In this work a new approach for the creation of Articulated Total Body (ATB) models for person-specific multi-body simulations is presented, with the main aim of overcoming limitations related to classical multi-ellipsoids ATB models, based on regression equations having only the weight and the height of the subject as input. The new methodology is based on a Statistical Shape Model (SSM), morphable according to up to 24 input parameters: the SSM was obtained from Principal Component Analysis (PCA), applied on a wide database of 3D human scans (CAESAR). The so obtained geometry can be segmented automatically to generate body segments with the respective inertial properties (mass, principal moments of inertia, and centres of mass location). The routine has been tested on a random set of 20 male subjects and the classical multi-ellipsoids models were compared to these in terms of inertial properties and 3D external geometry: the highest differences were registered at the abdomen and the thighs for what concerns the mass (60%), principal moments (75%) and centres of mass (50 mm) properties; the trunk, the shoulder and the calves are the most critical areas for the external geometry (average distance between the anthropomorphic and ellipsoids models equal to 50 mm). A contribution has been made to build person-specific multibody models. This is a valuable method since approximations made by multi-ellipsoidal models have resulted to be relevant at specific body areas, and personalised models can be a support to design and to forensic analyses.

In the last decade, a rapidly growing number of operando spectroscopy analyses have helped unravelling the electrochemical mechanism of lithium and post-lithium battery materials. The corresponding experiments usually produce large datasets containing many tens or hundreds of spectra. This considerable amount of data is calling for a suitable strategy for their treatment in a reliable way and within reasonable time frame. To this end, an alternative and innovating approach allowing one to extract all meaningful information from such data is the use of chemometric tools such as Principal Component Analysis (PCA) and multivariate curve resolution (MCR). PCA is generally used to discover the minimal particular structures in multivariate spectral data sets. In the case of operando spectroscopy data, it can be used to determine the number of independent components contributing to a complete series of collected spectra during electrochemical cycling. The number of principal components determined by PCA can then be used as the basis for MCR analysis, which allows the stepwise reconstruction of the “real” spectral components without needing any pre-existing model or any presumptive information about the system. In this paper, we will show how such approach can be effectively applied to different techniques, such as Mössbauer spectroscopy, X-ray absorption spectroscopy or transmission soft X-ray microscopy, for the comprehension of the electrochemical mechanisms in battery studies.