bluuebunny/crossref_metadata_2025_split huggingface dataset:
An adaptive weighted magnetic anomaly detection based on full magnetic gradient orthonormal basis function
Authors: Yan Youyu , Liu Jianguo , Shen Siyuan , Yan Shenggang 
Published: September 2024

Abstract
Magnetic gradient tensor has enjoyed great popularity in magnetic anomaly detection recently. The detection method using the full magnetic gradient orthonormal basis function has been proven to be effective in extracting the target’s magnetic anomaly signal. However, the six energy components obtained by the FMG-OBF detector vary with true energy terms (influenced by the orientation of the target’s magnetic moment and the moving trajectories), noise energy terms and middle terms, which largely affects the performance of the total energy. In this paper, an adaptive weighting FMG-OBF method, which is based on the sigmoid function to assign appropriate weights to normalized energy components according to their detection performance, is proposed to tackle this concern. Experiments have demonstrated that the proposed method has a higher signal-to-noise ratio from the perspective of signal energy and is less affected by the OTMM compared with the original method.
A Hamilton–Jacobi approach to nonlocal kinetic equations
Authors: Loy Nadia , Perthame Benoît 
Published: September 2024

Abstract
Highly concentrated patterns have been observed in a spatially heterogeneous, nonlocal, kinetic model with BGK type operators implementing a velocity-jump process for cell migration, directed by the nonlocal sensing of either an external signal or the cell population density itself. We describe, in an asymptotic regime, the precise profile of these concentrations which, at the macroscale, are Dirac masses. Because Dirac concentrations look like Gaussian potentials, we use the Hopf–Cole transform to calculate the potential adapted to the problem. This potential, as in other similar situations, is obtained through the viscosity solutions of a Hamilton–Jacobi equation. We begin with the linear case, when the heterogeneous external signal is given, and we show that the concentration profile obtained after the diffusion approximation is not correct and is a simple eikonal approximation of the true H–J equation. Its heterogeneous nature leads us to develop a new analysis of the implicit equation defining the Hamiltonian and a new condition to circumvent the ‘dimensionality problem’. In the nonlinear case, when the signal occurs from the cell density itself, it is shown that the already observed linear instability (pattern formation) occurs when the Hamiltonian is convex-concave, a striking new feature of our approach.
Energy expenditure prediction in preschool children: a machine learning approach using accelerometry and external validation
Authors: Coyle-Asbil Hannah J , Burk Lukas , Brandes Mirko , Brandes Berit , Buck Christoph , Wright Marvin N , Vallis Lori Ann 
Published: September 2024

Abstract

Objective. This study aimed to develop convolutional neural networks (CNNs) models to predict the energy expenditure (EE) of children from raw accelerometer data. Additionally, this study sought to external validation of the CNN models in addition to the linear regression (LM), random forest (RF), and full connected neural network (FcNN) models published in Steenbock et al (2019 J. Meas. Phys. Behav.
2 94–102). Approach. Included in this study were 41 German children (3.0–6.99 years) for the training and internal validation who were equipped with GENEActiv, GT3X+, and activPAL accelerometers. The external validation dataset consisted of 39 Canadian children (3.0–5.99 years) that were equipped with OPAL, GT9X, GENEActiv, and GT3X+ accelerometers. EE was recorded simultaneously in both datasets using a portable metabolic unit. The protocols consisted of a semi-structured activities ranging from low to high intensities. The root mean square error (RMSE) values were calculated and used to evaluate model performances. Main results. (1) The CNNs outperformed the LM (13.17%–23.81% lower mean RMSE values), FcNN (8.13%–27.27% lower RMSE values) and the RF models (3.59%–18.84% lower RMSE values) in the internal dataset. (2) In contrast, it was found that when applied to the external Canadian dataset, the CNN models had consistently higher RMSE values compared to the LM, FcNN, and RF. Significance. Although CNNs can enhance EE prediction accuracy, their ability to generalize to new datasets and accelerometer brands/models, is more limited compared to LM, RF, and FcNN models.
Enhancing P-wave localization for accurate detection of second-degree and third-degree atrioventricular conduction blocks
Authors: Liu Wenjing , Yan Li , Huang Yangcheng , Yin Ziyi , Wang Mingjie , Cai Wenjie 
Published: September 2024

Abstract

Objective. This paper tackles the challenge of accurately detecting second-degree and third-degree atrioventricular block (AVB) in electrocardiogram (ECG) signals through automated algorithms. The inaccurate detection of P-waves poses a difficulty in this process. To address this limitation, we propose a reliable method that significantly improves the performances of AVB detection by precisely localizing P-waves. Approach. Our proposed P-WaveNet utilized an attention mechanism to extract spatial and temporal features, and employs a bidirectional long short-term memory module to capture inter-temporal dependencies within the ECG signal. To overcome the scarcity of data for second-degree and third-degree AVB (2AVB,3AVB), a mathematical approach was employed to synthesize pseudo-data. By combining P-wave positions identified by the P-WaveNet with key medical features such as RR interval rhythm and PR intervals, we established a classification rule enabling automatic AVB detection. Main results. The P-WaveNet achieved an F1 score of 93.62% and 91.42% for P-wave localization on the QT Dataset and Lobachevsky University dataset datasets, respectively. In the BUTPDB dataset, the F1 scores for P-wave localization in ECG signals with 2AVB and 3AVB were 98.29% and 62.65%, respectively. Across two independent datasets, the AVB detection algorithm achieved F1 scores of 83.33% and 84.15% for 2AVB and 3AVB, respectively. Significance. Our proposed P-WaveNet demonstrates accurate identification of P-waves in complex ECGs, significantly enhancing AVB detection efficacy. This paper’s contributions stem from the fusion of medical expertise with data augmentation techniques and ECG classification. The proposed P-WaveNet demonstrates potential clinical applicability.
Breakdown modes of capacitively coupled plasma: I. Transitions from glow discharge to multipactor
Authors: Wu Hao , An Ran , Zhong Dong , Jiang Wei , Zhang Ya 
Published: September 2024

Abstract
This paper is the first in a two-part series that simulates the breakdown modes of capacitively coupled plasmas (CCPs) across various pressures using an implicit particle-in-cell/Monte Carlo collision model. The study identifies several discharge modes in the lower pressure region, including three self-sustained discharges: glow discharge, normal multipactor, and abnormal multipactor, as well as various non-self-sustained discharges. This part focuses on the formation and transitions of self-sustained modes, specifically detailing how the glow discharge transitions into an abnormal multipactor below 2 mTorr, which then evolves into a normal multipactor within a narrow voltage range. The characteristics and formation processes of these self-sustained discharges are explored, emphasizing their reliance on higher electron emission rates and high-frequency RF power. The emergence of multipactor discharges in the 60 MHz range potentially expands the gas discharge theory and broadens the application scope of CCPs.
Experimental testing of additively manufactured embedded fiber optic smart devices for clean energy applications
Authors: Donlan Connor F , Cabral Arturo , Carasik Lane B , Boulanger Andrew J , Derek Rountree S 
Published: September 2024

Abstract
An additively manufactured prototype smart device was created to investigate in-flow temperature distributions using embedded high-definition fiber optic sensors within a component for clean energy systems. The devices were created using Ultrasonic Additive Manufacturing to create a unique embedded pathway within a flow conditioner for the high-definition fiber optic sensors to be placed within. The fibers used allowed for temperature measurements to be taken every 0.65 mm along the fiber. The high-resolution fibers were thermally calibrated enable the 2D reconstruction of the temperature profile in the flow path of the structure. This is due to the temperature-related strain response of the material and strain measurements of the fibers. Hot airflow testing of these devices showed the ability to identify localized temperature differences in the flow. The observed strain response within the smart device had higher strain concentrations in the thicker web regions than in the thinner web regions. These higher strain regions resulted in higher uncertainties for the temperature responses. Further calibration is needed to improve the accuracy of the smart devices, specifically within the inner web structures of a flow straightening component. Further investigations of the devices within flow showed the temperature sensing to be independent of the effects of flow velocity. The devices were able to distinguish temperature differences within single and two-phase flow and showed local sensitivity to the temperature changes with the identification of hot and cold spots. The presented results showed the viability of the smart device for obtaining detailed temperature distributions using common industrial components. Eventually, the goal for these smart devices will be to withstand higher temperature and pressure environments such as those experienced in nuclear, fusion, and concentrated solar energy systems.
Topological coindices and QSPR analysis for some potential drugs used in lung cancer treatment via CoM and CoNM-polynomials
Authors: Sağlam Özkan Yeşim , Kara Yeliz 
Published: September 2024

Abstract
Topological indices are used to convert a chemical structure into a real number, usually to study the physicochemical and biological properties of molecules. The groundwork is prepared for the interpretation of the obtained data by processing with Quantitative Structure Property/Activity Relationship (QSPR/QSAR). In this study, the drugs lorlatinib, gefitinib, sotorasib, pralsetinib, crizotinib, adagrasib, alectinib, brigatinib, dacomitinib and entrectinib, which are potential to be used in the treatment of lung cancer, are discussed. Topological coindices are calculated with the help of CoM and CoNM polynomials obtained with the graph structures of these drugs. The relationship between topological coindices and physicochemical properties such as evaporation enthalpy, flash point, molar refraction, polarisation, surface tension, molar volume are investigated by QSPR analysis. At this stage, linear, logarithmic and quadratic regression methods have been used. The results show that the values of these topological indices are highly correlated with certain physicochemical properties of the used some drugs in the treatment of lung cancer. In addition, using comparative analysis, the actual values and the values calculated with the help of topological indices have been examined in terms of predictive ability. The findings of this search demonstrate topological indices’ potential as tools for cancer drug discovery and design.
Bipartite quantum features influenced by pure and mixed disorders
Authors: Noman Muhammad , Kenfack Lionel Tenemeza , Cui Wei 
Published: September 2024

Abstract
This study explores quantum features, namely negativity, quantum-memory entropic uncertainty, entropic coherence, and linear entropy in a two-qubit mixture system comprising two Bell states under various classical environments. Furthermore, we assume including pure noise (colored pink, colored brown, Gaussian) and mixed noise (brown-Gaussian, pink-Gaussian) characterizing the classical environments. The influence of the purity parameter of the system along with the inclusive noise parameters is systematically analyzed. The entropic coherence and negativity functions remain sensitive to the noise showing larger revivals compared to the quantum-memory and coherence functions. The interplay between different types of noise leads to interesting dynamics, such as the suppression of Gaussian noise’s Markovian behavior by non-Markovian brown noise. Interestingly, the non-Markovian traits of pink noise are highly suppressed when combined with Gaussian noise. Besides, in mixed noise scenarios, non-regular fluctuations contrast with regular revivals in pure noise cases. The brown-Gaussian mixed noise case reveals the suppression of Gaussian noise’s Markovian behavior by non-Markovian brown noise, influencing overall dynamics. Notably, this scenario shows slight entanglement generation, unusual under classical environments, highlighting unique dynamics in mixed noise scenarios.
Rogue waves of the Nizhnik-Novikov-Veselov equation via self-mapping transformation
Authors: Zhang Jie-Fang , Jin Mei-Zhen , Zhang Zhao 
Published: September 2024

Abstract
This paper introduces a new two-dimensional self-mapping transformation applied to the Nizhnik-Novikov-Veselov equation, resulting in the generation of numerous rogue wave solutions. We discover that temporal-localized and spatiotemporal-localized two-dimensional rogue waves respectively. Notably, these rogue waves emerge from a zero background and subsequently exhibit both algebraic and exponential decay patterns. The proposed technique offers a potential tool for constructing rogue-like waves within (2+1)-dimensional nonlinear wave frameworks. The findings presented here serve as a robust mathematical foundation for advancing both theoretical understanding and practical applications of rogue waves.
Dynamically tunable highly sensitive sensor based on graphene and black phosphorus composite metamaterial
Authors: Xu Yiping , Wang Ziyi , Yang Yang , Huang Xin , Zeng Xiaodong , Cheng Shubo , Yi Zao , Yao Shunyu , Liu Lian , Zhou Jiayi , Duan Yunxuan 
Published: September 2024

Abstract
Focusing on the realization of multiple-detection-point sensors in the mid-infrared band, a dynamically tunable high-sensitive index refraction sensor based on graphene and black phosphorus (BP) composite metamaterial is proposed. By adjusting the height of the grating, the strength of the structural plasmon-induced absorption (PIA) can be enhanced within a certain range. The energy transfer mechanism of the system is analyzed by the coupled mode theory (CMT), the theoretical data of CMT fit well with the FDTD simulation results, which proves that the analysis of the system using the CMT model is correct. The resonant wavelengths of the PIA can be efficiently modulated by changing the Fermi level of graphene and the carrier density of BP. It is worth mentioning that under different environmental refractive indexes, our proposed system has excellent sensing characteristics in the mid-infrared band. The maximum sensitivity and the maximum figure of merit (FOM) are up to 5.174 μm/RIU and 26.449, respectively. This research could play an important role in mid-infrared optical sensors.
Broadband low-loss polarization-insensitive graphene-integrated photonic crystal fiber modulators in the near-infrared range
Authors: Ye Longfang , Su Siyi , Zhang Yong , Zhu Chunhui , Zhuo Jianliang , Liu Qing Huo 
Published: September 2024

Abstract
The amplitude modulator with the function of converting electric signal to optical amplitude modulation is a key component in high-speed optical communication links. In this paper, we demonstrate a variety of two-dimensional graphene-integrated photonic crystal fiber modulators for efficient near-infrared amplitude modulation with ultra-low insertion loss. Taking the advantages of the perfect total internal reflection of the porous photonic crystal fiber and flexible tunability of the graphene conductivity, the modulation depth of 0.075 dB μm−1 with a low optical loss of 2.13 × 10−4 dB μm−1, a long propagation length of ∼20 mm and polarization-insensitive characteristics in a wide wavelength of 1.3 to 1.8 μm can be achieved. This high-performance modulator may have great potential applications in various high-speed telecommunication, interconnection, and graphene-based integrated photonic systems.
Electromagnetic modeling and analysis based on multilayer domain structure for GMI behavior in high frequency
Authors: Wang Y F , Feng T F , Li Y L , Makhnovskiy D P , Eggers T , Phan M H , Qin F X 
Published: September 2024

Abstract
Existing theoretical models on the frequency dependence of the magnetoimpedance (MI) in ferromagnetic microwires primarily describe the MI phenomenon at the limiting cases of lower MHz (<several hundred MHz) or higher GHz (>several GHz) ranges. However, in the intermediate region between these two ranges, known as the transition region, MI curves undergo complex transformations. These transformations have been documented in the literature, but their underlying causes remain poorly understood. Unambiguous knowledge of the domain structure and its correlation with MI properties is essential for elucidating this behavior. In this study, we have, for the first time, observed the inner core magnetic structure of Co-based microwires and revealed its relationship with the high-frequency MI effect. A distinct magnetic structure comprising longitudinal domains in the inner core (IC), circular domains in the outer shell (OS), and a transition region (TR) has been identified. This structure originates from compositional gradients and residual stresses during microwire fabrication. The IC/TR/OS structure manifests in the complex transformations of the MI behavior, exhibiting a turning point at GHz frequencies before the characteristic double MI peak. We developed a multilayer planar model that considers this more realistic magnetic structure, including the TR layer. This model successfully reproduces the key features of the MI curves and provides deeper insights into the high-frequency MI phenomenon. Our findings pave the way for optimizing the sensing capabilities of Co-based ferromagnetic microwires and demonstrate the potential of using high-frequency MI measurements to map their magnetic microstructures.
Synthesis and characterization of ZnO nanoparticles prepared by green routes: controlling morphologies by maintaining pH
Authors: Sharma Surabhi , Singh Archana Kumari , Singh Satya Pal , Singh Rajnish Kumar 
Published: September 2024

Abstract
The synthesis of metal oxide semiconductor nanoparticles has attracted much attention in recent past. Nanoparticles are broadly used in solar energy conversion, catalysis, varistors, gas sensors, and non-linear optics, etc. Due to their wide band gap properties, zinc oxide nanoparticles are widely used in numerous applications. Zinc oxide nanoparticles have a wide band gap of approximately 3.3 eV. In this work, we have reported synthesis and characterization of ZnO nanoparticles having rod-like, spherical and flower-like structures. We have used zinc acetate dehydrate [Zn(CH3COO)2. 2H2O] and aqueous extract of Dahlia Pinnata leaves and deionized water. Dahlia Pinnata leaves extract has not been previously used to prepare ZnO NPs. It serves as a reducing and capping agent. We have analyzed the presence of chemicals in the extract using FTIR, Raman and NMR spectroscopy techniques. It is found that the unique morphology of ZnO NPs flower-like structures, enhances it's sensing properties in comparison to the spherical ones. We have used UV–vis-nir spectroscopy, x-ray diffraction (XRD), Fourier Transform Infrared Microscopy(FTIR), and Scanning Electron Microscope (SEM) analysis and have explored the opto-electronic properties of ZnO nanoparticles and have correlated with their structural and morphological properties.
A well production prediction method based on blending heterogeneous ensemble learning optimized by OOA
Authors: Zhang Zhanquan 
Published: September 2024

Abstract
Oil well production prediction is an important research content in oilfield development, and constructing a scientific and good prediction model is a key issue. In this paper, a blending ensemble learning oil well production prediction model combining Random Forest, LGBM, and TCN is established and optimized by the Osprey optimization algorithm. Firstly, ANOVA and Pearson’s correlation coefficient is used to filter the relevant features affecting the well yield prediction, the autocorrelation coefficient is used to determine the lag order of the well yield prediction, and RF, LGBM, and TCN networks with good performance are selected as the base learners. Linear regression is used as the meta-learning to form the blending ensemble learning model, and the Osprey optimization algorithm is used to optimize the whole blending ensemble learning model. The Osprey optimization algorithm is used to optimize the whole blending model, and the optimal hyperparameters are derived and brought into the ensemble learning prediction model. After the experiment, it is shown that the ensemble oil well production prediction model can reduce the MAE by about 12.75% compared with the single prediction model, and can effectively predict the future production of oil wells.
Modeling of biomass gasification polygeneration
Authors: Liu Jiang , Dai Pengrui , Jiang Weiting 
Published: September 2024

Abstract
A single biomass gasification technology often struggles to simultaneously meet the requirements of efficiency, economy, and environmental sustainability. Hence, this paper establishes a biomass gasification polygeneration model, integrating biomass gasification with the subsequent utilization of syngas. The system comprises parallel chemical and power units, enabling polygeneration activities such as methane production and electricity generation through syngas derived from gasification units. Within the system, syngas are split: a portion is directed to the methane synthesis unit for methane production, while the remainder is sent to the combined cycle unit for electricity generation. Concurrently, the tail gas produced after methane synthesis enters the combined cycle unit. Throughout this process, by-product steam is utilized for grade matching in each section of the chemical synthesis side, with any excess steam utilized for steam turbine power generation. Using the established polygeneration model, this study examines the influence of gasification temperature on the performance of the polygeneration system.
Stability study of a two-DOF piezoelectric energy harvester
Authors: Li Yuanhua , Han Minglei 
Published: September 2024

Abstract
Self-adaptive or self-adjusting vibration energy harvesting has the advantage of high-power density in multi-directional vibration excitations and has entered the vision of researchers. Due to the presence of a two-DOF cantilever beam, there is potential instability in the self-adaptive or self-adjusting vibration energy harvesting system. This paper takes a direction self-adaptive vibration energy harvester (DSVEH) as an example to study the influence of system parameters on stability. It is found that the larger the centroid offset distance, the more stable the system is; The greater the resistance torque, the more stable the system is. In practical applications, system parameters should be matched reasonably. This study can be used for relevant parameters optimization of direction self-adaptive vibration energy harvesting systems.