Depth-Sensing Indentation as a Micro- and Nanomechanical Approach to Characterisation of Mechanical Properties of Soft, Biological, and Biomimetic Materials

Classical methods of material testing become extremely complicated or impossible at micro-/nanoscale. At the same time, depth-sensing indentation (DSI) can be applied without much change at various length scales. However, interpretation of the DSI data needs to be done carefully, as length-scale dependent effects, such as adhesion, should be taken into account. This review paper is focused on different DSI approaches and factors that can lead to erroneous results, if conventional DSI methods are used for micro-/nanomechanical testing, or testing soft materials. We also review our recent advances in the development of a method that intrinsically takes adhesion effects in DSI into account: the Borodich-Galanov (BG) method, and its extended variant (eBG). The BG/eBG methods can be considered a framework made of the experimental part (DSI by means of spherical indenters), and the data processing part (data fitting based on the mathematical model of the experiment), with such distinctive features as intrinsic model-based account of adhesion, the ability to simultaneously estimate elastic and adhesive properties of materials, and non-destructive nature

Eddy Current Response to Three-Dimensional Flaws by the Boundary Element Method

In planning an inspection procedure, or in designing parts with flaw detectability as a design goal, it is essential that the engineer have available some form of model for estimating the probability of flaw detection. In the past this need has been met, with varying degrees of success, by relying on experience in the inspection of similar parts, sometimes supplemented by experimental testing. With the rapid advances in computer technology in recent years, it is now feasible to consider replacing, or at least enhancing, such practices with predictions based on numerical simulation of the flaw detection process [1]

Production of positive controls for calcivirus-specific PCR using recombinant baculiovirus technology

Recent advances in our knowledge of the genetic structure of human caliciviruses (HuCVs) and small round-structured viruses (SRSVs) have led to the development of polymerase chain reaction (PCR)-based molecular tests specific for these viruses. These methods have been developed to detect a number of human pathogenic viruses in environmental samples including water, sewage and shellfish. HuCVs and SRSVs are not culturable, and no animal model is currently available. Therefore there is no convenient method of preparing viruses for study or for reagent production. One problem facing those attempting to use PCR-based methods for the detection of HuCVs and SRSVs is the lack of a suitable positive control substrate. This is particularly important when screening complex samples in which the levels of inhibitors present may significantly interfere with amplificiation. Regions within the RNA polymerase regions of two genetically distinct human caliciviruses have been amplified and used to produce recombinant baculoviruses which express RNA corresponding to the calicivirus polymerase. This RNA is being investigated as a positive control substrate for PCR testing, using current diagnostic primer sets. Recombinant baculovirus technology will enable efficient and cost-effective production of large quantities of positive control RNA with a specific known genotype. We consider the development of these systems as essential for successful screening and monitoring applications

Understanding Prior Bias and Choice Paralysis in Transformer-based Language Representation Models through Four Experimental Probes

Recent work on transformer-based neural networks has led to impressive advances on multiple-choice natural language understanding (NLU) problems, such as Question Answering (QA) and abductive reasoning. Despite these advances, there is limited work still on understanding whether these models respond to perturbed multiple-choice instances in a sufficiently robust manner that would allow them to be trusted in real-world situations. We present four confusion probes, inspired by similar phenomena first identified in the behavioral science community, to test for problems such as prior bias and choice paralysis. Experimentally, we probe a widely used transformer-based multiple-choice NLU system using four established benchmark datasets. Here we show that the model exhibits significant prior bias and to a lesser, but still highly significant degree, choice paralysis, in addition to other problems. Our results suggest that stronger testing protocols and additional benchmarks may be necessary before the language models are used in front-facing systems or decision making with real world consequences