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In the realm of welding production, optimizing parameters for enhanced quality and efficiency remains a challenge. This study introduces a novel approach using ABAQUS for simulating the thermal–mechanical behavior in T-shaped double-sided welded joints of Q345 steel. Initial simulations were experimentally validated. A range of welding parameters was then established, and the "Unifrnd" function helped sample within this range. This study considers eight welding parameters as input variables for an artificial neural network (ANN), trained to predict residual stress and deformation. Optimization algorithms NSGA-II (non-dominated sorting genetic algorithm-II) and MOPSO (multi-objective particle swarm optimization) algorithm were employed to refine the ANN's outputs. Notably, the Pareto front revealed an optimal balance between minimizing residual stress and deformation. While single optimization achieved up to 5.12% reduction in residual stress and over 50% in deformations, the tri-objective optimization resulted in a more balanced reduction (about 1.88% in residual stress and over 20% in deformations). This highlights the need for parameters weighting in decision-making. The findings demonstrate the effectiveness of integrating ANN with optimization algorithms for welding parameter optimization, with implications for improved welding practices. [ABSTRACT FROM AUTHOR]

This work aims to explore crashworthiness of square aluminum/CFRP tubes with different hybrid schemes under quasi-static axial loading condition, and maximize their crashworthiness by performing multi-objective discrete robust optimization design. In the present study, metal/CFRP hybrid tubes with two hybrid schemes and their corresponding individual tubes are manufactured. The crushing process and crashworthiness indictors of all specimens are experimentally investigated under quasi-static axial crushing load. According to the experimental results, the AC hybrid tubes, made of outer aluminum square tube and internally adhered CFRP layers, show excellent energy-absorbing characteristics. Subsequently, numerical simulations are performed for the AC hybrid tubes to further investigate the effects of aluminum wall thickness, CFRP layer number and ply orientation on the energy-absorbing characteristics. It is found that the total energy absorption increases with the increasing tubal wall thickness of aluminum and CFRP layer, and ply orientation makes great contribution to the overall deformation modes and energy-absorbing characteristics of AC hybrid structures. Finally, a multi-objective discrete robust optimization has been conducted to optimize AC hybrid structures for the optimal crashworthy performance. [ABSTRACT FROM AUTHOR]

Snoring affects 57 % of men, 40 % of women, and 27 % of children in the USA. Besides, snoring is highly correlated with obstructive sleep apnoea (OSA), which is characterised by loud and frequent snoring. OSA is also closely associated with various life-threatening diseases such as sudden cardiac arrest and is regarded as a grave medical ailment. Preliminary studies have shown that in the USA, OSA affects over 34 % of men and 14 % of women. In recent years, polysomnography has increasingly been used to diagnose OSA. However, due to its drawbacks such as being time-consuming and costly, intelligent audio analysis of snoring has emerged as an alternative method. Considering the higher demand for identifying the excitation location of snoring in clinical practice, we utilised the Munich-Passau Snore Sound Corpus (MPSSC) snoring database which classifies the snoring excitation location into four categories. Nonetheless, the problem of small samples remains in the MPSSC database due to factors such as privacy concerns and difficulties in accurate labelling. In fact, accurately labelled medical data that can be used for machine learning is often scarce, especially for rare diseases. In view of this, Model-Agnostic Meta-Learning (MAML), a small sample method based on meta-learning, is used to classify snore signals with less resources in this work. The experimental results indicate that even when using only the ESC-50 dataset (non-snoring sound signals) as the data for meta-training, we are able to achieve an unweighted average recall of 60.2 % on the test dataset after fine-tuning on just 36 instances of snoring from the development part of the MPSSC dataset. While our results only exceed the baseline by 4.4 %, they still demonstrate that even with fine-tuning on a few instances of snoring, our model can outperform the baseline. This implies that the MAML algorithm can effectively tackle the low-resource problem even with limited data resources. [ABSTRACT FROM AUTHOR]

Tri-spine horseshoe crabs (HSCs) Tachypleus tridentatus have been facing the threat of population depletion for decades, and the physiology and microbiology of their early life stages are lacking. To explore what directs the change of juvenile T. tridentatus gut microbiota and how gut microbiota change, by using 16S rRNA sequencing of gut samples we detected the intestinal microbiome of juvenile HSCs and compared the impact of initial molting and initial feeding, as well as the effect of environment. Results showed that the predominant phyla in the gut microbial community of juvenile HSCs are Proteobacteria and Bacteroidetes. The richness and diversity of intestinal microbes greatly decreased after initial molting. Microbial-mediated functions predicted by PICRUSt showed that "Signal Transduction", "Cellular Processes and Signaling", "Infective Diseases" and "Digestive System" pathways significantly increased in 2nd instars. As for the effect of environment, the connection between living environment and the intestinal microbiome started to manifest after initial molting. Unexpectedly, initial feeding treatment slightly affected the intestinal microbiome of T. tridentatus in the early life stage, whereas the effect of initial molting was significant. The present study provided the first insight into the gut microbiota of T. tridentatus, and the findings led a new sight to explain what guide the change of gut microbiota. [ABSTRACT FROM AUTHOR]

A putative chrysovirus recovered from Brassica campestris var. purpurea and provisionally named 'Brassica campestris chrysovirus 1' (BrcCV1) was sequenced. The genome of the putative BrcCV1 consists of three double-stranded RNAs (dsRNAs) comprising 3,639 (dsRNA 1), 3,567 (dsRNA 2) and 3,337 (dsRNA 3) base pairs, respectively, each containing a single open reading frame (ORF 1-3). The putative proteins encoded by ORF 1-3 show homologies to RdRp, CP and chryso-P3 of approved or tentative chrysoviruses. In addition, the three dsRNAs of BrcCV1 contain highly conserved 5' and 3' untranslated regions (UTRs) in a way similar to known chrysoviruses. In a phylogenetic tree based on the conserved amino acid sequences of the RdRps of chrysoviruses, totiviruses and partitiviruses, the putative BrcCV1 formed a separate clade with Raphanus sativus chrysovirus 1 (RasCV1), a putative trisegmented, plant-infecting chrysovirus, in the family Chrysoviridae. [ABSTRACT FROM AUTHOR]