Conformational variability and heterogeneity are crucial determinants of the function of biological macromolecules. The possibility of accessing this information experimentally suffers from severe under-determination of the problem, since there are a few experimental observables to be accounted for by a (potentially) infinite number of available conformational states. Several computational methods have been proposed over the years in order to circumvent this theoretically insurmountable obstacle.

Invasive species cause huge amounts of environmental, economic, social and cultural damage in Europe and worldwide. Improving measures to control them is an ongoing challenge, and mathematical modeling and optimization are becoming increasingly popular as a tool to assist management (1; 2; 4). We analyse an optimal control model for the control of invasive species which aims to find the best temporal resource allocation strategy for the population reduction, under a budget constraint (3).

In a typical secure communication system, messages undergo two different encodings: an error-correcting code is applied at the physical layer to ensure correct reception by the addressee (integrity), while at an upper protocol layer cryptography is leveraged to enforce secrecy with respect to eavesdroppers (confidentiality).

In a recent paper, we have introduced new sets of Sheffer and Brenke polynomial sequences based on higher order Bell numbers. In this paper, by using a more compact notation, we show another family of exponential polynomials belonging to the Sheffer class, called, for shortness, Sheffer-Bell polynomials. Furthermore, we introduce a set of logarithmic numbers, which are the counterpart of Bell numbers and their extensions.

This study describes a semi-empirical model developed to estimate volumetric soil moisture (<mml:semantics>theta v</mml:semantics>) in bare soils during the dry season (March-May) using C-band (5.42 GHz) synthetic aperture radar (SAR) imagery acquired from the Sentinel-1 European satellite platform at a 20 m spatial resolution. The semi-empirical model was developed using backscatter coefficient (<mml:semantics>sigma degrees dB</mml:semantics>) and in situ soil moisture collected from Siruguppa taluk (sub-district) in the Karnataka state of India.

We study numerically the effect of thermal fluctuations and of variable fluid-substrate interactions on the spontaneous dewetting of thin liquid films. To this aim, we use a recently developed lattice Boltzmann method for thin liquid film flows, equipped with a properly devised stochastic term. While it is known that thermal fluctuations yield shorter rupture times, we show that this is a general feature of hydrophilic substrates, irrespective of the contact angle $\theta$. The ratio between deterministic and stochastic rupture times, though, decreases with $\theta$.

This work presents a methodology for the short-term forecast of intense rainfall based on a neural network and the integration of Global Navigation and Positioning System (GNSS) and meteorological data. Precipitable water vapor (PWV) derived from GNSS is combined with surface pressure, surface temperature and relative humidity obtained continuously from a ground-based meteorological station. Five years of GNSS data from one station in Lisbon, Portugal, are processed. Data for precipitation forecast are also collected from the meteorological station.

In order to solve Prandtl--type equations we propose a collocation--quadrature method based on de la Vallée Poussin (briefly VP) filtered interpolation at Chebyshev nodes. Uniform convergence and stability are proved in a couple of Holder--Zygmund spaces of locally continuous functions. With respect to classical methods based on Lagrange interpolation at the same collocation nodes, we succeed in reproducing the optimal convergence rates of the L2 case and cut off the typical log factor which seemed inevitable dealing with uniform norms.