bluuebunny/crossref_metadata_2025 huggingface dataset:
Carbon Nanotori Reinforced Lubricants in Plastic Deformation Processes
Authors: Taha-Tijerina Jose Jaime , Martínez Juan Manuel , Euresti Daniel , Arquieta-Guillén Patsy Yessenia
Published: April 2022

This research presents the effects of carbon nanotori structures (CNst) dispersed as reinforcement for metal-working and metal-forming lubricants. Synthetic (SL) and deep drawing (DD) nanolubricants were prepared following a two-step method at 0.01 wt.%, 0.05 wt.%, and 0.10 wt.% filler fractions. Slight increases in viscosity (<6%) for nanolubricants were observed as filler fraction was increased through various measured temperatures. Tribological behavior of nanolubricants displayed superb improvements under antiwear and extreme pressure conditions. The load carrying capacity (poz) increased by 16% and 22% at merely 0.01 wt.% CNst reinforcement and up to 73% and 107% at 0.10 wt.% filler fraction for SL and DD nanolubricants, respectively, compared to conventional materials. Additionally, at 0.10 wt.% wear scar evaluations showed a highest benefit of 16% and 24%, for SL and DD nanolubricants, respectively. This enhancement is attributed to diverse mechanisms such as rolling/sliding and load bearing effects, tribofilm formation, and CNst tribosintering behavior (at high pressures) onto metallic surfaces due to nanostructures size and morphology and their interlayer relationship among conventional lubricants.
Generation of A Space-Variant Vector Beam with Catenary-Shaped Polarization States
Authors: Wang Junjie , Pu Mingbo , Jin Jinjin , Zhang Fei , Liu Ling , Kong Weijie , Li Xiong , Guo Yinghui , Luo Xiangang
Published: April 2022

We demonstrate the generation of a space-variant vector beam with catenary-shaped polarization states based on the polarization interferometry. With a spatial light modulator and a common path interferometric configuration, two orthogonally circularly polarized beams with different phase modulation overlap each other, yielding the vector beams. In addition, the polarization states of this vector beam are scalable to the arbitrary spatial distribution because of its great flexibility and universal applicability. It is expected that this vector beam may have many potential and intriguing applications in the micro/nano material processing, liquid crystal elements fabrication and optical micro-manipulation, and so on.
Investigation of Exfoliation Efficiency of 6H-SiC Implanted Sequentially with He+ and H2+ Ions
Authors: You Guoqiang , Lin Haipeng , Qu Yanfeng , Hao Jie , You Suyuan , Li Bingsheng
Published: April 2022

Silicon carbide (SiC) is a promising material used in the advanced semiconductor industry. Fabricating SiC-on-insulator via H implantation is a good method. He and H co-implantation into Si can efficiently enhance exfoliation efficiency compared to only H implantation. In this study, 6H-SiC single crystals were implanted with He+ and H2+ dual beams at room temperature, followed by annealing at 1100 °C for 15 min, and irradiations with 60 keV He ions with a fluence of 1.5 × 1016 ions/cm−2 or 5.0 × 1016 ions/cm−2 and 100 keV H2+ ions with a fluence of 5 × 1016 ions/cm−2 were carried out. The lattice disorder was characterized by both Raman spectroscopy and transmission electron microscopy. The intensity of Raman peaks decreased with increasing fluence. No Raman shift or new phases were found. A very high numerical density of bubbles was observed as compared to single H or He implantation. Moreover, stacking faults, Frank loops and tangled dislocations were formed in the damaged layer. Surface exfoliation was inhibited by co-implantation. A possible reason for this is an increase in fracture toughness and a decrease in elastic out-of-plane strain due to dense bubbles and stacking faults.
Special Issue: Biopolymer-Based Materials for Biomedical Engineering
Authors: Oliveira Joaquim M. , Ribeiro Viviana P. , Reis Rui L.
Published: April 2022

In the field of tissue engineering and regenerative medicine (TERM), the use of traditional biomaterials capable of integrating the host tissue to promote the healing and regenerative process while it degrades has become less and less a focus of inspiration [...]
Influence of Rock Dust Additives as Fine Aggregate Replacement on Properties of Cement Composites—A Review
Authors: Dobiszewska Magdalena , Bagcal Orlando , Beycioğlu Ahmet , Goulias Dimitrios , Köksal Fuat , Niedostatkiewicz Maciej , Ürünveren Hüsamettin
Published: April 2022

Concrete production consumes enormous amounts of fossil fuels, raw materials, and is energy intensive. Therefore, scientific research is being conducted worldwide regarding the possibility of using by-products in the production of concrete. The objective is not only to identify substitutes for cement clinker, but also to identify materials that can be used as aggregate in mortar and concrete productions. Among the potential alternative materials that can be used in cement composite production is rock dust of different geological origin. However, some adversarial effects may be encountered when using rock dust regarding the properties and durability of mortars and concrete. Therefore, comprehensive research is needed to evaluate the adequacy of rock dust use in cementitious composite production. This paper presents a comprehensive review of the scientific findings from past studies concerning the use of various geological origins of rock dust in the production of mortars and concrete. The influence of rock dust as a replacement of fine aggregates on cementitious composites was analyzed and evaluated. In this assessment and review, fresh concrete and mortar properties, i.e., workability, segregation, and bleeding, mechanical properties, and the durability of hardened concrete and mortar were considered.
Biomedical Membrane of Fish Collagen/Gellan Gum Containing Bone Graft Materials
Authors: Kim Jin , Lee Chang-Moon , Moon Seong-Yong , Jeong Young-IL , Kim Chun Sung , Lee Sook-Young
Published: April 2022

The development of a guided bone regeneration (GBR) membrane with non-mammalian fish collagen has the advantage of low risk for transmission of infectious diseases in tissue regeneration. In this work, a fish collagen/gellan gum and bone graft material (FC/GG-BGM) composite GBR membrane were fabricated through solution blending and casting procedures in a vacuum. The membranes were characterized using Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscopy observation (SEM), and atomic force microscope (AFM) analyses. FT-IR results suggested that ionic interactions were formed between FC and GG both in composite powder and membranes. In vivo experiments showed that these FC/GG-BGM composite membranes could generate osteoblast minerals and promote loose bone calcification, thus accelerating bone regeneration. At 2 weeks, the defected site of rats treated with FC/GG-BGM membrane (0.377 ± 0.012 mm3) showed higher regeneration than that of rats treated with the bovine collagen membrane (0.290 ± 0.015 mm3) and control rats without membrane (0.160 ± 0.008 mm3). Compared with bovine collagen membrane, the FC/GG-BGM composite membrane displays better bone regeneration ability. Therefore, FC/GG-BGM composite membrane is suitable as a GBR membrane for bone regeneration.
Influence of Additives on Microstructure and Mechanical Properties of Alumina Ceramics
Authors: Wang Weili , Chen Jianqi , Sun Xiaoning , Sun Guoxun , Liang Yanjie , Bi Jianqiang
Published: April 2022

Alumina is one of the most commonly used and researched structural ceramic because of its excellent properties. However, its intrinsic brittleness is the fatal drawback, which hinders it from wider applications. How to improve its fracture toughness as well as the bending strength is always challenging for material researchers. In this paper, alumina matrix composites were fabricated by hot-pressing, in which some additives, including zirconia, alumina platelets, and MXene, were incorporated. The influence of the introduced additives on their microstructure and mechanical properties was investigated. Compare with the monolithic alumina, both bending strength and fracture toughness of all samples were improved greatly. Incorporation of zirconia was beneficial to the mechanical properties due to the phase-transformation strengthening and toughening mechanism. While alumina platelets resulted in high fracture toughness because of the self-toughening of elongated grains. The synergistic effect of alumina platelets and MXene enormously improved the fracture toughness from 2.9 ± 0.3 MPa·m1/2 for monolithic alumina to 7.5 ± 0.4 MPa·m1/2 for the composite, which was increased by 159%. This work will provide useful references for the fabrication of high-strength and high-toughness alumina ceramics by introducing additives properly.
Root Causes and Mechanisms of Failure of Wind Turbine Blades: Overview
Authors: Mishnaevsky Leon
Published: April 2022

A review of the root causes and mechanisms of damage and failure to wind turbine blades is presented in this paper. In particular, the mechanisms of leading edge erosion, adhesive joint degradation, trailing edge failure, buckling and blade collapse phenomena are considered. Methods of investigation of different damage mechanisms are reviewed, including full scale testing, post-mortem analysis, incident reports, computational simulations and sub-component testing. The most endangered regions of blades include the protruding parts (tip, leading edges), tapered and transitional areas and bond lines/adhesives. Computational models of different blade damage mechanisms are discussed. The role of manufacturing defects (voids, debonding, waviness, other deviations) for the failure mechanisms of wind turbine blades is highlighted. It is concluded that the strength and durability of wind turbine blades is controlled to a large degree by the strength of adhesive joints, interfaces and thin layers (interlaminar layers, adhesives) in the blade. Possible solutions to mitigate various blade damage mechanisms are discussed.
Numerical Analysis of Standing Waves Phenomenon of Aircraft Tires
Authors: Gan Yu , Fang Xingbo , Wei Xiaohui
Published: April 2022

In this paper, the generation mechanisms of standing waves on aircraft tires are discussed by comparing the time-domain model and FEA model. Unlike passenger car tires, aircraft tires accelerate to landing speed sharply when the airplane lands. According to the tire structure, the detailed finite element model of the aircraft tires is established in ABAQUS. The tire model runs on a 1.7 m spinning drum and accelerates to 300 km/h in 0.3 s. The proposed finite element model is verified by comparing the simulation results under inflation and static load with the experimental results. Similarly, by analyzing and comparing the calculated values of the time-domain model and FEA model, the variation of standing wave wavelength at different speeds is studied. In addition, the stress and strain field of the aircraft tires standing wave is analyzed. According to the definition of tire standing wave, a method for determining critical speed is proposed. Finally, the effects of tire inflation pressure and vertical load on the occurrence of standing waves were studied.
A Saturation-Based Method for Primary Resonance Control of Flexible Manipulator
Authors: Geng Ruihai , Bian Yushu , Zhang Liang , Guo Yizhu
Published: April 2022

When primary resonance occurs, even a small external disturbance can abruptly excite large amplitude vibration and deteriorate the working performance of a flexible manipulator. Most active control methods are effective for non-resonant vibration but not for primary resonance. In view of this, this paper puts forward a new nonlinear saturation-based control method to suppress the primary resonance of a flexible manipulator considering complicated rigid-flexible coupling and modal coupling. A vibration absorber with variable stiffness/damping is designed to establish an energy exchange channel for saturation. A novel idea of modal coupling enhancement is suggested to improve saturation performance by strengthening the coupling relationship between the mode of the vibration absorber and the controlled mode of the flexible manipulator. Through stability analysis on the primary resonance response of the flexible manipulator with the vibration absorber, the saturation mechanism is successfully established and the effectiveness of the saturation control algorithm is validated. On this basis, several important indexes are extracted and employed to optimize saturation control. Finally, a series of virtual prototyping simulations and experiments are conducted to verify the feasibility of the suggested saturation-based control method. This research will contribute to the primary resonance suppression of a flexible manipulator under a complex external excitation environment.
Minimalistic Logit Model as an Effective Tool for Predicting the Risk of Financial Distress in the Visegrad Group
Authors: Pavlicko Michal , Mazanec Jaroslav
Published: April 2022

Predicting financial distress is one of the most well-known issues in corporate finance. Investors and other stakeholders often use prediction models as relevant tools for identifying weaknesses to eliminate potential threats to business partners. This paper aims to present an effective logistic regression model for a one-year-ahead prediction of financial distress with the minimum set of predictors as a part of risk management. The paper is motivated by various works dealing with the curse of dimensionality phenomenon and the observation that the increasing number of logit-model predictors does not improve the prediction—on the contrary. Monitoring the significance of improvement in the stepwise growth of the predictor set is used to identify the minimal set. Logistic regression with cross-validation is involved in the modelling process. The proposed model is compared with other logit-based models used regionally or globally on the same large dataset, which underlines the model validity and robustness. The proposed logit model contains only two significant predictors and achieves excellent performance metrics compared to other models. The added value of the article lies in a simple application for managers, investors, creditors, financial institutions, and others with a reliable classification of companies into healthy and unhealthy company groups.
Revisiting the Boundary Value Problem for Uniformly Transversely Loaded Hollow Annular Membrane Structures: Improvement of the Out-of-Plane Equilibrium Equation
Authors: Zhang Qi , Li Xue , He Xiao-Ting , Sun Jun-Yi
Published: April 2022

In a previous work by the same authors, a hollow annular membrane structure loaded transversely and uniformly was proposed, and its closed-form solution was presented; its anticipated use is for designing elastic shells of revolution. Since the height–span ratio of shells of revolution is generally desired to be as large as possible, to meet the need for high interior space, especially for the as-small-as-possible horizontal thrust at the base of shells of revolution, the closed-form solution should be able to cover annular membranes with a large deflection–outer radius ratio. However, the previously presented closed-form solution cannot meet such an ability requirement, because the previous out-of-plane equilibrium equation used the assumption of a small deflection–outer radius ratio. In this study, the out-of-plane equilibrium equation is re-established without the assumption of a small deflection–outer radius ratio, and a new and more refined closed-form solution is presented. The new closed-form solution is numerically discussed regarding its convergence and effectiveness, and compared with the old one. The new and old closed-form solutions agree quite closely for lightly loaded cases but diverge as the load intensifies. Differences in deflections, especially in stresses, are very significant when the deflection–outer radius ratio exceeds 1/4, indicating that, in this case, the new closed-form solution should be adopted in preference to the old one.
A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin
Authors: Ye Zi , Wu Yinxu , Chen Hui , Pan Yi , Jiang Qingshan
Published: April 2022

Cryptocurrencies can be considered as mathematical money. As the most famous cryptocurrency, the Bitcoin price forecasting model is one of the popular mathematical models in financial technology because of its large price fluctuations and complexity. This paper proposes a novel ensemble deep learning model to predict Bitcoin’s next 30 min prices by using price data, technical indicators and sentiment indexes, which integrates two kinds of neural networks, long short-term memory (LSTM) and gate recurrent unit (GRU), with stacking ensemble technique to improve the accuracy of decision. Because of the real-time updates of comments on social media, this paper uses social media texts instead of news websites as the source data of public opinion. It is processed by linguistic statistical method to form the sentiment indexes. Meanwhile, as a financial market forecasting model, the model selects the technical indicators as input as well. Real data from September 2017 to January 2021 is used to train and evaluate the model. The experimental results show that the near-real time prediction has a better performance, with a mean absolute error (MAE) 88.74% better than the daily prediction. The purpose of this work is to explain our solution and show that the ensemble method has better performance and can better help investors in making the right investment decision than other traditional models.
Categorification of Integer Sequences via Brauer Configuration Algebras and the Four Subspace Problem
Authors: Moreno Cañadas Agustín , Fernández Espinosa Pedro Fernando , Agudelo Muñetón Natalia
Published: April 2022

The four subspace problem is a known matrix problem, which is equivalent to determining all the indecomposable representations of a poset consisting of four incomparable points. In this paper, we use solutions of this problem and invariants associated with indecomposable projective modules with some suitable Brauer configuration algebras to categorify the integer sequence encoded in the OEIS as A100705 and some related integer sequences.
Numerical Method for a Filtration Model Involving a Nonlinear Partial Integro-Differential Equation
Authors: Baigereyev Dossan , Omariyeva Dinara , Temirbekov Nurlan , Yergaliyev Yerlan , Boranbek Kulzhamila
Published: April 2022

In this paper, we propose an efficient numerical method for solving an initial boundary value problem for a coupled system of equations consisting of a nonlinear parabolic partial integro-differential equation and an elliptic equation with a nonlinear term. This problem has an important applied significance in petroleum engineering and finds application in modeling two-phase nonequilibrium fluid flows in a porous medium with a generalized nonequilibrium law. The construction of the numerical method is based on employing the finite element method in the spatial direction and the finite difference approximation to the time derivative. Newton’s method and the second-order approximation formula are applied for the treatment of nonlinear terms. The stability and convergence of the discrete scheme as well as the convergence of the iterative process is rigorously proven. Numerical tests are conducted to confirm the theoretical analysis. The constructed method is applied to study the two-phase nonequilibrium flow of an incompressible fluid in a porous medium. In addition, we present two examples of models allowing for prediction of the behavior of a fluid flow in a porous medium that are reduced to solving the nonlinear integro-differential equations studied in the paper.
A Regularized Graph Neural Network Based on Approximate Fractional Order Gradients
Authors: Liu Zijian , Wang Yaning , Luo Yang , Luo Chunbo
Published: April 2022

Graph representation learning is a significant challenge in graph signal processing (GSP). The flourishing development of graph neural networks (GNNs) provides effective representations for GSP. To effectively learn from graph signals, we propose a regularized graph neural network based on approximate fractional order gradients (FGNN). The regularized graph neural network propagates the information between neighboring nodes. The approximation strategy for calculating fractional order derivatives avoids falling into fractional order extrema and overcomes the high computational complexity of fractional order derivatives. We further prove that such an approximation is feasible and FGNN is unbiased towards the global optimization solution. Extensive experiments on citation and community networks show that the proposed FGNN has improved recognition accuracy and convergence speed than vanilla FGNN. The five datasets of different sizes and domains confirm the great scalability of our proposed method.
Towards Avoiding Cascading Failures in Transmission Expansion Planning of Modern Active Power Systems Using Hybrid Snake-Sine Cosine Optimization Algorithm
Authors: Rawa Muhyaddin
Published: April 2022

In this paper, a transmission expansion planning (TEP) model is proposed to guarantee the resilience of power systems and mitigate cascading failures’ impacts. The energy storage systems and fault current limiters’ planning models are integrated into the TEP problem to minimize cascading outages and comply with short-circuit current reliability constraints. Most studies in the literature adopt a single strategy to simulate power systems’ cascading failures that may not be enough to guarantee networks’ resilience. This work elaborates on two scenarios for initiating cascading failures to study the impact of various initiating events on the planned system’s strength and the projects required. The TEP problem is formulated as a non-linear, non-convex large-scale problem. To avoid linearization issues and enhance meta-heuristics performance, a hybridization of two meta-heuristic techniques, namely snake optimizer and sine cosine algorithm (SO-SCA), is proposed to solve the problem. Two hybridization strategies are suggested to improve the exploration and exploitation stages. Defining future loads growth is essential for TEP. Hence, a load forecasting technique based on SO-SCA is investigated and compared with some methods reported in the literature. The results obtained proved the efficiency of the proposed approach in predicting load growth. TEP’s calculations were carried out on the Garver and the IEEE 24-bus system. The results demonstrated the superiority of the hybrid SO-SCA in solving the TEP problem. Moreover, the projects required to expand networks differed according to the type of cascading failures’ initiating scenario.
Influence of Context on Greatest Common Divisor Problem Solving: A Qualitative Study
Authors: Martinez Silvia , Valverde Jose C.
Published: April 2022

This paper presents results from a study about problem solving related to the concept of the greatest common divisor with secondary school students. The perspective of the analysis is the meaningful learning of the constructivist theory. The main objectives are to assess the students’ competence in the resolution of such problems and analyze if the difficulties in the acquisition of this competence are influenced by the kinds of magnitudes or the context of the problem. The results suggest that some contexts generate more difficulties to perform the use of the greatest common divisor. Moreover, some erroneous patterns have been detected. On one hand, students tend to relate and confuse the concepts of greatest common divisor and lowest common multiple. On the other hand, they have a predisposition to simplify problems, performing only the operation to obtain the greatest common divisor, and without thinking that additional arithmetic operations can be performed.
An Evolutionary Game Analysis of Periodical Fluctuation in Food Safety Supervision
Authors: Sun Jiaqin , Fan Ruguo , Yang Zhou
Published: April 2022

Periodical fluctuation is a common phenomenon in food safety supervision. The existing literature on China’s food safety supervision mainly analyzes periodical fluctuation by statistical methods. This paper provides a theoretical explanation by building an evolutionary game model between food enterprises and supervision institutions under bounded rationality. The “Sanlu milk powder” food safety incident is taken as a typical example to conduct numerical simulations of the food safety supervision game. Moreover, the determining factors in the periodical fluctuation in food safety supervision are analyzed in detail by numerical simulations, including the initial states and benefit–cost parameters. The results show that the periodical fluctuation and probability of supervision failure are influenced by the initial states. Supervision institutions should discard historical path dependence and adjust their supervision-intensity timing according to its actual effects. In addition, blindly increasing rewards or punishments cannot effectively restrain the fluctuation or reduce food safety incidents. To reduce the occurrence of food safety incidents and decrease periodical fluctuation, supervision institutions should reduce supervision costs by using information technology, establish strict food safety standards to eliminate “small-workshop” enterprises, be more aware of risks and appropriately overestimate the added benefits for food enterprises of becoming involved in illegal production.
Algebraic Systems with Positive Coefficients and Positive Solutions
Authors: Acu Ana Maria , Raşa Ioan , Şteopoaie Ancuţa Emilia
Published: April 2022

The paper is devoted to the existence, uniqueness and nonuniqueness of positive solutions to nonlinear algebraic systems of equations with positive coefficients. Such systems appear in large numbers of applications, such as steady-state equations in continuous and discrete dynamical models, Dirichlet problems, difference equations, boundary value problems, periodic solutions and numerical solutions for differential equations. We apply Brouwer’s fixed point theorem, Krasnoselskii’s fixed point theorem and monotone iterative methods in order to extend some known results and to obtain new results. We relax some hypotheses used in the literature concerning the strict monotonicity of the involved functions. We show that, in some cases, the unique positive solution can be obtained by a monotone increasing iterative method or by a monotone decreasing iterative method. As a consequence of one of our results, we recover the existence of a non-negative solution of the Leontief system and describe a monotone iterative method to find it.
A Comparative Study of Forecasting Electricity Consumption Using Machine Learning Models
Authors: Lee Madeline , Ser Yee , Selvachandran Ganeshsree , Thong Pham , Cuong Le , Son Le , Tuan Nguyen , Gerogiannis Vassilis
Published: April 2022

Production of electricity from the burning of fossil fuels has caused an increase in the emission of greenhouse gases. In the long run, greenhouse gases cause harm to the environment. To reduce these gases, it is important to accurately forecast electricity production, supply and consumption. Forecasting of electricity consumption is, in particular, useful for minimizing problems of overproduction and oversupply of electricity. This research study focuses on forecasting electricity consumption based on time series data using different artificial intelligence and metaheuristic methods. The aim of the study is to determine which model among the artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), least squares support vector machines (LSSVMs) and fuzzy time series (FTS) produces the highest level of accuracy in forecasting electricity consumption. The variables considered in this research include the monthly electricity consumption over the years for different countries. The monthly electricity consumption data for seven countries, namely, Norway, Switzerland, Malaysia, Egypt, Algeria, Bulgaria and Kenya, for 10 years were used in this research. The performance of all of the models was evaluated and compared using error metrics such as the root mean squared error (RMSE), average forecasting error (AFE) and performance parameter (PP). The differences in the results obtained via the different methods are analyzed and discussed, and it is shown that the different models performed better for different countries in different forecasting periods. Overall, it was found that the FTS model performed the best for most of the countries studied compared to the other three models. The research results can allow electricity management companies to have better strategic planning when deciding on the optimal levels of electricity production and supply, with the overall aim of preventing surpluses or shortages in the electricity supply.
Investigation of 2D Seismic DDA Method for Numerical Simulation of Shaking Table Test of Rock Mass Engineering
Authors: Fu Xiaodong , Kang Jingyu , Sheng Qian , Zheng Lu , Du Wenjie , Ding Haifeng
Published: April 2022

Since the basic theory of the discontinue deformation analysis (DDA) method was proposed, the DDA open source has gone through a long development process. At present, different kinds of programs have been widely applied in rock mass engineering such as slope, dam, and tunnel. This paper introduces the solution principle of DDA motion equations in detail, as well as the development status of the 2D open-source program. Numerical simulation of shaking table test of rock mass engineering using 2D DDA program is highlighted, and investigations of seismic wave pre-processing and seismic input method are carried out. First, based on the Newmark integration scheme, the integration algorithms of synthetic or measured seismic wave time history, correction function of seismic wave, and DDA simulation are unified. Then, three seismic input methods are implanted in the DDA program, and the applicability of various seismic input methods is discussed. On this basis, using the improved seismic 2D DDA program, a shaking table test of typical rock mass engineering is simulated. Through the comparison between the theoretical/test data and simulation results, the reliability of the improved DDA program in seismic response analysis is verified; the large mass method and the large stiffness method are more suitable for rigid foundation, such as shaking table test; the propagation of the seismic wave presents a significant amplification effect due to the reflection, refraction, and diffraction in the tunnel. The research results provide DDA theory and an open-source program for analyzing the seismic response of rock mass engineering.
Bistability and Chaos Emergence in Spontaneous Dynamics of Astrocytic Calcium Concentration
Authors: Pankratova Evgeniya , Sinitsina Maria , Gordleeva Susanna , Kazantsev Victor
Published: April 2022

In this work, we consider a mathematical model describing spontaneous calcium signaling in astrocytes. Based on biologically relevant principles, this model simulates experimentally observed calcium oscillations and can predict the emergence of complicated dynamics. Using analytical and numerical analysis, various attracting sets were found and investigated. Employing bifurcation theory analysis, we examined steady state solutions, bistability, simple and complicated periodic limit cycles and also chaotic attractors. We found that astrocytes possess a variety of complex dynamical modes, including chaos and multistability, that can further provide different modulations of neuronal circuits, enhancing their plasticity and flexibility.
Meanings Expressed by Primary Schoolchildren When Solving a Partitioning Task
Authors: Castro-Rodríguez Elena , Ferreira Marisel , Montoro Ana B. , Ruiz-Hidalgo Juan F.
Published: April 2022

This study entailed an in-depth exploration of the meanings identified by a group of 105 fourth year primary schoolchildren when solving a task involving partitioning. The research was based on a semantic triangle consisting of a conceptual structure, representation systems, and sense. The content of children’s answers was analysed qualitatively. One of the most prominent findings was that purposes or usages were recognised based on multiple strategies, new categories of which, not envisaged in earlier research, were defined. Most of the students deployed graphic, verbal, and numerical representation and established relationships among them. Concepts such as the part-whole relationship and fractioning appeared in their description of conceptual structure, although errors were detected in terms of inequities and confusion between numerator and denominator.
Static and Dynamic Analysis of 6-DOF Quasi-Zero-Stiffness Vibration Isolation Platform Based on Leaf Spring Structure
Authors: Wang Zhen , He Chuanlin , Xu Yan , Li Dong , Liang Zhanyuan , Ding Wei , Kou Lei
Published: April 2022

Multi-degree-of-freedom isolator with low stiffness is a fair prospect in engineering application. In this paper, a novel 6-DOF QZS vibration isolation platform based on leaf spring structure is presented. Its bearing capacity is provided through four leaf springs, and the quasi-zero-stiffness is realized by the force balance between the central spring and the suspension spring. 6-DOF vibration isolation is realized by the ball-hinge fixed design of a leaf spring. Through static and dynamic analysis, the following conclusions are brought. The stiffness of the leaf spring and the deformation of the central spring under static load are directly proportional to the bearing capacity of the isolation table. Besides, in order to ensure that the stiffness of the system is close to zero, the stiffness of the suspension spring and the inner spring should be as similar as possible. The vertical and horizontal displacement transmissibility tests of the isolation platform are carried out, in which the jumping phenomenon in the QZS vibration isolation platform is analyzed. By improving the damping of the structure and the length of the suspension spring, the dynamic vibration isolation process of the system can be more stable, the transmissibility can be reduced, and the vibration isolation effect can be enhanced.
Artificial Neural Based Speed and Flux Estimators for Induction Machine Drives with Matlab/Simulink
Authors: Diab Ahmed A. Zaki , Elsawy Mohammed A. , Denis Kotin A. , Alkhalaf Salem , Ali Ziad M.
Published: April 2022

In this paper, an Artificial Neural Network (ANN) for accurate estimation of the speed and flux for induction motor (IM) drives has been presented for industrial applications such as electric vehicles (EVs). Two ANN estimators have been designed, one for the rotor speed estimation and the other for the stator and rotor flux estimation. The input training data has been collected based on the currents and voltage data, while the output training data of the speed and stator and rotor fluxes has been established based on the measured speed and flux estimator-based mathematical model of the IM. The designed ANN estimators can overcome the problem of the parameter’s variations and drift integration problems. Matlab/Simulink has been used to develop and test the ANN estimators. The results prove the ANN estimators’ effectiveness under various operation conditions.
The Dynamics between Structural Conditions and Entrepreneurship in Europe: Feature Extraction and System GMM Approaches
Authors: Borges Ana , Correia Aldina , Costa e Silva Eliana , Carvalho Glória
Published: April 2022

Structural conditions and population characteristics of countries have been identified in the literature as factors for an individual to become, or to have intentions of becoming, an entrepreneur. However, this is still a subject under research, which has become increasingly relevant and could be crucial in the current challenges of European countries. In this work, the factors for entrepreneurial intentions and entrepreneurship activity are studied. More precisely, the structural conditions of European countries, which has changed over the last two decades, is analysed. The aim is to describe this behaviour and to state the main conditions for developing entrepreneurship activities and the intentions to become an entrepreneur. To achieve this purpose, feature extraction, namely, principal component analysis and dynamic longitudinal approaches are used. In particular, we propose that the system-generalised method of moments (GMM) model is adequate in this situation. The results suggest that the structure of the European framework conditions for entrepreneurship, obtained using the Factor Analysis year by year, is quite diversified until 2008, while after 2008, it is more stable. Moreover, it is concluded that the conditions associated with entrepreneurial intentions and entrepreneurial activity differ between these two time periods. Hence, the dynamic aspect of the structural conditions that affect entrepreneurial activities or intentions should be acknowledged.
Fourth Cumulant Bound of Multivariate Normal Approximation on General Functionals of Gaussian Fields
Authors: Kim Yoon-Tae , Park Hyun-Suk
Published: April 2022

We develop a technique for obtaining the fourth moment bound on the normal approximation of F, where F is an Rd-valued random vector whose components are functionals of Gaussian fields. This study transcends the case of vectors of multiple stochastic integrals, which has been the subject of research so far. We perform this task by investigating the relationship between the expectations of two operators Γ and Γ*. Here, the operator Γ was introduced in Noreddine and Nourdin (2011) [On the Gaussian approximation of vector-valued multiple integrals. J. Multi. Anal.], and Γ* is a muilti-dimensional version of the operator used in Kim and Park (2018) [An Edgeworth expansion for functionals of Gaussian fields and its applications, stoch. proc. their Appl.]. In the specific case where F is a random variable belonging to the vector-valued multiple integrals, the conditions in the general case of F for the fourth moment bound are naturally satisfied and our method yields a better estimate than that obtained by the previous methods. In the case of d=1, the method developed here shows that, even in the case of general functionals of Gaussian fields, the fourth moment theorem holds without conditions for the multi-dimensional case.
COVID Asymmetric Impact on the Risk Premium of Developed and Emerging Countries’ Stock Markets
Authors: Núñez-Mora José Antonio , Santillán-Salgado Roberto Joaquín , Contreras-Valdez Mario Iván
Published: April 2022

We estimated the stock market risk premium during the COVID-19 pandemic with a GARCH-in-Mean (GARCH-M)(1,1) model. The analysis then explored the presence of regime changes using a two-regime Markov-Switching GARCH (MS GARCH)(1,1) model. The sample we used included the stock market indexes of nine countries from three geographical regions, including: North America (Canada, USA, and Mexico), South America (Brazil and Argentina), and Asia (Japan, South Korea, Hong Kong, and Singapore), over two periods: (a) pre-COVID (from 1 January 2015 to 31 December 2019); and (b) COVID (from 1 January 2020 to 31 December 2021). Our GARCH-M(1,1) estimation results indicate that the more developed countries’ stock markets experienced an important increase in their risk premium during the COVID period, likely explained by the massive government anticyclical policies. By contrast, developing countries’ stock markets, particularly in Latin America, experienced a reduction, and in some cases, even a total loss of the risk premium effect. From the perspective of investors and portfolio risk managers, the identification of high and low volatility periods and their estimated probability of occurrence is useful for the characterization of stress scenarios and the design of emerging strategies. For governments and central bankers, the implementation of different policies should respond to the more likely scenarios but should also be prepared to respond to other less likely scenarios. Institutional preparedness to respond to as many different scenarios as may be identified with the use of MS GARCH models can make their interventions more successful. This work presents an objective example of how the use of MS GARCH models may be of use to practitioners in both the financial industry and government. We confirmed that the results of a two-regime MS GARCH model are superior to those obtained from a single-regime model.
Phytoplankton Toxins and Their Potential Therapeutic Applications: A Journey toward the Quest for Potent Pharmaceuticals
Authors: Pradhan Biswajita , Ki Jang-Seu
Published: April 2022

Phytoplankton are prominent organisms that contain numerous bioactive substances and secondary metabolites, including toxins, which can be valuable to pharmaceutical, nutraceutical, and biotechnological industries. Studies on toxins produced by phytoplankton such as cyanobacteria, diatoms, and dinoflagellates have become more prevalent in recent years and have sparked much interest in this field of research. Because of their richness and complexity, they have great potential as medicinal remedies and biological exploratory probes. Unfortunately, such toxins are still at the preclinical and clinical stages of development. Phytoplankton toxins are harmful to other organisms and are hazardous to animals and human health. However, they may be effective as therapeutic pharmacological agents for numerous disorders, including dyslipidemia, obesity, cancer, diabetes, and hypertension. In this review, we have focused on the properties of different toxins produced by phytoplankton, as well as their beneficial effects and potential biomedical applications. The anticancer properties exhibited by phytoplankton toxins are mainly attributed to their apoptotic effects. As a result, phytoplankton toxins are a promising strategy for avoiding postponement or cancer treatment. Moreover, they also displayed promising applications in other ailments and diseases such as Alzheimer’s disease, diabetes, AIDS, fungal, bacterial, schizophrenia, inflammation, allergy, osteoporosis, asthma, and pain. Preclinical and clinical applications of phytoplankton toxins, as well as future directions of their enhanced nano-formulations for improved clinical efficacy, have also been reviewed.
Sinuhirtone A, An Uncommon 17,19-Dinorxeniaphyllanoid, and Nine Related New Terpenoids from the Hainan Soft Coral Sinularia hirta
Authors: Chen Zi-Hui , Lu Si-Qi , Han Guan-Ying , Li Xu-Wen , Guo Yue-Wei
Published: April 2022

Chemical investigation of the Hainan soft coral Sinularia hirta resulted in the isolation and identification of a library of sixteen structurally diverse terpenoids, including a dinorditerpenoid with an uncommon 17,19-dinorxeniaphyllane skeleton, namely sinuhirtone A (7), six new xeniaphyllane-type diterpenoids (1–6), one new norxeniaphyllanoid (8), two new norcaryophyllene-type sesquiterpenoids (9 and 10), together with six known related compounds (11–16). Compounds 1–3 are three new furanone-containing xeniaphyllane-type diterpenoids. The structures of the new compounds, including their absolute configurations, were determined by extensive spectroscopic analysis and a series of quantum chemical calculations, including quantum mechanical-nuclear magnetic resonance (QM–NMR), time-dependent density functional theory–electronic circular dichroism (TDDFT–ECD), and optical rotatory dispersion (ORD) methods. A plausible biosynthetic connection between new compounds 1–9 was also proposed. New compounds 2–4, 7, and 8 were evaluated for in vitro cytotoxicity against four cancer cell lines.
Cardiac Biomarkers in Patients with Acute Pulmonary Embolism
Authors: Janisset Luc , Castan Maxime , Poenou Géraldine , Lachand Raphael , Mismetti Patrick , Viallon Alain , Bertoletti Laurent
Published: April 2022

Pulmonary embolism is a frequent and potentially fatal disease. The major challenge of initial management lies in prognostic stratification. Since 2014, the European recommendations on the diagnosis and management of acute pulmonary embolism are based on assessing the risk stratification regarding hemodynamic status first, then on a combined risk assessment model using a clinical score, an imaging evaluation of right heart size and the concentration of a serum cardiac biomarker. Usual biomarkers cover cardiac ischemia (troponin and derivates) and dilatation (BNP and derivates). The aim of this review is to offer a practical update on the role of the Troponins and BNPs families of biomarkers and the prognosis of pulmonary embolism, and furthermore, to provide a brief overview of their place in current management.
The Role of Tissue Geometry in Spinal Cord Regeneration
Authors: Pettigrew David , Singh Niharika , Kirthivasan Sabarish , Crutcher Keith
Published: April 2022

Unlike peripheral nerves, axonal regeneration is limited following injury to the spinal cord. While there may be reduced regenerative potential of injured neurons, the central nervous system (CNS) white matter environment appears to be more significant in limiting regrowth. Several factors may inhibit regeneration, and their neutralization can modestly enhance regrowth. However, most investigations have not considered the cytoarchitecture of spinal cord white matter. Several lines of investigation demonstrate that axonal regeneration is enhanced by maintaining, repairing, or reconstituting the parallel geometry of the spinal cord white matter. In this review, we focus on environmental factors that have been implicated as putative inhibitors of axonal regeneration and the evidence that their organization may be an important determinant in whether they inhibit or promote regeneration. Consideration of tissue geometry may be important for developing successful strategies to promote spinal cord regeneration.
Romanian Version of the Oral Health Values Scale: Adaptation and Validation
Authors: Balgiu Beatrice Adriana , Sfeatcu Ruxandra , Mihai Christina , Lupușoru Mircea , Bucur Mirela Veronica , Tribus Laura
Published: April 2022

Background and Objectives: Oral health values are an indicator of how people decide their priorities for oral health—an integral part of general health. The purpose of the study was the validation of the Oral Health Values Scale (OHVS), which measures the extent to which individuals invest in their oral health and which includes four factors: Professional dental care, Appearance, Flossing, and Retention of teeth. Materials and Methods: A cross-sectional study was conducted on a sample of adults (n = 869; Mean age = 32.98) who completed the online questionnaire set. The OHVS was translated into Romanian through the forward-backward translation procedure. The construct validity was assessed through a confirmatory factor analysis (CFA) based on the maximum likelihood estimation method. The convergent validity was assessed by associating the OHVS with scales that evaluate the oral health quality of life (OHIP-14), the attitude towards one’s dentist (R-DBS), oral self-care (DNS), and general health literacy (GHL). The internal consistency was examined using Cronbach’s α and McDonald’s ω. Results: The CFA supports the four-factor model of the scale. The OHVS total score and its subscales are negatively associated with the impact of oral health on the quality of life (except for the Appearance subscale) and with a distrust in the dentist’s treatments; they are positively associated with oral self-care and general health literacy. The gender difference shows females have higher scores on all four subscales. The internal consistency is good for Appearance, Flossing, and the OHVS total score, but weak for Professional dental care and Retention. Conclusions: OHVS is a valid instrument in the Romanian context that can enrich the set of tools that contribute to oral health research, especially in epidemiological studies.