A Peridynamics-Based Micromechanical Modeling Approach for Random Heterogeneous Structural Materials

This paper presents a peridynamics-based micromechanical analysis framework that can efficiently handle material failure for random heterogeneous structural materials. In contrast to conventional continuum-based approaches, this method can handle discontinuities such as fracture without requiring supplemental mathematical relations. The framework presented here generates representative unit cells based on microstructural information on the material and assigns distinct material behavior to the constituent phases in the random heterogenous microstructures. The framework incorporates spontaneous failure initiation/propagation based on the critical stretch criterion in peridynamics and predicts effective constitutive response of the material. The current framework is applied to a metallic particulate-reinforced cementitious composite. The simulated mechanical responses show excellent match with experimental observations signifying efficacy of the peridynamics-based micromechanical framework for heterogenous composites. Thus, the multiscale peridynamics-based framework can efficiently facilitate microstructure guided material design for a large class of inclusion-modified random heterogenous materials.

Total Synthesis of Tiacumicin B: Study of the Challenging β-Selective Glycosylations*

We give a full account of the total synthesis of tiacumicin B (Tcn-B), a natural glycosylated macrolide with remarkable antibiotic properties. Our strategy is based on our experience with the synthesis of the tiacumicin B aglycone and on unique 1,2-cis-glycosylation steps. We used sulfoxide anomeric leaving-groups in combination with a remote 3-O-picoloyl group on the donors that allowed highly β-selective rhamnosylation and noviosylation that rely on H-bond-mediated aglycone delivery. The rhamnosylated C1-C3 fragment was anchored to the C4-C19 aglycone fragment by a Suzuki-Miyaura cross-coupling. Ring-size-selective Shiina macrolactonization provided a semiglycosylated aglycone that was engaged directly in the noviolysation step with a virtually total β-selectivity. Finally, a novel deprotection method was devised for the removal of a 2-naphthylmethyl ether on a phenol, and efficient removal of all the protecting groups provided synthetic tiacumicin B.

An adaptive, interacting, cluster-based model for predicting the transmission dynamics of COVID-19

The SARS-CoV-2 driven disease COVID-19 is pandemic with increasing human and monetary costs. COVID-19 has put an unexpected and inordinate degree of pressure on healthcare systems of strong and fragile countries alike. To launch both containment and mitigation measures, each country requires estimates of COVID-19 incidence as such preparedness allows agencies to plan efficient resource allocation and to design control strategies. Here, we have developed a new adaptive, interacting, and cluster-based mathematical model to predict the granular trajectory of COVID-19. We have analyzed incidence data from three currently afflicted countries of Italy, the United States of America, and India. We show that our approach predicts state-wise COVID-19 spread for each country with reasonable accuracy. We show that Rt, as the effective reproduction number, exhibits significant spatial variations in these countries. However, by accounting for the spatial variation of Rt in an adaptive fashion, the predictive model provides estimates of the possible asymptomatic and undetected COVID-19 cases, both of which are key contributors in COVID-19 transmission. We have applied our methodology to make detailed predictions for COVID19 incidences at the district and state level in India. Finally, to make the models available to the public at large, we have developed a web-based dashboard, namely "Predictions and Assessment of Corona Infections and Transmission in India" (PRACRITI, see http://pracriti.iitd.ac.in), which provides the detailed Rt values and a three-week forecast of COVID cases.