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Öğe A comparative review of ORC and R-ORC technologies in terms of energy, exergy, and economic performance(Elsevier Ltd, 2024) Damarseckin, Serdal; Junior Kane, Sebe Yves; Atiz, Ayhan; Karakilcik, Mehmet; Sogukpinar, Haci; Bozkurt, Ismail; Oyucu, SaadinThis review examines Organic Rankine Cycle (ORC) technology, which generates electricity using organic fluids at low temperature ranges. To enhance the efficiency of basic ORC systems, they are often adapted into Regenerative Organic Rankine Cycle (R-ORC) systems. The review highlights the dimensions of economic, energy, and exergy efficiency, which are critical for practical application. Factors like the choice of working fluid, heat source temperature, and heat exchanger efficiency significantly affect economic feasibility; suboptimal choices can reduce returns and hinder project viability. Strategic decisions can improve economic outcomes and make ORC technology more appealing, as improved efficiency often leads to better economic performance through increased energy output and reduced operational costs. ORC and R-ORC systems promote sustainable energy production by enhancing energy efficiency in various applications, including geothermal power plants, industrial waste heat recovery, biomass energy production, and solar power plants. By enabling electricity generation even at low temperatures, these systems efficiently utilize existing energy sources, reduce dependence on fossil fuels, and minimize environmental impacts, thus providing both economic and ecological benefits. Additionally, when the studies conducted are examined, R-ORC exhibits higher performance than basic ORC. R-ORC is significantly superior to ORC in terms of both energy and exergy efficiency. Specifically, in terms of energy efficiency, R-ORC has been found to be 1.83 %–25.5 % more efficient. Regarding exergy efficiency, R-ORC demonstrates approximately 7.69 % better performance. Furthermore, due to these increases in efficiency, it has been determined that R-ORC also provides a more positive economic contribution compared to ORC. Thus, comparisons between ORC and R-ORC systems play a significant role in sustainable energy production and offer valuable guidance for future research. The limitations of ORC and R-ORC systems include limited efficiency due to low temperature differentials, the environmental impact of the organic fluids used, and high costs. © 2024 The AuthorsÖğe A Comparative Study of AI Methods on Renewable Energy Prediction for Smart Grids: Case of Turkey(Mdpi, 2024) Unsal, Derya Betul; Aksoz, Ahmet; Oyucu, Saadin; Guerrero, Josep M.; Guler, MerveFossil fuels still have emerged as the predominant energy source for power generation on a global scale. In recent years, Turkey has experienced a notable decrease in the production of coal and natural gas energy, juxtaposed with a significant rise in the production of renewable energy sources. The study employed neural networks, ANNs (artificial neural networks), and LSTM (long short-term memory), as well as CNN (convolutional neural network) and hybrid CNN-LSTM designs, to assess Turkey's energy potential. Real-time outcomes were produced by integrating these models with meteorological data. The objective was to design strategies for enhancing performance by comparing various models of outcomes. The data collected for Turkey as a whole are based on average values. Machine learning approaches were employed to mitigate the error rate seen in the acquired outcomes. Comparisons were conducted across light gradient boosting machine (LightGBM), gradient boosting regressor (GBR), and random forest regressor (RF) techniques, which represent machine learning models, alongside deep learning models. Based on the findings of the comparative analyses, it was determined that the machine learning model, LightGBM, exhibited the most favorable performance in enhancing the accuracy of predictions. Conversely, the hybrid model, CNN-LSTM, had the greatest rate of inaccuracy. This study will serve as a guide for renewable energy researchers, especially in developing countries such as Turkey that have not switched to a smart grid system.Öğe Advancing Electric Vehicle Infrastructure: A Review and Exploration of Battery-Assisted DC Fast Charging Stations(Mdpi, 2024) Aksoz, Ahmet; Asal, Burcak; Bicer, Emre; Oyucu, Saadin; Gencturk, Merve; Golestan, SaeedConcerns over fossil fuel depletion, fluctuating fuel prices, and CO2 emissions have accelerated the development of electric vehicle (EV) technologies. This article reviews advancements in EV fast charging technology and explores the development of battery-assisted DC fast charging stations to address the limitations of traditional chargers. Our proposed approach integrates battery storage, allowing chargers to operate independently of the electric grid by storing electrical energy during off-peak hours and releasing it during peak times. This reduces dependence on grid power and enhances grid stability. Moreover, the transformer-less, modular design of the proposed solution offers greater flexibility, scalability, and reduced installation costs. Additionally, the use of smart energy management systems, incorporating artificial intelligence and machine learning techniques to dynamically adjust charging rates, will be discussed to optimize efficiency and cost-effectiveness.Öğe Analysis of SARIMA Models for Forecasting Electricity Demand(IEEE, 2024) Aksoz, Ahmet; Oyucu, Saadin; Bicer, Emre; Bayindir, RamazanThis article presents an in-depth evaluation of electricity consumption predictions using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. Leveraging historical electricity consumption data, the SARIMA model demonstrates a commendable ability to forecast future consumption patterns. Our analysis reveals a strong alignment between the model's predictions and actual consumption data, affirming the efficacy of time series modeling in capturing complex energy consumption dynamics. Notably, while the model excels in predicting near-term consumption trends, uncertainties widen for long-term forecasts, prompting critical reflections on the model's evolving accuracy and reliability over time. For future research endeavors, we recommend comparing the performance of diverse time series models to discern optimal modeling approaches. Further optimization of model parameters stands as a paramount endeavor to refine prediction accuracy and mitigate uncertainties. Specifically, efforts to identify and address potential overfitting or underfitting tendencies within the model are advised. Additionally, leveraging supplementary data sources and integrating seasonal factors could bolster the reliability of future predictions, expanding the model's predictive scope and ensuring more robust and precise forecasts.Öğe Comparative Analysis of Commonly Used Machine Learning Approaches for Li-Ion Battery Performance Prediction and Management in Electric Vehicles(Mdpi, 2024) Oyucu, Saadin; Dogan, Ferdi; Aksoz, Ahmet; Bicer, EmreThe significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are crucial in terms of the popularity of EVs. The accurate prediction and management of LIBs in EVs are essential, and machine learning-based methods have been explored in order to estimate parameters such as the state of charge (SoC), the state of health (SoH), and the state of power (SoP). Various machine learning techniques, including support vector machines, decision trees, and deep learning, have been employed for predicting LIB states. This study proposes a methodology for comparative analysis, focusing on classical and deep learning approaches, and discusses enhancements to the LSTM (long short-term memory) and Bi-LSTM (bidirectional long short-term memory) methods. Evaluation metrics such as MSE, MAE, RMSE, and R-squared are applied to assess the proposed methods' performances. The study aims to contribute to technological advancements in the electric vehicle industry by predicting the performance of LIBs. The structure of the rest of the study is outlined, covering materials and methods, LIB data preparation, analysis, the proposal of machine learning models, evaluations, and concluding remarks, with recommendations for future studies.Öğe Discharge Capacity Estimation for Li-Ion Batteries: A Comparative Study(Mdpi, 2024) Oyucu, Saadin; Dumen, Sezer; Duru, Iremnur; Aksoz, Ahmet; Bicer, EmreLi-ion batteries are integral to various applications, ranging from electric vehicles to mobile devices, because of their high energy density and user friendliness. The assessment of the Li-ion state of heath stands as a crucial research domain, aiming to innovate safer and more effective battery management systems that can predict and promptly report any operational discrepancies. To achieve this, an array of machine learning (ML) and artificial intelligence (AI) methodologies have been employed to analyze data from Li-ion batteries, facilitating the estimation of critical parameters like state of charge (SoC) and state of health (SoH). The continuous enhancement of ML and AI algorithm efficiency remains a pivotal focus of scholarly inquiry. Our study distinguishes itself by separately evaluating traditional machine learning frameworks and advanced deep learning paradigms to determine their respective efficacy in predictive modeling. We dissected the performances of an assortment of models, spanning from conventional ML techniques to sophisticated, hybrid deep learning constructs. Our investigation provides a granular analysis of each model's utility, promoting an informed and strategic integration of ML and AI in Li-ion battery state of health prognostics. Specifically, a utilization of machine learning algorithms such as Random Forests (RFs) and eXtreme Gradient Boosting (XGBoost), alongside regression models like Elastic Net and foundational neural network approaches including Multilayer Perceptron (MLP) were studied. Furthermore, our research investigated the enhancement of time series analysis using intricate models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and their outcomes with those of hybrid models, including a RNN-long short-term memory (LSTM), CNN-LSTM, CNN-Gated Recurrent Unit (GRU) and RNN-GRU. Comparative evaluations reveal that the RNN-LSTM configuration achieved a Mean Squared Error (MSE) of 0.043, R-Squared of 0.758, Root Mean Square Error (RMSE) of 0.208, and Mean Absolute Error (MAE) of 0.124, whereas the CNN-LSTM framework reported an MSE of 0.039, R-Squared of 0.782, RMSE of 0.197, and MAE of 0.122, underscoring the potential of deep learning-based hybrid models in advancing the accuracy of battery state of health assessments.Öğe Ensemble Learning in Li-Ion Battery Management Systems: Focus on Voting Regression for Capacity Estimation(Institute of Electrical and Electronics Engineers Inc., 2024) Asal, Burcak; Oyucu, Saadin; Aksoz, AhmetAccurate estimation of discharge capacity in lithium-ion batteries is very important for optimizing their performance and longevity and directly affects the efficiency of battery management systems (BMS). Traditional models frequently encounter challenges in handling the non-linearities and complex interdependencies inherent in battery behavior. This study introduces a robust voting regression-based approach that combines multiple regression models to improve prediction accuracy and reliability. We employ four different regression configurations, Multi-Layer Perceptron (MLP), Random Forest (RF), Linear Regression and K-Nearest Neighbor (KNN) to form a combined estimator through voting mechanisms. The robustness of our voting-based approach is further validated through extensive experimentation on two real-world battery datasets including various operational conditions. Our results show that the voting regression approach we propose provides stable and accurate performance, making it a viable tool for different BMS applications. We also discuss the potential practical implications of our proposed voting approach and suggest directions for future research to further refine and adapt this approach to different types of battery technologies and configurations. © 2024 IEEE.Öğe Estimating Smart Grid Stability with Hybrid RNN plus LSTM Deep Learning Approach(IEEE, 2024) Oyucu, Saadin; Sagiroglu, Seref; Aksoz, Ahmet; Bicer, EmreSmart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of RNN to recognize relationships between consecutive data points with LSTM's capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently.Öğe Fake Voice Detection: A Hybrid CNN-LSTM Based Deep Learning Approach(Institute of Electrical and Electronics Engineers Inc., 2024) Oyucu, Saadin; Çelimli, Derya Betül Ünsal; Aksöz, AhmetThis study focuses on developing and evaluating a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) based deep learning model for detecting fake voice recordings. The proposed model addresses the critical issue of artificial intelligence-generated speech mimicking human voices, which can potentially be used for malicious purposes, thereby endangering individuals' privacy and safety. A comprehensive dataset comprising 5,889 real and 5,889 fake voice samples was utilized for this research. The dataset underwent rigorous preprocessing, including segmentation into fixed-length windows and normalization. The hybrid CNN-LSTM model was then trained and validated systematically involving exploratory data analysis and extensive hyperparameter tuning. The experimental results demonstrated that the proposed model achieved an accuracy of 99.2%, an F1 score of 99.2%, a recall of 99.4%, and a precision of 99.0%, indicating its robust performance in distinguishing between real and fake voices. The findings underscore the potential of the hybrid CNN-LSTM model as a powerful tool for safeguarding digital communications against the growing threat of fake voices. © 2024 IEEE.Öğe Hybrid AI-Powered Real-Time Distributed Denial of Service Detection and Traffic Monitoring for Software-Defined-Based Vehicular Ad Hoc Networks: A New Paradigm for Securing Intelligent Transportation Networks(MDPI, 2024) Polat, Onur; Oyucu, Saadin; Turkoglu, Muammer; Polat, Hueseyin; Aksoz, Ahmet; Yardimci, FahriVehicular Ad Hoc Networks (VANETs) are wireless networks that improve traffic efficiency, safety, and comfort for smart vehicle users. However, with the rise of smart and electric vehicles, traditional VANETs struggle with issues like scalability, management, energy efficiency, and dynamic pricing. Software Defined Networking (SDN) can help address these challenges by centralizing network control. The integration of SDN with VANETs, forming Software Defined-based VANETs (SD-VANETs), shows promise for intelligent transportation, particularly with autonomous vehicles. Nevertheless, SD-VANETs are susceptible to cyberattacks, especially Distributed Denial of Service (DDoS) attacks, making cybersecurity a crucial consideration for their future development. This study proposes a security system that incorporates a hybrid artificial intelligence model to detect DDoS attacks targeting the SDN controller in SD-VANET architecture. The proposed system is designed to operate as a module within the SDN controller, enabling the detection of DDoS attacks. The proposed attack detection methodology involves the collection of network traffic data, data processing, and the classification of these data. This methodology is based on a hybrid artificial intelligence model that combines a one-dimensional Convolutional Neural Network (1D-CNN) and Decision Tree models. According to experimental results, the proposed attack detection system identified that approximately 90% of the traffic in the SD-VANET network under DDoS attack consisted of malicious DDoS traffic flows. These results demonstrate that the proposed security system provides a promising solution for detecting DDoS attacks targeting the SD-VANET architecture.Öğe Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning(MDPI, 2025) Dogan, Ferdi; Oyucu, Saadin; Unsal, Derya Betul; Aksoz, Ahmet; Vafaeipour, MajidThe real-time prediction of energy production is essential for effective energy management and planning. Forecasts are essential in various areas, including the efficient utilization of energy resources, the provision of energy flexibility services, decision-making amidst uncertainty, the balancing of supply and demand, and the optimization of online energy systems. This study examines the use of tree-based ensemble learning models for renewable energy production prediction, focusing on environmental factors such as temperature, pressure, and humidity. The study's primary contribution lies in demonstrating the effectiveness of the bagged trees model in reducing overfitting and achieving higher accuracy compared to other models, while maintaining computational efficiency. The results indicate that less sophisticated models are inadequate for accurately representing complex datasets. The results evaluate the effectiveness of machine learning methods in delivering valuable insights for energy sectors managing environmental conditions and predicting renewable energy sourcesÖğe Integrating Machine Learning and MLOps for Wind Energy Forecasting: A Comparative Analysis and Optimization Study on Türkiye's Wind Data(Mdpi, 2024) Oyucu, Saadin; Aksoz, AhmetThis study conducted a detailed comparative analysis of various machine learning models to enhance wind energy forecasts, including linear regression, decision tree, random forest, gradient boosting machine, XGBoost, LightGBM, and CatBoost. Furthermore, it developed an end-to-end MLOps pipeline leveraging SCADA data from a wind turbine in T & uuml;rkiye. This research not only compared models using the RMSE metric for selection and optimization but also explored in detail the impact of integrating machine learning with MLOps on the precision of energy production forecasts. It investigated the suitability and efficiency of ML models in predicting wind energy with MLOps integration. The study explored ways to improve LightGBM algorithm performance through hyperparameter tuning and Docker utilization. It also highlighted challenges in speeding up MLOps development and deployment processes. Model performance was assessed using the RMSE metric, conducting a comparative evaluation across different models. The findings revealed that the RMSE values among the regression models ranged from 460 kW to 192 kW. Focusing on enhancing LightGBM, the research decreased the RMSE value to 190.34 kW. Despite facing technical and operational hurdles, the implementation of MLOps was proven to enhance the speed (latency of 9 ms), reliability (through Docker encapsulation), and scalability (using Docker swarm) of machine learning endeavors.Öğe Interpreting CNN-RNN Hybrid Model-Based Ensemble Learning with Explainable Artificial Intelligence to Predict the Performance of Li-Ion Batteries in Drone Flights(MDPI, 2024) Ersoz, Betul; Oyucu, Saadin; Aksoz, Ahmet; Sagiroglu, Seref; Bicer, EmreLi-ion batteries are important in modern technology, especially for drones, due to their high energy density, long cycle life, and lightweight properties. Predicting their performance is crucial for enhancing drone flight safety, optimizing operations, and reducing costs. This involves using advanced techniques like machine learning (e.g., Convolutional Neural Network-CNNs, Recurrent Neural Network-RNNs), statistical modeling (e.g., Kalman Filtering), and explainable AI (e.g., SHAP, LIME, PDP) to forecast battery behavior, extend battery life, and improve drone efficiency. The study aims to develop a CNN-RNN-based ensemble model, enhanced with explainable AI, to predict key battery metrics during drone flights. The model's predictions will aid in enhancing battery performance via continuous, data-driven monitoring, improve drone safety, optimize operations, and reduce greenhouse gas emissions through advanced recycling methods. In the present study, comparisons are made for the behaviors of two different drone Li-ion batteries, numbered 92 and 129. The ensemble model in Drone 92 showed the best performance with MAE (0.00032), RMSE (0.00067), and R2 (0.98665) scores. Similarly, the ensemble model in Drone 129 showed the best performance with MAE (0.00030), RMSE (0.00044), and R2 (0.98094) performance metrics. Similar performance results are obtained in the two predictions. However, drone 129 has a minimally lower error rate. When the Partial Dependence Plots results, which are one of the explainable AI (XAI) techniques, are interpreted with the decision tree algorithm, the effect of the Current (A) value on the model estimations in both drone flights is quite evident. When the current value is around -4, the model is more sensitive and shows more changes. This study will establish benchmarks for future research and foster advancements in drone and battery technologies through extensive testing.Öğe Multi-Stage Learning Framework Using Convolutional Neural Network and Decision Tree-Based Classification for Detection of DDoS Pandemic Attacks in SDN-Based SCADA Systems(Mdpi, 2024) Polat, Onur; Turkoglu, Muammer; Polat, Huseyin; Oyucu, Saadin; Uzen, Huseyin; Yardimci, Fahri; Aksoz, AhmetSupervisory Control and Data Acquisition (SCADA) systems, which play a critical role in monitoring, managing, and controlling industrial processes, face flexibility, scalability, and management difficulties arising from traditional network structures. Software-defined networking (SDN) offers a new opportunity to overcome the challenges traditional SCADA networks face, based on the concept of separating the control and data plane. Although integrating the SDN architecture into SCADA systems offers many advantages, it cannot address security concerns against cyber-attacks such as a distributed denial of service (DDoS). The fact that SDN has centralized management and programmability features causes attackers to carry out attacks that specifically target the SDN controller and data plane. If DDoS attacks against the SDN-based SCADA network are not detected and precautions are not taken, they can cause chaos and have terrible consequences. By detecting a possible DDoS attack at an early stage, security measures that can reduce the impact of the attack can be taken immediately, and the likelihood of being a direct victim of the attack decreases. This study proposes a multi-stage learning model using a 1-dimensional convolutional neural network (1D-CNN) and decision tree-based classification to detect DDoS attacks in SDN-based SCADA systems effectively. A new dataset containing various attack scenarios on a specific experimental network topology was created to be used in the training and testing phases of this model. According to the experimental results of this study, the proposed model achieved a 97.8% accuracy rate in DDoS-attack detection. The proposed multi-stage learning model shows that high-performance results can be achieved in detecting DDoS attacks against SDN-based SCADA systems.Öğe Optimizing Lithium-Ion Battery Performance: Integrating Machine Learning and Explainable AI for Enhanced Energy Management(Mdpi, 2024) Oyucu, Saadin; Ersoz, Betul; Sagiroglu, Seref; Aksoz, Ahmet; Bicer, EmreManaging the capacity of lithium-ion batteries (LiBs) accurately, particularly in large-scale applications, enhances the cost-effectiveness of energy storage systems. Less frequent replacement or maintenance of LiBs results in cost savings in the long term. Therefore, in this study, AdaBoost, gradient boosting, XGBoost, LightGBM, CatBoost, and ensemble learning models were employed to predict the discharge capacity of LiBs. The prediction performances of each model were compared based on mean absolute error (MAE), mean squared error (MSE), and R-squared values. The research findings reveal that the LightGBM model exhibited the lowest MAE (0.103) and MSE (0.019) values and the highest R-squared (0.887) value, thus demonstrating the strongest correlation in predictions. Gradient boosting and XGBoost models showed similar performance levels but ranked just below LightGBM. The competitive performance of the ensemble model indicates that combining multiple models could lead to an overall performance improvement. Furthermore, the study incorporates an analysis of key features affecting model predictions using SHAP (Shapley additive explanations) values within the framework of explainable artificial intelligence (XAI). This analysis evaluates the impact of features such as temperature, cycle index, voltage, and current on predictions, revealing a significant effect of temperature on discharge capacity. The results of this study emphasize the potential of machine learning models in LiB management within the XAI framework and demonstrate how these technologies could play a strategic role in optimizing energy storage systems.Öğe The Role of Machine Learning in Enhancing Battery Management for Drone Operations: A Focus on SoH Prediction Using Ensemble Learning Techniques(MDPI, 2024) Cetinus, Buesra; Oyucu, Saadin; Aksoz, Ahmet; Bicer, EmreThis study considers the significance of drones in various civilian applications, emphasizing battery-operated drones and their advantages and limitations, and highlights the importance of energy consumption, battery capacity, and the state of health of batteries in ensuring efficient drone operation and endurance. It also describes a robust testing methodology used to determine battery SoH accurately, considering discharge rates and using machine learning algorithms for analysis. Machine learning techniques, including classical regression models and Ensemble Learning methods, were developed and calibrated using experimental UAV data to predict SoH accurately. Evaluation metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) assess model performance, highlighting the balance between model complexity and generalization. The results demonstrated improved SoH predictions with machine learning models, though complexities may lead to overfitting challenges. The transition from simpler regression models to intricate Ensemble Learning methods is meticulously described, including an assessment of each model's strengths and limitations. Among the Ensemble Learning methods, Bagging, GBR, XGBoost, LightGBM, and stacking were studied. The stacking technique demonstrated promising results: for Flight 92 an RMSE of 0.03% and an MAE of 1.64% were observed, while for Flight 129 the RMSE was 0.66% and the MAE stood at 1.46%.Öğe Unveiling the wind energy future of Türkiye with policies technologies and potential(Elsevier Ltd, 2025) Sogukpinar, Haci; Bozkurt, Ismail; Oyucu, Saadin; Aksoz, AhmetTürkiye has set ambitious renewable energy targets aligned with the European Union Green Deal, aiming for 55 % renewables by 2035 and finally, zero-emission by 2050 so for this target a total installed capacity of 120 GW in wind and solar energy by 2035. In line with Türkiye's target of 120 GW of installed power in wind and solar energy by 2035, the sector aims to invest 5 GW of wind annually. This target will ensure that all wind investments to be put into operation in Türkiye by 2035 will reach a total of 50 GW and the wind-generated electricity will reach 138 TW-hours per year. As of the end of 2024, the total installed power has reached 13 GW and new installations continue rapidly. The wind potential of Türkiye is 118,683 MWe for wind speeds of over 6.8 m/s at a height of 50 m and the total WEP (Wind Energy Potential) of Türkiye is 48,000 MWe for regions with wind speeds of over 7 m/s. Offshore wind potential is calculated as 17,393 MWe, and these data are excluded from the land wind potential. Preparations for offshore wind power plants are ongoing, with an installation target of 5000 MWe for 2035. This study reviews Türkiye's wind energy potential, the evolution of its installations, and the policies driving this growth, concluding with recommendations for achieving its ambitious targets. © 2025 The Authors
