Lagrangian and Hamiltonian formalisms for coupled higher-order elements: theory,modeling, simulation

Nonlinear Dynamics - In this work, the definition of the constitutive relation of a classical higher-order two-terminal element from Chua’s table is extended to the coupled element. The way...     

Detecting and classifying outliers in big functional data

Advances in Data Analysis and Classification - We propose two new outlier detection methods, for identifying and classifying different types of outliers in (big) functional data sets. The proposed...     

Distributionally robust stochastic programs with side information based on trimmings

Mathematical Programming - We consider stochastic programs conditional on some covariate information, where the only knowledge of the possible relationship between the uncertain parameters and the...     

Electrochemical behavior of methylene blue at bare and DNA-modified silver solid amalgam electrodes

Journal of Solid State Electrochemistry - Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used for a voltammetric study of methylene blue (MB) at a mercury meniscus-modified...     

Molecular QTAIM Topology Is Sensitive to Relativistic Corrections

The topology of the molecular electron density of benzene dithiol gold cluster complex Au₄−S−C₆H₄−S′−Au′₄ changed when relativistic corrections were made and the structure was close to a minimum of the Born–Oppenheimer energy surface. Specifically, new bond paths between hydrogen atoms on the benzene ring and gold atoms appeared, indicating that there is a favorable interaction between these atoms at the relativistic level. This is consistent with the observation that gold becomes a better electron acceptor when relativistic corrections are applied. In addition to relativistic effects, here, we establish the sensitivity of molecular topology to basis sets and convergence thresholds for geometry optimization.     

Rapid Detection of AIB1 in Breast Cancer Cells Based on Aptamer-Functionalized Nanomotors

Herein, we report ultrasound-propelled graphene-oxide coated gold nanowire motors, functionalized with fluorescein-labeled DNA aptamers (FAM-AIB1-apt), for qualitative detection of overexpressed AIB1 oncoproteins in MCF-7 breast cancer cells. The movement of nanomotors under the ultrasound field facilitated intracellular uptake and resulted in a faster aptamer binding with the target protein and thus faster fluorescence recovery. The propulsion behavior of the aptamer functionalized nanomotors greatly enhanced the fluorescence intensity compared to static conditions. The new aptamer@nanomotor-based strategy offers considerable potential for further development of sensing methodologies towards diagnosis of breast cancer.     

Rapid and easy quantification of elemental sulphur in aqueous samples from biological reactors: the turbidimetric method revisited

Recent developments in wastewater treatment have led to a renewed interest to obtain elemental sulphur (S°) as a by-product from bioreactors. However, practical studies are limited by the gap of adequate analytical techniques for its determination. This paper provides a statistical study and matrix effect evaluation of an adapted spectrophotometric method for routine S° analyses in aqueous samples, based on a methodology previously described by Hart (1961). Four complex matrices were tested: domestic sewage and effluent samples from three different bioreactors. Tested performance criteria included linearity, matrix effect, limit of detection and quantification and S° recovery. Results were linear (R² = 0.99994) in the studied range (5 to 100 mg S° L^?1) and no matrix effect was observed. The accuracy was based on recovery values that varied from 100% to 106%. The colloidal S° separation and extraction protocol was also considered suitable for aqueous samples, reaching more than 99.0% of S° recovery.     

Code2vect: An efficient heterogenous data classifier and nonlinear regression technique

The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm.     

The Lp dual Minkowski problem for p > 1 and q > 0

General $L_p$ dual curvature measures have recently been introduced by Lutwak, Yang and Zhang [24]. These new measures unify several other geometric measures of the Brunn–Minkowski theory and the dual Brunn–Minkowski theory. $L_p$ dual curvature measures arise from qth dual intrinsic volumes by means of Alexandrov-type variational formulas. Lutwak, Yang and Zhang [24] formulated the $L_p$ dual Minkowski problem, which concerns the characterization of $L_p$ dual curvature measures. In this paper, we solve the existence part of the $L_p$ dual Minkowski problem for $p>1$ and $q>0$, and we also discuss the regularity of the solution.