bluuebunny/crossref_metadata_2025_split huggingface dataset:
Leveraging ontochains for distributed public transit ticketing: An investigation with the system for ticketing ubiquity with blockchains
Authors: Preece Joseph D. , Morris Christopher R. , Easton John M. 
Published: May 2024

AbstractTransport ticketing systems are crucial for enabling seamless, efficient, and sustainable mobility. However, traditional ticketing systems face limitations such as ticket fraud, lack of interoperability, and the inability to adapt to changes in the dynamic transport networks they issue tickets for. This paper presents new approaches to the system for ticketing ubiquity with blockchains (STUB), a novel smart transport ticketing solution that employs ontochains, a hybrid data structure combining blockchains and ontologies to form a type of distributed knowledge graph. STUB aims to address these limitations by providing a secure, transparent, and flexible platform for ticket issuance, validation, and management. We describe the key components and workflow of the STUB system, highlighting the use of transport network ontologies for modelling complex relationships within transportation systems and blockchain technologies for transport network ontology's state. Additionally, the implementation of Merkle proofs for efficient and secure validation between on‐chain and off‐chain ontological data is discussed. Finally, a simulated toy example is used as a lightweight proof‐of‐concept to demonstrate these capabilities. The proposed STUB system has the potential to significantly impact the future of transportation ticketing by offering a more seamless, interoperable, and user‐friendly experience whilst addressing the challenges associated with traditional ticketing systems.
Blockchain end user adoption and societal challenges: Exploring privacy, rights, and security dimensions
Authors: Virani Salman Saleem 
Published: May 2024

AbstractThe focus of this review article is on the societal problems and end user acceptance of blockchain technology. The paper begins by outlining the importance of blockchain in modernizing trust and data management systems and highlighting its rapid spread across numerous industries. In‐depth analysis of the adoption‐influencing aspects is done, which also lists the advantages and typical end‐user problems. It examines the privacy implications, restrictions on pseudonymity, and function of technologies that improve privacy, such as zero‐knowledge proofs, while also exploring the legal and regulatory environment around blockchain, putting a focus on digital identity, intellectual property, and data ownership. It also evaluates blockchain security features, such as flaws and risks associated with smart contracts, discusses best practices for boosting security, discusses the societal effects of blockchain, and makes suggestions for legislators, companies, and scholars. The use of blockchain technology and its effects on privacy, rights, and security are discussed in real‐world case studies as well.
A 4‐GHz actively driven ultra‐low phase noise mode‐locked fibre laser
Authors: Yentur Abdulkadir , Keskin Mehmet Ziya , Ozdur Ibrahim 
Published: May 2024

AbstractThis work demonstrates an actively driven ultra‐low phase noise mode‐locked laser built with all polarization‐maintaining components. The output of the laser is an optical pulse train at 4‐GHz repetition rate and 1.63‐ps pulse width. The absolute phase noise and timing jitter of the photo detected signal is measured and found to be –140 dBc/Hz at 10‐kHz offset frequency and 10.21 fs (integrated from 100 Hz to 10 MHz).
Named entity recognition model based on Multi‐BiLSTM and competition mechanism
Authors: Zhou Yang , Zeng Haoyang , Zhang Wei , Liu Jiacheng 
Published: May 2024

AbstractA named entity recognition (NER) model based on multiple bidirectional long short‐term memory networks (Multi‐BiLSTM) and competition mechanism (CM) is proposed. This model includes three parts: word vectorization module, learning module and application module. In the word vectorization module, the word vectors containing semantic features are generated based on BERT (Bidirectional Encoder Representations from Transformers). In the learning module, the multiple small‐scale BiLSTM are used to extract the contextual features of the word vectors synchronously. In the application module, the competition mechanism is adopted to select the relatively better models obtained in the learning module for recognizing entities. The results show the proposed model can reduce the parameters in the learning process, and the NER performance of the proposed model is better than that of the BERT‐BiLSTM‐CRF model. For example, the F1 can be increased by 1.02% and 0.39% for CoNLL2003 and Chinese resume datasets respectively.
Digital implementation method and research on thermal life loss of power transformers
Authors: Dongxue Li , Yan Liu , Jialin Jiang , Yongteng Jing , Zhonghua Lv , Guolong Chen , Ran Liu 
Published: May 2024

AbstractThe report of the 20th National Congress of the Communist Party of China pointed out that it is necessary to accelerate the construction of digital China, and actively develop equipment intelligence, digitalization, and high‐end equipment manufacturing. With the construction of a new round of digital China, the safety of digital technology and high‐end equipment is particularly important for power grid. Transformer is an important link of power grid operation. The destructive thermal failure of transformer has become a hot research issue in the power industry. At the same time, there are more and more power electronic equipment in the power grid, which makes the regulation and control more and more complicated. Taking a 1000 kVA oil‐immersed transformer as an example, the magnetic, current, and thermal multi field coupling numerical analysis method is first used to simulate and analyse the operation status of the transformer. By comparing the simulation results with the monitoring data results, the error of the results is controlled within 5%. Finally, the multi‐state characteristic parameters of the transformer are monitored through sensors, and the numerical simulation analysis results are integrated with the state monitoring results to build a transformer thermal life loss assessment system. The method in this paper can evaluate and analyse the running state of transformer in real time, which is of great significance for the power company to formulate the treatment measures.
A metaheuristic algorithm for regulating virtual inertia of a standalone microgrid incorporating electric vehicles
Authors: Mishra Debayani , Maharana Manoj Kumar , Kar Manoj Kumar , Nayak Anurekha , Islam Md. Minarul , Ustun Taha Selim 
Published: May 2024

AbstractModern electrical networks, particularly microgrids, have seen a sharp rise in acquiring non‐conventional sources. The inertia of the microgrid decreases drastically because RESs have been used in place of traditional synchronous generators. The reduced inertia negatively impacts the dynamics and performance of the microgrid with RESs, which decreases the microgrid's stability, especially in operation on an island. The primary purpose of this research study is to enhance the dynamic security of an island microgrid by merging an electric vehicle with a frequency control technique based on virtual inertia control. A Proportional Integral Derivative Filter Constant (PIDFN) controller optimally created using the Modified Differential Evolution (MDE) method served as the foundation for control in the virtual inertia control loop. The effectiveness of the MDE‐based PIDFN controller was examined considering the diverse operational scenarios are compared and contrasted with those of traditional methods Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO)‐based PIDFN controllers. Real‐time wind and solar power statistics and random load fluctuations were incorporated to provide realistic simulation settings. The outcomes demonstrate that the MDE‐based PIDFN controller performs better in reference frequency tracking and reducing frequency disturbances than the other optimization strategies.
The SRG/eROSITA All-Sky Survey
Authors: Clerc N. , Comparat J. , Seppi R. , Artis E. , Bahar Y. E. , Balzer F. , Bulbul E. , Dauser T. , Garrel C. , Ghirardini V. , Grandis S. , Kirsch C. , Kluge M. , Liu A. , Pacaud F. , Ramos-Ceja M. E. , Reiprich T. H. , Sanders J. , Wilms J. , Zhang X. 
Published: May 2024

Aims. Characterising galaxy cluster populations from a catalogue of sources selected in astronomical surveys requires knowledge of sample incompleteness, known as the selection function. The first All-Sky Survey (eRASS1) by eROSITA on board Spectrum Roentgen Gamma (SRG) has enabled the collection of large samples of galaxy clusters detected in the soft X-ray band over the western Galactic hemisphere. The driving goal consists in constraining cosmological parameters, which puts stringent requirements on the accuracy and flexibility of explainable selection function models.
Methods. We used a large set of mock observations of the eRASS1 survey and we processed simulated data identically to the real eRASS1 events. We matched detected sources to simulated clusters and we associated detections to intrinsic cluster properties. We trained a series of models to build selection functions depending only on observable surface brightness data. We developed a second series of models relying on global cluster characteristics such as X-ray luminosity, flux, and the expected instrumental count rate as well as on morphological properties. We validated our models using our simulations and we ranked them according to selected performance metrics. We validated the models with datasets of clusters detected in X-rays and via the Sunyaev–Zeldovich effect. We present the complete Bayesian population modelling framework developed for this purpose.
Results. Our results reveal the surface brightness characteristics most relevant to cluster selection in the eRASS1 sample, in particular the ambiguous role of central surface brightness at the scale of the instrument resolution. We have produced a series of user-friendly selection function models and demonstrated their validity and their limitations. Our selection function for bright sources reproduces the catalogue matches with external datasets well. We discuss potential inconsistencies in the selection models at a low signal-to-noise revealed by comparison with a deep X-ray sample acquired by eROSITA during its performance verification phase.
Conclusions. Detailed modelling of the eRASS1 galaxy cluster selection function is made possible by reformulating selection into a classification problem. Our models are used in the first eRASS1 cosmological analysis and in sample studies of eRASS1 cluster and groups. These models are crucial for science with eROSITA cluster samples and our new methods pave the way for further investigation of faint cluster selection effects.
Galaxies’ properties in the Fundamental Plane across time
Authors: D’Onofrio M. , Chiosi C. 
Published: May 2024

Context. Using the Illustris-1 and IllustrisTNG-100 simulations, we investigate the properties of the Fundamental Plane (FP), which is the correlation between the effective radius Re, the effective surface intensity Ie, and the central stellar velocity dispersion σ of galaxies, at different cosmic epochs.
Aims. Our aim is to study the properties of galaxies in the FP and its projections across time, adopting samples covering different intervals of mass. We would like to demonstrate that the position of a galaxy in the FP space strongly depends on its degree of evolution, which might be represented by the β and $ L^\prime_0 $
parameters entering the L = $ L^\prime_0 $
(t)σβ(t) law.
Methods. Starting from the comparison of the basic relations among the structural parameters of artificial and real galaxies at low redshift, we obtain the fit of the FP and its coefficients at different cosmic epochs for samples of different mass limits. Then, we analyze the dependence of the galaxy position in the FP space as a function of the β parameter and the star formation rate (SFR).
Results. We find that: (1) the coefficients of the FP change with the mass range of the galaxy sample; (2) the low luminous and less massive galaxies do not share the same FP of the bright massive galaxies; (3) the scatter around the fitted FP is quite small at any epoch and increases when the mass interval increases; (4) the distribution of galaxies in the FP space strongly depends on the β values (i.e., on the degree of virialization and the star formation rate).
Conclusions. The FP is a complex surface that is well approximated by a plane only when galaxies share similar masses and condition of virialization.
Efficacy of Three-Dimensional Printing in the Management of Frontal Bone Trauma
Authors: Kassem Mohamed , Hagras Mahmoud Abd , Elsheikh Mohamed Nasser , Erfan Fathhe Ali , Amer Mohamed 
Published: May 2024

Abstract
Objectives Craniomaxillofacial (CMF) trauma surgery is difficult because of its unique architecture and vast range of injuries in the head and neck area. This study sought to determine the potential of employing preoperative three-dimensional (3D)-printed models to improve frontal fracture healing outcomes.
Methods This prospective cohort clinical trial involved 20 patients who were surgically fitted and had a frontal bone fracture, as evidenced by computed tomography. The patients were separated into two groups: Group A: patients with frontal bone trauma reduced using 3D printing; and Group B: patients with frontal bone trauma reduced without 3D printing.
Results Compared to Group B, Group A had a considerably shorter operational time (p < 0.001). The esthetic results, complications, and functional outcomes were not significantly different between the two groups. All patients in Groups A and B underwent accurate radiographic evaluations (correct placement of the titanium mesh).
Conclusion 3D printing in preoperative planning improves frontal fracture repair with respect to operative length but does not reduce intraoperative blood loss or improve postoperative function compared with normal management.
Predicting Postoperative Pain and Opioid Use with Machine Learning Applied to Longitudinal Electronic Health Record and Wearable Data
Authors: Soley Nidhi , Speed Traci J. , Xie Anping , Taylor Casey Overby 
Published: May 2024

Abstract
Background Managing acute postoperative pain and minimizing chronic opioid use are crucial for patient recovery and long-term well-being.
Objectives This study explored using preoperative electronic health record (EHR) and wearable device data for machine-learning models that predict postoperative acute pain and chronic opioid use.
Methods The study cohort consisted of approximately 347 All of Us Research Program participants who underwent one of eight surgical procedures and shared EHR and wearable device data. We developed four machine learning models and used the Shapley additive explanations (SHAP) technique to identify the most relevant predictors of acute pain and chronic opioid use.
Results The stacking ensemble model achieved the highest accuracy in predicting acute pain (0.68) and chronic opioid use (0.89). The area under the curve score for severe pain versus other pain was highest (0.88) when predicting acute postoperative pain. Values of logistic regression, random forest, extreme gradient boosting, and stacking ensemble ranged from 0.74 to 0.90 when predicting postoperative chronic opioid use. Variables from wearable devices played a prominent role in predicting both outcomes.
Conclusion SHAP detection of individual risk factors for severe pain can help health care providers tailor pain management plans. Accurate prediction of postoperative chronic opioid use before surgery can help mitigate the risk for the outcomes we studied. Prediction can also reduce the chances of opioid overuse and dependence. Such mitigation can promote safer and more effective pain control for patients during their recovery.
Neurological Findings and a Brief Review of the Current Literature in a Severe Case of Aicardi-Goutières Syndrome Due to an IFIH1 Mutation
Authors: Železnik Mojca , Vesnaver Tina Vipotnik , Neubauer David , Soltirovska-Šalamon Aneta 
Published: May 2024

AbstractAicardi-Goutières syndrome (AGS) is a rare genetic early-onset progressive encephalopathy with variable clinical manifestations. The IFIH1 mutation has been confirmed to be responsible for type I interferon production and activation of the Janus kinase signaling pathway. We herein stress neurological observations and neuroimaging findings in a severe case report of an infant with AGS type 7 due to an IFIH1 mutation who was diagnosed in the first month of life. We also review neurological characteristics of IFIH1 mutations through recent literature.
A Prospective Study to Evaluate the Prevalence of Microsatellite Instability in Endometrial Carcinoma by using Immunohistochemistry for Mismatch Repair Proteins as a Surrogate Marker
Authors: Solanki Prithviraj K. , Mandolkar Mahesh , Kulkarni Padmaj 
Published: May 2024

Abstract
Aim Use of immunohistochemistry for mismatch repair (MMR) proteins to identify the prevalence of microsatellite instability (MSI) in cases of endometrial carcinoma and its subsequent correlation with various histopathological parameters.
Materials and Methods The expression of MMR proteins, viz PMS2, MLH1, MSH2, and MSH6, were assessed in 114 endometrial cancer cases by immunohistochemistry using Dako EnVision FLEX system, on paraffin blocks of tumor tissue fixed in 10% formalin.
Results We studied 114 endometrial cases for MMR protein expression, of which the majority were of endometrioid histologic subtype (n = 93, 81.6%), whereas the remainder comprised serous carcinoma (n = 12, 10.5%), clear cell carcinoma (n = 1, 0.9%), carcinosarcoma (n = 5, 4.4%), and dedifferentiated uterine carcinoma (n = 3, 2.6%). Twenty-one (18%) of these cases were found to be deficient for MMR proteins, of which 20 were of endometrioid histologic subtype and only 1 was dedifferentiated uterine carcinoma. Loss of MMR protein expression occurred in pairs of either PMS2 and MLH1 or MSH2 and MSH6.
Conclusion MSI is one of the major molecular pathways contributing to tumorigenesis in endometrial carcinomas. Immunohistochemistry for MMR proteins is a highly sensitive and cost-effective alternative for molecular testing for MSI. It is also a great tool for screening patients for Lynch syndrome. Immunohistochemical testing for MMR should be offered to all patients of endometrial cancers.
Assessment of Preoperative Anxiety Utilizing Amsterdam Preoperative Anxiety and Information Scale in Patients Undergoing Intracranial Tumor Surgery: An Exploratory Study
Authors: Chandra Vikram , Goel Nishant , Kumari Ranjeeta , Agrawal Sanjay 
Published: May 2024

Abstract
Background Preoperative anxiety in neurosurgical patients is high due to life-threatening illness and uncertainty of the surgical outcome. This study assessed preoperative anxiety and its influencing factors in patients undergoing intracranial tumor surgeries utilizing the Amsterdam Preoperative Anxiety and Information Scale (APAIS).
Methods One-hundred twenty patients, 18 to 65 years of age, of either sex, American Society of Anesthesiologists (ASA) grades I/II posted for elective craniotomy and tumor excision, were selected for the study. Various components of the APAIS were explained, and the level of anxiety and need for information were noted. The level of anxiety and depression by HADS (Hospital Anxiety and Depression Scale) score was also evaluated.
Results The mean age was 38.00 ± 12.15 years. About 57.5% of the patients were male. Eighteen (15%) patients had an educational qualification of class 10, 60(50%) of 10 + 2, 36 (30%) were graduates, and 6 (5%) had a postgraduate qualification.The mean APAIS for total anxiety was 11.10 ± 1.79; the score for need for information was 6.28 ± 1.61. Mean total anxiety scores were higher in female patients (11.96 vs. 10.46, p < 0.001). Higher anxiety was observed in ASA status I patients (11.33 vs. 10.539 ASA I vs. II, p = 0.020). Significantly higher anxiety was observed in patients educated till class 10 (p = 0.012). A significantly higher need for information was observed in postgraduates (p = 0.012). Eighty (66.7%) patients had clinical anxiety, and 35 (29.2%) had clinical depression on the HADS score.
Conclusion Neurosurgical patients have higher anxiety due to intracranial pathology, site of surgery, and unpredictability of outcome. Demographic and clinical variables do affect anxiety levels.
Volunteer translators in non-governmental organizations: exploring their identity and power through discourse analysis
Authors: Sánchez Ramos María del Mar 
Published: May 2024

AbstractThe need for immediate communication in a world characterized by an ever-increasing flow of information has led to a rapid increase in volunteer translation in recent decades. While previous studies have focused on aspects such as volunteer translators’ motivation and the attendant ethical issues of voluntary translation, comparatively little research has explored the phenomenon from a discursive perspective. The research reported in this article explored the discursive construction of the volunteer translator in non-governmental organizations (NGOs) active in Spain using critical discourse analysis. Drawn from analysis of 25 semi-structured individual interviews with volunteer translators in five different NGOs, the findings reveal that volunteer translators are seen either as an agent and actor of social change who facilitates communication and possesses a strong sense of identity and social status, or as someone who is interfering in a sector traditionally limited to professional translators and who consequently has a weaker sense of identity and social status. This suggests that the role of the volunteer translator currently lacks definition, and that further research is needed to better understand the relationship between volunteer and professional translators in the 21st century.
More romantic or more realistic: trajectories and influencing factors of romantic love among Chinese college students from entering college to graduation
Authors: Liu Xinqiao , Ji Xinyu , Zhang Yifan 
Published: May 2024

AbstractCollege is a significant transition in the growth of individuals, and romantic relationships play an important role in the future development of individuals. Understanding young people’s attitudes toward love is helpful for formulating strategies to guide them to form correct attitudes toward love, promote the formation of positive and healthy attitudes toward marriage and life. This study aimed to explore the trajectories of college students’ attitudes toward love, reveal the development law of their attitudes toward love, and identify the influencing factors that lead to changes in their attitudes toward love. Descriptive and correlation analyses were performed on participants’ attitudes toward love, followed by utilization of a growth mixture model to explore latent trajectory classes and a multinomial logistic regression model to analyze influencing factors across trajectories.The results indicated that romantic attitudes could be divided into three groups: “high-increasing”, “low-decreasing” and “low-increasing”, and realistic attitudes could be divided into three groups: “high-increasing”, “low-increasing” and “high-decreasing”. Gender, major, extroversion, and hometown location were factors influencing whether students fell into different trajectory classes. Overall, society and colleges should pay more attention to changes in college students’ attitudes toward love, provide timely intervention and guidance, and prevent them from suffering from behavioral, physical, and psychological problems.