bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nanoporous gold with microporous structure prepared by sodium dodecyl sulfate-mediated electrochemical dealloying
Authors: Li Yaning , Li Guangzhong , Wang Jian 
Published: December 2023

Abstract
Nanoporous gold (NPG) is a promising catalytic material for the oxidation of CO and methanol applications. However, NPGs are prone to extensive macroscopic cracking that often decrease mechanic properties of NPGs and depresses their catalytic action. To produce crack-free NPG with an ultra-finer porosity in room temperature, the anionic surfactant sodium dodecyl sulfate (SDS) was added in electrochemical dealloying process. SDS has the effect of reducing the surface diffusion of gold which hinder the initial coarsening of ligaments and prevents interior silver atoms from being exposed and dissolved. As a result, the pore and ligment size are finer, but higher residual silver of NPG samples. NPG with pore size down to 2 nm and the ligament 4.0 nm was successfully fabricated with 13.32 mM SDS in perchloric acid solution. The surface diffusion coefficient of Au atoms was 1.6 × 10−24 m2·s−1, nearly 3 orders of magnitude smaller than that of Au atoms in the absence of SDS (2.8 × 10−21 m2·s−1). Nanoindentation results demonstrated that high residual silver content made NPG samples harder and stiffer，the specific surface areas of NPG with 6.66 mM SDS was 190 m2 g−1 by BET. This work provided very important clues on how to control the crack free ultrafine nanoporous structure of other materials.
Fast prediction of patient-specific organ doses in brain CT scans using support vector regression algorithm
Authors: Shao Wencheng , Lin Xin , Yi Yanling , Huang Ying , Qu Liangyong , Zhuo Weihai , Liu Haikuan 
Published: December 2023

Abstract

Objectives. This study aims to develop a method for predicting patient-specific head organ doses by training a support vector regression (SVR) model based on radiomics features and graphics processing unit (GPU)-calculated reference doses. Methods. In this study, 237 patients who underwent brain CT scans were selected, and their CT data were transferred to an autosegmentation software to segment head regions of interest (ROIs). Subsequently, radiomics features were extracted from the CT data and ROIs, and the benchmark organ doses were computed using fast GPU-accelerated Monte Carlo (MC) simulations. The SVR organ dose prediction model was then trained using the radiomics features and benchmark doses. For the predicted organ doses, the relative root mean squared error (RRMSE), mean absolute percentage error (MAPE), and coefficient of determination (R
2) were evaluated. The robustness of organ dose prediction was verified by changing the patient samples on the training and test sets randomly. Results. For all head organs, the maximal difference between the reference and predicted dose was less than 1 mGy. For the brain, the organ dose was predicted with an absolute error of 1.3%, and the R
2 reached up to 0.88. For the eyes and lens, the organ doses predicted by SVR achieved an RRMSE of less than 13%, the MAPE ranged from 4.5% to 5.5%, and the R
2 values were more than 0.7. Conclusions. Patient-specific head organ doses from CT examinations can be predicted within one second with high accuracy, speed, and robustness by training an SVR using radiomics features.
Esophageal balloon catheter system identification to improve respiratory effort time features and amplitude determination
Authors: Xia Yu Hao Wang , Victor Jr Marcus Henrique , Morais Caio César Araújo , Costa Eduardo Leite Vieira , Amato Marcelo Britto Passos 
Published: December 2023

Abstract

Objective. Understanding a patient’s respiratory effort and mechanics is essential for the provision of individualized care during mechanical ventilation. However, measurement of transpulmonary pressure (the difference between airway and pleural pressures) is not easily performed in practice. While airway pressures are available on most mechanical ventilators, pleural pressures are measured indirectly by an esophageal balloon catheter. In many cases, esophageal pressure readings take other phenomena into account and are not a reliable measure of pleural pressure. Approach. A system identification approach was applied to provide accurate pleural measures from esophageal pressure readings. First, we used a closed pressurized chamber to stimulate an esophageal balloon and model its dynamics. Second, we created a simplified version of an artificial lung and tried the model with different ventilation configurations. For validation, data from 11 patients (five male and six female) were used to estimate respiratory effort profile and patient mechanics. Main results. After correcting the dynamic response of the balloon catheter, the estimates of resistance and compliance and the corresponding respiratory effort waveform were improved when compared with the adjusted quantities in the test bench. The performance of the estimated model was evaluated using the respiratory pause/occlusion maneuver, demonstrating improved agreement between the airway and esophageal pressure waveforms when using the normalized mean squared error metric. Using the corrected muscle pressure waveform, we detected start and peak times 130 ± 50 ms earlier and a peak amplitude 2.04 ± 1.46 cmH2O higher than the corresponding estimates from esophageal catheter readings. Significance. Compensating the acquired measurements with system identification techniques makes the readings more accurate, possibly better portraying the patient’s situation for individualization of ventilation therapy.
A forward-inverse dynamic model for the hydraulic damping(magnetorheological) actuator based on cyclic stress–strain
Authors: Ma Baizhou , Huang Hui , Huang Jiabo , Lin Xiufang , Huang Qiufang , Chen Shumei 
Published: December 2023

Abstract
Magnetorheological dampers (MRDs) are applied to hydraulic systems, which not only improve the underdamped characteristics of valve-controlled cylinder systems, but also help hydraulic actuators to resist high load impact. However, the high power density leads to the complexity of the internal flow channel of the damper, which seriously affects the output accuracy of the damping force. It can lead to the fact that existing dynamics models cannot accurately describe the hysteresis characteristics of the MRD. Therefore, this study proposes a simple and general dynamic model of MRD, which solves the problem that existing models are complex and difficult to invert. Firstly, the hydraulic damping actuator with the series MRD is taken as the research object. Based on the stress–strain hysteresis characteristics under the cyclic constitutive model, the hyperbolic tangent curve is reorganized and normalized. It can accurately describe the yield formation and yield dissipation stages of the hysteresis loop. Secondly, the relationship between the parameters of the dynamic model and the current is obtained according to the mechanical experimental data. Then the inverse model of the MRD is established by using the method of section-backstepping. Finally, in the static experiment, the mean absolute percentage error (MAPE) of the force at different velocity is less than 7.5%; in the dynamic experimental test, the MAPE of the force is 9.7%. The inverse dynamics model is verified to have high tracking performance under both static and dynamic forces. And it also indirectly confirms the effectiveness of the forward model.
On the testability of the Károlyházy model
Authors: Figurato Laria , Bassi Angelo , Donadi Sandro 
Published: December 2023

Abstract
Károlyházy’s original proposal, suggesting that space-time fluctuations could be a source of decoherence in space, faced a significant challenge due to an unexpectedly high emission of radiation (13 orders of magnitude more than what was observed in the latest experiment). To address this issue, we reevaluated Károlyházy’s assumption that the stochastic metric fluctuation must adhere to a wave equation. By considering more general correlation functions of space-time fluctuations, we resolve the problem and consequently revive the aforementioned proposal.
The rise of rationality in blockchain dynamics
Authors: Di Antonio Gabriele , Vinci Gianni Valerio , Pietronero Luciano , Javarone Marco Alberto 
Published: December 2023

Abstract
Taking informed decisions, namely acting rationally, is an individual attitude of paramount relevance in nature and human societies. In this work, we study how rationality spreads in a community. To this end, through an agent-based model, we analyse the dynamics of a population whose individuals, endowed with a rational attitude controlled by a numerical parameter, play a simple game. The latter consists of multiple strategies, each associated with a given reward. The proposed model is then used as a benchmark for studying the behaviour of Bitcoin users, inferred by analysing transactions recorded in the Blockchain. Remarkably, a population undergoing a sharp transition from irrational to rational attitudes shows a behavioural pattern similar to that of Bitcoin users, whose rationality showed up as soon as their cryptocurrency became worth just a few cents (USD). To conclude, a behavioural analysis that relies on an entropy measure combined with a simple agent-based model allows us to detect the rise of rationality across a community. Although further investigations are essential to corroborate our results, we deem the proposed approach could also get used for studying other social phenomena and behaviours.
Dislocation reduction in an MOVPE-grown CdTe/Si epilayer by ex-situ annealing and its effect on the performances of gamma ray detectors fabricated
Authors: Chaudhari B S , Niraula M , Okumura R , Maruyama T 
Published: December 2023

Abstract
We studied ex situ thermal cycle annealing on metalorganic vapor phase epitaxy (MOVPE) grown CdTe on (211) Si substrates for dislocation density reduction and its effect on the performance of gamma-ray detector fabricated. The ex situ annealing was performed by varying temperatures from 600 °C to 1000 °C for different anneal durations and cycles varied from 1 to 7 in a hydrogen environment. Gamma-ray detector was fabricated in a p-CdTe/n-CdTe/n+-Si heterojunction diode structure. Dislocation densities were evaluated by the etch pit density (EPD) technique. Dislocation densities were decreased when the annealing temperature was increased above 800 °C. This is due to the annealing enhancing dislocations’ glide motion, which annihilate and reduce their density. The device property could be improved by subjecting the annealing during the early stage of the growth. Devices subjected to annealing showed lower dark current and improved gamma detection property when compared to the devices that were not subjected to annealing during their fabrications.
QTN-VQC: an end-to-end learning framework for quantum neural networks
Authors: Qi Jun , Yang Chao-Han , Chen Pin-Yu 
Published: December 2023

Abstract
This work focuses on investigating an end-to-end learning approach for quantum neural networks (QNN) on noisy intermediate-scale quantum devices. The proposed model combines a quantum tensor network (QTN) with a variational quantum circuit (VQC), resulting in a QTN-VQC architecture. This architecture integrates a QTN with a horizontal or vertical structure related to the implementation of quantum circuits for a tensor-train network. The study provides theoretical insights into the quantum advantages of the end-to-end learning pipeline based on QTN-VQC from two perspectives. The first perspective refers to the theoretical understanding of QTN-VQC with upper bounds on the empirical error, examining its learnability and generalization powers; The second perspective focuses on using the QTN-VQC architecture to alleviate the Barren Plateau problem in the training stage. Our experimental simulation on CPU/GPUs is performed on a handwritten digit classification dataset to corroborate our proposed methods in this work.
First-principles study of bismuthene as a high energy density and excellent rate performance anode material for potassium-ion batteries
Authors: Pan Wenfeng , Ning Suiting , Chen Lili , Chen Zhiquan 
Published: December 2023

Abstract
Potassium-ion batteries (KIBs), with their abundance of resources, lower cost, high ion conductivity, and comparable redox potential, hold potential as an alternative to lithium-ion batteries (LIBs) for large-scale energy storage. Nonetheless, the scarcity of high-performance electrode materials remains a major obstacle in the advancement of KIBs. Here, the viability of bismuthene as an anode material for KIBs was systematically investigated using first-principles calculations. We found that bismuthene exhibits a maximum adsorption capacity of two layers of K atoms, offering a moderate theoretical capacity of 256.5 mAh g−1. Additionally, the adsorption of K atoms on bismuthene leads to a notable enhancement in the electronic conductivity. Moreover, the ultralow average open circuit voltage (0.17 V) and diffusion barrier (0.02 eV) of K on bismuthene monolayer along the zigzag direction, suggesting a high energy density and outstanding rate performance of batteries. Hence, bismuthene demonstrates remarkable potential as a high-performance KIBs anode material, making it a hopeful contender in the field of energy storage.
A theoretical study of ferrocene based on combined configuration interaction singles (CIS) and time-dependent density functional theory (TDDFT) approach
Authors: Trivedi Ravi , Bhattacharyya Pritam 
Published: December 2023

Abstract
The state-of-the art density functional theory (DFT) is used to clearly resolve the two parallel cyclopentadienyl rings of ferrocene, which are either staggered (D5d symmetry) or eclipsed (D5h symmetry), in their ground-state conformation. Present result revealed that the eclipsed conformer with D5h point group represents the true minimum ground state structure of ferrocene. Natural population analysis is used to determine how atomic charge is distributed across different atoms of ferrocene D5h conformer and also the distribution of electrons in the core, valence, and Rydberg sub-shells. It is further investigated in potential energy scan that the rotation of the dihedral angle δ from 0° to 3π/5 will reproduce three times D5h or D5d conformers periodically as the period of 2π/5 due to the pentagonal structure of the CP ring. Further to examine optical spectra in the ultraviolet-visible (UV–vis) range, configuration interaction single (CIS) and time-dependent density functional theory (TDDFT) have conducted which help in locating the significant electronic shifts between different energy levels. Absorption spectra for high spin states were also generated in order to comprehend the characteristics of low-lying spin excitation. According to our estimates, the greatest absorption intensity is restricted to an energy range of 4–6 eV. Knowledge of ferrocene conformers will improve the research on other metallocenes and their derivatives, which have applications in biotechnology, nanotechnology, and solar technology.
Constraining the halo-ISM connection through multi-transition carbon monoxide line-intensity mapping
Authors: Chung Dongwoo T. 
Published: December 2023

Abstract
Line-intensity mapping (LIM) surveys will characterise the cosmological large-scale structure of emissivity in a range of atomic and molecular spectral lines, but existing literature rarely considers whether these surveys can recover excitation properties of the tracer gas species, such as the carbon monoxide (CO) molecule. Combining basic empirical and physical assumptions with the off-the-shelf Radex radiative transfer code or a Gaussian process emulator of Radex outputs, we devise a basic dark matter halo model for CO emission by tying bulk CO properties to halo properties, exposing physical variables governing CO excitation as free parameters. The CO Mapping Array Project (COMAP) is working towards a multi-band survey programme to observe both CO(1–0) and CO(2–1) at z ∼ 7. We show that this programme, as well as a further `Triple Deluxe' extension to higher frequencies covering CO(3–2), is fundamentally capable of successfully recovering the connection between halo mass and CO abundances, and constraining the molecular gas kinetic temperature and density within the star-forming interstellar medium in ways that single-transition CO LIM cannot. Given a fiducial thermal pressure of ∼ 104 K cm-3 for molecular gas in halos of ∼ 1010
M
⊙, simulated multi-band COMAP surveys successfully recover the thermal pressure within 68% interval half-widths of 0.5–0.6 dex. Construction of multi-frequency LIM instrumentation to access multiple CO transitions is crucial in harnessing this capability, as part of a cosmic statistical probe of gas metallicity, dust chemistry, and other physical parameters in star-forming regions of the first galaxies and proto-galaxies out of reionisation.
AstroSer: Leveraging Deep Learning for Efficient Content-based Retrieval in Massive Solar-observation Images
Authors: Wu Shichao , Liu Yingbo , Yang Lei , Liu Xiaoying , Li Xingxu , Xiang Yongyuan , Gong Yunyu 
Published: December 2023

Abstract
Rapid and proficient data retrieval is an essential component of modern astronomical research. In this paper, we address the challenge of retrieving astronomical image content by leveraging state-of-the-art deep learning techniques. We have designed a retrieval model, HybridVR, that integrates the capabilities of the deep learning models ResNet50 and VGG16 and have used it to extract key features of solar activity and solar environmental characteristics from observed images. This model enables efficient image matching and allows for content-based image retrieval (CBIR). Experimental results demonstrate that the model can achieve up to 98% similarity during CBIR while exhibiting adaptability and scalability. Our work has implications for astronomical research, data management, and education, and it can contribute to optimizing the utilization of astronomical image data. It also serves as a useful example of the application of deep learning technology in the field of astronomy.
Unsupervised learning of stationary and switching dynamical system models from Poisson observations
Authors: Song Christian Y , Shanechi Maryam M 
Published: December 2023

Abstract

Objective. Investigating neural population dynamics underlying behavior requires learning accurate models of the recorded spiking activity, which can be modeled with a Poisson observation distribution. Switching dynamical system models can offer both explanatory power and interpretability by piecing together successive regimes of simpler dynamics to capture more complex ones. However, in many cases, reliable regime labels are not available, thus demanding accurate unsupervised learning methods for Poisson observations. Existing learning methods, however, rely on inference of latent states in neural activity using the Laplace approximation, which may not capture the broader properties of densities and may lead to inaccurate learning. Thus, there is a need for new inference methods that can enable accurate model learning. Approach. To achieve accurate model learning, we derive a novel inference method based on deterministic sampling for Poisson observations called the Poisson Cubature Filter (PCF) and embed it in an unsupervised learning framework. This method takes a minimum mean squared error approach to estimation. Terms that are difficult to find analytically for Poisson observations are approximated in a novel way with deterministic sampling based on numerical integration and cubature rules. Main results. PCF enabled accurate unsupervised learning in both stationary and switching dynamical systems and largely outperformed prior Laplace approximation-based learning methods in both simulations and motor cortical spiking data recorded during a reaching task. These improvements were larger for smaller data sizes, showing that PCF-based learning was more data efficient and enabled more reliable regime identification. In experimental data and unsupervised with respect to behavior, PCF-based learning uncovered interpretable behavior-relevant regimes unlike prior learning methods. Significance. The developed unsupervised learning methods for switching dynamical systems can accurately uncover latent regimes and states in population spiking activity, with important applications in both basic neuroscience and neurotechnology.
Defining Ideal Double Eyelids With a Morphometric Analysis in Asians
Authors: Chi Yarong , Li Zhujun , Jin Lin , Yu Nanze , Huang Jiuzuo , Long Xiao 
Published: December 2023

Abstract

Background
Double eyelids are always considered crucial aesthetic symbols. Despite numerous studies conducted on the attractiveness of double eyelids, there remains a dearth of research on quantitative and morphological evaluation of ideal double eyelids.

Objectives
In this we study aimed to investigate the optimal height and morphological characteristics of ideal double eyelids.

Methods
Participants were presented with a total of 9 images, consisting of 1 single eyelid image and 8 double eyelid images, featuring 2 distinct shapes and 4 varied pretarsal shows. Respondents were instructed to assign scores ranging from 1 (least attractive) to 5 (most attractive) for each image. Subsequently, the scores for each image were analyzed based on population demographics, followed by the calculation of aesthetic metrics.

Results
The whole cohort deemed images with a 2-mm fold to be more attractive than 1 mm (P &lt; .001), followed by 3 mm and 0 mm (single eyelid), and finally, 4 mm. Morphologically, significant differences were found between images with the same pretarsal shows of 3 mm (P &lt; .001) and 4 mm (P = .026). Most subgroup analysis results were aligned with those of the cohort, with gender being the most significant factor in distinguishing double eyelid aesthetics. Additionally, aesthetic characteristics of 2-mm folds were found to be comparable to appealing double eyelids in previous studies.

Conclusions
In this study we validated the optimal heights and morphology of double eyelids, thereby addressing the existing gap in aesthetic studies on double eyelids. These findings hold significant implications for surgical planning, effect assessment, and other periocular procedures related to upper blepharoplasty.

Level of Evidence: 4