Solvent effects on charge spatial extent in DNA and implications for transfer

To clarify the role played by water in facilitating long-range DNA charge transport, carefully designed, state-of-the-art, self-interaction corrected density-functional quantum mechanical and molecular mechanical (SIC-QM/MM) simulations are performed for the first time on two ionized adenine:thymine bridge models in explicit water solvent at finite temperature. For random solvent configurations, the charge is partially delocalized. However, a charge localization on different, well-separated adenines can be induced and is correlated with a restructuring of their first solvation shells. Thus, the importance of water in the mechanism of long-range charge transport is explicitly demonstrated, and the microscopic conditions for a charge localization are revealed.     

Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures

The differential diagnosis of epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) may be difficult, due to the lack of distinctive clinical features. The interictal electroencephalographic (EEG) signal may also be normal in patients with ES. Innovative diagnostic tools that exploit non-linear EEG analysis and deep learning (DL) could provide important support to physicians for clinical diagnosis. In this work, 18 patients with new-onset ES (12 males, 6 females) and 18 patients with video-recorded PNES (2 males, 16 females) with normal interictal EEG at visual inspection were enrolled. None of them was taking psychotropic drugs. A convolutional neural network (CNN) scheme using DL classification was designed to classify the two categories of subjects (ES vs. PNES). The proposed architecture performs an EEG time-frequency transformation and a classification step with a CNN. The CNN was able to classify the EEG recordings of subjects with ES vs. subjects with PNES with 94.4% accuracy. CNN provided high performance in the assigned binary classification when compared to standard learning algorithms (multi-layer perceptron, support vector machine, linear discriminant analysis and quadratic discriminant analysis). In order to interpret how the CNN achieved this performance, information theoretical analysis was carried out. Specifically, the permutation entropy (PE) of the feature maps was evaluated and compared in the two classes. The achieved results, although preliminary, encourage the use of these innovative techniques to support neurologists in early diagnoses.     

The Renewed Role of Sweep Functions in Noisy Shortcuts to Adiabaticity

We study the robustness of different sweep protocols for accelerated adiabaticity following in the presence of static errors and of dissipative and dephasing phenomena. While in the noise-free case, counterdiabatic driving is, by definition, insensitive to the form of the original sweep function, this property may be lost when the quantum system is open. We indeed observe that, according to the decay and dephasing channels investigated here, the performance of the system becomes highly dependent on the sweep function. Our findings are relevant for the experimental implementation of robust shortcuts-to-adiabaticity techniques for the control of quantum systems.     

Dynamics and non-equilibrium steady state in a system of coupled harmonic oscillators

A system of two coupled oscillators, each of them coupled to an independent reservoir, is analysed. The analytical solution of the non-rotating wave master equation is obtained in the high-temperature and weak coupling limits. No thermal entanglement is found in the high-temperature limit. In the weak coupling limit the system converges to an entangled non-equilibrium steady state. A critical temperature for the appearance of quantum correlations is found.     

A Large Deviation Perspective on Ratio Observables in Reset Processes: Robustness of Rate Functions

Journal of Statistical Physics - We study large deviations of a ratio observable in discrete-time reset processes. The ratio takes the form of a current divided by the number of reset steps and as...     

Shakedown analysis of limited-ductility structures

Summary The paper deals with the shakedown analysis problem of discrete elastoplastic structures, subjected to variable and repeated quasi-static loads, account being given to the limited structural ductility by imposing a-priori limit values to some relevant parameters of plastic deformation. The key-concept of limited-ductility load amplifier is assessed and the problem is given a unified formulation as a nonlinear mathematical programming problem, where ductility constraints — of generalized character — are obtained by using a so-called perturbation method which leads to a-priori upper bounds of the relevant plastic deformation measures. Lower bounds to the load amplifier are so obtained and three different computationals stratagies are herein outlined: the linear parametric programming one is applied to three case-study structures.

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Sommario La memoria affronta lo studio del problema di adattamento elastoplastico di strutture discrete, sottoposte a carichi comunque variabili in modo quasi-statico, tenendo conto di un comportamento a limitata duttilità della struttura, il quale richiede che uno o più parametri della deformazione plastica risultino non superiori di assegnati valori. Del problema di analisi posto, utilizzando il concetto di moltiplicatore dei carichi per l'adattamento con limitata duttilità, viene data una formulazione unitaria come problema di programmazione matematica non lineare, i cui vincoli di duttilità sono ottenuti con tecniche perturbative di maggiorazione a-priori delle deformazioni plastiche. Il metodo conduce a limiti inferiori del moltiplicatore dei carichi dipendenti dal livello assegnato alla deformazione plastica. Tre differenti strategie computazionali vengono delineate per la soluzione del problema, una delle quali di tipo programmazione lineare parametrica viene applicata a tre esempi.

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Ricerca finanziata dal CNR e dal MPI.     

A convolutional neural network approach to calibrating the rotation axis for X‐ray computed tomography

This paper presents an algorithm to calibrate the center‐of‐rotation for X‐ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential for reducing or removing other artifacts caused by instrument instability, detector non‐linearity, etc. An open‐source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.     

Characterization of bronze Roman coins of the fifth century called <Emphasis Type=Italic>nummi</Emphasis> through different analytical techniques

Mixing of different organic charge-transfer-complexes (CT-complexes) might allow the adjustment of the optical and morphological properties of the resulting material system. In this work, a study of two CT-complexes, mixed by thermal coevaporation at different concentrations by substituting only the acceptor molecules, is presented. Electron diffraction patterns, which were collected on samples of the ternary system of the prototypical CT-complexes DBTTF-TCNQ and DBTTF-F₄TCNQ do not show any indication of a mixed crystalline phase or novel crystalline order. X-ray diffraction measurements additionally confirm the phase separation in the ternary system. However, upon mixing of the complexes, the degree of crystallinity of the individual phases is reduced. This effect correlates with the mixing ratio of the CT-complexes in the ternary compound. Furthermore, we do not observe a shift or the appearance of new peaks in the infrared spectra of (DBTTF-TCNQ) _x :(DBTTF-F₄TCNQ)_1?x . Hence, there is no indication for a pronounced or novel chemical interaction between the individual CT-complexes in the mixed compound.     

Electrochemical characterization of core@shell CoFe2O4/Au composite

Quantum dots (QDs) have received considerable attention due to their unique optical and electrical properties. Although substantial research has focused on the potential applications and toxicological impacts of QDs, far less effort has been directed toward understanding their fate and transport in the environment. In this work, the effect of four coatings, polyethylene glycol functionalized polymer (PEGP), carboxyl derivatized polymer (COOHP), linoleic acid (LA), and polyacrylic acid-octylamine (PAA-OA), on the transport and retention of QDs in porous media were evaluated under environmentally relevant conditions. Aqueous QD suspensions (ca. 10 nM) were introduced into water-saturated columns packed with 40–50 mesh Ottawa sand at a pore-water velocity of 7.6 m/day. At an ionic strength (IS) of 3 mM and pH of 7, PEGP-coated QDs were completely retained within the column, while more than 60 % of COOHP-coated QDs were transported through a column run under identical conditions. When PAA-OA and LA were used as coatings, effluent QD recoveries increased to more than 65 and 89 % of the injected mass, respectively. Additionally, a decrease in pH from 9.5 to 5.0, or an increase of IS from 0 to 30 mM reduced the eluted mass of PAA-OA-coated QDs by more than 2 and 15 times, respectively. The relative mobility of coated QDs (LA > PAA-OA > COOHP > PEGP) was consistent with total interaction energy profiles between QDs and sand surfaces calculated based on Derjaguin–Landau–Verwey–Overbeek (DLVO) theory. At an IS of 3 mM (NaCl) and pH 7, a linear correlation was obtained between the fraction of eluted QDs and the magnitude of the primary interaction energy barrier. These findings demonstrate the strong dependence of QD transport on coating type and indicate that interaction energies based on DLVO theory can be used to predict the relative mobility of QDs in porous media.