bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the stall vortex and vibration characteristics in multi-stage pump under natural flow condition
Authors: Zhou Runze , Liu Houlin , Dong Liang , Ooi Kim Tiow , Dai Cui , Hua Runan
Published: August 2023

When a vessel reaches a specific speed, the circulating pump in the cooling system stops driving. It relies only on the kinetic energy its speed generates to provide cooling seawater for the condenser. This cooling strategy is known as natural flow cooling. Under natural flow conditions, the impeller rotates passively due to flow impact, and the circulating pump serves as a significant resistance component in the cooling system. The unstable flow within a pump is primarily attributed to the varying scales of stall vortex, which can induce vibration and potentially result in severe damage to components. To investigate the generation of stall vortex and vibration characteristics under natural flow conditions, the numerical simulation method was employed to study the structure and motion characteristics of stall vortex in multi-stage pumps at different flow rates. Moreover, vibration signals were collected through a natural flow experiment, and the obtained data were analyzed using empirical mode decomposition with singular value decomposition and wavelet transform methods. The result indicates that natural flow conditions can be categorized into impeller stuck and passive rotation. During passive rotation, the speed increases linearly with the flow rate. Thus, the blade inlet's attack angle remains consistent at different flow rates, making the internal flow features similar. The stretching and bending of the stall vortex structure primarily cause the variation of the vorticity. Additionally, the internal structure and motion pattern of stall vortex exhibit similarities. The excitation force increases continuously with the flow rate and is related to the effects of flow losses and turbulent kinetic energy. Vibration energy caused by stall vortex and rotor–stator interference is mainly concentrated in the low-frequency band.
Retdeficiency decreases neural crest progenitor proliferation and restricts fate potential during enteric nervous system development
Authors: Vincent Elizabeth , Chatterjee Sumantra , Cannon Gabrielle H. , Auer Dallas , Ross Holly , Chakravarti Aravinda , Goff Loyal A.
Published: August 2023

The receptor tyrosine kinase RET plays a critical role in the fate specification of enteric neural crest–derived cells (ENCDCs) during enteric nervous system (ENS) development.RETloss of function (LoF) is associated with Hirschsprung disease (HSCR), which is marked by aganglionosis of the gastrointestinal (GI) tract. Although the major phenotypic consequences and the underlying transcriptional changes fromRetLoF in the developing ENS have been described, cell type- and state-specific effects are unknown. We performed single-cell RNA sequencing on an enriched population of ENCDCs from the developing GI tract ofRetnull heterozygous and homozygous mice at embryonic day (E)12.5 and E14.5. We demonstrate four significant findings: 1)Ret-expressing ENCDCs are a heterogeneous population comprising ENS progenitors as well as glial- and neuronal-committed cells; 2) neurons committed to a predominantly inhibitory motor neuron developmental trajectory are not produced underRetLoF, leaving behind a mostly excitatory motor neuron developmental program; 3) expression patterns of HSCR-associated andRetgene regulatory network genes are impacted byRetLoF; and 4)Retdeficiency leads to precocious differentiation and reduction in the number of proliferating ENS precursors. Our results support a model in whichRetcontributes to multiple distinct cellular phenotypes during development of the ENS, including the specification of inhibitory neuron subtypes, cell cycle dynamics of ENS progenitors, and the developmental timing of neuronal and glial commitment.
Lagrangian large eddy simulations via physics-informed machine learning
Authors: Tian Yifeng , Woodward Michael , Stepanov Mikhail , Fryer Chris , Hyett Criston , Livescu Daniel , Chertkov Michael
Published: August 2023

High-Reynolds number homogeneous isotropic turbulence (HIT) is fully described within the Navier–Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as large eddy simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about effects of subgrid scales on the resolved scales. Here, we take an alternative approach and design LES heuristics stated in terms of Lagrangian particles moving with the flow. Our Lagrangian LES, thus L-LES, is described by equations generalizing the weakly compressible smoothed particle hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from direct numerical simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The subgrid-scale contributions are modeled separately with physical constraints to account for the effects from unresolved scales. We build the resulting model under the differentiable programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our L-LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.
Structural insights into the interaction of three Y-shaped ligands with PI3Kα
Authors: Zhou Qingtong , Liu Xiao , Neri Dario , Li Wenxin , Favalli Nicholas , Bassi Gabriele , Yang Su , Yang Dehua , Vogt Peter K. , Wang Ming-Wei
Published: August 2023

Class IA phosphoinositide 3-kinase alpha (PI3Kα) is an important drug target because it is one of the most frequently mutated proteins in human cancers. However, small molecule inhibitors currently on the market or under development have safety concerns due to a lack of selectivity. Therefore, other chemical scaffolds or unique mechanisms of catalytic kinase inhibition are needed. Here, we report the cryo-electron microscopy structures of wild-type PI3Kα, the dimer of p110α and p85α, in complex with three Y-shaped ligands [cpd16 (compound 16), cpd17 (compound 17), and cpd18 (compound 18)] of different affinities and no inhibitory effect on the kinase activity. Unlike ATP-competitive inhibitors, cpd17 adopts a Y-shaped conformation with one arm inserted into a binding pocket formed by R770 and W780 and the other arm lodged in the ATP-binding pocket at an angle that is different from that of the ATP phosphate tail. Such a special interaction induces a conformation of PI3Kα resembling that of the unliganded protein. These observations were confirmed with two isomers (cpd16 and cpd18). Further analysis of these Y-shaped ligands revealed the structural basis of differential binding affinities caused by stereo- or regiochemical modifications. Our results may offer a different direction toward the design of therapeutic agents against PI3Kα.
Influence of boron segregation on the hot ductility of T23 steel CGHAZ
Authors: Zhang Dongdong , Wang Xue , Li Yong
Published: August 2023

Boron segregates in a non-equilibrium segregation mode at lath, packet, block boundaries as well as prior austenite grain boundaries (PAGBs) in 2.25Cr1.6WVNb steel coarse grain heat-affected zone (CGHAZ) during the thermal cycle, and tends to segregate only at PAGBs in the same mode during the high-temperature tensile test. Boron segregation along PAGB increases GB plasticity but not inter-granular strength. This is because the boron segregation at PAGBs occupies the vacancies and other defect positions, which inhibits the nucleation and growth of micro-voids, and reduces plasticity consumption. As a result, the hot ductility of CGHAZ is enhanced.
Physics-informed machine learning models for the prediction of transient temperature distribution of ferritic steel in directed energy deposition by cold metal transfer
Authors: Kumar Amritesh , Sarma Ritam , Bag Swarup , Srivastava V. C. , Kapil Sajan
Published: August 2023

In-situ monitoring of the additive layer characteristics in the directed energy deposition (DED) process by any contact technology is cumbersome. A well-tested finite element (FE) model is often employed to extract transient temperature distribution during deposition. However, the numerical model pertaining to each deposition attribute is computationally expensive. In the present work, we have generated a dataset through an experimentally validated thermal model, and further multiple machine learning (ML) algorithms are applied to train datasets. Models with an accuracy of more than 99% are utilised for the prediction of transient temperature distribution. The validation of deposition attributes using experiments and numerical model suggests that the physics-informed machine learning models for cold metal transfer can be applied in the DED process.
NME3 binds to phosphatidic acid and mediates PLD6-induced mitochondrial tethering
Authors: Su You-An , Chiu Hsin-Yi , Chang Yu-Chen , Sung Chieh-Ju , Chen Chih-Wei , Tei Reika , Huang Xuang-Rong , Hsu Shao-Chun , Lin Shan-Shan , Wang Hsien-Chu , Lin Yu-Chun , Hsu Jui-Cheng , Bauer Hermann , Feng Yuxi , Baskin Jeremy M. , Chang Zee-Fen , Liu Ya-Wen
Published: August 2023

Mitochondria are dynamic organelles regulated by fission and fusion processes. The fusion of membranes requires elaborative coordination of proteins and lipids and is particularly crucial for the function and quality control of mitochondria. Phosphatidic acid (PA) on the mitochondrial outer membrane generated by PLD6 facilitates the fusion of mitochondria. However, how PA promotes mitochondrial fusion remains unclear. Here, we show that a mitochondrial outer membrane protein, NME3, is required for PLD6-induced mitochondrial tethering or clustering. NME3 is enriched at the contact interface of two closely positioned mitochondria depending on PLD6, and NME3 binds directly to PA-exposed lipid packing defects via its N-terminal amphipathic helix. The PA binding function and hexamerization confer NME3 mitochondrial tethering activity. Importantly, nutrient starvation enhances the enrichment efficiency of NME3 at the mitochondrial contact interface, and the tethering ability of NME3 contributes to fusion efficiency. Together, our findings demonstrate NME3 as a tethering protein promoting selective fusion between PLD6-remodeled mitochondria for quality control.
Effect of structural properties in the electrical impedance spectrum of multi-walled carbon nanotube/graphite/polyester ternary composites
Authors: Belhimria R , Samir Z , Boukheir S , Soreto Teixeira S , Len A , Szakál A , Achour M E , Anson-Casaos A , El Hasnaoui M , Costa L C , Gonzalez-Dominguez J M
Published: August 2023

The aim of this study is to establish a connection or correlation between the electrical and structural properties of ternary composites, which were prepared by adding multi-walled carbon nanotubes (MWCNT) and graphite (Gr) as conductive fillers to an insulating polyester matrix. The study utilized small-angle neutron scattering, infrared and Raman spectroscopy for structural characterization. Electrical measurements were conducted within a frequency range of 100 Hz to 1 MHz and a temperature range of 200–380 K. The alternating current (AC) conductivity showed a frequency dependence following Jonscher’s power law. The temperature dependence of the AC conductivity suggested that the electrical conduction within the material is a thermally activated process. The temperature-dependent behavior of the direct current (DC) conductivity suggests that below and above the critical temperature, there is a pronounced positive and negative temperature coefficient of resistivity, respectively. In comparison to MWCNT/polyester or Gr/polyester composites, the percolation threshold of the MWCNT/Gr/polyester composite is much lower. Additionally, applying the Arrhenius equation to analyze the temperature dependence of DC conductivity reveals that the inclusion of MWCNT and Gr in the polyester matrix results in a decrease in activation energy.
Emergence of field-induced memory effect in spin ices
Authors: Yadav Pramod K , Upadhyay Rajnikant , Kumar Rahul , Nukala Pavan , Upadhyay Chandan
Published: August 2023

Out-of-equilibrium investigation of strongly correlated materials deciphers the hidden equilibrium properties. Herein, we have investigated the out-of-equilibrium magnetic properties of polycrystalline Dy2Ti2O7 and Ho2Ti2O7 spin ices. Our experimental findings reveal the emergence of magnetic field-induced anomalous hysteresis observed solely in temperature-and magnetic field-dependent AC susceptibility measurements. The observed memory effect (anomalous thermomagnetic hysteresis) exhibits a strong dependence on both thermal and non-thermal driving variables. Owing to the non-collinear spin structure, the applied DC bias magnetic field produces quenched disorder sites in the cooperative Ising spin matrix and suppresses the spin–phonon coupling. These quench disorders create a dynamic spin correlation, having slow spin relaxation and quick decay time, which additionally contribute to AC susceptibility. The initial conditions and measurement protocol decide the magnitude and sign of this dynamical term contributing to AC susceptibility. It is being suggested that such out-of-equilibrium properties arise from the combined influences of geometric frustration, disorder, and the cooperative nature of spin dynamics exhibited by these materials.
Wayside wheelset lateral motion detection and vehicle hunting instability evaluation
Authors: Peng Xinyu , Zeng Jing , Wang Jianbin , Li Dadi , Wang Qunsheng
Published: August 2023

Wheel profile wear, or bogie suspension failure will result in hunting instability of the vehicle system which will lead to dramatic changes in the lateral motion of the wheelset. The occurrence of hunting instability will worsen the ride performance of the vehicle or even cause the danger of wheelset derailment. Thus, the in-time and effective detection of wheelset lateral motion is very important for the vehicle hunting stability evaluation. By installing a certain amount of displacement sensors on the rail side along the track and detecting the distance between the wheel and rail when the wheelset passing by, the lateral displacements of the wheelset in measuring points can be obtained. The detection method of wheelset lateral motion by the wayside is studied, which includes the measuring principle, structure design, sensor installation, data acquisition and data analysis, and the application of the method to on-site detection for typical instability cases are demonstrated. At the same time, innovate on the existing on-board hunting instability monitoring method, proposed an evaluation method of hunting instability for wayside lateral movement monitoring system. The results show that: installing 20 eddy current sensors on both sides of the straight track to measure the lateral displacement of the wheelset can meet the requirement of detecting the vehicle hunting instability. At the same time, analyzing the measurement data and fitting the maximum value of the signal collected by each sensor can obtain the lateral trajectory of the wheelset. The wheelset lateral displacement can be used to monitor the occurrence of vehicle hunting instability if periodic motion is detected. The amplitude of the motion can determine the hunting instability level, and the frequency can be used to describe the wheel wear level and calculate the wheel/rail contact equivalent conicity. This method is able to provide a reliable data support for the vehicle operation safety. The proposed monitoring method and monitoring index are also of reference value to the development of other wayside hunting instability monitoring systems.
Detection and cancellation method of impulse noise in gearbox non-stationary vibration signals—a case study of port ship unloader
Authors: Yang Chen , Li Hongkun , Cao Shunxin , Zhang Kongliang
Published: August 2023

This work proposes a signal preprocessing framework that cancels out impulse noise in the non-stationary gearbox vibration signals of a ship unloader, which reduce the interference of non-cyclic impulses on the damage detection. The algorithm involves three main steps: (i) Preliminary localization of impulse noise, including suppressing components related to normal machine operation, segmenting the signal, and localizing abnormal sub-segment signal based on higher-order statistical criteria. (ii) Detection of impulse boundaries. The influence of random impulse noise on the signal envelope spectrum is studied, and an impulse indicator is constructed to accurately detect the boundaries of impulse noise in abnormal sub-segment signals. (iii) Cancellation of impulse noise. Replace the identified random impulse with Gaussian white noise. Laboratory and industrial data confirm the excellent performance of the proposed algorithm. It can accurately identify and cancels out the non-cyclic impulse noise in the non-stationary gearbox vibration signal, which facilitates the subsequent damage detection.
Diverse long-term potentiation and depression based on multilevel LiSiO
x
memristor for neuromorphic computing
Authors: Wu Zeyu , Li Zewen , Lin Xin , Shan Xin , Chen Gang , Yang Chen , Zhao Xuanyu , Sun Zheng , Hu Kai , Wang Fang , Ren Tianling , Song Zhitang , Zhang Kailiang
Published: August 2023

Memristor-based neuromorphic computing is expected to overcome the bottleneck of von Neumann architecture. An artificial synaptic device with continuous conductance variation is essential for implementing bioinspired neuromorphic systems. In this work, a memristor based on Pt/LiSiO
x
/TiN structure is developed to emulate an artificial synapse, which shows non-volatile multilevel resistance state memory behavior. Moreover, the high nonlinearity caused by abrupt changes in the set process is optimized by adjusting the initial resistance. 100 levels of continuously modulated conductance states are achieved and the nonlinearity factors are reduced to 1.31. The significant improvement is attributed to the decrease in the Schottky barrier height and the evolution of the conductive filaments. Finally, due to the improved linearity of the long-term potentiation/long-term depression behaviors in LiSiO
x
memristor, a robust recognition rate (∼94.58%) is achieved for pattern recognition with the modified National Institute of Standards and Technology handwriting database. The Pt/LiSiO
x
/TiN memristor shows significant potential in high-performance multilevel data storage and neuromorphic computing systems.
Consistency of muscle fibers directionality in human thigh derived from diffusion-weighted MRI
Authors: Shusharina Nadya , Nguyen Christopher
Published: August 2023

Objective. Diffusion-weighted MR imaging (DW-MRI) is known to quantify muscle fiber directionality and thus may be useful for radiotherapy target definition in sarcomas. Here, we investigate the variability of tissue anisotropy derived from diffusion tensor (DT) in the human thigh to establish the baseline parameters and protocols for DW-MRI acquisition for future studies in sarcoma patients. Approach. We recruited ten healthy volunteers to acquire diffusion-weighted MR images of the left and right thigh. DW-MRI data were used to reconstruct DT eigenvectors within each individual thigh muscle. Deviations of the principal eigenvector from its mean were calculated for different experimental conditions. Main results. Within the majority of muscles in most subjects, the mode of the histogram of the angular deviation of the principal eigenvector of the water DT from its muscle-averaged value did not exceed 20°. On average for all subjects, the mode ranged from 15° to 24°. Deviations much larger than 20° were observed in muscles far from the RF coil, including cases with significant amounts of subcutaneous fat and muscle deformation under its own weight. Significance. Our study is a robust characterization of angular deviations of muscle fiber directionality in the thigh as determined by DW-MRI. We show that an appropriate choice of experimental conditions reduces the variability of the observed directionality. Precise determination of tissue directionality will enable reproducible models of microscopic tumor spread, with future application in defining the clinical target volume for soft tissue sarcoma.
Extracting lung contour deformation features with deep learning for internal target motion tracking: a preliminary study
Authors: Zhang Jie , Wang Yajuan , Bai Xue , Chen Ming
Published: August 2023

Objective. To propose lung contour deformation features (LCDFs) as a surrogate to estimate the thoracic internal target motion, and to report their performance by correlating with the changing body using a cascade ensemble model (CEM). LCDFs, correlated to the respiration driver, are employed without patient-specific motion data sampling and additional training before treatment. Approach. LCDFs are extracted by matching lung contours via an encoder–decoder deep learning model. CEM estimates LCDFs from the currently captured body, and then uses the estimated LCDFs to track internal target motion. The accuracy of the proposed LCDFs and CEM were evaluated using 48 targets’ motion data, and compared with other published methods. Main results. LCDFs estimated the internal targets with a localization error of 2.6 ± 1.0 mm (average ± standard deviation). CEM reached a localization error of 4.7 ± 0.9 mm and a real-time performance of 256.9 ± 6.0 ms. With no internal anatomy knowledge, they achieved a small accuracy difference (of 0.34∼1.10 mm for LCDFs and of 0.43∼1.75 mm for CEM at the 95% confidence level) with a patient-specific lung biomechanical model and the deformable image registration models. Significance. The results demonstrated the effectiveness of LCDFs and CEM on tracking target motion. LCDFs and CEM are non-invasive, and require no patient-specific training before treatment. They show potential for broad applications.
Multi-task contrastive learning for semi-supervised medical image segmentation with multi-scale uncertainty estimation
Authors: Xing Chengcheng , Dong Haoji , Xi Heran , Ma Jiquan , Zhu Jinghua
Published: August 2023

Objective. Automated medical image segmentation is vital for the prevention and treatment of disease. However, medical data commonly exhibit class imbalance in practical applications, which may lead to unclear boundaries of specific classes and make it difficult to effectively segment certain tail classes in the results of semi-supervised medical image segmentation. Approach. We propose a novel multi-task contrastive learning framework for semi-supervised medical image segmentation with multi-scale uncertainty estimation. Specifically, the framework includes a student-teacher model. We introduce global image-level contrastive learning in the encoder to address the class imbalance and local pixel-level contrastive learning in the decoder to achieve intra-class aggregation and inter-class separation. Furthermore, we propose a multi-scale uncertainty-aware consistency loss to reduce noise caused by pseudo-label bias. Main results. Experiments on three public datasets ACDC, LA and LiTs show that our method achieves higher segmentation performance compared with state-of-the-art semi-supervised segmentation methods. Significance. The multi-task contrastive learning in our method facilitates the negative impact of class imbalance and achieves better classification results. The multi-scale uncertainty estimation encourages consistent predictions for the same input under different perturbations, motivating the teacher model to generate high-quality pseudo-labels. Code is available at https://github.com/msctransu/MCSSMU.git.
A Bayesian formulation for perturbed current tomography in tokamaks
Authors: Bohlsen N , Hole M
Published: August 2023

An initial investigation into the application of Bayesian inference to the reconstruction of the spatial distribution of current perturbations in tokamaks from diagnostic signals is presented. Previous work in Bayesian equilibrium current tomography is extended to the case of a complex phasor representation of harmonically time varying current distributions. A forward function to predict the response of magnetic diagnostics is constructed using only electrodynamics and not reduced models of ideal MHD. The extension of this forward function to incorporate a fully kinetic model of the plasma state is suggested. The response of soft x-ray diagnostics, and the motional Stark effect diagnostic to the current perturbations are also predicted and the integration of all diagnostics into a single estimate of the current perturbation is proposed. Simulations with synthetic diagnostics in simple geometry demonstrate that the perturbed current distribution can be reconstructed subject to prior assumptions regarding solution smoothness.
Spatiotemporal corona discharge characteristics of nanoelectrode: array carbon nanotubes
Authors: Li Dingchen , Li Chuan , Li Jiawei , Xiao Menghan , Wang Pengyu , Liu Zhi , Zhang Ming , Yang Yong , Yu Kexun
Published: August 2023

Corona discharge is a widely-used phenomenon that requires a sharp electrode to generate a strong electric field (106 V m−1) at high voltages (typically in the tens of kV). The advent of nanoelectrodes has overcome the technical limitations of traditional electrodes, dramatically improving the density of discharge points and enabling low voltage (several kV) corona discharges with nanometer-sized tips. Consequently, nanoelectrode discharge technology has the potential to revolutionize the miniaturization of plasma equipment in the future. However, research on the discharge characteristics of nanoelectrodes is still relatively sparse. This paper focuses on an array of carbon nanotubes (ACNTs) and proposes a numerical simulation model based on the hybrid hydrodynamics model and ion migration model. The accuracy and efficiency of this model are demonstrated by a high degree of agreement between the results from numerical simulations and experiments. In addition, the corona discharge characteristics of ACNTs are studied and discussed, particularly the spatiotemporal evolution of charged particles near the tip. This paper may provide a method of analysis for optimizing and broadly applying nanoelectrodes.
Optimization of Nb/Al-AlO
x
/Nb Josephson junctions through wafer-scale anodic oxidation: a systematic characterization and performance analysis
Authors: Chen Jian , Wang Zhenyu , Xu Da , Qiao Huan , Li Jinjin , Zhong Qing , Wang Shijian , Zeng Jiusun , Cai Jinhui , Zhang Mingyu , Wang Yeru , Li Xu , Zhong Yuan , Cao Wenhui , Wang Xueshen
Published: August 2023

The Nb/Al-AlO
x
/Nb (SIS) Josephson junction is a crucial component in many types of superconducting devices. However, it can be easily damaged during the plasma fabrication processes. Anodic oxidation is an effective method for protecting SIS junctions by oxidizing Nb and Al from the junction profile. We used a custom wafer-scale anodic oxidation system and a neutral electrolyte to study the oxidation process of Nb films and Nb-Al(-AlO
x
)-Nb junctions. The oxidation process was thoroughly characterized by considering factors such as morphology and electrical properties. Anodization spectroscopy revealed varying oxidation sections from the top Nb layer to the bottom layer, extending across the Al-AlO
x
interlayer. This indicates that a 4 nm Al layer is sufficient to cover the surface of the bottom Nb film. High-resolution transmission electron microscopy and energy dispersive spectroscopy revealed that the Nb2O5 layer produced from the oxidation of the bottom Nb layer penetrated the Al2O3 layer and migrated to the top surface as the oxidation voltage increased. The top Nb layer of the SIS junction was also subjected to oxidation, despite the presence of a protective photoresist. Following the anodic oxidation process, the entire wafer surface was coated with an insulating Nb2O5 film. This film provided protection for the SIS junctions during the subsequent microfabrication process. The fabricated junction array, consisting of 128 junctions, demonstrated uniform electrical properties benefiting from the anodic oxidation process. This systematic analysis will further the research and practical applications of SIS junctions.
Floquet topological superconductors with many Majorana edge modes: topological invariants, entanglement spectrum and bulk-edge correspondence
Authors: Wu Hailing , Wu Shenlin , Zhou Longwen
Published: August 2023

One-dimensional (1D) Floquet topological superconductors possess two types of degenerate Majorana edge modes at zero andπquasienergies, leaving more room for the design of boundary time crystals and quantum computing schemes than their static counterparts. In this work, we discover Floquet superconducting phases with large topological invariants and arbitrarily many Majorana edge modes in periodically driven Kitaev chains (KCs). Topological winding numbers defined for the Floquet operator and Floquet entanglement Hamiltonian are found to generate consistent predictions about the phase diagram, bulk-edge correspondence and numbers of zero andπMajorana edge modes of the system under different driving protocols. The bipartite entanglement entropy further shows non-analytic behaviors around the topological transition point between different Floquet superconducting phases. These general features are demonstrated by investigating the KC with periodically kicked pairing or hopping amplitudes. Our discovery reveals the rich topological phases and many Majorana edge modes that could be brought about by periodic driving fields in 1D superconducting systems. It further introduces a unified description for a class of Floquet topological superconductors from their quasienergy bands and entanglement properties.
The impact of quantized magnetic pressure on the stimulated Brillouin scattering of electromagnetic waves
Authors: Rozina Ch , Maroosh A , Poedts S , Shah H A
Published: August 2023

Within the frame work of Landau quantization theory of Fermi gas, we formulate here the exotic physics of magnetic stimulated Brillouin scattering instability (MSBS) arising due to the nonlinear interaction of high frequency electromagnetic waves (EMWs) with degenerate, strongly magnetized electron-ion plasma. Quantum magneto hydrodynamic model (QMHD) is followed to develop the basic differential equations of quantized magnetosonic waves (QMWs) in the presence of super strong magnetic (SSH) field, whereas Maxwell equations are used to derive the governing differential equation of pump EMWs. The nonlinear interaction of EMWs and QMWs is addressed by employing the phasor matching technique. The obtained dispersion relation of MSBS shows that for a fixed density of fermions, the SSH field alone suppresses the MSBS instability as a function of quantized magneto ion velocity (C

He
) and the Alfven speed (V

A
) via three-wave decay and modulational instabilities. However, for particular condition the MSBS instability is found to increase as a function of SSH field. Next, the analytical results are verified numerically and graphically for soft x-rays in the environment of neutron star. The present MSBS analysis may be critical in neutron stars, radio pulsars and magnetars having super strong magnetic field i.e. even larger than the quantum threshold value i.e, H ∼ 4.4 × 1013
G, or in any application where the enhancement or suppression of SBS may be important.
Analysis of electromagnetic waves reflected by re-entry plasma sheath based on CSO-FDTD
Authors: Zhang Jie , Li Miao , Han Bing
Published: August 2023

A transmission model of terahertz waves in the plasma sheath is established based on a cascaded finite time domain difference method and analyzed the transmission characteristics of electromagnetic waves under the action of an external magnetic field and collision frequency. The flow field around the hypersonic aircraft is simulated and the characteristics of the flow field as the altitude changes of the aircraft are analyzed based on the flow field distribution of the RAM-C (Rapid Attenuation Measurement) vehicle. The results show the transmission characteristics of terahertz waves in plasma that will be affected by flight height, plasma parameters, external magnetic field, and collision frequency. In addition, under the action of an external magnetic field and the different collision frequency, the transmission characteristics of the left-handed terahertz wave will be improved. The application of the magnetic field will introduce an absorption peak in the right-handed terahertz wave, and it will move to the high frequency direction with the increase of the magnetic induction intensity.
On the dynamic mode-III crack in the elastic continuum consisting of sandy properties
Authors: Yadav Ram Prasad , Renu
Published: August 2023

The aim of the present investigation is to come up with a mathematical model for the dynamic mode III crack in an elastic continuum consisting of dry sandy properties. For the analysis of the propagating mode-III crack due to the propagating Love-type wave in an elastic continuum consisting of dry sandy properties, the results for the stress intensity factor (SIF) and crack opening displacement (COD) have been developed in closed form. The complex variable integral transform mathematical methods consisting of the two-sided Fourier integral transform, along with the Weiner-Hopf technique, serve as a salient feature of the adopted mathematical treatment for determining the SIF and COD in the closed form. Moreover, the expressions for the SIF and COD for the limiting static mode-III crack have also been obtained and deduced for special cases of elastic medium. The effects of existing parameters, viz. dry sandiness parameter, depth of crack, crack velocity on the SIF and COD . The effect of physical materials parameters has been shown numerically as well as graphically.
Localized waves and interaction solutions to an integrable variable coefficients Jimbo-Miwa equation
Authors: Liu Jinzhou , Yan Xinying , Jin Meng , Xin Xiangpeng
Published: August 2023

In this paper, the reduced variable coefficients Jimbo-Miwa (vcJM) equation is studied. Firstly, the integrability of the reduced vcJM equation is verified by Painlevé analysis. Based on the Hirota bilinear method and the long wave limit method, the N-soliton solutions, rational and semirational solutions of the vcJM equation are obtained. By choosing different parameters and coefficient functions, some of different kinds of local waves, including of solition, breather wave and lumps, of the equation are obtained. Furthermore, the interaction solutions between different local waves are obtained. The dynamical behavior of the interaction between different local waves is studied by modifying the time parameters and the process is displayed by figures.
511 keV line and primordial black holes from first-order phase transitions
Authors: Tseng Po-Yan , Yeh Yu-Min
Published: August 2023

511 keV gamma-ray excess from the Galactic center is a long
lasting anomaly without satisfying astrophysical explanation.
Hawking evaporation of hypothetical primordial black hole (PBH) with
mass 1.0 × 10-17 ≲ M
PBH/M
⊙ ≲ 
8.0 × 10-17 and fractional abundance 10-3 ≲ 
f
PBH ≲ 1.0, gives rise substantial
non-relativistic electrons/positrons annihilating into diphoton,
well reproduces the 511 keV line. However, it is obscure of the
mechanism behind to form PBH with meteoritical mass in the early
Universe. In this work, we investigated the production mechanism of
PBHs through a cosmological first-order phase transition induced by
quartic effective thermal potential via a scalar field in dark
sector. We found the phase transition with vacuum energy,
 𝒪(1) ≲ B
1/4/MeV ≲ 𝒪 (100), produces the desired monochromatic PBH mass and
abundance fraction. Correlated signatures of gravitational wave and
extragalactic gamma-ray, respectively coming from phase transition
and black hole evaporation, are within μAres and
AMEGO/e-ASTROGAM/COSI/XGIS-THESUS projected sensitivities. Finally,
we include the PBH mass function from FOPT and found it can not
improve the explanation to 511 keV excess.
New MGCAMB tests of gravity with CosmoMC and Cobaya
Authors: Wang Zhuangfei , Mirpoorian Seyed Hamidreza , Pogosian Levon , Silvestri Alessandra , Zhao Gong-Bo
Published: August 2023

We present a new version of MGCAMB, a patch for the Einstein-Boltzmann solver CAMB for cosmological tests of gravity. New features include a new cubic-spline parameterization allowing for a simultaneous reconstruction of μ, Σ and the dark energy density fraction Ω
X
as functions of redshift, the option to work with a direct implementation of μ, Σ (instead of converting to μ, γ first), along with the option to test models with a scalar field coupled only to dark matter, and the option to include dark energy perturbations when working with w ≠ -1 backgrounds, to restore consistency with CAMB in the GR limit. This version of MGCAMB comes with a Python wrapper to run it directly from the Python interface, an implementation in the latest version of CosmoMC, and can be used with Cobaya.
Constraining an Einstein-Maxwell-dilaton-axion black hole at the Galactic Center with the orbit of the S2 star
Authors: Fernández Fernández Rebeca , Della Monica Riccardo , de Martino Ivan
Published: August 2023

We derive new constraints on the dilaton parameter appearing in the spherically-symmetric
black hole solution of Einstein-Maxwell-dilaton-axion gravity, by studying the geodesic motion of
the S2 star in the Galactic Center. Einstein-Maxwell-dilaton-axion black holes represent a
compelling alternative to the standard black hole paradigm in General Relativity. This theory
emerges from the low energy effective action of the heterotic string theory and has been proven to
predict peculiar observational features from the direct imaging of black hole shadows. At a
fundamental level, Einstein-Maxwell-dilaton-axion includes additional electromagnetic, dilatonic
and axionic fields coupled to the space-time metric. When considering charged non-rotating black
hole solutions, the additional fields endow the metric with one extra parameter b, called
dilaton parameter, that is theoretically bound to 0 < b < M. Using publicly available astrometric
data for S2 we derive an upper bound on b ≲ 12M at 95% confidence level and we
demonstrate that only including the measurement of the relativistic orbital precession for S2 is
sufficient to reduce this bound to b ≲ 1.4M at the same confidence level. Additionally,
using a mock data mimicking future observations of S2 with the GRAVITY interferometer, we show
that improved astrometric precision can help further narrow down the allowed dilaton parameter
range to b ≲ 0.033M after monitoring the S2 orbit for one and a half period.
Transverse-field XY spin chain with the competing long-range interactions: multi-criticality around the XX-symmetric point
Authors: Nishiyama Yoshihiro
Published: August 2023

The transverse-field XY spin chain with competing antiferromagnetic long-range interactions,

J
r

∝
1

/

r
α

(r: distance between spins), and the exponent α was investigated numerically. The main concern is to clarify the character of the transverse-field-driven phase transition for the small-α regime around the XX-symmetric point, η = 0 (η: XY-anisotropy parameter). To cope with the negative-sign problem, we employed the exact-diagonalization method, which enables us to evaluate the fidelity susceptibility χ

F
. Because the fidelity susceptibility does not assume any order parameter after a phase transition, it detects the multi-criticality around the XX-symmetric point in a systematic manner. As a preliminary survey, with η = 0.5 and α = 2 fixed, we made the scaling analysis of χ

F
. The scaling behavior of χ

F
shows that the transverse-field-driven phase transition belongs to the 2D-Ising universality class. Thereby, with the properly crossover-scaled η, the χ

F
data is cast into the crossover scaling formula for the small-α regime. The multi-critical exponents fed into the scaling formula are argued through referring to related studies.
Chicken Manure Disease Recognition Model Based on Improved ResNeXt50
Authors: Chen Xiuzhe , Yang Xiaoxia
Published: August 2023

The feces of sick chickens often show a certain degree of morbidity. It is an effective method to identify sick chickens by deep learning. Concerning the environmental factors of chicken manure shooting that have a great influence, this paper proposes the introduction of a mixed attention mechanism into the ResNeXt50 network model and applies it to the chicken manure data set. While using residual structure learning, the model enhances the perception and learning ability of the input image. The results showed that the overall accuracy of the ResNeXt50-3A model on the test set reached 97.4%, which was 17.3, 1.5, 28.6, and 1.5 percentage points higher than the RegNetY_400MF, Vgg19, ViT (Vision Transformer) and ResNeXt50 models respectively. The ResNeXt50-3A model has high recognition accuracy and can provide support for the accurate identification of diseased chickens in all aspects of chicken breeding, replacing manual detection of diseased chickens.
Optimization of BP Neural Network for Wind Power Output Prediction Based on Particle Swarm Optimization Algorithm
Authors: Liu Kai , Su Hongsheng
Published: August 2023

Because wind power has strong uncertainty and uncontrollability, there are great difficulties in the process of grid connection and power dispatching. The existing prediction models have some shortcomings, such as insufficient precision and poor convergence. In this paper, an improved short-term prediction model is proposed. The correlation coefficient is determined by the grey correlation method, and then the optimal coefficient is calculated by optimizing the backpropagation network using a particle swarm optimization algorithm. Comparing the prediction results of the new model with those of a single BP network and the POS optimization BP network, it can be concluded that the new model is more precise than the other models.
Application of Improved Genetic Algorithm and Ant Colony Algorithm in Multi-objective Path Planning
Authors: Pang Xueting , Chen Chao , Tong Xin , Shi Liangxiong , Liu Xingzhu
Published: August 2023

Due to the shortcomings of GA in path planning, there are too many control variables and it is easy to fall into local areas. We introduce the idea of a gene bank and store the new chromosomes generated each time in the gene bank. The idea of linear regression is proposed to predict the probability of crossover and mutation of the next generation. At the same time, a method of increasing chromosome diversity is proposed, which avoids the problem that it is difficult to set the control variable parameters reasonably and fall into local optimum when the GA is applied in path planning. To overcome the shortcomings of ACO in path planning, an ACO with improved heuristic function and transition probability was proposed, and the method of the real-time environment was given. Through the traveling salesman problem, the improved GA and ACO are combined to apply to path planning. The experimental results show that the fusion of GA and ACO in this paper is superior to ordinary GA and ACO in all aspects of path planning.
Automatic Bridge Detection of SAR Images Based on Interpretable Deep Learning Algorithm
Authors: Li Zhonghao , Chen Lifu , Long Fengqi , Li Zengqi , Jina Haixin
Published: August 2023

As a typical feature target in SAR images, the accurate detection and localization of bridge targets are of important research value for the management of transportation facilities and battlefield reconnaissance. Since most of the current bridge target detection algorithms use horizontal frames, they cannot achieve accurate localization of bridges. There is a large amount of background information in the data set annotation, which is not conducive to the training of the network. In this paper, an in-depth analysis of the interpretable deep learning algorithm is presented to show that the use of rotating detection frames is more beneficial to the automatic detection of targets with large aspect ratios such as bridges. The algorithm framework first performs feature extraction of bridge targets through the Yolov5 network, and then passes the feature maps containing target information into the proposed regression prediction visualization module to obtain detection result maps and heat maps. In the experiments, the SAR images of the Foshan area are used to verify the results, and the advantages of the rotating detection frame in terms of localization and inspection performance are analysed by comparing the IoU and the heat map.
Extraction of Building from Remote Sensing Imagery Base on Multi-Attention L-CAFSFM
Authors: Yuan Fuxiang , Jin Huazhong , Guo Xinyi , Bao Zhixi
Published: August 2023

The extracted building information can be widely applied in urban planning, land resource management, and other related fields. This paper proposes a novel method for building extraction, which aims to improve the accuracy of the extraction process. The method combines a bi-directional feature pyramid with a location-channel attention feature serial fusion module (L-CAFSFM). By using the ResNeXt101 network, more precise and abundant building features are extracted. The L-CAFSFM combines and calculates the adjacent two-level feature maps, and the iteration process from high-level to low-level and from low-level to high-level enhances the feature extraction ability of the model at different scales and levels. We use the DenseCRF algorithm to refine the correlation between pixels. The performance of our method is evaluated on the Wuhan University building dataset (WHU), and the experimental results show that the precision, F-score, recall rate, and IoU of our method are 94.94%, 94.32%, 93.70%, and 89.25%, respectively. Compared with the baseline network, our method achieves a more accurate performance in extracting buildings from high-resolution images. The proposed method can be widely applied in urban planning, land resource management, and other related fields.
The Application of Classifier Model Based on FA-SVM Algorithm in Speech Recognition
Authors: Wang Haijun , Su Jianna , Jin Tao , Zhang Xiaojiao , Qiao Zhili
Published: August 2023

In recent years, speech recognition technology has been increasingly widely used. How to improve the robustness of speech recognition systems has become the focus of researchers. This paper proposes an accent recognition model based on the Firefly algorithm to optimize the support vector machine. Firstly, a set of penalty factors and kernel parameters in the support vector machine are taken as firefly individuals, and the important parameters affecting the performance of the support vector machine are optimized by the firefly algorithm. A Firefly-Support vector machine (FA-SVM) model is established and applied to accent recognition. The example analysis shows that the FA-SVM accent recognition model has taller classification accuracy and faster running speed than the PSO-SVM and GA-SVM classifier models.