bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dual-antenna GNSS-aided robust MEMS IMU initial alignment under the earth-centered-earth-fixed frame
Authors: Zhang Siyu , Tang Weiming , Deng Chenlong , Li Yangyang 
Published: October 2023

AbstractThe micro-electro-mechanical system (MEMS), with its small size, low cost and low power, has attracted widespread attentions in recent year, and the initial alignment of the inertial measurement unit (IMU) is an indispensable step before the navigation computation. In the traditional alignment methods with only the velocity measurements, the yaw angle is subject to divergence under low dynamic scenarios, and the measurements are also susceptible to the external environments. Furthermore, the navigation frame (nframe) is usually chosen as the reference frame. Although intuitive, it is more computationally complex compared with the earth-centered-earth-fixed frame (eframe). Therefore, to improve the alignment performance and computational efficiency, a robust MEMS IMU initial alignment method with yaw assistance under theeframe is proposed. In this method, to address the issues of the yaw divergency and computational inefficiency in the traditional methods, the relationship between the yaw angle, derived from dual-antenna global navigation satellite system baseline solution, and IMU error is derived and incorporated into the state space model witheframe as the reference frame. Besides, a robust extended Kalman filtering is adopted to further improve the system’s performance of outlier resistance, whose robust factors are determined by the Institute of Geodesy and Geophysics Ⅲ model. To validate the performance of the proposed method, two field experiments with the velocity of about 0.7 m s−1and 1.5 m s−1was conducted, and the collected data were processed for contrastive analysis. The results show that the effectiveness of the proposed method at suppressing yaw angle divergence and improving the alignment accuracy by resisting outliers under low dynamic scenarios compared with the traditional method. Moreover, the elapsed time is reduced by about 11% when the reference frame in data processing is switched from thenframe to theeframe.
Application of local and global interval embedding algorithms in uncertain system processes
Authors: Li Ning , Ding Hua , Sun Xiaochun , Liu Zeping , Pu Guoshu 
Published: October 2023

Abstract
The data collected during the fault monitoring of industrial processes are often uncertain because of measurement noise, sensor error, and complex working environments. Currently, most of this uncertainty is transformed into interval-value data for processing. However, the traditional interval fault monitoring method (TIFMM) only considers the global information of the interval-valued data and only rarely considers the local neighborhood information, which can characterize the topological relationship between the interval-valued data points, so that the sample points with some nonlinear relationship still maintain this nonlinear relationship after dimensionality reduction. Therefore, to take into account both local and global information, a new interval-valued data projection algorithm called local and global interval embedding algorithms (LGIEA) is proposed in this paper. First, the measurement error estimation method, based on the principle of reasonable granularity, converts the imprecise variable measurement values into interval-value data that can reflect uncertainty. Then, an interval-matrix feature extraction method based on local and global information fusion, proposed in this paper, extracts process features by minimizing local scattering and maximizing global scattering of interval-valued data. Thereby, it preserves the optimal local neighborhood and global information in low-dimensional space. In addition, four statistical indicators and corresponding variable-contribution graphs have been defined to use online fault monitoring and fault variable identification. Finally, the superiority of the LGIEA was verified using simulation data of the Tennessee Eastman process and actual operating data of a shearer in an inclined-ditch coal mine. The experimental results show that, Compared with TIFMM such as midpoint-radius principal component analysis (PCA), complete information PCA and spectral radius-based interval PCA, proposed method significantly reduces the error alarm rate and the false monitoring rate and improves the accuracy of data classification. It also has better fault identification accuracy and robustness.
Quantum phase transitions in the anti-Jaynes-Cummings triangle model
Authors: Cheng Jia-Ming , Zhang Yong-Chang , Zhou Xiang-Fa , Zhou Zheng-Wei 
Published: October 2023

Abstract
We carefully investigate the comprehensive impact of atom-cavity interaction and artificial magnetic fields on quantum phase transitions of anti-Jaynes-Cummings triangle model in the infinite frequency limit. We discover that ground states of the optical field can be a gapped normal phase (NP) or three kinds of gapless superradiant phases with infinite degeneracy. When the atom-cavity coupling is weak, the optical field is in a NP, which is a vacuum with no photons. Otherwise, it will stay at one of the superradiant phases: a normal superradiant phase without photon currents and another two chiral superradiant phases with opposite photon currents. The former only breaks the continuous U(1) symmetry and its gapless excitations are normal Goldstone modes. Nevertheless, the latter, mainly induced by an external synthetic gauge field, break both the continuous U(1) symmetry and chiral symmetry, thereby corresponding gapless excitations are chiral Goldstone modes. In addition, we also propose a detecting scheme to distinguish these superradiant phases.
A quantum feature selection framework via ground state preparation
Authors: Wang Lu , Chen Zhao-Yun , Le Feng-Yu , Yu Zhi-Qian , Xue Cheng , Zhuang Xi-Ning , Yan Qing , Yang Yang , Wu Yu-Chun , Guo Guo-Ping 
Published: October 2023

Abstract
Traditional feature selection methods face the challenges of increasing time complexity and local optima. In previous works, many classical feature selection methods were accelerated through quantum algorithms. However, these approaches still inherit the constraints of these classical methods as they do not address the issue of local minima. Here, we propose a novel quantum feature selection framework based on the classifier’s result, which utilizes Hamiltonian encoding and a ground state preparation algorithm. Numerical experiments are conducted on real-world datasets from the finance and medicine domains. Moreover, the results demonstrate that the proposed method produces the same or better classification accuracy on the classifier than the original data without feature selection. Overall, our approach presents a promising solution to feature selection using quantum computing.
Effect of Matching Algorithm and Profile Shape on Pulsar Pulse Time of Arrival Uncertainties
Authors: Wang J. , Verbiest J. P. W. , Shaifullah G. M. , Yuan J. P. 
Published: October 2023

Abstract
For high-precision pulsar timing analysis and low-frequency gravitational wave detection, it is essential to accurately determine pulsar pulse times of arrival (ToAs) and associated uncertainties. To measure the ToAs and their uncertainties, various cross-correlation-based techniques can be employed. We develop methodologies to investigate the impact of the template-matching method, profile shape, signal-to-noise ratio of both template and observation on ToA uncertainties. These methodologies are then applied to data from the International Pulsar Timing Array. We demonstrate that the Fourier domain Markov chain Monte Carlo method is generally superior to other methods, while the Gaussian interpolation shift method outperforms other methods in certain cases, such as profiles with large duty cycles or smooth profiles without sharp features. However, it is important to note that our study focuses solely on ToA uncertainty, and the optimal method for determining both ToA and ToA uncertainty may differ.
A Comparison of Co-temporal Vector Magnetograms Obtained with HMI/SDO and SP/Hinode
Authors: Zhang Mei , Zhang Haocheng , Jiang Chengqing 
Published: October 2023

Abstract
Accurate measurement of magnetic fields is very important for understanding the formation and evolution of solar magnetic fields. Currently, there are two types of solar magnetic field measurement instruments: filter-based magnetographs and Stokes polarimeters. The former gives high temporal resolution magnetograms and the latter provides more accurate measurements of magnetic fields. Calibrating the magnetograms obtained by filter-based magnetographs with those obtained by Stokes polarimeters is a good way to combine the advantages of the two types. Our previous studies have shown that, compared to the magnetograms obtained by the Spectro-Polarimeter (SP) on board Hinode, those magnetograms obtained by both the filter-based Solar Magnetic Field Telescope (SMFT) of the Huairou Solar Observing Station and by the filter-based Michelson Doppler Imager (MDI) aboard SOHO have underestimated the flux densities in their magnetograms and systematic center-to-limb variations present in the magnetograms of both instruments. Here, using a sample of 75 vector magnetograms of stable alpha sunspots, we compare the vector magnetograms obtained by the Helioseismic and Magnetic Imager (HMI) aboard Solar Dynamics Observatory (SDO) with co-temporal vector magnetograms acquired by SP/Hinode. Our analysis shows that both the longitudinal and transverse flux densities in the HMI/SDO magnetograms are very close to those in the SP/Hinode magnetograms and the systematic center-to-limb variations in the HMI/SDO magnetograms are very minor. Our study suggests that using a filter-based magnetograph to construct a low spectral resolution Stokes profile, as done by HMI/SDO, can largely remove the disadvantages of the filter-type measurements and yet still possess the advantage of high temporal resolution.
A study on flexible bio-based conductive foam for pressure sensing and electromagnetic interference applications
Authors: Selvaraj Vinoth Kumar , Subramanian Jeyanthi , P Krishna Rajeev 
Published: October 2023

Abstract
Day by day gadgets are taking an irreplaceable role in our life. Humans are now depending on electronic gadgets. This increased usage and utility of these electronic gadgets increased the radiation; hence, it is important to research materials for better absorption of these radiations. In this research work, we are developing an electromagnetic interference shielding material. We used polyurethane foam (PU foam), an insulating material that has been filled with nanofillers Polyaniline (PANi), Zinc Oxide (ZnO), and MWCNT. The PU foam used was castor oil-based instead of going with petroleum-based. This work aims to achieve a potential material for electromagnetic interference shielding and sensing material that should be bio-degradable at a low price. The samples were fabricated using the taguchi method in the design of the experiment. This helps to reduce time consumption and provides more accurate results. Once the samples were fabricated, it was subjected to morphological study SEM and EDAX. EMI and conductivity were also carried out. The EMI experiment was done using setup model N5230A PNA-L. The conductivity test is done. GRA relational statistics was utilized to find the interrelation between the two output responses in the taguchi. The experiment concludes that the samples synthesized with 2 wt% of PANi, 300 rpm mixing rotation, and 10 min. Sonication time, provide the best conductivity of 900 S m−1 and EMI SE of 34.38 dB. The best result for conductivity is for sample 8. The maximum conductivity value is 900 S m−1. The lightweight flexible conductive foams can be used in the application of biosensors.
Silicon elastomer as flexible substrate: dielectric characterization and applications for wearable antenna
Authors: Iftikhar Adnan , Naseer Noaman , Yildiz Solen Kumbay , Gokcen Dincer , Fida Adnan , Shafique Muhammad Farhan , Saka Birsen 
Published: October 2023

Abstract
In this paper, low-cost mold silicone and silicone elastomers are investigated as substrates for the realization of flexible antennas. A methodical dielectric characterization is carried out, followed by a detailed explanation of the manufacturing process of the silicone elastomers. The prepared silicone elastomer substrates are also subjected to mechanical tests to ensure flexibility and robustness. The mechanical tests corroborated the utilization of the prepared silicone elastomers for the flexible antennas. Silicone has limited adhesion to metal, so when producing a silicone substrate, a 0.5 mm deep cavity is created with a negative impression of the intended metal component. Consequently, the metal layer is embedded within the silicon substrate, aligning the top surface of the metal flush with the silicone substrate edges. The radio frequency (RF) structure incorporates ridges within the silicone substrate to form a gap, effectively securing the metal on the surface of the silicone. Finally, to prevent the metal from falling from the silicone substrate, Kapton tape is laminated on the substrate. The wrapping of the Kapton tape additionally provides protection from moisture since the silicone elastomer substrate is prone to moisture absorption. The proposed technique is experimentally verified by designing and prototyping a coplanar patch antenna using copper and conductive woven fiber on the silicone substrate. The simulation analysis and experimentation results authenticated the effectiveness of the proposed technique to design a flexible antenna on the silicone elastomer substrates. It is also concluded that the conductive woven fiber-based prototype offers higher flexibility as compared to the copper-based prototype. It is also clinched that there exists a trade-off in flexibility and performance characteristics due to the conductivity and texture difference between the copper and conductive woven fiber.
Decoding of imagined speech electroencephalography neural signals using transfer learning method
Authors: Mahapatra Nrushingh Charan , Bhuyan Prachet 
Published: October 2023

Abstract
The use of brain-computer interfaces to produce imagined speech from brain waves has the potential to assist individuals with difficulty producing speech or communicating silently. The decoding of covert speech has been observed to have limited efficacy due to the diverse nature of the associated measured brain waves and the limited number of covert speech databases. As a result, traditional machine learning algorithms for learning and inference are challenging, and one of the real alternatives could be to leverage transfer of learning. The main goals of this research were to create a new deep learning (DL) framework for decoding imagined speech electroencephalography (EEG) signals tasks using transfer learning and to transfer the model learning of the source task of an imagined speech EEG dataset to the model training on the target task of another imagined speech EEG dataset, essentially the cross-task learning transfer of discriminative characteristics of the source task to the target task of imagined speech. The experiment was carried out using two distinct open-access EEG datasets, FEIS and KaraOne, that recorded the imagined speech classes of neural signals from multiple individuals. The target FEIS model and the target KaraOne model for multiclass classification exhibit overall accuracy of 89.01% and 82.35%, respectively, according to the proposed transfer learning. The experiment results indicate that the cross-task deep transfer learning design reliably classifies the imagined speech EEG signals by applying the source task learning to the target task learning. The findings suggest the feasibility of a consistent strategy for classifying multiclass imagined speech with transfer learning, which could thereby open up the possibility of future investigation into cross-task imagined speech classification knowledge usability for generalization of new imagined speech prompts.
Bimetallic copper-based nanowires and the means to create next-generation stable transparent electrodes
Authors: Križan Anđela , Zimny Kevin , Guyonnet Alexandre , Idowu Emmanuel Opeyemi , Duguet Etienne , Plissonneau Marie , d’Alençon Lauriane , Le Mercier Thierry , Tréguer-Delapierre Mona 
Published: October 2023

Abstract
Metallic nanowire percolating networks are one of the promising alternatives to conventional transparent conducting electrodes. Among the conductive metals, copper appears as a relevant alternative to develop electrodes in a more sustainable and economical way (abundance of the supplies, geo-political risks regarding the supplies, environmental impact, and cost). However, Cu nanowires suffer from high instability in air, and one of the ways to increase stability as well as to boost properties related to transparent electrodes is to combine the Cu with another metal, resulting in bimetallic nanowires. Even though the field of fabrication of nanoalloys has been advancing at a rapid pace in the last two decades, binary nanowires are difficult to produce due to a wide range of parameters that must be aligned in regard to metals that are being combined, such as surface energy of the bulk metal, atomic radii, crystal lattice matching, redox potentials, etc. In this review, we present the current research landscape in making Cu-based bimetallic nanowires for the development of metal nanowire networks with high oxidation resistance. This analysis allows identifying the most promising bimetallic materials for obtaining highly efficient, robust, and cost-effective electrodes.
AGR - 1 A Case Study of Cognitive Reserve and Wernicke's Aphasia Status-Post Insular Stroke
Authors: Wright Madison E , Kinoshita Lisa M 
Published: October 2023

Abstract

Objective
The case is an 88-year-old, woman, formerly a successful surgeon for decades, with Wernicke’s aphasia due to dominant insular stroke. Our evaluation explored her deficits in the context of the heterogeneous presentations of insular strokes and how speech rehabilitation has impacted her language abilities given her cognitive reserve prior to her stroke.

Method
The patient sustained a left MCA embolic stroke in 2020. Neurodiagnostics revealed an infarct in the insula, dominant temporal, frontal, and parietal lobes. Baseline language assessment was completed. Results included moderate–severe receptive language impairment; intact fluency; deficits in writing and repetition; and significant paraphasias.

Results
Neuropsychological assessment demonstrated prominent deficits in repetition with paraphasias and irritability. Auditory learning, attention, and working memory were invalid due to deficits in repetition. She was diagnosed with mild vascular neurocognitive disorder and conduction aphasia.

Conclusions
Despite comprehension having improved, the patient’s paraphasias resemble the presentation of Wernicke’s aphasia. Interestingly, there were some consistencies in paraphasic errors, such as consistently swapping certain words or letters, demonstrating diminished but not absent volition of speech. Story memory was the most effective measure of auditory memory given repetition deficits. Despite repetition deficits commonly localizing to the supramarginal gyrus, her repetition difficulty is likely related to language network disruption due to her insular infarct. Rehabilitation for comprehension was successful despite age at time of injury. Consistent with previous research, results suggest despite cognitive reserve, repetition is resistant to rehabilitation; however, in this case, paraphasic errors were also resistant. Rehabilitation should include a behavioral component for insular stroke patients.
A - 1 Acculturation’s Effect on Neuropsychological Test Performance: a Systematic Review
Authors: Puente Antonio N , Deng Xinyue , Baker Melissa , Puente Antonio E 
Published: October 2023

Abstract

Objective
Acculturation is not thought to reflect one’s brain function; however, numerous studies have shown that less acculturated adults perform worse on neuropsychological tests. This study systematically reviews the effect of acculturation on neuropsychological tests in healthy adults.

Data Selection
This systematic review was registered in Prospero (CRD42020196578) with the defined review procedures. It included studies with healthy adults and relational data between neuropsychological test scores and acculturation. 13,945 studies were screened studies and 8 met inclusion criteria.

Data Synthesis
After accounting for demographic factors (including education), acculturation was a positive predictor of performance on tests of dementia screening, processing speed, attention, executive functioning, language, and memory. This relationship remained after accounting for both demographic factors and English Fluency, and specifically on tests of IQ, language, and visuoconstruction.

Conclusion
These results support measuring and considering acculturation when interpreting both verbal and “non-verbal” test results. At a minimum acculturation should be factored into test selection, normative selection, and clinical judgment. Looking forward it is hoped that new norms and tests will be developed to eliminate the effect of one’s culture on test performance.
A - 2 The Effect of English Language Fluency, Acculturation, and Length of Stay in the U.S. on Cognitive Functioning in Mexican Americans
Authors: Dimmick Andrew , Su Charlie , Raffiudin Hanan , Callahan Jennifer L 
Published: October 2023

Abstract

Objective
Evidence suggests that Hispanic and Latino individuals perform worse on some measures of cognitive functioning than non-Hispanic White individuals. Given this finding, several possible explanations have been posited, including differences in English fluency and acculturation. The purpose of this study was to investigate the effect of English fluency and acculturation on older Mexican American individuals’ cognitive testing performance.

Method
Participants (M age = 63.44; 66.1% female) were Mexican American (n = 899) community members participating in the Health and Aging Brain Study: Health Disparities study. Information gathered included demographic information, attained education level, language use/fluency, acculturation level, and estimated premorbid functioning (reading score) in addition to cognitive functioning performance (MMSE).

Results
Controlling for education and estimated premorbid functioning, both longer stay in the U.S. (β = 1.12, p = 0.006) and more acculturation (β = 1.00, p = 0.035) significantly predicted better cognitive testing performance. Furthermore, after controlling for education and estimated premorbid functioning, using English as a primary language resulted in significantly better testing performance than using Spanish as a primary language and being English fluent (p = 0.006) or non-fluent (p &lt; 0.001). English fluent individuals that used Spanish as their primary language performed significantly better (p &lt; 0.001) than those that were not English fluent and used Spanish as their primary language.

Conclusion
Our findings suggest that factors associated with U.S. acculturation, including stay in the U.S. and English fluency, influence cognitive testing performance. Additional cultural considerations and adaptions may be needed for current neuropsychology measures, such as cognitive testing instruments.