bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Association Between Temporomandibular‐Related Quality of Life and Oral Behaviours: A Cross‐Sectional Study in Patients With Temporomandibular Disorders
Authors: Xiao Chu‐Qiao , Zhang Jing , Luo Wen‐Xin , Jiang Nan , Zhang Li‐Ming , Yang Hao‐Lun , Wang Xiao‐Yi , Yue Yuan , Xiong Xin 
Published: November 2024

ABSTRACTObjectivesOral behaviours (OB) are some oral overuse behaviours which could be observed in patients with temporomandibular disorders (TMD). This study aims to investigate the association between TMD‐related quality of life and OB to enhance understanding of these behaviours.MethodsA total of 319 participants diagnosed with TMD were included in this research. According to the diagnostic criteria for TMD, patients were divided into three subgroups: pain‐related TMD (PT, n = 71), intra‐articular TMD (IT, n = 91) and combined TMD (CT, n = 157). Participants completed the oral behaviour checklist (OBC), the oral health impact profile for TMD (OHIP‐TMD), the generalised anxiety disorder 7‐item (GAD‐7) and the patient health questionnaire 9‐item (PHQ‐9), and demographic characteristics were collected from medical records.ResultsTMD patients with high OBC scores (25–84 points) had higher scores in six of the seven OHIP‐TMD domains (all p < 0.05). Logistic regression analysis indicated that OHIP‐TMD scores (OR = 1.03), younger age (OR = 0.95), education (OR = 2.80–3.10), and PHQ‐9 scores (OR = 1.14) were significantly positively correlated with OBC scores (p < 0.05). After adjusting for confounding factors, the psychological discomfort (OR = 1.09) and psychological disability (OR = 1.10) domains of the OHIP‐TMD were identified as independent factors associated with higher OBC scores.ConclusionsPatients with more frequent OB exhibited a lower quality of life across nearly all OHIP‐TMD domains. Higher OHIP‐TMD scores and specific psychological domains were independently associated with a higher frequency of OB. The causal relationship between quality of life and OB in TMD patients requires further investigation.
The relationship between sleep difficulties and externalizing and internalizing problems in children and adolescents with mental illness
Authors: van Tetering Emilie M. A. , Mies Gabry W. , Klip Helen , Pillen Sigrid , Muskens Jet B. , Polderman Tinca J. C. , van der Mheen Malindi , Staal Wouter G. , Pieters Sara 
Published: November 2024

SummarySleep difficulties are presumably a transdiagnostic factor in the complex aetiology of psychiatric disorders in youth. This study assessed the prevalence of sleep difficulties in children and adolescents seeking specialized psychiatric care, examined the relationships of internalizing and externalizing problems, and considered the moderating role of sex and age on these relationships. Parent‐reported data on difficulties initiating sleep, difficulties maintaining sleep, early morning awakenings and daytime fatigue from a large sample of children and adolescents referred for specialized psychiatric care (n = 4638; < 18 years) were used to estimate prevalence rates. To examine associations between these sleep difficulties and internalizing/externalizing problems, multiple linear regression analyses were conducted on available data (n = 3768) stratified in three age groups (1.5–5 years; 6–11 years; 12–18 years). Overall prevalence, i.e. at least one sleep difficulty was reported to be often or always present, was 65%. Difficulties initiating sleep occurred the most, closely followed by daytime fatigue. In all age groups, sleep difficulties were positively related to internalizing and externalizing problems. In young children and school‐age children, age moderated the interaction between sleep difficulties and internalizing problems. To conclude, prevalence rates of sleep difficulties in children with mental illness appear higher than it has been reported in the general youth population, especially difficulties initiating sleep and daytime fatigue. We observed that the associations between internalizing problems and sleep difficulties in young children and school‐age children seemed to be amplified with age, suggesting a negative, bidirectional, spiral in development.
The Single‐Dose Pharmacokinetics of a Compounded Levetiracetam Formulation and Bioequivalence to a Commercial Formulation in Healthy Dogs
Authors: Paushter Aaron M. , Foss Kari D. , Reinhart Jennifer M. , Forsythe Lauren E. , Hague Devon W. 
Published: November 2024

ABSTRACTLevetiracetam (LEV) is an anti‐epileptic drug used extra‐label in dogs. Commercially available extended‐release formulations (LEV‐ER), administered twice daily, cannot be crushed or split, limiting their use in small dogs. A compounded LEV‐ER formulation (PO‐COMP) can purportedly be partitioned without loss of extended‐release properties. The aims of this study were to establish the pharmacokinetic parameters of PO‐COMP, divided at the tablet score, and determine the bioequivalence of partitioned PO‐COMP to an intact commercially available Food and Drug Administration‐approved human oral generic formulation of LEV‐ER (PO‐COMM). In a randomized crossover design, 12 healthy dogs received a single IV dose (30 mg/kg) of IV‐COMM, a single oral dose (500 mg) of intact PO‐COMM, or a single oral dose (500 mg) of partitioned PO‐COMP and underwent serial measurement of plasma LEV concentrations over 24 h. PO‐COMP was bioequivalent to PO‐COMM using the 90% confidence interval method for maximum concentration (−3.2% difference [CI −7.4% to −1.1%]) and area under the curve (−14.4% difference [CI −17.8% to 10.8%]). PO‐COMP may improve medication adherence and seizure control relative to immediate‐release LEV, which requires three times daily dosing. Efficacy studies of PO‐COMP are warranted.
Influencing Factors of Nurses’ Well‐Being in Critical Care During Pandemic Era: A Systematic Review
Authors: Musio Maria Emma , Russo Marta , Barbieri Martina , Moro Andrea , Zanini Milko , Sasso Loredana , Bagnasco Annamaria , Catania Gianluca 
Published: November 2024

ABSTRACTAimTo identify factors protecting and hindering the well‐being of critical care nurses during the COVID‐19 pandemic.BackgroundThe unique work challenges of critical care nurses can undermine their professional and mental well‐being; as evidenced by the prevailing literature on burnout, compassion fatigue, and moral distress. The COVID‐19 pandemic has seen these professionals on the front lines and has raised many questions about professional well‐being. Identifying the factors that protect and hinder the well‐being of critical care nurses would help to develop the strategies necessary to reduce worrying phenomena associated with professional malaise.EvaluationA systematic review was conducted using electronic databases including PubMed, CINAHL, Scopus, Cochrane, PsycINFO, and Web on Science.Key issuesAfter full text analysis, 25 papers were included in the current systematic review. Factors influencing the well‐being of critical care nurses have been grouped into “Protective” and “Hindering.”ConclusionsThis review shows that critical care nurses’ well‐being is influenced by factors like personal resilience and supportive work environments, which enhance their experience. Challenges include sociodemographic issues and the COVID‐19 pandemic's impact. Addressing these factors is crucial for their well‐being and the effectiveness of healthcare systems.Implications for clinical practiceThe well‐being of nurses is intricately linked to the quality and security of patient care, ultimately influencing clinical outcomes. This review delves deeply into the multifaceted factors that affect the professional well‐being of critical care nurses during the COVID‐19 pandemic. Recognizing these elements is critical for directing health policy toward the development of initiatives that bolster healthcare workers’ welfare. Prioritizing the professional well‐being of nurses is imperative, as it is fundamental in mitigating the increasing inclination towards job turnover, a challenge that is profoundly impacting the healthcare sector.
Legal status and refugees' perceptions of institutional justice: The role of communication quality
Authors: Frank Emily , Nivorozhkin Anton 
Published: November 2024

AbstractWhat factors influence refugees' perceptions of justice in bureaucratic institutions? As global migration movements draw increasing attention, migrants' experiences as constituents in destination countries merit further research. Drawing evidence from the 2018 survey of refugees participating in the German Socio‐Economic Panel, this article examines the role of legal status in shaping perceptions of justice at government offices. Our findings highlight a stark contrast: refugees with unstable legal statuses often perceive bureaucratic proceedings as less just compared to those with firmer legal standings. However, refugees' perceptions of a more positive encounter their encounters with street‐level bureaucrats can act as a buffer against the negative effects of legal status on perceptions of justice at government offices. These insights underscore a pressing policy implication: asylum procedures, currently marked by ambiguity and delays, could benefit significantly from enhanced communication quality on the part of street‐level bureaucrats.
Pilot Free Trade Zone Establishment and New Firm Entry: Evidence from China
Authors: Jiang Lingduo , Lu Jing , Yang Yang 
Published: November 2024

ABSTRACTThis article investigates the effect of pilot free trade zones (FTZs) on new firm entry. Using firm registration data in China from 2003 to 2020, we find that the establishment of FTZs has a significant and persistent positive impact on new firm entry, and it is attributed to the capital and talent agglomeration induced by FTZs. Heterogeneous analyses show that FTZs primarily stimulate the entry of firms in high‐tech industries and domestic private firms, and the effect of FTZs is more pronounced in prefectures with higher institutional innovation and those in coastal areas. Moreover, FTZs exhibit positive spillover effects on the emergence of new firms in surrounding regions, suggesting that the establishment of FTZs has empowered coordinated regional development.
Evolved Structures: Primification Using Load Lines
Authors: Sadler Elliot , McClelland Ryan , Ho Nhut , Li Bingbing 
Published: November 2024

Abstract
In this study, the simplification of complex AI generated geometry for increasing compatibility with legacy analysis tools is explored. Beginning with a general discussion on the problems facing engineers in the production of non-uniform parts as well as problems with testing complex geometries with conventional FEA tools, it is established that analysis sectors can benefit from part geometry that not only simplifies and unifies its shape, but also reduces it into its primary load paths. The workflow used to convert entities from complex into primitives is explored, as well as the result of FEA testing on both a baseline and a modified structure. The importance of 3D sketches and their use in both testing scenarios and fabrication efforts is discussed, and the generation of these 3D sketches is shown as a part of the workflow. The adaptation of part geometry from organic generative design to simple load path to easily manufacturable node and tube structures is shown. The workflow for developing a 3D load path sketch is shown, and an example of an application of the process is explored, from generation of the evolved structure to its transformation to a carbon fiber tube and node configuration.
Ontology-Based Context-Aware Data Analytics in Additive Manufacturing
Authors: Park Yeun , Witherell Paul , Cho Hyunbo 
Published: November 2024

Abstract
Recent advances in Additive Manufacturing (AM), particularly in production scenarios, have been largely driven by insights achieved through data analytics. AM has greatly benefited from the increasingly large amounts of data generated during the design to product transformation. Despite the large amounts of data that can be generated from each build, the variations that can occur between builds create challenges in data reuse.
Advances in data analytics are coming in the form of advanced machine learning algorithms, and are often associated with deep learning, where large amounts of data are analyzed, and interpretations are made. While such algorithms are increasingly adept at solving complex problems, solutions are highly dependent on the data used to train the algorithms and thus often subject to unwanted bias. Contextualization of data can help limit unintended bias and improve the probability of attaining viable results.
Data contextualization often comes through domain context. This work describes the development of an AM ontology to support context-aware data analytics. The Additive Manufacturing Data Analytics Ontology, or AMDA Ontology, is developed to facilitate the contextualization of AM data through the representation of explicit AM concepts throughout the design to product transformation and the encoding of inhering relationships within the concepts. The AM concepts are accompanied by a suite of concepts representative of the necessary modeling, simulation, and analytics terms to create links between AM data, AM data analytics opportunities, and appropriate machine learning algorithms. The early results indicate that AMDA ontology has the ability to facilitate key correlations between AM data and the analytic opportunities to enhance the design to product transformation of AM parts.
Dynamic Modeling, Simulation, and Experimental Validation of the Power Take-Off Mechanism of an Oscillating Hydrofoil-Based Hydrokinetic Energy Converter
Authors: Vijayasankar Vishnu , Zhu Joseph , Bart-Smith Hilary , Zuo Lei 
Published: November 2024

Abstract
The study introduces the dynamic analysis of the Power Take-Off (PTO) mechanism of a Bio-Inspired Renewable Energy (BIRE) device aimed at revolutionizing riverine power generation. Inspired by the dynamics of fish swimming, an oscillating hydrofoil is utilized to harvest energy, which is converted via a Mechanical Motion Rectifier (MMR). This innovative PTO mechanism transmutes oscillatory motion into unidirectional rotation, ensuring high conversion efficiency. A ball screw-based MMR PTO was comprehensively examined using dry lab tests on an Instron machine and compared with a rack and pinion-based MMR PTO. The model prediction showed a commendable match with experimental data, with a primary focus on capturing frictional behaviors. Crucial findings indicated the prominence of dry friction, attributed to possible gear misalignments, which, when addressed, can significantly bolster the system’s mechanical efficiency. At an optimal load of 1 Ω, the PTO achieved an efficiency of 75%. Additionally, the distinct orientation of the force vs. displacement loop sheds light on inherent system dynamics, suggesting avenues for future refinements. This research lays the groundwork for a highly efficient and adaptable mechanism designed for harnessing riverine hydrokinetics.
Multi-Trip Algorithm for Multi-Depot Rural Postman Problem With Rechargeable Vehicles
Authors: Sathyamurthy Eashwar , Azarm Shapour , Herrmann Jeffrey W. 
Published: November 2024

Abstract
Autonomous vehicles have emerged as transformative technologies with vast implications across various domains, including surveillance. However, they are constrained by battery limitations, which can hinder their usability and efficiency. Designing algorithms to efficiently utilize them optimizing their battery usage, is crucial to unlocking their full potential. This paper addresses this challenge by studying an extension of the rural postman problem with multiple depots and rechargeable and reusable vehicles that can make multiple trips but have limited battery capacity. It presents a new Mixed Integer Linear Programming (MILP) formulation to find optimal solutions to the problem and proposes a new heuristic called the multi-trip (MT) algorithm. The paper describes the results of a study that tested the MT algorithm, other heuristics, and the MILP formulation on both benchmark instances and larger, real-world instances generated from road maps. Our results show that the MT algorithm found feasible solutions for every instance and produced better solutions than heuristics from the literature on 38 of the 39 instances that were tested. Due to its requirements for memory and computing time, the Gurobi optimizer used for solving the MILP formulation could not find optimal solutions for the largest instances that we tested. This research contributes to enhancing the efficiency and effectiveness of battery-operated unmanned by developing new heuristics to address the challenge of battery limitations.
Fast Computation of Collision-Free Cutter Postures Using Discrete Representation of Workpiece Shape
Authors: Inui Masatomo , Umezu Nobuyuki 
Published: November 2024

Abstract
In the manufacturing of complex-shaped parts, particularly using simultaneous 5-axis machining technology, as machining progresses, excess material is removed, and the workpiece shape becomes smaller. Therefore, the holder and workpiece are less likely to collide, and the range of possible tool postures increases. However, in the quest for automation, incorporating such a tool posture “margin” into calculating machining commands has not yet been possible. The final goal of this research is to realize a software system that can automatically generate commands for the 5-axis machining considering the shape change of the workpiece. The workpiece shape in the machining process can be computed using a geometric machining simulation. In the simulation, discrete methods, such as voxel or dexel models, are commonly used to represent the workpiece shape. This study developed a fast method to calculate a safe tool posture for such a discrete workpiece model without holder collisions. Our method uses the parallel processing function of the graphics processing unit to accelerate the computation. The software was successfully developed and its effectiveness was verified through several computational experiments.
A Novel Connectivity Index for Microstructures Imperfection Detection and Rectification in Multiscale Structure Design
Authors: Rastegarzadeh Sina , Huang Jida 
Published: November 2024

Abstract
Inspired by natural designs, microstructures exhibit remarkable properties, which drive interest in creating metamaterials with extraordinary traits. However, imperfections within microstructures and poor connectivity at the microscale level can significantly impact their performance and reliability. Achieving proper connectivity between microstructural elements and detecting structural imperfections within the microstructures pose challenges in multiscale design optimization. While using a connectivity index (CI) to quantify the topological connectivity between microstructures has been explored previously, prior approaches have limitations in identifying microstructures with complex curved geometries between adjacent units. To alleviate this issue, we present a novel CI in this study. The proposed CI goes beyond conventional methods by focusing on surface interfaces and internal microstructural irregularities. Through numerical investigations, we successfully connected distinct types of microstructures well by integrating the introduced CI with the functional-gradation scheme. We also demonstrate that the presented CI can serve as a metric to identify sharp changes or imperfections within microstructures. We evaluate the performance of the introduced index against other connectivity indices using diverse microstructural examples. Experimental findings provide valuable insights into the fundamental aspects of imperfection detection and rectification in microstructures within the multiscale design, paving the way for developing more robust and reliable materials in engineering applications.
Finite-Volume Physics-Informed U-Net for Flow Field Reconstruction With Sparse Data
Authors: Zhu Tong , Liu Dehao , Lu Yanglong 
Published: November 2024

Abstract
Fluid dynamics is governed by partial differential equations (PDEs) which are solved numerically. The limitations of traditional methods in data assimilation hinder their effective engagement with experiments. Physics-informed neural network (PINN) has emerged as a hybrid data-physics-driven model for convective problems. However, the approach suffers from low accuracy and poor efficiency due to the way of incorporating PDEs. In this work, a novel convolutional neural network framework integrating finite volume method (FVM) is developed to address the challenge. The interface variables of the grid are predicted by the neural network for the first time, rather than a complex procedure in FVM. The physical law is then learned by minimizing the residual of the discretized conservative form of PDEs. A comparison between this model and the original PINN regarding prediction accuracy demonstrates the superiority of embedding PDEs through FVM. The effects of sampling strategies and quantities are studied. The result confirms the model’s capability to utilize sparsely available internal measurement data. Furthermore, the model performs well even in scenarios where partial initial and boundary conditions are absent.
Towards Domain-Adaptive, Resolution-Free 3D Topology Optimization With Neural Implicit Fields
Authors: Heyrani Nobari Amin , Regenwetter Lyle , Ahmed Faez 
Published: November 2024

Abstract
Topology optimization is a ubiquitous task in engineering design, involving the optimal distribution of material in a prescribed spatial domain. Recently, data-driven methods such as deep generative AI models have been proposed as an alternative to iterative optimization methods. However, existing data-driven approaches are often trained on datasets using fixed grid resolutions and domain shapes, reducing their applicability to different resolutions or different domain shapes.
In this paper, we introduce two key innovations — a fast TO solver and a neural implicit field architecture to address these limitations.
First, we introduce a fast, parallelizable, iterative GPU-based TO solver optimized for high-throughput dataset generation for 3D unstructured meshes. Our solver generated 122K optimized 3D topologies, an order of magnitude more than the largest existing public dataset. Second, we introduce a new resolution-free data-driven method for 3D topologies using neural fields, called NITO-3D. A single NITO-3D model trains and predicts for a variety of resolutions and aspect ratios. By also eliminating the need for computationally intensive physical field conditioning, NITO-3D offers a faster, more flexible alternative for 3D topology optimization. On average, NITO-3D generates topologies roughly 2000 times faster and with only 0.3% higher compliance than state-of-the-art iterative solvers. With 10 steps of iterative fine-tuning, NITO-3D is on average 15 times faster and generates topologies that are under 0.1% more compliant than SIMP’s. We open-source all data and code associated with this work.