Design of a Flow-Through Voltammetric Sensor Based on an Antimony-Modified Silver Electrode for Determining Lithol Rubine B in Cosmetics

Lithol Rubine B (LRB; the disodium salt of 3-hydroxy-4-[(4-methyl-2-sulfophenyl) azo]-2-naphthalenecarboxylic acid) was detected using high-performance liquid chromatography with an electrochemical (antimony film on silver) detector (HPLC-ECD). For direct current (DC) mode, with the current at a constant potential, and measurements with suitable experimental parameters, a linear concentration from 0.125 to 1.80 μg/mL was found. The detection limit of our method was approximately 2.0 ng/mL. An antimony-modified silver detector was used to demonstrate that LRB is electrochemically reduced in acidic media and to analyze commercial cosmetics to determine their LRB content. Findings using HPLC-ECD and HPLC with an ultraviolet detector were comparable

Simulation of the shape memory effect in a NiTi nano model system

The shape memory behavior of a NiTi nanoparticle is analyzed by molecular dynamics simulations. After a detailed description of the equilibrium structures of the used model potential, the multi variant martensitic ground state, which depends on the geometry of the particle, is discussed. Tensile load is applied, changing the variant configuration to a single domain state with a remanent strain after unloading. Heating the particle leads to a shape memory effect without a phase transition to the austenite, but by variant reorientation and twin boundary formation at a certain temperature. These processes are described by stress-strain and strain-temperature curves, together with a visualization of the microstructure of the nanoparticle. Results are presented for five different Ni concentrations in the vicinity of 50%, showing for example, that small deviations from this ideal composition can influence the critical temperature for shape recovery significantly.Comment: 7 pages, 4 figures; accepted for publication in the "Journal of Alloys and Compounds

Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets

A vast amount of images has been generated due to the diversity and digitalization of devices for image acquisition. However, the gap between low-level visual features and high-level semantic representations has been a major concern that hinders retrieval accuracy. A retrieval method based on the transfer learning model and the relevance feedback technique was formulated in this study to optimize the dynamic trade-off between the structural complexity and retrieval performance of the small- and medium-scale content-based image retrieval (CBIR) system. First, the pretrained deep learning model was fine-tuned to extract features from target datasets. Then, the target dataset was clustered into the relative and irrelative image library by exploring the Bayes classifier. Next, the support vector machine (SVM) classifier was used to retrieve similar images in the relative library. Finally, the relevance feedback technique was employed to update the parameters of both classifiers iteratively until the request for the retrieval was met. Results demonstrate that the proposed method achieves 95.87% in classification index F1 - Score, which surpasses that of the suboptimal approach DCNN-BSVM by 6.76%. The performance of the proposed method is superior to that of other approaches considering retrieval criteria as average precision, average recall, and mean average precision. The study indicates that the Bayes + SVM combined classifier accomplishes the optimal quantities more efficiently than only either Bayes or SVM classifier under the transfer learning framework. Transfer learning skillfully excels training from scratch considering the feature extraction modes. This study provides a certain reference for other insights on applications of small- and medium-scale CBIR systems with inadequate samples

Pyrolysis of wild cyanophyta from Chaohu lake for bio-oil

To solve the environmental problems caused by the algae, pyrolysis experiment was studied to produce bio-oil with the wild cyanophyta from Chaohu lake for the first time. The results showed that the suitable temperature, carrier gas flow rate, and the smaller particle size were better for liquid products generation, the liquid (bio-oil) yield obtained maximum (66 %) at temperature of 450 oC, carried gas flow rate of 50 mL/min and particle size of less than 0.25 mm. The main ingredients of liquid product from cyanophyta pyrolysis consisted of hydrocarbons, nitrogenous compounds, acids and other organic compounds (such as alcohols, phenols esters and non-identified materials). Acid content was the highest and greatly affected by temperature. The content of hydrocarbons was about 15%

HERA data and DGLAP evolution: theory and phenomenology

We examine critically the evidence for deviations from next-to-leading order perturbative DGLAP evolution in HERA data. We briefly review the status of perturbative small-x resummation and of global determinations of parton distributions. We show that the geometric scaling properties of HERA data are consistent with DGLAP evolution, which is also strongly supported by the double asymptotic scaling properties of the data. However, backward--evolution of parton distributions into the low x, low Q^2 region does show evidence of deviations between the observed behaviour and the next-to-leading order predictions. These deviations cannot be explained by missing next-to-next-to-leading order perturbative terms, and are consistent with perturbative small-x resummation.Comment: Fig. 8 corrected. Published in NP

Using Prosody to Classify Discourse Relations

Comunicació presentada a: The 18th Annual Conference of the International Speech Communication Association (INTERSPEECH 2017), celebrada a Estocolm, Suència, del 20 al 24 d'agost de 2017.This work aims to explore the correlation between the discourse structure of a spoken monologue and its prosody by predicting discourse relations from different prosodic attributes. For this purpose, a corpus of semi-spontaneous monologues in English has been automatically annotated according to the Rhetorical Structure Theory, which models coherence in text via rhetorical relations. From corresponding audio files, prosodic features such as pitch, intensity, and speech rate have been extracted from different contexts of a relation. Supervised classification tasks using Support Vector Machines have been performed to find relationships between prosodic features and rhetorical relations. Preliminary results show that intensity combined with other features extracted from intra- and intersegmental environments is the feature with the highest predictability for a discourse relation. The prediction of rhetorical relations from prosodic features and their combinations is straightforwardly applicable to several tasks such as speech understanding or generation. Moreover, the knowledge of how rhetorical relations should be marked in terms of prosody will serve as a basis to improve speech synthesis applications and make voices sound more natural and expressive.This work is part of the KRISTINA project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Grant Agreement number 645012. The second author is partially funded by the Spanish Ministry of Economy, Industry and Competitiveness through the Ramón y Cajal program. The third and fourth authors are partially funded by ANPCYT PICT 2014-1561, and the Air Force Office of Scientific Research, Air Force Material Command, USAF under Award No. FA9550-15-1-0055

Physical Information Neural Networks for Solving High-index Differential-algebraic Equation Systems Based on Radau Methods

As is well known, differential algebraic equations (DAEs), which are able to describe dynamic changes and underlying constraints, have been widely applied in engineering fields such as fluid dynamics, multi-body dynamics, mechanical systems and control theory. In practical physical modeling within these domains, the systems often generate high-index DAEs. Classical implicit numerical methods typically result in varying order reduction of numerical accuracy when solving high-index systems.~Recently, the physics-informed neural network (PINN) has gained attention for solving DAE systems. However, it faces challenges like the inability to directly solve high-index systems, lower predictive accuracy, and weaker generalization capabilities. In this paper, we propose a PINN computational framework, combined Radau IIA numerical method with a neural network structure via the attention mechanisms, to directly solve high-index DAEs. Furthermore, we employ a domain decomposition strategy to enhance solution accuracy. We conduct numerical experiments with two classical high-index systems as illustrative examples, investigating how different orders of the Radau IIA method affect the accuracy of neural network solutions. The experimental results demonstrate that the PINN based on a 5th-order Radau IIA method achieves the highest level of system accuracy. Specifically, the absolute errors for all differential variables remains as low as $10^{-6}$, and the absolute errors for algebraic variables is maintained at $10^{-5}$, surpassing the results found in existing literature. Therefore, our method exhibits excellent computational accuracy and strong generalization capabilities, providing a feasible approach for the high-precision solution of larger-scale DAEs with higher indices or challenging high-dimensional partial differential algebraic equation systems

Oral delivery of camptothecin using cyclodextrin/poly(anhydride) nanoparticles

Camptothecin (CPT), a molecule that shows powerful anticancer activity, is still not used in clinic due to its high hydrophobicity and poor active form's stability. In order to solve these drawbacks, the combination between poly(anhydride) nanoparticles and cyclodextrins was evaluated. CPT-loaded nanoparticles, prepared in the presence of 2-hydroxypropyl-β-cyclodextrin, (HPCD-NP) displayed a mean size close to 170nm and a payload of 50μg per mg (25 times higher than the one of the control nanoparticles). CPT was not released from nanoparticles under gastric conditions. However, under intestinal conditions, about 50% of the drug content was released as a burst, whereas the remained drug was released following a zero-order kinetic. Pharmacokinetic studies revealed that the CPT plasma levels, from orally administered nanoparticles, were high and sustained up to 48h. The CPT oral bioavailability was 7-fold higher than the value obtained with the control, whereas its clearance was significantly lower than for the aqueous suspension. These observations may be directly related to a prolonged residence time of nanoparticles in close contact with the intestinal epithelium, the presence of the cyclodextrin that decreases the CPT transformation into its inactive form and the generation of an acidic microenvironment during the degradation of the poly(anhydride) that would prevent the transformation of the active lactone into the inactive carboxylate conformation