Efficient method for aeroelastic tailoring of composite wing to minimize gust response

Aeroelastic tailoring of laminated composite structure demands relatively high computational time especially for dynamic problem. This paper presents an efficient method for aeroelastic dynamic response analysis with significantly reduced computational time. In this method, a relationship is established between the maximum aeroelastic response and quasi-steady deflection of a wing subject to a dynamic loading. Based on this relationship, the time consuming dynamic response can be approximated by a quasi-steady deflection analysis in a large proportion of the optimization process. This method has been applied to the aeroelastic tailoring of a composite wing of a tailless aircraft for minimum gust response. The results have shown that 20%–36% gust response reduction has been achieved for this case. The computational time of the optimization process has been reduced by 90% at the cost of accuracy reduction of 2~4% comparing with the traditional dynamic response analysis

Effects of different hypertonic resuscitations on traumatic brain injuries and cranioencephalic trauma: A single centre, retrospective analysis

Purpose: To compare the efficacies of 3 % (w/v) hypertonic saline, 20 % (w/v) mannitol, and 10 % (w/v) mannitol plus 10 % (v/v) glycerol in the management of intracranial hypertension.Methods: Patients with intracranial pressure > 20 mmHg received 3 % (w/v) hypertonic saline (HT cohort, n = 78) or 20 % w/v mannitol (MT cohort, n = 82) or 10 % (w/v) mannitol plus 10 % (v/v) glycerol (MG cohort, n = 73) until intracranial pressure was reduced below 15 mmHg. Neurologic outcomes, hemodynamic parameters, and clinical biochemistry were evaluated as indices of intracranial pressure and pathological parameters.Results: Serum sodium levels and serum osmolarity were significantly increased by 3 % (w/ v) hypertonic saline, relative to the other hypertonic resuscitations. At the end of 1 h observation period, 60 (77 %), 36 (44 %), and 41 (56 %) of patients from HT, MT, and MG cohorts, respectively, had their cerebral perfusion pressure successfully maintained at > 70 mmHg. At the end of 1 h observation period, intracranial pressure ≤ 20 mmHg was successfully maintained in 78 (100 %), 81 (99 %), and 73 (100 %) patients from HT, MT, and MG cohorts, respectively. The mean values of arterial pressure of patients in HT, MT, and MG cohorts were increased after 1 h, 15 min, and 30 min of interventions, respectively.Conclusion: These results indicate that 3 % (w/v) hypertonic saline was the most rapid and most effective resuscitation for the management of intracranial hypertension in traumatic brain injuries or cranioencephalic trauma

Efficient Method for Aeroelastic Tailoring of Composite Wing to Minimize Gust Response

Aeroelastic tailoring of laminated composite structure demands relatively high computational time especially for dynamic problem. This paper presents an efficient method for aeroelastic dynamic response analysis with significantly reduced computational time. In this method, a relationship is established between the maximum aeroelastic response and quasi-steady deflection of a wing subject to a dynamic loading. Based on this relationship, the time consuming dynamic response can be approximated by a quasi-steady deflection analysis in a large proportion of the optimization process. This method has been applied to the aeroelastic tailoring of a composite wing of a tailless aircraft for minimum gust response. The results have shown that 20%-36% gust response reduction has been achieved for this case. The computational time of the optimization process has been reduced by 90% at the cost of accuracy reduction of 2∼4% comparing with the traditional dynamic response analysis

Reduced neural responses to reward reflect anhedonia and inattention: an ERP study

An inhibited neural response to reward is typical of clinical depression and can predict an individual's overall depressive symptoms. However, the mechanism underlying this are unclear. Previous studies have found that anhedonia and inattention may mediate the relationship between reward sensitivity and depressive symptoms. Therefore, this study aimed to verify the relationship between reward sensitivity and overall depressive symptoms in a depressive tendency sample as well as to explore the mechanism underlying the ability of neural responses to reward to predict overall depressive symptoms via a mediation model. Sixty-four participants (33 with depressive tendencies and 31 without; dichotomized by BDI-II) finished simple gambling tasks while their event-related potential components (ERPs) were recorded and compared. Linear regression was conducted to verify the predictive effect of ERPs on overall depressive symptoms. A multiple mediator model was used, with anhedonia and distractibility as mediators reward sensitivity and overall depressive symptoms. The amplitude of reward positivity (ΔRewP) was greater in healthy controls compared to those with depressive tendencies (p = 0.006). Both the gain-locked ERP component (b = − 1.183, p = 0.007) and the ΔRewP (b = − 0.991, p = 0.024) could significantly negatively predict overall depressive symptoms even after controlling for all anxiety symptoms. The indirect effects of anhedonia and distractibility were significant (both confidence intervals did not contain 0) while the direct effect of reward sensitivity on depressive symptom was not significant (lower confidence interval = − 0.320, upper confidence interval = 0.065). Individuals with depressive tendencies display impaired neural responses to reward compared to healthy controls and reduced individual neural responses to reward may reflect the different biotypes of depression such as anhedonia and inattention.publishedVersio

Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning

Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many research efforts but remains non-trivial challenges. One major challenge is that existing GAL strategies may introduce semantic confusion to the selected training set, particularly when graphs are noisy. Specifically, most existing methods assume all aggregating features to be helpful, ignoring the semantically negative effect between inter-class edges under the message-passing mechanism. In this work, we present Semantic-aware Active learning framework for Graphs (SAG) to mitigate the semantic confusion problem. Pairwise similarities and dissimilarities of nodes with semantic features are introduced to jointly evaluate the node influence. A new prototype-based criterion and query policy are also designed to maintain diversity and class balance of the selected nodes, respectively. Extensive experiments on the public benchmark graphs and a real-world financial dataset demonstrate that SAG significantly improves node classification performances and consistently outperforms previous methods. Moreover, comprehensive analysis and ablation study also verify the effectiveness of the proposed framework.Comment: Accepted by CIKM 202

Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods

With the popularity of cryptocurrencies and the remarkable development of blockchain technology, decentralized applications emerged as a revolutionary force for the Internet. Meanwhile, decentralized applications have also attracted intense attention from the online gambling community, with more and more decentralized gambling platforms created through the help of smart contracts. Compared with conventional gambling platforms, decentralized gambling have transparent rules and a low participation threshold, attracting a substantial number of gamblers. In order to discover gambling behaviors and identify the contracts and addresses involved in gambling, we propose a tool termed ETHGamDet. The tool is able to automatically detect the smart contracts and addresses involved in gambling by scrutinizing the smart contract code and address transaction records. Interestingly, we present a novel LightGBM model with memory components, which possesses the ability to learn from its own misclassifications. As a side contribution, we construct and release a large-scale gambling dataset at https://github.com/AwesomeHuang/Bitcoin-Gambling-Dataset to facilitate future research in this field. Empirically, ETHGamDet achieves a F1-score of 0.72 and 0.89 in address classification and contract classification respectively, and offers novel and interesting insights

A novel nanoparticle drug delivery system based on PEGylated hemoglobin for cancer therapy

Proteins such as albumin, gelatin, casein, transferrin, and collagen are widely used as drug delivery systems. However, only albumin-based paclitaxel (PTX) formulation AbraxaneVR (PTX-albumin NPs prepared by nab-technology) has been successfully developed for treating metastatic breast cancer clinically due to abundant materials, simple industrial scale-up process, and well tumor-targeting ability. Hemoglobin (Hb) is another protein used for drug delivery with similar advantages. In this study, we successfully synthesized PEG-Hb nanoparticles loading with PTX based on previously well-established acid-denatured method. PEG-Hb-PTX NPs showed enhanced cellular uptake and great cellular inhibition ability in vitro. Moreover, our animal study showed that PEGylated NPs greatly accumulated in tumor tissues and exhibited excellent anticancer activity in vivo. We found that PEG-Hb-PTX NPs possess a better in vivo antitumor effect than the commercially available TaxolVR formulation. We believe that PEG-Hb has great potential as an efficient drug delivery system for further clinic study