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"Rogers, Simon"
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Human body
Provides an easy-reference, graphically illustrated introduction to the human body and how it functions to sustain life, including digestion, reproduction, and the senses.
Book
In search of physical meaning: defining transient parameters for nonlinear viscoelasticity
A complete set of model-independent viscoelastic functions for understanding responses to transient nonlinear rheological tests is presented, using large-amplitude oscillatory shear strain as a model nonlinear protocol. The derivation makes no assumptions about symmetries, and is therefore applicable to the responses to any input, allowing researchers to unambiguously define time-dependent moduli, viscosities, compliances, fluidities, and normal stress coefficients. A legend for interpreting the dynamic trajectories in modulus space is provided, along with explicit definitions of the rates at which the moduli change. These provide a quantitative mechanism to identify when, and by how much, a material response stiffens, softens, thickens, or thins while being deformed. In addition to providing analytical expressions for the moduli, the derivation
requires
the definition of a conceptually new term. This means there exist three, not two, time-dependent nonlinear viscoelastic functions by which any response can be fully described. The third function accounts for nonlinear properties such as yield stresses and the shifting of the strain equilibrium. This complete analysis scheme is unique in making a distinction between the strains in the lab and material frames. The quantitative sequence of physical process analysis, which is fully developed in this work, allows for comprehensive physical interpretations of responses to transient deformations of any kind to be made, including the steady alternance responses to large-amplitude oscillatory shear (LAOS), time-dependent oscillatory shear startup responses, and thixotropic and anti-thixotropic responses.
Journal Article
A first course in machine learning
Offering a complete introduction to the fundamental concepts underlying machine learning theory, this text presents modern methods and mathematical foundations needed to enable further study. The text requires minimal mathematical prerequisites, making it appropriate for students who are new to the field.
Book
Elucidating the G″ overshoot in soft materials with a yield transition via a time-resolved experimental strain decomposition
Materials that exhibit yielding behavior are used in many applications, from spreadable foods and cosmetics to direct write three-dimensional printing inks and filled rubbers. Their key design feature is the ability to transition behaviorally from solid to fluid under sufficient load or deformation. Despite its widespread applications, little is known about the dynamics of yielding in real processes, as the nonequilibrium nature of the transition impedes understanding. We demonstrate an iteratively punctuated rheological protocol that combines strain-controlled oscillatory shear with stress-controlled recovery tests. This technique provides an experimental decomposition of recoverable and unrecoverable strains, allowing for solid-like and fluid-like contributions to a yield stress material’s behavior to be separated in a time-resolved manner. Using this protocol, we investigate the overshoot in loss modulus seen in materials that yield. We show that this phenomenon is caused by the transition from primarily solid-like, viscoelastic dissipation in the linear regime to primarily fluid-like, plastic flow at larger amplitudes. We compare and contrast this with a viscoelastic liquidwith no yielding behavior, where the contribution to energy dissipation from viscous flow dominates over the entire range of amplitudes tested.
Journal Article
Digital rheometer twins
Precise and reliable prediction of soft and structured materials’ behavior under flowing conditions is of great interest to academics and industrial researchers alike. The classical route to achieving this goal is to construct constitutive relations that, through simplifying assumptions, approximate the time- and rate-dependent stress response of a complex fluid to an imposed deformation.The parameters of these simplified models are then identified by suitable rheological testing. The accuracy of each model is limited by the assumptions made in its construction, and, to a lesser extent, the ability to determine numerical values of parameters from the experimental data. In this work, we leverage advances in machine learning methodologies to construct rheology-informed graph neural networks (RhiGNets) that are capable of learning the hidden rheology of a complex fluid through a limited number of experiments. A multifidelity approach is then taken to combine limited additional experimental data with the RhiGNet predictions to develop “digital rheometers” that can be used in place of a physical instrument.
Journal Article
Optimal conditions for pre-shearing thixotropic or aging soft materials
Pre-shearing is widely recognized as a necessary step to guarantee repeatability in rheological studies of thixotropic or aging soft materials. When one-directional pre-shear protocols are used, unrecovered elastic strain which leads to biased material states that are not always relaxed because of the build-up of structure during the relaxation process. We propose a way of guaranteeing unbiased material states by incorporating recovery steps, consisting of steps of strain opposing the initial direction of shearing, into any pre-shear protocol. Using such a multi-step pre-shear protocol, we show that it is possible to produce identical results from shearing in the positive and negative directions for the same magnitude of rate after pre-shearing. We further show how this idea of unbiased material states can be used to obtain unbiased results for other fundamental rheological experiments such as flow curves and frequency sweeps. By performing the new pre-shear protocol for every single measurement point of a flow curve or frequency sweep, it is possible to obtain data which is not affected by previous data collection, which leads to material responses with simple and clear shear histories.
Journal Article
Professional delays in referral of patients with mouth cancer: six case histories
Professional delay is an important delay in referral of patients with suspected mouth cancer. Missing the possibility of cancer might not only result in worse outcomes in respect to function and survival, but also have medicolegal implications. The aim of this article was to review a consecutive cohort of patients over a two-year period with mouth cancer diagnosis and identify those with professional delay and illustrate the main types of presentations using short case histories. The multi-disciplinary team records were used to identify case notes of a two-year (2019 and 2020) consecutive cohort of patients diagnosed with mouth cancer, including referrals from primary and secondary care. Professional delay was considered if red flag symptoms were not referred within two weeks or if there was initial misdiagnosis. In total, 246 patients with mouth cancer were discussed with the multi-disciplinary team: 35 had delay in referral or misdiagnosis of mouth cancer. Six common scenarios were identified: 1) sudden onset paraesthesia; 2) dental abscess; 3) temporomandibular joint dysfunction syndrome (TMJD) and abscess; 4) TMJD; 5) trauma/facial fracture; and 6) non-healing socket following dental extraction. To conclude, it can be difficult to accurately diagnose mouth cancer in primary dental and medical care and an index of suspicion is essential in order to minimise the possibility of professional delay.
Key points
Professional delay is an uncommon but important aspect of delay in referral of patients with suspected mouth cancer.
Professional delay can occur when the symptoms are misinterpreted as a common non-cancer condition, such as a dental abscess, temporomandibular joint dysfunction syndrome or a non-healing socket following dental extraction.
Greater awareness of red flag symptoms in both general dental and medical practitioners is needed to ensure prompt referral and early detection of mouth cancer.
Journal Article
Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships
Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm—Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds.
Journal Article