Bottleneck Detection in Modular Construction Factories Using Computer Vision.

The construction industry is increasingly adopting off-site and modular construction methods due to the advantages offered in terms of safety, quality, and productivity for construction projects. Despite the advantages promised by this method of construction, modular construction factories still rely on manually-intensive work, which can lead to highly variable cycle times. As a result, these factories experience bottlenecks in production that can reduce productivity and cause delays to modular integrated construction projects. To remedy this effect, computer vision-based methods have been proposed to monitor the progress of work in modular construction factories. However, these methods fail to account for changes in the appearance of the modular units during production, they are difficult to adapt to other stations and factories, and they require a significant amount of annotation effort. Due to these drawbacks, this paper proposes a computer vision-based progress monitoring method that is easy to adapt to different stations and factories and relies only on two image annotations per station. In doing so, the Scale-invariant feature transform (SIFT) method is used to identify the presence of modular units at workstations, and the Mask R-CNN deep learning-based method is used to identify active workstations. This information was synthesized using a near real-time data-driven bottleneck identification method suited for assembly lines in modular construction factories. This framework was successfully validated using 420 h of surveillance videos of a production line in a modular construction factory in the U.S., providing 96% accuracy in identifying the occupancy of the workstations and an F-1 Score of 89% in identifying the state of each station on the production line. The extracted active and inactive durations were successfully used via a data-driven bottleneck detection method to detect bottleneck stations inside a modular construction factory. The implementation of this method in factories can lead to continuous and comprehensive monitoring of the production line and prevent delays by timely identification of bottlenecks.

Archaeal assemblages inhabiting temperate mixed forest soil fluctuate in taxon composition and spatial distribution over time.

This study explored the persistence and spatial distribution of a diverse Archaeal assemblage inhabiting a temperate mixed forest ecosystem. Persistence under native conditions was measured from 2001 to 2010, 2011, and 2012 by comparison of 16S rRNA gene clone libraries. The Archaeal assemblages at each of these time points were found to be significantly different (AMOVA, P < 0.01), and the nature of this difference was dependent on taxonomic rank. For example, the cosmopolitan genus g_Ca. Nitrososphaera (I.1b) was detected at all time points, but within this taxon the abundance of s_SCA1145, s_SCA1170, and s_Ca. N. gargensis fluctuated over time. In addition, spatial heterogeneity (patchiness) was measured at these time points using 1D TRFLP-SSCP fingerprinting to screen soil samples covering multiple spatial scales. This included soil collected from small volumes of 3 cubic centimeters to larger scales-over a surface area of 50 m(2), plots located 1.3 km apart, and a separate locality 23 km away. The spatial distribution of Archaea in these samples changed over time, and while g_Ca. Nitrososphaera (I.1b) was dominant over larger scales, patches were found at smaller scales that were dominated by other taxa. This study measured the degree of change for Archaeal taxon composition and patchiness over time in temperate mixed forest soil.

One-dimensional TRFLP-SSCP is an effective DNA fingerprinting strategy for soil Archaea that is able to simultaneously differentiate broad taxonomic clades based on terminal fragment length polymorphisms and closely related sequences based on single stranded conformation polymorphisms.

DNA fingerprinting methods provide a means to rapidly compare microbial assemblages from environmental samples without the need to first cultivate species in the laboratory. The profiles generated by these techniques are able to identify statistically significant temporal and spatial patterns, correlations to environmental gradients, and biological variability to estimate the number of replicates for clone libraries or next generation sequencing (NGS) surveys. Here we describe an improved DNA fingerprinting technique that combines terminal restriction fragment length polymorphisms (TRFLP) and single stranded conformation polymorphisms (SSCP) so that both can be used to profile a sample simultaneously rather than requiring two sequential steps as in traditional two-dimensional (2-D) gel electrophoresis. For the purpose of profiling Archaeal 16S rRNA genes from soil, the dynamic range of this combined 1-D TRFLP-SSCP approach was superior to TRFLP and SSCP. 1-D TRFLP-SSCP was able to distinguish broad taxonomic clades with genetic distances greater than 10%, such as Euryarchaeota and the Thaumarchaeal clades g_Ca. Nitrososphaera (formerly 1.1b) and o_NRP-J (formerly 1.1c) better than SSCP. In addition, 1-D TRFLP-SSCP was able to simultaneously distinguish closely related clades within a genus such as s_SCA1145 and s_SCA1170 better than TRFLP. We also tested the utility of 1-D TRFLP-SSCP fingerprinting of environmental assemblages by comparing this method to the generation of a 16S rRNA clone library of soil Archaea from a restored Tallgrass prairie. This study shows 1-D TRFLP-SSCP fingerprinting provides a rapid and phylogenetically informative screen of Archaeal 16S rRNA genes in soil samples.