Fracture behavior and mechanical,thermal, and rheological properties of biodegradable films extruded by flat die and calender

The development of biodegradable materials for tailored applications, particularly in the field of polymeric films and sheets, is a challenging technological goal as well as a contribution to help protect the environment. Poly(lactic) acid (PLA) is a promising substitute for several oil-based polymers; however, to overcome its thermal and mechanical drawbacks, researchers have developed solutions such as blending PLA with polybutylene adipate terephthalate (PBAT), which is capable of increasing the ductility of the final material. In this study, PLA/PBAT binary blends, with minimum possible content of nonrenewable materials, were examined from processing, thermal, morphological, and rheological perspective. An optimized PLA/PBAT ratio was chosen as the polymeric basis to obtain a biodegradable formulation by adding a biobased plasticizer and appropriate fillers to produce a micrometer film with tailored flexibility and tear resistance. The processing technology involved flat-die extrusion, followed by calendering. The tearing resistance of the produced film was investigated, and the results were compared with literature data. A study on the essential work of fracture was implemented to explore the mode III out-of-plane fracture resistance starting from a trouser tear test.     

The Landscape of the Spiked Tensor Model

We consider the problem of estimating a large rank-one tensor u ^⊗k ∈ (ℝⁿ)^⊗k , k ≥ 3 , in Gaussian noise. Earlier work characterized a critical signal-to-noise ratio λ  _Bayes = O(1) above which an ideal estimator achieves strictly positive correlation with the unknown vector of interest. Remarkably, no polynomial-time algorithm is known that achieved this goal unless λ ≥ Cn^{(k − 2)/4} , and even powerful semidefinite programming relaxations appear to fail for 1 ≪ λ ≪ n^{(k − 2)/4} . In order to elucidate this behavior, we consider the maximum likelihood estimator, which requires maximizing a degree-k homogeneous polynomial over the unit sphere in n dimensions. We compute the expected number of critical points and local maxima of this objective function and show that it is exponential in the dimensions n , and give exact formulas for the exponential growth rate. We show that (for λ larger than a constant) critical points are either very close to the unknown vector u or are confined in a band of width Θ(λ^{−1/(k − 1)}) around the maximum circle that is orthogonal to u . For local maxima, this band shrinks to be of size Θ(λ^{−1/(k − 2)}) . These “uninformative” local maxima are likely to cause the failure of optimization algorithms. © 2019 Wiley Periodicals, Inc.     

Chain spaces via Clifford algebras

In this note we show that the chain space belonging to a quadric can be embedded into the chain geometry over a Clifford algebra via a generalized stereographic projection.     

Primal cutting plane algorithms revisited

Dual fractional cutting plane algorithms, in which cutting planes are used to iteratively tighten a linear relaxation of an integer program, are well-known and form the basis of the highly successful branch-and-cut method. It is rather less well-known that various primal cutting plane algorithms were developed in the 1960s, for example by Young. In a primal algorithm, the main role of the cutting planes is to enable a feasible solution to the original problem to be improved. Research on these algorithms has been almost non-existent.  In this paper we argue for a re-examination of these primal methods. We describe a new primal algorithm for pure 0-1 problems based on strong valid inequalities and give some encouraging computational results. Possible extensions to the case of general mixed-integer programs are also discussed.     

Estimation consistante des paramètres d'un modèle non linéaire pour des données fonctionnelles discrétisées aléatoirement

We study a class of nonlinear regression models for scalar or vectorial response when the explanatory variable is a function. We introduce a consistent estimator of the parameters of models in this class when functions are evaluated at randomly chosen observation points. To cite this article: F. Rossi, B. Conan-Guez, C. R. Acad. Sci. Paris, Ser. I 340 (2005).