bluuebunny/crossref_metadata_2025_split huggingface dataset:
A REVIEW OF THE LITERATURE ON DIGITAL ENTREPRENEURSHIP
Authors: Medfouni Hynda , Mekarssi Lemya , Djeffal Khalid 
Published: November 2024

Objective: This study aims to comprehensively review digital entrepreneurship (DE) research, identifying key themes, trends, and future research directions.

Theoretical Framework: The review draws upon frameworks including entrepreneurial ecosystem theory, digital platform theory, and theories of entrepreneurial intention formation to understand DE dynamics.

Method: A systematic and bibliometric analysis of 276 English-language articles published between 2000 and 2023 from the Scopus database was conducted. VOSviewer software analyzed publication trends, keywords, and citations.

Results and Discussion: DE research has significantly grown, particularly in the last five years. Five thematic clusters emerged: DE ecosystems, cultivating digital entrepreneurs, entrepreneurship in the digital economy, exploring new frontiers, and sustainable, competitive digital enterprises. The study highlights influential works and emerging areas such as digital platforms, entrepreneurial intention, and DE education. The discussion contextualizes these findings within the theoretical frameworks and their implications.

Research Implications: This research provides valuable insights for researchers, educators, and policymakers. It emphasizes the need for further research on the impact of digital technologies on entrepreneurial ecosystems, individual entrepreneurial intentions, and the role of education in fostering DE.

Originality/Value: This study contributes by offering a comprehensive and up-to-date overview of DE research, identifying knowledge gaps, and proposing a future research agenda. The systematic approach utilizing bibliometric techniques provides a robust analysis of the DE literature, enhancing understanding of this evolving field.
Non-invasive Streptococcus pneumoniae infections are associated with different serotypes than invasive infections, Belgium, 2020 to 2023
Authors: Passaris Ioannis , Depickère Stéphanie , Braeye Toon , Mukovnikova Marina , Vodolazkaia Alexandra , Abels Chloé , Cuypers Lize , Desmet Stefanie , Ceyssens Pieter-Jan ,   NIPD study group Belgium
Published: November 2024

Background
Despite widely implemented pneumococcal vaccination programmes, Streptococcus pneumoniae remains a global risk for human health. Streptococcus pneumoniae can cause invasive (IPD) or non-invasive pneumococcal disease (NIPD). Surveillance is mainly focusing on IPD, assessing the full impact of pneumococcal vaccination programmes on pneumococcal disease is challenging.

Aim
We aimed to prospectively investigate serotype distribution and antimicrobial resistance (AMR) of S. pneumoniae isolates from patients with NIPD and compare with data on IPD isolates and with a 2007–2008 dataset on NIPD.

Methods
Between September 2020 and April 2023, we collected isolates and patient data from patients with NIPD from 23 clinical laboratories in Belgium. Capsular typing was performed by a validated Fourier-Transform Infrared spectroscopic method, and AMR was assessed with broth microdilution, using the European Committee on Antimicrobial Susceptibility Testing (EUCAST) clinical breakpoints.

Results
We received S. pneumoniae isolates from 1,008 patients with lower respiratory tract infections (n = 760), otitis media (n = 190) and sinusitis (n = 58). Serotype 3 was the most prevalent serotype among the NIPD isolates. Serotypes not included in the 20-valent pneumococcal conjugate vaccine (PCV20) were significantly more common among the NIPD than among the IPD isolates. Antimicrobial resistance levels were significantly higher among the NIPD isolates (n = 539; 2020–2022) compared with the IPD isolates (n = 2,344; 2021–2022). Resistance to several β-lactam antimicrobials had increased significantly compared with 15 years before.

Conclusions
The NIPD isolates were strongly associated with non-vaccine serotypes and with increased AMR levels. This underlines the importance of continued NIPD surveillance for informed policy making on vaccination programmes.
Technical Analysis of the Indonesian Stock Market with Gated Recurrent Unit and Temporal Convolutional Network
Authors: Aisyah Siti , Angraini Yenni , Sadik Kusman , Sartono Bagus , Dito Gerry Alfa 
Published: November 2024

Big data is essential in the age of 4.0 industry as it becomes the basis of decision making. Deep learning research in the last few years has been proven effective in understanding complex big data patterns, especially in the finance sector. The rapid growth of the Indonesian stock market in the last 20 years, which was driven by globalization, prompted fluctuation in the Bursa Efek Jakarta (JKSE) which was influenced by stock prices, commodity prices, and exchange rate. This study identifies the main indicators of Indonesian stock market crisis, applies and compares deep learning models, particularly Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN), in predicting stock prices. This study identified 20 JKSE crisis points between the 2002-2023 period with average return value at around -6%. All variables correlated positively with JKSE, with SET.BK as the highest correlated variable in lag 0. The American and European stock market, commodity price, and exchange rate tend to show a pattern opposite to the JKSE crisis. Predictor variables such as STI, HIS, KLSE, KS11, SET.BK, PSEI.PS, RUT, and USDIDR are chosen based on significant cross correlation and average return plot. Hyperparameter tuning and cross validation within a 3 years window concluded that the GRU model is accurate and efficient, with RMSE value at 43.35568 and MAE value at 33.66909 in the validation data.
Machine Learning Techniques for Heart Disease Prediction Using a Multi-Algorithm Approach
Authors: Biddinika Muhammad Kunta , Masitha Alya , Herman Herman , Fatimah Vita Arfiana Nurul 
Published: November 2024

This analysis explores the efficiency of machine learning systems for heart disease identification through a multi-algorithm approach. The main objective is to identify the best performing algorithm for accurate disease prediction, improving clinical decision making. Using criteria including accuracy, precision, recall, F1 score, and recall, the study assessed four algorithms: Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT). The results show that Random Forest outperforms the others, achieving 86.23% precision, 93.76% recall, 89.84% F1 score, and 88.41% accuracy. Random Forest gets an AUC ROC result of 0.94, so Random Forest is considered a superior model in this scenario, especially because it has higher accuracy. The algorithms showed a strong balance between sensitivity and specificity. Decision Tree showed reasonable performance with a precision of 84.18% and a recall of 90.27%, while Naïve Bayes recorded a precision of 87.68% and a recall of 87.03%. SVM showed a precision of 87.40% and a recall of 84.78%, indicating some limitations in capturing positive cases. The novelty of this study lies in the comparative analysis of several algorithms to optimize the heart disease prediction model for clinical use. The random forest algorithm is one of the choices, but there is still a medical standard for classifying people as either indicating or not experiencing heart failure, according to the study.
Research on Autonomous Navigation of Unmanned Aerial Vehicles Based on Correlation-Based Image Comparison Methods
Authors: Belyaev P. Yu. , Zikratov I. A. 
Published: November 2024

Relevance. Autonomous navigation of unmanned aerial vehicles (UAVs) is one of the key challenges in the modern aerospace industry. Specifically, for small UAVs, the task of autonomous navigation becomes even more complex due to limitations in computational resources and sensor capabilities. Optimizing navigation methods under such conditions is a pressing issue that requires solutions capable of providing high performance with minimal resource consumption.Objective. Improving the efficiency of autonomous navigation for small UAVs by using correlation methods for image comparison. Achieving this objective is linked to the development and evaluation of algorithms that provide high-speed and accurate navigation with limited computational resources. The work uses methods such as the autocorrelation function, Pearson correlation, the structural similarity index, and a simple neural network for image comparison.Solution. The research showed that the autocorrelation-based approach demonstrates the best performance under low computational resources. It ensures high-speed processing of the entire image and shows optimal detection accuracy. Compared to other methods presented in the study, the autocorrelation approach is capable of working not only with noise in the "reference" map but also of using alternative areas with altered patterns as detected regions.The scientific novelty of the study is determined by the systematic comparison of various methods applied to the task of image comparison for small UAVs with limited computational resources. Unlike well-known works in the field of correlation-extreme systems, this research focuses on the use of a "reference map" and a search area, representing two different images of the same terrain taken from different sources. This is a key difference, as most methods are not highly efficient in processing such images where patterns may differ significantly.The practical significance of the developed algorithm lies in the fact that the proposed autocorrelation-based method can be used by developers of autonomous small UAV control systems to reduce computational load and increase data processing speed.
Constant Values Distribution Investigation in the C Programs Source Code
Authors: Izrailov K. E. 
Published: November 2024

Currently, software engineering plays a key role in software development, one of the criteria for the development of which is the investigation of its factology and various scientific and practical patterns. An important aspect of this area is the logic of program execution, operating with internal data, and, in particular, constant values, the identification of patterns in which actualizes this research. The main applications of this pattern include obtaining fundamental knowledge about algorithms, creating new and expanding existing metrics for evaluating and comparing program code, developing methods for its optimization, using it in genetic programming, etc.The purpose of this article is to obtain the frequency distribution of constant values in the source code of programs in the C programming language.The essence of the presented approach is to create a method for statistical analysis of the text of the source codes of programs contained in the ExeBench dataset (which consists of a huge amount of source code of functions in the C programming language, their assembler code for various processor architectures, compilation errors and other information).The proposed method is based on the use of algorithms for lexical and syntactic analysis of source code functions, semantic definition of constant types, and conversion of the recording of programming language symbols into the corresponding numeric or string values.The method has an implementation in the form of a software tool in the Python programming language, given in the form of an intuitive pseudocode. Experiments using this prototype allowed us to obtain the desired distribution of constant values for the source code of programs in the C programming language. Analysis of the obtained results allowed us to make a number of important theoretical and practical conclusions regarding the most frequently used constants, the correspondence of the obtained distribution to the Zipf law and its proximity to the exponential function, the anomalous appearance of a number of constants in the Top 50, etc.The scientific novelty of the proposed approach lies in the fact that the distribution of constant values for the source code of programs in the C programming language is obtained for the first time.The theoretical significance consists in obtaining new fundamental knowledge regarding the features and patterns of source code constructions, which can be extended to other programming languages.The practical significance consists in applying the distribution to a wide range of tasks, including the author's genetic reverse engineering, which in itself is a qualitatively new direction.
Application of the Gray Wolf Optimization Algorithm and Neural Networks for Solving Discrete Problems
Authors: Lisov A. A. , Vozmilov A. G. , Gundarev K. A. , Kulganatov A. Z. 
Published: November 2024

Relevance. In recent decades, metaheuristic optimization methods have become popular for solving complex problems that require searching for global extrema. Algorithms such as genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), as well as more modern approaches such as cat pack optimization (CSO) and gray wolf pack optimization (GWO) demonstrate high efficiency, but their application is often limited by the conditions of continuity and differentiability of the objective functions. This is a challenge when solving problems with discrete data, where such requirements are not met. In this context, the search for methods that allow adapting metaheuristic algorithms to work with discrete functions is of particular relevance.Aim. The study is aimed at testing the hypothesis about the possibility of using a neural network trained on a limited set of discrete data as an approximation of a function sufficient for the correct execution of the GWO algorithm when searching for a global minimum. The implementation of this hypothesis can significantly expand the scope of GWO, making it available for a wider range of problems where functions are defined on discrete sets.Methods. The study is based on the analysis of existing approaches and experimental verification of the hypothesis on two test functions: a linear function and a Booth function, which are widely used as standards for evaluating the performance of optimization algorithms. Numerical experiments were conducted using neural networks as an approximating model to obtain the results. Solution. During the experiments, an analysis of the applicability of neural networks for approximating discrete functions was carried out, which showed the success of this approach. It was found that neural networks can approximate discrete functions with high accuracy, creating conditions for a successful search for a global minimum using the GWO algorithm.Novelty. For the first time, a hypothesis was proposed and tested on the use of neural networks for approximating objective functions in metaheuristic optimization problems on discrete data. This direction has not previously received due coverage in the scientific literature, which adds significance to the obtained results and confirms the effectiveness of the proposed approach.Practical significance. The results of the study open up new prospects for the application of algorithms such as GWO in optimization problems based on discrete data, expanding the capabilities of metaheuristic methods and facilitating their implementation in a wider class of applied problems, including problems where the use of other methods is limited.
The Efficiency Assessment of the Blockchain Networks Adaptive Algorithm as Part of a Multi-Consensus System on Communication Networks
Authors: Pomogalova A. V. 
Published: November 2024

The relevance of the approaches and solutions proposed in the work is due to the desire for distributed computing and decentralization of system modules using blockchain technology in order to increase the autonomy, security and independence of system components. The implementation of blockchain technology for decentralization is hampered by the lack of flexibility in terms of the algorithm for making a single decision in the asynchronous system ‒ consensus, which affects the amount of generated network traffic and the requirements for the hardware component. Due to the changing nature of the transmitted traffic, the load on the communication channel, the time of day of traffic transmission, the equipment, and the network protocols used, the development of a single universal consensus algorithm is not possible, since it depends on a very wide range of variable and specific parameters characteristic of different tasks. The introduction of adaptation mechanisms to current network conditions and a change in the consensus algorithm without losing information and with a satisfactory level of delays would provide sufficient flexibility for further implementation in existing telecommunication systems. The purpose of this paper is to evaluate the effectiveness of the proposed adaptive algorithm for choosing a consensus for blockchain networks.The essence of the presented algorithm is to change the consensus algorithm on a section of the blockchain network upon reaching certain network conditions, which allows regulating the amount of generated load on the communication network to reduce the loss of transaction blocks and the subsequent delay in their processing. The proposed efficiency assessment model is based on mathematical modeling methods, differential equation analysis, queueing theory and network graphs. The analysis of the results showed the efficiency of the proposed model when comparing the results of the analytical calculation and the experiment. The scientific novelty of the proposed approach is to developing an adaptive consensus selection algorithm, as opposed to developing a universal consensus algorithm, and the efficiency assessment model of the proposed algorithm on the communication network, taking into account a number of parameters characterizing the devices and the section of the communication network.The theoretical significance is in the universality of the proposed efficiency assessment model for calculating the synchronization speed of all nodes of the blockchain network with specified network parameters.The practical significance of the proposed algorithm and efficiency assessment method lies in the formation of new approaches and opportunities for integrating blockchain technology into modern communication networks, abstracting from the problem of consensus selection in changing conditions of the communication network section.
The Method of Measuring the Occupancy of the Radio Frequency Spectrum, Varying According to the Daily Cycle
Authors: Tokarev A. B. , Ostankov A. V. , Polev V. Yu. 
Published: November 2024

Relevance. Management of the use of the radio frequency spectrum requires taking into account the actual occupancy of radio channels and frequency bands. However, the patterns of occupancy change underlying the International Telecommunication Union (ITU-R) Radiocommunication Sector documents do not fully meet the needs of practice. Some of the ITU-R recommendations are focused on estimating local occupancy for short time intervals; other recommendations involve measurements in stationary radio channels, although only a small part of real radio channels can be counted on to have the constant occupancy throughout the time axis. At the same time, the activities of many organizations, and therefore the resources they use, are subject to a daily cycle of activity, which allows us to recommend for consideration a model of occupancy change in accordance with the daily cycle. The aim of the work is to develop a methodology for collecting information and forming an estimate of the daily occupancy change for the analyzed radio channels. Methods used. The development of recommendations for the collection of data for occupancy monitoring is based on the practical approaches of radio monitoring services and classical methods of statistical analysis. Novelty. In this paper, a new model for changing the occupancy of radio frequency channels in accordance with the daily cycle is proposed for use, as well as a statistically justified method for measuring occupancy.Result. The proposed methodology allows us to obtain a practically oriented assessment of occupancy changes, which does not require replacing the fleet of equipment traditionally used by radio monitoring services; the necessary changes can be implemented with relatively simple software modification. The paper provides preliminary recommendations for ensuring the accuracy and reliability of measurements, based on a traditionally used mathematical apparatus, which involves limiting the relative measurement error. This entails the need for very long measurements for the most important low-occupied radio channels. So, it is necessary to look for alternative measurement accuracy requirements that would meet the real needs of radio monitoring services.Practical significance. The acceptance of the developed methodology will eliminate the currently existing contradiction between the theoretical provisions underlying the ITU recommendations and the practice of conducting radio-controlled measurements.
A Set of Models for Device Positioning in Sixth Generation Networks. Part 2. Review of Algorithms and Accuracy Assessment
Authors: Fokin G. A. 
Published: November 2024

Relevance. This work is the second part of a series devoted to the study of a set of positioning models in sixth-generation terahertz networks and solves the problems of systematizing algorithms and assessing the accuracy of determining the location of a user device depending on the configuration and size of the antenna array at the base station.Purpose. Within the framework of the scientific problem of searching for means of achieving decimeter accuracy of coordinate estimates, outlined in the first part of the cycle, the analysis of accuracy assessment models, a review of algorithms and ways of their optimization, as well as a numerical experiment, performed in this study serve the purpose of justifying the configuration and dimensions of the antenna array used at the base station.The research method is an analytical review of the state of the problem based on current scientific publications, conceptual modeling, categorical approach, expert combination, comparative analysis, formalization, mathematical and simulation modeling.Solution / results. The paper presents models for assessing the accuracy of positioning in 6G terahertz networks, formalizes the relationship between primary measurements and coordinate estimates for multi-position and single-position positioning in the near and far zones. It provides an overview of algorithms for geometric positioning and positioning with training for cases of one-stage and two-stage processing; analyzes the specifics of implementing algorithms for simultaneous tracking and map construction. It provides an analysis of the features of optimizing algorithms in offline and online modes. Simulation modeling is used to assess the accuracy for a scenario of territorial distribution with direct visibility and ideal synchronization.Novelty. Using simulation modeling tools, the achievement of decimeter accuracy of coordinate and orientation estimates of 1° in the terahertz range for a far-field model using a 1 GHz band and a composite antenna array of more than half a thousand elements has been scientifically substantiated.The theoretical significance lies in establishing the dependence of the accuracy of coordinate and orientation estimates of the device on the configuration and dimensions of the antenna array at the base station.The practical significance of the developed simulation model lies in the numerical justification of the limits of device positioning accuracy in sixth-generation networks depending on the antenna array used at the base station for a given scenario.