bluuebunny/crossref_metadata_2025_split huggingface dataset:
Superfluid fraction tensor of a two-dimensional supersolid
Authors: Blakie P B 
Published: April 2024

Abstract
We investigate the superfluid fraction of crystalline stationary states within the framework of mean-field Gross–Pitaevskii theory. Our primary focus is on a two-dimensional Bose–Einstein condensate with a non-local soft-core interaction, where the superfluid fraction is described by a rank-2 tensor. We then calculate the superfluid fraction tensor for crystalline states exhibiting triangular, square, and stripe geometries across a broad range of interaction parameters. Factors leading to an anisotropic superfluid fraction tensor are also considered. We also refine the Leggett bounds for the superfluid fraction of the 2D system. We systematically compare these bounds to our full numerical results, and other results in the literature. This work is of direct relevance to other supersolid systems of current interest, such as supersolids produced using dipolar Bose–Einstein condensates.
Weak abnormal acoustic signal enhancement and recognition using squeeze-and-excitation attention based denoising convolutional neural network during high-dam flood discharging
Authors: Lian Jijian , Xu Wenliang , Liang Chao , Liu Fang , Wang Runxi 
Published: April 2024

Abstract
Acoustic signals (particularly cavitation acoustic signals) generated during the flood discharge of high dams are highly sensitive to various abnormal situations, whereas weak abnormal signal recognition under strong discharge-noise interference is extremely challenging. Based on the prototype and model experiments, the related abnormal acoustic signals and discharge noise were recorded to construct datasets. Subsequently, using the framework of the deep neural network (DNN) speech enhancement method, a squeeze-and-excitation attention based denoising convolutional neural network (DnCNN) based method for weak abnormal acoustic signal enhancement and recognition was proposed and verified using two case studies of cavitation acoustic signal enhancement and multicategory acoustic signal enhancement and recognition. Compared with the DnCNN method and traditional signal processing methods (such as wavelet, empirical mode decomposition, least mean square, and recursive least square), the proposed method achieved excellent signal enhancement performance after training based on limited prior knowledge of signal and noise. It also demonstrated good generalization ability and robustness in multicategory tasks, which significantly improved the abnormal signal recognition accuracy. This study provides technical support for the practical application of acoustic monitoring based on DNN for safety during the flood discharge of high dams.
OxySaver: highly conserving demand oxygen delivery system with increased throughput
Authors: Haque Syed Razwanul , Saha Shovon Sudan , Chowdhury Hasib Ahmed , Nesa Meherun , Choudhury Ju-un Nahar , Choudhury Dewan Ahmad Fawzul Kabir , Rahman Tanzilur 
Published: April 2024

Abstract
Since the beginning of the twentieth century, oxygen has been an essential therapeutic drug. Patients with chronic lung disease and other conditions rely on supplemental oxygen provided by oxygen cylinders for treatment. Because of the sudden increase in oxygen demand caused by the COVID-19 pandemic, combined with a lack of oxygen supply, new methods of oxygen delivery have been investigated as a potential solution. We propose OxySaver, a novel low-cost (∼60$) demand oxygen delivery system that may significantly reduce oxygen consumption. The proposed system consists of a pressure sensor that detects the patient’s demand for oxygen, a normally open solenoid valve that switches the supply ON/OFF on demand, and a processing unit that ensures the supply of oxygen only during the patients’ inhalation. It is simple to install using existing oxygen cylinders and oxygen pipelines in hospitals. The preliminary prototype tests have been promising, with OxySaver able to save up to 67% more oxygen than conventional methods. OxySaver’s pulse setting can reach 18 LPM, as opposed to the 0.2–4 LPM typically found in oxygen concentrators. The system only needs 3.72 W at 50 bpm, which is less than other similar devices. OxySaver in multiplexing mode has the potential to reduce the logistical challenges associated with oxygen refilling and transport. The device is now ready for clinical testing at the bedside.
Similarity indicator and CG-CGAN prediction model for remaining useful life of rolling bearings
Authors: Yang Liu , Binbin Dan , Cancan Yi , Shuhang Li , Xuguo Yan , Han Xiao 
Published: April 2024

Abstract
To tackle the challenges of performing early fault warning and improving the prediction accuracy for the remaining useful life (RUL) of rolling bearings, this paper proposes a similarity health indicator and a predictive model of CG-conditional generative adversarial network (CGAN), which relies on a CGAN that combines one-dimensional convolutional neural network (CNN) with a bidirectional gate recurrent unit (Bi-GRU). This framework provides a comprehensive theoretical foundation for RUL prediction of rolling bearings. The similarity health indicator allows for early fault warning of rolling bearings without expert knowledge. Within the CGAN framework, the inclusion of constraints guides the generation of samples in a more targeted manner. Additionally, the proposed CG-CGAN model incorporates Bi-GRU to consider both forward and backward information, thus improving the precision of RUL forecasting. Firstly, the similarity indicator between the vibration signals of the rolling bearing over its full life span and the standard vibration signals (healthy status) is calculated. This indicator helps to determine the early deterioration points of the rolling bearings. Secondly, the feature matrix composed of traditional health indicators and similarity health indicator, is utilized to train and test the proposed CG-CGAN model for RUL prediction. Finally, to corroborate the efficacy of the proposed method, two sets of real experiment data of rolling bearing accelerated life from the Intelligent Maintenance Systems (IMS) are utilized. Experimental findings substantiate that the proposed similarity health indicator offers early fault alerts and precisely delineates the performance diminution of the rolling bearing. Furthermore, the put-forward CG-CGAN model achieves high-precision RUL prediction of rolling bearing.
The accuracy of vehicle position and heading angle improvement based on dual-antenna GNSS/INS/Barometer integration using extended Kalman filter
Authors: Jiao Hongyuan , Xu Xiangbo , Chen Shao , Guo Ningyan , Yu Zhibin 
Published: April 2024

Abstract
In the field of autonomous driving progresses, obtaining the precise position and attitude of vehicles through measurement technology is vital. An integrated GNSS/INS/Barometer (Global Navigation Satellite System/Inertial Navigation System/Barometer) fusion method is proposed to improve vehicle positioning and heading angle accuracy. An extended Kalman filter (EKF) based on horizontal dilution of precision (HDOP) is designed to fuse the INS error and the GNSS measurement. Furthermore, GNSS dual antennas are utilized to increase a state variable in EKF based on HDOP in addition to position and velocity. The kinematic constraint of zero velocity in the lateral and vertical directions of vehicle movement is used to measure the mounting angle to reduce error. The position and heading angle accuracy of the proposed method are 0.483 m and 1.358°, which represent reductions of 37.03% and 43.86% compared to the conventional EKF method, respectively. The experimental results demonstrate that the proposed method can measure accurate position and heading angle in both open and occluded environments.
Analysis of measurement uncertainty for verification/calibration of particulate matter measuring instruments using the gravimetric method
Authors: Duong Nam Thanh , Tran Tung Son , Tran Hoa Thi , Nguyen Giang Hoang 
Published: April 2024

Abstract
As demand for particulate matter (PM) monitoring increases for scientific and regulatory purposes, the need for an accurate calibration process for these instruments will also increase. This article focuses on studying the factors that cause measurement uncertainty during the calibration process with the calibration facility (ManDust) for reference gravimetric instruments. Measurement uncertainty in this article included type A and type B uncertainties. Type A uncertainty mostly included repeatability and reproducibility while volume uncertainty predominately contributed to type B uncertainty. To clearly examine how these factors contributed to the combined uncertainty, the calibration process was carried out at a high concentration range from 0.4 to 1.6 mg m−3. In general, the measurement uncertainty ranged from 1.95% to 6.65%. Further analysis showed that type A uncertainty contributed more to the total uncertainty than type B. In particular, uncertainty from volume calculation and measurement contributed over 80% of type B uncertainty. During the PM filter weighing process, the uncertainty during the conditioning process contributed the most to type B uncertainty. This article also conducted a sensitivity analysis and found that besides volume measurement, the filter weighing procedure was also sensitive to changes even though it only contributed less than 5% of type B uncertainty. Using high-quality devices and careful handling during these two procedures should be emphasized to reduce the uncertainty of the calibration process.
Anisotropic sensing based on single ReS2 flake for VOCs discrimination
Authors: Xie Yuan , Zhang Zhe , Meng Fanying , Huo Shida , Hu Xiaodong , Niu Pingjuan , Wu Enxiu 
Published: April 2024

Abstract
Selective and sensitive detection of volatile organic compounds (VOCs) holds paramount importance in real-world applications. This study proposes an innovative approach utilizing a single ReS2 field-effect transistor (FET) characterized by distinct in-plane anisotropy, specifically tailored for VOC recognition. The unique responses of ReS2, endowed with robust in-plane anisotropic properties, demonstrate significant difference along the a-axis and b-axis directions when exposed to four kinds of VOCs: acetone, methanol, ethanol, and IPA. Remarkably, the responses of ReS2 were significantly magnified under ultraviolet (UV) illumination, particularly in the case of acetone, where the response amplified by 10–15 times and the detection limit decreasing from 70 to 4 ppm compared to the dark conditions. Exploiting the discernible variances in responses along the a-axis and b-axis under both UV and dark conditions, the data points of acetone, ethanol, methanol and IPA gases were clearly separated in the principal component space without any overlap through principal component analysis, indicating that the single ReS2 FET has a high ability to distinguish various gas species. The exploration of anisotropic sensing materials and light excitation strategies can be applied to a broad range of sensing platforms based on two-dimensional materials for practical applications.
Influence of the RF voltage amplitude on the space- and time-resolved properties of RF–LF dielectric barrier discharges in α–γ mode
Authors: Robert Raphaël , Sadeghi Nader , Hagelaar Gerjan , Stafford Luc , Massines Françoise 
Published: April 2024

Abstract
This work reports the results of an experimental and modeling study on dual-frequency Ar–NH3 dielectric barrier discharges (DBDs) exhibiting the α–γ transition. A combination of space- and time-resolved optical absorption and emission spectroscopy is used to record spatio-temporal mappings of the Ar metastable number density, Ar 750.4 nm line emission intensity, and electron–Ar bremsstrahlung continuum emission intensity. With the increase of the radio frequency (RF) voltage amplitude in a 50 kHz–5 MHz DBD, maximum populations of Ar excited species (1s and 2p states, linked to the population of high-energy electrons) observed in the γ mode decrease and appear earlier in the low-frequency cycle. On the other hand, the density of the bulk electrons, monitored from the continuum emission intensity, increases, with a more prominent rise in the RF-α mode than in the γ regime. Such behaviors are consistent with the predictions of 1D fluid model and results from a decrease of the gas voltage required for self-maintenance of the cathode sheath in the γ breakdown.
Non-locality of conjugation symmetry: characterization and examples in quantum network sensing
Authors: Miyazaki Jisho , Akibue Seiseki 
Published: April 2024

Abstract
Some quantum information processing protocols necessitate quantum operations that are invariant under complex conjugation. In this study, we analyze the non-local resources necessary for implementing conjugation-symmetric measurements on multipartite quantum networks. We derive conditions under which a given multipartite conjugation can have locally implementable symmetric measurements. In particular, a family of numbers called the ‘magic-basis spectrum’ comprehensively characterizes the local measurability of a given 2-qubit conjugation, as well as any other properties that are invariant under local unitary transformations. We also explore the non-local resources required for optimal measurements on known quantum sensor networks by using their conjugation symmetries as a guide.
Novel competitive fractional chaotic circuits interaction networks with hierarchical structure and encryption application
Authors: El-Mesady A , Elsonbaty Amr , El-Shafai Walid , Kamal F M 
Published: April 2024

Abstract
In the realm of complex networks, the challenge of ensuring secure communication amidst the vulnerabilities of conventional encryption methods has become increasingly critical. This study delves into the complex realm of synchronized behaviors in networks, employing fractional-order chaotic circuits within hierarchically structured competitive interaction networks to enhance encryption security, particularly for medical image transmission. We propose a novel paradigm that transcends traditional synchronization methods used across various disciplines, from engineering to social sciences, by unveiling the intricate dynamics of how units within networks share interactions. Our approach leverages the unique properties of fractional chaos and network hierarchy, demonstrating that the proposed model, characterized by multi-directed links and competitive strategies, significantly improves synchronization. Through detailed analysis, including bifurcation diagrams and Lyapunov exponent plots, we uncover the optimal configurations of coupling strength and fractional order that lead to enhanced network synchronization. This synchronization is pivotal for our encryption application, showcasing a high level of security and privacy in the transmission of medical images. The encryption technique benefits from the network’s complex and synchronized dynamics, rendering it a formidable challenge for potential attackers to decipher the encrypted data. While our findings offer a promising mechanism for creating robust communication networks capable of securing sensitive medical data, the implications of our work extend beyond this application. The successful application of fractional-order chaotic circuits sets a groundwork for securing diverse types of data transmissions against the evolving landscape of cyber threats. This research not only marks a significant advancement in network security but also opens new avenues for applying these principles across a spectrum of fields where data security and privacy are paramount.
Competition of systematically optimized deep neural networks for the estimation of tensile behavior of aluminum-magnesium alloy
Authors: Mokhtari Mir Abolfazl , Nikzad Mohammad Hossein , Jalalvand Meysam 
Published: April 2024

Abstract
Aluminum-magnesium (Al-Mg) alloys are prevalently employed within the aerospace sector. This research engaged a suite of deep learning approaches, encompassing the Artificial Neural Network (ANN), Gated Recurrent Unit (GRU) networks, Long-Short Term Memory (LSTM), and simple Recurrent Neural Network (RNN) to evaluate their predictive efficacy regarding the tensile strength and stiffness of Al-Mg alloys obtained from molecular dynamics simulation. The Taguchi method was initially applied to refine the architecture of each deep neural network (DNN), followed by a comparative analysis of their optimized configurations. The findings of this investigation revealed that the refined simple RNN and LSTM models exhibited superior predictive accuracy for estimating the strength and stiffness of the alloy, respectively. Moreover, the study elucidated that DNNs equipped with memory capabilities outstripped traditional ANNs in forecasting the tensile properties of Al-Mg alloys.
Double-graviton production from Standard Model plasma
Authors: Ghiglieri J. , Laine M. , Schütte-Engel J. , Speranza E. 
Published: April 2024

Abstract
The thermal plasma filling the early universe generated a stochastic
gravitational wave background that peaks in the microwave frequency
range today. If the graviton production rate is expressed as a series in a
fine-structure constant, α, and the temperature over the Planck mass,
T
2/m
pl
2, then the lowest-order contributions come from single
(∼αT
2/m
pl
2) and double (∼T
4/m
pl
4)
graviton production via 2 → 2 scatterings.
We show that in the Standard Model,
single-graviton production dominates if the maximal
temperature is smaller than
4 × 1018 GeV. This justifies previous calculations
which relied solely on single-graviton production.
We mention Beyond the Standard Model scenarios in which the single and
double-graviton contributions could be of comparable magnitudes.
Finally, we elaborate on what these results imply for the range of
applicability of General Relativity as an effective theory.
TEMPLATES: A Robust Outlier Rejection Method for JWST/NIRSpec Integral Field Spectroscopy
Authors: Hutchison Taylor A. , Welch Brian D. , Rigby Jane R. , Olivier Grace M. , Birkin Jack E. , Phadke Kedar A. , Khullar Gourav , Rauscher Bernard J. , Sharon Keren , Aravena Manuel , Bayliss Matthew B. , Elicker Lauren A. , Kim Seonwoo , Solimano Manuel , Vieira Joaquin D. , Vizgan David ,   On Behalf of the JWST TEMPLATES Early Release Science Team
Published: April 2024

Abstract
We describe a custom outlier rejection algorithm for JWST/NIRSpec integral field spectroscopy. This method uses a layered sigma clipping approach that adapts clipping thresholds based upon the spatial profile of the science target. We find that this algorithm produces a robust outlier rejection while simultaneously preserving the signal of the science target. Originally developed as a response to unsatisfactory initial performance of the jwst pipeline outlier detection step, this method works either as a standalone solution, or as a supplement to the current pipeline software. Comparing leftover (i.e., not flagged) artifacts with the current pipeline’s outlier detection step, we find that our method results in one fifth as many residual artifacts as the jwst pipeline. However, we find a combination of both methods removes nearly all artifacts—an approach that takes advantage of both our algorithm’s robust outlier rejection and the pipeline’s use of individual dithers. This combined approach is what the TEMPLATES Early Release Science team has converged upon for our NIRSpec observations. Finally, we publicly release the code and Jupyter notebooks for the custom outlier rejection algorithm.
VLBI with SKA: Possible Arrays and Astrometric Science
Authors: Li Yingjie , Xu Ye , Li Jingjing , Bian Shuaibo , Lin Zehao , Hao Chaojie , Liu Dejian 
Published: April 2024

Abstract
The next generation of very long baseline interferometry (VLBI) is stepping into the era of microarcsecond (μas) astronomy, and pushing astronomy, especially astrometry, to new heights. VLBI with the Square Kilometre Array (SKA), SKA-VLBI, will increase current sensitivity by an order of magnitude, and reach astrometric precision routinely below 10 μas, even challenging 1 μas. This advancement allows precise parallax and proper motion measurements of various celestial objects. Such improvements can be used to study objects (including isolated objects, and binary or multiple systems) in different stellar stages (such as star formation, main-sequence stars, asymptotic giant branch stars, pulsars, black holes, white dwarfs, etc.), unveil the structure and evolution of complex systems (such as the Milky Way), benchmark the international celestial reference frame, and reveal cosmic expansion. Furthermore, the theory of general relativity can also be tested with SKA-VLBI using precise measurements of light deflection under the gravitational fields of different solar system objects and the perihelion precession of solar system objects.
Controlling neural activity: LPV modelling of optogenetically actuated Wilson–Cowan model
*
Authors: Martínez S , Sánchez-Peña R S , García-Violini D 
Published: April 2024

Abstract

Objective. This paper aims to bridge the gap between neurophysiology and automatic control methodologies by redefining the Wilson–Cowan (WC) model as a control-oriented linear parameter-varying (LPV) system. A novel approach is presented that allows for the application of a control strategy to modulate and track neural activity. Approach. The WC model is redefined as a control-oriented LPV system in this study. The LPV modelling framework is leveraged to design an LPV controller, which is used to regulate and manipulate neural dynamics. Main results. Promising outcomes, in understanding and controlling neural processes through the synergistic combination of control-oriented modelling and estimation, are obtained in this study. An LPV controller demonstrates to be effective in regulating neural activity. Significance. The presented methodology effectively induces neural patterns, taking into account optogenetic actuation. The combination of control strategies with neurophysiology provides valuable insights into neural dynamics. The proposed approach opens up new possibilities for using control techniques to study and influence brain functions, which can have key implications in neuroscience and medicine. By means of a model-based controller which accounts for non-linearities, noise and uncertainty, neural signals can be induced on brain structures.
Chicken Meat and Beef Identification in UV-Vis Absorbance Spectrum by Applying Savitzky-Golay Method
Authors: Dewi M F , Prihhapso Y , Setianingrum L , Hamidah M , Firdaus M Y , Nurwidya A I , Pramudya T , Majid F ‘A , Jannah L L , Ivo P , Darmawan N , Rahardjo S 
Published: April 2024

Abstract
Spectroscopy is one of the techniques suitable for validation of the quality, safety, and hygiene of meat due to some advantages, such as rapid detection capability, ease of use, and non-destructive measurement. Utilizing an ultraviolet-visible (UV-Vis) spectrometer, with a wavelength range of 200 nm to 1100 nm, this paper proposes a method for processing spectrum data profiling of chicken meat and beef. A 360-time reflectance spectrum was acquired from each chicken breast and beef loin at the optimum integration time of 60 ms. Subsequently, the spectrum was converted into an absorbance spectrum, and the apparent noise was excluded by applying the Savitzky-Golay (SG) method. The result shows the difference in spectrum between chicken meat and beef in which the chicken meat has two peaks at the wavelength of 284.79 nm and 422.69 nm, meanwhile, the beef shows three peaks at wavelengths of 282.46 nm, 419.03 nm, and 577.97 nm. It was found that the SG method enhanced the absorbance spectrum, showing the difference in spectrum behaviors in the wavelength range of 450∼600 nm. Consequently, to shorten the computational time, building a light, cheap, and compact prototype reading device within this specific wavelength range will be the next target in the near future.