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2006 research outputs found
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Endemic coexistence and competition of virus variants under partial cross-immunity
Get PDFIn this study, we developed a mathematical framework, based on the SIR model, to study the dynamics of two competing virus variants with different characteristics of transmissibility, immune escape, and cross-immunity. The model includes variant-specific transmission and recovery rates and enables flexible parameterization of partial and waning cross-immunity. We conducted stability and bifurcation analyses and numerical simulations to explore the conditions of coexistence, dominance, and extinction of the variants, studying variations in epidemiological parameters that affect endemic prevalence and infection ratios. Our results indicated that transmission rates, levels of crossimmunity, and immunity waning rates are critical in determining disease outcomes, which influence variant prevalence and competitive dynamics. The sensitivity analysis provided the relative importance of these parameters and provided valuable insight into designing intervention strategies. This work contributes to furthering our understanding of multi-variant epidemic dynamics and lays the bedrock for tackling complex interactions involving arising virus variants, finding applications in real-world public health planning
Метаверсте тауар белгілерін пайдалану мен қорғаудың жекелеген мәселелері
Get PDFМақалада метаверс кеңістігінде тауар белгісін қолдану және қорғау мәселесі қарастырылады. Метаверс–бизнестің, шығармашылықтың және пайдаланушылардың арасында өзара қарым-қатынас орнатудың бірегей мүмкіндіктерін ашатын жаңа виртуалды кеңістік болып табылады. Мақалада метаверс түсінігімен NFT түсінігі қарастырылған, сонымен қатар берілген түсініктер мысалдар арқылы тереңірек түсіндірілген. Мақалада виртуалды кеңістік аясында зияткерлік меншіктің объектілері, оның ішінде тауар белгілерін пайдаланудың ерекшеліктері талданады. Авторлар әртүрлі виртуалды ойын платформаларының (Roblox, Fortnite, SecondLife, т.б.) мысалдары арқылы тауар белгілерінің қолданудан туындайтын тәуекелдер мен бұзушылықтарды қарастырады. Практикалық кейстер мен мысалдар, оның ішінде шетелдік тәжірибе зерделеніп, тауар белгісіне құқықтың бұзылу фактілеріне құқықтық тұрғыдан баға беріледі. Авторлар шынайы әлемдегі және виртуалды әлемдегі тауар белгісі қорғаудың маңызды аспектілеріне тоқталып, NFT арқылы тауарлар мен қызметтерді сату мәселесінің өзекті тұстарын анықтауға тырысады. Виртуалды кеңістікте қолданылатын тауар белгілерін тіркеудің кейбір мәселелері қарастырылады. Тауар белгілерін Тауарлар мен қызметтердің халықаралық жіктемесінің жекелеген сыныптарында тіркеу тиімділігі жөнінде қорытындылар жасалады. Жұмыста зияткерлік меншікке құқықты қорғау тәжірибесін жетілдіру жолдары ұсынылады
Forecasting Student Academic Performance Using Machine Learning
No full textEducational data mining depends on accurate student academic outcome forecasting to detect studentswho need help early and receive specific support. Traditional linear models have been used extensively yetthey fail to detect the intricate non-linear patterns which exist in student achievement data. The evaluationof machine learning algorithms and their features for student outcome prediction in Portuguese secondaryeducation remains insufficient because of missing systematic assessments. The research investigates howLinear Regression and Random Forest and K-Nearest Neighbors perform when predicting Portugueselanguage grades from 649 student records containing 30 demographic and social and academic attributes.The evaluation of model performance used three established metrics which included Mean Squared Error(MSE) and R-Squared (R²) and Mean Absolute Error (MAE). The results showed Linear Regressionproduced the most accurate predictions through its lowest MSE (9.00) and MAE (2.30) values but its weakR² value (0.01) indicated poor explanatory power. The error rates of Random Forest matched those of LinearRegression (MSE = 9.48 and MAE = 2.34) yet its negative R² (-0.04) indicated poor generalization becauseof irrelevant features and suboptimal hyperparameters. The KNN model showed the worst results (MSE =11.10 and MAE = 2.57 and R² = -0.21) because it failed to detect important patterns without additionaloptimization. The results show that educational prediction tasks require both optimal feature selectionand parameter adjustment for successful results. The research shows that linear models perform betterthan complex methods in specific situations yet optimized non-linear models demonstrate superior abilityto understand student achievement complexity. The research provides essential guidelines for developingbetter feature engineering and machine learning approaches to predict educational result
Plagiarism types and detection methods: a systematic survey of algorithms in text analysis
No full textPlagiarism in academic and creative writing continues to be a significant challenge, driven by the exponential growth of digital content. This paper presents a systematic survey of various types of plagiarism and the detection algorithms employed in text analysis. We categorize plagiarism into distinct types, including verbatim, paraphrasing, translation, and idea-based plagiarism, discussing the nuances that make detection complex. This survey critically evaluates existing literature, contrasting traditional methods like string-matching with advanced machine learning, natural language processing, and deep learning approaches. We highlight notable works focusing on cross-language plagiarism detection, source code plagiarism, and intrinsic detection techniques, identifying their contributions and limitations. Additionally, this paper explores emerging challenges such as detecting cross-language plagiarism and AI-generated content. By synthesizing the current landscape and emphasizing recent advancements, we aim to guide future research directions and enhance the robustness of plagiarism detection systems across various domains
«The Chinese Digital Payment Market: A study on Alipay's Strategic Position and User Acceptance»
Get PDFThe digital payment landscape in China has experienced a remarkable transformation, driven largely by platforms like Alipay. Since its inception in 2004 by Alibaba Group, Alipay has grown into one of the most dominant players in the Chinese market, influencing not only payment systems but also the global expansion of financial services. This report examines Alipay’s strategic positioning, user adoption, and competitive advantage in the Chinese digital payments ecosystem. Alipay's success can be attributed to several factors, including the network effect, technological innovation, and strategic partnerships. The platform has continuously evolved, offering a variety of services beyond payments, such as wealth management and loans, which have further entrenched its position in users' daily lives. Alipay’s dominance is further solidified by its ability to retain high levels of user trust, convenience, and security, which are critical in the financial technology sector. The research focuses on Alipay’s strategic development, user engagement strategies, and comparison with competitors like WeChat Pay. Additionally, a survey of Kazakh students studying in China provides insight into Alipay’s cross-border use through Kaspi.kz, highlighting its adoption in international markets. This report concludes that Alipay’s dominance is not only a result of its innovation but also its ability to build a strong and loyal user base. Its network effects, coupled with user-friendly features and strong security measures, have positioned it as a leader in the global digital payments industr
Повышение удовлетворенности медицинского персонала (на примере Восточно-Казахстанской областной больницы)
No full textМагистерская диссертация посвящена анализу факторов, влияющих на удовлетворенность медицинского персонала, и разработке практических рекомендаций по её повышению. В качестве объекта исследования выбрана Восточно-Казахстанская областная больница. Работа включает теоретический обзор подходов к управлению персоналом, исследование системы мотивации и поощрения в условиях казахстанского здравоохранения, а также анализ влияния удовлетворенности сотрудников на показатели качества медицинских услуг. На основе количественного и качественного анализа предложены меры по улучшению системы стимулирования, направленные на снижение текучести кадров, уменьшение числа медицинских ошибок и повышение эффективности работы. Практическая значимость диссертации заключается в возможности внедрения предложенных рекомендаций в деятельность медицинских организаций
Retention of Administrative Staff in Higher Education Institutions in Kazakhstan
Get PDFAdministrative staff retention in higher education institutions (HEIs) is an important issue due to its impact on an institution's effectiveness. At first glance, it may seem that factors such as low wages and limited career opportunities may seem like the main difficulties. Still, the main reasons influencing employees' decisions to stay or quit remain poorly studied. The purpose of this study was to explore how working conditions affect the turnover of administrative staff and identify the primary problems they face in Kazakhstani universities. Topics such as job satisfaction, recognition, leadership style, career perspectives, salary, and human resource policy were investigated to determine their influence on employee retention. The study used a qualitative methodology, during which semi-structured interviews were conducted with ten administrative staff members from a private and an autonomous university. Thematic analysis was used to identify the patterns in data. The results of the study showed that although the working conditions were generally acceptable, the lack or slow career growth, tension in the team, salary and hierarchical decision-making structures contributed to dissatisfaction. Employees of autonomous institutions noted higher social benefits, while employees of private universities noted the flexibility of working conditions. Despite some differences between the two types of universities, the wish for career advancement was common to all participants. The study suggests recommendations tailored to specific conditions for improving staff retention, including more structured career paths at the beginning of employment
Development and optimization ofphysics-informed neural networks for solving partial differential equations
No full textThis study investigates the application of physics-informed neural networks (PINNs) for solving Poissonequations in both 1D and 2D domains and compares them with finite difference method. Additionally, thestudy explores the capability of multi-task learning with PINNs, where the network not only predicts thesolution but also estimates unknown parameters. In the case of a second-order differential equation witha varying coefficient, PINNs successfully approximated both the source term and the varying coefficientwhile achieving low training loss. The model demonstrated excellent generalization capabilities and accuratereconstruction of the underlying system parameters, showing the potential of PINNs in complex physicalsimulations