bluuebunny/crossref_metadata_2025_split huggingface dataset:
Theatre as an engine for German-Swedish cultural transfer in the early twentieth century
Authors: Brandau Nina 
Published: October 2024

Abstract
It was not only literary exchange and travel writing that flourished between Germany and Scandinavia around
1900. The theatre sector was also influenced by increasing artistic mobility that facilitated the transfer of ideas on modern
theatre across Europe. This chapter examines selected guest performances and directorships of the German theatre director Max
Reinhardt and the actor Alexander Moissi in Stockholm between 1915 and 1921. Expanding the traditional idea of travel writing,
the artists used guest performances as a means of allowing ideas to travel to a new national context. Using cultural transfer
and mobility theories, the aim of this contribution is to explore how Reinhardt’s and Moissi’s mobile acts brought cultural
and aesthetic ideas to Sweden and which factors – social, political, aesthetic as well as infrastructural – influenced the
transfer process. This is done with a content analysis of newspaper articles as primary sources. With the help of these
sources, I will illustrate that Reinhardt’s and Moissi’s visits to Sweden were only successful because of a well-developed
German-Scandinavian network that had been built beforehand. It is argued that the artistic ideas that they transferred to
Sweden were mainly affected by socio-cultural and infrastructural developments, while political ideologies were of secondary
importance.
Exile, travel narrative and cultural transfer in Négar Djavadi’s Désorientale (2016)
Authors: den Toonder Jeanette 
Published: October 2024

Abstract
This chapter aims to analyse how the intersection of travel and exile in Négar Djavadi’s novel
Désorientale (2016) motivates cultural exchanges through movements of conflict and contact. After having
established a framework of exile and travel in relation to narrative, cultural encounters and transfer, the analysis firstly
focuses on the metaphorical and physical levels of travel and exile as developed in the novel. Exile and travel are
intertwined and a necessary part of the female protagonist’s life and offer the possibility to create a travel narrative where
she connects her Iranian past and French present. The second part examines the confrontation with her sexual identity and the
understanding of her hybrid body as a place of encounter between different cultures. By acknowledging the disorientation of
her exiled body, the protagonist/narrator is capable of establishing relationships with others and to share her experience.
The final section further discusses the importance of meaningful encounters by elaborating on the connection between the
narrator and the implied reader that allow for critical reflection and transfer of insights, emotions and ideas. The analysis
demonstrates the role of the exiled narrator as cultural transmitter. This chapter further contributes to the study of travel
writing and cultural transfer by offering new perspectives on how the genre develops in the twenty-first century as an effect
of migration.
A Weibo local network growth model constructed from the perspective of following–followed
Authors: Nian 年 Fu-Zhong 福忠 , Yao 姚 Ran-Qing 然庆 
Published: October 2024

Abstract
In order to explore the evolution process of the Weibo local network, this study first defines four factors influencing the evolution of the Weibo network. On this basis, the BA scale-free network model was enhanced by incorporating these four factors and accounting for directionality, resulting in a Weibo local network evolution model based on user attributes and behavioral similarity. The model’s validity was validated by comparing simulation results with real data. The findings indicate that the Weibo local network exhibits both small-world characteristics and distinctive features. The results show that the Weibo local network exhibits both small-world characteristics and distinctive properties. The in-degree distribution follows a mixed pattern of exponential and power-law distributions, the degree-degree shows isomatching, and both the in-degree centrality and eigenvector centrality values are relatively low. This research contributes to our understanding of user behaviour in the Weibo network, and provides a structural basis for exploring the impact of Weibo network structure on information dissemination.
MPDDF: Multi perspective dual data flow model for time series forecasting
Authors: Yang Baoshun 
Published: October 2024

Abstract
Multivariate time series forecasting tasks are widely used in social production and daily life, and making accurate predictions for the future can help enterprises take preventive measures for upcoming events. The prediction method based on deep neural networks has become the main method used in time series forecasting tasks. In order to improve the performance of the prediction model, this paper designs methods to improve the prediction performance of the model from the perspectives of multi-dimensional, time-domain, and data distribution in multivariate time series data. Using graph neural networks to establish dependencies between multi-dimensional data and perform feature extraction, using dual flow learning networks for time-domain feature extraction, and preventing overfitting of the model during training, and using learnable bias values to mitigate the impact of time series data distribution shift. By conducting experiments on the model in five different application scenarios and comparing it with advanced time series forecasting models, an average improvement of 7.50% was achieved in the MSE index and 6.53% in the MAE index, demonstrating the effectiveness of the model designed in this paper.
Dynamic analysis of six-bar tensegrity-based robot
Authors: Ji Zhifei , Du Zhengyin , Lin Min , Wang Weiqi , Yan Wenduan 
Published: October 2024

Abstract
The tensegrity-based robot is a hot research topic in the research field of robotics. The dynamics of the robot, describing the specific motion patterns under the influence of forces and torques, play an important role in the application of robotics. In addition, the dynamics should be considered when revealing the internal mechanisms of robots and predicting their dynamic behaviors. In this work, a dynamic model for a rolling six-bar tensegrity-based robot is established. In terms of kinematic representation, quaternions are chosen as the tool for attitude description, simplifying the derivation process of the complex three-dimensional rotational kinematics calculations. Quaternions play a crucial role in mathematical processing for robot attitude control and path planning due to their effective representation of three-dimensional space rotations. The classic Newton-Euler dynamic framework was adopted to conduct in-depth and detailed studies on the dynamic characteristics of the robot under various force conditions and motion states, with a particular focus on rolling dynamic analysis. Special attention was paid to the dynamic response mechanisms of each component of the robot under the combined action of internal and external forces and torques.
A helmet detection algorithm based on improved Yolov8n
Authors: Nie Yanping , Xie Xiaoyun 
Published: October 2024

Abstract
To address the issues of false positives and missed detections in current safety helmet algorithms, we propose a new detection method using an enhanced yolov8n model. An enhanced Yolov8n-based algorithm for detecting safety helmets is proposed. In this experiment, a new module called C2f_ECA is proposed. This module can extract channel information and enhance the performance of the network by replacing the original loss function of the algorithm with the EIoU loss function. The aspect ratio loss term is now determined by the difference between the predicted dimensions of width and height and those of the minimum bounding box. This modification speeds up the convergence of predicted boxes and improves their regression accuracy. In addition, a dataset of safety helmets is constructed in complex scenarios. The proposed algorithm enhances Yolov8n’s mAP50 by 1.6% and average accuracy by 1.6% compared to the original and traditional Yolov8 algorithms, respectively. It can efficiently address the issues of false positives and missed detections of safety helmets.
Counterfactual-based two-stage trajectory prediction framework for autonomous vehicle navigation systems
Authors: Ren Jiaxing 
Published: October 2024

Abstract
Accurate prediction of a vehicle’s trajectory is essential for autonomous driving and robotic operations in a complex, dynamic environment. However, current methods that rely on historical and environmental cues to predict future trajectories make the inner distance between testing and training environments often overlooked. To address this issue, we propose a counterfactual analysis method specifically designed for vehicle trajectory prediction. We begin by constructing a causal graph that includes historical trajectories, future trajectories, and environmental interactions. This graph captures the causal relationships between these elements. Our approach directly intervenes on the trajectory itself, effectively cutting off the inference from the environment to the trajectory. By doing so, we mitigate the negative impact of environmental bias. Furthermore, by comparing observed and intervened trajectory clues, we highlight the influence of environmental distance. Our goal is to improve the accuracy of trajectory prediction. Our counterfactual analysis module integrates seamlessly into various baseline prediction models. Experimental data demonstrates that our method outperforms others on publicly available Argoverse2 motion forecasting benchmarks.
An improved whale algorithm for real-time boundary processing and its convergence analysis
Authors: Yu Kaixuan , Liu Yongsheng , Zhang Hepei , Gao Shichen , Li Shuao , Wan Xueyu , Bao Peng 
Published: October 2024

Abstract
To tackle the deficiencies in the whale algorithm, including its weak stability, sluggish convergence rate, and tendency to get trapped in local optima, an adapted whale optimization algorithm has been created, which introduces quasi-opposition learning and a real-time boundary processing mechanism. In the enhanced algorithm, a quasi-opposition learning mechanism is implemented to broaden the search scope and augment the variety of the population during the initial phases of iteration. This approach also helps to guarantee the convergence of the algorithm in the later phases of iteration. The unified boundary processing method of delay concentration is improved to solve the problem that a large amount of convergence damages diversity if a large number of transgressive individuals are unified and centralized. The algorithm flow is given and the asymptotic fitness and the asymptotic global optimal solution are proved. The time complexity is analyzed theoretically. Ultimately, six recognized algorithms were utilized to simulate the CEC 2017 function set across different scales. Findings indicate that the enhanced algorithm significantly boosts convergence rate, optimization precision, and solution robustness, demonstrating strong convergence characteristics.
An analysis of the factors affecting Community-Based Tourism associated with Indigenous Knowledge of the Khmer ethnic people minority in An Giang Province, Vietnam
Authors: Ha N T N , Lien T T K 
Published: October 2024

Abstract
Community-Based Tourism has gained popularity around the world in recent years because of its benefits and impact on local communities. In this trend, ethnic minority communities, which comprise distinct and unique Indigenous Knowledge Systems, are becoming more appealing and popular tourist destinations. While the community tourism model based on Indigenous Knowledge is gaining popularity, enough study has not been conducted to analyze the elements influencing this approach. The purpose of this study is to assess factors influencing the growth of community-based tourism in An Giang province using traditional Khmer ethnic minority expertise. In this study, quantitative methods were performed on 196 tourists who had visited this model. According to the research findings, four categories of factors influence the development of this model: the Community-Based Tourism key factor (1), the Community-Based Tourism unique factors (2), the Community-Based Tourism Supporting factors (3), and the Indigenous Knowledge Systems factor (4).
Governance Practices in Mitigating Human-Elephant Conflicts in Dong Nai Province, Vietnam
Authors: Ngoc P T B , Huong T T T 
Published: October 2024

Abstract
Human-elephant conflicts (HEC) have become a significant concern due to their implications for both biodiversity conservation and human livelihoods. It has become a serious issue in Asian countries, particularly in Vietnam. This paper explores the effective governance strategies and good practices to mitigate human-elephant conflict in Dong Nai province, Vietnam. Through participatory observation, in-depth interviews, and secondary data analysis, this article aims to identify and evaluate the practices that have been employed to mitigate these conflicts. The participatory observation was conducted over ten years, from 2013 to 2023, and 28 interviews were carried out with conservation experts, local government officials and residents. As a result, the study identified the causes, solutions, and outcomes of 10 years of governance practices addressing HEC. It also found an increase in the elephant population and improvement in their health status in Dong Nai. Importantly, the study found that good governance practices have reduced conflict and improved local livelihoods. Finally, this article proposes a governance model for human-elephant conflict mitigation operated by a triangular connection between the state, the community, and conservation groups in initial strategies related to minimizing human-elephant conflict. This model aims to achieve two main goals: protecting elephant herds, increasing their population, and safeguarding agricultural production areas. These findings provide insights for policymakers, conservationists, and local communities in their efforts to achieve harmonious coexistence between humans and elephants.
Scalable quantum detector tomography by high-performance computing
Authors: Schapeler Timon , Schade Robert , Lass Michael , Plessl Christian , Bartley Tim J 
Published: October 2024

Abstract
At large scales, quantum systems may become advantageous over their classical counterparts at performing certain tasks. Developing tools to analyze these systems at the relevant scales, in a manner consistent with quantum mechanics, is therefore critical to benchmarking performance and characterizing their operation. While classical computational approaches cannot perform like-for-like computations of quantum systems beyond a certain scale, classical high-performance computing (HPC) may nevertheless be useful for precisely these characterization and certification tasks. By developing open-source customized algorithms using HPC, we perform quantum tomography on a megascale quantum photonic detector covering a Hilbert space of 106. This requires finding 108 elements of the matrix corresponding to the positive operator valued measure, the quantum description of the detector, and is achieved in minutes of computation time. Moreover, by exploiting the structure of the problem, we achieve highly efficient parallel scaling, paving the way for quantum objects up to a system size of 1012 elements to be reconstructed using this method. In general, this shows that a consistent quantum mechanical description of quantum phenomena is applicable at everyday scales. More concretely, this enables the reconstruction of large-scale quantum sources, processes and detectors used in computation and sampling tasks, which may be necessary to prove their nonclassical character or quantum computational advantage.
Design and test on high-gap wheeled agricultural chassis for harvesting broccoli
Authors: Xu Huimin , Chen Yijin , Hu Miaodan , Yu Gaohong , Zheng Chao , Zhang Zhou , Wu Tianhao , Zhao Xiong 
Published: October 2024

Abstract
According to the planting mode of broccoli and the growth characteristics of broccoli, a kind of high-gap agricultural chassis with structural reliability and strong stability was designed for broccoli picking. Firstly, the dynamics and kinematics of the chassis were analyzed, and then we analyzed the stability and completed the finite element analysis of the key components. Finally, the simulation and field test were carried out. Thus, the main performance parameters of the chassis are determined. The overall power, driving power consumption, maximum endurance time, maximum driving distance, maximum power storage, and maximum battery power were 5.959kw, 1.84kw, 3 h, 21 km, 5.63kwh, and 6.552kw respectively. The results of the slope climbing and ridge driving test were better than those of the simulation test. The actual slope climbing effect of the chassis accords with the calculation of theoretical value, and it can climb the soil slope of 25°. These findings proved the stability of the high-gap agricultural chassis in the field and indicated that the chassis can be applied to small fields in hilly and mountainous regions.
Intelligent aerodynamic identification method based on wavelet transform and deep learning
Authors: Ma Guilin , Dai Jun , Hou Zhenyan , Li Shichao , Liu Xiangyun , He Mingao , Wen Fengqian , Chen Wei 
Published: October 2024

Abstract
Wind tunnel force testing is particularly crucial for the aerodynamic characteristics of hypersonic vehicles, and the intelligent and high-precision aerodynamic identification technology serves as the key supporting technology. Due to the short effective test time of the pulse combustion wind tunnel, the inertial force vibration generated by the force measurement system is difficult to be attenuated completely at the start of the wind tunnel. At the same time, there are many interference signals in the output signal during the test, which makes it difficult to achieve high-precision aerodynamic load identification. Therefore, in order to eliminate the influence of interference signals on aerodynamic identification accuracy and obtain accurate aerodynamic loads, this paper introduces an intelligent aerodynamic identification method based on wavelet transform and deep learning. Firstly, meyer wavelet is selected as the wavelet basis, and signal processing is performed on the output signal through wavelet transform to eliminate interference signals and improve signal quality. Then, through RNN-GRU network model to identify the load. The reliability of the method is verified by the rod balance test platform. The results show that the method can effectively reduce the influence of interference signal, and the relative error between the identified load and the real load is about 2%. At the same time, the identification results of the method are compared with those of the mean value method, which further verifies the accuracy of the proposed method.
Enhancing tribological performance of AISI 52100: the role of laser surface texturing in dry and lubricated conditions
Authors: Turali Narayana , Saleem Shahid , Muddada Venkatesh , Naik Deepak Kumar , Sreeramulu Dowluru , Duppala Azad , Dilawar Mohammed , Radha Kalyani 
Published: October 2024

Abstract
Laser surface texturing (LST) is a valuable technique in surface engineering that offers significant improvements in the tribological behavior of metals and alloys. This study investigates the synergistic effect of incorporating hexagonal boron nitride (h-BN) as a solid lubricant with laser surface texturing on the tribological performance of AISI 52100. Dimple textures with varying pitch distances (100, 150, and 200 μm) were created on the surfaces of the mentioned materials using LST. The friction coefficient and wear behavior of both non-textured surface (NTS) and textured surfaces (TS) were analyzed under dry sliding (DS) and lubricating sliding (LS) conditions. Wear tests were conducted using a universal tribometer under a load of 10 N, a sliding frequency of 10 Hz, and a sliding time of 1250 s. The results demonstrate that the presence of dimple textures significantly influences the tribological behavior of the materials. In DS, the wear coefficient exhibits a significant reduction compared to NTS, with decreases of 88.1% for TSP-100, 28.5% for TSP-150, and 76.1% for TSP-200. In LS, the TSP-L-100 (h-BN NPS) surface demonstrates the lowest average wear coefficient among all textured surfaces. Specifically, the TSP-L-100 surface shows a remarkable 97.61% reduction in average wear coefficient compared to NTS. This study demonstrates the potential of LST combined with h-BN as a solid lubricant in improving the tribological properties of metals, offering valuable insights for surface engineering applications in various industries.
Axonal damage and inflammation response are biological correlates of decline in small-world values: a cohort study in autosomal dominant Alzheimer’s disease
Authors: Vermunt Lisa , Sutphen Courtney L , Dicks Ellen , de Leeuw Diederick M , Allegri Ricardo F , Berman Sarah B , Cash David M , Chhatwal Jasmeer P , Cruchaga Carlos , Day Gregory S , Ewers Michael , Farlow Martin R , Fox Nick C , Ghetti Bernardino , Graff-Radford Neill R , Hassenstab Jason , Jucker Mathias , Karch Celeste M , Kuhle Jens , Laske Christoph , Levin Johannes , Masters Colin L , McDade Eric , Mori Hiroshi , Morris John C , Perrin Richard J , Preische Oliver , Schofield Peter R , Suárez-Calvet Marc , Xiong Chengjie , Scheltens Philip , Teunissen Charlotte E , Visser Pieter Jelle , Bateman Randall J , Benzinger Tammie L S , Fagan Anne M , Gordon Brian A , Tijms Betty M 
Published: October 2024

Abstract
The grey matter of the brain develops and declines in coordinated patterns during the lifespan. Such covariation patterns of grey matter structure can be quantified as grey matter networks, which can be measured with magnetic resonance imaging. In Alzheimer’s disease, the global organization of grey matter networks becomes more random, which is captured by a decline in the small-world coefficient. Such decline in the small-world value has been robustly associated with cognitive decline across clinical stages of Alzheimer’s disease. The biological mechanisms causing this decline in small-world values remain unknown. Cerebrospinal fluid (CSF) protein biomarkers are available for studying diverse pathological mechanisms in humans and can provide insight into decline. We investigated the relationships between 10 CSF proteins and small-world coefficient in mutation carriers (N = 219) and non-carriers (N = 136) of the Dominantly Inherited Alzheimer Network Observational study. Abnormalities in Amyloid beta, Tau, synaptic (Synaptosome associated protein-25, Neurogranin) and neuronal calcium-sensor protein (Visinin-like protein-1) preceded loss of small-world coefficient by several years, while increased levels in CSF markers for inflammation (Chitinase-3-like protein 1) and axonal injury (Neurofilament light) co-occurred with decreasing small-world values. This suggests that axonal loss and inflammation play a role in structural grey matter network changes.