bluuebunny/crossref_metadata_2025_split huggingface dataset:
Insight of hydrogen evolution reaction in slab SnO2 loaded with transition metal atoms
Authors: Sun Xueqin , Huang Le , Liao Haijun , Xiao Wenbo , Li Jingbo 
Published: May 2024

Abstract
The utilization of hydrogen energy has emerged as a promising solution for clean and sustainable energy sources. The development of cost-effective catalysts with high activity and stability is crucial for efficient hydrogen production. In this work, we investigated the hydrogen evolution reaction (HER) catalytic activity of single transition metal atom (TM = V, Cr, Mn, Fe, Co, Ni) on slab SnO2 by using density functional theory. Our results revealed that the catalytic activity of the slab SnO2 can be significantly enhanced by loading the transition atom. By calculating the Gibbs free energies and exchange current densities in different adsorption configurations of TM-SnO2, single-atom catalyst (SAC) of Mn-loaded SAC exhibits excellent catalytic performance, characterized by a low Gibbs free energy barrier (−0.05 eV). Introducing a TM on the SnO2 surface breaks its local symmetry, while the strong coupling between the metal and H atoms enhances catalytic performance. The synergetic effect of symmetry breaking and metal-H interaction boosts overall catalytic activity. This work not only proposes a novel non-platinum HER catalyst based on SnO2 but also lays a solid foundation for future applications of SnO2-based catalysts.
Improved performance of ternary organic solar cells based on both bulk and pseudo-planar heterojunction active layer
Authors: Zheng Qiao , Zhou Hui , Guo Zhaohuang , Zhuang Jinyong , Deng Hui , Wu Jionghua , Zhang Caixia , Wang Weihuang , Cheng Shuying 
Published: May 2024

Abstract
The high photovoltaic conversion efficiency (PCE) of ternary organic solar cells (OSCs) based on PTB7-Th, PC71BM and IEICO-4F material is obtained, the short-circuit current density (J
SC), fill factor (FF), and open-circuit voltage is 25.90 mA cm−2, 73.20% and 0.71 V, respectively. PCE is as high as 13.53%. It is the highest PCE of ternary OSCs based on PTB7-Th, PC71BM and IEICO-4F materials for all we know. The narrow bandgap material of IEICO-4F is deposited on PTB7-Th:PC71BM bulk heterojunction (BHJ) by layer-by-layer process. We constructed the dual bandgap active layer both BHJ and pseudo-planar heterojunction (P-PHJ), it could be defined as ternary BHJ/P-PHJ of OSCs. This type of OSCs is not only the complementary bandgap material of the active layer, but also increasing the donor/acceptor (D/A) interface. The excitons generation and collection of the device are increased leading a higher J
SC and FF. The semitransparent OSCs (ST-OSCs) is prepared by varying the thickness of Ag electrode and PCE can reach 9.70%, and the average visible light transmittance and light use efficiency of ST-OSCs are improved effectively.
Absolutely continuous invariant measures for random dynamical systems of beta-transformations
Authors: Suzuki Shintaro 
Published: May 2024

Abstract
We consider an independent and identically distributed (i.i.d.) random dynamical system of simple linear transformations on the unit interval

T

β

(
x
)
=
β
x

(mod 1),

x
∈
[
0
,
1
]

, β > 0, which are the so-called beta-transformations. For such a random dynamical system, including the case that it is generated by uncountably many maps, we give an explicit formula for the density function of a unique stationary measure under the assumption that the random dynamics is expanding in mean. As an application, in the case that the random dynamics is generated by finitely many maps and the maps are chosen according to a Bernoulli measure, we show that the density function is analytic as a function of parameter in the Bernoulli measure and give its derivative explicitly. Furthermore, for a non-i.i.d. random dynamical system of beta-transformations, we also give an explicit formula for the random densities of a unique absolutely continuous invariant measure under a certain strong expanding condition or under the assumption that the maps randomly chosen are close to the beta-transformation for a non-simple number in the sense of parameter β.
Numerical investigation of two-dimensional Fokker-Planck equation in inflationary models: importance of slow-roll parameters
Authors: Hong Deog Ki , Jiang Jie , Yeom Dong-han 
Published: May 2024

Abstract
In this study, we generalize the Fokker-Planck equation to two-dimensional cases, including potential functions with periodic boundary conditions and piecewise-defined structures, to analyze the probability distribution in multi-field inflationary models. We employ the spectral method for spatial derivatives and the Crank-Nicolson method for the time evolution to solve the equation numerically for the slow-roll inflation. We find that the distribution in the Fokker-Planck equation was determined by the two-dimensional potential combined slow-roll parameters. And the volume weighting effect makes the distribution in the Fokker-Planck Volume equation is determined by the potential.
Analysis of BOSS galaxy data with weighted skew-spectra
Authors: Chen Shu-Fan , Chakraborty Priyesh , Dvorkin Cora 
Published: May 2024

Abstract
We present the first application of the weighted skew-spectra to analyze non-Gaussian
information in galaxy survey data. Using the tree-level galaxy skew-spectra together with the
one-loop power spectrum multipoles, we analyze the Sloan Digital Sky Survey (SDSS)-III Baryon
Oscillation Spectroscopic Survey (BOSS) galaxy clustering data, and target our search towards the
equilateral bispectrum shape of primordial non-Gaussianity. We use the Effective Field Theory
model for the galaxy power spectrum and bispectrum, and account for systematic effects, such as
the survey geometry. From our likelihood analysis, we find f
NL
equil
= -34+296
-334 at 68% CL, consistent with previous works, while systematic errors
from our treatment of the survey geometry lead to an unreliable estimation of f
NL
ortho.
We further constrain the bias and counterterm parameters, while keeping the cosmology
fixed to Planck 2018 values. As a check, we also validate our analysis pipeline using the
Nseries simulation suite.
Impact of half-wave plate systematics on the measurement of CMB B-mode polarization
Authors: Monelli Marta , Komatsu Eiichiro , Ghigna Tommaso , Matsumura Tomotake , Pisano Giampaolo , Takaku Ryota 
Published: May 2024

Abstract
Polarization of the cosmic microwave background (CMB) can help probe the fundamental physics behind cosmic inflation via the measurement of primordial B modes.
As this requires exquisite control over instrumental systematics, some next-generation CMB experiments plan to use a rotating half-wave plate (HWP) as polarization modulator.
However, the HWP non-idealities, if not properly treated in the analysis, can result in additional systematics.
In this paper, we present a simple, semi-analytical end-to-end model to propagate the HWP non-idealities through the macro-steps that make up any CMB experiment (observation of multi-frequency maps, foreground cleaning, and power spectra estimation) and compute the HWP-induced bias on the estimated tensor-to-scalar ratio, r.
We find that the effective polarization efficiency of the HWP suppresses the polarization signal, leading to an underestimation of r.
Laboratory measurements of the properties of the HWP can be used to calibrate this effect, but we show how gain calibration of the CMB temperature can also be used to partially mitigate it.
On the basis of our findings, we present a set of recommendations for the HWP design that can help maximize the benefits of gain calibration.
From inflation to dark matter halo profiles: the impact of primordial non-Gaussianities on the central density cusp
Authors: Stahl Clément , Mai Nicolas , Famaey Benoit , Dubois Yohan , Ibata Rodrigo 
Published: May 2024

Abstract
It has recently been shown that local primordial non-Gaussianities (PNG) with significant amplitude (|f
NL| ∼ 1000), at small (Mpc) scales, can help in forming simulated galaxies with more disky baryonic kinematics than in the Gaussian case, while generating matter power spectra that can differ by up to 20% from the Gaussian case at non-linear scales. Here, we explore in detail the consequences of such small-scale PNG on the dark matter halo profiles. We show in particular that, for negative f
NL, dark matter halos formed in collisionless simulations are not always well described by the traditional Navarro-Frenk-White (NFW) profiles, as supported by their sparsity distribution. We conclude that NFW profiles are not as clear attractors for the density profiles of dark matter halos in the presence of PNG than in the case of a Gaussian contrast density field. We show how alternatives to the NFW profile can describe halos both in the Gaussian and non-Gaussian cases. From the combination of our sparsity analysis and the quality of the adjustments of the density profiles with a minimal extension to NFW, we conclude that z = 1 halos carry the most interesting information about PNG.
Steel surface defect detection algorithm based on ESI-YOLOv8
Authors: Zhang Xinrong , Wang Yanlong , Fang Huaisong 
Published: May 2024

Abstract
To enhance the precision of detecting defects on steel plate surfaces and diminish the incidences of false detection and leakage, the ESI-YOLOv8 algorithm is introduced. This algorithm introduces a novel EP module and integrates the large separation convolutional attention module and the spatial pyramid pooling module to propose the SPPF-LSKA module. Additionally, the original CIOU loss function is replaced with the INNER-CIOU loss function. The EP module minimizes redundant computations and model parameters to optimize efficiency and simultaneously increases the multi-scale fusion mechanism to expand the sensory field. The SPPF-LSKA module reduces computational complexity, accelerates model operation speed, and improves detection accuracy. Additionally, the INNER-CIOU loss function can improve detection speed and model accuracy by controlling the scale size of the auxiliary border.The results of the experiment indicate that, following the improvements made, the algorithm’s detection accuracy has increased to 78%, which is 3.7% higher than the original YOLOv8. Furthermore, the model parameters were reduced, and the verification was conducted using the CoCo dataset, resulting in an average accuracy of 77.8%. In conclusion, the algorithm has demonstrated its ability to perform steel plate surface defect detection with efficiency and accuracy.
Phase I quality control framework for monitoring organ-at-risk dose
Authors: Sivabhaskar Sruthi , Buatti Jacob S , Yeh Arthur B , Papanikolaou Niko , Roy Arkajyoti 
Published: May 2024

Abstract

Objective. The aim of this work was to develop a Phase I control chart framework for the recently proposed multivariate risk-adjusted Hotelling’s

T

2

chart. Although this control chart alone can identify most patients receiving extreme organ-at-risk (OAR) dose, it is restricted by underlying distributional assumptions, making it sensitive to extreme observations in the sample, as is typically found in radiotherapy plan quality data such as dose-volume histogram (DVH) points. This can lead to slightly poor-quality plans that should have been identified as out-of-control (OC) to be signaled in-control (IC). Approach. We develop a robust iterative control chart framework to identify all OC patients with abnormally high OAR dose and improve them via re-optimization to achieve an IC sample prior to establishing the Phase I control chart, which can be used to monitor future treatment plans. Main Results. Eighty head-and-neck patients were used in this study. After the first iteration, P14, P67, and P68 were detected as OC for high brainstem dose, warranting re-optimization aimed to reduce brainstem dose without worsening other planning criteria. The DVH and control chart were updated after re-optimization. On the second iteration, P14, P67, and P68 were IC, but P40 was identified as OC. After re-optimizing P40’s plan and updating the DVH and control chart, P40 was IC, but P14* (P14’s re-optimized plan) and P62 were flagged as OC. P14* could not be re-optimized without worsening target coverage, so only P62 was re-optimized. Ultimately, a fully IC sample was achieved. Multiple iterations were needed to identify and improve all OC patients, and to establish a more robust control limit to monitor future treatment plans. Significance. The iterative procedure resulted in a fully IC sample of patients. With this sample, a more robust Phase I control chart that can monitor OAR doses of new plans was established.
Vision-aided grasp classification: design and evaluation of compact CNN for prosthetic hands
Authors: Sharma Udit , Vasamsetti Srikanth , Chander Sekar Anup , Datta Banibrata 
Published: May 2024

Abstract
Powered prosthetic hands capable of executing various grasp patterns are highly sought-after solutions for upper limb amputees. A crucial requirement for such prosthetic hands is the accurate identification of the intended grasp pattern and subsequent activation of the prosthetic digits accordingly. Vision-based grasp classification techniques offer improved coordination between amputees and prosthetic hands without physical contact. Deep learning methods, particularly Convolutional Neural Networks (CNNs), are utilized to process visual information for classification. The key challenge lies in developing a model that can effectively generalize across various object shapes and accurately classify grasp classes. To address this, a compact CNN model named GraspCNet is proposed, specifically designed for grasp classification in prosthetic hands. The use of separable convolutions reduces the computational burden, making it potentially suitable for real-time applications on embedded systems. The GraspCNet model is designed to learn and generalize from object shapes, allowing it to effectively classify unseen objects beyond those included in the training dataset. The proposed model was trained and tested using various standard object data sets. A cross-validation strategy has been adopted to perform better in seen and unseen object class scenarios. The average accuracy achieved was 82.22% and 75.48% in the case of seen, and unseen object classes respectively. In computer-based real-time experiments, the GraspCNet model achieved an accuracy of 69%. A comparative analysis with state-of-the-art techniques revealed that the proposed GraspCNet model outperformed most benchmark techniques and demonstrated comparable performance with the DcnnGrasp method. The compact nature of the GraspCNet model suggests its potential for integration with other sensing modalities in prosthetic hands.
Machine learning-aided evaluation of oxidative strength of cold atmospheric plasma-treated water
Authors: Irmak Seyma Ecem , Ozdemir Gizem Dilara , Ozdemir Mehmet Akif , Ercan Utku Kürşat 
Published: May 2024

Abstract
Plasma medicine is gaining attraction in the medical field, particularly the use of cold atmospheric plasma (CAP) in biomedicine. The chemistry of the plasma is complex, and the reactive oxygen species (ROS) within it are the basis for the biological effect of CAP on the target. Understanding how the oxidative power of ROS responds to diverse plasma parameters is vital for standardizing the effective application of CAP. The proven applicability of machine learning (ML) in the field of medicine is encouraging, as it can also be applied in the field of plasma medicine to correlate the oxidative strength of plasma-treated water (PTW) according to different parameters. In this study, plasma-treated water was mixed with potassium iodide-starch reagent for color formation that could be linked to the oxidative capacity of PTW. Corresponding images were captured resulting from the exposure of the color-forming agent to water treated with plasma for different time points. Several ML models were trained to distinguish the color changes sourced by the oxidative strength of ROS. The AdaBoost Classifier (ABC) algorithm demonstrated better performance among the classification models used by extracting color-based features from the images. Our results, with a test accuracy of 63.5%, might carry a potential for future standardization in the field of plasma medicine with an automated system that can be created to interpret the oxidative properties of ROS in different plasma treatment parameters via ML.
Unraveling the environmental consequences of trade openness in South Africa: a novel approach using ARDL modeling
Authors: Lanhui Wang , Ibrahim Abubakar Sadiq 
Published: May 2024

Abstract
Free trade and environmental sustainability are currently top economic and environmental research priorities. While numerous theories connect trade openness with environmental quality, empirical evidence often fails to support these claims. Using data spanning from 1980 to 2020, our study examines the effect of trade openness on CO2 emissions in South Africa. By employing a novel ARDL modeling framework, our analysis confirms the presence of the Environmental Kuznets Curve (EKC) hypothesis in South Africa. Our findings reveal that while GDP square enhances environmental quality, trade openness and economic growth have a degrading effect over time. Additionally, the study identifies that energy consumption, FDI, and value-added activities all contribute to environmental degradation. Findings also highlights the influence of institutional quality on the environment, demonstrating that political stability and control of corruption lead to increased CO2 emissions, while the rule of law reduces CO2 emissions. The research suggested that the potential of green economies should be leveraged in developing renewable energy, sustainable development, the recycling industry, and green financing sectors. A shift in economic activity in this direction will thus foster long-term economic growth and sustainable development.
Beam shaping in light-sheet microscopy: an experimental analysis
Authors: Hüpfel Manuel , Nienhaus Gerd Ulrich 
Published: May 2024

Abstract
Thanks to its unique optical sectioning capability, light-sheet fluorescence microscopy has proven to be a powerful technique for volumetric imaging of entire model organisms with high spatial and temporal resolution. For light sheet generation with scanned laser beams, holographic beam shaping offers precise control over the optical fields exciting the fluorescence. Various illumination schemes have been proposed, aiming for best image quality with regard to axial resolution, optical sectioning, illumination homogeneity and photobleaching while at the same time retaining a large field of view. Here, we have engineered and characterized a variety of beams and analyzed their imaging performance by using phantom samples and zebrafish embryos. These data may assist researchers to select the light sheet best suited to the imaging application at hand.
Fault detection through discrete wavelet transforms and radial basis function neural network in shunt compensated distribution systems
Authors: Joshi Minesh K , Patel R R 
Published: May 2024

Abstract
This paper discusses a critical study of fault detection, classification, and identification in distribution systems compensated through a distribution static synchronous compensator (D-STATCOM) using discrete wavelet transforms (DWT) and a radial basis function neural network (RBFNN). The efficacy of the proportional integral (PI) controller is discussed in the simulation during normal, voltage sag, and swell conditions. Then, the Daubechies mother wavelet (db4) is used here to extract and decompose the fault current signals because of its high accuracy of detection with less processing time. The models are subjected to different conditions of faults, such as line to ground (LG), line to line (LL), double line to ground (LLG), and three phases (LLL and LLLG) with different fault resistance. The novelty of the scheme is that DWT and RBFNN techniques were compared and proven effective in demonstrating the faulty conditions. To validate the proposed approach, a simulation study is carried out in MATLAB with various operating conditions, and it is shown that the proposed method can depend on abundantly protecting the distribution grid from faulty conditions.
Transient state analysis of rehabilitated RC beams using finite element modelling and prediction using an artificial neural network
Authors: R Surya Prakash , N Parthasarathi 
Published: May 2024

Abstract
The present research develops and verifies a simpler numerical approach for analyzing the thermal transient state of rehabilitated concrete beams reinforced with various types of FRP (fiber-reinforced polymer) subjected to high temperatures and specifically built as under-reinforced concrete beams. This approach offers a straightforward, efficient, and exact instrument for numerical analysis. The proposed analytical technique has been validated by load-displacement curves and cross-section temperature data, indicating its dependability and practicality. Subsequently, the validated approach was used to examine the impact of significant variables on the outcome and restoration of FRP-reinforced concrete beams at high temperatures. The methodology gives the Comparing conventional and CFRP, GFRP, AFRP reinforced beams using beam, truss, and shell elements. Thermal and UDL loads were applied, mesh at 25 mm × 25 mm. Transient analysis contrasts performance via displacement and temperature. The temperature versus displacement curve shows the FRP comparisons. Identifying the critical temperature before failure is crucial, emphasizing the curve’s significance in assessing structural performance and potential failure points. Nodal temperatures ranged 939 °C–963 °C (rehabilitated) versus 958 °C (conventional). 200 °C difference affects thermal boundary conditions for structural analysis and Conventional peaks at 320 °C, while AFRP, GFRP, and CFRP reach 358 °C, 385 °C, and 390 °C respectively. CFRP lasts 2400 min. Neural network models demonstrate effective generalizability, enabling satisfactory predictions of RC beam rehabilitation with CFRP laminates within the study’s parameter range.
On-demand quantum light sources for underwater communications
Authors: Scognamiglio Dominic , Gale Angus , Al-Juboori Ali , Toth Milos , Aharonovich Igor 
Published: May 2024

Abstract
Quantum communication has been at the forefront of modern research for decades, however it is severely hampered in underwater applications, where the properties of water absorb nearly all useful optical wavelengths and prevent them from propagating more than, in most cases, a few metres. This research reports on-demand quantum light sources, suitable for underwater optical communication. The single photon emitters, which can be engineered using an electron beam, are based on impurities in hexagonal boron nitride. They have a zero phonon line at ∼436 nm, near the minimum value of water absorption and are shown to suffer negligible transmission and purity loss when travelling through water channels. These emitters are also shown to possess exceptional underwater transmission properties compared to emitters at other optical wavelengths and are utilised in a completely secure quantum key distribution experiment with rates of kbits s−1.