bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quantify point defects in monolayer tungsten diselenide
Authors: Ding Sujuan , Lin Fang , Jin Chuanhong 
Published: March 2021

Abstract
Point defects may significantly influence the electrical and optoelectronic properties of two-dimensional (2D) tungsten diselenide (WSe2), while precise information about point defects distribution (e.g. species and concentration) in monolayer (ML-) WSe2 are hard to obtain. In this letter, we tried to partly fill this knowledge gap via performing quantitative and statistical analysis of intrinsic point defects in WSe2 monolayers prepared by three so-called main-stream approaches i.e. mechanical exfoliation (ME), chemical vapor deposition (CVD), and molecular beam epitaxy (MBE), which are promising for providing high-quality samples. Via a conjunction of statistic atomic-resolution annular dark-field scanning transmission electron microscopy imaging, software-based automated defect identification and counting, together with image simulations, defect species and concentrations were quantitatively determined. Seven types of intrinsic point defects were identified in ML-WSe2 and the most dominant one is selenium mono-vacancy (VSe) (corresponding to one Se atom missing), irrespective of the synthetic route and growth conditions. Exact contents and diversity of point defects depend on the specific preparation method: CVD grown ML-WSe2 is the most defective (for example, the density of VSe reaches 1.48% in atomic ratio), followed by ME (∼0.85 at% for VSe) and MBE grown samples (∼0.49 at% for VSe). Our results, though still with limited sampling, provide preliminary quantitative information of point defects in ML-WSe2, which can serve as a reference to achieve the precisely controlled large-scale sample growth and establish the structure-property relationship of 2D transition-metal dichalcogenides materials.
PANI coupled hierarchical Bi2S3 nanoflowers based hybrid nanocomposite for enhanced thermoelectric performance
Authors: Sharma Surbhi , Singh Huidrom Hemojit , Kumar Sunil , Khare Neeraj 
Published: March 2021

Abstract
Bismuth sulfide (Bi2S3) is a promising material for thermoelectric applications owing to its non-toxicity and high abundance of bismuth (Bi) and sulfur (S) elements on earth. However, its low electrical conductivity drastically reduces the value of the figure of merit (ZT). In this work, we have synthesized three-dimensional (3D) hierarchical Bi2S3 nanoflowers (NFs) by the hydrothermal route and further incorporated them with conducting polymer polyaniline (PANI) by simple chemisorption method. We have investigated the thermoelectric properties of the as-prepared Bi2S3 NFs and PANI/Bi2S3 nanocomposite samples and it is demonstrated that the incorporation of the PANI matrix with the 3D hierarchical Bi2S3 NFs provides a conducting substrate for the easy transport of the electrons and reduces the barrier height at the interface, resulting in ∼62% increment in the electrical conductivity as compared to Bi2S3 NFs. Moreover, a decrement in the thermal conductivity of the PANI/Bi2S3 nanocomposite is observed as compared to pristine Bi2S3 NFs due to the increased phonon scattering at the interfaces facilitated by the hierarchical morphology of the NFs. Furthermore, an increment in the electrical conductivity and simultaneous decrement in the thermal conductivity results in an overall ∼20% increment in the figure of merit (ZT) for PANI/Bi2S3 nanocomposite as compared to pristine Bi2S3 NFs. The work highlights an effective strategy of coupling 3D hierarchical metal chalcogenide with conducting polymer for optimizing their thermoelectric properties.
Neutrino mass and asymmetric dark matter: study with inert Higgs doublet and high scale validity
Authors: Dutta Banik Amit , Roshan Rishav , Sil Arunansu 
Published: March 2021

Abstract
We consider an inert Higgs doublet (IHD) extension of the Standard Model accompanied with three right handed neutrinos and a dark sector, consisting of a singlet fermion and a scalar, in order to provide a common framework for dark matter, leptognesis and neutrino mass. While the Yukawa coupling of the right handed neutrinos with IHD (having mass in the intermediate regime: 80–500 GeV) is responsible for explaining the observed baryon asymmetry through leptogenesis, its coupling with the dark sector explains the dark matter relic density. The presence of IHD also explains the neutrino mass through radiative correction. We find that study of the high scale validity of the model in this context becomes crucial as it restricts the parameter space significantly. It turns out that there exists a small, but non-zero contribution to the relic density of DM from IHD too. Considering all the constraints from dark matter, leptogenesis, neutrino mass and high scale validity of the model, we perform a study to find out the viable parameter space.
Modeling the marked spectrum of matter and biased tracers in real- and redshift-space
Authors: Philcox Oliver H.E. , Aviles Alejandro , Massara Elena 
Published: March 2021

Abstract
We present the one-loop perturbation theory for the power spectrum of the marked density field of matter and biased tracers in real- and redshift-space. The statistic has been shown to yield impressive constraints on cosmological parameters; to exploit this, we require an accurate and computationally inexpensive theoretical model. Comparison with N-body simulations demonstrates that linear theory fails on all scales, but inclusion of one-loop Effective Field Theory terms gives a substantial improvement, with ∼ 5% accuracy at z = 1. The expansion is less convergent in redshift-space (achieving ∼ 10% accuracy), but there are significant improvements for biased tracers due to the freedom in the bias coefficients. The large-scale theory contains non-negligible contributions from all perturbative orders; we suggest a reorganization of the theory that contains all terms relevant on large-scales, discussing both its explicit form at one-loop and structure at infinite-loop. This motivates a low-k correction term, leading to a model that is sub-percent accurate on large scales, albeit with the inclusion of two (three) free coefficients in real- (redshift-)space. We further consider the effects of massive neutrinos, showing that beyond-EdS corrections to the perturbative kernels are negligible in practice. It remains to see whether the purported gains in cosmological parameters remain valid for biased tracers and can be captured by the theoretical model.
Tidal disruption near black holes
and their mimickers
Authors: Banerjee Pritam , Paul Suvankar , Shaikh Rajibul , Sarkar Tapobrata 
Published: March 2021

Abstract
Black holes and wormholes are solutions of Einstein's field equations, both of which, from afar, can look like a
central mass. We show here that although at large distances both behave like Newtonian
objects, close to the event horizon or to the throat, black holes and wormholes have
different tidal effects on stars, due to their respective geometries.
We quantify this difference
by a numerical procedure in the Schwarzschild black hole and the exponential wormhole backgrounds,
and compare the peak fallback rates of tidal debris in these geometries.
The tidal disruption rates in these backgrounds are also computed. It is shown that these quantities are
a few times higher for wormholes, compared to the black hole cases.
Statistically-anisotropic tensor bispectrum from inflation
Authors: Hiramatsu Takashi , Murai Kai , Obata Ippei , Yokoyama Shuichiro 
Published: March 2021

Abstract
We develop a possibility of generating tensor non-Gaussianity in a kind of anisotropic inflation, where a (1) gauge field is kinetically coupled to a spectator scalar field.
Owing to this coupling, the coherent mode of the electric field appears and softly breaks the isotropy of the Universe.
We compute the bispectrum of linearly-polarized tensor perturbations sourced by the gauge field and find that it is strongly red-tilted
and has distinctive statistical anisotropies including higher-order multipole moments.
Interestingly, the tensor bispectra with the specific combinations of linear polarization modes are dominant, and their amplitudes depend on the different sets of multipole moments.
This new type of statistically-anisotropic tensor non-Gaussianity can be potentially testable with the upcoming cosmic microwave background B-mode polarization experiments.
Electron acoustic solitary waves in unmagnetized nonthermal plasmas
Authors: Khalid Muhammad , Khan Aqil , Khan Mohsin , Khan Daud , Ahmad Sheraz , Ata-ur-Rahman  
Published: March 2021

Abstract
Electron acoustic (EA) solitary waves (SWs) are studied in an unmagnetized plasma consisting of hot electrons (following Cairns-Tsalli distribution), inertial cold electrons, and stationary ions. By employing a reductive perturbation technique (RPT), the nonlinear Korteweg–de Vries (KdV) equation is derived and its SW solution is analyzed. Here, the effects of plasma parameters such as the nonextensivity parameter (q), the nonthermality of electrons (α), and the cold-to-hot electron density ratio (β) are investigated.
A convolutional neural network to identify motor units from high-density surface electromyography signals in real time
Authors: Wen Yue , Avrillon Simon , Hernandez-Pavon Julio C , Kim Sangjoon J , Hug François , Pons José L 
Published: March 2021

Abstract

Objectives. This paper aims to investigate the feasibility and the validity of applying deep convolutional neural networks (CNN) to identify motor unit (MU) spike trains and estimate the neural drive to muscles from high-density electromyography (HD-EMG) signals in real time. Two distinct deep CNNs are compared with the convolution kernel compensation (CKC) algorithm using simulated and experimentally recorded signals. The effects of window size and step size of the input HD-EMG signals are also investigated. Approach. The MU spike trains were first identified with the CKC algorithm. The HD-EMG signals and spike trains were used to train the deep CNN. Then, the deep CNN decomposed the HD-EMG signals into MU discharge times in real time. Two CNN approaches are compared with the CKC: (a) multiple single-output deep CNN (SO-DCNN) with one MU decomposed per network, and (b) one multiple-output deep CNN (MO-DCNN) to decompose all MUs (up to 23) with one network. Main results. The MO-DCNN outperformed the SO-DCNN in terms of training time (3.2–21.4 s epoch−1 vs 6.5–47.8 s epoch−1, respectively) and prediction time (0.04 vs 0.27 s sample−1, respectively). The optimal window size and step size for MO-DCNN were 120 and 20 data points, respectively. It results in sensitivity of 98% and 85% with simulated and experimentally recorded HD-EMG signals, respectively. There is a high cross-correlation coefficient between the neural drive estimated with CKC and that estimated with MO-DCNN (range of r-value across conditions: 0.88–0.95). Significance. We demonstrate the feasibility and the validity of using deep CNN to accurately identify MU activity from HD-EMG with a latency lower than 80 ms, which falls within the lower bound of the human electromechanical delay. This method opens many opportunities for using the neural drive to interface humans with assistive devices.
Large deviations of the Lyapunov exponent in 2D matrix Langevin dynamics with applications to one-dimensional Anderson localization models
Authors: Monthus Cécile 
Published: March 2021

Abstract
For the 2D matrix Langevin dynamics that correspond to the continuous-time limit of the products of some 2 × 2 random matrices, the finite-time Lyapunov exponent can be written as an additive functional of the associated Riccati process submitted to some Langevin dynamics on the infinite periodic ring. Its large deviations properties can thus be analyzed from two points of view that are equivalent in the end by consistency but give different perspectives. In the first approach, one starts from the large deviations at level 2.5 for the joint probability of the empirical density and empirical current of the Riccati process, and one then performs the appropriate Euler–Lagrange optimization in order to compute the cumulant generating function of the Lyapunov exponent. In the second approach, this cumulant generating function is obtained from the spectral analysis of the appropriate tilted Fokker–Planck operator. The associated conditioned process obtained via the generalization of Doob’s h-transform allows one to clarify the equivalence with the first approach. Finally, applications to one-dimensional Anderson localization models are described in order to explicitly obtain the first cumulants of the finite-size Lyapunov exponent.
Note on generalized symmetries, gapless excitations, generalized symmetry protected topological states, and anomaly
Authors: Jian Chao-Ming , Xu Cenke 
Published: March 2021

Abstract
We consider quantum many body systems with generalized symmetries, such as the higher form symmetries introduced recently, and the ‘tensor symmetry’. We consider a general form of lattice Hamiltonians which allow a certain level of nonlocality. Based on the assumption of dual generalized symmetries, we explicitly construct low energy excited states. We also derive the ’t Hooft anomaly for the general Hamiltonians after ‘gauging’ the dual generalized symmetries. A 3d system with dual anomalous 1-form symmetries can be viewed as the boundary of a 4d generalized symmetry protected topological (SPT) state with 1-form symmetries. We also present a prototype example of a 4d SPT state with mixed 1-form and 0-form symmetry topological response theory as well as its physical construction. The boundary of this SPT state can be a 3d anomalous QED state, or an anomalous 1-form symmetry-enriched topological order. Insights are gained by dimensional compatification/reduction. After dimensional compactification, the 3d system with N pairs of dual 1-form symmetries reduces to a 1d system with 2N pairs of dual U(1) global symmetries, which is the boundary of an ordinary 2d SPT state, while the 3d system with tensor symmetry reduces to a 1d Lifshitz theory, which is protected by the center of mass conservation of the system.
The use of Kahoot in Trigonometric material during the pandemic Covid-19
Authors: Akhsani Lukmanul , Ahmad  , Nugroho Yanuarto Wanda 
Published: March 2021

AbstractMathematics is an abstract subject. Many students do not stay long in learning mathematics. The Covid-19 pandemic conditions require students to study at home. This makes students learn to be more independent. There needs to be innovation in learning. The use of Kahoot can assist teachers in making variations in learning. The use of kahoot can motivate students to learn mathematics. This study shows a positive response to the use of kahoot. Students enjoy using kahoot. Kahoot is one of the variations in the online learning they follow
Analogical reasoning on two dimensional geometry
Authors: Gunawan  , Supriatna  , Annisa Nur Fauzi Dea 
Published: March 2021

Abstract
The aim of this study was to describe the mathematical analogy ability of the eighth-grade students of SMP Negeri 1 Purbalingga based on multiple intelligences. This study used a qualitative description method by conducting investiga tions and providing an overview of the state of the object/research subject based on facts. The research subjects were grade VIII H students of SMP Negeri 1 Purbalingga in the 2019/2020 school year who were divided heterogeneously from class A- I. The technique used n this study was purposive sampling with the aim of selecting 2 students from each of the multiple intelligences, namely linguistic intelligences, logical-mathematical intelligences, and visual -spatial intelligences. The data collection method of this research was carried out using a questionnaire, tests, interviews, and documentation. The data analysis technique used was the Miles and Huberman model and triangulation techniques were also used to analyze the test validity. The results of this study found that students with linguistic intelligence were able to identify answers, determine answers, but were not able to associate. In addition, students with logical-mathematical intelligence were able to identify answers, determine answers, and determine similarities. On the other hand, students with visual-spatial intelligence were able to identify answers, determine answers, but were not able to find problems related to mathematical operations and also were not able to write answers related to questions that required sentences.
Mathematics thinking ability in metaphorical based on personality type
Authors: Purwanto Joko , Muhammad Malim , Novia Ulfah Erin , Rukijah Tien 
Published: March 2021

Abstract
This study aims to describe students’ metaphorical mathematical thinking skills based on sanguinis and melancholy personality types in one-variable linear equation system (OLES). The data source of this research is the seventh-grade students of SMP Negeri 2 Kalibagor. Respondents in this study were taken using the purposive sampling method, there are two types of personality taken as respondents, namely the sanguinis personality type and the melancholic personality type. The methods used to collect data in this study were personality type tests, metaphorical thinking tests, and interviews. Based on the results of the research that has been done, it can be concluded that students with the sanguinis type of personality are able to fulfill four of the six stages of metaphorical thinking abilities. The stages that are fulfilled are Connect, Relate, Explore, and Transform, while the stages that are not fulfilled are the Analyze and Experiment stages. Students with melancholy personality types are able to fulfill all stages of metaphorical thinking skills, namely Connect, Relate, Explore, Analyze, Transform, and Experiment.
TPACK in mathematics teacher education: Are teachers ready to teach for ICT literacy?
Authors: Nugroho Yanuarto Wanda , Mistima Maat Siti , Husnin Hazrati 
Published: March 2021

Abstract
TPACK is an effective combination of technological content knowledge, pedagogical knowledge, and content knowledge to understand the understanding of how the structure, representation, and adaptation of a topic, problem, and content are adapted according to the interests, technology, and abilities of teachers and presented in teaching. Mastery of teachers’ ICT literacy is not possible without the TPACK that underlies the use of ICT. This article employed the relationship between TPACK and ICT literacy among Indonesian Mathematics Teachers in Senior School background. This research used a quantitative approach which focused on the survey as the chosen research design. The research procedures: 1) finding factor analysis of each construct; TPACK and ICT literacy with Exploratory Factor Analysis; 2) constructs the instrument by Confirmatory Factor Analysis to get a fit model; 3) structural Equation Model for the relationship between TPACK and ICT literacy. The model criteria obtained the fit index test; Chi-Square, Chi-Square/df TLI, CFI, NFI, and RMSEA. The results of the study revealed that the TPACK construct consisted of 7 valid sub-constructs and ICT literacy consisted of 4 valid sub-constructs. Whereas, the analysis of the data shows a significant relationship between TPACK and ICT literacy with a strong contribution significant relationship between TPACK and ICT literacy (β = .566).
Geometry From Lambitu tribe Etnomatematics
Authors: Mariamah  , Muslim  , Amrullah  , Prayitno Sudi , Badu Kusuma Anggun 
Published: March 2021

Abstract
The Lambitu tribe has a traditional house as a cultural heritage of ancestors since 1620 M. The traditional house is uma lengge and uma jompa. The existence of this traditional house is very important for the Lambitu tribe community, which functions as a residence as well as a granary. This traditional house is already very well known among the wider community as well as being a tourist destination. The community do not realize that this traditional form of home has mathematical values, which should be used as a source of learning mathematics for students in the learning process at school. Therefore, the purpose of this study was to identify the ethnomatematics form of lengge and jompa traditional houses. The results showed that uma lengge and uma jompa had geometric shapes such as two-dimensional and three-dimensional shapes. The two-dimensional shapes like square, trapezium, isosceles triangle, right triangle, and rectangles, while the three-dimensional shapes consisted of isosceles trapezium, cubes and rectangular prism. Based on the results of this study, it is important to be used as a reference for teachers and students a s well a s teaching materials that are contextual and contain local wisdom local culture.
Mathematical problem-solving skills on relation and function through Model-Eliciting Activities (MEAs)
Authors: Rusliah Nur , Handican Rhomiy , Laswadi  , Deswita Ria , Oktafia Mesi 
Published: March 2021

Abstract
Mathematics relation and function is one of important topics in junior high school. This study aims to see differences in the problem-solving a bilities of junior high school students who use the Model-Eliciting Activities (MEAs) and conventional models in learning mathematics relation and function. This research is a quantitative study using a quasi-experimental approach with a non-equivalent control group design. The population in this study was all of the seventh-grade students at a State Junior High School in Sungai Penuh, Jambi, Indonesia, and the sample was two groups experimental group and control group. The data collection technique used is a test of mathematical problem-solving skills. The data were analyzed by using he independent t-test to find comparisons or differences. The research result showed that there were differences in the mathematical problem-solving skills of junior high school students who used MEAs and other students who did not use MEAs. This difference occurs as a result of different treatment received by students, by MEAs students are required to be more active in understanding and formulating problems.
Mathematical knowledge for early childhood teaching: A deep insight on how pre-service teachers prepare mathematical activities
Authors: Gilar Jatisunda Mohamad , Hidayanti Maria , Lita  , Salim Nahdi Dede , Cahyaningsih Ujiati , Suciawati Vici 
Published: March 2021

Abstract
Geometry concepts are essential to teaching in early childhood. Engaging early childhood in geometric thinking and spatial reasoning can support their overall mathematical and cognitive development. Preparing the mathematics learning process in early childhood, especially the concept of geometry, requires careful planning by paying attention to various aspects both from the psychological side of students and in terms of mathematics content. Early childhood education teachers must have the skills to plan math activities. The research objective was to see the ability of prospective early childhood education teachers to do math activities. The research method used is phenomenology, which examines the experiences of pre-service early childhood education teachers after attending the “Mathematics and Cognitive Development” lecture. The research results were obtained when designing the learning process of pre-service early childhood education teachers by paying attention to the aspects of providing meaningful experiences through games in introducing the concept of geometry.