (DS) was 0.57 ± 0.10, gradient index (GI) was 2.69 ± 0.30, and homogeneity index (HI) was 1.79 ± 0.09. Compared to the 3D Dense U-Net, CDS-CNN predictions demonstrated a 3.5% improvement in TC, and CDS-CNN's dose prediction yielded better outcomes than the 3D Dense U-Net across all evaluation criteria. The experimental results demonstrated that the proposed CDS-CNN model outperformed other models in predicting GKRS dose distributions, with predictions closely matching the TPS doses.

An intelligent model for efficient load forecasting and sustainable energy management in sustainable microgrids

Microgrids have emerged as a promising solution for enhancing energy sustainability and resilience in localized energy distribution systems. Efficient energy management and accurate load forecasting are one of the critical aspects for improving the operation of microgrids. Various approaches for energy prediction and load forecasting using statistical models are discussed in the literature. In this work, a novel energy management framework that incorporates machine learning (ML) techniques is presented for an accurate prediction of solar and wind energy generation. The anticipated approach also emphasizes time series-based load forecasting in microgrids with precise estimation of State of Charge (SoC) of battery. A unique feature of the proposed framework is that utilizes historical load data and employs time series analysis coupled with different ML models to forecast the load demand in a commercial microgrids scenario. In this work, Long Short-Term Memory (LSTM) and Linear Regression (LR) models are employed for an experimental analysis to study the proposed framework under three different cases, such as (i) prediction of energy generation, (ii) load demand forecasting and, (iii) prediction of SoC of battery. The results show that the Random Forest (RF) and LSTM models performs well for energy prediction and load forecasting respectively. On the other hand, the Artificial Neural Network (ANN) model exhibited superior accuracy in terms of SoC estimation. Further, in this work, a Graphical User Interface (GUI) is developed for evaluating the efficacy of the proposed energy management framework.

Findings on celestial pole offsets predictions in the second earth orientation parameters prediction comparison campaign (2nd EOP PCC)

In 2021, the International Earth Rotation and Reference Systems Service (IERS) established a working group tasked with conducting the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) to assess the current accuracy of EOP forecasts. From September 2021 to December 2022, EOP predictions submitted by participants from various institutes worldwide were systematically collected and evaluated. This article summarizes the campaign's outcomes, concentrating on the forecasts of the dX, dY, and dψ, dε components of celestial pole offsets (CPO). After detailing the campaign participants and the methodologies employed, we conduct an in-depth analysis of the collected forecasts. We examine the discrepancies between observed and predicted CPO values and analyze their statistical characteristics such as mean, standard deviation, and range. To evaluate CPO forecasts, we computed the mean absolute error (MAE) using the IERS EOP 14 C04 solution as the reference dataset. We then compared the results obtained with forecasts provided by the IERS. The main goal of this study was to show the influence of different methods used on predictions accuracy. Depending on the evaluated prediction approach, the MAE values computed for day 10 of forecast were between 0.03 and 0.16 mas for dX, between 0.03 and 0.12 mas for dY, between 0.07 and 0.91 mas for dψ, and between 0.04 and 0.41 mas for dε. For day 30 of prediction, the corresponding MAE values ranged between 0.03 and 0.12 for dX, and between 0.03 and 0.14 mas for dY. This research shows that machine learning algorithms are the most promising approach in CPO forecasting and provide the highest prediction accuracy (0.06 mas for dX and 0.08 mas for dY for day 10 of prediction).Graphical abstract

A survey on machine learning system for intraductal papillary mucinous neoplasms detection

IPMN cysts, a pre-malignant risk to the pancreas, have the potential to develop into pancreatic cancer. Accurately identifying and evaluating the risk level is crucial for planning an efficient treatment strategy. However, this task is immensely challenging due to the varied and irregular shapes, textures, and sizes of IPMN cysts, as well as those of the pancreas itself. In this study, we introduce a new computer-aided diagnostic approach for classifying IPMN risk levels based on multi-contrast MRI scans. The proposed analysis framework comprises an efficient volumetric self-adapting segmentation strategy for delineating the pancreas, followed by a newly developed deep learning-based classification scheme incorporating a radiomics-based predictive approach. To evaluate the proposed decision-fusion model, we use multi-centre datasets and multi-contrast MRI scans, aiming to achieve superior performance compared to the current state of the art in this field. The ablation studies illustrate the importance of both radiomics and deep learning modules in achieving a new state-of-the-art (SOTA) performance compared to international guidelines and published studies (81.9% vs 61.3% in accuracy). These key findings carry significant implications for clinical decision-making, potentially revolutionizing the way IPMN risk levels are classified. Through a series of rigorous experiments on multi-centre datasets (involving more MRI scans from five centers), we attained unprecedented performance levels with moderate accuracy. The code will be made available upon publication.

Machine-learning-based modeling of saturated flow boiling in pin-fin micro heat sinks with expanding flow passages

For high potential flow-boiling-based thermal management systems, to better understand the underlying flow physics and to present an effective predictive approach have critical importance. Different from the existing literature, this study, for the first time, takes the machine learning (ML) algorithms into consideration for flow boiling in expanding type micro-pin-fin heat sinks (ETMPFHS). A new database including saturated flow boiling data in ETMPFHS is obtained for various operational conditions. Mass flux (G = 150, 210, 270 and 330 kg m−2 s−1), inlet temperature (Ti = 40, 49, 58, 67 and 76 °C) and effective heat flux (approximately, qeff″= 241 to 460 kW m−2) are the variable parameters. In this study, advanced ML algorithms including Support Vector Machine (SVM), Artificial Neural Network (ANN), Regression Trees (RT) and Linear Regression (LR) are used. It is concluded that, for flow boiling in ETMPFHS, the ANN emerges as the most effective model for prediction of htp, ΔT, and ΔP, followed by SVM, while RT and LR present poorer results in terms of predictive accuracy and reliability. Trends of predictions of both the ANN and SVM nearly overlap the experimental data; while both the RT and LR show different trends against the experimental results.

Computational and experimental characterisation of a new (R)-camphor-Thiazolidinone derivative: A combined approach for structure optimisation and activity prediction

This study describes the synthesis, characterisation, in-silico exploration, and theoretical investigation of a novel 5-Methyl-2-(((1S,4R)-1,7,7-trimethylbicyclo[2.2.1]heptan-2-ylidene)hydrazono)thiazolidin-4-one 3. The structure of the synthesised derivative was successfully confirmed using high-resolution mass spectrometry (HRMS) and 1H and 13C nuclear magnetic resonance (NMR) spectroscopy. In addition, a concise theoretical study was employed to elucidate the experimentally observed stability of the compound. On the computational level, this investigation utilised DFT, molecular docking, and molecular dynamics simulations for the (R)-camphor-thiazolidinone derivative. The optimised structure of compound 3 was predicted with the DFT/B3LYP method with the 6–311++G(d,p) basis set. The theoretical vibrational modes were assigned and found to be consistent with the observed FT-IR spectrum. The simulated NMR values exhibited good agreement with the experimental NMR chemical shifts, with the deviations of 0.5113 and 3.816 ppm for the 1H and 13C, respectively. The UV–vis spectra indicated the n → π* and π → π* transitions in compound 3, with confirmation of intermolecular charge transfer (ICT) from the FMOs and NBO analyses. The MEP surface, Mulliken, and NPA analyses confirm the presence of reactive sites within compound 3. NCI-RDG studies revealed the presence of van der Waals (vdW) interactions and the absence of hydrogen bonding within the studied molecule. The molecular docking analysis indicates that compound 3 exhibits a strong inhibitory effect on the targeted protein 6HQO, which, in turn, is associated with breast cancer. The molecular dynamics analysis validates the accuracy of the docking results.

An experimental validation of a filtering approach for prompt gamma prediction in a research proton treatment planning system

Objective. Prompt gamma (PG) radiation generated from nuclear reactions between protons and tissue nuclei can be employed for range verification in proton therapy. A typical clinical workflow for PG range verification compares the detected PG profile with a predicted one. Recently, a novel analytical PG prediction algorithm based on the so-called filtering formalism has been proposed and implemented in a research version of RayStation (RaySearch Laboratories AB), which is a widely adopted treatment planning system. This work validates the performance of the filtering PG prediction approach. Approach. The said algorithm is validated against experimental data and benchmarked with another well-established PG prediction algorithm implemented in a MATLAB-based software REGGUI. Furthermore, a new workflow based on several PG profile quality criteria and analytical methods is proposed for data selection. The workflow also calculates sensitivity and specificity information, which can help practitioners to decide on irradiation course interruption during treatment and monitor spot selection at the treatment planning stage. With the proposed workflow, the comparison can be performed on a limited number of selected high-quality irradiation spots without neighbouring-spot aggregation. Main results. The mean shifts between the experimental data and the predicted PG detection (PGD) profiles (ΔPGD) by the two algorithms are estimated to be 1.5±2.1 mm and −0.6±2.2 mm for the filtering and REGGUI prediction methods, respectively. The ΔPGD difference between two algorithms is observed to be consistent with the beam model difference within uncertainty. However, the filtering approach requires a much shorter computation time compared to the REGGUI approach. Significance. The novel filtering approach is successfully validated against experimental data and another widely used PG prediction algorithm. The workflow designed in this work selects spots with high-quality PGD shift calculation results, and performs sensitivity and specificity analyses to assist clinical decisions.

New Guidelines for the Treatment of the Alveolar Septum in Immediate Dentoalveolar Restoration Technique Associated with Osseodensification: A Case Series.

Achieving initial implant stability at the molar extraction site can be challenging due to bone width, quality, and anatomical limitations like the maxillary sinus and inferior alveolar nerve. The implant placement should achieve precise centralization with the interradicular septum to facilitate implant stabilization and preservation/regeneration of the alveolar ridge post-extraction with bone grafting. Immediate non-occlusal crown placement aids peri-implant tissue maturation for the desired outcome. This retrospective series introduces guidelines for treating sockets with alveolar septum types. The approach involves immediate dentoalveolar restoration (IDR) and osseodensification (OD) with an autogenous graft for bone preservation. A new protocol for the treatment of the molar interradicular septum during immediate implant placement and/or alveolar ridge preservation/reconstruction was applied in 12 cases. Preoperative and postoperative cone-beam computed tomographic examinations were performed. Socket width was measured and compared between timepoints. The mean preoperative and postoperative (mean, 23.58 ± 9.70 months) socket widths were 9.51 ± 0.40 and 11.16 ± 0.30 mm, respectively (17.35% increase; p <0.05). IDR with OD is a predictable approach to the treatment of molar sockets during implant placement.

Adaptive machine learning for forecasting in wind energy: A dynamic, multi-algorithmic approach for short and long-term predictions

This study elucidates the formulation and validation of a dynamic hybrid model for wind energy forecasting, with a particular emphasis on its capability for both short-term and long-term predictive accuracy. The model is predicated on the assimilation of time-series data from past wind energy generation and employs a triad of machine learning algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN). Empirical data, harvested from a 2 MW grid-connected wind turbine, served as the basis for the training and validation phases. A comparative evaluation methodology was devised to scrutinize the performance of each constituent algorithm across a diverse array of metrics. This evaluation framework facilitated the identification of individual algorithmic limitations, which were subsequently mitigated through the implementation of a dynamic switching mechanism within the hybrid model. This innovative feature enables the model to adaptively select the most efficacious forecasting technique based on historical performance data. The hybrid model demonstrated superior forecasting accuracy in both, short-term energy forecasts at 15-min intervals over a day, and in broad, long-term. It recorded a Normalized Mean Absolute Error (NMAE) of 5.54 %, which is notably lower than the NMAE range of 5.65 %–9.22 % observed in other tested models, and significantly better than the average NMAE found in the literature, which spans from 6.73 % to 10.07 %. Such versatility renders it invaluable for grid operators and wind farm management, aiding in both operational and strategic planning. The study's findings not only contribute to the existing body of knowledge in renewable energy forecasting but also suggest the hybrid model's broader applicability in various other predictive analytics domains.

TSpred: a robust prediction framework for TCR-epitope interactions using paired chain TCR sequence data.

Prediction of T-cell receptor (TCR)-epitope interactions is important for many applications in biomedical research, such as cancer immunotherapy and vaccine design. The prediction of TCR-epitope interactions remains challenging especially for novel epitopes, due to the scarcity of available data. We propose TSpred, a new deep learning approach for the pan-specific prediction of TCR binding specificity based on paired chain TCR data. We develop a robust model that generalizes well to unseen epitopes by combining the predictive power of CNN and the attention mechanism. In particular, we design a reciprocal attention mechanism which focuses on extracting the patterns underlying TCR-epitope interactions. Upon a comprehensive evaluation of our model, we find that TSpred achieves state-of-the-art performances in both seen and unseen epitope specificity prediction tasks. Also, compared to other predictors, TSpred is more robust to bias related to peptide imbalance in the dataset. In addition, the reciprocal attention component of our model allows for model interpretability by capturing structurally important binding regions. Results indicate that TSpred is a robust and reliable method for the task of TCR-epitope binding prediction. Source code is available at https://github.com/ha01994/TSpred. Supplementary data are available at Bioinformatics online.

An Inventive Approach for Simultaneous Prediction of Mean Fragmentation Size and Peak Particle Velocity Using Futuristic Datasets Through Improved Techniques of Genetic XG Boost Algorithm

An Inventive Approach for Simultaneous Prediction of Mean Fragmentation Size and Peak Particle Velocity Using Futuristic Datasets Through Improved Techniques of Genetic XG Boost Algorithm

Development of partial least squares regression with discriminant analysis for software bug prediction

Many prediction models and approaches have been introduced during the past decades that try to forecast bugged code elements based on static source code metrics, change and history metrics, or both. However, there is still no universal best solution to this problem, as most suitable features and models vary from dataset to dataset and depend on the context in which we use them. Therefore, novel approaches and further studies on this topic are highly necessary.In this paper, we employ a chemometric approach – Partial Least Squares with Discriminant Analysis (PLS-DA) – for predicting bug prone Classes in Java programs using static source code metrics. PLS-DA is successfully applied within the field of chemometrics, but to our best knowledge, it has never been used before in the software maintenance domain for predicting bugs. In addition, we have used rigorous statistical treatments and evaluation for representing the software engineering results.We show that our PLS-DA based prediction model achieves superior performances compared to the state-of-the-art approaches (i.e. F-measure of 0.44–0.47 at 90% confidence level) when no data re-sampling applied and comparable to others when applying up-sampling on the largest open bug dataset, while training the model is significantly faster, thus finding optimal parameters is much easier. In terms of completeness, which measures the amount of bugs contained in the Java Classes predicted to be defective, PLS-DA outperforms every other algorithm: it found 69.3% and 79.4% of the total bugs with no re-sampling and up-sampling, respectively.

Deep Learning Approaches for Power Prediction in Wind–Solar Tower Systems

Wind–solar towers are a relatively new method of capturing renewable energy from solar and wind power. Solar radiation is collected and heated air is forced to move through the tower. The thermal updraft propels a wind turbine to generate electricity. Furthermore, the top of the tower’s vortex generators produces a pressure differential, which intensifies the updraft. Data were gathered from a wind–solar tower system prototype developed and established at Kyushu University in Japan. Aiming to predict the power output of the system, while knowing a set of features, the data were evaluated and utilized to build a regression model. Sensitivity analysis guided the feature selection process. Several machine learning models were utilized in this study, and the most appropriate model was chosen based on prediction quality and temporal criteria. We started with a simple linear regression model but it was inaccurate. By adding some non-linearity through using polynomial regression of the second order, the accuracy increased considerably sufficiently. Moreover, deep neural networks were trained and tested to enhance the power prediction performance. These networks performed very well, having the most powerful prediction capabilities, with a coefficient of determination R2=0.99734 after hyper-parameter tuning. A 1-D convolutional neural network achieved less accuracy with R2=0.99647, but is still considered a competitive model. A reduced model was introduced trading off some accuracy (R2=0.9916) for significantly reduced data collection requirements and effort.

Smart predictions of petrophysical formation pore pressure via robust data-driven intelligent models

AbstractPredicting pore pressure in the formation is crucial for assessing reservoir geomechanical characteristics, designing drilling schemes/mud programs, and strategies to enhance oil recovery. Accurate predictions are vital for safe and cost-effective exploration and development. Recent research has seen the emergence of intelligent models utilizing machine learning (ML) and deep learning (DL) algorithms, offering promising outcomes. However, there remains a need to identify the most accurate and dependable model among these. This study aims to address this gap by comparing the performance of various ML and DL models, as reported in existing literature, to determine the optimal approach for pore pressure prediction. The sorted machine learning (ML) and deep learning (DL) regression algorithms used for the comparative analysis are decision tree (DT), extreme gradient boosting (XGBoost), random forest (RF), recurrent neural network (RNN), and convolutional neural network (CNN). A total dataset of 22,539 is gathered from five wells (15/9-F-1 A, 15/9-F-1 B, 15/9-F-11 A, 15/9-F-11 T2, and 15/9-F-14) drilled at North-sea Volve oil field, Norway. The first four wells are used to train and test the ML and DL algorithm, and the remaining well (15/9-F-14) is used to evaluate the best-performing algorithm’s universality in predicting pore pressure at the field of study. Seven different petrophysical parameters are used as input parameters to develop the predictive models. Statistical performance metrics are carried out to analyze the applied ML and DL performance. Based on performance indicators, the RF algorithm showed superior results compared to other predictive models with R2 and RMSE values of 0.97 and 2.70 MPa, respectively. Furthermore, the best-performing predictive model with low prediction error RMSE value is applied to the other well dataset from the field of study to access the universality of the RF algorithm to predict pore pressure in the field of study. The results of the universality analysis show a satisfactory prediction accuracy with R2 and RMSE values of 0.905 and 6.48 MPa, respectively.

Prioritizing Amazon Forest conservation: Assessing potential biomass under climate change

This study aims to identify the capacity of intact forests to maintain Above Ground Biomass (AGB) under new climate conditions. Using a predictive approach under different climate scenarios (SSP1–2.6 and SSP5–8.5), and considering the mean of General Circulation Models (GCMs) for the period (2021–2040), it was revealed that the regions with the greatest capacity for AGB in the optimistic scenario are concentrated in the northwest and southeast regions, covering the basins of the Negro, Xingu and Tapajós rivers. In the SSP5–8.5 scenario, although potential AGB gains are concentrated in the same regions, the spatial distribution is more restricted and dispersed. Indigenous Landsemerge as areas with the greatest potential AGB gains in both climate scenarios. However, these predictions do not include the impact of extreme climate events, such as storms, severe droughts, and wildfires, which could lead to an underestimation of actual biomass variations.