bluuebunny/crossref_metadata_2025_split huggingface dataset:
2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines
Authors: Virani Salim S. , Newby L. Kristin , Arnold Suzanne V. , Bittner Vera , Brewer LaPrincess C. , Demeter Susan Halli , Dixon Dave L. , Fearon William F. , Hess Beverly , Johnson Heather M. , Kazi Dhruv S. , Kolte Dhaval , Kumbhani Dharam J. , LoFaso Jim , Mahtta Dhruv , Mark Daniel B. , Minissian Margo , Navar Ann Marie , Patel Amit R. , Piano Mariann R. , Rodriguez Fatima , Talbot Amy W. , Taqueti Viviany R. , Thomas Randal J. , van Diepen Sean , Wiggins Barbara , Williams Marlene S. 
Published: July 2023

AIM:
The “2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease” provides an update to and consolidates new evidence since the “2012 ACCF/AHA/ACP/AATS/PCNA/SCAI/STS Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease” and the corresponding “2014 ACC/AHA/AATS/PCNA/SCAI/STS Focused Update of the Guideline for the Diagnosis and Management of Patients With Stable Ischemic Heart Disease.”

METHODS:
A comprehensive literature search was conducted from September 2021 to May 2022. Clinical studies, systematic reviews and meta-analyses, and other evidence conducted on human participants were identified that were published in English from MEDLINE (through PubMed), EMBASE, the Cochrane Library, Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline.

STRUCTURE:
This guideline provides an evidenced-based and patient-centered approach to management of patients with chronic coronary disease, considering social determinants of health and incorporating the principles of shared decision-making and team-based care. Relevant topics include general approaches to treatment decisions, guideline-directed management and therapy to reduce symptoms and future cardiovascular events, decision-making pertaining to revascularization in patients with chronic coronary disease, recommendations for management in special populations, patient follow-up and monitoring, evidence gaps, and areas in need of future research. Where applicable, and based on availability of cost-effectiveness data, cost–value recommendations are also provided for clinicians. Many recommendations from previously published guidelines have been updated with new evidence, and new recommendations have been created when supported by published data.
Longitudinal Pathways Between Physical Activity, Depression, and Perceived Stress in Peripheral Artery Disease
Authors: Yazgan Idil , Bartlett Victoria , Romain Gaëlle , Cleman Jacob , Petersen-Crair Pamela , Spertus John A. , Hardt Madeleine , Mena-Hurtado Carlos , Smolderen Kim G. 
Published: July 2023

BACKGROUND:
One-fifth of the patients with peripheral artery disease (PAD) experience depression and stress. Depression and stress may impact patients’ abilities to be physically active, a key recommendation for supporting overall PAD management to improve symptoms and reduce the risk of cardiovascular events. We aimed to study interrelationships between 1-year longitudinal trajectories of depression, stress, and physical activity following a PAD diagnosis.

METHODS:
Patients with new or worsening PAD symptoms enrolled at 10 US PORTRAIT study (Patient-Centered Outcomes Related to Treatment Practices in Peripheral Arterial Disease: Investigating Trajectories) vascular specialty clinics (CT, LA, MI, MO, NC, OH, and RI) were assessed at baseline, 3, 6, and 12 months between June 2, 2011 and December 3, 2015. Depressive symptoms were measured with the 8-item Patient Health Questionnaire, perceived stress with the 4-item Perceived Stress Scale and physical activity with items from the INTERHEART study. Path analysis was used to examine the longitudinal relationship between depression and physical activity and perceived stress and physical activity.

RESULTS:
A total of 766 patients were included (mean age of 68.2 [±9.4] years; 57.7% male). Overall, 17.8% reported significant depressive symptoms, 36.0% experienced increased perceived stress, and 44.1% were sedentary upon PAD diagnosis. A decrease in physical activity preceded a rise in subsequent depressive symptoms (β ranges −0.45 [95% CI, −0.80 to −0.09]; −0.81 [95% CI, −1.19 to 0.42]) over the course of 1 year. Low physical activity scores at the initial presentation were followed by high perceived stress at 3 months (β=−0.44 [95% CI, −0.80 to −0.07]).

CONCLUSIONS:
In symptomatic PAD, a decrease in physical activity was followed by an increased risk of depressive symptoms and perceived stress at subsequent intervals over the course of 1 year following PAD diagnosis and treatment. Integrated behavioral health approaches for PAD, addressing physical activity and managing depression or distress, are indicated as collective PAD treatment goals.
Decision-based learning for teaching arterial blood gas analysis
Authors: Tesseyman Sheri , Poulsen Tracy , Rainsdon-Meek Samantha , Leary Heather , Sorensen Ursula , Plummer Kenneth 
Published: July 2023

AbstractObjectivesThis case study explored implementation of a Decision-Based Learning (DBL) tool for teaching arterial blood gas (ABG) analysis to nursing students.MethodsFor this mixed-methods study, ABG problems in a DBL model were solved by nursing students. Students answered a survey about their experience with DBL. Quantitative survey results are reported with descriptive statistics. Open-ended questions and instructor and student interview data were qualitatively analyzed.ResultsStudents had a positive experience with DBL and gained self-efficacy regarding ABG analysis. The tool was engaging, simple to use, and not overly time-consuming.ConclusionsDBL can be a useful tool for teaching ABG analysis to nursing students. Implications for an international audience nursing students everywhere benefit from understanding ABG analysis. DBL is a promising tool that can be used in any location with digital resources.
Direct observation of coordinated assembly of individual native centromeric nucleosomes
Authors: Popchock Andrew R , Larson Joshua D , Dubrulle Julien , Asbury Charles L , Biggins Sue 
Published: July 2023

AbstractEukaryotic chromosome segregation requires the kinetochore, a megadalton‐sized machine that forms on specialized centromeric chromatin containing CENP‐A, a histone H3 variant. CENP‐A deposition requires a chaperone protein HJURP that targets it to the centromere, but it has remained unclear whether HJURP has additional functions beyond CENP‐A targeting and why high AT DNA content, which disfavors nucleosome assembly, is widely conserved at centromeres. To overcome the difficulties of studying nucleosome formation in vivo, we developed a microscopy assay that enables direct observation of de novo centromeric nucleosome recruitment and maintenance with single molecule resolution. Using this assay, we discover that CENP‐A can arrive at centromeres without its dedicated centromere‐specific chaperone HJURP, but stable incorporation depends on HJURP and additional DNA‐binding proteins of the inner kinetochore. We also show that homopolymer AT runs in the yeast centromeres are essential for efficient CENP‐A deposition. Together, our findings reveal requirements for stable nucleosome formation and provide a foundation for further studies of the assembly and dynamics of native kinetochore complexes.
Optimization of Machine Learning in Predicting Fracture Behaviour of Materials in AI
Authors: Joshi Gaurav 
Published: July 2023

Machine learning has revolutionized various fields, including material science and engineering, by enabling accurate predictions and optimization of complex phenomena. One such area is the prediction of fracture behaviour in materials, which plays a crucial role in ensuring structural integrity and safety in numerous applications. This abstract focuses on the optimization of machine learning techniques for predicting fracture behaviour in materials using artificial intelligence (AI). The primary objective of this research is to develop a robust and efficient machine learning model that can accurately predict the fracture behaviour of materials. To achieve this, a comprehensive dataset comprising various material properties, such as strength, ductility, and microstructural features, is collected. Additionally, fracture-related data, including fracture toughness, crack propagation rates, and failure modes, are also incorporated into the dataset. Several machines learning algorithms, including decision trees, random forests, support vector machines, and neural networks, are employed to train and evaluate the predictive models. The models are optimized by tuning hyperparameters and selecting the most relevant features through feature selection techniques. Furthermore, advanced optimization algorithms, such as genetic algorithms and particle swarm optimization, are utilized to enhance the performance of the machine learning models.
To ensure the generalizability and robustness of the developed models, cross-validation techniques and extensive testing on independent datasets are conducted. The accuracy and performance of the models are assessed through various evaluation metrics, such as mean squared error, accuracy, and precision-recall curves. Comparative analyses are performed to determine the most suitable machine learning algorithm for predicting fracture behaviour in materials. The results demonstrate that the optimized machine learning models exhibit high accuracy and reliability in predicting fracture behaviour. The developed models can effectively capture the complex relationships between material properties and fracture characteristics, enabling the identification of critical parameters that influence fracture behaviour. This research contributes to the advancement of AI in material science and engineering by providing valuable insights into the prediction and optimization of fracture behaviour in materials.
In the optimization of machine learning techniques for predicting fracture behaviour in materials using AI. The developed models offer accurate predictions and valuable insights into the fracture characteristics of materials, aiding in the design and optimization of structural components with enhanced safety and performance. The application of machine learning in material science continues to evolve, and this research paves the way for further advancements in the field.
Development of Smoothing Algorithm for Stabilization of the Material and Fluid–Solid Interaction Problems Using Machine Learning
Authors: Dogra Vaishally 
Published: July 2023

In the application of machine learning algorithms has gained significant attention in various scientific and engineering disciplines. This study focuses on the development of a novel smoothing algorithm utilizing machine learning techniques for the stabilization of material and fluid-solid interaction problems. The interaction between materials and fluids presents complex challenges in numerous fields, including civil engineering, biomechanics, and aerospace engineering. Traditional numerical methods often struggle to accurately model and simulate these intricate interactions, leading to unstable and unreliable results. Therefore, the incorporation of machine learning techniques into the computational analysis of such problems has the potential to improve accuracy and efficiency.
The proposed smoothing algorithm aims to address these challenges by leveraging the power of machine learning. It involves training a model to recognize and capture the underlying patterns and behaviors of the material and fluid-solid interactions. The algorithm employs advanced techniques such as artificial neural networks and deep learning architectures to learn from the available data, adaptively adjust the simulation parameters, and stabilize the computation process. The development process encompasses several stages. Initially, a comprehensive dataset is collected, comprising a wide range of material and fluid-solid interaction scenarios. The dataset includes information on the properties of the materials, fluid dynamics, and the resulting solid responses. This data is then used to train the machine learning model, enabling it to learn the underlying physics and behavior of the interaction process. The algorithm operates in real-time during simulations, continuously adjusting and refining the solution to maintain stability. By effectively addressing the instability issues commonly encountered in material and fluid-solid interaction problems, the proposed algorithm enhances the reliability and accuracy of the simulation results.
The effectiveness of the developed smoothing algorithm is validated through extensive numerical experiments and comparisons with traditional methods. Performance metrics such as stability, accuracy, computational efficiency, and convergence are carefully assessed to evaluate the algorithm's effectiveness in stabilizing material and fluid-solid interaction problems. This research presents a pioneering approach in utilizing machine learning techniques to develop a novel smoothing algorithm for stabilizing material and fluid-solid interaction problems. The results obtained from this study have the potential to significantly advance the accuracy and reliability of computational simulations in various engineering and scientific domains, enabling more robust design and analysis of structures and systems affected by material and fluid-solid interactions.
Development of a Machine Learning Model for Predicting Fracture Behaviour of Materials Using AI
Authors: Srivastava Neeraj 
Published: July 2023

Fracture behaviour prediction of materials is a critical aspect in various industries, including aerospace, automotive, and manufacturing. Accurate prediction of fracture behaviour can aid in designing robust materials and structures, enhancing safety, and optimizing performance. In this study, we propose the development of a machine learning model for predicting fracture behaviour of materials using artificial intelligence (AI) techniques. The methodology involves the collection and pre-processing of a comprehensive dataset comprising material properties, structural characteristics, and fracture behaviour observations. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, are employed to train and optimize the predictive model. Feature engineering techniques are utilized to extract relevant features and reduce dimensionality. The model's performance is evaluated using appropriate metrics, including accuracy, precision, recall, and F1-score.
The significance lies in the potential to provide accurate and efficient predictions of fracture behaviour, thereby enabling informed decision-making in material selection, design, and performance optimization. By leveraging AI techniques, we aim to overcome the limitations of traditional fracture prediction methods that rely on empirical models or complex numerical simulations. Developing a machine learning model for fracture behaviour prediction, evaluating the performance of different algorithms and feature engineering techniques, and assessing the practical implications and benefits of the developed model in real-world applications. Introducing a novel approach to predicting fracture behaviour using AI techniques. The results of this study have the potential to enhance the understanding of material fracture mechanisms and pave the way for improved material design and performance optimization.
Optimization of Isogeometric Analysis-Based Topology Optimization Design with Global Stress Constraint in AI
Authors: Negi Pankaj 
Published: July 2023

Topology optimization is a powerful technique used in engineering design to optimize the material distribution within a given design domain, resulting in improved structural performance. Isogeometric analysis (IGA) has emerged as a promising approach for topology optimization due to its ability to seamlessly integrate geometric design and analysis. The traditional formulation of IGA-based topology optimization focuses on minimizing compliance, neglecting stress constraints that are crucial for ensuring structural integrity. In propose an optimization framework that combines IGA-based topology optimization and artificial intelligence (AI) to optimize designs with a global stress constraint. The objective is to simultaneously minimize the compliance and control the maximum stress in the structure. The proposed framework leverages the power of AI algorithms, such as deep neural networks, to efficiently search for the optimal material distribution that satisfies the stress constraint.
The optimization process begins with the generation of an initial design based on the isogeometric representation. The design is then iteratively updated using an AI-driven optimization algorithm that incorporates the global stress constraint. The AI algorithm learns from the stress distribution patterns in the design domain and guides the optimization towards finding an optimal material distribution that minimizes compliance while ensuring the maximum stress does not exceed the predefined limit. To evaluate the performance of the proposed approach, several numerical experiments and comparisons with traditional methods are conducted. The results demonstrate that the combination of IGA-based topology optimization and AI significantly improves the structural performance by effectively controlling stress levels while achieving better compliance values. The optimized designs exhibit enhanced strength and structural efficiency compared to those obtained using conventional methods.
The integration of AI algorithms with IGA-based topology optimization offers a promising avenue for tackling complex engineering design problems with stress constraints. The proposed framework provides engineers with a powerful tool to design lightweight and structurally robust components. Furthermore, the application of AI in topology optimization can significantly reduce the computational cost and accelerate the design process. Overall, this research contributes to advancing the field of structural optimization by integrating AI-driven approaches into isogeometric analysis-based topology optimization for enhanced design efficiency and performance.
Optimization of Compatible Meshfree Quadrature Rule for Nonlocal Problems with Applications to Peri Dynamics Study
Authors: Dogra Vaishally 
Published: July 2023

The optimization of a compatible meshfree quadrature rule for nonlocal problems with applications to Peridynamics is investigated in this study. Peridynamics is a nonlocal continuum mechanics theory that models material behavior based on interactions between material points within a finite neighbourhood. In Peridynamics, the accurate evaluation of nonlocal integral equations is crucial for obtaining reliable and efficient solutions. Traditional numerical integration methods, such as Gaussian quadrature, are not directly applicable to nonlocal problems due to their local nature. Hence, the development of a compatible meshfree quadrature rule that can effectively handle nonlocal interactions is of great importance. The objective it to optimize a quadrature rule that accurately captures the nonlocal interactions in Peridynamics while maintaining computational efficiency. The optimization process involves the selection of appropriate quadrature points and weights that minimize the quadrature error and maximize the computational efficiency. Various optimization techniques, such as genetic algorithms, particle swarm optimization, or machine learning algorithms, are explored to search for an optimal quadrature rule. The optimized quadrature rule is then applied to several Peridynamics problems, including fracture mechanics, material failure, and dynamic response analysis. The performance of the optimized quadrature rule is evaluated by comparing the results with those obtained using traditional quadrature methods and analytical solutions, when available. The accuracy, stability, and computational efficiency of the optimized quadrature rule are analysed and discussed. The findings provide valuable insights into the development and optimization of compatible meshfree quadrature rules for nonlocal problems, particularly in the context of Peridynamics. The optimized quadrature rule offers improved accuracy in capturing nonlocal interactions and reduces the computational cost compared to traditional methods. The applications of the optimized quadrature rule to various Peridynamics problems demonstrate its effectiveness and reliability.
The implications of it extend beyond Peridynamics and can be applicable to other nonlocal models in the field of computational mechanics. The optimized quadrature rule has the potential to enhance the accuracy and efficiency of numerical simulations involving nonlocal phenomena, enabling more reliable predictions of material behavior and structural response. The findings of this contribute to the advancement of numerical methods for nonlocal problems and provide a foundation for further research and developments in the field of compatible meshfree quadrature rules for nonlocal problems.
Single-session Intervention on Growth Mindset on Negative Emotions for University Student Mental Health (U-SIGMA): A Protocol of Two-armed Randomized Controlled Trial
Authors: ZHU Shimin , HU Yuxi , QI Di , QIN Nan , CHI Xinli , LUO Jiawen , WU Jie , HUANG Hua , WU Qiaobing , YU Lu , NI Shiguang , Hamilton Kyra , TSE Samson 
Published: July 2023

Abstract
Background
The university years are a developmentally crucial phase and a peak period for the onset of mental disorders. The beliefs about the changeability of negative emotion may play an important role in help-seeking. The brief digital growth mindset intervention is potentially scalable and acceptable to enhance adaptive coping and help-seeking for mental health needs in university students. We adapted the Single-session Intervention on Growth Mindset for adolescents (SIGMA) to be applied in university students (U-SIGMA). This protocol introduces a two-armed waitlist randomized controlled trial study to examine the effectiveness and acceptability of U-SIGMA in promoting help-seeking among university students in the Greater Bay Area.
Methods
University students (N = 250, ages 18–25) from universities in the Greater Bay Area will be randomized to either the brief digital growth mindset intervention group or the waitlist control group. Participants will report on the mindsets of negative emotions, perceived control over anxiety, attitude toward help-seeking, physical activity, hopelessness, psychological well-being, depression, anxiety, and perceived stress at baseline, the 2-week and 8-week follow-up through web-based surveys. A 30-minute digital intervention will be implemented in the intervention group, with a pre- and post-intervention survey collecting intervention feedback, while the control group will receive the link for intervention after 8 weeks.
Discussion
This protocol introduces the implementation plan of U-SIMGA in multi-cities of the Greater Bay Area. The findings are expected to help provide pioneer evidence for the effectiveness and acceptability of the brief digital intervention for university students in the Chinese context and beyond and contribute to the development of accessible and effective prevention and early intervention for university students’ mental health.
Trial registration:
The study is registered with HKU Clinical Trials Registry: HKUCTR-3012; Registered 14 April 2023.