bluuebunny/crossref_metadata_2025 huggingface dataset:
Загроза гелієвого дефіциту і можлива участь України в її подоланні
Authors: Лукін Олександр Юхимович , Онищенко Володимир Олександрович
Published: August 2024

У статті проаналізовано гелієносність нафтогазових родовищ, розташованих на території України. Показано, що вміст гелію в зонах інтенсивного газонакопичення в середньому перевищує 0,05—0,1 %. На думку авторів, отримання в Україні гелієвого концентрату з газової суміші вуглеводневих родовищ є економічно доцільним, що особливо важливо в умовах загрози дефіциту гелію у світі.
Influence of unheated rooms on thermal comfort and energy efficiency of adjacent premises
Authors: Yurkevych Yurii , Savchenko Olena , Voznyak Orest , Datsko Oleksandra
Published: August 2024

The research was conducted for three types of houses, including a panel house built before 1991, a brick house built in 2006, and a brick house built in 2022. It was established that the temperature difference, which is decisive when assessing the sanitary and hygienic condition of the premises, does not exceed the normative values for buildings built after 2006. In the building since 1991, the sanitary and hygienic indicators of the heated room deteriorate when the outside air temperature drops below −3 °C. At the same time, the fulfillment of the first condition of comfort in the room occurs at indoor air temperatures higher than the standard value, therefore, under the calculated conditions, residents feel discomfort when staying in such a room.
Prosztatarákban a vérkép a diagnózis időpontjában nem prediktív a túlélésre
Authors: Tóth Zoltán , Fadgyas-Freyler Petra , Tordé Ákos , Horváth László , Vásárhelyi Barna , Gyarmati Béla
Published: August 2024

Bevezetés: Számos megfigyelés utal arra, hogy bizonyos daganatok
esetében a vérkép egyes elemei előre jelzik a beteg túlélését.
Célkitűzés: Prosztatarákos betegeknél a
neutrophil-lymphocyta arány (NLR), a thrombocyta-lymphocyta arány (PLR), a
fehérvérsejtszám (WBC), illetve a túlélési idő hossza közötti kapcsolat
értékelése. Módszer: Az Uzsoki Utcai Kórház Általános Urológiai
Osztályán 2000 és 2005 között diagnosztizált prosztatarákos betegeknél a
klinikai adatok (diagnóziskor az életkor, a prosztataspecifikus antigén [PSA]
szintje, a TNM-pontérték, a Gleason-score, társbetegségek, valamint a vérkép
elemei) és a túlélési idő közötti kapcsolat elemzése Cox-regresszióval.
Eredmények: Elemzésünkben 97 beteg adatait dolgoztuk fel
két évtizeddel az ellátási esemény után; közülük 82 hunyt el, 15-en még mindig
élnek. A prosztatarák diagnózisának időpontjában meghatározott vérkép egyetlen
eleme sem különbözött a két csoport (elhunytak és túlélők) között. A túlélési
idő hosszára a diagnózis időpontjában szignifikáns hatást csak a beteg életkora
(p = 0,004), a PSA-szint (p = 0,033) és a Gleason-score (p = 0,033) gyakorolt,
ezeken túl a vérkép egyetlen vizsgált paramétere sem befolyásolta azt. A
túlélési idő alapján képzett alcsoportokban (1, 2, 5 és 10 éven belül elhunytak)
sem tértek el a vérképparaméterek. Megbeszélés: Az eddig
korábban az irodalomban azonosított tényezők (PSA, Gleason-pontérték, életkor)
mellett a rutin vérkép elemei nincsenek kapcsolatban a túléléssel az általános
prosztatarákos populációban. Következtetés: Prosztatarákban a
vérkép elemei alapján nem jelezhető előre, hogy a prosztatarák diagnózisát
követő átlagosan 20 év után melyik az a beteg, aki túlél, illetve hogy az
elhunyt betegek esetében mennyi lesz a tényleges túlélési idő hossza. Orv Hetil.
2024; 165(34): 1319–1324.
True calm
Authors: Silaban Stevy
Published: August 2024

.
Improving Banknote Authentication Accuracy through
Logistic Regression and Data Preprocessing
Authors: Singh Abhishek , Hasan2 Zohaib , Sharma Saurabh
Published: August 2024

This study employs a logistic regression model to classify the authenticity of banknotes based on their
variance, skewness, kurtosis, and entropy features. Using a dataset from the UCI Machine Learning Repository, the
model demonstrates high accuracy in distinguishing between authentic and forged banknotes. The dataset is
preprocessed, split into training and test sets, and scaled for optimal performance. The trained logistic regression model
achieves an accuracy of 98.36%, illustrating its effectiveness. Additionally, the model's prediction capabilities are
validated with new banknote data. This research underscores the potential of machine learning in financial fraud
detection.
Analyzing Sentiment and Emotion in Omicron-Related Social Media Content: An NLP Perspective
Authors: Singh Abhishek , Hasan2 Zohaib , Paranjape Vishal
Published: August 2024

This paper presents an approach to sentiment analysis using various text processing techniques on a
dataset of textual data related to the Omicron variant. The study applies Natural Language Processing (NLP) methods,
including data cleaning, stemming, and stopword removal, to preprocess the text. Subsequently, it employs Word
Cloud visualizations to explore the frequency of words and hashtags in the dataset. Sentiment analysis is performed
using the VADER sentiment analysis tool to categorize the sentiments expressed in the text into positive, negative, and
neutral categories. The aggregated sentiment scores are analyzed to determine the overall sentiment trend within the
dataset. The results indicate the predominant sentiment as positive, with detailed insights into the distribution of
sentiments. The paper highlights the effectiveness of combining NLP techniques with sentiment analysis for
understanding public opinion and trends in textual data.
Optimizing Federated Learning Techniques for Advanced Decentralized AI Systems
Authors: Sharma Saurabh , Hasan2 Zohaib , Paranjape Vishal
Published: August 2024

Machine learning (ML) models have become indispensable in extracting insights and promoting
innovation across diverse fields due to the emergence of large data. Historically, the creation of ML models has relied
on gathering data in a centralized manner, which has raised notable concerns around privacy and security. This is
because sensitive data needs to be transferred to a central place. Decentralized machine learning resolves these
concerns by allowing the training of machine learning models on numerous devices without centralizing the data. This
study explores the intricacies of decentralized machine learning (ML) training, with a focus on methodologies and
algorithms that prioritize privacy preservation. Federated Learning (FL) is a fundamental method in decentralized
machine learning that enables training models using data from decentralized sources while guaranteeing data secrecy.
Local devices in Florida engage in data calculations and solely transmit model updates to a central server. The central
server then combines these updates to improve the global model. This paper investigates the difficulties associated with
decentralized training, including the burden of communication, the diversity of data and devices, and the potential for
adversarial attacks. Additionally, it evaluates existing solutions and suggests novel approaches to enhance the
effectiveness and safety of decentralized machine learning frameworks. The proposed method attains a precision of
97.6%, a mean absolute error (MAE) of 0.403, and a root mean square error (RMSE) of 0.203. The results emphasize
the capability of distributed machine learning in creating artificial intelligence systems that protect privacy, can be
expanded easily, and are dependable.
Machine Learning and Collaborative FilteringBased Book Recommendation System
Authors: Paranjape Vishal , Sharma Saurabh , Hasan Zohaib
Published: August 2024

Nowadays, the volume of information available on the internet has significantly increased, and people
need tools to find and access relevant information. One such tool is a recommendation system. These systems help
users navigate quickly and obtain the necessary information efficiently. Recommendation systems are an effective
software technique to address this issue. They can be used in various settings, including libraries. This paper proposes a
Book Recommendation System using Collaborative Filtering and Content-Based Algorithms to recommend books
to users based on their preferences and the ratings provided by other users. The proposed system will allow users to
view and search for books, publications, and genres by category using the Support Vector Machine. SVM will
list the top-rated books based on the input subject name and provide ratings. It will also ensure the maintenance of user
privacy.
Analyzing COVID-19 Case Fatality Ratios:
Death Rates and Testing Metrics across
Various Nations
Authors: Sharma Saurabh , Singh Abhishek , Paranjape Vishal , Hasan Zohaib
Published: August 2024

This study examines the COVID-19 case fatality ratios in different nations by utilizing data from
Worldometer. The approach involves assessing mortality rates by considering the ratio of tests performed per
confirmed case and the overall number of reported cases. The research investigates the relationship between the quality
of testing and the reported fatality rates by specifically examining nations with significant numbers of cases. The
findings emphasize notable disparities in mortality rates, especially among nations with varying testing capabilities.
The study also assesses the influence of thorough testing on the precision of reported death rates, offering insights into
the efficacy of public health measures in managing the epidemic.
A Comprehensive Study on Reinforcement Learning for High-Precision Robotic Systems
Authors: Patel Abhishek , Verma Saurabh , Pali Pankaj , Tiwari Harsh , Kanojiya Sagar
Published: August 2024

Reinforcement Learning (RL) has demonstrated significant potential in enhancing the capabilities of
robotic systems through adaptive learning and decision-making. This paper presents a novel approach for robotic
control and navigation utilizing RL algorithms. The proposed method achieved a remarkable accuracy of 96.9%,
indicating robust performance in various robotic tasks. Additionally, the method demonstrated a mean absolute error
(MAE) of 0.309 and a root mean square error (RMSE) of 0.107, reflecting precise and reliable predictions in the
control and navigation processes. The paper provides a comprehensive overview of the RL principles applied, discusses
the implementation details, and highlights the effectiveness of the proposed method through empirical results and case
studies. The findings underscore the transformative potential of RL in advancing robotic systems and outline directions
for future research in this evolving domain.
NLP and its Components: A Detailed Discussion
Authors: Hasan Zohaib , Vishwakarma Zeba , Pateriya Nidhi
Published: August 2024

Natural Language Processing (NLP) encompasses computational techniques for processing and
analyzing human language, primarily through Natural Language Understanding (NLU) and Natural Language
Generation (NLG). NLU focuses on interpreting language by analyzing phonology (sounds), morphology (word
structures), syntax (sentence structures), semantics (meaning), and pragmatics (context). These processes enable
machines to comprehend the nuances of human language for accurate interpretation and response generation.
NLG, in contrast, involves producing human-like text from structured data. It includes content determination
(identifying relevant information), text planning (organizing information), sentence planning (constructing
grammatically correct sentences), and surface realization (generating the final text). NLG is crucial for applications like
automated report generation and chat bots, where coherent and contextually appropriate responses are essential.
The synergy between NLU and NLG underpins many NLP applications. In machine translation, NLU interprets the
source text, while NLG generates the translated text. In question-answering systems, NLU processes the query, and
NLG formulates the response. Deep learning advancements have significantly enhanced both NLU and NLG, enabling
more sophisticated and human-like interactions.
This paper explores the components and processes of NLU and NLG, offering a comprehensive understanding of their
mechanisms and the advancements driving modern NLP systems.
Enhancing User Engagement through Hybrid Algorithms in Movie Recommendation Systems
Authors: Paranjape Vishal , Sharma Saurabh , Hasan Zohaib
Published: August 2024

The Netflix Movie Recommendation System is a sophisticated algorithmic solution designed to enhance
user experience by providing personalized movie suggestions. This system utilizes a combination of collaborative
filtering, content-based filtering, and hybrid approaches to predict and recommend movies that users are likely to enjoy
based on their viewing history and preferences. Collaborative filtering leverages the collective behavior of users,
identifying patterns and similarities in viewing habits to suggest films. Content-based filtering, on the other hand,
analyzes the attributes of movies, such as genre, director, and cast, to recommend titles with similar characteristics. The
hybrid approach integrates both methods to overcome individual limitations and improve recommendation accuracy.
Netflix employs advanced machine learning techniques, including deep learning and matrix factorization, to handle
vast amounts of data and continuously refine the recommendation process. The system also incorporates user feedback,
ratings, and implicit signals such as viewing time and interaction patterns to dynamically adapt to changing user
preferences. This multi-faceted recommendation engine is critical in retaining user engagement, reducing churn, and
enhancing the overall viewing experience on the platform. Through constant innovation and optimization, Netflix aims
to deliver highly relevant and enjoyable content to its diverse global audience.
Improving Real-Time Pollution Prediction Using
Dimensionality Reduction and KNN Algorithms
Authors: Sharma Saurabh , Paranjape Vishal , Singh Abhishek
Published: August 2024

This study introduces an innovative method that uses machine learning to forecast air quality. It use a
large dataset that includes a range of environmental parameters. The methodology incorporates standard scaling,
Principal Component Analysis (PCA) for reducing dimensionality, and the K-Nearest Neighbours (KNN) algorithm to
improve predicting accuracy. The model demonstrates an impressive overall accuracy of 95%, surpassing conventional
approaches. This method provides a strong and effective solution for predicting air quality in real-time, making a
substantial contribution to proactive environmental management and safeguarding public health. The findings highlight
the capacity of sophisticated machine learning methods to tackle the issues related to urban air pollution.
A Bio-Inspired Approach to Enhancing
Machine Learning: Integrating Spiking
Neural Networks
Authors: Verma Saurabh , Paranjape Vishal , Sharma Saurabh
Published: August 2024

In this study, we investigate the integration of bio-inspired neural networks, specifically spiking neural
networks (SNNs), into conventional machine learning frameworks. Known for their energy efficiency and ability to
process temporal information, SNNs present unique advantages over traditional artificial neural networks (ANNs). This
paper explores recent advancements in SNNs, focusing on methodologies such as spike-timing-dependent plasticity
(STDP), hybrid conversion techniques, and learnable membrane time constants [1][2]. We evaluate the application of
SNNs in visual categorization, digit recognition, and biomedical imaging [3][4]. A comprehensive literature review
identifies current strengths and lim
Leveraging Real-Time Health Analytics to
Upgrade Wearable Technology for Proactive
Healthcare Management
Authors: Sharma Saurabh , Hasan Zohaib , Paranjape Vishal , Verma Saurabh
Published: August 2024

By combining wearable technology with real-time health analytics, healthcare can be revolutionized
through the ability to continuously monitor and proactively control health factors. This study describes the creation of
sophisticated algorithms specifically built to handle and evaluate health data from wearable devices in real-time. The
main objective is to identify abnormalities and forecast probable health problems before they reach a critical stage. The
suggested method exhibits a notable level of precision, as seen by performance measures that indicate an accuracy of
94.8%, a Root Mean Squared Error (RMSE) of 0.208, and a Mean Absolute Error (MAE) of 0.406. The results
highlight the efficacy of the suggested algorithms in detecting subtle trends and anomalies in health measures, thereby
enabling prompt interventions and enhancing patient outcomes. The results emphasize the potential of wearable health
monitoring and real-time analytics in connecting the process of gathering data with practical and useful information,
ultimately promoting a more proactive and tailored approach to healthcare.
Advancing AutoML: Integrating Traditional Techniques with Neural Network Architectures for
Enhanced Predictive Performance
Authors: Sharma Saurabh , Hasan Zohaib , Paranjape Vishal
Published: August 2024

Machine learning (ML) has rapidly progressed and is now extensively used in diverse fields such as
healthcare, finance, and more. Although there have been significant advancements, the process of creating ML models
that are successful is still intricate and demanding in terms of resources. It necessitates a considerable level of
competence in data science and specialized knowledge in the relevant field. The emergence of Automated Machine
Learning (AutoML) has been a response to this obstacle, with the goal of making ML accessible to a wider audience by
automating the entire process of applying ML to practical problems. This study introduces an innovative AutoML
approach that relies on sophisticated machine learning methods. The proposed framework utilizes cutting-edge
algorithms to optimize different stages of the ML pipeline, guaranteeing the creation of high-performing models with
minimum human involvement. The approach combines conventional machine learning techniques with state-of-the-art
neural network structures to provide reliable and scalable solutions. The experimental findings validate the
effectiveness of the suggested AutoML solution, attaining a 97.6% accuracy, a mean absolute error (MAE) of 0.403,
and a root mean square error (RMSE) of 0.203. The results demonstrate that the suggested solution outperforms
existing methods, showing its potential to greatly lower the difficulty of using machine learning applications while also
providing a strong basis for future research in automated and interpretable machine learning.
A Data-Driven Framework for Environmental Monitoring: Integrating Machine Learning and Analytics
Authors: Sharma Saurabh , Paranjape Vishal
Published: August 2024

Effective environmental monitoring and conservation activities are very important, as evidenced by the
quickening rate of environmental degradation and the pressing need for sustainable management of natural resources.
Scale, accuracy, and real-time data gathering are three areas where traditional approaches frequently fall short.
Artificial Intelligence (AI) has become a disruptive force in this setting, providing novel solutions that greatly improve
the capacity to observe, evaluate, and deal with environmental concerns. Artificial intelligence (AI)-driven
technologies, such as machine learning, computer vision, and data analytics, have the potential to completely transform
environmental monitoring by making it possible to gather and analyse enormous amounts of data from a variety of
sources, such as sensor networks, satellite imagery, and citizen science projects. Examining the most recent
developments in AI applications for several environmental domains, including pollution management, biodiversity
conservation, and climate change mitigation, this study investigates the role of AI in advancing environmental
monitoring and conservation efforts. The study also addresses the difficulties and moral issues surrounding the
application of AI in these settings, highlighting the necessity of interdisciplinary cooperation and strong regulatory
frameworks to guarantee the ethical and efficient application of AI technology. The accuracy, mean absolute error
(MAE), and root mean square error (RMSE) of the suggested approach are 96.2%, 0.401, and 0.204, respectively. AI
can help create more resilient and sustainable ecosystems, which will ultimately benefit the world and its people, by
being included into environmental initiatives.
Experimental Hybrid Approach for Improving the
Quality of Personalized Product Recommendation
Systems with Deep Learning
Authors: Sharma Saurabh , Paranjape Vishal
Published: August 2024

Deep learning has recently made significant advancements in recommender systems. Various methods,
including hybrid recommendation systems, content-based recommendations, and deep learning, have been employed to
enhance recommendation accuracy. Over the past decade, the proliferation of big data has led to a rapid increase in the
amount of available data on the internet. However, dealing with complex and large data sets makes it challenging for
many users to quickly find the information they need. At this juncture, recommendation systems play a crucial role in
addressing the issue of data overload. The development of recommendation algorithms has been particularly driven by
the growth of the e-commerce industry. Traditional single recommendation algorithms face challenges such as data
sparsity, long-tail items, and cold starts. Hybrid recommendation algorithms can effectively mitigate some of these
limitations. In response to these challenges, this paper proposes an experimental hybrid approach to improve the quality
of personalized product recommendations using deep learning, specifically an Improved Attention-Convolutional
Neural Network (IA-CNN). This method aims to address the shortcomings of single collaborative models. The
algorithm first employs a comprehensive approach, combining product- and user-based collaborative filtering strategies
to generalize and categorize the output results. This methodology captures detailed and abstract nonlinear interactions
between users and products through advanced deep learning techniques. Finally, experiments were designed to test the
algorithm's effectiveness. The IA-CNN algorithm was evaluated against a benchmark algorithm using the Amazon
product rating dataset. The results demonstrate that the proposed IA-CNN algorithm outperforms the benchmark in
rating prediction on the test dataset
Evaluación de una aplicación construida mediante Classcraft para el aprendizaje de técnicas de integración
Authors: Puerta Marín Valentina , Ríos Parra Juan José , Pulgarín León Jonathan , Hincapié Torres Diana Carolina , Ruiz Vélez Leidy Mariana , Castañeda Ramírez Sergio
Published: August 2024

The objective of this article is to evaluate an application built using Classcraft for learning integration techniques and determine its effectiveness in terms of learning and participant motivation. The evaluation of the application contributes to understanding the potential of game-based apps as effective teaching tools. The methodology used in the evaluation consisted of administering a survey to the participants to gather information about their experience with the application. The survey consisted of qualitative questions, where the evaluated aspects included the perception of the design of the application, its formative nature, as well as open-ended questions that allowed students to express positive aspects and areas for improvement. The results of this work indicate that the application was well-received by the participants, most participants reporting a positive evaluation, reduced stress, clarity in the exercise development, acquisition of new knowledge, motivation, and skill development. In conclusion, the evaluation results support the participants' positive perception of the integration topic and demonstrate the potential of game-based apps as effective teaching tools. These findings suggest that gamification is a promising strategy to improve assessment, learning, and motivation in the context of teaching integration techniques.
Realidad aumentada para el fortalecimiento del pensamiento matemático geométrico espacial
Authors: Barragán Sánchez Cristian Camilo , Diaz León Julián Andrés , Renteria Vera Jorge Amado
Published: August 2024

The ninth-grade students of the El Sagrado Corazón de Jesús Hermanas Bethlemitas School (Bucaramanga-Colombia) present low academic performance in spatial geometric mathematical thinking. In this context, a didactic strategy supported by an augmented reality application was designed, from a mixed approach, descriptive scope, and type of quasi-experimental design in groups 9-1 and 9.2. To do this, a methodology was followed that included the diagnosis, designs, implementation, and evaluation phases. The results of the entry and exit tests were 60 and 85 points respectively; the student t hypothesis test with a significance level of 5% yielded values lees than 0.1%, rejecting the null hypothesis. In this sense, there is a statically significant difference between the entry and exit test. Consequently, it is concluded that the pedagogical intervention generated a significant effect in strengthening mathematical and geometric spatial thinking in ninth grade students.
Políticas institucionales de investigación en educación superior
Authors: Daza-Orozco Carlos Eduardo , Forero-Londoño Óscar Fernando , Merchán-Merchán Martha Andrea
Published: August 2024

In Ibero-America, studies on "institutional research policies" in "higher education" constitute a little explored or emerging field; the absence of publications on the subject evokes multi-causal factors such as the lack of maturation of administrative structures oriented to the development of science, technology and innovation -CTeI in the countries, low valuation of knowledge in society, insufficient budgetary and infrastructural capacities in the territories; the instability in the governance of Latin American nations and the volatility in the regulations oriented to research development; makes that for the most part, a divulgative scenario composed of succinct cases with annualized statistical data and without periodicity/transcendence in time is established. This article describes the findings found in the systematic review of the scientific literature on institutional research policies in higher education published from 2014 to 2023. The methodology adopted the guidelines of the PRISMA Statement method and the Cochrane Collaboration manual, to establish a map of motivations, thematizations, and theoretical and methodological contributions; from the deconstruction of records through the MAXQDAÒ qualitative analysis software. The results, discussions, and conclusions are framed in a humanistic perspective of "science and technology studies -ECT" that seek in / from institutional research policies an opportunity to constitute a "research culture" at the various levels of higher education oriented/evaluated from representational, regulatory, identity, productive and usability of knowledge elements.
Incidencia de los textos expositivos relacionados con temas de electromagnetismo en los componentes afectivos de los escolares
Authors: Padilla Berdugo Rodolfo Antonio , Iriarte Díaz-Granados Fernando Segundo , Amador López Jorge Alberto , Olivo Franco José Luís
Published: August 2024

In this research, the incidence of expository texts related to electromagnetism topics on the affective components of schoolchildren was analyzed. For this, three texts related to the battery, the alternator and the electric bell were selected and validated and 80 students from 7th and 8th grade of the secondary education level of a private educational institution of a mixed nature were chosen as part of the sample. located in the metropolitan area of ​​Barranquilla-Colombia, which were distributed by simple and systematic sampling in 4 experimental groups. After reading the texts, an expository text comprehension test was applied, the results of which allowed the formation of focus groups so that data could be collected through a questionnaire and a semi-structured interview. With the use of the NVivo software, it was known that stress, motivation/demotivation, curiosity, astonishment, satisfaction, boredom and interest/disinterest were present during the reading of the texts. It is necessary to consider these results; Well, in fact, expository texts due to their characteristics have an impact on student learning
Conflicts of interest in the hiring of non-audit services provided by audit firms: are the anti-conflict corporate policies adopted in Brazil adequate?
Authors: Rigueto Alessandra , Coutinho e Silva Adolfo Henrique
Published: August 2024

The present study analyzed the policies and procedures adopted to prevent conflicts of interest situations in Brazilian publicly traded companies that hired the same audit firm to perform both the financial statement audit and other types of non-audit services in the fiscal year 2020. Using content analysis technique, the study examined information available in the Management Reports for the year ended December 2020. which are mandatory disclosures to the market, and in the Policies for Hiring Non-Audit Services of their Independent Auditors valid in the year 2020. optionally submitted to the Securities and Exchange Commission. The observed results suggest a low level of detail in the information provided by companies, as most use standardized and concise texts in their respective Management Reports. Through best practices, it is noted that the Policies for Hiring Non-Audit Services of their Independent Auditors in the majority of analyzed companies identify the Audit Committee as responsible for service contracts. Some companies include a list of prohibited services and establish percentage limits for non-audit services to be provided. As a contribution, the study aimed to outline the policies adopted by companies in response to regulatory requirements and highlight some existing evidence in the disclosed information.
Relação entre os gastos públicos e eficiência educacional de municípios catarinenses
Authors: Graupner Thaisa Caroline , Hein Adriana Kroenke , Hein Nelson
Published: August 2024

A educação pública de alta qualidade desempenha um papel crucial na mitigação das disparidades socioeconômicas e na promoção do desenvolvimento regional. O presente estudo tem como objetivo analisar a relação entre os gastos públicos e a eficiência educacional de municípios catarinenses. Utilizando dados referentes aos anos de 2017 e 2019, este artigo se baseia no Índice de Desenvolvimento da Educação Básica (IDEB) como métrica principal, analisando uma amostra composta por 262 municípios catarinenses. Métodos estatísticos, incluindo Regressão Linear Múltipla e a técnica de análise multicritério TOPSIS, foram empregados para avaliar a eficiência dos gastos educacionais e classificar os municípios em termos de eficácia. Os resultados indicam que um PIB per capita mais elevado não se correlaciona necessariamente com um desempenho educacional superior, evidenciando a complexidade dos fatores que influenciam a eficiência educacional. Variáveis como o Gasto Médio e o Índice de Desenvolvimento Humano (IDH) emergiram como determinantes significativos do IDEB, destacando sua importância na melhoria dos indicadores educacionais municipais. A conclusão deste estudo oferece insights valiosos para pesquisadores e gestores públicos, fornecendo subsídios para a avaliação da política educacional e embasando decisões que visam otimizar a alocação de recursos públicos, com o intuito de promover uma gestão mais eficiente e equitativa do sistema educacional.
Quantitative multi-target amplicon sequencing workflow v1
Authors: Buczek Mateusz , Prus-Frankowska Monika , Łukasik Piotr
Published: August 2024

High-throughput sequencing of marker gene amplicons is a powerful tool for the discovery of biodiversity, at different levels. However, the method has multiple caveats. Here, we describe in detail a versatile laboratory protocol for high-throughput and cost-effective characterization of microbiota across large numbers of wild insects. By simultaneously targeting different marker regions, in a quantitative manner, the protocol enables the determination of insect host identity and reconstruction of bacterial diversity, alongside bacterial quantification combined with rigorous contamination control. It can be easily extended to encompass other marker regions and customized depending on specific needs, research questions, or specimen properties. The protocols encompasses DNA extraction from individual specimens (specimen homogenization, homogenate purification) and Illumina amplicon library preparation for two target regions: insect mitochondrial cytochrome oxidase I gene and the V4 region of bacterial 16S rRNA gene. It also explores the sequencing options and briefly outlines bioinformatics solutions that are published elsewhere. The protocol is labor- and cost-effective and relies on readily available reagents and equipment. It is highly customizable: we suggest alternative solutions, including omission of certain optional elements, or addition of alternative reagents and approaches. To date, we have successfully applied the protocol to nearly 20,000 individual insects, obtaining insights into microbial distribution, diversity, and transmission across multiple populations or large multi-species community subsamples of wild insects.
Bilgisayar Destekli Öğrenme Yöntemi Kullanılarak Yapılan Lisansüstü Tez Çalışmalarının İncelenmesi
Authors: Şahin Ramazan , Yazıcı Mustafa
Published: August 2024

Bu çalışmanın amacı Fen Bilimleri Eğitimi alanında bilgisayar destekli öğrenme yöntemi kullanılarak yapılan lisansüstü tez çalışmalarının çeşitli değişkenler açısından incelenmesidir. Bu amaç doğrultusunda Ulusal Tez Merkezi’nde yayınlanan lisansüstü tez çalışmaları taranmış olup ulaşılan çalışmalar betimsel içerik analizi yöntemiyle analiz edilmiştir. 2005-2020 yılları arasında yapılmış lisansüstü çalışmalar araştırmanın evrenini, sınıf uygulaması içeren 45 yüksek lisans ve yedi doktora olmak üzere erişime açık toplam 52 lisansüstü tez çalışması ise araştırmanın örneklemini oluşturmaktadır. Ulaşılan tez çalışmaları; yayımlandıkları yıllara, amaçlarına, çalışmalarda kullanılan bilgisayar destekli öğrenme tekniklerine, araştırma yöntem ve desenlerine, örneklem durumlarına, konu, süre, veri toplama araçlarına, verileri analiz tekniklerine, bilgisayar destekli öğrenme araçlarına ve sonuçlara göre analiz edilmiştir. Araştırmalardan elde edilen veriler yüzde ve frekans tablosu haline getirilerek çözümlenmiştir. Bulgular incelendiğinde bilgisayar destekli öğrenme yönteminin daha çok akademik başarıya etkisinin araştırıldığı, örneklem grubunun ortaokul seviyesinde yoğunlaştığı, veri toplama aracı olarak fen bilimleri akademik başarı testinin kullanıldığı ve çalışmalarda deneysel desenlerin daha sık tercih edildiği görülmektedir. Ayrıca veri analiz tekniği olarak bağımsız örneklem t testinin, konu alanı olarak fiziksel olaylar konu alanının ve bilgisayar destekli öğretim tekniği olarak ise animasyon tekniğinin daha fazla kullanıldığı görülmektedir. Gelecekte yapılacak araştırmaların fiziksel olaylar konularının yanında farklı konularda ve nicel yöntemlerin yanında nitel yöntemlere de daha fazla yer verilmesi gerektiği yönünde önerilerde bulunulmuştur.
Technology-Supported Self-Regulated Language Learning: A Systematic Review
Authors: Yalçın Ceyda
Published: August 2024

In this study, studies on technology-supported self-regulated language learning conducted between 2011 and 2022 are examined in terms of subject and focus points, applied techniques, and research findings. Thirty studies are addressed in this context. The findings of numerous original studies are synthesized and merged by using systematic review. The content analysis method was used to analyze the data. With this method, the data were coded by categorizing them into themes. The findings demonstrated that using technology in self-regulated language learning improves and promotes students' language learning in affective, linguistic, metacognitive, cognitive, and sociocultural areas. Thematically, studies focused mostly on affective and linguistic aspects. The research primarily used quantitative and mixed approaches as methodology. The findings revealed that, from an affective standpoint, the use of technology favorably influences learners' attitudes, motivation, and perceptions toward self-regulated language learning. Linguistically learners' language skills improved and language-learning outcomes progressed positively. In metacognitive terms, students' ability to plan their own learning increased. It is determined that learning cooperatively in the sociocultural sense improves self-regulated language learning. In terms of cognition, using cognitive strategies is found to support technology-supported self-regulated language learning. The study ends with some implications for doing more practical studies on the use of technology in self-regulated language learning.
Eğitimde Yenilikçi Bir Yardımcı: Sohbet Robotlarının Öğretimdeki Yeri ve Geleceğiyle İlgili Bir İnceleme
Authors: Altun Emrah , Seferoğlu Süleyman Sadi
Published: August 2024

Bu çalışmanın amacı, sohbet robotlarının eğitim-öğretim ortamlarındaki potansiyel katkılarını derinlemesine incelemektir. Çalışmanın bir diğer amacı da eğitimde sohbet robotlarının kullanımıyla ilgili Türkçe alanyazındaki bilgi birikimini zenginleştirmek ve ülkemizde bu alanda yapılacak çalışmalara örnek oluşturmaktır. Araştırmada nitel araştırma yöntemlerinde doküman incelemesi tekniği kullanılmıştır. Bu bağlamda, Web of Science (WoS) Core Collection veritabanında yer alan hakemli bilimsel yayınlar esas alınmıştır. Çalışma sonucunda, sohbet robotlarının çeşitli eğitim-öğretim uygulamalarına etkili bir şekilde entegre edilebileceği ve farklı pedagojik işlevleriyle eğitim süreçlerine katkıda bulunabileceği ortaya çıkmıştır. Öte yandan eğitimde sohbet robotlarının kullanılmasına yönelik çeşitli eleştiriler ve tartışmalar ile risklerin mevcut olduğu sonucuna ulaşılmıştır. Ulaşılan bu sonuçların, sohbet robotlarının ülkemizde eğitime entegre edilmesi konusunda paydaşlara yol gösterici olacağı umulmaktadır. Bunların yanında ulaşılan sonuçların, sohbet robotlarının hangi eğitim uygulamalarında ve nasıl kullanılabileceğiyle ilgili olarak ipuçları içerdiği düşünülmektedir. Çalışmada ulaşılan sonuçlar doğrultusunda, özellikle eleştiri, tartışma ve risklerin ülkemiz dinamikleri göz önüne alınarak nasıl ele alınabileceği gibi hususlarda çeşitli önerilerde bulunulmuştur.
Yapay Zekâ İle Üretilen Metinlerin Yabancı Dil Olarak Türkçe Öğretiminde Okuma Becerisine Yönelik Kullanılabilirliği: ChatGPT-3.5 Örneği
Authors: Katı Tuba Nur , Can Uğur
Published: August 2024

Günümüz dünyasında, yapay zekâ araçları, farklı amaçlarla yaygın bir şekilde kullanılmaktadır. Bunlardan bazıları, yabancı dil öğrenme sürecinde öğrencilere ve öğretmenlere yardımcı olmayı mümkün kılacak şekilde tasarlanmıştır. Bu araçlar, öğrencilerin farklı dil becerilerini geliştirmek için materyaller sunabilmektedir. Bu çalışmada, ChatGPT-3.5 kullanılarak yabancılara Türkçe öğretimi alanına ilişkin seviyelere yönelik oluşturulan metinlerin ders materyali olarak kullanılabilirliği tartışılarak ChatGPT-3.5’in seviyelere uygun metin oluşturabilme durumunu tespit etmek amaçlanmaktadır. ChatGPT, OpenAI tarafından geliştirilen, doğal dil işleme alanında önemli bir başarı olarak kabul edilen bir yapay zekâ modelidir ve insanlarla etkileşim kurarak onların doğal dil kullanımına uygun şekilde cevaplar vermek için tasarlanmıştır. Araştırmada, “ChatGPT-3.5’in yabancı dil olarak Türkçe öğretimi alanında seviyelere uygun metinler üretebilme durumu nedir?” sorusu temel alınmıştır. Çalışmada nitel araştırma yaklaşımlarından durum çalışması yöntemi kullanılmıştır. ChatGPT-3.5 tarafından üretilen sekiz metin 13 alan uzmanından görüş alınarak; dilbilgisi yapısı, sözvarlığı unsurları, seviyeye uygunluğu, konuya uygunluk ve seviyelere göre kullanım tercihi kategorilerinde incelenmiştir. Metinlerin metinsellik özelliği taşıyıp taşımadıkları, metinsellik ölçütleri bakımından tartışılmıştır. Elde edilen bulgular doğrultusunda seviyelere yönelik üretilmesi hedeflenen 8 metnin 4’ünün hedeflenen seviyeye uygun kabul edilebileceği; 4’ünün ise hedeflenen seviyeye uygun olmadığı ve ChatGPT-3.5 versiyonunun seviyelere yönelik Türkçe metin oluşturma başarı oranının %50 olduğu sonucuna ulaşılmıştır.
Türkiye’de Matematik Eğitiminde Dijital Oyunların Kullanımı: Bir Sistematik Derleme Çalışması
Authors: Aydın Cabir Serhat , Ata Rıdvan
Published: August 2024

Günümüzde büyük bir kitleye sahip olan dijital oyunlar araştırmacıların dikkatini çekmiş ve matematik eğitiminde karşılaşılan sorunların dijital oyunlar aracılığıyla çözülmesi fikrini ortaya çıkarmıştır. Bu fikirden doğan dijital oyun tabanlı öğrenme yöntemi etkili öğretimin yanı sıra ilgi ve motivasyonu arttırmayı hedeflemektedir. Bu derlemede Türkiye’de matematik dersinde dijital oyunların kullanılmasına ilişkin çalışmalar incelenmiştir. Çalışmanın amacı dijital oyun tabanlı öğrenme yöntemini inceleyen çalışmaların bulgularının sentezini sunmak ve ileride bu konuyla ilgili yapılacak olan çalışmalara rehber bir çalışma sunmaktır. Çalışmada sistematik derleme yöntemi kullanılmıştır. Tarama YÖK Tez ve Dergipark veri tabanlarında yapılmıştır. Tarama sonucunda 35 bilimsel çalışma bu kapsamda incelenmek için seçilmiştir. Seçilen çalışmalar genel yapıları, dijital oyunların yapıları ve öğretime yönelik özellikleri olmak üzere 3 başlık altında incelenmiştir. Verilerin analizinde betimsel istatistiklerden frekans ve yüzde kullanılmıştır. Çalışmaların genel yapıları incelendiğinde dijital oyun tabanlı öğrenme yöntemiyle ilgili araştırmaların son yıllarda arttığı, deneysel ve yarı deneysel desenlerin daha çok tercih edildiği ayrıca katılımcıların çoğunluk olarak ortaokul öğrencilerinden oluştuğu görülmektedir. Araştırmalarda kullanılan dijital oyunlar incelendiğinde, araştırmacıların çalışmalarında daha çok hazır oyun kullanmayı ve eğitsel dijital oyun kullanmayı tercih ettikleri görülmektedir. Mevcut dijital oyunların hem sayı hem de içerik bakımından yetersiz olduğu bir diğer bulgudur. Çalışmaların öğretime yönelik özellikleri incelendiğinde dijital oyun tabanlı öğrenme yönteminin matematik başarısı, matematiğe yönelik bilişsel becerileri ve matematiğe yönelik tutumları olumlu yönde etkilediği görülmüştür. İncelenen çalışmalarda dijital oyunlar daha çok derste pekiştirme yapmak amacıyla kullanılmıştır. Zaman sorunu, sınıf yönetimi zorluğu, dijital oyun yetersizliği, dijital oyun bağımlılığı, tasarımsal ve donanımsal eksiklikler araştırmacıların karşılaştıkları sorunlardır.
Impact of comorbid pathology on the cytokine cascade in the population of a large industrial city (using Chelyabinsk as an example)
Authors: Simbirtsev Andrey S. , Ryabova Liana V. , Savchuk Ksenia S.
Published: August 2024

BACKGROUND: The steady increase in the number of patients with comorbid pathology in large industrial cities necessitates the study of the immune response in this category of patients.
AIM: To investigate the cytokine profile of residents of a large industrial city (using Chelyabinsk as an example) depending on the presence of comorbid pathology (type 2 diabetes mellitus, obesity, hypertension).
MATERIALS AND METHODS: The cross-sectional study included 61 people, including 39 people with type 2 diabetes mellitus and 22 clinically healthy volunteers. The multiplex assessment method was used to evaluate the concentrations of 11 cytokines in the blood serum: GM-CSF, IFN-γ, IL-2, IL-4, IL-5, IL-8, IL-12p70, IL-13, IL-17A, MIP-1β, TNF-α (pg/ml).
RESULTS: Determination of the concentration of cytokines in the blood serum of patients with comorbid pathology in Chelyabinsk revealed a number of features, consisting in the predominance of proinflammatory cytokines and chemokines (IFN-γ, TNF-α, IL-8, MIP-1β, IL-12p70, IL-17A) and a shift in the Th1/Th2 balance towards the proinflammatory phenotype Th1.
CONCLUSIONS: The obtained results confirm the presence of inflammation in comorbid pathology (type 2 diabetes mellitus, obesity, hypertension) in the population of Chelyabinsk.
Topical issues of diagnosis and treatment of arthropathy associated with tonsillitis.
(Literature review)
Authors: Krykov Andrey I. , Klimenko Alesya A. , Tovmasyan Anna S. , Ramazanov Said R. , Golovatyk Anastasiya A. , Stepanova Maria M.
Published: August 2024

The problem of chronic tonsillitis (CT) and its complications remains one of the most urgent in modern otorhinolaryngology today. The high probability of developing associated diseases, such as rheumatic heart disease, nonspecific arthritis and glomerulonephritis, emphasizes the high importance of this nosological unit both in the practice of an otorhinolaryngologist and doctors of other specialties. Reactive arthritis occupies a special place among the complications of CT. Acute rheumatic fever (ARF) used to be the most common disease that was associated with streptococcal tonsillitis and was accompanied by reactive arthritis. Since 1959, patients with ARL have become less common, but cases of "aseptic" arthritis in patients with acute tonsillitis continued to be recorded, however, they did not have any other major criteria to confirm the diagnosis of ARL. This type of arthritis is called "Post-streptococcal reactive arthritis" (PSRA). Despite the fact that this disease has been known since the middle of the XIX century, there is very little information about its etiology and pathogenesis in the literature. The main symptoms of PSRA are usually similar to the articular syndrome in classical reactive arthritis and are clinically manifested by acute asymmetric non-migrating polyarthritis. Laboratory diagnosis of PSRA, in particular, the determination of titers of streptococcal antibodies plays an important role in confirming the diagnosis. A special place is occupied by the differential diagnosis of PSRA with rheumatic diseases (RH). The main diagnostic laboratory markers of RH are antinuclear antibodies, rheumatoid factor and antibodies to citrullinated proteins. Joint syndrome in PSRA is often stopped when nonsteroidal anti-inflammatory drugs (NSAIDs) are prescribed only in combination with antibacterial drugs. If arthritis does not respond to long-term treatment with antibiotics and NSAIDs, and especially if the patient has a history of recurrent CT, a planned tonsillectomy is required to prevent the development of a chronic form of tonsillitis-associated arthritis. The article presents an analytical review of domestic and foreign publications on the topic of CT complicated by reactive arthritis. The possible diagnostic methods necessary to confirm the diagnosis and exclude a wide range of arthritis of a different etiology are described.
Drug interaction and safety of rivaroxaban use based on the global database Vigibase
Authors: Kondrakhin Andrey
Published: August 2024

A summary of the reports is presented, which are included in the database of the World Health Organization's international drug monitoring program as of March 23, 2022 on the use of rivaroxaban, recommended for the use of the prevention of thrombotic complications and the most often combined drug found in clinical practice. Cases of drug interaction were analyzed and safety analysis of combination therapy was carried out. This material is presented for doctors, researchers, health regulators.