bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automated classification of online reviews of otolaryngologists
Authors: Stenzel Jake G. , Schultz Nicholas R. , Marino Michael J. 
Published: November 2024

AbstractObjectivesThe study aimed to extract online comments of otolaryngologists in the 20 most populated cities in the United States from healthgrades.com, develop and validate a natural language processing (NLP) logistic regression algorithm for automated text classification of reviews into 10 categories, and compare 1‐ and 5‐star reviews in directly‐physician‐related and non‐physician‐related categories.Methods1977 1‐star and 12,682 5‐star reviews were collected. The primary investigator manually categorized a training dataset of 324 1‐star and 909 5‐star reviews, while a validation subset of 100 5‐star and 50 1‐star reviews underwent dual manual categorization. Using scikit‐learn, an NLP algorithm was trained and validated on the subsets, with F1 scores evaluating text classification accuracy against manual categorization. The algorithm was then applied to the entire dataset with comparison of review categorization according to 1‐ and 5‐star reviews.ResultsF1 scores for NLP validation ranged between 0.71 and 0.97. Significant associations emerged between 1‐star reviews and treatment plan, accessibility, wait time, office scheduling, billing, and facilities. 5‐star reviews were associated with surgery/procedure, bedside manner, and staff/mid‐levels.ConclusionThe study successfully validated an NLP text classification system for categorizing online physician reviews. Positive reviews were found to have an association with directly‐physician related context. 1‐star reviews were related to treatment plan, accessibility, wait time, office scheduling, billing, and facilities. This method of text classification effectively discerned the nuances of human‐written text, providing valuable insights into online healthcare feedback that is scalable.Level of evidence: Level 3
Study of Global Seawater Corrosivity Classification Based on Marine Environmental Factors Data
Authors: Zhang Penghui , Liu Shaotong , Ding Kangkang , Wu Shuai , Fan Lin 
Published: November 2024

ABSTRACTTo evaluate the global seawater corrosivity and ensure the service safety of marine equipment, the methods of seawater corrosivity classification, which contained the standard metal corrosion rates method and the environmental factors evaluation method, are proposed and then applied to the seawater corrosivity classification in typical China seas. By comparing the results from both methods, the feasibility of the environmental factors evaluation method based on the grey relational model is verified. Furthermore, with the collection and processing of typical seawater factors data of global ocean, the corrosivity of global seawater is classified into six grades with the corrosion test data of carbon steel as baseline, and its distribution in different months is visualized by means of ArcGIS technology. The results show that the sea areas with high corrosivity are mainly located at equatorial and tropical sea areas, and for carbon steel, seawater temperature was the main influence on seawater corrosivity.
Advances in Self‐Healing Perovskite Solar Cells Enabled by Dynamic Polymer Bonds
Authors: Yuan Qisong , Chen Juxiang , Shi Chengyu , Shi Xiangrong , Sun Chenyu , Jiang Bo 
Published: November 2024

AbstractThis comprehensive review addresses the self‐healing phenomenon in perovskite solar cells (PSCs), emphasizing the reversible reactions of dynamic bonds as the pivotal mechanism. The crucial role of polymers in both enhancing the inherent properties of perovskite and inducing self‐healing phenomena in grain boundaries of perovskite films are exhibited. The review initiates with an exploration of the various stability problems that PSCs encounter, underscoring the imperative to develop PSCs with extended lifespans capable of self‐heal following damage from moisture and mechanical stress. Owing to the strong compatibility brought by polymer characteristics, many additive strategies can be employed in self‐healing PSCs through artful molecular design. These strategies aim to limit ion migration, prevent moisture ingress, alleviate mechanical stress, and enhance charge carrier transport. By scrutinizing the conditions, efficiency, and types of self‐healing behavior, the review encapsulates the principles of dynamic bonds in the polymers of self‐healing PSCs. The meticulously designed polymers not only improve the lifespan of PSCs through the action of dynamic bonds but also enhance their environmental stability through functional groups. In addition, an outlook on self‐healing PSCs is provided, offering strategic guidance for future research directions in this specialized area.
A Bibliometric Exploration of Multiple Criteria Decision Aid and Clustering—A Conceptual Taxonomy
Authors: Delias Pavlos , Doumpos Michalis 
Published: November 2024

ABSTRACTThis work explores the intersection of Multiple Criteria Decision Aid (MCDA) and clustering techniques, revealing unexploited potential and novel perspectives arising from their integration, challenging their conventional separation. It serves as a compass, guiding researchers through a bibliometric exploration and a conceptual taxonomy consolidating existing knowledge. Employing a two‐fold methodology, we first sketch the field's contours through a bibliometric lens, uncovering its intellectual structure, thematic landscape, and social dynamics. Then, using science mapping techniques like co‐word analysis, historiography, and collaboration network analysis, we examine patterns, revealing an interconnected mosaic of concepts. Our findings unveil a natural grouping into three categories: (1) Mixed‐yet‐not‐integrated approaches, explores sequential applications—clustering followed by MCDA or vice versa—where one method precedes and informs the other. (2) ‘Relational/ordered clustering’ leveraging criteria dependency to refine structures. (3) Using MCDA to improve clustering mechanics through similarity metrics, domain knowledge incorporation, and robustness. We conclusively propose a taxonomy along three axes: Units of Analysis, Instrumentalisation, and Objective. The key takeaway emphasises the collaborative potential of MCDA, envisioning a landscape where the integration of MCDA and clustering not only enhances existing methodologies but also spawns innovative paradigms, fostering a symbiotic relationship that transcends conventional boundaries.
Atmospheric icing meteorological parameter study using field experiments and simulation
Authors: Han Xingbo , Virk Muhammad , Asif Hamza , Mäkynen Anssi , Juttula Harri , Molkoselkä Eero , Kaikkonen Ville A. 
Published: November 2024

AbstractAtmospheric icing on ground structures is a concern from design, operation, and safety perspectives. Supercooled water droplets size and liquid water content (LWC) are important weather parameters to better understand the ice accretion physics on ground structures. Most existing studies are based on measurements at high altitude. The study is based on the field results of a specific event (from 9:30 to 22:27 h on October 29, 2022) in Arctic region of northern Norway. The data from this event are presented and used for analytical validation and simulation. Field measurements of different meteorological weather parameters including the droplet size and LWC are carried out leading to recording of resultant atmospheric ice load and intensity. A comprehensive study is also carried out to validate droplet collision efficiency and ice load using the existing analytical model ISO‐12494 and computational fluid dynamics (CFD)–based numerical simulations. Furthermore, the differences in icing simulation using parameters such as median volume diameter (MVD), Langmuir B –J as alternatives to the actual droplet size distribution (DSD) spectrum are also analyzed. The results show that under natural meteorological conditions, the characteristics of water DSD change in real time. Using MVD alone to calculate the water droplet collision efficiency on circular cylinders can lead to significant errors. Accurately selecting the Langmuir distribution as a substitute for the actual DSD can reduce simulation errors to within 5%. Compared to the analytical model, the numerical simulations result better reflects the collision characteristics of water droplets of different sizes on the cylindrical object.
Spectrally selective and interleaved water imaging and fat imaging (siWIFI)
Authors: Shin Soo Hyun , Tang Qingbo , Carl Michael , Athertya Jiyo S. , Suprana Arya , Ma Yajun 
Published: November 2024

AbstractPurposeTo develop a novel imaging sequence that independently acquires water and fat images while being inherently insensitive to motion.MethodsThe new sequence, termed spectrally selective and interleaved water imaging and fat imaging (siWIFI), uses a narrow bandwidth RF pulse for selective excitation of water and fat separately. The interleaved acquisition method ensures that the obtained water and fat images are inherently coregistered. A radial sampling strategy further reduces motion‐induced artifacts. Phantoms with lipid concentrations ranging from 0% to 50% were scanned to measure fat fraction. Moreover, healthy volunteers were scanned to assess the in vivo feasibility of fat fraction measurement at the hip, knee, and liver. In vivo fat fraction measurements were compared with those from vendor‐provided iterative decomposition of water and fat with echo asymmetry and least‐squares estimation (IDEAL) scans. Furthermore, a magnetization transfer (MT) preparation module was incorporated to demonstrate the feasibility of simultaneous measurement of fat fraction and MT ratio utilizing the siWIFI framework.ResultsThe phantom fat fractions measured by siWIFI showed excellent correlation with lipid concentrations (R2 = 0.9995, p < 0.0001). In vivo studies demonstrated that the fat fractions obtained from siWIFI were comparable to those from IDEAL. Additionally, siWIFI demonstrates reduced motion artifacts from pulsatile flow in knee imaging compared to IDEAL scans and exhibits less sensitivity to respiratory motion in liver imaging compared to IDEAL scans without breath‐hold. The knee imaging study demonstrated that MT‐prepared siWIFI is capable of generating fat fraction and MT ratio maps simultaneously.ConclusionThe proposed siWIFI sequence allows selective water–fat imaging and quantification with reduced motion artifacts.
Development of cefepime‐induced encephalopathy in a patient with depression and rectal cancer: A case report
Authors: Yamaguchi Junji , Sadahiro Ryoichi , Wada Saho , Nishikawa Eri , Terada Tatsuto , Nakahara Rika , Matsuoka Hiromichi 
Published: November 2024

AbstractBackgroundCefepime, a fourth‐generation cephalosporin, has neurotoxic side effects such as encephalopathy. Baseline conditions, including blood–brain barrier (BBB) impairment and renal dysfunction, are known to associate with elevated central nervous concentration of cefepime. Although BBB dysfunction occurs with depression or cancer, currently, neither is regarded as a risk factor for cefepime‐induced encephalopathy.Case PresentationA 79‐year‐old woman with a history of depression and rectal cancer was hospitalized for a bacterial liver abscess. Brain metastasis and other causes for delirium were excluded, and no renal dysfunction was observed. However, 11 days after cefepime and metronidazole administration, the patient suddenly developed confusion, disorientation, and myoclonus, with no apparent changes on brain magnetic resonance imaging. Electroencephalography revealed a consistent tri‐phasic wave pattern. Clinical symptoms were well consistent with cefepime‐induced encephalopathy; hence, cefepime and metronidazole were discontinued, followed by rapid physical and mental recovery, with no aftereffects.ConclusionsIn terms of BBB dysfunction, depression and cancer might be possible occult risk factors for cefepime‐induced encephalopathy. Doctors need to pay attention to encephalopathy risk when administering cefepime in patients with depression or cancer because the psychiatric symptoms of encephalopathy, depression, and delirium from other causes are often confusing, leading to misdiagnosis and a poor prognosis.
Ni‐Co Bimetallic Catalysts Supported on Mixed Oxides (Sc‐Ce‐Zr) for Enhanced Methane Dry Reforming
Authors: Abasaeed Ahmed E. , Ibrahim Ahmed A. , Fakeeha Anis H. , Bayazed Mohammed O. , Amer Mabrook S. , Abu‐Dahrieh Jehad K. , Al‐Fatesh Ahmed S. 
Published: November 2024

AbstractDry methane reforming (DRM) presents a viable pathway for converting greenhouse gases into useful syngas. Nevertheless, the procedure requires robust and reasonably priced catalysts. This study explored using cost‐effective cobalt and nickel combined into a single catalyst with different metal ratios. The reaction was conducted in a fixed reactor at 700 °C. The findings indicate that the incorporation of cobalt significantly enhances catalyst performance by preventing metal sintering, improving metal dispersion, and promoting beneficial metal‐support interactions. The best‐performing catalyst (3.75Ni+1.25Co‐ScCeZr) achieved a good conversion rate of CH4 and CO2 at 46.8 %, and 60 % respectively after 330 minutes while maintaining good stability. The TGA and CO2‐TPD analysis results show that the addition of Co to Ni reduces carbon formation, and increases the amount of strong basic sites and isolated O2− species, and the total amount of CO2 desorbed. These results collectively highlight the potential of cobalt‐nickel catalysts for practical DRM applications and contribute to developing sustainable energy technologies.
An Analysis of Contextual Factors Influencing women's Candidacy in Local Elections: Insights From Italy
Authors: Benati Igor , Falavigna Greta , Sella Lisa 
Published: November 2024

ABSTRACTExtensive research showed that the gender gap in electoral participation, both active and passive, is linked to cultural and socio‐economic aspects that can combine in diverse ways across different territories, leading to regional disparities. The present study analyzes how such heterogeneity in cultural and socio‐economic conditions interacts with the level of female participation in political competition in Italy, as measured by the proportion of female candidates in municipal elections from 2009 to 2016. Focusing on the gender dimension of candidacy in local elections, the study investigates its territorial determinants, particularly the role of the social and economic context. The research is innovative in two ways. First, it utilizes a large original panel dataset covering gender aspects of electoral candidatures at the municipal level as well as many socio‐economic contextual characteristics. Second, it employs a specification of the Correlated Random Effects Within‐Between model, which allows to disentangle the effect of both time‐variant and time‐invariant information within a panel data framework. Finally, the time period examined is of interest as it encompasses the Italian electoral reform known as Law 215/2012, which introduced a gender list quota at the municipal level. The results of the study suggest that policies to reduce the gender gap must aim not only at introducing quotas for women's representation but also at acting on all those economic and social factors that limit the presence of women in electoral candidacy.
Analytical considerations of the load‐deflection behavior in fibers during a filament winding process
Authors: Steinweller Christina , Hartmann Stefan 
Published: November 2024

AbstractFilament winding is a manufacturing process used in the construction of fiber composite structures, where a thermoset resin impregnated bunch of fibers is deposited on a rotating mandrel. The evolving stress and strain states due to thermal and mechanical loads during the manufacturing process are of particular interest to avoid subsequent failure of the parts. In this first approach, the mechanical load within the fiber, mainly driven by the pre‐stressed fiber tension, is investigated. Thus, the development of the fiber tension and the pressure on the mandrel as a function of the number of windings is examined. Therefore, models based on the theory of large and small deformations are derived under certain assumptions, resulting in a boundary value problem. In case of large deformations, the boundary value problem using a three‐dimensional formulation based on incompressible, isotropic hyperelasticity does not yield a simple solution. Using a small deformation formulation on the basis of a one‐dimensional consideration an analytical solution can be found. The problems are described in curvilinear coordinates. From the derived models, the required quantities including the fiber tension, the pressure on the mandrel, also as a function of the number of windings, and the fiber elongation can be calculated.
Quantifying uncertainty in neural network predictions of forced vibrations
Authors: Westmeier Tobias , Hetzler Hartmut , Kreuter Daniel Christopher , Seitz Philipp , Chalvatzaki Georgia 
Published: November 2024

AbstractThe prediction of forced vibrations in nonlinear systems is a common task in science and engineering, which can be tackled using various methodologies. A classical approach is based on solving differential (algebraic) equations derived from physical laws ('first principles'). Alternatively, Artificial Neural Networks (ANNs) may be applied, which rely on learning the dynamics of a system from given data. However, a fundamental limitation of ANNs is their lack of transparency, making it difficult to understand and trust the model's predictions. In this contribution, we follow a hybrid modelling approach combining a data‐based prediction using a stabilised Autoregressive Neural Network (s‐ARNN) with a priori knowledge from first principles. Moreover, aleatoric and epistemic uncertainty is quantified by a combination of mean‐variance estimation (MVE) and deep ensembles. Validating this approach for a classical Duffing oscillator suggests that the MVE ensemble is the most accurate and reliable method for prediction accuracy and uncertainty quantification. These findings underscore the significance of understanding uncertainties in deep ANNs and the potential of our method in improving the reliability of predictive nonlinear system modelling. We also demonstrate that including partially known dynamics can further increase accuracy, highlighting the importance of combining ANNs and physical laws.
A brief review of reduced order models using intrusive and non‐intrusive techniques
Authors: Padula Guglielmo , Girfoglio Michele , Rozza Gianlugi 
Published: November 2024

AbstractReduced Order Models (ROMs) have gained a great attention by the scientific community in the last years thanks to their capabilities of significantly reducing the computational cost of the numerical simulations, which is a crucial objective in applications like real time control and shape optimization. This contribution aims to provide a brief overview about such a topic. We discuss both a classic intrusive framework based on a Galerkin projection technique and hybrid/non‐intrusive approaches, including Physics Informed Neural Networks (PINN), purely Data‐Driven Neural Networks (NN), Radial Basis Functions (RBF), Dynamic Mode Decomposition (DMD) and Gaussian Process Regression (GPR). We also briefly mention geometrical parametrization and dimensionality reduction methods like Active Subspaces (ASs). Then we test the performance of such approaches in terms of efficiency and accuracy against three academic test cases, the lid driven cavity, the flow past a cylinder and the geometrically parametrized Stanford Bunny. Moreover, we also briefly present some preliminary results related to a more complex case involving an industrial application.
An Overview of the Mechanical Behavior of Nanocomposites Considering the Interphase
Authors: Raoux Nicolas , Benelfellah Abdelkibir , AÏT Hocine Nourredine 
Published: November 2024

ABSTRACTOver the past decade, nanocomposites with polymer matrices have garnered significant attention for their ability to enhance properties with minimal inclusion volume, thanks to nanoscale reinforcement. The key factor distinguishing them from microcomposites is the interphase, an area of interaction where matrix properties are influenced by the reinforcement. This induces a dependence on particle size that sets nanocomposites apart. Observation of the interphase is achieved through experimental methods like atomic force microscopy (AFM) and numerical approaches such as molecular dynamics (MD) modeling. Indirect determination involves the comparison of experimental results with predictive models, which may be numerical or analytical homogenization schemes. The widely used double inclusion scheme focuses on nanocomposites, but for a comprehensive mechanical behavior prediction, elastic behavior alone is not sufficient. Factors in the matrix (plasticity, viscosity, temperature) and morphology (dispersion, surface treatment, bonding) must be considered. This review explores various methods in the literature that address these aspects.
Impact of Non‐Pharmacological Cognitive Interventions on Real‐World Daily Function in Children With Cancer: A Systematic Review
Authors: L'Hotta Allison J. , Martin‐Giacalone Bailey , Zink Jennifer , Fung Anna , Myers Andrew , Lipsey Kim , Brick Rachelle 
Published: November 2024

ABSTRACTChildhood cancer survivors (CCS) frequently experience cognitive challenges, which impact their ability to participate in functional activities. This systematic review examines the effects of nonpharmacological cognitive interventions on functional outcomes (e.g., activities of daily living). We systematically searched eight databases (e.g., PubMed, EMBASE) from 2012 to 2023. Two team members independently screened articles and extracted article, intervention, sample characteristics, and outcome data. We assessed intervention reporting with the Template for Intervention Description and Replication. Fourteen studies met inclusion criteria, representing 12 unique trials. Four of the six trials evaluating Cogmed, a computerized working memory training program, demonstrated functional benefits; small to large effects were observed for academic achievement (Cohen's d = 0.28–0.87) and decreased inattention in daily life (d = −0.36 and −0.98). Other interventions included exergaming, math or reading interventions, occupational therapy, and neurofeedback. Less than half of studies reported on intervention personalization (n = 5), fidelity assessment (n = 4), or cost (n = 1). Study heterogeneity limited our ability to meta‐analyze results for functional outcomes. Limited evidence and gaps in quality of intervention reporting are barriers to addressing the cognitive challenges of CCS. Standardizing functional outcome measurement, identifying effective interventions, and improving the quality of intervention reporting could accelerate the translation of intervention research to clinical practice.