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We present a non-Dyson fourth-order algebraic diagrammatic construction formulation of the electron propagator, featuring the distinct IP- and EA-ADC(4) schemes for the treatment of ionization and electron attachment processes. The algebraic expressions have been derived automatically using the intermediate state representation approach and implemented in the Q-Chem quantum-chemical program package. The performance of the novel methods is assessed with respect to high-level reference data for ionization potentials and electron affinities of closed- and open-shell systems. While only minor improvements over the corresponding third-order methods are observed for one-hole ionization and one-particle electron attachment processes from closed-shell systems (MAE = 0.27 eV and MAE = 0.05 eV), a significantly enhanced performance is found in case of open-shell reference states (MAE = 0.11 eV and MAE = 0.02 eV). A particularly appealing feature of the novel methods is their accurate treatment of satellite transitions. For closed-shell reference states, we obtain accuracies of MAE = 0.81 eV and MAE = 0.27 eV, while in case of open-shell reference states, mean absolute errors of MAE = 0.15 eV and MAE = 0.27 eV are found.
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http://dx.doi.org/10.1021/acs.jpca.4c03037 | DOI Listing |
Front Physiol
July 2025
Department of General Medicine, The First People's Hospital of Zhengzhou, Zhengzhou, Henan, China.
Background: With global aging and lifestyle changes, carotid atherosclerotic plaques are a major cause of cerebrovascular disease and ischemic stroke. However, ultrasound images suffer from high noise, low contrast, and blurred edges, making it difficult for traditional image processing methods to accurately extract plaque information.
Objective: To establish a deep learning-based DualPlaqueNet model for semantic segmentation and size prediction of plaques in carotid ultrasound images, thereby providing comprehensive and accurate auxiliary information for clinical risk assessment and personalized diagnosis and treatment.
J Cannabis Res
July 2025
Chemistry Program, Faculty of Science and Technology, Suratthani Rajabhat University, Surat Thani, 84100, Thailand.
Background: With the growing interest in the therapeutic applications of L., understanding how extraction techniques influence its antioxidant potential and phytochemical composition is essential for optimizing product quality and functionality.
Methods: This study evaluated the impact of various oil-based extraction methods—specifically using medium-chain triglycerides (MCT)—on the antioxidant activity and cannabinoid content of sugar leaf, leaf, and root.
Sci Rep
July 2025
School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, 600127, India.
The inherent variability of wind and solar energy introduces fluctuations in power generation, making accurate forecasting essential for maintaining the grid's stability. This study addresses key research gaps in wind energy forecasting, including the inability of traditional statistical models to capture complex, nonlinear temporal patterns, the underutilization of real-time, location-specific data, the lack of comparative analyses across diverse models and datasets, and the absence of systematic model selection strategies for future forecasting. To overcome these limitations, this study applies advanced machine learning (ML) and deep learning (DL) techniques with systematic hyperparameter tuning to enhance predictive performance.
View Article and Find Full Text PDFSci Data
May 2025
Beijing Institute of Remote Sensing Information, 100011, Beijing, China.
Global warming and urbanization serve as critical research themes in fine-scale climate studies, particularly in developed cities. This study aims to provide a high spatiotemporal resolution dataset of near-surface air temperatures for densely developed urban areas. The dataset comprises daily maximum, minimum, and mean temperatures for the summer months (June to August) from 2019 to 2023, at a spatial resolution of 100 m, across the Jiangbei climate zone in China.
View Article and Find Full Text PDFPhys Med Biol
May 2025
Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
This study investigates key factors influencing deep learning-based dose prediction models for head and neck cancer radiation therapy. The goal is to evaluate model accuracy, robustness, and computational efficiency, and to identify key components necessary for optimal performance.We systematically analyze the impact of input and dose grid resolution, input type, loss function, model architecture, and noise on model performance.
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