bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhanced Application of Principal Component Analysis in Machine Learning for Imputation of Missing Traffic Data
Authors: Choi Yoon-Young , Shon Heeseung , Byon Young-Ji , Kim Dong-Kyu , Kang Seungmo
Published: May 2019

Missing value imputation approaches have been widely used to support and maintain the quality of traffic data. Although the spatiotemporal dependency-based approaches can improve the imputation performance for large and continuous missing patterns, additionally considering traffic states can lead to more reliable results. In order to improve the imputation performances further, a section-based approach is also needed. This study proposes a novel approach for identifying traffic-states of different spots of road sections that comprise, namely, a section-based traffic state (SBTS), and determining their spatiotemporal dependencies customized for each SBTS, for missing value imputations. A principal component analysis (PCA) was employed, and angles obtained from the first principal component were used to identify the SBTSs. The pre-processing was combined with a support vector machine for developing the imputation model. It was found that the segmentation of the SBTS using the angles and considering the spatiotemporal dependency for each state by the proposed approach outperformed other existing models.
Plasma-Activation of Larger Liquid Volumes by an Inductively-Limited Discharge for Antimicrobial Purposes
Authors: Schmidt Michael , Hahn Veronika , Altrock Beke , Gerling Torsten , Gerber Ioana Cristina , Weltmann Klaus-Dieter , von Woedtke Thomas
Published: May 2019

A new configuration of a discharge chamber and power source for the treatment of up to 1 L of liquid is presented. A leakage transformer, energizing two metal electrodes positioned above the liquid, limits the discharge current inductively by utilizing the weak magnetic coupling between the primary and secondary coils. No additional means to avoid arcing (electric short-circuiting), e.g., dielectric barriers or resistors, are needed. By using this technique, exceeding the breakdown voltage leads to the formation of transient spark discharges, producing non-thermal plasma (NTP). These discharges effected significant changes in the properties of the treated liquids (distilled water, physiological saline solution, and tap water). Considerable concentrations of nitrite and nitrate were detected after the plasma treatment. Furthermore, all tested liquids gained strong antibacterial efficacy which was shown by inactivating suspended Escherichia coli and Staphylococcus aureus. Plasma-treated tap water had the strongest effect, which is shown for the first time. Additionally, the pH-value of tap water did not decrease during the plasma treatment, and its conductivity increased less than for the other tested liquids.
Multi-Objective Defocus Robust Source and Mask Optimization Using Sensitive Penalty
Authors: Wei Pengzhi , Li Yanqiu , Li Tie , Sheng Naiyuan , Li Enze , Sun Yiyu
Published: May 2019

The continuous decrease in the size of lithographic technology nodes has led to the development of source and mask optimization (SMO) and also to the control of defocus becoming stringent in the actual lithography process. Due to multi-factor impact, defocusing is always changeable and uncertain in the real exposure process. But conventional SMO assumes the lithography system is ideal, which only compensates the optical proximity effect (OPE) in the best focus plane. Therefore, to solve the inverse lithography problem with more uniformity of pattern in different defocus variations, we proposed a defocus robust SMO (DRSMO) approach that is driven by a defocus sensitivity penalty function for the first time. This multi-objective optimization samples a wide range of defocus disturbances and it can be proceeded by the mini-batch gradient descent (MBGD) algorithm effectively. The simulation results showed that a more robust defocus source and mask can be designed through DRSMO optimization. The defocus sensitivity factor sβ maximally decreased 63.5% compared to conventional SMO, and due to the low error sensitivity and the depth of defocus (DOF), the process window (PW) was further enlarged effectively. Compared to conventional SMO, the exposure latitude (EL) maximally increased from 4.5% to 10.5% and DOF maximally increased 54.5% (EL = 5%), which proved the validity of the DRSMO method in improving the focusing performance.
Classification of Marine Vessels with Multi-Feature Structure Fusion
Authors: Zhang Erhu , Wang Kelu , Lin Guangfeng
Published: May 2019

The classification of marine vessels is one of the important problems of maritime traffic. To fully exploit the complementarity between different features and to more effectively identify marine vessels, a novel feature structure fusion method based on spectral regression discriminant analysis (SF-SRDA) was proposed. Firstly, we selected the different convolutional neural network features that better describe the characteristics of ships, and constructed the features based on graphs by the similarity metric. Then we weighed the concatenate multi-feature and fused their structures according to the linear relationship assumption. Finally, we constructed the optimization formula to solve the fusion features and structure by using spectral regression discriminant analyses. Experiments on the VAIS dataset show that the proposed SF-SRDA method can reduce the feature dimension from the original 102,400 dimensions to 5 dimensions, that the classification accuracy of visible images can reach 87.60%, and that that of the infrared image can reach 74.68% at daytime. The experimental results demonstrate that the proposed method can not only extract the optimal features from the original redundant feature space, but also greatly reduce the dimensions of the feature. Furthermore, the classification performance of SF-SRDA also gets a promising result.
Overview on the Evolution of Laser Welding of Vascular and Nervous Tissues
Authors: Gomes Diogo Francisco , Galvão Ivan , Loja Maria Amélia Ramos
Published: May 2019

Laser welding presents a core position in the health sector. This process has had an outstanding impact on the surgical procedures from many medical areas, such as on vascular and nervous surgeries. The aim of the present research is to present an overview on the evolution of laser welding of vascular and nervous tissues. These surgeries present many advantages, such as an absence of foreign-body reactions and aneurysms and good tensile strengths. However, despite the sutureless nature of the process, complementary sutures have been applied to support the procedure success. An important concern in vascular and nervous laser welding is the thermal damage. The development of temperature-controlled feedback systems has reduced this concern with a very precise control of the laser parameters. The bonding strength of vascular and nerve laser welds can be enhanced with the application of solder solutions, bonding materials, and laser-activated dyes. Alternative techniques to laser welding, such as photochemical tissue bonding and electrosurgical high-frequency technologies, have also been tested for vascular and nervous repairs.
Establishment of a Numerical Model to Design an Electro-Stimulating System for a Porcine Mandibular Critical Size Defect
Authors: Raben Hendrikje , Kämmerer Peer W. , Bader Rainer , van Rienen Ursula
Published: May 2019

Electrical stimulation is a promising therapeutic approach for the regeneration of large bone defects. Innovative electrically stimulating implants for critical size defects in the lower jaw are under development and need to be optimized in silico and tested in vivo prior to application. In this context, numerical modelling and simulation are useful tools in the design process. In this study, a numerical model of an electrically stimulated minipig mandible was established to find optimal stimulation parameters that allow for a maximum area of beneficially stimulated tissue. Finite-element simulations were performed to determine the stimulation impact of the proposed implant design and to optimize the electric field distribution resulting from sinusoidal low-frequency ( f = 20 Hz ) electric stimulation. Optimal stimulation parameters of the electrode length h el = 25 m m and the stimulation potential φ stim = 0.5 V were determined. These parameter sets shall be applied in future in vivo validation studies. Furthermore, our results suggest that changing tissue properties during the course of the healing process might make a feedback-controlled stimulation system necessary.
Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification
Authors: He Lin , Chen Xianjun , Li Jun , Xie Xiaofeng
Published: May 2019

Manifold learning is a powerful dimensionality reduction tool for a hyperspectral image (HSI) classification to relieve the curse of dimensionality and to reveal the intrinsic low-dimensional manifold. However, a specific characteristic of HSIs, i.e., irregular spatial dependency, is not taken into consideration in the method design, which can yield many spatially homogenous subregions in an HSI scence. Conventional manifold learning methods, such as a locality preserving projection (LPP), pursue a unified projection on the entire HSI, while neglecting the local homogeneities on the HSI manifold caused by those spatially homogenous subregions. In this work, we propose a novel multiscale superpixelwise LPP (MSuperLPP) for HSI classification to overcome the challenge. First, we partition an HSI into homogeneous subregions with a multiscale superpixel segmentation. Then, on each scale, subregion specific LPPs and the associated preliminary classifications are performed. Finally, we aggregate the classification results from all scales using a decision fusion strategy to achieve the final result. Experimental results on three real hyperspectral data sets validate the effectiveness of our method.
Dynamic Response Analysis of a Simply Supported Double-Beam System under Successive Moving Loads
Authors: Jiang Lizhong , Zhang Yuntai , Feng Yulin , Zhou Wangbao , Tan Zhihua
Published: May 2019

The dynamic response of a simply supported double-beam system under moving loads was studied. First, in order to reduce the difficulty of solving the equation, a finite sin-Fourier transform was used to transform the infinite-degree-of-freedom double-beam system into a superimposed two-degrees-of-freedom system. Second, Duhamel’s integral was used to obtain the analytical expression of Fourier amplitude spectrum function considering the initial conditions. Finally, based on finite sin-Fourier inverse transform, the analytical expression of dynamic response of a simply supported double-beam system under moving loads was deduced. The dynamic response under successive moving loads was calculated by the analytical method and the general FEM software ANSYS. The analysis results show that the analytical method calculation results are consistent with ANSYS’ calculation, thus validating the analytical calculation method. The simply supported double-beam system had multiple critical speeds, and the flexural rigidity significantly affected both peak vertical displacement and critical speed.
Reactive Black 5 Degradation on Manganese Oxides Supported on Sodium Hydroxide Modified Graphene Oxide
Authors: Saroyan Hayarpi , Ntagiou Dimitra , Rekos Kyriazis , Deliyanni Eleni
Published: May 2019

Sodium hydroxide-modified graphene oxide was used as manganese oxides support for the preparation of nanocomposites via a one-pot preparation route for the degradation of Reactive Black 5. The nanocomposites were characterized for their structure by X-ray diffraction, for their textural properties by Nitrogen adsorption, and for their surface chemistry by Fourier transform infrared spectroscopy, potentiometric titration, and thermal analysis measurements. The nanocomposites prepared showed to possess high activity for the degradation/oxidation of Reactive Black 5 at ambient conditions, without light irradiation, which was higher than that of the precursors manganese oxides and can be attributed to the synergistic effect of the manganese oxides and the modified graphene oxide.
Solids Content of Black Liquor Measured by Online Time-Domain NMR
Authors: Nikolskaya Ekaterina , Janhunen Petri , Haapalainen Mikko , Hiltunen Yrjö
Published: May 2019

Black liquor, a valuable by-product of the pulp production process, is used for the recovery of chemicals and serves as an energy source for the pulp mill. Before entering the recovery unit, black liquor runs through several stages of evaporation, wherein the solids content (SC) can be used to control the evaporation effectiveness. In the current study, the time-domain nuclear magnetic resonance (TD-NMR) technique was applied to determine the SC of black liquor. The TD-NMR system was modified for flowing samples, so that the black liquor could be pumped through the system, followed by the measurement of the spin-spin relaxation rate, R2. A temperature correction was also applied to reduce deviations in the R2 caused by the sample temperature. The SC was calculated based on a linear model between the R2 and the SC values determined gravimetrically, where good agreement was shown. The online TD-NMR system was tested at a pulp mill for the SC estimation of weak black liquor over seven days without any fouling, which demonstrated the feasibility of the method in a harsh industrial environment. Therefore, the potential of the TD-NMR technology as a technique for controlling the black liquor evaporation process was demonstrated.
Chronic Disease Prediction Using Character-Recurrent Neural Network in The Presence of Missing Information
Authors: Kim Changgyun , Son Youngdoo , Youm Sekyoung
Published: May 2019

The aim of this study was to predict chronic diseases in individual patients using a character-recurrent neural network (Char-RNN), which is a deep learning model that treats data in each class as a word when a large portion of its input values is missing. An advantage of Char-RNN is that it does not require any additional imputation method because it implicitly infers missing values considering the relationship with nearby data points. We applied Char-RNN to classify cases in the Korea National Health and Nutrition Examination Survey (KNHANES) VI as normal status and five chronic diseases: hypertension, stroke, angina pectoris, myocardial infarction, and diabetes mellitus. We also employed a multilayer perceptron network for the same task for comparison. The results show higher accuracy for Char-RNN than for the conventional multilayer perceptron model. Char-RNN showed remarkable performance in finding patients with hypertension and stroke. The present study utilized the KNHANES VI data to demonstrate a practical approach to predicting and managing chronic diseases with partially observed information.
Automatic Digital Modulation Classification Based on Curriculum Learning
Authors: Zhang Min , Yu Zhongwei , Wang Hai , Qin Hongbo , Zhao Wei , Liu Yan
Published: May 2019

Neural network shows great potential in modulation classification because of its excellent accuracy and achievability but overfitting and memorizing data noise often happen in previous researches on automatic digital modulation classifier. To solve this problem, we utilize two neural networks, namely MentorNet and StudentNet, to construct an automatic modulation classifier, which possesses great performance on the test set with −18–20 dB signal-to-noise ratio (SNR). The MentorNet supervises the training of StudentNet according to curriculum learning, and deals with the overfitting problem in StudentNet. The proposed classifier is verified in several test sets containing additive white Gaussian noise (AWGN), Rayleigh fading, carrier frequency offset and phase offset. Experimental results reveal that the overall accuracy of this classifier for common eleven modulation types was up to 99.3% while the inter-class accuracy could be up to 100%, which was much higher than many other classifiers. Besides, in the presence of interferences, the overall accuracy of this novel classifier still could reach 90% at 10 dB SNR indicting its excellent robustness, which makes it suitable for applications like military electronic warfare.
Line Shapes in a Magnetic Field: Trajectory Modifications I: Electrons
Authors: Alexiou Spiros
Published: May 2019

In recent work, the effect of a magnetic field on the line shapes via the modification of electron perturber trajectories was considered. In the present paper we revisit this idea using a variation of the Collision-time Statistics method, in order to account for a l l relevant perturbers. We also obtain line profiles for the hydrogen L α line for conditions of astrophysical interest. Although the Collision-time statistics method works for both electrons and ions, we apply a simplification here that results in an excessive number of ions having to be simulated. As a result, the present, simplified version, is typically only appropriate for electrons.
An Efficient Class of Traub–Steffensen-Type Methods for Computing Multiple Zeros
Authors: Kumar Deepak , Sharma Janak Raj , Cesarano Clemente
Published: May 2019

Numerous higher-order methods with derivative evaluations are accessible in the literature for computing multiple zeros. However, higher-order methods without derivatives are very rare for multiple zeros. Encouraged by this fact, we present a family of third-order derivative-free iterative methods for multiple zeros that require only evaluations of three functions per iteration. Convergence of the proposed class is demonstrated by means of using a graphical tool, namely basins of attraction. Applicability of the methods is demonstrated through numerical experimentation on different functions that illustrates the efficient behavior. Comparison of numerical results shows that the presented iterative methods are good competitors to the existing techniques.
Best Approximation Results in Various Frameworks
Authors: Sabar Taoufik , Bassou Abdelhafid , Aamri Mohamed
Published: May 2019

We first provide a best proximity point result for quasi-noncyclic relatively nonexpansive mappings in the setting of dualistic partial metric spaces. Then, those spaces will be endowed with convexity and a result for a cyclic mapping will be obtained. Afterwards, we prove a best proximity point result for tricyclic mappings in the framework of the newly introduced extended partial S b -metric spaces. In this way, we obtain extensions of some results in the literature.
Casein-Conjugated Gold Nanoparticles for Amperometric Detection of Leishmania infantum
Authors: Diouani Mohamed Fethi , Ouerghi Oussama , Belgacem Kamel , Sayhi Maher , Ionescu Radu , Laouini Dhafer
Published: May 2019

Sensitive and reliable approaches targeting the detection of Leishmania are critical for effective early diagnosis and treatment of leishmaniasis. In this frame, this paper describes a rapid quantification assay to detect Leishmania parasites based on the combination of the electrocatalytic ability of gold nanoparticles (AuNPs) to act as a catalyst for the hydrogen formation reaction along with the specificity of the interaction between casein and the major surface protease of the Leishmania parasite, GP63. First, pure and casein-modified AuNPs were prepared and characterized by scanning electron microscopy and ultraviolet–visible spectroscopy. Then, casein-conjugated AuNPs were incubated with Leishsmania parasites in solution; the formed complex was collected by centrifugation, treated by acidic solution, and the pelleted AuNPs were placed on screen-printed carbon electrodes (SPCEs) and chronoamperometric measurements were carried out. Our results suggest that it is possible to detect Leishmania parasites, with a limit less than 1 parasite/mL. A linear response over a wide concentration interval, ranging from 2 × 10−2 to 2 × 105 parasites/mL, was achieved. Additionally, a pretreatment of Leishmania parasites with Amphotericin B, diminished their interaction with casein. This findings and methodology are very useful for drug efficacy assessment.
Faecal Scent as a Novel Non-Invasive Biomarker to Discriminate between Coeliac Disease and Refractory Coeliac Disease: A Proof of Principle Study
Authors: Rouvroye Maxine D. , Wicaksono Alfian , Bosch Sofie , Savelkoul Edo , Covington James A. , Beaumont Hanneke , Mulder Chris J. , Bouma Gerd , de Meij Tim G.J. , de Boer Nanne K.H.
Published: May 2019

Currently, the gold standard for diagnosis of coeliac disease (CD) is based on serology and gastroduodenoscopy with histology of duodenal mucosal biopsies. The aim of this study was to evaluate the potential of faecal volatile organic compounds (VOCs) analysis as a novel, non-invasive tool to discriminate between CD in remission in patients on a gluten-free diet (GFD), refractory coeliac disease (RCD) and controls without CD. Patients with an established diagnosis of CD on a GFD, RCD and healthy controls (HC) were instructed to collect a faecal sample. All subjects completed questionnaires on clinical symptoms, lifestyle and dietary information. Faecal VOCs were measured using gas chromatography-ion mobility spectrometry. A total of 13 CD, 7 RCD and 10 HC were included. A significant difference in VOC profiles between CD and RCD patients (area under the curve (AUC) ± 95% CI: 0.91 (0.79–1) p = 0.000) and between CD and HC (AUC ± 95% CI: 0.71 (0.51–0.91) p = 0.0254) was observed. We found no significant differences between faecal VOC patterns of HC and RCD. Based on faecal VOCs, CD could be discriminated from RCD and HC. This implies that faecal VOC analysis may hold potential as a novel non-invasive biomarker for RCD. Future studies should encompass a larger cohort to further investigate and validate this prior to application in clinical practice.
Selection of (Green) Roof Systems: A Sustainability-Based Multi-Criteria Analysis
Authors: Rosasco Paolo , Perini Katia
Published: May 2019

A wide diffusion of green envelopes in cities can be an opportunity to improve urban environment conditions and reduce negative effects of climate change. The green roof system is a widespread solution adopted all over the world due to the relative simplicity of installation and the large private and social benefits provided. Despite this, some factors hinder the diffusion of the green roof system, not only economic factors (due to the higher installation costs compare to a traditional roof solution), but also technical factors connected to lack of knowledge. The present paper investigates the factors influencing designers in the choice of a building roof systems, comparing a traditional solution and a greening system. The involvement of architects, engineers, and researchers allows the selection of the most important factors. Results of the study identifies their priority, and through a sustainability-based multicriteria analysis, the role played by each one in the decision process. This approach provides interesting hints to identify effective strategies to support a wider diffusion of greening systems for urban resilience.
Clinical Evaluation of CA72-4 for Screening Gastric Cancer in a Healthy Population: A Multicenter Retrospective Study
Authors: Hu Ping-Jen , Chen Ming-Yao , Wu Ming-Shun , Lin Ying-Chin , Shih Ping-Hsiao , Lai Chih-Ho , Lin Hwai-Jeng
Published: May 2019

Early detection is important for improving the survival rate of patients with gastric cancer (GC). Serum tumor markers have been widely used for detecting GC. However, their clinical values remain controversial. This study aims to investigate the role of serum cancer antigen 72-4 (CA72-4) in the diagnosis of GC in a healthy population. A total of 7757 adults who underwent upper gastrointestinal endoscopy and serum CA72-4 level measurement in multicenters in Taiwan from January 2006 to August 2016 were recruited in this retrospective study. Risk factors for GC, serum tumor markers, and esophagogastroduodenoscopy (EGD) findings were evaluated. High serum levels of CA72-4 were found in 7.2% of healthy adults. CA72-4 level showed lower sensitivity (33.3%) but higher specificity (92.8%); however, the positive predictive value was quite low (0.18%). After adjustment of clinical risk factors for GC using EGD findings, gastric ulcer (adjusted odds ratio (aOR) = 2.11), gastric polyps (aOR = 1.42), and atrophic gastritis (aOR = 1.27) were significantly associated with high serum CA72-4 levels. Furthermore, both age (OR = 1.01) and Helicobacter pylori infection (OR = 1.44) exhibited a significant association with high serum CA72-4 levels. These results indicate that routine screening of CA72-4 levels for diagnosing GC in asymptomatic patients may be ineffective due to low sensitivity and low positive predictive value. The clinical utility of EGD findings along with serum CA72-4 level for screening healthy individuals with GC is warranted.
Combining Planar Laser-Induced Fluorescence with Stagnation Point Flows for Small Single-Crystal Model Catalysts: CO Oxidation on a Pd(100)
Authors: Zhou Jianfeng , Matera Sebastian , Pfaff Sebastian , Blomberg Sara , Lundgren Edvin , Zetterberg Johan
Published: May 2019

A stagnation flow reactor has been designed and characterized for both experimental and modeling studies of single-crystal model catalysts in heterogeneous catalysis. Using CO oxidation over a Pd(100) single crystal as a showcase, we have employed planar laser-induced fluorescence (PLIF) to visualize the CO2 distribution over the catalyst under reaction conditions and subsequently used the 2D spatially resolved gas phase data to characterize the stagnation flow reactor. From a comparison of the experimental data and the stagnation flow model, it was found that characteristic stagnation flow can be achieved with the reactor. Furthermore, the combined stagnation flow/PLIF/modeling approach makes it possible to estimate the turnover frequency (TOF) of the catalytic surface from the measured CO2 concentration profiles above the surface and to predict the CO2, CO and O2 concentrations at the surface under reaction conditions.
Facile Mechanochemical Synthesis of Nickel/Graphene Oxide Nanocomposites with Unique and Tunable Morphology: Applications in Heterogeneous Catalysis and Supercapacitors
Authors: Gopiraman Mayakrishnan , Saravanamoorthy Somasundaram , Deng Dian , Ilangovan Andivelu , Kim Ick Soo , Chung Ill Min
Published: May 2019

In this study, a very simple and highly effective mechanochemical preparation method was developed for the preparation of Ni nanoparticles supported graphene oxide (GO) nanocomposites (Ni/GO, where Ni is a composition of Ni(OH)2, NiOOH, NiO, Ni2O3 and NiO2), 3 wt% NiO/GO (Ni/GO-1) and 8 wt% NiO/GO(Ni/GO-2). The developed method is not only very simple and efficient, but also, the morphology of Ni/GO nanocomposites can be tuned by simply varying the metal loading. Morphology and specific surface area of the resultant Ni/GO nanocomposites were investigated by mean of AFM, HR-TEM and BET. Chemical sate and factual content of Ni in Ni/GO-1 and Ni/GO-2, and the presence of defective sites in Ni-nanocomposites were investigated in detail. To our delight, the prepared Ni/GO-2 demonstrated superior catalytic activity toward the reduction of 2- and 4-nitrophenol in water with high rate constant (kapp) of 35.4 × 10−3 s−1. To the best of our knowledge, this is the best efficient Ni-based graphene nanocomposites for the reduction of 2- and 4-NP reported to date. The Ni/GO-1 and Ni/GO-2 demonstrated an excellent reusability; no loss in its catalytic activity was noticed, even after 10th cycle. Surprisingly the Ni/GO-2 as electrode material exhibited an excellent specific capacitance of 461 F/g in 6 M KOH at a scan rate of 5 mV. Moreover, the Ni/GO nanocomposites were found to possess poor electrical resistance and high stability (no significant change in the specific capacitance even after 1000 cycles).
Inhibition of Hedgehog Signaling in Fibroblasts, Pancreatic, and Lung Tumor Cells by Oxy186, an Oxysterol Analogue with Drug-Like Properties
Authors: Wang Feng , Stappenbeck Frank , Parhami Farhad
Published: May 2019

The widespread involvement of the Hedgehog (Hh) signaling pathway in human malignancies has motivated the clinical development of Smoothened (Smo) antagonists, such as vismodegib and sonidegib. However, Smo antagonists have failed to benefit patients suffering from Hh pathway-dependent solid tumors, such as pancreatic, colorectal, or ovarian cancer. Hh-dependent cancers are often driven by activating mutations that occur downstream of Smo and directly activate the transcription factors known as glioma-associated oncogenes (Gli1-3). Hence, the direct targeting of Gli could be a more effective strategy for achieving disease modification compared to Smo antagonism. In this study, we report on the biological and pharmacological evaluation of Oxy186, a semisynthetic oxysterol analogue, as a novel inhibitor of Hh signaling acting downstream of Smo, with encouraging drug-like properties. Oxy186 exhibits strong inhibition of ligand-induced Hh signaling in NIH3T3-E1 fibroblasts, as well as in constitutively activated Hh signaling in Suppressor of Fused (Sufu) null mouse embryonic fibroblast (MEF) cells. Oxy186 also inhibits Gli1 transcriptional activity in NIH3T3-E1 cells expressing exogenous Gli1 and Gli-dependent reporter constructs. Furthermore, Oxy186 suppresses Hh signaling in PANC-1 cells, a human pancreatic ductal adenocarcinoma (PDAC) tumor cell line, as well as PANC-1 cell proliferation in vitro, and in human lung cancer cell lines, A549 and H2039.
Characterization of Lipid and Lipid Droplet Metabolism in Human HCC
Authors: Berndt Nikolaus , Eckstein Johannes , Heucke Niklas , Gajowski Robert , Stockmann Martin , Meierhofer David , Holzhütter Hermann-Georg
Published: May 2019

Human hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults and the most common cause of death in people with cirrhosis. While previous metabolic studies of HCC have mainly focused on the glucose metabolism (Warburg effect), less attention has been paid to tumor-specific features of the lipid metabolism. Here, we applied a computational approach to analyze major pathways of fatty acid utilization in individual HCC. To this end, we used protein intensity profiles of eleven human HCCs to parameterize tumor-specific kinetic models of cellular lipid metabolism including formation, enlargement, and degradation of lipid droplets (LDs). Our analysis reveals significant inter-tumor differences in the lipid metabolism. The majority of HCCs show a reduced uptake of fatty acids and decreased rate of β-oxidation, however, some HCCs display a completely different metabolic phenotype characterized by high rates of β-oxidation. Despite reduced fatty acid uptake in the majority of HCCs, the content of triacylglycerol is significantly enlarged compared to the tumor-adjacent tissue. This is due to tumor-specific expression profiles of regulatory proteins decorating the surface of LDs and controlling their turnover. Our simulations suggest that HCCs characterized by a very high content of triglycerides comprise regulatory peculiarities that render them susceptible to selective drug targeting without affecting healthy tissue.
In Vitro Corrosion and Bioactivity Performance of Surface-Treated Ti-20Nb-13Zr Alloys for Orthopedic Applications
Authors: Arumugam Madhan Kumar , Hussein Mohamed A. , Yusuf Adesina Akeem , Al-Aqeeli Nasser
Published: May 2019

The influence of surface treatments on the microstructure, in vitro bioactivity and corrosion protection performance of newly fabricated Ti-20Nb-13Zr (TNZ) alloys was evaluated in simulated body fluid (SBF). The TNZ alloy specimens were treated with separate aqueous solutions of NaOH and H2O2 and with a mixture of both, followed by thermal treatment. The nanoporous network surface structure observed in H2O2-treated and alkali-treated specimens was entirely different from the rod-like morphology observed in alkali hydrogen peroxide-treated specimens. XRD results revealed the formation of TiO2 and sodium titanate layers on the TNZ specimens during surface treatments. The water contact angle results implied that the surface-treated specimens exhibited improved surface hydrophilicity, which probably improved the bioactivity of the TNZ specimens. The in vitro corrosion protection performance of the surface-treated TNZ specimens was analyzed using electrochemical corrosion testing in SBF, and the obtained results indicated that the surface-treated specimens exhibited improved corrosion resistance performance compared to that of the bare TNZ specimen. The in vitro bioactivity of the treated TNZ specimens was assessed by soaking in SBF, and all the investigated treated specimens showed numerous apatite nucleation spheres within 3 days of immersion in SBF.
Fibroblasts Collagen Production and Histological Alterations in Hereditary Gingival Fibromatosis
Authors: Roman-Malo Lourdes , Bullon Beatriz , de Miguel Manuel , Bullon Pedro
Published: May 2019

Hereditary gingival fibromatosis is a disorder for which the etiology remains unknown. We aimed to evaluate the fibroblasts and histological alterations to give new clues. A father and a daughter of a family showing gingival hereditary fibromatosis were treated, and gingival biopsies were obtained. A histological study revealed dense fibrous tissue, basal lamina disruption, and epithelial cell migration into the connective tissue. Fibroblasts were cultured from the father and daughter and compared with those from a healthy control patient. The results of the biochemical analysis showed increased collagen synthesis, reduced antioxidant CoQ10 content, and high levels of lipid peroxidation. Additionally, fibroblasts culture incubation with the oxidant H2O2 increased collagen levels that have been reduced by the addition of the antioxidant CoQ10. We conclude that some fibroblasts metabolic alterations play a significant role in initiating and maintaining persistent fibrotic tissue. Oxidative stress influences the fibroblasts collagen production and could play a particular role in the pathogenesis of hereditary gingival fibromatosis.
Summoning, No-Signalling and Relativistic Bit Commitments
Authors: Kent Adrian
Published: May 2019

Summoning is a task between two parties, Alice and Bob, with distributed networks of agents in space-time. Bob gives Alice a random quantum state, known to him but not her, at some point. She is required to return the state at some later point, belonging to a subset defined by communications received from Bob at other points. Many results about summoning, including the impossibility of unrestricted summoning tasks and the necessary conditions for specific types of summoning tasks to be possible, follow directly from the quantum no-cloning theorem and the relativistic no-superluminal-signalling principle. The impossibility of cloning devices can be derived from the impossibility of superluminal signalling and the projection postulate, together with assumptions about the devices’ location-independent functioning. In this qualified sense, known summoning results follow from the causal structure of space-time and the properties of quantum measurements. Bounds on the fidelity of approximate cloning can be similarly derived. Bit commitment protocols and other cryptographic protocols based on the no-summoning theorem can thus be proven secure against some classes of post-quantum but non-signalling adversaries.
Nonadditive Entropies and Complex Systems
Authors: Rapisarda Andrea , Thurner Stefan , Tsallis Constantino
Published: May 2019

An entropic functional S is said additive if it satisfies, for any two probabilistically independent systems A and B, that S ( A + B ) = S ( A ) + S ( B ) [...]
Comparing the Use of Two Different Model Approaches on Students’ Understanding of DNA Models
Authors: Mierdel Julia , Bogner Franz X.
Published: May 2019

As effective methods to foster students’ understanding of scientific models in science education are needed, increased reflection on thinking about models is regarded as a relevant competence associated with scientific literacy. Our study focuses on the influence of model-based approaches (modeling vs. model viewing) in an out-of-school laboratory module on the students’ understanding of scientific models. A mixed method design examines three subsections of the construct: (1) students’ reasoning about multiple models in science, (2) students’ understanding of models as exact replicas, and (3) students’ understanding of the changing nature of models. There were 293 ninth graders from Bavarian grammar schools that participated in our hands-on module using creative model-based tasks. An open-ended test item evaluated the students’ understanding of “multiple models” (MM). We defined five categories with a majority of students arguing that the individuality of DNA structure leads to various DNA models (modelers = 36.3%, model viewers = 41.1%). Additionally, when applying two subscales of the quantitative instrument Students’ Understanding of Models in Science (SUMS) at three testing points (before, after, and delayed-after participation), a short- and mid-term decrease for the subscale “models as exact replicas” (ER) appeared, while mean scores increased short- and mid-term for the subscale “the changing nature of models” (CNM). Despite the lack of differences between the two approaches, a positive impact of model-based learning on students’ understanding of scientific models was observed.
Risk-Constrained Stochastic Scheduling of a Grid-Connected Hybrid Microgrid with Variable Wind Power Generation
Authors: Vahedipour-Dahraie Mostafa , Rashidizadeh-Kermani Homa , Anvari-Moghaddam Amjad
Published: May 2019

This paper presents a risk-constrained scheduling optimization model for a grid-connected hybrid microgrid including demand response (DR), electric vehicles (EVs), variable wind power generation and dispatchable generation units. The proposed model determines optimal scheduling of dispatchable units, interactions with the main grid as well as adjustable responsive loads and EVs demand to maximize the expected microgrid operator’s profit under different scenarios. The uncertainties of day-ahead (DA) market prices, wind power production and demands of customers and EVs are considered in this study. To address these uncertainties, conditional value-at-risk (CVaR) as a risk measurement tool is added to the optimization model to evaluate the risk of profit loss and to indicate decision attitudes in different conditions. The proposed method is finally applied to a typical hybrid microgrid with flexible demand-side resources and its applicability and effectives are verified over different working conditions with uncertainties.
The Tabu_Genetic Algorithm: A Novel Method for Hyper-Parameter Optimization of Learning Algorithms
Authors: Guo Baosu , Hu Jingwen , Wu Wenwen , Peng Qingjin , Wu Fenghe
Published: May 2019

Machine learning algorithms have been widely used to deal with a variety of practical problems such as computer vision and speech processing. But the performance of machine learning algorithms is primarily affected by their hyper-parameters, as without good hyper-parameter values the performance of these algorithms will be very poor. Unfortunately, for complex machine learning models like deep neural networks, it is very difficult to determine their hyper-parameters. Therefore, it is of great significance to develop an efficient algorithm for hyper-parameter automatic optimization. In this paper, a novel hyper-parameter optimization methodology is presented to combine the advantages of a Genetic Algorithm and Tabu Search to achieve the efficient search for hyper-parameters of learning algorithms. This method is defined as the Tabu_Genetic Algorithm. In order to verify the performance of the proposed algorithm, two sets of contrast experiments are conducted. The Tabu_Genetic Algorithm and other four methods are simultaneously used to search for good values of hyper-parameters of deep convolutional neural networks. Experimental results show that, compared to Random Search and Bayesian optimization methods, the proposed Tabu_Genetic Algorithm finds a better model in less time. Whether in a low-dimensional or high-dimensional space, the Tabu_Genetic Algorithm has better search capabilities as an effective method for finding the hyper-parameters of learning algorithms. The presented method in this paper provides a new solution for solving the hyper-parameters optimization problem of complex machine learning models, which will provide machine learning algorithms with better performance when solving practical problems.
Compact Printable Inverted-M Shaped Chipless RFID Tag Using Dual-Polarized Excitation
Authors: Abdulkawi Wazie M. , Sheta Abdel-Fattah A. , Issa Khaled , Alshebeili Saleh A.
Published: May 2019

A novel and compact dual-polarized (DP) chipless radio-frequency identification (RFID) tag is presented in this paper. This tag can read both vertical and horizontal orientations within its frequency band, which improves the robustness and detection capability of the RFID system. The proposed tag makes use of the slot length variation encoding technique to improve the encoding capacity. This technique can duplicate the encoding capacity, thereby reducing the overall tag size by almost 50%. In particular, the proposed tag has an encoding capacity of 20 bits in the 3–8 GHz frequency band and achieves data density of around 15.15 bits/cm2. Three prototypes are fabricated and tested outside an anechoic chamber. Furthermore, one tag is tested at different distances (10 cm, 30 cm, and 60 cm) from the reader and the measured results are compared. The simulated and measured results are in reasonable agreement, with acceptable shifts at some frequencies due to fabrication and experimental errors.
Four-State Coupled-Line Resonator for Chipless RFID Tags Application
Authors: Abdulkawi Wazie M. , Sheta Abdel-Fattah A.
Published: May 2019

A novel quad-state coupled-line microstrip resonator is proposed for compact chipless radio frequency identification (RFID) tags. The proposed resonator can be reconfigured to present one of four possible states: 00, 01, 10, and 11, representing, no resonance, resonance at f2, resonance at f1, and resonance at both f1 and f2, respectively. The frequency span between f2 and f1 can be easily controlled, thereby reducing the required spectrum. Moreover, the proposed technique allows the storage of a large amount of data in a compact size to reduce the cost per bit. A multi-resonator prototype consisting of six resonators is designed, analyzed, and experimentally characterized. This prototype is implemented on the RT Duroid 5880 substrate with a dielectric constant of 2.2, loss tangent of 0.0009, and thickness of 0.79 mm. The designed configuration can be reconfigured for 46 codes. Two complete the RFID tags, including the six resonators and two orthogonally polarized transmitting and receiving antennas, are implemented and tested. The first tag code is designed for all ones, 111111111111, and the second tag is designed as 101010101010 code. Experimental results show good agreement with the simulation.
Improvement of Energy Efficiency and Control Performance of Cooling System Fan Applied to Industry 4.0 Data Center
Authors: Ko Jae-Sub , Huh Jun-Ho , Kim Jong-Chan
Published: May 2019

This paper proposes a control method to improve the energy efficiency and performance of cooling fans used for cooling. In Industry 4.0, a large number of digital data are used, and a large number of data centers are created to handle these data. These data centers consist of information technology (IT) equipment, power systems, and cooling systems. The cooling system is essential to prevent failure and malfunction of the IT equipment, which consumes a considerable amount of energy. This paper proposes a method to reduce the energy used in such cooling systems and to improve the temperature control performance. This paper proposes an fuzzy proportional integral(FPI) controller that controls the input value of the proportional integral(PI) controller by the fuzzy controller according to the operation state, a VFPI (Variable Fuzzy Proportional Integral) controller that adjusts the gain value of the fuzzy controller, and a variable fuzzy proportion integration-variable limit (VFPI-VL) controller that adjusts the limit value of the fuzzy controller’s output value. These controllers control the fan applied to the cooling system and compare the energy consumed and temperature control performance. When the PI controller consumes 100% of the power consumed, the FPI is 50.5%, the VFPI controller is 44.3%, and the VFPI-VL is 32.6%. The power consumption is greatly reduced. In addition, the VFPI-VL controller is the lowest in temperature variation, which improves the energy efficiency and performance of the cooling system using a fan. The methods presented in this paper can not only be applied to fans for cooling, but also to variable speed systems for various purposes and improvement of performance and efficiency can be expected.
Analysis and Design of Functional Device for Vehicular Cloud Computing
Authors: Wu Guilu , Li Sha , Wang Shujun , Jiang Yutong , Li Zhengquan
Published: May 2019

Relay technology application becomes prevalent nowadays, as it can effectively extend the communication distance, especially for vehicular networks with a limited communication range. Combined with vehicular cloud (VC), transmission efficiency can be improved by offloading partial data. Hence, designing a vehicle relay algorithm and implementation embedded vehicle device is critical. In this paper, VC is considered to deal with the complexity computation in our proposed system model. Without a loss of generality, an end-to-end vehicle communication with one assisted vehicle is analyzed firstly on a transmission link based on VC. Here, the signal-to-noise ratio (SNR) on the receiving end and link outage probability is obtained to enhance the link reliability. The VC computing helps us further simplify computational complexity. Subsequently, an embedded vehicle-enabled device is designed to achieve the optimal path relay selection in realistic vehicular environments. In the functional device framework, we display an optimal path relay selection algorithm according to the link quality. Finally, the performance of the transmission link on the outage probability related with SNR is verified in the simulation results. Meanwhile, the effect of the relay gain is also analyzed. The application of a vehicle-enabled embedded device could improve the performance of vehicular networks.
Zi-CAM: A Power and Resource Efficient Binary Content-Addressable Memory on FPGAs
Authors: Irfan Muhammad , Ullah Zahid , C. C. Cheung Ray
Published: May 2019

Content-addressable memory (CAM) is a type of associative memory, which returns the address of a given search input in one clock cycle. Many designs are available to emulate the CAM functionality inside the re-configurable hardware, field-programmable gate arrays (FPGAs), using static random-access memory (SRAM) and flip-flops. FPGA-based CAMs are becoming popular due to the rapid growth in software defined networks (SDNs), which uses CAM for packet classification. Emulated designs of CAM consume much dynamic power owing to a high amount of switching activity and computation involved in finding the address of the search key. In this paper, we present a power and resource efficient binary CAM architecture, Zi-CAM, which consumes less power and uses fewer resources than the available architectures of SRAM-based CAM on FPGAs. Zi-CAM consists of two main blocks. RAM block (RB) is activated when there is a sequence of repeating zeros in the input search word; otherwise, lookup tables (LUT) block (LB) is activated. Zi-CAM is implemented on Xilinx Virtex-6 FPGA for the size 64 × 36 which improved power consumption and hardware cost by 30 and 32%, respectively, compared to the available FPGA-based CAMs.