bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of optical properties of uniaxial hyperbolic nanospheres via artificial neural network
Authors: Arumona A E , Johnson A 
Published: September 2024

Abstract
In this study, absorption and scattering multilayer perceptron models are developed and validated to predict the optical spectra of uniaxial hyperbolic nanospheres. The finite difference time domain method is used to generate the dataset of the absorption and scattering optical spectra. The models’ optimal performance is achieved for 5 hidden layers and 80 neurons for the absorption and the same hidden layers with 120 neurons for the scattering. The predictions by the model on the test dataset give a low average mean squared error of 0.000145 for the absorption and 0.00071 for the scattering. We also performed a robustness test by using parameters outside the initial parameters used for the training and the predictions are in good agreement with the actual datasets for both absorption and scattering. This research shows the application of artificial neural networks to predict the optical properties of hyperbolic materials and lays the groundwork for developing more complicated neural network models to predict complex phenomena in hyperbolic metamaterials, which are faster and computationally less expensive than using conventional simulation methods. Hyperbolic metamaterials offer unique optical properties that can be used to design new optical devices.
Wave patterns of the coupled nonlinear Schrödinger equations in photonic crystal fibers with four-wave mixing
Authors: Houwe Alphonse , Abbagari Souleymanou , Akinyemi Lanre , Metwally Ahmed Sayed M , Doka Serge Y 
Published: September 2024

Abstract
In this paper, we examine the behavior of modulation instability within photonic crystals. The model employed is the coherent coupled nonlinear Schrödinger equation, incorporating weak birefringence and four-wave mixing, which arises at the edge of the optical mode. The linear analysis is used to derive the modulation instability spectrum.
Throughout the modulation instability spectrum, we identify both stable and unstable modes, thereby confirming the breakdown of the plane wave. For certain four-wave mixing parameters, the amplitude of the modulation instability spectrum and its bandwidths expand, creating an opening for localized structures to emerge. Another aspect of this study has been demonstrated in normal and anomalous dispersion regimes where an increasing initial amplitude of the plane wave is fulfilled. Specifically, numerical simulations highlight the occurrence of Benjamin-Feir instability, where wave patterns emerge under the influence of four-wave mixing. Additionally, solitonic waves are generated, demonstrating the presence of Akhmediev breathers and other modulated structures, confirming that photonic crystals with four-wave mixing are conducive to these formations. The findings from this study could inform future research in the development of nonlinear photonic waveguides.
ALP anarchy
Authors: Chadha-Day Francesca , Maxwell James , Turner Jessica 
Published: September 2024

Abstract
String theory models generically predict the existence of multiple axion-like particle (ALP) fields, yet the majority of both theoretical and experimental works have assumed only one ALP. In this paper, we discuss the phenomenology of systems with multiple ALPs that can undergo oscillations akin to neutrino oscillations. Motivated by this effect, we extend the `anarchy' framework, which has been used to predict neutrino oscillation parameters, to generate the parameters of many ALP systems. We explore the phenomenology of these ALP anarchy models in some of the leading ALP search strategies, including the CERN Axion Solar Telescope, magnetic white dwarfs and the gamma-ray spectra of distant blazars. We include both the ALP-photon and the ALP-electron coupling. We find that ALP anarchy models predict drastically different results than single ALP models.
Jet tagging with more-interaction particle transformer*
Authors: Wu 吴 Yifan 佚凡 , Wang 王 Kun 坤 , Li 李 Congqiao 聪乔 , Qu 曲 Huilin 慧麟 , Zhu 朱 Jingya 经亚 
Published: September 2024

Abstract
In this paper, we introduce the More-Interaction Particle Transformer (MIParT), a novel deep-learning neural network designed for jet tagging. This framework incorporates our own design, the More-Interaction Attention (MIA) mechanism, which increases the dimensionality of particle interaction embeddings. We tested MIParT using the top tagging and quark-gluon datasets. Our results show that MIParT not only matches the accuracy and AUC of LorentzNet and a series of Lorentz-equivariant methods, but also significantly outperforms the ParT model in background rejection. Specifically, it improves background rejection by approximately 25% with a signal efficiency of 30% on the top tagging dataset and by 3% on the quark-gluon dataset. Additionally, MIParT requires only 30% of the parameters and 53% of the computational complexity needed by ParT, proving that high performance can be achieved with reduced model complexity. For very large datasets, we double the dimension of particle embeddings, referring to this variant as MIParT-Large (MIParT-L). We found that MIParT-L can further capitalize on the knowledge from large datasets. From a model pre-trained on the 100M JetClass dataset, the background rejection performance of fine-tuned MIParT-L improves by 39% on the top tagging dataset and by 6% on the quark-gluon dataset, surpassing that of fine-tuned ParT. Specifically, the background rejection of fine-tuned MIParT-L improves by an additional 2% compared to that of fine-tuned ParT. These results suggest that MIParT has the potential to increase the efficiency of benchmarks for jet tagging and event identification in particle physics.
Filter banks guided correlational convolutional neural network for SSVEPs based BCI classification
Authors: Wen Xin , Jia Shuting , Han Dan , Dong Yanqing , Gao Chengxin , Cao Ruochen , Hao Yanrong , Guo Yuxiang , Cao Rui 
Published: September 2024

Abstract

Objective. In the field of steady-state visual evoked potential brain computer interfaces (SSVEP-BCIs) research, convolutional neural networks (CNNs) have gradually been proved to be an effective method. Whereas, majority works apply the frequency domain characteristics in long time window to train the network, thus lead to insufficient performance of those networks in short time window. Furthermore, only the frequency domain information for classification lacks of other task-related information. Approach. To address these issues, we propose a time-frequency domain generalized filter-bank convolutional neural network (FBCNN-G) to improve the SSVEP-BCIs classification performance. The network integrates multiple frequency information of electroencephalogram (EEG) with template and predefined prior of sine-cosine signals to perform feature extraction, which contains correlation analyses in both template and signal aspects. Then the classification is performed at the end of the network. In addition, the method proposes the use of filter banks divided into specific frequency bands as pre-filters in the network to fully consider the fundamental and harmonic frequency characteristics of the signal. Main results. The proposed FBCNN-G model is compared with other methods on the public dataset Benchmark. The results manifest that this model has higher accuracy of character recognition accuracy and information transfer rates in several time windows. Particularly, in the 0.2 s time window, the mean accuracy of the proposed method reaches

62.02
%
±
5.12
%

, indicating its superior performance. Significance. The proposed FBCNN-G model is critical for the exploitation of SSVEP-BCIs character recognition models.
A multi-network model of Parkinson’s disease tremor: exploring the finger-dimmer-switch theory and role of dopamine in thalamic self-inhibition
Authors: Sadeghi Fatemeh , Popova Mariia , Páscoa Dos Santos Francisco , Zittel Simone , Hilgetag Claus C 
Published: September 2024

Abstract

Background. Tremor is a cardinal symptom of Parkinson’s disease (PD) that manifests itself through complex oscillatory activity across multiple neuronal populations. According to the finger-dimmer-switch (FDS) theory, tremor is triggered by transient pathological activity in the basal ganglia-thalamo-cortical (BTC) network (the finger) and transitions into an oscillatory form within the inner circuitry of the thalamus (the switch). The cerebello-thalamo-cortical (CTC) network (the dimmer) is then involved in sustaining and amplifying tremor amplitude. In this study, we aimed to investigate the generation and progression dynamics of PD tremor oscillations by developing a comprehensive and interacting FDS model that transitions sequentially from healthy to PD to tremor and then to tremor-off state. Methods. We constructed a computational model consisting of 700 neurons in 11 regions of BTC, CTC, and thalamic networks. Transition from healthy to PD state was simulated through modulating dopaminergic synaptic connections; and further from PD to tremor and tremor-off by modulating projections between the thalamic reticular nucleus (TRN), anterior ventrolateral nucleus (VLa), and posterior ventrolateral nucleus (VLp). Results. Sustained oscillations in the frequency range of PD tremor emerged in thalamic VLp (5 Hz) and cerebellar dentate nucleus (3 Hz). Increasing self-inhibition in the thalamus through dopaminergic modulation significantly decreased tremor amplitude. Conclusion/Significance. Our results confirm the mechanistic power of the FDS theory in describing the PD tremor phenomenon and emphasize the role of dopaminergic modulation on thalamic self-inhibition. These insights pave the way for novel therapeutic strategies aimed at reducing the tremor by strengthening thalamic self-inhibition, particularly in dopamine-resistant patients.
A comprehensive survey of evolutionary algorithms and metaheuristics in brain EEG-based applications
Authors: Arif Muhammad , ur Rehman Faizan , Sekanina Lukas , Malik Aamir Saeed 
Published: September 2024

Abstract
Electroencephalography (EEG) has emerged as a primary non-invasive and mobile modality for understanding the complex workings of the human brain, providing invaluable insights into cognitive processes, neurological disorders, and brain–computer interfaces. Nevertheless, the volume of EEG data, the presence of artifacts, the selection of optimal channels, and the need for feature extraction from EEG data present considerable challenges in achieving meaningful and distinguishing outcomes for machine learning algorithms utilized to process EEG data. Consequently, the demand for sophisticated optimization techniques has become imperative to overcome these hurdles effectively. Evolutionary algorithms (EAs) and other nature-inspired metaheuristics have been applied as powerful design and optimization tools in recent years, showcasing their significance in addressing various design and optimization problems relevant to brain EEG-based applications. This paper presents a comprehensive survey highlighting the importance of EAs and other metaheuristics in EEG-based applications. The survey is organized according to the main areas where EAs have been applied, namely artifact mitigation, channel selection, feature extraction, feature selection, and signal classification. Finally, the current challenges and future aspects of EAs in the context of EEG-based applications are discussed.
Evaluation of radiation detection sensors and quality assurance relative dosimeters fabricated using methylammonium lead iodide for brachytherapy
Authors: Yang S.W. , Shin Y.H. , Park S.K. 
Published: September 2024

Abstract
Methylammonium lead iodide (MAPbI3) perovskite is a
well-known organic-inorganic lead halide perovskite that has gained
much attention because of its advantages such as a high absorption
coefficient for radiation absorption, high carrier mobility, high
defect tolerance, and outstanding solution processability. In this
study, we aimed to fabricate radiation detection sensors and
relative dosimeters using MAPbI3 and evaluate their
applicability as quality assurance (QA) equipment for
brachytherapy. MAPbI3 film dosimeters of various thicknesses
were fabricated, and their responses to Iridium-192 source were
evaluated to determine the suitable thickness for radiation
detection sensors. Additionally, the ability to use the fabricated
QA relative dosimeter for brachytherapy was determined based on the
results of reproducibility and linearity evaluations. The evaluation
of the reproducibility of the fabricated MAPbI3 radiation
detection sensors with thicknesses of 150 and 225 μm showed
standard deviations of 0.51% and 1.14%, respectively, which met
the evaluation criterion of ≤ 1.5%. The evaluation of
linearity indicated R2 values of 0.9999 and 0.9998,
respectively, which met the criterion of ≥ 0.9990. The results
clearly demonstrated that the MAPbI3 dosimeter fabricated
during this study may be used as a QA relative dosimeter for
brachytherapy.
Inter-product biases in extreme precipitation duration and frequency across China
Authors: Lu Jiayi , Wang Kaicun , Wu Guocan , Ye Aizhong , Mao Yuna 
Published: September 2024

Abstract
Accurately delineating the duration and frequency characteristics of extreme precipitation is vital for assessing climate change risks. This study reassesses the spatiotemporal variations in the frequency and persistence indices of extreme precipitation from 2001 to 2019 across thirteen widely used precipitation datasets. We quantify the inter-product biases using common accuracy indices based on reference data, derived from a national observation network of over 2400 stations. Regarding the duration of extreme precipitation, represented by consecutive dry days (CDD) and consecutive wet days (CWD), gauge-based datasets generally demonstrate better accuracy. Satellite retrieval datasets tend to overestimate CDD (4.58%) and CWD (60.50%) at continental scale. Meanwhile, reanalysis and fusion datasets tend to underestimate CDD (−30.27% and −15.39%, respectively) and overestimate CWD (148.44% and 93.41%, respectively). In terms of frequency indices, represented by the number of heavy precipitation days (R10MM) and the number of very heavy precipitation days (R20MM), gauge-based, satellite retrieval, and fusion datasets show weak biases in R10MM (all below 3.5%), while reanalysis datasets indicate substantial overestimation (33.62%). In the case of R20MM, there is an improvement in the performance of reanalysis datasets, while the performance of other datasets declines. However, almost all datasets fail to consistently capture variations in the Tibetan Plateau and Xinjiang regions, where gauge stations are limited and terrain is complex. Furthermore, multiple datasets present significant discrepancies in temporal trends from 2001 to 2019. Remote sensing datasets tend to overestimate CDD, while reanalysis datasets generally show persistent underestimation of CDD and persistent overestimation of other indices. This research contributes to guiding the application and improvement of global precipitation datasets in extreme precipitation studies.
Connection formulae in the collision limit I: case studies in Lifshitz geometry
Authors: Zhao Hao , Zhu Rui-Dong 
Published: September 2024

Abstract
The connection formulae provide a systematic way to compute physical quantities, such as the quasinormal modes, Green functions, in blackhole perturbation theories. In this work, we test whether it is possible to consistently take the collision limit, which brings two or more regular singularities into an irregular one, of the connection formulae, and we provide some supportive evidence for it.
General properties of the response function in a class of solvable non-equilibrium models
Authors: Corberi F , Smaldone L 
Published: September 2024

Abstract
We study the non-equilibrium response function

R

i
j

(
t
,

t

′

)

, namely the variation of the local magnetization

⟨

S
i

(
t
)
⟩

on site i at time t as an effect of a perturbation applied at the earlier time t′ on site j, in a class of solvable spin models characterized by the vanishing of the so-called asymmetry. This class encompasses both systems brought out of equilibrium by the variation of a thermodynamic control parameter, as after a temperature quench, or intrinsically out of equilibrium models with violation of detailed balance. The one-dimensional Ising model and the voter model (on an arbitrary graph) are prototypical examples of these two situations which are used here as guiding examples. Defining the fluctuation-dissipation ratio

X

i
j

(
t
,

t

′

)
=
β

R

i
j

/

(
∂

G

i
j

/

∂

t

′

)

, where

G

i
j

(
t
,

t

′

)
=
⟨

S
i

(
t
)

S
j

(

t

′

)
⟩

is the spin–spin correlation function and β is a parameter regulating the strength of the perturbation (corresponding to the inverse temperature when detailed balance holds), we show that, in the quite general case of a kinetics obeying dynamical scaling, on equal sites this quantity has a universal form

X

i
i

(
t
,

t

′

)
=
(
t
+

t

′

)

/

(
2
t
)

, whereas

lim

t
→
∞

X

i
j

(
t
,

t

′

)
=
1

/

2

for any ij couple. The specific case of voter models with long-range interactions is thoroughly discussed.
Phycoremediation of tofu wastewater using a mixed culture of Chlorella vulgaris and Nannochloropsis oculate with salicylic acid elicitor
Authors: Taufikurahman T , Irene J , Melani L 
Published: September 2024

Abstract
Wastewater from tofu industry contains suspended and organic matter which, if discharged directly into the waters, can degrade water quality. One method of wastewater treatment that can be done is phycoremediation, which utilizes the activity of microalgae which can assimilate N and P in tofu wastewater as a source of nutrients for growth. The addition of salicylic acid as elicitor can increase microalgal biomass, although administration of salicylic acid at high concentrations potentially could inhibit microalgae growth. Therefore, it is necessary to know the right concentration of salicylic acid which could increase microalgae biomass. In this study, cultivation of mixed microalgae Chlorella vulgaris, and Nannochloropsis oculata, with a ratio of 1:1 (v/v) was carried out for 7 days at the Open Raceway Pond (ORP) with a working volume of 55 L. The growth medium used was tofu wastewater with a concentration of 16% (v/v) of the working volume. On the fifth day, variations were made with the addition of salicylic acid 0 μM (control), 20 μM, and 200 μM. The use of mixed cultures was expected to increase resistance to various abiotic and biotic conditions, as well as optimize resource utilization. Parameters measured included specific growth rate, pH, initial and final levels of nitrate, orthophosphate, BOD, and COD. Mixed culture of C. vulgaris and N. oculata with the addition of 20 μM salicylic acid produced the best growth, with a specific growth rate of 0.66/day. The addition of 20 μM salicylic acid also resulted in the highest reduction in wastewater parameters, with the percentage of reductions in nitrate concentration (57.67%), orthophosphate (44.09%), BOD (62.48%), and COD (56.09%). The result showed that phycoremediation of tofu wastewater can be best carried out by utilizing a mixed culture of C. vulgaris and N. oculata with the addition of 20 μM salicylic acid.
Confined mass transport in two-dimensional capillary
Authors: Li Baowen , Wang Jiajun , Zhu Junliang , Tan Wang , Ji Jiahui , Yin Jun , Li Xuemei , Guo Wanlin 
Published: September 2024

Abstract
Over the past decade, nanofluidics has undergone significant expansion, propelled by advances in crafting artificial channels at nanometric and sub-nanometric scales with diverse geometries. Central to this domain, two-dimensional capillaries have risen as a pivotal research platform, marked by their angstrom-level precision, unparalleled wall surface smoothness, and clearly defined surface charge states. Their advent has profoundly deepened our understanding of mass transport dynamics, spanning gases, water molecules, and ions, shedding light on the complex interactions among various influencing factors and revealing a range of previously undiscovered physical phenomena. This review delves into the development of 2D capillaries, the principal fluid transport phenomena observed within, and the critical elements that affect these processes. We also touch on a fascinating discovery-the quantum liquid friction seen in water moving over carbon surfaces. In anticipation of future explorations in nanofluidics, we envision a trajectory aimed at emulating the efficiency levels of biological ion channels, setting the stage for a new era of scientific inquiry and technological innovation.
Harnessing the potential of electrospun TiO2 nanofibers and nanoparticles enriched with natural dyes: a path towards affordable aolutions for low-cost electronic devices
Authors: Mhetre Harshada , Chendake Yogesh , Nayyar Manan , Kumar Piyush , Sorte Sarvesh , Naik Nithesh , S A Salmataj , Hiremath Pavan 
Published: September 2024

Abstract
This study evaluates the morphological effects of TiO2 nanoparticles, nanofibers, and a bilayer configuration on electronic devices, such as Dye-Sensitized Solar Cells (DSSCs) and UV sensors. Cost-efficient natural dyes—curcumin, coffee beans, and banana peel—were used as sensitizers for nanomaterial films. TiO2 nanoparticles were synthesized using the sol–gel technique, while nanofibers were produced via electrospinning. Characterization techniques, including Scanning Electron Microscopy (SEM), Energy Dispersive x-ray Spectroscopy (EDS), and x-ray Diffraction (XRD), confirmed the formation and dimensions of the TiO2 nanostructures. UV–visible spectroscopy was used to determine the optical properties of the samples. TiO2 nanofibers and nanoparticles exhibited high surface-area-to-volume ratios, with nanofibers having a diameter of 20 nm and particles measuring 50 nm. A binder-free, low-temperature paste was prepared using TiO2 nanoparticles and nanofibers to develop thin films. The turmeric dye showed peak absorption at 470 nm with a band gap energy of 2.06 eV when loaded on a TiO2 bilayer film. This study aims to develop electronic devices that reduce costs and enhance performance by using low-cost, efficient, and economically viable dyes. TiO2 nanofiber and nanoparticle films show promise for cost-effective and high-performance electronic devices.