Technical-economic and environmental protection performance evaluation of a novel hybrid solar-nuclear thermally coupled power and desalination plant.

Clean energy complementary system can reduce environmental pollution effectively and is considered as a future energy development direction. In this paper, an innovative solar-nuclear thermally coupled power and desalination plant for electricity and freshwater productions is proposed. As solar power and nuclear power are combined, this multi-energy system is a clean energy system and basically has no emissions of soot, sulfur oxides, carbon dioxide, and nitrogen oxides. The operating behavior assessment results of the multi-energy system show that the power generation and freshwater production systems can operate synergistically. The electric power and corresponding efficiency of the multi-energy system are 290.7 MW and 38.2%, in which the solar proportion is about 38.1%. The daily freshwater production of the multi-energy system is 3761.3 t. The economic assessment results reveal that the levelized costs of electricity and freshwater of the multi-energy system are 0.361 yuan/(kWh) and 1.645 yuan/t. The environmental protection analysis results show that in contrast with a coal-fired system, the annual emission reductions of soot, sulfur oxides, carbon dioxide, and nitrogen oxides of the multi-energy system are 7350.94 t, 12,634.42 t, 513,034.14 t, and 11,945.28 t, revealing a significant environmental protection performance.

Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study.

BACKGROUND: Determining the grade and molecular marker status of intramedullary gliomas is important for assessing treatment outcomes and prognosis. Invasive biopsy for pathology usually carries a high risk of tissue damage, especially to the spinal cord, and there are currently no non-invasive strategies to identify the pathological type of intramedullary gliomas. Therefore, this study aimed to develop a non-invasive machine learning model to assist doctors in identifying the intramedullary glioma grade and mutation status of molecular markers. METHODS: A total of 461 patients from two institutions were included, and their sagittal (SAG) and transverse (TRA) T2-weighted magnetic resonance imaging scans and clinical data were acquired preoperatively. We employed a transformer-based deep learning model to automatically segment lesions in the SAG and TRA phases and extract their radiomics features. Different feature representations were fed into the proposed neural networks and compared with those of other mainstream models. RESULTS: The dice similarity coefficients of the Swin transformer in the SAG and TRA phases were 0.8697 and 0.8738, respectively. The results demonstrated that the best performance was obtained in our proposed neural networks based on multimodal fusion (SAG-TRA-clinical) features. In the external validation cohort, the areas under the receiver operating characteristic curve for graded (WHO I-II or WHO III-IV), alpha thalassemia/mental retardation syndrome X-linked (ATRX) status, and tumor protein p53 (P53) status prediction tasks were 0.8431, 0.7622, and 0.7954, respectively. CONCLUSIONS: This study reports a novel machine learning strategy that, for the first time, is based on multimodal features to predict the ATRX and P53 mutation status and grades of intramedullary gliomas. The generalized application of these models could non-invasively provide more tumor-specific pathological information for determining the treatment and prognosis of intramedullary gliomas.

Automatic and Efficient Prediction of Hematoma Expansion in Patients with Hypertensive Intracerebral Hemorrhage Using Deep Learning Based on CT Images.

Patients with hypertensive intracerebral hemorrhage (ICH) have a high hematoma expansion (HE) incidence. Noninvasive prediction HE helps doctors take effective measures to prevent accidents. This study retrospectively analyzed 253 cases of hypertensive intraparenchymal hematoma. Baseline non-contrast-enhanced CT scans (NECTs) were collected at admission and compared with subsequent CTs to determine the presence of HE. An end-to-end deep learning method based on CT was proposed to automatically segment the hematoma region, region of interest (ROI) feature extraction, and HE prediction. A variety of algorithms were employed for comparison. U-Net with attention performs best in the task of segmenting hematomas, with the mean Intersection overUnion (mIoU) of 0.9025. ResNet-34 achieves the most robust generalization capability in HE prediction, with an area under the receiver operating characteristic curve (AUC) of 0.9267, an accuracy of 0.8827, and an F1 score of 0.8644. The proposed method is superior to other mainstream models, which will facilitate accurate, efficient, and automated HE prediction.