bluuebunny/crossref_metadata_2025_split huggingface dataset:
Superconducting properties of commercial REBCO-coated conductors with artificial pinning centers
Authors: Tsuchiya Kiyosumi , Wang Xudong , Fujita Shinji , Ichinose Ataru , Yamada Kyohei , Terashima Akio , Kikuchi Akihiro 
Published: August 2021

Abstract
Over the last 20 years, technologies for manufacturing rare-earth barium copper oxide (REBCO)-coated conductors have undergone a steady development. Currently, the properties of these conductors are reasonably stable owing to the intensive efforts of the manufacturers. However, for high-field magnet applications, such as the magnets used in nuclear magnetic resonance instruments, accelerators, and fusion reactors, further enhancements in the current-carrying capabilities and/or the current densities of the conductors under a high magnetic field are necessary. Recently, several conductors doped with artificial pinning centers (APCs) have become commercially available, primarily from four manufacturers: Fujikura, Shanghai ST, SuperOx, and SuperPower. In this study, we characterized these relatively new conductors from the viewpoint of a magnet designer. We measured the critical currents (I
c) of full-size 4 mm wide conductors in a wide field range at 4.2 K and 77 K; we also measured the critical temperatures. The measurement results showed that the I
c values at 4.2 K under perpendicular fields for these conductors are significantly greater than those of non-APC conductors; for the 4 mm wide conductors, the I
c values are in the range of 300–740 A and 450–1000 A at 18 T and 12 T, respectively. Furthermore, we clarified that the non-Cu current density (J
c) at 4.2 K for some of the investigated conductors is more than twice the J
c of the recent Nb3Sn conductors in fields exceeding 15 T. In the investigated commercial REBCO-APC conductors, the highest layer J
c of ∼60 kA mm−2 (at 18 T and 4.2 K) was noted. We also investigated the I
c–B relationship at 4.2 K for the recent REBCO-APC conductors.
Carbon-based neural electrodes: promises and challenges
Authors: Devi Mamta , Vomero Maria , Fuhrer Erwin , Castagnola Elisa , Gueli Calogero , Nimbalkar Surabhi , Hirabayashi Mieko , Kassegne Sam , Stieglitz Thomas , Sharma Swati 
Published: August 2021

Abstract
Neural electrodes are primary functional elements of neuroelectronic devices designed to record neural activity based on electrochemical signals. These electrodes may also be utilized for electrically stimulating the neural cells, such that their response can be simultaneously recorded. In addition to being medically safe, the electrode material should be electrically conductive and electrochemically stable under harsh biological environments. Mechanical flexibility and conformability, resistance to crack formation and compatibility with common microfabrication techniques are equally desirable properties. Traditionally, (noble) metals have been the preferred for neural electrode applications due to their proven biosafety and a relatively high electrical conductivity. Carbon is a recent addition to this list, which is far superior in terms of its electrochemical stability and corrosion resistance. Carbon has also enabled 3D electrode fabrication as opposed to the thin-film based 2D structures. One of carbon’s peculiar aspects is its availability in a wide range of allotropes with specialized properties that render it highly versatile. These variations, however, also make it difficult to understand carbon itself as a unique material, and thus, each allotrope is often regarded independently. Some carbon types have already shown promising results in bioelectronic medicine, while many others remain potential candidates. In this topical review, we first provide a broad overview of the neuroelectronic devices and the basic requirements of an electrode material. We subsequently discuss the carbon family of materials and their properties that are useful in neural applications. Examples of devices fabricated using bulk and nano carbon materials are reviewed and critically compared. We then summarize the challenges, future prospects and next-generation carbon technology that can be helpful in the field of neural sciences. The article aims at providing a common platform to neuroscientists, electrochemists, biologists, microsystems engineers and carbon scientists to enable active and comprehensive efforts directed towards carbon-based neuroelectronic device fabrication.
Higher moment of portfolio optimization with Polynomial Goal Programming approach
Authors: Siew Lam Weng , Jaaman Saiful Hafizah , Hoe Lam Weng 
Published: August 2021

Abstract
The mean-variance (MV) model has been introduced in portfolio optimization to minimize the risk and achieve the target rate of return in the investment. However, the higher moment skewness and kurtosis are not considered in this model. The investors prefer portfolio with high skewness value and low kurtosis value so that the probability of getting extreme negative rates of return will be reduced. Therefore, the MV model has been extended to the mean-variance-skewness-kurtosis (MVSK) model by incorporating the skewness and kurtosis factor. The objective of this study is to construct the optimal portfolio of the MVSK model by using the polynomial goal programming (GP) approach. The data of this study comprises technology companies that listed in Malaysian stock market. In the fourth industrial revolution, technology companies play an important role in the development of a country. The results of this study show that the optimal portfolio of MVSK model outperforms the MV model by giving higher portfolio skewness value and lower portfolio kurtosis value. This study is significant because the investors can maximize the portfolio skewness value and minimize the portfolio kurtosis value with the MVSK model.
A new system of singular integral equations for a curvilinear crack in bonded materials
Authors: Hamzah KB , Nik Long NMA , Senu N , Eshkuvatov ZK 
Published: August 2021

Abstract
The modified complex potentials (MCPs) functions are used to develop a new system of singular integral equations (SIEs) for a curvilinear crack in the upper part of bonded materials subjected to shear mode stress with the help of continuity conditions for resultant force and displacement functions. The unknown dislocation distribution function is mapped into a square root singularity function by using curved length coordinate method and the traction along the crack as the right hand term. The Gaussian quadrature rules were used to obtain the numerical solution for a new system of SIEs in order to compute the nondimensional stress intensity factors (SIFs) for these problems. Our results agree with those of the previous works. The findings have revealed that the nondimensional SIFs depend on the elastic constant ratio, crack geometries and the position of the cracks.
Effects of Movement Control Order on mitigating spread of COVID-19 in the early phase of the outbreak in Malaysia
Authors: Yatim Siti Ainor Mohd , Kim Peter S 
Published: August 2021

Abstract
We propose a mathematical model to investigate the early outbreak of COVID-19 in Malaysia. The model emphasizes the role of the government’s Movement Control Orders (MCOs) as population-wide lockdown measures and the potential benefit of mass testing on disease spread. We fit this model to the reported active COVID-19 cases to estimate model parameters. We also assume transmission rates change with respect to stages of MCOs and compare the differences in rates to assess the effectiveness of different levels of MCO restrictions. The estimated parameters match the observed data well, and our results suggest a slowing of the trajectory of COVID-19 outbreak in the country, indicating that the series of MCOs taken to counter COVID-19 transmission are having a significant positive effect.
A review of mathematical modelling of blood flow in human circulatory system
Authors: Khalid A K , Othman Z S , M Shafee CT M N 
Published: August 2021

Abstract
An interface between biology and mathematics has initiated and fostered new mathematical areas, where the ideas from biology and mathematics are synergistically applied. Study of fluid dynamics plays a significant role in fluid flow inside the human body, and modeling of blood flow is an important field in circulatory physics. However, models have been developed are very complex with three-dimensional analysis. This project presents a novel and simple mathematical model of blood flow. Assuming blood is a Newtonian fluid which is governed by the Navier-Stokes equations and continuity equation and with making use of the Navier-Stokes equation, a simple differential equation called as the circulatory system equation is derived. Then by applying the ogical assumptions on this model, the general mathematical model of the normal blood flow rate is developed. Using Poisuelli’s equation, the sirculatory system equation is also used to develop a model for blood pressure. These two models are then analyzed against surface, pressure gradient and the vessel’s length using MATLAB.
Entropy generation and natural convection of nanofluids in a trapezoidal cavity having an innersolid cylinder
Authors: Ishak Muhamad S. , Alsabery Ammar I. , Hashim Ishak 
Published: August 2021

Abstract
A numerical analysis of entropy generation and natural convection in a trapezoidal cavity with an internal solid cylinder filled with Al2O3 – water nanofluid has been investigated using finite difference method. The bottom wall is thermally insulated while the left and right walls were cooled isothermally. Remainder of these walls are kept adiabatic. Particular factors have been focused on the effects of Rayleigh number, dimensionless radius of the solid cylinder and nanoparticles of volume fraction on streamlines, isotherms, isentropic, local and average Nusselt number. Obtained results have demonstrated that the Rayleigh number and size of the solid cylinder are important control parameters for optimizing heat transfer and Bejan number.
Interactions of Soliton in Weakly Nonlocal Nonlinear Media
Authors: Aklan N A B , Faizar F A , Umarov B A 
Published: August 2021

Abstract
Solitary waves or solitons is a nonlinear phenomenon which has been studied intensively due to its application in solid-state matter such as Bose-Einstein condensates state, plasma physics, optical fibers and nematic liquid crystal. In particular, the study of nonlinear phenomena occurs in the structure of waves gained interest of scholars since their discovery by John Russell in 1844. The Nonlinear Schrödinger Equation (NLSE) is the theoretical framework for the investigation of nonlinear pulse propagation in optical fibers. Nonlocality can be found in an underlying transport mechanisms or long-range forces like electrostatic interactions in liquid crystals and many-body interactions with matter waves in Bose-Einstein condensate or plasma waves. The length of optical beam width and length of response function are used to classify nonlocality in optical materials. The nonlocality can be categorized as weak nonlocal if the width of the optical beam broader than the length of response function and if the width of the optical beam is narrower than the length of response function, it is considered as highly nonlocal. This work investigates the interactions of solitons in a weakly nonlocal Cubic NLSE with Gaussian external potential. The variational approximation (VA) method was employed to solve non integrable NLSE to ordinary differential equation (ODE). The soliton parameters and the computational program are used to simulate the propagation of the soliton width and its center-of-mass position. In the presence of Gaussian external potential, the soliton may be transmitted, reflected or trapped based on the critical velocity and potential strength. Direct numerical simulation of Cubic NLSE is programmed to verify the results of approximation method. Good agreement is achieved between the direct numerical solution and VA method results.
Estimation of the microalgae growth using the optimization approach
Authors: Ling K S , Jamaian S S , Zulkifli F H 
Published: August 2021

Abstract
This study estimates the parameters value of the microalgae growth kinetic model which are Monod, Tessier and modified Moser model using the Levenberg-Marquardt method, by minimizing the sum of squares error. The optimal control problem for microalgae growth in photobioreactor is formulated and solved by the optimal input design method and taking into account the parametric sensitivities in order to obtain the control function. Hence, the fourth-order Runge-Kutta method is implemented to predict the microalgae biomass concentration by solving the state equations of the optimal control problem. The result shows the Tessier model best describes the growth of microalgae with the produced biomass concentration of weight 1.813 × 1010
g.
Z-number-based conjoint analysis method for analyzing decision-makers’ preference levels in attribute ratings
Authors: Razalli Nur Anis Fatihah , Sulaiman Nor Hashimah 
Published: August 2021

Abstract
Fuzzy Conjoint Analysis Method (FCAM) is widely used in multi-attribute decision making areas particularly in analyzing decision makers’ preferences towards attributes. Discrete fuzzy sets are commonly used to define the membership function of linguistic values. Nevertheless, the use of discrete fuzzy sets in representing human judgment and preference levels may not be sufficient in describing human preference defined on continuous scale. In FCAM, discrete fuzzy sets or fuzzy numbers are commonly used to represent the linguistic terms that merely describe ratings or levels of satisfaction on attributes based on decision makers’ opinions or preferences. The certainty elements in terms of confidence level associated with decision makers’ judgment and preferences are seldom considered in FCAM. Thus, in this study, Z-numbers which are composed of ratings with certainty components (in the form of confidence level) are integrated in the existing FCAM procedure. In the proposed procedure of Z-number based Conjoint Analysis Method (Z-CAM), preferences described in the form of ratings with confidence levels are expressed in the form Z-numbers. The existing FCAM procedure is slightly modified so as to cater the Z-number input data. Z-CAM has an additional feature in comparison to the existing FCAM whereby in addition to ranking the attributes, the former could also produce overall ratings supported with confidence levels.
Development of a linguistic model of a reforming unit of a catalytic reforming plant
Authors: Orazbayev B , Zhumadillayeva A , Orazbayeva K , Umarov T , Dyussekeyev K , Kurmangaziyeva L 
Published: August 2021

Abstract
The influence of the main parameters of the reforming process on the quantity and quality of the produced product-catalyst-has been investigated. On the basis of the proposed linguistic modelling algorithm, a linguistic model has been built that describes: the effect of the temperature of the reforming reactor on the yield of catalyst and the stability of the catalyst. The possibility of constructing a linguistic model that estimates the influence of the feed rate of raw materials on the quantity and quality (octane number) of catalyst, a component of motor fuel, has been shown.
A modified predictive model for colour changes in French fries during frying
Authors: Othman Siti Nabihah , Jamil Norazaliza Mohd 
Published: August 2021

Abstract
During frying, heat and mass transfer phenomena happen and cause the physiochemical changes that affect the colour of french fries. Moisture content, oil content, and colour are important quality parameters in frying french fries, while temperature, frying time, and sample thickness will affect the french fries. In this study, we developed a modified mathematical model for colour changes of French fries during frying. The colour changes were formulated using a first-order ordinary differential equation that was solved using the 4th order Runge-Kutta method in the MATLAB software. The formulation for rate constant was modified using the Arrhenius equation and the sum squared error (SSE) of the proposed model was compared with the SSE of existing models. The colour was evaluated based on two parameters which are oil temperature (150°C, 170°C, 190°C) during frying and sample thickness (5 mm,10 mm, 15 mm) of french fries. The results showed that incorporating the factor of moisture into the model provides a better prediction of lightness and yellowness of french fries during frying. Overall, we conclude that moisture plays a significant role in the colour changing of french fries.
A systematic review of Bezier-like Triangular in surface reconstruction
Authors: Azman Anis Nur’ain , Hadi Normi Abdul , Nasir Mohd Agos Salim 
Published: August 2021

AbstractThe application of spline triangular surface has recently been critically discussed in Computer Aided Geometry Design (CAGD). This type of surface has significantly contributed to many important areas especially in reconstructing medical images such as bone and organs. The reconstructed surface is used in further study for example behavioural analysis, safety analysis and surgical planning. Many successful methods have been developed for example Bezier, Ball and Timmer triangular surfaces. In this paper, a comprehensive review of the literature on the triangular surface is presented. The advantages and disadvantages of existing methods on triangular surface are discussed and highlighted in this paper. The methods will be compared in terms of basis function development and the results of produced surface are shown visually. The main objective of this paper is to assist researchers in deciding the best method to be used in surface reconstruction.
Numerical simulations of agent navigation via Half-Sweep Modified Two-Parameter Over-Relaxation (HSMTOR)
Authors: Musli F A , Sulaiman J , Saudi A 
Published: August 2021

Abstract
The research on the efficiency of route navigation has been continuously developing. Especially, the capability of the generated route to provide a collision-free route for an agent to move in a particular environment. Thus, this study attempts to solve the route navigational problem iteratively via a numerical method. A new method called Half-Sweep Modified Two-Parameter Over-Relaxation (HSMTOR) is used to solve the navigational problems. For numerical simulation purposes, HSMTOR is used to obtain Laplace’s equation solutions called harmonic functions. A gradient descent search algorithm then utilizes the harmonic functions to provide a smooth and collision-free route for an agent to commute inside the environment. In addition, the formulation of the HSMTOR iterative method is presented. Several numerical experiments and simulations are conducted in order to verify the efficiency of the proposed method. The result shows that the proposed method performed better than the existing methods such as full-, half-sweep for Modified Successive Over-Relaxation, Modified Accelerated Over-Relaxation and Modified Two-Parameter Over-Relaxation respectively (FSMSOR, HSMSOR, FSMAOR, HSMAOR and FSMTOR).
An overview: on path planning optimization criteria and mobile robot navigation
Authors: Atiyah Anis Naema , Adzhar Noraziah , Jaini Nor Izzati 
Published: August 2021

Abstract
Mobile robots are growing more significant from time to time and have been applied to many fields such as agriculture, space, and even human life. It could improve mobile robot navigation efficiency, ensure path planning safety and smoothness, minimize time execution, etc. The main focus of mobile robots is to have the most optimal functions. An intelligent mobile robot is required to travel autonomously in various environments, static and dynamic. This paper article presents the optimization criteria for mobile robot path planning to figure out the most optimal mobile robot criteria to fulfill, including modeling analysis, path planning and implementation. Path length and path smoothness are the most parameters used in optimization in mobile robot path planning. Based on path planning, the mobile robot navigation is divided into three categories: global navigation, local navigation and personal navigation. Then, we review each category and finally summarize the categories in a map and discuss the future research strategies.
Convergence properties of 3-point block Adams method with one off-step point for ODEs
Authors: Soomro H , Zainuddin N , Daud H 
Published: August 2021

Abstract
Numerous critical and intricate systems from various disciplines of sciences are developed by means of differential equations. Analytical methods are frequently complex or unfeasible to execute for problems due to the difficulty of these systems, therefore, numerical methods are the way out. To integrate differential equations, the most common approaches are single step and multistep techniques. A well-known multistep method Adams formula is one of the appropriate methods for solving non-stiff ordinary differential equation (ODEs). Present research emphases of 3-Point Block Method with one off-step point using Adams Moulton Formula for finding the solution intended for the system of non-stiff first order ODEs. Block method has been derived by considering the Adams Formula. The development of this method will compute the three solution values at xn+1
, x
n+2 and x
n+3 comprising of one off-step point at x
n+2.5 using constant step size. The advantage of adding up one off-step point in the implicit Adams method will lead us towards better accuracy. This paper is shaped up with the derivation of the formula followed by its convergence properties by using MATHEMATICA software. As a result, the method is consistent and zero-stable, which implies convergence.
Power outage prediction by using logistic regression and decision tree
Authors: Saidi Alia Yasmin Nor , Ramli Nor Azuana , Muhammad Noryanti , Awalin Lilik Jamilatul 
Published: August 2021

Abstract
The occurrence of the power outage caused inconvenience to the customers including the energy suppliers. There are various factors that can trigger the power outage such as lightning, weather or animal. In this paper, the power outage prediction has been performed by using the datasets provided which are lightning data and tripping report. The machine learning method was carried out to predict the power outage occurrence by using the Classification Learner App in MATLAB. Before performing the machine learning method, the data went through the data pre-processing to ensure the data is clean and the significant feature for prediction can be selected to run in the Classification Learner App. The results of this research have shown that Fine Tree is the most suitable model to be used for the prediction of power outage. The results have been compared by using the Area Under Curve (AUC) in Receiving Operating Characteristic (ROC). Logistic Regression and Coarse Tree shows the lowest value of AUC compared to other model and Fine Tree has the highest value of AUC.