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   https://doi.org/10.1007/s00332-022-09780-2  

   García Trillos, Nicolás ; Morales, Javier ( June 2022 , Journal of Nonlinear Science)
2. Semi-paracrystallinity in semi-conducting polymers

   https://doi.org/10.1039/D1MH01349A  

   Marina, Sara ; Gutierrez-Fernandez, Edgar ; Gutierrez, Junkal ; Gobbi, Marco ; Ramos, Nicolás ; Solano, Eduardo ; Rech, Jeromy ; You, Wei ; Hueso, Luis ; Tercjak, Agnieszka ; et al ( April 2022 , Materials Horizons)

   Precise determination of structural organization of semi-conducting polymers is of paramount importance for the further development of these materials in organic electronic technologies. Yet, prior characterization of some of the best-performing materials for transistor and photovoltaic applications, which are based on polymers with rigid backbones, often resulted in conundrums in which X-ray scattering and microscopy yielded seemingly contradicting results. Here we solve the paradox by introducing a new structural model, i.e. , semi-paracrystalline organization. The model establishes that the microstructure of these materials relies on a dense array of small paracrystalline domains embedded in a more disordered matrix. Thus, the overall structural order relies on two parameters: the novel concept of degree of paracrystallinity ( i.e. , paracrystalline volume/mass fraction, introduced here for the first time) and the lattice distortion parameter of paracrystalline domains ( g -parameter from X-ray scattering). Structural parameters of the model are correlated with long-range charge carrier transport, revealing that charge transport in semi-paracrystalline materials is particularly sensitive to the interconnection of paracrystalline domains. 

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3. A divergence-free semi-implicit finite volume scheme for ideal, viscous, and resistive magnetohydrodynamics: A divergence-free semi-implicit finite volume scheme for ideal, viscous and resistive magnetohydrodynamics

   https://doi.org/10.1002/fld.4681  

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4. On semi-empirical decomposition of multidecadal climate variability into forced and internally generated components: SEMI-EMPIRICAL DECOMPOSITION OF MULTIDECADAL CLIMATE VARIABILITY

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   https://doi.org/10.1109/ssci50451.2021.9660093  

   Ozdogan, Emrecan ; Sabin, Norman C. ; Gracie, Thomas ; Portley, Steven ; Halac, Mali ; Coard, Thomas ; Trimble, William ; Sokhansanj, Bahrad ; Rosen, Gail ; Polikar, Robi ( January 2022 , 2021 IEEE Symposium Series on Computational Intelligence (SSCI))

   Genome sequencing generates large volumes of data and hence requires increasingly higher computational resources. The growing data problem is even more acute in metagenomics applications, where data from an environmental sample include many organisms instead of just one for the common single organism sequencing. Traditional taxonomic classification and clustering approaches and platforms - while designed to be computationally efficient - are not capable of incrementally updating a previously trained system when new data arrive, which then requires complete re-training with the augmented (old plus new) data. Such complete retraining is inefficient and leads to poor utilization of computational resources. An ability to update a classification system with only new data offers a much lower run-time as new data are presented, and does not require the approach to be re-trained on the entire previous dataset. In this paper, we propose Incremental VSEARCH (I-VSEARCH) and its semi-supervised version for taxonomic classification, as well as a threshold independent VSEARCH (TI-VSEARCH) as wrappers around VSEARCH, a well-established (unsupervised) clustering algorithm for metagenomics. We show - on a 16S rRNA gene dataset - that I-VSEARCH, running incrementally only on the new batches of data that become available over time, does not lose any accuracy over VSEARCH that runs on the full data, while providing attractive computational benefits. 

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