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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]

O‐linked‐β‐N‐acetylglucosamine (O‐GlcNAcylation) is a distinctive posttranslational protein modification involving the coordinated action of O‐GlcNAc transferase and O‐GlcNAcase, primarily targeting serine or threonine residues in various proteins. This modification impacts protein functionality, influencing stability, protein–protein interactions, and localization. Its interaction with other modifications such as phosphorylation and ubiquitination is becoming increasingly evident. Dysregulation of O‐GlcNAcylation is associated with numerous human diseases, including diabetes, nervous system degeneration, and cancers. This review extensively explores the regulatory mechanisms of O‐GlcNAcylation, its effects on cellular physiology, and its role in the pathogenesis of diseases. It examines the implications of aberrant O‐GlcNAcylation in diabetes and tumorigenesis, highlighting novel insights into its potential role in cardiovascular diseases. The review also discusses the interplay of O‐GlcNAcylation with other protein modifications and its impact on cell growth and metabolism. By synthesizing current research, this review elucidates the multifaceted roles of O‐GlcNAcylation, providing a comprehensive reference for future studies. It underscores the potential of targeting the O‐GlcNAcylation cycle in developing novel therapeutic strategies for various pathologies. [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]

Turbidity is an important parameter in monitoring water quality, and thus attracts significant attention. Changes in the various components and constituent elements of water directly affect turbidity measurements. The turbidity of water is generally quantified by measuring the absorbance or scattering characteristics of substances suspended in it. The complex composition of environmental water bodies complicates the determination of factors influencing their turbidity. Controlled experiments that quantitatively analyze the effect of a single factor on the measurement of turbidity in the laboratory are an important means to improve the accuracy of turbidity assessment. Properties of suspended materials in a water column that may affect its measured turbidity include the concentration of algae, particle size, and the color of soluble substances, etc. The laboratory experiments reported here used Chlorella as an example to investigate the effect of algal concentration on turbidity measurement. The results are as follows. When the turbidity is low (100 NTU), the average relative error between the theoretical and practical absorbance is about 37.52%, which decreases to 19.20% at 100–200 NTU and 5.16% at 200–400 NTU. The characteristic spectral bands sensitive to turbidity (680 nm) and Chlorella (240 nm) were selected, and the theoretical and practical turbidity results were analyzed. The average relative errors of mixed liquids of less than 100, 100–200, and 200–400 NTU are 65.07%, 34.18%, and 3.95%, respectively. Therefore, the concentration of Chlorella significantly affects the measured turbidity, and results in a more complex effect at low turbidity (<100 NTU). Combining the analysis of absorbance peak values and characteristic spectral bands, we can assess the turbidity changes in different components, and through calibration, information regarding the concentration and variation of different components in water bodies can be obtained. The results of this research could improve the accuracy of on-site measurement of the concentrations of different components suspended in water, and also facilitate the development of new turbidity sensors. [ABSTRACT FROM AUTHOR]

Kawasaki disease (KD) is an acute self‐limiting vasculitis with coronary complications, usually occurring in children. The incidence of KD in children is increasing year by year, mainly in East Asian countries, but relatively stably in Europe and America. Although studies on KD have been reported, the pathogenesis of KD is unknown. With the development of high‐throughput sequencing technology, growing number of regulatory noncoding RNAs (ncRNAs) including microRNA (miRNA), long noncoding RNA (lncRNA), and circular RNA (circRNA) have been identified to involved in KD. However, the role of ncRNAs in KD has not been comprehensively elucidated. Therefore, it is significative to study the regulatory role of ncRNA in KD, which might help to uncover new and effective therapeutic strategies for KD. In this review, we summarize recent studies on ncRNA in KD from the perspectives of immune disorders, inflammatory disorders, and endothelial dysfunction, and highlight the potential of ncRNAs as therapeutic targets for KD. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have received considerable attention in dealing with severe environmental and energy crises. The capacity planning of public charging stations has been a major factor in facilitating the wide market penetration of EVs. In this paper, we present an optimization model for charging station capacity planning to maximize the fuzzy quality of service (FQoS) considering queuing behavior, blocking reliability, and multiple charging options classified by battery technical specifications. The uncertainty of the EV arrival and service time are taken into account and described as fuzzy numbers characterized by triangular membership functions. Meanwhile, an $\alpha$ -cuts-based algorithm is proposed to defuzzify the FQoS. Finally, the numerical results illustrate that a more robust plan can be obtained by accounting for FQoS. The contribution of the proposed model allows decision-makers and operators to plan the capacity of charging stations with fuzzy EV arrival rate and service rate and provide a better service for customers with different charging options. [ABSTRACT FROM AUTHOR]

Broiler behavior is closely related to the breeding environment. Therefore, studying broiler behavior helps breeding farm workers to better carry out welfare breeding. In the breeding environment of yellow feather broilers, temperature, humidity, and ammonia concentration are the main factors that affect the behavior of the broilers. This study used a multichromatic aberration model to process the color images of yellow feather broilers to extract the activity feature of the broilers at different periods, utilized the Cb component of YCbCr color model and the b component of Lab color model to remove background litter in images, and employed the Q component of YIQ color model to remove the feeders and the drinkers from the image. The segmented images were constructed into an accumulator to generate a heat map of yellow feather broilers' activity. Then, the correlation between the activity and the temperature and humidity index (THI) and the correlation between the activity and ammonia concentration were explored. The experiment found that the activity of the broilers was significantly positively correlated with ammonia concentration (P < 0.05), indicating that the activity of yellow feather broilers increased with ammonia concentration ascending. Besides, the THI in the broiler chamber had a significant positive correlation with the ammonia data (P < 0.01), indicating that when the THI in the broiler chamber increases, the ammonia concentration rises. The research provides a direction for exploring the impact of THI and ammonia concentration on the performance of yellow feather broilers. At the same time, it provides a theoretical basis for the early warning and judgment of broiler breeding by farm workers in the future. [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]

In an actual industrial or military operations environment, a multi-state system (MSS) consisting of multi-state components often needs to perform multiple missions in succession. To improve the probability of the system successfully completing the next mission, all the maintenance activities need to be performed during maintenance breaks between any two consecutive missions under limited maintenance resources. In such case, selective maintenance is a widely used maintenance policy. As a typical discrete mathematics problem, selective maintenance has received widespread attention. In this work, a selective maintenance model considering human reliability for multi-component systems is investigated. Each maintenance worker can be in one of multiple discrete working levels due to their human error probability (HEP). The state of components after maintenance is assumed to be random and follow an identified probability distribution. To solve the problem, this paper proposes a human reliability model and a method to determine the state distribution of components after maintenance. The objective of selective maintenance scheduling is to find the maintenance action with the optimal reliability for each component in a maintenance break subject to constraints of time and cost. In place of an enumerative method, a genetic algorithm (GA) is employed to solve the complicated optimization problem taking human reliability into account. The results show the importance of considering human reliability in selective maintenance scheduling for an MSS. [ABSTRACT FROM AUTHOR]