bluuebunny/crossref_metadata_2025 huggingface dataset:
Crack-Resistant Amino Resin Flame-Retardant Coatings Using Waterborne Polyurethane as a Co-Binder Resin
Authors: He Yanrong , Wu Yuzhang , Qu Wei , Zhang Jingpeng
Published: June 2022

Surface cracking is a major issue in amino resin-based flame-retardant coatings, which can be reduced by mixing flexible resins into the coatings. In this study, flexible waterborne polyurethane (WPU) was added into a melamine-modified, urea-formaldehyde, resin-based intumescent flame retardant (MUF-IFR) coating. A molecular chain of WPU was inserted into the MUF network and formed a WPU/MUF-semi-IPN structure. The cracking resistance of the coating was gradually enhanced with the increase in WPU content. When the WPU content exceeded 25% of the total resin, there were no cracks in the coatings after crack-resistance tests. The coatings before and after toughening showed good transparency on wood surfaces. The influence of WPU on char formation and flame retardant properties were explored by TGA, SEM, and cone calorimetry. The results showed that the decomposition of WPU occurred before char formation, which decreased the integrity of the coating and damaged the compactness of the char. Therefore, the addition of WPU reduced the expansion height and the barrier capacity of the char as well as the flame retardant properties of the coating. When the amount of WPU was 25% of the total resin, compared to the non-WPU coating, the average heat release rate in 300 s (AveHRR300s) and the total heat release at 300 s (THR300s) of the samples were increased by 45.8% and 35.7%, respectively. However, compared to the naked wood, the peak heat release rate (pHRR1), AveHRR300s, and THR300s of the samples with the coating containing 25% WPU were decreased by 64.2%, 39.0%, and 39.7%, respectively. Therefore, the thermal stability of WPU affected char formation. The amount of WPU added should be chosen to be the amount that was added just before the coating cracked.
Achieving Good Temperature Stability of Dielectric Constant by Constructing Composition Gradient in (Pb1−x,Lax)(Zr0.65,Ti0.35)O3 Multilayer Thin Films
Authors: Wu Ming , Xiao Yanan , Yan Yu , Liu Yongbin , Li Huaqiang , Gao Jinghui , Zhong Lisheng , Lou Xiaojie
Published: June 2022

Ferroelectrics with a high dielectric constant are ideal materials for the fabrication of miniaturized and integrated electronic devices. However, the dielectric constant of ferroelectrics varies significantly with the change of temperature, which is detrimental to the working stability of electronic devices. This work demonstrates a new strategy to design a ferroelectric dielectric with a high temperature stability, that is, the design of a multilayer relaxor ferroelectric thin film with a composition gradient. As a result, the fabricated up-graded (Pb,La)(Zr0.65,Ti0.35)O3 multilayer thin film showed a superior temperature stability of the dielectric constant, with variation less than 7% in the temperature range from 30 °C to 200 °C, and more importantly, the variation was less than 2.5% in the temperature range from 75 °C to 200 °C. This work not only develops a dielectric material with superior temperature stability, but also demonstrates a promising method to enhance the temperature stability of ferroelectrics.
Effect of Sr Deficiency on Electrical Conductivity of Yb-Doped Strontium Zirconate
Authors: Khaliullina Adelya , Meshcherskikh Anastasia , Pankratov Aleksander , Dunyushkina Liliya
Published: June 2022

The effect of Sr-deficiency on microstructure, phase composition and electrical conductivity of SrxZr0.95Yb0.05O3-δ (x = 0.94–1.00) was investigated via X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray spectroscopy and impedance spectroscopy. The samples were synthesized by a chemical solution method and sintered at 1600 °C. According to X-ray diffraction data, the samples with x = 0.96–1.00 were single-phase oxides possessing an orthorhombic perovskite-type structure; while zirconia-based minor phases arouse at x = 0.94, which was confirmed by the electron microscopy. Sr stoichiometry was shown to influence the electrical conductivity. The highest total and bulk conductivities, 6–10−4 Scm−1 and 3–10−3 Scm−1, respectively, at 600 °C in humid air (pH2O = 3.2 kPa), were observed for the x = 0.98 composition. In the temperature range of 300–600 °C, the conductivity of the samples with x = 0.96–1.00 increased with the increase in humidity, which indicates a significant contribution of protonic defects to the charge transport. Electrical conductivity of SrxZr0.95Yb0.05O3-δ was discussed in terms of the defect formation model and the secondary phases precipitation.
Recovery of Zinc and Iron from Steel Mill Dust—An Overview of Available Technologies
Authors: Xue Yang , Hao Xiansheng , Liu Xiaoming , Zhang Na
Published: June 2022

The global shortage of zinc mines makes the extraction of zinc from zinc-containing wastes a hot research topic. Most kinds of steel mill dust (SMD) cannot be directly returned to the ironmaking and steelmaking processes due to their zinc content. A large amount of SMD produced during steelmaking has become a major challenge for steel plants due to environmental pollution, health issues and land scarcity. Lots of processes for recovering metals from SMD have been developed to comprehensively utilize them and solve these environmental problems. Zinc in SMD can be recovered by these technologies, and the residue can be used as raw material for ironmaking. In this work, the sources and characteristics of SMD from different processes were analyzed firstly. Then, the mechanisms of physical, hydrometallurgical, pyrometallurgical and pyrometallurgy–hydrometallurgy combined processes for SMD disposal are presented, and these methods are compared in terms of energy consumption, process complexity and industrial application. Finally, suggestions and prospects for utilization of SMD are put forward.
Biochar Synthesis from Mineral- and Ash-Rich Waste Biomass, Part 1: Investigation of Thermal Decomposition Mechanism during Slow Pyrolysis
Authors: Nair Rahul Ramesh , Mondal Moni Mohan , Srinivasan Shanmugham Venkatachalam , Weichgrebe Dirk
Published: June 2022

Synthesizing biochar from mineral- and ash-rich waste biomass (MWB), a by-product of human activities in urban areas, can result in renewable and versatile multi-functional materials, which can also cater to the need of solid waste management. Hybridizing biochar with minerals, silicates, and metals is widely investigated to improve parent functionalities. MWB intrinsically possesses such foreign materials. The pyrolysis of such MWB is kinetically complex and requires detailed investigation. Using TGA-FTIR, this study investigates and compares the kinetics and decomposition mechanism during pyrolysis of three types of MWB: (i) mineral-rich banana peduncle (BP), (ii) ash-rich sewage sludge (SS), and (iii) mineral and ash-rich anaerobic digestate (AD). The results show that the pyrolysis of BP, SS, and AD is exothermic, catalyzed by its mineral content, with heat of pyrolysis 5480, 4066, and 1286 kJ/kg, respectively. The pyrolysis favors char formation kinetics mainly releasing CO2 and H2O. The secondary tar reactions initiate from ≈318 °C (BP), 481 °C (SS), and 376 °C (AD). Moreover, negative apparent activation energies are intrinsic to their kinetics after 313 °C (BP), 448 °C (SS), and 339 °C (AD). The results can support in tailoring and controlling sustainable biochar synthesis from slow pyrolysis of MWB.
Development of a Flexible Metamaterial Film with High EM Wave Absorptivity by Numerical and Experimental Methods
Authors: Cheng Chin-Hsiang , Chen Yi-Shen , Tsai Hsin-Yu , Liang Yu-Ling , Lin David T. W. , Chen Yitung
Published: June 2022

The present study is intended to develop and test a cost-effective and efficient printing method for fabricating flexible metamaterial film with high electromagnetic wave absorptivity. The film can be easily applied to the surfaces with curved aspects. Firstly, numerical parametric study of the absorption characteristics of the film is performed for the range of frequency varying from 2.0 to 9.0 GHz based on commercial software package. Secondly, the flexible metamaterial films are fabricated, and experiments are conducted. The flexible metamaterial film consists of a flexible dielectric film made of polyimide (PI) and an array of split-ring resonators. The split-ring resonators of different geometric dimensions are fabricated on the PI film surface by using a silver nanoparticles ink jet printer. The performance of the flexible structure is then measured and dependence of operation frequency with higher absorptivity on the dimensions of the split-ring resonators is investigated. A comparison between the numerical and experimental data shows that the numerical predictions of the operation frequency with higher absorptivity closely agree with the experimental data.
Study on Wear Mechanism of Helical Gear by Three-Body Abrasive Based on Impact Load
Authors: Yuan Wei , Wang Haotian , Guo Qianjian , Wang Wenhua , Zhu Yuqi , Yu Jie , Yang Xianhai
Published: June 2022

This study aimed to explore the wear characteristics and evolution mechanisms of large-scale wind power gears under the impact load of particles of the three-body abrasive Al2O3 (0.2 mg/mL) from four aspects: oil analysis, vibration analysis, amount of gear wear, and tooth-surface-wear profile analysis. A magnetic powder brake was used to simulate the actual working conditions. Combined with the abrasive particle monitoring and the morphology analysis of the tooth-surface-wear scar, by setting quantitative hard particles in the lubricating oil, the gears are mainly operated in the abrasive wear state, and wear monitoring and wear degree analysis are carried out for the whole life cycle of the gears. Oil samples were observed and qualitatively analyzed using a particle counter, a single ferrograph, a metallographic microscope, and a scanning electron microscope. The experiments demonstrate that the initial hard particles have a greater impact in the early wear stage of the gears (<20 h), and abrasive particle concentration increases by 30%. This means that Al2O3 particles accelerate the gear wear during the running-in period. The loading method of the impact load on the oblique gear exacerbates the abrasion particle wear and expands the stress concentration, which reduces the surface of large milling particles on the surface, and reduces the width of the tooth (the part above the pitch line is severely worn), which causes the gear to break into failure. The research provides help for analyzing the mechanism of abrasive wear of gears and predicting wear life.
The Application of a Hybrid Method for the Identification of Elastic–Plastic Material Parameters
Authors: Potrzeszcz-Sut Beata , Dudzik Agnieszka
Published: June 2022

The indentation test is a popular method for the investigation of the mechanical properties of materials. The technique, which combines traditional indentation tests with mapping the shape of the imprint, provides more data describing the material parameters. In this paper, such methodology is employed for estimating the selected material parameters described by Ramberg–Osgood’s law, i.e., Young’s modulus, the yield point, and the material hardening exponent. Two combined identification methods were used: the P-A procedure, in which the material parameters are identified on the basis of the coordinates of the indentation curves, and the P-C procedure, which uses the coordinates describing the imprint profile. The inverse problem was solved by neural networks. The results of numerical indentation tests—pairs of coordinates describing the indentation curves and imprint profiles—were used as input data for the networks. In order to reduce the size of the input vector, a simple and effective method of approximating the branches of the curves was proposed. In the Results Section, we show the performance of the approximation as a data reduction mechanism on a synthetic dataset. The sparse model generated by the presented approach is also shown to efficiently reconstruct the data while minimizing error in the prediction of the mentioned material parameters. Our approach appeared to consistently provide better performance on the testing datasets with considerably easier computation than the principal component analysis compression results available in the literature.
Development of a Formability Prediction Model for Aluminium Sandwich Panels with Polymer Core
Authors: Liu Xiaochuan , Di Bozhou , Yu Xiangnan , Liu Heli , Dhawan Saksham , Politis Denis J. , Kopec Mateusz , Wang Liliang
Published: June 2022

In the present work, the compatibility relationship on the failure criteria between aluminium and polymer was established, and a mechanics-based model for a three-layered sandwich panel was developed based on the M-K model to predict its Forming Limit Diagram (FLD). A case study for a sandwich panel consisting of face layers from AA5754 aluminium alloy and a core layer from polyvinylidene difluoride (PVDF) was subsequently conducted, suggesting that the loading path of aluminium was linear and independent of the punch radius, while the risk for failure of PVDF increased with a decreasing radius and an increasing strain ratio. Therefore, the developed formability model would be conducive to the safety evaluation on the plastic forming and critical failure of composite sandwich panels.
Modeling Hydrodynamic Charge Transport in Graphene
Authors: Gungor Arif Can , Koepfli Stefan M. , Baumann Michael , Ibili Hande , Smajic Jasmin , Leuthold Juerg
Published: June 2022

Graphene has exceptional electronic properties, such as zero band gap, massless carriers, and high mobility. These exotic carrier properties enable the design and development of unique graphene devices. However, traditional semiconductor solvers based on drift-diffusion equations are not capable of modeling and simulating the charge distribution and transport in graphene, accurately, to its full extent. The effects of charge inertia, viscosity, collective charge movement, contact doping, etc., cannot be accounted for by the conventional Poisson-drift-diffusion models, due to the underlying assumptions and simplifications. Therefore, this article proposes two mathematical models to analyze and simulate graphene-based devices. The first model is based on a modified nonlinear Poisson’s equation, which solves for the Fermi level and charge distribution electrostatically on graphene, by considering gating and contact doping. The second proposed solver focuses on the transport of the carriers by solving a hydrodynamic model. Furthermore, this model is applied to a Tesla-valve structure, where the viscosity and collective motion of the carriers play an important role, giving rise to rectification. These two models allow us to model unique electronic properties of graphene that could be paramount for the design of future graphene devices.
Influence of Age and Harvesting Season on The Tensile Strength of Bamboo-Fibre-Reinforced Epoxy Composites
Authors: Dessalegn Yalew , Singh Balkeshwar , van Vuure Aart W. , Rajhi Ali A. , Ahmed Gulam Mohammed Sayeed , Hossain Nazia
Published: June 2022

The purpose of this study was to measure the strength of various bamboo fibres and their epoxy composites based on the bamboo ages and harvesting seasons. Three representative samples of 1–3-year-old bamboo plants were collected in November and February. Bamboo fibres and their epoxy composites had the highest tensile strength and Young’s modulus at 2 years old and in November. The back-calculated tensile strengths using the “rule of mixture” of Injibara, Kombolcha, and Mekaneselam bamboo-fibre-reinforced epoxy composites were 548 ± 40–422 ± 33 MPa, 496 ± 16–339 ± 30 MPa, and 541 ± 21–399 ± 55 MPa, whereas the back-calculated Young’s moduli using the “rule of mixture” were 48 ± 5–37 ± 3 GPa, 36 ± 4–25 ± 3 GPa, and 44 ± 2–40 ± 2 GPa, respectively. The tensile strengths of the Injibara, Kombolcha, and Mekaneselam bamboo-fibre-reinforced epoxy composites were 227 ± 14–171 ± 22 MPa, 255 ± 18–129 ± 15 MPa, and 206 ± 19–151 ± 11 MPa, whereas Young’s moduli were 21 ± 2.9–16 ± 4.24 GPa, 18 ± 0.8–11 ± 0.51 GPa, and 18 ± 0.85–16 ± 0.82 GPa respectively. The highest to the lowest tensile strengths and Young’s moduli of bamboo fibres and their epoxy composites were Injibara, Mekaneselam, and Kombolcha, which were the local regional area names from these fibres were extracted. The intended functional application of the current research study is the automobile industries of headliners, which substitute the conventional materials of glass fibres.
Bonding of Clear Aligner Composite Attachments to Ceramic Materials: An In Vitro Study
Authors: Alsaud Bashair A. , Hajjaj Maher S. , Masoud Ahmad I. , Abou Neel Ensanya A. , Abuelenain Dalia A. , Linjawi Amal I.
Published: June 2022

Background: We aim to evaluate the effect of surface conditioning, bonding agents and composite types on surface roughness (SR) and shear bond strength (SBS) of clear aligner composite attachments bonded to ceramics. Methods: One hundred and eighty IPS e.max CAD specimens were prepared. For SR, 60 specimens were divided according to surface conditioning (n = 15) into four groups: control, 9.6% hydrofluoric acid (HFA), 37% phosphoric acid (PhA), air abrasion (AA). SR was measured using a Profilometer and Atomic Force Microscopy. For SBS, 120 specimens were divided according to conditioning methods (n = 40) (9.6% HFA and 37% PhA or AA), then according to bonding agents (n = 20) (Assure universal bond (AUB) or Single bond universal (SBU)) and then according to composite type (n = 10): Filtek™ Z350 and Filtek™ Z350 XT flowable composite. SBS was measured using Instron testing machine. Descriptive and group comparison were calculated (p < 0.05). Results: AA had the highest SR, while the control had the lowest SR (p < 0.05). HFA had the highest, but insignificant SBS, followed by AA (p > 0.05). AUB had higher SBS than SBU (p < 0.001). Filtek™ Z350 produced higher SBS than Filtek™ Z350 XT flowable composite (p < 0.01). Conclusion: The combination of AA, AUB, and Filtek Z350 produced the highest SBS, followed by HFA, AUB, and Filtek Z350.
Humidity Sensing Applications of Lead-Free Halide Perovskite Nanomaterials
Authors: Tambwe Kevin , Ross Natasha , Baker Priscilla , Bui Thanh-Tuân , Goubard Fabrice
Published: June 2022

Over the past decade, perovskite-based nanomaterials have gained notoriety within the scientific community and have been used for a variety of viable applications. The unique structural properties of these materials, namely good direct bandgap, low density of defects, large absorption coefficient, high sensitivity, long charge carrier lifetime, good selectivity, acceptable stability at room temperature, and good diffusion length have prompted researchers to explore their potential applications in photovoltaics, light-emitting devices, transistors, sensors, and other areas. Perovskite-based devices have shown very excellent sensing performances to numerous chemical and biological compounds in both solid and liquid mediums. When used in sensing devices, Perovskite nanomaterials are for the most part able to detect O2, NO2, CO2, H2O, and other smaller molecules. This review article looks at the use of lead-free halide perovskite materials for humidity sensing. A complete description of the underlying mechanisms and charge transport characteristics that are necessary for a thorough comprehension of the sensing performance will be provided. An overview of considerations and potential recommendations for the creation of new lead-free perovskite nanostructure-based sensors is presented.
Preparation and Properties of Modified Phenylethynyl Terminated Polyimide with Neodymium Oxide
Authors: Zhang Peng , Liu Hansong , Yao Yilun , Yang Tao , Sun Jinsong , Zhong Xiangyu , Bao Jianwen , Zhao Yan , Chen Xiangbao
Published: June 2022

Modified phenylethynyl terminated polyimides (PIs) were successfully prepared by using neodymium oxide (Nd2O3) via high-speed stirring and ultrasonic dispersion methods. In addition, the structure and properties of the Nd2O3-modified imide oligomers as well as the thermo-oxidative stability of the modified polyimides (PI/Nd2O3 hybrid) and its modification mechanism were investigated in detail. The thermogravimetric analysis (TGA) results indicated that the 5% decomposition temperature (Td5%) of the PI/Nd2O3 hybrids improved from 557 °C to 575 °C, which was also verified by the TGA-IR tests. Meanwhile, the weight loss rate of the PI/Nd2O3 hybrids significantly decreased by 28% to 31% compared to that of pure PI under isothermal aging at 350 °C for 450 h when the added content of Nd2O3 was between 0.4 wt% and 1 wt%, showing outstanding thermo-oxidative stability. Moreover, the mechanism of the enhanced thermo-oxidative stability for the modified PIs was analyzed by scanning electron microscopy (SEM) and X-ray diffraction (XRD).
Recycling of Waste Toner Powder as Adsorbent to Remove Aqueous Heavy Metals
Authors: Fernández Begoña , Ayala Julia , del Valle Elena , Martínez-Blanco David , Castañón Ana María , Menéndez-Aguado Juan M.
Published: June 2022

The removal of Cd2+, Zn2+ and Ni2+ from metal solutions onto waste toner power (WTP) was investigated. The influence of parameters such as pH, contact time, initial metal concentration and adsorbent dosage was studied in batch adsorption experiments. Batch equilibrium experiments showed that the highest removal efficiency for Zn2+ and Cd2+ occurs at pH 7, while pH 5 is the most suitable for Ni2+ removal. The amount of metal removed (mg/g) improved when increasing the initial concentration, and sorption of heavy metals reached equilibrium in 24 h. Metals’ uptake increased with increasing adsorbent dosage. The adsorption isotherms of Zn2+, Cd2+ and Ni2+ onto WTP fit the Langmuir better than the Freundlich model with correlation coefficient R2 values ranging from 0.998 to 0.968 and 0.989 to 0.881, respectively. The data showed that the maximum adsorption capacity of heavy metals, amax, ranged from 2.42 to 1.61 mg/g, from 6.22 to 2.01 mg/g and from 3.49 to 2.56 mg/g for Ni2+, Zn2+ and Cd2+, respectively, with the three WTPs used in this study. This adsorbent can potentially be used to remove metal ions from wastewater.
Experimental Study on the Cracking and Mechanical Properties of Lime Soil with Different Slaking Conditions of Newly Repaired Earthen City Walls
Authors: Yue Jianwei , Su Huicong , Song Xiao , Xu Xiangchun , Zhao Limin , Zhao Gang , Li Peng , Chen Ying
Published: June 2022

In this paper, a method to control the lime reaction by different slaking conditions is proposed to reduce the occurrence of cracks in newly repaired earthen city walls. The effects and mechanisms of the slaking time (0 h, 12 h, 24 h, 48 h and 72 h), lime content (10%, 15% and 20%), and moisture content (14%, 18% and 22%) on the cracking and mechanical properties of lime soil were analyzed by the test results of surface cracks, triaxial compression, particle gradation, pH value, X-ray diffraction and scanning electron microscope. The results show that proper slaking of lime soil specimens can reduce surface cracks and improve mechanical properties. After 12 h of appropriate slaking, the crack rate of the lime soil with 20% content decreased by 97.13%, the cohesion increased by 20.27%, and the internal friction angle decreased by 11.27%. However, the mechanical properties decreased when the slaking time was too long. After 72 h of slaking, the cohesion of 20% lime soil decreased by 8.21% and the internal friction angle increased by 2.82%. Further analysis shows that the appropriate slaking conditions can regulate the reaction rate and alkali environment, control the lime produced cementitious substances, improve the particle gradation and further reduce the occurrence of surface cracks. These results provide a basis for the restoration technology of newly repaired earthen city walls.
Automated Process Planning for Embossing and Functionally Grading Materials via Site-Specific Control in Large-Format Metal-Based Additive Manufacturing
Authors: Borish Michael , Gibson Brian T. , Adkins Cameron , Mhatre Paritosh
Published: June 2022

The potential for site-specific, process-parameter control is an attribute of additive manufacturing (AM) that makes it highly attractive as a manufacturing process. The research interest in the functionally grading material properties of numerous AM processes has been high for years. However, one of the issues that slows developmental progress in this area is process planning. It is not uncommon for manual programming methods and bespoke solutions to be utilized for site-specific control efforts. This article presents the development of slicing software that contains a fully automated process planning approach for enabling through-thickness, process-parameter control for a range of AM processes. The technique includes the use of parent and child geometries for controlling the locations of site-specific parameters, which are overlayed onto unmodified toolpaths, i.e., a vector-based planning approach is used in which additional information, such as melt pool size for large-scale metal AM processes, is assigned to the vectors. This technique has the potential for macro- and micro-structural modifications to printed objects. A proof-of-principle experiment is highlighted in which this technique was used to generate dynamic bead geometries that were deposited to induce a novel surface embossing effect, and additional software examples are presented that highlight software support for more complex objects.
Magnetic, Electrical, and Physical Properties Evolution in Fe3O4 Nanofiller Reinforced Aluminium Matrix Composite Produced by Powder Metallurgy Method
Authors: Ashrafi Negin , Mohamed Ariff Azmah Hanim , Jung Dong-Won , Sarraf Masoud , Foroughi Javad , Sulaiman Shamsuddin , Hong Tang Sai
Published: June 2022

An investigation into the addition of different weight percentages of Fe3O4 nanoparticles to find the optimum wt.% and its effect on the microstructure, thermal, magnetic, and electrical properties of aluminum matrix composite was conducted using the powder metallurgy method. The purpose of this research was to develop magnetic properties in aluminum. Based on the obtained results, the value of density, hardness, and saturation magnetization (Ms) from 2.33 g/cm3, 43 HV and 2.49 emu/g for Al-10 Fe3O4 reached a maximum value of 3.29 g/cm3, 47 HV and 13.06 emu/g for the Al-35 Fe3O4 which showed an improvement of 41.2%, 9.3%, and 424.5%, respectively. The maximum and minimum coercivity (Hc) was 231.87 G for Al-10 Fe3O4 and 142.34 G for Al-35 Fe3O4. Moreover, the thermal conductivity and electrical resistivity at a high weight percentage (35wt.%) were 159 w/mK, 9.9 × 10−4 Ω·m, and the highest compressive strength was 133 Mpa.
Mechanical Design and a Novel Structural Optimization Approach for Hexapod Walking Robots
Authors: Burkus Ervin , Odry Ákos , Awrejcewicz Jan , Kecskés István , Odry Péter
Published: June 2022

This paper presents a novel model-based structural optimization approach for the efficient electromechanical development of hexapod robots. First, a hexapod-design-related analysis of both optimization objectives and relevant parameters is conducted based on the derived dynamical model of the robot. A multi-objective optimization goal is proposed, which minimizes energy consumption, unwanted body motion and differences between joint torques. Then, an optimization framework is established, which utilizes a sophisticated strategy to handle the optimization problems characterized by a large set of parameters. As a result, a satisfactory result is efficiently obtained with fewer iterations. The research determines the optimal parameter set for hexapod robots, contributing to significant increases in a robot’s walking range, suppressed robot body vibrations, and both balanced and appropriate motor loads. The modular design of the proposed simulation model also offers flexibility, allowing for the optimization of other electromechanical properties of hexapod robots. The presented research focuses on the mechatronic design of the Szabad(ka)-III hexapod robot and is based on the previously validated Szabad(ka)-II hexapod robot model.
Practical Secret Image Sharing Based on the Chinese Remainder Theorem
Authors: Li Longlong , Lu Yuliang , Liu Lintao , Sun Yuyuan , Wang Jiayu
Published: June 2022

Compared with Shamir’s original secret image sharing (SIS), the Chinese-remainder-theorem-based SIS (CRTSIS) generally has the advantages of a lower computation complexity, lossless recovery and no auxiliary encryption. However, general CRTSIS is neither perfect nor ideal, resulting in a narrower range of share pixels than that of secret pixels. In this paper, we propose a practical and lossless CRTSIS based on Asmuth and Bloom’s threshold algorithm. To adapt the original scheme for grayscale images, our scheme shares the high seven bits of each pixel and utilizes the least significant bit (LSB) matching technique to embed the LSBs into the random integer that is generated in the sharing phase. The chosen moduli are all greater than 255 and the share pixels are in the range of [0, 255] by a screening operation. The generated share pixel values are evenly distributed in the range of [0, 255] and the selection of (k,n) threshold is much more flexible, which significantly improves the practicality of CRTSIS. Since color images in RGB mode are made up of three channels, it is easy to extend the scheme to color images. Theoretical analysis and experiments are given to validate the effectiveness of the proposed scheme.
A Novel Multi-Objective Hybrid Election Algorithm for Higher-Order Random Satisfiability in Discrete Hopfield Neural Network
Authors: Karim Syed Anayet , Kasihmuddin Mohd Shareduwan Mohd , Sathasivam Saratha , Mansor Mohd. Asyraf , Jamaludin Siti Zulaikha Mohd , Amin Md Rabiol
Published: June 2022

Hybridized algorithms are commonly employed to improve the performance of any existing method. However, an optimal learning algorithm composed of evolutionary and swarm intelligence can radically improve the quality of the final neuron states and has not received creative attention yet. Considering this issue, this paper presents a novel metaheuristics algorithm combined with several objectives—introduced as the Hybrid Election Algorithm (HEA)—with great results in solving optimization and combinatorial problems over a binary search space. The core and underpinning ideas of this proposed HEA are inspired by socio-political phenomena, consisting of creative and powerful mechanisms to achieve the optimal result. A non-systematic logical structure can find a better phenomenon in the study of logic programming. In this regard, a non-systematic structure known as Random k Satisfiability (RANkSAT) with higher-order is hosted here to overcome the interpretability and dissimilarity compared to a systematic, logical structure in a Discrete Hopfield Neural Network (DHNN). The novelty of this study is to introduce a new multi-objective Hybrid Election Algorithm that achieves the highest fitness value and can boost the storage capacity of DHNN along with a diversified logical structure embedded with RANkSAT representation. To attain such goals, the proposed algorithm tested four different types of algorithms, such as evolutionary types (Genetic Algorithm (GA)), swarm intelligence types (Artificial Bee Colony algorithm), population-based (traditional Election Algorithm (EA)) and the Exhaustive Search (ES) model. To check the performance of the proposed HEA model, several performance metrics, such as training–testing, energy, similarity analysis and statistical analysis, such as the Friedman test with convergence analysis, have been examined and analyzed. Based on the experimental and statistical results, the proposed HEA model outperformed all the mentioned four models in this research.
A Symmetric/Asymmetric Bimodal Extension Based on the Logistic Distribution: Properties, Simulation and Applications
Authors: Cortés Isaac E. , Venegas Osvaldo , Gómez Héctor W.
Published: June 2022

In this paper, we introduce bimodal extensions, one symmetric and one asymmetric, of the logistic distribution. We define this new density and study some basic properties. We draw inferences from the moment estimator and maximum likelihood approaches. We present a simulation study to assess the behaviour of the moment and maximum likelihood estimators. We also study the singularity of the Fisher information matrix for particular cases. We offer applications in real data and compare them with a mixture of logistics distributions.
A New Operator for Meromorphic Functions
Authors: Güney Hatun Özlem , Breaz Daniel , Owa Shigeyoshi
Published: June 2022

Let Σ be the class of functions f(z) of the form f(z)=1z+∑k=0∞akzk, which are analytic in the punctured disk. Using the differentiations and integrations, new operator Dnf(z) is introduced for f(z)∈Σ. The object of the present paper is to discuss some interesting properties for Dnf(z) and some properties concerned with different boundary points of the open unit disk. Moreover, some simple examples for our results are shown.
Exemplar-Based Sketch Colorization with Cross-Domain Dense Semantic Correspondence
Authors: Cui Jinrong , Zhong Haowei , Liu Hailong , Fu Yulu
Published: June 2022

This paper aims to solve the task of coloring a sketch image given a ready-colored exemplar image. Conventional exemplar-based colorization methods tend to transfer styles from reference images to grayscale images by employing image analogy techniques or establishing semantic correspondences. However, their practical capabilities are limited when semantic correspondences are elusive. This is the case with coloring for sketches (where semantic correspondences are challenging to find) since it contains only edge information of the object and usually contains much noise. To address this, we present a framework for exemplar-based sketch colorization tasks that synthesizes colored images from sketch input and reference input in a distinct domain. Generally, we jointly proposed our domain alignment network, where the dense semantic correspondence can be established, with a simple but valuable adversarial strategy, that we term the structural and colorific conditions. Furthermore, we proposed to utilize a self-attention mechanism for style transfer from exemplar to sketch. It facilitates the establishment of dense semantic correspondence, which we term the spatially corresponding semantic transfer module. We demonstrate the effectiveness of our proposed method in several sketch-related translation tasks via quantitative and qualitative evaluation.
XAI-Fall: Explainable AI for Fall Detection on Wearable Devices Using Sequence Models and XAI Techniques
Authors: Mankodiya Harsh , Jadav Dhairya , Gupta Rajesh , Tanwar Sudeep , Alharbi Abdullah , Tolba Amr , Neagu Bogdan-Constantin , Raboaca Maria Simona
Published: June 2022

A fall detection system is vital for the safety of older people, as it contacts emergency services when it detects a person has fallen. There have been various approaches to detect falls, such as using a single tri-axial accelerometer to detect falls or fixing sensors on the walls of a room to detect falls in a particular area. These approaches have two major drawbacks: either (i) they use a single sensor, which is insufficient to detect falls, or (ii) they are attached to a wall that does not detect a person falling outside its region. Hence, to provide a robust method for detecting falls, the proposed approach uses three different sensors for fall detection, which are placed at five different locations on the subject’s body to gather the data used for training purposes. The UMAFall dataset is used to attain sensor readings to train the models for fall detection. Five models are trained corresponding to the five sensors models, and a majority voting classifier is used to determine the output. Accuracy of 93.5%, 93.5%, 97.2%, 94.6%, and 93.1% is achieved on each of the five sensors models, and 92.54% is the overall accuracy achieved by the majority voting classifier. The XAI technique called LIME is incorporated into the system in order to explain the model’s outputs and improve the model’s interpretability.
Explainable Machine Learning for Longitudinal Multi-Omic Microbiome
Authors: Laccourreye Paula , Bielza Concha , Larrañaga Pedro
Published: June 2022

Over the years, research studies have shown there is a key connection between the microbial community in the gut, genes, and immune system. Understanding this association may help discover the cause of complex chronic idiopathic disorders such as inflammatory bowel disease. Even though important efforts have been put into the field, the functions, dynamics, and causation of dysbiosis state performed by the microbial community remains unclear. Machine learning models can help elucidate important connections and relationships between microbes in the human host. Our study aims to extend the current knowledge of associations between the human microbiome and health and disease through the application of dynamic Bayesian networks to describe the temporal variation of the gut microbiota and dynamic relationships between taxonomic entities and clinical variables. We develop a set of preprocessing steps to clean, filter, select, integrate, and model informative metagenomics, metatranscriptomics, and metabolomics longitudinal data from the Human Microbiome Project. This study accomplishes novel network models with satisfactory predictive performance (accuracy = 0.648) for each inflammatory bowel disease state, validating Bayesian networks as a framework for developing interpretable models to help understand the basic ways the different biological entities (taxa, genes, metabolites) interact with each other in a given environment (human gut) over time. These findings can serve as a starting point to advance the discovery of novel therapeutic approaches and new biomarkers for precision medicine.
Robust Variable Selection Based on Penalized Composite Quantile Regression for High-Dimensional Single-Index Models
Authors: Song Yunquan , Li Zitong , Fang Minglu
Published: June 2022

The single-index model is an intuitive extension of the linear regression model. It has been increasingly popular due to its flexibility in modeling. In this work, we focus on the estimators of the parameters and the unknown link function for the single-index model in a high-dimensional situation. The SCAD and Laplace error penalty (LEP)-based penalized composite quantile regression estimators, which could realize variable selection and estimation simultaneously, are proposed; a practical iterative algorithm is introduced to obtain the efficient and robust estimators. The choices of the tuning parameters, the bandwidth, and the initial values are also discussed. Furthermore, under some mild conditions, we show the large sample properties and oracle property of the SCAD and Laplace penalized composite quantile regression estimators. Finally, we evaluated the performances of the proposed estimators by two numerical simulations and a real data application.
A Masked Self-Supervised Pretraining Method for Face Parsing
Authors: Li Zhuang , Cao Leilei , Wang Hongbin , Xu Lihong
Published: June 2022

Face Parsing aims to partition the face into different semantic parts, which can be applied into many downstream tasks, e.g., face mask up, face swapping, and face animation. With the popularity of cameras, it is easier to acquire facial images. However, pixel-wise manually labeling is time-consuming and labor-intensive, which motivates us to explore the unlabeled data. In this paper, we present a self-supervised learning method attempting to make full use of the unlabeled facial images for face parsing. In particular, we randomly mask some patches in the central area of facial images, and the model is required to reconstruct the masked patches. This self-supervised pretraining is capable of making the model capture facial feature representations through these unlabeled data. After self-supervised pretraining, the model is fine-tuned on a few labeled data for the face parsing task. Experimental results show that the model achieves better performance for face parsing assisted by the self-supervised pretraining, which greatly decreases the labeling cost. Our approach achieves 74.41 mIoU on the LaPa test set fine-tuned on only 0.2% of the labeled data of the whole training data, surpassing the model that is directly trained by a large margin of +5.02 mIoU. In addition, our approach achieves a new state-of-the-art on the LaPa and CelebAMask-HQ test set.
Numerical Stability and Performance of Semi-Explicit and Semi-Implicit Predictor–Corrector Methods
Authors: Beuken Loïc , Cheffert Olivier , Tutueva Aleksandra , Butusov Denis , Legat Vincent
Published: June 2022

Semi-implicit multistep methods are an efficient tool for solving large-scale ODE systems. This recently emerged technique is based on modified Adams–Bashforth–Moulton (ABM) methods. In this paper, we introduce new semi-explicit and semi-implicit predictor–corrector methods based on the backward differentiation formula and Adams–Bashforth methods. We provide a thorough study of the numerical stability and performance of new methods and compare their stability with semi-explicit and semi-implicit Adams–Bashforth–Moulton methods and their performance with conventional linear multistep methods: Adams–Bashforth, Adams–Moulton, and BDF. The numerical stability of the investigated methods was assessed by plotting stability regions and their performances were assessed by plotting error versus CPU time plots. The mathematical developments leading to the increase in numerical stability and performance are carefully reported. The obtained results show the potential superiority of semi-explicit and semi-implicit methods over conventional linear multistep algorithms.
epSFEM: A Julia-Based Software Package of Parallel Incremental Smoothed Finite Element Method (S-FEM) for Elastic-Plastic Problems
Authors: Zhou Meijun , Qin Jiayu , Huo Zenan , Giampaolo Fabio , Mei Gang
Published: June 2022

In this paper, a parallel Smoothed Finite Element Method (S-FEM) package epSFEM using incremental theory to solve elastoplastic problems is developed by employing the Julia language on a multicore CPU. The S-FEM, a new numerical method combining the Finite Element Method (FEM) and strain smoothing technique, was proposed by Liu G.R. in recent years. The S-FEM model is softer than the FEM model for identical grid structures, has lower sensitivity to mesh distortion, and usually produces more accurate solutions and a higher convergence speed. Julia, as an efficient, user-friendly and open-source programming language, balances computational performance, programming difficulty and code readability. We validate the performance of the epSFEM with two sets of benchmark tests. The benchmark results indicate that (1) the calculation accuracy of epSFEM is higher than that of the FEM when employing the same mesh model; (2) the commercial FEM software requires 10,619 s to calculate an elastoplastic model consisting of approximately 2.45 million triangular elements, while in comparison, epSFEM requires only 5876.3 s for the same computational model; and (3) epSFEM executed in parallel on a 24-core CPU is approximately 10.6 times faster than the corresponding serial version.
Vehicular-Network-Intrusion Detection Based on a Mosaic-Coded Convolutional Neural Network
Authors: Hu Rong , Wu Zhongying , Xu Yong , Lai Taotao
Published: June 2022

With the development of Internet of Vehicles (IoV) technology, the car is no longer a closed individual. It exchanges information with an external network, communicating through the vehicle-mounted network (VMN), which, inevitably, gives rise to security problems. Attackers can intrude on the VMN, using a wireless network or vehicle-mounted interface devices. To prevent such attacks, various intrusion-detection methods have been proposed, including convolutional neural network (CNN) ones. However, the existing CNN method was not able to best use the CNN’s capability, of extracting two-dimensional graph-like data, and, at the same time, to reflect the time connections among the sequential data. Therefore, this paper proposed a novel CNN model, based on two-dimensional Mosaic pattern coding, for anomaly detection. It can not only make full use of the ability of a CNN to extract grid data but also maintain the sequential time relationship of it. Simulations showed that this method could, effectively, distinguish attacks from the normal information on the vehicular network, improve the reliability of the system’s discrimination, and, at the same time, meet the real-time requirement of detection.
Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)
Authors: Rohan Ali
Published: June 2022

In the current Industry 4.0 revolution, prognostics and health management (PHM) is an emerging field of research. The difficulty of obtaining data from electromechanical systems in an industrial setting increases proportionally with the scale and accessibility of the automated industry, resulting in a less interpolated PHM system. To put it another way, the development of an accurate PHM system for each industrial system necessitates a unique dataset acquired under specified conditions. In most circumstances, obtaining this one-of-a-kind dataset is difficult, and the resulting dataset has a significant imbalance, a lack of certain useful information, and contains multi-domain knowledge. To address those issues, this paper provides a fault detection and diagnosis system that evaluates and preprocesses imbalanced, scarce, multi-domain (ISMD) data acquired from an industrial robot, utilizing signal processing (SP) techniques and deep learning-based (DL) domain knowledge transfer. The domain knowledge transfer is used to produce a synthetic dataset with a high interpolation rate that contains all the useful information about each domain. For domain knowledge transfer and data generation, continuous wavelet transform (CWT) with a generative adversarial network (GAN) was used, as well as a convolutional neural network (CNN), to test the suggested methodology using transfer learning and categorize several faults. The proposed methodology was tested on a real experimental bench that included an industrial robot created by Hyundai Robotics. This test had a satisfactory outcome with a 99.7% (highest) classification accuracy achieved by transfer learning on several CNN benchmark models.
The Effect of Von Willebrand Disease on Pregnancy, Delivery, and Postpartum Period: A Retrospective Observational Study
Authors: Sladič Mateja , Verdenik Ivan , Smrkolj Špela
Published: June 2022

Background and Objectives Several reports indicate that women with von Willebrand disease (VWD) are at an increased risk of bleeding and other complications during pregnancy and childbirth. The aim of this study was to investigate the effect of VWD on the course of pregnancy, childbirth, and the postpartum period. Materials and Methods This was a retrospective study that compared many variables between women with VWD (n = 26) and women without VWD (n = 297,111) who gave birth between 2002 and 2016 in Slovenia. Data were obtained from the Slovenian National Perinatal Information System. Results Women with VWD were not more likely to have a miscarriage, vaginal bleeding during pregnancy, anemia, intrauterine growth restriction, or imminent premature labor. However, women with VWD were more likely to experience childbirth trauma-related bleeding (OR, 10.7; 95% CI: 1.4, 78.9), primary postpartum hemorrhage (OR, 3.7; 95% CI: 0.9, 15.8), and require blood transfusion after childbirth (OR, 16.3; 95% CI: 2.2, 120.3). No cases of stillbirth or early neonatal death were observed in women with VWD. Conclusion Although women with VWD did not demonstrate an increased risk of vaginal bleeding during pregnancy or poor fetal outcomes, they had a higher risk of primary postpartum hemorrhage and requiring blood transfusion.