ABSTRACT
The importance of construction automation has grown worldwide, aiming to deliver new machineries for the automation of roads, tunnels, bridges, buildings and earth-work construction. This need is mainly driven by (i) the shortage and rising costs of skilled workers, (ii) the tremendous increased needs for new infrastructures to serve the daily activities and (iii) the immense demand for maintenance of ageing infrastructure. Shotcrete (sprayed concrete) is increasingly becoming popular technology among contractors and builders, as its application is extremely economical and flexible as the growth in construction repairs in developed countries demand excessive automation of concrete placement. Even if shotcrete technology is heavily mechanized, the actual application is still performed manually at a large extend. RoBétArméEuropean project targets the Construction 4.0 transformation of the construction with shotcrete with the adoption of breakthrough technologies such as sensors, augmented reality systems, high-performance computing, additive manufacturing, advanced materials, autonomous robots and simulation systems, technologies that have already been studied and applied so far in Industry 4.0. The paper at hand showcases the development of a novel robotic system with advanced perception, cognition and digitization capabilities for the automation of all phases of shotcrete application. In particular, the challenges and barriers in shotcrete automation are presented and the RoBétArmésuggested solutions are outlined. We introduce a basic conceptual architecture of the system to be developed and we demonstrate the four application scenarios on which the system is designated to operate.
The RoBétArmé European project targets the Construction 4.0 transformation of the construction with shotcrete with the adoption of breakthrough technologies such as sensors, augmented reality systems, high-performance computing, additive manufacturing, advanced materials, autonomous robots and simulation systems, technologies that have already been studied and applied so far in Industry 4.0. This paper showcases a case study on which novel robotic systems will be developed for the automation of shotecrete application. The outcomes of this research can be widely used in other application technologies related to the construction domain.
ABSTRACT
In order to ensure food safety, screening food samples for the presence of pathogens has been categorised as a legal testing item throughout the globe. One of the most prevalent zoonotic bacteria transmitted through dairy milk is Staphylococcus aureus. Given the limitations of the conventional detection methods, in the current study we desigined a competitive lateral flow immune assay (LFIA) using colloidal silver nanoparticles derived from mango leaves for the detection of Staphylococcus aureus in cow milk. SpA, a recombinant protein of Staphylococcus aureus, was used to raised hyperimmune sera used for developing the assay followed by conjugation with the synthesized nanoparticles. To increase the specificity of the assay, the milk samples were prenriched with selective agar exclusively require for Staphyloccocus aureus. The assay was found to be completed within 7-8 h by observing test and control lines in LFIA strips. The developed assay was found to specifically detect the bacteria as low as 1000 cfu/ml of milk samples. With a total 230 number of raw and clinical mastitis milk samples, the assay was validated and achieved relative accuracy, specificity, and sensitivity values of 97.39, 98.03, and 96.1%, respectively. The developed LFIA, which uses economically feasible and stable silver nanoparticles derived from mango leaves, has the potential for routine screening of milk samples for the presence of Staphylococcus aureus, especially in low-resource settings, allowing for early diagnosis, which facilitates effective treatment for the dairy animals and prevents the transmission of the disease in consumers.
