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According to this study, the ECAP process with different BP is performed on the engineering magnesium alloy ( AZ31 ). The effect of BP on the strain condition and flow paths may differ as the statically deformed portion of the sample passes through the strain zone. The BP application of 25, 50 and 100 MPa brings about the forward typical shear rotation of structural components in the horizontal direction. The complex (20 x 20 x 200) mm 3 ECAP engine is applied with movable outer walls and specially designed sliding bottoms for low friction (moving die). The flow model is made by a cubic flow net of the DEFORM software tool on the workpiece. Structural features and initial micro-hardness measurements made on the x and y planes of the workpiece also received a special attention. Since BP leads to increased density the micro-hardness could be improved and ascribed to the change in texture.

Research in human-associated microbiomes often involves the analysis of taxonomic count tables generated via high-throughput sequencing. It is difficult to apply statistical tools as the data is high-dimensional, sparse, and compositional. An approachable way to alleviate high-dimensionality and sparsity is to aggregate variables into pre-defined sets. Set-based analysis is ubiquitous in the genomics literature and has demonstrable impact on improving interpretability and power of downstream analysis. Unfortunately, there is a lack of sophisticated set-based analysis methods specific to microbiome taxonomic data, where current practice often employs abundance summation as a technique for aggregation. This approach prevents comparison across sets of different sizes, does not preserve inter-sample distances, and amplifies protocol bias. Here, we attempt to fill this gap with a new single-sample taxon enrichment method that uses a novel log-ratio formulation based on the competitive null hypothesis commonly used in the enrichment analysis literature. Our approach, titled competitive balances for taxonomic enrichment analysis (CBEA), generates sample-specific enrichment scores as the scaled log-ratio of the subcomposition defined by taxa within a set and the subcomposition defined by its complement. We provide sample-level significance testing by estimating an empirical null distribution of our test statistic with valid p-values. Herein, we demonstrate, using both real data applications and simulations, that CBEA controls for type I error, even under high sparsity and high inter-taxa correlation scenarios. Additionally, CBEA provides informative scores that can be inputs to downstream analyses such as prediction tasks.

There has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.

Background The infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationship within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life. Results Stool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks ( n  = 158) and 12-months ( n  = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis (6 weeks: p  = 0.056; 12 months: p  = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks: p  = 0.376; 12 months: p  = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R 2 values demonstrated poor predictive performance across all models assessed (avg: − 5.06% -- 6 weeks; − 3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg: 0.344–6 weeks; 0.265–12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations. Conclusions Our results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.

We examine how operational changes in customer flows in retail stores affect the rate of COVID-19 transmission. We combine a model of customer movement with two models of disease transmission: direct exposure when two customers are in close proximity and wake exposure when one customer is in the airflow behind another customer. We find that the effectiveness of some operational interventions is sensitive to the primary mode of transmission. Restricting customer flow to one-way movement is highly effective if direct exposure is the dominant mode of transmission. In particular, the rate of direct transmission under full compliance with one-way movement is less than one-third the rate under two-way movement. Directing customers to follow one-way flow, however, is not effective if wake exposure dominates. We find that two other interventions—reducing the speed variance of customers and throughput control—can be effective whether direct or wake transmission is dominant. We also examine the trade-off between customer throughput and the risk of infection to customers, and we show how the optimal throughput rate drops rapidly as the population prevalence rises.

Ground penetrating radar (GPR) has been widely utilized for non-destructive inspection of civil infrastructure systems such as bridges and tunnels. However, the identification of GPR signatures poses significant challenges due to the overlapped multiple objects. To overcome the obstacle, we proposed an innovative Mask R-CNN based network considering spatial relationship between GPR signatures. Firstly, to capture the spatial relationship of overlapping signatures, we introduced an improved intersection over union considering central distance and aspect ratio between GPR signatures. Secondly, we further modified the Non-Maximum Suppression and enhanced the corresponding anchor generative mechanism. To validate the proposed method, we conducted testing on GPR scans obtained from real data from a bridge. The results demonstrate that the proposed method not only accurately detects GPR signatures, but also significantly outperforms existing Mask R-CNN in terms of segmenting overlapped GPR signature. Specially, the proposed method achieved an average accuracy of 46.8% in the segmentation task, marking a substantial advancement in the field.

This Special Issue of the Robotica is on recent advances in field and service robotics with a focus on the use of robotic and autonomous technologies to handle tasks in harsh environments and tasks that involve the multirobot cooperation and human–robot interactions.

This paper presents an ensemble learning approach to predict the active power losses during the allocation and sizing of distributed generation (DG) units in power distribution networks. The forecast model incorporates the Gradient Boosting Machine Regression (GBMR) to estimate DG location, bus voltages, DG size, and active losses without conventional power flow calculations. The results demonstrate that the suggested estimations of power losses and DG sizing are effective, practical, and adaptable for power system management. The accuracy of the proposed model has been validated using key performance metrics and tested on the standard IEEE 33 bus system. In the case of fixed load, the GBMR outperforms other machine learning techniques with the R-squared 0.9997, with a very low mean absolute percentage error (MAPE) (0.2216%) and a root mean square error (RMSE) of 1.0673 in predicting active power losses. This approach is promising in enabling grid operators to effectively manage DG unit integration of distributed energy resources from precise and reliable estimates of the power loss.

Vietnam is experiencing an epidemiologic transition with an increased prevalence of non-communicable diseases. The country needs novel, large-scale, and sustainable interventions to improve hypertension control. We report the 12 month follow-up results of a cluster randomized feasibility trial in Hung Yen province, Vietnam, which evaluated the feasibility and acceptability of two community-based interventions to improve hypertension control: a \"storytelling\" and a didactic intervention. The storytelling intervention included stories in the patients' own words about coping with hypertension and didactic content about the importance of healthy lifestyle behaviors in controlling elevated blood pressure levels. The didactic intervention included only didactic content, which were general recommendations for managing several important risk factors for hypertension and other non-communicable diseases. The storytelling intervention was delivered by two DVDs three months apart; the didactic intervention included only one DVD. The trial was conducted in patients with poorly controlled hypertension from 4 communes (communities), which were equally randomized to the two interventions. The mean age of the 160 patients was 66 years and 54% were men. Between baseline enrollment and the 12 month follow-up, mean systolic blood pressure declined by 10.8 mmHg (95% CI: 6.5-14.9) in the storytelling group and by 5.8 mmHg (95% CI: 1.6-10.0) in the didactic content group. The storytelling group also experienced more improvement in several health behaviors, including increased levels of physical activity and reduced consumption of salt and alcohol. We observed considerable long-term beneficial effects of both interventions, especially of our storytelling intervention, among patients with inadequately controlled hypertension. A large scale randomized trial should more systematically compare the short and long-term effectiveness of the two interventions in controlling hypertension. ClinicalTrials.gov: NCT02483780.

Background Prenatal exposure to arsenic has been linked to a range of adverse health conditions in later life. Such fetal origins of disease are frequently the result of environmental effects on the epigenome, leading to long-term alterations in gene expression. Several studies have demonstrated effects of prenatal arsenic exposure on DNA methylation; however the impact of arsenic on the generation and decoding of post-translational histone modifications (PTHMs) is less well characterized, and has not been studied in the context of prenatal human exposures. Methods In the current study, we examined the effect of exposure to low-to-moderate levels of arsenic in a US birth cohort, on the expression of 138 genes encoding key epigenetic regulators in the fetal portion of the placenta. Our candidate genes included readers, writers and erasers of PTHMs, and chromatin remodelers. Results Arsenic exposure was associated with the expression of 27 of the 138 epigenetic genes analyzed. When the cohort was stratified by fetal sex, arsenic exposure was associated with the expression of 40 genes in male fetal placenta, and only 3 non-overlapping genes in female fetal placenta. In particular, we identified an inverse relationship between arsenic exposure and expression of the gene encoding the histone methyltransferase, PRDM6 ( p  < 0.001). Mutation of PRDM6 has been linked to the congenital heart defect, patent ductus arteriosus. Conclusions Our findings suggest that prenatal arsenic exposure may have sex-specific effects on the fetal epigenome, which could plausibly contribute to its subsequent health impacts.