bluuebunny/crossref_metadata_2025 huggingface dataset:
Correlations between defect propensity and dynamical heterogeneities in supercooled water
Authors: Verde Alejandro R. , Alarcón Laureano M. , Appignanesi Gustavo A.
Published: February 2023

A salient feature of supercooled liquids consists in the dramatic dynamical slowdown they undergo as temperature decreases while no significant structural change is evident. These systems also present dynamical heterogeneities (DH): certain molecules, spatially arranged in clusters, relax various orders of magnitude faster than the others. However, again, no static quantity (such as structural or energetic measures) shows strong direct correlations with such fast-moving molecules. In turn, the dynamic propensity approach, an indirect measure that quantifies the tendency of the molecules to move in a given structural configuration, has revealed that dynamical constraints, indeed, originate from the initial structure. Nevertheless, this approach is not able to elicit which structural quantity is, in fact, responsible for such a behavior. In an effort to remove dynamics from its definition in favor of a static quantity, an energy-based propensity has also been developed for supercooled water, but it could only find positive correlations between the lowest-energy and the least-mobile molecules, while no correlations could be found for those more relevant mobile molecules involved in the DH clusters responsible for the system’s structural relaxation. Thus, in this work, we shall define a defect propensity measure based on a recently introduced structural index that accurately characterizes water structural defects. We shall show that this defect propensity measure provides positive correlations with dynamic propensity, being also able to account for the fast-moving molecules responsible for the structural relaxation. Moreover, time dependent correlations will show that defect propensity represents an appropriate early-time predictor of the long-time dynamical heterogeneity.
Does variation in composition affect dynamics when approaching the eutectic composition?
Authors: Chatterjee Srijan , Chowdhury Tubai , Bagchi Sayan
Published: February 2023

Deep eutectic solvent is a mixture of two or more components, mixed in a certain molar ratio, such that the mixture melts at a temperature lower than individual substances. In this work, we have used a combination of ultrafast vibrational spectroscopy and molecular dynamics simulations to investigate the microscopic structure and dynamics of a deep eutectic solvent (1:2 choline chloride: ethylene glycol) at and around the eutectic composition. In particular, we have compared the spectral diffusion and orientational relaxation dynamics of these systems with varying compositions. Our results show that although the time-averaged solvent structures around a dissolved solute are comparable across compositions, both the solvent fluctuations and solute reorientation dynamics show distinct differences. We show that these subtle changes in solute and solvent dynamics with changing compositions arise from the variations in the fluctuations of the different intercomponent hydrogen bonds.
Controlling quantum phases of electrons and excitons in moiré superlattices
Authors: Zhang Lifu , Ni Ruihao , Zhou You
Published: February 2023

Moiré lattices formed in twisted and lattice-mismatched van der Waals heterostructures have emerged as a platform to engineer the novel electronic and excitonic states at the nanoscale. This Perspective reviews the materials science of moiré heterostructures with a focus on the structural properties of the interface and its structural–property relationships. We first review the studies of the atomic relaxation and domain structures in moiré superlattices and how these structural studies provide critical insights into understanding the behaviors of quantum-confined electrons and excitons. We discuss the general frameworks to manipulate moiré structures and how such control can be harnessed for engineering new phases of matter and simulating various quantum phenomena. Finally, we discuss routes toward large-scale moiré heterostructures and give an outlook on their applications in quantum electronics and optoelectronics. Special emphasis will be placed on the challenges and opportunities of the reliable fabrication and dynamical manipulation of moiré heterostructures.
Atmospheric pressure plasma jet impinging on fiber arrays: Penetration pattern determined by fiber spacing
Authors: Kong Xianghao , Li Haoyi , Yang Weimin , Li Sisi , Yang Dezheng , Ning Wenjun , Wang Ruixue
Published: February 2023

Atmospheric pressure cold plasmas have great potential for surface functionalization, and the interaction between the plasmas and fibers is essential to understand the underlying physics. In this Letter, the penetration pattern and mechanism of an atmospheric pressure plasma jet (APPJ) interacting with different spacing fiber scaffolds were studied by both experiment and modeling. The intensified charge coupled device images showed that APPJ induced a radial surface streamer on the windward side of the scaffold and another axial forward streamer on the opposite side. Propagation distance of both the radial and axial streamers diminished as the spacing decreased. Similar trends were predicted by a 2D fluid model. The simulation results indicated that the high electrical field carried by the streamer head in APPJ was allowed to pass through the gap for large spacing scaffold, while it was blocked by high intensity charges at small spacing. Instead, one axial streamer was generated in the latter case. The physical insight on the penetration mechanism of plasma jet interacting with fiber array in this Letter may contribute to improve treatment uniformity of plasma technology.
Origin of oxygen-redox and transition metals dissolution in Ni-rich LixNi0.8Co0.1Mn0.1O2 cathode
Authors: Cai Chun , Zhang Dantong , Zhang Qi , Chen Kunfeng , Hua Wenchao , Peng Chao , Xue Dongfeng
Published: February 2023

Recently, Ni-rich LiNixCoyMn1−x−yO2 (x ≥ 0.8) draw significant research attention as cathode materials in lithium-ion batteries due to their superiority in energy density. However, the oxygen release and the transition metals (TMs) dissolution during the (dis)charging process lead to serious safety issues and capacity loss, which highly prevent its application. In this work, we systematically explored the stability of lattice oxygen and TM sites in LiNi0.8Co0.1Mn0.1O2(NCM811) cathode via investigating various vacancy formations during lithiation/delithiation, and properties such as the number of unpaired spins (NUS), net charges, and d band center were comprehensively studied. In the process of delithiation (x = 1 → 0.75 → 0), the vacancy formation energy of lattice oxygen [Evac(O)] has been identified to follow the order of Evac(O–Mn) > Evac(O–Co) > Evac(O–Ni), and Evac(TMs) shows a consistent trend with the sequence of Evac(Mn) > Evac(Co) > Evac(Ni), demonstrating the importance of Mn to stabilize the structural skeleton. Furthermore, the |NUS| and net charge are proved to be good descriptors for measuring Evac(O/TMs), which show linear correlations with Evac(O) and Evac(TMs), respectively. Li vacancy plays a pivotal role on Evac(O/TMs). Evac(O/TMs) at x = 0.75 vary extremely between the NiCoMnO layer (NCM layer) and the NiO layer (Ni layer), which correlates well with |NUS| and net charge in the NCM layer but aggregates in a small region in the Ni layer due to the effect of Li vacancies. In general, this work provides an in-depth understanding of the instability of lattice oxygen and transition metal sites on the (104) surface of Ni-rich NCM811, which might give new insights into oxygen release and transition metal dissolution in this system.
On the origin of spanwise vortex deformations during the secondary instability stage in compressible mixing layers
Authors: Bai Yuping , Yan Peiwen , Yu Yifei , Zhao Quanbin , Chong Daotong , Yan Junjie
Published: February 2023

The three-dimensionality of turbulence initiates with spanwise vortex deformations associated with the amplification of three-dimensional disturbance modes. However, the origin of spanwise vortex deformations is still not well understood. In this paper, compressible mixing layers are performed via direct numerical simulation (DNS). Two typical types of secondary instabilities producing spanwise vortex deformations are of consideration: fundamental instability and subharmonic instability. Based on the fast Fourier transform and DNS data, a low-rank velocity model v0 is obtained to demonstrate that spanwise vortex deformations are originated from a linear superposition of fundamental norm mode, a pair of fundamental or subharmonic oblique modes, and the mean mode. Through observing flow structures of the above norm and oblique modes, a striking feature is found that the velocity model v0 containing deformed spanwise vortices can be decomposed into three new velocity models v1, v2, and v3 containing relatively simplified counterparts (spanwise or oblique vortices). Then, the instability mechanism of the latter vortices is explored by analyzing the position relationship between the function of the generalized inflection points and cores of relatively simplified vortices. We find that an inviscid inflectional instability mechanism is responsible for the formation of spanwise and oblique vortices. Based on the above findings, a view is first proposed that spanwise vortex deformations with aligned and staggered patterns are a joint result of the parametric resonant mechanism and the inviscid inflectional instability mechanism.
Pre-trained combustion model and transfer learning in thermoacoustic instability
Authors: Qin Ziyu , Wang Xinyao , Han Xiao , Lin Yuzhen , Zhou Yuchen
Published: February 2023

In this paper, deep learning is involved to comprehend thermoacoustic instability more deeply and achieve early warning more reliably. Flame images and pressure series are acquired in model combustors. A total of seven data domains are obtained by changing the combustor structural parameters. Then, the pre-trained model TIPE (Thermoacoustic Image-Pressure Encoder), containing an image encoder with ResNet architecture and a pressure encoder with transformer architecture, is trained through the contrastive self-supervised task of aligning the image and pressure signals in the embedding space. Furthermore, transfer learning in thermoacoustic instability prediction is performed based on k-nearest neighbors. Results show that the pre-trained model can better resist the negative effect caused by class imbalance. The weighted F1 score of the pre-trained model is 6.72% and 2.61% larger than supervised models in zero-shot transfer and few-shot transfer, respectively. It is inferred that the more generic features encoded by TIPE result in superior generalization in comparison with traditional supervised methods. Moreover, our proposed method is insensitive to the thresholds of determining thermoacoustic states. Principal component analysis reveals the physical interpretability preliminarily through the connection between feature principal components and pressure fluctuation amplitudes. Finally, the key spatial region of flame images and temporal interval of pressure series are visualized by class activation map and global attention scores.
Wavelet-based adaptive large-eddy simulation of supersonic channel flow with different thermal boundary conditions
Authors: De Stefano Giuliano
Published: February 2023

This work investigates the effect of different thermal wall boundary conditions on the wavelet-based adaptive large-eddy simulation of supersonic turbulent channel flow. The compressible flow governing equations are expressed in terms of wavelet-based Favre-filtered variables and are supplied with the anisotropic minimum dissipation closure model. Various computations are performed, where the resolved temperature field is constrained by either Dirichlet (isothermal) or Neumann (adiabatic) boundary conditions at the walls. The turbulence diagnostics include mean flow features and turbulent fluctuations statistics. The successful comparison with reference direct numerical simulations demonstrates the validity and the efficiency of the wavelet-based adaptive approach for wall-bounded turbulent compressible flow, regardless of the thermal boundary conditions that are imposed.
Functional traits in Myrteae species: the role of habitat heterogeneity and genus in humid and seasonal tropical environments
Authors: Santos Paulo Sérgio Neves dos , Rossatto Davi Rodrigo , Oliveira Marla Ibrahim Uehbe de , Couto-Santos Ana Paula Lima do , Funch Ligia Silveira
Published: February 2023

Context Morphological traits allow for plant persistence in various environments differing in edaphic and climatic aspects, and their values are normally the result of the diversity of genera and species occurring in a certain region. Myrteae, a widespread tribe of Myrtaceae, is found in varied environmental conditions ranging from dry to wet environments; thus, it is a potential study group for understanding trait variation. Aims Considering that functional-trait variation can be selected by environmental filters, and also be affected by phylogenetic aspects, we used the diversity of the Myrteae group present in different vegetation types in north-eastern Brazil to understand the role of the environment and genus in trait variation. Methods Two morphological traits, namely, wood density and specific leaf area, were sampled in 29 species from 6 genera in the following four vegetation types: savanna, seasonally dry tropical forest, rupestrian grassland and humid forest. We assessed how variation in the two traits was related to genus and environmental conditions. Key results The studied species showed trait variation depending on the environment and genus. Wood density was mainly determined by genus, whereas specific leaf area was mostly influenced by the environment. Conclusion The most important determinant of interspecific trait variation differed between wood density and specific leaf area in this study. Wood density was strongly related to genus, suggesting it is a conserved trait in Myrteae, whereas specific leaf area tended to be more variable. Implications Our results contribute to understanding the causes of morphological and genetic variability in Myrteae.
Histamine H
2
receptor deficit in glutamatergic neurons contributes to the pathogenesis of schizophrenia
Authors: Ma Qianyi , Jiang Lei , Chen Han , An Dadao , Ping Yiting , Wang Yujia , Dai Haibin , Zhang Xiangnan , Wang Yi , Chen Zhong , Hu Weiwei
Published: February 2023

Schizophrenia is a serious mental disorder, and existing antipsychotic drugs show limited efficacy and cause unwanted side effects. The development of glutamatergic drugs for schizophrenia is currently challenging. Most functions of histamine in the brain are mediated by the histamine H
1
receptor; however, the role of the H
2
 receptor (H
2
R) is not quite clear, especially in schizophrenia. Here, we found that expression of H
2
R in glutamatergic neurons of the frontal cortex was decreased in schizophrenia patients. Selective knockout of the H
2
R gene (
Hrh2
) in glutamatergic neurons (

CaMKIIα-Cre; Hrh2 
fl/fl

) induced schizophrenia-like phenotypes including sensorimotor gating deficits, increased susceptibility to hyperactivity, social withdrawal, anhedonia, and impaired working memory, as well as decreased firing of glutamatergic neurons in the medial prefrontal cortex (mPFC) in in vivo electrophysiological tests. Selective knockdown of H
2
R in glutamatergic neurons in the mPFC but not those in the hippocampus also mimicked these schizophrenia-like phenotypes. Furthermore, electrophysiology experiments established that H
2
R deficiency decreased the firing of glutamatergic neurons by enhancing the current through hyperpolarization-activated cyclic nucleotide-gated channels. In addition, either H
2
R overexpression in glutamatergic neurons or H
2
R agonism in the mPFC counteracted schizophrenia-like phenotypes in an MK-801-induced mouse model of schizophrenia. Taken together, our results suggest that deficit of H
2
R in mPFC glutamatergic neurons may be pivotal to the pathogenesis of schizophrenia and that H
2
R agonists can be regarded as potentially efficacious medications for schizophrenia therapy. The findings also provide evidence for enriching the conventional glutamate hypothesis for the pathogenesis of schizophrenia and improve the understanding of the functional role of H
2
R in the brain, especially in glutamatergic neurons.
Video-rate Raman-based metabolic imaging by Airy light-sheet illumination and photon-sparse detection
Authors: Dunn Lochlann , Luo Haokun , Subedi Nava R. , Kasu Ramachandran , McDonald Armando G. , Christodoulides Demetrios N. , Vasdekis Andreas E.
Published: February 2023

Despite its massive potential, Raman imaging represents just a modest fraction of all research and clinical microscopy to date. This is due to the ultralow Raman scattering cross-sections of most biomolecules that impose low-light or photon-sparse conditions. Bioimaging under such conditions is suboptimal, as it either results in ultralow frame rates or requires increased levels of irradiance. Here, we overcome this tradeoff by introducing Raman imaging that operates at both video rates and 1,000-fold lower irradiance than state-of-the-art methods. To accomplish this, we deployed a judicially designed Airy light-sheet microscope to efficiently image large specimen regions. Further, we implemented subphoton per pixel image acquisition and reconstruction to confront issues arising from photon sparsity at just millisecond integrations. We demonstrate the versatility of our approach by imaging a variety of samples, including the three-dimensional (3D) metabolic activity of single microbial cells and the underlying cell-to-cell variability. To image such small-scale targets, we again harnessed photon sparsity to increase magnification without a field-of-view penalty, thus, overcoming another key limitation in modern light-sheet microscopy.
Bayesian inference in ring attractor networks
Authors: Kutschireiter Anna , Basnak Melanie A. , Wilson Rachel I. , Drugowitsch Jan
Published: February 2023

Working memories are thought to be held in attractor networks in the brain. These attractors should keep track of the uncertainty associated with each memory, so as to weigh it properly against conflicting new evidence. However, conventional attractors do not represent uncertainty. Here, we show how uncertainty could be incorporated into an attractor, specifically a ring attractor that encodes head direction. First, we introduce a rigorous normative framework (the circular Kalman filter) for benchmarking the performance of a ring attractor under conditions of uncertainty. Next, we show that the recurrent connections within a conventional ring attractor can be retuned to match this benchmark. This allows the amplitude of network activity to grow in response to confirmatory evidence, while shrinking in response to poor-quality or strongly conflicting evidence. This “Bayesian ring attractor” performs near-optimal angular path integration and evidence accumulation. Indeed, we show that a Bayesian ring attractor is consistently more accurate than a conventional ring attractor. Moreover, near-optimal performance can be achieved without exact tuning of the network connections. Finally, we use large-scale connectome data to show that the network can achieve near-optimal performance even after we incorporate biological constraints. Our work demonstrates how attractors can implement a dynamic Bayesian inference algorithm in a biologically plausible manner, and it makes testable predictions with direct relevance to the head direction system as well as any neural system that tracks direction, orientation, or periodic rhythms.
Control of tissue oxygenation by S-nitrosohemoglobin in human subjects
Authors: Reynolds James D. , Posina Kanna , Zhu Lin , Jenkins Trevor , Matto Faisal , Hausladen Alfred , Kashyap Vikram , Schilz Robert , Zhang Rongli , Mannick Joan , Klickstein Lloyd , Premont Richard T. , Stamler Jonathan S.
Published: February 2023

S-Nitrosohemoglobin (SNO-Hb) is unique among vasodilators in coupling blood flow to tissue oxygen requirements, thus fulfilling an essential function of the microcirculation. However, this essential physiology has not been tested clinically. Reactive hyperemia following limb ischemia/occlusion is a standard clinical test of microcirculatory function, which has been ascribed to endothelial nitric oxide (NO). However, endothelial NO does not control blood flow governing tissue oxygenation, presenting a major quandary. Here we show in mice and humans that reactive hyperemic responses (i.e., reoxygenation rates following brief ischemia/occlusion) are in fact dependent on SNO-Hb. First, mice deficient in SNO-Hb (i.e., carrying C93A mutant Hb refractory to S-nitrosylation) showed blunted muscle reoxygenation rates and persistent limb ischemia during reactive hyperemia testing. Second, in a diverse group of humans—including healthy subjects and patients with various microcirculatory disorders—strong correlations were found between limb reoxygenation rates following occlusion and both arterial SNO-Hb levels (n = 25;
P 
= 0.042) and SNO-Hb/total HbNO ratios (n = 25;
P 
= 0.009). Secondary analyses showed that patients with peripheral artery disease had significantly reduced SNO-Hb levels and blunted limb reoxygenation rates compared with healthy controls (n = 8 to 11/group;
P 
< 0.05). Low SNO-Hb levels were also observed in sickle cell disease, where occlusive hyperemic testing was deemed contraindicated. Altogether, our findings provide both genetic and clinical support for the role of red blood cells in a standard test of microvascular function. Our results also suggest that SNO-Hb is a biomarker and mediator of blood flow governing tissue oxygenation. Thus, increases in SNO-Hb may improve tissue oxygenation in patients with microcirculatory disorders.
The right ear advantage – a perspective from speech perception in noise test
Authors: Sendesen Eser , Colak Hasan , Korkut Yagız , Yalcınkaya Eda , Sennaroglu Gonca
Published: February 2023

Objective:

So far, binaural summation for speech stimuli has been less evaluated in the literature, without the head shadow effect and spatial release from masking. Moreover, the right ear advantage has been shown mostly in the dichotic listening situation, but with conflicting results in the monaural/binaural listening condition. Therefore, the present study aimed to evaluate binaural hearing advantage utilizing only binaural summation without other factors and to evaluate the right ear advantage in the monaural listening situation.

Methods:

Thirty-seven participants with normal hearing in the range of 0.125–20 kHz participated current study. Evaluation of speech perception in noise (SPIN) performances were conducted by Matrix test. Participants assessed in three different conditions including the right ear condition, the left ear condition and the binaural condition. Test order of the ears was counterbalanced and noises presented ipsilateral for monaural conditions.

Results:

The lowest signal-to-noise ratio (SNR) values needed for understanding speech in noise were statistically significantly lower in binaural listening conditions compared to monaural listening conditions. In monaural listening conditions, the SNR value needed to understand speech in noise in the right ear was statistically significantly lower than in the left ear.

Conclusion:

The results showed that the rehabilitation/habilitation of hearing-impairment patient should consider binaural amplification options to improve SPIN. In case of the symmetric hearing loss with the encountering compulsory situations which bring about the necessity to use only one ear, then right ear preference for amplification devices can provide better SPIN performance.
A comparative study on the characteristics of nanosecond laser ablation zinc and acrylonitrile butadiene styrene targets
Authors: Xu Yongfeng , Yang Liang , Zhou Dongjian , Li Qingwei , Shi Wenbo , Jin Yuqi
Published: February 2023

In this study, the influence of laser energy and pressure on propulsion performance of zinc and acrylonitrile butadiene styrene (ABS) is investigated by impulse measurement, fast exposure images, spectral diagnostics and target ablation. A Q-switched Nd:YAG laser with the wavelength of 1064 nm and pulse width of 6 ns is employed. The impulse and coupling coefficient generated by laser ablation ABS are greater than that of Zn, and they exhibit a similar variation trend with pressure. However, at higher pressure levels, the change in impulse versus laser energy is not completely coincident between Zn and ABS samples. The target property plays a significant role in the generation and propagation of plume related to the plasma parameters such as electron density and temperature. The temporal evolution images indicate that the plasma plume of laser-induced Zn presents a faster decay in comparison with that of ABS, which is ascribed to the fact that the gas temperature of ABS is higher than the electron temperature of Zn plasma in the local thermodynamical equilibrium. Also, the electron density is lower for Zn due to the rapid heat diffusion and higher ablation threshold of metal. It is found that the surface absorption is dominant for metal because the ablated crater of Zn performs larger diameter and shallower depth. On the contrary, the shrinkage in diameter but enhancement in depth of crater is observed from ABS surface, and the ablation mass is larger, suggesting the obvious volume absorption for polymer. The results reveal that the target property can engender an important effect on the energy conversion between laser, target and plasma.
Field-induced second-harmonic generation induced by distorted soft chiral crystal
Authors: Chen Hui-Yu , Araoka Fumito , Lee Fang-Yu , Lee Chi , Chen Jui Jiun
Published: February 2023

The nonlinear optical response of soft chiral crystal system-blue-phase (BP) liquid crystals was studied experimentally using a second-harmonic-generation (SHG) microscope. With the aid of the SHG microscope (SHM), the internal coupling between the polarization and structural deformation was visualized in a short time. In this study, a fringing field, formed at the electrode edges, causes lattice deformation of the cubic BPs, which contributes to the flexoelectric-optic response and field-induced SHG at low frequencies. Using the SHM, we can observe the spatial distribution of the induced polarization in the BPs, and the mean SHG intensity of the cubic BP depends quadratically on the strength of the electric field at a lower value. As the applied electric field increases, the structure of the BPs transfers to the chiral nematic phase (N*), and then the SHG intensity remains constant. Compared to the mean intensities of the SHG signal in N* and the different BPs in the low electric field, the SHG signal caused by the lattice deformation in BPs is weaker in N* and depends on the cubic structure of the BPs. The experimental results demonstrate that through the SHM, the influence of the inhomogeneous electric field on the BPs can be exhibited clearly because the response of the SHG signal in BPs is sensitive to field-induced lattice deformation and phase transitions between the BPs and chiral nematic. This will help us elucidate the mechanism of the secondary electro-optical response in BPs and for further improvement and development of high-performance photonic devices using BPs.
Synapse with versatility based on the Pt /LaMnO3/Pt heterojunction
Authors: Ma Lu , Yin Yanfeng , Jia Caihong , Zhang Weifeng
Published: February 2023

The realization of highly efficient neuromorphic computing necessitates the development of fast artificial synaptic devices. Mott insulator artificial synapses, in particular, provide tremendous potential for ultrafast neuromorphic devices. In this work, based on Pt/LaMnO3/Pt heterostructures, a variety of synaptic plasticity has been realized, including paired-pulse facilitation/depression, spike rate-dependent plasticity (SRDP) and four types of spike time-dependent plasticity. Furthermore, Bienenstock–Cooper–Munro learning rules with a sliding frequency threshold have been found from SRDP. These findings make a significant contribution to brain-like neuromorphic computing.
World beyond the nearest neighbors
Authors: Egami Takeshi , Ryu Chae Woo
Published: February 2023

The structure beyond the nearest neighbor atoms in liquid and glass is characterized by the medium-range order (MRO). In the conventional approach, the MRO is considered to result directly from the short-range order (SRO) in the nearest neighbors. To this bottom–up approach starting with the SRO, we propose to add a top–down approach in which global collective forces drive liquid to form density waves. The two approaches are in conflict with each other, and the compromise produces the structure with the MRO. The driving force to produce density waves provides the stability and stiffness to the MRO, and controls various mechanical properties. This dual framework provides a novel perspective for description of the structure and dynamics of liquid and glass.
The local dark matter distribution in self-interacting dark matter halos
Authors: Rahimi Elham , Vienneau Evan , Bozorgnia Nassim , Robertson Andrew
Published: February 2023

We study the effects of dark matter self-interactions on the local dark matter distribution in selected Milky Way-like galaxies in the EAGLE hydrodynamical simulations. The simulations were run with two different self-interacting dark matter models, a constant and velocity-dependent self-interaction cross-section.
We find that the local dark matter velocity distribution of the Milky Way-like halos in the simulations with dark matter self-interactions and baryons are generally similar to those extracted from cold collisionless dark matter simulations with baryons. In both cases, the local dark matter speed distributions agree well with their best fit Maxwellian distributions. Including baryons in the simulations with or without dark matter self-interactions increases the local dark matter density and shifts the dark matter speed distributions to higher speeds. To study the implications for direct detection, we compute the dark matter halo integrals obtained directly from the simulations and compare them to those obtained from the best fit Maxwellian velocity distribution. We find that a Maxwellian distribution provides a good fit to the halo integrals of most halos, without any significant difference between the results of different dark matter self-interaction models.
Simulating neutrino echoes induced by secret neutrino interactions
Authors: Carpio Jose Alonso , Murase Kohta
Published: February 2023

New neutrino interactions beyond the Standard Model (BSM) have been of much interest in not only particle physics but also cosmology and astroparticle physics. We numerically investigate the time delay distribution of astrophysical neutrinos that interact with the cosmic neutrino background. Using the Monte Carlo method, we develop a framework that enables us to simulate the time-dependent energy spectra of high-energy neutrinos that experience even multiple scatterings en route and to handle the sharp increase in the cross section at the resonance energy. As an example, we focus on the case of secret neutrino interactions with a scalar mediator.
While we find the excellent agreement between analytical and simulation results for small optical depths, our simulations enable us to study optically thick cases that are not described by the simplest analytic estimates.
Our simulations are used to understand effects of cosmological redshifts, neutrino spectra and flavors.
The developments will be useful for probing BSM neutrino interactions with not only current neutrino detectors such as IceCube and Super-Kamiokande but also future neutrino detectors such as IceCube-Gen2 and Hyper-Kamiokande.
Electric-field control of perpendicular magnetic anisotropy by resistive switching via electrochemical metallization
Authors: Yuan Yuan , Wei Lu-Jun , Lu Yu , Liu Ruo-Bai , Liu Tian-Yu , Chen Jia-Rui , You Biao , Zhang Wei , Wu Di , Du Jun
Published: February 2023

Electric-field control of perpendicular magnetic anisotropy (PMA) is a feasible way to manipulate perpendicular magnetization, which is of great importance for realizing energy-efficient spintronics. Here, we propose a novel approach to accomplish this task at room temperature by resistive switching (RS) via electrochemical metallization (ECM) in a device with the stack of Si/SiO2/Ta/Pt/Ag/Mn-doped ZnO (MZO)/Pt/Co/Pt/ITO. By applying certain voltages, the device could be set at high-resistance-state (HRS) and low-resistance-state (LRS), accompanied with a larger and a smaller coercivity (H
C), respectively, which demonstrates a nonvolatile E-field control of PMA. Based on our previous studies and the present control experiments, the electric modulation of PMA can be briefly explained as follows. At LRS, the Ag conductive filaments form and pass through the entire MZO layer and finally reach the Pt/Co/Pt sandwich, leading to weakening of PMA and reduction of H
C. In contrast, at HRS, most of the Ag filaments dissolve and leave away from the Pt/Co/Pt sandwich, causing partial recovery of PMA and an increase of H
C. This work provides a new clue to designing low-power spintronic devices based on PMA films.
Proton irradiation-induced dynamic characteristics on high performance GaN/AlGaN/GaN Schottky barrier diodes
Authors: Zhang Tao , Li Ruo-Han , Su Kai , Su Hua-Ke , Lv Yue-Guang , Xu Sheng-Rui , Zhang Jin-Cheng , Hao Yue
Published: February 2023

Dynamic characteristics of the single-crystal GaN-passivated lateral AlGaN/GaN Schottky barrier diodes (SBDs) treated with proton irradiation are investigated. Radiation-induced changes including idealized Schottky interface and slightly degraded on-resistance (R
ON) are observed under 10-MeV proton irradiation at a fluence of 1014 cm−2. Because of the existing negative polarization charges induced at GaN/AlGaN interface, the dynamic ON-resistance (R
ON,dyn) shows negligible degradation after a 1000-s-long forward current stress of 50 mA to devices with and without being irradiated by protons. Furthermore, the normalized R
ON,dyn increases by only 14% that of the initial case after a 100-s-long bias of −600 V has been applied to the irradiated devices. The high-performance lateral AlGaN/GaN SBDs with tungsten as anode metal and in-situ single-crystal GaN as passivation layer show a great potential application in the harsh radiation environment of space.
Beyond linear neural envelope tracking: a mutual information approach
Authors: De Clercq Pieter , Vanthornhout Jonas , Vandermosten Maaike , Francart Tom
Published: February 2023

Objective. The human brain tracks the temporal envelope of speech, which contains essential cues for speech understanding. Linear models are the most common tool to study neural envelope tracking. However, information on how speech is processed can be lost since nonlinear relations are precluded. Analysis based on mutual information (MI), on the other hand, can detect both linear and nonlinear relations and is gradually becoming more popular in the field of neural envelope tracking. Yet, several different approaches to calculating MI are applied with no consensus on which approach to use. Furthermore, the added value of nonlinear techniques remains a subject of debate in the field. The present paper aims to resolve these open questions. Approach. We analyzed electroencephalography (EEG) data of participants listening to continuous speech and applied MI analyses and linear models. Main results. Comparing the different MI approaches, we conclude that results are most reliable and robust using the Gaussian copula approach, which first transforms the data to standard Gaussians. With this approach, the MI analysis is a valid technique for studying neural envelope tracking. Like linear models, it allows spatial and temporal interpretations of speech processing, peak latency analyses, and applications to multiple EEG channels combined. In a final analysis, we tested whether nonlinear components were present in the neural response to the envelope by first removing all linear components in the data. We robustly detected nonlinear components on the single-subject level using the MI analysis. Significance. We demonstrate that the human brain processes speech in a nonlinear way. Unlike linear models, the MI analysis detects such nonlinear relations, proving its added value to neural envelope tracking. In addition, the MI analysis retains spatial and temporal characteristics of speech processing, an advantage lost when using more complex (nonlinear) deep neural networks.
Temporal Design of Art Painting Landscape Based on Ant Colony Optimization Algorithm
Authors: Jiang Qiuyue
Published: February 2023

In the long history of painting, art research in each period will also reflect on the current situation and future improvement. Inspired by the group behavior of natural insect communities, to find the shortest path between insect nests and food, a simulated evolutionary algorithm ant colony algorithm is proposed to simulate the foraging behavior of natural ant colonies. This paper studies the temporal design based on ant colony optimization algorithm. Time is an inseparable part of landscape design. With the continuous progress of time, various landscape characteristics have also been formed. The purpose of this paper is to provide theoretical and practical support for the improvement of art painting landscape design from the perspective of time. The ant colony optimization algorithm is used to analyze and analyze the values of the algorithm parameters.
A Quantitative Plant Type Recognition algorithm based on Gene Expression Programming
Authors: Deng Qingying , Deng Song
Published: February 2023

Efficient and accurate plant type identification is an important foundation for biological research. The existing traditional machine learning classification algorithms have the defects of low classification accuracy in solving plant type recognition and cannot quantitatively give the corresponding class recognition model. In this paper, we propose a Quantitative Plant Type Recognition algorithm based on Gene Expression Programming (QPTR-GEP). Firstly, the plant type data set to be processed is preprocessed; then, the GEP population corresponding to plant type identification is constructed, and the corresponding quantitative identification function expressions are obtained after genetic operations such as mutation, recombination and transposition; finally, the classification expression with the highest fitness is used as the classifier corresponding to a single plant type, and the classifier model integrating the data set containing all plant type is put into the data set for plant type identification. Experimental results on the open source Iris demonstrate that our proposed algorithm outperforms SVM and Decision Tree algorithms for plant type identification.
Analysis and Discussion on Emergency Rescue Inspection Case of Machine Roomless Elevator by Data
Authors: Wang Lei
Published: February 2023

This paper introduces the principle and method of emergency rescue for machine room-less elevator, analyzes an inspection case due to the failure of the electric release function, studies and proposes the importance of monitoring the door lock signal by the electric release device through the electrical working principle, and analyzes the failure reason and safety hidden danger of the emergency rescue device for machine room-less elevator.
Sentiment recognition model of EEG signals combined with one-dimensional convolution and BiBASRU-AT
Authors: Zhu Feiyu , Wang Jiehua , Ding Weiping , Xie Tian , Han Qiuyun
Published: February 2023

To address the problems of low accuracy of EEG emotion sentiment and insufficient feature extraction ability of recurrent model, an EEG sentiment recognition model combining one-dimensional convolution and BiBASRU-AT is proposed. The data set is preprocessed in segments to expand the number of samples, and 62 channel local emotional features are extracted from one-dimensional convolution; The built-in self-attention simple recurrent unit is constructed to capture the multi-channel fusion features and the dependence between channels. The soft attention mechanism identifies the key features that have a great impact on the identification of emotional tendencies, and the linear layer outputs the positive, neutral and negative emotion recognition results. The experimental results on the EEG data set(SEED) show that the model achieves an average classification accuracy of 90.24%, which is higher than the excellent deep learning model compared with the experiment. The built-in self-attention simple recurrent unit has stronger feature capture ability, which proves the effectiveness of the model.
Research on Location Routing Problem of Multi-type Precooling Facilities Based on Bi-level Programming Model
Authors: Wang Xuping , Zhang Chiyi , Yang Huimin , Li Ya
Published: February 2023

Compared with developed countries, the decay rate of fresh agricultural products in China is still high, which makes precooling problem in the first mile widely concerned by the government and enterprises. At the same time, new precooling facilities such as modular fixed precooling warehouses and mobile precooling vehicles have become effective tools to solve the precooling problem. Combined with the national conditions of scattered agricultural production and small farmers occupying the main body in China, it is necessary to unify the layout of fixed precooling facilities and mobile precooling facilities. In this paper, a bi-level programming based multi-type precooling facilities location-routing model is constructed from the perspective of precooling service providers. An extensive numerical experiment is carried on to show the efficiency of the model and algorithm in different problem sizes.
Investigation on Thermodynamic Stability of 10H and 14H Long Period Stacking Ordered Phase in Mg-Zn-Y Alloy
Authors: Yu Zhiqing , Li Xiaotao , Ma Zhenning , Wang Xun , Li Xing
Published: February 2023

In this paper, enthalpies of formation and electronic structures of long period stacking ordered phases in ternary Mg-Zn-Y alloys are calculated by first-principles. Through calculations of the enthalpies of formation, it was found that enthalpies of formation of 10H phase and 14H phase are negative, and the enthalpy of formation of 10H phase less than that of 14H phase. It shows that both phases can form stable structure. The energy of transformation calculations show that 14H phase is more stable than 10H phase. The analysis of electronic structure shows that the difference between the structural stability of 10H phase and 14H phase is due to the different number of bonding electrons at the lower energy level of Fermi level. More bonding electrons determine that 14H phase has better stability than 10H phase.
Construction Measures for Defect Reinforcement of Concrete Structures in Nanshan Water Purification Plant
Authors: Hu Cong , Yao Siqi , Ouyang Xuanri , Yu Mian , Xu Junpeng , Xie Shaoqi , Li Shumin
Published: February 2023

The concrete material due to conveniently, price is low wait for a reason, is currently the mainstream architecture materials, concrete is subject to corrosion in the process of serving, however, especially in wastewater treatment, water purification and other service of concrete structures under complicated environment, especially the strength degradation, the service life of the structure and the structure of security is extremely unfavorable. In this paper, the structural defects of the primary sedimentation tank of a set of system in Shenzhen Nanshan Water purification Plant are detected, the geological survey results and related construction experience are analyzed, and the anti-seepage reinforcement construction measures are put forward.
An Intelligent Compound Prediction Model Based on WOA and ELM
Authors: Wan Chunlu , Xu Kang , Wang Jie
Published: February 2023

In order to reduce the prediction error of Air Quality Index (AQI) by Extreme Learning Machine (ELM), an Intelligent Composite Prediction Model (ICPM) is proposed. ICPM uses an Improved Whale Optimization Algorithm (IWOA) to find the ELM parameters. IWOA introduces logarithmically varying nonlinear control factors and cosine varying adaptive weighting factors to balance local exploitation with global search capabilities. Prediction of AQI combined with daily historical data of air quality in Henan Province (2019), it is proved that ICPM has better prediction performance and generalization performance than ELM and other models.
Research on holographic display and technology application of art museum based on immersive design
Authors: Yu Deng , Yao Wang
Published: February 2023

With the rapid development of science and technology, artificial intelligence digital technology has gradually entered human life and combined with art. It has become a major trend of future exhibitions to create immersive exhibitions through the intervention of digital technology and artificial intelligence in art museums. The immersive art exhibition is to change the sensory conditions of the audience through the intervention of digital media, from the original visual experience to the fusion of various senses such as “sight, hearing and touch”, so that the audience can enter a new world of works experience. the immersive exhibition composed of the combination of technology and art has become an important exhibition method to attract audiences. The creation of immersive exhibitions relies on the application of technologies such as Front-projected Holographic Display and Augmented Reality. At present, immersive exhibitions around the world are emerging in an endless stream. Under the general trend of information exhibition, how to combine works of art with digital media and endow them with connotation, and how digital technology is involved in the exhibition to form the final immersive effect, are issues worth considering at present.
A Method for the Fusion of Results from RFID and VLPR
Authors: Wang Jia , Zhang Hong , Zou Huayi , Jiang Suiping
Published: February 2023

Radio Frequency Identification (RFID) and Vehicle License Plate Recognition (VLPR) are two main methods for vehicle identification. How to fusion the recognition results of the two methods is a problem that needs to be solved in practice. The existing fusion technology usually uses trigger acquisition for precise matching, or uses one recognition result as a reference to perform matching in another result set, while these technology does not provide a clearly algorithm or has certain limitations, and is not universal. A matching algorithm is presented in this paper, which takes advantage of the similarity metric between the results from RFID and VLPR, thus eliminating the drawbacks of existing match methods. The algorithm calculates the similarity metric of the results from RFID and VLPR by the vehicle license plate character graphics similarity, then find matching results from the two recognition results in a specific time window, finally work out precise matching result or similar matching result. The presented algorithm is simple but effective, with no need to triggering RFID and VLPR simultaneously. The algorithm can output both precise matches and similar matches. Through the verification results in the real road scene, the performance in the license plate similarity calculation and fusion matching process is consistent with expectations, which shows the effectiveness of the algorithm.