bluuebunny/crossref_metadata_2025 huggingface dataset:
In Situ EPR Characterization of a Cobalt Oxide Water Oxidation Catalyst at Neutral pH
Authors: Kutin Yury , Cox Nicholas , Lubitz Wolfgang , Schnegg Alexander , Rüdiger Olaf
Published: November 2019

Here we report an in situ electron paramagnetic resonance (EPR) study of a low-cost, high-stability cobalt oxide electrodeposited material (Co-Pi) that oxidizes water at neutral pH and low over-potential, representing a promising system for future large-scale water splitting applications. Using CW X-band EPR we can follow the film formation from a Co(NO3)2 solution in phosphate buffer and quantify Co uptake into the catalytic film. As deposited, the film shows predominantly a Co(II) EPR signal, which converts into a Co(IV) signal as the electrode potential is increased. A purpose-built spectroelectrochemical cell allowed us to quantify the extent of Co(II) to Co(IV) conversion as a function of potential bias under operating conditions. Consistent with its role as an intermediate, Co(IV) is formed at potentials commensurate with electrocatalytic O2 evolution (+1.2 V, vs. SHE). The EPR resonance position of the Co(IV) species shifts to higher fields as the potential is increased above 1.2 V. Such a shift of the Co(IV) signal may be assigned to changes in the local Co structure, displaying a more distorted ligand field or more ligand radical character, suggesting it is this subset of sites that represents the catalytically ‘active’ component. The described spectroelectrochemical approach provides new information on catalyst function and reaction pathways of water oxidation.
Organocatalysis and Beyond: Activating Reactions with Two Catalytic Species
Authors: Sinibaldi Arianna , Nori Valeria , Baschieri Andrea , Fini Francesco , Arcadi Antonio , Carlone Armando
Published: November 2019

Since the beginning of the millennium, organocatalysis has been gaining a predominant role in asymmetric synthesis and it is, nowadays, a foundation of catalysis. Synergistic catalysis, combining two or more different catalytic cycles acting in concert, exploits the vast knowledge acquired in organocatalysis and other fields to perform reactions that would be otherwise impossible. Merging organocatalysis with photo-, metallo- and organocatalysis itself, researchers have ingeniously devised a range of activations. This feature review, focusing on selected synergistic catalytic approaches, aims to provide a flavor of the creativity and innovation in the area, showing ground-breaking examples of organocatalysts, such as proline derivatives, hydrogen bond-mediated, Cinchona alkaloids or phosphoric acids catalysts, which work cooperatively with different catalytic partners.
A Polypyrrole-Modified Pd-Ag Bimetallic Electrode for the Electrocatalytic Reduction of 4-Chlorophenol
Authors: Wei Xuefeng , Zeng Laiyuan , Lu Weiwei , Miao Juan , Zhang Ruichang , Zhou Ming , Zhang Jun
Published: November 2019

A polypyrrole-modified bimetallic electrode composed of Pd-Ag on a Ti substrate (Pd-Ag/PPY/Ti) was successfully prepared via a chemical deposition method, and was applied to the electrocatalytic hydrodechlorination of 4-chlorophenol (4-CP) in aqueous solution. The electrode was characterized by cyclic voltammetry (CV), scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). Various influences on the dechlorination efficiency of 4-chlorophenol, including applied current, initial pH value, and temperature, were studied. The dechlorination efficiency of 4-CP reached 94% within 120 min under the optimum conditions, i.e., a dechlorination current of 6 mA, an initial pH of 2.30, and a temperature of 303 K. The apparent activation energy of the dechlorination of 4-CP by the Pd-Ag/PPY/Ti electrode was calculated to be 49.6 kJ/mol. The equivalent conversion rate constant kPd was 0.63 L.gPd−1·min−1, which was higher than the findings presented in comparable literature. Thus, a highly effective bimetallic electrode with promising application prospects and low Pd loading was fabricated.
Regulation of RXR-RAR Heterodimers by RXR- and RAR-Specific Ligands and Their Combinations
Authors: le Maire Albane , Teyssier Catherine , Balaguer Patrick , Bourguet William , Germain Pierre
Published: November 2019

The three subtypes (α, β, and γ) of the retinoic acid receptor (RAR) are ligand-dependent transcription factors that mediate retinoic acid signaling by forming heterodimers with the retinoid X receptor (RXR). Heterodimers are functional units that bind ligands (retinoids), transcriptional co-regulators and DNA, to regulate gene networks controlling cell growth, differentiation, and death. Using biochemical, crystallographic, and cellular approaches, we have set out to explore the spectrum of possibilities to regulate RXR-RAR heterodimer-dependent transcription through various pharmacological classes of RAR- and RXR- specific ligands, alone or in combination. We reveal the molecular details by which these compounds direct specificity and functionality of RXR-RAR heterodimers. Among these ligands, we have reevaluated and improved the molecular and structural definition of compounds CD2665, Ro41-5253, LE135, or LG100754, highlighting novel functional features of these molecules. Our analysis reveals a model of RXR-RAR heterodimer action in which each subunit retains its intrinsic properties in terms of ligand and co-regulator binding. However, their interplay upon the combined action of RAR- and RXR-ligands allows for the fine tuning of heterodimer activity. It also stresses the importance of accurate ligand characterization to use synthetic selective retinoids appropriately and avoid data misinterpretations.
rDNA Clusters Make Contact with Genes that Are Involved in Differentiation and Cancer and Change Contacts after Heat Shock Treatment
Authors: Tchurikov Nickolai A. , Fedoseeva Daria M. , Klushevskaya Elena S. , Slovohotov Ivan Y. , Chechetkin Vladimir R. , Kravatsky Yuri V. , Kretova Olga V.
Published: November 2019

Human rDNA clusters form numerous contacts with different chromosomal regions as evidenced by chromosome conformation capture data. Heterochromatization of rDNA genes leads to heterochromatization in different chromosomal regions coupled with the activation of the transcription of genes related to differentiation. These data suggest a role for rDNA clusters in the regulation of many human genes. However, the genes that reside within the rDNA-contacting regions have not been identified. The purpose of this study was to detect and characterize the regions where rDNA clusters make frequent contacts and to identify and categorize genes located in these regions. We analyzed the regions that contact rDNA using 4C data and show that these regions are enriched with genes specifying transcription factors and non-coding RNAs involved in differentiation and development. The rDNA-contacting genes are involved in neuronal development and are associated with different cancers. Heat shock treatment led to dramatic changes in the pattern of rDNA-contacting sites, especially in the regions possessing long stretches of H3K27ac marks. Whole-genome analysis of rDNA-contacting sites revealed specific epigenetic marks and the transcription sites of 20–100 nt non-coding RNAs in these regions. The rDNA-contacting genes jointly regulate many genes that are involved in the control of transcription by RNA polymerase II and the development of neurons. Our data suggest a role for rDNA clusters in the differentiation of human cells and carcinogenesis.
VEGF-A-Cleavage by FSAP and Inhibition of Neo-Vascularization
Authors: Uslu Özgür , Herold Joerg , Kanse Sandip
Published: November 2019

Alternative splicing leads to the secretion of multiple forms of vascular endothelial growth factor-A (VEGF-A) that differ in their activity profiles with respect to neovascularization. FSAP (factor VII activating protease) is the zymogen form of a plasma protease that is activated (FSAPa) upon tissue injury via the release of histones. The purpose of the study was to determine if FSAPa regulates VEGF-A activity in vitro and in vivo. FSAP bound to VEGF165, but not VEGF121, and VEGF165 was cleaved in its neuropilin/proteoglycan binding domain. VEGF165 cleavage did not alter its binding to VEGF receptors but diminished its binding to neuropilin. The stimulatory effects of VEGF165 on endothelial cell proliferation, migration, and signal transduction were not altered by FSAP. Similarly, proliferation of VEGF receptor-expressing BAF3 cells, in response to VEGF165, was not modulated by FSAP. In the mouse matrigel model of angiogenesis, FSAP decreased the ability of VEGF165, basic fibroblast growth factor (bFGF), and their combination, to induce neovascularization. Lack of endogenous FSAP in mice did not influence neovascularization. Thus, FSAP inhibited VEGF165-mediated angiogenesis in the matrigel model in vivo, where VEGF’s interaction with the matrix and its diffusion are important.
Emerging Roles of Long Non-Coding RNAs as Drivers of Brain Evolution
Authors: Zimmer-Bensch Geraldine
Published: November 2019

Mammalian genomes encode tens of thousands of long-noncoding RNAs (lncRNAs), which are capable of interactions with DNA, RNA and protein molecules, thereby enabling a variety of transcriptional and post-transcriptional regulatory activities. Strikingly, about 40% of lncRNAs are expressed specifically in the brain with precisely regulated temporal and spatial expression patterns. In stark contrast to the highly conserved repertoire of protein-coding genes, thousands of lncRNAs have newly appeared during primate nervous system evolution with hundreds of human-specific lncRNAs. Their evolvable nature and the myriad of potential functions make lncRNAs ideal candidates for drivers of human brain evolution. The human brain displays the largest relative volume of any animal species and the most remarkable cognitive abilities. In addition to brain size, structural reorganization and adaptive changes represent crucial hallmarks of human brain evolution. lncRNAs are increasingly reported to be involved in neurodevelopmental processes suggested to underlie human brain evolution, including proliferation, neurite outgrowth and synaptogenesis, as well as in neuroplasticity. Hence, evolutionary human brain adaptations are proposed to be essentially driven by lncRNAs, which will be discussed in this review.
Perinatal Mesenchymal Stromal Cells and Their Possible Contribution to Fetal-Maternal Tolerance
Authors: Magatti Marta , Stefani Francesca Romana , Papait Andrea , Cargnoni Anna , Masserdotti Alice , Silini Antonietta Rosa , Parolini Ornella
Published: November 2019

During pregnancy, a successful coexistence between the mother and the semi-allogenic fetus occurs which requires a dynamic immune system to guarantee an efficient immune protection against possible infections and tolerance toward fetal antigens. The mechanism of fetal-maternal tolerance is still an open question. There is growing in vitro and in vivo evidence that mesenchymal stromal cells (MSC) which are present in perinatal tissues have a prominent role in generating a functional microenvironment critical to a successful pregnancy. This review highlights the immunomodulatory properties of perinatal MSC and their impact on the major immune cell subsets present in the uterus during pregnancy, such as natural killer cells, antigen-presenting cells (macrophages and dendritic cells), and T cells. Here, we discuss the current understanding and the possible contribution of perinatal MSC in the establishment of fetal-maternal tolerance, providing a new perspective on the physiology of gestation.
One-Step Method for Preparing Dispersive Tea Polyphenol/Graphene Nanosheets Enhanced with Anticorrosion Performance
Authors: Guo Youwei , Xiao Guoqing , Zhang Wei , Zhang Nange , Chen Chunling , Fan Yi , Li Hongjie , Liu Xuewei , He Yi
Published: November 2019

Water-dispersible and anticorrosion nanocomposites have attracted extensive attention. In this study, tea polyphenol (TP)/graphene (GE) was fabricated with a one-step route. The preparation and modification of graphene nanosheets was carried out by graphene employing tea polyphenols as reduction and functionalization reagents. Our study adopted a nontoxic reductant without an extra functionalization reagent. This method is convenient, inexpensive, and environmentally friendly. The final functionalized graphene nanosheets had a single-layer structure. For evaluating performance, Raman spectroscopy was adapted for evaluating π–π interactions between TP and graphene. Elemental and structural composition was analyzed using X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). Sample morphology was characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Results indicated that the TP could effectively augment the dispersive performance of graphene in the solution. The durable anticorrosion capacity of the epoxy matrix noticeably increased after adding the appropriate amount of tea polyphenols–graphene (TPG) (0.3 wt.%). Electrochemical impedance spectroscopy (EIS) studies showed that the impedance of artificial defects was enhanced. The anticorrosion property was attributed to the uniform dispersion of graphene by adding TP.
Influence of Polymer Composition and Substrate on the Performance of Bioinspired Coatings with Antibacterial Activity
Authors: Chiloeches Alberto , Echeverría Coro , Fernández-García Marta , Muñoz-Bonilla Alexandra
Published: November 2019

A series of methacrylic copolymers bearing thiazolium cationic groups and catechol moieties were evaluated as antibacterial coatings on a variety of materials including aluminum and plastics such as polycarbonate, poly(methyl methacrylate), and silicone rubber. The thermal properties of the copolymers were first studied by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The cationic copolymers were thermally stable up to 200 °C and presented glass transition temperatures values well above 100 °C; thus, an acceptable thermal behavior for typical biomedical applications. The cationic copolymers with variable content of the adhesive anchoring N-(3,4-dihydroxyphenethyl) methacrylamide (DOMA) units were coated onto the metal and polymeric substrates by drop casting and the adhesive properties of the obtained coatings were further evaluated as a function of DOMA content and substrate. Optical profilometry, attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectra, and antimicrobial studies reveal that the coatings adhere stronger to metal substrates than to the polymeric substrates. The copolymers with higher content of DOMA, 24 mol.%, resist solvent erosion treatment when coated onto all substrates and exhibit antimicrobial activity against Gram-positive S. aureus bacteria after this erosion treatment. In contrast, copolymers with low content, 9 mol.% of DOMA, only remain attached onto the aluminum metal substrate after solvent treatment, while on polymeric substrates the coatings are almost removed and do not show any efficacy against S. aureus bacteria.
Effect of Transglutaminase Cross-Linking in Protein Isolates from a Mixture of Two Quinoa Varieties with Chitosan on the Physicochemical Properties of Edible Films
Authors: Escamilla-García Monserrat , Delgado-Sánchez Luis Felipe , Ríos-Romo Raquel Adriana , García-Almendárez Blanca E. , Calderón-Domínguez Georgina , Méndez-Méndez Juan Vicente , Amaro-Reyes Aldo , Di Pierro Prospero , Regalado-González Carlos
Published: November 2019

The growing demand for minimally processed foods with a long shelf life and environmentally friendly materials has forced industry to develop new technologies for food preservation and handling. The use of edible films has emerged as an alternative solution to this problem, and mixtures of carbohydrates and proteins, may be formulated to improve their properties. The objective of this work was to evaluate the effect of protein cross-linking with transglutaminase (TG) of two varieties of quinoa protein isolate (Chenopodium quinoa) [Willd (QW), and Pasankalla (QP)] on the physicochemical and barrier properties of edible films based on chitosan (CT)-quinoa protein. The evaluated properties were water vapor permeability (WVP), solubility, adsorption, roughness determined by atomic force microscopy, and the interactions among the main film components determined by Raman spectroscopy. The results indicated that TG interacted with lysine of QW and QP. CT:QW (1:5, w/w) showed the lowest solubility (14.02 ± 2.17% w/w). WVP varied with the composition of the mixture. The WVP of CT:quinoa protein ranged from 2.85 to 9.95 × 10−11 g cm Pa−1 cm−2 s−1 without TG, whereas adding TG reduced this range to 2.42–4.69 × 10−11 g cm Pa−1 cm−2 s−1. The addition of TG to CT:QP (1:10, w/w) reduced the film surface roughness from 8.0 ± 0.5 nm to 4.4 ± 0.3 nm. According to the sorption isotherm, the addition of TG to CT-QW films improved their stability [monolayer (Xm) = 0.13 ± 0.02 %]. Films with a higher amount of cross-linking showed the highest improvement in the evaluated physical properties, but interactions among proteins that were catalyzed by TG depended on the protein source and profile.
Assessments of Facial Muscle Thickness by Ultrasound in Younger Adults: Absolute and Relative Reliability
Authors: Abe Takashi , Spitz Robert W. , Wong Vickie , Viana Ricardo B. , Yamada Yujiro , Bell Zachary W. , Chatakondi Raksha N. , Loenneke Jeremy P.
Published: November 2019

The absolute reliability (i.e., standard error of measurement and minimal difference) of a measurement is important to consider when assessing training effects. However, the absolute reliability for ultrasound measured facial muscle thickness had not been investigated. In order to examine the absolute and relative reliability of measuring facial muscles, 98 healthy, young, and middle-aged adults (18–40 years) had ultrasound measurements taken twice, separated by an average of three days. Six facial muscles were selected to determine the reliability of facial muscle thickness. The relative reliability (ICC3,1) ranged from 0.425 for the orbicularis oris (inferior) to 0.943 for the frontalis muscle. The absolute reliability (minimal difference) ranged from 0.25 mm for the orbicularis oculi to 1.82 mm for the masseter. The percentage minimal difference was 22%, 25%, 26%, 29%, 21%, and 10% for the frontalis, orbicularis oculi, orbicularis oris (superior), orbicularis oris (inferior), depressor anguli oris, and masseter, respectively. Our results indicated that the relative reliability was similar to that observed previously. The absolute reliability indicated that the measurement error associated with measuring muscle thickness of the face may be greater than that of the trunk/limb muscles. This may be related to the difficulty of accurately determining the borders of each muscle.
Electronic Structure of Oxygen-Deficient SrTiO3 and Sr2TiO4
Authors: Al-Zubi Ali , Bihlmayer Gustav , Blügel Stefan
Published: November 2019

The conductive behavior of the perovskite SrTiO 3 is strongly influenced by the presence of oxygen vacancies in this material, therefore the identification of such defects with spectroscopic methods is of high importance. We use density functional theory to characterize the defect-induced states in SrTiO 3 and Sr 2 TiO 4 . Their signatures at the surface, the visibility for scanning tunneling spectroscopy and locally conductive atomic force microscopy, and the core-level shifts observed on Ti atoms in the vicinity of the defect are studied. In particular, we find that the exact location of the defect state (e.g., in SrO or TiO 2 planes relative to the surface) are decisive for their visibility for scanning-probe methods. Moreover, the usual distinction between Ti 3 + and Ti 2 + species, which can occur near defects or their aggregates, cannot be directly translated in characteristic shifts of the core levels. The width of the defect-induced in-gap states is found to depend critically on the arrangement of the defects. This also has consequences for the spectroscopic signatures observed in so-called resistive switching phenomena.
Avalanches in Compressed Sandstone: Crackling Noise under Confinement
Authors: Zhao Yunfeng , Liu Hanlong , Xie Kainan , Salje Ekhard K.H. , Jiang Xiang
Published: November 2019

The acoustic emission, AE, from avalanches of local cracks and microstructural changes of sandstone under confined compression have been reported. These avalanches soften the underlying minerals and play a key role as indicators for the prediction of geo-engineering disasters, such as mining collapses, rock outbursts caused by high ground stress, and man-made quakes by fracking. Compressed sandstone is a model material for the investigation of avalanches. The avalanche energies, amplitudes, and waiting times show the probability distributions that allow us to distinguish between three compression stages; namely, (I) pre-failure, (II) correlated failure, and (III) post-failure. The energy of stage I and stage II is power-law distributed and scale invariant, while post-failure experiments show power laws with high exponential damping (friction). The scaling behavior is close to the predictions of a mean-field (MF) model (stage II) and a force-integrated mean-field model (stage I). Confinement shifts the value of the energy exponent closer to the MF prediction. Omori’s law and waiting time distributions are independent of stress during the compression; their scaling exponents are very similar to those found in seismological studies.
Molecular–Statistical Theory for the Description of Re-Entrant Ferroelectric Phase
Authors: Emelyanenko Alexander V.
Published: November 2019

The re-entrant ferroelectric phase (Sm- C re * ) is investigated in the framework of a molecular–statistical approach. It was found that anticlinic synpolar along the smectic layer normal phase can arise below the antiferroelectric phase (Sm- C A * ) in the temperature scale, and we suggest this phase to be Sm- C re * . We have shown that in the vicinity of Sm- C A * –Sm- C re * phase transition temperature, a very small electric field can cause a transition into the bidomain synclinic phase, where the helical pitch is unwound and the tilt planes have contributions either along or against the electric field. The helical rotation, elasticity and deformation of the Sm- C * , Sm- C A * and Sm- C re * structures without electric field or in the presence of electric field, as well as the dielectric response, are investigated. It is shown that Sm- C re * can arise solely due to the dipole–dipole interaction, and thus, in contrast to the conventional (improper) ferroelectric Sm- C * , appears to be the proper ferroelectric phase.
Breast Ultrasound Image Synthesis using Deep Convolutional Generative Adversarial Networks
Authors: Fujioka Tomoyuki , Mori Mio , Kubota Kazunori , Kikuchi Yuka , Katsuta Leona , Adachi Mio , Oda Goshi , Nakagawa Tsuyoshi , Kitazume Yoshio , Tateishi Ukihide
Published: November 2019

Deep convolutional generative adversarial networks (DCGANs) are newly developed tools for generating synthesized images. To determine the clinical utility of synthesized images, we generated breast ultrasound images and assessed their quality and clinical value. After retrospectively collecting 528 images of 144 benign masses and 529 images of 216 malignant masses in the breasts, synthesized images were generated using a DCGAN with 50, 100, 200, 500, and 1000 epochs. The synthesized (n = 20) and original (n = 40) images were evaluated by two radiologists, who scored them for overall quality, definition of anatomic structures, and visualization of the masses on a five-point scale. They also scored the possibility of images being original. Although there was no significant difference between the images synthesized with 1000 and 500 epochs, the latter were evaluated as being of higher quality than all other images. Moreover, 2.5%, 0%, 12.5%, 37.5%, and 22.5% of the images synthesized with 50, 100, 200, 500, and 1000 epochs, respectively, and 14% of the original images were indistinguishable from one another. Interobserver agreement was very good (|r| = 0.708–0.825, p < 0.001). Therefore, DCGAN can generate high-quality and realistic synthesized breast ultrasound images that are indistinguishable from the original images.
A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data
Authors: Chang Wenbing , Liu Yinglai , Xiao Yiyong , Yuan Xinglong , Xu Xingxing , Zhang Siyue , Zhou Shenghan
Published: November 2019

The outcomes of hypertension refer to the death or serious complications (such as myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes of hypertension are very concerning for patients and doctors, and are ideally avoided. However, there is no satisfactory method for predicting the outcomes of hypertension. Therefore, this paper proposes a prediction method for outcomes based on physical examination indicators of hypertension patients. In this work, we divide the patients’ outcome prediction into two steps. The first step is to extract the key features from the patients’ many physical examination indicators. The second step is to use the key features extracted from the first step to predict the patients’ outcomes. To this end, we propose a model combining recursive feature elimination with a cross-validation method and classification algorithm. In the first step, we use the recursive feature elimination algorithm to rank the importance of all features, and then extract the optimal features subset using cross-validation. In the second step, we use four classification algorithms (support vector machine (SVM), C4.5 decision tree, random forest (RF), and extreme gradient boosting (XGBoost)) to accurately predict patient outcomes by using their optimal features subset. The selected model prediction performance evaluation metrics are accuracy, F1 measure, and area under receiver operating characteristic curve. The 10-fold cross-validation shows that C4.5, RF, and XGBoost can achieve very good prediction results with a small number of features, and the classifier after recursive feature elimination with cross-validation feature selection has better prediction performance. Among the four classifiers, XGBoost has the best prediction performance, and its accuracy, F1, and area under receiver operating characteristic curve (AUC) values are 94.36%, 0.875, and 0.927, respectively, using the optimal features subset. This article’s prediction of hypertension outcomes contributes to the in-depth study of hypertension complications and has strong practical significance.
Identify Risk Pattern of E-Bike Riders in China Based on Machine Learning Framework
Authors: Wang Chen , Kou Siyuan , Song Yanchao
Published: November 2019

In this paper, the risk pattern of e-bike riders in China was examined, based on tree-structured machine learning techniques. Three-year crash/violation data were acquired from the Kunshan traffic police department, China. Firstly, high-risk (HR) electric bicycle (e-bike) riders were defined as those with at-fault crash involvement, while others (i.e., non-at-fault or without crash involvement) were considered as non-high-risk (NHR) riders, based on quasi-induced exposure theory. Then, for e-bike riders, their demographics and previous violation-related features were developed based on the crash/violation records. After that, a systematic machine learning (ML) framework was proposed so as to capture the complex risk patterns of those e-bike riders. An ensemble sampling method was selected to deal with the imbalanced datasets. Four tree-structured machine learning methods were compared, and a gradient boost decision tree (GBDT) appeared to be the best. The feature importance and partial dependence were further examined. Interesting findings include the following: (1) tree-structured ML models are able to capture complex risk patterns and interpret them properly; (2) spatial-temporal violation features were found as important indicators of high-risk e-bike riders; and (3) violation behavior features appeared to be more effective than violation punishment-related features, in terms of identifying high-risk e-bike riders. In general, the proposed ML framework is able to identify the complex crash risk pattern of e-bike riders. This paper provides useful insights for policy-makers and traffic practitioners regarding e-bike safety improvement in China.
Invariant Image-Based Currency Denomination Recognition Using Local Entropy and Range Filters
Authors: Anwar Hafeez , Ullah Farman , Iqbal Asif , Ul Hasnain Anees , Ur Rehman Ata , Bell Peter , Kwak Daehan
Published: November 2019

We perform image-based denomination recognition of the Pakistani currency notes. There are a total of seven different denominations in the current series of Pakistani notes. Apart from color and texture, these notes differ from one another mainly due to their aspect ratios. Our aim is to exploit this single feature to attain an image-based recognition that is invariant to the most common image variations found in currency notes images. Among others, the most notable image variations are caused by the difference in positions and in-plane orientations of the currency notes in images. While most of the proposed methods for currency denomination recognition only focus on attaining higher recognition rates, our aim is more complex, i.e., attaining a high recognition rate in the presence of image variations. Since, the aspect ratio of a currency note is invariant to such differences, an image-based recognition of currency notes based on aspect ratio is more likely to be translation- and rotation-invariant. Therefore, we adapt a two step procedure that first extracts a currency note from the homogeneous image background via local entropy and range filters. Then, the aspect ratio of the extracted currency note is calculated to determine its denomination. To validate our proposed method, we gathered a new dataset with the largest and most diverse collection of Pakistani currency notes, where each image contains either a single or multiple notes at arbitrary positions and orientations. We attain an overall average recognition rate of 99% which is very encouraging for our method, which relies on a single feature and is suited for real-time applications. Consequently, the method may be extended to other international and historical currencies, which makes it suitable for business and digital humanities applications.
Toward Inferring Potts Models for Phylogenetically Correlated Sequence Data
Authors: Rodriguez Horta Edwin , Barrat-Charlaix Pierre , Weigt Martin
Published: November 2019

Global coevolutionary models of protein families have become increasingly popular due to their capacity to predict residue–residue contacts from sequence information, but also to predict fitness effects of amino acid substitutions or to infer protein–protein interactions. The central idea in these models is to construct a probability distribution, a Potts model, that reproduces single and pairwise frequencies of amino acids found in natural sequences of the protein family. This approach treats sequences from the family as independent samples, completely ignoring phylogenetic relations between them. This simplification is known to lead to potentially biased estimates of the parameters of the model, decreasing their biological relevance. Current workarounds for this problem, such as reweighting sequences, are poorly understood and not principled. Here, we propose an inference scheme that takes the phylogeny of a protein family into account in order to correct biases in estimating the frequencies of amino acids. Using artificial data, we show that a Potts model inferred using these corrected frequencies performs better in predicting contacts and fitness effect of mutations. First, only partially successful tests on real protein data are presented, too.
Variational Autoencoder Reconstruction of Complex Many-Body Physics
Authors: Luchnikov Ilia A. , Ryzhov Alexander , Stas Pieter-Jan , Filippov Sergey N. , Ouerdane Henni
Published: November 2019

Thermodynamics is a theory of principles that permits a basic description of the macroscopic properties of a rich variety of complex systems from traditional ones, such as crystalline solids, gases, liquids, and thermal machines, to more intricate systems such as living organisms and black holes to name a few. Physical quantities of interest, or equilibrium state variables, are linked together in equations of state to give information on the studied system, including phase transitions, as energy in the forms of work and heat, and/or matter are exchanged with its environment, thus generating entropy. A more accurate description requires different frameworks, namely, statistical mechanics and quantum physics to explore in depth the microscopic properties of physical systems and relate them to their macroscopic properties. These frameworks also allow to go beyond equilibrium situations. Given the notably increasing complexity of mathematical models to study realistic systems, and their coupling to their environment that constrains their dynamics, both analytical approaches and numerical methods that build on these models show limitations in scope or applicability. On the other hand, machine learning, i.e., data-driven, methods prove to be increasingly efficient for the study of complex quantum systems. Deep neural networks, in particular, have been successfully applied to many-body quantum dynamics simulations and to quantum matter phase characterization. In the present work, we show how to use a variational autoencoder (VAE)—a state-of-the-art tool in the field of deep learning for the simulation of probability distributions of complex systems. More precisely, we transform a quantum mechanical problem of many-body state reconstruction into a statistical problem, suitable for VAE, by using informationally complete positive operator-valued measure. We show, with the paradigmatic quantum Ising model in a transverse magnetic field, that the ground-state physics, such as, e.g., magnetization and other mean values of observables, of a whole class of quantum many-body systems can be reconstructed by using VAE learning of tomographic data for different parameters of the Hamiltonian, and even if the system undergoes a quantum phase transition. We also discuss challenges related to our approach as entropy calculations pose particular difficulties.
Secure Retrospective Interference Alignment
Authors: Seif Mohamed , Tandon Ravi , Li Ming
Published: November 2019

In this paper, the K-user interference channel with secrecy constraints is considered with delayed channel state information at transmitters (CSIT). We propose a novel secure retrospective interference alignment scheme in which the transmitters carefully mix information symbols with artificial noises to ensure confidentiality. Achieving positive secure degrees of freedom (SDoF) is challenging due to the delayed nature of CSIT, and the distributed nature of the transmitters. Our scheme works over two phases: Phase one, in which each transmitter sends information symbols mixed with artificial noises, and repeats such transmission over multiple rounds. In the next phase, each transmitter uses the delayed CSIT of the previous phase and sends a function of the net interference and artificial noises (generated in previous phase), which is simultaneously useful for all receivers. These phases are designed to ensure the decodability of the desired messages while satisfying the secrecy constraints. We present our achievable scheme for three models, namely: (1) K-user interference channel with confidential messages (IC-CM), and we show that 1 2 ( K − 6 ) SDoF is achievable; (2) K-user interference channel with an external eavesdropper (IC-EE); and (3) K-user IC with confidential messages and an external eavesdropper (IC-CM-EE). We show that for the K-user IC-EE, 1 2 ( K − 3 ) SDoF is achievable, and for the K-user IC-CM-EE, 1 2 ( K − 6 ) is achievable. To the best of our knowledge, this is the first result on the K-user interference channel with secrecy constrained models and delayed CSIT that achieves an SDoF which scales with K , square-root of number of users.
A Regulated Charge Pump with Extremely Low Output Ripple
Authors: Navidi Mir Mohammad , Graham David W.
Published: November 2019

In this paper, we present a regulated charge pump with extremely low output ripple (<1 mV) that can be used for accurate programming of nonvolatile memory. We present a technique to include a low-drop-out regulator inside the charge-pump regulation loop to reduce the ripple. This charge pump was fabricated in a 0.35 μ m standard CMOS process. The die area of this charge pump is 0.163 mm 2 . While operating from a 2.5 V supply, this charge pump generates regulated voltages up to 10 V.
Incremental Low Rank Noise Reduction for Robust Infrared Tracking of Body Temperature during Medical Imaging
Authors: Yousefi Bardia , Memarzadeh Sharifipour Hossein , Eskandari Mana , Ibarra-Castanedo Clemente , Laurendeau Denis , Watts Raymond , Klein Matthieu , Maldague Xavier P. V.
Published: November 2019

Thermal imagery for monitoring of body temperature provides a powerful tool to decrease health risks (e.g., burning) for patients during medical imaging (e.g., magnetic resonance imaging). The presented approach discusses an experiment to simulate radiology conditions with infrared imaging along with an automatic thermal monitoring/tracking system. The thermal tracking system uses an incremental low-rank noise reduction applying incremental singular value decomposition (SVD) and applies color based clustering for initialization of the region of interest (ROI) boundary. Then a particle filter tracks the ROI(s) from the entire thermal stream (video sequence). The thermal database contains 15 subjects in two positions (i.e., sitting, and lying) in front of thermal camera. This dataset is created to verify the robustness of our method with respect to motion-artifacts and in presence of additive noise (2–20%—salt and pepper noise). The proposed approach was tested for the infrared images in the dataset and was able to successfully measure and track the ROI continuously (100% detecting and tracking the temperature of participants), and provided considerable robustness against noise (unchanged accuracy even in 20% additive noise), which shows promising performance.
Selected Rolling Bearing Fault Diagnostic Methods in Wheel Embedded Permanent Magnet Brushless Direct Current Motors
Authors: Skora Marcin , Ewert Pawel , Kowalski Czeslaw T.
Published: November 2019

In recent years, the number of outer rotor permanent magnet brushless direct current (PM BLDC) motor drives has been intensively growing. Due to the specifics of drive operation, bearing faults are especially common, which results in motor stoppage. In a number of these types of motor applications, the monitoring and diagnostics of bearing conditions is relatively rare. This article presents the results of research aimed at searching for simple and simultaneously effective methods for assessing the condition of bearings that can be built into the drive control system. In the experimental research, four vibration signal processing methods were analysed with regards to the identification accuracy of fault symptoms in the geometric elements of bearings (characteristic frequencies). The results are presented for three cases of bearing faults and compared with a new bearing, they were obtained based on a vibration signal analysis using the classical fast Fourier transform (FFT), Fourier transform of signal absolute values, Fourier transform of an envelope signal obtained using the Hilbert transform, and the Fourier transform of a signal filtered with the Teager–Kaiser energy operator (TKEO).
Environmental Impact Evaluation of Distributed Renewable Energy System Based on Life Cycle Assessment and Fuzzy Rough Sets
Authors: Li Chengzhou , Wang Ningling , Zhang Hongyuan , Liu Qingxin , Chai Youguo , Shen Xiaohu , Yang Zhiping , Yang Yongping
Published: November 2019

The distributed renewable energy system, integrating various renewable energy resources, is a significant energy supply technology within energy internet. It is an effective way to meet increasingly growing demand for energy conservation and environmental damage reduction in energy generation and energy utilization. In this paper, the life cycle assessment (LCA) method and fuzzy rough sets (FRS) theory are combined to build an environmental evaluation model for a distributed renewable energy system. The ReCiPe2016 method is selected to calculate the environmental effect scores of the distributed energy system, and the FRS is utilized to identify the crucial activities and exchanges during its life cycle from cradle to grave. The generalized evaluation method is applied to a real-world case study, a typical distributed energy system located in Yanqing District, Beijing, China, which is composed of wind power, small-scale hydropower, photovoltaic, centralized solar thermal power plant and a biogas power plant. The results show that the environmental effect of per kWh power derived from the distributed renewable energy system is 2.06 × 10−3 species disappeared per year, 9.88 × 10−3 disability-adjusted life years, and 1.75 × 10−3 USD loss on fossil resources extraction, and further in the uncertainty analysis, it is found that the environmental load can be reduced effectively and efficiently by improving life span and annual utilization hour of power generation technologies and technology upgrade for wind turbine and photovoltaic plants. The results show that the proposed evaluation method could fast evaluate the environmental effects of a distributed energy system while the uncertainty analysis with FRS successfully and effectively identifies the key element and link among its life span.
Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection
Authors: Yuan Tongke , Sun Zhifeng , Ma Shihao
Published: November 2019

The fault diagnosis and prediction technology of wind turbines are of great significance for increasing the power generation and reducing the downtime of wind turbines. However, most of the current fault detection approaches are realized by setting a single alarm threshold. Considering the complicated working conditions of wind farms, such methods are prone to ignore the fault, send out a false alarm, or leave insufficient troubleshooting time. In this work, we propose a gearbox fault prediction approach of wind turbines based on the supervisory control and data acquisition (SCADA) data. A stacking model composed of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBOOST) was constructed as the normal behavior model to describe the normal conditions of the wind turbines. We used the Mahalanobis distance (MD) instead of the residual to measure the deviation of the current state from the normal conditions of the turbines. By inputting the MD series into the proposed change-point detection algorithm, we can obtain the change point at which the fault symptom begins to appear, and thus achieving the fault prediction of the gearbox. The proposed approach is validated on the historical data of 5 wind turbines in a wind farm, which proves its effectiveness to detect the fault in advance.
Environmental Performance of Emerging Photovoltaic Technologies: Assessment of the Status Quo and Future Prospects Based on a Meta-Analysis of Life-Cycle Assessment Studies
Authors: Weyand Steffi , Wittich Carolin , Schebek Liselotte
Published: November 2019

Emerging photovoltaic technologies are expected to have lower environmental impacts during their life cycle due to their extremely thin-film technology and resulting material savings. The environmental impacts of four emerging photovoltaics were investigated based on a meta-analysis of life-cycle assessment (LCA) studies, comprising a systematic review and harmonization approach of five key indicators to describe the environmental status quo and future prospects. The status quo was analyzed based on a material-related functional unit of 1 watt-peak of the photovoltaic cell. For future prospects, the functional unit of 1 kWh of generated electricity was used, including assumptions on the use phase, notably on the lifetime. The results of the status quo show that organic photovoltaic technology is the most mature emerging photovoltaic technology with a competitive environmental performance, while perovskites have a low performance, attributed to the early stage of development and inefficient manufacturing on the laboratory scale. The results of future prospects identified improvements of efficiency, lifetime, and manufacturing with regard to environmental performance based on sensitivity and scenario analyses. The developed harmonization approach supports the use of LCA in the early stages of technology development in a structured way to reduce uncertainty and extract significant information during development.
Storage and Climatic Controlling Factors of Litter Standing Crop Carbon in the Shrublands of the Tibetan Plateau
Authors: Nie Xiuqing , Wang Dong , Yang Lucun , Zhou Guoying
Published: November 2019

Litter is an important component of terrestrial ecosystems and plays a significant role in carbon cycles. Quantifying regional-scale patterns of litter standing crop distribution will improve our understanding of the mechanisms of the terrestrial carbon cycle, and thus enable accurate predictions of the responses of the terrestrial carbon cycle to future climate change. In this study, we aimed to estimate the storage and climatic controlling factors of litter standing crop carbon in the Tibetan Plateau shrublands. We investigated litter standing crop carbon storage and its controlling factors, using a litter survey at 65 shrublands sites across the Tibetan Plateau from 2011–2013. Ordinary least squares regression analyses were conducted to estimate the relationships between litter standing crop carbon, longitude, and latitude. Multiple linear regressions were used to evaluate relationships among litter standing crop carbon, mean annual temperature (MAT), mean annual precipitation (MAP), and aboveground biomass. The litter standing crop carbon storage was 10.93 Tg C, 7.40 Tg C, and 3.53 Tg C in desert shrublands and alpine shrublands, respectively. Litter standing crop carbon decreased with longitude, and was stable with increasing latitude. Most (80%) of the litter standing crop was stored in branches, with only 20% stored in foliage in the shrublands on the Tibetan Plateau. The conversion coefficient was 0.44 for litter standing crop to litter standing crop carbon, and 0.39 and 0.45 for foliage and branch litter standing crop to foliage and branch litter standing crop carbon, respectively. Aboveground biomass can accelerate more inputs of litter and has a positive effect on litter standing crop carbon. MAT had a positive effect on litter standing crop carbon due to stimulating more input of aboveground biomass. However, MAP had a negative relationship with litter standing crop carbon by enhancing litter decomposition.