bluuebunny/crossref_metadata_2025_split huggingface dataset:
Developing predictive models for HIV Drug resistance: A genomic and AI approach
Authors: Charles Chukwudalu Ebulue  , Ogochukwu Virginia Ekkeh  , Ogochukwu Roseline Ebulue  , Chukwunonso Sylvester Ekesiobi  
Published: May 2024

This paper proposes a novel approach to combating HIV drug resistance through the development of predictive models leveraging genomic data and artificial intelligence (AI). With the increasing prevalence of drug-resistant strains of HIV, there is a critical need for innovative strategies to predict and manage resistance mutations, thereby optimizing treatment outcomes and prolonging the efficacy of antiretroviral therapy (ART). Drawing on advances in genomics and AI, this study outlines a conceptual framework for the development of predictive models that can identify potential drug-resistance mutations in HIV genomes and inform clinical decision-making. The proposed framework integrates genomic data from HIV-infected individuals with AI algorithms capable of learning complex patterns within the data. By analyzing genomic sequences obtained from HIV-positive patients, the models aim to identify genetic variations associated with drug resistance, predict the likelihood of resistance development, and guide the selection of appropriate treatment regimens. This approach holds promise for personalized medicine in HIV care, enabling clinicians to tailor therapy based on an individual's genetic profile and risk of resistance. Key components of the conceptual framework include data preprocessing to extract relevant genomic features, model training using machine learning techniques such as deep learning and ensemble methods, and validation of predictive performance through cross-validation and independent testing. Furthermore, the integration of clinical data, such as treatment history and viral load measurements, enhances the predictive accuracy of the models and provides valuable insights into treatment response dynamics.The development of predictive models for HIV drug resistance represents a paradigm shift in HIV care, offering a proactive approach to treatment management and surveillance. By leveraging genomic and AI technologies, healthcare providers can anticipate and address emerging resistance mutations before they compromise treatment efficacy. Ultimately, the implementation of predictive models holds the potential to improve patient outcomes, reduce the transmission of drug-resistant HIV strains, and advance the global fight against HIV/AIDS.
Keywords: Developing, Predictive Models, HIV Drug Resistance, Genomic, AI Approach.
Study on the contact performance of the variable hyperbolic circular arc tooth trace cylindrical gear with installation errors
Authors: Ma Dengqiu , Jiang Bing , Bao Lingli , Ye Zhenhuan , Liu Yongping 
Published: May 2024

Abstract. Installation errors are important factor for the contact performance of the variable hyperbolic circular arc tooth trace (VH-CATT) cylindrical gear; the study of the relationship between installation errors and contact performances is beneficial for gear vibration and noise reduction. Firstly, based on the meshing theory, the tooth surface equation and contact ellipse of the VH-CATT cylindrical gear were deduced, and the tooth surface imprinting experiment was realized. Next, the tooth contact analysis model of the VH-CATT cylindrical gear was developed to investigate the influence of the installation errors on the elliptical contact area. Then, the calculation formulas of the tooth surface gap and contact point flexibility matrix were carried out to develop the load tooth contact analysis model of the VH-CATT cylindrical gear. Finally, the influence of the installation errors on the load distribution was investigated. Research shows that the elliptical contact area of the VH-CATT cylindrical gear is a line contact within a certain range and is located in the middle section of the tooth width. The elliptical contact area of the VH-CATT cylindrical gear increases or decreases rapidly when meshing in and out, and all the installation errors have a certain influence on the elliptical contact area, except for the center distance error. The single-tooth meshing area with the installation errors is greater than that of the ideal gear pair. The load distribution area with the installation errors deviates from the middle section, except for the center distance error. The installation rotation errors around the x and y axis have a certain influence on the maximum load of the single-tooth meshing area. The research results provide a basis for improving the bearing capacity of the VH-CATT cylindrical gear and optimizing the design.
Knowledge and Practice of BDS Graduates Regarding Antibiotic Prophylaxis in Infective Endocaraditis
Authors: Ahamad Saleem Uzair , Jouth Choala Simran , Bashir Nafiah , Siddiqui Usama , Rehman Basheer 
Published: May 2024

Objective: The objective of the study was to find out the BDS graduates’ knowledge regarding antibiotic prophylaxis in infective endocarditis according to Amerian Dental Association guidelines.Methodology: A questionnaire, containing 28 questions, was distributed among BDS graduates who were serving at Khyber College of Dentistry during the period between April 2018 to June 2018.Results: In total 116 questionnaires were distributed among which 85 graduates responded back with response rate of 73.27%.Most graduates used multiple sources to gain knowledge. Most graduates (92.45%) knew that patients with previous history of endocarditis and patients suffering from rheumatic heart disease are the conditions which need antibiotic prophylaxis. In case of procedures which need antibiotic prophylaxis the highest percentage of correct answers was for periodontal surgery (98.11%) followed by extraction (92.45%). Regarding regime of antibiotics most graduate were of the option that penicillin is the first choice of drug and 1g of it should be given.Conclusion: Most of the graduates had an inadequate knowledge regarding use of antibiotic prophylaxis against infective endocarditis and were unaware of the current guidelines, so there should be trainings and workshops to refresh their knowledge regarding current practices.
MINIMIZING TEMPERATURE DEVIATIONS IN RUBBER MIXING PROCESS BY USING ARTIFICIAL NEURAL NETWORKS
Authors: Lukas Marco , Leineweber Sebastian , Reitz Birger , Overmeyer Ludger , Aschemann Alexander , Klie Benjamin , Giese Ulrich 
Published: May 2024

ABSTRACT
Rubber mixing is a complex manufacturing process that poses challenges for process control due to the high number of control variables, including mixing parameter settings, rheological behavior, compound viscosity, and batch-dependent material variations. Already small deviations from the control variables can influence the compound properties, leading to increased scrap rates. To address these challenges, this paper introduces an artificial intelligence–based approach to enhance process control in rubber mixing by predicting mixing temperatures from input variables. The proposed method uses feedforward neural networks (FFNs) to enable early identification of batch-specific temperature deviations, thereby enabling systematic improvements with each new application. The FFN was trained on a diverse dataset encompassing various rubber recipes and batches. Post-training, the FFN demonstrated remarkable accuracy, achieving a mean absolute percentage error of 1.00% on the training dataset and 1.44% on the validation dataset, thereby showcasing its efficacy in predicting temperature fluctuations within the mixing process. Consequently, the FFN can determine the relevant input variables necessary to achieve specific mixing temperatures, providing a foundation for an automated control system in rubber mixing process. This paper outlines the system architecture of the FFN tailored for rubber mixing and provides a comprehensive overview of the experimental results.
Interactive Creation and Performance of Digital Music Based on Virtual Reality Technology
Authors: Leiyan Jiang  
Published: May 2024

It explores the application of Virtual Reality (VR) technology to the development and performance of digital music on the e-MARZ platform, which promotes Malaysian arts and culture. Following the ADDIE model's systematic approach, the emphasis is on the Design phase, which involves conceptualizing and realizing the goal for interactive digital music experiences. Through painstaking attention to user requirements and immersive design concepts, e-MARZ strives to provide entertaining and informative experiences that inspire creativity and enthusiasm for Malaysian music. Using VR technology, users are transported to virtual spaces where they can explore, compose, and play music in novel ways. By adhering to user-centred design principles and pedagogical effectiveness, e-MARZ seeks to give a transforming trip across the realms of digital music within a virtual landscape, enriching the cultural tapestry of Malaysian arts and music. The results of the usability show that the e-MARZ module effectively addressed the usability needs of both professionals and high school students, offering an immersive and engaging learning experience in Malaysian arts and music
Movie Scene Simulation and Rendering Based on Virtual Reality Technology
Authors: Yuanyuan Ji  
Published: May 2024

Movie scene simulation and rendering with virtual reality (VR) technology have emerged as a game-changing method in the film industry, providing filmmakers with unparalleled creative opportunities and immersive storytelling experiences. This article investigates the use of VR technology in movie scene simulation and rendering, with a focus on sophisticated modelling techniques and rendering methods. Filmmakers can effectively control scene complexity and enhance rendering speed by utilizing VR-based modelling approaches such as multi-resolution decomposition and level of detail (LOD) rendering. These modelling techniques enable the development of highly complex and realistic virtual worlds, allowing filmmakers to bring their creative visions to life with unparalleled precision. In addition, VR-based rendering technologies, such as viewpoint-based geometric chunking rendering, improve frame rates and overall scene rendering quality, resulting in a seamless and immersive watching experience for audiences. Using VR technology, filmmakers can push the boundaries of traditional filmmaking, create fascinating cinematic experiences, and reimagine narrative in the digital age. This research explores the revolutionary impact of VR technology on movie scene simulation and rendering, emphasizing its potential to change the way films are created, seen, and appreciated by audiences throughout the world. Finally, they compare and assess the scene effect, LOD detail level, and interactive experience effect before and after simplification. scene design for virtual reality technologies generally scores 5-6 on each of the measures, whereas common technology's scenario design usually gets 3 to 4, showing whether the scenario design of virtual reality technology works properly.
Deep Reinforcement Learning based channel allocation (DRLCA) in Cognitive Radio Networks
Authors: M. Narender Pavan  
Published: May 2024

The Spectrum is a scarce resource in wireless networks, hence these resources need to be perfectly channelized for their better utilization. By the study of the previous decades, it is found that the spectrum is colossally underutilized and the main reason for the underutilization found out to be the policies that are fixed and not dynamic. The dynamic spectrum allocation of frequency bands may overcome this problem. Cognitive radio provides an important concept that can used to solve the problem of underutilization of spectrum. Reinforcement learning is a key technique that is widely used to learn the spectrum allocation behaviour and maximize the system’s efficiency. Therefore, in this work, we have designed and developed a Reinforcement learning-based model to allocate the channels among secondary users. Also, a Deep Reinforcement learning-based channel allocation algorithm (DRLCA) has been proposed. The proposed DRLCA is compared with existing JPCRL [1]. In our algorithm, the Python libraries were used for simulation. From the simulation results and analysis, it is found that the DRLCA outperforms the JPCRL in terms of channel utilization by 5%.
Predictive Analytics and Machine Learning in Disease Diagnosis: A Review of Recent Advances
Authors: Kannan Vishwanatth  
Published: May 2024

Artificial intelligence (AI) refers to the utilisation of computer information to exhibit intelligent behaviour with minimal human intervention, whereas machine learning (ML) is seen as a subset of AI methodologies. Typically, this form of intelligence is widely recognised to have originated with the advent of robotics. Given the slow progression of diseases, it is crucial to make early predictions and administer appropriate medication. Hence, it is imperative to present a decision model that may aid in the diagnosis of chronic illnesses and forecast future patient prognoses. The primary objective of this study is to investigate the application of Predictive Analytics and ML in the field of Disease Diagnosis. The study will focus on reviewing the latest advancements in this area. This work specifically emphasises the significance of ML prediction models in disease diagnosis within the AI field, amidst other approaches available. The study utilises a qualitative research methodology. ML has been prominent in the medical field as it offers methods for analysing disease-related data, as indicated by this study. ML algorithms are crucial in attaining early disease detection. Another crucial finding in this research is that the accuracy and performance of the model can be enhanced by employing an alternative technique to generate a single ensemble model.
Practice and Exploration of University English Teaching Reform Based on Virtual Reality Technology
Authors: Yuan Siyuan 
Published: May 2024

The university English teaching, the integration of virtual reality (VR) technology represents a groundbreaking frontier poised to revolutionize traditional pedagogical approaches. As education evolves to meet the demands of a digital age, incorporating immersive technologies like VR offers transformative opportunities to enhance learning experiences and outcomes. In the dynamic landscape of university English teaching, the integration of cutting-edge technologies like Augmented Virtual Feature Classification (AVFC) heralds a transformative era of pedagogical innovation. AVFC represents a pioneering approach that harnesses the power of virtual reality (VR) technology to reshape traditional teaching methodologies. By leveraging the power of VR, AVFC redefines traditional teaching methodologies by immersing students in virtual environments enriched with augmented features. These features augment the learning experience by providing interactive elements, visual aids, and simulated scenarios tailored to enhance language learning. The implementation of AVFC has yielded remarkable results, demonstrating its effectiveness in driving positive learning outcomes. By immersing students in virtual environments enriched with augmented features, AVFC offers an unprecedented level of engagement and interactivity in language learning. AVFC implementation has demonstrated a 30% increase in student engagement and a 25% improvement in language proficiency compared to conventional teaching methods, highlighting its effectiveness in driving positive learning outcomes.
Corpus-based Research on English-Chinese Translation Teaching Combining Vocabulary Learning and Practice
Authors: Nan Zhang  
Published: May 2024

As the global economy is developing, real time translation among major influential languages such as English, Chinese has become a necessity. Translation from English to Chinese is difficult since Chinese has a different grammar and ambiguous word boundaries. Furthermore, there are issues with the current Chinese-English machine translation, including difficult-to-understand extended sentences and inaccurate word translation. To address this drawback, the methodology for more precisely categorizing vocabulary in both English and Chinese is described in this research. The data are collected from UM-Corpus news dataset. Then, using SEGBOT: Neural Text Segmentation method, English words are segmented from the sentences available on the dataset. Afterward, the data are fed to pre-processing. In pre-processing segment; words missing in translation from the news dataset are eliminated and enhances the input data utilizing Iterated Square-Root Cubature Kalman Filter. The outcome from the pre-processing data is transferred to the Pyramidal Convolution Shuffle Attention Neural Network (PCSANN). The word order, grammatical structure, consistent style and smooth flow for English to Chinese translation are successfully classified by using PCSANN. The Waterwheel Plant Algorithm (WWPA) is used to optimize the weight parameter of PCSANN. The proposed PCSANN-WWPA is applied in python working platform. Performance metrics, like accuracy, precision, F1-score, and recall are examined to compute proposed method. The gained results of the proposed PCSANN-WWPA method attains higher accuracy of 16.65%, 18.85%, and 17.89%, higher sensitivity of 16.34%, 12.23%, and 18.54% and higher precision of 14.89%, 16.89%, and 18.23%. The proposed ECTT-PCSANN-WWPA method is compared with the existing methods such as ECTT-RNN, ECTT-DNN, and ECTT-MLPNN models respectively.
Diagnosis of Medical Images Using Convolutional Neural Networks
Authors: Yogita K. Desai  
Published: May 2024

Medical image diagnosis using Convolutional Neural Networks (CNNs) has emerged as a viable way to improve the accuracy and efficiency of disease identification and categorization in clinical settings. In this study, they look at how CNNs can be used to diagnose lung nodules from chest X-ray pictures, to provide insights into the technology's performance and future clinical applications. A dataset of 10,000 tagged chest X-ray pictures showing both benign and malignant lung nodules was obtained and preprocessed using standard methods. The dataset was used to construct and train a proprietary CNN architecture, which was then rigorously evaluated on distinct training, validation, and test sets. The CNN model showed good accuracy (94.8%), sensitivity (92.1%), specificity (96.5%), precision, recall, F1 score, and area under the ROC curve (AUC), indicating its robustness and generalization ability. These findings show that CNN-based diagnostic tools may help radiologists and physicians discover and diagnose lung cancer earlier, improving patient outcomes and optimizing healthcare delivery. However, difficulties such as interpretability, data privacy, and regulatory approval must be addressed before CNNs can be fully utilized in medical imaging. This study emphasizes CNNs' transformative significance in diagnostic medicine and the necessity for additional research and development to realize their full potential in clinical practice.
Соціальне підприємництво як основа сталого розвитку територіальних громад
Authors: Рудаченко О. О. , Коненко В. В. 
Published: May 2024

В статті проаналізовано роль соціального підприємництва у забезпеченні сталого розвитку територіальних громад, регіонів та країни, загалом. Доведено, що важливість проведення дослідження сталого розвитку територіальних громад в Україні надзвичайно висока, оскільки це є вирішальним фактором для забезпечення якісного життя мешканців та підвищення їх благополуччя. Здійснення сталого розвитку передбачає збалансоване використання ресурсів, охорону навколишнього середовища, розвиток ефективної інфраструктури та надання соціальних послуг. Авторами розглядається поняття соціального підприємництва та його значення у контексті сталого розвитку, зокрема в контексті розвитку місцевих громад через поєднання соціальної місії з підприємницькою діяльністю соціальні суб’єкти підприємництва здатні вирішувати соціальні проблеми та сприяти економічному зростанню на місцевому, регіональному та державному рівнях. Доведено, що досягнення місії соціального підприємництва здійснюється різними способами. Зазвичай, суб’єкти соціального підприємництва продають власні товари та організовують соціальні, культурні або спортивні заходи, проекти та програми. Сформована бізнес-модель формування соціального підприємництва, яка спрямована на створення інноваційного підходу до соціального підприємництва, що дозволить досягти суспільних користей та економічного успіху одночасно. Така модель базується на принципах сталого розвитку та включає наступні аспекти: створення соціально-орієнтованих бізнес-стратегій; партнерство з громадськими організаціями; залучення інвестицій; використання інноваційних цифрових технологій; розвиток ринку соціального підприємництва. Таким чином, дослідження підкреслює важливість підтримки та сприяння соціальному підприємництву як ключового інструменту для досягнення сталого розвитку територіальних громад.
Методичний підхід до оцінки стратегії управління конкурентоспроможністю компаній зі страхування життя
Authors: Новоселецька А. О. 
Published: May 2024

В статті обґрунтована важливість діяльності страхових компаній для суспільства та їх вплив на економічну безпеку. Метою статті є формування методичного підходу до оцінки стратегії управління конкурентоспроможністю компаній зі страхування життя, який дасть змогу розробити заходи, що направлені на підвищення фінансової стабільності страховиків. Дослідження показало значне зменшення кількості страхових компаній в Україні, що може призвести до негативних наслідків, таких як обмеження доступності страхових послуг та підвищення ризиків для інвесторів. Запропоновано розробити нові методи та підходи, які дадуть змогу забезпечити стабільність страхових компаній. У статті також представлено методичний підхід до оцінки стратегії управління конкурентоспроможністю страхових компаній зі страхування життя. Такий підхід включає в себе п'ять основних етапів, що спрямовані на вдосконалення управлінських рішень та підвищення конкурентоспроможності компаній у даній сфері, а саме: аналіз поточного стану; розробка та реалізація стратегії; визначення пріоритетних напрямів підвищення конкурентоспроможності; впровадження заходів; контроль та оцінка результатів. В дослідженні також сформована політика соціально-економічної відповідальності, що означає прийняття страховою компанією зобов'язань щодо сприяння позитивним соціальним та економічним змінам у суспільстві, покращення якості життя людей. В рамках запропонованого підходу сформовано основні напрямки підвищення конкурентоспроможності страхового ринку України, до яких віднесено: розвиток інноваційних послуг; підвищення якості обслуговування клієнтів; оптимізація внутрішніх процесів; розвиток кадрового потенціалу; розширення ринкових можливостей; розширення цифрових можливостей; активне реагування на зміни в законодавстві; управління ризиками та фінансовою стабільністю. Сформовано комплексний підхід, який враховує різноманітні аспекти управління, включаючи стратегічне планування, аналіз ринкового середовища, оцінку конкурентного потенціалу та розробку ефективних маркетингових стратегій.