Data Organization Research Articles

BLOOM : CLOUD OPTIMIZATION TOOL

Bloom: Cloud Optimization tool" endeavors to harness the power of the Ant Colony Optimization (ACO) algorithm to address the challenge of organizing unstructured input data in cloud storage systems. The primary objective is to optimize cloud storage utilization and minimize associated billing expenses. Through the utilization of ACO, the solution aims to transform unorganized and unstructured data into a structured format, facilitating efficient storage allocation and enhancing data management practices. By leveraging predictive insights derived from the structured data, the solution empowers both cloud service providers and users to make informed decisions regarding data storage optimization. Ultimately, this approach not only streamlines data organization processes but also aids in maximizing cost-effectiveness by ensuring that cloud resources are allocated optimally based on usage patterns and predicted future demands. The integration of ACO into the solution offers a promising avenue for addressing the complexities inherent in managing vast amounts of unstructured data in cloud environments, thereby paving the way for improved efficiency and cost savings.

Possibilities in teaching planning to make mathematical knowledge accessible to students with autism spectrum disorder

This article presents part of the analysis conducted in a study to indicate and substantiate possibilities for planning mathematics teaching in the initial years of basic education for students with autism spectrum disorder. Engeström’s activity theory guided the investigation as a theoretical and methodological tool, directing data organization and the design of the analyses. This qualitative study had activities prepared by teachers as its data source. The research revealed that, for the teaching process to effectively contribute to mathematics learning by students with autism spectrum disorder, elements that allow the planning of teaching activities based on the construction of meanings of the mathematical objects studied and the development of skills that precede them must be considered.

Perception and needs: a qualitative study on sense of job security among nurses in central and western China

AimsTo explore nurses’ perceptions of sense of job security and their needs to improve it.DesignA descriptive qualitative study employed an in-depth, in-person interview from February to April in 2021. The...

ВІД АНАЛІЗУ ТЕКСТУ ДО МОДЕЛЮВАННЯ ПРИРОДНОЇ МОВИ: КОМПЛЕКСНЕ ДОСЛІДЖЕННЯ

The article presents a comprehensive analysis of modern techniques and technologies used to analyze the tonality of textual data. Through a series of experiments, basic machine and deep learning algorithms such as sentiment analysis were developed to automatically detect and classify emotional tones in texts. Practical applications of these methods are also explored, ranging from social network monitoring to expert analysis and analysis of user reviews. Next, the article delves into the classification of textual data using modern machine and deep learning methods. This section of the paper reviews several classification models, including the Bayesian most-narrow classifier, support vector methods, and neural networks, highlighting their advantages and limitations. In addition, it emphasizes the importance of implementing text classification in various fields, including social network analysis, news article categorization, and automated document processing. In addition, clustering of similar textual data is considered for further analysis. Various clustering algorithms, such as k-means, hierarchical clustering, and spectral clustering, are compared, with special emphasis on their application to large text corpora. A demonstration of the practical application of text clustering, including data organization, topic search, and style identification in paper works, was also demonstrated. Moving on, the article considers the processing of individual thematic structures in textual data and its further analysis. An in-depth analysis of topic modeling techniques, such as the realized Dirichlet distribution (LDA) model, as well as its capabilities and limitations, is explored. Practical applications of topic modeling are demonstrated in various ways, including text collection analysis, news trend detection, and automatic document categorization. Finally, the article discusses some challenges and future prospects for the development of thematic modeling.

Program of All-Inclusive Care for the Elderly: an untapped setting for research to advance pain care in older persons.

The Program of All-Inclusive Care for the Elderly (PACE) is a community-based care model in the United States that provides comprehensive health and social services to frail, nursing home-eligible adults aged 55 years and older. PACE organizations aim to support adequate pain control in their participants, yet few evidence-based pain interventions have been adopted or integrated into this setting. This article provides a roadmap for researchers who are interested in collaborating with PACE organizations to embed and evaluate evidence-based pain tools and interventions. We situate our discussion within the Consolidated Framework for Implementation Research (CFIR), a meta-theoretical framework that considers multi-level influences to implementation and evaluation of evidence-based programs. Within each CFIR domain, we identify key factors informed by our own work that merit consideration by research teams and PACE collaborators. Inner setting components pertain to the organizational culture of each PACE organization, the type and quality of electronic health record data, and availability of staff to assist with data abstraction. Outer setting components include external policies and regulations by the National PACE Association and audits conducted by the Centers for Medicare and Medicaid Services, which have implications for research participant recruitment and enrollment. Individual-level characteristics of PACE organization leaders include their receptivity toward new innovations and perceived ability to implement them. Forming and sustaining research-PACE partnerships to deliver evidence-based pain interventions pain will require attention to multi-level factors that may influence future uptake and provides a way to improve the health and well-being of patients served by these programs.

Security and Ownership in User-Defined Data Meshes

Data meshes are an approach to data architecture and organization that treats data as a product and focuses on decentralizing data ownership and access. It has recently emerged as a field that presents quite a few challenges related to data ownership, governance, security, monitoring, and observability. To address these challenges, this paper introduces an innovative algorithmic framework leveraging data blueprints to enable the dynamic creation of data meshes and data products in response to user requests, ensuring that stakeholders have access to specific portions of the data mesh as needed. Ownership and governance concerns are addressed through a unique mechanism involving Blockchain and Non-Fungible Tokens (NFTs). This facilitates the secure and transparent transfer of data ownership, with the ability to mint time-based NFTs. By combining these advancements with the fundamental tenets of data meshes, this research offers a comprehensive solution to the challenges surrounding data ownership and governance. It empowers stakeholders to navigate the complexities of data management within a decentralized architecture, ensuring a secure, efficient, and user-centric approach to data utilization. The proposed framework is demonstrated using real-world data from a poultry meat production factory.

Integration of an online application to optimise multi-disciplinary meetings: a retrospective analysis.

Multi-disciplinary Meetings (MDM) play a crucial role in complex patient care, involving input from various specialties to formulate evidence-based management plans. However, traditional unidirectional approaches and reliance on manual processes have led to inefficiencies in the MDM pathway. This study identified and aimed to improve four critical moments where Information Communication Technologies (ICTs) could enhance the MDM process. Initial referral, information synthesis, meeting presentation and the creation of actionable/auditable items. Microsoft Office Forms, a customisable survey platform, was implemented to streamline MDM processes. Forms were created to gather patient information, develop agendas, and track outcomes. Automation through Excel scripting further optimised data organisation and agenda creation. Referrals using Forms takes an average of 7min and 21s. Over 15months the submission time has reduced and is trending towards under 5min for each referral. The system's scalability has allowed 1744 cases to be discussed over a 15-month period. Active departments using Forms is expanding to seven from two prior to the pilot. Microsoft Office Forms proved to be a valuable and adaptable tool for MDMs, offering benefits such as streamlined information gathering, real-time collaboration, and scalability. The study highlights the potential of existing tools within Microsoft licenses for healthcare process optimisation, providing a cost-effective and customisable solution for MDM agendas. While recognising some limitations, the study concludes that leveraging Microsoft Office Forms can significantly improve system efficiency in a multi-disciplinary setting.

PyOKR: A Semi-Automated Method for Quantifying Optokinetic Reflex Tracking Ability.

The study of behavioral responses to visual stimuli is a key component of understanding visual system function. One notable response is the optokinetic reflex (OKR), a highly conserved innate behavior necessary for image stabilization on the retina. The OKR provides a robust readout of image tracking ability and has been extensively studied to understand visual system circuitry and function in animals from different genetic backgrounds. The OKR consists of two phases: a slow tracking phase as the eye follows a stimulus to the edge of the visual plane and a compensatory fast phase saccade that resets the position of the eye in the orbit. Previous methods of tracking gain quantification, although reliable, are labor intensive and can be subjective or arbitrarily derived. To obtain more rapid and reproducible quantification of eye tracking ability, we have developed a novelsemi-automated analysis program, PyOKR, that allows for quantification of two-dimensional eye tracking motion in response to any directional stimulus, in addition to being adaptable to any type of video-oculography equipment. This method provides automated filtering, selection of slow tracking phases, modeling of vertical and horizontal eye vectors, quantification of eye movement gains relative to stimulus speed, and organization of resultant data into a usable spreadsheet for statistical and graphical comparisons. This quantitative and streamlined analysis pipeline, readily accessible via PyPI import, provides a fast and direct measurement of OKR responses, thereby facilitating the study of visual behavioral responses.

Transformation of Financial Accounting to Management Accounting in the Era of Artificial Intelligence

With the rapid development of the Internet, the era of artificial intelligence has become a trend in today's society, ushering in an intelligent revolution in various industries. The advent of the era of artificial intelligence has laid a solid information foundation and environmental support for the development and progress of the information age, which will greatly impact the traditional working methods and approaches of financial accountants. Basic and repetitive tasks such as issuing invoices and expense reimbursement approvals can be replaced by intelligent machines, allowing financial professionals to focus more on management accounting. The application of artificial intelligence technology can not only improve the efficiency of financial accountants, but also promote the transition from financial accounting to management accounting. Therefore, how to utilize artificial intelligence to assist financial professionals in management accounting work is an inevitable requirement for the development of the intelligent era, which presents more demands for management accounting. Based on this background, the development process of enterprises needs to transform the financial management model, enabling the technical aspects of data collection and organization to move towards more advanced and efficient directions. This article analyzes and studies the strategies for using artificial intelligence to help financial accountants acquire management accounting capabilities, achieve the transformation of financial professionals into management accountants, and provide support for the development of enterprises in the era of artificial intelligence.

Retraction: "Evaluation of the neuroprotective effects of electromagnetic fields and coenzyme Q10 on hippocampal injury in mouse".

Retraction: "Evaluation of the neuroprotective effects of electromagnetic fields and coenzyme Q10 on hippocampal injury in mouse" by Mansoureh Soleimani, Fereshteh Golab, Akram Alizadeh, Sara Rigi, Zeinab Nazarian Samani, Gelareh Vahabzadeh, Tahmineh Peirovi, Maryam Sarbishegi, Majid Katebi, Fereshteh Azedi, J Cell Physiol 2019, 234: 18720-18730. The above article, published online on 1 April 2019 in Wiley Online Library (https://onlinelibrary.wiley.com/doi/full/10.1002/jcp.28512) has been retracted by agreement between the journal's Editor in Chief, Alexander Hutchison, and the Wiley Periodicals LLC. The retraction has been agreed following an investigation based on allegations raised by third parties. Image elements in Figures 3 and 5 were found to have been duplicated and/or published elsewhere in a different scientific context by the same author group. The authors have admitted significant errors in figure compilation due to the simultaneous execution of experiments for both this article and their previous publication, resulting in complications with organization and storage of data. Accordingly, the editors consider the conclusions of this article to be invalid. The authors have been informed of the decision to retract the article but disagree with it.

Association between gestational diabetes and cardiovascular disease within 24 months postpartum

BackgroundCardiovascular disease is the leading cause of death among women in the United States. It is well established that gestational diabetes mellitus (GDM) is associated with an overall lifetime increased risk of cardiometabolic disease, even among those without intercurrent type 2 diabetes. However, the association between GDM and short-term risk of cardiovascular disease (CVD) is unclear. Establishing short-term risks of CVD for patients with GDM has significant potential to inform early screening and targeted intervention strategies to reduce premature cardiovascular morbidity among women. ObjectiveWe aimed to estimate the risk of cardiovascular disease diagnoses in the first 24 months postpartum among patients with GDM compared with patients without GDM. Study DesignOur longitudinal population-based study included pregnant individuals with deliveries during 2007-2019 in the Maine Health Data Organization's All Payer Claims Data. We excluded records with gestational age <20 weeks and deliveries with non-Maine residence, multifetal gestation, those without insurance in the month of delivery or the 3 months before pregnancy, those with implausible time to next pregnancy (<60 days), pre-gestational diabetes mellitus, and any pre-pregnancy diagnosis of the cardiovascular conditions being examined postpartum. GDM and CVD (heart failure, ischemic heart disease, arrhythmia/cardiac arrest, cardiomyopathy, cerebrovascular disease/stroke, and new chronic hypertension) were identified by ICD 9/10 diagnosis codes. Cox proportional hazards models were used to estimate hazard ratios (HR), adjusting for potential confounding factors. We assessed whether the association between GDM and chronic hypertension was mediated by intercurrent diabetes mellitus. ResultsOf the 84,746 pregnancies examined, the cumulative risk of CVD within 24 months postpartum for those with GDM vs. without GDM was 0.13% vs. 0.20% for heart failure, 0.16% vs. 0.14% for ischemic heart disease, 0.60% vs. 0.44% for cerebrovascular disease/stroke, 0.22% vs. 0.16% for arrhythmia/cardiac arrest, 0.20% vs. 0.20% for cardiomyopathy, and 4.19% vs. 1.83% for new chronic hypertension. After adjusting for potential confounders, those with GDM have an increased risk of new chronic hypertension (adjusted hazard ratio (aHR) 1.56 (95% CI 1.32-1.86)) within the first 24 months postpartum as compared to those without GDM. There was no association between GDM and ischemic heart disease (aHR 0.75 (95% CI 0.34-1.65)), cerebrovascular disease/stroke (aHR 1.13 (95% CI 0.78-1.66)), arrhythmia/cardiac arrest (aHR 1.16 (95% CI 0.59-2.29)), or cardiomyopathy (aHR 0.75 (95% CI 0.40-1.41)) within the first 24 months postpartum. Those with GDM appeared to have a decreased risk of heart failure within 24 months postpartum, aHR 0.45 (95% CI (0.21-0.98)). Our mediation analyses estimated that 28% of the effect of GDM on new chronic hypertension was mediated through intercurrent diabetes mellitus. ConclusionPatients with GDM have a significantly increased risk of new chronic hypertension as early as 24 months postpartum. The majority of this effect was not due to the development of diabetes mellitus. Our findings suggest that all women with GDM need careful monitoring and screening for new chronic hypertension in the first 2 years postpartum.

AI-Driven Precision Clothing Classification: Revolutionizing Online Fashion Retailing with Hybrid Two-Objective Learning

In the ever-expanding online fashion market, businesses in the clothing sales sector are presented with substantial growth opportunities. To utilize this potential, it is crucial to implement effective methods for accurately identifying clothing items. This entails a deep understanding of customer preferences, niche markets, tailored sales strategies, and an improved user experience. Artificial intelligence (AI) systems that can recognize and categorize clothing items play a crucial role in achieving these objectives, empowering businesses to boost sales and gain valuable customer insights. However, the challenge lies in accurately classifying diverse attire items in a rapidly evolving fashion landscape. Variations in styles, colors, and patterns make it difficult to consistently categorize clothing. Additionally, the quality of images provided by users varies widely, and background clutter can further complicate the task of accurate classification. Existing systems may struggle to provide the level of accuracy needed to meet customer expectations. To address these challenges, a meticulous dataset preparation process is essential. This includes careful data organization, the application of background removal techniques such as the GrabCut Algorithm, and resizing images for uniformity. The proposed solution involves a hybrid approach, combining the strengths of the ResNet152 and EfficientNetB7 architectures. This fusion of techniques aims to create a classification system capable of reliably distinguishing between various clothing items. The key innovation in this study is the development of a Two-Objective Learning model that leverages the capabilities of both ResNet152 and EfficientNetB7 architectures. This fusion approach enhances the accuracy of clothing item classification. The meticulously prepared dataset serves as the foundation for this model, ensuring that it can handle diverse clothing items effectively. The proposed methodology promises a novel approach to image identification and feature extraction, leading to impressive classification accuracy of 94%, coupled with stability and robustness.

Design and Implementation of Educonnect: A Cloud Native Application for Educational Institutions

Abstract: In modern software environments, a paradigm known as the MERN stack has emerged as an important force due to its breadth of development and management capabilities. Leveraging native MERN (MongoDB, Express.js, React.js, Node.js) software development, deployment, and management techniques with Cloud services, "Educonnect" emerges as an innovative solution for educational institutions seeking efficient and cost-effective data management. This paper deals with the implementation of a cloud native web application Educonnect after a dexterous development strategy to create a cloud native web application. The main focus of this cloud native application is to provide a solution for data organization and collection along with an effective way of sending communication to the students by using cloud services

Empowering Posyandu Cadres Through Positive Parenting Psychoeducation to Safeguard Children From Stunting

Indonesia’s demographic bonus for the millennial generation is starting to increase. However, the quality of nutrition and health of Indonesian children is threatened by cases of stunting in several regions in Indonesia without specialized assistance from posyandu cadres as the frontline educators for parents. This community service research aimed to explore posyandu cadres' understanding of the value of positive parenting to prevent stunting through a Psychoeducation Program design. This activity program involved psychoeducational interventions using lectures, discussion, and role-play of positive parenting practices. The Positive Parenting Psychoeducation Activities were carried out involving 39 Posyandu Cadres in Ringu Rara Village, Lamboya Sub-District, West Sumba District, East Nusa Tenggara Province (N = 20) and Posyandu Cadres in Tirtoadi Village, Mlati Sub-District, Sleman District, Special Region of Yogyakarta Province (N = 19). A psychoeducation program evaluation using the open-ended questionnaire was administered before (pre-test) and after (post-test) lecture sessions, discussion sessions, and Positive Parenting training sessions. Subsequently, the trainers continued with Focused Group Discussion sessions for the follow-up stage of the training. Using a qualitative approach to thematic analysis techniques and cross-tabulations, the authors searched for themes that emerged from the questionnaire responses data and recorded focus group discussion transcripts. Besides, the data organization was also carried out with the help of the MAXQDA 2020 Application. The thematic analysis results generated three main themes around Positive Parenting Psychoeducation activities, namely Posyandu Cadres with their tasks and challenges, Positive Parenting to prevent stunting, and Positive Parenting Psychoeducation follow-up plans. The conclusion drawn from the results of this activity was that the family-household factor of parenting patterns can influence stunting. Posyandu cadres need positive parenting psychoeducation to be able to share information with, assist, monitor, and help parents of children with stunting indications.

ANALYSIS OF DATA ORGANIZATION SYSTEM ARCHITECTURES OF THE SMART EUROPEAN REGION "CENTER OF EUROPE" BASED ON THE DATA LAKE TECHNOLOGICAL SOLUTION

In the contemporary landscape of digital transformation, the concept of a "smart region" emerges as a key strategy for regional development, utilizing data integration technologies to enhance the quality of life, optimize service delivery, and promote sustainable growth. The designation of the "Centre of Europe" as a smart region is justified by its strategic geographical location, encompassing Transcarpathia and adjacent areas of neighboring countries, coupled with a relatively low population density. This unique context creates compelling premises for rethinking traditional urban frameworks of smart cities on a broader scale, transforming them into a comprehensive smart region model tailored to the specific needs and opportunities of Central Europe. The rationale for implementing the "Centre of Europe" as a smart region stems from its advantageous position at the heart of the continent, which generates unprecedented opportunities for cross-border collaboration, innovation, and connectivity. The low population density of the region further highlights the potential for the adoption of advanced technological solutions capable of addressing a wide array of challenges and needs. By achieving the status of a smart region, the "Centre of Europe" aspires to become a leading example of how digital and technological innovations can be utilized to create an inclusive, efficient, and progressive regional ecosystem. Objective: The primary objective of this research is to conduct a comparative analysis of advanced data organization system architectures and their role in facilitating the transformation of the "Centre of Europe" into an intelligent European region. It also aims to explore the capabilities of utilizing data lakes for the effective management of large data sets and regional development, taking into account the unique geographical and demographic characteristics of the region. The subject of the study encompasses the architectures of big data organization systems in the smart region of the "Centre of Europe". The object of the study involves the processes of storing and processing large data within the smart region. Main characteristics: This article illuminates the main aspects of implementing big data processing technologies in the smart region of the "Centre of Europe", necessitating scalable and flexible solutions for large data storage, the use of efficient data management practices, and the integration of advanced technologies such as cloud computing, the Internet of Things (IoT), and artificial intelligence (AI) to enhance data processing and analytical capabilities. Furthermore, recommendations for regional authorities on adopting these technologies have been formulated, highlighting the importance of collaboration, support for investments in the region's digital infrastructure as key components for achieving the smart region vision. The construction of modern, high-tech data storage facilities using the Data Lake architecture enables the simultaneous use and processing of large volumes of data from various fields and sources. The authors have proposed conceptual models for forming data complexes from different functional areas of the smart region.

Structured Data Ontology for AI in Industrial Asset Condition Monitoring

This paper proposes an ontology for prognostics and health management (PHM) applications involving sensor networks monitoring industrial machinery. Deep learning methods show promise for the development of autonomous PHM systems but require vast quantities of structured and representative data to realize their potential. PHM systems involve unique and specialized data characterized by time and context, and thus benefit from tailored data management systems. Furthermore, the use of dissimilar standards and practices with respect to database structure and data organization is a hinderance to interoperability. To address this, this paper presents a robust, structured data ontology and schema that is designed to accommodate a wide breadth of PHM applications. The inclusion of contextual and temporal data increases its value for developing and deploying enhanced ML-driven PHM systems. Challenges around balancing the competing priorities of structure and flexibility are discussed. The proposed schema provides the benefits of a relational schema with some provisions for noSQL-like flexibility in areas where PMH applications demand it. The selection of a database engine for implementation is also discussed, and the proposed ontology is demonstrated using a Postgres database. An instance of the database was loaded with large auto-generated fictitious data via multiple Python scripts. CRUD (create, read, update, delete) operations are demonstrated with several queries that answer common PHM questions.

Exploring dimensions of metadata quality assessment: A scoping review

Assessing metadata is of paramount importance for several critical reasons. Metadata plays a pivotal role in various aspects, including data retrieval and search, data organization, interoperability, data preservation, and the overall user experience. The purpose of this scoping review is to identify the most commonly measured dimensions of metadata quality in existing studies on metadata quality assessment. The study also investigates the types of data sources and countries contributing most to the literature on metadata quality assessment and the types of documents used to communicate their findings. The methodology involves the application of PRISMA model for qualitatively evaluating 55 studies on metadata quality assessment. The co-occurrence analysis is made on the title and abstract of selected articles using VOSviewer 1.6.18 version, visualization software. The review found that completeness, accuracy, consistency, accessibility, conformance, provenance, and timeliness are commonly used dimensions in metadata quality assessment. However, there is no consensus on their exact definition and measurement, indicating a need for further investigation into less commonly assessed quality dimensions. Digital repositories and open government data are the most commonly studied data sources, with the United States being the leading contributor and journal articles being the most commonly used document type. The cluster analysis based on co-occurrence of terms in title and abstract found three research areas, “Metadata Quality Assessment,” “Metadata Quality Dimensions,” and “Metadata Quality Applications, Frameworks, and Approaches” as prominent areas of research. The originality of the study lies in its methodology that involves rigorous screening of articles on metadata quality. It is a first attempt to qualitatively synthesize literature on metadata quality. The article emphasizes the importance of metadata quality research and the need to improve the flexibility of metadata quality assessment tools to facilitate better metadata quality assurance measures.

A new model construction based on the knowledge graph for mining elite polyphenotype genes in crops.

Identifying polyphenotype genes that simultaneously regulate important agronomic traits (e.g., plant height, yield, and disease resistance) is critical for developing novel high-quality crop varieties. Predicting the associations between genes and traits requires the organization and analysis of multi-dimensional scientific data. The existing methods for establishing the relationships between genomic data and phenotypic data can only elucidate the associations between genes and individual traits. However, there are relatively few methods for detecting elite polyphenotype genes. In this study, a knowledge graph for traits regulating-genes was constructed by collecting data from the PubMed database and eight other databases related to the staple food crops rice, maize, and wheat as well as the model plant Arabidopsis thaliana. On the basis of the knowledge graph, a model for predicting traits regulating-genes was constructed by combining the data attributes of the gene nodes and the topological relationship attributes of the gene nodes. Additionally, a scoring method for predicting the genes regulating specific traits was developed to screen for elite polyphenotype genes. A total of 125,591 nodes and 547,224 semantic relationships were included in the knowledge graph. The accuracy of the knowledge graph-based model for predicting traits regulating-genes was 0.89, the precision rate was 0.91, the recall rate was 0.96, and the F1 value was 0.94. Moreover, 4,447 polyphenotype genes for 31 trait combinations were identified, among which the rice polyphenotype gene IPA1 and the A. thaliana polyphenotype gene CUC2 were verified via a literature search. Furthermore, the wheat gene TraesCS5A02G275900 was revealed as a potential polyphenotype gene that will need to be further characterized. Meanwhile, the result of venn diagram analysis between the polyphenotype gene datasets (consists of genes that are predicted by our model) and the transcriptome gene datasets (consists of genes that were differential expression in response to disease, drought or salt) showed approximately 70% and 54% polyphenotype genes were identified in the transcriptome datasets of Arabidopsis and rice, respectively. The application of the model driven by knowledge graph for predicting traits regulating-genes represents a novel method for detecting elite polyphenotype genes.

PARTICIPAÇÃO E MOBILIZAÇÃO DOS FÓRUNS DE EDUCAÇÃO DE JOVENS E ADULTOS NO YOUTUBE DURANTE A PANDEMIA DA COVID-19: ANÁLISE METODOLÓGICA

In the pandemic context, it has become essential to maintain the actions of social movements, including those related to Education. In this sense, the Forums for Youth and Adult Education (EJA) have maintained and strengthened agendas related to EJA, denouncing, claiming, and creating networks of solidarity through live broadcasts on YouTube. Based on ongoing research on the mobilization of Youth and Adult Education Forums on YouTube and the narratives of individuals during the Covid-19 pandemic in the years 2020 and 2021, the article points out methodological aspects adopted based on Content Analysis. The article introduces the research topic on the performance of EJA Forums in live broadcasts and their interaction with individuals from different sectors concerned with the learners of EJA. The techniques used to access these materials are then presented, advancing in the stages of the methodology used. The article explores the process of data collection and organization, essential in the pre-analysis stage of Content Analysis. The account of the procedures for data collection demonstrates the paths taken to create a method in which efforts were made to reconcile web and programming tools, which were found to be essential for conducting the research, confirming the importance of learning about the transformations brought about by digital technologies.

Artificial Intelligence Methods in Automated Unmanned Aerial Vehicles Control Systems

One of the main problems in ensuring the unmanned aerial systems (UAS) safety and control performance indicators is the operational analysis organization of heterogeneous data coming from on-board sensors and the formation of adequate recommendations and decisions on their basis of flight missions implementation. In recent years, there have been many research papers devoted to solving this problem using artificial intelligence (AI) methods. The article discusses AI methods for using in tasks related to UAS. We have described the sources of information to generate the data necessary for the application of AI methods. We have classified typical tasks of computer vision and navigation systems for solving using AI methods. We have analyzed the generally accepted classification of AI methods within the scope of the research subject. At the same time, special attention is paid to the features of AI methods that allow solving many well-known problems of recognition, approximation, optimization for UAS target and navigation tasks realization and effective operator support. In particular, we have considered neural networks, decision trees, support vector machines, k-nearest neighbors, genetic, ant colony algorithms, artificial immune systems. Currently, the hardware allows integrating complex algorithms based on these methods on board and widely using them in flight missions. The results of the study conducted as part of the review are illustrated by examples from the scientific publications.