bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Online Operational Modal Analysis Method for Vibration Control for Linear Time-Varying Structure
Authors: Wang Cheng , Huang Haiyang , Lai Xiongming , Chen Jianwei
Published: December 2019

From the viewpoint of vibration control, if the amplitude of the main frequencies of the vibration response can be reduced, the vibration energy of the structure is greatly reduced. Modal parameters, including modal shapes, natural frequencies, and damping ratios, can reflect the dynamics of the structure and can be used to control the vibration. This paper integrates the idea of “forgetting factor weighting” into eigenvector recursive principal component analysis, and then proposes an operational modal analysis (OMA) method that uses eigenvector recursive PCA with a forgetting factor (ERPCAWF). The proposed method can identify the transient natural frequencies and transient modal shapes online and realtime using only nonstationary vibration response signals. The identified modal parameters are also suitable for online, real-time health monitoring and fault diagnosis. Finally, the modal identification results from a three-degree-of-freedom weakly damped linear time-varying structure shows that the ERPCAWF-based OMA method can effectively identify transient modal parameters online using only nonstationary response signals. The results also show that the ERPCAWF-based approach is faster, requires less memory space, and achieves higher identification accuracy and greater stability than autocorrelation matrix recursive PCA with a forgetting factor-based OMA.
Effects of Boron Content on Microstructure and Wear Properties of FeCoCrNiBx High-Entropy Alloy Coating by Laser Cladding
Authors: Liu Dezheng , Zhao Jing , Li Yan , Zhu Wenli , Lin Liangxu
Published: December 2019

The FeCoCrNiBx high-entropy alloy (HEA) coatings with three different boron (B) contents were synthesized on Q245R steel (American grade: SA515 Gr60) by laser cladding deposition technology. Effects of B content on the microstructure and wear properties of FeCoCrNiBx HEA coating were investigated. In this study, the phase composition, microstructure, micro-hardness, and wear resistance (rolling friction) were investigated by X-ray diffraction (XRD), a scanning electron microscope (SEM), a micro hardness tester, and a roller friction wear tester, respectively. The FeCoCrNiBx coatings exhibited a typical dendritic and interdendritic structure, and the microstructure was refined with the increase of B content. Additionally, the coatings were found to be a simple face-centered cubic (FCC) solid solution with borides. In terms of mechanical properties, the hardness and wear resistance ability of the coating can be enhanced with the increase of the B content, and the maximum hardness value of three HEA coatings reached around 1025 HV0.2, which is higher than the hardness of the substrate material. It is suggested that the present fabricated HEA coatings possess potentials in application of wear resistance structures for Q245R steel.
A Back Propagation Neural Network Model Optimized by Mind Evolutionary Algorithm for Estimating Cd, Cr, and Pb Concentrations in Soils Using Vis-NIR Diffuse Reflectance Spectroscopy
Authors: Wang Xi , An Shi , Xu Yaqing , Hou Huping , Chen Fuyao , Yang Yongjun , Zhang Shaoliang , Liu Run
Published: December 2019

Visible and near infrared spectroscopy is an effective method for monitoring the content of heavy metals in soil. However, due to the difference between polluted soil with phytoremediation and without phytoremediation, the common estimation model cannot meet accuracy requirements. To solve this problem, combined with an ecological restoration experiment for soil contamination using the plant Neyraudia reynaudiana, this study explored the feasibility of using a hyperspectral technology to estimate the heavy metal content (Cd, Cr, and Pb) of soil under phytoremediation. A total of 108 surface soil samples (from depths of 0–20 cm) were collected. Inversion models were established using partial least squares regression (PLSR) and the back propagation neural network optimized by a mind evolutionary algorithm (MEA-BPNN). The results revealed that: (1) modeling with derivative-transformed spectra can effectively enhance the correlation between soil spectral reflectance and heavy metal content. (2) Compared with the BP neural network model, the estimation accuracy (R2) was improved from 0.728, 0.737, and 0.675 to 0.873, 0.884, and 0.857 using the MEA-BP neural network model. The residual prediction deviation (RPD) values for the three heavy metals Cd, Cr, and Pb using the MEA-BPNN model were 2.114, 3.000, and 2.560, respectively. Among them, the estimated model of Cd was an excellent prediction. (3) Compared with PLSR, the model prediction results established by the MEA-BP neural network had higher estimation accuracy. In summary, the use of diffuse reflectance spectroscopy to predict heavy metal content provides a theoretical basis for further study of the large-scale monitoring of soil heavy-metal pollution and its remediation evaluation in the polluted area, which is of great significance.
Risk Management for the Reliability of Robotic Assisted Treatment of Non-resectable Liver Tumors
Authors: Pisla Doina , Calin Vaida , Birlescu Iosif , Hajjar Nadim Al , Gherman Bogdan , Radu Corina , Plitea Nicolae
Published: December 2019

Hepatic cancers represent an important worldwide health issue where surgery alone in most cases is not a feasible therapeutic solution since most tumors are non-resectable. Despite targeted therapies showing positive results in other areas of cancer treatment, in the case of liver tumors, no low-risk delivery methods have been identified. Based on a risk assessment approach, this paper proposes a technical solution in the form of a robotic system capable of achieving a reliable delivery method for targeted treatment, focusing on the patient safety and therapeutic efficiency. The design of the robotic system starts from the definition of the design constraints with respect to the medical protocol. An analytical hierarchy process is used to prioritize the data correlated with the technical characteristics of a new robotic system, aiming to minimize risks associated with the medical procedure. In a four-phase quality function deployment, the technical solution is evaluated with respect to the quality characteristics, functions, subsystems, and components aiming to achieve a safe and reliable system with high therapeutic efficiency. The results lead to the concept of HeRo, a parallel robotic system for the reliable targeted treatment of non-resectable liver tumors.
Ultra-Thin Metamaterial Beam Splitters
Authors: Zhang Xu , Davis James E. , Güney Durdu Ö.
Published: December 2019

Here, we present blueprints for three types of ultra-thin beam splitters based on versatile fishnet metamaterial structures at the 1.55 μ m optical communication wavelength. The thicknesses of the designed polarizing beam splitter and partially polarizing beam splitter are 1/26 of the free-space wavelength, while the thickness of the non-polarizing beam splitter is 1/13 of the free-space wavelength. Numerical simulations show that, compared to other miniaturization approaches including popular dielectric metasurfaces, metal-based metamaterial approach can provide much thinner beam splitters with reasonable performance. Such beam splitters can enable miniaturization of conventional and advanced quantum photonic systems towards higher density, scalability, and functionality.
Integer-and Fractional-Order Integral and Derivative Two-Port Summations: Practical Design Considerations
Authors: Sotner Roman , Domansky Ondrej , Jerabek Jan , Herencsar Norbert , Petrzela Jiri , Andriukaitis Darius
Published: December 2019

This paper targets on the design and analysis of specific types of transfer functions obtained by the summing operation of integer-order and fractional-order two-port responses. Various operations provided by fractional-order, two-terminal devices have been studied recently. However, this topic needs to be further studied, and the topologies need to be analyzed in order to extend the state of the art. The studied topology utilizes the passive solution of a constant-phase element (with order equal to 0.5) implemented by parallel resistor–capacitor circuit (RC) sections operating as a fractional-order two-port. The integer-order part is implemented by operational amplifier-based lossless integrators and differentiators in branches with electronically adjustable gain, useful for time constant tuning. Four possible cases of the fractional-order and integer-order two-port interconnections are analyzed analytically, by PSpice simulations and also experimentally in the frequency range between 10 Hz and 1 MHz. Standard discrete active components are used in this design for laboratory verification. Practical recommendations for construction and also particular solutions overcoming possible issues with instability and DC offsets are also given. Experimental and simulated results are in good agreement with theory.
Supercritical CO2 Mixtures for Advanced Brayton Power Cycles in Line-Focusing Solar Power Plants
Authors: Valencia-Chapi Robert , Coco-Enríquez Luis , Muñoz-Antón Javier
Published: December 2019

This work quantifies the impact of using sCO2-mixtures (s-CO2/He, s-CO2/Kr, s-CO2/H2S, s-CO2/CH4, s-CO2/C2H6, s-CO2/C3H8, s-CO2/C4H8, s-CO2/C4H10, s-CO2/C5H10, s-CO2/C5H12 and s-CO2/C6H6) as the working fluid in the supercritical CO2 recompression Brayton cycle coupled with line-focusing solar power plants (with parabolic trough collectors (PTC) or linear Fresnel (LF)). Design parameters assessed are the solar plant performance at the design point, heat exchange dimensions, solar field aperture area, and cost variations in relation with admixtures mole fraction. The adopted methodology for the plant performance calculation is setting a constant heat recuperator total conductance (UAtotal). The main conclusion of this work is that the power cycle thermodynamic efficiency improves by about 3–4%, on a scale comparable to increasing the turbine inlet temperature when the cycle utilizes the mentioned sCO2-mixtures as the working fluid. On one hand, the substances He, Kr, CH4, and C2H6 reduce the critical temperature to approximately 273.15 K; in this scenario, the thermal efficiency is improved from 49% to 53% with pure s-CO2. This solution is very suitable for concentrated solar power plants coupled to s-CO2 Brayton power cycles (CSP-sCO2) with night sky cooling. On the other hand, when adopting an air-cooled heat exchanger (dry-cooling) as the ultimate heat sink, the critical temperatures studied at compressor inlet are from 318.15 K to 333.15 K, for this scenario other substances (C3H8, C4H8, C4H10, C5H10, C5H12 and C6H6) were analyzed. Thermodynamic results confirmed that the Brayton cycle efficiency also increased by about 3–4%. Since the ambient temperature variation plays an important role in solar power plants with dry-cooling systems, a CIT sensitivity analysis was also conducted, which constitutes the first approach to defining the optimum working fluid mixture for a given operating condition.
Full-Scale Train Derailment Testing and Analysis of Post-Derailment Behavior of Casting Bogie
Authors: Bae Hyun-Ung , Moon Jiho , Lim Seung-Jae , Park Jong-Chan , Lim Nam-Hyoung
Published: December 2019

In this study, a full-scale train bogie derailment test was conducted. For this, test methodologies to describe the wheel-climbing derailment of the train bogie and to obtain accurate test data were proposed. The derailment test was performed with the casting bogie for a freight train and a Rheda 2000 concrete track. Two different derailment velocities (28.08 km/h and 55.05 km/h) were considered. From the test, it was found that humps in the concrete track affected the post-derailment behavior of the bogie when the derailment velocity was 28.08 km/h. For a higher derailment velocity (55.05 km/h), significant lateral movement of the derailed bogie was observed. This lateral movement was first controlled by wheel–rail contact, followed by contact with the containment wall. Finally, the train was returned to the track center.
Study on Shear Mechanism of Bolted Jointed Rocks: Experiments and CZM-Based FEM Simulations
Authors: Zhang Shubo , Wang Gang , Jiang Yujing , WU Xianlong , Li Genxiao , He Peng , Yu Junhong , Sun Linlin
Published: December 2019

Based on the underground jointed rock of the Huangdao water sealed oil depot in China, the shear failure mechanism of bolted jointed rock is studied through laboratory experiments and numerical simulation. Laboratory experiments are performed to explore the shear behavior of bolted jointed rock with different joint roughness. Our results show that using high strength bolts is beneficial to improving the shear strength of the jointed rock, but the high strength of bolts can also lead to the rock fracture, which should be avoided. For this particular project site, experimental results indicate that 15% elongation is the best. In addition, a new numerical simulation method with CZM (cohesive zone model) used for modeling the shearing process of bolted jointed rock is proposed. It can reasonably describe the characteristics of jointed rock as a discontinuous medium, and bolt as a continuous medium, that replicate well the shearing process. The numerical model is then verified by comparing the experiment results, and it can be effectively be applied to the simulation of joint shearing process. Finally, we use this simulation method to explore the shear failure mechanism of bolted joints, and find that the root cause of rock failure is the deformation mismatch between the bolt and the surrounding rock. The tensile stress between them eventually causes the rock to fracture near the bolt hole.
Fully Differential Chopper-Stabilized Multipath Current-Feedback Instrumentation Amplifier with R-2R DAC Offset Adjustment for Resistive Bridge Sensors
Authors: Kwon Yongsu , Kim Hyungseup , Kim Jaesung , Han Kwonsang , You Donggeun , Heo Hyunwoo , Cho Dong-il “Dan” , Ko Hyoungho
Published: December 2019

A fully differential multipath current-feedback instrumentation amplifier (CFIA) for a resistive bridge sensor readout integrated circuit (IC) is proposed. To reduce the CFIA’s own offset and 1/f noise, a chopper stabilization technique is implemented. To attenuate the output ripple caused by chopper up-modulation, a ripple reduction loop (RRL) is employed. A multipath architecture is implemented to compensate for the notch in the chopping frequency band of the transfer function. To prevent performance degradation resulting from external offset, a 12-bit R-2R digital-to-analog converter (DAC) is employed. The proposed CFIA has an adjustable gain of 16–44 dB with 5-bit programmable resistors. The proposed resistive sensor readout IC is implemented in a 0.18 μm complementary metal-oxide-semiconductor (CMOS) process. The CFIA draws 169 μA currents from a 3.3 V supply. The simulated input-referred noise and noise efficiency factor (NEF) are 28.3 nV/√Hz and 14.2, respectively. The simulated common-mode rejection ratio (CMRR) is 162 dB, and the power supply rejection ratio (PSRR) is 112 dB.
Cellulose Nanofibers and Other Biopolymers for Biomedical Applications. A Review
Authors: Moohan John , Stewart Sarah A. , Espinosa Eduardo , Rosal Antonio , Rodríguez Alejandro , Larrañeta Eneko , Donnelly Ryan F. , Domínguez-Robles Juan
Published: December 2019

Biopolymers are materials synthesised or derived from natural sources, such as plants, animals, microorganisms or any other living organism. The use of these polymers has grown significantly in recent years as industry shifts away from unsustainable fossil fuel resources and looks towards a softer and more sustainable environmental approach. This review article covers the main classes of biopolymers: Polysaccharides, proteins, microbial-derived and lignin. In addition, an overview of the leading biomedical applications of biopolymers is also provided, which includes tissue engineering, medical implants, wound dressings, and the delivery of bioactive molecules. The future clinical applications of biopolymers are vast, due to their inherent biocompatibility, biodegradability and low immunogenicity. All properties which their synthetic counterparts do not share.
A New Method of Low Amplitude Signal Detection and Its Application in Acoustic Emission
Authors: Agletdinov Einar , Merson Dmitry , Vinogradov Alexei
Published: December 2019

A novel methodology is proposed to enhance the reliability of detection of low amplitude transients in a noisy time series. Such time series often arise in a wide range of practical situations where different sensors are used for condition monitoring of mechanical systems, integrity assessment of industrial facilities and/or microseismicity studies. In all these cases, the early and reliable detection of possible damage is of paramount importance and is practically limited by detectability of transient signals on the background of random noise. The proposed triggering algorithm is based on a logarithmic derivative of the power spectral density function. It was tested on the synthetic data, which mimics the actual ultrasonic acoustic emission signal recorded continuously with different signal-to-noise ratios (SNR). Considerable advantages of the proposed method over established fixed amplitude threshold and STA/LTA (Short Time Average / Long Time Average) techniques are demonstrated in comparative tests.
Properties of the Surface Layer After Trochoidal Milling and Brushing: Experimental Study and Artificial Neural Network Simulation
Authors: Kulisz Monika , Zagórski Ireneusz , Matuszak Jakub , Kłonica Mariusz
Published: December 2019

The aim of this study was to investigate the effect of milling and brushing cutting data settings on the surface geometry and energy parameters of two Mg alloy substrates: AZ91D and AZ31. In milling, the cutting speed and the trochoidal step were modified (vc = 400–1200 m/min and str = 5–30%) to investigate how they affect selected 2D (Rz, Rku, Rsk, RSm, Ra) and 3D (Sa, Sz, Sku, Ssk) roughness parameters. The brushing treatment was carried out at constant parameters: n = 5000 rev/min, vf = 300 mm/min, ap = 0.5 mm. The surface roughness of specimens was assessed with the Ra, Rz, and RSm parameters. The effects of the two treatments on the workpiece surface were analyzed comparatively. It was found that the roughness properties of the machined surface may be improved by the application of a carbide milling cutter and ceramic brush. The use of different machining data was also shown to impact the surface free energy and its polar component of Mg alloy specimens. Complementary to the results from the experimental part of the study, the investigated machining processes were modelled by means of statistical artificial neural networks (the radial basis function and multi-layered perceptron). The artificial neural networks (ANNs) were shown to perform well as a tool for the prediction of Mg alloy surface roughness parameters and the maximum height of the profile (Rz) after milling and brushing.
Fingering Instability of a Gravity-Driven Thin Film Flowing Down a Vertical Tube with Wall Slippage
Authors: Ma Chicheng , Hu Shuaizhao , Dong Guangxu , Li Bo
Published: December 2019

Inspired by the antiwetting property of pitcher plants, specialists have designed different functional material with slippery surfaces, and a directional slippery surface has been fabricated. This paper considers a gravity-driven liquid film coating the interior surface of a vertical tube, and different slippery lengths in the azimuthal direction and the axial direction are taken into account. The evolution equation of coating flow is derived using the thin film model, and time responses for two dimensional flow are calculated. Linear stability analysis (LSA) is given based on the traveling wave solutions, demonstrating that the axial slippery effect suppresses the flow instability and causes a larger traveling wave speed. Simultaneously, the azimuthal slippery effect plays a destabilizing role for perturbations with small wavenumbers and it is stabilizing for large wavenumbers. Direct simulations of the fingering flow patterns agree well with the linear stability analysis. Our results offer insight into the influence of wall slippage on the flow stability of liquid in petroleum engineering.
Energy-Saving Performance and Production Accuracy of the Direct-Pressure Tire Curing Technology with an Expandable Steel Internal Mold
Authors: Zhang Jinyun , Wang Bogang , Liu Xiaoying , Cheng Lisheng , Yan Hua , Ding Quanyong , Tan Jing , Yang Weimin
Published: December 2019

Due to the low thermal conductivity and low rigidity of the rubber bladder, the traditional tire curing process faces problems such as low efficiency, high energy consumption, and low production accuracy. To eliminate defects, this work presents a novel direct-pressure curing technology (DPCT) with a steel internal mold heated by electromagnetic induction. Special equipment featuring this novel technology was developed and used for trial-production of tire with a specific size. The energy consumption of sample tires was measured for comparison between the new technology and the traditional one. Nonuniformity and unbalance of tires are tested, meanwhile, physical properties of tread and sidewall parts of cured tires are tested. Furthermore, a finite element analysis (FEA) is carried out to investigate the heating rate of the new curing technology and to optimize the curing process. According to the results, with the new curing technology, the energy consumption per cure cycle is cut down by about 86%, while the curing efficiency and the tensile strength of sidewall part of the cured tire are improved by 22.5% and increased by 13.9%, respectively. In addition, the radial force variation (RFV), couple unbalance mass and curing temperature difference are also reduced by 16.8%, 37%, and 8 °C, respectively. These results suggest that DPCT has excellent energy-saving performance and production accuracy.
Object Detection with Low Capacity GPU Systems Using Improved Faster R-CNN
Authors: Körez Atakan , Barışçı Necaattin
Published: December 2019

Object detection in remote sensing images has been frequently used in a wide range of areas such as land planning, city monitoring, traffic monitoring, and agricultural applications. It is essential in the field of aerial and satellite image analysis but it is also a challenge. To overcome this challenging problem, there are many object detection models using convolutional neural networks (CNN). The deformable convolutional structure has been introduced to eliminate the disadvantage of the fixed grid structure of the convolutional neural networks. In this study, a multi-scale Faster R-CNN method based on deformable convolution is proposed for single/low graphics processing unit (GPU) systems. Weight standardization (WS) is used instead of batch normalization (BN) to make the proposed model more efficient for a small batch size (1 img/per GPU) on single GPU systems. Experiments were conducted on the publicly available 10-class geospatial object detection (NWPU-VHR 10) dataset to evaluate the object detection performance of the proposed model. Experiment results show that our model achieved a 92.3 mAP. This is a 1.7% mAP increase when compared to the best results in the models using the same dataset.
Using the Importance–Satisfaction Model and Service Quality Performance Matrix to Improve Long-Term Care Service Quality in Taiwan
Authors: Chen Shun-Hsing , Pai Fan-Yun , Yeh Tsu-Ming
Published: December 2019

The present study integrates the importance–satisfaction (I-S) model and service quality performance matrix (SQPM) to examine long-term care (LTC) service demands and satisfaction improvement. Many scholars have used a single model to explore project improvement. Each model has advantages, but we think they are too subjective and suggest that it is best to integrate models to determine what should be improved. We established quality attributes of service demands based on more than two sessions of discussions and expert consultations with LTC service users (older adults). The final questionnaire was divided into three parts: a demand survey, satisfaction survey, and demographics survey, and 292 valid questionnaires were collected. The questionnaire items were summarized with means and standard deviations. In this study, if only the I-S model was used to examine LTC in Taiwan, then seven service elements of the system would need to be improved. However, if only the SQPM method was used, then 16 service elements would need to be improved. Only seven service elements were identified by both methods. When time and resources are limited, it is not feasible to take comprehensiveness into account. When many projects must be improved and it is impossible to implement them at the same time, improvement priorities need to be developed. Taiwan lacks sufficient LTC resources, so it is impossible to provide enough resources for all those who need care. To use resources efficiently, the I-S model and SQPM were integrated in this study to identify areas for improvement.
Bulletproof Performance of Composite Plate Fabricated Using Shear Thickening Fluid and Natural Fiber Paper
Authors: Cho Hyeonho , Lee Jongsuk , Hong Sungjin , Kim Sunghan
Published: December 2019

In the munitions industry, there have been considerable efforts spent to develop low-cost, simply fabricated, easily wearable, and biocompatible bulletproof armors. Recently, long fiber-reinforced composites and shear thickening fluids (STFs) were inceptively utilized to improve bulletproof performance with solid or fabric materials. In this study, Hanji, a cornstarch suspension, Korean traditional long fiber paper, and a well-known STF, respectively, were examined for bulletproof applications to evaluate their own effects on bulletproof performance; tests were carried out in the field and finite element analysis (FEA) was performed to evaluate the behavior of materials regarding with perforated clay areas from in-field tests. It was found that both Hanji and STF influenced the bullet penetration by two factors, namely the momentum of bullet and stress propagation. The cornstarch suspension, rather than Hanji, showed outstanding performance in decreasing the linear velocity of the bullet and minimized the stress propagation to the protecting object. Thus, although STF performed a key role in bulletproof performance, Hanji also proved to be a suitable material as an exterior covering for absorbing the initial impact stress and maintaining the durability and stability of the armor itself.
Implementing AutoML in Educational Data Mining for Prediction Tasks
Authors: Tsiakmaki Maria , Kostopoulos Georgios , Kotsiantis Sotiris , Ragos Omiros
Published: December 2019

Educational Data Mining (EDM) has emerged over the last two decades, concerning with the development and implementation of data mining methods in order to facilitate the analysis of vast amounts of data originating from a wide variety of educational contexts. Predicting students’ progression and learning outcomes, such as dropout, performance and course grades, is regarded among the most important tasks of the EDM field. Therefore, applying appropriate machine learning algorithms for building accurate predictive models is of outmost importance for both educators and data scientists. Considering the high-dimensional input space and the complexity of machine learning algorithms, the process of building accurate and robust learning models requires advanced data science skills, while is time-consuming and error-prone in most cases. In addition, choosing the proper method for a given problem formulation and configuring the optimal parameters’ values for a specific model is a demanding task, whilst it is often very difficult to understand and explain the produced results. In this context, the main purpose of the present study is to examine the potential use of advanced machine learning strategies on educational settings from the perspective of hyperparameter optimization. More specifically, we investigate the effectiveness of automated Machine Learning (autoML) for the task of predicting students’ learning outcomes based on their participation in online learning platforms. At the same time, we limit the search space to tree-based and rule-based models in order to achieving transparent and interpretable results. To this end, a plethora of experiments were carried out, revealing that autoML tools achieve consistently superior results. Hopefully our work will help nonexpert users (e.g., educators and instructors) in the field of EDM to conduct experiments with appropriate automated parameter configurations, thus achieving highly accurate and comprehensible results.
Characteristic Length and Time Scales of the Highly Forward Scattering of Photons in Random Media
Authors: Fujii Hiroyuki , Ueno Moegi , Kobayashi Kazumichi , Watanabe Masao
Published: December 2019

Background: Elucidation of the highly forward scattering of photons in random media such as biological tissue is crucial for further developments of optical imaging using photon transport models. We evaluated length and time scales of the photon scattering in three-dimensional media. Methods: We employed analytical solutions of the time-dependent radiative transfer, M-th order delta-Eddington, and photon diffusion equations (RTE, dEM, and PDE). We calculated the fluence rates at different source-detector distances and optical properties. Results: We found that the zeroth order dEM and PDE, which approximate the highly forward scattering to the isotropic scattering, are valid in longer length and time scales than approximately 10 / μ t ′ and 40 / μ t ′ v , respectively, where μ t ′ is the reduced transport coefficient and v the speed of light in a medium. The first and second order dEM, which approximate the highly forward-peaked phase function by the first two and three Legendre moments, are valid in the longer scales than approximately 4.0 / μ t ′ and 6.3 / μ t ′ v ; 2.8 / μ t ′ and 3.5 / μ t ′ v , respectively. The boundary conditions less influence the length scales, while they reduce the times scales from those for bulk at the longer length scale than approximately 4.0 / μ t ′ . Conclusion: Our findings are useful for constructions of accurate and efficient photon transport models. We evaluated length and time scales of the highly forward scattering of photons in various kinds of three-dimensional random media by analytical solutions of the radiative transfer, M-th order delta-Eddington, and photon diffusion equations.
Large-Scale Grid Integration of Renewable Energy Resources with a Double Synchronous Controller
Authors: Mehrasa Majid , Pouresmaeil Edris , Soltani Hamid , Blaabjerg Frede , Calado Maria R. A. , Catalão João P. S.
Published: December 2019

This paper provides virtual inertia and mechanical power-based double synchronous controller (DSC) for power converters based on the d- and q-components of the converter current to assure the stable operation of the grid with the penetration of large-scale renewable energy resources (RERs). The DSC is projected based on emulating both the inertia and mechanical power variables of the synchronous generators (SGs), and its performance is compared with a non-synchronous controller (NSC) that is without these emulations. The main contributions of the DSC are providing a large margin of stability for the power grid with a wide area of low and high values of virtual inertia, also improving significantly power grid stability (PGS) with changing properly the embedded virtual variables of inertia, mechanical power, and also mechanical power error. Also, decoupling features of the proposed DSC in which both d and q components are completely involved with the characteristics of SGs as well as the relationship between the interfaced converter and dynamic models of SGs are other important contributions of the DSC over the existing control methods. Embedding some coefficients for the proposed DSC to show its robustness against the unknown intrinsic property of parameters is another contribution in this paper. Moreover, several transfer functions are achieved and analyzed that confirm a more stable performance of the emulated controller in comparison with the NSC for power-sharing characteristics. Simulation results confirm the superiority of the proposed DSC in comparison with other existing control techniques, e.g., the NSC techniques.
The Influence of Thermo-Mechanical Activation of Bentonite on the Mechanical and Durability Performance of Concrete
Authors: Rehman Safi Ur , Yaqub Muhammad , Noman Muhammad , Ali Babar , Ayaz Khan Muhammad Nasir , Fahad Muhammad , Muneeb Abid Malik , Gul Akhtar
Published: December 2019

Despite presenting a very high global warming toll, Portland cement concrete is the most widely used construction material in the world. The eco-efficiency, economy, and the overall mechanical and durability performances of concrete can be improved by incorporating supplementary cementitious materials (SCMs) as partial substitutions to ordinary Portland cement (OPC). Naturally found bentonite possesses pozzolanic properties and has very low carbon footprint compared to OPC. By applying activation techniques, the reactivity of bentonite can be improved, and its incorporation levels can be maximized. In this study, the influence of mechanical and thermo-mechanical activation of bentonite is investigated on properties of concrete. Bentonite was used for 0%, 10%, 15%, 20%, 25%, 30%, and 35% mass replacements of OPC. Mechanical (compressive strength and split tensile strength) and durability (water absorption, sorptivity coefficient, and acid attack resistance) properties were studied. Results of experimental testing revealed that, concrete containing bentonite showed good mechanical performance, while durability was significantly improved relative to control mix. Application of thermo-mechanical activation can enhance the incorporation levels of bentonite in concrete. At 15% and 25%, bentonite produced optimum results for mechanical and thermo-mechanical activation, respectively. Bentonite inclusion is more beneficial to the durability than the mechanical strength of concrete.
Intelligent Microarray Data Analysis through Non-negative Matrix Factorization to Study Human Multiple Myeloma Cell Lines
Authors: Casalino Gabriella , Coluccia Mauro , Pati Maria L. , Pannunzio Alessandra , Vacca Angelo , Scilimati Antonio , Perrone Maria G.
Published: December 2019

Microarray data are a kind of numerical non-negative data used to collect gene expression profiles. Since the number of genes in DNA is huge, they are usually high dimensional, therefore they require dimensionality reduction and clustering techniques to extract useful information. In this paper we use NMF, non-negative matrix factorization, to analyze microarray data, and also develop “intelligent” results visualization with the aim to facilitate the analysis of the domain experts. For this purpose, a case study based on the analysis of the gene expression profiles (GEPs), representative of the human multiple myeloma diseases, was investigated in 40 human myeloma cell lines (HMCLs). The aim of the experiments was to study the genes involved in arachidonic acid metabolism in order to detect gene patterns that possibly could be connected to the different gene expression profiles of multiple myeloma. NMF results have been verified by western blotting analysis in six HMCLs of proteins expressed by some of the most abundantly expressed genes. The experiments showed the effectiveness of NMF in intelligently analyzing microarray data.
Special Issue on Laser Ultrasonics
Authors: Gusev Vitalyi E. , Shen Zhonghua , Murray Todd W.
Published: December 2019

The field of laser ultrasonics encompasses fundamental research on laser–matter interactions, as well as applications of opto-acoustic and acousto-optic phenomena in industry and biomedicine [...]
LeSSA: A Unified Framework based on Lexicons and Semi-Supervised Learning Approaches for Textual Sentiment Classification
Authors: Khan Jawad , Lee Young-Koo
Published: December 2019

Sentiment Analysis (SA) is an active research area. SA aims to classify the online unstructured user-generated contents (UUGC) into positive and negative classes. A reliable training data is vital to learn a sentiment classifier for textual sentiment classification, but due to domain heterogeneity, manually construction of reliable labeled sentiment corpora is a laborious and time-consuming task. In the absence of enough labeled data, the alternative usage of sentiment lexicons and semi-supervised learning approaches for sentiment classification have substantially attracted the attention of the research community. However, state-of-the-art techniques for semi-supervised sentiment classification present research challenges expressed in questions like the following. How to effectively utilize the concealed significant information in the unstructured data? How to learn the model while considering the most effective sentiment features? How to remove the noise and redundant features? How to refine the initial training data for initial model learning as the random selection may lead to performance degradation? Besides, mainly existing lexicons have trouble with word coverage, which may ignore key domain-specific sentiment words. Further research is required to improve the sentiment lexicons for textual sentiment classification. In order to address such research issues, in this paper, we propose a novel unified sentiment analysis framework for textual sentiment classification called LeSSA. Our main contributions are threefold. (a) lexicon construction, generating quality and wide coverage sentiment lexicon. (b) training classification models based on a high-quality training dataset generated by using k-mean clustering, active learning, self-learning, and co-training algorithms. (c) classification fusion, whereby the predictions from numerous learners are confluences to determine final sentiment polarity based on majority voting, and (d) practicality, that is, we validate our claim while applying our model on benchmark datasets. The empirical evaluation of multiple domain benchmark datasets demonstrates that the proposed framework outperforms existing semi-supervised learning techniques in terms of classification accuracy.
Micro Particle Sizing Using Hilbert Transform Time Domain Signal Analysis Method in Self-Mixing Interferometry
Authors: Zhao Yu , Zhang Menglei , Zhang Chen , Yang Wuxiong , Chen Tao , Perchoux Julien , Ramírez-Miquet Evelio E. , Moreira Raul da Costa
Published: December 2019

The present work envisages the development of a novel and low-cost self-mixing interferometry (SMI) technology-based single particle sensing system in a microchannel chip for real time single micro-scale particle sizing. We proposed a novel theoretical framework to describe the impulse SMI signal expression in the time domain induced by a flowing particle. Using Hilbert transform, the interferometric fringe number of the impulse SMI signal was retrieved precisely for particle size discrimination. For the ease of particle sensing, a hydrodynamic focusing microfluidic channel was employed by varying the flow rate ratio between the sample stream and the sheath liquid, and the particle stream of a controllable width was formed very easily. The experimental results presented good agreement with the theoretical values, providing a 300 nm resolution for the particle sizing measurement.
Design and Simulation of a Capacity Management Model Using a Digital Twin Approach Based on the Viable System Model: Case Study of an Automotive Plant
Authors: Gallego-García Sergio , Reschke Jan , García-García Manuel
Published: December 2019

Matching supply capacity and customer demand is challenging for companies. Practitioners often fail due to a lack of information or delays in the decision-making process. Moreover, researchers fail to holistically consider demand patterns and their dynamics over time. Thus, the aim of this study is to propose a holistic approach for manufacturing organizations to change or manage their capacity. The viable system model was applied in this study. The focus of the research is the clustering of manufacturing and assembly companies. The goal of the developed capacity management model is to be able to react to all potential demand scenarios by making decisions regarding labor and correct investments and in the right moment based on the needed information. To ensure this, demand data series are analyzed enabling autonomous decision-making. In conclusion, the proposed approach enables companies to have internal mechanisms to increase their adaptability and reactivity to customer demands. In order to prove the conceptual model, a simulation of an automotive plant case study was performed, comparing it to classical approaches.
Obstacle Avoidance Drone by Deep Reinforcement Learning and Its Racing with Human Pilot
Authors: Shin Sang-Yun , Kang Yong-Won , Kim Yong-Guk
Published: December 2019

Drones with obstacle avoidance capabilities have attracted much attention from researchers recently. They typically adopt either supervised learning or reinforcement learning (RL) for training their networks. The drawback of supervised learning is that labeling of the massive dataset is laborious and time-consuming, whereas RL aims to overcome such a problem by letting an agent learn with the data from its environment. The present study aims to utilize diverse RL within two categories: (1) discrete action space and (2) continuous action space. The former has the advantage in optimization for vision datasets, but such actions can lead to unnatural behavior. For the latter, we propose a U-net based segmentation model with an actor-critic network. Performance is compared between these RL algorithms with three different environments such as the woodland, block world, and the arena world, as well as racing with human pilots. Results suggest that our best continuous algorithm easily outperformed the discrete ones and yet was similar to an expert pilot.
Charlotte Froese Fischer—Her Work and Her Impact
Authors: Hibbert Alan
Published: December 2019

Charlotte Froese Fischer has been at the forefront of research in atomic structure theory for over 60 years. She has developed many of the methods currently used by researchers and has written associated computer programs which have been published and hence made accessible to the research community. Throughout her career, she has consistently encouraged and mentored young scientists, enabling them to embark on independent careers of their own. This article provides an overview of the methods and codes she has developed, some large-scale calculations she has undertaken, and some insight into the impact she has had on young scientists, and the leadership she continues to show as she reaches her 90th birthday.
Sensor Fault Detection and Isolation for Degrading Lithium-Ion Batteries in Electric Vehicles Using Parameter Estimation with Recursive Least Squares
Authors: Tran Manh-Kien , Fowler Michael
Published: December 2019

With the increase in usage of electric vehicles (EVs), the demand for Lithium-ion (Li-ion) batteries is also on the rise. The battery management system (BMS) plays an important role in ensuring the safe and reliable operation of the battery in EVs. Sensor faults in the BMS can have significant negative effects on the system, hence it is important to diagnose these faults in real-time. Existing sensor fault detection and isolation (FDI) methods have not considered battery degradation. Degradation can affect the long-term performance of the battery and cause false fault detection. This paper presents a model-based sensor FDI scheme for a Li-ion cell undergoing degradation. The proposed scheme uses the recursive least squares (RLS) method to estimate the equivalent circuit model (ECM) parameters in real time. The estimated ECM parameters are put through weighted moving average (WMA) filters, and then cumulative sum control charts (CUSUM) are implemented to detect any significant deviation between unfiltered and filtered data, which would indicate a fault. The current and voltage faults are isolated based on the responsiveness of the parameters when each fault occurs. The proposed FDI scheme is then validated through conducting a series of experiments and simulations.
Impact Effect of Methyl Tertiary-Butyl Ether “Twelve Months Vapor Inhalation Study in Rats”
Authors: Sarhan Osama M. , Jain Antrix , Mutwally Hamed M. A. , Osman Gamal H. , Yun Jung Sung , Issa Tawfik , Elmogy Mohamed
Published: December 2019

We investigated the early risk of developing cancer by inhalation of low doses (60 µL/day) of methyl tertiary butyl ether (MTBE) vapors using protein SDS-PAGE and LC-MS/MS analysis of rat sera. Furthermore, histological alterations were assessed in the trachea and lungs of 60 adult male Wistar rats. SDS-PAGE of blood sera showed three protein bands corresponding to 29, 28, and 21 kDa. Mass spectroscopy was used to identify these three bands. The upper and middle protein bands showed homology to carbonic anhydrase 2 (CA II), whereas the lower protein band showed homology with peroxiredoxin 2. We found that exposure to MTBE resulted in histopathological alterations in the trachea and the lungs. The histological anomalies of trachea and lung showed that the lumen of trachea, bronchi, and air alveoli packed with free and necrotic epithelial cells (epithelialization). The tracheal lamina propria of lung demonstrated aggregation of lymphoid cells, lymphoid hyperplasia, hemorrhage, adenomas, fibroid degeneration, steatosis, foam cells, severe inflammatory cells with monocytic infiltration, edema, hemorrhage. Occluded, congested, and hypertrophied lung arteries in addition, degenerated thyroid follicles, were observed. The hyaline cartilage displayed degeneration, deformation, and abnormal protrusion. In conclusion, our results suggest that inhalation of very low concentrations of the gasoline additive MTBE could induce an increase in protein levels and resulted in histopathological alterations of the trachea and the lungs.
Clinical Trials with Herbal Products for the Prevention of Dental Caries and Their Quality: A Scoping Study
Authors: Ancuceanu Robert , Anghel Adriana Iuliana , Ionescu Camelia , Hovaneț Marilena Viorica , Cojocaru-Toma Maria , Dinu Mihaela
Published: December 2019

It is currently recognized that an injudicious strategy about caries in the last decades has been not only focusing of research mostly in children, but also the narrow focusing on fluoride, because despite sufficient availability of fluoride in water and oral healthcare products, caries levels escalate steadily as people get older and caries remain a main public health issue to be settled. In the last two decades the scientific community intensified efforts of exploring other products for caries prevention, herbal products being one of these approaches. Preliminary evidence indicated that clinical trials for caries prevention with herbal products are heterogeneous in design, quality and products evaluated, we therefore performed a scoping review intended to explore the main characteristics of such clinical trials. From an initial collection of 1986 unique papers from different literature databases, 56 articles satisfied the inclusion and exclusion criteria. The species investigated, dosage forms, study designs, duration of intervention, controls, endpoints, quality of reporting, and risk of bias are discussed. Of the trials reviewed here, 85.71% reported positive results but given the methodological flaws and biases affecting them, it is difficult to conclude on the efficacy of those products based on the studies published thus far.