Capacity Model Research Articles

Model for end-stage liver disease-dependent prognostic capacity of platelet-to-lymphocyte ratio following liver transplantation for hepatocellular carcinoma

BackgroundTo improve liver organ allocation, the model for end-stage liver disease (MELD) score was adopted in candidates reflecting the severity of liver disease and the physical condition of patients. Inflammatory markers are prognostic factors for various cancers and play prognostic roles in patients after liver transplantation (LT) for hepatocellular carcinoma (HCC). Researchers focused more on pre-LT inflammatory markers, while the role of dynamic change of these inflammatory markers is still unknown. The purpose of this study was to estimate the prognostic value of pre-LT and post-LT inflammatory markers. Material and methodsWe collected the pre-LT complete blood count and the post-LT result with highest count of white blood cells within 48 h. Platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio and systemic immune-inflammation index were calculated, and their prognostic roles were analyzed for their MELD scores. ResultsThis retrospective two-center cohort study enrolled 290 patients after LT for HCC. Multivariate analysis identified pre-LT PLR as independent risk factor for recurrence-free survival (RFS) [HR (95%CI): 1.002 (1.000–1.003), p = 0.023]. A high pre-LT PLR or post-LT PLR were associated with poorer RFS (p < 0.001 and p = 0.004, respectively). Based on the MELD scores, the pre-LT PLR value was able to predict the RFS in high MELD group (p < 0.001) but had no predictive power in low MELD group (p = 0.076). On the contrary, the post-LT PLR value was better to predict the overall RFS value in low MELD group (p = 0.007) but could not predict the overall RFS value in high MELD group (p = 0.136). ConclusionsBoth pre-LT PLR and post-LT PLR demonstrated prognostic value in patients following LT for HCC. Monitoring PLR values based on the MELD score can improve the predictive prognosis and more effectively guide the individual decisions for the postoperative intervention.

Finite element analyses of burst capacities of corroded pipelines containing pinhole-in-corrosion defects

This study carries out extensive parametric three-dimensional elasto-plastic finite element analysis to evaluate the burst capacity of oil and gas pipelines containing pinhole-in-corrosion (PIC) defects. The analysis results reveal that the burst capacity of the PIC defect is insensitive to the pinhole diameter but largely affected by the depth of the pinhole and length of the general corrosion. A pinhole located at the centre of the general corrosion is found to have a larger impact on the burst capacity than the same pinhole located near the edge of the general corrosion. The observed size and location effects on the burst capacity are attributed to the bulging deformation of a corroded pipeline under internal pressure. The findings of this study provide the basis for developing a practical, accurate engineering burst capacity model for PIC defects.

Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing

Studying urban heat islands holds significance for the sustainable development of cities. This comprehensive study analyzed the temporal characteristics of a Surface Urban Heat Island and Canopy Layer Heat Island by employing Moderate-Resolution Imaging Spectroradiometer image data spanning from 2003 to 2020 over Beijing, China. Leveraging the Gaussian capacity model, the geometrical characteristics of the Surface Urban Heat Island and Canopy Layer Heat Island, such as intensity, center, direction, and range, were examined among three different timescales of day, month, and year. Results indicate that the intensities of the Surface Urban Heat Island and Canopy Layer Heat Island tend to have bigger seasonal variations during winter nights and summer daytime. In addition, at night the centers of Surface Urban Heat Island and Canopy Layer Heat Island are mainly concentrated in the range of 116.3°~116.4° E in longitude and 39.90°~39.95° N in latitude, while during the daytime they are more scattered, mainly in the range of 116.2°~116.5° E in longitude and 39.7°~40.0° N in latitude. In the hot season, the center of the heat island moves east to north, while in the cold season it moves west to south. Monthly average ellipse areas of Surface Urban Heat Island and Canopy Layer Heat Island vary more during the day than that at night, the maximum daytime differences were 2662 km2 and 2293 km2, while the maximum nighttime differences were 484 km2 and 265 km2. Overall, the average area is increasing, with the heat island center moving eastward and deflecting towards the northeast-southwest direction. The expansion of urban areas will continue to influence the movement and extent of heat islands. The study offers insights to inform strategies for mitigating urban heat islands.

Quantifying the runoff reduction benefits of the ‘Grain for Green’ Programme using the VIC model

ABSTRACT Variable infiltration capacity model is used to quantitatively analyse the runoff reduction benefits of three basins on the Loess Plateau since the implementation of the ‘Grain for Green’ programme. The programme played an important role, contributing to >60% of the runoff variation in the different basins. Furthermore, for every 1000 km2 increase in cumulative forest area in basins, the rate of runoff coefficient reduction changed by −30%, −37.3% and 169% in the late stage of the programme compared to the early stage, respectively (‘-’ represents a decrease in reduction rate). This was closely related to the restoration of vegetation.

A Numerical Model for the Bearing Capacity of Anchoring Interface in Composite Rock Mass by A Modified Nonlinear Bond-Slip Model

A Numerical Model for the Bearing Capacity of Anchoring Interface in Composite Rock Mass by A Modified Nonlinear Bond-Slip Model

Assessment of hydrocarbon degradation capacity and kinetic modeling of Chlorella vulgaris and Scenedesmus quadricauda for crude oil phycoremediation under mixotrophic conditions

Crude oil spills imperil aquatic ecosystems globally, prompting innovative solutions such as microalgae-based bioremediation. This study explores the potential of Chlorella vulgaris and Scenedesmus quadricauda, for crude oil spill phycoremediation under mixotrophic conditions and varying crude oil concentrations (0.5–2%). C. vulgaris demonstrated notable resilience, thriving up to 1% crude oil exposure, while S. quadricauda adapted to lower concentrations. Optimal growth for both was observed at 0.5% exposure. Chlorophyll a content in C. vulgaris increases at 0.5% exposure but declines above 1%, while a decline was noticeable in chlorophyll b in treatment groups above 1%. Carotenoid levels varied, displaying the highest levels at higher concentrations above 1.5%. Similarly, S. quadricauda showed increased chlorophyll a content at 0.5% exposure, with stable carotenoid levels and a decline in chlorophyll b content at higher concentrations. GC/MS analyses indicated C. vulgaris efficiently degraded aliphatic compounds like decane and tridecane, surpassing S. quadricauda in degrading both aliphatic and aromatic hydrocarbons. Growth kinetics was best represented by the modified Gompertz and logistic models. These findings highlight the species-specific adaptability and optimal concentration for microalgae to degrade crude oil effectively, advancing phycoremediation processes and strategies critical for environmental restoration.

EMR-HRNet: A Multi-Scale Feature Fusion Network for Landslide Segmentation from Remote Sensing Images.

Landslides constitute a significant hazard to human life, safety and natural resources. Traditional landslide investigation methods demand considerable human effort and expertise. To address this issue, this study introduces an innovative landslide segmentation framework, EMR-HRNet, aimed at enhancing accuracy. Initially, a novel data augmentation technique, CenterRep, is proposed, not only augmenting the training dataset but also enabling the model to more effectively capture the intricate features of landslides. Furthermore, this paper integrates a RefConv and Multi-Dconv Head Transposed Attention (RMA) feature pyramid structure into the HRNet model, augmenting the model's capacity for semantic recognition and expression at various levels. Last, the incorporation of the Dilated Efficient Multi-Scale Attention (DEMA) block substantially widens the model's receptive field, bolstering its capability to discern local features. Rigorous evaluations on the Bijie dataset and the Sichuan and surrounding area dataset demonstrate that EMR-HRNet outperforms other advanced semantic segmentation models, achieving mIoU scores of 81.70% and 71.68%, respectively. Additionally, ablation studies conducted across the comprehensive dataset further corroborate the enhancements' efficacy. The results indicate that EMR-HRNet excels in processing satellite and UAV remote sensing imagery, showcasing its significant potential in multi-source optical remote sensing for landslide segmentation.

NnSegNeXt: A 3D Convolutional Network for Brain Tissue Segmentation Based on Quality Evaluation.

Accurate and automated segmentation of brain tissue images can significantly streamline clinical diagnosis and analysis. Manual delineation needs improvement due to its laborious and repetitive nature, while automated techniques encounter challenges stemming from disparities in magnetic resonance imaging (MRI) acquisition equipment and accurate labeling. Existing software packages, such as FSL and FreeSurfer, do not fully replace ground truth segmentation, highlighting the need for an efficient segmentation tool. To better capture the essence of cerebral tissue, we introduce nnSegNeXt, an innovative segmentation architecture built upon the foundations of quality assessment. This pioneering framework effectively addresses the challenges posed by missing and inaccurate annotations. To enhance the model's discriminative capacity, we integrate a 3D convolutional attention mechanism instead of conventional convolutional blocks, enabling simultaneous encoding of contextual information through the incorporation of multiscale convolutional features. Our methodology was evaluated on four multi-site T1-weighted MRI datasets from diverse sources, magnetic field strengths, scanning parameters, temporal instances, and neuropsychiatric conditions. Empirical evaluations on the HCP, SALD, and IXI datasets reveal that nnSegNeXt surpasses the esteemed nnUNet, achieving Dice coefficients of 0.992, 0.987, and 0.989, respectively, and demonstrating superior generalizability across four distinct projects with Dice coefficients ranging from 0.967 to 0.983. Additionally, extensive ablation studies have been implemented to corroborate the effectiveness of the proposed model. These findings represent a notable advancement in brain tissue analysis, suggesting that nnSegNeXt holds the promise to significantly refine clinical workflows.

Motivation on training transfer: a moderated moderation model of personal capacity for transfer and gender

Purpose Previous research suggests that motivation to transfer is related to the effective transfer of training, but less is known about the boundary conditions of this relationship. Therefore, this study aims to propose that motivation to transfer is more strongly related to training transfer when employees have a stronger personal capacity for transfer. The author also hypothesizes that this two-way interaction is further moderated by gender. Design/methodology/approach Using a cross-sectional design and a sample of 257 Tunisian workers, a three-way interaction model with the PROCESS macro (Hayes, 2018) was used to test the hypotheses. Findings Results show that workers respond to motivation to transfer with increased training transfer. Personal capacity for transfer and gender moderated the proposed relationship. Thus, the interaction effect between motivation to transfer and personal capacity for transfer is significant for women but not significant for men. More specifically, motivation to transfer is more likely to predict training transfer among women with high personal capacity for transfer than among those with low. Originality/value This study adds to the literature on motivation to transfer and training transfer by explaining two boundary conditions in this relationship and by recognizing the conditional interaction effect of gender on the moderating effect of personal capacity for transfer.

Low-carbon scheduling research of integrated energy system based on Stackelberg game under sharing mode

To achieve a win-win situation among multiple stakeholders within the integrated energy system, this paper proposes a low-carbon scheduling research of integrated energy system based on Stackelberg game under sharing mode. Firstly, an operation framework of integrated energy system based on leader-follower game was established, and a model of integrated energy system operator and user coalition was established considering carbon trading mechanism. Secondly, bi-directional long short-term memory is used to predict the travel data of electric vehicles, and a schedulable capacity model for electric vehicle cluster as shared energy storage was established. Then, an energy trading optimization model based on leader-follower game was constructed for integrated energy system operator and user coalition, and an improved Shapley value is adopted to realize cost allocation within user coalition. Finally, based on the demonstration project of Sino-Singapore Tianjin Eco-City is selected as the research object to make example analysis. The calculation results show this paper can achieve a reasonable benefit allocation among multiple participants, reduce carbon emissions of integrated energy system, and increase renewable energy accommodation capacity. The relevant achievements are a preliminary exploration for an effective mode of sharing economy in integrated energy system.

Technical Note for a Dataset of U.S. School Bus Fleets

This technical note describes the methods used to create a dataset of the composition of school bus fleets in the United States. The dataset contains data from 46 states and the District of Columbia, with information including the school district that the school bus serves, its model year, fuel type, manufacturer, seating capacity and ownership model. WRI researchers compiled the dataset by submitting records requests to state governments between March and November 2022.

On the Sensitivity of Future Hydrology in the Colorado River to the Selection of the Precipitation Partitioning Method

AbstractThe accurate partitioning of precipitation is important for snowpack and streamflow simulations in mountainous regions. However, most hydrologic models employ a partitioning method based on near‐surface air temperature (Ta) and the sensitivity of hydrologic simulations to the selection of the precipitation partitioning method (PPM) under climate change has been underexplored. To address this, we compared simulations using two PPMs, a conventional Ta‐based scheme and a wet‐bulb temperature (Tw)‐based approach (TA and TW) with the Variable Infiltration Capacity model. Our study focused on the Colorado River Basin (CRB) which is vulnerable to future snowfall and streamflow reductions due to climate warming. Historical simulations demonstrated the improved performance of the TW scheme in simulating snowfall fraction (SF) and snow water equivalent (SWE) in relation to in situ observations and a gridded SWE product. Subsequently, we evaluated the influence of the PPM selection on snowpack and streamflow projections from eight climate models under two emissions scenarios. The ensemble median results revealed more substantial decline in annual SF with the TW scheme as compared to the TA method (−12% vs. −9%) from the historical (1976–2005) to the far future (2066–2095) period, especially at lower elevations. Hydrologic simulations showed that the TA scheme underestimated reductions in SWE and streamflow (Q) under warming as compared to the TW scheme. This finding has important implications on future projections for the CRB and in other mountainous basins where climate warming can shift conditions from snowfall to rainfall.

Predicting drug-target interactions using matrix factorization with self-paced learning and dual similarity information.

Drug repositioning (DR) refers to a method used to find new targets for existing drugs. This method can effectively reduce the development cost of drugs, save time on drug development, and reduce the risks of drug design. The traditional experimental methods related to DR are time-consuming, expensive, and have a high failure rate. Several computational methods have been developed with the increase in data volume and computing power. In the last decade, matrix factorization (MF) methods have been widely used in DR issues. However, these methods still have some challenges. (1) The model easily falls into a bad local optimal solution due to the high noise and high missing rate in the data. (2) Single similarity information makes the learning power of the model insufficient in terms of identifying the potential associations accurately. We proposed self-paced learning with dual similarity information and MF (SPLDMF), which introduced the self-paced learning method and more information related to drugs and targets into the model to improve prediction performance. Combining self-paced learning first can effectively alleviate the model prone to fall into a bad local optimal solution because of the high noise and high data missing rate. Then, we incorporated more data into the model to improve the model's capacity for learning. Our model achieved the best results on each dataset tested. For example, the area under the receiver operating characteristic curve and the precision-recall curve of SPLDMF was 0.982 and 0.815, respectively, outperforming the state-of-the-art methods. The experimental results on five benchmark datasets and two extended datasets demonstrated the effectiveness of our approach in predicting drug-target interactions.

CNN-Based Machine Tool Monitoring with STFT Image Analysis

Abstract: In the realm of machine tool operation, effective condition monitoring holds predominant importance to ensure operational reliability and safety. Leveraging deep learning methodologies, particularly convolutional neural networks (CNN), for defect identification has gained significant attention. However, inherent challenges persist, including the extraction of salient features and potential information loss while extracting the features from raw vibration signals. In response, this study proposes an intelligent approach for condition monitoring in machine tools, integrating short-time Fourier transform and convolutional neural networks (STFT-CNN). The process entails using STFT to convert one-dimensional vibration signals into time-frequency pictures, which are then fed into the STFT-CNN model to acquire and identify fault features. Furthermore, the study explores optimizing STFT parameters, such as window type, window width, and translation overlap width, across various typical window functions to improve the effectiveness of the transformation process. Within the STFT-CNN model, the utilization of stacked double convolutional layers aims to augment the model's nonlinear expression capacity, thereby facilitating robust fault diagnosis capabilities in machine tool condition monitoring applications

Evaluation of side and tip resistances for barrette piles using CYCU/Barrette/Side&Tip/64

This study focuses on evaluating the side and tip resistances for barrette piles under compression loading. An extensive dataset from field load tests, designated as CYCU/Barrette/Side&Tip/64, was utilized for analysis. These data were categorized into drained and undrained soils, based on the predominant soil conditions along the pile shaft. In contrast, tip resistance depended on the soil (drained or undrained) or rock condition at the pile tip. Eight interpretation methods were employed to evaluate the measured side and tip resistances of each load test. The predicted side resistance was calculated using the classical α and β methods developed for more common piles such as drilled shafts. For the prediction of tip resistance, end-bearing capacity models for a drilled shaft resting on soil or socketed in rock are considered. Subsequently, a comparison was made between the measured and predicted capacities. Based on these analyses, it was observed that the measured side resistance is the main contributor to the overall capacity of barrette piles. The percentage of measured side resistance ranges from around 80% to 90%. In addition, the predicted side resistance calculated using the α and β methods is smaller than the measured side resistance interpreted using the L2 criterion. To reduce this prediction bias, the adhesion factor (α) and stress factors (K/Ko) for barrette piles were adjusted. For the tip resistance, the trend is opposite – predicted values are larger than the measured values for barrette piles resting on soil or socketed in rock. Another approach to correct for prediction bias called the generalized model factor is presented.

An interpretable online prediction method for remaining useful life of lithium-ion batteries

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is advantageous for maintaining the stability of electrical systems. In this paper, an interpretable online method which can reflect capacity regeneration is proposed to accurately estimate the RUL. Firstly, four health indicators (HIs) are extracted from the charging and discharging process for online prediction. Then, the HIs model is trained using support vector regression to obtain future features. And the capacity model of Gaussian process regression (GPR) is trained and analyzed by Shapley additive explanation (SHAP). Meanwhile, the state space for capacity prediction is constructed with the addition of Gaussian non-white noise to simulate the capacity regeneration. And the modified predicted HIs and noise are obtained by unscented Kalman filter. Finally, according to SHAP explainer, the predicted HIs acting as the baseline and the modified HIs containing information on capacity regeneration are chosen to predict RUL. In addition, the bounds of confidence intervals (CIs) are calculated separately to reflect the regenerated capacity. The experimental results demonstrate that the proposed online method can achieve high accuracy and effectively capture the capacity regeneration. The absolute error of failure RUL is below 5 and the minimum confidence interval is only 2.

Experimental study and theoretical prediction of axial compression behavior in PMC-reinforced CFST columns with void defects

Polymer-modified concrete (PMC) is a promising seismic reinforcement material to improve the ultimate load carrying capacity and ductility of structures. This study investigates the reinforcing effects of PMC on voided concrete-filled steel tube (CFST) columns through axial compression tests on 21 specimens. A meticulous analysis, incorporating void arch height, steel tube thickness, and PMC reinforcement, elucidates failure modes, load-deformation behavior, and strain characteristics. The experimental results demonstrate that PMC reinforcements significantly enhance the structural behavior and load-bearing capacity, improve ductility, and prevent material detachment from excellent bonding and collaborative working properties. A novel calculation model for ultimate load capacity and lateral pressure coefficient of voided CFST columns is developed and validated. Results reveal a distinct improvement in specimen performance post PMC reinforcement, with load-displacement curves resembling intact specimens, mitigating the impact of void defects. Furthermore, PMC-reinforced specimens exhibit effective recovery of load-bearing capacity and ductility, offering a cost-effective alternative to steel tube thickening. Changes in strain and Poisson's ratio curves underscore PMC's ability to restore biaxial compression stress, enhancing stability. Prediction models, rooted in the Double Shear Unified Strength Theory, accurately anticipate ultimate bearing capacity and lateral pressure coefficient, bolstering practical engineering design and repair endeavors. In summation, this study provides comprehensive insights into PMC-reinforced voided CFST columns, facilitating informed structural enhancements and repairs.

New recellularizable cardio-matrix scaffold as a base for alternative in vitro models for the cardiotoxicity risk prediction

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): SID 227944/22 DOR 2331157/23 Background Cardiotoxicity is the occurrence of cardiac dysfunction induced by pathophysiological stimuli as a result of toxic effects. A considerable number of drugs, including chemotherapics, antibiotics antidepressants but also antiarrhythmics, could cause an alteration of cardiac physiology, and then cardiac damage. These include disturbances in ventricular repolarization and QT interval, arrhythmias, bradycardia, tachycardia, decreases in left ventricular ejection fraction, and congestive heart failure. Precinical evaluation of drug cardiotoxicity has used animal models, which tend to be expensive, have lo throughput, and have limitations as not ever faithfully reflect the human pathophysiology, while current two-dimensional cellular models revealed to be not sufficient and specific. Purpose The aim of this study is to develop a myocardial tissue-like scaffold, in order to realize an efficient and easy-replicable in vitro model for drug cardiotoxicity prediction. This must be able to mimic the morphological, cellular, and electrophysiological complexity of the intact adult human myocardium. Material and Methods Starting from fresh porcine hearts, cardiac specimens were isolated from left ventricles using a scalpel, sectioned with a vibratome and punched with a biopsy puncher obtaining myocardial patches. Thus, samples were decellularized through a novel serial decellularization treatment based on osmotic shock and detergents. The decellularization procedure exploited a new automated, dynamic perfusion device for a standardized and optimized removal of cardiac cells and non-ECM proteins. These samples were analysed for decellularization effectiveness by DNA quantification, histology and immunofluorescence staining to evaluate cell removal and extracellular matrix integrity. Moreover, they were tested for cytocompatibility using human mesenchymal stem cells and cardiac progenitors, also derived from induced pluripotent stem cells. Cellular adhesion, behaviour, vitality, proliferation, and possible apoptotic effects were tested, too. Results In order to advance a robust bioelectronics model, an automated platform was developed to generate a three-dimensional, bioengineered replica of human myocardial tissue. With respect to the common decellularization protocol characterized by mild agitation, results based on the automated, dynamic treatments have shown a superior ability to obtain acellular, biocompatible scaffolds in terms of cell elements’ removal and extracellular matrix preservation, as well as time- and cost-effectiveness. Indeed, these decellularized scaffolds allow cell penetration, adhesion and survival. Conclusions Further experiments will be focused on evaluating functional bioengineered myocardial tissues with electrophysiological and molecular analysis to evaluate this new model's throughput capacity. Finally, the validation of these bioelectronic platforms will be performed with drugs with known cardiotoxic effects.

Soil environmental carrying capacity and its spatial high-precision accounting framework

Human activity intensity should be controlled within the carrying capacity of soil units, which is crucial for environmental sustainability. However, the existing assessment methods for soil environmental carrying capacity (SECC) rarely consider the relationship between human activity intensity and pollutant emissions, making it difficult to provide effective early warning of human activity intensity. Moreover, there is a lack of spatial high-precision accounting methods for SECC. This study first established a spatial soil environmental capacity (SEC) model based on the pollutant thresholds corresponding to the specific protection target. Next, a spatial net-input flux model was proposed based on soil pollutants' input/output fluxes. Then, the quantitative relationship between human activity intensity and pollutant emissions was established and further incorporated into the SECC model. Finally, the spatial high-precision accounting framework of SECC was proposed. The methodology was used to assess the SECC for the copper production capacity in a typical copper smelting area in China. The results showed that (i) the average SECs for Cu, Cd, Pb, Zn, As and Cr are 427.89, 16.84, 306.41, 376.8, 71.63, and 392.7 kg hm−2, respectively; (ii) heavy metal (HM) concentrations and land-use types jointly influence the spatial distribution pattern of SEC; (iii) atmospheric deposition is the dominant HM input pathway and the high net-input fluxes are mainly located in the southeast of the study area; (iv) with the current human activity intensity for 50 years, the average SECs for Cu, Cd, Pb, Zn, As and Cr are 202.31, 1.71, 20.9, 66.15, 36.73, and 3 kg hm−2, respectively; and (v) to maintain the protection target at the acceptable risk level within 50 years, the SECC for the increased copper production capacity is 1.53 × 106 t. This study provided an effective tool for early warning of human activity intensity.

Parabolic Modeling Forecasts of Space and Time European Hydropower Production

Renewable sources of energy production are some of the main targets today to protect the environment through reduced fossil fuel consumption and CO2 emissions. Alongside wind, solar, marine, biomass and nuclear sources, hydropower is among the oldest but still not fully explored renewable energy sources. Compared with other sources like wind and solar, hydropower is more stable and consistent, offering increased predictability. Even so, it should be analyzed considering water flow, dams capacity, climate change, irrigation, navigation, and so on. The aim of this study is to propose a forecast model of hydropower production capacity and identify long-term trends. The curve fit forecast parabolic model was applied to 33 European countries for time series data from 1990 to 2021. Space-time cube ArcGIS representation in 2D and 3D offers visualization of the prediction and model confidence rate. The quadratic trajectory fit the raw data for 14 countries, validated by visual check, and in 20 countries, validated by FMRSE 10% threshold from the maximal value. The quadratic model choice is good for forecasting future values of hydropower electric capacity in 22 countries, with accuracy confirmed by the VMRSE 10% threshold from the maximal value. Seven local outliers were identified, with only one validated as a global outlier based on the Generalized Extreme Studentized Deviate (GESD) test at a 5% maximal number of outliers and a 90% confidence level. This result achieves our objective of estimating a level with a high degree of occurrence and offering a reliable forecast of hydropower production capacity. All European countries show a growing trend in the short term, but the trends show a stagnation or decrease if policies do not consider intensive growth through new technology integration and digital adoption. Unfortunately, Europe does not have extensive growth potential compared with Asia–Pacific. Public policies must boost hybrid hydro–wind or hydro–solar systems and intensive technical solutions.