bluuebunny/crossref_metadata_2025_split huggingface dataset:
Enhancing AI Chatbot Responses in Health Care: The SMART Prompt Structure in Head and Neck Surgery
Authors: Vaira Luigi Angelo , Lechien Jerome R. , Abbate Vincenzo , Gabriele Guido , Frosolini Andrea , De Vito Andrea , Maniaci Antonino , Mayo‐Yáñez Miguel , Boscolo‐Rizzo Paolo , Saibene Alberto Maria , Maglitto Fabio , Salzano Giovanni , Califano Gianluigi , Troise Stefania , Chiesa‐Estomba Carlos Miguel , De Riu Giacomo 
Published: January 2025

AbstractObjectiveThis study aims to evaluate the impact of prompt construction on the quality of artificial intelligence (AI) chatbot responses in the context of head and neck surgery.Study DesignObservational and evaluative study.SettingAn international collaboration involving 16 researchers from 11 European centers specializing in head and neck surgery.MethodsA total of 24 questions, divided into clinical scenarios, theoretical questions, and patient inquiries, were developed. These questions were entered into ChatGPT‐4o both with and without the use of a structured prompt format, known as SMART (Seeker, Mission, AI Role, Register, Targeted Question). The AI‐generated responses were evaluated by experienced head and neck surgeons using the Quality Analysis of Medical Artificial Intelligence instrument (QAMAI), which assesses accuracy, clarity, relevance, completeness, source quality, and usefulness.ResultsThe responses generated using the SMART prompt scored significantly higher across all QAMAI dimensions compared to those without contextualized prompts. Median QAMAI scores for SMART prompts were 27.5 (interquartile range [IQR] 25‐29) versus 24 (IQR 21.8‐25) for unstructured prompts (P < .001). Clinical scenarios and patient inquiries showed the most significant improvements, while theoretical questions also benefited, but to a lesser extent. The AI's source quality improved notably with the SMART prompt, particularly in theoretical questions.ConclusionThis study suggests that the structured SMART prompt format significantly enhances the quality of AI chatbot responses in head and neck surgery. This approach improves the accuracy, relevance, and completeness of AI‐generated information, underscoring the importance of well‐constructed prompts in clinical applications. Further research is warranted to explore the applicability of SMART prompts across different medical specialties and AI platforms.
Regulatory role of DBC1 in inflammation and autoimmune diseases
Authors: Wu Jinzhi , Yang Fan , Xu Guanhua , Ma Xinlei , Lin Jin , Chen Weiqian 
Published: January 2025

AbstractDeleted in breast cancer 1 (DBC1) is a nuclear protein that plays an important role in apoptosis, DNA repair, and epigenetic modifications. Extensive studies have highlighted the importance of DBC1 in tumorigenesis. Surprisingly, it was recently found that DBC1 works as an immune regulator in the immune system and in autoimmune diseases. This study systematically reviewed how DBC1 exerts its function and assumed a possible role of DBC1 in autoimmunity. The search strategy included the following terms: (1) (DBC1) OR (CCAR2) and (2) ((DBC1) OR (CCAR2)) AND ((immunity) OR (autoimmune diseases)). The above terms were entered into PUBMED, covering the period from 2002 to 2024, and articles investigating or involving functional studies of DBC1 were evaluated. Subsequently, a systematic review was conducted to investigate the potential role of DBC1 in autoimmunity. Our findings indicate that DBC1 exhibits dual pro‐inflammatory and anti‐inflammatory functions, primarily achieved through the direct or indirect modulation of key immunoinflammatory proteins, including silent information regulation 2 homolog‐1, histone deacetylase 3, SUVH9, and signal transducer and activator of transcription (STAT). DBC1 influences cytokine production and immune cell activity. However, most of these studies were based on oncological diseases, and direct exploration of the role of DBC1 in autoimmune diseases remains limited. Further research is necessary to fully understand the precise immunomodulatory function of DBC1 in autoimmune diseases, as well as its underlying mechanism and physiological effects. In conclusion, DBC1 regulates apoptosis, autophagy, aging, and epigenetic modifications, can potentially be an upstream molecule of the Janus kinase/STAT signaling pathway, and is worthy of further comprehensive and in‐depth evaluation.
Effect of Laminectomy Methods on the Surgical Safety of Automatic Laminectomy Robot
Authors: Li Ya , Lu Da , Wu Xueqing , Lu Jiahao , Xu Yangyang , Zhang Shijia , Zhang Le , Pei Baoqing 
Published: January 2025

ABSTRACTBackgroundThe efficacy of laminectomy procedures is contingent on the method of resection. The objective of this study was to investigate the impact of different methods of resection on the surgical safety of automated laminectomy robots, an area that remains uncharted.MethodsLamina resection surgeries using both drilling and layer‐by‐layer methods, are performed on ovine spinal samples. An analysis of the force and lateral deviation at the end of the robotic arm is conducted.ResultsFor the drilling and layer‐by‐layer lamina resection methods, the average peak force for the drilling method is 1.21 N, and deviations are within 1 mm. For the layer‐by‐layer resection method, the peak force reaches 2.39 N, and deviations are within 1.6 mm.ConclusionDuring the drilling resection, the ultrasonic osteotome experiences less force and minimal lateral deviation. This method demonstrates higher precision and safety in laminar resection surgeries. The drilling method should be the primary choice for robot manufacturers.
Retrieval of soil moisture using a decomposition‐based model and optical‐thermal model with Sentinel‐1 and Landsat‐8 images
Authors: Moghaddas Mohammad 
Published: January 2025

AbstractWater content of the soil has a significant role to play in the hydrological cycle and environmental processes. This study includes two phases: (1) approximation of soil moisture at the farm level based on optical‐thermal images of Landsat‐8 satellite and (2) retrieval of soil moisture by the dual‐polarized basis decomposition model with the help of approximate soil moisture that is obtained by the optical‐thermal model. In this research, two mechanisms, volume scattering and surface scattering, are considered. Furthermore, in order to model surface scattering, Bragg matrix has been used. The proposed radar model estimates soil moisture without using ground data, although few ground measurements have been used in the optical‐thermal model. The Carlson triangular model has been used to approximate soil moisture using optical‐thermal images. Three indices, normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), and moisture stress index (MSI), have been used in the optical‐thermal model. Two ground soil moisture datasets are used in this study: (1) Cook Agronomy Farm (CAF) soil moisture data located in the United States and (2) real‐time in situ soil monitoring for agriculture (RISMA) soil moisture data located in Canada. The radar model (base decomposition model) achieved a lowest root mean square error (RMSE) of 3.33% and a highest of 11.21%, showing strong accuracy in soil moisture retrieval. The optical‐thermal model had a slightly higher minimum RMSE of 4.04% and a maximum of 9.68%. These results suggest that the radar model generally outperforms the optical‐thermal model, making it more reliable for accurate soil moisture estimation in agricultural applications, which is crucial for optimizing irrigation and managing resources.
LD‐informed deep learning for Alzheimer's gene loci detection using WGS data
Authors: Jo Taeho , Bice Paula , Nho Kwangsik , Saykin Andrew J. ,   Alzheimer's Disease Sequencing Project
Published: January 2025

AbstractINTRODUCTIONThe exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large‐scale high throughput sequencing data. This study presents Deep‐Block, a multi‐stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three‐stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk.METHODSThe Deep‐Block was applied to a large‐scale whole genome sequencing (WGS) dataset from the Alzheimer's Disease Sequencing Project (ADSP), comprising 7416 non‐Hispanic white (NHW) participants (3150 cognitively normal older adults (CN), 4266 AD).RESULTS30,218 LD blocks were identified and then ranked based on their relevance with Alzheimer's disease. Subsequently, the Deep‐Block identified novel SNPs within the top 1500 LD blocks and confirmed previously known variants, including APOE rs429358 and rs769449. Expression Quantitative Trait Loci (eQTL) analysis across 13 brain regions provided functional evidence for the identified variants. The results were cross‐validated against established AD‐associated loci from the European Alzheimer's and Dementia Biobank (EADB) and the GWAS catalog.DISCUSSIONThe Deep‐Block framework effectively processes large‐scale high throughput sequencing data while preserving SNP interactions during dimensionality reduction, minimizing bias and information loss. The framework's findings are supported by tissue‐specific eQTL evidence across brain regions, indicating the functional relevance of the identified variants. Additionally, the Deep‐Block approach has identified both known and novel genetic variants, enhancing our understanding of the genetic architecture and demonstrating its potential for application in large‐scale sequencing studies.Highlights
Growing genomic datasets require advanced tools to identify genetic loci in sequencing.
Deep‐Block, a novel AI framework, was used to process large‐scale ADSP WGS data.
Deep‐Block identified both known and novel AD‐associated genetic loci.
rs429358 (APOE) was key; rs11556505 (TOMM40), rs34342646 (NECTIN2) were significant.
The AI framework uses biological knowledge to enhance detection of Alzheimer's loci.
Effective models for generalized Newtonian fluids through a thin porous medium following the Carreau law
Authors: Anguiano María , Bonnivard Matthieu , Suárez‐Grau Francisco J. 
Published: January 2025

AbstractWe consider the flow of a generalized Newtonian fluid through a thin porous medium of thickness , perforated by periodically distributed solid cylinders of size . We assume that the fluid is described by the 3D incompressible Stokes system, with a non‐linear viscosity following the Carreau law of flow index , and scaled by a factor , where . Generalizing (Anguiano M.: et al. Q. J. Mech. Math., 75(1), 1–27 (2022)), where the particular case and was addressed, we perform a new and complete study on the asymptotic behavior of the fluid as goes to zero. Depending on and the flow index , using homogenization techniques, we derive and rigorously justify different effective linear and non‐linear lower‐dimensional Darcy's laws. Finally, using a finite element method, we study numerically the influence of the rheological parameters of the fluid and of the shape of the solid obstacles on the behavior of the effective systems.
Thermal performance of DispersedInorganic magnetic hybrid nanomaterials into mixed convective flow through flexible porous disks
Authors: Faisal Muhammad , Qureshi Muhammad Zubair Akbar , Shah Nehad Ali 
Published: January 2025

AbstractThis study applies advanced AI techniques, including machine learning algorithms, to explore the numerically unsteady laminar flow of viscous and incompressible fluids in coaxially swirled porous disks, with applications in engineering sciences. Our focus encompasses the effects of magnetic hybrid nanomaterials and the dynamic behaviors associated with expanding/contracting and injection/suction. Utilizing single‐phase simulations, we address nonlinear coupled ordinary differential equations set against appropriate boundary conditions. Key parameters of our study include permeability and relaxation, as well as the influence of chemical reactions and mixed convection on fluid behaviors. The thermophysical properties of Al2O3/Cu nanoparticles have been by varying their morphological aspects. For the hybrid nanofluids flow, the aggregation of nanoparticle volume fraction has been designed critically in conjunction with an energy and mass transfer equation. Because dimensionless ordinary differential equations are employed, the obtained expression is transmuted using the obliging transformation technique. The desired nonlinear system of ODEs is implemented using an accurate numerical method. Our findings reveal significant impacts of chemical reaction parameters on the Sherwood number and a marked increase in the skin friction coefficient, Nusselt number, and Sherwood number as nanoparticle volume fraction rises from 2% to 7%.
Formulation of the extended plane wave expansion for in‐plane and out‐of‐plane vibrations in viscoelastic phononic thin plates under a constant temperature field
Authors: Santos Mateus C. P. dos , Filho Cimark G. Sandes , Miranda Edson J. P. de , Sinatora Amilton 
Published: January 2025

AbstractIn this study, we develop an extended plane wave expansion method to determine the complex elastic band gaps for both in‐plane and out‐of‐plane vibrations in a viscoelastic phononic crystal (PnC) thin plate model under a constant temperature field. The periodic structure of the thin plate comprises a unit cell (UC) consisting of an epoxy matrix and circular steel inclusions. In this configuration, we consider the viscoelastic effects described by the generalized Maxwell model arising from the epoxy matrix. Furthermore, we incorporate thermal effects in the UC, accounting for the linear dependence of the Young's modulus on the temperature of the epoxy matrix, assuming the absence of heat flow in the dimensions of the UC. We present a band structure illustrating the displacement of the real and imaginary parts of the complex band structure for evanescent waves arising from the viscoelastic PnC thin plate, considering both in‐plane and out‐of‐plane vibrations.
MHD nanofluid flow over a thin needle: Impact of second‐order slip and variable thermal properties
Authors: Bilal M. , Batool Atika , Mehmood Yasir , Ramzan M. 
Published: January 2025

AbstractThe complexity of second‐order slip flow phenomena demonstrated by magnetohydrodynamics (MHD) nanofluids passing through a thin needle with dynamically changing thermal characteristics is explored in this article. It delves deeply into the mutual effects of thermophoretic and Brownian motion and analyzes the implications of MHD. Specifically, temperature‐dependent variations in the viscosity and thermal conductivity of the nanofluid are observed, and the boundary contact is subject to a strict zero mass flow constraint. Employing transformative methodologies, the governing equations are transmuted into a set of intricate ordinary differential equations (ODEs), systematically tackled using the sophisticated MATLAB bvp4c algorithm to furnish precise numerical solutions. This research provides important new understandings of the complex dynamics of nanofluids surrounding thin needles, clarifying their relevance in many engineering contexts. The study reveals that the rate of heat transfer is enhanced for the higher surface needle thickness, and the fluid with higher viscosity yields a decrease in the temperature of the fluid.
On Generic Topological Embeddings
Authors: Kubiś Wiesław , Kucharski Andrzej , Turek Sławomir 
Published: January 2025

Abstract
We show that an embedding of a fixed 0-dimensional compact space K into the Čech–Stone remainder

$$\omega ^*$$

ω
∗

as a nowhere dense P-set is the unique generic limit, a special object in the category consisting of all continuous maps from K to compact metric spaces. Using categorical Fraïssé theory, we obtain some well-known theorems about Čech–Stone remainders of infinite discrete spaces. Furthermore, we prove several results concerning universality and homogeneity of the space

$$\kappa ^\kappa $$

κ
κ

, with

$$\kappa $$

κ

a regular uncountable cardinal, using generalized ultrametrics.
Advanced hybrid laminates: elastomer integration for optimized mechanical properties
Authors: Raimondi Luca , Bernardi Federico 
Published: January 2025

Abstract
Interleaving elastomeric films into polymeric composite materials is a promising technological solution to manufacture components with localized functionalities. To optimize processing time and reduce testing costs, there is an urgent need for modeling strategies to predict the effect of hybridization based on the fundamental properties of singular constituents. In this work, three different laminates with varying numbers and positions of elastomeric layers were manufactured and mechanically tested in flexural configuration. The digital image correlation (DIC) technique is employed to evaluate the displacements and the strain field on the surface of the sample. A numerical framework for the prediction of the mechanical response, including damage initiation and evolution, was developed and validated against experimental data. The numerical results showed significant agreement with the experiments, reporting a maximum mismatch of about 10% in strain distribution and about 2% in the ultimate load. Additionally, degradation trends in the load vs. deflection curves were always consistent. Analysis of the fractured surface and predicted failure modes further demonstrated the reliability of the method.
A metal 3D-printed non-assembly mandrel for profile bending
Authors: Kotzian Tobias , Frohn-Sörensen Peter , Nebeling Daniel , Engel Bernd 
Published: January 2025

Abstract
In the overall regime of production technologies, bending processes allow production of complex parts and profiles in a precise, flexible, and economical way. It is important to manufacture tools which are universally applicable and easy to manufacture, since modern industries demand flexibility in terms of larger varieties or smaller batch sizes. In the past, it was difficult and time-consuming to manufacture bending tools, especially mandrels, due to their high geometric complexity and tight assembly of swivel joints. In this paper, we present a new design approach for the bending mandrel which is suitable for metal additive manufacturing that incorporates non-assembly mechanisms of joints. The hinges are systematically developed with particular focus on achieving a minimized gap and resultant clearance. As a test case, a mandrel is 3D printed by using the laser powder bed fusion technique and successfully tested in the rotary draw bending process parameterized for narrow bends in stainless steel tubes. In the light of these results, a solution is formulated for ease in manufacturing and faster rate of tool production, in this case bending mandrel. This methodology facilitates the production of small batch sizes and large product variants in bending processes. The research presented in this paper serves as a forward step towards economical production of complex parts in a highly flexible manner.
Percolation for 2D Classical Heisenberg Model and Exit Sets of Vector Valued GFF
Authors: Aru Juhan , Garban Christophe , Sepúlveda Avelio 
Published: January 2025

Abstract
Our motivation in this paper is twofold. First, we study the geometry of a class of exploration sets, called exit sets, which are naturally associated with a 2D vector-valued Gaussian Free Field :

$$\phi : \mathbb {Z}^2 \rightarrow \mathbb {R}^N, N\ge 1$$

ϕ
:

Z

2

→

R

N

,
N
≥
1

. We prove that, somewhat surprisingly, these sets are a.s. degenerate as long as

$$N\ge 2$$

N
≥
2

, while they are conjectured to be macroscopic and fractal when

$$N=1$$

N
=
1

. This analysis allows us, when

$$N\ge 2$$

N
≥
2

, to understand the percolation properties of the level sets of

$$\{ \Vert \phi (x)\Vert _{{2}}, x\in \mathbb {Z}^2\}$$

{
‖
ϕ

(
x
)

‖
2

,
x
∈

Z

2

}

and leads us to our second main motivation in this work: if one projects a spin

$$O(N+1)$$

O
(
N
+
1
)

model (the case

$$N=2$$

N
=
2

corresponds to the classical Heisenberg model) down to a spin O(N) model, we end up with a spin O(N) in a quenched disorder given by random conductances on

$$\mathbb {Z}^2$$

Z

2

. Using the exit sets of the N-vector-valued GFF, we obtain a local and geometric description of this random disorder in the limit

$$\beta \rightarrow \infty $$

β
→
∞

. This allows us in particular to revisit a series of celebrated works by Patrascioiu and Seiler (J Stat Phys 69(3):573–595, 1992, Nucl Phys B Proc Suppl 30:184–191, 1993, J Stat Phys 106(3):811–826, 2002) which argued against Polyakov’s prediction that spin

$$O(N+1)$$

O
(
N
+
1
)

model is massive at all temperatures as long as

$$N\ge 2$$

N
≥
2

(Polyakov in Phys Lett B 59(1):79–81, 1975). We make part of their arguments rigorous and more importantly we provide the following counter-example: we build ergodic environments of (arbitrary) high conductances with (arbitrary) small and disconnected regions of low conductances in which, despite the predominance of high conductances, the XY model remains massive. Of independent interest, we prove that at high

$$\beta $$

β

, the fluctuations of a classical Heisenberg model near a north pointing spin are given by a

$$N=2$$

N
=
2

vectorial GFF. This is implicit for example in Polyakov (1975) but we give here the first (non-trivial) rigorous proof. Also, independently of the recent work Dubédat and Falconet (Random clusters in the villain and xy models, arXiv preprint arXiv:2210.03620, 2022), we show that two-point correlation functions of the spin O(N) model can be given in terms of certain percolation events in the cable graph for any

$$N\ge 1$$

N
≥
1

.
Morphological analysis of inosculated connections in weeping figs: insights on density, geometry, fiber structures, and compositional variations
Authors: Wang Xiuli , Gard Wolfgang , Mosleh Yasmine , van de Kuilen Jan-Willem 
Published: January 2025

Abstract
Trees exhibit adaptability in response to external loads, which allows them to form an inosculated connection (self-growing connection) with a neighboring tree. Such connections have the mechanical potential to build living tree structures. Although qualitative studies have studied this phenomenon, quantitative analysis of its growth features remains limited. Self-growing connections fused by weeping figs (Ficus benjamina L.) are utilized to study growth features. X-ray scanning and optical microscopy techniques are employed to investigate parameters including density, geometry, fiber structures, and material compositions. Key findings demonstrate that the fused region of a connection has a larger volume and a higher density on the intersected surface. Microscopic analysis identifies that the enlarged wood in the fused area is tension wood characterized by G-layers. The key component that connects trees is referred to as merged fibers, and the pattern of their distribution is found to be mainly in the outer layer of the larger cross-angle of a connection. At the cellular level, crystals within cells are identified in the fused region, implying possible mechanical stresses the interface has experienced. The findings in self-growing connections can serve as inspiration for structural design in living structures, biomimicry, bioinspired structures, and advancements in bioeconomics.