bluuebunny/crossref_metadata_2025 huggingface dataset:
Rheological and Tribological Study of Polyethylsiloxane with SiO2 Nanoparticles Additive
Authors: Dembelova Tuyana , Badmaev Badma , Makarova Dagzama , Mashanov Aleksandr , Mishigdorzhiyn Undrakh
Published: December 2022

Nowadays, much attention is paid to the creation of high-performance lubricants with improved properties through the use of ultrafine nanopowders. The paper shows the results of studying the viscoelastic properties of samples of silicon dioxide nanoparticle suspensions based on polyethylsiloxane (silicone oil) by the acoustic resonance method. The method is based on a study of the additional coupling effect on the resonance characteristics of the piezoelectric resonator. The values of the shear modulus and the tangent of the mechanical loss angle were calculated. The interaction between polymer molecules and nanoparticles was characterized by infrared spectroscopy. The influence of silicon dioxide nanoparticles (as a nano-additive) on the performance characteristics of polyethylsiloxane lubricant is presented. The results of determining the friction coefficient from the sliding speed show an increase in the tear strength of the lubricating film, leading to improved tribological properties.
Synthesis and Crystal Structure of 3-(4-Cyano-3-nitro-1H-pyrazol-5-yl)-4-nitrofurazan: Comparison of the Influence of the NO2 and CN Groups on Crystal Packing and Density
Authors: Suponitsky Kyrill Yu. , Strizhenko Kirill V.
Published: December 2022

The title compound was synthesized and characterized by IR and NMR spectroscopy and single crystal X-ray diffraction. The analysis of the crystal packing of the title compound and its analog, bearing a nitro group instead of a nitrile one, allowed a direct comparison of two common explosophoric groups: CN and NO2. By using ΔOED-based densification approach, it is shown that the CN group is lighter in mass, less dense, and participates in intermolecular bonding to a lesser extent in comparison to the NO2 group. As a result, the cyano compound has a lower density and a looser crystal packing than the nitro analog.
Metal/Graphene Composites: A Review on the Simulation of Fabrication and Study of Mechanical Properties
Authors: Baimova Julia A. , Shcherbinin Stepan A.
Published: December 2022

Although carbon materials, particularly graphene and carbon nanotubes, are widely used to reinforce metal matrix composites, understanding the fabrication process and connection between morphology and mechanical properties is still not understood well. This review discusses the relevant literature concerning the simulation of graphene/metal composites and their mechanical properties. This review demonstrates the promising role of simulation of composite fabrication and their properties. Further, results from the revised studies suggest that morphology and fabrication techniques play the most crucial roles in property improvements. The presented results can open up the way for developing new nanocomposites based on the combination of metal and graphene components. It is shown that computer simulation is a possible and practical way to understand the effect of the morphology of graphene reinforcement and strengthening mechanisms.
The Effect of Particle Necks on the Mechanical Properties of Aerogels
Authors: Ratke Lorenz , Rege Ameya , Aney Shivangi
Published: December 2022

Mechanical properties of open-porous materials are often described by constructing a cellular network with beams of constant cross sections as the struts of the cells. Such models have been applied to describe, for example, thermal and mechanical properties of aerogels. However, in many aerogels, the pore walls or the skeletal network is better described as a pearl-necklace, in which the particles making up the network appear as a string of pearls. In this paper, we investigate the effect of neck sizes on the mechanical properties of such pore walls. We present an analytical and a numerical solution by modeling these walls as corrugated beams and study the subsequent deviations from the classical scaling theory. Additionally, a full numerical model of such pearl-necklace-like walls with concave necks of varying sizes are simulated. The results of the numerical model are shown to be in good agreement with those resulting from the computational one.
Performance Enhancement of Direct Torque and Rotor Flux Control (DTRFC) of a Three-Phase Induction Motor over the Entire Speed Range: Experimental Validation
Authors: Alshbib Mussaab M. , Elgbaily Mohamed Mussa , Alsofyani Ibrahim Mohd , Anayi Fatih
Published: December 2022

This paper proposes a robust and effective method of direct torque and rotor flux control (DTRFC) strategy for an induction motor (IM). The described scheme ensures the elimination of uncontrollable angles (UCAs) over the entire speed range. This means that each voltage vector (VV) produces the required effect of both torque and flux without any counteracting effect. First, the behaviour of the DTRFC algorithm was analysed at low and high speeds in terms of determining the values of UCAs. Through the analysis, it was found that the basic scheme suffered from UCAs at medium and high speeds. Accordingly, a special strategy for medium and high speeds with 18 sub-sectors (SSs) was proposed while maintaining the basic 6 sectors strategy for low speed. The transition speed between the two strategies was determined to ensure the absence of UCAs over the whole speed range. The simulation results of the proposed method were obtained in the MATLAB/Simulink environment. Furthermore, to verify the effectiveness of this method, a dSPACE-based experimental induction motor DTRFC drive system was accomplished.
Comparison of Drivetrain Topologies for an Ankle Gait Cycle
Authors: Joos Yentl , Sergeant Peter , Vansompel Hendrik , Verstraten Tom
Published: December 2022

Powered prosthesis actuation is a field where energy efficiency and mass are important characteristics. The motion requirements of high torque at low speed and low torque at high speed are difficult to effectively combine in a single electric motor. A possibility is to use an oversized direct–drive motor that can deliver the peak torque. However, this results in a heavy actuation system and low overall efficiency. A common practice is combining a smaller electric motor with a gear reduction system. In the literature, novel redundant electric actuation systems have been presented with or without locking mechanisms to mitigate this problem. In this work, we provide a comparison of multiple electrical actuation systems composed of electric motors, gear reduction systems and locking mechanisms. This is done using the gait cycle of a human ankle as a case study. An electric motor with a double stator and a single rotor combined with a gearbox shows the most promising overall results when taking energy loss, total mass and complexity of the system into account.
Innovative Design and Kinematic Characteristics Analysis of Floating Mobile Chassis of Inspection Robot
Authors: Wang Chengjun , Wang Zhihui , Hu Haixia , Li Long
Published: December 2022

In view of the problems that exist in the working plane of the inspection robot equipped with precision instruments that cannot always maintain a stable state when moving on a complex road surface, a floating mobile chassis was designed based on the Teoriya Resheniya Izobreatatelskikh Zadatch (TRIZ) theory, and the floating suspension device was also optimized based on the substance field. The kinematic model of the floating mobile chassis was established, and the obstacle-surmounting analysis has been carried out on complex road conditions such as the boss and trench. The dynamic model and mobile performance evaluation model of the obstacle crossing wheel are established. The prototype of the non-floating mobile chassis and the prototype of the floating mobile chassis were respectively established in ADAMS, and the motion comparison simulation analysis of boss, trench crossing and complex road conditions were also carried out. The results showed that the floating mobile chassis has strong adaptive performance, and the stability of the working plane can always be maintained when crossing obstacles.
Fault Diagnosis of Mine Ventilator Bearing Based on Improved Variational Mode Decomposition and Density Peak Clustering
Authors: Zhang Xi , Wang Hongju , Li Xuehui , Gao Shoujun , Guo Kui , Wei Yingle
Published: December 2022

The mine ventilator plays a role in protecting the life safety of underground workers, which is very significant to the production and development of coal mines. In total, 70% of ventilator failures are mechanical failures, and bearing failures are the most likely to occur in mechanical failures, which are also difficult to find. In order to identify fan bearing faults accurately, this paper proposes a fault diagnosis method based on improved variational mode decomposition and density peak clustering. First, the variational mode decomposition’s modal number K and secondary penalty factor α are chosen employing the improved sparrow optimization process. The bearing vibration signal is decomposed by the variational mode decomposition algorithm with optimized parameters. To create the characteristic vector, the multi-scale permutation entropy of the fourth order intrinsic mode function is determined. Then, the characteristic matrix is dimensionally reduced by kernel principal component analysis, and the two-dimensional matrix after dimensionality reduction is divided by density peak clustering method to find the clustering center of the training sample features. Lastly, the membership degree is assessed using the normalized clustering distance between the characteristic matrix of the test sample and the cluster center of the training sample. The accuracy of bearing fault identification on the self-constructed experimental platform can reach 100%, which verifies the effectiveness and potential of the proposed method.
Crowning Method on Bearing Supporting Large Wind Turbine Spindle Considering the Flexibility of Structure of Shaft System
Authors: Liu Xiangyang , Niu Rongjun , Wang Bin , Zhang Shuai , Cui Yongcun , Zhang Zhanli
Published: December 2022

To meet the precision design of bearings on large wind turbine spindles, a crowning method of bearing on wind turbine spindles considering the flexibility of the support structure is proposed. Firstly, a finite element (FE) model of the shaft system with a flexible structure is constructed by connecting the shaft and bearing through constraint equations (CE) and multi-point constraint (MPC) algorithms and replacing the bearing rollers with nonlinear spring elements and dampers. Then, the Newmark integration algorithm is used to solve the model and analyze the effect of the structure’s rigidity on the load distribution of bearings. Then, perform convergence analysis of the sequences of the spring load distribution using a high-pass filter based on fast Fourier transform (FFT) and root mean square error (RMSE) to obtain a suitable number of replacement springs. Finally, a sub-model of the upwind bearing is constructed with structured mesh. With the maximum Von Mises stress of the roller profile as the design target, the optimal logarithmic crowning of the roller and its tolerance zone under the given working conditions are obtained. The results show that the FE model of the shaft system proposed has good convergence. The FE model of the shaft system considering the flexibility of the support structure can obtain more accurate load distributions of bearings and can make the accurate crowning design of the bearing rollers based on the actual working conditions. This provides support for the precision design of bearings in large shaft systems.
The Assembly of Grid-type Lanthanide Cluster
Authors: Li Jinsong , Zhang Fan , Guo Xuefeng , Liu Dan , Wu Jianfeng
Published: December 2022

A dicompartmental Schiff base ligand was synthesized and used for the assembly of a lanthanide grid-like complex. Dinuclear Dy2 and tetranuclear Dy4 complexes were isolated from the reaction of the ligand with different dysprosium salt. Single crystal X-ray diffractions show that the two DyIII ions in Dy2 are adopted in the N3O coordination pockets of the ligand and further coordinated by water molecules, whereas, for Dy4, the four DyIII ions are clamped by four ligands through their terminal N3O coordination pockets, forming a grid-type assembly. Magnetic studies reveal that complex Dy2 shows field-induced single-molecule magnetic behavior under 1000 Oe dc field, complex Dy4 shows fast relaxation under zero field and field-induced single-molecule magnet (SMM) behavior under 500 Oe. The difference in the magnetic relaxation is related to the various deprotonation of the ligand and distinct topology of the assemblies.
Old Discovery Leading to New Era: Metabolic Imaging of Cancer with Deuterium MRI
Authors: Ding Hao , Haroon Athar , Wan Simon , Niendorf Thoralf , Adeleke Sola
Published: December 2022

The understanding of metabolic compartments involved in the survival, growth and invasion of tumours is important for modern cancer research. Deuterium metabolic spectroscopy (DMS) and metabolic imaging (DMI) have been demonstrated as robust, straightforward tools for visualising tumour metabolism in vivo. However, for them to become part of the cancer patient’s management pathway in a clinical setting, there remain many obstacles to overcome. Technological advancement in magnetic resonance imaging hardware and processing is needed. Further justification of DMI’s potential also requires more human study and multidisciplinary collaboration.
Kinetics and the Effect of Thermal Treatments on the Martensitic Transformation and Magnetic Properties in Ni49Mn32Ga19 Ferromagnetic Shape Memory Ribbons
Authors: Tolea Felicia , Popescu Bogdan , Bartha Cristina , Enculescu Monica , Tolea Mugurel , Sofronie Mihaela
Published: December 2022

In our work, the kinetics of martensitic transformations and the influence of thermal treatments on martensitic transformations, as well as the related magnetic properties of the Ni49Mn32Ga19 ferromagnetic shape memory melt-spun ribbons, have been investigated. Thermal treatments at 673 K for 1, 4 and 8 h can be considered an instrument for fine-tuning the performance parameters of alloys. One-hour thermal treatments promote an improvement in the crystallinity of these otherwise highly textured ribbons, reducing internal defects and stress induced by the melt-spinning technique. Longer thermal treatments induce an important magnetization rise concomitantly with a slight and continuous increase in martensitic temperatures and transformation enthalpy. The activation energy, evaluated from differential scanning calorimeter experimental data with a Friedman model, significantly increases after thermal treatments as a result of the multi-phase coexistence and stabilization of the non-modulated martensitic phase, which increases the reverse martensitic transformation hindrance.
Rethinking the Teaching of University Statistics: Challenges and Opportunities Learned from the Colombia–UK Dialogue
Authors: Méndez-Romero Rafael Alberto , Carter Jackie , Carrerá-Martínez Sofía , Suavita-Ramírez María Angélica , Higgins Vanessa
Published: December 2022

The aim of this paper is first to examine, through a qualitative analysis of statistics syllabi, the current state of statistical education in a sample of universities in Colombia. The focus is on statistics teaching in degrees for economics and business administration students. The results from the qualitative analysis reflect a preponderance of traditional and didactic teaching methods centered on the teacher, not on the student. The second aim is to present findings from a case study that has developed an innovative pedagogical intervention, called a data fellows program, from the University of Manchester, United Kingdom, which evidences opportunities for how statistics can be taught effectively to non-STEM majors. Further, the data fellows model has also been explored in the context of developing statistical and data skills capacities in Latin America. We reflect on how the lessons from the UK case study could open up opportunities for rethinking the teaching of statistics in Colombia through developing data projects and experiential learning to practice statistics in the real world.
Lifts of a Quarter-Symmetric Metric Connection from a Sasakian Manifold to Its Tangent Bundle
Authors: Khan Mohammad Nazrul Islam , De Uday Chand , Velimirović Ljubica S.
Published: December 2022

The objective of this paper is to explore the complete lifts of a quarter-symmetric metric connection from a Sasakian manifold to its tangent bundle. A relationship between the Riemannian connection and the quarter-symmetric metric connection from a Sasakian manifold to its tangent bundle was established. Some theorems on the curvature tensor and the projective curvature tensor of a Sasakian manifold with respect to the quarter-symmetric metric connection to its tangent bundle were proved. Finally, locally ϕ-symmetric Sasakian manifolds with respect to the quarter-symmetric metric connection to its tangent bundle were studied.
Investigation of the Convergence of a Multi-Grid Algorithm for Solving the Task of Pressure in the Thin Lubricating Film of the Non-Newtonian Fluid
Authors: Zadorozhnaya Elena , Levanov Igor , Mukhortov Igor , Hudyakov Vlad
Published: December 2022

The article describes a multi-grid algorithm for integrating the Reynolds equation for hydrodynamic pressures in the lubricating film of a heavy-loaded journal bearing. This equation is the basic one in solving the tasks of designing friction units of piston- and rotary machines. Lubrication sources of various configurations in the form of grooves and holes located on the friction surfaces were taken into account. The version of the multi-grid algorithm developed by the authors is based on Brandt’s work. At each level of grids, not only the convergence of the solution is controlled, but also the rate of convergence. The pressure equation was approximated by finite differences using the control volume method and passed to a system of algebraic equations, which were solved by the Seidel method. Bessel formulas were used as the interpolation operator. The function for taking into account the non-Newtonian properties of the lubricant is based on the power law. Comparison of the developed algorithm with other versions showed high efficiency. The use of multi-grid algorithms makes it possible to perform multi-variant calculations of the dynamics of heavily loaded bearings. As a result of the calculations, the characteristics of the connecting rod bearing of the heat engine, as well as the multilayer bearing of the turbocharger, are presented.
IC-SNN: Optimal ANN2SNN Conversion at Low Latency
Authors: Li Cuixia , Shang Zhiquan , Shi Li , Gao Wenlong , Zhang Shuyan
Published: December 2022

The spiking neural network (SNN) has attracted the attention of many researchers because of its low energy consumption and strong bionics. However, when the network conversion method is used to solve the difficulty of network training caused by its discrete, too-long inference time, it may hinder the practical application of SNN. This paper proposes a novel model named the SNN with Initialized Membrane Potential and Coding Compensation (IC-SNN) to solve this problem. The model focuses on the effect of residual membrane potential and rate encoding on the target SNN. After analyzing the conversion error and the information loss caused by the encoding method under the low time step, we propose a new initial membrane potential setting method and coding compensation scheme. The model can enable the network to still achieve high accuracy under a low number of time steps by eliminating residual membrane potential and encoding errors in the SNN. Finally, experimental results based on public datasets CIFAR10 and CIFAR100 also demonstrate that the model can still achieve competitive classification accuracy in 32 time steps.
C1-Cubic Quasi-Interpolation Splines over a CT Refinement of a Type-1 Triangulation
Authors: Benharzallah Haithem , Mennouni Abdelaziz , Barrera Domingo
Published: December 2022

C1 continuous quasi-interpolating splines are constructed over Clough–Tocher refinement of a type-1 triangulation. Their Bernstein–Bézier coefficients are directly defined from the known values of the function to be approximated, so that a set of appropriate basis functions is not required. The resulting quasi-interpolation operators reproduce cubic polynomials. Some numerical tests are given in order to show the performance of the approximation scheme.
New Explicit Oscillation Criteria for First-Order Differential Equations with Several Non-Monotone Delays
Authors: Attia Emad , El-Matary Bassant
Published: December 2022

The oscillation of a first-order differential equation with several non-monotone delays is proposed. We extend the works of Kwong (1991) and Sficas and Stavroulakis (2003) for equations with several delays. Our results not only essentially improve but also generalize a large number of the existing ones. Using some numerical examples, we illustrate the applicability and effectiveness of our results over many known results in the literature.
Availability Optimization Decision Support Design System for Different Repairable n-Stage Mixed Systems
Authors: Liu Gia-Shie , Lin Kuo-Ping
Published: December 2022

This study attempts to propose an availability optimization decision support design system for repairable n-stage mixed systems, in which different combinations of subsystems, such as parallel, standby, and k-out-of-q, are connected in series configuration. The enumeration method, tabu search, simulated annealing, non-equilibrium simulated annealing, and the modified redundancy allocation heuristic combined with a modified genetic algorithm will be proposed to solve the system availability optimization problem and further determine the appropriate system configuration design. Several simulated cases are conducted by following the procedural flow of the proposed availability optimization decision support design system to reach the optimal allocations of the component redundancy amount, the optimal repair rates, and the optimal failure rates of all subsystems to minimize the total system cost under several configuration constraints for different repairable n-stage mixed systems. Simulated results display that the proposed availability optimization decision support design system can definitely take advantage of different component redundancy system designs, including the parallel-series system, n-stage standby system, n-stage k-out-of-q system, and n-stage mixed system, to save a lot of cost and meet the high level of the system availability requirement compared to the n-stage single component series system. Additionally, the results for all proposed combined methods also show that the parallel-series system can obviously reach the same level of system availability requirement with less system total cost, in contrast to the n-stage standby system, by presuming the identical deteriorating probability for both the operating components and the standby components. The performance comparisons of five proposed combined methods for four proposed system configurations are analyzed comprehensively. It can be concluded that the performances of the modified redundancy allocation heuristic method, combined with a modified genetic algorithm on the criteria of the optimal system costs for four proposed system configurations, are not only superior to the other four combined methods, but also to the performances on the criteria of CPU running time for four proposed system configurations.
Guidance Law for Autonomous Takeoff and Landing of Unmanned Helicopter on Mobile Platform Based on Asymmetric Tracking Differentiator
Authors: Wang Zian , Gong Zheng , Yang Yang , Liu Yongzhen , Cai Pengcheng , Zhang Chengxi
Published: December 2022

For some flight missions, such as autonomous landing on mobile platforms, the demands on indicators such as target-tracking accuracy and so on are relatively high. To achieve this, a guidance system with excellent precision is necessary. An asymmetric tracking differentiator based on a tracking differentiator is proposed to establish the guidance system. On the basis of the proposed asymmetric tracking differentiator, an altitudinal and horizontal helicopter guidance system structure is designed. In this paper, a guidance law is designed in order to meet the accuracy and precision requirements in the autonomous landing and transition process. Apart from that, a plane-motion-guidance law is also designed to realize static and dynamic point tracking, linear route tracking and circular route tracking to improve the trajectory smoothness and accuracy. Finally, simulations of the autonomous landing process on moving platforms, including three stages, namely approaching, tracking and landing, are completed. The application effects and precision of the autonomous landing guidance algorithm under different wave heights and period conditions are analyzed through the obtained simulation curves.
Algorithms for Quantum Computation: The Derivatives of Discontinuous Functions
Authors: Gerck Ed
Published: December 2022

We hope this work allows one to calculate large prime numbers directly, not by trial-and-error, but following a physical law. We report—contrary to conventional assumptions—that differentiation of discontinuous functions (DDF) exists in the set Q, which becomes central to algorithms for quantum computation. DDF have been thought to exist not in the classical sense, but using distributions. However, DDF using distributions still is defined in terms of mathematical real-numbers (MRN), and do not address the Problem of Closure, here investigated. These facts lead to contradictions using MRN, solved by this work, providing a new unbounded class of physical solutions using physical numbers in quantum mechanics (QM), that were always there (just hidden), allowing DDF without distributions, or MRN. It is worthwhile to see this only in mathematics, to avoid the prejudices found in physics, as this reforms both general relativity and QM. This confirms the opinions of Nicolas Gisin that MRN are non-computable with probability 1, and Niels Bohr that physics is not reality, it is a fitting story about reality. Mathematics can get closer to reality, surprisingly. We just have to base mathematics on nature, not on how it defines nature.
Numerical Methods That Preserve a Lyapunov Function for Ordinary Differential Equations
Authors: Hernández-Solano Yadira , Atencia Miguel
Published: December 2022

The paper studies numerical methods that preserve a Lyapunov function of a dynamical system, i.e., numerical approximations whose energy decreases, just like in the original differential equation. With this aim, a discrete gradient method is implemented for the numerical integration of a system of ordinary differential equations. In principle, this procedure yields first-order methods, but the analysis paves the way for the design of higher-order methods. As a case in point, the proposed method is applied to the Duffing equation without external forcing, considering that, in this case, preserving the Lyapunov function is more important than the accuracy of particular trajectories. Results are validated by means of numerical experiments, where the discrete gradient method is compared to standard Runge–Kutta methods. As predicted by the theory, discrete gradient methods preserve the Lyapunov function, whereas conventional methods fail to do so, since either periodic solutions appear or the energy does not decrease. Moreover, the discrete gradient method outperforms conventional schemes when these do preserve the Lyapunov function, in terms of computational cost; thus, the proposed method is promising.
A Robust Learning Methodology for Uncertainty-Aware Scientific Machine Learning Models
Authors: Costa Erbet Almeida , Rebello Carine de Menezes , Fontana Márcio , Schnitman Leizer , Nogueira Idelfonso Bessa dos Reis
Published: December 2022

Robust learning is an important issue in Scientific Machine Learning (SciML). There are several works in the literature addressing this topic. However, there is an increasing demand for methods that can simultaneously consider all the different uncertainty components involved in SciML model identification. Hence, this work proposes a comprehensive methodology for uncertainty evaluation of the SciML that also considers several possible sources of uncertainties involved in the identification process. The uncertainties considered in the proposed method are the absence of a theory, causal models, sensitivity to data corruption or imperfection, and computational effort. Therefore, it is possible to provide an overall strategy for uncertainty-aware models in the SciML field. The methodology is validated through a case study developing a soft sensor for a polymerization reactor. The first step is to build the nonlinear model parameter probability distribution (PDF) by Bayesian inference. The second step is to obtain the machine learning model uncertainty by Monte Carlo simulations. In the first step, a PDF with 30,000 samples is built. In the second step, the uncertainty of the machine learning model is evaluated by sampling 10,000 values through Monte Carlo simulation. The results demonstrate that the identified soft sensors are robust to uncertainties, corroborating the consistency of the proposed approach.
Lie Symmetry Analysis of the Aw–Rascle–Zhang Model for Traffic State Estimation
Authors: Paliathanasis Andronikos , Leach Peter G. L.
Published: December 2022

We extend our analysis on the Lie symmetries in fluid dynamics to the case of macroscopic traffic estimation models. In particular we study the Aw–Rascle–Zhang model for traffic estimation, which consists of two hyperbolic first-order partial differential equations. The Lie symmetries, the one-dimensional optimal system and the corresponding Lie invariants are determined. Specifically, we find that the admitted Lie symmetries form the four-dimensional Lie algebra A4,12. The resulting one-dimensional optimal system is consisted by seven one-dimensional Lie algebras. Finally, we apply the Lie symmetries in order to define similarity transformations and derive new analytic solutions for the traffic model. The qualitative behaviour of the solutions is discussed.
Dynamics of a Hybrid HIV/AIDS Model with Age-Structured, Self-Protection and Media Coverage
Authors: Wang Yaping , Hu Lin , Nie Linfei
Published: December 2022

Taking into account the effects of the heterogeneity of the population and media coverage on disease transmission, in this paper, a hybrid HIV/AIDS model with age-structure, self-protection awareness and media coverage is formulated, which is made up of five partial differential equations (PDEs) and one ordinary differential equation (ODE). We establish the existence of the solution associated with the hybrid system and prove that the solution is unique, bounded and positive utilizing the semigroup approach. Based on the basic reproduction number R0, the threshold dynamics of this model are rigorously investigated, that is, there always is a unique disease-free steady state E0 and it is globally stable when R0<1, that is, the disease dies out. Further, there exists a unique endemic steady state E* and it is locally stable when R0>1 and some additional technical conditions are met. In addition, the uniform persistence of this hybrid system is demonstrated for R0>1, which means that the disease remains at the endemic level for a long time, which is not discussed in other age-structured infectious disease articles. Numerical simulations are also given to explain the main theoretical results, which suggest that age variability is a non-negligible factor in HIV/AIDS transmission, that is, the moment and scale of HIV/AIDS outbreaks are diverse for people of different ages, and media coverage can encourage people to take steps to avoid potential infection and control the spread of the disease.
Advance Dynamic Panel Second-Generation Two-Step System Generalized Method of Movement Modeling: Applications in Economic Stability-Shadow Economy Nexus with a Special Case of Kingdom of Saudi Arabia
Authors: Rehman Mohd Ziaur , Khan Shabeer , Ali Mohsin , Rehman Faheem Ur , Alonazi Wadi B. , Aljuaid Mohammed
Published: December 2022

Applying an advance dynamic panel second-generation two-step system generalized method of movement modeling, this study endeavors to bridge the gap in the literature by examining the association between shadow economy and economic stability in multiple countries with a special case of the Kingdom of Saudi Arabia. The study has been motivated by the evidence that the shadow economy is a persistent source of the decline in tax revenue, which is the main source of funds for any economy to run welfare packages for the public. In addition, it also impacts the economy’s financial stability and amplifies the losses. Saudi Arabia is selected as a special case given its global significance and participation in the group of the fastest growing economies in the world. Thus, this study finds a negative association between the shadow economy and economic stability in the case of the full sample and a sub sample of Saudi Arabia. In other words, our study supports the point of view of those scholars who claim that the shadow economy is harmful to the economy. Based on the empirical findings, the study recommends that policymakers need to give importance to the shadow economy while formulating economic stability policies in the case of a full sample, especially in Saudi Arabia. Similarly, the robustness of the results is tested using different model specifications and alternative estimation techniques.
Tail Value-at-Risk-Based Expectiles for Extreme Risks and Their Application in Distributionally Robust Portfolio Selections
Authors: Chen Haoyu , Fan Kun
Published: December 2022

Empirical evidence suggests that financial risk has a heavy-tailed profile. Motivated by recent advances in the generalized quantile risk measure, we propose the tail value-at-risk (TVaR)-based expectile, which can capture the tail risk compared with the classic expectile. In addition to showing that the risk measure is well-defined, the properties of TVaR-based expectiles as risk measures were also studied. In particular, we give the equivalent characterization of the coherency. For extreme risks, usually modeled by a regularly varying survival function, the asymptotic expansion of a TVaR-based expectile (with respect to quantiles) was studied. In addition, motivated by recent advances in distributionally robust optimization in portfolio selections, we give the closed-form of the worst-case TVaR-based expectile based on moment information. Based on this closed form of the worst-case TVaR-based expectile, the distributionally robust portfolio selection problem is reduced to a convex quadratic program. Numerical results are also presented to illustrate the performance of the new risk measure compared with classic risk measures, such as tail value-at-risk-based expectiles.
Modelling the Impact of World Oil Prices and the Mining and Quarrying Sector on the United Arab Emirates’ GDP
Authors: Al Humssi Ahmad , Petrovskaya Maria , Abueva Milana
Published: December 2022

In this research, we aimed to model the impact of world oil prices on the gross domestic product of the United Arab Emirates (UAE). The objective of the study was to determine the transmission mechanism of the influence of the changing oil price within the macroeconomic indicators of the UAE. In this study, we analysed the impact of world oil prices and the crude oil sector on economic growth in the UAE for the period of 2001–2020 by applying ADF, OLS, ARDL, and Granger causality techniques. The results also showed the direct impact of the changes in oil prices on the GDP of the UAE in the short and long terms; in other words, a decline in oil prices could pose a threat to the economic security of the UAE in the long term if appropriate corrective measures are not taken. In order to avoid these negative consequences of the oil price crisis, in this study, we emphasize that the only alternative to exporting oil is to diversify economic sources for long-term development and increase the efficiency of non-oil sectors.
A Review of the Key Technology in a Blockchain Building Decentralized Trust Platform
Authors: Deng Weichu , Huang Teng , Wang Haiyang
Published: December 2022

Currently, the trust mechanisms of various Internet application platforms are still built under the orders of centralized authorities. This centralized trust mechanism generally suffers from problems such as excessive power of central nodes, single point of failure and data privacy leakage. Blockchain is a new type of distributed data architecture with non-tamperability, openness and transparency, and traceability, which can achieve secure and trustworthy sharing of data without the participation of third-party authorities. The decentralized trust mechanism built based on the blockchain provides a new research paradigm with broad development prospects to solve the problem of establishing reliable information sharing under the environmental conditions of incomplete reliability in finance, healthcare, energy, and data security. In response to the issues exposed by centralized trust mechanisms in recent years, based on the critical technology of blockchain, this paper surveys the relevant literature around the vital issue of building a decentralized and secure trust mechanism. First, the decentralized trust mechanism architecture is sorted out by comparing different decentralized platforms. The blockchain is divided into the data layer, network layer, consensus layer, contract layer and application layer, which correspond to the theory, implementation, operation, extension, and application of the decentralized trust mechanism of a blockchain, a district-centric platform. Secondly, the principles and technologies of blockchain are elaborated in detail, focusing on the underlying principles, consensus algorithms, and smart contracts. Finally, blockchain problems and development directions are summarized in light of relevant literature.
Analysis of Drag Coefficients around Objects Created Using Log-Aesthetic Curves
Authors: Wo Mei Seen , Gobithaasan R.U. , Miura Kenjiro T. , Loy Kak Choon , Harun Fatimah Noor
Published: December 2022

A fair curve with exceptional properties, called the log-aesthetic curves (LAC) has been extensively studied for aesthetic design implementations. However, its implementation in terms of functional design, particularly hydrodynamic design, remains mostly unexplored. This study examines the effect of the shape parameter α of LAC on the drag generated in an incompressible fluid flow, simulated using a semi-implicit backward difference formula coupled with P2−P1 Taylor–Hood finite elements. An algorithm was developed to create LAC hydrofoils that were used in this study. We analyzed the drag coefficients of 47 LAC hydrofoils of three sizes with various shapes in fluid flows with Reynolds numbers of 30, 40, and 100, respectively. We found that streamlined LAC shapes with negative α values, of which curvature with respect to turning angle are almost linear, produce the lowest drag in the incompressible flow simulations. It also found that the thickness of LAC objects can be varied to obtain similar drag coefficients for different Reynolds numbers. Via cluster analysis, it is found that the distribution of drag coefficients does not rely solely on the Reynolds number, but also on the thickness of the hydrofoil.
Analysis and Forecasting of Sales Funnels
Authors: Griva Egor , Butorina Irina , Sidorov Anatoly , Senchenko Pavel
Published: December 2022

This article discusses the use of analysis and forecasting methods for sales funnels to help further decision-making. A number of objective and subjective factors preventing the wide use of various sales funnel forecasting methods are described. It has been substantiated that due to a large number of external and internal factors, perfect forecasting results cannot be obtained. It has been proved that the most accurate and suitable methods for the forecasting of sales funnels are the methods included in the group of time series forecasting methods. Recommendations have been developed to improve some of the methods that significantly increase the accuracy of the forecasted data. Using the data received from different organizations, it was possible to empirically verify the accuracy of the forecast values. The obtained results of analysis and forecasting were used for testing the methods of searching the optimal scenarios of achieving the forecast indicators.
Increasing the Effectiveness of Network Intrusion Detection Systems (NIDSs) by Using Multiplex Networks and Visibility Graphs
Authors: Iglesias Perez Sergio , Criado Regino
Published: December 2022

In this paper, we present a new approach to NIDS deployment based on machine learning. This new approach is based on detecting attackers by analyzing the relationship between computers over time. The basic idea that we rely on is that the behaviors of attackers’ computers are different from those of other computers, because the timings and durations of their connections are different and therefore easy to detect. This approach does not analyze each network packet statistically. It analyzes, over a period of time, all traffic to obtain temporal behaviors and to determine if the IP is an attacker instead of that packet. IP behavior analysis reduces drastically the number of alerts generated. Our approach collects all interactions between computers, transforms them into time series, classifies them, and assembles them into a complex temporal behavioral network. This process results in the complex characteristics of each computer that allow us to detect which are the attackers’ addresses. To reduce the computational efforts of previous approaches, we propose to use visibility graphs instead of other time series classification methods, based on signal processing techniques. This new approach, in contrast to previous approaches, uses visibility graphs and reduces the computational time for time series classification. However, the accuracy of the model is maintained.
HistoSSL: Self-Supervised Representation Learning for Classifying Histopathology Images
Authors: Jin Xu , Huang Teng , Wen Ke , Chi Mengxian , An Hong
Published: December 2022

The success of image classification depends on copious annotated images for training. Annotating histopathology images is costly and laborious. Although several successful self-supervised representation learning approaches have been introduced, they are still insufficient to consider the unique characteristics of histopathology images. In this work, we propose the novel histopathology-oriented self-supervised representation learning framework (HistoSSL) to efficiently extract representations from unlabeled histopathology images at three levels: global, cell, and stain. The model transfers remarkably to downstream tasks: colorectal tissue phenotyping on the NCTCRC dataset and breast cancer metastasis recognition on the CAMELYON16 dataset. HistoSSL achieved higher accuracies than state-of-the-art self-supervised learning approaches, which proved the robustness of the learned representations.
MedDeblur: Medical Image Deblurring with Residual Dense Spatial-Asymmetric Attention
Authors: Sharif S. M. A. , Naqvi Rizwan Ali , Mehmood Zahid , Hussain Jamil , Ali Ahsan , Lee Seung-Won
Published: December 2022

Medical image acquisition devices are susceptible to producing blurry images due to respiratory and patient movement. Despite having a notable impact on such blind-motion deblurring, medical image deblurring is still underexposed. This study proposes an end-to-end scale-recurrent deep network to learn the deblurring from multi-modal medical images. The proposed network comprises a novel residual dense block with spatial-asymmetric attention to recover salient information while learning medical image deblurring. The performance of the proposed methods has been densely evaluated and compared with the existing deblurring methods. The experimental results demonstrate that the proposed method can remove blur from medical images without illustrating visually disturbing artifacts. Furthermore, it outperforms the deep deblurring methods in qualitative and quantitative evaluation by a noticeable margin. The applicability of the proposed method has also been verified by incorporating it into various medical image analysis tasks such as segmentation and detection. The proposed deblurring method helps accelerate the performance of such medical image analysis tasks by removing blur from blurry medical inputs.
Cell-like P Systems with Channel States and Synchronization Rule
Authors: Jiang Suxia , Liang Tao , Xu Bowen , Shen Zhichao , Zhu Xiaoliang , Wang Yanfeng
Published: December 2022

Cell-like P systems with channel states and symport/antiport rules (CCS P systems) are a type of nondeterministic parallel biological computing model, where there exists a channel between adjacent regions and there is a state on each channel to control the execution of symport/antiport rules. In this work, a synchronization rule is introduced into CCS P systems, a variant of CCS P systems called CCS P systems with synchronization rule (CCSs P systems) is proposed. The universality of CCSs P systems with only uniport (symport or antiport) rules is investigated. By simulating the register machine, we proved that CCSs P systems have the ability to simulate any Turing machine in the following three cases: having two membranes, two channel states and using symport rules of length at most 2; having one membrane, three channel states and using symport rules of length at most 2; and having one membrane, two channel states and using antiport rules of length at most 3.
Fine-Dust-Induced Skin Inflammation: Low-Molecular-Weight Fucoidan Protects Keratinocytes and Underlying Fibroblasts in an Integrated Culture Model
Authors: Kirindage Kirinde Gedara Isuru Sandanuwan , Jayasinghe Arachchige Maheshika Kumari , Cho Namki , Cho Seok Ho , Yoo Hee Min , Fernando Ilekuttige Priyan Shanura , Ahn Ginnae
Published: December 2022

Prolonged exposure to fine dust (FD) increases the risk of skin inflammation. Stimulated epidermal cells release growth factors into their extracellular environment, which can induce inflammation in dermal cells. Algae are considered rich sources of bioactive materials. The present study emphasized the effect of low-molecular-weight fucoidan isolated from Sargassum confusum (LMF) against FD-induced inflammation in HaCaT keratinocytes and underneath fibroblasts (HDFs) in an integrated culture model. HDFs were treated with media from FD-stimulated HaCaT with LMF treatments (preconditioned media). The results suggested that FD increased the oxidative stress in HaCaT, thereby increasing the sub-G1 phase of the cell cycle up to 587%, as revealed via flow cytometric analysis. With preconditioned media, HDFs also displayed oxidative stress; however, the increase in the sub-G1 phase was insignificant compared with HaCaT. LMF dose-dependently regulated the NF-κB/MAPK signaling in HaCaT. Furthermore, significant downregulation in NF-κB/MAPK signaling, as well as inflammatory cytokines, tissue inhibitors of metalloproteinases, matrix metalloproteinases, and reduction in relative elastase and collagenase activities related to the extracellular matrix degeneration were observed in HDFs with a preconditioned media treatment. Therefore, we concluded that HDFs were protected from inflammation by preconditioned media. Continued research on tissue culture and in vivo studies may reveal the therapeutic potential of LMF.