Metal ion interactions with DNA have far-reaching implications in biochemistry and DNA nanotechnology. Ag+ is uniquely interesting because it binds exclusively to the bases rather than the backbone of DNA, without the toxicity of Hg2+. In contrast to prior studies of Ag+ incorporation into double-stranded DNA, we remove the constraints of Watson-Crick pairing by focusing on homo-base DNA oligomers of the canonical bases. High resolution electro-spray ionization mass spectrometry reveals an unanticipated Ag+-mediated pairing of guanine homo-base strands, with higher stability than canonical guanine-cytosine pairing. By exploring unrestricted binding geometries, quantum chemical calculations find that Ag+ bridges between non-canonical sites on guanine bases. Circular dichroism spectroscopy shows that the Ag+-mediated structuring of guanine homobase strands persists to at least 90 °C under conditions for which canonical guanine-cytosine duplexes melt below 20 °C. These findings are promising for DNA nanotechnology and metal-ion based biomedical science.
We used finite element analyses (FEA) on Abaqus to study flexural properties of additive manufactured beams using polylactic acid (PLA) polymer. Experimental stress–strain data from flexural testing are used to define elastic–plastic properties of the material in the computation software. The flexural experiments are used to validate the FEA approach suggested. The method provides good results of deflection and stress with errors well below 10% in most of the cases. Therefore, by using the proposed approach, costs related to repeated experimental works can be avoided. In addition, the flexural rigidities of the additive manufactured beams are studied. Five different beam stiffener designs (diamond, honeycomb, square, triangular and wiggle) are studied based on beam bending theory. The force–deflection data from the flexural tests are used to determine the area moments of inertia of the beams. The honeycomb stiffener showed the highest force–deflection behaviour that led to the highest calculated area moment of inertia. However, with the lowest force–deflection behaviour, the square stiffener had the lowest calculated area moment of inertia.
Industry has always been in the pursuit of becoming more economically efficient and the current focus has been to reduce human labour using modern technologies. Even with cutting edge technologies, which range from packaging robots to AI for fault detection, there is still some ambiguity on the aims of some new systems, namely, whether they are automated or autonomous. In this paper, we indicate the distinctions between automated and autonomous systems as well as review the current literature and identify the core challenges for creating learning mechanisms of autonomous agents. We discuss using different types of extended realities, such as digital twins, how to train reinforcement learning agents to learn specific tasks through generalisation. Once generalisation is achieved, we discuss how these can be used to develop self-learning agents. We then introduce self-play scenarios and how they can be used to teach self-learning agents through a supportive environment that focuses on how the agents can adapt to different environments. We introduce an initial prototype of our ideas by solving a multi-armed bandit problem using two ε-greedy algorithms. Further, we discuss future applications in the industrial management realm and propose a modular architecture for improving the decision-making process via autonomous agents.
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