bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial Intelligence in Pancreatic Imaging: A Systematic Review
Authors: Podină Nicoleta , Gheorghe Elena Codruța , Constantin Alina , Cazacu Irina , Croitoru Vlad , Gheorghe Cristian , Balaban Daniel Vasile , Jinga Mariana , Țieranu Cristian George , Săftoiu Adrian 
Published: January 2025

ABSTRACTThe rising incidence of pancreatic diseases, including acute and chronic pancreatitis and various pancreatic neoplasms, poses a significant global health challenge. Pancreatic ductal adenocarcinoma (PDAC) for example, has a high mortality rate due to late‐stage diagnosis and its inaccessible location. Advances in imaging technologies, though improving diagnostic capabilities, still necessitate biopsy confirmation. Artificial intelligence, particularly machine learning and deep learning, has emerged as a revolutionary force in healthcare, enhancing diagnostic precision and personalizing treatment. This narrative review explores Artificial intelligence's role in pancreatic imaging, its technological advancements, clinical applications, and associated challenges. Following the PRISMA‐DTA guidelines, a comprehensive search of databases including PubMed, Scopus, and Cochrane Library was conducted, focusing on Artificial intelligence, machine learning, deep learning, and radiomics in pancreatic imaging. Articles involving human subjects, written in English, and published up to March 31, 2024, were included. The review process involved title and abstract screening, followed by full‐text review and refinement based on relevance and novelty. Recent Artificial intelligence advancements have shown promise in detecting and diagnosing pancreatic diseases. Deep learning techniques, particularly convolutional neural networks (CNNs), have been effective in detecting and segmenting pancreatic tissues as well as differentiating between benign and malignant lesions. Deep learning algorithms have also been used to predict survival time, recurrence risk, and therapy response in pancreatic cancer patients. Radiomics approaches, extracting quantitative features from imaging modalities such as CT, MRI, and endoscopic ultrasound, have enhanced the accuracy of these deep learning models. Despite the potential of Artificial intelligence in pancreatic imaging, challenges such as legal and ethical considerations, algorithm transparency, and data security remain. This review underscores the transformative potential of Artificial intelligence in enhancing the diagnosis and treatment of pancreatic diseases, ultimately aiming to improve patient outcomes and survival rates.
Linear Dependencies Among Cubic Partition Numbers
Authors: Ballantine Cristina , Merca Mircea 
Published: January 2025

Abstract
A cubic partition is an integer partition in which the even parts can occur in two distinct colors. In this paper, we denote the number of cubic partitions of n by a(n) and define

$$a_m(n)$$

a
m

(
n
)

as the number of cubic partitions of n in which only even parts that are not congruent to 0 modulo

$$2^m$$

2
m

can appear in two colors. By leveraging classical results on truncated theta series, we introduce a collection of linear relations associated with the cubic partition functions a(n) and

$$a_m(n)$$

a
m

(
n
)

. This context reveals intriguing connections among ordinary partitions, overpartitions,

$$2^m$$

2
m

-regular partitions, pod-partitions and cubic partitions. A complete characterization of the congruences of

$$a_2(n)$$

a
2

(
n
)

modulo 2 and 4 is provided.
Assessing uncertainty in image-based monitoring: addressing false positives, false negatives, and base rate bias in structural health evaluation
Authors: Plevris Vagelis 
Published: January 2025

Abstract
This study explores the limitations of image-based structural health monitoring (SHM) techniques in detecting structural damage. Leveraging machine learning and computer vision, image-based SHM offers a scalable and efficient alternative to manual inspections. However, its reliability is impacted by challenges such as false positives, false negatives, and environmental variability, particularly in low base rate damage scenarios. The Base Rate Bias plays a significant role, as low probabilities of actual damage often lead to misinterpretation of positive results. This study uses both Bayesian analysis and a frequentist approach to evaluate the precision of damage detection systems, revealing that even highly accurate models can yield misleading results when the occurrence of damage is rare. Strategies for mitigating these limitations are discussed, including hybrid systems that combine multiple data sources, human-in-the-loop approaches for critical assessments, and improving the quality of training data. These findings provide essential insights into the practical applicability of image-based SHM techniques, highlighting both their potential and their limitations for real-world infrastructure monitoring.
Effectiveness and cost-effectiveness of interventions to increase knowledge and awareness of attention deficit hyperactivity disorder: a systematic review
Authors: McKenna Kaitlyn , Wanni Arachchige Dona Sithara , Gold Lisa , Silk Tim J. , Le Ha N. D. 
Published: January 2025

Abstract
Various interventions, including caregiver education, psychoeducation, teacher and clinician training and behavioral management embedded with education, are available to enhance awareness and knowledge among caregivers, teachers, and clinicians. This review synthesizes evidence on the effectiveness and cost-effectiveness of interventions to increase ADHD awareness and knowledge for caregivers, clinicians, and teachers. Peer-reviewed literature was identified through the systematic searches of six databases: MEDLINE Complete, APA PsycInfo, CINAHL Complete, ERIC, Global Health and EconLit. Two independent reviewers completed a two-stage screening process and quality assessment. Results are presented as a narrative synthesis. Economic evaluation study cost data were converted for comparability. Of the 5963 results, 28 studies were included. Most interventions were found to be effective in increasing the awareness and knowledge of ADHD among caregivers, clinicians, and teachers. Clinician knowledge and capacity to assess, diagnose and treat ADHD were improved. Of the economic evaluation studies, interventions were promising in parent education, and behavioral management and psychosocial therapy interventions were found to be cost-effective. The review provides important insights about effective and cost-effective interventions to increase ADHD awareness and knowledge. These insights are critical to effectively plan services/interventions for families and children with ADHD. Further research in economic evaluation of interventions is needed to assist policy-decision making.
Reducing the effect of face orientation using FaceMesh landmarks in drowsiness estimation based on facial thermal images
Authors: Nomura Ayaka , Yoshida Atsushi , Nagumo Kent , Nozawa Akio 
Published: January 2025

Abstract
In this study, facial skin temperature distribution (FSTD) is focused on as a new driver monitoring index. FSTD is an autonomic index that can be measured remotely. Studies have been conducted to estimate drowsiness based on FSTD using modelng methods such as CNN, a type of deep learning, and sparse modeling, which can be trained with a small amount of data. These studies, however, only evaluated front-facing facial thermal images. FaceMesh is a model that extracts 478 3D facial feature landmarks from a 2D face image. In contrast to conventional models that extract only 68 facial feature landmarks, FaceMesh can extract facial feature landmarks for the entire face, including the cheeks, forehead, and other areas of the face that are in the blind spots. This study aims to improve the accuracy of drowsiness estimation by applying FaceMesh to automatically detect tilted faces and not including tilted images in the training data. As a result, the method proposed in this study improved drowsiness estimation accuracy by about 6% compared to the old method, which did not take face orientation into account.
Čapek’s Argument for the Reality of Temporal Passage
Authors: Kügler Peter 
Published: January 2025

Abstract
The antirealist position on temporal passage is that time exists but does not pass. Antirealists either claim that experiences of passage represent something that does not exist or that these experiences do not represent passage. This paper reconstructs and defends an argument for the reality of passage by Milič Čapek that is based on the idea of mental passage, the passage of experience itself. The belief that mental passage exists is introspectively justified. This justification is not undermined by perceptual illusions and is also compatible with antirealist explanations of why the belief arises. Both physicalism and psychophysical dualism justify the premise that mental passage implies physical passage. Arguments by Huw Price and Simon Prosser purport to show that experiences do not represent physical passage. However, these arguments rely on thought experiments that presuppose the non-existence of mental passage, and therefore miss the fact that mental passage enables the representation of physical passage.
Selling out the city? Airbnb’s amateur and professional listings across cities, time, and hotspots
Authors: Kirchner Stefan , Pohl Simon C. 
Published: January 2025

Abstract
In the early days of its existence, Airbnb was heralded as a champion of the “sharing economy”, enabling amateur hosts to offer short-term lodging to a global community of guests. Since then, public debates and research have highlighted the rise of professional hosts as an indication for the commercialization of Airbnb. However, it is neither well understood how Airbnb listings developed across cities and time nor how they are distributed across local hotspots. The paper conceptualizes the Airbnb marketplace in its spatial constitution, as a network space brokering accommodation at specific places within designated city territories. It asks who dominates the digital marketplace across cities and time, distinguishing between professional and amateur listings. Based on an extensive dataset of 45 cities all over the world over a period of eight years, the paper investigates the distribution of amateur and professional listings. The article then focuses on the spatial diffusion of Airbnb listings in four selected cities (Amsterdam, Berlin, London, San Francisco) which represent diverse regulatory approaches, to identify local hotspots and establish who dominates these hotspots. The results show a worldwide trend of professional listings rising to dominate Airbnb, alongside few pockets of amateur marketplaces. Within cities, amateurs prevail only in local hotspots in specific cities—which, moreover, tend to be found only in peripheral locations. The results provide rich empirical insights into the commercialization and diversity of Airbnb as a network space around the world.
Metastable Microstructural States During Short-Term Heat Treatment of the High-Speed Steel PM HS 3-3-4: Modeling and Experimental Validation
Authors: Müller Stefan , Benito Santiago , Schuppener Jannik , Weber Sebastian 
Published: January 2025

Abstract
Short-term heat treatments of tool steels, such as inductive hardening, target the martensitic hardening of functional surfaces from a metastable microstructural state of the multiphase system. Understanding the kinetics of the carbide dissolution, precipitation, growth, and coarsening, and their effect on the austenitic matrix is, thus, instrumental to designing optimal heat treatments. The present work investigates the influence of such processes on the powder-metallurgically produced high-speed steel DIN EN 1.3377 (PM HS 3-3-4). Starting from the as-delivered soft-annealed state, the microstructural evolution during the heat treatment was investigated. The heat treatments were simulated experimentally in a quenching dilatometer and numerically within the software package MatCalc. SEM and XRD provided detailed information about the microstructure, whereas hardness measurements deliver insights into mechanical properties. The developed MatCalc model can predict the carbide dissolution and the associated change in the chemical composition of the matrix during austenitizing as a function of temperature and time. The results in terms of the measured and computed martensite start temperature demonstrated good agreement between the experimental and simulation campaigns.
Intrinsic High-Strain-Rate Softening in a High-Strength Quenching and Partitioning Steel
Authors: Huang C. P. , Wang M. , Zhu K. Y. , Perlade Astrid , Huang M. X. 
Published: January 2025

Abstract
An anomalous high-strain-rate softening phenomenon was demonstrated in a high-strength quenching and partitioning (Q&P) steel. It was found that the high-strain-rate softening in the Q&P steel is caused by the intrinsic carbon-dislocation interaction, rather than transformation induced plasticity effect, dislocation density difference, or adiabatic heating. The key mechanism is ascribed to the disappeared Cottrell drag force during high-strain-rate deformation, which originates from carbon-dislocation interaction and is present during quasi-static deformation.
Should adrenal incidentaloma patients be evaluated for muscle mass, function, and quality? A cross-sectional study
Authors: Alkan Samet , Guney Sedat Can , Akcura Can , Ozdemir Nilufer , Hekimsoy Zeliha 
Published: January 2025

Abstract

Purpose
Our study evaluated skeletal muscle mass, function and quality among mild autonomous cortisol secretion (MACS) patients and non-functioning adrenal incidentaloma (NFAI) patients in comparison with the control group without adrenal mass.

Methods
63 NFAI (49 female, 14 male) and 31 MACS (24 female, 7 male) patients were included in the study. As the control group, 44 patients (31 women, 13 men) who were known to have no radiological adrenal pathology on computed tomography or magnetic resonance imaging performed for other reasons were selected. After recording the laboratory parameters of the patients, anthropometric measurements, handgrip strength test with dynamometer, SARC-F survey and bioelectrical impedance analysis (BIA) measurements were performed.

Results
There was no statistical difference among the groups in terms of age, gender, and BMI parameters. Handgrip strength (HGS), skeletal muscle mass (SMM) index (SMM/BMI), and skeletal muscle quality (HGS/SMM), values used to evaluate muscle strength and quality, were found to be significantly lower in both the MACS and NFAI groups compared to the control group (p = 0.004, p = 0.012 and p = 0.034 respectively). This significance was also present in women subgroup analyses (p = 0.002, p = 0.037 and p = 0.039 respectively), but these parameters lost their statistical significance in men. In the correlation analysis of the female subgroup, 24-h free urine cortisol value was inversely proportional to skeletal muscle quality (rs = -0.417, p = 0.008).

Conclusion
Our study showed that there is a decrease in muscle mass and function in female AI patients, and this decrease is more severe in MACS patients. These results may suggest that mild cortisol excess also has negative effects on skeletal muscle metabolism.
Revolutionizing acute myeloid leukemia treatment: a systematic review of immune-based therapies
Authors: Ebinama Ugochi , George Binsah 
Published: January 2025

Abstract
The established protocol for the management of acute myeloid leukemia (AML) has traditionally involved the administration of induction chemotherapy, followed by consolidation chemotherapy, and subsequent allogeneic stem cell transplantation for eligible patients. However, the prognosis for individuals with relapsed and refractory AML remains unfavorable. In response to the necessity for more efficacious therapeutic modalities, targeted immunotherapy has emerged as a promising advancement in AML treatment. This comprehensive review article specifically examines classical unconjugated and toxin-conjugated monoclonal antibodies, which are currently in the preclinical phase or undergoing evaluation in clinical trials. The review delves into the proposed mechanisms through which these monoclonal antibodies elicit anti-tumor activity and identifies the challenges associated with designing targeted immunotherapy. The review focuses on targeting specific antigens in AML, including FLT3/CD125, CLL-1, CD33, CD38, CD47, CD70, and CD123.
Material characterization of three liquid interlayers for laminated glass solutions
Authors: Hänig Julian , Gutjahr Alina , Bukieda Paulina , Engelmann Michael 
Published: January 2025

Abstract
The demand for versatile and visually appealing glazed structures has been increasing in recent years. To meet contemporary architectural needs, a wider range of functionalities is required, including smart, bendable and thin laminated glazing. In particular, cold poured liquid optically clear adhesives (LOCA) are gaining prominence as interlayers for complex curved cold bending and smart integration, where conventional interlayer films impose limitations. Furthermore, new applications for cold bending of thin glass with LOCA for complex free-form geometries are currently under development. This paper presents the investigation of three LOCA for application as interlayers in laminated glass. Within a comprehensive experimental study, essential material properties were investigated. The research focuses on the viscoelastic, tensile, and creep properties of the LOCA. Additionally, the study considers how the materials age under radiation, high temperature and high moisture in accordance with the product standard for laminated glass. All results are summarized to provide a wide-ranging data base for dimensioning laminated glass made with LOCA. The investigations proof a wide-ranging suitability for laminated glass. The mechanical properties of the LOCA are comparable to conventional interlayer films. Two materials behave medium stiff to stiff and one is more flexible. All LOCA provide ductility which makes them suitable for laminated safety glass applications. High performances paired with the autoclave-free processing LOCA drives for new fields of applications for architectural glazings.
Biological insights into alleviating heavy metal toxicity through the simultaneous supply of biochar biofilters derived from the Mikania micrantha and molybdenum nanoparticles
Authors: Rana Muhammad Shoaib , Chen Hongyu , Deng Shiwen , Imran Muhammad , Abdellah Yousif Abdelrahman Yousif , Li Wanting , Lin Jiayu , Li Jiaxi , Wang Ruilong 
Published: January 2025

Abstract
Cadmium (Cd) contamination in the environment is widespread, making it crucial to reduce Cd accumulation in cereal crops like wheat. However, strategies that not only mitigate Cd pollution but also address other environmental challenges, such as invasive species management, remain unclear. This study introduces an innovative approach combining molybdenum nanoparticles (Mo NPs, 1 µM) and biochar biofilters derived from the invasive plant Mikania micrantha (IPMM), targeting the biochemical and molecular responses of wheat under Cd stress (100 µM). Our findings showed that this novel combination significantly improved wheat physiological characteristics, growth, root architecture, elemental profile, osmoregulation, carotenoid, chlorophyll, gas exchange, and photosynthetic efficiency. Remarkably, simultaneous supply of IPMM biochar biofilters and Mo NPs substantially modulated the Cd translocation, reducing its accumulation in root (30.54%) and shoot (53.59%). Additionally, this strategy not only preserved mesophyll cell structures and the membrane integrity, but also strengthened and activated the oxidative defense systems through the regulation of genetic expressions. This synergistic approach advances the Cd alleviating techniques and offers a sustainable solution for utilizing invasive plants as a potential resource. By addressing both heavy metal pollution and ecological challenges, it provides a promising solution for safer crop production in Cd-contaminated environments.

Graphical Abstract
Open science 2.0: revolutionizing spatiotemporal data sharing and collaboration
Authors: Wang Siqin , Huang Xiao , Zhang Mengxi , Bao Shuming , Liu Lingbo , Fu Xiaokang , Zhang Ting , Song Yongze , Kedron Peter , Wilson John , Ye Xinyue , Yang Chaowei , Guan Wendy 
Published: January 2025

Abstract
The Spatial Data Lab (SDL) project is a collaborative initiative by the Center for Geographic Analysis at Harvard University, KNIME, Future Data Lab, China Data Institute, and George Mason University. Co-sponsored by the NSF IUCRC Spatiotemporal Innovation Center, SDL aims to advance applied research in spatiotemporal studies across various domains such as business, environment, health, mobility, and more. The project focuses on developing an open-source infrastructure for data linkage, analysis, and collaboration. Key objectives include building spatiotemporal data services, a reproducible, replicable, and expandable (RRE) platform, and workflow-driven data analysis tools to support research case studies. Additionally, SDL promotes spatiotemporal data science training, cross-party collaboration, and the creation of geospatial tools that foster inclusivity, transparency, and ethical practices. Guided by an academic advisory committee of world-renowned scholars, the project is laying the foundation for a more open, effective, and robust scientific enterprise.
Prediction of mechanical properties of eco-friendly concrete using machine learning algorithms and partial dependence plot analysis
Authors: Roy Tonmoy , Das Pobithra , Jagirdar Ravi , Shhabat Mousa , Abdullah Md Shahriar , Kashem Abul , Rahman Raiyan 
Published: January 2025

Abstract
Rice husk ash concrete (RHAC) shows promise as a beneficial supplementary material in concrete. However, determining mechanical properties such as compressive strength (CS) and splitting tensile strength (STS) of RHAC through conventional lab-scale methods is laborious and time-consuming. In this research, seven important variables were selected as inputs to predict CS and STS using machine learning (ML) models, including Gaussian Process Regression (GPR), Random Forest Regression (RFR), and Decision Tree Regression (DTR) with grid search optimization. The result presented revealed that selected machine learning models provide well accuracy for CS and STS estimates. Among these, the DTR model demonstrated superior performance, with CS prediction R2, RMSE, MAE, and MAPE values of 0.964, 3.314, 2.225, and 5.068, at the testing stage respectively. For STS at the testing stage, DTR achieved R2 of 0.969, RMSE of 0.177, MAE of 0.1322, and MAPE of 3.413. GPR and RFR models also performed well, with R2 values of 0.9434 and 0.9530 for CS prediction. The partial dependence plot (PDP) analysis revealed the optimal mix design parameters for achieving the desired strength. These results offer valuable insights for sustainable construction, allowing engineers to efficiently predict and optimize material performance, reducing the reliance on time-consuming lab methods.