bluuebunny/crossref_metadata_2025_split huggingface dataset:
High-precision optical fiber pressure sensor using frequency-modulated continuous-wave laser interference
Authors: Bai Lang , Zheng Gang , Sun Bin , Zhang Xiongxing , Sheng Qiming , Han Yuan 
Published: February 2021

This work presents a high-precision fiber optic pressure sensor based on frequency-modulated continuous-wave (FMCW) laser interference. The pressure sensor is primarily composed of a diaphragm-type Fabry–Pérot (F–P) cavity, with the diaphragm fabricated using high-elasticity SUS631 stainless steel. The external air pressure causes the center of the elastic diaphragm to deform, and this deformation results in a change in the F–P cavity length. The FMCW laser-interference method was used to demodulate the change in the length of the cavity and realize high-precision pressure measurements. The experimental results showed that when the pressure measurement is in the range of 0 kPa–600 kPa, an accuracy of 3.8 Pa can be obtained. In addition, the sensor had very good linearity with pressure change (R2 = 0.999 94), repeatability, and stability.
Multi-energy calibration of a PILATUS3 CdTe detector for hard x-ray measurements of magnetically confined fusion plasmas
Authors: Barbui T. , Delgado-Aparicio L. F. , Pablant N. , Disch C. , Luethi B. , Pilet N. , Stratton B. , VanMeter P. 
Published: February 2021

A multi-energy hard x-ray pin-hole camera based on the PILATUS3 X 100K-M CdTe detector has been developed at the Princeton Plasma Physics Laboratory for installation on the Tungsten Environment in Steady State Tokamak. This camera will be employed to study thermal plasma features such as electron temperature as well as non-thermal effects such as fast electron tails produced by a lower hybrid radiofrequency current drive and the birth of runaway electrons. The innovative aspect of the system lies in the possibility of setting the threshold energy independently for each of the ∼100k pixels of the detector. This feature allows for the measurement of the x-ray emission in multiple energy ranges with adequate space and time resolution (∼1 cm, 2 ms) and coarse energy resolution. In this work, the energy dependence of each pixel was calibrated within the range 15 keV–100 keV using a tungsten x-ray tube and emission from a variety of fluorescence targets (from yttrium to uranium). The data corresponding to pairs of Kα emission lines are fit to the characteristic responsivity (“S-curve”), which describes the detector sensitivity across the 64 possible energy threshold values for each pixel; this novel capability is explored by fine-tuning the voltage of a six-bit digital-analog converter after the charge-sensitive amplifier for each of the ∼100k pixels. This work presents the results of the calibration including a statistical analysis. It was found that the achievable energy resolution is mainly limited by the width of the S-curve to 3 keV–10 keV for threshold energies up to 50 keV, and to ≥20 keV for energies above 60 keV.
Machine learning for magnetic phase diagrams and inverse scattering problems
Authors: Samarakoon Anjana M , Alan Tennant D 
Published: February 2021

Abstract
Machine learning promises to deliver powerful new approaches to neutron scattering from magnetic materials. Large scale simulations provide the means to realise this with approaches including spin-wave, Landau Lifshitz, and Monte Carlo methods. These approaches are shown to be effective at simulating magnetic structures and dynamics in a wide range of materials. Using large numbers of simulations the effectiveness of machine learning approaches are assessed. Principal component analysis and nonlinear autoencoders are considered with the latter found to provide a high degree of compression and to be highly suited to neutron scattering problems. Agglomerative heirarchical clustering in the latent space is shown to be effective at extracting phase diagrams of behavior and features in an automated way that aid understanding and interpretation. The autoencoders are also well suited to optimizing model parameters and were found to be highly advantageous over conventional fitting approaches including being tolerant of artifacts in untreated data. The potential of machine learning to automate complex data analysis tasks including the inversion of neutron scattering data into models and the processing of large volumes of multidimensional data is assessed. Directions for future developments are considered and machine learning argued to have high potential for impact on neutron science generally.
Interferometric device for the in-process measurement of diameter variation in the manufacture of ultraprecise spheres
Authors: Meeß Rudolf , Dontsov Dennis , Langlotz Enrico 
Published: February 2021

Abstract
A novel device is presented which is designed for in-process measurements of the variation of the diameter of highly reflective spheres. Silicon spheres have been used for the new definition of the International System of Units (SI). Many spheres have to be processed, and the form of these objects, and thus the manufacturing process’s stability, needs to be controlled every day. Commercially available measurement equipment and even state-of-the-art spherical interferometers have reached their limits in terms of resolution, uncertainty, the complexity of their handling routines, measurement time and even financial investment. A novel setup has thus been designed after considering and selecting a special mechanical setup with a minimal measurement loop, stable optical sensors and a handling strategy which avoids collision and contact with the very valuable, superpolished spherical objects. Thus, the design minimizes the influence of the environment and reduces the measurement time at an equator with sub-nanometre resolution to 3 min. In addition, the analysis time is reduced to less than a minute.
Transmural recordings of gastrointestinal electrical activity using a spatially-dense microelectrode array
Authors: Nagahawatte Nipuni D , Paskaranandavadivel Niranchan , Angeli-Gordon Timothy R , Cheng Leo K , Avci Recep 
Published: February 2021

Abstract

Objective. High-resolution serosal recordings provide detailed information about the bioelectrical conduction patterns in the gastrointestinal (GI) tract. However, equivalent knowledge about the electrical activity through the GI tract wall remains largely unknown. This study aims to capture and quantify the bioelectrical activity across the wall of the GI tract. Approach. A needle-based microelectrode array was used to measure the bioelectrical activity across the GI wall in vivo. Quantitative and qualitative evaluations of transmural slow wave characteristics were carried out in comparison to the serosal slow wave features, through which the period, amplitude, and SNR metrics were quantified and statistically compared. Main results. Identical periods of 4.7 ± 0.3 s with amplitudes of 0.17 ± 0.04 mV versus 0.31 ± 0.1 mV and signal to noise ratios of 5.5 ± 1.3 dB versus 14.4 ± 1.1 dB were observed for transmural and serosal layers, respectively. Four different slow wave morphologies were observed across the transmural layers of the GI wall. Similar amplitudes were observed for all morphology types, and Type 1 and Type 2 were of the highest prevalence, dominating the outer and inner layers. Type 2 was exclusive to the middle layer while Type 4 was primarily observed in the middle layer as well. Significance. This study demonstrates the validity of new methodologies for measuring transmural slow wave activation in the GI wall and can now be applied to investigate the source and origin of GI dysrhythmias leading to dysmotility, and to validate novel therapeutics for GI health and disease.
Frustrated magnetism of the triangular-lattice antiferromagnets α-CrOOH and α-CrOOD
Authors: Liu Jiabin , Liu Benqiong , Yuan Long , Li Boqiang , Xie Lei , Chen Xiping , Zhang Hongxia , Xu Daye , Tong Wei , Wang Jinchen , Li Yuesheng 
Published: February 2021

Abstract
We report a comprehensive investigation of the frustrated magnetism on the delafossite oxides, α-CrOOH and α-CrOOD, which experimentally realize the S = 3/2 nearly-Heisenberg antiferromagnetic (J
1 > 0) model on a triangular lattice with weak single-ion anisotropy (D). The electron spin resonance (ESR), neutron scattering, and specific heat (C
m) measurements on both α-CrOOH and α-CrOOD consistently indicate that the long-range 120° Néel order is significantly suppressed and both systems are in the vicinity of a spin-liquid phase with C
m ∼ T
2 at low temperatures. The strength of D is quantitatively determined from fitting the high-temperature ESR linewidth and magnetic susceptibility, and its minus sign (D < 0, easy-axis type) is suggested by the low-energy (E ⩽ 2 meV) spin excitations. This easy-axis anisotropy (D/J
1 ∼ −5%) competes with the 120° Néel order and thus enhances the quantum spin fluctuations at low temperatures.
Classification of 13 original rock samples by laser induced breakdown spectroscopy
Authors: Wang Chong , Wang Jing , Wang Jing , Du Huan , Wang Jinghua 
Published: February 2021

Abstract
Rock classification plays a very important role in geological research. In order to simulate rock classification under unmanned conditions, we selected 13 kinds of rock samples and obtained their classification from spectral information, without any pretreatment, by using laser-induced breakdown spectroscopy (LIBS). Firstly, we collected all the characteristic regions of each rock and used principal component analysis to reduce the dimension of each LIBS spectral signal, to improve the accuracy and speed of the classification algorithm. Secondly, three classification algorithms were used to classify dimension-reduced spectral data, namely linear discriminant analysis, random forest classification and support vector machine (SVM). At the same time, the classification results were evaluated by confusion matrix. The final average classification accuracy was 27%, 91% and 100%, respectively, showing that the SVM algorithm can be applied to the LIBS classification of rocks.
Tunable mode-locked fiber laser to generate ultrashort cylindrical vector beams
Authors: Zhang Tao , Hu Haifeng , Chen Jian , Zhan Qiwen 
Published: February 2021

Abstract
In this work, we report a tunable mode-locked fiber laser that can produce ultrashort laser pulses with polarization vortex. Two vortex wave plates (VWPs) are employed in this laser to convert the intracavity beam states between the Gaussian mode and cylindrical vector mode. By adjusting the angle between a diffraction grating and the light beam in the cavity, the center wavelength of the mode-locked pulse can be tuned from 1015 nm to 1030 nm. Mode-locked pulses with full width at half maximum of 1.51 ps with the maximum single pulse energy of 2.2 nJ have been obtained. Through synchronously adjusting the orientations of the two VWPs, the output mode can be switched between radial polarization and azimuthal polarization states. The output vortex pulse from the tunable mode-locked fiber laser may find many applications such as optical manipulation, imaging and laser micro-machining.
Characterizations of Completely Nnc
(Weakly Nnc
)-irresolute Functions via Nnce-open Sets
Authors: Vadivel A. , Navuluri Mohanarao , Thangaraja P. 
Published: February 2021

Abstract
The purpose of present this paper is to introduce and investigate two new classes of Nnc
-irresolute functions called completely Nnce-irresolute functions and completely weakly Nnce-irresolute functions in topological spaces by using the concept of Nnce-open sets. Several new characterizations and interesting properties concerning completely Nnce-irresolute functions and completely weakly Nnce-irresolute functions are obtained. Furthermore the relationships between these functions and some other well-known types of functions are also given.
Predicting heart failure using data mining with Rough set theory and Fuzzy Petri Net
Authors: Meher Taj S. , Sudha M. , Kumaravel A. 
Published: February 2021

Abstract
The Rough Set Theory (RST) is a method that has proven its efficiency and simplicity in machine learning and successfully developing now a day’s vastly and rapidly. Fuzzy Petri nets (FPNs) are a potential modelling technique, which is used for knowledge representation and reasoning of rule-based expert systems. Though, RST has the efficiency in dimension reduction, it has to be proved with evidence by creating model using FPN with rule based reasoning. In this paper the induction of decision rules by RST executed with Fuzzy Petri Nets (FPN) is analyzed in the sense how it performed better than other data mining classifiers. The rule-based classifiers like jrip, part R, zero R are used for the comparison purposes. The knowledge captured from the rules through the best reduct has performed with the efficiency of RST and formulating rules by FPN. This paper experiments the Heart failure data to investigate the decision making from the rules generated by LERS system of RST with the approach of FPN. The heart failures during the follow up period of the patents are predicted with the pattern recognized form the data using the above process and the best evaluators are found during the experiments were pictured.
Zagreb Indices of Edge Based on IB Product
Authors: Pattabiraman K. , Suganya T. 
Published: February 2021

Abstract
A topological index is a numerical representation of a chemical structure which correlates certain physico-chemical characteristic of chemical compounds besides its numerical values. Zagreb index is the first degree based topological index which is proposed by Gutman et al.[10]in 1972. In this paper, we obtain the exact formulae for the Zagreb indices of new types and its coindices of two new types of graph operation based on indhu-bala product which is defined by Indulal and Balakrishnan [14] in 2016.
Agricultural Farming Survey Using Iot
Authors: Shali Mrs. A. , Sangeerani Devi Mrs. A. , Kavitha Mrs. D. 
Published: February 2021

Abstract
Web of things (IoT) is a promising innovation which gives productive and solid arrangements towards the modernization of a few spaces. IoT based arrangements are being created to naturally keep up and screen rural homesteads with negligible human association. The article presents numerous parts of advancements associated with the space of IoT in farming. It clarifies the significant segments of IoT based keen cultivating. A thorough conversation on network advances utilized in IoT based horticulture has been introduced, that includes network design and layers, network geographies utilized, and conventions. Moreover, the association of IoT based farming frameworks with pertinent advances including distributed computing, huge information stockpiling and examination has likewise been introduced. Likewise, security issues in IoT farming have been featured. A rundown of advanced mobile phone based and sensor based applications created for various parts of ranch the executives has likewise been introduced. Finally, the guidelines and approaches made by a few nations to normalize IoT based agribusiness have been introduced alongside barely any accessible examples of overcoming adversity.
On Nnc Zα
-Open Sets, NncA*L sets and Decompositions of Continuity
Authors: Balasubramaniyan K. , Gobikrishnan A. , Vadivel A. 
Published: February 2021

Abstract
In this paper, we introduce and study the notion of NncZα
-open sets and some properties of this class of sets are investigated. Also, we introduce the class of NncA*L sets via NncZα
-open sets. Further, by using these sets, a new decompositions of continuous functions are presented.
Homotopy Perturbation Method for Mathematical Modelling of Dengue Fever
Authors: Rekha S , Balaganesan P , Renuka J 
Published: February 2021

AbstractDengue fever is a viral mosquito-transmitted infection that has become a major international infection in recent years. The leading cause of disease and death in tropical and sub-tropical regions is a public health concern. Models from mathematical epidemiology, such as the classical SIR-model and its variants, are used to characterize the spread of Dengue in a given population. The mathematical modelling of Dengue Fever is formulated into a first-order nonlinear differential equation. Homotopy Perturbation Method approaches the analytical solution of the model (HPM), and also simulation results are identified. Finally, the analytical solutions, simulation results are compared, and satisfactory agreement is noted.
An example and experience of a mathematical modelling activity: the hip roof problem
Authors: Aziz T A , Makmuri  
Published: February 2021

Abstract
There is a growing body of literature that recognizes the importance of mathematical modelling which deal with real world problems in mathematics school. The present study set out to investigate potential uses of the Hip Roof problem to introduce a mathematical modelling activity to Indonesian students. This study was exploratory and interpretative in nature. The Hip Roof problem was originally developed by the researcher and was assigned to six groups of undergraduate students enrolled at one public university in Jakarta, Indonesia. Test administration and interview were conducted to describe their work. The works were analyzed descriptively. The result indicates that the problem is feasible with undergraduate students and the participants of the study appreciate the problem included in school mathematics to enhance their ability to implement mathematics in their real life. In solving the problem, the participants used their personal mathematical knowledge.
Mathematical study for an infectious disease with awareness-based SIS-M model
Authors: Latifah S , Aldila D , Giyarti W , Tasman H 
Published: February 2021

Abstract
In this article, we discuss a mathematical model of Susceptible-Infected-Susceptible-Media (SIS-M), which considers the level of human awareness on certain presence of disease. To construct the model, we divided the population based on their health status into susceptible individuals unaware and aware of the disease and the infectious individuals. The level of awareness included private awareness associated with direct contacts between unaware and aware populations, global awareness due to reported cases of infection, and regular awareness campaigns from media or policy makers. The dynamical behaviour of the model was analysed rigorously. The disease-free equilibrium point, the endemic equilibrium point, and the basic reproduction number were shown in this model analytically and numerically. We found that the disease-free equilibrium point was locally asymptotically stable if R
0 < 1, and unstable if R
0 > 1. From the sensitivity analysis of R
0, it was found that there was a minimum intensity for the awareness campaign so that the level of awareness manifested in the efforts of individuals to protect themselves from disease successfully eradicate the disease from the community. This result indicates that the efforts of individuals in protecting themselves can have a massive effect on the existence of the disease in the community.
The effect of Titanium Dioxide (TiO2) based metallic catalytic converter on the four-stroke motorcycle engine performance
Authors: Warju W , Drastiawati N S , Ariyanto S R , Nurtanto M 
Published: February 2021

Abstract
Many environmentally friendly automotive technologies have been developed to support the blue sky program. Among the several innovations, metallic catalytic converter (MCC) technology used in environmentally friendly motorcycle exhausts system with titanium dioxide (TiO2) is one of the innovations available to reduce the rise in air pollution due to exhaust emissions. In terms of reducing exhaust emissions, this technology certainly does not doubt its ability. But the problem is that not many researchers have examined the effect of the application of these technologies on engine performance. Thus, the high aim of this study is to analyze the effectiveness of the use of TiO2 based MCC on the four-stroke motorcycle engine performance. This type of experimental research uses a Yamaha Vixion motorcycle as an object of research. Engine performance testing is performed based on SAE J1349, namely Engine Power Test Code-Spark Ignition and Compression Ignition-Net Power Rating. The equipment used in this study is the inertia of the chassis dynamometer, digital tachometer, digital thermometer, humidity meter, and blower. The independent variables in this study are the standard muffler and modified muffler with TiO2 based MCC. The control variables are engine speed, engine oil temperature, ambient air temperature and humidity, and Pertamax fuel, while the dependent variable is engine performance (torque, power, and fuel consumption). Data analysis techniques using quantitative descriptive methods. This study found that the use of environmentally friendly motorcycle mufflers with TiO2 based MCC technology could increase the torque of the Yamaha Vixion motorcycles. In line with these results, vehicle power also increases, and fuel consumption is relatively economical if the TiO2-based MCC is compared to standard exhaust.
Guided inquiry blended learning tools (GI-BL) for school magnetic matter in junior high school to improve students’ scientific literacy
Authors: Gunawan G , Jufri A W , Nisrina N , Al-Idrus A , Ramdani A , Harjono A 
Published: February 2021

AbstractThis article discusses the use of learning tools in blended learning with a guided inquiry model in magnetic matter for junior high schools. Based on technological and educational developments, this learning tool was integrated with blended learning. The GI-BL (Guided Inquiry-Blended Learning) learning tools consist of magnetic textbooks, student worksheets, lesson plans and instruments for scientific literacy. The quasi-experimental method in this study used a pre-test post-test control group design. The GI-BL learning tools were developed and validated in terms of content as well as the instruments. Testing the effectiveness of this learning tool was carried out on 163 samples of junior high school students. GI-BL learning tools were developed to improve students’ scientific literacy, especially in solving problems related to magnetism-related phenomena. The data on the effectiveness of the GIBL learning tools were analysed using N-gain. Based on the results of the N-gain analysis, it appears that students who learn with GIBL learning tools can improve scientific literacy. The highest increase occurred in indicators describing scientific phenomena and in understanding the concept of Lorentz Force.