bluuebunny/crossref_metadata_2025 huggingface dataset:
Sensitivity of EGFR/HER-2 Positive Cells Isolated from Ascitic Fluid of Advanced Ovarian Cancer Patients to EGFR/HER-2 Inhibitors
Authors: Chitcholtan Kenny , Harker Dianne , Simcock Bryony , Sykes Peter
Published: March 2020

Background: advanced ovarian cancer often presents with ascites. These ascites contain small clusters of cancer cells, which may contribute greatly to the metastatic potential of ovarian cancer in the peritoneal cavity. Therefore, understanding the unique protein expressions of this cell population will provide vital information for the development of tailored, targeted treatment. In this study, we isolate floating ovarian cancer cells from ovarian cancer patient ascitic fluid and use these cells to document that the expression of EGFR/HER-2 proteins may be essential for the growth and survival of these floating cancer cell clusters. Methods: ascitic fluid-derived cells were isolated from ascitic fluid by using Ficoll separation. Cells were cultured in a non-adherent condition for six days. The protein level of EGFR, HER-2, AKT, and ERK and their phosphorylation in ovarian cancer cell lines were determined by immunofluorescence. The immunofluorescent staining for proteins presented in ascitic fluid-derived cells determined the intensity profile of each protein using Carl Zeiss Blue software. Results: Isolated ovarian cancer cells from ascitic fluid have a measurable level of EGFR and HER-2 proteins. The inhibition of EGFR and EGFR/HER-2 positive cells with gefitinib and canertinib selectively disrupts cell viability and the protein level of EGFR, HER-2, AKT and ERK and their respective phosphorylation status. In addition, the dual EGFR/HER-2 inhibitor canertinib demonstrates greater anti-tumour effects than gefitinib in EGFR/HER-2 positive cells. Conclusion: These studies reveal an important role of multiple activation of receptor tyrosine kinases in floating ovarian cancer cells, as well as the importance of a dual EGFR/HER-2 inhibitor used as alternative adjuvant therapy in advanced ovarian cancer patients.
An Application of Isogeometric Analysis and Boundary Integral Element Method for Solving Nonlinear Contact Problems
Authors: Camacho Gutiérrez Stephanie Virginia , Jáuregui Correa Juan Carlos , Dominguez-Gonzalez Aurelio , Gómez-Loenzo Roberto Augusto
Published: March 2020

A novel method to solve nonlinear contact between two bodies with plane strain behavior is presented in this paper. This method is based on the Boundary Integral Equation (BIE) and the Isogeometric Analysis (IGA). Unlike works that divide the boundary into elements, this method evaluates it as a single element, reducing the degrees of freedom in the solution. Moreover, the Particle Swarm Optimization Algorithm (PSO) was used in order to estimate the deformation when two bodies are in contact and there is penetration between them. The obtained results were compared with the Finite Element Method (FEM) and the Hertz contact equation.
Wireless-Based Identification and Model Updating of a Skewed Highway Bridge for Structural Health Monitoring
Authors: He Leqia , Reynders Edwin , García-Palacios Jaime H. , Carlo Marano Giuseppe , Briseghella Bruno , De Roeck Guido
Published: March 2020

Vibration-based monitoring was performed on a short-span skewed highway bridge on the basis of wireless measurements. By means of operational modal analysis, highly accurate modal results (frequencies and mode shapes) were extracted by using a self-developed wireless acquisition system, for which the performance was verified in the field. In order to reproduce the experimental modal characteristics, a refined finite element model was manually tuned to reduce the idealization errors and then updated with the sensitivity method to reduce the parametric errors. It was found that to build a reliable Finite element (FE) model for application in structural health monitoring, the effects of superelevation and boundary conditions of a skewed bridge should be taken into account carefully.
Dynamic Range Compression and the Semantic Descriptor Aggressive
Authors: Moore Austin
Published: March 2020

In popular music productions, the lead vocal is often the main focus of the mix and engineers will work to impart creative colouration onto this source. This paper conducts listening experiments to test if there is a correlation between perceived distortion and the descriptor “aggressive”, which is often used to describe the sonic signature of Universal Audio 1176, a much-used dynamic range compressor in professional music production. The results from this study show compression settings that impart audible distortion are perceived as aggressive by the listener, and there is a strong correlation between the subjective listener scores for distorted and aggressive. Additionally, it was shown there is a strong correlation between compression settings rated with high aggressive scores and the audio feature roughness.
Electrical Tomography: A Review of Configurations, and Application to Fibre Flow Suspensions Characterisation
Authors: Faia Pedro , Silva Rui , Rasteiro Maria G. , Garcia Fernando
Published: March 2020

Understanding the behaviour of suspension flows continues to be a subject of great interest considering its industrial relevance, regardless of the long time and effort dedicated to it by the scientific and industrial communities. Information about several flow characteristics, such as flow regimen, relative velocity between phases, and spatial distribution of the phases, are essential for the development of exact models for description of processes involving pulp suspension. Among the diverse non-invasive techniques for flow characterisation that have been reported in the literature for obtaining experimental data about suspension flow in different processes, Electrical Tomography is one of the most interesting, since it presents perhaps the best compromise among cost, portability, and, above all, safety of handling (indeed there is no need to use radiation, which requires special care when using it). In this paper, a brief review and comparison between existing technologies for pulp suspension flow monitoring will be presented, together with their strengths and weaknesses. Emphasis is given to Electrical Tomography, because it offers the above-mentioned compromise and thus was the strategy adopted by the authors to characterise different flow processes (solid–liquid, liquid–liquid, fibres, etc.). The produced portable EIT system is described, and examples of results of its use for pulp suspension flow characterisation are reported and discussed.
Sparse Low-Rank Based Signal Analysis Method for Bearing Fault Feature Extraction
Authors: Wang Baoxiang , Liao Yuhe , Duan Rongkai , Zhang Xining
Published: March 2020

The condition monitoring of rolling element bearings (REBs) is essential to maintain the reliable operation of rotating machinery, and the difficulty lies in how to estimate fault information from the raw signal that is always overwhelmed by severe background noise and other interferences. The method based on a sparse model has attracted increasing attention because it can capture deep-level fault features. However, when processing a signal with complex components and weak fault features, the performance of sparse model-based methods is often not ideal. In this work, the fault information-based sparse low-rank algorithm (FISLRA) is proposed to abstract the fault information from a noisy signal interfered with by background noise and external interference. Concretely, a sparse and low-rank model is formulated in the time-frequency domain. Then, a fast-converging algorithm is derived based on the alternating direction method of multipliers (ADMM) to solve the formulated model. Moreover, to further highlight the periodical transients, a correlated kurtosis-based thresholding (CKT) scheme proposed in this paper is also incorporated to solve the proposed low-rank spares model. The superiority of the proposed FISLRA over the traditional sparse low-rank model (TSLRM) and spectral kurtosis (SK) is proved by simulation analysis. In addition, two experimental signals collected from a bearing test rig are utilized to demonstrate the efficiency of the proposed FISLRA in fault detection. The results illustrate that compared to the TSLRM method, FISLRA can effectively extract periodical fault transients even when harmonic components (HCs) are present in the noisy signal.
On the Efficiency of OpenACC-aided GPU-Based FDTD Approach: Application to Lightning Electromagnetic Fields
Authors: Mohammadi Sajad , Karami Hamidreza , Azadifar Mohammad , Rachidi Farhad
Published: March 2020

An open accelerator (OpenACC)-aided graphics processing unit (GPU)-based finite difference time domain (FDTD) method is presented for the first time for the 3D evaluation of lightning radiated electromagnetic fields along a complex terrain with arbitrary topography. The OpenACC directive-based programming model is used to enhance the computational performance, and the results are compared with those obtained by using a CPU-based model. It is shown that OpenACC GPUs can provide very accurate results, and they are more than 20 times faster than CPUs. The presented results support the use of OpenACC not only in relation to lightning electromagnetics problems, but also to large-scale realistic electromagnetic compatibility (EMC) applications in which computation time efficiency is a critical factor.
Transfer Learning Strategies for Deep Learning-based PHM Algorithms
Authors: Yang Fan , Zhang Wenjin , Tao Laifa , Ma Jian
Published: March 2020

As we enter the era of big data, we have to face big data generated by industrial systems that are massive, diverse, high-speed, and variability. In order to effectively deal with big data possessing these characteristics, deep learning technology has been widely used. However, the existing methods require great human involvement that is heavily depend on domain expertise and may thus be non-representative and biased from task to similar task, so for a wide variety of prognostic and health management (PHM) tasks, how to apply the developed deep learning algorithms to similar tasks to reduce the amount of development and data collection costs has become an urgent problem. Based on the idea of transfer learning and the structures of deep learning PHM algorithms, this paper proposes two transfer strategies via transferring different elements of deep learning PHM algorithms, analyzes the possible transfer scenarios in practical application, and proposes transfer strategies applicable in each scenario. At the end of this paper, the deep learning algorithm of bearing fault diagnosis based on convolutional neural networks (CNN) is transferred based on the proposed method, which was carried out under different working conditions and for different objects, respectively. The experiments verify the value and effectiveness of the proposed method and give the best choice of transfer strategy.
Comparison of Equilibrium-Stage and Rate-Based Models of a Packed Column for Tar Absorption Using Vegetable Oil
Authors: Almoslh Adel , Alobaid Falah , Heinze Christian , Epple Bernd
Published: March 2020

In this study two mathematical models, rate-based and equilibrium-stage models in Aspen Plus process simulator, were used to simulate the tar absorption processes using soybean oil as a solvent in a research lab-scale experiment. The matching between simulation results and experimental data shows a good agreement. The simulation results predicted by the rate-based model show a higher level of agreement than the equilibrium model compared with the experimental data. Analysis study of tar absorption process was carried out which revealed the effect of temperature and flow rate on the soybean oil, and height-packed bed on tar removal efficiency. The methodology of selecting the optimum (most economical) operation conditions has also been performed in this study.
Development of a Group Method of Data Handling Technique to Forecast Iron Ore Price
Authors: Li Diyuan , Moghaddam Mohammad Reza , Monjezi Masoud , Jahed Armaghani Danial , Mehrdanesh Amirhossein
Published: March 2020

Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting the global iron ore price. Considering the high number of effective parameters and existence of complex relationship among them, artificial intelligence-based approaches can be employed to predict iron ore price. In this paper, a new intelligence system namely group method of data handling (GMDH) was developed and introduced to predict the price of iron ore. For comparison purposes, four other techniques i.e., autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural network (ANN), and classification and regression tree (CART) were developed for prediction of monthly iron ore price. Then, using testing datasets, the developed models were validated and their performance capacities were compared. The results showed that performance prediction of the GMDH model is significantly better than other predictive models based on four performance indices i.e., root mean square error, variance account for (VAF), mean absolute error, and mean absolute percentage error. Results of VAF (97.89%, 90.81%, 80.95%, 55.02%, and 23.87% for GMDH, SVR, ANN, CART, and ARIMA models, respectively) revealed that the GMDH technique is able to predict iron ore price with higher degree of accuracy compared to the other techniques.
Highly Curved Lane Detection Algorithms Based on Kalman Filter
Authors: Dorj Byambaa , Hossain Sabir , Lee Deok-Jin
Published: March 2020

The purpose of the self-driving car is to minimize the number casualties of traffic accidents. One of the effects of traffic accidents is an improper speed of a car, especially at the road turn. If we can make the anticipation of the road turn, it is possible to avoid traffic accidents. This paper presents a cutting edge curve lane detection algorithm based on the Kalman filter for the self-driving car. It uses parabola equation and circle equation models inside the Kalman filter to estimate parameters of a using curve lane. The proposed algorithm was tested with a self-driving vehicle. Experiment results show that the curve lane detection algorithm has a high success rate. The paper also presents simulation results of the autonomous vehicle with the feature to control steering and speed using the results of the full curve lane detection algorithm.
Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations
Authors: van Wijk Rob C. , Ayoun Alsoud Rami , Lennernäs Hans , Simonsson Ulrika S. H.
Published: March 2020

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.
Leakage Current Reduction in Single-Phase Grid-Connected Inverters—A Review
Authors: Estévez-Bén Adyr A. , Alvarez-Diazcomas Alfredo , Macias-Bobadilla Gonzalo , Rodríguez-Reséndiz Juvenal
Published: March 2020

The rise in renewable energy has increased the use of DC/AC converters, which transform the direct current to alternating current. These devices, generally called inverters, are mainly used as an interface between clean energy and the grid. It is estimated that 21% of the global electricity generation capacity from renewable sources is supplied by photovoltaic systems. In these systems, a transformer to ensure grid isolation is used. Nevertheless, the transformer makes the system expensive, heavy, bulky and reduces its efficiency. Therefore, transformerless schemes are used to eliminate the mentioned disadvantages. One of the main drawbacks of transformerless topologies is the presence of a leakage current between the physical earth of the grid and the parasitic capacitances of the photovoltaic module terminals. The leakage current depends on the value of the parasitic capacitances of the panel and the common-mode voltage. At the same time, the common-mode voltage depends on the modulation strategy used. Therefore, by the manipulation of the modulation technique, is accomplished a decrease in the leakage current. However, the connection standards for photovoltaic inverters establish a maximum total harmonic distortion of 5%. In this paper an analysis of the common-mode voltage and its influence on the value of the leakage current is described. The main topologies and strategies used to reduce the leakage current in transformerless schemes are summarized, highlighting advantages and disadvantages and establishing points of comparison with similar topologies. A comparative table with the most important aspects of each converter is shown based on number of components, modes of operation, type of modulation strategy used, and the leakage current value obtained. It is important to mention that analyzed topologies present a variation of the leakage current between 0 to 180 mA. Finally, the trends, problems, and researches on transformerless grid-connected PV systems are discussed.
Rosas, the Storyless, and Roles
Authors: Haidu Rachel
Published: March 2020

Analyzing the early work of Anne Teresa De Keersmaeker—and particularly Rosas danst Rosas (1983)—this article examines the notion of “the storyless” in relation to the role: that pillar of dance, and especially choreography, which enables the individuation, transmission, and exchange of each dancer’s part. Set in relation to the principles of serial music that De Keersmaeker had already explored in Fase (1982) and to the idea of seriality itself, the role provides a way to consider how “storylessness” could be both emancipatory and feminist. It responds to identitarian models of feminist argumentation by suggesting that a virtue of certain forms of abstraction lies in the pleasurable ways that dance’s roles demonstrate the circulability and exchangeability of the self.
Comparison of Remote Sensing based Multi-Source ET Models over Cropland in a Semi-Humid Region of China
Authors: Zhuang Qifeng , Wang Hao , Xu Yuqi
Published: March 2020

The estimation of cropland evapotranspiration (ET) is essential for agriculture water management, drought monitoring, and yield forecast. Remote sensing-based multi-source ET models have been widely applied and validated in the semi-arid region of China. However, careful investigation of the models’ performances for different crop types (winter wheat and summer maize) over the semi-humid region is still necessary. This study used remote sensing data (Landsat 8 and ASTER) and compared three mainstream multi-source ET models: (i) the two-source energy balance model, i.e., TSEB; (ii) the Penman-Monteith based four-source model, i.e., 4s-PM; (iii) the Priestley Taylor-Jet Propulsion Laboratory ET algorithm, i.e., PT-JPL. The measurements of the eddy-covariance (EC) flux tower located in Guantao county of North China were used to validate the models. The results showed that the TSEB model performed the best in estimating latent heat flux (LE) of maize, with an RMSE of 75.0 W/m2 and an R2 of 0.9, and the 4s-PM model had the highest accuracy of LE estimation for wheat, with an RMSE of 61.0 W/m2 and an R2 of 0.91. The LE spatial distribution comparison indicated that the PT-JPL model had more capacity to exhibit crop ET heterogeneity. The major environmental factors affecting ET varied with crop types and crop growth stages. Without taking soil moisture into account, the 4s-PM and TSEB models overestimated LE under water deficit in the maturation stage of wheat. The plant moisture stress based on vegetation index in the PT-JPL model underestimated the evaporation in the maturation stage while the cropland was still wet.
Impact of Air–Wave–Sea Coupling on the Simulation of Offshore Wind and Wave Energy Potentials
Authors: Wu Lichuan , Shao Mingming , Sahlée Erik
Published: March 2020

Offshore wind and wave energy potentials are commonly simulated by atmosphere and wave stand-alone models, in which the Atmosphere–Wave–Ocean (AWO) dynamical coupling processes are neglected. Based on four experiments (simulated by UU-CM, Uppsala University-Coupled model) with four different coupling configurations between atmosphere, waves, and ocean, we found that the simulations of the wind power density (WPD) and wave potential energy (WPE) are sensitive to the AWO interaction processes over the North and Baltic Seas; in particular, to the atmosphere–ocean coupling processes. Adding all coupling processes can change more than 25% of the WPE but only less than 5% of the WPD in four chosen coastal areas. The impact of the AWO coupling processes on the WPE and WPD changes significantly with the distance off the shoreline, and the influences vary with regions. From the simulations used in this study, we conclude that the AWO coupling processes should be considered in the simulation of WPE and WPD.
Contribution of Neurotrophins to the Immune System Regulation and Possible Connection to Alcohol Addiction
Authors: Kozlov Evgenii M. , Grechko Andrey V. , Chegodaev Yegor S. , Wu Wei-Kai , Orekhov Alexander N.
Published: March 2020

The first references to neurotrophic factors date back to the middle of the 20th century when the nerve growth factor (NGF) was first discovered. Later studies delivered a large amount of data on neurotrophic factors. However, many questions regarding neurotrophin signaling still remain unanswered. One of the principal topics in neurotrophin research is their role in the immune system regulation. Another important research question is the possible involvement of neurotrophin signaling in the pathological processes associated with alcoholism. Among known neurotrophins, NT-4 remains the least studied and appears to be involved in alcoholism and chronic stress pathogenesis. In this review we discuss known neurotrophin signaling cascades mediated by different neurotrophin receptors, as well as provide a generalization of the data regarding the influence of neurotrophins NGF, BDNF, and NT-4 on the immune system and their potential contribution to the pathogenesis of alcoholism.
Osteoarthritis and Toll-Like Receptors: When Innate Immunity Meets Chondrocyte Apoptosis
Authors: Barreto Goncalo , Manninen Mikko , K. Eklund Kari
Published: March 2020

Osteoarthritis (OA) has long been viewed as a degenerative disease of cartilage, but accumulating evidence indicates that inflammation has a critical role in its pathogenesis. In particular, chondrocyte-mediated inflammatory responses triggered by the activation of innate immune receptors by alarmins (also known as danger signals) are thought to be involved. Thus, toll-like receptors (TLRs) and their signaling pathways are of particular interest. Recent reports suggest that among the TLR-induced innate immune responses, apoptosis is one of the critical events. Apoptosis is of particular importance, given that chondrocyte death is a dominant feature in OA. This review focuses on the role of TLR signaling in chondrocytes and the role of TLR activation in chondrocyte apoptosis. The functional relevance of TLR and TLR-triggered apoptosis in OA are discussed as well as their relevance as candidates for novel disease-modifying OA drugs (DMOADs).
Role of PA2G4P4 pseudogene in bladder cancer tumorigenesis
Authors: Pisapia Laura , Terreri Sara , Barba Pasquale , Mastroianni Marianna , Donnini Maria , Mercadante Vincenzo , Palmieri Alessandro , Verze Paolo , Mirone Vincenzo , Altieri Vincenzo , Califano Gianluigi , Liguori Giovanna Lucia , Strazzullo Maria , Cimmino Amelia , Del Pozzo Giovanna
Published: March 2020

Background: Many pseudogenes possess biological activities and play important roles in the pathogenesis of various types of cancer including bladder cancer (BlCa), which still lacks suitable molecular biomarkers. Recently, pseudogenes were found to be significantly enriched in a pan-cancer classification based on the Cancer Genome Atlas gene expression data. Among them, the top-ranking pseudogene was the proliferation-associated 2G4 pseudogene 4 (PA2G4P4). Methods: Genomic and transcript features of PA2G4P4 were determined by GeneBank database analysis followed by 5’ RACE experiments. Therefore, we conducted a retrospective molecular study on a cohort of 45 patients of BlCa. PA2G4P4 expression was measured by RT-qPCR, whereas PA2G4P4 transcript distribution was analyzed by in situ hybridization on both normal and cancerous histological sections and compared to the immunolocalization of its parental PA2G4/EBP1 protein. Finally, we tested the effects of PA2G4P4 depletion on proliferation, migration, and death of BlCa cells. Results: We showed for the first time PA2G4P4 overexpression in BlCa tissues and in cell lines. PA2G4P4 distribution strictly overlaps PA2G4/EBP1 protein localization. Moreover, we showed that PA2G4P4 knockdown affects both proliferation and migration of BlCa cells, highlighting its potential oncogenic role. Conclusions: PA2G4P4 may play a functional role as an oncogene in BlCa development, suggesting it as a good candidate for future investigation and new clinical applications.
Movidea: A Software Package for Automatic Video Analysis of Movements in Infants at Risk for Neurodevelopmental Disorders
Authors: Baccinelli Walter , Bulgheroni Maria , Simonetti Valentina , Fulceri Francesca , Caruso Angela , Gila Letizia , Scattoni Maria Luisa
Published: March 2020

Early detecting the presence of neurodevelopmental disorders plays an important role in the effectiveness of the treatment. In this paper, we present a novel tool to extract motion features using single camera video recordings of infants. The Movidea software was developed to allow the operator to track the movement of end-effectors of infants in free moving conditions and extract movement features automatically. Movidea was used by different operators to analyze a set of video recordings and its performance was evaluated. The results showed that Movidea performance did not vary with the operator, and the tracking was also stable in home-video recordings. Even if the setup allowed for a two-dimensional analysis, most of the informative content of the movement was maintained. The reliability of the measures and features extracted, as well as the easiness of use, may boost the uptake of the proposed solution in clinical settings. Movidea overcomes the current limitation in the clinical practice in early detection of neurodevelopmental disorders by providing objective measures based on reliable data, and adds a new tool for the motor analysis of infants through unobtrusive technology.
Comparison of Spatial Layout of Tall Buildings Clustered in Circular, Rectangular, and Linear Geographical Areas and Impact on Skyline
Authors: Puspitasari Ayu Wandira , Kwon Jongwook
Published: March 2020

Modern cities with tall building clusters can create powerful and distinctive features on the skyline more so than those with scattered tall buildings. In terms of their role in the city, tall building clusters can improve the image of the city, provide for high population density, and distinguish the urban centers. However, the planning of tall building clusters needs to be conducted using in-depth analysis in response to the spatial context to create an attractive skyline. This research attempts to compare different layouts of tall building clusters organized in circular, rectangular, and linear geographical areas. Their impact on the skyline was determined by analyzing the visibility and height transitions of these tall building clusters. Grasshopper was used to calculate the degree of surface visibility of these tall buildings from observers in urban spaces. To quantify the height transition of the cluster, the obstructed buildings were identified and mapped on the skyline viewed from a specific viewpoint. The results showed that the linear cluster had high visibility, followed by the circular and rectangular clusters. Decreasing the heights from the center to the periphery supported the focal point of the cluster.
Biolubricants from Rapeseed and Castor Oil Transesterification by Using Titanium Isopropoxide as a Catalyst: Production and Characterization
Authors: Encinar José , Nogales-Delgado Sergio , Sánchez Nuria , González Juan
Published: March 2020

The transesterification of rapeseed and castor oil methyl esters with different alcohols (2-ethyl-1-hexanol, 1-heptanol and 4-methyl-2-pentanol) and titanium isopropoxide as a catalyst, to produce biolubricants, was carried out. Parameters such as temperature, alcohol/methyl ester molar ratio, and catalyst concentration were studied to optimize the process. The reaction evolution was monitored with the decrease in FAME concentration by gas chromatography. In general, the reaction was almost complete in two hours, obtaining over 93% conversions. All the variables studied influenced on the reaction yields. Once the optimum conditions for the maximum conversion and minimum costs were selected, a characterization of the biolubricants obtained, along with the study of the influence of the kind of alcohol used, was carried out. The biolubricants had some properties that were better than mineral lubricants (flash points between 222 and 271 °C), needing the use of additives when they do not comply with the standards (low viscosity for rapeseed biolubricant, for instance). There was a clear influence of fatty acids of raw materials (oleic and ricinoleic acids as majority fatty acids in rapeseed and castor oil, respectively) and the structure of the alcohol used on the final features of the biolubricants.
Dynamically Operated Fischer-Tropsch Synthesis in PtL-Part 1: System Response on Intermittent Feed
Authors: Loewert Marcel , Pfeifer Peter
Published: March 2020

Society is facing serious challenges to reduce CO2 emissions. Effective change requires the use of advanced chemical catalyst and reactor systems to utilize renewable feedstocks. One pathway to long-term energy storage is its transformation into high quality, low-emission and CO2-neutral fuels. Performance of technologies such as the Fischer-Tropsch reaction can be maximized using the inherent advantages of microstructured packed bed reactors. Advantages arise not only from high conversion and productivity, but from its capability to resolve the natural fluctuation of renewable sources. This work highlights and evaluates a system for dynamic feed gas and temperature changes in a pilot scale Fischer-Tropsch synthesis unit for up to 7 L of product per day. Dead times were determined for non-reactive and reactive mode at individual positions in the setup. Oscillating conditions were applied to investigate responses with regard to gaseous and liquid products. The system was stable at short cycle times of 8 min. Neither of the periodic changes showed negative effects on the process performance. Findings even suggest this technology’s capability for effective, small-to-medium-scale applications with periodically changing process parameters. The second part of this work focuses on the application of a real-time photovoltaics profile to the given system.
Performance on Bone Regeneration of a Silver Nanoparticle Delivery System Based on Natural Rubber Membrane NRL-AgNP
Authors: Marques Leonardo , Martinez Gabriela , Guidelli Éder , Tamashiro Jacqueline , Segato Rosimeire , Payão Spencer L. M. , Baffa Oswaldo , Kinoshita Angela
Published: March 2020

NRL-AgNP was developed bringing important properties of natural rubber as occlusive membrane with antimicrobial activity of silver nanoparticles. Biological aspects, such as cell viability, tissue reaction, and occlusive membrane performance of NRL-AgNP, are presented. In addition, in vivo degradation was investigated by Fourier Transform Infrared Spectroscopy (FTIR). The cell viability test was performed in mesenchymal stem cells of human deciduous dental pulp seeded with the new material. Tissue reaction was tested through subcutaneous implant of NRL-AgNP and compared to Polytetrafluoroethylene (PTFE) at the dorsum of rats. The performance of the NRL-AgNP as an occlusive membrane in Guided Bone Regeneration (GBR) was tested in full thickness critical size bone defects (8 mm) in rat calvaria. Cell viability was 98.8% for NRL-AgNP and did not result in statistically significant differences compared to negative control (p > 0.05 Kruskal–Wallis). All materials presented similar tissue reaction (p > 0.05). In the GBR experiment, the defects covered with NRL-AgNP presented a more advanced stage of bone regeneration in comparison with non-treated defects. The FTIR spectra of NRL-AgNP before and after implantation showed no degradation of NRL-AgNP membranes. These results are in favor of the NRL-AgNP use as an occlusive membrane for GBR.
Microstructure and Oxidation Resistance of Cr–Al–Si Alloys for High-Temperature Applications
Authors: Kim Jeong-Min , Kim Chae-Young
Published: March 2020

Cr–Al alloys are attracting much attention as heat- and corrosion-resistant coating materials due to their excellent high-temperature properties. In order to investigate the effect of aluminum content on the microstructure and oxidation resistance of Cr–Al–Si alloys, cast specimens were prepared by using a vacuum-arc melting furnace, and high-temperature oxidation tests were conducted with the specimens, for 1 h, at 1100 °C, in air. In the case of cast microstructure of Cr–Al–Si alloys, it consists mainly of Cr single phase, up to 5 at.% Al, and AlCr phases were additionally formed in alloys containing 10% Al or more. In the specimen with 20% Al added, CrSi phase was also found in addition to the AlCr phase. The weight change of the specimens heated for 1 h, at 1100 °C, indicated that all had excellent oxidation resistance. However, when the Al content was less than 10%, the weight gain tended to be a little lower than that of 10% or more.
Local Structure Analysis on Si-Containing DLC Films Based on the Measurement of C K-Edge and Si K-Edge X-ray Absorption Spectra
Authors: Kanda Kazuhiro , Suzuki Shuto , Niibe Masahito , Hasegawa Takayuki , Suzuki Tsuneo , Saitoh Hedetoshi
Published: March 2020

In this paper, the local structure of silicon-containing diamond-like carbon (Si-DLC) films is discussed based on the measurement of C K-edge and Si K-edge near-edge x-ray absorption fine structure (NEXAFS) spectra using the synchrotron radiation of 11 types of Si-DLC film fabricated with various synthesis methods and having different elemental compositions. In the C K-edge NEXAFS spectra of the Si-DLC films, the σ* band shrunk and shifted to the lower-energy side, and the π* peak broadened with an increase in the Si content in the Si-DLC films. However, there were no significant changes observed in the Si K-edge NEXAFS spectra with an increase in the Si content. These results indicate that Si–Si bonding is not formed with precedence in Si-DLC film.
Growth of Single-Crystal Cd0.9Zn0.1Te Ingots Using Pressure Controlled Bridgman Method
Authors: Yang Fan , Jie Wanqi , Wang Miao , Sun Xiaolong , Jia Ningbo , Yin Liying , Zhou Boru , Wang Tao
Published: March 2020

We report growth of single-crystal Cd0.9Zn0.1Te ingots while using the pressure-controlled Bridgman method. The Cd pressure was controlled during growth to suppress its evaporation from the melt and reduce the size of Te inclusions in the as-grown crystals. The accelerated crucible rotation technique (ACRT) was used to suppress constitutional supercooling. The fast accelerating and slow decelerating rotation speeds were optimized. Two-inch Cd0.9Zn0.1Te single-crystal ingots without grain boundaries or twins were grown reproducibly. Glow discharge mass spectrometry results indicate the effective segregation coefficients of Zn and In dopants are 1.24 and 0.18, respectively. The full width half maximum (FWHM) of X-ray rocking curve was approximately 22.5 ″, and the IR transmittance was approximately 61%, indicating high crystallinity. The mean size of the Te inclusions was approximately 13.4 μm. Single-crystal wafers were cut into 5 × 5 × 2 mm3 slices and then used to fabricate gamma ray detectors. The energy resolution and peak-to-valley ratio maps were constructed while using 59.5 keV gamma ray measurements, which proved the high uniformity of detection performance.
Data-Sets for Indoor Photovoltaic Behavior in Low Lighting Conditions
Authors: Masoudinejad Mojtaba
Published: March 2020

Analysis of voltage–current behavior of photovoltaic modules is a critical part of their modeling. Parameter identification of these models demands data from them, measured in realistic environments. In spite of advancement in modeling methodologies under solar lighting, few analyses have been focused on indoor photovoltaics. Lack of accurate and reproducible data as a major challenge in this field is addressed here. A high accuracy measurement setup for evaluation and analysis of indoor photovoltaic modules is explained. By use of this system, different modules are measured under diverse environmental conditions. These measurements are structured in data-sets that can be used for either analysis of physical environment effects and modeling or development of specific parameter identification methods in low light intensity conditions.
Profiling of Circulating microRNAs in Prostate Cancer Reveals Diagnostic Biomarker Potential
Authors: Fredsøe Jacob , Rasmussen Anne K. I. , Mouritzen Peter , Bjerre Marianne T. , Østergren Peter , Fode Mikkel , Borre Michael , Sørensen Karina D.
Published: March 2020

Early detection of prostate cancer (PC) is paramount as localized disease is generally curable, while metastatic PC is generally incurable. There is a need for improved, minimally invasive biomarkers as current diagnostic tools are inaccurate, leading to extensive overtreatment while still missing some clinically significant cancers. Consequently, we profiled the expression levels of 92 selected microRNAs by RT-qPCR in plasma samples from 753 patients, representing multiple stages of PC and non-cancer controls. First, we compared plasma miRNA levels in patients with benign prostatic hyperplasia (BPH) or localized prostate cancer (LPC), versus advanced prostate cancer (APC). We identified several dysregulated microRNAs with a large overlap of 59 up/down-regulated microRNAs between BPH versus APC and LPC versus APC. Besides identifying several novel PC-associated dysregulated microRNAs in plasma, we confirmed the previously reported upregulation of miR-375 and downregulation of miR-146a-5p. Next, by randomly splitting our dataset into a training and test set, we identified and successfully validated a novel four microRNA diagnostic ratio model, termed bCaP (miR-375*miR-33a-5p/miR-16-5p*miR-409-3p). Combined in a model with prostate specific antigen (PSA), digital rectal examination status, and age, bCaP predicted the outcomes of transrectal ultrasound (TRUS)-guided biopsies (negative vs. positive) with greater accuracy than PSA alone (Training: area under the curve (AUC), model = 0.84; AUC, PSA = 0.63. Test set: AUC, model = 0.67; AUC, PSA = 0.56). It may be possible in the future to use this simple and minimally invasive bCaP test in combination with existing clinical parameters for a more accurate selection of patients for prostate biopsy.
OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception
Authors: Mimouna Amira , Alouani Ihsen , Ben Khalifa Anouar , El Hillali Yassin , Taleb-Ahmed Abdelmalik , Menhaj Atika , Ouahabi Abdeldjalil , Ben Amara Najoua Essoukri
Published: March 2020

A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research.
Inversion-Based Approach for Detection and Isolation of Faults in Switched Linear Systems
Authors: Silveira Alexandre Miguel , Araújo Rui Esteves
Published: March 2020

This paper addresses the problem of the left inversion of switched linear systems from a diagnostics perspective. The problem of left inversion is to reconstruct the input of a system with the knowledge of its output, whose differentiation is usually required. In the case of this work, the objective is to reconstruct the system’s unknown inputs, based on the knowledge of its outputs, switching sequence and known inputs. With the inverse model of the switched linear system, a real-time Fault Detection and Isolation (FDI) algorithm with an integrated Fuzzy Logic System (FLS) that is capable of detecting and isolating abrupt faults occurring in the system is developed. In order to attenuate the effects of unknown disturbances and noise at the output of the inverse model, a smoothing strategy is also used. The results are illustrated with an example. The performance of the method is validated experimentally in a dc-dc boost converter, using a low-cost microcontroller, without any additional components.
10-GHz Fully Differential Sallen–Key Lowpass Biquad Filters in 55nm SiGe BiCMOS Technology
Authors: Centurelli Francesco , Monsurrò Pietro , Scotti Giuseppe , Tommasino Pasquale , Trifiletti Alessandro
Published: March 2020

Multi-GHz lowpass filters are key components for many RF applications and are required for the implementation of integrated high-speed analog-to-digital and digital-to-analog converters and optical communication systems. In the last two decades, integrated filters in the Multi-GHz range have been implemented using III-V or SiGe technologies. In all cases in which the size of passive components is a concern, inductorless designs are preferred. Furthermore, due to the recent development of high-speed and high-resolution data converters, highly linear multi-GHz filters are required more and more. Classical open loop topologies are not able to achieve high linearity, and closed loop filters are preferred in all applications where linearity is a key requirement. In this work, we present a fully differential BiCMOS implementation of the classical Sallen Key filter, which is able to operate up to about 10 GHz by exploiting both the bipolar and MOS transistors of a commercial 55-nm BiCMOS technology. The layout of the biquad filter has been implemented, and the results of post-layout simulations are reported. The biquad stage exhibits excellent SFDR (64 dB) and dynamic range (about 50 dB) due to the closed loop operation, and good power efficiency (0.94 pW/Hz/pole) with respect to comparable active inductorless lowpass filters reported in the literature. Moreover, unlike other filters, it exploits the different active devices offered by commercial SiGe BiCMOS technologies. Parametric and Monte Carlo simulations are also included to assess the robustness of the proposed biquad filter against PVT and mismatch variations.
Accurate Closed-Form Solution for Moving Underwater Vehicle Localization Using Two-Way Travel Time
Authors: Jia Tianyi , Wang Haiyan , Shen Xiaohong , Yan Yongsheng
Published: March 2020

Position information is essential for an underwater vehicle that can affect the performance of many other applications. Vehicle motion during the two-way travel time (TWTT) using an acoustic positioning system can affect the localization accuracy of the estimators based on the traditional static model. A new time measurement model for moving vehicle localization is presented, which compensates for the vehicle motion. The Cramér–Rao lower bounds (CRLBs) of the proposed model are derived for two different cases, where the depth of the underwater vehicle is unknown or known. Then, closed-form solutions for the two cases using the proposed model are derived and the solutions are shown analytically to reach the CRLBs. Simulations collaborate the theoretical performance of the proposed estimators and the moving model significantly improves the localization accuracy in comparison with the static model.
Learning Effective Skeletal Representations on RGB Video for Fine-Grained Human Action Quality Assessment
Authors: Lei Qing , Zhang Hong-Bo , Du Ji-Xiang , Hsiao Tsung-Chih , Chen Chih-Cheng
Published: March 2020

In this paper, we propose an integrated action classification and regression learning framework for the fine-grained human action quality assessment of RGB videos. On the basis of 2D skeleton data obtained per frame of RGB video sequences, we present an effective representation of joint trajectories to train action classifiers and a class-specific regression model for a fine-grained assessment of the quality of human actions. To manage the challenge of view changes due to camera motion, we develop a self-similarity feature descriptor extracted from joint trajectories and a joint displacement sequence to represent dynamic patterns of the movement and posture of the human body. To weigh the impact of joints for different action categories, a class-specific regression model is developed to obtain effective fine-grained assessment functions. In the testing stage, with the supervision of the action classifier’s output, the regression model of a specific action category is selected to assess the quality of skeleton motion extracted from the action video. We take advantage of the discrimination of the action classifier and the viewpoint invariance of the self-similarity feature to boost the performance of the learning-based quality assessment method in a realistic scene. We evaluate our proposed method using diving and figure skating videos of the publicly available MIT Olympic Scoring dataset, and gymnastic vaulting videos of the recent benchmark University of Nevada Las Vegas (UNLV) Olympic Scoring dataset. The experimental results show that the proposed method achieved an improved performance, which is measured by the mean rank correlation coefficient between the predicted regression scores and the ground truths.