Work

DATA-DRIVEN MATERIALS SCIENCE:

Mining Artifacts in Mycelium SEM Micrographs
Preprint (in submission): arXiv:2103.07573

Author: T Stona de Almeida

Abstract: Mycelium is a promising biomaterial based on fungal mycelium, a highly porous, nanofibrous structure. Scanning electron micrographs are used to characterize its network, but the currently available tools for nanofibrous microstructures do not contemplate the particularities of biomaterials. The adoption of a software for artificial nanofibrous microstructure for mycelium characterization adds the uncertainty of imaging artifact formation to the analysis. The reported work combines supervised and unsupervised machine learning methods to automate the identification of artifacts in the mapped pores of mycelium microstructure.

Keywords: Machine learning; unsupervised learning; image processing; mycelium; microstructure informatics.


Mycelium-based gradient porous structures: a structure-property correlation
no preprint available (in submission)

Authors: J Bie-Kaplan, FZ Chaggan, Z Corey, E Gawronska, P Manimuda, G McIntyre, E Olivero, T Stona, PC Nalam, O Wodo

Abstract: Mycelium has the potential to form a natural gradient porous structure. This paper, for the first time, presents the detailed structure-property characterization at the micro-length scales. We show the structural variation of mycelium along the growth direction. We quantify the structure with a series of structural descriptors and report the gradual changes in the morphologies along with the growth directions. Our results demonstrate the potential to use mycelium as a gradient structure for multiple applications, including membranes, bio-scaffolds, and structural material.

Keywords: Gradient porous networks; mycelium; micromechanics; image processing; microstructure informatics.


INDUSTRIAL MATHEMATICS:

ZIP Code versus Georeference
Mathematics in Industry Reports (2021), Cambridge Open Engage, doi:10.33774/miir-2021-4lgsp-v2

Authors: JL Bazán Guzmán, TS de Almeida, MM Ferreira, DCF Guzmán, F Louzada, M Miranda, AL Mota, S Rangel, CM Russo, LA Santos, MO Santos, F Toledo

Abstract: When dealing with predictive modeling of credit-granting, different types of attributes are used: Cadastral, Behavioral, Business / Proposal, Credit Bureaux, in addition to Public, Private or Subsidiaries Sources. The Postal Address Code (Código de Endereçamento Postal CEP in Portuguese) in Brazil, in particular, has a unique contribution capacity (uncorrelated with most other attributes in general) and reasonably good predictive power. CEP is frequently used by truncating its numeric representation, considering the first d digits, for example. In this report, a preliminary methodology is proposed, aiming to elaborate clustering sets of CEPs by considering the information of clients' defaults over a period of time. Additionally, we tested the number of clusters obtained using the Information Value criterion. Promising solutions are obtained using statistical and optimizing approaches. Other methodologies are suggested and could be complementary with the principal methodology proposed.

Keywords: Classification; clustering; geospatial data; optimization; credit risk; machine learning; computational geometry; information value.


APPLIED MATHEMATICAL ANALYSIS:

Convex Geometric Reasoning for Crystalline Energies
Caspian Journal of Computational & Mathematical Engineering, 2016, 51-62 ( arXiv:2102.12683 )

Author: T Stona de Almeida

Abstract: The present work revisits the classical Wulff problem restricted to crystalline integrands, a class of surface energies that gives rise to finitely faceted crystals. The general proof of the Wulff theorem was given by J.E. Taylor (1978) by methods of Geometric Measure Theory. This work follows a simpler and direct way through Minkowski Theory by taking advantage of the convex properties of the considered Wulff shapes.

Keywords: Wulff shape; energy minimization; anisotropy; surface energy; geometric inequality.

MSC: 35A15-Variational methods; 49J40-Variational inequalities; 49K20-Problems involving PDEs; 49Q10-Shape optimization other than minimal surfaces; 52B60-Isoperimetric problems for polytopes; 82D25-Crystals