bluuebunny/crossref_metadata_2025_split huggingface dataset:
Correlation of factor XIII subunit A with factor XIII activity in a population of parturient women
Authors: Frevert M. L. , Bürgi J. , Brun R. , Hothorn T. , Rösslein M. , Ochsenbein-Kölble N. , Haslinger C. , Korte W. 
Published: November 2024

Abstract
Background
The role of factor XIII in acute bleeding situations is gaining more and more importance. It has previously been shown that prepartum factor XIII activity has a significant impact on postpartum blood loss. Whether factor XIII antigen behaves in a similar manner is unknown. As postpartum hemorrhage is one of the leading causes of maternal morbidity and mortality worldwide and factor XIII antigen determination might be available more readily in some centers as compared to factor XIII activity, this is an important question to answer, especially in the emergency situation of a postpartum hemorrhage.

Objective
To assess the correlation of prepartum factor XIII antigen with prepartum factor XIII activity and to evaluate the correlation between prepartum factor XIII antigen on measured postpartum blood loss.

Methods
This is a secondary analysis of a prospective cohort study which analyzed the impact of prepartum blood coagulation factor XIII activity on postpartum blood loss in 1300 women at the University Hospital Zurich, Switzerland between October 2015 and November 2016 (“PPH-1300 study”). Blood loss was quantified using a previously validated technique. The association of factor XIII activity and factor XIII antigen was assessed by means of a Spearman rank correlation and differences were displayed using Bland–Altman plot and Passing–Bablok regression. The effect of the prepartum factor XIII antigen on blood loss was estimated by continuous outcome logistic regression.

Results
Prepartum factor XIII activity significantly correlated with prepartum factor XIII antigen (Spearman rank correlation coefficient for prepartum values 0.89, p < 0.001 and postpartum values 0.902, p < 0.001). Elevated values of prepartum factor XIII antigen showed a trend toward lower measured postpartum blood loss.

Conclusion
The correlation of factor XIII activity with factor XIII antigen (subunit A) in a large real-world sample as well as an association of prepartum factor XIII antigen and postpartum blood loss is observed. Factor XIII antigen determination, a highly automatable test, could be useful in emergency situations such as a PPH (as well as other bleeding situations) if the determination of factor XIII activity is not possible. To evaluate whether FXIII replenishment reduces blood loss is the focus of ongoing studies.
Patterns of patient-reported outcomes (PROs) in a diverse group of gynecologic cancer survivors
Authors: Gerrity Charlotte , Sinno Abdulrahman , Natori Akina , Sookdeo Vandana , MacIntyre Jessica , George Sophia , Calfa Carmen , Crane Tracy E. , Penedo Frank J. , Schlumbrecht Matthew 
Published: November 2024

Abstract
Objectives
Racial and ethnic disparities in patient-reported outcomes (PROs) among gynecologic cancer survivors are not well studied. We evaluated whether individual-level characteristics were associated with PROs in diverse gynecologic cancer survivors.

Methods
Gynecologic cancer patients in an ambulatory oncology clinic completed a psychosocial and practical needs assessment before their appointments through the electronic medical record (EMR) patient portal. Assessments were available in English and Spanish. Fatigue, pain, physical function, depression, and anxiety were assessed with Patient-Reported Outcomes Measurement Information System (PROMIS®) computer adaptive tests, and health-related quality of life was assessed by FACT-G7. PROs were dichotomized based on severity (normal/mild vs moderate/severe). Demographic and clinical information was collected. Analyses were performed using Chi-square, t-tests, and Kruskal–Wallis tests.

Results
A total of 582 women completed the assessment; 20% (n = 116) were racial minorities, and 54.5% (n = 310) were Hispanic. A total of 192 (32.8%) completed the assessments in Spanish. Hispanic patients had lower mean fatigue scores (49.31 vs 51.74, p = 0.01), and patients whose preferred language was Spanish had lower mean depression (47.63 vs 48.97, p = 0.05) and fatigue scores (48.27 vs 51.27, p < 0.01). There were no significant differences in the severity of PROs by race, ethnicity, or preferred language. QOL scores were worse in patients with high symptom severity for anxiety (p = 0.04) and physical functioning (p < 0.01). Current smokers had worse physical functioning (13.4% vs 6.5%, p = 0.03).

Conclusions
We found no significant differences in severity of PROs by race, ethnicity, or preferred language. Quality of life scores were worse for patients with high symptom severity for physical functioning and anxiety.
Pairwise interaction of in-line spheroids settling in a linearly stratified fluid
Authors: Abdal Abdullah M. , Kahouadji Lyes , Shin Seungwon , Chergui Jalel , Juric Damir , Caulfield Colm-Cille P. , Matar Omar K. 
Published: November 2024

AbstractThis study investigates the transport of particles in density-stratified fluids, a prevalent natural phenomenon. In the ocean, particles and marine snow descend through fluids with significant density variations due to salinity and temperature gradients. Such heterogeneity in the background fluid affects the settling or rising rates of particles, often leading to accumulation at transitional density layers. Previous research has primarily focused on spherical particles, examining their isolated motion, pairwise interactions, and collective transport in stratified fluids. This work, however, extends the investigation to the interaction between two spheroidal particles settling in-line in a linearly stratified fluid. This study employs an immersed-boundary technique to perform particle-resolved numerical simulations in a three-dimensional Cartesian domain. The results showcase the effects of varying the stratification strength through the Froude number, the particles’ aspect ratios, and the initial separation distance between the particles on the interaction dynamics between the settling spheroids.
Accelerated-gradient-based generalized Levenberg–Marquardt method with oracle complexity bound and local quadratic convergence
Authors: Marumo Naoki , Okuno Takayuki , Takeda Akiko 
Published: November 2024

AbstractMinimizing the sum of a convex function and a composite function appears in various fields. The generalized Levenberg–Marquardt (LM) method, also known as the prox-linear method, has been developed for such optimization problems. The method iteratively solves strongly convex subproblems with a damping term. This study proposes a new generalized LM method for solving the problem with a smooth composite function. The method enjoys three theoretical guarantees: iteration complexity bound, oracle complexity bound, and local convergence under a Hölderian growth condition. The local convergence results include local quadratic convergence under the quadratic growth condition; this is the first to extend the classical result for least-squares problems to a general smooth composite function. In addition, this is the first LM method with both an oracle complexity bound and local quadratic convergence under standard assumptions. These results are achieved by carefully controlling the damping parameter and solving the subproblems by the accelerated proximal gradient method equipped with a particular termination condition. Experimental results show that the proposed method performs well in practice for several instances, including classification with a neural network and nonnegative matrix factorization.
Drug pricing models, no ‘one-size-fits-all’ approach: a systematic review and critical evaluation of pricing models in an evolving pharmaceutical landscape
Authors: Manders Evert A. , van den Berg Sibren , de Visser Saco J. , Hollak Carla E. M. 
Published: November 2024

AbstractAccess to new medicines is crucial for patients but increasingly sparks discussion due to high prices. Simultaneously, the growing emphasis on specialized products and uncertainty surrounding the long-term effectiveness of new drug classes brought to the market underscore the need for innovative pricing approaches. A systematic literature review of pharmaceutical pricing models, accompanied by a critical appraisal, was conducted to offer insights contributing to novel approaches balancing sustainable pharmaceutical innovation with affordability and accessibility for patients. Six different pricing models are identified: value based pricing, basic cost-based pricing, and four more comprehensive pricing models incorporating numerous elements: the cancer-drug-pricing model, AIM model, (Nuijtens) discounted cash flow, and the real-option rate of return method. Although there are many similarities among the models, each has unique assumptions for implementation. For instance, all models except for the standard incremental cost-effectiveness ratio and basic cost-based pricing consider the number of eligible patients and the remaining patent period. Only the AIM model and the Nuijtens discounted cash flow model use lump sums. Both the latter and the real-option rate of return method explicitly include the cost of capital as a major cost-based component. Recognizing the diverse applications of each model highlights the need for more differential and dynamic pricing tailored to the characteristics and therapeutic areas of each drug. Additionally, the study underscores the importance of cost transparency in achieving this goal. Consequently, these findings can help stakeholders develop sustainable and affordable drug pricing mechanisms that address the complexities of the ever-changing pharmaceutical landscape.
Diagnostic Performance of a Next-Generation Virtual/Augmented Reality Headset: A Pilot Study of Diverticulitis on CT
Authors: Murphy Paul M. , An Julie Y. , Wojdyla Luke M. , Searleman Adam C. , Khurana Aman , Loehfelm Thomas W. , Fowler Kathryn J. , Longhurst Christopher A. 
Published: November 2024

AbstractNext-generation virtual/augmented reality (VR/AR) headsets may rival the desktop computer systems that are approved for clinical interpretation of radiologic images, but require validation for high-resolution low-luminance diagnoses like diverticulitis. The primary aim of this study is to compare diagnostic performance for detecting diverticulitis on CT between radiologists using a headset versus a desktop. The secondary aim is to survey participating radiologists about the usage of both devices. This pilot study retrospectively included 110 patients (mean age 64 ± 14 years, 62 women) who had abdomen/pelvis CT scans for which the report mentioned the presence or absence of diverticulitis. Scans were dichotomized and matched by time, for a total of 55 cases with diverticulitis and 55 controls with no diverticulitis. Six radiologists were oriented to the VR/AR headset (Apple Vision Pro) and viewer app (Visage Ease VP) using ten scans. They each scored 100 unknown scans on a 6-level scale for diverticulitis (1 = no diverticulitis, 6 = diverticulitis) on the headset and then on a desktop. Time per case was recorded. Finally, they completed a survey using 5-level scales about the ease of use of the headset and viewer app (1 = difficult, 5 = easy), about their experience with the headset (1 = bad, 5 = good), and about their preference between devices (1 = desktop, 5 = headset). Summary statistics and multi-reader multi-case ROC curves were calculated. The AUC (and 95% confidence interval) for diverticulitis was 0.93 (0.88–0.97) with the headset and 0.94 (0.91–0.98) with the desktop (p = 0.40). The median (and first-third quartiles) of time per case was 57 (41–76) seconds for the headset and 31 (22–64) seconds for the desktop (p < 0.001). Average survey scores ranged from 3.3 to 5 for ease of use, from 3 to 4.7 for experience, and from 2.2 to 3.3 for preference. Diagnostic performance for detecting diverticulitis on CT was similar between the next-generation VR/AR headset and desktop. Ease of use, experience, and preference varied across different aspects of the devices and among radiologists.
Deep learning for surgical instrument recognition and segmentation in robotic-assisted surgeries: a systematic review
Authors: Ahmed Fatimaelzahraa Ali , Yousef Mahmoud , Ahmed Mariam Ali , Ali Hasan Omar , Mahboob Anns , Ali Hazrat , Shah Zubair , Aboumarzouk Omar , Al Ansari Abdulla , Balakrishnan Shidin 
Published: November 2024

AbstractApplying deep learning (DL) for annotating surgical instruments in robot-assisted minimally invasive surgeries (MIS) represents a significant advancement in surgical technology. This systematic review examines 48 studies that utilize advanced DL methods and architectures. These sophisticated DL models have shown notable improvements in the precision and efficiency of detecting and segmenting surgical tools. The enhanced capabilities of these models support various clinical applications, including real-time intraoperative guidance, comprehensive postoperative evaluations, and objective assessments of surgical skills. By accurately identifying and segmenting surgical instruments in video data, DL models provide detailed feedback to surgeons, thereby improving surgical outcomes and reducing complication risks. Furthermore, the application of DL in surgical education is transformative. The review underscores the significant impact of DL on improving the accuracy of skill assessments and the overall quality of surgical training programs. However, implementing DL in surgical tool detection and segmentation faces challenges, such as the need for large, accurately annotated datasets to train these models effectively. The manual annotation process is labor-intensive and time-consuming, posing a significant bottleneck. Future research should focus on automating the detection and segmentation process and enhancing the robustness of DL models against environmental variations. Expanding the application of DL models across various surgical specialties will be essential to fully realize this technology’s potential. Integrating DL with other emerging technologies, such as augmented reality (AR), also offers promising opportunities to further enhance the precision and efficacy of surgical procedures.
Artificial intelligence-based optimization techniques for optimal reactive power dispatch problem: a contemporary survey, experiments, and analysis
Authors: Abdel-Basset Mohamed , Mohamed Reda , Hezam Ibrahim M. , Sallam Karam M. , Alshamrani Ahmad M. , Hameed Ibrahim A. 
Published: November 2024

AbstractThe optimization challenge known as the optimal reactive power dispatch (ORPD) problem is of utmost importance in the electric power system owing to its substantial impact on stability, cost-effectiveness, and security. Several metaheuristic algorithms have been developed to address this challenge, but they all suffer from either being stuck in local minima, having an insufficiently fast convergence rate, or having a prohibitively high computational cost. Therefore, in this study, the performance of four recently published metaheuristic algorithms, namely the mantis search algorithm (MSA), spider wasp optimizer (SWO), nutcracker optimization algorithm (NOA), and artificial gorilla optimizer (GTO), is assessed to solve this problem with the purpose of minimizing power losses and voltage deviation. These algorithms were chosen due to the robustness of their local optimality avoidance and convergence speed acceleration mechanisms. In addition, a modified variant of NOA, known as MNOA, is herein proposed to further improve its performance. This modified variant does not combine the information of the newly generated solution with the current solution to avoid falling into local minima and accelerate the convergence speed. However, MNOA still needs further improvement to strengthen its performance for large-scale problems, so it is integrated with a newly proposed improvement mechanism to promote its exploration and exploitation operators; this hybrid variant was called HNOA. These proposed algorithms are used to estimate potential solutions to the ORPD problem in small-scale, medium-scale, and large-scale systems and are being tested and validated on the IEEE 14-bus, IEEE 39-bus, IEEE 57-bus, IEEE 118-bus, and IEEE 300-bus electrical power systems. In comparison to eight rival optimizers, HNOA is superior for large-scale systems (IEEE 118-bus and 300-bus systems) at optimizing power losses and voltage deviation; MNOA performs better for medium-scale systems (IEEE 57-bus); and MSA excels for small-scale systems (IEEE 14-bus and 39-bus systems).
Uncovering suggestions in MOOC discussion forums: a transformer-based approach
Authors: Reina Sánchez Karen , Vaca Serrano Gonzalo , Arbáizar Gómez Juan Pedro , Duran-Heras Alfonso 
Published: November 2024

AbstractThe field of natural language processing has experienced significant advances in recent years, but these advances have not yet resulted in improved analytics for instructors on MOOC platforms. Valuable information, such as suggestions, is generated in the comment forums of these courses, but due to their volume, manual processing is often impractical. This study examines the feasibility of fine-tuning and effectively utilizing state-of-the-art deep learning models to identify comments that contain suggestions in MOOC forums. The main challenges encountered are the lack of labeled datasets from the MOOC context for fine-tuning classification models and the soaring computational cost of this training. For this study, we manually collected and labeled 2228 comments in Spanish and English from 5 MOOCs and scraped 1.4 million MOOC reviews from 3 platforms. We fine-tuned and evaluated 4 pretrained models based on the transformer architecture and 3 traditional machine learning models to compare their effectiveness in the suggestion mining task in this domain. Transformer-based models proved to be highly effective in this task/domain combination, achieving performance levels that matched or exceeded those deemed appropriate in other contexts and were significantly greater than those achieved by traditional models. Domain adaptation led to improved linguistic understanding of the target domain; however, in this project, this approach did not translate into an observable improvement in suggestion mining. The automated identification of comments that can be labeled as suggestions can result in considerable time savings for instructors, especially considering that less than a quarter of the analyzed comments contain suggestions.
Martingale Hardy Orlicz–Lorentz–Karamata spaces and applications in Fourier analysis
Authors: Hao Z. , Weisz F. 
Published: November 2024

Abstract
We summarize some results as well as we prove some new results about the Orlicz–Lorentz–Karamata spaces and martingale Hardy Orlicz–Lorentz–Karamata spaces. More precisely, Doob's maximal inequality for submartingales and Burkholder–Davis–Gundy inequality are presented. We also show some fundamental martingale inequalities and modular inequalities. Additionally, based on atomic decompositions, duality theorems and fractional integral operators are discussed. As applications in Fourier analysis, we consider the Walsh–Fourier series on Orlicz–Lorentz–Karamata spaces. The dyadic maximal operators on martingale Hardy Orlicz–Lorentz–Karamata spaces are presented. The boundedness of maximal Fejér operator is proved, which further implies some convergence results of the Fejér means.
Carbon emission reducing and financing strategies of capital-constrained supply chains using equity financing under different power structures
Authors: Qin Juanjuan , Lyu Xiaochen , Fu Huiping , Wang Ziping 
Published: November 2024

AbstractThis study considers a low-carbon supply chain model comprised of a capital-constrained manufacturer and a retailer under a cap-and-trade system. These two parties can choose from two financing modes: bank financing or mixed financing, in which the latter combines bank financing with equity financing. Two decision-making power structures are formulated, namely the Nash game model and the retailer-led Stackelberg game model. Subsequently, a comparative analysis of equilibrium outcomes for both parties within these power structures is performed, yielding the following insights: (1) In the bank financing mode, retailers find greater profitability in adopting dominance. When emission reduction efficiency is high, manufacturers obtain more profit within a Nash game context. However, when emission reduction efficiency is low, manufacturers accrue more profit in a Stackelberg game context. In the mixed financing mode without cap-and-trade regulations, supply chain participants do not express a preference for any specific game structure. (2) Without cap-and-trade regulation, in both the Nash and Stackelberg game models, the supply chain participants find more profitability in mixed financing when carbon emission reduction efficiency is high. Meanwhile, bank financing proves more profitable when the carbon emission reduction is low. Moreover, this study investigates the impact of wholesale and retail prices of low-carbon products on the financing strategies of the supply chain participants under different power structures and financing modes. Additionally, extended models are considered, including the manufacturer-led Stackelberg game structure and the capital-constrained manufacturer with a certain amount of initial capital.
Near-Field Mixing in a Coaxial Dual Swirled Injector
Authors: Marragou Sylvain , Guiberti Thibault Frédéric , Poinsot Thierry , Schuller Thierry 
Published: November 2024

Abstract
Improving mixing between two coaxial swirled jets is a subject of interest for the development of next generations of fuel injectors. This is particularly crucial for hydrogen injectors, where the separate introduction of fuel and oxidizer is preferred to mitigate the risk of flashback. Raman scattering is used to measure the mean compositions and to examine how mixing between fuel and air streams evolves along the axial direction in the near-field of the injector outlet. The parameters kept constant include the swirl level

$$S_e = 0.67$$

S
e

=
0.67

in the annular channel, the injector dimensions, and the composition of the oxidizer stream, which is air. Experiments are carried out in cold flow conditions for different compositions of the central stream, including hydrogen and methane but also helium and argon. Three dimensionless mixing parameters are identified, the velocity ratio

$$u_e/u_i$$

u
e

/

u
i

between the external stream and internal stream, the density ratio

$$\rho _e/\rho _i$$

ρ
e

/

ρ
i

between the two fluids, and the inner swirl level

$$S_i$$

S
i

in the central channel. Adding swirl to the central jet significantly enhances mixing between the two streams very close to the injector outlet. Mixing also increases with higher velocity ratios

$$u_e/u_i$$

u
e

/

u
i

, independently of the inner swirl. Additionally, higher density ratios

$$\rho _e/\rho _i$$

ρ
e

/

ρ
i

enhance mixing between the two streams only in the case without swirl conferred to the central flow. A model is proposed for coaxial swirled jets, yielding a dimensionless mixing progress parameter that only depends on the velocity ratio

$$u_e/u_i$$

u
e

/

u
i

and geometrical features of the swirling flow that can be determined by examining the structure of the velocity field. Comparing the model with experiments, it is shown to perform effectively across the entire range of velocity ratios

$$0.6 \le u_e/u_i \le 3.8$$

0.6
≤

u
e

/

u
i

≤
3.8

, density ratios

$$0.7 \le \rho _e/\rho _i \le 14.4$$

0.7
≤

ρ
e

/

ρ
i

≤
14.4

, and inner swirl levels

$$0.0 \le S_i \le 0.9$$

0.0
≤

S
i

≤
0.9

. This law may be used to facilitate the design of coaxial swirled injectors.