bluuebunny/crossref_metadata_2025 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.
Health and economic geography in the US
Authors: Χριστόπουλος Κωνσταντίνος
Published: July 2023

Παρά την ύπαρξη συσχέτισης μεταξύ υγείας και πλούτου, οι πολιτικές υγείας τείνουν να αγνοούν τους κοινωνικούς και οικονομικούς μηχανισμούς πίσω από τις ανισότητες στον τομέα της υγείας. Σε αυτή τη διατριβή εξετάζονται δύο θέματα: η πρόωρη θνησιμότητα και οι κυβερνητικές παρεμβάσεις για την αντιμετώπιση των επιπτώσεων του COVID-19. Συγκεκριμένα, πρώτα μελετάται εάν η πρόωρη θνησιμότητα, μετρούμενη με τα χρόνια πιθανής απώλειας ζωής (YPLL), συγκλίνει μεταξύ των πολιτειών των ΗΠΑ, ανά φύλο και ανά φυλή. Με βάση αυτά τα αποτελέσματα, εξετάζεται επίσης ποιες αιτίες θνησιμότητας, καθώς και δαπάνες για την υγεία, μπορεί να οδήγησαν σε απόκλιση στα YPLL. Για το σκοπό αυτό γίνεται χρήση μιας νέας μεθοδολογίας σύγκλισης για τα έτη 1979–2017. Τα ευρήματα δείχνουν ότι για τους άνδρες και τους μαύρους, όλες οι πολιτείες των ΗΠΑ συγκλίνουν σε μια σταθερή κατάσταση ως προς στα YPLL, ενώ για τις γυναίκες, τους λευκούς και τον συνολικό πληθυσμό, οι πολιτείες σχηματίζουν ομάδες σύγκλισης. Αυτές οι ομάδες διαφέρουν κυρίως σε βρεφική, καρδιαγγειακή και θνησιμότητα από ακούσιους τραυματισμούς, με αυτές με τα λιγότερα YPLL να βρίσκονται κυρίως στη δυτική και ανατολική ακτή. Συμπερασματικά, οι θάνατοι που μπορούν να προληφθούν φαίνεται να είναι τα κύρια αίτια της πρόωρης θνησιμότητας ενώ οι δαπάνες για την υγεία δεν φαίνεται να διαδραματίζουν τόσο σημαντικό ρόλο. Δεύτερον, μελετάται η συσχέτιση μεταξύ των κυβερνητικών μέτρων για την αντιμετώπιση των επιπτώσεων της πανδημίας COVID-19 και της σχέσης ισότητας-αποτελεσματικότητας και πιο συγκεκριμένα της μισθολογικής ισότητας. Για το σκοπό της ανάλυσης χρησιμοποιήθηκαν διαστρωματικά δεδομένα από τις πολιτείες των ΗΠΑ για το έτος 2020 και υποδείγματα χωρικής οικονομετρίας. Το κύριο εύρημα είναι ότι η συσχέτιση των μέτρων που έλαβαν οι πολιτειακές κυβερνήσεις για την αντιμετώπιση του COVID-19 με την προαναφερθείσα σχέση (σχέση ισότητας-αποτελεσματικότητας), διαφέρει ανάλογα με το εισόδημα των Πολιτειών. Επιπλέον, βρέθηκε μια σχέση ανεστραμμένου-U μεταξύ της μισθολογικής ανισότητας και της αποτελεσματικότητας. Αυτές οι ετερογενείς επιπτώσεις ενδέχεται να διαδραματίσουν σημαντικό ρόλο στην περιφερειακή ολοκλήρωση.
Variety of CSR practices during the covid-19 pandemic
Authors: Safakul Fatahullah Zihad Safakul Fatahullah
Published: July 2023

This study aims to identify various CSR practices during covid-19, especially CSR practices that focuses on reducing the economic impact on vulnerable communities. The results of this there are various CSR practices carried out by big companies in Indonesia in order to create a culture of shared prosperity through positive interactions with employees, families and communities around the company to improve the quality of life together during the oveid-19 pandemic. Corporate CSR during the pandemic covid-19 has a freat opportunity to bring out the best innovations in making social contributions which mean as a from of long-term development in achieving a balance between profitability and harmony with its various stakeholders. Meanwhile, examples of companies that taken in this study is the practice of CSR carried out by Grab, PT. Pertamina. This matter shows that CSR programs during a pandemic are aimed at vulnerable groups of people.
Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting
Authors: Tariq Muhammad Usman , Ismail Shuhaida Binti , Babar Muhammad , Ahmad Ashir
Published: July 2023

The pandemic has significantly affected many countries including the USA, UK, Asia, the Middle East and Africa region, and many other countries. Similarly, it has substantially affected Malaysia, making it crucial to develop efficient and precise forecasting tools for guiding public health policies and approaches. Our study is based on advanced deep-learning models to predict the SARS-CoV-2 cases. We evaluate the performance of Long Short-Term Memory (LSTM), Bi-directional LSTM, Convolutional Neural Networks (CNN), CNN-LSTM, Multilayer Perceptron, Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN). We trained these models and assessed them using a detailed dataset of confirmed cases, demographic data, and pertinent socio-economic factors. Our research aims to determine the most reliable and accurate model for forecasting SARS-CoV-2 cases in the region. We were able to test and optimize deep learning models to predict cases, with each model displaying diverse levels of accuracy and precision. A comprehensive evaluation of the models’ performance discloses the most appropriate architecture for Malaysia’s specific situation. This study supports ongoing efforts to combat the pandemic by offering valuable insights into the application of sophisticated deep-learning models for precise and timely SARS-CoV-2 case predictions. The findings hold considerable implications for public health decision-making, empowering authorities to create targeted and data-driven interventions to limit the virus’s spread and minimize its effects on Malaysia’s population.
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

Objectives

This case study explored implementation of a Decision-Based Learning (DBL) tool for teaching arterial blood gas (ABG) analysis to nursing students.

Methods

For 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.

Results

Students 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.

Conclusions

DBL 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

Eukaryotic 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.
El viaje hacia el exilio libanés de los refugiados pa lestinos: La Hijra
Authors: Velasco Muñoz Rosa
Published: July 2023

El presente artículo se centra en el resultado más trágico de la partición del territorio de Palestina y de la limpieza étnica premeditada llevada a cabo por el movimiento sionista: el éxodo de la población árabe palestina y, más concretamente, el “Viaje” (Hijra) que una parte considerable de estos ciudadanos, se vieron obligados a realizar hacia el exilio libanés a partir del verano de 1947 y hasta finales de 1949. Las tres oleadas perfectamente diferenciadas en las que se fue desarrollando este éxodo masivo y las condiciones específicas bajo las que se fue produciendo, estuvieron relacionadas tanto con el pueblo o la ciudad de procedencia de los que partían, debido básicamente a la expansión del terror estratégicamente manejado por las diferentes milicias sionistas y sus dirigentes, como con la clase social a la que pertenecían los refugiados. AbstractThe present article is focused mainly on the most tragic result that meant the partition of the territory of Palestine and the deliberated ethnic cleansing that was carried out by the Zionist movement: the Arab exodus from Palestine and specifically, the “Trip” (Hijra) that a representative part of those citizens were forced to make towards the Lebanese exile from the summer 1947 until the end of 1949. The three waves, perfectly differentiated, where was being developed this massive exodus and the specific conditions that were being generated, were either related to the people or the city of origin where they came from: basically due to the spreading of terror, strategically handled by the different Zionist militias, as well as the social class which belonged to the refugees.
Narasi ekologi bahari dalam Pemanggil Kematian karya Jemmy Piran: Kajian ekokritik Buell
Authors: Ramadhan Zul Fitrah , Juanda Juanda , AJ Andi Agussalim
Published: July 2023

Penelitian ini mengangkat masalah kesadaran manusia dalam menjaga ekosistem yang semakin memprihatinkan. Tujuan penelitian ini menelusuri pelbagai fenomena ekologis bahari dalam narasi cerpen Pemanggil Kematian karya Jemmy Piran. Data primer penelitian bersumber dari laman Ruang Sastra (ruangsastra.com) dikaji menggunakan pendekatan ekokritik Buell. Ekokritik Buell menelusuri komponen alam nonmanusia, legitimasi kepentingan alam, tanggung jawab manusia pada alam, dan dinamisasi alam. Hasil penelitian menympulkan bahwa terdapat elemen nonmanusia yang ditemukan dalam cerpen karya Jemmy Piran, yaitu fenomena satwa laut, fenomena ekosistem laut, dan fenomena mistik laut. Legitimasi alam menunjukkan laut sebagai ekosistem yang menjadi pusat hukum tatanan dan adat masyarakat pesisir. Tanggung jawab pada alam ditunjukkan oleh sikap peduli kepada laut, beretika dalam menangkap ikan, menjaga kebersihan laut, dan menjaga satwa laut. Sedangkan dinamisasi alam tampak pada laut mengalami perubahan kondisi akibat pengaruh perilaku manusia yang menggunakan cara ilegal dalam menangkap ikan. Kesimpulan penelitian menunjukkan terdapat narasi mistik yang diimajinasikan pengarang melalui karya sastra (cerpen) sebagai model pewacanaan, yakni melalui kepercayaan terhadap sosok penjaga laut Harin Botan. Dengan kata lain, hal tersebut membuktikan jika budaya dan mitos masyarakat merupakan faktor pendukung kelestarian alam karena dengan kepercayaan tersebut masyarakat menjadi takut melakukan tindakan eksploitasi terhadap alam.
Kota dan perilaku parafilia dalam Sentimentalisme Calon Mayat: tubuh dan resistensi protagonis terhadap kota kapitalis Orde Baru
Authors: Muhtarom Imam
Published: July 2023

Penyimpangan perilaku para protagonis dalam Sentimentalisme Calon Mayat (2023) merupakan bagian tak terpisahkan dari sistem kota yang mekanis dan kapitalistis. Perilaku-perilaku aneh tokoh-tokoh dalam cerpen Sony Karsono adalah sebuah bentuk perlawanan warga kota terhadap lingkungan sosial yang mekanis dan kapitalistis. Resistensi aktan-aktan cerita menjadikan tubuh sebagai medium pembebasan kungkungan kota yang serba materialistis, membatasi, dan menindas. Artikel ini berfokus pada perilaku menyimpang karakter-karakter utama cerita demi menghadirkan pengalaman esensial kehidupan kota yang unik, tipikal, karakteristik urban yang dibangun secara kapitalis. Protagonis yang bersetubuh dengan mayat, kawin dengan boneka, menikmati bunuh diri, onani pada saat dengar lagu kebangsaan, menggubah sajak-sajak seks vulgar perkotaan ditengarai sebagai perilaku parafilia. Memanfaatkan paradigma David Harvey mengenai situasi spasial kota, artikel ini memahami jika pelbagai perilaku protagonis yang parafilis adalah sarana untuk melihat kehidupan manusia kota yang ditopang kapitalisme. Perilaku parafilia tokoh-tokoh utama cerpen diletakkan dalam kerangka perlawanan penghuni kota terhadap habitat sosial yang kapitalis. Sistem kota yang mekanis dan kapitalistis sengaja diciptakan oleh pihak rezim kuasa yang ingin menciptakan status quo demi mempertahankan kekuasaan dan dominasi. Cerpen-cerpen dalam Sentimentalisme Calon Mayat menawarkan pembacaan kota secara berbeda agar terlepas dari cengkeraman kuasa kota yang menindas.
role of Information System to measure the Cost and Performance Aware Scheduling Technique for Cloud Computing Environment
Authors: Ashabrawy Mohamed
Published: July 2023

The lot of interest has been put forth by researchers to improve workload scheduling in the cloud platform. However, the execution of scientific workflow on a cloud platform is time-consuming and expensive. Much research has been emphasised, as users are charged based on the usage hour, minimising processing time to reduce cost. However, the processing cost can be reduced by minimising energy consumption, especially when resources are heterogeneous; Minimal work has been done considering optimising cost with energy and processing time parameters to meet task Quality of Service (QoS) requirements. This paper presents cost and performance-aware workload scheduling (CPA-WS) techniques under a heterogeneous cloud platform. This paper presents a cost optimisation model through the minimisation of processing time and energy dissipation for the execution of the task. Experiments are conducted using two widely used workflows such as Inspiral and CyberShake. The outcome shows the CPA-WS significantly reduces energy, time, and cost compared to the standard workload scheduling model.
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.
Analysis of Smart Manufacturing Technologies for Industry Using AI Methods
Authors: Verma Deepak
Published: July 2023

Smart manufacturing technologies have gained significant attention in the industrial sector due to their potential to revolutionize traditional manufacturing processes. Among these technologies, artificial intelligence (AI) methods have emerged as powerful tools for enhancing efficiency, productivity, and decision-making in manufacturing operations. This paper presents an analysis of smart manufacturing technologies for industry using AI methods. The analysis focuses on the application of AI techniques such as machine learning, deep learning, and data analytics in various aspects of smart manufacturing, including predictive maintenance, process optimization, quality control, and supply chain management. The paper provides an overview of the key AI methods employed in smart manufacturing and discusses their benefits and challenges. It also highlights case studies and real-world implementations of AI-based smart manufacturing systems. The findings of this analysis demonstrate the significant contributions of AI methods in enabling intelligent and autonomous manufacturing systems. The paper concludes with insights into the future directions and potential impact of AI-driven smart manufacturing technologies in industry, emphasizing the importance of continued research and development in this field to unlock the full potential of smart manufacturing in the industry. Smart manufacturing technologies have revolutionized the industrial sector by enhancing productivity, efficiency, and flexibility. Artificial intelligence (AI) methods, such as machine learning and data analytics, play a crucial role in enabling smart manufacturing systems to optimize processes and make informed decisions. This research paper aims to analyse the application of AI methods in smart manufacturing technologies. The study explores various AI-based approaches used in different stages of smart manufacturing, including data acquisition, data analysis, process optimization, and predictive maintenance. The research provides insights into the benefits, challenges, and potential future developments of AI in smart manufacturing, offering valuable guidance for industries aiming to implement these technologies.
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.
Fingerprint Identification Using FPGA with a Newly Designed Architecture
Authors: Panwar Avnish
Published: July 2023

The Aadhaar project was recently introduced by the Unique Identification Authority of India (UIDAI) to provide a Unique Identification (UID) number to every resident of India. Biometric and demographic information are used to create a unique identification number (Aadhaar). You may use your Aadhaar number to get a subsidy on LPG cylinders, a driving licence, a PAN card and a bank account. The software industry is crucial to the success of the Aadhaar system. The suggested work, including the pre-processing stage, fingerprint recognition process, and fingerprint recognition system, is carried out across three platforms, including MATLAB, field programmable gate array (FPGA), and application specific integrated circuit (ASIC). In order to improve the processing speed, resource utilisation, power consumption, and recognition rate of the proposed job, the FPGA is programmed with two specialised hardware methods: the CORDIC algorithm and IP cores. The suggested work largely focuses on fixing the processing time and accuracy issues. The concept is converted into an ASIC using FPGA technology with the goal of reducing power consumption and resource utilisation without sacrificing performance. In contrast to FPGA implementation, which makes use of look-up tables (LUTs) and customizable logic blocks (CLBs), ASIC implementation makes use of the basic cells directly. When compared to FPGA design, ASIC development speeds up recognition by 20%.
Significance and Conservation of Birds of the Genus of Bee-eater (MEROPS) in Uzbekistan
Authors: Chorshanbiyevich Yangiboyev Elmurod , Ochilovna Shodiyeva Fotima , Rohmonkulovich Kholboev Fakhriddin , Shomurodovich Rakhimov Matnazar
Published: July 2023

The purpose of this article is to identify and evaluate the importance of birds of the genus Merops (European Bee-eater -Merops apiaster and Blue-cheeked Bee-eater -Merops persicus) in Uzbekistan, as well as to develop recommendations aimed at solving some problems associated with its conservation. As a result of the study, in the aspect of biocenotic relations, the natural significance of these species was revealed. The economic value is determined and analyzed in beekeeping farms. From the study of the remains of food contained in the stomachs, throats and nests of M.apiaster, a preliminary list of the main species of the food spectrum has been compiled. Based on the bioacoustic repellent "Korshun-8", a new, more effective bioacoustic repellent has been developed to repel bee-eaters. To solve some of the problems associated with the conservation of bee-eaters, recommendations have been developed aimed at preventing the illegal extermination of bee-eaters, preserving their nesting colonies, etc.
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

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.
Diagnostic Validity of Specific Immunoglobulin E Levels to alpha-gal in Alpha-gal Syndrome: A Cross-Sectional Analysis.
Authors: Sánchez Adrián Germán , Alonso-Llamazares Ana , García-González Fernando , Matala-Ahmed Bakai , Melgar-Reyes Ceny Solani , Antepara-Ercoreca Ignacio
Published: July 2023

Background: The diagnosis of Alpha-gal Syndrome (AGS) is based on the presence of symptoms after being exposed to potential sources of alpha-gal together with values ​​of specific IgE (sIgE) to alpha-gal ≥ 0.1 kUA/L or ≥0.35 kUA/L. The aim of this study was to evaluate the diagnostic validity of sIgE levels to alpha-gal ≥ 0.1 kUA/L for identifying AGS.
Methods: This was a cross-sectional analysis of adult patients with available data on sIgE levels to alpha-gal, classified into two groups according to the presence (Group 1) or absence (Group 2) of symptoms after being exposed to potential sources of alpha-gal. Values of sIgE to alpha-gal ≥ 0.1 kUA/l were considered a positive result. A descriptive analysis of internal and external validity parameters was performed in the entire population and adjusted by sex.
Results: The study included 33 individuals in Group 1 and 65 in Group 2, with a mean age of around 47 years. The analysis of internal validity parameters revealed a high sensitivity, specificity, and positive probability ratio, with higher sensitivity in men and higher specificity in women. The analysis of external validity parameters showed a high negative predictive value and global value in all populations and both sexes. However, the positive predictive value was relatively high in men, but low in women.
Conclusions: Our results suggest that sIgE levels ≥ 0.1 kUA/L may be a useful tool for the diagnosis of AGS, although other factors and diagnostic techniques should also be considered.
Comparison of post-operative pain and quality of life between total thoracoscopic surgery and conventional full-sternotomy for aortic valve replacement
Authors: Lin Zhiqin , Chen Xiujun , Dai Xiaofu , Xu Zheng , Chen Liangwan
Published: July 2023

Background
To compare the post-operative pain and quality of life of patients who underwent total thoracoscopic surgery (TTS) or conventional full-sternotomy (CFS) for aortic valve replacement (AVR).
Methods
We reviewed the records of 223 consecutive AVR patients with either TTS or CFS from January 2018 to December 2022. We used a visual analogue scale (VAS) and the Short Form-36 Health Survey (SF-36) to measure the post-operative pain and quality of life, respectively. We also compared the operative data and clinical outcomes between the two groups.
Results
The TTS group had lower adjusted mean VAS scores than the CFS group at all time points after surgery (at 1 to 3 days and at 3 and 6 months, p < .001 for all comparisons), indicating less pain. The TTS group also had higher mean SF-36 scores than the CFS group up to 6 months after surgery (p < .001 for all comparisons), indicating better quality of life. The operative time was similar between the two groups (p = .224), but the TTS group had longer cardiopulmonary bypass time and aortic cross-clamp time than the CFS group (p < .001). The TTS group had more pulmonary complications than the CFS group (p = .023). However, there were no significant differences in other major complications or mortality between the two groups.
Conclusions
TTS is a safe and effective alternative to CFS for AVR. TTS resulted in less pain and better quality of life, especially in the early recovery period. However, further prospective randomized controlled studies are needed to confirm our findings.
VIEWER: An electronic audit and feedback tool for the delivery of population health management applied to mental health care
Authors: Msosa Yamiko Joseph , Codling David , Wang Tao , Broadbent Matthew , Roberts Angus , Harland Robert , McGuire Philip , Stewart Rob , Dobson Richard JB
Published: July 2023

Background: Due to the burden and negative impacts of psychotic disorders, treatment alone is insufficient to reduce the gap in psychosis care thereby highlighting the need for greater investment in prevention strategies which are targeted towards an entire population to detect risk factors early and prevent adverse health outcomes at scale. This work evaluates the technical feasibility of implementing electronic audit and feedback (eA&F) to support population health management in mental health care by facilitating the visualisation of clinical health records to provide relevant summaries for eA&F.
Methods: A Participatory Design approach was adopted as an integral framework throughout the design and development of an eA&F platform. The eA&F platform, VIEWER, was evaluated for its technical feasibility. The feasibility testing of the platform was carried out with clinical teams in the context of three different clinical use cases to support eA&F for managing the psychosis patient population.
Results: An eA&F platform for supporting population health management was developed. The platform was used successfully for eA&F to support the identification of unmet needs, variations in care processes, and inequalities between groups of patients within the psychosis patient population.
Conclusions: It is technically feasible to develop and deploy an eA&F platform that supports population health management in secondary mental healthcare. Using advanced clinical analytics to integrate population-level data with routine clinical practice raises exciting possibilities of building more intelligent services that are adaptive to the needs of the population, and adopt a more proactive, preventative approach. The innovative eA&F platform gives clinicians the flexibility to look at clinical data that is relevant to them at the level of abstraction that is appropriate for the decision being made. By sharing the methods and steps taken in this work, useful insights can be provided to other researchers and practitioners that are developing similar digital health interventions.
An improved UAV image detection algorithm based on YOLOv5
Authors: Guo Yecai , Sun Jingdong , Saha Amitave
Published: July 2023

At present, existing small target detection methods suffer from low detection accuracy, high false detection rate and high leakage rate. In order to improve small target detection, this paper proposes the FSD-YOLOv5 algorithm, which has three improvements on the basis of the YOLOv5 algorithm. Firstly, the Focal EIoU is used instead of the original CIoU to improve the model convergence speed and regression accuracy; Secondly, in order to cope with the defects in the CNN architecture, we adopt a new CNN building block called SPD-Conv; Finally, to address the problem of the reduced or lost information of small objects in feature maps caused by downsampling in convolutional neural networks, we introduce feature reuse to increase the feature information of small objects in the feature maps. We evaluated FSD-YOLOv5 on the VisDrone-2019 dataset and compared its performance with YOLOv5. The results show that FSD-YOLOv5 achieves a detection accuracy of 36.3%, an improvement of 2.4% compared to the original algorithm. For small target detection, the FSD-YOLOv5 algorithm has better detection results than the YOLOv5 algorithm.