Hydrodynamic performance optimization of marine propellers based on fluid-structure coupling

Fiber-reinforced composites offer the benefits of high strength, high stiffness, lightweight, superior damping performance, and great design capability when compared to metal. The rigidity characteristics of the composite laminate in different directions may be adjusted to meet the requirements of the application by using appropriate materials and arranging the lay-up sequence. As a result, the purpose of this work is to explore the influence of lay-up type on propeller performance in terms of both hydrodynamic and structural performance. A transient fluid-structure interaction (FSI) algorithm based on the finite element method (FEM) combined with the computational fluid dynamics (CFD) technique is developed and used for the analysis of composite propellers. The hydrodynamic performance of the propeller is compared to that of a metallic material. Propeller propulsion efficiency, structural deformation, equivalent stress, and damage performance of different lay-up options under three different operating situations are compared. In addition, it is presented a parametric optimization approach to get the most appropriate lay-up program for composite blades with the best hydrodynamic properties and structural performance

CrossFusion: Interleaving Cross-modal Complementation for Noise-resistant 3D Object Detection

The combination of LiDAR and camera modalities is proven to be necessary and typical for 3D object detection according to recent studies. Existing fusion strategies tend to overly rely on the LiDAR modal in essence, which exploits the abundant semantics from the camera sensor insufficiently. However, existing methods cannot rely on information from other modalities because the corruption of LiDAR features results in a large domain gap. Following this, we propose CrossFusion, a more robust and noise-resistant scheme that makes full use of the camera and LiDAR features with the designed cross-modal complementation strategy. Extensive experiments we conducted show that our method not only outperforms the state-of-the-art methods under the setting without introducing an extra depth estimation network but also demonstrates our model's noise resistance without re-training for the specific malfunction scenarios by increasing 5.2\% mAP and 2.4\% NDS

An Effective Meaningful Way to Evaluate Survival Models

One straightforward metric to evaluate a survival prediction model is based on the Mean Absolute Error (MAE) -- the average of the absolute difference between the time predicted by the model and the true event time, over all subjects. Unfortunately, this is challenging because, in practice, the test set includes (right) censored individuals, meaning we do not know when a censored individual actually experienced the event. In this paper, we explore various metrics to estimate MAE for survival datasets that include (many) censored individuals. Moreover, we introduce a novel and effective approach for generating realistic semi-synthetic survival datasets to facilitate the evaluation of metrics. Our findings, based on the analysis of the semi-synthetic datasets, reveal that our proposed metric (MAE using pseudo-observations) is able to rank models accurately based on their performance, and often closely matches the true MAE -- in particular, is better than several alternative methods.Comment: Accepted to ICML 202

Recommending Themes for Ad Creative Design via Visual-Linguistic Representations

There is a perennial need in the online advertising industry to refresh ad creatives, i.e., images and text used for enticing online users towards a brand. Such refreshes are required to reduce the likelihood of ad fatigue among online users, and to incorporate insights from other successful campaigns in related product categories. Given a brand, to come up with themes for a new ad is a painstaking and time consuming process for creative strategists. Strategists typically draw inspiration from the images and text used for past ad campaigns, as well as world knowledge on the brands. To automatically infer ad themes via such multimodal sources of information in past ad campaigns, we propose a theme (keyphrase) recommender system for ad creative strategists. The theme recommender is based on aggregating results from a visual question answering (VQA) task, which ingests the following: (i) ad images, (ii) text associated with the ads as well as Wikipedia pages on the brands in the ads, and (iii) questions around the ad. We leverage transformer based cross-modality encoders to train visual-linguistic representations for our VQA task. We study two formulations for the VQA task along the lines of classification and ranking; via experiments on a public dataset, we show that cross-modal representations lead to significantly better classification accuracy and ranking precision-recall metrics. Cross-modal representations show better performance compared to separate image and text representations. In addition, the use of multimodal information shows a significant lift over using only textual or visual information.Comment: 7 pages, 8 figures, 2 tables, accepted by The Web Conference 202

Microstructure evolution and electrochemical properties of TiO 2 /Ti-35Nb-2Ta-3Zr micro/nano-composites fabricated by friction stir processing

Forming stable anti-corrosion surface layer and homogenized microstructure on the surface of material has become a major challenge in developing biomedical β titanium alloy. In the study, TiO 2 /Ti-35Nb-2Ta-3Zr anti-corrosion micro/nano-composites with different amount of TiO 2 particles were successfully fabricated by one-pass friction stir processing (FSP). The composition, microstructure and electrochemical properties of the material are characterized systematically. In particular, compact passive oxide films formed on surface of the material after electrochemical corrosion are elaborated from constituent, thickness and structural characteristics. Furthermore, the relationship between various FSP parameters, microstructure presented and corresponding corrosion resistance has been discussed in detail. The results show that TiO 2 /Ti-35Nb-2Ta-3Zr micro/nano-composite layers possess massive uniform β grains with homogeneous dispersive oxygen on the surface. Nanocrystallines surrounded by amorphous phases and α″ martensite accompanied with dislocations are discovered. TiO 2 /Ti-35Nb-2Ta-3Zr micro/nano-composite layers present outstanding corrosion resistance. More TiO 2 added and higher rotation speed promotes the optimization in corrosion resistance forming more compact passive films. The study displays the potential of a new micro/nano-composite with outstanding surface microstructure and corrosion resistance that serves better as a biomedical implant. © 2019 Elsevier Lt

Modelling biological age based on plasma peptides in Han Chinese adults

Age-related disease burdens increased over time, and whether plasma peptides can be used to accurately predict age in order to explain the variation in biological indicators remains inadequately understood. Here we first developed a biological age model based on plasma peptides in 1890 Chinese Han adults. Based on mass spectrometry, 84 peptides were detected with masses in the range of 0.6-10.0 kDa, and 13 of these peptides were identified as known amino acid sequences. Five of these thirteen plasma peptides, including fragments of apolipoprotein A-I (m/z 2883.99), fibrinogen alpha chain (m/z 3060.13), complement C3 (m/z 2190.59), complement C4-A (m/z 1898.21), and breast cancer type 2 susceptibility protein (m/z 1607.84) were finally included in the final model by performing a multivariate linear regression with stepwise selection. This biological age model accounted for 72.3% of the variation in chronological age. Furthermore, the linear correlation between the actual age and biological age was 0.851 (95% confidence interval: 0.836-0.864) and 0.842 (95% confidence interval: 0.810-0.869) in the training and validation sets, respectively. The biological age based on plasma peptides has potential positive effects on primary prevention, and its biological meaning warrants further investigation

The TTYH3/MK5 Positive Feedback Loop regulates Tumor Progression via GSK3-β/β-catenin signaling in HCC

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide, and identification of novel targets is necessary for its diagnosis and treatment. This study aimed to investigate the biological function and clinical significance of tweety homolog 3 (TTYH3) in HCC. TTYH3 overexpression promoted cell proliferation, migration, and invasion and inhibited HCCM3 and Hep3B cell apoptosis. TTYH3 promoted tumor formation and metastasis in vivo. TTYH3 upregulated calcium influx and intracellular chloride concentration, thereby promoting cellular migration and regulating epithelial-mesenchymal transition-related protein expression. The interaction between TTYH3 and MK5 was identified through co-immunoprecipitation assays and protein docking. TTYH3 promoted the expression of MK5, which then activated the GSK3β/β-catenin signaling pathway. MK5 knockdown attenuated the activation of GSK3β/β-catenin signaling by TTYH3. TTYH3 expression was regulated in a positive feedback manner. In clinical HCC samples, TTYH3 was upregulated in the HCC tissues compared to nontumor tissues. Furthermore, high TTYH3 expression was significantly correlated with poor patient survival. The CpG islands were hypomethylated in the promoter region of TTYH3 in HCC tissues. In conclusion, we identified TTYH3 regulates tumor development and progression via MK5/GSK3-β/β-catenin signaling in HCC and promotes itself expression in a positive feedback loop

Heritability enrichment of immunoglobulin G N-glycosylation in specific tissues

Genome-wide association studies (GWAS) have identified over 60 genetic loci associated with immunoglobulin G (IgG) N-glycosylation; however, the causal genes and their abundance in relevant tissues are uncertain. Leveraging data from GWAS summary statistics for 8,090 Europeans, and large-scale expression quantitative trait loci (eQTL) data from the genotype-tissue expression of 53 types of tissues (GTEx v7), we derived a linkage disequilibrium score for the specific expression of genes (LDSC-SEG) and conducted a transcriptome-wide association study (TWAS). We identified 55 gene associations whose predicted levels of expression were significantly associated with IgG N-glycosylation in 14 tissues. Three working scenarios, i.e., tissue-specific, pleiotropic, and coassociated, were observed for candidate genetic predisposition affecting IgG N-glycosylation traits. Furthermore, pathway enrichment showed several IgG N-glycosylation-related pathways, such as asparagine N-linked glycosylation, N-glycan biosynthesis and transport to the Golgi and subsequent modification. Through phenome-wide association studies (PheWAS), most genetic variants underlying TWAS hits were found to be correlated with health measures (height, waist-hip ratio, systolic blood pressure) and diseases, such as systemic lupus erythematosus, inflammatory bowel disease, and Parkinson’s disease, which are related to IgG N-glycosylation. Our study provides an atlas of genetic regulatory loci and their target genes within functionally relevant tissues, for further studies on the mechanisms of IgG N-glycosylation and its related diseases

Ghost in the Minecraft: Generally Capable Agents for Open-World Enviroments via Large Language Models with Text-based Knowledge and Memory

The captivating realm of Minecraft has attracted substantial research interest in recent years, serving as a rich platform for developing intelligent agents capable of functioning in open-world environments. However, the current research landscape predominantly focuses on specific objectives, such as the popular "ObtainDiamond" task, and has not yet shown effective generalization to a broader spectrum of tasks. Furthermore, the current leading success rate for the "ObtainDiamond" task stands at around 20%, highlighting the limitations of Reinforcement Learning (RL) based controllers used in existing methods. To tackle these challenges, we introduce Ghost in the Minecraft (GITM), a novel framework integrates Large Language Models (LLMs) with text-based knowledge and memory, aiming to create Generally Capable Agents (GCAs) in Minecraft. These agents, equipped with the logic and common sense capabilities of LLMs, can skillfully navigate complex, sparse-reward environments with text-based interactions. We develop a set of structured actions and leverage LLMs to generate action plans for the agents to execute. The resulting LLM-based agent markedly surpasses previous methods, achieving a remarkable improvement of +47.5% in success rate on the "ObtainDiamond" task, demonstrating superior robustness compared to traditional RL-based controllers. Notably, our agent is the first to procure all items in the Minecraft Overworld technology tree, demonstrating its extensive capabilities. GITM does not need any GPU for training, but a single CPU node with 32 CPU cores is enough. This research shows the potential of LLMs in developing capable agents for handling long-horizon, complex tasks and adapting to uncertainties in open-world environments. See the project website at https://github.com/OpenGVLab/GITM