The use of neural networks to characterise problematic arc sounds

Automation of electric arc welding has been at the centre of considerable debate and the subject of much research for several decades. One conclusion drawn from all this effort is that there seems to be no single system that can monitor all of the variables and subsequently, fully control any welding process. To date there has been considerable success in the development of seam tracking systems employing various sensing techniques, good progress has been made in the area of penetration measurement and worthwhile use has been made of the integration of expert systems and modelling software within these control domains. Skilled welders develop their own monitoring and control systems and it has been observed that part of this expertise is the ability to listen subconsciously to the sound of the arc and to alter the electrode position in response to an adverse change in arc noise. Attempts have been made to analyse these sounds using both conventional techniques and more recently expert systems, neither have delivered any usable information. This paper describes a new approach involving the use of neural networks in the identification of sounds which indicate that the welding system is drifting out of control

The real time analysis of acoustic weld emissions using neural networks

Artificial Neural Networks (ANNs) are becoming an increasingly viable computing tool in control scenarios where human expertise is so often required. The development of software emulations and dedicated VLSI devices is proving successful in real world applications where complex signal analysis, pattern recognition and discrimination are important factors. An established observation is that a skilled welder is able to monitor a manual arc welding process by subconsciously changing the position of the electrode in response to an adverse change in audible process noise. Expert systems applied to the analysis of chaotic acoustic emissions have failed to establish any salient information due to the inabilities of conventional architectures in processing vast quantities of erratic data at real time speeds. This paper describes the application of a hybrid ANN system, utilising a combination of multiple ANN architectures and conventional techniques, to establish system parameter acoustic signatures for subsequent on line control

The application of neural networks for the control of industrial arc welding

The use of automatic closed loop control is well established in all areas of manufacturing industry. New methods for measuring system variables, data processing and process control are being sought to improve system efficiency. Skilled welders are able to subconsciously monitor a manual arc welding process by listening to the sound and repositioning the electrode in response to a change in arc noise. This paper describes the real time monitoring of acoustic emissions from an automated submerged arc welding process and the application of Neural Networks to predict the point of instability of the process variables

The analysis of airborne acoustics of S.A.W. using neural networks

The analysis of acoustic emissions for machine health monitoring has made rapid advances in the last five years due to a revival of interest in the application of Artificial Neural Networks (ANNs). Complex signal analysis, which has often thwarted conventional statistical methods and expert systems, is now more possible with the introduction of 'neural' based computing methods. Acoustic emissions from welding processes are well documented. In particular, it has been established that a manual welder is capable of making intrinsic decisions concerning electrode position based on process noise. The analysis of time / amplitude signals and Fast Fourier Transforms (I-I-1s), within salient frequency bandwidths of the weld acoustic, has yielded erratic, unpredictable and noise polluted data. Extracting a meaningful interpretation from this data is computationally intensive when utilising standard statistical methods and leads to data explosions, especially when an 'on-line' corrective control signal is required. An Artificial Neural Network is 'trained' on examples from acquired data and performs a robust signal recognition task rather than relying on a programmed set of data samples as in the case of an expert system. This technique enables the network to generalise and, as a consequence, allows the input data to be erratic, erroneous and even incomplete. This research defines the development of a hybrid system, utilising high speed date capture and 141-1' computation for the signal pre-processing and a 'self organising' network paradigm to establish weld stability and real time corrective control of the process parameters. The paper describes a successful application of a Neural Network hybrid system to determine weld stability in submerged arc welding (S.A.W) through the interpretation of airborne acoustics

Evidence for Association between SH2B1 Gene Variants and Glycated Hemoglobin in Nondiabetic European American Young Adults: The Add Health Study

Glycated hemoglobin (HbA1c) is used to classify glycaemia and type 2 diabetes (T2D). Body mass index (BMI) is a predictor of HbA1c levels and T2D. We tested 43 established BMI and obesity loci for association with HbA1c in a nationally representative multiethnic sample of young adults from the National Longitudinal Study of Adolescent to Adult Health [Add Health: age 24–34 years; n = 5641 European Americans (EA); 1740 African Americans (AA); 1444 Hispanic Americans (HA)] without T2D, using two levels of covariate adjustment (Model 1: age, sex, smoking, and geographic region; Model 2: Model 1 covariates plus BMI). Bonferroni adjustment was made for 43 SNPs and we considered P < 0.0011 statistically significant. Means (SD) for HbA1c were 5.4% (0.3) in EA, 5.7% (0.4) in AA, and 5.5% (0.3) in HA. We observed significant evidence for association with HbA1c for two variants near SH2B1 in EA (rs4788102, P = 2.2 × 10−4; rs7359397, P = 9.8 × 10−4) for Model 1. Both results were attenuated after adjustment for BMI (rs4788102, P = 1.7 × 10−3; rs7359397, P = 4.6 × 10−3). No variant reached Bonferroni-corrected significance in AA or HA. These results suggest that SH2B1 polymorphisms are associated with HbA1c, largely independent of BMI, in EA young adults

Historical environmental change in Africa drives divergence and admixture of Aedes aegypti mosquitoes: a precursor to successful worldwide colonization?

Article PurchasedIncreasing globalization has promoted the spread of exotic species, including disease vectors. Understanding the evolutionary processes involved in such colonizations is both of intrinsic biological interest and important to predict and mitigate future disease risks. The Aedes aegypti mosquito is a major vector of dengue, chikungunya and Zika, the worldwide spread of which has been facilitated by Ae. aegypti's adaption to human-modified environments. Understanding the evolutionary processes involved in this invasion requires characterization of the genetic make-up of the source population(s). The application of approximate Bayesian computation (ABC) to sequence data from four nuclear and one mitochondrial marker revealed that African populations of Ae. aegypti best fit a demographic model of lineage diversification, historical admixture and recent population structuring. As ancestral Ae. aegypti were dependent on forests, this population history is consistent with the effects of forest fragmentation and expansion driven by Pleistocene climatic change. Alternatively, or additionally, historical human movement across the continent may have facilitated their recent spread and mixing. ABC analysis and haplotype networks support earlier inferences of a single out-of-Africa colonization event, while a cline of decreasing genetic diversity indicates that Ae. aegypti moved first from Africa to the Americas and then to Asia. ABC analysis was unable to verify this colonization route, possibly because the genetic signal of admixture obscures the true colonization pathway. By increasing genetic diversity and forming novel allelic combinations, divergence and historical admixture within Africa could have provided the adaptive potential needed for the successful worldwide spread of Ae. aegypti

A mitochondrial half-size ABC transporter is involved in cadmium tolerance in Chlamydomonas reinhardtii

Five cadmium-sensitive insertional mutants, all affected at the CDS1 ('cadmium-sensitive 1') locus, have been previously isolated in the unicellular green alga Chlamydomonas reinhardtii. We here describe the cloning of the Cds1 gene (8314 bp with 26 introns) and the corresponding cDNA. The Cds1 gene, strongly induced by cadmium, encodes a putative protein (CrCds1) of 1062 amino acid residues that belongs to the ATM/HMT subfamily of half-size ABC transporters. This subfamily includes both vacuolar HMT-type proteins transporting phytochelatin-cadmium complexes from the cytoplasm to the vacuole and mitochondrial ATM-type proteins involved in the maturation of cytosolic Fe/S proteins. Unlike the Delta sphmt1 cadmium-sensitive mutant of Schizosaccharomyces pombe that lacks a vacuolar HMT-type transporter, the cds1 mutant accumulates a high amount of phytochelatin-cadmium complexes. By epitope tagging, the CrCds1 protein was localized in the mitochondria. Even though mitochondria of cds1 do not accumulate important amounts of 'free' iron, the mutant cells are hypersensitive to high iron concentrations. Our data show for the first time that a mitochondrial ATM-like transporter plays a major role in tolerance to cadmium.Peer reviewe

Microevolution during the emergence of a monophasic Salmonella Typhimurium epidemic in the United Kingdom

Microevolutionary events associated with the emergence and clonal expansion of new 27 epidemic clones of bacterial pathogens hold the key to understanding the drivers of 28 epidemiological success. We describe a comparative whole genome sequence and 29 phylogenomic analysis of monophasic Salmonella Typhimurium isolates from the UK 30 and Italy from 2005-2012. Monophasic isolates from this time formed a single clade 31 distinct from recent monophasic epidemic clones described previously from North 32 America and Spain. The current UK monophasic epidemic clones encode a novel 33 genomic island encoding resistance to heavy metals (SGI-3), and composite transposon 34 encoding antibiotic resistance genes not present in other Typhimurium isolates, that 35 may have contributed to the epidemiological success. We also report a remarkable 36 degree of genotypic variation that accumulated during clonal expansion of a UK 37 epidemic including multiple independent acquisitions of a novel prophage carrying the 38 sopE gene and multiple deletion events affecting the phase II flagellin locus

Developing the Active Communities Tool to Implement the Community Guide's Built Environment Recommendation for Increasing Physical Activity

Physical activity is higher in communities that include supportive features for walking and bicycling. In 2016, the Community Preventive Services Task Force released a systematic review of built environment approaches to increase physical activity. The results of the review recommended approaches that combine interventions to improve pedestrian and bicycle transportation systems with land use and environmental design strategies. Because the recommendation was multifaceted, the Centers for Disease Control and Prevention determined that communities could benefit from an assessment tool to address the breadth of the Task Force recommendations. The purpose of this article is to describe the systematic approach used to develop the Active Communities Tool. First, we created and refined a logic model and community theory of change for tool development. Second, we reviewed existing community-based tools and abstracted key elements (item domains, advantages, disadvantages, updates, costs, permissions to use, and psychometrics) from 42 tools. The review indicated that no tool encompassed the breadth of the Community Guide recommendations for communities. Third, we developed a new tool and pilot tested its use with 9 diverse teams with public health and planning expertise. Final revisions followed from pilot team and expert input. The Active Communities Tool comprises 6 modules addressing all 8 interventions recommended by the Task Force. The tool is designed to help cross-sector teams create an action plan for improving community built environments that promote physical activity and may help to monitor progress toward achieving community conditions known to promote physical activity