Residual Stress Analyses in a Pipe Welding Simulation: 3D Pipe Versus Axi-symmetric Models

AbstractThis paper numerically studied the residual stress in a butt-welded steel pipe. A comparison of 3D pipe and axi-symmetric finite element model under the condition of same welding simulation parameters was carried out. The results showed that axi- symmetric model share similar residual stress distribution with 3D model in the condition of same heat source shape parameters. However, the stress values of the two concerned models were quite different. Meanwhile the scale of welding pool for 3D model was almost twice bigger than that of axi-symmetric model. Both welding experiment and simulation results of 3D model showed that peak temperature of welding pool along the welding path increased during the welding process, and welding pool width and depth also increased with the moving of heat source

A programmable topological photonic chip

Controlling topological phases of light has allowed experimental observations of abundant topological phenomena and development of robust photonic devices. The prospect of more sophisticated controls with topological photonic devices for practical implementations requires high-level programmability. Here, we demonstrate a fully programmable topological photonic chip with large-scale integration of silicon photonic nanocircuits and microresonators. Photonic artificial atoms and their interactions in our compound system can be individually addressed and controlled, therefore allowing arbitrary altering of structural parameters and geometrical configurations for the observations of dynamic topological phase transitions and diverse photonic topological insulators. By individually programming artificial atoms on the generic chip, it has allowed comprehensive statistic characterisations of topological robustness against relatively weak disorders, as well as counterintuitive topological Anderson phase transitions induced by strong disorders. Our generic topological photonic chip that can be rapidly reprogrammed to implement multifunctionalities, prototypes a flexible and versatile platform for possible applications across fundamental science and topological technologies

OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping

Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments. However, existing benchmarks tend to oversimplify the scene by solely focusing on lane perception tasks. Observing that human drivers rely on both lanes and traffic signals to operate their vehicles safely, we present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. The objective of the presented dataset is to advance research in understanding the structure of road scenes by examining the relationship between perceived entities, such as traffic elements and lanes. Leveraging existing datasets, OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. It comprises three primary sub-tasks, including the 3D lane detection inherited from OpenLane, accompanied by corresponding metrics to evaluate the model's performance. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.Comment: Accepted by NeurIPS 2023 Track on Datasets and Benchmarks | OpenLane-V2 Dataset: https://github.com/OpenDriveLab/OpenLane-V

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Predicting Diabetes On Phenotype and Genotype using Neural Networks

Diabetes is a long-standing disease caused by high blood sugar over a long period of time and one in every ten Americans has diabetes. The neural networks have gained attention in large-scale genetic research because of its ability in non-linear relationships. However, the data imbalance problem, which is caused by the disproportion between the number of disease samples and the number of healthy samples, will decrease the prediction accuracy. In this project, we tackle the data imbalance problem when predicting diabetes with genotype SNP data and phenotype data provided by UK BioBank. The dataset is highly skewed with healthy samples with the ratio of 20. We build a phenotype neural network and a genotype neural network, which uses the sampling techniques to counter the data imbalance problem before feeding the data to the neural networks. We found out that the phenotype neural network outperforms the genotype neural network and achieves 90% accuracy. We reach the conclusion that undersampling performs better than oversampling to counter the data imbalance problem in our dataset and the phenotype is better than the genotype when predicting diabetes. We also discover the key phenotype and genotype features that contributed most to our model prediction

Application of Correlation Pre-Filtering Neural Network to DNA Methylation Data: Biological Aging Prediction

We introduce the CPFNN (Correlation Pre-Filtering Neural Network) for biological age prediction based on blood DNA methylation data. The model is built on 20,000 top correlated DNA methylation features and trained by 1810 healthy samples from GEO database. The input data format and the instructions for parser and CPFNN model are detailed in this chapter. Followed by two potential uses, age acceleration detection and unknown age prediction are discussed

Learning Effective Geometry Representation from Videos for Self-Supervised Monocular Depth Estimation

Recent studies on self-supervised monocular depth estimation have achieved promising results, which are mainly based on the joint optimization of depth and pose estimation via high-level photometric loss. However, how to learn the latent and beneficial task-specific geometry representation from videos is still far from being explored. To tackle this issue, we propose two novel schemes to learn more effective representation from monocular videos: (i) an Inter-task Attention Model (IAM) to learn the geometric correlation representation between the depth and pose learning networks to make structure and motion information mutually beneficial; (ii) a Spatial-Temporal Memory Module (STMM) to exploit long-range geometric context representation among consecutive frames both spatially and temporally. Systematic ablation studies are conducted to demonstrate the effectiveness of each component. Evaluations on KITTI show that our method outperforms current state-of-the-art techniques

Integrated Metabolomic and Transcriptomic Analysis Reveals the Flavonoid Regulatory Network by Eutrema EsMYB90

Flavonoids are representative secondary metabolites with different metabolic functions in plants. Previous study found that ectopic expression of EsMYB90 from Eutremasalsugineum could strongly increase anthocyanin content in transgenic tobacco via regulating the expression of anthocyanin biosynthesis genes. In the present research, metabolome analysis showed that there existed 130 significantly differential metabolites, of which 23 metabolites enhanced more than 1000 times in EsMYB90 transgenic tobacco leaves relative to the control, and the top 10 of the increased metabolites included caffeic acid, cyanidin O-syringic acid, myricetin and naringin. A total of 50 markedly differential flavonoids including flavones (14), flavonols (13), flavone C-glycosides (9), flavanones (7), catechin derivatives (5), anthocyanins (1) and isoflavone (1) were identified, of which 46 metabolites were at a significantly enhanced level. Integrated analysis of metabolome and transcriptome revealed that ectopic expression of EsMYB90 in transgenic tobacco leaves is highly associated with the prominent up-regulation of 16 flavonoid metabolites and the corresponding 42 flavonoid biosynthesis structure genes in phenylpropanoid/flavonoid pathways. Dual luciferase assay documented that EsMYB90 strongly activated the transcription of NtANS and NtDFR genes via improving their promoter activity in transiently expressed tobacco leaves, suggesting that EsMYB90 functions as a key regulator on anthocyanin and flavonoid biosynthesis. Taken together, the crucial regulatory role of EsMYB90 on enhancing many flavonoid metabolite levels is clearly demonstrated via modulating flavonoid biosynthesis gene expression in the leaves of transgenic tobacco, which extends our understanding of the regulating mechanism of MYB transcription factor in the phenylpropanoid/flavonoid pathways and provides a new clue and tool for further investigation and genetic engineering of flavonoid metabolism in plants

Age Prediction by DNA Methylation in Neural Networks

Aging is traditionally thought to be caused by complex and interacting factors such as DNA methylation. The traditional formula of DNA methylation aging is based on linear models and little work has explored the effectiveness of neural networks, which can learn non-linear relationships. DNA methylation data typically consists of hundreds of thousands of feature space and a much less number of biological samples. This leads to overfitting and a poor generalization of neural networks. We propose Correlation Pre-Filtered Neural Network (CPFNN) that uses Spearman Correlation to pre-filter the input features before feeding them into neural networks. We compare CPFNN with the statistical regressions (i.e. Horvaths and Hannums formulas), the neural networks with LASSO regularization and elastic net regularization, and the Dropout Neural Networks. CPFNN outperforms these models by at least 1 year in term of Mean Absolute Error (MAE), with a MAE of 2.7 years. We also test for association between the epigenetic age with Schizophrenia and Down Syndrome (p=0.024 and p\u3c0.001, respectively). We discover that for a large number of candidate features, such as genome-wide DNA methylation data, a key factor in improving prediction accuracy is to appropriately weight features that are highly correlated with the outcome of interest