bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Impact of Nova Outbursts on the Chemical Abundance of the Interstellar Medium
Authors: He Guoli , Zhu Chunhua , Lü Guoliang , Li Lin , Guo Sufen , Liu Helei , Gao Jun 
Published: September 2024

Abstract
Nova outbursts are the results of thermonuclear runaways, which occur when sufficient material accretes on the surfaces of white dwarfs (WDs). Using the MESA code, we construct a detailed grid for carbon-oxygen and oxygen-neon-magnesium novae. By employing population synthesis methods, we conduct a statistical analysis of the distribution of novae in the Milky Way. In our models, on average, a typical nova system may undergo about 8000 eruptions and the Galactic nova rate is ∼130 yr−1. The C, N, and O elements in nova ejecta are strongly affected by the mixing degree between WD core and accreted material. Our results show that the average value of 12C/13C in nova ejecta is about an order of magnitude lower than that on the surface of a red giant, that for 16O/17O is about 5 times lower, and that for 14N/15N is about 1.5 times lower. The annual yields of 13C , 15N, and 17O from nova ejection are larger than those from AGB stars. This indicates that compared to a red giant, nova eruptions are a more important source of the odd-numbered nuclear elements of 13C , 15N, and 17O in the Galactic interstellar medium.
PD-ARnet: a deep learning approach for Parkinson’s disease diagnosis from resting-state fMRI
Authors: Li Guangyao , Song Yalin , Liang Mingyang , Yu Junyang , Zhai Rui 
Published: September 2024

Abstract

Objective. The clinical diagnosis of Parkinson’s disease (PD) relying on medical history, clinical symptoms, and signs is subjective and lacks sensitivity. Resting-state fMRI (rs-fMRI) has been demonstrated to be an effective biomarker for diagnosing PD. Approach. This study proposes a deep learning approach for the automatic diagnosis of PD using rs-fMRI, named PD-ARnet. Specifically, PD-ARnet utilizes Amplitude of Low Frequency Fluctuations and Regional Homogeneity extracted from rs-fMRI as inputs. The inputs are then processed through a developed dual-branch 3D feature extractor to perform advanced feature extraction. During this process, a Correlation-Driven weighting module is applied to capture complementary information from both features. Subsequently, the Attention-Enhanced fusion module is developed to effectively merge two types of features, and the fused features are input into a fully connected layer for automatic diagnosis classification. Main results. Using 145 samples from the PPMI dataset to evaluate the detection performance of PD-ARnet, the results indicated an average classification accuracy of 91.6% (95% confidence interval [CI]: 90.9%, 92.4%), precision of 94.7% (95% CI: 94.2%, 95.1%), recall of 86.2% (95% CI: 84.9%, 87.4%), F1 score of 90.2% (95% CI: 89.3%, 91.1%), and AUC of 92.8% (95% CI: 91.1%, 95.0%). Significance. The proposed method has the potential to become a clinical auxiliary diagnostic tool for PD, reducing subjectivity in the diagnostic process, and enhancing diagnostic efficiency and consistency.
Integrating spatial and temporal features for enhanced artifact removal in multi-channel EEG recordings
Authors: Yin Jin , Liu Aiping , Wang Lanlan , Qian Ruobing , Chen Xun 
Published: September 2024

Abstract

Objective. Various artifacts in electroencephalography (EEG) are a big hurdle to prevent brain–computer interfaces from real-life usage. Recently, deep learning-based EEG denoising methods have shown excellent performance. However, existing deep network designs inadequately leverage inter-channel relationships in processing multi-channel EEG signals. Typically, most methods process multi-channel signals in a channel-by-channel way. Considering the correlations among EEG channels during the same brain activity, this paper proposes utilizing channel relationships to enhance denoising performance. Approach. We explicitly model the inter-channel relationships using the self-attention mechanism, hypothesizing that these correlations can support and improve the denoising process. Specifically, we introduce a novel denoising network, named spatial-temporal fusion network (STFNet), which integrates stacked multi-dimension feature extractor to explicitly capture both temporal dependencies and spatial relationships. Main results. The proposed network exhibits superior denoising performance, with a 24.27% reduction in relative root mean squared error compared to other methods on a public benchmark. STFNet proves effective in cross-dataset denoising and downstream classification tasks, improving accuracy by 1.40%, while also offering fast processing on CPU. Significance. The experimental results demonstrate the importance of integrating spatial and temporal characteristics. The computational efficiency of STFNet makes it suitable for real-time applications and a potential tool for deployment in realistic environments.
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noise in the Robin Hood model
Authors: Singh Abha , Chhimpa Rahul , Yadav Avinash Chand 
Published: September 2024

Abstract
We consider the Robin Hood dynamics, a one-dimensional extremal self-organized critical model that describes the low-temperature creep phenomenon. One of the key quantities is the time evolution of the state variable (force noise). To understand the temporal correlations, we compute the power spectra of the local force fluctuations and apply finite-size scaling to get scaling functions and critical exponents. We find a signature of the

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noise for the local force with a nontrivial value of the spectral exponent

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. We also examine temporal fluctuations in the position of the extremal site and a local activity signal. We present results for different local interaction rules of the model.
An Intrusion Detection Model Based on a Residual Memory Convolutional Neural Network with Attention Mechanism
Authors: Liu Yuankai , Guo Feng , Zhao Qian , Wu Chuankun 
Published: September 2024

Abstract
As the utilization of IoT devices becomes more widespread, the variety of attacks targeting these devices is also increasing. Traditional intrusion detection systems in IoT environments often struggle to effectively recognize the diverse types of attacks. Therefore, this study proposes a Residual Memory Convolutional Neural Network (RMCNN) model incorporating an attention mechanism, aimed at improving the accuracy and efficiency of multi-class attack detection in IoT environments. The model begins by extracting spatial features from traffic data through Convolutional Neural Network (CNN) layers, and then captures dynamic changes in time series data using Gated Recurrent Unit (GRU). Subsequently, a multi-head attention mechanism is employed to reinforce focus on critical information. Finally, the outputs from the GRU are combined with those from the multi-head attention mechanism via residual connections, enhancing the model’s learning capabilities and improving the recognition accuracy of various attack types. Verified through experiments on the CICIOT2023 dataset, the model achieved an F1 score of 97.29%, indicating significant improvements in the detection performance of multi-class attacks and confirming its applicability and effectiveness in the field of IoT security.
A Registration Method Based on Planar Features Between BIM Model and Point Cloud
Authors: Wu Qiwen , Zhao Xi 
Published: September 2024

Abstract
In the digitization process of the construction industry, it is frequently necessary to use BIM as a digital model carrier, and the registration between BIM and point cloud is a crucial step in BIM applications. Most existing 3D registration methods, such as the ICP algorithm, are capable of aligning point clouds. However, these classical methods are subject to the influence of numerous points contained within the point cloud, relying heavily on point-to-point correlations. Consequently, it is challenging to extend these algorithms to register with other forms of spatial information, such as BIM models, beyond the point cloud. Considering that planes are basic geometric elements in building BIM models, this paper proposes a novel method for aligning BIM models with point clouds by matching planar features. The method extracts planes from point clouds using an enhanced region growing algorithm, directly parsing the planar geometric information from BIM in IFC format. After completing the matching of plane groups, the optimal solution is calculated using a weighted least squares method. The experimental results indicate that the proposed method can achieve successful registration between the BIM model and point cloud, with a lower RMSE of 5.73mm compared to the registration method using RANSAC+ICP on the same dataset.
Research and design of low-noise cooling fan for fuel cell vehicle
Authors: Zhu Jiale , Xu Guoqiang , You Yanghong , He Youwei 
Published: September 2024

Abstract
The main noise source of fuel cell vehicles comes from the cooling vehicle, which has the characteristics of high voltage and large air volume. However, the large air volume makes the fan noise level significantly increase, reaching about 70dB. Even if the car adopts a variety of physical noise reduction methods, the noise level inside the car is still high, seriously affecting the passenger ride experience. In recent years, related research combined with bionic technology to improve the fan blade by designing a new shape structure, so that it has similar characteristics to some organisms to achieve the purpose of noise reduction. In this paper, ANSYS-CFX software is used to simulate the fan efficiency and noise level under different blade numbers, predict its performance, and optimize its structure.
Research and optimization of heat transfer characteristics of heat pipe-coupled phase change energy storage system
Authors: Zhang Renping , Zhou Zuxiang 
Published: September 2024

Abstract
Heat pipe coupled Latent Heat Thermal Energy Storage (LHTES) is a commonly used technique for improving heat storage, due to its advantages such as heat conduction, isothermal, and uniform temperature. Adding fins to the heat pipe can enhance energy storage efficiency and system performance. Although previous research has looked into how heat pipe layouts affect LHTES, there is still a dearth of research on fin geometry optimization for boosted heat transfer. In this work, we used ANSYS Fluent to simulate the consequence of fin placement upon the heating capacity of a Phase Change Material (PCM) based LHTES system. Through an in-depth analysis of the heat transfer mechanisms, in an effort to quicken the PCM’s solidification process, we adjusted the fins’ length and spacing. The LHTES system’s overall solidification time was greatly shortened by the optimized model, going from 18800 seconds to 8500 seconds, achieving a 54.79% enhancement in thermal transfer efficiency.
Development of large-volume 130TeO2 bolometers for the CROSS 2β decay search experiment
Authors: Avignone F.T. , Barabash A.S. , Berest V. , Bergé L. , Calvo-Mozota J.M. , Carniti P. , Chapellier M. , Dafinei I. , Danevich F.A. , Dumoulin L. , Ferella F. , Ferri F. , Gallas A. , Giuliani A. , Gotti C. , Gras P. , Ianni A. , Imbert L. , Khalife H. , Kobychev V.V. , Konovalov S.I. , Loaiza P. , de Marcillac P. , Marnieros S. , Marrache-Kikuchi C.A. , Martinez M. , Nisi S. , Nones C. , Olivieri E. , Ortiz de Solórzano A. , Peinaud Y. , Pessina G. , Poda D.V. , Rosier Ph. , Scarpaci J.A. , Tretyak V.I. , Umatov V.I. , Zarytskyy M.M. , Zolotarova A. 
Published: September 2024

Abstract
We report on the development of thermal detectors based on
large-size tellurium dioxide crystals
(45 × 45 × 45 mm), containing tellurium enriched in
130Te to about 91%, for the CROSS double-beta decay
experiment. A powder used for the crystals growth was additionally
purified by the directional solidification method, resulting in the
reduction of the concentration of impurities by a factor 10, to a
few ppm of the total concentration of residual elements (the main
impurity is Fe). The purest part of the ingot (the first
∼ 200 mm, about 80% of the total length of the cylindrical
part of the ingot) was determined by scanning segregation profiles
of impurities and used for the 130TeO2 powder production
with no evidence of re-contamination. The crystal growth was
verified with precursors produced from a powder with natural Te
isotopic composition, and two small-size
(20 × 20 × 10 mm) samples were tested at a sea-level
laboratory showing high bolometric and spectrometric performance
together with acceptable 210Po content (below 10 mBq/kg). This
growth method was then applied for the production of six large cubic
130TeO2 crystals and 4 of them were taken randomly to be
characterized at the Canfranc underground laboratory, in the
CROSS-dedicated low-background cryogenic facility. Two
130TeO2 samples were coated with a thin,
𝒪(100 nm), metal film in form of Al layer (on 4 sides)
or AlPd grid (on a single side) to investigate the possibility to
tag surface events by pulse-shape discrimination. Similarly to the
small natural precursors, large-volume 130TeO2 bolometers
show high performance and even better internal purity (^210Po
activity ∼ 1 mBq/kg, while activities of 228Th and
226Ra are below 0.01 mBq/kg), satisfying requirements for the
CROSS and, potentially, next-generation experiments.
Isotopic labeling evidence shows faster carbon release from microbial residues than plant litter
Authors: Zhu Jingjing , Cai Yuhua , Wu Fuzhong , Zhang Jinyu , Zhang Xinying , Ni Xiangyin 
Published: September 2024

Abstract
Carbon (C) release from plant and microbial residues is a primary pathway of energy flow from photosynthetic and metabolic biomass to carbon dioxide (CO2) in terrestrial ecosystems. Traditional view show that microbial residue C is more resistant to decompose than plant litter because their smaller particle sizes could be preferentially occluded in microaggregates with less microbial accessibility. However, we still lack a quantitative assessment (i.e. isotopic C labeling) to isolate the progressive release of C fractions from both plant and microbial residues. Here we used a global data set of 117 decomposition experiments that traced the 13C or 14C release of isotopically labeled plant and microbial residues to estimate the C release rates and turnover times by using a first-order exponential kinetics model. The average C release rates of crop, grass and tree litter were 7.78, 3.79 and 2.11 yr−1, which were significantly lower than microbial residues (13.07 yr−1). Although C release rates of both plant and microbial residues were positively correlated with site temperature, the mean turnover time of microbial residues was 2–6 times lower than plant litter. We suggest that a constraint in microbial and plant residues leads to a predictable pattern of C release during terrestrial decomposition, which could be included in Earth system models.
Change in the sustainability of regional agricultural systems: based on an emergy decomposition analysis
Authors: Song Fei , Wu Haoyu , Sun Zihan , Bai Junbo , Su Fangli , Xu Deshen , Cao Chenchen , Li Haifu , Song Shuang , Liu Yi 
Published: September 2024

Abstract
In the context of rapid population growth and limited arable land resources, the agricultural system has to provide enough food in a sustainable way. Regional agricultural systems have good consistency in agricultural practices, management decisions, social economy, and climate, which is of great significance in ensuring food security. In this study, emergy analysis and the logarithmic mean divisia index method were integrated to construct an evaluation framework from the dimensions of socio-economic environment, resource environment, climate environment, and ecological environment. Then we evaluated and analyzed the changes in agricultural system sustainability from 1990 to 2019 in the mainstream of Liaohe River Basin, a typical agricultural basin in China. The results showed that the Emergy sustainability index (ESI) decreased from 0.17 to 0.14, and factors Δ Pt/Gt
(social and economic development level), Δ G/I (agricultural economic benefits), and Δ Gt/G (economic structure) from the socio-economic environment dimension had the greatest impact on changes in ESI. Moreover, society and economy affected the factors in the resource environmental dimensions through the allocation of policies and resources, which in turn directly affected ESI. The influence of factors from the climatic environment and ecological environment weakens as the ability to manage agricultural systems increases. The research provided a reference for the planning and management of sustaining agricultural systems at a regional scale.
Synthesis reveals heterogeneous changes in the metabolism and emission of greenhouse gases of drying rivers
Authors: Sepp Margot , González-Trujillo Juan David , Marcé Rafael , Sabater Sergi 
Published: September 2024

Abstract
More than half of the world’s rivers experience occasional, seasonal, or permanent drying, and this may increase because of climate change. Drying, i.e. severe reduction in water flow even leading to streambed desiccation, can have a profound impact on the available aquatic habitat, biodiversity, and functions of rivers. Yet, to date, it is unclear whether similar drying events in comparable climate zones result in similar changes in ecosystem processes, such as river metabolism or greenhouse gas (GHG) emissions. Here, we synthesise the detected effects of drying on gross primary production (GPP) and ecosystem respiration (ER), as well as on the emissions of GHGs (CO2, CH4, and N2O) in rivers and streams. We examined the current available scientific literature detailing the impact of drying on these variables when measured either in the field or in the laboratory. We extracted data from 30 studies analysing GPP and ER responses, and data on GHG emissions from another 35 studies. Then, we conducted a meta-analysis to determine whether the magnitude and direction of the effects varied across the systems and climate zones studied, or according to the type (natural or human-induced) and severity of drying. In general, drying enhanced GPP (under low flows) and CH4 emissions, and decreased CO2 and N2O emissions. The hydrological phases throughout streambed drying (low water flow, isolated pools, or desiccation) had differential effects on metabolism and GHG emissions. The effects of drying were generally more severe when it induced desiccation, rather than just periods of low flow. Desiccation strongly reduced GPP, likely because of the die-off of algae, while its negative effect on ER was smaller. Greater decrease in GPP than in ER under desiccation would lead to increase in CO2 emissions; our results showed accordingly that desiccation increased CO2 emissions. Furthermore, the magnitude and direction of the effects varied depending on the study type. Experimental studies conducted in micro- and mesocosms demonstrated greater effects than field studies, thus the extrapolation of results from these to real conditions should be done with caution. Overall, the effects’ direction was inconsistent across climate zones, except for the Mediterranean climate zone, where drying was showing a negative effect on both metabolism and GHG emissions. Our synthesis may contribute to identifying the worldwide trends and patterns of drying on riverine functions associated to global change impacts on river and stream ecosystems.
Special Department India (Sonderreferat Indien) of the German Foreign Ministry
Authors: Roy Baijayanti 
Published: September 2024

Abstract
This chapter examines the epistemology of ‘politically useful’ knowledge concerning India, produced at the Special Department India (Sonderreferat Indien; SRI) of the German Foreign Ministry, through an interface between certain wartime objectives of Nazi Germany and the diasporic Indian anti-colonialism represented by the militant nationalist Subhas Chandra Bose who lived in Berlin between 1941 and 1943. This chapter examines two projects undertaken by the SRI, which illustrate the collusion between knowledge production on India, Bose’s concerns, and the demands of Nazi politics: (a) fortnightly reports on the current developments in India, and (b) a series of German books on modern India. While the reports were to serve as guidelines for the German press in its coverage of Indian events, the books were manifestations of the Nazi government`s tactical expression of sympathy for Indian anti-colonialism as well as informal guidebooks for shaping German propaganda concerning India. These projects were pursued mainly under two functionaries of the SRI: the Indologist Ludwig Alsdorf and the former Social Democrat and trade unionist Franz Josef Furtwängler. Another aim of this chapter is to inspect the role of the Indian anti-colonialists involved in these two projects. While a few of them were allowed to present an occasional report, others were denied the privileged status of knowledge providers for the Nazi government. The chapter contends that the interracial relationships and the process of knowledge production, as well as the utilizable knowledge discourses were deeply influenced by certain racialized assumptions and political prejudices which overlapped between German Indological scholarship and Nazi Weltanschauung.
Introduction
Authors: Shoemaker David 
Published: September 2024

Abstract
This chapter motivates the book with the general motto assumed by most responsibility theorists that “to be a responsible agent is to be an apt candidate for responses like blame and praise.” The first worry about this motto is that blame doesn’t seem to have a unitary nature. We blame each other in multiple ways for multiple reasons. The second worry is that there’s been hardly any work on praise; indeed, most philosophers have assumed that, once we get clear on the nature of blame, then praise will simply be its positive counterpart. But as it turns out, there are many reasons to think not; indeed, there seem to be numerous asymmetries between them. What’s needed, therefore, is a systematic investigation into the nature of both blame and praise, as well as the nature of blameworthiness and praiseworthiness, and the remainder of the introduction lays out the plan of attack.
Policing and Citizen Trust in Kenya: How Community Policing Shapes Local Trust-Building and Collaboration
Authors: Mutahi Patrick , Höglund Kristine , Elfversson Emma 
Published: September 2024

Abstract
In contexts of high insecurity and mistrust in the police, how and why do local residents still choose to collaborate with the police, and what is the role of community policing in such considerations? Research on policing in Africa has emphasized the structural and macropolitical barriers to effective police reform, including institutionalized cultures of impunity and corruption. Less attention, however, has been paid to the contextual and relational dynamics that shape police-community collaboration. We argue that a relational perspective, which centres local residents’ interactions with police and community policing structures, provides novel insights into the challenges of policing reforms. This perspective also demonstrates how contingent and incremental trust can be built in very challenging circumstances. We study these dynamics in Karagita and Kaptembwo, two low-income urban settlements in Nakuru County, Kenya, that have experienced violent crime and repeated electoral violence. Despite considerable challenges of crime, police misconduct, and political interference in these settlements, our findings point to how positive everyday interaction and community policing structures can contribute to incremental improvements in police-community relationships. In contrast to existing work on African policing that primarily highlights the challenges of police reform, this study offers insights into when reform has the potential to be effective.