23 research outputs found

    Growth of 0.55eV-GaInAsSb Quaternary Alloy Films for a Thermophotovoltaic Device by Liquid Phase Epitaxy

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    Lattice matched Ga_(1-x)In_xAs_ySb_(1-y) quaternary alloy films for thermophotovoltaic cells were successfully grown on n-type GaSb substrates by liquid phase epitaxy. Mirror-like surfaces for the epitaxial layers were achieved and evaluated by atomic force microscopy. The composition of the Ga_(1-x)In_xAs_ySb_(1-y) layer was characterized by energy dispersive X-ray analysis with the result that x = 0.2, y = 0.17. The absorption edges of the Ga_(1-x)In_xAs_ySb_(1-y) films were determined to be 2. 256μm at room temperature by Fourier transform infrared transmission spectrum analysis, corresponding to an energy gap of 0.55eV. Hall measurements show that the highest obtained electron mobility in the undoped p-type samples is 512cm2~/(V·s) and the carrier density is 6. 1×10~(16)cm~(-3) at room temperature. Finally, GaInAsSb based thermophotovoltaic cells in different structures with quantum efficiency values of around 60% were fabricated and the spectrum response characteristics of the cells are discussed

    De-risking transient stability of AC/DC power systems based on ESS integration

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    Owing to the charging/discharging flexibility, electric storage system (ESS) is widely recognised as a promising technique that can be introduced to enhance transient stability of power systems. Here, a multi-objective ESS allocating and sizing approach is presented to de-risk the loss of stability for the AC/DC power system. The approach contains two major steps. Firstly, transient stability risk (TSR) is assessed based on the severity and probability of contingencies which is simulated according to the good point set sampling considering multiple uncertainties and probabilistic fault. In the second step, an allocating and sizing model is proposed for ESS to minimise the operational cost, while the AC/DC system is subject to the TSR. Additionally, strength Pareto evolutionary algorithm (SPEA2) is employed to solve the model, optimising ESS allocation and size as well as output of regular generators. A modified AC/DC test system is used to validate the presented approach. The results indicate that, with the appropriate planning strategy, ESS is able to significantly contribute to TSR improvement as it can assist in transient power balancing after a severe fault

    Biorefining fractionation of the Camellia oleifera Abel. hull into diverse bioproducts with a two-stage organosolv extraction

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    This study developed a two-stage organosolv extraction process for the production of diverse components from <i>tea oil fruit hull (TOFH)</i> – a common lignocellulosic byproduct of tea oil processing industry. Results showed that a mild <i>aqueous ethanol organosolv (AEO)</i> extraction isolated 83.8% of tea saponin and 81.5% of tannin from the TOFH. Further, a severe <i>atmospheric glycerol organosolv (AGO)</i> extraction recovered the residual tea saponin and tannin. Meantime, the AGO process removed 40% of the hemicellulose and 35% of the lignin. The solid residual after the two-stage AEO-AGO extraction had a high cellulose hydrolyzability (>95%), much higher than that (only 40%) of one-step AEO extracted solid residual. Structural features of these undissolved solids were characterized by SEM, AFM and FT-IR. The two-stage organosolv extraction showed good potential in biorefining of the agroforestry biomass into value added bioproducts

    Bioprocessing of tea oil fruit hull with acetic acid organosolv pretreatment in combination with alkaline H2O2

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    Abstract Background As a natural renewable biomass, the tea oil fruit hull (TOFH) mainly consists of lignocellulose, together with some bioactive substances. Our earlier work constructed a two-stage solvent-based process, including one aqueous ethanol organosolv extraction and an atmospheric glycerol organosolv (AGO) pretreatment, for bioprocessing of the TOFH into diverse bioproducts. However, the AGO pretreatment is not as selective as expected in removing the lignin from TOFH, resulting in the limited delignification and simultaneously high cellulose loss. Results In this study, acetic acid organosolv (AAO) pretreatment was optimized with experimental design to fractionate the TOFH selectively. Alkaline hydrogen peroxide (AHP) pretreatment was used for further delignification. Results indicate that the AAO–AHP pretreatment had an extremely good selectivity at component fractionation, resulting in 92% delignification and 88% hemicellulose removal, with 87% cellulose retention. The pretreated substrate presented a remarkable enzymatic hydrolysis of 85% for 48 h at a low cellulase loading of 3 FPU/g dry mass. The hydrolyzability was correlated with the composition and structure of substrates by using scanning electron microscopy, confocal laser scanning microscopy, and X-ray diffraction. Conclusion The mild AAO–AHP pretreatment is an environmentally benign and advantageous scheme for biorefinery of the agroforestry biomass into value-added bioproducts

    A Deep Learning-based Method to Extract Lumen and Media-Adventitia in Intravascular Ultrasound Images

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    Intravascular ultrasound (IVUS) imaging allows direct visualization of the coronary vessel wall and is suitable for assessing atherosclerosis and the degree of stenosis. Accurate segmentation and lumen and median-adventitia (MA) measurements from IVUS are essential for such a successful clinical evaluation. However, current automated segmentation by commercial software relies on manual corrections, which is time-consuming and user-dependent. We aim to develop a deep learning-based method using an encoder-decoder deep architecture to automatically and accurately extract both lumen and MA border. Inspired by the dual-path design of the state-of-the-art model IVUS-Net, our method named IVUS-U-Net++ achieved an extension of the U-Net++ model. More specifically, a feature pyramid network was added to the U-Net++ model, enabling the utilization of feature maps at different scales. Following the segmentation, the Pearson correlation and Bland-Altman analyses were performed to evaluate the correlations of 12 clinical parameters measured from our segmentation results and the ground truth. A dataset with 1746 IVUS images from 18 patients was used for training and testing. Our segmentation model at the patient level achieved a Jaccard measure (JM) of 0.9080 ± 0.0321 and a Hausdorff distance (HD) of 0.1484 ± 0.1584 mm for the lumen border; it achieved a JM of 0.9199 ± 0.0370 and an HD of 0.1781 ± 0.1906 mm for the MA border. The 12 clinical parameters measured from our segmentation results agreed well with those from the ground truth (all -values are smaller than .01). Our proposed method shows great promise for its clinical use in IVUS segmentation

    Semantic segmentation using deep learning to extract total extraocular muscles and optic nerve from orbital computed tomography images

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    Precise segmentation of total extraocular muscles (EOM) and optic nerve (ON) is essential to assess anatomical development and progression of thyroid-associated ophthalmopathy (TAO). To develop a semantic segmentation method to extract the total EOM and ON from orbital CT images in patients with suspected TAO. A total of 7879 images obtained from 97 subjects were enrolled in this study. 88 patients were randomly selected into the training/validation dataset, and the rest were put into the test dataset. Contours of the total EOM and ON in all patients were manually delineated by experienced radiologists as the ground truth. A three-dimensional(3D) end-to-end fully convolutional neural network called semantic V-net (SV-net) was developed for our segmentation task. Intersection over Union (IoU) was measured to evaluate the accuracy of the segmentation results, and Pearson correlation analysis was for evaluating the volumes measured from our segmentation results against those from the ground truth. It achieved an overall IoU of 0.8207 for the test dataset; the IoU was 0.7599 for the superior rectus muscle, 0.8183 for the lateral rectus muscle, 0.8481 for the medial rectus muscle, 0.8436 for the inferior rectus muscle and 0.8337 for the optic nerve. The volumes measured from our segmentation results agreed well with those from the ground truth(all R \u3e 0.98, P \u3c 0.0001). The qualitative and quantitative evaluations demonstrate excellent performance of our method in automatically extracting the total EOM and ON and measuring their volumes in orbital CT images. There is a great promise for clinical application to assess these anatomical structures for the diagnosis and prognosis of TAO

    Deep learning-based diagnosis of disease activity in patients with Graves’ orbitopathy using orbital SPECT/CT

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    Purpose: Orbital [99mTc]TcDTPA orbital single-photon emission computed tomography (SPECT)/CT is an important method for assessing inflammatory activity in patients with Graves’ orbitopathy (GO). However, interpreting the results requires substantial physician workload. We aim to propose an automated method called GO-Net to detect inflammatory activity in patients with GO. Materials and methods: GO-Net had two stages: (1) a semantic V-Net segmentation network (SV-Net) that extracts extraocular muscles (EOMs) in orbital CT images and (2) a convolutional neural network (CNN) that uses SPECT/CT images and the segmentation results to classify inflammatory activity. A total of 956 eyes from 478 patients with GO (active: 475; inactive: 481) at Xiangya Hospital of Central South University were investigated. For the segmentation task, five-fold cross-validation with 194 eyes was used for training and internal validation. For the classification task, 80% of the eye data were used for training and internal fivefold cross-validation, and the remaining 20% of the eye data were used for testing. The EOM regions of interest (ROIs) were manually drawn by two readers and reviewed by an experienced physician as ground truth for segmentation GO activity was diagnosed according to clinical activity scores (CASs) and the SPECT/CT images. Furthermore, results are interpreted and visualized using gradient-weighted class activation mapping (Grad-CAM). Results: The GO-Net model combining CT, SPECT, and EOM masks achieved a sensitivity of 84.63%, a specificity of 83.87%, and an area under the receiver operating curve (AUC) of 0.89 (p \u3c 0.01) on the test set for distinguishing active and inactive GO. Compared with the CT-only model, the GO-Net model showed superior diagnostic performance. Moreover, Grad-CAM demonstrated that the GO-Net model placed focus on the GO-active regions. For EOM segmentation, our segmentation model achieved a mean intersection over union (IOU) of 0.82. Conclusion: The proposed Go-Net model accurately detected GO activity and has great potential in the diagnosis of GO
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