bluuebunny/crossref_metadata_2025_split huggingface dataset:
Navigation methods using airlandscape data for small UAVs
Authors: Miroshnichenko A. P. , Mukhin I. E. 
Published: November 2024

The purpose of the research. UAVs are widely used in such fields as military, intelligence, and research. As a result, the issue of their use in electronic warfare conditions and the presence of various interference, both man-made and natural, is acute. Thus, the need for UAV navigation using visual data increases.The purpose is study of the main negative factors affecting the quality of aerial photography images and methods for eliminating them to construct a correct orthomosaic for UAV visual navigation.Methods. Mathematical methods are proposed to eliminate negative distortions in UAV camera images using various image transformation approaches. After applying these methods to the original images, corrected versions are obtained, which are used to construct the tile covering. Tiled coverage created from processed images provides continuous and uniform coverage of the area collected during the UAV’s flight. This allows you to obtain exact coordinates of images and objects on them.Results. An analysis of the main methods for eliminating negative factors that distort images during aerial photography for the visual navigation of UAVs, as well as a brief overview of the visual navigation methods themselves.Conclusion. To successfully implement visual navigation of a UAV, it is necessary to apply a number of methods for converting aerial photography images, as well as the use of certain algorithms for visual navigation. It is concluded that in addition to the use of mathematical and software algorithms, it will also be necessary to analyze and study the necessary computing power for the use of the entire hardware and software complex on board the UAV, taking into account its weight-dimensional properties.
Assessment of the reliability of sociological research on the Internet
Authors: Nesterovich S. A. , Brezhneva A. N. 
Published: November 2024

The purpose of research. The main goal of assessing reliability is to quantitatively substantiate the measure of confidence in a particular process. The article discusses the methodological and practical aspects of assessing the reliability of sociological research conducted on the Internet. Emphasis is placed on analyzing the specific problems encountered when using online surveys and other digital data collection methods. Issues of sample representativeness and a number of other quantitative indicators that affect the reliability of the assessment of sociological research are discussed.Methods. The article is devoted to methodological and practical aspects of assessing the reliability of sociological research conducted on the Internet. Issues of sample representativeness, weighting coefficients, data analysis, testing for systematic errors, and calculation of Cronbach’s alpha coefficient are discussed.Results. The main steps for calculating the reliability of sociological research conducted on the Internet are presented. Key steps include proper sample selection and adjustment, use of weights, performing statistical analysis, and estimating confidence intervals. These methods help minimize errors and increase the reliability of the data obtained.Conclusion. The proposed methods for assessing the reliability of the Internet will help improve the quality of data and the reliability of the results, namely, Online surveys can be subject to various types of biases, including self-selection of participants, when only certain groups of people decide to participate in the survey. Credibility assessment will promote fair and transparent research, which strengthens the trust of respondents and the public in research projects.
Algorithm for intelligent procesing of text information
Authors: Efanov S. V. , Ivanova E. N. , Chernetskaya I. E. 
Published: November 2024

The purpose of research. The goal of the research is to develop an intellectual processing algorithm for classifying text information. As the amount of information grows every day, it is necessary to quickly and efficiently separate significant from unimportant content. Therefore, the development of an intellectual processing algorithm for classifying text information is an urgent task.Methods. A method is proposed for classifying text information presented in one or more natural languages. It is based on 5 key stages: entering a task, accumulating a queue of tasks, processing the task, generating the result of processing the task, outputting the result. The input task is presented in the form of an http request, the body of which contains a file object. If the intensity of the input stream is greater than the processing speed, then an accumulation of tasks occurs. After selecting the active task (using the FIFO principle), it is processed. As a result of the transformations, the received data is decoded into a string using UTF-8 encoding. Processing refers to the process of categorization, when a search for patterns occurs in a line. Upon completion of rubrication, the result for the selected task is generated. From the accumulated result, a response to the original http request is formed, the body of which contains a list of found categories.Results. A method and algorithm for processing text data has been developed to determine the topics that are present in the input data set. The algorithm, implemented in software, allows you to work with text data in various languages.Conclusion. The software development of the text data classification algorithm was carried out in the C++ programming language using the Qt libraries version 5.11. This implementation showed a throughput of 1-5 MB per second (on a homogeneous input text data set). The algorithm allows you to correctly process damaged file formats.
Concept model and realization of object orientied system with metaprogramming
Authors: Chaplygin A. A. 
Published: November 2024

The purpose of the research is to develop a conceptual model of an object oriented system and implement it using a functional language with metaprogramming support. Methods. A conceptual model of an object oriented system was developed based on a dynamic object system with message passing and dynamic dispatch. The developed object oriented system includes functions for creating and deleting classes, creating and deleting object instances of a class, functions for working with object properties, functions for adding methods to a class and passing messages. Inheritance is also possible, when a child class will have the properties of the parent class and its own properties. Results. The object oriented system was implemented using a set of macros and helper functions. The basic data structure was a hash table with key access capabilities, which was also implemented using macros. When a class is created, an instance constructor is automatically generated, setting the initial values of the object’s fields. Inheritance is implemented as the inclusion of fields of a parent class in a child object. Polymorphism is implemented as dynamic dispatch: when processing a message, a handler method is searched. The resulting object oriented system, together with the hash table module, occupies 97 lines. Conclusion. As a result of the work, a conceptual model of an object oriented system was created and implemented, which includes the main functions of the object oriented programming paradigm. It has been shown that macros can be used to create a compact object oriented system without the need to add any new functions or capabilities to the functional language interpreter.
Information and computing system based on smart contracts for online voting
Authors: Gerasyov A. A. , Efremova I. N. , Arhipova I. S. , Kamalov R. N. 
Published: November 2024

The purpose of research.The actual problem in voting systems is not the prevention of fraud and the failure to ensure the integrity of elections. Therefore, the task of building a system of safe and transparent voting is urgent.The purpose of the research is design and develop an information and computing system based on smart contacts for online voting.Methods. The presented system has the form of a decentralized system that can overcome a number of limitations, such as the likelihood of human error, falsification of voting results and problems with accessibility, using blockchain technology, which provides protection against unauthorized access and transparent record keeping.The use of smart contract technology also ensures that the voting process will be automated, transparent and secure. Thanks to the ability to execute self-executing contracts based on predefined rules, smart contracts can eliminate the need for intermediaries, reduce the likelihood of errors and ensure a high degree of accountability.Results. In order to evaluate the effectiveness of the developed system, testing was carried out, after which a comparative analysis was made with other similar systems.The system can ensure that votes are counted accurately and cannot be manipulated, thereby increasing confidence in the results of the voting process. In addition, the system can potentially reduce the costs associated with traditional voting methods and improve accessibility for voters, since it can be accessed remotely via a web interface.In general, the system will be of interest to various organizations and individuals seeking to conduct safe and transparent voting processes.Conclusion. The use of this software product will provide security and convenience when participating in various online voting, reliable storage of votes and the provision of detailed statistics on the conducted voting.
Autonomous artificial intelligence for sorting the results of preventive radiological studies on the example of mammography
Authors: Vasilev Yu. A. , Arzamasov K. M. , Vladzymyrskyy A. V. , Kolsanov A. V. , Shulkin I. M. , Bobrovskaya T. M. , Pestrenin L. D. 
Published: November 2024

The purpose of research. Radiation diagnostics is central to the detection of malignant neoplasms. Recently, the implementation of screening programs has faced a number of obstacles, including staff shortages and limited funding. The introduction of artificial intelligence (AI)-based systems capable of absolutely accurate sorting of research into two categories - "normal" and "not normal", seems to be a promising solution to these problems. However, before they are widely used, it is critically important to verify their ability to guarantee the safety and high quality of the screening process. The aim of the study is to evaluate the possibility of using autonomous sorting of mammographic examination results in real clinical conditions. Methods. The study was carried out in 2 stages. At the first stage, 25,892 mammographic studies processed by the AI service were retrospectively analyzed. A ROC analysis of these results was carried out in order to assess the possibility of configuring the AI service for 100% sensitivity. At the prospective stage, the results of 82,372 mammograms were analyzed. All studies were processed by AI services configured for 100% sensitivity. The tasks of the AI services included the sorting of mammography results into the categories "normal" and "not normal". Next, the decisions of AI services and radiologists on categorization were compared. Results. According to the results of a retrospective study, when configuring the AI service for 100% sensitivity, the specificity was 39%. In the course of a prospective study, the proportion of defects (false attribution of research results to the "norm" category) was 0.08%, the specific weight of clinically significant defects in AI services was 0.02%, which is significantly lower than that of a radiologist. Conclusion. The use of autonomous sorting of mammographic research results in clinical practice is possible in order to optimize the diagnostic process during preventive measures, as well as under the condition of monitoring the quality of artificial intelligence technologies. Keywords: artificial intelligence, mammography, preventive examinations, radiation diagnostics. Conflict of interest: The author declares the absence of obvious and potential conflicts of interest related to the publication of this article.
Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods
Authors: Gray Magnus , Milanova Mariofanna , Wu Leihong 
Published: November 2024

Abstract

Background
Artificial intelligence (AI) is rapidly being adopted to build products and aid in the decision-making process across industries. However, AI systems have been shown to exhibit and even amplify biases, causing a growing concern among people worldwide. Thus, investigating methods of measuring and mitigating bias within these AI-powered tools is necessary.

Objective
In natural language processing applications, the word embedding association test (WEAT) is a popular method of measuring bias in input embeddings, a common area of measure bias in AI. However, certain limitations of the WEAT have been identified (ie, their nonrobust measure of bias and their reliance on predefined and limited groups of words or sentences), which may lead to inadequate measurements and evaluations of bias. Thus, this study takes a new approach at modifying this popular measure of bias, with a focus on making it more robust and applicable in other domains.

Methods
In this study, we introduce the SD-WEAT, which is a modified version of the WEAT that uses the SD of multiple permutations of the WEATs to calculate bias in input embeddings. With the SD-WEAT, we evaluated the biases and stability of several language embedding models, including Global Vectors for Word Representation (GloVe), Word2Vec, and bidirectional encoder representations from transformers (BERT).

Results
This method produces results comparable to those of the WEAT, with strong correlations between the methods’ bias scores or effect sizes (r=0.786) and P values (r=0.776), while addressing some of its largest limitations. More specifically, the SD-WEAT is more accessible, as it removes the need to predefine attribute groups, and because the SD-WEAT measures bias over multiple runs rather than one, it reduces the impact of outliers and sample size. Furthermore, the SD-WEAT was found to be more consistent and reliable than its predecessor.

Conclusions
Thus, the SD-WEAT shows promise for robustly measuring bias in the input embeddings fed to AI language models.
EDAI Framework for Integrating Equity, Diversity, and Inclusion Throughout the Lifecycle of AI to Improve Health and Oral Health Care: Qualitative Study
Authors: Abbasgholizadeh Rahimi Samira , Shrivastava Richa , Brown-Johnson Anita , Caidor Pascale , Davies Claire , Idrissi Janati Amal , Kengne Talla Pascaline , Madathil Sreenath , Willie Bettina M , Emami Elham 
Published: November 2024

Background
Recent studies have identified significant gaps in equity, diversity, and inclusion (EDI) considerations within the lifecycle of artificial intelligence (AI), spanning from data collection and problem definition to implementation stages. Despite the recognized need for integrating EDI principles, there is currently no existing guideline or framework to support this integration in the AI lifecycle.

Objective
This study aimed to address this gap by identifying EDI principles and indicators to be integrated into the AI lifecycle. The goal was to develop a comprehensive guiding framework to guide the development and implementation of future AI systems.

Methods
This study was conducted in 3 phases. In phase 1, a comprehensive systematic scoping review explored how EDI principles have been integrated into AI in health and oral health care settings. In phase 2, a multidisciplinary team was established, and two 2-day, in-person international workshops with over 60 representatives from diverse backgrounds, expertise, and communities were conducted. The workshops included plenary presentations, round table discussions, and focused group discussions. In phase 3, based on the workshops’ insights, the EDAI framework was developed and refined through iterative feedback from participants. The results of the initial systematic scoping review have been published separately, and this paper focuses on subsequent phases of the project, which is related to framework development.

Results
In this study, we developed the EDAI framework, a comprehensive guideline that integrates EDI principles and indicators throughout the entire AI lifecycle. This framework addresses existing gaps at various stages, from data collection to implementation, and focuses on individual, organizational, and systemic levels. Additionally, we identified both the facilitators and barriers to integrating EDI within the AI lifecycle in health and oral health care.

Conclusions
The developed EDAI framework provides a comprehensive, actionable guideline for integrating EDI principles into AI development and deployment. By facilitating the systematic incorporation of these principles, the framework supports the creation and implementation of AI systems that are not only technologically advanced but also sensitive to EDI principles.
Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study
Authors: Dahu Butros M , Khan Solaiman , Toubal Imad Eddine , Alshehri Mariam , Martinez-Villar Carlos I , Ogundele Olabode B , Sheets Lincoln R , Scott Grant J 
Published: November 2024

Background
The global obesity epidemic demands innovative approaches to understand its complex environmental and social determinants. Spatial technologies, such as geographic information systems, remote sensing, and spatial machine learning, offer new insights into this health issue. This study uses deep learning and spatial modeling to predict obesity rates for census tracts in Missouri.

Objective
This study aims to develop a scalable method for predicting obesity prevalence using deep convolutional neural networks applied to satellite imagery and geospatial analysis, focusing on 1052 census tracts in Missouri.

Methods
Our analysis followed 3 steps. First, Sentinel-2 satellite images were processed using the Residual Network-50 model to extract environmental features from 63,592 image chips (224×224 pixels). Second, these features were merged with obesity rate data from the Centers for Disease Control and Prevention for Missouri census tracts. Third, a spatial lag model was used to predict obesity rates and analyze the association between deep neural visual features and obesity prevalence. Spatial autocorrelation was used to identify clusters of obesity rates.

Results
Substantial spatial clustering of obesity rates was found across Missouri, with a Moran I value of 0.68, indicating similar obesity rates among neighboring census tracts. The spatial lag model demonstrated strong predictive performance, with an R2 of 0.93 and a spatial pseudo R2 of 0.92, explaining 93% of the variation in obesity rates. Local indicators from a spatial association analysis revealed regions with distinct high and low clusters of obesity, which were visualized through choropleth maps.

Conclusions
This study highlights the effectiveness of integrating deep convolutional neural networks and spatial modeling to predict obesity prevalence based on environmental features from satellite imagery. The model’s high accuracy and ability to capture spatial patterns offer valuable insights for public health interventions. Future work should expand the geographical scope and include socioeconomic data to further refine the model for broader applications in obesity research.
Características de las curvas de lactación en ovejas y factores que influyen en su variación: Revisión
Authors: Castillo-Hernández Gabriela , Ochoa-Alfaro Ana Erika , Ochoa-Cordero Manuel Antonio , Maldonado Jáquez Jorge Alonso , Torres-Hernández Glafiro 
Published: November 2024

Los programas de mejoramiento genético de las razas de ovejas lecheras han respondido a una creciente demanda y popularidad del mercado, especialmente para quesos elaborados con leche de oveja. Estos derivados de la leche son una fuente importante de sustancias bioactivas para la salud humana. Por lo tanto, el conocimiento de la producción de leche (PRODL) es muy importante y los factores que influyen en su variación. El patrón típico de la PRODL durante el período que una oveja está lactando se conoce como curva de lactación (CL), y éstas pueden ser típicas (CLT) o atípicas (CLA). Las CLT se caracterizan por alcanzar una PRODL máxima (pico de lactación, PL) a los pocos días después del parto, y posteriormente disminuye gradualmente hasta llegar al final de la lactación, o secado. Las CLA son aquellas que muestran alguna desviación del patrón normal. Es importante conocer la representación gráfica del comportamiento de la lactación, ya que, además de predecir la PRODL, permite identificar problemas de salud y alimentación, así como la posibilidad de seleccionar hembras que sobresalen en la PRODL. La persistencia de la lactación (PER) se ha definido como la tasa de disminución en la PRODL después de que se alcanzó el PL, y es muy deseable que las ovejas tengan una alta PER. Se han desarrollado modelos matemáticos para el estudio de las CL y PER. Existen factores genéticos y ambientales que influyen en las CL.
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Efecto del extracto acuoso de granos de café gastado como antioxidante en hamburguesas de cerdo crudas durante el almacenamiento refrigerado
Authors: Murillo Hernández Juan Luis , Vargas Sánchez Rey David , Torres Martínez Brisa del Mar , Huerta Leidenz Nelson , Torrescano Urrutia Gastón Ramón , Sánchez Escalante Armida 
Published: November 2024

Se estudió el efecto del extracto acuoso de granos de café gastado (CG) y el butilhidroxitolueno (BHT) sobre el deterioro del color, la oxidación de lípidos y el estado antioxidante de las hamburguesas de cerdo crudas durante el almacenamiento refrigerado (4 °C/9 días, en oscuridad). Se evaluó el contenido de polifenoles y la actividad antirradical del extracto de CG. Las hamburguesas de cerdo se evaluaron para determinar el pH, los parámetros de color y la oxidación de lípidos (OXL), así como la actividad antioxidante total de la carne. Los resultados mostraron que el extracto de CG es una fuente importante de polifenoles y ejerce actividad antioxidante. Su inclusión en muestras de carne mitigó los cambios indeseables en los valores de pH, color y OXL y aumentó la estabilidad antioxidante durante el almacenamiento (P<0.05). En conclusión, el uso del extracto de CG como antioxidante natural puede mejorar la calidad y la vida útil de las hamburguesas de cerdo crudas.
Penatalaksanaan Pemasangan Dental Implant pada kasus Kehilangan Gigi Posterior Tunggal
Authors: Kartikasari Halim Aristya , Poedjiastoeti Wiwiek 
Published: November 2024

Background: Tooth loss is one of the problems that patients complain about in dentist practice. Tooth loss is often found in adult patients, especially posterior teeth, i.e. premolar or molars. Implant restoration of the loss of one posterior tooth is the best option for now. Objective: To determine the good management of complication factors when installing dental implants on posterior teeth. Case and Management: A 23-year-old male patient came to RSGM-P FKG Usakti to make a fixed denture on the left lower back tooth that had been extracted due to a tooth fracture so that chewing could be better. The patient has no systemic disease, no history of allergies, and no bad habits. There are no complicating factors at the time of dental implant placement in dental area 37, so there is good osseointegration and proper crown insertion. Conclusion: Dental implant placement in the case of posterior single-tooth loss in this patient was not found to be difficult. Dental implant restoration with single-tooth loss is the best option for now with a success rate of 97% for dental implants with posterior single-tooth loss compared to other types of implant restoration.