bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of substrate temperature on the structural, static and dynamic magnetic properties of FeSi/MgO(001) films
Authors: Guo Xiao-bin , Lu Xiao-ling , Su Feng-chao , Qiu Wen-hai , Su Zheng , Xie Jia-feng 
Published: December 2021

Abstract
FeSi films with different substrate temperature (T
s) were deposited on MgO(001) substrates by radiofrequency magnetron sputtering. During the change of crystal structure from the amorphous to the epitaxial state, the magnetic anisotropy changed in three stages: dominant uniaxial magnetic anisotropy (T
s < 400 °C), enhanced cubic magnetocrystalline anisotropy (400 °C ⩽ T
s⩽ 600 °C) and weak cubic magnetocrystalline anisotropy (T
s = 700 °C and 800 °C). In addition, the resonance frequency ƒ
r first decreased and then reached its maximum value before finally disappearing due to the large coercivity field. These results demonstrate the correlation between the structure and static and dynamic magnetic properties of FeSi films, and provide an effective method for preparing soft films with deterministic uniaxial or cubic magnetic anisotropy for practical applications.
Recent advances in field effect transistor biosensor technology for cancer detection: a mini review
Authors: Chao Lemeng , Liang Ying , Hu Xiao , Shi Huanhuan , Xia Ting , Zhang Hong , Xia Huiling 
Published: December 2021

Abstract
Cancer is an incurable disease, and the treatment process is extremely painful. Early detection may ease the treatment process and prevent cancer from spreading beyond the primary disease area. However, conventional screening tests have long detection times and lack the required sensitivity for early detection. Consequently, traditional cancer biosensors, including amplification refractory mutation system, digital polymerase chain reaction, next generation sequencing, western blot, electrochemical, and mechanical biosensors, have been studied in recent years. Specifically, field effect transistor (FET) biosensors, are attractive pocketable devices with short detection time capabilities. Because FET biosensors have outstanding electrical and mechanical properties, FET biosensors have been studied for their efficacy in the early detection of cancer. Traditional detection methods of cancer biomarkers include the use of FET biosensors for the detection of cancer biomarkers, especially gene, antigen, and protein characteristics. This review presents the latest strategies in FET applications in cancer biosensing and compares their advantages and disadvantages regarding sensing principle, configuration, and performance. Especially, FET biosensors for the detection of cancer biomarkers, which include antibodies, nucleic acids, proteins are highlighted. Mechanical and electrical properties of FET devices and their effect on performance is discussed. This review provides a guiding role in the design and development of FET-based biosensors.
Plexcitonic strong coupling: unique features, applications, and challenges
Authors: Zhao Qian , Zhou Wen-Jie , Deng Yan-Hui , Zheng Ya-Qin , Shi Zhong-Hong , Kee Ang Lay , Zhou Zhang-Kai , Wu Lin 
Published: December 2021

Abstract
There have recently been remarkable achievements in turning light–matter interaction into strong-coupling quantum regime. In particular, room-temperature plexcitonic strong coupling in plasmon-exciton hybrid systems can bring promising benefits for fundamental and applied physics. Herein, we review theoretical insight and recent experimental achievements in plexcitonic strong coupling, and divide this review into two main parts. The first part briefly introduces the general field of strong coupling, including its origin and history, physical mechanisms and theoretical models, as well as recent advanced applications of strong coupling, such as quantum or biochemical devices enabled by optical strong coupling. The second part concentrates on plexcitonic strong coupling by introducing its unique features and new potentials (such as single-particle ultrastrong coupling, strong-coupling dynamics in femtosecond scale) and discusses the limitations and challenges of plexcitonic strong coupling. This will also be accompanied by potential solutions, such as microcavity-engineered plexcitonics, spectral hole burning effects and metamaterial-based strong coupling. Finally, we summarize and conclude this review, highlighting future research directions and promising applications.
Strain accuracy enhancement of stereo digital image correlation for object deformation with large rotations
Authors: Wu Rong , Zhao Shili , Liu Yi , Zhang Shuiqiang 
Published: December 2021

Abstract
We propose a full-field stereo digital image correlation (DIC) strain measurement method to overcome the poor accuracy while measuring the deformation under large rotations. Such a drawback comes from the failure to consider rotation movements of the deformed objects when calculating their strain values. To address this, we first used a DIC matching algorithm combined with a rotated subset and feature point detection to obtain displacement fields. By employing a singular value decomposition method, we can then calculate rotation matrices of the strain windows before and after deformations. Finally, to eliminate the strain errors caused by rotation, we introduced the rotation matrices into the classical pointwise least squares DIC strain calculation method. Both numerical simulations and experiments are performed, and the accuracy and effectiveness of the proposed method are confirmed by the experimental results.
Intelligent reading recognition method of a pointer meter based on deep learning in a real environment
Authors: Zhou Dengke , Yang Ying , Zhu Jie , Wang Ku 
Published: December 2021

Abstract
Accurate reading of a pointer meter is a crucial task in complex environments, such as substations, the military and aerospace. Current recognition algorithms are mainly used to identify the same type and a non-tilt meter, which has limited application in a real environment. In this paper, we propose a novel end-to-end intelligent reading method for a pointer meter based on deep learning, which locates the meter and extracts the pointer simultaneously without any prior information. In particular, the pointer is directly and precisely extracted using the designed semi-pointer detection method without any handcrafted features designed in advance, which avoids accumulated error caused by preprocessing. Based on the extracted panel object, including the semi-pointer, panel center and scale characters, the indicated value of the pointer is obtained by a local angle method, which achieves better performance than the traditional angle method by referring to the neighboring scale lines of the pointer. Experimental results demonstrate that the method is faster and more effective than some common methods. It is worth noting that this study has the advantage of being able to recognize pointer meters under complex conditions such as tilt, rotation, blur and illumination. This is acceptable for the actual application requirements in a real environment with a recognition accuracy of 99.20% and an average reference error of 0.34%.
Heterostructure of core–shell IrCo@IrCoO
x
as efficient and stable catalysts for oxygen evolution reaction
Authors: Ma Xiaoping , Deng Lili , Lu Manting , He Yi , Zou Shuai , Xin Yu 
Published: December 2021

Abstract
Although researches on non-noble metal electrocatalysts have been made some progress recently, their performance in proton exchange membrane water electrolyzer is still incomparable to that of noble-metal-based catalysts. Therefore, it is a more practical way to improve the utilization of precious metals in electrocatalysts for oxygen evolution reaction (OER) in the acidic medium. Herein, nanostructured IrCo@IrCoO
x
core–shell electrocatalysts composed of IrCo alloy core and IrCoO
x
shell were synthesized through a simple colloidally synthesis and calcination method. As expected, the hybrid IrCo-200 NPs with petal-like morphology show the best OER activities in acidic electrolytes. They deliver lower overpotential and better electrocatalytic kinetics than pristine IrCo alloy and commercial Ir/C, reaching a low overpotential (j = 10 mA cm−2) of 259 mV (versus RHE) and a Tafel slope of 59 mV dec−1. The IrCo-200 NPs displayed robust durability with life time of about 55 h in acidic solution under a large current density of 50 mA cm−2. The enhanced electrocatalytic activity may be associated with the unique metal/amorphous metal oxide core–shell heterostructure, allowing the improved charge transferability. Moreover, the *OH-rich amorphous shell functions as the active site for OER and prevents the further dissolution of the metallic core and thus ensures high stability.
Nitrogen-doped graphene quantum dots synthesized by femtosecond laser ablation in liquid from laser induced graphene
Authors: Shen Li , Zhou Sikun , Huang Fei , Zhou Hao , Zhang Hong , Wang Shutong , Zhou Shouhuan 
Published: December 2021

Abstract
In this work, graphene quantum dots (GQDs) were synthesized by femtosecond laser ablation in liquid using laser induced graphene as the carbon source. Nitrogen-doped graphene quantum dots (N-GQDs) were successfully synthesized by adding ammonia water to the graphene suspension. The GQDs/N-GQDs structure consist of a graphitic core with oxygen and nitrogen functionalities with particle size less than 10 nm, as demonstrated by x-ray photoelectron spectroscopy, Fourier infrared spectrometer spectroscopy, and transmission electron microscopy. The absorption peak, PL spectrum, and quantum yield of the N-GQDs were significantly enhanced compared with the undoped GQDs. Further, the possible mechanism of synthesis GQDs was discussed. Furthermore, the N-GQDs were used as a fluorescent probe for detection of Fe3+ ions. The N-GQDs may extend the application of graphene-based materials to bioimaging, sensor, and photoelectronic.
Novel electrolyte additive of graphene oxide for prolonging the lifespan of zinc-ion batteries
Authors: Wang Xuyang , Kirianova Alina V , Xu Xieyu , Liu Yanguang , Kapitanova Olesya O , Gallyamov Marat O 
Published: December 2021

Abstract
Aqueous zinc-ion batteries have attracted the attention of the industry due to their low cost, good environmental friendliness, and competitive gravimetric energy density. However, zinc anodes, similar to lithium, sodium and other alkali metal anodes, are also plagued by dendrite problems. Zinc dendrites can penetrate through polymer membranes, and even glass fiber membranes which seriously hinders the development and application of aqueous zinc-ion batteries. To resolve this issue, certain additives are required. Here we have synthesized an electrochemical graphene oxide with novel electrolyte based on tryptophan, which allows to obtain few-layered sheets with a remarkably uniform morphology, good aqueous solution dispersion, easy preparation and environmental friendliness. We used this electrochemical graphene oxide as an additive to the electrolyte for aqueous zinc-ion batteries. The results of phase-field model combined with experimental characterization revealed that the addition of this material effectively promotes the uniform distribution of the electric field and the Zn-ion concentration field, reduces the nucleation overpotential of Zn metal, and provides a more uniform deposition process on the metal surface and improved cyclability of the aqueous Zn-ion battery. The resultant Zn∣Zn symmetric battery with the electrochemical graphene oxide additive affords a stable Zn anode, which provided service for more than 500 h at 0.2 mA cm−2 and even more than 250 h at 1.0 mA cm−2. The Coulombic efficiency (98.7%) of Zn∣Cu half-cells and thus cyclability of aqueous Zn-ion batteries using electrochemical graphene oxide is significantly better compared to the additive-free electrolyte system. Therefore, our approach paves a promising avenue to foster the practical application of aqueous Zn-ion batteries for energy storage.
Ru-decorated gallium nitride nanotubes as chemical sensor for detection of purinethol drug: a density functional theory study
Authors: Menazea A A , Awwad Nasser S , Ibrahium Hala A , Delerkheiroehin P , Ali H Elhosiny 
Published: December 2021

Abstract
We employed density functional B3LYP to inspect the impact of Ru-decoration on a GaN nanotube (GaNNT) sensing performance in detection of purinethol (PT) drug. The interaction of the pristine GaNNT with the PT was found to be weak, and the sensing response is 4.3. Decorating an Ru atom into the GaNNT surface increases the adsorption energy (Ead) of PT from −6.5 to −23.6 kcal/mol. The sensing response significantly rises to 93.6 by Ru-decoration. A short recovery time of 15.7 s is found for the PT desorption from the Ru-decorated GaNNT surface at 298 K. The water solvent reduces Ead of PT to −20.1 kcal mol−1. Thus, it suggests that Ru-decorated GaNNT may be a highly sensitive PT sensor with a short recovery time.
Quantum coherence induced by a flux qubit coupled by a resonator coherent field through a two-photon interaction
Authors: Mohamed A-B A , Hessian H A , Al-Duais F S , Eleuch H 
Published: December 2021

Abstract
The intrinsic decoherence effects on a flux qubit coupled to a resonator through a two-photon interaction where the resonator field is initially in coherent and even coherent states are investigated. The qubit-resonator entanglement and coherence loss (mixedness) of the system and its subsystems are examined using entropy and negativity. The ability of the qubit-resonator interaction to generate quantum coherence (qubit-resonator entanglement and mixedness) is shown to be dependent on the initial cavity non-classicality, detuning, and decoherence. For larger values of the qubit-resonator detuning, the initial resonator non-classicality can enhance the generation and stability of quantum coherence. The decoherence degrades the qubit-resonator entanglement and destroys the sudden death-birth entanglement.
Computational and experimental investigations on electronic and optical response of oxygenated MoS2
Authors: Shakeel Mubashra , Iqbal M Javaid , Iftikhar Mamoor , Farooq Umer , Qureshi Muhammad Tauseef , Hussein A. Wahab M. A , Chand Ram , Affan Hira , Saleem Murtaza 
Published: December 2021

Abstract
The current study is concerned with the first principle investigations of the oxygenated MoS2 to explore the electronic and optical properties. We consider various oxygen concentrations (MoS2, MoS1.75O0.25, MoS1.5O0.5, MoSO) for the prediction of its impact on parameters using PBE-GGA approximation. A noticeable change in parameters has been observed in the visible regime with the introduction of oxygen content in the MoS2 structure. The results of the electronic properties suggest a string role of Mo d-states, S p-states, and O p-states which, however, show variations for various O concentrations near the Fermi level. The optical absorption spectra for oxygenated MoS2 show a blue shift in the visible range compared to the pure MoS2. The effect of oxygen can also be seen in the experimentally prepared MoS2 thin films as the variation of optical behavior can be seen. Refractive index decreases from 2.5 to ∼2. Similarly, absorption graphs show a lack of absorption phenomenon as the oxygen content increases. The role of oxygen brings appreciable changes in the optical parameters over the different energy ranges.
Deep learning reveals personalized spatial spectral abnormalities of high delta and low alpha bands in EEG of patients with early Parkinson’s disease
Authors: Chu Chunguang , Zhang Zhen , Wang Jiang , Liu Shang , Wang Fei , Sun Yanan , Han Xiaoxuan , Li Zhen , Zhu Xiaodong , Liu Chen 
Published: December 2021

Abstract

Objective. Parkinson’s disease (PD) is one of the most common neurodegenerative diseases, and early diagnosis is crucial to delay disease progression. The diagnosis of early PD has always been a difficult clinical problem due to the lack of reliable biomarkers. Electroencephalogram (EEG) is the most common clinical detection method, and studies have attempted to discover the EEG spectrum characteristics of early PD, but the reported conclusions are not uniform due to the heterogeneity of early PD patients. There is an urgent need for a more advanced algorithm to extract spectrum characteristics from EEG to satisfy the personalized requirements. Approach. The structured power spectral density with spatial distribution was used as the input of convolutional neural network (CNN). A visualization technique called gradient-weighted class activation mapping was used to extract the optimal frequency bands for identifying early PD. Based on the model visualization, we proposed a novel quantitative index of spectral characteristics, spatial-mapping relative power (SRP), to detect personalized abnormalities in the spatial spectral characteristics of EEG in early PD. Main results. We demonstrated the feasibility of applying CNN to identify the patients with early PD with an accuracy of 99.87% ± 0.03%. The models indicated the characteristic frequency bands (high-delta (3.5–4.5 Hz) and low-alpha (7.5–11 Hz) frequency bands) that are used to identify the early PD. The SRP of these two characteristic bands in early PD patients was significantly higher than that in the control group, and the abnormalities were consistent at the group and individual levels. Significance. This study provides a novel personalized detection algorithm based on deep learning to reveal the optimal frequency bands for identifying early PD and obtain the spatial frequency characteristics of early PD. The findings of this study will provide an effective reference for the auxiliary diagnosis of early PD in clinical practice.
The Accuracy of the RK-4 and RK-5 Technique as a Numerical Solution to the SEIRS Model of Online Game Addiction on Mathematics Students
Authors: Side Syafruddin , Saman Abdul , Ramadhan Nur Rezky , Sidjara Sahlan 
Published: December 2021

Abstract
This study aims to determine the accuracy of Runge kutta order-4 (RK-4) and order-5 (RK-4) as a solution to the SEIRS numerical model for online game addiction in mathematics students at Universitas Negeri Makassar. The model used in online game addiction is the Suspected-Exposed-Infected-Recovered-Suspected (SEIRS). This is a quantitative research with secondary data obtained from a closed questionnaire where students were given several answer choices. It starts by examining the SEIRS model of online game addiction, before determining general solutions using the RK-4 and RK-5 techniques. The model is then analyzed and simulated using RK-4 and RK-5 techniques, which are compared to determine the most accurate one. The results show that the SEIRS simulation model is accurate for predicting the number of online game addictions. Furthermore, the Runge-Kutta technique can be used to observe the trend of increasing cases of online games among students. The numerical simulation showed that the RK-4 technique is more accurate than the RK-5 when the population is larger. The simulation results of the SEIRS model using MAPLE provided an estimated increase in online game addiction that can be used by students and their parents to limit the number of permits associated with launching online games.
Mathematics Learning for Students with Special Needs During the Covid-19 Pandemic
Authors: Kadarisma G , Juandi D 
Published: December 2021

AbstractStudents with special needs need special attention when learning during this pandemic because of the limitations they have, especially in learning mathematics. Therefore, the purpose of this study is to describe how mathematics learning for children with special needs during the Covid-19 pandemic is implemented. Therefore, the method in this research is the descriptive qualitative method. The data collection technique used in this research is the study of documentation and interviews with respect to their respective guidelines. Qualitative data were obtained through interviews with teachers accompanying inclusive students as key informants. Documentation is done to see an overview of the teaching materials used by the teacher. The subjects in this study were special guidance teachers for special needs students as many as 2 people from 2 different inclusive schools in the city of Bandung. The results of this study are that. At the beginning of the pandemic, Alam Bandung School implemented online learning, but the rules applied at the school became chaotic, students could not focus during video conferencing, and students’ cognitive abilities decreased, especially in mathematics. Therefore, learning is carried out offline even though it is only 3 days filled with therapy, deepening material and assignments for home. At Mutiara Bunda School, at the beginning of the pandemic, online learning was carried out using Google Meetings and task collection through Google Classroom. Teachers design their own learning tools such as home learning guides for each special needs students so that learning objectives can still be achieved even though they are studying from home.
Knot Semantic Logic and Deep Structure of Statements
Authors: Ja’faruddin  , Chen Wen-Haw , Khaerati  
Published: December 2021

Abstract
This article aims to analyze the deep structure of sentences by applying the knot semantic logic principle in the surface structure of the sentences. This article will help to judge some controversial statements that have legal and social effects. The knot semantic logic theory is a novel approach to explaining a literature’s symmetry structure. Chiasm, parallelism, and concentricity are all examples of symmetry structure. This new theory introduces a new method for detecting symmetry in words, sentences, verses, essays, chapters, and entire books. This analysis will use Knot semantic logic point of view. The methodology used in the analysis has two main procedures: (1) classification of the structure and (2) analyzing some examples from some official/formal statements and controversial statements using Paltridge’s classification of thematic progression and knot semantic logic. The result of this research is that the valid argument has surface and Deep Structure. Some fallacies statements such as presumption, generalization, distortion, and deletion have a deep structure that forms ring composition (Type I of knots semantic logic) and parallelizations (Type III of knots semantic logic). This research results generate a new idea about perspective truth, Original perspective domain, and Response perspective domain
“CoVid” Analysis using Online-Based Problem-Solving Approach on Informatics Mathematics Subject
Authors: Ashari N W , Salwah  , Ma’rufi  
Published: December 2021

AbstractDuring the COVID-19 pandemic, lecturers carry out online learning and this condition also force student to learn independently. One alternative solution given is online-based problem-solving approach. This approach are suitable implemented in the mathematics Informatics course. The Approach challenges students to be able to express every result of thinking, transform information into various forms of representation, as well as the ability to adapt to online learning during this pandemic. This research is case study research that aims to analyse the Communication Skills, Visual thinking, and Adaptability (COVid) of students in the mathematics informatics course through Online-Based Problem-Solving Approach. This course examines combinatorics and pigeon holes principle. This lesson is useful in the context of making software that requires certain calculations and logic. The subjects of this study were second semester students of the 2020/2021 academic year at the Informatics Study Program at Univeritas Cokroaminoto Palopo. Data was collected by using assignments, questionnaires, and interviews to obtain valid data. The results show that only a few students are able to express every result of thinking, transform information into various forms of representation, as well as the ability to adapt to online learning during this pandemic.