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PURPOSE: To observe the growth and resolution of decompression gas bubbles in the spinal cord of live rats in real time using MRI. METHODS: We constructed an MRI-compatible pressure chamber system to visualize gas bubble dynamics in deep tissues in real time. The system pressurizes and depressurizes rodents inside an MRI scanner and monitors their respiratory rate, heart rate, and body temperature while providing gaseous anesthesia under pressure during the experiments. RESULTS: We observed the formation of decompression gas bubbles in the spinal cord of rats after compression to 7.1 bar absolute and rapid decompression inside the MRI scanner while maintaining continuous gaseous anesthesia and vital monitoring. CONCLUSION: We have shown the direct observation of decompression gas bubble formation in real time by MRI in live, anesthetized rats.
Assuntos
Imageamento por Ressonância Magnética , Animais , Ratos , Imageamento por Ressonância Magnética/métodos , Ratos Sprague-Dawley , Gases , Sistemas Computacionais , Medula Espinal/diagnóstico por imagem , Compressão da Medula Espinal/diagnóstico por imagem , Desenho de Equipamento , MasculinoRESUMO
PURPOSE: This study aimed to assess the margin for the planning target volume (PTV) using the Van Herk formula. We then validated the proposed margin by real-time magnetic resonance imaging (MRI). METHODS: An analysis of cone-beam computed tomography (CBCT) data from early glottic cancer patients was performed to evaluate organ motion. Deformed clinical target volumes (CTV) after rigid registration were acquired using the Velocity program (Varian Medical Systems, Palo Alto, CA, USA). Systematic (Σ) and random errors (σ) were evaluated. The margin for the PTV was defined as 2.5â¯Σâ¯+ 0.7â¯σ according to the Van Herk formula. To validate this margin, we accrued healthy volunteers. Sagittal real-time cine MRI was conducted using the ViewRay system (ViewRay Inc., Oakwood Village, OH, USA). Within the obtained sagittal images, the vocal cord was delineated. The movement of the vocal cord was summed up and considered as the internal target volume (ITV). We then assessed the degree of overlap between the ITV and the PTV (vocal cord plus margins) by calculating the volume overlap ratio, represented as (ITVâ©PTV)/ITV. RESULTS: CBCTs of 17 early glottic patients were analyzed. Σ and σ were 0.55 and 0.57 for left-right (LR), 0.70 and 0.60 for anterior-posterior (AP), and 1.84 and 1.04 for superior-inferior (SI), respectively. The calculated margin was 1.8â¯mm (LR), 2.2â¯mm (AP), and 5.3â¯mm (SI). Four healthy volunteers participated for validation. A margin of 3â¯mm (AP) and 5â¯mm (SI) was applied to the vocal cord as the PTV. The average volume overlap ratio between ITV and PTV was 0.92 (range 0.85-0.99) without swallowing and 0.77 (range 0.70-0.88) with swallowing. CONCLUSION: By evaluating organ motion by using CBCT, the margin was 1.8 (LR), 2.2 (AP), and 5.3â¯mm (SI). The margin acquired using CBCT fitted well in real-time cine MRI. Given that swallowing during radiotherapy can result in a substantial displacement, it is crucial to consider strategies aimed at minimizing swallowing and related motion.
Assuntos
Tomografia Computadorizada de Feixe Cônico , Glote , Neoplasias Laríngeas , Imagem Cinética por Ressonância Magnética , Humanos , Tomografia Computadorizada de Feixe Cônico/métodos , Imagem Cinética por Ressonância Magnética/métodos , Glote/diagnóstico por imagem , Masculino , Neoplasias Laríngeas/diagnóstico por imagem , Neoplasias Laríngeas/radioterapia , Pessoa de Meia-Idade , Feminino , Adulto , Idoso , Movimentos dos Órgãos , Sistemas Computacionais , Planejamento da Radioterapia Assistida por Computador/métodos , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
BACKGROUND: Lesion durability and transmurality are crucial for successful radiofrequency (RF) ablation. This study provides a model of real-time RF lesion visualization and insights into the role of underlying parameters, as local impedance (LI). METHODS: A force-sensing, LI-sensing catheter was used for lesion creation in an ex vivo model involving cross-sections of porcine cardiac preparations. During 60 s of RF application, one measurement per second was performed regarding lesion size and available ablation parameters. In total, 1847 measurements from n = 36 lesions were performed. Power (20-50 W) and contact force (1-5 g, 10-15 g, 20-25 g) were systematically alternated. RESULTS: Lesion formation was most prominent in the first seconds of RF application during which nonlinear lesion growth was observed (max. 1.08 mm/s for lesion depth and 2.71 mm/s for lesion diameter). Power levels determined the extent of lesion formation in the early phase. After 20 s, lesion size growth velocity approaches 0.1 mm/s at all power levels. LI changes were also highest in the first seconds (up to - 12 Ω/s) and decreased to less than - 0.1Ω/s after prolonged application. CONCLUSION: Lesion formation in irrigated RF ablation is a nonlinear process. Final lesion size resulting from an RF application is mainly influenced by high rates of lesion growth in the first seconds of ablation. LI seems to be a good surrogate for differentiating changes in lesion formation.
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Ablação por Cateter , Suínos , Animais , Ablação por Cateter/métodos , Sistemas Computacionais , Impedância Elétrica , Técnicas In Vitro , Ablação por Radiofrequência/métodos , Cirurgia Assistida por Computador/métodosRESUMO
As artificial intelligence (AI) expands its presence in healthcare, particularly within emergency medicine (EM), there is growing urgency to explore the ethical and practical considerations surrounding its adoption. AI holds the potential to revolutionize how emergency physicians (EPs) make clinical decisions, but AI's complexity often surpasses EPs' capacity to provide patients with informed consent regarding its use. This article underscores the crucial need to address the ethical pitfalls of AI in EM. Patient autonomy necessitates that EPs engage in conversations with patients about whether to use AI in their evaluation and treatment. As clinical AI integration expands, this discussion should become an integral part of the informed consent process, aligning with ethical and legal requirements. The rapid availability of AI programs, fueled by vast electronic health record (EHR) datasets, has led to increased pressure on hospitals and clinicians to embrace clinical AI without comprehensive system evaluation. However, the evolving landscape of AI technology outpaces our ability to anticipate its impact on medical practice and patient care. The central question arises: Are EPs equipped with the necessary knowledge to offer well-informed consent regarding clinical AI? Collaborative efforts between EPs, bioethicists, AI researchers, and healthcare administrators are essential for the development and implementation of optimal AI practices in EM. To facilitate informed consent about AI, EPs should understand at least seven key areas: (1) how AI systems operate; (2) whether AI systems are understandable and trustworthy; (3) the limitations of and errors AI systems make; (4) how disagreements between the EP and AI are resolved; (5) whether the patient's personally identifiable information (PII) and the AI computer systems will be secure; (6) if the AI system functions reliably (has been validated); and (7) if the AI program exhibits bias. This article addresses each of these critical issues, aiming to empower EPs with the knowledge required to navigate the intersection of AI and informed consent in EM.
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Inteligência Artificial , Medicina de Emergência , Humanos , Comunicação , Sistemas Computacionais , Consentimento Livre e EsclarecidoRESUMO
Transcranial ultrasound imaging assumes a growing significance in the detection and monitoring of intracranial lesions and cerebral blood flow. Accurate solution of partial differential equation (PDE) is one of the prerequisites for obtaining transcranial ultrasound wavefields. Grid-based numerical solvers such as finite difference (FD) and finite element methods have limitations including high computational costs and discretization errors. Purely data-driven methods have relatively high demands on training datasets. The fact that physics-informed neural network can only target the same model limits its application. In addition, compared to time-domain approaches, frequency-domain solutions offer advantages of reducing computational complexity and enabling stable and accurate inversions. Therefore, we introduce a framework called FD-embedded UNet (FEUNet) for solving frequency-domain transcranial ultrasound wavefields. The PDE error is calculated using the optimal 9-point FD operator, and it is integrated with the data-driven error to jointly guide the network iterations. We showcase the effectiveness of this approach through experiments involving idealized skull and brain models. FEUNet demonstrates versatility in handling various input scenarios and excels in enhancing prediction accuracy, especially with limited datasets and noisy information. Finally, we provide an overview of the advantages, limitations, and potential avenues for future research in this study.
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Sistemas Computacionais , Cabeça , Ultrassonografia , Redes Neurais de Computação , CrânioRESUMO
Detecting violent behavior in videos to ensure public safety and security poses a significant challenge. Precisely identifying and categorizing instances of violence in real-life closed-circuit television, which vary across specifications and locations, requires comprehensive understanding and processing of the sequential information embedded in these videos. This study aims to introduce a model that adeptly grasps the spatiotemporal context of videos within diverse settings and specifications of violent scenarios. We propose a method to accurately capture spatiotemporal features linked to violent behaviors using optical flow and RGB data. The approach leverages a Conv3D-based ResNet-3D model as the foundational network, capable of handling high-dimensional video data. The efficiency and accuracy of violence detection are enhanced by integrating an attention mechanism, which assigns greater weight to the most crucial frames within the RGB and optical-flow sequences during instances of violence. Our model was evaluated on the UBI-Fight, Hockey, Crowd, and Movie-Fights datasets; the proposed method outperformed existing state-of-the-art techniques, achieving area under the curve scores of 95.4, 98.1, 94.5, and 100.0 on the respective datasets. Moreover, this research not only has the potential to be applied in real-time surveillance systems but also promises to contribute to a broader spectrum of research in video analysis and understanding.
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Fluxo Óptico , Violência , Sistemas ComputacionaisRESUMO
This study is related to Smart Aqua Farm, which combines artificial intelligence (AI) and Internet of things (IoT) technology. This study aimed to monitor fish growth in indoor aquaculture while automatically measuring the average size and area in real time. Automatic fish size measurement technology is one of the essential elements for unmanned aquaculture. Under the condition of labor shortage, operators have much fatigue because they use a primitive method that samples the size and weight of fish just before fish shipment and measures them directly by humans. When this kind of process is automated, the operator's fatigue can be significantly reduced. Above all, after measuring the fish growth, predicting the final fish shipment date is possible by estimating how much feed and time are required until the fish becomes the desired size. In this study, a video camera and a developed light-emitting grid panel were installed in indoor aquaculture to acquire images of fish, and the size measurement of a mock-up fish was implemented using the proposed method.
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Aquicultura , Inteligência Artificial , Humanos , Animais , Aquicultura/métodos , Peixes , Sistemas Computacionais , TecnologiaRESUMO
In the field of unmanned systems, the combination of artificial intelligence with self-operating functionalities is becoming increasingly important. This study introduces a new method for autonomously detecting humans in indoor environments using unmanned aerial vehicles, utilizing the advanced techniques of a deep learning framework commonly known as "You Only Look Once" (YOLO). The key contribution of this research is the development of a new model (YOLO-IHD), specifically designed for human detection in indoor using drones. This model is created using a unique dataset gathered from aerial vehicle footage in various indoor environments. It significantly improves the accuracy of detecting people in these complex environments. The model achieves a notable advancement in autonomous monitoring and search-and-rescue operations, highlighting its importance for tasks that require precise human detection. The improved performance of the new model is due to its optimized convolutional layers and an attention mechanism that process complex visual data from indoor environments. This results in more dependable operation in critical situations like disaster response and indoor rescue missions. Moreover, when combined with an accelerating processing library, the model shows enhanced real-time detection capabilities and operates effectively in a real-world environment with a custom designed indoor drone. This research lays the groundwork for future enhancements designed to significantly increase the model's accuracy and the reliability of indoor human detection in real-time drone applications.
Assuntos
Inteligência Artificial , Dispositivos Aéreos não Tripulados , Humanos , Reprodutibilidade dos Testes , Sistemas Computacionais , CulturaRESUMO
Machine learning (ML) is a well-known subfield of artificial intelligence (AI) that aims at developing algorithms and statistical models able to empower computer systems to automatically adapt to a specific task through experience or learning from data [...].
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Inteligência Artificial , Aprendizado de Máquina , Algoritmos , Sistemas Computacionais , Modelos EstatísticosRESUMO
To properly control the network of the power system and ensure its protection, Phasor measurement units (PMUs) must be used to monitor the network's operation. PMUs can provide synchronized real-time measurements. These measurements can be used for state estimation, fault detection and diagnosis, and other grid control applications. Conventional state estimation methods use weighting factors to balance the different types of measurements, and zero injection measurements can lead to large weighting factors that can introduce computational errors. The offered methods are designed to ensure that these zero injection criteria can be strictly satisfied while calculating the voltage profile and observability of the various distribution networks without sacrificing computing efficiency. The proposed method's viability is assessed using standard IEEE distribution networks. MATLAB coding is used to simulate the case analyses. Overall, the study provides a valuable contribution to the field of power distribution system monitoring and control by simplifying the process of determining the optimal locations for PMUs in a distribution network and assessing the impact of ZI buses on the voltage profile of the system.
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Sistemas Computacionais , Tecnologia , InjeçõesRESUMO
Modeling and recognizing events in complex systems through machine learning techniques is a challenging task. Especially if the model is constrained to be explainable and interpretable, while ensuring high levels of accuracy. In this paper, we adopt a bilinear logistic regression model in which the parameters are trained in a data-driven fashion on a real-world dataset of power grid failure data. The bilinear white-box model - grounded on a specific neural architecture - has been proven effective in classifying faulty states with a performance comparable to several classifiers in technical literature. Additionally, the low computational complexity of the bilinear model, in terms of the number of free parameters, allows gaining insights into the fault phenomenon correlating the events that impact the power grid (exogenous causes) with its constitutive characteristics, thence eliciting the relational information hidden in the data. The proposed model is also able to estimate a vulnerability vector that can be associated, as a suitable characteristic "label", to power grid components, opening the way, as will be deeply demonstrated in the following, not only to predictive maintenance programs or condition monitoring tasks but also to risk assessment and scenario analyses in line with the explainable AI paradigm.
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Sistemas Computacionais , Aprendizado de Máquina , Modelos LogísticosRESUMO
The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. The integration of EVs into the grid has increased the demand for power while maintaining the grid's balance and efficiency. Demand Side Management (DSM) plays a pivotal role in this system, ensuring that the grid can accommodate the additional load demand without compromising stability or necessitating costly infrastructure upgrades. In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. The objective functions are constructed using various optimization strategies, such as the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA). This algorithm-based DSM method is simulated using MATLAB/Simulink in different cases and loads, such as residential and Information Technology (IT) sector loads. The results show that the peak load has been reduced from 4.5 MW to 2.6 MW, and the minimum load has been raised from 0.5 MW to 1.2 MW, successfully reducing the gap between peak and low points. Additionally, the performance of each algorithm was compared in terms of the difference between peak and valley points, computation time, and convergence rate to achieve the best fitness value.
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Algoritmos , Sistemas Computacionais , Fontes de Energia Elétrica , Eletricidade , ÍndiaRESUMO
Modern industrial processes are characterized by extensive, multiple operation units, and strong coupled correlation of subsystems. Fault detection of large-scale processes is still a challenging problem, especially for tandem plant-wide processes in multiple fields such as water treatment process. In this paper, a novel distributed graph attention network-bidirectional long short-term memory (D-GATBLSTM) fault detection model is proposed for large-scale industrial processes. Firstly, a multi-node knowledge graph (MNKG) is constructed using a joint data and knowledge driven strategy. Secondly, for large-scale industrial process, a global feature extractor of graph attention networks (GATs) is constructed, on the basis of which, sub-blocks are decomposed based on MNKG. Then, local feature extractors of bidirectional long short-term memory (Bi-LSTM) for each sub-block are constructed, in which correlations among multiple sub-blocks are considered. Finally, a multi-subblock fusion collaborative prediction model is constructed and the comprehensive fault detection results are given by the grid search method. The effectiveness of our D-GATBLSTM is exemplified in a secure water treatment process case, where it outperforms baseline models compared, with 27% improvement in precision, 15% increase in recall, and overall F-score enhancement of 0.22.
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Sistemas Computacionais , Reconhecimento Automatizado de Padrão , Conhecimento , Memória de Longo Prazo , Rememoração MentalRESUMO
Banking and stock markets consider gold to be an important component of their economic and financial status. There are various factors that influence the gold price trend and its fluctuations. Accurate and reliable prediction of the gold price is an essential part of financial and portfolio management. Moreover, it could provide insights about potential buy and sell points in order to prevent financial damages and reduce the risk of investment. In this paper, different architectures of deep neural network (DNN) have been proposed based on long short-term memory (LSTM) and convolutional-based neural networks (CNN) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. An illustrative dataset from the closing gold prices for 44 years, from 1978 to 2021, is provided to demonstrate the effectiveness and feasibility of this method. The grid search technique finds the optimal set of DNNs' parameters. Furthermore, to assess the efficiency of DNN models, three statistical indices of RMSE, RMAE, and coefficient of determination (R2), were calculated for the test set. Results indicate that the proposed hybrid model (CNN-Bi-LSTM) outperforms other models in total bias, capturing extreme values and obtaining promising results. In this model, CNN is used to extract features of input dataset. Furthermore, Bi-LSTM uses CNN's outputs to predict the daily closing gold price.
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Sistemas Computacionais , Ouro , Investimentos em Saúde , Memória de Longo Prazo , Redes Neurais de ComputaçãoRESUMO
High-voltage dry-type bushings, serving as the crucial junctions in DC power transmission, represent equipment with the highest failure rate on the DC primary side, underscoring the critical importance of monitoring their condition. Presently, numerical simulation methods are commonly employed to assess the internal state of bushings. However, due to limitations in the efficiency of multi-physics field computations, the guidance provided by numerical simulation results in the field of power equipment condition assessment is relatively weak. This paper focuses on solving the electrical-thermal coupling in high-voltage dry-type bushings. Addressing the most widely used tetrahedral mesh in numerical computations, we propose an efficient solution method based on the concept of "smooth domains." This method involves partitioning the volume centroids of the elements into multiple smooth domains within the computational domain. Electric and thermal conduction matrix calculations occur within these smooth domains, rather than within the grid or element interiors. This approach eliminates the need for traditional element mapping and complex volume integration. To demonstrate the effectiveness of this method, we use high-voltage dry-type bushings as a case study, comparing the performance of our approach with traditional finite element algorithms. We verify the algorithm's computational efficiency and apply it to the analysis of typical temperature anomalies in bushings, further illustrating its suitability for electrical equipment condition assessment.
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Algoritmos , Eletricidade , Simulação por Computador , Temperatura , Sistemas Computacionais , Análise de Elementos FinitosRESUMO
In steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems, traditional flickering stimulation patterns face challenges in achieving a trade-off in both BCI performance and visual comfort across various frequency bands. To investigate the optimal stimulation paradigms with high performance and high comfort for each frequency band, this study systematically compared the characteristics of SSVEP and user experience of different stimulation paradigms with a wide stimulation frequency range of 1-60 Hz. The findings suggest that, for a better balance between system performance and user experience, ON and OFF grid stimuli with a Weber contrast of 50% can be utilized as alternatives to traditional flickering stimulation paradigms in the frequency band of 1-25 Hz. In the 25-35 Hz range, uniform flicker stimuli with the same 50% contrast are more suitable. In the higher frequency band, traditional uniform flicker stimuli with a high 300% contrast are preferred. These results are significant for developing high performance and user-friendly SSVEP-based BCI systems.
Assuntos
Interfaces Cérebro-Computador , Potenciais Evocados Visuais , Humanos , Estimulação Luminosa/métodos , Eletroencefalografia/métodos , Sistemas ComputacionaisRESUMO
Ultra-short-term power load forecasting is beneficial to improve the economic efficiency of power systems and ensure the safe and stable operation of power grids. As the volatility and randomness of loads in power systems, make it difficult to achieve accurate and reliable power load forecasting, a sequence-to-sequence based learning framework is proposed to learn feature information in different dimensions synchronously. Convolutional Neural Networks(CNN) Combined with Bidirectional Long Short Term Memory(BiLSTM) Networks is constructed in the encoder to extract the correlated timing features embedded in external factors affecting power loads. The parallel BiLSTM network is constructed in the decoder to mine the power load timing information in different regions separately. The multi-headed attention mechanism is introduced to fuse the BiLSTM hidden layer state information in different components to further highlight the key information representation. The load forecastion results in different regions are output through the fully connected layer. The model proposed in this paper has the advantage of high forecastion accuracy through the example analysis of real power load data.
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Sistemas Computacionais , Aprendizagem , Memória de Longo Prazo , Redes Neurais de Computação , PrevisõesRESUMO
There are global efforts to deploy Electric Vehicles (EVs) because of the role they promise to play in energy transition. These efforts underscore the e-mobility paradigm, representing an interplay between renewable energy resources, smart technologies, and networked transportation. However, there are concerns that these initiatives could burden the electricity grid due to increased demand. Hence, the need for accurate short-term load forecasting is pivotal for the efficient planning, operation, and control of the grid and associated power systems. This study presents robust models for forecasting half-hourly and hourly loads in the UK's power system. The work leverages machine learning techniques such as Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR) to develop robust prediction models using the net imports dataset from 2010 to 2020. The models were evaluated based on metrics like Root Mean Square Error (RMSE), Mean Absolute Prediction Error (MAPE), Mean Absolute Deviation (MAD), and the Correlation of Determination (R2). For half-hourly forecasts, SVR performed best with an R-value of 99.85%, followed closely by GPR and ANN. But, for hourly forecasts, ANN led with an R-value of 99.71%. The findings affirm the reliability and precision of machine learning methods in short-term load forecasting, particularly highlighting the superior accuracy of the SVR model for half-hourly forecasts and the ANN model for hourly forecasts.
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Benchmarking , Sistemas Computacionais , Reprodutibilidade dos Testes , Eletricidade , Reino Unido , PrevisõesRESUMO
The interconnected power system connects the power grids of different regions through transmission lines, achieving power interconnection and resource sharing. However, data is transmitted through open power networks and is more susceptible to network attacks. To improve the stability of interconnected power systems under deception attacks, three scenarios of system security load frequency control were studied. Based on the construction of a dynamic model of load frequency control, an event-triggered strategy was used to reduce the communication frequency between nodes, resulting in a reduction in the amount of network transmission data. A sliding mode controller was constructed to solve the problem of event-triggered sliding mode security load frequency control. Elastic event-triggered sliding mode load frequency control for interconnected power systems under mixed attacks. The simulation results showed that using the load frequency control model triggered by events, the load frequency deviation of the interconnected power system can be stabilized at around 12 seconds, effectively saving the cost of network resources. Under the regulation of the load frequency control model based on sliding mode control, the interconnected power system stabilized in 10 seconds, reducing the load of network transmission. The elastic event-triggered sliding mode load frequency control model can ensure stable transmission of power data under various attacks and has good anti-interference performance. The results of this study have played an important role in achieving the stability of power resource supply. Compared with previous studies on individual power systems, this study solves the attack problem of interconnected power systems and considers the frequency control problem of system security loads under mixed attacks, enabling the system to recover stability faster.
Assuntos
Comunicação , Sistemas Computacionais , Simulação por Computador , Fontes de Energia Elétrica , EnganaçãoRESUMO
Developing novel EV chargers is crucial for accelerating Electric Vehicle (EV) adoption, mitigating range anxiety, and fostering technological advancements that enhance charging efficiency and grid integration. These advancements address current challenges and contribute to a more sustainable and convenient future of electric mobility. This paper explores the performance dynamics of a solar-integrated charging system. It outlines a simulation study on harnessing solar energy as the primary Direct Current (DC) EV charging source. The approach incorporates an Energy Storage System (ESS) to address solar intermittencies and mitigate photovoltaic (PV) mismatch losses. Executed through MATLAB, the system integrates key components, including solar PV panels, the ESS, a DC charger, and an EV battery. The study finds that a change in solar irradiance from 400 W/m2 to 1000 W/m2 resulted in a substantial 47% increase in the output power of the solar PV system. Simultaneously, the ESS shows a 38% boost in output power under similar conditions, with the assessments conducted at a room temperature of 25°C. The results emphasize that optimal solar panel placement with higher irradiance levels is essential to leverage integrated solar energy EV chargers. The research also illuminates the positive correlation between elevated irradiance levels and the EV battery's State of Charge (SOC). This correlation underscores the efficiency gains achievable through enhanced solar power absorption, facilitating more effective and expedited EV charging.