Several sets of energetic particle diagnostics, including a set of neutron flux monitoring systems, a solid-state neutral particle analyzer and a fast ion loss probe (FILP), have been used to investigate the energetic...
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Several sets of energetic particle diagnostics, including a set of neutron flux monitoring systems, a solid-state neutral particle analyzer and a fast ion loss probe (FILP), have been used to investigate the energetic ion losses induced by the long-lived saturated internal mode (LLM) in the HL-2A tokamak. Clear experimental evidence for different levels of energetic ion losses induced by LLM, sawtooth and minor disruption has been observed. A numerical calculation for the evolution of neutron emissions was carried out with the FBURN code, and it shows that the neutron emission drop rate linearly increases with the LLM amplitude and no threshold perturbation amplitude exists, illustrating that the loss mechanism for LLM induced energetic ion loss is dominantly convective. In addition, measurement results of the FILP demonstrate that LLM tends to expel energetic ions with relatively low energy (E < 27 keV) and high pitch angle (? > 60(?)), and can suppress the prompt loss of energetic ions with high energy and low pitch angle to a certain degree. Furthermore, the physical process for LLM induced energetic ion loss can be explained by orbit calculations, which show that LLM induced lost energetic ions will transport from center to peripheral region first, and then get lost out of plasma. The experimental observations are successfully reproduced by calculations using the ORBIT code combined with both the NUBEAM code and the MARS-K code. The paper clearly describes the whole physical process of LLM induced energetic ion loss for the first time in the HL-2A tokamak.
In this paper, a unified computational framework towards medical image explanation is proposed to promote the ability of computers on understanding and interpreting medical images. Four complementary modules are inclu...
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ISBN:
(纸本)9783030374464;9783030374457
In this paper, a unified computational framework towards medical image explanation is proposed to promote the ability of computers on understanding and interpreting medical images. Four complementary modules are included, such as the construction of Medical Image-Text Joint Embedding (MITE) based on large-scale medical images and related texts;a Medical Image Semantic Association (MISA) mechanism based on the MITE multimodal knowledge representation;a Hierarchical Medical Image Caption (HMIC) module that is visually understandable to radiologists;and a language-independent medical imaging report generation prototype system by integrating the HMIC and transfer learning method. As an initial study of automatic medical image explanation, preliminary experiments were carried out to verify the feasibility of the proposed framework, including the extraction of large scale medical image-text pairs, semantic concept detection from medical images, and automatic medical imaging reports generation. However, there is still a great challenge to produce medical image interpretations clinically usable, and further research is needed to empower machines explaining medical images like a human being.
An oxic-anoxic-oxic(O-A-O)system followed by coagulation and ozonation processes was used to study the treatment of coking *** the O-A-O process,the removals of NH4+-N,total nitrogen and COD were 91.5-93.3%,91.3-92.6%...
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An oxic-anoxic-oxic(O-A-O)system followed by coagulation and ozonation processes was used to study the treatment of coking *** the O-A-O process,the removals of NH4+-N,total nitrogen and COD were 91.5-93.3%,91.3-92.6%and 89.1-93.8%,respectively when employing hydraulic residence times of 60 h for the biochemical *** removal of NH4+-N was obtained due to the placement of an aerobic tank in front of A-O system which can mitigate the inhibitory effect of toxic compounds in coking wastewater on nitrifying *** of methanol into the anoxic reactor greatly increased the removal of total nitrogen,indicating that denitrifiers can hardly use organic compounds in coking wastewater as carbon source for *** values of the effluent from the O-A-O system were still higher than 260 mg/L even with a prolonged time of 160 h mainly due to the high refractory properties of residual compounds in the *** subsequent coagulation and ozonation processes resulted in the COD removal of 91.5%-93.3%and reduced the relative abundance of large molecular weight(MW)organics(>1 kDa)from 55.8%to 20.93%with the ozone,PAC and PAM dosages of 100,150 and 4 mg/L *** these conditions,the COD value and concentration of polycyclic aromatic hydrocarbons in the final effluent were less than 80 and 0.05 mg/L,respectively,which meet the requirement of the Chinese emission *** results indicate that the combined technology of O-A-O process,coagulation and ozonation is a reliable way for the treatment of coking wastewater.
Semantic concept detection contributes to machine understanding and learning from medical images;it also plays an important role in image reading and image-assisted diagnosis. In this study, the problem of detecting h...
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ISBN:
(数字)9783030285777
ISBN:
(纸本)9783030285777;9783030285760
Semantic concept detection contributes to machine understanding and learning from medical images;it also plays an important role in image reading and image-assisted diagnosis. In this study, the problem of detecting high-frequency concepts from medical images was transformed into a multilabel classification task. The transfer learning method based on convolutional neural networks (CNNs) was used to recognize high-frequency medical concepts. The image retrieval-based topic modelling method was used to obtain the semantically related concepts from images similar to the given medical images. Our group participated in the concept detection subtasks that were launched by ImageCLEFcaption 2018 and ImageCLEFmed Caption 2019. In the 2018 task, the CNN-based transfer learning method achieved an F1 score of 0.0928, while the retrieval-based topic model achieved an F1 score of 0.0907. Although the latter method recalled some low-frequency concepts, it heavily depended on the image retrieval results. For the latter 2019 task, we proposed body part-based pre-classification strategies and achieved an F1 score of 0.2235. The results indicated that the transfer learning-based multi-label classification method was more robust in high-frequency concept detection across different data sets, but there is still much room for improvement in large-scale open semantic concept detection research.
In Ref.[1],Eq.(8)has a typo,the following replacement should be done.γ_(bb)/2rδ/δr(rδ(r,t)/δr=2γ_(bb)/R_(bb)-Þ(t,t)-Ⅱ[h[r,t)](bubble=drop-bubble=drop)The publisher regrets an error in the original–article...
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In Ref.[1],Eq.(8)has a typo,the following replacement should be done.γ_(bb)/2rδ/δr(rδ(r,t)/δr=2γ_(bb)/R_(bb)-Þ(t,t)-Ⅱ[h[r,t)](bubble=drop-bubble=drop)The publisher regrets an error in the original–article,and the sentence that explained the equation“Eqs.(8)–(10)show the augmented Young–Laplace equation for the interactions of gas bubbles or liquid droplets in different configurations,where Rb is the bubble/drop radius,Rp is the particle radius,Rbp=(1/Rb+1/Rp)1.
Background: Recent studies have elucidated the instrumental role of microRNAs (miRNAs) in neuropathic pain (NP) progression. As one member of miRNAs, miR-130a-3p has been proved as a mediator in inflammation and neuro...
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Background: Recent studies have elucidated the instrumental role of microRNAs (miRNAs) in neuropathic pain (NP) progression. As one member of miRNAs, miR-130a-3p has been proved as a mediator in inflammation and neuronal maturation. The present study attempted to elucidate what effect miR-130a-3p exerts on NP. Materials and methods: The miR-130a-3p expression in the spinal cord tissues of rat with spinal cord compression injury (SCI) and LPS-induced BV2 microglia was determined with RT-PCR, which was further applied to analyze the clinical relevance between miR-130a-3p and neuropathic pain. Besides, the expression of IGF-1, IL-1 beta, IL-6, and TNF-alpha in the spinal cord tissues of rats was measured using RT-PCR and ELISA after intrathecal injection of miR-130a-3p inhibitors and tail vein injection of IGF-1 low-expression lentivirus (Lv-shIGF-1). Further, neuronal apoptosis (labeled by Caspase3) and microglial activation (labeled by Iba1) were examined by immunohistochemistry (IHC), and the levels of IGF-1, IGF-1R, NF-kappa B were determined by western blot. Additionally, bioinformatic was employed to analyze the potential target genes of miR-130a-3p. Furthermore, the dual luciferase activity assay and RNA immunoprecipitation assay were conducted to further substantiate whether miR-130a-3p targets IGF-1. Results: In comparison with the sham group, the miR-130a-3p expression was remarkably up-regulated in the spinal cord lesions of SCI rats. The ELISA results showed that inhibiting the miR-130a-3p significantly reduced NP symptoms of SCI rats, mitigated neuronal apoptosis, microglial activation, repressed NF-kappa B phosphorylation and the IL-1 beta, IL-6 and TNF-alpha expressions in SCI rats. Contrarily, downregulation of miR-130a-3p increased IGF-1 and IGF-1R expression. What's more, we observed the same effects in BV2 microglia. In addition, the bioinformatics analysis showed that miR-130-3p targeted at the 3'-untranslated region of IGF-1 and inhibiting its expr
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