bluuebunny/crossref_metadata_2025_split huggingface dataset:
Curve-Skeleton Extraction from 3D Objects Using Spiral Patch
Authors: SAYED Abdul Rahman EL , Chakik Abdallah El , Samad Mahmoud , ALABBOUD hassan 
Published: November 2023

Abstract

The process of extracting a curve-skeleton from a 3D object is critical for various computer vision applications, including 3D object matching and recognition, 3D object segmentation, 3D animation, and 3D surface reconstruction. In this paper, we present a novel and efficient method for extracting the skeleton from general 3D objects based on a weight graph representation. Our method defines a unique spiral patch for each graph vertex, representing the shortest loop over the surface contour starting from the vertex. We construct a curve skeleton graph (CSG) using these spiral patches, which accurately represents the skeleton. We demonstrate the robustness of our method through various experiments, including noise reduction and different transformations. Furthermore, we showcase two practical applications based on our method: shape partitioning and gesture recognition. Our approach outperforms existing methods, demonstrating superior accuracy and efficiency. Overall, our proposed method presents a robust and efficient solution for extracting a curve-skeleton from general 3D objects, with promising applications in various computer vision fields.
A Software Architecting for Quantum Machine Learning Platform in Noisy Intermediate-Scale Quantum Era
Authors: Yu Wenbin , Chen Yuhao , Zhang Chengjun , Chen Yadang , Wei Hongyu , Chen Zongyuan , Zhang Yifan 
Published: November 2023

Abstract
In the current Noisy Intermediate-Scale Quantum (NISQ) era, despite quantum computing is challenged by scale limitations and noise, it still holds immense potential to accelerate machine learning for specific problems. However, we have found that existing machine learning platforms do not adequately take into account the properties of quantum computing, resulting in a lack of an integrated quantum machine learning environment. Therefore, this article proposes a software architecture specifically designed for quantum machine learning platform, which aims to provide development tools that offer full life-cycle support for quantum machine learning. We first analyze the software requirements of quantum machine learning platform by using user story method, and then construct the platform architecture blueprint by using multi-view software architecture method. To validate the effectiveness of the architecture we proposed, we used this architecture as a specification to implement quantum reinforcement learning (QRL) and construct a quantum convolutional neural network (QNN). The results indicate that our architecture can effectively support the fusion of quantum computing and machine learning. We believe that this software architecture will propel the development of quantum machine learning in the NISQ era, bringing new opportunities to quantum computing.
An Occupational Mechanical Job Exposure Index based on five Norwegian nationwide Surveys of Living Conditions on work environment
Authors: Hermansen Åsmund 
Published: November 2023

Abstract
Objectives
Nordic register data are not collected for research purposes, and various dimensions of working conditions are typically missing in register-based research. One way to address the issue of missing information on the work environment in register data is to utilize a job exposure matrix (JEM). The purpose of this paper is to document and provide access to a Mechanical Job Exposure Matrix (JEM) and a validated Occupational Mechanical Job Exposure Index based on the constructed JEM, allowing researchers to utilize the index in register-based research. The JEM and the Occupational Mechanical Job Exposure Index were created using data from five nationwide Surveys of Living Conditions on work environment conducted in Norway in 2006, 2009, 2013, 2016, and 2019, encompassing a total of 43,977 respondents. The ultimate aim of constructing the index was to create a comprehensive measure of mechanical job exposures for use in future analyses of Norwegian register data.
Data description
This paper provides the scripts documenting the construction of the Mechanical Job Exposure Matrix (JEM) and the Occupational Mechanical Job Exposure Index, as well as a data file including the matrix and the index. A script for merging the matrix and index to register data is also provided.
A Randomized Ensemble Based on Distinguishing Output Labellings]{A Randomized Ensemble Based on Distinguishing Output Labellings
Authors: Calle-Romero Mario , Martínez-Muñoz Gonzalo 
Published: November 2023

Abstract
This article proposes and analyzes a novel classification model trained to distinguish correctly from incorrectly labeled instances. The proposed method transforms any classification problem from a task of producing the correct label to a task of deciding if a given label is correct. To do so, a new dataset is created in which an attribute is added with a randomized label suggestion for each instance. The model then learns if the suggested labels are correct or not.The method builds an ensemble of decision trees trained on different randomized versions of this classification task.The proposed ensemble is validated with several experiments with the aim of giving some insights on the algorithm performance, especially its hyper-parameters, and to compare its performance with benchmark models. The experiments, carried on a variety of datasets, real-world and synthetic,shows that the proposed model achieves competitive results with respect to state-of-the-art learning models of the literature such as Random Forest.
Is cyber hygiene a remedy to IPTV infringement? A study of online streaming behaviours and cybersecurity practices
Authors: Shah Rajiv , Cemiloglu Deniz , Yucel Cagatay , Ali Raian , Katos Vasilis 
Published: November 2023

Abstract
Spurred by the rapid modernisation of the sector and advent of Internet Protocol Television (IPTV), Audio visual (AV) piracy is at epidemic levels, with interventions having limited effect. The dominant theme in interventions to date have been around personal deterrence (i.e., the threat of legal action) and have not considered other factors that may influence an individual’s decision to consume infringing content. In this paper, we consider psychological factors, including perceptions around risk-taking, security behaviours, problematic internet use and personality traits to gain a comprehensive understanding of factors influencing engagement with IPTV, and the potential implications for cybersecurity. For this purpose, a survey was conducted with 283 participants living in the UK (age range 18 - 74, male 104), and an integrated structural equation model was constructed. Our findings showed a positive relationship between security behaviours and the perceived risk of viewing IPTV, and a negative relationship between the dark personality triad and perceived risk of viewing IPTV. They suggest that security behaviours fully mediate the relationship between problematic internet use and IPTV risk taking, indicating a potential new path forward for anti-piracy interventions with greater efficacy.
Evaluating a Sigmoid Dark Energy Model to Explain the Hubble Tension
Authors: Torres-Arzayus Sergio , Delgado-Correal Camilo , Higuera-G. Mario-A. , Rueda-Blanco Sebastian 
Published: November 2023

Abstract
In this study we analyze Type Ia supernovae (SNe Ia) data sourced from the Pantheon+ compilation \citep{pantheon} to investigate late-time physics effects influencing theexpansion history, \(H(z)\) , at redshifts \((z &#x003C; 2)\) . Our focus centers on a time-varying dark energy (DE) model that introduces a rapid transition in the equation of state, at a specific redshift, \(z_a\) , from the baseline, \(\Lambda = -1\) , value to the present value, \(w_0\) , through the implementation of a sigmoid function.The constraints obtained for the DE sigmoid phenomenological parametrization have broad applicability for dynamic DE models that invoke late-time physics.Our analysis indicates that the sigmoid model provides a slightly better, though not statistically significant, fit to the SNe Pantheon+ data compared to the standard \lcdm\alone. The fit results, assuminga flat geometry and maintaining \(\Omega_m\) constant at the 2018-Planck value of $0.3153$, are as follows: \(H_0 = 73.3^{+0.2}_{-0.6} \) \ksmpc\, \(w_{0} = -0.95^{+0.15}_{-0.02} \), \(z_a = 0.8 \pm 0.46\). The errors represent statistical uncertainties only.The available SN dataset lacks sufficient statistical power to distinguish between the baseline \lcdm\and the alternative sigmoid models.A feature of interest offered by the sigmoid model is that it identifies a specific redshift, $z_a = 0.8$, where a potential transitionin the equation of state could have occurred. The sigmoid model does not favor a DE in the phantom region ($w_0 < -1$). Further constraints to the dynamic DE model have been obtained using CMB data to compute the distance to the last scattering surface. While the sigmoid DE modeldoes not completely resolve the $H_0$ tension, it offers a transition mechanismthat can still play a role alongside other potential solutions.
Predictive Model Suggests Increase of Blue Streamer Discharges due to Climate Change
Authors: Husbjerg Lasse Skaaning , Chanrion Olivier , Neubert Torsten , Li Dongshuai , Stendel Martin , Kaas Eigil , Reglero Victor 
Published: November 2023

Abstract
Blue streamer discharges are corona flashes usually originating from the upper part of thunderclouds and are associated with more severe weather systems than lightning. When observed from space they present with strong 337.0 nm emissions and essentially no 777.4 nm component. We built a model based on measurements by the Atmosphere-Space Interactions Monitor (ASIM) on the International Space Station (ISS) which allows us to predict where these events occur with only ERA5 reanalysis data as inputs. Running the model for even years in the period 1980 to 2022 we show that the production of blue streamer discharges increases in a warming climate at a rate of \((7 \pm 2)% K^{-1}\). Changes in the geographical distribution of the events show that the increase is centered mainly in central Africa with smaller increases in Australia, eastern North America and eastern Europe.
Research on fire early warning index system of coal mine goaf based on multi-parameter fusion
Authors: Wang Beifang , Lv Yuanhao , Liu Chunbao 
Published: November 2023

Abstract
In order to effectively prevent and control goaf fire in coal mines, a goaf fire early warning index system based on multi-parameter fusion was established. So that the temperature characteristic index for early fire warning in Goaf could be found, thermogravimetric tests and programmed heating tests must be used to see how often coal spontaneously burns. Additionally, combined the temperature characteristic index and the Graham coefficient yields the primary gas index. The auxiliary gas index, which exhibits a significant correlation with goaf fire, was calculated through the utilisation of the grey correlation methodology. Furthermore, the integration of the early warning temperature index and other gas indicators was accomplished through the use of the D-S evidence theory. Additionally, the outcome of the integration process involved the establishment of a multi-parameter integration-based system that served as an early fire warning signal for coal mining sites. The validation of the early warning index system's effectiveness was then conducted by a programmed heating test. The results suggested that the process of coal spontaneous combustion could be divided into six discrete phases, specifically the latent phase, oxidation phase, critical phase, pyrolysis phase, fission phase, and combustion phase. The identification of these stages was based on the distinctive temperatures associated with spontaneous combustion of coal, which have been observed at 31.7°C, 54.8°C, 153.7°C, 204.5°C, and 241.6°C. 100Δ(CO)/ΔO2(%) can be used as the principal indicator of gas within the Goaf region, hence facilitating the prompt identification of fires. The compounds C3H8/CH4, C3H8/C2H6, C2H4/C2H6, and C2H2 possess the capability to function as additional gas indicators for the timely identification of goaf fires. The utilisation of the D-S evidence theory in weight distribution provides support for the creation of an early warning index system for goaf fires, which relied on the fusion of many parameters. The reliability of the early warning index system was shown by means of a controlled heating experiment.
Total intracorporeal laparoscopic ileal ureter replacement in a single position for ureteral stricture based on membrane anatomy
Authors: Wu Guohao , Li Haomin , Luo Feng , Zheng Handa , Yuegao Yuanzhi , Xie Lishan , Luo Huilan , Chen Zhihui , Ye Dongming , Lai Caiyong 
Published: November 2023

Abstract
Purpose The aim of this study was to present our initial experience and prove the feasibility of total intracorporeal laparoscopic ileal ureter replacement (TILIUR) in a single position for ureteral stricture based on membrane anatomy.
Materials and methods Between January 2021 and April 2023, six patients underwent TILIUR in a single position for ureteral strictures based on membrane anatomy. All patients with a past medical history underwent radical hysterectomy with bilateral pelvic lymph node dissection as well as extensive ureteral stricture due to radiotherapy. The procedure is performed completely laparoscopically. Dissection of the digestive system as well as ureteral stricture or renal pelvis is based on membrane anatomy. The surgery is performed in a single position.
Results TILIUR in a single position for ureteral stricture based on membrane anatomy was successfully performed without open conversion in all patients. Among the 6 patients, 3 patients underwent combined ileal ureter replacement (IUR) and abdominal wall ostomy, 2 underwent unilateral IUR, and 1 underwent bilateral IUR. The mean length of the ileal substitution was 22.83 cm (range: 15-28). The average operative time was 458 ± 72.77 minutes (range 385-575 minutes), and the average intraoperative blood loss was 158 mL (range 50-400 mL). The median postoperative hospital stay was 15.1 d (range: 8–32). The median duration of postoperative follow-up was 15 months (range: 3-29 days). The success rate was 100%.
Conclusions TILIUR in a single position may be a promising option for ureteral stricture based on membrane anatomy in selected patients. Moreover, it has a positive effect on patients with renal insufficiency and urinary incontinence. Although IUR is difficult and risky, proficient surgeons can perform the procedure safely and effectively.
Independent Travelers’ Knowledge-Sharing Willingness in Travel Social Networking Sites
Authors: Chen Yi-Fen , Chou Shu-Hui , Jou Tzu-I 
Published: November 2023

Abstract
Purpose
With the rise of the Internet and the emergence of the global social community, independent travelers are increasingly engaging in real-time interaction and knowledge sharing through travel social networking sites. Existing studies on travel social networking sites has primarily focused on the community’s willingness to use them. Therefore, this study explored independent travelers’ intention to share knowledge using travel social networking sites by examining their innovative personality traits and community identity.
Methods
The study employed an online questionnaire, and 480 valid questionnaire surveys were analyzed using structural equation modeling and confirmatory factor analysis.
Results
The results show that independent travelers’ innovative personality traits affect subjective knowledge and perceived risk; community identification affects trust and sense of belonging; trust affects perceived risk, sense of belonging, and subjective knowledge; and sense of belonging affect tourism knowledge-sharing willingness.
Conclusion
These results are beneficial for travel industry managers in developing marketing strategies and managing travel social networking sites.
Effects of salt concentration on top wall bubble injection in a turbulent channel flow: Bubble dynamics and wall drag reduction
Authors: Biswas Subhajit , Govardhan Raghuraman N. 
Published: November 2023

Abstract
Most applications related to bubble drag reduction (BDR) occur in contaminated environments where the presence of different surface active agents modify bubble coalescence and hence, affect flow drag. Although there have been studies on bubble drag modifications with salt/surfactant, the effects of systematic variation in salt/surfactant concentration on bubble dynamics and drag remain relatively unexplored. Driven by this motivation, in the present work, we experimentally investigate the effects of salt concentration on the bubble dynamics and drag modification in a fully developed horizontal turbulent channel flow for top wall bubble injection, over a wide range of salt concentrations (0< M <0.08, moles/liter), channel Reynolds number (22,500< Re <65,000), and injected bubble void fraction (0< α <0.16). The injected bubbles interact with the flow in the turbulent channel and as they move downstream reach an equilibrium state between the bubbly phase and the fully developed carrier phase that persists further downstream. The equilibrium state of the bubble dynamics is captured by high-speed visualizations and the corresponding drag is obtained from stream-wise pressure drop measurements within the channel. Increasing salt concentration levels is seen to lead to reduction in bubble coalescence and consequently in bubble size that modifies bubble deformability, migration, and distribution near the top wall, with the changes being dependent on the Re and α values. At low Re ≈ 22, 500, the addition of salt leads to a dramatic reduction in bubble sizes (∼ 100 microns) from the very large coalesced bubbles (∼ cm) seen in the no-salt cases, with consequent changes in the bubble dynamics and increased drag (up to 70%). The reduction in bubble sizes with salt addition leads to an increase in drag with salt concentration, which saturates beyond a critical salt concentration (MCritical). With increasing Re, due to smaller time available for coalescence, the bubbles are relatively small even in the absence of salt and hence, the bubble dynamics is found to be less susceptible to modifications by the addition of salt, unlike at low Re, and hence, results in a smaller increase in drag and a lower MCritical. At the largest Re of about 65,000, both the bubble dynamics and drag are found to be very similar in both the no-salt and salt cases indicating that MCritical ≈ 0. Our observations suggest that at low Re, the experiments in typical laboratory freshwater conditions will not predict the bubble dynamics aspects and flow modifications in contaminated environments, while it can be a good indicator at large Re. The present results can thus help deepen our understanding on bubbly flow applications in contaminated environments, such as those that occur in bubble induced drag reductions in ships and pipelines.
Medical Improvisation: Impact of Training on Medical Students’ Experience with Communication on Clinical Rotations - A Single-Center Mixed-Methods Study
Authors: Kukora Stephanie , Batell Brittany , Ravi Nithin , Zikmund-Fisher Brian J. , Thompson Christopher , Mullan Patricia B. 
Published: November 2023

Abstract
Background: Effectively preparing medical students to communicate with patients, families, and teams remains a challenge in medical education. Although techniques from medical improvisation have been introduced to teach communication skills in this context, limited evidence exists regarding how such training elicits student involvement, impacts clinical students’ communication skills, and affects medical students’ authentic clinical care interactions.
Objective: To implement a medical improvisation-based educational training in the pre-clinical year and gather medical students’ reflections following one year of clinical rotations.
Methods: We recruited medical student volunteers to participate in an established medical improvisation curriculum, complete course evaluations, and provide progressive reflections as they began and continued their clinical medical training. Course duration was 2 hours per week for 5 weeks, prior to beginning clinical rotations. Participants completed anonymous, electronic evaluations via Qualtrics software before and after the course, which included structured (closed-ended) and open-ended questions. One year later, after students completed core clinical rotations, we conducted semi-structured interviews and performed thematic analysis on transcripts.
Results: 23 of 24 recruited students completed the course and surveys. Pre/post-course comparison survey data show significant improvements in reported comfort with different communication situations. 11 students (48%) participated in follow-up interviews. All interviewees reported they found the course valuable and, universally, would recommend it. In interviews, students noted that skills improved by medical improvisation included flexibility, “yes, and” approach, accepting mistakes, listening, and confidence in unknown situations. Many interviewees felt medical improvisation was helpful to their professional development, gave them strategies for challenging conversations in real-life practice, and supported personal growth and wellness and building friendships.
Conclusion: Medical students exposed to medical improvisation prior to clinical rotations noted both immediate benefits in communication skills and long-lasting improvement in specific domains of communication.
SSADLog: Whole Lifecycle Tuning Anomaly Detection with Small Sample Logs
Authors: Zhou Zhisheng , Zhou Meixiu , Wu Axin , Xia Jie , Liu Weiping 
Published: November 2023

Abstract
Log messages play a critical role in system analysis and issue resolution, particularly in complex software-intensive systems that demand high availability and quality assurance. However, log-based anomaly detection faces three major challenges. Firstly, millions of log data poses a significant labeling challenge. Secondly, log data tends to exhibit a severe class imbalance. Thirdly, the task of anomaly detection in such massive datasets requires both high accuracy and efficiency. Numerous deep learning based methods have been proposed to tackle those challenges. Yet, a comprehensive solution that effectively addresses all these issues has remained elusive. Through careful examination of log messages from stable systems, we find a consistency principle: the number of unique anomaly logs is consistently small. Based on this principle, we present a novel framework called ''Whole Lifecycle Tuning Anomaly Detection with Small Sample Logs'' (SSADLog). SSADLog introduces a hyper-efficient log data pre-processing method that generates a representative subset of small sample logs. It leverages a pre-trained bidirectional encoder representations from transformers (BERT) language model to create contextual word embeddings. Furthermore, a semi-supervised fine-tuning process is employed for enhancing detection accuracy. A distinctive feature of SSADLog is its ability to fine-tune language models with small samples, achieving high-performance iterations in just approximately 30 minutes. Extensive experimental evaluations show that SSADLog greatly reduces the effort to detect anomaly log messages from millions of daily new logs and outperforms the previous representative methods across various log datasets in terms of precision, recall, and F1 score.