bluuebunny/crossref_metadata_2025 huggingface dataset:
An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings
Authors: Margaris Dionisis , Spiliotopoulos Dimitris , Karagiorgos Gregory , Vassilakis Costas
Published: July 2020

Collaborative filtering algorithms formulate personalized recommendations for a user, first by analysing already entered ratings to identify other users with similar tastes to the user (termed as near neighbours), and then using the opinions of the near neighbours to predict which items the target user would like. However, in sparse datasets, too few near neighbours can be identified, resulting in low accuracy predictions and even a total inability to formulate personalized predictions. This paper addresses the sparsity problem by presenting an algorithm that uses robust predictions, that is predictions deemed as highly probable to be accurate, as derived ratings. Thus, the density of sparse datasets increases, and improved rating prediction coverage and accuracy are achieved. The proposed algorithm, termed as CFDR, is extensively evaluated using (1) seven widely-used collaborative filtering datasets, (2) the two most widely-used correlation metrics in collaborative filtering research, namely the Pearson correlation coefficient and the cosine similarity, and (3) the two most widely-used error metrics in collaborative filtering, namely the mean absolute error and the root mean square error. The evaluation results show that, by successfully increasing the density of the datasets, the capacity of collaborative filtering systems to formulate personalized and accurate recommendations is considerably improved.
Uniparental Inheritance of Salinity Tolerance and Beneficial Phytochemicals in Rice
Authors: Huong Can Thu , Anh Truong Thi Tu , Dat Tran Dang , Dang Khanh Tran , Dang Xuan Tran
Published: July 2020

Salinity stress is one of the most problematic constraints to significantly reduce rice productivity. The Saltol QTL (quantitative trait locus) has been known as one among many principal genes/QTLs responsible for salinity tolerance in rice. However, the introgression of the Saltol QTL from the donor (male) into the recipient (female) cultivars induces great recessions from the progeny generation, which results in heavy fieldwork and greater cost and time required for breeding. In this study, the F1 generation of the cross TBR1 (female cultivar, salinity tolerant) × KD18 (male cultivar, salinity susceptible) was preliminarily treated with N-methyl-N-nitrosourea (MNU) to induce the mutants M1. Results on physiological traits show that all the M2 (self-pollinated from M1) and M3 (self-pollinated from M2) individuals obtain salinity tolerant levels as the recurrent TBR1. Twelve SSR (simple sequence repeat) markers involved in the Saltol QTL (RM493, RM562, RM10694, RM10720, RM10793, RM10852, RM13197, RM201, RM149, RM508, RM587, and RM589) and other markers related to yield-contributing traits and disease resistance, as well as water and nitrogen use, have efficacy that is polymorphic. The phenotype and genotype analyses indicate that the salinity tolerant Saltol QTL, growth parameter, grain yield and quality, pest resistance, water and nitrogen use efficacy, and beneficial phytochemicals including antioxidants, momilactone A (MA) and momilactone B (MB) are uniparentally inherited from the recurrent (female) TBR1 cultivar and stabilized in the M2 and M3 generations. Further MNU applications should be examined to induce the uniparental inheritance of other salinity tolerant genes such as OsCPK17, OsRMC, OsNHX1, OsHKT1;5 to target rice cultivars. However, the mechanism of inducing this novel uniparental inheritance for salinity tolerance by MNU application needs elaboration.
Analysing the Initial Bacterial Adhesion to Evaluate the Performance of Antifouling Surfaces
Authors: Alves Patrícia , Moreira Joana Maria , Miranda João Mário , Mergulhão Filipe José
Published: July 2020

The aim of this work was to study the initial events of Escherichia coli adhesion to polydimethylsiloxane, which is critical for the development of antifouling surfaces. A parallel plate flow cell was used to perform the initial adhesion experiments under controlled hydrodynamic conditions (shear rates ranging between 8 and 100/s), mimicking biomedical scenarios. Initial adhesion studies capture more accurately the cell-surface interactions as in later stages, incoming cells may interact with the surface but also with already adhered cells. Adhesion rates were calculated and results shown that after some time (between 5 and 9 min), these rates decreased (by 55% on average), from the initial values for all tested conditions. The common explanation for this decrease is the occurrence of hydrodynamic blocking, where the area behind each adhered cell is screened from incoming cells. This was investigated using a pair correlation map from which two-dimensional histograms showing the density probability function were constructed. The results highlighted a lower density probability (below 4.0 × 10−4) of the presence of cells around a given cell under different shear rates irrespectively of the radial direction. A shadowing area behind the already adhered cells was not observed, indicating that hydrodynamic blocking was not occurring and therefore it could not be the cause for the decreases in cell adhesion rates. Afterward, cell transport rates from the bulk solution to the surface were estimated using the Smoluchowski-Levich approximation and values in the range of 80–170 cells/cm2.s were obtained. The drag forces that adhered cells have to withstand were also estimated and values in the range of 3–50 × 10−14 N were determined. Although mass transport increases with the flow rate, drag forces also increase and the relative importance of these factors may change in different conditions. This work demonstrates that adjustment of operational parameters in initial adhesion experiments may be required to avoid hydrodynamic blocking, in order to obtain reliable data about cell-surface interactions that can be used in the development of more efficient antifouling surfaces.
New Advances in the Determination of Free and Bound Phenolic Compounds of Banana Passion Fruit Pulp (Passiflora tripartita, var. Mollissima (Kunth) L.H. Bailey) and Their In Vitro Antioxidant and Hypoglycemic Capacities
Authors: Giambanelli Elisa , Gómez-Caravaca Ana Maria , Ruiz-Torralba Arancha , Guerra-Hernández Eduardo Jesús , Figueroa-Hurtado Jorge Geovanny , García-Villanova Belén , Verardo Vito
Published: July 2020

Banana passion fruit (Passiflora tripartite L.H. Bailey) is a lesser known species of the genus Passiflora. This fruit typically grows in the Andean region of Ecuador and it is locally known as tumbo, taxo or curuba. The juice of this fruit is highly appreciated in South America. Extracts of banana passion fruit were characterized for their content levels of free and bound phenolic compounds by high performance liquid chromatography coupled to high resolution mass spectrometry detector (HPLC-ESI-TOF-MS). A total of 82 polar compounds classified as phenolic acid derivatives, organic acids, benzophenones, flavan-3-ols, flavonols and flavones were detected in the extracts. The total phenolic content was 2356 mg 100 g−1 dry matter, with the bound phenolic fraction representing 37.7% of total amounts. Flavan-3-ols, such as (epi)catechin, (epi)azfelechin and their derivatives, were the main phenolic compounds in the free phenolic fraction; however, phenolic acids represented the most abundant class of bound phenolic extracts. The antioxidant and hypoglycemic capacities reported for banana passion fruit were higher than for other fruits. To our knowledge, this is the first time that bound phenolic compounds have been described in banana passion fruit pulp.
Antioxidant and Anti-inflammatory Effect of Nrf2 Inducer Dimethyl Fumarate in Neurodegenerative Diseases
Authors: Scuderi Sarah A. , Ardizzone Alessio , Paterniti Irene , Esposito Emanuela , Campolo Michela
Published: July 2020

Neurodegenerative diseases (NDs) represents debilitating conditions characterized by degeneration of neuronal cells in specific brain areas, causing disability and death in patients. In the pathophysiology of NDs, oxidative stress, apoptosis and neuroinflammation have a key role, as demonstrated by in vivo and in vitro models. Therefore, the use of molecules with antioxidant and anti-inflammatory activities represents a possible strategy for the treatment of NDs. Many studies demonstrated the beneficial effects of fumaric acid esters (FAEs) to counteract neuroinflammation and oxidative stress. Among these molecules, dimethyl fumarate (DMF) showed a valid therapeutic approach to slow down neurodegeneration and relieve symptoms in patients with NDs. DMF is a methyl ester of fumaric acid and acts as modulator of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway as well as nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) translocation. Therefore, this review aims to examine the potential beneficial effects of DMF to counteract oxidative stress and inflammation in patients with NDs.
Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence
Authors: Al-hababi Mohammed Ali Mohammed , Khan Muhammad Bilal , Al-Turjman Fadi , Zhao Nan , Yang Xiaodong
Published: July 2020

Non-contact health care monitoring is a unique feature in the emerging 5G networks that is achieved by exploiting artificial intelligence (AI). The ratio of the number of health care problems and patients is increasing exponentially and creating burgeoning data. The integration of AI and Internet of things (IoT) systems enables us to increase the huge volume of data to be generated. The approach by which AI is applied to the IoT systems enhances the intelligence of the health care system. In post-surgery monitoring of the patient, timely consultation is essential before further loss. Unfortunately, even after the advice of the doctor to the patient, he/she may forget to perform the activity in the correct way, which may lead to complications in recovery. In this research, the idea is to design a non-contact sensing testbed using AI for the classification of post-surgery activities. Universal software-defined radio peripheral (USRP) is utilized to collect the data of spinal cord operated patients during weight lifting activity. The wireless channel state information (WCSI) is extracted by using orthogonal frequency division multiplexing (OFDM) technique. AI applies machine learning to classify the correct and wrong way of weight lifting activity that was considered for experimental analysis. The accuracy achieved by the proposed testbed by using a fine K-nearest neighbor (FKNN) algorithm is 99.6%.
New Indices for Evaluating Vibration Characteristics of Flexible-Joint Robots
Authors: Zhang Ping , Li Yuwen
Published: July 2020

Structural vibration is a significant consideration for robotic applications such as machining where the robot is subject to large dynamic loading. Aiming at providing an efficient means to evaluating the vibration characteristics of industrial robots for these applications, this work proposes two new indices to quantify the elastic displacement of the tool mounted on the robot caused by the vibrations induced by external process loading for flexible-joint robots. For this purpose, a structural dynamic model is first developed to derive the frequency responses of the tool displacement. Then, the displacement-force and displacement-torque frequency response ratios are defined, which represent the mapping from the amplitudes of an external harmonic force and torque to the amplitude of tool displacement respectively. The upper bounds of the two ratios are used as evaluation indices for the vibration characteristics of the robot, which represent the worst situation of the tool displacement due to harmonic excitation with amplitude of unit force and unit torque respectively. With these indices, an efficient method is provided to predict whether the tool misalignment caused by periodic loading is acceptable for process quality requirement. Numerical simulation demonstrates the effectiveness of the proposed method for a robotic riveting system being developed for aerospace assembly.
A Novel Single-Channel Arrangement in Chirp Transform Spectrometer for High-Resolution Spectrum Detection
Authors: Zhao Quan , Tong Ling , Gao Bo
Published: July 2020

Chirp transform spectrometer (CTS) has become a powerful tool widely used in spectral analysis nowadays. In this paper, a novel single-channel structure of Chirp transform spectrometer for high-resolution spectrum detection is developed. By adding an additional signal source, a mixer, a power divider, and a combiner, one channel of the classical CTS is replaced and the matching problem between the two channels is avoided. The circuit principle and characteristics of the novel single-channel structure are presented. Two simulation models with ideal devices and nonideal devices for the classical two-channel CTS structure and the improved single-channel structure are built to verify the effectiveness of the novel structure. Simulation results show that the time distributions of the obtained compression pulses resulting from the improved structure are the same as that from the classical two-channel CTS. The obtained amplitudes and spectral resolution are almost the same in both structures with ideal devices. The introduced nonideal devices mainly influence the amplitudes of the output pulses. In addition, an experiment with hardware implementation is verified on real chains. The influence of nonideal devices is measured and analyzed. Compared to the classical two-channel structure of CTS, the modified one-channel arrangement avoids the matching problem between different channels and saves devices.
Digital Volume Correlation Applied to X-ray Micro-Tomography Images in Uniaxial Creep Tests on Anisotropic Clayey Rock
Authors: Shi Hailing , Hosdez Jerome , Rougelot Thomas , Xie Shouyi , Shao Jianfu , Talandier Jean , Lacidogna Giuseppe
Published: July 2020

Creep tests are commonly performed to characterize time-dependent deformation of geological materials. Classical measuring methods are not suitable for long term tests and not able to provide full three-dimensional strain fields. In this study, Digital Volume Correlation (DVC) is applied to X-ray micro-tomography (XRMT) images from creep tests on a hard clayey rock. In situ uniaxial compression creep tests are performed under different levels of stress and with different loading orientations with respect to the structural anisotropy of rock. Based on the XRMT images taken during the creep tests, DVC is applied to compute the full three dimensional strain fields and global averages strains of tested samples. The effects of bedding planes and hard inclusions on the non-uniform distribution of strains are analyzed.
Magneto-Rheological Elastomer Composites. A Review
Authors: Samal Sneha , Škodová Marcela , Abate Lorenzo , Blanco Ignazio
Published: July 2020

Magneto-rheological elastomer (MRE) composites belong to the category of smart materials whose mechanical properties can be governed by an external magnetic field. This behavior makes MRE composites largely used in the areas of vibration dampers and absorbers in mechanical systems. MRE composites are conventionally constituted by an elastomeric matrix with embedded filler particles. The aim of this review is to present the most outstanding advances on the rheological performances of MRE composites. Their distribution, arrangement, wettability within an elastomer matrix, and their contribution towards the performance of mechanical response when subjected to a magnetic field are evaluated. Particular attention is devoted to the understanding of their internal micro-structures, filler–filler adhesion, filler–matrix adhesion, and viscoelastic behavior of the MRE composite under static (valve), compressive (squeeze), and dynamic (shear) mode.
Corona Effect Influence on the Lightning Performance of Overhead Distribution Lines
Authors: Mestriner Daniele , Brignone Massimo
Published: July 2020

Overhead distribution lines can be seriously damaged from lightning events because both direct and indirect events can cause flashovers along the line. The lightning performance of such power lines is usually computed neglecting the effect of corona discharge along the conductors: in particular, the corona discharge determined by the indirect lightning event is taken into account only by few researchers because it can have meaningful impacts only in few cases. However, when we deal with overhead distribution lines with high Critical Flashover value (CFO) and small diameters, the corona discharge caused by indirect events has to be taken into account. This paper shows the effects of corona discharge in the lightning performance computation of overhead distribution lines. The analysis will involve different configurations in terms of line diameter and air conditions, focusing on the negative effect of corona discharge in the number of dangerous events that determine line flashovers.
An Analytical Solution for Longitudinal Impedance of a Large-Diameter Floating Pile in Soil with Radial Heterogeneity and Viscous-Type Damping
Authors: Meng Kun , Cui Chunyi , Liang Zhimeng , Li Haijiang , Pei Huafu
Published: July 2020

An analytical model is presented for solving the longitudinal complex impedance of a large-diameter floating pile in viscoelastic surrounding soil with radial heterogeneity and viscous-type damping, taking the effect of three-dimensional wave propagation of soil and lateral inertia of the pile shaft into account. The corresponding analytical solution for longitudinal impedance is also derived and validated via comparisons with existing solutions. The influences of the pile length, Poisson’s ratio of the pile shaft and the viscous damping coefficient, as well as the degree and radius of disturbed surrounding soil, on the longitudinal impedance of the pile shaft are examined by performing parametric analyses. It is demonstrated that the proposed analytical model and solution are suitable for the longitudinal vibration problem of a large-diameter pile and radially inhomogeneous surrounding soil, especially when the pile slenderness is low. In addition, the present solution can be easily degenerated to describe the longitudinal vibration problem relating to a large-diameter floating pile in radially homogenous soil or a pile with fixed-end supports.
Experimental Comparison of Diesel and Crude Rapeseed Oil Combustion in a Swirl Burner
Authors: Józsa Viktor
Published: July 2020

In pursuing maximum energy efficiency, local utilization of various crude fuels came into view. The present paper compares the combustion characteristics of standard diesel oil and crude rapeseed oil; the latter is an excellent model for high-viscosity liquid fuels. The combustion tests were performed in a 15 kW atmospheric turbulent swirl burner; the liquid fuels were atomized by a plain-jet airblast atomizer. Firstly, the acoustic signal is evaluated, since the instabilities of swirl combustion are accompanied by characteristic pressure fluctuations. The spectral analysis was performed by Wavelet transform, which fits excellently to the acoustic spectrum of combustion noise. This multi-scale technique features increased spectral resolution at lower frequencies at the expense of lower temporal resolution, providing excellent performance at both low-frequency, well-localized components and high-frequency, broadband phenomena. The joint probability density function of two characteristic frequencies was plotted with the result that flame acoustics match for the two fuels. Secondly, their pollutant emissions were compared and evaluated under similar conditions with the conclusion that crude rapeseed oil can substitute diesel oil in a limited operating range. Note that the distinct material properties already mean differences in all atomization, evaporation, and mixing characteristics, hence, the latter result is not intuitive.
Automated Classification of Blood Loss from Transurethral Resection of the Prostate Surgery Videos Using Deep Learning Technique
Authors: Chen Jian-Wen , Lin Wan-Ju , Lin Chun-Yuan , Hung Che-Lun , Hou Chen-Pang , Cho Ching-Che , Young Hong-Tsu , Tang Chuan-Yi
Published: July 2020

Transurethral resection of the prostate (TURP) is a surgical removal of obstructing prostate tissue. The total bleeding area is used to determine the performance of the TURP surgery. Although the traditional method for the detection of bleeding areas provides accurate results, it cannot detect them in time for surgery diagnosis. Moreover, it is easily disturbed to judge bleeding areas for experienced physicians because a red light pattern arising from the surgical cutting loop often appears on the images. Recently, the automatic computer-aided technique and artificial intelligence deep learning are broadly used in medical image recognition, which can effectively extract the desired features to reduce the burden of physicians and increase the accuracy of diagnosis. In this study, we integrated two state-of-the-art deep learning techniques for recognizing and extracting the red light areas arising from the cutting loop in the TURP surgery. First, the ResNet-50 model was used to recognize the red light pattern appearing in the chipped frames of the surgery videos. Then, the proposed Res-Unet model was used to segment the areas with the red light pattern and remove these areas. Finally, the hue, saturation, and value color space were used to classify the four levels of the blood loss under the circumstances of non-red light pattern images. The experiments have shown that the proposed Res-Unet model achieves higher accuracy than other segmentation algorithms in classifying the images with the red and non-red lights, and is able to extract the red light patterns and effectively remove them in the TURP surgery images. The proposed approaches presented here are capable of obtaining the level classifications of blood loss, which are helpful for physicians in diagnosis.
Sound Event Detection Using Derivative Features in Deep Neural Networks
Authors: Kwak Jin-Yeol , Chung Yong-Joo
Published: July 2020

We propose using derivative features for sound event detection based on deep neural networks. As input to the networks, we used log-mel-filterbank and its first and second derivative features for each frame of the audio signal. Two deep neural networks were used to evaluate the effectiveness of these derivative features. Specifically, a convolutional recurrent neural network (CRNN) was constructed by combining a convolutional neural network and a recurrent neural networks (RNN) followed by a feed-forward neural network (FNN) acting as a classification layer. In addition, a mean-teacher model based on an attention CRNN was used. Both models had an average pooling layer at the output so that weakly labeled and unlabeled audio data may be used during model training. Under the various training conditions, depending on the neural network architecture and training set, the use of derivative features resulted in a consistent performance improvement by using the derivative features. Experiments on audio data from the Detection and Classification of Acoustic Scenes and Events 2018 and 2019 challenges indicated that a maximum relative improvement of 16.9% was obtained in terms of the F-score.
LSUN-Stanford Car Dataset: Enhancing Large-Scale Car Image Datasets Using Deep Learning for Usage in GAN Training
Authors: Kramberger Tin , Potočnik Božidar
Published: July 2020

Currently there is no publicly available adequate dataset that could be used for training Generative Adversarial Networks (GANs) on car images. All available car datasets differ in noise, pose, and zoom levels. Thus, the objective of this work was to create an improved car image dataset that would be better suited for GAN training. To improve the performance of the GAN, we coupled the LSUN and Stanford car datasets. A new merged dataset was then pruned in order to adjust zoom levels and reduce the noise of images. This process resulted in fewer images that could be used for training, with increased quality though. This pruned dataset was evaluated by training the StyleGAN with original settings. Pruning the combined LSUN and Stanford datasets resulted in 2,067,710 images of cars with less noise and more adjusted zoom levels. The training of the StyleGAN on the LSUN-Stanford car dataset proved to be superior to the training with just the LSUN dataset by 3.7% using the Fréchet Inception Distance (FID) as a metric. Results pointed out that the proposed LSUN-Stanford car dataset is more consistent and better suited for training GAN neural networks than other currently available large car datasets.
Root Canal Morphology of the Permanent Mandibular Incisors by Cone Beam Computed Tomography: A Systematic Review
Authors: Herrero-Hernández Silvia , López-Valverde Nansi , Bravo Manuel , Valencia de Pablo Óliver , Peix-Sánchez Manuel , Flores-Fraile Javier , Ramírez Juan Manuel , Macedo de Sousa Bruno , López-Valverde Antonio
Published: July 2020

Knowledge of dental anatomy through the assessment of the anatomic variations of each tooth’s root canal system is essential to undertake endodontic therapy. The aim of this systematic review was to analyze the different studies on the internal morphology of permanent mandibular incisors where Cone-Beam Computed Tomography (CBCT) X-ray imaging is used. Pubmed, CENTRAL, Wiley Library and Web of Science electronic databases were searched for scientific studies included until March 2020. The terms used in the search were: “permanent mandibular incisors”, “root canal morphology” and “cone-beam computed tomography”. The search was limited to studies whose aim was the analysis of the morphology of the root canal system evaluating the parameters of methodology, population, sample, number and configuration. A total of 19 studies met the inclusion criteria. There was a noticeable lack of unanimity in the setting adjustments of each of the CBCT devices used. The presence of two root canals varied from 0.4% to 45%. The most frequent configurations were Vertucci’s Types I, III, II, V, IV, VII and VI. Type VIII configuration was non-existent. CBCT revealed the existence of anatomical symmetry patterns, and there was no unanimity of criteria regarding the presence of a second root canal. Results concerning the presence of a second root canal in the mandibular incisors differ widely, with a possible influence of the geographic area where the study was conducted. The prevalence of a second canal is higher in mandibular lateral incisors than in mandibular central incisors. There was no direct relationship between voxel size (0.125–0.3 mm) and increased prevalence of a second canal.
Incremental Dilations Using CNN for Brain Tumor Classification
Authors: Roy Sanjiban Sekhar , Rodrigues Nishant , Taguchi Y-h.
Published: July 2020

Brain tumor classification is a challenging task in the field of medical image processing. Technology has now enabled medical doctors to have additional aid for diagnosis. We aim to classify brain tumors using MRI images, which were collected from anonymous patients and artificial brain simulators. In this article, we carry out a comparative study between Simple Artificial Neural Networks with dropout, Basic Convolutional Neural Networks (CNN), and Dilated Convolutional Neural Networks. The experimental results shed light on the high classification performance (accuracy 97%) of Dilated CNN. On the other hand, Dilated CNN suffers from the gridding phenomenon. An incremental, even number dilation rate takes advantage of the reduced computational overhead and also overcomes the adverse effects of gridding. Comparative analysis between different combinations of dilation rates for the different convolution layers, help validate the results. The computational overhead in terms of efficiency for training the model to reach an acceptable threshold accuracy of 90% is another parameter to compare the model performance.
An Overview of Reinforcement Learning Methods for Variable Speed Limit Control
Authors: Kušić Krešimir , Ivanjko Edouard , Gregurić Martin , Miletić Mladen
Published: July 2020

Variable Speed Limit (VSL) control systems are widely studied as solutions for improving safety and throughput on urban motorways. Machine learning techniques, specifically Reinforcement Learning (RL) methods, are a promising alternative for setting up VSL since they can learn and react to different traffic situations without knowing the explicit model of the motorway dynamics. However, the efficiency of combined RL-VSL is highly related to the class of the used RL algorithm, and description of the managed motorway section in which the RL-VSL agent sets the appropriate speed limits. Currently, there is no existing overview of RL algorithm applications in the domain of VSL. Therefore, a comprehensive survey on the state of the art of RL-VSL is presented. Best practices are summarized, and new viewpoints and future research directions, including an overview of current open research questions are presented.
Evaluating Machine Learning Classification Using Sorted Missing Percentage Technique Based on Missing Data
Authors: Hung Che-Yu , Jiang Bernard C. , Wang Chien-Chih
Published: July 2020

Missing data are common in industrial sensor readings owing to system updates and unequal radio-frequency periods. Existing methods addressing missing data through imputation may not always be appropriate. This study presented a sorted missing percentages technique for filtering attributes when building machine learning classification models using sensor readings with missing data. Signal detection theory was employed to evaluate the distinguishing ability of resulting models. To evaluate its performance, the proposed technique was applied to a publicly available air pressure system dataset, which then was used to build several classifiers. The experimental results indicated that the proposed technique allowed a logistic regression model to achieve the best accuracy score (99.56%) and a better distinguishing ability (response bias of 0.0013, adjusted response bias of 0.0044, and decision criterion of −1.8994) compared with the methods applied to the same dataset and reported in papers published between 2016 and 2019 March on binary classification, wherein attributes with more than 20% of missing data were filtered out. The proposed technique is suitable for industrial sensor data analysis and can be applied to the scenarios dealing with missing data owing to unequal radio-frequency periods or a system being updated with new fields.
High Definition Map-Based Localization Using ADAS Environment Sensors for Application to Automated Driving Vehicles
Authors: Shin Donghoon , Park Kang-moon , Park Manbok
Published: July 2020

This paper presents high definition (HD) map-based localization using advanced driver assistance system (ADAS) environment sensors for application to automated driving vehicles. A variety of autonomous driving technologies are being developed using expensive and high-performance sensors, but limitations exist due to several practical issues. In respect of the application of autonomous driving cars in the near future, it is necessary to ensure autonomous driving performance by effectively utilizing sensors that are already installed for ADAS purposes. Additionally, the most common localization algorithm, which is usually used lane information only, has a highly unstable disadvantage in the absence of that information. Therefore, it is essential to ensure localization performance with other road features such as guardrails when there are no lane markings. In this study, we would like to propose a localization algorithm that could be implemented in the near future by using low-cost sensors and HD maps. The proposed localization algorithm consists of several sections: environment feature representation with low-cost sensors, digital map analysis and application, position correction based on map-matching, designated validation gates, and extended Kalman filter (EKF)-based localization filtering and fusion. Lane information is detected by monocular vision in front of the vehicle. A guardrail is perceived by radar by distinguishing low-speed object measurements and by accumulating several steps to extract wall features. These lane and guardrail information are able to correct the host vehicle position by using the iterative closest point (ICP) algorithm. The rigid transformation between the digital high definition map (HD map) and environment features is calculated through ICP matching. Each corrected vehicle position by map-matching is selected and merged based on EKF with double updating. The proposed algorithm was verified through simulation based on actual driving log data.
Deep Matrix Factorization Approach for Collaborative Filtering Recommender Systems
Authors: Lara-Cabrera Raúl , González-Prieto Ángel , Ortega Fernando
Published: July 2020

Providing useful information to the users by recommending highly demanded products and services is a fundamental part of the business of many top tier companies. Recommender Systems make use of many sources of information to provide users with accurate predictions and novel recommendations of items. Here we propose, DeepMF, a novel collaborative filtering method that combines the Deep Learning paradigm with Matrix Factorization (MF) to improve the quality of both predictions and recommendations made to the user. Specifically, DeepMF performs successive refinements of a MF model with a layered architecture that uses the acquired knowledge in a layer as input for subsequent layers. Experimental results showed that the quality of both the predictions and recommendations of DeepMF overcome the baselines.
Analysis of Narrow-Line Laser Cooling and Trapping of Sr Atoms in Microgravity Environments
Authors: Ren Jie , Liu Hui , Lu Xiaotong , Chang Hong
Published: July 2020

Obtaining ultracold alkaline earth(-like) atoms in space encounters the problem of performing narrow-line laser cooling in microgravity environments ( μ -gEs). This paper reports an analysis of the magneto-optical trap (MOT) based on the narrow-line transition in 88 Sr, while paying special attention to the role of the gravity. This analysis suggests the MOTs based on narrow-line transitions cannot be cold and dense enough in a μ -gE. We thus propose a strategy: that one can use a dual-frequency MOT to realize a low-temperature, high density, and high transfer efficiency, narrow-line red MOT in μ -gEs.
H2/H∞ Output Tracking Control with a Performance Compensator for Aeroengines
Authors: Chen Qianjing , Huang Jinquan , Pan Muxuan , Lu Feng
Published: July 2020

A robust output tracking controller is necessary for the safe and reliable operation of aeroengines. This paper aims at developing an H 2 / H ∞ output tracking approach for aeroengines. In order to improve the tracking performance of the traditional robust tracker, the proposed control structure is designed as a combination of a nominal controller and a compensator. Concretely, an H 2 / H ∞ nominal controller is derived from game algebraic Raccati equation (GARE), which facilitates establishing a compensator for the system. Since the reference is usually unknown in advance for practical application, the proposed compensator is calculated online according to the nominal controller and the current reference. The solvability of the compensator and the stability of the system is guaranteed for both stable and bounded unstable references. Simulation examples for a turbofan engine are provided to demonstrate the effectiveness of the proposed algorithm.
Impact of the Static Magnetic Field on Growth, Pigments, Osmolytes, Nitric Oxide, Hydrogen Sulfide, Phenylalanine Ammonia-Lyase Activity, Antioxidant Defense System, and Yield in Lettuce
Authors: Abdel Latef Arafat Abdel Hamed , Dawood Mona F. A. , Hassanpour Halimeh , Rezayian Maryam , Younes Nabil A.
Published: July 2020

Magnetic fields are an unavoidable physical factor affecting living organisms. Lettuce seeds (Lactuca sativa var. cabitat L.) were subjected to various intensities of the static magnetic field (SMF) viz., MF0 (control), SMF1 (0.44 Tesla (T), SMF2 (0.77 T), and SMF3 (1 T) for three exposure times (1, 2, and 3 h). SMF-treated seedlings showed induction in growth parameters and metabolism comparing to control. All photosynthetic pigments were induced markedly under SMF, especially chlorophyll a. SMF at different intensities boosted osmolytes, non-enzymatic antioxidants, and the phenylalanine ammonia-lyase activity over non-magnetized seedlings. Oxidative damage criteria viz., hydrogen peroxide, superoxide radical, and lipid peroxidation, as well as polyphenol oxidase activity, were kept at low values under SMF-treated seeds relative to control, especially SMF2. Electron donors to antioxidant enzymes including nitrate reductase, nitric oxide, and hydrogen sulfide induced via SMF exposure and consequently the activities of superoxide dismutase, glutathione-S-transferases, catalase, and peroxidases family enzymes were also stimulated under SMF, whatever the intensity or the exposure period applied. All these regulations reflected on the enhancement of lettuce yield production which reached 50% over the control at SMF3. Our findings offered that SMF-seed priming is an innovative and low-cost strategy that can improve the growth, bioactive constituents, and yield of lettuce.
Thykamine Extracts from Spinach Reduce Acute Inflammation In Vivo and Downregulate Phlogogenic Functions of Human Blood Neutrophils In Vitro
Authors: Beaupré Vickie , Boucher Nathalie , Desgagné-Penix Isabel
Published: July 2020

The anti-inflammatory and antioxidant role of Thykamine, a botanical extract of thylakoides obtained from spinach leaves, has been investigated in animal and cellular models. The oxidative properties have been proven by inhibiting NO production (>98%) in J774A.1 cells and by protecting a linoelic acid emulsion subjected to lipid peroxidation caused by AAPH. Thykamine injected intraperitoneally to rats reduced the inflammatory process of (TNBS)-induced colitis and carrageenan-induced paw edema. As neutrophils are the first cells to migrate to inflammatory sites, the influence of Thykamine on the primary neutrophil functions were studied. Thykamine dose-dependent reduced neutrophil chemiotaxis, phagocytosis, and degranulation. No change in the release of LDH by neutrophils on Thykamine was recorded. Thykamine inhibited by 85% the neutrophil production of O2−. A superoxide recovery activity was observed on a zymography demonstrating a SOD-like enzyme on Thykamine extracts. Spontaneous fluorescence provided by carotenoid and chlorophyll pigments (488/675 nm) detected Thykamine on the surface, in the cytoplasm (mainly central where Golgi are present) and weakly in the nucleus of neutrophils. The results argue that SOD and pigments found in Thykamine are part of its antioxidant and anti-inflammatory properties shown in in vivo and in vitro models of inflammation.
H2 Production from Catalytic Methane Decomposition Using Fe/x-ZrO2 and Fe-Ni/(x-ZrO2) (x = 0, La2O3, WO3) Catalysts
Authors: Al-Mubaddel Fahad , Kasim Samsudeen , Ibrahim Ahmed A. , Al-Awadi Abdulrhman S. , Fakeeha Anis H. , Al-Fatesh Ahmed S.
Published: July 2020

An environmentally-benign way of producing hydrogen is methane decomposition. This study focused on methane decomposition using Fe and Fe-Ni catalysts, which were dispersed over different supports by the wet-impregnation method. We observed the effect of modifying ZrO2 with La2O3 and WO3 in terms of H2 yield and carbon deposits. The modification led to a higher H2 yield in all cases and WO3-modified support gave the highest yield of about 90% and was stable throughout the reaction period. The reaction conditions were at 1 atm, 800 °C, and 4000 mL(hgcat)−1 space velocity. Adding Ni to Fe/x-ZrO2 gave a higher H2 yield and stability for ZrO2 and La2O3 + ZrO2-supported catalysts whose prior performances and stabilities were very poor. Catalyst samples were analyzed by characterization techniques like X-ray diffraction (XRD), nitrogen physisorption, temperature-programmed reduction (TPR), thermo-gravimetric analysis (TGA), and Raman spectroscopy. The phases of iron and the supports were identified using XRD while the BET revealed a significant decrease in the specific surface areas of fresh catalysts relative to supports. A progressive change in Fe’s oxidation state from Fe3+ to Fe0 was observed from the H2-TPR results. The carbon deposits on Fe/ZrO2 and Fe/La2O3 + ZrO2 are mainly amorphous, while Fe/WO3 + ZrO2 and Fe-Ni/x-ZrO2 are characterized by graphitic carbon.
Bimetallic Substituted Ceria: An Alternative Approach to Ligand-Free Heck-Mizoroki Cross-Coupling Reactions
Authors: Vundla Zanele P. , Friedrich Holger B.
Published: July 2020

This work describes Ce0.89Zr0.03Pd0.08O2-δ as a heterogeneous catalyst for Heck-Mizoroki reactions. The material was synthesised by urea-assisted solution combustion to give a zirconium-stabilised cerium fluorite structure, with a fraction of palladium incorporated into the host structure. Characterisation techniques included ICP-OES, P-XRD and electron microscopy. The catalyst illustrated a high TOF of 1860 h−1 for the cross-coupling of iodobenzene with methyl acrylate, when trimethylamine (TEA) was used as a base and dimethylformamide (DMF) as the solvent at 130 °C. To establish the activity of coupling pairs, screening was limited to aryliodobenzenes, with various electronic properties, to determine the influence of aryliodobenzene electronic density on the trans product yield. Electron-donating substituents showed good yields, while electron-withdrawing groups had lower yields. Furthermore, various classes of electron-deficient olefins were screened to determine any effect on the trans product yield. Electron-deficient olefins showed higher yields with regard to the trans product than neutral styrene.
Carbon Dioxide Reforming of Methane over Ni Supported SiO2: Influence of the Preparation Method on the Resulting Structural Properties and Catalytic Activity
Authors: Ren Hua-Ping , Tian Shao-Peng , Ding Si-Yi , Huang Gui-Qiu , Zhu Min , Ma Qiang , Song Wen-Qi , Zhao Yu-Zhen , Miao Zongcheng , Wang Wei
Published: July 2020

Ni-C/SiO2 and Ni-G/SiO2 catalysts were prepared by a complexed-impregnation method using citric acid and glycine as complexing agents, respectively. Ni/SiO2 was also prepared by the conventional incipient impregnation method. All the catalysts were comparatively tested for carbon dioxide reforming of methane (CDR) at P = 1.0 atm, T = 750 °C, CO2/CH4 = 1.0, and GHSV = 60,000 mL·g−1·h−1. The results showed that Ni-C/SiO2 and Ni-G/SiO2 exhibited better CDR performance, especially regarding stability, than Ni/SiO2. The conversions of CH4 and CO2 were kept constant above 82% and 87% after 20 h of reaction over Ni-C/SiO2 and Ni-G/SiO2 while they were decreased from 81% and 88% to 56% and 59%, respectively, over the Ni/SiO2. The characterization results of the catalysts before and after the reaction showed that the particle size and the distribution of Ni, as well as the interactions between Ni and the support were significantly influenced by the preparation method. As a result, an excellent resistance to the coking deposition and the anti-sintering of Ni was obtained over the Ni-C/SiO2 and Ni-G/SiO2, leading to a highly active and stable CDR performance.
Graphite–Metal Oxide Composites as Potential Anodic Catalysts for Microbial Fuel Cells
Authors: Chorbadzhiyska Elitsa , Bardarov Ivo , Hubenova Yolina , Mitov Mario
Published: July 2020

In this study, graphite–metal oxide (Gr–MO) composites were produced and explored as potential anodic catalysts for microbial fuel cells. Fe2O3, Fe3O4, or Mn3O4 were used as a catalyst precursor. The morphology and structure of the fabricated materials were analyzed by scanning electron microscopy and X-ray diffraction, respectively, and their corrosion resistance was examined by linear voltammetry. The manufactured Gr–MO electrodes were tested at applied constant potential +0.2 V (vs. Ag/AgCl) in the presence of pure culture Pseudomonas putida 1046 used as a model biocatalyst. The obtained data showed that the applied poising resulted in a generation of anodic currents, which gradually increased during the long-term experiments, indicating a formation of electroactive biofilms on the electrode surfaces. All composite electrodes exhibited higher electrocatalytic activity compared to the non-modified graphite. The highest current density (ca. 100 mA.m−2), exceeding over eight-fold that with graphite, was achieved with Gr–Mn3O4. The additional analyses performed by cyclic voltammetry and electrochemical impedance spectroscopy supported the changes in the electrochemical activity and revealed substantial differences in the mechanism of current generation processes with the use of different catalysts.
Numerical Investigation of Ventilation Air Methane Catalytic Combustion in Circular Straight and Helical Coil Channels with Twisted Tape Insert in Catalytic-Monolith Reactors
Authors: Chaedir Benitta A. , Kurnia Jundika C. , Chen Lianjun , Jiang Lishuai , Sasmito Agus P.
Published: July 2020

In a catalytic combustion of ventilation air methane, one of the key factors determining the reactor performance is the geometry of the reactor. It should be designed to provide maximum energy conversion at minimum catalyst usage and operating cost. This numerical study is conducted to investigate the catalytic combustion of ventilation air methane from a gassy underground mine in a circular straight and helical reactor channel with twisted tape insert. A three-dimensional computational fluid dynamics model which considers conservation of mass, momentum, energy, and species together with chemical reactions, and constitutive relations for species properties and reactions kinetics was developed and validated against the previously published data. The effect of several key factors affecting the catalytic combustion performance such as inlet Reynolds number, twisted tape ratio, and reactor length are evaluated to obtain the optimum reactor parameters. For evaluation purpose, the reaction performance of the studied reactors will be compared to the straight reactor without twisted tape which is set as a baseline. The results give a firm confirmation on the superior performance of the reactors with twisted tape insert as compared to those without. In addition, it is found that helical reactors generate higher net power as compared to their respective straight reactor counterpart despite having lower FoM due to larger catalyst area. Interestingly, the higher twisting ratio offers better performance in terms of net power as well as FoM. Overall, the results highlight the potential of twisted tape insert application in catalytic combustion.
Enantioselective Transesterification of Allyl Alcohols with (E)-4-Arylbut-3-en-2-ol Motif by Immobilized Lecitase™ Ultra
Authors: Leśniarek Aleksandra , Chojnacka Anna , Drozd Radosław , Szymańska Magdalena , Gładkowski Witold
Published: July 2020

Lecitase™ Ultra was immobilized on four different supports and tested for the first time as the biocatalyst in the kinetic resolution of racemic allyl alcohols with the (E)-4-arylbut-3-en-2-ol system in the process of transesterification. The most effective biocatalyst turned out to be the enzyme immobilized on agarose activated with cyanogen bromide (LU-CNBr). The best results (E > 200, ees and eep = 95–99%) were obtained for (E)-4-phenylbut-3-en-2-ol and its analog with a 2,5-dimethylphenyl ring whereas the lowest ee of kinetic resolution products (90%) was achieved for the substrate with a 4-methoxyphenyl substituent. For all substrates, (R)-enantiomers were esterified faster than their (S)-antipodes. The results showed that LU-CNBr is a versatile biocatalyst, showing high activity and enantioselectivity in a wide range of organic solvents in the presence of commonly used acyl donors. High operational stability of LU-CNBr allows it to be reused in three subsequent reaction cycles without negative effects on the efficiency and enantioselectivity of transesterification. This biocatalyst can become attractive to the commercial lipases in the process of the kinetic resolution of allyl alcohols.