This study explores the intricate connections among financial technology (FinTech), artificial intelligence (AI), and eco-friendly markets in the US, shedding light on their dynamic interplay and implications for sustainable investment and policy strategies. Specifically, our research delves into the transformative roles of FinTech and AI in broadening financial access, fostering green financing initiatives, and aligning financial practices with environmentally conscious objectives. We also investigate market reactions among the AI, FinTech, non-greenwashing, and eco-friendly markets during exogenous shocks, offering valuable insights into these markets' interconnectedness. An innovative connectedness approach, the R decomposed measures, is employed to capture the contemporaneous and lagged spillover effects using daily data from December 19, 2017, to November 1, 2023. We also focus on constructing a minimum connectedness portfolio using the time-varying parameter vector autoregressive approach. The findings reveal significant volatility connectivity within these intergroups, emphasizing the need for sustainable tech finance policies and real-time monitoring systems to address market fluctuations. Overall, this study contributes to an underexplored area by providing empirical evidence and valuable implications for scholars and policymakers, and can help in guiding sustainable investment and policy strategies aligned with zero-emissions agendas.
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http://dx.doi.org/10.1016/j.jenvman.2024.120977 | DOI Listing |
Comput Med Imaging Graph
January 2025
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; National Key Laboratory of Kidney Diseases, Beijing 100853, China. Electronic address:
In clinical optical molecular imaging, the need for real-time high frame rates and low excitation doses to ensure patient safety inherently increases susceptibility to detection noise. Faced with the challenge of image degradation caused by severe noise, image denoising is essential for mitigating the trade-off between acquisition cost and image quality. However, prevailing deep learning methods exhibit uncontrollable and suboptimal performance with limited interpretability, primarily due to neglecting underlying physical model and frequency information.
View Article and Find Full Text PDFEur J Radiol
January 2025
Department of Medicine 3, Friedrich-Alexander-University Erlangen-Nürnberg and Universitätsklinikum Erlangen, Erlangen, Germany; Institute of Medical Physics, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany. Electronic address:
Objectives: Contrast agents are frequently administered in computed tomography (CT) scans used for opportunistic screening of osteoporosis. The objective of this study is to compare the impact of contrast-related bone mineral density (BMD) increase between phantom-based and internal CT calibration techniques.
Materials And Methods: Phantom-based and internal CT calibration techniques were used to determine trabecular BMD in 93 existing clinical CT scans of the lumbar spine of 34 subjects, scanned before and after administration of contrast agents.
J Neurosurg
January 2025
1Department of Neurosurgery, St. Olav's University Hospital, Trondheim, Norway.
Objective: The extent of resection (EOR) and postoperative residual tumor (RT) volume are prognostic factors in glioblastoma. Calculations of EOR and RT rely on accurate tumor segmentations. Raidionics is an open-access software that enables automatic segmentation of preoperative and early postoperative glioblastoma using pretrained deep learning models.
View Article and Find Full Text PDFJCO Precis Oncol
January 2025
Translational Research Support Office, National Cancer Center Hospital East, Chiba, Japan.
Purpose: Human epidermal growth factor receptor 2 (HER2)-targeted therapies have shown promise in treating -amplified metastatic colorectal cancer (mCRC). Identifying optimal biomarkers for treatment decisions remains challenging. This study explores the potential of artificial intelligence (AI) in predicting treatment responses to trastuzumab plus pertuzumab (TP) in patients with -amplified mCRC from the phase II TRIUMPH trial.
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