bluuebunny/crossref_metadata_2025_split huggingface dataset:
The gas-sensing mechanism of Nb2C monolayer for SF6 decomposition based on the DFT study
Authors: Yang Dingqian , He Dandong , Lu Detao , Yuan Manman , Zeng Wen , Zhou Qu 
Published: October 2024

Abstract
During fault analysis of gas-insulated switchgear (GIS), continuous monitoring of the gases produced by the decomposition of SF6 is critical to the safe operation of the equipment. Although a variety of gas detection technologies are currently available on the market, low-power gas detection devices for SF6 decomposition products are still in the development stage, and technological advances in this area are of great significance for improving the reliability of GIS systems. Based on the density functional theory (DFT), the Nb2C crystal surface structure was established and six adsorption structures of SO2, H2S, SOF2, SO2F2, HF and CO gas molecules on Nb2C crystal surface were constructed by geometrical optimization in this paper. The gas-sensitive properties of each adsorption system were explored in terms of adsorption energy, charge transfer, electron density, density of states and recovery time. The results showed that the Nb2C crystal surface was unfit HF and CO gases detection, both of which showed weak physical adsorption; the adsorption energies of SO2, H2S, SOF2, and SO2F2 on the Nb2C crystal surface were −1.846 eV, −1.081 eV, −5.270 eV, and −10.582 eV, respectively, and all of them were strong chemical adsorption. It was further shown by theoretical recovery time calculations that the Nb2C crystal surface can act as SOF2 and SO2F2 gas scavengers, and are able to desorb H2S (4.69 s) and SO2 (2.26 s) gases by appropriately increasing the temperature, but the Nb2C crystal surface is more suitable for use as a low-power gas-sensitive material for H2S gas detection. Therefore, Nb2C is anticipated to be a promising material for high response, low-power consumption and fast recovery for H2S gas detection in SF6 decomposition gas.
Conversations in the dark: cross-correlating birefringence and LSS to constrain axions
Authors: Arcari S. , Bartolo N. , Greco A. , Gruppuso A. , Lattanzi M. , Natoli P. 
Published: October 2024

Abstract
Unveiling the dark sector of the Universe is one of the leading efforts in theoretical physics. Among the many models proposed, axions and axion-like particles stand out due to their solid theoretical foundation, capacity to contribute significantly to both dark matter and dark energy, and potential to address the small-scale crisis of ΛCDM. Moreover, these pseudo-scalar fields couple to the electromagnetic sector through a Chern-Simons parity-violating term, leading to a rotation of the plane of linearly polarized waves, namely cosmic birefringence. We explore the impact of the axion-parameters on anisotropic birefringence and study, for the first time, its cross-correlation with the spatial distribution of galaxies, focusing on ultralight axions with masses 10-33 eV ≤ mϕ
≤ 10-28 eV. Through this novel approach, we investigate the axion-parameter space in the mass mϕ
and initial misalignment angle θi
, within the framework of early dark energy models, and constrain the axion-photon coupling gϕγ required to achieve unity in the signal-to-noise ratio of the underlying cross-correlation, computed with the instrument specifications of Euclid and forthcoming CMB-polarization data. Our findings reveal that for masses below 10-32 eV and initial misalignment angles greater in absolute value than π/4, the signal-to-noise ratio not only exceeds unity but also surpasses that achievable from the auto-correlation of birefringence alone (up to a factor 7), highlighting the informative potential of this new probe. Additionally, given the late-time evolution of these low-mass axions, the signal stems from the epoch of reionization, providing an excellent tool to single out the birefringence generated during this period.
Polarization study of P-wave charmonium radiative decay into a light vector meson at e
+
e
− collider experiment*
Authors: Chen 陈 Yong-Qing 永清 , Hong 洪 Peng-Cheng 鹏程 , Chen 陈 Zhuo 琢 , Shan 单 Wei 葳 , Song 宋 Wei-Min 维民 
Published: October 2024

Abstract
In this paper, a formalism is presented for the helicity amplitude analysis of the decays

(subscripts 1 and 2 are used to distinguish the two radiative photons), and the polarization expressions of the P-wave charmonia

and the vector mesons

for experimental measurements at an electron-positron collider. In addition, we derive formulae for the angular distributions of

to extract the degree of transverse polarization

of

pairs with symmetric beam energy as well as the ratios of two helicity amplitudes x (in

decays) and

(in

decays), which represent the relative magnitudes of transverse to longitudinal polarization amplitude. The results are validated by Monte Carlo simulation. Finally, the statistical sensitivity of

, x, and y are estimated based on the large

data samples collected at current and proposed future

collider experiments.
Applying statistical learning theory to deep learning
Authors: Gerbelot Cédric , Karagulyan Avetik , Karp Stefani , Ravichandran Kavya , Stern Menachem , Srebro Nathan 
Published: October 2024

Abstract
Although statistical learning theory provides a robust framework to understand supervised learning, many theoretical aspects of deep learning remain unclear;in particular, how different architectures may lead to inductive bias when trained using gradient-based methods. The goal of these lectures is to provide an overview of some of the main questions that arise when attempting to understand deep learning from a learning theory perspective. After a brief reminder on statistical learning theory and stochastic optimization, we discuss implicit bias in the context of benign overfitting. We then move to a general description of the mirror descent algorithm, showing how we may go back and forth between a parameter space and the corresponding function space for a given learning problem, as well as how the geometry of the learning problem may be represented by a metric tensor. Building on this framework, we provide a detailed study of the implicit bias of gradient descent on linear diagonal networks for various regression tasks, showing how the loss function, scale of parameters at initialization and depth of the network may lead to various forms of implicit bias; in particular, transitioning between kernel and feature learning regimes.
Application of Improved Classification Algorithm with Binary Tree Support Vector Machine in Power Grid Prediction
Authors: Chen Yingjun 
Published: October 2024

Abstract
To further improve the accuracy of the power grid prediction, the paper proposes an improved binary tree classification algorithm with the SVM and predicts the real time power grid. Based on the basic principles of SVM, the common multi-classifier classification algorithm and its characteristics are summarized. Incorporating the advantages of existing classification algorithms, an improved binary tree classification algorithm was used, addressing the limitations of conventional methods. From the simulation results, it can be seen that the improved binary tree algorithm can shorten the test time from the original 0.285 seconds to 0.267 seconds, and the measured category can also be increased from the original 94.42 to 95.27. This shows that the improved binary tree can not only improve the practicability of the support vector machine algorithm, but also better improve the ability of real-time prediction of power grid.
An Industrial Micro Parts Recognition Technology Based on Improved Yolov8
Authors: Hao Yafei , Jia Feng 
Published: October 2024

Abstract
Aiming at the recognition and classification problem of micro assembly parts in industrial production, the Yolov8 object detection algorithm was used as the base model to improve the C2f class in the backbone. And apply the improved object detection algorithm to the recognition field of detecting assembly parts. By collecting images of gears, nails, and screws from various angles on Roboflow, a training set and a validation set containing three types of gears, nails, and screws are formed. At the same time, in order to improve the robustness of the Yolov8 object detection algorithm, data processing methods such as adding noise and image rotation were adopted for the samples in the data. Due to the use of Yolov8 as the base model, a TXT text file containing label prediction box position information was formed after labelling the dataset on Roboflow. Based on Python (3.10.12) and cloud platform, improved Yolov8 object detection algorithm was trained and network validation was carried out. Use TXT files as input for the model to obtain and learn features of gears, nails, and screws. After 200 epochs, the mAP for detecting gears, nails, and screws increased by about 3% compared to the base model.
Research on UAV Multi-Source Information Monitoring System for Indoor Space
Authors: Zhou Yuqi , Yan Guochuang , Du Jiang , Niu Ze , Xue Lei , Liao Yingdong 
Published: October 2024

Abstract
As an important component of unmanned equipment, unmanned aerial vehicle (UAV) is becoming a research hotspot. Taking digital technology as the core, taking small rotary-wing UAV as the research object and indoor space as the test scene, this paper constructs a multi-source information monitoring system, and elaborates its hardware and software architecture. A multi-source information monitoring system is constructed, and its hardware and software architecture are described in detail. The system is equipped with core functions such as three-dimensional posture display and model combination, and is also capable of monitoring, storing and correlating multi-source data such as vibration, noise and position. The results show that the system can integrate at least 2 types of sensors, and can process at least 5 types of test process data, and the three-dimensional positioning accuracy of indoor targets is better than 10 cm. The system supplements the test means and methods for unmanned equipment.
Designing of Ecological Model Predictive Control Algorithm for Electric Trucks Platoons with Optimal Energy Consumption
Authors: Yang Jinhan , Zhang Rui , Xia Peng , Xu Jingwen 
Published: October 2024

Abstract
In response to the problem of motor vehicle exhaust emissions pollution, the development of new energy in China’s truck industry will be an inevitable trend. At present, the penetration rate and market share of pure electric trucks are constantly increasing. The research on electric truck platoons has strong practical significance. This article focuses on the ecological control strategy of pure electric truck platoons. Firstly, a platoon model of four electric trucks is established in Trucksim. The leading vehicle designs an ecological optimal speed dynamic programming algorithm, and in terms of following vehicle tracking control, the battery SOC state motor speed is considered to be improved, Designed an ecological model predictive control algorithm for electric truck platoon with optimal energy consumption. Using real road slope data in the mining area, a Trucksim/Matlab/Simulink joint simulation was conducted. The simulation results showed that compared with traditional constant speed cruise control, the proposed ecological predictive cruise control algorithm for pure electric commercial vehicle platoons saved 9.2577% of the overall battery SOC.
Gm-APD Lidar Distance Image Recovery Based on Intensity Image Target Edge Guidance
Authors: Fang Ye , Wang Chunyang , Xie Da 
Published: October 2024

Abstract
Gm-APD LiDAR in the detection of long-distance targets, the laser by the atmospheric effects of attenuation is serious, so that the target echo sparse, easy to cause the target distance like edge missing, details of fuzzy and other problems, the traditional method can’t make targeted treatment of the above problems. Therefore, this paper proposes a Gm-APD LIDAR distance image recovery method based on intensity image target edge guidance. The method introduces one-dimensional intensity information as an aid, combines the inverse distance weighting function to guide the reconstruction of the target edge contour information of the distance image, and then applies morphological image processing to correct the target distance value in a partition, and recovers a distance image with sharp edges and clear details. Experiments are carried out on the distance targets, and the results show that the method improves the target restoration by 19.21% and the peak signal-to-noise ratio by 3.12% compared with the concave morphology restoration algorithm, which verifies the advantages and effectiveness of the method proposed in this paper.
Design and Implementation of Image Acquisition and Signal Transmission Functions based on FPGA
Authors: Li Lifeng , Zhang Hui , Zhang Hongying , Deng Fangyuan 
Published: October 2024

Abstract
In recent years, video network monitoring systems have evolved rapidly, from the first generation of digital-analogue systems to the fully IP-based systems. Based on the FPGA core, this design focuses on image acquisition and information transmission. The FPGA expansion board contains several peripheral modules, including the OV7670 camera captures images, DRAM stores data, and FIFO handles cross-clock domain signal transmission. While the reference clock is 40ns, in this system, the clock period is 20ns, making two clock cycles equal to one reference clock cycle. Then, two counters are used, one is the reference clock and another is the line synchronization (cnt1) to count 800 clock cycles per line. The end of a line is indicated by end_cnt0, and FIFO data is stored in SDRAM. Configuring the VGA display driver allows image data to be transmitted and displayed on the VGA screen. The system features a simple structure, high resource utilization, and strong reliability.
Study on Calculation of Regulated Neutron Spectrum Based on Moderator with Multi-dimensional Features
Authors: Cao Chenglong , Cao Pei , Sun Ming , Chen Huaijin , Zhang Haiyan 
Published: October 2024

Abstract
Neutron transport simulation is one of the key techniques for calculating the regulated neutron spectrum, which is extremely accurate but comes with a significant time loss and is difficult to achieve convergence when there is deep penetration. Accurate regulation of the neutron spectrum is essential for research on experimental neutronics. In this study, a fast way to compute the regulated neutron spectrum using a moderator with multi-dimensional features was developed. The response matrix with different dimensions was generated for each moderator after it first created a moderator database by classifying common shielding materials based on various dimensions (such as shape, size, substance, etc.). Finally, neutron transport modeling enabled the speedy and precise estimation of regulated neutron spectrum via matrix transformation. The results demonstrated that the mean square error (MSE) of the normalized regulated neutron spectra computed using the Monte Carlo approach and the proposed technique were able to reach the order of 10−6. The anticipated method was confirmed by equating the results with those of the Monte Carlo method under a regulation system consisting of moderators of different dimensions. Furthermore, the MSE of the regulated neutron spectrum in the case of deep penetration of the simplified thermal reactor TRIGA of the two methods in the convergent energy domain could also reach the order of 10−6, and in the divergent domain, the suggested method in this study was able to give a heuristic neutron spectrum.
Multiple pathways towards sustainable development goals and climate targets
Authors: Soergel Bjoern , Rauner Sebastian , Daioglou Vassilis , Weindl Isabelle , Mastrucci Alessio , Carrer Fabio , Kikstra Jarmo , Ambrósio Geanderson , Aguiar Ana Paula Dutra , Baumstark Lavinia , Bodirsky Benjamin Leon , Bos Astrid , Dietrich Jan Philipp , Dirnaichner Alois , Doelman Jonathan C , Hasse Robin , Hernandez Ariel , Hoppe Johanna , Humpenöder Florian , Iacobuţă Gabriela Ileana , Keppler Dorothee , Koch Johannes , Luderer Gunnar , Lotze-Campen Hermann , Pehl Michaja , Poblete-Cazenave Miguel , Popp Alexander , Remy Merle , van Zeist Willem-Jan , Cornell Sarah , Dombrowsky Ines , Hertwich Edgar G , Schmidt Falk , van Ruijven Bas , van Vuuren Detlef , Kriegler Elmar 
Published: October 2024

Abstract
The UN sustainable development goals (SDGs) and the Paris climate target require a holistic transformation towards human well-being within planetary boundaries. However, there are growing debates on how to best pursue these targets. Proposed transformation strategies include market- and technology-driven green-growth, shifting towards a sufficiency-oriented post-growth economy, and a transformation driven primarily by strong government action. Here we quantify three alternative sustainable development pathways (SDPs), Economy-driven Innovation, Resilient Communities, and Managing the Global Commons, that reflect these different societal strategies. We compare the quantifications from two integrated assessment models and two sectoral models of the buildings and materials sectors across a broad set of indicators for sustainable development and climate action. Our global multi-scenario and multi-model analysis shows that all three SDPs enable substantial progress towards the human development goals of the SDGs. They simultaneously limit global warming and prevent further environmental degradation, with the sufficiency-oriented Resilient Communities scenario showing the lowest peak warming and lowest reliance on carbon dioxide removal as well as the largest improvements in biodiversity intactness. The SDPs also alleviate the concerns about the biogeophysical and technological feasibility of narrowly-focused climate change mitigation scenarios. However, the shifts in energy and food consumption patterns assumed in the SDPs, ranging from moderate in Economy-driven Innovation to very ambitious in Resilient Communities, also lead to increased challenges regarding socio-cultural feasibility.
Widespread forest-savanna coexistence but limited bistability at a landscape scale in Central Africa
Authors: Zwaan Aart , Staal Arie , Beest Mariska te , Rietkerk Max 
Published: October 2024

Abstract
Tropical forest and savanna frequently coexist under the same climatic conditions, which has led to the hypothesis that they could represent alternative ecosystem states, stabilized by internal feedbacks. An implication of this hypothesis is that forest and savanna may be bistable and exhibit tipping behavior in response to changing conditions. However, we pose that the local presence of forest and savanna within coexistence landscapes is not sufficient evidence that these are alternative stable states at larger ecosystem scales. Therefore, we explore forest-savanna coexistence and bistability at landscape scale in Central Africa. Using remote sensing data on tree cover, we classify 0.1° × 0.1° (approx. 10 × 10 km) landscapes as homogeneous forest, homogeneous savanna, or coexistence, and analyze the roles of climate, topography and soil sand content in driving their distributions. We find that local coexistence of forest and savanna within landscapes is common and occurs for the whole range of mean annual precipitation in our study area. At low precipitation, however, coexistence increases with topographic roughness and is therefore likely driven by local redistribution of resources rather than internal feedbacks. Coexistence within intermediate and high precipitation landscapes remains unexplained by the studied variables, and may be caused either by heterogeneity in unmeasured drivers or by feedback-driven bistability. At landscape scale, the precipitation ranges for which homogeneous forest and savanna occur have only limited overlap, and this overlap can be largely explained by other external drivers, such as seasonality, soil sand content, and topography. This lack of evidence that homogeneous forest and savanna in Central Africa are alternative ecosystem states at this landscape scale means that transitions between them may be mostly local, resulting in coexistence states. Therefore, we conclude that the likelihood of large-scale tipping between homogeneous forest and savanna ecosystems may be lower than previously thought.
Enlightenment
Authors: Parslow Christopher 
Published: October 2024

Abstract
The prosciutto’s distinction of being not only the earliest known Roman portable sundial but of presenting such an unusual form ensured that it featured prominently in more scientific studies of ancient time keeping while becoming a popular museum attraction. This chapter traces the prosciutto’s treatment in astronomical studies through the early nineteenth century, along with the remarkable misconceptions and errors that continued to hobble its proper understanding and tracks the evolution of museology that enhanced its accessibility. It highlights the more detailed mathematical analyses of it and catalogs the variety of ways it has been interpreted down to the modern day.
Mandan Dandies: Trade, Intimacy and Ornamental Excess in a Destroyed 1832 Portrait by George Catlin
Authors: Naessens Luke 
Published: October 2024

Abstract
This essay considers the role played by economics and sexuality in the development of the imaginary of US settler colonialism by considering George Catlin’s destroyed 1832 portrait of an anonymous Mandan ‘dandy’. Catlin’s representation of Indigenous dandies likely informed Charles Baudelaire’s account of the dandy as an avatar of the transition to modernity, but during the gay and Indigenous liberation movements of the 1970s, the Mandan dandy was reclaimed as evidence of pre-colonial queer sexualities. Synthesising these conceptions, the essay argues that this figure illuminates the material conditions of modernity in the fur trade: an unstable assemblage of contradictory Indigenous and Euro-American systems of economic exchange, political power and sexual intimacy. The dandy figures a form of queerness that was conceived as an effect of these socioeconomic conditions, which Catlin condemned as perverse: a queerness that lay at the foundations of settler colonialism rather than in opposition to it.
MicroRNA mediates the effects of food factors
Authors: Kumazoe Motofumi , Tachibana Hirofumi 
Published: October 2024

Abstract
Food factors elicit physiological effects by interfering with the central dogma system, including DNA methylation, replication, transcription, and translation. MicroRNAs (miRNAs) are noncoding short RNAs that are ∼20 nucleotides long and play a crucial role in the regulation of mRNA levels and translation processes. Importantly, miRNAs can be delivered to different locations in nanovesicles. However, little is known about their roles as mediators of the effects of food factors. This review introduces recent findings on the role of miRNAs in the beneficial effects of food factors, including green tea polyphenols and soybean isoflavones, and discusses the importance of miRNAs as mediators of the beneficial effects of food.