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- MacIntosh, Bradley J., et al.
(author)
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Radiological features of brain hemorrhage through automated segmentation from computed tomography in stroke and traumatic brain injury
- 2023
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In: Frontiers in Neurology. - 1664-2295. ; 14
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Journal article (peer-reviewed)abstract
- Introduction: Radiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study. Methods: Non-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Hemorrhage location frequency maps were generated. The number of estimated haematoma clusters was correlated with the total haematoma volume. Ground truth expert annotations were available for one ICH site; hence, a comparison was made with the estimated haematoma volumes. Segmentation volume estimates were used in a receiver operator characteristics (ROC) analysis for all samples (i.e., bleed detected) and then specifically for one site with few TBI-bleed cases. Results: The hemorrhage frequency maps showed spatial patterns of estimated lesions consistent with ICH or TBI-bleed presentations. There was a positive correlation between the estimated number of clusters and total haematoma volume for each site (correlation range: 0.45–0.74; each p-value < 0.01) and evidence of ICH between-site differences. Relative to hand-drawn annotations for one ICH site, the VIOLA-AI segmentation mask achieved a median Dice Similarity Coefficient of 0.82 (interquartile range: 0.78 and 0.83), resulting in a small overestimate in the haematoma volume by a median of 0.47 mL (interquartile range: 0.04 and 1.75 mL). The bleed detection ROC analysis for the whole sample gave a high area-under-the-curve (AUC) of 0.92 (with sensitivity and specificity of 83.28% and 95.41%); however, when considering only the mild head injury site, the TBI-bleed detection gave an AUC of 0.70. Discussion: An open-source segmentation tool was used to visualize hemorrhage locations across multiple data sources and revealed quantitative hemorrhage site differences. The automated total hemorrhage volume estimate correlated with a per-participant hemorrhage cluster count. ROC results were moderate-to-high. The VIOLA-AI tool had promising results and might be useful for various types of intracranial hemorrhage.
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| 2. |
- Sun, Ruirui, et al.
(author)
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Experimental and theoretical study on the dissociative photoionization of methyl methacrylate
- 2017
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In: Journal of Physics B. - : IOP Publishing. - 0953-4075 .- 1361-6455. ; 50:23
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Journal article (peer-reviewed)abstract
- The photoionization of methyl methacrylate and dissociation of its cation have been investigated by tunable vacuum ultraviolet synchrotron radiation coupled with time-of-flight mass spectrometer in the photon energy region of 9.0-15.5 eV. The ionization energy of methyl methacrylate and the appearance energies (AEs) for major fragments, C5H7O2+, C5H6O+, C4H5O2+,C4H5O+, C3H3O+ (C4H7+), C3H5+, C3H4+, C2H3O2+, and CH3+ are determined to be 9.76, 10.30, 10.66, 10.51, 11.17, 10.51, 10.74, 12.88, 12.73, 12.52, and 12.82 eV, respectively, by measurement of the photoionization efficiency curves. Possible formation pathways of the major fragments are proposed based on comparison of experimental AEs and energies predicted by ab initio G3B3 calculations. Transition states and intermediates involved in the dissociation channels are also located. The majority of the proposed channels occur through isomerization prior to dissociation. Hydrogen shift and ring closing/opening are found to be the dominant processes during photofragmentaion of methyl methacrylate.
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| 3. |
- Wang, Wenke, et al.
(author)
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Risk management of green supply chains for agricultural products based on social network evaluation framework
- 2024
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In: Business Strategy and the Environment. - : WILEY. - 0964-4733 .- 1099-0836.
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Journal article (peer-reviewed)abstract
- The green supply chain of agricultural products (GSCAP) is a key link for rural revitalization and sustainable development in China. However, it faces various risks from internal and external environments that threaten its performance and stability. This paper proposes a novel framework and system for identifying and evaluating the main risks in the GSCAP from the perspective of agricultural enterprises. The framework combines social network analysis (SNA) and an improved technique for order preference by similarity to an ideal solution (TOPSIS) method. SNA is used to analyze the correlations and influences among different types of risks, while the improved TOPSIS method is used to rank the risks of different GSCAPs and identify the key risks in each supply chain. The framework and system are verified by a case study of CDYBIT, a leading platform of food safety big data service in China. The results show that the supermarket supply chain has the highest risk, followed by the group catering supply chain, and the five-star hotel supply chain has the lowest risk. The main risk factors for each supply chain are also discussed, and some suggestions for risk management are provided. This paper contributes to the literature by providing a comprehensive and systematic risk assessment framework and system for the GSCAP, which can help agricultural enterprises improve their risk awareness and response capabilities.
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