bluuebunny/crossref_metadata_2025_split huggingface dataset:
Epidemiology and clinical outcomes of hospitalized Hispanic patients with IBD: results of a large national cohort study
Authors: Zhornitskiy Alex , Zhornitsky Felicia , Rasheed Waqas , Mao Eric J. 
Published: February 2025

Abstract

Introduction
Inflammatory bowel disease (IBD) has historically been seen as predominantly affecting non-Hispanic Whites (NHW). Hispanics are the largest minority group in the USA, yet they remain grossly underrepresented in studies of IBD. With this study, we aimed to better understand the epidemiology of hospitalized Hispanic patients with IBD in the US.

Methods
This was a retrospective cohort study utilizing the National Inpatient Sample, the largest publicly available all-payer inpatient care database in the United States. We compared demographics, hospitalization characteristics, clinical outcomes, and year-to-year trends from 2016 to 2020 in Hispanic and NHW with a primary diagnosis of inflammatory bowel disease, Crohn’s disease, or ulcerative colitis.

Results
NHWs hospitalized with a primary diagnosis of IBD had significantly higher rates of hospitalization than Hispanics (122.67 vs 71.12, P < 0.01). While hospitalized Hispanics with IBD are more likely to be in the lowest quartile for household income (31.6% vs 19.3%, P < 0.01), have a younger median age (37.0 vs 45.0, P < 0.01), and be uninsured (4.3% vs 8.8%, P < 0.01) compared to NHW. Length of admission was similar, yet NHWs had higher rates of mortality (0.3% vs 0.2%, P = 0.01), while total charges for hospitalizations were significantly higher for Hispanic patients (P < 0.01).

Discussion
To our knowledge, this is one of the largest US-based studies of Hispanics with IBD. Our findings suggest that among hospitalized IBD patients, Hispanics are more likely to be younger, uninsured, have a lower household income, and are less likely to undergo surgery while having higher hospital charges.
A clockmaker’s mathematics: a technology-based approach to the mathematical works of Jost Bürgi (1552–1632)
Authors: Moosbrugger Damian 
Published: February 2025

Abstract
In this article, I propose a new approach to analyze the interrelations between mathematics and technology. It has the potential to contribute methodologically to both the fields of history of mathematics as well as the study of computational technologies in the current context. Based on the conception of mathematics as a contingent human practice, I claim that the practical engagement with technology not only subjects new fields, materials, and problems to mathematical scrutiny but might even shape mathematics from within. To illustrate my approach and corroborate my thesis, I present a historical case study on the mathematical works of the Swiss clock- and instrument-maker Jost Bürgi (1552–1632). Besides being a practicing artisan, he left three mathematical treatises. The advancements in fine metal working at his time, exemplified in clockwork mechanisms and measuring instruments, not only motivated and directed Bürgi’s mathematical inquiries. Instead, I argue that the interaction with these technical apparatuses in practice has shaped the internal structure and workings of his mathematics, that is, its entities, justifications, presentations, proofs, and procedures. The close analysis of some aspects of his oeuvre, especially his notion(s) of the sine, his way of explaining the occurrence of multiple solutions in algebra, and his visual depiction of the bridging of ten in his logarithmic computational tool, reveals a potential integration of the experience and practical knowledge of a clockmaker into mathematics. I therefore make the point that his mathematical writings portray a clockmaker’s mathematics.
Efficacy of cannabinoids for the prophylaxis of chemotherapy-induced nausea and vomiting—a systematic review and meta-analysis
Authors: Chow Ronald , Basu Anna , Kaur Jagdeep , Hui David , Im James , Prsic Elizabeth , Boldt Gabriel , Lock Michael , Eng Lawson , Ng Terry L. , Zimmermann Camilla , Scotte Florian 
Published: February 2025

Abstract

Background
Cannabinoids have potential efficacy as prophylaxis for chemotherapy-induced nausea and vomiting (CINV), but no recent meta-analysis has reported on their relative efficacy compared to other antiemetics. The aim of this meta-analysis is to examine the relative efficacy of cannabinoids for prophylaxis of CINV.

Methods
A literature search was conducted in OVID Medline, EMBASE, and Cochrane Central Register of Controlled Trials from inception up until March 2024. Articles were included if they reported on complete response, no nausea, no vomiting or no use of rescue medications, and were randomized controlled trials with cannabinoids in one arm. Meta-analysis was conducted for each endpoint and for a composite endpoint amalgamating existing endpoints. Subgroup analyses by medication used in control arm and by study design were conducted. Cumulative and leave-one-out analysis was also conducted. Type I error was set at 0.05.

Results
A total of 26 studies were included in this meta-analysis, of which 23 were published before the 2000s. Nearly half of the included studies had some concern for bias. Cannabinoid had superior overall CINV control compared to placebo (RR 2.65, 95% CI 1.70–4.12, I
2 = 0.00%). However, there was no difference between cannabinoid and active treatment alternatives (most using dated single-agent regimens) for any outcomes. A recent phase II/III trial demonstrated superior efficacy of THC:CBD for secondary prevention of CINV when used as adjunctive therapy alongside modern antiemetic regimens, albeit mostly without olanzapine.

Conclusions
There is scant evidence for efficacy of cannabinoids for CINV in the era of triple and quadruple antiemetics. Although THC:CBD showed promised in a recent trial, further trials should examine its safety and efficacy in the context of regimens containing olanzapine.
A cybersecurity risk assessment methodology for industrial automation control systems
Authors: Brancati Francesco , Mongelli Diamantea , Mariotti Francesco , Lollini Paolo 
Published: February 2025

Abstract
Industrial automation control systems (IACS) are employed in current critical infrastructures and industrial plants spanning very different domains, and the transformation process towards Industry 4.0 is further increasing the dependencies on such systems. Since IACS can be exposed to malicious threats that could lead to catastrophic consequences, it is extremely important to assess the cybersecurity risk of these systems, to identify the possible threats, their impact, likelihood, and possible countermeasures. The ISA/IEC 62443 series of standards is suited for the design and security risk analysis of IACS, and has been submitted to the International Standards on Auditing and International Electrotechnical Commission for global adoption as international standards. In this paper, we focus on the zone and conduit requirement 5 (ZCR 5) of the 62443-3-2 part of the standard, which provides the steps for detailed cybersecurity risk assessment of IACS. These steps are fundamental to identify threats related to the system, determine the risk associated with them, and derive appropriate countermeasures. We provide a methodology for conducting a detailed risk assessment of IACS that is compliant with all the steps of the ZCR 5 and integrates the following features: (i) capability to manage the complexity of the assessment process, (ii) capability to select tailored countermeasures for critical assets through the identification of attack paths, (iii) explicit involvement of the asset owner in the key steps of the assessment process, and (iv) tool-supported. We illustrate the methodology by applying it to a case study of a power plant using gas turbines.
Weed Tolerance and Suppressive Ability of Potato Cultivars to Natural Weed Infestations
Authors: Siddiqui Ahmad Omid , Jabran Khawar 
Published: February 2025

Abstract
Concerns such as herbicide-resistant weeds and environmental pollution have increased an interest in using weed-competitive crop cultivars for weed management. Potato plants are highly vulnerable to weed competition, especially during early and late growing seasons. In this context, a two-year field experiment assessed weed tolerance and suppressive ability of 15 genetically diverse potato cultivars to natural weed infestations. The results showed that weed interference significantly reduced potato growth and yield. For instance, weed competition reduced plant height by 10–37%, plant fresh weight by 21–44%, plant dry weight by 15–40%, and yield by 15–48%. Potato cultivars varied in their ability to suppress and tolerate weeds, with weed dry weight ranging from 146.7 g m−2 in Lady Olympia to 428.3 g m−2 in Arizona and tuber yield from 13.84 t ha−1 in Electra to 25.15 t ha−1 in Agria under weedy conditions. Key traits of weed-suppressive cultivars included early emergence, rapid canopy development, and greater plant biomass. Cultivars with greater plant height and higher yields were generally more weed tolerant. Lady Olympia demonstrated the highest weed suppression among the evaluated cultivars, reducing weed dry weight by 60% or more, making it suitable for organic farming systems. Agria exhibited superior weed tolerance with minimal yield loss, making it ideal for conventional high-yield farming systems.
Community health learning experiences of Colombian undergraduate medical students. A phenomenographic research study
Authors: Jaimes-Peñuela Claudia Liliana , Lamus-Lemus Francisco , Reinoso-Chávez Natalia 
Published: February 2025

Abstract
Medical students’ perceptions of their community learning experiences can provide valuable insights for evolving and improving healthcare professional education curricula to better respond to health needs. This study aims to explore the learning experiences of undergraduate medical students in a community health course at a Colombian University. Fifteen students who had completed the Family Medicine and Community Health course participated in this qualitative study, employing a phenomenographic approach and interview technique. The phenomenographic analysis, informed by the transformative service-learning theoretical framework, resulted in the construction of categories of description, organized hierarchically, based on levels of understanding. The findings revealed five main community health learning experiences: Fulfilling the requirements, Educating the community, Solving health problems, Joint construction, and Personal transformation. Students experienced learning in diverse hierarchical learning levels, with their understanding around community health evolving over time. Transformative learning experiences were identified among only some students, indicating the potential for everyone to reach such levels of complexity and depth. Educationally critical aspects for achieving more complex levels of learning include exposure to intense and major challenges in longer periods of community services, reflection guided by teacher in intentional pedagogical spaces, feedback, and meaningful relationships between key stakeholders. These findings hold significance for medical programs that offer community medicine or related courses, since they present pedagogical opportunities to create and improve similar learning contexts in other domains.
Quantum deep learning in neuroinformatics: a systematic review
Authors: Orka Nabil Anan , Awal Md. Abdul , Liò Pietro , Pogrebna Ganna , Ross Allen G. , Moni Mohammad Ali 
Published: February 2025

Abstract
Neuroinformatics involves replicating and detecting intricate brain activities through computational models, where deep learning plays a foundational role. Our systematic review explores quantum deep learning (QDL), an emerging deep learning sub-field, to assess whether quantum-based approaches outperform classical approaches in brain data learning tasks. This review is a pioneering effort to compare these deep learning domains. In addition, we survey neuroinformatics and its various subdomains to understand the current state of the field and where QDL stands relative to recent advancements. Our statistical analysis of tumor classification studies (n = 16) reveals that QDL models achieved a mean accuracy of 0.9701 (95% CI 0.9533–0.9868), slightly outperforming classical models with a mean accuracy of 0.9650 (95% CI 0.9475–0.9825). We observed similar trends across Alzheimer’s diagnosis, stroke lesion detection, cognitive state monitoring, and brain age prediction, with QDL demonstrating better performance in metrics such as F1-score, dice coefficient, and RMSE. Our findings, paired with prior documented quantum advantages, highlight QDL’s promise in healthcare applications as quantum technology evolves. Our discussion outlines existing research gaps with the intent of encouraging further investigation in this developing field.
A conceptual framework for sustainable location, inventory and transportation problems in a supply chain context
Authors: Becerra Pablo , Mula Josefa , Sanchis Raquel 
Published: February 2025

Abstract
Sustainability in supply chains (SC) is a topic that has been widely discussed recently and, similarly to the quest to develop green SCs, it does not contemplate the integration of economic and social dimensions of sustainable SC management (SSCM). This article proposes a conceptual framework to provide key factors that act as a valuable tool to further develop quantitative models of location, inventory and transportation (LIT) problems in SSCs. The objectives of this paper are to: (i) review the literature by focusing on conceptual frameworks for sustainable SC; (ii) map and construct the conceptual framework blocks by describing each element and dimension; and (iii) validate the conceptual framework. The conceptual framework comprises five main blocks: SC structures, sustainable inputs, quantitative models, inventory policies and sustainability objectives. These five blocks are then formed by different dimensions and elements, which are described in this paper and validated in a mining case study. The results show that no previous studies propose a reference conceptual framework to model a sustainable LIT problem in a CLSC context, which is the main novelty of this paper. Finally, the conceptual framework is validated and applied as a diagnostic tool in two copper mining companies.
Retrospective temporal resolution interpolation alters myocardial strain quantification on compressed sensing cine CMR
Authors: Grob Leonard , Schwerzmann Yann , Kaiser Dario , Jung Bernd , Schweizer Thilo , Huettenmoser Stefan P. , Dozio Scilla , Huber Adrian T. , Boscolo Berto Martina , Gräni Christoph , Guensch Dominik P. , Fischer Kady 
Published: February 2025

Abstract
Compressed sensing (CS) is a rapidly developing technique that can acquire functional cines of the heart within seconds while free-breathing and it is ideal for assessing cardiac function in non-typical conditions such as when patients are sedated or anaesthetized or undergoing stress exams. CS cines can further include retrospective temporal resolution interpolation (INTPTR) to improve the frame count per heartbeat, and the impact of INTPTR on biventricular functional measurements is unknown. We investigated the impact of INTPTR on left and right ventricular volumetry and strain measurements of CS cines. Nineteen patients with 51 different CS acquisitions were prospectively enrolled. CS cines were acquired at rest, under adenosine stress, oxygen inhalation or while under general anaesthesia with mechanical ventilation. From the same CS acquisition, a dataset with and without INTPTR were generated by the scanner. The outputs were separated and analysed by blinded readers for left and right ventricular volumetry, as well as systolic and diastolic strain parameters using feature-tracking techniques. Measurements were compared between the INTPTR and non-INTPTR outputs. Similar measurements were obtained for biventricular volumes and ejection fraction independent of INTPTR. Peak strain was significantly underestimated on INTPTR cines for both longitudinal and circumferential orientations (p < 0.01). Nevertheless, good-to-excellent correlations were observed between the two measurements (r > 0.65, p < 0.01), and there was still a high area under the curve (AUC ≥ 0.95, p < 0.01) for detecting abnormal patients defined by strain analysis on the standard segmented cine. INTPTR especially negatively influenced strain rates analysis, as many strain rate curves were deemed unusable with this technique. These findings were consistent independent if the patient was in a resting, stress or anaesthetized condition. Although INTPTR is a feature which improves temporal resolution on CS cines, quantification of biventricular strain and strain rates is not feasible or comparable, thus, feature tracking analysis should be performed on non-INTPTR data. However, volumetry and ejection fraction analysis are consistent independent of which output is analysed.
An Empirical Evaluation of Distance Metrics in Hierarchical Risk Parity Methods for Asset Allocation
Authors: Salas-Molina Francisco , Pla-Santamaria David , Garcia-Bernabeu Ana , Hilario-Caballero Adolfo 
Published: February 2025

Abstract
Hierarchical Risk Parity methods address instability, concentration, and underperformance in asset allocation by taking advantage of machine learning techniques to build a diversified portfolio. HRP methods produce a hierarchical structure to the correlation between assets by means of tree clustering that results in a reorganization of the covariance matrix of returns. However, HRP admits multiple variations in terms of clustering algorithms and distance metrics. In this paper, we evaluate the out-of-sample performance of alternative hierarchical distance metrics for clustering purposes using real stock markets in three different market scenarios: bull market, sideways trend, and bear market. We pay special attention to the mean-variance performance of the output portfolios as an estimation of the ability of alternative methods to estimate future return and risk. Our results show that correlation-based metrics provide better performance than non-correlation metrics. In addition, HRP methods outperform quadratic optimizers in two of the three stock market scenarios.
Multicomponent Multimethod Assessment of Emotional Change in Psychotherapy Research: Initial Validation of a Neurobehavioral Paradigm
Authors: Grandjean Loris , Beuchat Hélène , Pascual-Leone Antonio , Martin-Soelch Chantal , Draganski Bogdan , Kramer Ueli 
Published: February 2025

Abstract
Self-contempt and emotional arousal are two key concepts associated with psychological distress but have been little studied in a daily life context. This work explores the use of individualized self-contemptuous stimuli extracted from a self-critical two-chair dialogue into an fMRI scanner. 28 female controls participated in psychological investigations (at three time points) and a self-critical emotion-eliciting two-chair dialogue followed by an fMRI assessment. We observed the neurofunctional activation during this task and compared neural activation during the exposition to self-critical individualized stimuli versus negative non-individualized stimuli. We also investigated emotional arousal change during the psychological session. The fMRI data analysis showed no significant difference in activation between the first and second fMRI assessments. We found no significant activation when comparing the neural activation between the exposition to self-contemptuous individualized stimuli and non-individualized negative stimuli. Controls do show an increased self-reported emotional arousal when expressing self-contempt. Our neurobehavioral design seems promising as proof of concept in combining an analogue psychotherapy session and an fMRI session to investigate expressed self-contempt and emotional arousal in healthy controls. Using this design in clinical populations seems feasible and may be important in clinical populations known for emotional difficulties such as BPD.
Agent-based modelling of older adult needs for autonomous mobility-on-demand: a case study in Winnipeg, Canada
Authors: Prédhumeau Manon , Manley Ed 
Published: February 2025

Abstract
As the populations continue to age across many nations, ensuring accessible and efficient transportation options for older adults has become an increasingly important concern. Autonomous Mobility-on-Demand (AMoD) systems have emerged as a potential solution to address the needs faced by older adults in their daily mobility. However, estimation of older adult mobility needs, and how they vary over space and time, is crucial for effective planning and implementation of such service, and conventional four-step approaches lack the granularity to fully account for these needs. To address this challenge, we propose an agent-based model of older adults mobility demand in Winnipeg, Canada. The model is built for 2022 using primarily open data, and is implemented in the Multi-Agent Transport Simulation toolkit. After calibration to accurately reproduce observed travel behaviours, a new AMoD service is tested in simulation and its potential adoption among Winnipeg older adults is explored. The model can help policy makers to estimate the needs of the elderly populations for door-to-door transportation and can guide the design of AMoD transport systems.
Estimating minimum wage effects on alcohol abuse and wages with alternative likely affected samples and models
Authors: Leigh J. Paul , Du Juan 
Published: February 2025

Abstract
We estimated effects of minimum wages on workers’ wages and alcohol abuse with samples defined by education and wages and using traditional two-way-fixed-effects (TWFE) and new heterogeneous, Stacked, models that allow for staggered treatment timing. Alcohol abuse was measured with binary indicators of heavy and binge drinking and count data on days of binge drinking. Longitudinal data were drawn from the Panel Study of Income Dynamics, 2005–2019. We investigated two samples of “likely affected” groups: the first—and most popular in the literature—was based on education i.e. people with at most a high school diploma; the second was based on wages, i.e. workers whose wages were ≤ 150% of minimum wages. Models included individual fixed effects. We found strong evidence that minimum wages lifted workers’ actual wages when “likely affected” was defined by wages but no evidence when “likely affected” was defined by education in both two-way-fixed-effects and Stacked models and across a variety of samples. We found most correlations between minimum wages and drinking were insignificant. While we found weak, suggestive evidence that minimum wages might reduce days of binge drinking, overall, we could not conclude that there was strong evidence that minimum wages either encouraged or discouraged alcohol abuse or, for that matter, that there was no relationship. Our results underscored the importance of considering alternative models before reaching conclusions; had we relied solely on TWFE, we may have concluded that minimum wages decreased alcohol abuse. Finally, our findings raise questions about the credibility of the many minimum-wage-and-health studies that rely on educational categories to define “likely affected.”