Exploring the opportunity for applying digital twins in the healthcare context is an emerging research area that has the potential to support more personalized care. A recognized aspect in cancer care is the need for more personalized treatment planning to complement the recent advances in precision medicine. In this article, we present a classification of digital twins into Grey Box, Surrogate, and Black Box models using systems and mathematical modeling theory. We then explore one possible approach, namely a Black Box classification for incorporating the use of digital twins in the context of personalized uterine cancer care. This article presents one of the first attempts to use digital twins in this capacity and represents an amalgamation of three key domains: clinical, digital health, and computer science, respectively.

Pulsed Eddy Current (PEC) sensing is used for Non-Destructive Evaluation (NDE) of the structural integrity of metallic structures in the aircraft, railway, oil and gas sectors. Urban water utilities also have extensive large ferromagnetic structures in the form of critical pressure pipe systems made of grey cast iron, ductile cast iron and mild steel. The associated material properties render NDE of these pipes by means of electromagnetic sensing a necessity. In recent years PEC sensing has established itself as a state-of-the-art NDE technique in the critical water pipe sector. This paper presents advancements to PEC inspection in view of the specific information demanded from water utilities along with the challenges encountered in this sector. Operating principles of the sensor architecture suitable for application on critical pipes are presented with the associated sensor design and calibration strategy. A Gaussian process-based approach is applied to model a functional relationship between a PEC signal feature and critical pipe wall thickness. A case study demonstrates the sensor’s behaviour on a grey cast iron pipe and discusses the implications of the observed results and challenges relating to this application.

Thickness quantification of conductive ferromagnetic materials has become a common necessity in present-day structural health monitoring and infrastructure maintenance. Recent research has found Pulsed Eddy Current (PEC) sensing, especially the detector-coil-based PEC sensor architecture, to effectively serve as a nondestructive sensing technique for this purpose. As a result, several methods of varying complexity have been proposed in recent years to extract PEC signal features, against which conductive ferromagnetic material thickness behaves as a function, in return enabling thickness quantification owing to functional behaviours. It can be seen that almost all features specifically proposed in the literature for the purpose of conductive ferromagnetic material-thickness quantification are in some way related to the diffusion time constant of eddy currents. This paper examines the relevant feature-extraction methods through a controlled experiment in which the methods are applied to a single set of experimentally captured PEC signals, and provides a review by discussing the quality of the extractable features, and their functional behaviours for thickness quantification, along with computational time taken for feature extraction. Along with this paper, the set of PEC signals and some MATLAB codes for feature extraction are provided as supplementary materials for interested readers.

Presented in this paper is a method of simulating photovoltaic modules by taking the manufacturer specified data at standard test conditions as inputs. A method for estimating unknown parameters of a simplified version of the single diode solar cell model corresponding to a given photovoltaic panel is described. In the proposed approach, the equivalent circuit represents an entire solar panel, composed of several solar cells. A new method, which is simple, is also proposed to identify the ideality factor of the equivalent diode along with the series resistance. Estimated unknowns are used in generating module output curves at different environmental conditions. The generated curves are compared with manufacturer's data. The curves are also compared with curves generated with an alternative method available in the literature. The superiority of the proposed technique is demonstrated.