bluuebunny/crossref_metadata_2025 huggingface dataset:
Baicalein Inhibits Benzo[a]pyrene-Induced Toxic Response by Downregulating Src Phosphorylation and by Upregulating NRF2-HMOX1 System
Authors: Tanaka Yuka , Ito Takamichi , Tsuji Gaku , Furue Masutaka
Published: June 2020

Benzo[a]pyrene (BaP), a major environmental pollutant, activates aryl hydrocarbon receptor (AHR), induces its cytoplasmic-to-nuclear translocation and upregulates the production of cytochrome P450 1A1 (CYP1A1), a xenobiotic metabolizing enzyme which metabolize BaP. The BaP-AHR-CYP1A1 axis generates reactive oxygen species (ROS) and induces proinflammatory cytokines. Although the anti-inflammatory phytochemical baicalein (BAI) is known to inhibit the BaP-AHR-mediated CYP1A1 expression, its subcellular signaling remains elusive. In this study, normal human epidermal keratinocytes and HaCaT keratinocytes were treated with BAI, BaP, or BAI + BaP, and assessed for the CYP1A1 expression, antioxidative pathways, ROS generation, and proinflammatory cytokine expressions. BAI and BAI-containing herbal medicine Wogon and Oren-gedoku-to could inhibit the BaP-induced CYP1A1 expression. In addition, BAI activated antioxidative system nuclear factor-erythroid 2-related factor-2 (NRF2) and heme oxygenase 1 (HMOX1), leading the reduction of BaP-induced ROS production. The BaP-induced IL1A and IL1B was also downregulated by BAI. BAI inhibited the phosphorylation of Src, a component of AHR cytoplasmic complex, which eventually interfered with the cytoplasmic-to-nuclear translocation of AHR. These results indicate that BAI and BAI-containing herbal drugs may be useful for inhibiting the toxic effects of BaP via dual AHR-CYP1A1-inhibiting and NRF2-HMOX1-activating activities.
Design of Heterogenous Two-Element Array Antenna on an Electrically Thick Substrate for High Isolation and Low Pattern Correlation Using Modal Difference in Radiation Patterns
Authors: Lim Tae Heung , Jang Byung-Jun , Choo Hosung
Published: June 2020

In this paper, we propose a novel design of a two-element array antenna on an electrical thick substrate with an extremely narrow array distance. The proposed array consists of a rectangular ring patch printed on a thick substrate and a monopole wire in the center of the substrate. Each element has a modal difference in the radiation pattern, causing high isolation and low correlation between the array elements. From the measurement results, the monopole and patch elements exhibit reflection coefficients of −10 dB and −10.7 dB with peak gains of 3.8 dBi and 6.1 dBi, respectively, at 1.6 GHz. The mutual coupling between the two elements is −20.7 dB. For modal analysis of the antenna pattern, spherical mode decomposition is performed on the radiation patterns of the two elements, and low envelope correlation coefficient levels below 16% are maintained. We also investigate the antijamming performance using a power inversion algorithm in a practical pattern nulling application; a null depth of −47.7 dB and a null width of 33.2° are obtained when the interference signal arrives at the elevation angle of 45°.
Influence of Maximum Aggregate Grain Size on the Strength Properties and Modulus of Elasticity of Concrete
Authors: Góra Jacek , Szafraniec Małgorzata
Published: June 2020

Depending on the dimensions of concrete elements, aggregates of different grain sizes are used for the building structures. Taking this fact into account, the authors of the paper have undertaken in their work an issue concerning the analysis of the influence of maximum aggregate grain size on the strength properties and modulus of elasticity of concrete. This is also due to the fact that few published research results are available in this area. In this paper, the influence of the maximum grain size on the basic strength and deformation properties of concrete is discussed. The research concerns both concretes and gravel aggregates used for their construction with maximum grain sizes of 8 mm, 16 mm and 31.5 mm. The values of the compressive and splitting tensile strength, brittleness and modulus of elasticity of concretes with w/c = 0.45 were analysed. The analysis showed that the strength properties are proportional not only to the maximum size of aggregate grain, but also to the crushing strength of the aggregate. There were no analogous relations found with respect to the modulus of elasticity of the tested concretes. Tensile strength was particularly susceptible to the observed changes.
Interval Type-2 Fuzzy Inference System Based on Closest Point of Approach for Collision Avoidance between Ships
Authors: Ohn Sung Wook , Namgung Ho
Published: June 2020

According to International Regulations for Preventing Collision at Sea, collision avoidance started from assessing the collision risk. In particular, the radar was mentioned as suitable equipment for observation and analysis of the collision risk. Thus, many researches have been conducted by utilizing the radar. Fuzzy Inference System based on Type-1 Fuzzy Logic (T1FIS) using Distance to Closest Point of Approach ( D C P A ) and Time to Closest Point of Approach ( T C P A ) computed via the radar has been largely used for assessing the collision risk. However, the T1FIS had significant limitations on the membership function not including linguistic and numerical uncertainties. In order to solve the issue, we developed the Fuzzy Inference System based on Interval Type-2 Fuzzy Logic (IT2FIS) as follows: (i) the T1FIS was selected among proposed methods based on the type-1 fuzzy logic; (ii) we extended the T1FIS into the IT2FIS by gradually increasing the Footprint of Uncertainty (FOU) size taking into consideration symmetry, and (iii) numerical simulations were conducted for performance validation. As a result, the IT2FIS using the FOU size “±5%” (i.e., interval 10% between upper membership function and lower membership function) not only computed the appropriate and linear collision risk index smoothly until near-collision situation but also help to overcome uncertainties that exist in real navigation environments.
Optimizing Replaced Nozzle Diameter of Abrasive Blasting Systems Using Experiment Technique Design
Authors: Vu Ngoc-Pi , Le Xuan-Hung , Nguyen Dinh-Ngoc , Luu Anh-Tung , Nguyen Thanh-Tu , Tran Ngoc-Giang , Tran Thi-Hong , Muthuramalingam Thangaraj
Published: June 2020

The blasting technique has been broadly utilized to prepare new surfaces for painting, engraving, etc., in industry. In fact, the minimization of the cleaning cost system is essential. However, the knowledge of this technique in terms of optimization has been poor so far. The aim of this study is to find the optimum replaced boron carbide nozzle diameter for the abrasive blasting system. The basis of the study is to find the replaced nozzle diameter to minimize the cost of the blasting system. Seven main parameters are adopted for examining their influences on the response and the optimum replaced nozzle diameter. The design of experiment (DOE) technique is utilized by using Minitab@19. The results reveal that the initial nozzle diameter has the strongest impact on the optimum replaced nozzle diameter. Furthermore, the proposed regression model has been found to be entirely insistent to the experiments. The utilization of this model can provide an effective way to simplify the calculation of the optimum initial nozzle diameter for boron carbide.
Free Vibration Analysis of Functionally Gradient Sandwich Composite Plate Embedded SMA Wires in Surface Layer
Authors: Huang Yizhe , Li Lin , Xu Zhichao , Li Chaopeng , Mao Kuanmin
Published: June 2020

In this paper, a new type of composite gradient sandwich plate structure is proposed, which embeds the pre-strained shape memory alloy (SMA) into the surface layer and the core layer composed of epoxy resin and graphite-reinforced materials. In the core layer, graphite-reinforced material has a continuous gradient distribution along the thickness direction of the sandwich plate. Dynamic behavior of composite gradient sandwich plate in thermal environment is investigated. The equations of motion and frequency equation are derived based on the Reddy shear deformation theory and the constitutive equation for a composite sandwich plate, via the Hamilton principle. Some analytical study is depicted to provide an insight into the effects of volume fraction of material composition, gradient distribution of graphite in the core layer, and pre-strain of SMA in the surface layer on the dynamic behavior of a sandwich composite plate. This study investigates the modal performance of a sandwich composite plate with two aspects, a gradient core layer of graphite-reinforced material and surface layer-embedded SMA wires, which provide a new design idea for dynamic behavior of sandwich plates.
The Ethanol Oxidation Reaction Performance of Carbon-Supported PtRuRh Nanorods
Authors: Huang Tzu-Hsi , Bhalothia Dinesh , Lin Shuan , Huang Yu-Rewi , Wang Kuan-Wen
Published: June 2020

In this study, carbon-supported Pt-based catalysts, including PtRu, PtRh, and PtRuRh nanorods (NRs), were prepared by the formic acid reduction method for ethanol oxidation reaction (EOR) application. The aspect ratio of all experimental NRs is 4.6. The X-ray photoelectron spectroscopy and H2-temperature-programmed reduction results confirm that the ternary PtRuRh has oxygen-containing species (OCS), including PtOx, RuOx and RhOx, on its surface and shows high EOR current density at 0.6 V. The corresponding physical structure results indicate that the surface OCS can enhance the adsorption of ethanol through bi-functional mechanism and thereby promote the EOR activity. On the other hand, the chronoamperometry (CA) results imply that the ternary PtRuRh has the highest mass activity, specific activity, and stability among all catalysts. The aforementioned pieces of evidence reveal that the presence of OCS facilitates the oxidation of adsorbed intermediates, such as CO or CHx, which prevents the Pt active sites from poisoning and thus simultaneously improves the current density and durability of PtRuRh NRs in EOR.
Nonlinear Elastic Wave Energy Imaging for the Detection and Localization of In-Sight and Out-of-Sight Defects in Composites
Authors: Segers Joost , Hedayatrasa Saeid , Poelman Gaétan , Van Paepegem Wim , Kersemans Mathias
Published: June 2020

In this study, both linear and nonlinear vibrational defect imaging is performed for a cross-ply carbon fiber-reinforced polymer (CFRP) plate with artificial delaminations and for a quasi-isotropic CFRP with delaminations at the edge. The measured broadband chirp vibrational response is decomposed into different components: the linear response and the nonlinear response in terms of the higher harmonics. This decomposition is performed using the short-time Fourier transformation combined with bandpass filtering in the time-frequency domain. The linear and nonlinear vibrational response of the defect is analyzed by calculation of the defect-to-background ratio. Damage maps are created using band power calculation, which does not require any user-input nor prior information about the inspected sample. It is shown that the damage map resulting from the linear band power shows high sensitivity to shallow defects, while the damage map associated to the nonlinear band power shows a high sensitivity to both shallow and deep defects. Finally, a baseline-free framework is proposed for the detection and localization of out-of-sight damage. The damage is localized by source localization of the observed nonlinear wave components in the wavenumber domain.
HYDRUS-1D Simulation of Nitrogen Dynamics in Rainfed Sweet Corn Production
Authors: Iqbal Mazhar , Kamal Md Rowshon , Mohd Soom Mohd Amin , Yamin Muhammad , Fazly M. Mohd , Che Man Hasfalina , Muhammed Hadi Hamaaziz
Published: June 2020

Nitrogen loss from agricultural fields results in contamination of ground and surface water resources due to leaching and runoff, respectively. Nitrogen transport dynamics vary significantly among agricultural fields of different climates, especially in the tropical climate. This study intended to evaluate the rainfall impact on nitrogen distribution and losses under tropical rain-fed conditions. The study was carried out in a sweet corn field for two growing seasons at the Malaysian Agricultural Research and Development Institute (MARDI) research field. The HYDRUS-1D numerical model was used to simulate nitrogen transport dynamics in this study. The observed nitrogen concentrations were used for calibration and validation of the model. Total nitrogen input to sweet corn was 120 kg/ha for both seasons. Nitrogen losses through surface runoff and leaching were dominating pathways. Surface runoff accounted for 35.3% and 22.2% of total nitrogen input during the first and second seasons, respectively. The leaching loss at 60 cm depth accounted for 4.0% (first season) and 18.5% (second season). The crop N uptake was 37.5% and 24.9% during the first and second seasons, respectively. Nitrate was the dominant form of N uptake by the crop that accounted for 83.6% (first season) and 78.5% (second season). The HYDRUS-1D simulation results of nitrogen concentrations and fluxes were found in good agreement with observed data. The overall results of simulation justified the HYDRUS-1D for improved fertilizer use in the tropical climate.
Robust Control for Active Suspension of Hub-Driven Electric Vehicles Subject to in-Wheel Motor Magnetic Force Oscillation
Authors: Wu Hang , Zheng Ling , Li Yinong , Zhang Zhida , Yu Yinghong
Published: June 2020

In this paper, after investigating the coupling effect in a permanent magnet synchronous in-wheel motor, a robust control method for active suspension of hub-driven electric vehicles (EVs) to enhance the performance of the in-wheel motor and the vehicle is proposed. Based on the electric vehicle model addressing the coupling effect between the electromagnetic excitation of the permanent magnet synchronous motor (PMSM) and the transient dynamics in EVs, the influence of the coupling effect on the motor and the vehicle performance is analyzed. The results reflect that the coupling effect in in-wheel motors intensifies the magnetic force oscillation, aggravates the eccentricity of the rotor, deteriorates the motor operation performance, and worsens the ride comfort. To suppress the magnetic force oscillation in motor and enhance the vehicle comfort, the active suspension system considering five aspects of suspension performance is introduced. Simultaneously, on the basis of Lyapunov stability theory, a reliable robust Hꝏ controller considering model uncertainties, actuator failure and electromagnetic force interference is designed. The simulation results reflect that the robust Hꝏ feedback controller can not only achieve better ride comfort, but also restrain the coupling effect in the motor. Meanwhile the other requirements such as the road holding capability, the actuator limitation, and the suspension deflection are also maintained. The proposed robust control method demonstrates a potential application in the practice of EV control.
Comparison of Instance Selection and Construction Methods with Various Classifiers
Authors: Blachnik Marcin , Kordos Mirosław
Published: June 2020

Instance selection and construction methods were originally designed to improve the performance of the k-nearest neighbors classifier by increasing its speed and improving the classification accuracy. These goals were achieved by eliminating redundant and noisy samples, thus reducing the size of the training set. In this paper, the performance of instance selection methods is investigated in terms of classification accuracy and reduction of training set size. The classification accuracy of the following classifiers is evaluated: decision trees, random forest, Naive Bayes, linear model, support vector machine and k-nearest neighbors. The obtained results indicate that for the most of the classifiers compressing the training set affects prediction performance and only a small group of instance selection methods can be recommended as a general purpose preprocessing step. These are learning vector quantization based algorithms, along with the Drop2 and Drop3. Other methods are less efficient or provide low compression ratio.
Application of Adaptive Tuned Magneto-Rheological Elastomer for Vibration Reduction of a Plate by a Variable-Unbalance Excitation
Authors: Jeong Un-Chang
Published: June 2020

The present study on vibration reduction in systems wherein the excitation frequency is variable designed and fabricated a magnetorheological elastomer (MRE)-based tunable dynamic vibration absorber and evaluated its performance in an experimental manner. The design of an MRE-based adaptive tuned dynamic vibration absorber (ATDVA) involves designing two parts: stiffness and mass. Before designing the MRE-based ATDVA, this study determined the resonance frequency of a target object for vibration reduction. For the design of the ATDVA’s stiffness part, the thickness of specimens was determined by measuring the rate of variation of the MRE’s shear modulus with respect to the MRE’s thickness. The design of the mass part was optimized using sensitivity analysis and genetic algorithms after the derivation of formulas for its magnetic field and mass. Further, upon the application of an electric current to the MRE, its stiffness was measured so that the stiffness of the designed MRE-based ATDVA could be tuned accordingly. Finally, the vibration-reducing performance of the MRE-based ATDVA was evaluated to determine the applicability of the vibration absorber under the condition of variable-frequency excitation.
A Microbial Screening in Silico Method for the Fitness Step Evaluation in Evolutionary Algorithms
Authors: Gargantilla Becerra A. , Lahoz-Beltra R.
Published: June 2020

One of the most delicate stages of an evolutionary algorithm is the evaluation of the goodness of the solutions by some procedure providing a fitness value. However, although there are general rules, it is not always easy to find an appropriate evaluation function for a given problem. In the biological realm, today, there is a variety of experimental methods under the name of microbial screening to identify and select bacteria from their traits, as well as to obtain their fitness. In this paper, we show how given an optimization problem, a colony of synthetic bacteria or bacterial agents is able to evaluate the fitness of candidate solutions by building an evaluation function. The evaluation function is obtained simulating, in silico, a bacterial colony conducting the laboratory methods used in microbiology, biotechnology and synthetic biology to measure microbial fitness. Once the evaluation function is built, it is included in the code of the genetic algorithm as part of the fitness routine. The practical use of this approach is illustrated in two classic optimization problems. In silico routines have been programmed in Gro, a cell programming language oriented to synthetic biology, and can easily be customized to many other optimization problems.
Deformation Characteristics and Control Method of Kilometer-Depth Roadways in a Nickel Mine: A Case Study
Authors: Li Guang , Ma Fengshan , Guo Jie , Zhao Haijun
Published: June 2020

Deformation failure and support methods of roadways have always been critical issues in mining production and safety, especially for roadways buried in complex engineering geological conditions. To resolve these support issues of kilometer-depth roadways under high ground stress and broken rock mass, a case study on the roadways in the No. 2 mining area of Jinchuan Mine, China, is presented in this paper. Based on a detailed field survey, the deformation characteristics of the roadways and failure modes of supporting structures were investigated. It was found that the horizontal deformations were serious, and the primary support was not able to control the surrounding rock well. Additionally, a broken rock zone test was carried out, which indicated that a zonal disintegration phenomenon occurred around the roadways and the maximum depth of the fractured zone was more than 4.8 m. In order to effectively limit the deformation in the roadways, a new support scheme called the “multistage anchorage + concrete-filled steel tube” was put forward. To further assess the support behavior of the new method, we selected a test roadway in the research area, and numerical simulations and in-situ monitoring were conducted. The findings suggest that the roadway’s serious deformation under high ground stress and broken rock mass could be successfully controlled by the new control method, which can provide a reference for other engineering solutions under similar geological conditions.
Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network
Authors: Lin Yung-Chih , Wu Kung-Da , Shih Wei-Cheng , Hsu Pao-Kai , Hung Jui-Pin
Published: June 2020

This study presents surface roughness modeling for machined parts based on cutting parameters (spindle speed, cutting depth, and feed rate) and machining vibration in the end milling process. Prediction models were developed using multiple regression analysis and an artificial neural network (ANN) modeling approach. To reduce the effect of chatter, machining tests were conducted under varying cutting parameters as defined in the stable regions of the milling tool. The surface roughness and machining vibration level are modeled with nonlinear quadratic forms based on the cutting parameters and their interactions through multiple regression analysis methods, respectively. Analysis of variance was employed to determine the significance of cutting parameters on surface roughness. The results show that the combined effects of spindle speed and cutting depth significantly influence surface roughness. The comparison between the prediction performance of the multiple regression and neural network-based models reveal that the ANN models achieve higher prediction accuracy for all training data with R = 0.96 and root mean square error (RMSE) = 3.0% compared with regression models with R = 0.82 and RMSE = 7.57%. Independent machining tests were conducted to validate the predictive models; the results conclude that the ANN model based on cutting parameters with machining vibration has a higher average prediction accuracy (93.14%) than those of models with three cutting parameters. Finally, the feasibility of the predictive model as the base to develop an online surface roughness recognition system has been successfully demonstrated based on contour surface milling test. This study reveals that the predictive models derived on the cutting conditions with consideration of machining stability can ensure the prediction accuracy for application in milling process.
Improvement of the Performance-Based Seismic Design Method of Cable Supported Bridges with the Resilient-Friction Base Isolation Systems
Authors: Gil Heungbae , Han Kyoungbong , Gong Junho , Cho Dooyong
Published: June 2020

In areas of civil engineering, the resilient friction base isolator (R-FBI) system has been used due to its enhanced isolation performance under seismic excitations. However, because nonlinear behavior of the R-FBI should be reflected in seismic design, effective stiffness (Keff) of the R-FBI is uniformly applied at both peak ground acceleration (PGA) of 0.08 g and 0.154 g which use a multimodal response spectrum (RS) method analysis. For rational seismic design of bridges, it should be required to evaluate the dynamics of the R-FBI from in-field tests and to improve the seismic design procedure based on the performance level of the bridges. The objective of this study is to evaluate the dynamics of the R-FBI and to suggest the performance-based seismic design method for cable-supported bridges with the R-FBI. From the comparison between the experiments’ results and modal shape analyses, the modal shape analyses using primary (Ku) or infinite stiffness (fixed end) showed a great agreement with the experimental results compared to the application of Keff in the shape analysis. Additionally, the RS or nonlinear time history method analyses by the PGA levels should be applied by reflecting the dynamic characteristics of the R-FBI for the reasonable and efficient seismic design.
Motion Planning and Coordinated Control of Underwater Vehicle-Manipulator Systems with Inertial Delay Control and Fuzzy Compensator
Authors: Han Han , Wei Yanhui , Ye Xiufen , Liu Wenzhi
Published: June 2020

This paper presents new motion planning and robust coordinated control schemes for trajectory tracking of the underwater vehicle-manipulator system (UVMS) subjected to model uncertainties, time-varying external disturbances, payload and sensory noises. A redundancy resolution technique with a new secondary task and nonlinear function is proposed to generate trajectories for the vehicle and manipulator. In this way, the vehicle attitude and manipulator position are aligned in such a way that the interactive forces are reduced. To resist sensory measurement noises, an extended Kalman filter (EKF) is utilized to estimate the UVMS states. Using these estimates, a tracking controller based on feedback Linearization with both the joint-space and task-space tracking errors is proposed. Moreover, the inertial delay control (IDC) is incorporated in the proposed control scheme to estimate the lumped uncertainties and disturbances. In addition, a fuzzy compensator based on these estimates via IDC is introduced for reducing the undesired effects of perturbations. Trajectory tracking tasks on a five-degrees-of-freedom (5-DOF) underwater vehicle equipped with a 3-DOF manipulator are numerically simulated. The comparative results demonstrate the performance of the proposed controller in terms of tracking errors, energy consumption and robustness against uncertainties and disturbances.
Maneuver-Based Objectification of User Comfort Affecting Aspects of Driving Style of Autonomous Vehicle Concepts
Authors: Schockenhoff Ferdinand , Nehse Hannes , Lienkamp Markus
Published: June 2020

Driving maneuvers try to objectify user needs regarding the driving dynamics for a vehicle concept. As autonomous vehicles will not be driven by people, the driving style that merges the individual aspects of driving dynamics, like user comfort, will be part of the vehicle concept itself. New driving maneuvers are, therefore, necessary to objectify the driving style of autonomous vehicle concepts with all its interdependencies relating to the individual aspects. This paper presents a methodology to design such driving maneuvers and includes a pilot study and a user study. As an example, the methodology was applied to the parameters of user comfort and travel time. The driven maneuvers resulted in statistical equations to objectify the interdependencies of these two aspects. Finally, this paper provides an outlook for needed maneuvers in order to tackle the entire driving style with its multidimensional facets.
Economic Feasibility of Underground Pumped Storage Hydropower Plants Providing Ancillary Services
Authors: Menéndez Javier , Fernández-Oro Jesús Manuel , Loredo Jorge
Published: June 2020

The electricity generated by some renewable energy sources (RESs) is difficult to forecast; therefore, large-scale energy storage systems (ESSs) are required for balancing supply and demand. Unlike conventional pumped storage hydropower (PSH) systems, underground pumped storage hydropower (UPSH) plants are not limited by topography and produce low environmental impacts. In this paper, a deterministic model has been conducted for three UPSH plants in order to evaluate the economic feasibility when considering daily turbine cycle times at full load (DTCs) between 4 and 10 h. In the model, the day-ahead and the ancillary services markets have been compared to maximize the price spread between the electricity generated and consumed. Secondary regulation, deviation management and tertiary regulation services have been analyzed to maximize the income and minimize the cost for purchasing energy. The capital costs of an open-loop UPSH plant have been estimated for the case of using the existing infrastructure and for the case of excavating new tunnels as lower reservoirs. The net present value (NPV), internal rate of return (IRR) and payback period (PB) have been obtained in all scenarios. The results obtained show that the energy generation and the annual generation cycles decrease when the DTC increases from 4 to 10 h, while the NPV and the IRR increase due to investment costs. The investment cost of a 219 MW UPSH plant using the existing infrastructure reaches 366.96 M€, while the NPV, IRR and PB reached 185 M€, 7.10% and 15 years, respectively, participating in the ancillary services markets and considering a DTC of 8 h.
Uptake of Potentially Toxic Elements by Four Plant Species Suitable for Phytoremediation of Turin Urban Soils
Authors: Gaggero Elisa , Malandrino Mery , Fabbri Debora , Bordiglia Giorgio , Fusconi Anna , Mucciarelli Marco , Inaudi Paolo , Calza Paola
Published: June 2020

This study investigated the concentrations of 22 elements in two Turin urban soils located in the city center (Campana Street garden (CA)) and in a peripheral area (Nobile Park (NOB)). The former was found contaminated by Pb, Zn, Ba, Cr and Ni and, to a lower degree, by As, Co, Cu and Cd, while the latter showed high concentrations of Co, Cr and Ni. The nature of Cr, Ni and Co in both sites is mainly geogenic, whereas the high content of Pb, Zn, Ba, As, Cu and Cd in the CA soil is probably due to exposure to atmospheric deposition linked to emissions from motor vehicles, domestic and industrial burning of fossil fuels and industrial emissions. We evaluated the uptake of potentially toxic elements (PTEs) by four plant species suitable for phytoremediation (Brassica juncea, Helianthus annuus, Zea mays and Pteris vittata) in controlled conditions in CA and NOB soils in order to assess their efficiency in the absorption of PTEs and suitability to restore the CA site. Results highlighted a different uptake ability of the plants according to the considered element; for example, Brassica juncea demonstrated a great capability in cadmium uptake. The effect of a soil improver, derived from the composting of green and organic waste, on absorption efficiency was also studied and it altered uptake preferences of specific elements by the tested plant species, thus suggesting that its use has to be evaluated according to the target.
Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography
Authors: de la Fuente Castillo Víctor , Díaz-Álvarez Alberto , Manso-Callejo Miguel-Ángel , Serradilla García Francisco
Published: June 2020

Photogrammetry involves aerial photography of the Earth’s surface and subsequently processing the images to provide a more accurate depiction of the area (Orthophotography). It is used by the Spanish Instituto Geográfico Nacional to update road cartography but requires a significant amount of manual labor due to the need to perform visual inspection of all tiled images. Deep learning techniques (artificial neural networks with more than one hidden layer) can perform road detection but it is still unclear how to find the optimal network architecture. Our main goal is the automatic design of deep neural network architectures with grammar-guided genetic programming. In this kind of evolutive algorithm, all the population individuals (here candidate network architectures) are constrained to rules specified by a grammar that defines valid and useful structural patterns to guide the search process. Grammar used includes well-known complex structures (e.g., Inception-like modules) combined with a custom designed mutation operator (dynamically links the mutation probability to structural diversity). Pilot results show that the system is able to design models for road detection that obtain test accuracies similar to that reached by state-of-the-art models when evaluated over a dataset from the Spanish National Aerial Orthophotography Plan.
Electromagnetic Testing of Moisture Separation Reheater Tube based on Multivariate Singular Spectral Analysis
Authors: Luong Van Su , Le Minhhuy , Nguyen Khoa Dang , Le Dang-Khanh , Lee Jinyi
Published: June 2020

Moisture separator reheater (MSR) tubing systems are an important part of a pressurized-water power plant to increase the efficiency of the heat transfer rate. The MSR tubes are finned tubes which are made of ferritic stainless steel (SS439) with a high strength and corrosion resistance characteristics. However, corrosion can appear along with the fins after a long period of operation of the MSR tubes that requires nondestructive testing (NDT) of the MSR tubes’ periodically. Electromagnetic testing (ET) is an efficient NDT method for the inspection of far-side corrosion in the MSR tubes. However, the ET sensor signal is affected by signal noise from the fins. Material degradation that make it challenging to inspect and evaluate the corrosion. In this study, we proposed three ET methods, including magnetic flux leakage testing, eddy current testing and partial saturation eddy current testing, and incorporated with a multivariate singular spectral analysis (MSSA) filter to improve the detectability of the corrosion in the MSR tubes. The proposed MSSA filter was compared with the multivariate wavelet transform filter and Gabor transform filter, and the results showed more efficient and stable results of the MSSA filter in the extraction of the corrosion signal.
Multiresolution Speech Enhancement Based on Proposed Circular Nested Microphone Array in Combination with Sub-Band Affine Projection Algorithm
Authors: Dehghan Firoozabadi Ali , Irarrazaval Pablo , Adasme Pablo , Zabala-Blanco David , Durney Hugo , Sanhueza Miguel , Palacios-Játiva Pablo , Azurdia-Meza Cesar
Published: June 2020

Speech enhancement is one of the most important fields in audio and speech signal processing. The speech enhancement methods are divided into the single and multi-channel algorithms. The multi-channel methods increase the speech enhancement performance by providing more information with the use of more microphones. In addition, spatial aliasing is one of the destructive factors in speech enhancement strategies. In this article, we first propose a uniform circular nested microphone array (CNMA) for data recording. The microphone array increases the accuracy of the speech processing methods by increasing the information. Moreover, the proposed nested structure eliminates the spatial aliasing between microphone signals. The circular shape in the proposed nested microphone array implements the speech enhancement algorithm with the same probability for the speakers in all directions. In addition, the speech signal information is different in frequency bands, where the sub-band processing is proposed by the use of the analysis filter bank. The frequency resolution is increased in low frequency components by implementing the proposed filter bank. Then, the affine projection algorithm (APA) is implemented as an adaptive filter on sub-bands that were obtained by the proposed nested microphone array and analysis filter bank. This algorithm adaptively enhances the noisy speech signal. Next, the synthesis filters are implemented for reconstructing the enhanced speech signal. The proposed circular nested microphone array in combination with the sub-band affine projection algorithm (CNMA-SBAPA) is compared with the least mean square (LMS), recursive least square (RLS), traditional APA, distributed multichannel Wiener filter (DB-MWF), and multichannel nonnegative matrix factorization-minimum variance distortionless response (MNMF-MVDR) in terms of the segmental signal-to-noise ratio (SegSNR), perceptual evaluation of speech quality (PESQ), mean opinion score (MOS), short-time objective intelligibility (STOI), and speed of convergence on real and simulated data for white and colored noises. In all scenarios, the proposed method has high accuracy at different levels and noise types by the lower distortion in comparison with other works and, furthermore, the speed of convergence is higher than the compared researches.
Classification of Heart Sounds Using Convolutional Neural Network
Authors: Li Fan , Tang Hong , Shang Shang , Mathiak Klaus , Cong Fengyu
Published: June 2020

Heart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global average pooling layer to obtain global information about the feature maps and avoid overfitting. Considering the class imbalance, the class weights were set in the loss function during the training process to improve the classification algorithm’s performance. Stratified five-fold cross-validation was used to evaluate the performance of the proposed method. The mean accuracy, sensitivity, specificity and Matthews correlation coefficient observed on the PhysioNet/CinC Challenge 2016 dataset were 86.8%, 87%, 86.6% and 72.1% respectively. The proposed algorithm’s performance achieves an appropriate trade-off between sensitivity and specificity.
Engineering-Aided Inventive Surgery
Authors: Belfiore Nicola Pio , Scorza Andrea , Ursi Pietro
Published: June 2020

This Editorial presents a new Special Issue dedicated to some old and new interdisciplinary areas of cooperation between engineering and surgery. The first two sections offer some food for thought, in terms of a brief introductory and general review of the past, present, future and visionary perspectives of the synergy between engineering and surgery. The last section presents a very short and reasoned review of the contributions that have been included in the present Special Issue. Given the vastness of the topic that this Special Issue deals with, we hope that our effort may have offered a stimulus, albeit small, to the development of cooperation between engineering and surgery.
Model Predictive Control of Non-Linear Systems Using Tensor Flow-Based Models
Authors: Antão Rómulo , Antunes José , Mota Alexandre , Escadas Martins Rui
Published: June 2020

The present paper proposes an approach for the development of a non-linear model-based predictive controller (NMPC) using a non-linear process model based on Artificial Neural Networks (ANNs). This work exploits recent trends on ANN literature using a TensorFlow implementation and shows how they can be efficiently used as support for closed-loop control systems. Furthermore, it evaluates how the generalization capability problems of neural networks can be efficiently overcome when the model that supports the control algorithm is used outside of its initial training conditions. The process’s transient response performance and steady-state error are parameters under focus and will be evaluated using a MATLAB’s Simulink implementation of a Coupled Tank Liquid Level controller and a Yeast Fermentation Reaction Temperature controller, two well-known benchmark systems for non-linear control problems.
Learning Translation-Based Knowledge Graph Embeddings by N-Pair Translation Loss
Authors: Song Hyun-Je , Kim A-Yeong , Park Seong-Bae
Published: June 2020

Translation-based knowledge graph embeddings learn vector representations of entities and relations by treating relations as translation operators over the entities in an embedding space. Since the translation is represented through a score function, translation-based embeddings are trained in general by minimizing a margin-based ranking loss, which assigns a low score to positive triples and a high score to negative triples. However, this type of embedding suffers from slow convergence and poor local optima because the loss adopts only one pair of a positive and a negative triple at a single update of learning parameters. Therefore, this paper proposes the N-pair translation loss that considers multiple negative triples at one update. The N-pair translation loss employs multiple negative triples as well as one positive triple and allows the positive triple to be compared against the multiple negative triples at each parameter update. As a result, it becomes possible to obtain better vector representations rapidly. The experimental results on link prediction prove that the proposed loss helps to quickly converge toward good optima at the early stage of training.
A Novel Deep Learning Approach for Tropical Cyclone Track Prediction Based on Auto-Encoder and Gated Recurrent Unit Networks
Authors: Lian Jie , Dong Pingping , Zhang Yuping , Pan Jianguo
Published: June 2020

Under global climate change, the frequency of typhoons and their strong wind, heavy rain, and storm surge increase, seriously threatening the life and property of human society. However, traditional tropical cyclone track prediction methods have difficulties in processing large amounts of complex data in terms of prediction efficiency and accuracy. Recently, deep learning methods have shown a potential capability to process complex data efficiently and accurately. In this paper, we propose a novel data-driven approach based on auto-encoder (AE) and gated recurrent unit (GRU) models to forecast tropical cyclone landing locations using the historical tropical cyclone tracks and various meteorological attributes. This approach fuses a data preprocessing layer, an AE layer, and a GRU layer with a customized batch process. The model is trained on a real-world tropical cyclone dataset from the years 1945–2017. Through a comparison with existing forecasting methods, the results verified that our proposed model performed around 15%, 42%, and 56% better than the Numerical Weather Prediction model (NWP) in 24, 48, and 72 h forecasts, and 27%, 13%, 17%, and 17% better than RNN, AE-RNN, GRU, and LSTM, respectively, in 24 h forecasts, using the absolute position error. In addition, a comparison of the meteorological variables indicated that the variable maximum sustained wind speed had the most significant effect on tropical cyclone track prediction.
Design of Capacitive Paddy Moisture Sensor Based on Electrical Impedance Spectroscopy Analysis
Authors: Song Qi , Wei Xinhua , Sun Weihong , Lu Zemin , Tao Tao
Published: June 2020

The intelligent control of the paddy drying process is an prominent development trend and the accurate detection of paddy moisture content is the basis for the intelligent control of the paddy drying process. For the design of a paddy moisture sensor, electrical impedance spectroscopy experiments of the paddy variety Wu Yu Jing 3 (Oryza sativa L.) were conducted to optimize the excitation frequency and establish a paddy moisture detection model. By analyzing the change of paddy capacitance in moisture ranging from 14% to 29% over the frequency ranging from 100 Hz to 1 MHz with temperature ranging from 40 °C to 80 °C, 2 kHz is selected as the excitation frequency for paddy moisture detection in this study. Two-thirds of the experimental data points (i.e., 55 experiments points) at 2 kHz were modeled by regression analysis and the remaining one-third of the experimental data points (i.e., 28 experiments points) were used to evaluate the paddy moisture detection models. The R2 of the paddy moisture detection model was 0.933 and the standard deviation of fitting test was 1.14%. A paddy moisture sensor was designed, and the standard deviation of the detection residual was 1.23%, which was close to the standard deviation 1.14% in the fitting test.
A Novel Experimental Study on the Effects of Soil and Faults’ Properties on Tunnels Induced by Normal and Reverse Faults
Authors: Ghafari Mehdi , Nahazanan Haslinda , Md Yusoff Zainuddin , Nik Daud Nik Norsyahariati
Published: June 2020

Due to the world population increasing considerably, there is a need for efficient public transportation, such as the subway. However, it has become a major concern to geotechnical engineers that the development and construction of subways are held underground where faults exist, as it will be a major risk to any structure if the fault is still active. Several seismic events, such as the earthquakes in Taiwan in 1999, China in 2008, and Malaysia (Sabah) in 2015, caused by fault ruptures, signify the importance of this study. In this paper, a physical model of 1000 mm in height, 3000 mm in length, and 1000 mm in width, which is the largest single gravity (1g) model for simulation faults (normal and reverse) ever built, was fabricated to evaluate the influence of various soil properties, various fault angles, and tunnel depths on tunnels affected by normal and reverse faults. The effects of various soil properties, such as water content, particle size, cohesion, and friction angle, had revealed major changes (approximately by 34%, 39%, 64%, and 39%, respectively) in tunnel displacements. Results also showed that increasing of fault angle could increase the tunnel displacement as much as two times. In addition, when a tunnel is located close to the ground surface, 22% less displacement was found to have occurred to the tunnel. With the results obtained from the physical model, simulation had been made using plane strain and axial symmetry (PLAXIS) software. The comparison made between rock and soft soil showed that soft soil imposed two times more displacements than rock, and an existence of foundation in soft soil and rock can decrease the tunnel displacements by 6% and 4%, respectively. This paper asserts that besides the structural design of a tunnel, the geotechnical design also has a major impact on the safety and robustness of the tunnel, in which aspects such as soil properties, tunnel depth, and fault angle have a strong influence on tunnel damages which were not considered in previous research, despite their importance.
Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea
Authors: Nasir Mohammad , Sadollah Ali , Yoon Jin Hee , Geem Zong Woo
Published: June 2020

Harmony Search (HS) is a music-inspired optimization algorithm for solving complex optimization problems that imitate the musical improvisational process. This paper reviews the potential of applying the HS algorithm in three countries, China, South Korea, and Japan. The applications represent several disciplines in fields of study such as computer science, mathematics, electrical/electronic, mechanical, chemical, civil, and industrial engineering. We anticipate an increasing number of HS applications from these countries in near future.
DroidPortrait: Android Malware Portrait Construction Based on Multidimensional Behavior Analysis
Authors: Su Xin , Xiao Lijun , Li Wenjia , Liu Xuchong , Li Kuan-Ching , Liang Wei
Published: June 2020

Recently, security incidents such as sensitive data leakage and video/audio hardware control caused by Android malware have raised severe security issues that threaten Android users, so thus behavior analysis and detection research researches of malicious Android applications have become a hot topic. However, the behavioral portrait of Android malware that can depict the behavior of Android malware is not approached in previous literature. To fill this gap, we propose DroidPortrait, an Android malware multi-dimensional behavioral portrait construction approach. We take the behavior of Android malware as an entry point and extract an informative behavior dataset that includes static and dynamic behavior from Android malware. Next, aiming at Android malware that contains different kinds of behaviors, a behavioral tag is defined then combined with a machine learning (ML) algorithm to implement the correlation of these behavioral tags. Android malware behavioral portrait architecture based on behavior analysis and its design is investigated, as also an optimized random forest algorithm is conceived then combined with Android malware behavioral portrait to detect Android malware. The evaluation findings indicate the DroidPortrait can depict behavioral characteristics of Android malware comprehensive and detect them with high performance.