bluuebunny/crossref_metadata_2025_split huggingface dataset:
Absorption-transmission-diffusion-type radar cross section reduction metasurfaces based on frequency selective absorber and polarization conversion chessboard structure
Authors: Li Jianxiao , Tian Jiawei , Qiu Riquan , Yao Xin , Liu Qian , Chen Haiyan , Han Tiancheng , Yin Liangjun , Liang Difei , Xie Jianliang 
Published: February 2024

Abstract
Absorption-transmission-diffusion-type radar cross section (RCS) reduction metasurfaces based on the frequency selective absorber and the polarization conversion chessboard structure are proposed in this paper. The RCS reduction metasurfaces consist of three parts, i.e. the top layer realized by a cross-shaped meander line loaded with lumped resistors for low-frequency absorption and high-frequency transmission, the mid-layer constructed by angular ring vias frequency selective surface (FSS) sandwiched with two square patches FSS for broadband transmission in mid-frequency zone, and the bottom layer employed chessboard metasurfaces composed of dual-arrow type polarization conversion units for scattering cancellation in high frequency zone. The physical mechanisms of the RCS reduction metasurfaces with multifunctional electromagnetic controlling including wave absorption, bandpass transmission, polarization reversal, and phase cancellation are also discussed and analyzed. Simulation and experimental results demonstrate a reflection of lower than −10 dB for normal incident linearly polarized waves over a frequency range of 2.5 GHz–18 GHz, with a relative bandwidth of 151.22%; transmission passband properties with insertion loss better to −3 dB in the frequency range of 5.62 GHz–11.0 GHz, with a relative bandwidth of 64.74%. Due to the resonance-coupling-resonance principle, the broadband transmission with low insertion loss was obtained. The RCS properties of the proposed multi-functional metasurfaces were further analyzed by numerical simulation and experimental measurement, and the results showed that RCS reduction of over 10 dB was achieved for normal incident different polarized waves at the frequency range of 2.5 GHz to 17 GHz. Even in the case of normal incidence, there was a deviation between the reflection characteristics and RCS reduction. The RCS reduction metasurfaces are expected to be employed in applications where multi mechanism collaborative control of electromagnetic waves is of extreme importance.
A decoupling calculation method of separating temperature component from the nano-scale metal film thickness measurement output by the eddy current sensor
Authors: Wang Jinlong , Li Hongkai , Zhang Tong , Li Zihan 
Published: February 2024

Abstract
The eddy current method has been widely applied in the field of nano-scale metal film thickness detection. The output signal of an eddy current sensor is generally tiny in practice, and it is easily affected by the ambient temperature variation, which results in a decrease in the measurement accuracy. How to separate the temperature effect on film thickness measurement for achieving a high precision is a major problem. Therefore, a coupling model of an eddy current sensor with an electromagnetic field and a temperature field is established in this study, and the influence of the film thickness and temperature on coil impedance is calculated. It is found that the inductance and resistance of the coil vary monotonically as thickness and temperature with a measure of linearity in a certain range, as well as the real part and imaginary part of the output voltage by using an AC bridge. Then a film thickness-temperature decoupling calculation method is proposed, and a group of linear calibration intervals are further divided considering the linearity and measurement accuracy. According to the calculation results, it is confirmed that the method can decouple the two, and accomplish a higher accuracy. In addition, the method is verified by a series of experiments, and the variation trend of real and imaginary parts of output voltage with thickness and temperature is consistent with simulation results. At the same time, it is feasible to realize a synchronous detection of metal film thickness and ambient temperature in a certain range.
Moment tensor potential for static and dynamic investigations of screw dislocations in bcc Nb
Authors: Zotov Nikolay , Gubaev Konstantin , Wörner Julian , Grabowski Blazej 
Published: February 2024

Abstract
A new machine-learning interatomic potential, specifically a moment tensor potential (MTP), is developed for the study of screw-dislocation properties in body-centered-cubic (bcc) Nb in the thermally- and stress-assisted temperature regime. Importantly, configurations with straight screw dislocations and with kink pairs are included in the training set. The resulting MTP reproduces with near density-functional theory (DFT) accuracy a broad range of physical properties of bcc Nb, in particular, the Peierls barrier and the compact screw-dislocation core structure. Moreover, it accurately reproduces the energy of the easy core and the twinning-anti-twinning asymmetry of the critical resolved shear stress (CRSS). Thereby, the developed MTP enables large-scale molecular dynamics simulations with near DFT accuracy of properties such as for example the Peierls stress, the critical waiting time for the onset of screw dislocation movement, atomic trajectories of screw dislocation migration, as well as the temperature dependence of the CRSS. A critical assessment of previous results obtained with classical embedded atom method potentials thus becomes possible.
On uniqueness properties of solutions of the generalized fourth-order Schrödinger equations
Authors: Lee Zachary , Yu Xueying 
Published: February 2024

Abstract
In this paper, we study uniqueness properties of solutions to the generalized fourth-order Schrödinger equations in any dimension d of the following forms,

i

∂
t

u
+

∑

j
=
1

d

∂

x
j

4

u
=
V

t
,
x

u
,
and
i

∂
t

u
+

∑

j
=
1

d

∂

x
j

4

u
+
F

u
,

u
‾

=
0.

We show that a linear solution u with fast enough decay in certain Sobolev spaces at two different times has to be trivial. Consequently, if the difference between two nonlinear solutions u
1 and u
2 decays sufficiently fast at two different times, it implies that

u
1

≡

u
2

.
Embedding machine learning based toxicity models within radiotherapy treatment plan optimization
Authors: Maragno Donato , Buti Gregory , Birbil Ş. İlker , Liao Zhongxing , Bortfeld Thomas , den Hertog Dick , Ajdari Ali 
Published: February 2024

Abstract

Objective. This study addresses radiation-induced toxicity (RIT) challenges in radiotherapy (RT) by developing a personalized treatment planning framework. It leverages patient-specific data and dosimetric information to create an optimization model that limits adverse side effects using constraints learned from historical data. Approach. The study uses the optimization with constraint learning (OCL) framework, incorporating patient-specific factors into the optimization process. It consists of three steps: optimizing the baseline treatment plan using population-wide dosimetric constraints; training a machine learning (ML) model to estimate the patient’s RIT for the baseline plan; and adapting the treatment plan to minimize RIT using ML-learned patient-specific constraints. Various predictive models, including classification trees, ensembles of trees, and neural networks, are applied to predict the probability of grade 2+ radiation pneumonitis (RP2+) for non-small cell lung (NSCLC) cancer patients three months post-RT. The methodology is assessed with four high RP2+ risk NSCLC patients, with the goal of optimizing the dose distribution to constrain the RP2+ outcome below a pre-specified threshold. Conventional and OCL-enhanced plans are compared based on dosimetric parameters and predicted RP2+ risk. Sensitivity analysis on risk thresholds and data uncertainty is performed using a toy NSCLC case. Main results. Experiments show the methodology’s capacity to directly incorporate all predictive models into RT treatment planning. In the four patients studied, mean lung dose and V20 were reduced by an average of 1.78 Gy and 3.66%, resulting in an average RP2+ risk reduction from 95% to 42%. Notably, this reduction maintains tumor coverage, although in two cases, sparing the lung slightly increased spinal cord max-dose (0.23 and 0.79 Gy). Significance. By integrating patient-specific information into learned constraints, the study significantly reduces adverse side effects like RP2+ without compromising target coverage. This unified framework bridges the gap between predicting toxicities and optimizing treatment plans in personalized RT decision-making.
A neoclassical validation of balanced and unbalanced rotations on EAST H-mode discharges
Authors: Bae Cheonho , Jin Yifei , Lyu Bo , Hao Baolong , Li Yingying , Zhang Xinjun , Liu Haiqing , Zhang Hongming , Wang Fudi , Fu Jia , Fu Jing , Huang Juan , Zeng Long , Zang Qing , Li Yichao , He Liang , Lu Dian 
Published: February 2024

Abstract
Predicting residual stress (RS) contribution to intrinsic rotation is one of the major challenges in the study of momentum transport in tokamaks. One efficient experimental means of quantifying RS torque magnitude is to generate radially-flat and near-zero rotation profiles, termed ‘balanced rotations’ in this work, using counter-Ip
Neutral Beam Injections (NBIs) to effectively cancel the torques from co-Ip
NBIs. One remaining question, however, is on whether or not the attained velocity profile is well zeroed and flat enough so that the predicted RS torques based on perfect balance assumption can be used for further studies such as fitting of diffusive and convective coefficients to match with experiments. This article presents a neoclassical means of validating the attained balanced and unbalanced rotations at EAST to consequently validate the predicted RS torque profiles, using TRANSP/NUBEAM and a recently-developed neoclassical rotation/transport code TransROTA (Bae et al 2024 Comput. Phys. Commun.
296 108992). Both balanced and unbalanced EAST H-mode discharges are analyzed to find that the suggested neoclassical validation methodology successfully validates attained balanced rotations. It also finds that neoclassical gyroviscous (NGV) torque serves as the balance-breaking mechanism for axisymmetric plasmas and the balance-breakings start from the core and propagate towards the edge. This work also suggests the possibility of using the neoclassical methodology to find locally-balanced rotations at an elevated velocity range near ∼20 km s−1, which implies possible scaling of RS torques up to a certain elevated velocity range.
Numerical simulation of streamer, pressure wave, and vortex induced by nanosecond pulsed surface dielectric barrier discharges
Authors: Zhang Jiao , Tang Weiwei , Wang Yanhui , Wang Dezhen 
Published: February 2024

Abstract
In this study, a two-dimensional fluid model is employed to simulate the streamer, pressure wave, and vortex in surface dielectric barrier discharge driven by nanosecond pulse voltage (ns-SDBD). It comprises a numerical model with two interconnected modules: discharge dynamics and gas flow dynamics. These modules are coupled through the physical variables including ‘EHD force’, ‘thermal source’, ‘velocity field’, ‘gas temperature’, and ‘gas pressure’. Our research primarily focuses on the underlying physical mechanisms of pressure waves and vortices for plasma-based flow control. The generation of pressure waves is attributed to the rapid gas heating by pulsed discharge, whereas the formation and development of the vortex are related to the ionic wind (EHD effect) provided by the plasma. To thoroughly understand and optimize flow control performance, an investigation into the effects of various discharge parameters, such as voltage amplitude and polarity, is conducted. Additionally, several SDBD modules are arranged in series, each featuring a dual three-electrode configuration. Subsequently, the dynamic behaviors of multiple streamers, pressure waves, and vortices, along with their interactions, are explored.
Multiple asymmetric couplings induced unconventional corner mode in topolectrical circuits
Authors: Jiang Hengxuan , Wang Xiumei , Chen Jie , Zhou Xingping 
Published: February 2024

Abstract
We investigate the emergence of unconventional corner mode in a two-dimensional (2D) topolectrical circuits induced by asymmetric couplings. The non-Hermitian skin effect of two kinked one-dimensional (1D) lattices with multiple asymmetric couplings are explored. Then we extend to the 2D model, derive conditions for the non-Hermitian hybrid skin effect and show how the corner modes are formed by non-reciprocal pumping based on 1D topological modes. We provide explicit electrical circuit setups for realizing our observations via realistic LTspice simulation. Moreover, we show the time varying behaviors of voltage distributions to confirm our results. Our study may help to extend the knowledge on building the topological corner modes in the non-Hermitian presence.
Quantum dot laser dynamics and external filtered modes under the influence of double-filtered optical feedback
Authors: Mahdi Alaa S , Al Husseini Hussein B 
Published: February 2024

Abstract
We study the dynamics and solution structure of a semiconductor quantum dot (QD) laser system that gets delayed FOF from two filter loops: the double-filtered optical feedback (DFOF) laser. Two filters are utilized in optical communication applications to regulate and stabilize the laser output, which served as the inspiration for this study. The entire mathematical model of the DFOF laser consists of delay differential equations for the inversion of the QD laser with the two filters. We focus on continuous-wave DFOF QD laser systems because reliable laser source operation is essential for optical communication applications. These basic responses are called external filtered modes (EFMs), and they significantly affect the stability and structure of the EFMs as well as the laser’s performance in comparison to the single FOF QD laser. The parameter C

eff
is a measure of the interference between the two filter fields, and it is identified as a key to the EFM structure. To analyse how the structure and stability of the EFMs depend on all the filter and feedback loop parameters, we make extensive use of the EFM surface in the (Ω(ω
s), C

eff
, Y

s
)-space of frequency s, filter phase difference C

eff
, and population inversion Y

s
of the QD laser. In general, the EFM surface is the natural object that should be taken into account to comprehend the dynamical characteristics of the DFOF laser. In general, our study involved searching for regions in the QD laser output with stable amplitude and frequency. The time delay suppression is dependent on the spectral width Λ of the filter and how far it is from the solitary laser frequency, according to numerical calculations. In addition to studying the dynamic behavior using a bifurcation diagram at the filter working areas, the results showed two stages of operation due to the occurrence of phase entanglement stochastic.
Single photon emission from in situ created nitrogen-vacancies in chemical vapor deposition grown single crystal diamond
Authors: Shukla Vivek K , Poswal H K , Kala A , Achanta V G , Majumder A , Saha K , Rai Padmnabh 
Published: February 2024

Abstract
The work reports the manipulation of nitrogen-vacancy centres in chemical vapor deposition grown single crystal diamonds by incorporation of nitrogen, boron and oxygen during the growth process. Single photon emission measurements were performed on diamond samples by using continuous wave and pulsed laser of wavelength 532 nm. The second order auto-correlation measurement

0.3

<
g

2

(
0
)
<
0.6

on nitrogen incorporated diamond sample shows the anti-bunching behavior of photons emerging from the population of one- and two-color centres. However, the correlation measurement

0.6
<

g

2

(
0
)
<
0.8

on oxygen and boron incorporated diamond samples show the anti-bunching behaviour of photons due to population of two- and three-color centres present in the samples. The result opens the possibilities of in situ development of nitrogen-vacancy centres in diamond during the growth process for next generation quantum computing devices.
Dynamical model of antibiotic responses linking expression of resistance genes to metabolism explains emergence of heterogeneity during drug exposures
Authors: Stevanovic Mirjana , Teuber Carvalho João Pedro , Bittihn Philip , Schultz Daniel 
Published: February 2024

Abstract
Antibiotic responses in bacteria are highly dynamic and heterogeneous, with sudden exposure of bacterial colonies to high drug doses resulting in the coexistence of recovered and arrested cells. The dynamics of the response is determined by regulatory circuits controlling the expression of resistance genes, which are in turn modulated by the drug’s action on cell growth and metabolism. Despite advances in understanding gene regulation at the molecular level, we still lack a framework to describe how feedback mechanisms resulting from the interdependence between expression of resistance and cell metabolism can amplify naturally occurring noise and create heterogeneity at the population level. To understand how this interplay affects cell survival upon exposure, we constructed a mathematical model of the dynamics of antibiotic responses that links metabolism and regulation of gene expression, based on the tetracycline resistance tet operon in E. coli. We use this model to interpret measurements of growth and expression of resistance in microfluidic experiments, both in single cells and in biofilms. We also implemented a stochastic model of the drug response, to show that exposure to high drug levels results in large variations of recovery times and heterogeneity at the population level. We show that stochasticity is important to determine how nutrient quality affects cell survival during exposure to high drug concentrations. A quantitative description of how microbes respond to antibiotics in dynamical environments is crucial to understand population-level behaviors such as biofilms and pathogenesis.
The effect of lookahead on phase transition in migration of three species with cyclic predator–prey relations
Authors: Kayacan O , Middendorf M 
Published: February 2024

Abstract
A three-species predator–prey system with cyclic predator–prey relations (also called the rock–paper–scissors game) on a one-dimensional lattice where all individuals migrate in the same direction is studied. Each individual can look ahead within a certain range and can stop its migration when too many predators occur within its lookahead range. Simulation experiments revealed that the three species can coexist within a wide range of model parameter values, providing insights into the dynamical phase transition between coexisting and single phases.
Graph sampling based deep metric learning for cross-view geo-localization
Authors: Jia Haozhang 
Published: February 2024

Abstract
Cross-view geo-localization has emerged as a novel computer vision task that has garnered increasing attention. This is primarily attributed to its practical significance in the domains of drone navigation and drone-view localization. Moreover, the work is particularly demanding due to its inherent requirement for cross-domain matching. There are generally two ways to train a neural network to match similar satellite and drone-view images: presentation learning with classifiers and identity loss, and metric learning with pairwise matching within mini-batches. The first takes extra computing and memory costs in large-scale learning, so this paper follows a person-reidentification method called QAConv-GS, and implements a graph sampler to mine the hardest data to form mini-batches, and a QAConv module with extra attention layers appended to compute similarity between image pairs. Batch-wise OHEM triplet loss is then used for model training. With these implementations and adaptions combined, this paper significantly improves the state of the art on the challenging University-1652 dataset.
Review: Recent advances for the diffusion model
Authors: Wei Yufeng 
Published: February 2024

Abstract
As the generative model technology becomes more and more popular, more and more people have invested in the research of the current State-of-the-art (SOTA) generative model-diffusion model. This paper reviews all SOTA generation models using the diffusion model for text-to-image generation since the emergence of the diffusion model, including the denoising diffusion probabilistic model (DDPM), DALL·E model, imagen model, stable diffusion model, and diffusion transformer architecture (DiT) model. In the theoretical section, the basic principles behind the diffusion model are reviewed in detail in the way of mathematical calculation, including the training process of the model and the mathematical principles behind the sampling process. Moreover, this paper focuses on the technical characteristics of these models and various improvements made after model iteration, such as model structure optimization, more efficient and accurate training methods, and the application of other optimization techniques widely used in the field of deep learning to diffusion models. In the end, the technical route of the development of the diffusion model is summarized, and some predictions are made.
Asymptotic feature pyramid based YOLOv5s for birds detection
Authors: Cai Jiajie , Huang Han , Song Feiyang 
Published: February 2024

Abstract
The detection of all sorts of birds has become increasingly important in the fields of ecological balance and biological protection. To tackle the problems of low accuracy, high omission rate and low detection confidence levels in the application of artificial intelligence and deep learning in bird detection, this paper proposes a bird detection method that leverages the YOLOv5s model and incorporates the asymptotic feature pyramid network module (AFPN). Diverging from the conventional pyramid network module (FPN), AFPN offers a more efficient solution, characterized by reduced computation time and memory consumption. It also minimizes conflicts that may arise during feature training. To further enhance the model’s performance and efficiency, the project introduces an object detection regression loss function along with several distinct loss functions. These functions are employed to train and analyze a standardized dataset, enabling the identification of an optimal solution. Through rigorous model construction, training, and repeated testing, notable improvements have been achieved in the accuracy rate and other relevant indicators, meeting the necessary application standards. This refined method exhibits great potential for fulfilling diverse bird detection requirements.