bluuebunny/crossref_metadata_2025_split huggingface dataset:
On the uncertain intensity estimate of the 1859 Carrington storm
Authors: Love Jeffrey J. , Rigler E. Joshua , Hayakawa Hisashi , Mursula Kalevi 
Published: May 2024

A study is made of the intensity of the Carrington magnetic storm of September 1859 as inferred from visual measurements of horizontal-component geomagnetic disturbance made at the Colaba observatory in India. Using data from modern observatories, a lognormal statistical model of storm intensity is developed, to characterize the maximum-negative value of the storm-time disturbance index (maximum –Dst) versus geomagnetic disturbance recorded at low-latitude observatories during magnetic storms. With this model and a recently published presentation of the Colaba data, the most likely maximum –Dst of the Carrington storm and its credibility interval are estimated. A related model is used to examine individual Colaba disturbance values reported for the Carrington storm. Results indicate that only about one in a million storms with maximum –Dst like the Carrington storm would result in local disturbance greater than that reported from Colaba. This indicates that either the Colaba data were affected by magnetospheric-ionospheric current systems in addition to the ring current, or there might be something wrong with the Colaba data. If the most extreme Colaba disturbance value is included in the analysis, then, of all hypothetical storms generating the hourly average disturbance recorded at Colaba during the Carrington storm, the median maximum –Dst = 964 nT, with a 68% credibility interval of [855,1087] nT. If the most extreme Colaba disturbance value is excluded from the analysis, then the median maximum –Dst = 866 nT, with a 68% credibility interval of [768,977] nT. The widths of these intervals indicate that estimates of the occurrence frequency of Carrington-class storms are very uncertain, as are related estimates of risk for modern technological systems.
Necrotizing Enterocolitis-Associated Acute Kidney Injury—Transforming the Paradigm
Authors: Garg Padma P. , Shenberger Jeffrey , South Andrew M. , Garg Parvesh M. 
Published: May 2024

Necrotizing enterocolitis (NEC) is one of the most common conditions requiring emergency surgery in the neonatal intensive care unit and is associated with a septic shock-like state contributing to multiorgan dysfunction. NEC affects 6 to 10% of very low-birth-weight infants and remains a leading cause of death. The occurrence of severe acute kidney injury (AKI) following surgical NEC is a harbinger of multiple morbidities. This review presents current evidence about the clinical impact of NEC-associated AKI on the clinical outcomes. Studies evaluating nephroprotective strategies to prevent AKI and its consequences are greatly needed to improve the postoperative recovery and clinical outcomes in neonates with NEC. Future observational studies and clinical trials in preterm infants with NEC prioritize measuring short-term (AKI) and longer term (chronic kidney disease) kidney outcomes.
Key Points

Impact
Emotions unveiled: detecting COVID-19 fake news on social media
Authors: Farhoudinia Bahareh , Ozturkcan Selcen , Kasap Nihat 
Published: May 2024

AbstractThe COVID-19 pandemic has highlighted the pernicious effects of fake news, underscoring the critical need for researchers and practitioners to detect and mitigate its spread. In this paper, we examined the importance of detecting fake news and incorporated sentiment and emotional features to detect this type of news. Specifically, we compared the sentiments and emotions associated with fake and real news using a COVID-19 Twitter dataset with labeled categories. By utilizing different sentiment and emotion lexicons, we extracted sentiments categorized as positive, negative, and neutral and eight basic emotions, anticipation, anger, joy, sadness, surprise, fear, trust, and disgust. Our analysis revealed that fake news tends to elicit more negative emotions than real news. Therefore, we propose that negative emotions could serve as vital features in developing fake news detection models. To test this hypothesis, we compared the performance metrics of three machine learning models: random forest, support vector machine (SVM), and Naïve Bayes. We evaluated the models’ effectiveness with and without emotional features. Our results demonstrated that integrating emotional features into these models substantially improved the detection performance, resulting in a more robust and reliable ability to detect fake news on social media. In this paper, we propose the use of novel features and methods that enhance the field of fake news detection. Our findings underscore the crucial role of emotions in detecting fake news and provide valuable insights into how machine-learning models can be trained to recognize these features.
The experiences of healthcare providers who refer to a campus-based pharmacy clinic: a qualitative analysis
Authors: Wheeler Tara F. , Leitch Sharon , Marra Carlo A. 
Published: May 2024

Introduction The University of Otago School of Pharmacy Clinic (the Clinic) is a campus-based non-dispensing clinic that offers consultation-based medicines optimisation services to patients. Aim This project aims to understand the experiences and opinions of healthcare providers who have referred patients to the School of Pharmacy Clinic, specifically: their motivation for referring patients; how the Clinic impacts providers, patients and the wider health system; provider satisfaction; and opportunities for further collaboration. Methods Semi-structured interviews were used to collect data from 15 participants who represented five health professions. An inductive reflexive thematic analysis approach was used to analyse the dataset from which codes and themes were developed. Normalisation Process Theory (NPT) was used to structure the interview guide and as a framework to present themes. Results Seven themes were developed; ‘Perceptions of Pharmacists’ (Coherence), ‘Motivators for Engagement’ and ‘Barriers to Engagement’ (Cognitive Participation), ‘Utility of Pharmacist Feedback’ and ‘Opportunities’ (Collective Action) and ‘Referrers’ Experiences’ and ‘Patient-centred Care’ (Reflexive Action). Discussion Healthcare providers described predominantly positive experiences. Medically complex cases and patients requiring medicines education were most likely to be referred for consultation. Engaging with the Clinic presented valuable opportunities for interprofessional collaborative practice and continuing professional education. Referrers would like more regular contact with Clinic pharmacists to encourage interprofessional collaborative relationships. Patients were thought to benefit from their pharmacist’s clinical expertise, time, patient-centred approach and subsequent medication and health optimisation. Integration of Clinic pharmacists into specialist outpatient clinics at Dunedin Hospital may broaden the scope and improve efficiency of their services.
A cell bank paradigm for preclinical evaluation of an analogous cellular product for an allogeneic cell therapy
Authors: Nordberg Rachel C , Donahue Ryan P , Espinosa M Gabriela , Salinas Evelia Y , Hu Jerry C , Athanasiou Kyriacos A 
Published: May 2024

Abstract
Toward the translation of allogeneic cell therapy products, cell banks are needed not only to manufacture the final human product but also during the preclinical evaluation of an animal-based analogous cellular product (ACP). These cell banks need to be established at both the master cell bank (MCB) level and the working cell bank (WCB) level. Inasmuch as most of the development of cell therapy products is at academic centers, it is imperative that academic researchers understand how to establish MCBs and WCBs within an academic environment. To illustrate this process, using articular cartilage as the model, a cell bank for an ACP was developed (MCBs at passage 2, WCBs at passage 5) to produce self-assembled neocartilage for preclinical evaluation (constructs at passage 7). The cell bank system is estimated to be able to produce between 160 000 and 400 000 constructs for each of the six MCBs. Overall, the ACP cell bank yielded constructs that are analogous to the intended human product, which is critical toward conducting preclinical evaluations of the ACP for inclusion in an Investigational New Drug application to the FDA.
Measuring human perception of residential built environment through street view image and deep learning
Authors: Meng Yumeng , Sun Dong , Lyu Mei , Niu Jianing , Fukuda Hiroatsu 
Published: May 2024

Abstract
As an important part of the urban built environment, streets exploring the influence mechanism between the built environment and human perception. It is one of the issues in building healthy cities. In this study, the residential streets of Zhongshan Distict, Dalian were selected as the study site, including Mountain Low-rise Neighborhood, Old Mid-rise Neighborhood, and Modern High-rise Neighborhood. Meanwhile, spatial measurement and human perception perception evaluation of the street environment were based on Deep learning and street view image (SVI). The study used human perceptions as dependent variables, and physical features as the independent variables. Finally, two regression models of positive and negative perceptions were established to analyze the relationship between them. The results showed that in the three types of neighborhood, positive perception was mainly focused on Mountain Low-rise Neighborhood; Negative perception was mainly focused on Old Mid-rise Neighborhood. Greenness, Openness, Natural Landscape, Natural to artificial ratio of the horizontal interface, and Natural to artificial ratio of the vertical interface had a positive influence on positive perception. Pedestrian occurrence rate, Enclosure, and Vehicle Occurrence rate had a negative influence on negative emotive. Greenness was the physical feature that most affected human perception. This study provided a method for objectively evaluating the quality of the street built environment. It is important for promoting the quality of residential streets and public mental health.
STG-MTL: scalable task grouping for multi-task learning using data maps
Authors: Sherif Ammar , Abid Abubakar , Elattar Mustafa , ElHelw Mohamed 
Published: May 2024

Abstract
Multi-Task Learning (MTL) is a powerful technique that has gained popularity due to its performance improvement over traditional Single-Task Learning (STL). However, MTL is often challenging because there is an exponential number of possible task groupings, which can make it difficult to choose the best one because some groupings might produce performance degradation due to negative interference between tasks. That is why existing solutions are severely suffering from scalability issues, limiting any practical application. In our paper, we propose a new data-driven method that addresses these challenges and provides a scalable and modular solution for classification task grouping based on a re-proposed data-driven features, Data Maps, which capture the training dynamics for each classification task during the MTL training. Through a theoretical comparison with other techniques, we manage to show that our approach has the superior scalability. Our experiments show a better performance and verify the method’s effectiveness, even on an unprecedented number of tasks (up to 100 tasks on CIFAR100). Being the first to work on such number of tasks, our comparisons on the resulting grouping shows similar grouping to the mentioned in the dataset, CIFAR100. Finally, we provide a modular implementation
3

3

https://github.com/ammarSherif/STG-MTL.
for easier integration and testing, with examples from multiple datasets and tasks.
Global system errors to simultaneously improve the identification of subsystems with mixed data Gaussian process regression
Authors: LaMack Cameron J , Schearer Eric M 
Published: May 2024

Abstract
This paper explores the use of Gaussian process regression for system identification in control engineering. It introduces two novel approaches that utilize the data from a measured global system error. The paper demonstrates these approaches by identifying a simulated system with three subsystems, a one degree of freedom mass with two antagonist muscles. The first approach uses this whole-system error data alone, achieving accuracy on the same order of magnitude as subsystem-specific data (

9.28
±
0.87
N
vs.
6.96
±
0.32
N

of total model errors). This is significant, as it shows that the same data set can be used to identify unique subsystems, as opposed to requiring a set of data descriptive of only a single subsystem. The second approach demonstrated in this paper mixes traditional subsystem-specific data with the whole system error data, achieving up to 98.71% model improvement.
Journey over destination: dynamic sensor placement enhances generalization
Authors: Marcato Agnese , Guiltinan Eric , Viswanathan Hari , O’Malley Daniel , Lubbers Nicholas , Santos Javier E 
Published: May 2024

Abstract
Reconstructing complex, high-dimensional global fields from limited data points is a challenge across various scientific and industrial domains. This is particularly important for recovering spatio-temporal fields using sensor data from, for example, laboratory-based scientific experiments, weather forecasting, or drone surveys. Given the prohibitive costs of specialized sensors and the inaccessibility of certain regions of the domain, achieving full field coverage is typically not feasible. Therefore, the development of machine learning algorithms trained to reconstruct fields given a limited dataset is of critical importance. In this study, we introduce a general approach that employs moving sensors to enhance data exploitation during the training of an attention based neural network, thereby improving field reconstruction. The training of sensor locations is accomplished using an end-to-end workflow, ensuring differentiability in the interpolation of field values associated to the sensors, and is simple to implement using differentiable programming. Additionally, we have incorporated a correction mechanism to prevent sensors from entering invalid regions within the domain. We evaluated our method using two distinct datasets; the results show that our approach enhances learning, as evidenced by improved test scores.
Water adsorption kinetics on graphene controlled by surface modification of supporting substrates
Authors: Hirose Takumu , Ochi Naoto , Nouchi Ryo 
Published: May 2024

Abstract
Sensing layers with an increased affinity for water molecules are essential for the development of highly sensitive humidity sensors. Graphene possesses superior electrical properties that make it suitable for the fabrication of low-noise miniaturized sensors. However, the enhancement of water affinity by introducing surface defects such as covalently attached hydrophilic groups reduces the electrical conductivity of graphene. In this study, we exploit the wetting transparency of graphene to increase its water affinity without introducing defects. Kinetic measurements using a Kelvin probe with a large-diameter tip showed that the rate constant of water adsorption was higher for graphene deposited on a hydrophilic substrate. These findings suggest that the wetting transparency of graphene can be exploited to reduce defect introduction into the graphene sensing layer, and has potential applications in sensor technologies.
Doctrines in Motion
Authors: Feitoza Pedro 
Published: May 2024

Abstract
This chapter examines the thinking and ideologies of Brazilian evangelicals between 1860–1910. It recovers the ways Protestants responded to the First Vatican Council (1869–1870), the papacy of Pius IX, the Religious Question in Brazil (1872–1875), and the secular ideologies of the time. The chapter concentrates on some influential tracts by Émile de Laveleye and William Gladstone, translated into Portuguese, discussing issues of religion, modernity, and race, and the publications of the Brazilian Society of Evangelical Tracts (1883–1897). Evangelicals built upon these religious and social debates to familiarize their publics with Protestant doctrines and ideas. The chapter also analyzes the tracts produced by Plymouth Brethren independent missionaries in Brazil and Portugal and their critique of the Brazilian religious landscape.
The role of European chemical manufacturing companies in promoting effective communication of conditions of safe use by workers
Authors: Urbanus Jan , Tjoe Nij Evelyn , Tietz Cornelia 
Published: May 2024

Abstract
In 2006, the revised chemicals management legislation mandated that manufacturers of hazardous chemical substances conduct risk assessments for the entire substance life cycle. Additionally, they must communicate use-specific safe handling advice (exposure scenarios) to their customer, as annex to the Safety Data Sheet (SDS). Despite significant efforts to develop workable solutions for chemical mixtures, this goal has not yet been fully achieved. Therefore, a Cefic research project (LRI B23) was commissioned on how to ensure meaningful health risk communication for workers across supply chains. The research project determined that risk-based safe use advice generated by manufacturers, often does not reach the intended end-user and was seen as not tailored to specific user needs. Recipients of the advice are also not prepared to act based on information developed by suppliers. From an industry perspective, the complexity of supply chains and substance life cycles are considered major barriers for effective safe use communication. Exposure scenarios for substance use in industrial work environments are often perceived as adding little value compared to existing safe use arrangements required by other health, safety, and environmental legislation applicable to employers and duty-holders. To attain meaningful use-specific safe handling advice for workers, including those at non-industrial premises who may benefit most from such advice, knowledge transfer and close collaboration between manufacturers and formulators remain key elements, supported by enhanced regulatory appreciation.
Ecological correlates of population genetics in Linum suffruticosum, an heterostylous polyploid and taxonomic complex endemic to the Western Mediterranean Basin
Authors: Vanrell Maria Antònia , Novaes Letícia R , Afonso Ana , Arroyo Juan , Simón-Porcar Violeta 
Published: May 2024

Abstract
Linum suffruticosum s.l. is a taxonomic complex widespread in the Western Mediterranean basin. The complex is characterized by a high phenotypic and cytogenetic diversity, and by a unique three-dimensional heterostyly system that makes it an obligate outcrosser. We studied the patterns of genetic diversity and structure of populations throughout the entire distribution of L. suffruticosum s.l. with microsatellite markers. We analysed their relationships with various biological and ecological variables, including the morph ratio and sex organ reciprocity of populations measured with a novel multi-dimensional method. Populations consistently showed an approximate 1:1 morph ratio with high sex organ reciprocity and high genetic diversity. We found high genetic differentiation of populations, showing a pattern of isolation by distance. The Rif mountains in NW Africa were the most important genetic barrier. The taxonomic treatment within the group was not related to the genetic differentiation of populations, but to their environmental differentiation. Genetic diversity was unrelated to latitude, elevation, population size, niche suitability or breeding system. However, there was a clear influence of ploidy level on the genetic diversity of populations, and a seeming centre–periphery pattern in its distribution. Our results suggest that polyploidization events, high outcrossing rates, isolation by distance and important geographical barriers to gene flow have played major roles in the microevolutionary history of this species complex.
Assessing pressure wave components for aortic stiffness monitoring through spectral regression learning
Authors: Aghilinejad Arian , Gharib Morteza 
Published: May 2024

Abstract

Aims
The ageing process notably induces structural changes in the arterial system, primarily manifesting as increased aortic stiffness, a precursor to cardiovascular events. While wave separation analysis is a robust tool for decomposing the components of blood pressure waveform, its relationship with cardiovascular events, such as aortic stiffening, is incompletely understood. Furthermore, its applicability has been limited due to the need for concurrent measurements of pressure and flow. Our aim in this study addresses this gap by introducing a spectral regression learning method for pressure-only wave separation analysis.

Methods and results
Leveraging data from the Framingham Heart Study (2640 individuals, 55% women), we evaluate the accuracy of pressure-only estimates, their interchangeability with a reference method based on ultrasound-derived flow waves, and their association with carotid-femoral pulse wave velocity (PWV). Method-derived estimates are strongly correlated with the reference ones for forward wave amplitude (R2=0.91), backward wave amplitude (R2=0.88), and reflection index (R2=0.87) and moderately correlated with a time delay between forward and backward waves (R2=0.38). The proposed pressure-only method shows interchangeability with the reference method through covariate analysis. Adjusting for age, sex, body size, mean blood pressure, and heart rate, the results suggest that both pressure-only and pressure-flow evaluations of wave separation parameters yield similar model performances for predicting carotid-femoral PWV, with forward wave amplitude being the only significant factor (P &lt; 0.001; 95% confidence interval, 0.056–0.097).

Conclusion
We propose an interchangeable pressure-only wave separation analysis method and demonstrate its clinical applicability in capturing aortic stiffening. The proposed method provides a valuable non-invasive tool for assessing cardiovascular health.
Dementia-Friendly Transportation Services: A Scoping Review
Authors: Lanthier-Labonté Stéphanie , Chaudhury Habib , Wong Joey , Hung Lillian 
Published: May 2024

Abstract

Background and Objectives
Transportation is an important component of dementia-friendly communities. People living with dementia have the right to access transportation services to keep their mobility in their community, which is essential for independence, well-being, and quality of life. This scoping review maps the literature on dementia-friendly transportation services and explores their characteristics to inform future development and research.

Research Design and Methods
Empirical quantitative and qualitative studies in English or French that informed on transportation services for people living with dementia in the community were searched in 15 databases. Two authors independently screened records and charted relevant characteristics from selected publications. Important findings were summarized with a narrative synthesis approach.

Results
Thirty-five studies informed on important dimensions of transportation services in urban and/or rural context: availability, accessibility, acceptability, adaptability, and affordability. Important insights were identified: the importance of staff training and attitude, and the challenges of availability of affordable services in the rural context. Emerging policy and program intervention areas include the need for access to quiet areas in transit hubs, training to use mass public transit and mobility management.

Discussion and Implications
The literature on dementia-friendly transportation services is important in the expansion and maintenance of mobility for people living with dementia in the community. The scope of the existing scientific literature remains limited. Although several studies indicated a clear need for better access to dementia-friendly transportation services, the best practices still need to be demonstrated in the scientific literature.