bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Factors Influencing Students' Learning Success Rate in National Examinations
Authors: Hasanah Uswatun
Published: March 2024

The National Examination (UN) is a crucial assessment tool in many educational systems worldwide, determining students' academic achievements and educational progress. This study aims to analyze the factors influencing students' success rates in the National Examination. Utilizing a qualitative approach and conducting a comprehensive review of existing literature through library research, this study investigates various factors that impact students' performance in the National Examination. The analysis includes factors such as socio-economic background, access to educational resources, teaching quality, study habits, and psychological factors like motivation and self-efficacy. The findings reveal that socio- economic status significantly influences students' preparation and performance in the National Examination. Moreover, access to educational resources and quality teaching positively correlate with students' success rates. Additionally, students' study habits, including time management, self-discipline, and effective learning strategies, play a vital role in determining their performance in the examination. Furthermore, psychological factors such as motivation, self-confidence, and perceived academic competence contribute significantly to students' success in the National Examination. By understanding these factors, educators and policymakers can develop targeted interventions and strategies to improve students' preparation and performance in the National Examination, thereby enhancing overall educational outcomes and promoting equity in education.
Legal Pluralism and Indigenous Justice Systems: An Anthropological Analysis
Authors: Tanjung Muhammad Arifin
Published: March 2024

Legal pluralism refers to the coexistence of multiple legal systems within a single society, often including both state-based and indigenous or customary justice systems. This article provides an anthropological analysis of legal pluralism and indigenous justice systems, exploring the complexities and implications of their interaction. Drawing on qualitative methods, literature studies, and library research, the paper examines how legal pluralism shapes social norms, power dynamics, and access to justice in diverse cultural contexts. It critically evaluates the challenges and opportunities presented by the recognition and integration of indigenous justice systems into broader legal frameworks, highlighting issues of cultural relativism, human rights, and legal reform. Through an interdisciplinary approach, the article offers insights into the complex dynamics of legal pluralism and indigenous justice, shedding light on the ways in which these systems intersect and influence each other. By examining case studies from various regions and communities, the research underscores the importance of understanding the cultural, historical, and socio- political factors that shape legal pluralism and indigenous justice systems. Ultimately, this analysis contributes to broader discussions on legal diversity, social justice, and the role of anthropology in understanding legal phenomena.
Crisis Management in Education: Strategies for Resilience and Adaptation in Diverse Cultural Contexts
Authors: Johnson Katherine Reynold
Published: March 2024

Crisis management in education is a critical aspect of ensuring the resilience and adaptation of educational systems, particularly in diverse cultural contexts. This article explores various strategies employed to address crises in education and examines their effectiveness across different cultural settings. Through qualitative methods, literature reviews, and library research, this study investigates how educational institutions respond to and recover from crises while considering the cultural nuances that influence these processes. In conclusion, crisis management in education requires a multifaceted approach that integrates proactive planning, resilience-building efforts, cultural sensitivity, and effective communication strategies. By adopting these strategies, educational institutions can enhance their capacity to respond to crises and promote resilience and adaptation in diverse cultural contexts. This study contributes to the literature by highlighting the importance of cultural considerations in crisis management and providing insights into best practices for ensuring the safety and well-being of students and staff in times of crisis.
Anthropological Perspectives on Criminal Behavior: Understanding Cultural Contexts
Authors: Zubaedah Putri Amalia , Hafizi Royyan
Published: March 2024

Anthropological Perspectives on Criminal Behavior: Understanding Cultural Contexts delves into the intricate relationship between culture and criminal behavior, employing qualitative methods and extensive literature review. This interdisciplinary study explores how cultural contexts shape and influence criminal behavior, providing insights into diverse societal norms, values, and practices. By examining various ethnographic studies and theoretical frameworks, this research aims to elucidate the complex interplay between culture, social structures, and individual actions that contribute to criminality. Through an in-depth analysis of existing literature, this article identifies key themes and patterns in anthropological research on criminal behavior, shedding light on cultural factors that underpin deviant conduct across different societies. The findings highlight the importance of understanding cultural contexts in addressing crime and developing effective interventions that are sensitive to diverse cultural perspectives. Ultimately, this study contributes to the advancement of anthropological knowledge on criminal behavior and informs future research and policy initiatives aimed at promoting social justice and reducing crime rates.
Money talks! The role of parents’ discussion of money for preschoolers’ math knowledge
Authors: Duong Shirley , Elliott Leanne E. , Sidoti Olivia , Bachman Heather J. , Libertus Melissa E. , Votruba-Drzal Elizabeth
Published: March 2024

Children’s participation in cultural, everyday practices and social interactions involving math and money can contribute to the development of their knowledge and skills in these domains. Further work is needed to uncover what features of these activities, such as aspects of the conversations that may occur (e.g., number and money talk), facilitate and/or are shaped by children’s understanding of money concepts and skills. The present study examined the extent to which parents engaged in conversations about numbers and money with their four-year-old children during pretend grocery play and the relations to children’s math skills. We found that talk about price labeling and exchanging currency or goods occurred most frequently and that money and number talk were not significantly related to children’s broader math skills. However, parents’ money talk was positively associated with children’s money-related math skills, and this association was driven by the co-occurrence of talk about money and numbers. Our results suggest that parent-child conversations in familiar contexts such as grocery shopping provide rich opportunities to discuss culturally relevant practices surrounding money and practice math skills in the context of monetary exchanges. Thus, it is critical to consider how existing family practices and everyday contexts support children’s early math learning.
A brief, multiple-choice assessment of mature number sense is strongly correlated with more resource-intensive measures
Authors: Kirkland Patrick K. , Guang Claire , Otuonye Chineme , McNeil Nicole M.
Published: March 2024

Students who exhibit mature number sense make sense of numbers and operations, use reasoning to notice patterns, and flexibly choose effective problem-solving strategies (McIntosh et al., 1997, https://ro.ecu.edu.au/ecuworks/6819). Due to its dispositional nature, mature number sense is typically measured through in-depth interviews or tests of strategy usage. Yet, the lack of an efficient, rigorously developed measure has made it difficult to collect systematic, replicable evidence on students’ mature number sense. To address this, we developed a brief assessment of mature number sense. The present study provides additional convergent evidence of validity for this measure with US students in grades 3-8 (8–14 years old). We compared middle school (N = 40) and upper elementary school (N = 41) scores from the brief assessment with an established, time-intensive measure (Yang, 2019, https://doi.org/10.1007/s10649-018-9874-8) and an in-depth interview of student strategy usage (Markovits & Sowder, 1994, https://doi.org/10.2307/749290). We found strong correlations (r > 0.7) across all three measures, and this held even when controlling for students’ arithmetic scores (pr > 0.6). Researchers and educators can now use the brief assessment to investigate students’ mathematical thinking and advance knowledge of a key aspect of mathematical cognition.
Ausfalloption Karlsruhe
Authors: Machura Zora , Weickert Jakob
Published: March 2024

Antidemokratische Kräfte erstarken – und mit ihnen die parlamentarische Obstruktion in Bund und Ländern. Wenn solche Kräfte in den anstehenden Landtagswahlen mehr als ein Drittel oder gar die Mehrheit der Stimmen gewinnen, stellt sich mit Dringlichkeit die Frage, wie die Landesverfassungsgerichte funktionsfähig bleiben können. Eine derzeit diskutierte Möglichkeit besteht darin, die für das Bundesverfassungsgericht diskutierten Mechanismen auch auf die Landesverfassungsgerichtsbarkeit zu übertragen. Eine andere Option wäre es, den Weg zu beschreiten, den das Grundgesetz in Art. 99 GG vorgezeichnet hat: die Organleihe.
Development and Application of a Metaverse-based Maker Education Program to Cultivate Human Rights Sensitivity
Authors:   The Korean Society for Artificial Intelligence, Cho Yoonhee , Park Hyungbin
Published: March 2024

This research attempted to investigate the effect of cultivating human rights sensitivity when a metaverse-based maker education program was developed to cultivate human rights sensitivity and applied to elementary school students, and the research questions were set as follows. First, convergent education elements related to human rights sensitivity are extracted from moral and social studies. Second, a metaverse-based maker education program teaching and learning model is devised to cultivate human rights sensitivity based on the design thinking model. Third, a metaverse-based maker education program is developed and applied to foster human rights sensitivity. Fourth, the effect of the metaverse-based maker education program on cultivating human rights sensitivity is analyzed. As a result of the study, it was confirmed that there was a significant improvement in the human rights sensitivity of learners after application compared to before and after application of this education program. The results of this study are expected to be widely used as basic data for metaverse-based maker education to cultivate human rights sensitivity of elementary school students in the future.
Research on the Intervention Path of Social Work in the Governance of Cults
Authors:   Yixin Publisher, Zhao Rujuan , Li Zhihao
Published: March 2024

The illegal activities of cult organizations have brought enormous human, material, and economic losses to the country and people, and have caused irreparable personal harm to innocent people. The repeated occurrence of self harm or violence against others caused by cults that harm society shows that the remnants of cults have not been completely eliminated. Under the joint crackdown of multiple government departments and systematic operation, large-scale cult organizations have collapsed, but sporadic and small-scale cult activities are difficult to completely eradicate. Therefore, eliminating the breeding ground for cults and gradually cleaning up the problem of cult governance in cult organizations should be highly valued by the Party and the government, and preventing sporadic cults from resurgence through various opportunities. In terms of cult governance, the government has invested a large amount of governance resources, severely cracking down on cults and transforming cult followers, while safeguarding the basic rights of individuals involved in cults and guiding them to return to the community. From the professional perspective of social work, using the theoretical perspective of social work to analyze the problems of cults, and proposing the use of the three major professional methods of social work to participate in the governance of cults and the transformation of cult believers, promoting better solutions to cult governance issues.
Optimizing Resource Allocation in Cloud Infrastructure through AI Automation: A Comparative Study
Authors: Karamthulla Musarath Jahan , Narkarunai Jesu , Malaiyappan Arasu , Tillu Ravish
Published: March 2024

Optimizing resource allocation in cloud infrastructure is paramount for ensuring efficient utilization of computing resources and minimizing operational costs. With the proliferation of diverse workloads and dynamic user demands, manual resource management becomes increasingly challenging. In this context, artificial intelligence (AI) automation emerges as a promising approach to enhance resource allocation efficiency. This paper presents a comparative study of various AI techniques applied to optimize resource allocation in cloud environments. We explore the efficacy of machine learning, evolutionary algorithms, and deep reinforcement learning methods in dynamically allocating resources to meet performance objectives while minimizing costs. Through a comprehensive evaluation of these approaches using real-world datasets and simulation experiments, we highlight their strengths, limitations, and comparative performance. Our findings provide valuable insights into the effectiveness of AI-driven resource allocation strategies, enabling cloud providers and practitioners to make informed decisions for enhancing cloud infrastructure management
Facial and Dental Injuries in Karate
Authors: Vidovic-Stesevic Vesna , Verna Carlalberta , Krastl Gabriel , Kühl Sebastian , Filippi Andreas
Published: March 2024

Karate is a martial art that carries a high trauma risk. Trauma-related Swiss and European karate data are currently unavailable. This survey seeks to increase knowledge of the incidence of traumatic facial and dental injuries, their emergency management, awareness of tooth rescue boxes, the use of mouthguards and their modifications. Interviews were conducted with 420 karate fighters from 43 European countries using a standardized questionnaire. All the participants were semi-professionals. The data were evaluated with respect to gender, kumite level (where a karate practitioner trains against an adversary), and country. Of the 420 fighters interviewed, 213 had experienced facial trauma and 44 had already had dental trauma. A total of 192 athletes had hurt their opponent by inflicting a facial or dental injury, and 290 knew about the possibility of tooth replantation following an avulsion. Only 50 interviewees knew about tooth rescue boxes. Nearly all the individuals interviewed wore a mouthguard (n = 412), and 178 of them had made their own modifications to the guard. The results of the present survey suggest that more information and education in wearing protective gear are required to reduce the incidence of dental injuries in karate.
Modeling the relationship between prices and output, taking into account complementarity and substitution of products on the example of the "input-output" tables of the USA
Authors: Внуков И.А.
Published: March 2024

Статья посвящена моделированию межотраслевого баланса и анализу зависимости между ценами и выпуском отраслей. Исследование основывается на модифицированной методологии межотраслевого баланса, предложенной авторами, которая включает спрос на продукцию со стороны конечного потребителя. Модель подбирает коэффициенты спроса, исходя из уровней выпуска отраслей. Работа таких моделей не была изучена в литературе. Авторы провели анализ данных мировых таблиц затрат-выпуска в период с 2000 по 2014 гг. на примере США. Рассмотрена динамика цен и общего объема выпуска за анализируемый период. В результате исследования была выявлена слабая незначимая корреляция между отраслями, а также выделены кластеры отраслей по степени взаимосвязи цены и выпуска. В этих кластерах была обнаружена средняя отрицательная связь. Модельные расчеты согласуются с реальными данными по выпуску, однако менее согласованы по конечному потреблению. Это указывает на то, что модель может быть улучшена для более точного прогнозирования конечного потребления. Данная работа поднимает вопросы для экономистов и специалистов в области моделирования межотраслевого баланса для исследования эффектов комплементарности и субституции продукции отраслей. Предложен новый подход к анализу зависимости между ценами и выпуском отраслей.

The article is devoted to modeling the intersectoral balance and analyzing the relationship between prices and output of industries. The study is based on a modified methodology of intersectoral balance proposed by the authors, which includes the demand for products from the end consumer. The model selects demand coefficients based on the output levels of industries. The work of such models has not been studied in the literature. The authors analyzed data from global input-output tables in the period from 2000 to 2014 using the example of the United States. The dynamics of prices and total output over the analyzed period are considered. As a result of the study, a weak insignificant correlation between industries was revealed, and clusters of industries were identified according to the degree of correlation between price and output. An average negative association was found in these clusters. The model calculations are consistent with real data on output, but less consistent on final consumption. This indicates that the model can be improved to more accurately predict final consumption. This work raises questions for economists and specialists in the field of cross-industry balance modeling to study the effects of complementarity and substitution of industry products. A new approach to the analysis of the relationship between prices and output of industries is proposed
Análisis de los procesos estratégicos de la asociación agraria bananera fincas de El Oro
Authors: Del Rosario Rodríguez Emily Andrea , Vite Cevallos Harry , Carvajal Romero Héctor
Published: March 2024

In this article we make an analysis of the strategic processes based on the job satisfaction of the administrative, financial and operational area of the Agrarian Banana Association Fincas de El Oro in which we are going to use the dimensions: the nature and content of the work, group work, individual stimulation and effort, working conditions, welfare conditions, which were called indicators in this research that was descriptive of a transversal type, which was carried out at a single moment with the presence or absence of the result variable to be studied and without being altered or replaced. This was done through a survey of the workers of the different departments and it was concluded that there is a commitment on the part of the personnel to the company since they are aware that the results obtained by their work, both individually and in groups, can significantly influence the company and for this reason a norm must be implemented that allows the orientation, coordination, simplification and unification of the criteria to standardize the norms of the services offered with quality and safety.
Estrategia de implementación del aprendizaje en línea en el Centro de Formación en Docencia Universitaria
Authors: Alfonso Moreira Yaquelín , Juanes Giraud Blas Yoel
Published: March 2024

This article results from an investigation carried out from the current technological condition in which virtuality is carried out in the university; so that students and teachers can have opportunities for interaction. The objective is: to lead the training process of the students / teachers that stimulates the development of digital competences and the promotion of a university culture from this learning modality. It was decided to work with two analysis units, students and teachers. In the presentation, an online learning implementation strategy is generated as a result, which is structured by three dimensions with their corresponding stages, which allows the precision of actions, organizes, guides and evaluates the educational practice so that they learn by doing teachers and students. The strategy was submitted to the criteria of 10 experts. In the case of teacher training, they need to experience this process as part of their professional development. Achieving digital skills involves a change of thinking; that in the university it acquires a particular connotation, because its professors already have a basic knowledge of virtuality.
Text-Based Content Analysis on Social Media Using Topic Modelling to Support Digital Marketing
Authors: Buana Gandhi Surya , Tyasnurita Raras , Puspita Nindita Cahya , Vinarti Retno Aulia , Mahananto Faizal
Published: March 2024

This study aims to create Social Media Analytics (SMA) tools to help Digital Marketers or Content Creators create content topics for creating text-based Instagram content and support digital marketing strategy. Since no SMA tools can provide topic discovery for text-based Instagram content, this research aims to make an SMA tool. The data requirements to make an SMA tool include content text, content caption text, likes, comments, upload time, and content category obtained through the Instascrapper. The method used in this study is the Topic Modelling method using the Latent Dirichlet Allocation (LDA) approach to find the most dominant topic in the content. Optical Character Recognition (OCR) performs an image transformation process to extract text from text-based Instagram content images. The results of SMA tool creation are tested on three expert users, which shows that 93% of test participants could use the SMA to find topic references, and 85% can still be used by users even though they find it difficult. Since the test result shows that SMA tools still need development, for further research, SMA tools can focus on developing the user experience to increase the value of user acceptance by paying attention to the ease of the SMA tools. Also, SMA tools can focus on target users such as Data Analysts, Business Intelligence Analysts, or others within a company to support decision-making for the marketing department.
Distributed Aerial Image Stitching on Multiple Processors using Message Passing Interface
Authors: Ramadhan Alif Wicaksana , Aulia Fira , Dewi Ni Made Lintang Asvini , Winarno Idris , Sukaridhoto Sritrusta
Published: March 2024

This study investigates the potential of using Message Passing Interface (MPI) parallelization to enhance the speed of the image stitching process. The image stitching process involves combining multiple images to create a seamless panoramic view. This research explores the potential benefits of segmenting photos into distributed tasks among several identical processor nodes to expedite the stitching process. However, it is crucial to consider that increasing the number of nodes may introduce a trade-off between the speed and quality of the stitching process. The initial experiments were conducted without MPI, resulting in a stitching time of 1506.63 seconds. Subsequently, the researchers employed MPI parallelization on two computer nodes, which reduced the stitching time to 624 seconds. Further improvement was observed when four computer nodes were used, resulting in a stitching time of 346.8 seconds. These findings highlight the potential benefits of MPI parallelization for image stitching tasks. The reduced stitching time achieved through parallelization demonstrates the ability to accelerate the overall stitching process. However, it is essential to carefully consider the trade-off between speed and quality when determining the optimal number of nodes to employ. By effectively distributing the workload across multiple nodes, researchers and practitioners can take advantage of the parallel processing capabilities offered by MPI to expedite image stitching tasks. Future studies could explore additional optimization techniques and evaluate the impact on speed and quality to achieve an optimal balance in real-world applications.
NasiQu: Designing Mobile Applications with the Concept of Social Entrepreneurship for Hunger People Using Agile Methods
Authors: Hidayat Hendra , Yulastri Asmar , Susanto Perengki , Ardi Zadrian , Yustisia Henny
Published: March 2024

Entrepreneurship is becoming an essential part of everyday life today. But the social problems of society, especially cases of hunger, have also become an essential issue to date. Digital entrepreneurship has become a trend recently, with many digital business platforms and emerging applications. However, combining digital entrepreneurship with social activities becomes more attractive, which we know as Digital Social Entrepreneurship. This study describes and explains the stages in designing NasiQu, a mobile social entrepreneurship application, to see how agile can make digital social entrepreneurship interesting by involving people's sense of concern for people in need, in this case, hungry people. The Agile method, one of many used in software development, is the one that is being used. The Agile method is a short-term system development approach that calls for quick adaptation and developers who can work with any change. The results of this product from NasiQu can facilitate donations in the form of packaged rice to those who need food; in the case of this implementation, it is still specifically for orphans. In this application, there are three users, namely donors, admins, and partners. All these users have different roles and application usage flows. In addition, this application makes food donation activities more effective and can be done anywhere and anytime. It is hoped that the ongoing implementation of this activity will help many people who need food and impact opening new job opportunities.
Elevated Novice Developer Productivity and Self-efficacy by Promoting UX Journey in Software Requirement Elicitation
Authors: Kusuma Wahyu Andhyka , Jantan Azrul Hazri , Admodisastro Novia Indriaty , Norowi Noris
Published: March 2024

This study explores the effectiveness of the UX Journey methodology in increasing developer productivity and self-efficacy. Materials: The UX journey, consisting of around 30 activities, offers a user-centric approach to developing solutions, with 86 volunteer respondents from 505 populations. Method: Through a comparative analysis of developer productivity metrics and the General Self-Efficacy Scale questionnaire, this study investigates the impact of UX Journey on self-efficacy before and after implementation. Results: The study's findings reveal a significant positive correlation between UX Journey and increased productivity and an association between self-efficacy variables. By incorporating a comprehensive set of activities and a user-centric approach, the UX Journey enables developers to navigate the design process efficiently while gaining a deeper understanding of user needs. The positive correlation between the UX Journey and increased productivity, as well as the relationships between self-efficacy variables, emphasize the value of this methodology in fostering practical design thinking. Implication for Further Research: While this study has limitations regarding sample size and contextual specificity, it provides valuable insight into the benefits of UX Journey and paves the way for further research. In addition, the study focused on specific design projects within a particular context, which might restrict the broader applicability of the results. Significant results indicate that the proposed method is as effective as the elicitation method in general, with the advantage that the developer can understand the needs and empathy of the users. UX journeys can enhance the design process and foster a deeper understanding of users' needs across multiple domains.
A Comparative Analysis of Combination of CNN-Based Models with Ensemble Learning on Imbalanced Data
Authors: Gao Xiaoling , Ramli Muhammad Izzad , Syed Zainal Ariffin Syed Mohd Zahid
Published: March 2024

This study investigates the usefulness of the Synthetic Minority Oversampling Technique (SMOTE) in conjunction with convolutional neural network (CNN) models, which include both single and ensemble classifiers. The objective of this research is to handle the difficulty of multi-class imbalanced image classification. The application of SMOTE in imbalanced picture datasets is still underexplored, even though CNNs have been shown to be successful in image classification and that ensemble learning approaches have improved their performance. To investigate whether or not SMOTE can increase classification accuracy and other performance measures when combined with CNN-based classifiers, our research makes use of a CIFAR-10 dataset that has been artificially step-imbalanced and has varying imbalanced ratios. We conducted experiments using five distinct models, namely AdaBoost, XGBoost, standalone CNN, CNN-AdaBoost, and CNN-XGBoost, on datasets that were either imbalanced or SMOTE-balanced. Metrics such as accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC) were included in the evaluation process. The findings indicate that SMOTE dramatically improves the accuracy of minority classes, and that the combination of ensemble classifiers with CNNs and oversampling techniques significantly improves overall classification performance, particularly in situations when there is a high-class imbalance. When it comes to enhancing imbalanced classification tasks, this study demonstrates the potential of merging oversampling techniques with CNN-based ensemble classifiers to minimize the impacts of class imbalance in picture datasets. This suggests a promising direction for future research in this area.
Convolutional Neural Networks-Based For Predicting Aerodynamic Coefficient Of Airfoils At Ultra-Low Reynolds Number
Authors: Kasman Alief Sadlie , Zikri Arizal Akbar , Fariduzzaman Fariduzzaman , Srigutomo Wahyu
Published: March 2024

Many applications, including airplane design, wind turbines, and heat transmission, use symmetric or asymmetric airfoils. Engineers employ these airfoil shapes to optimize performance and efficiency. Each airfoil has a unique set of aerodynamic coefficients that must be calculated to maximize the airfoil design. Engineers utilize numerous ways to calculate coefficients, such as lift and drag. One of the methods is the prediction method, which effectively reduces time and cost. This study's training dataset is obtained from particle-based numerical computation using the Lattice Boltzmann Method (LBM). Then, Convolutional Neural Networks (CNN) are used as a prediction method to get the aerodynamic coefficients of airfoils for lift and drag based on two different Reynolds numbers. In CNN, airfoil geometry representation is essential. The Signed Distance Function (SDF) was used to convert airfoil geometry into RGB pictures. On the other hand, the SDF method cannot explain different flow conditions; in this case, it is represented by the Reynolds number (Re). Therefore, we propose a Text-based Watermarking Method (TWM) to differentiate between Re = 500 and Re = 1000. Each airfoil representation was trained and tested to generate each prediction model using a modified LeNet-5. The computation results show that using CNN with TWM on SDF to define the Reynolds numbers could predict the lift and drag coefficients with varying angles of attack. Future research can focus on generalizations to different aerodynamic aspects and practical applications in complex scenarios.
Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm
Authors: Zhukabayeva Tamara , Adamova Aigul , Ven-Tsen Khu , Nurlan Zhanserik , Mardenov Yerik , Karabayev Nurdaulet
Published: March 2024

The imperfection of existing intrusion detection methods and the changing nature of malicious actions on the attacker's part led to the Internet of Things (IoT) network interaction in an unsafe state. The actual problem of improving the technology of the IOT is counteracting malicious network impacts. In this regard, research and development aimed at creating effective tools for solving applied problems within the framework of this problem are becoming increasingly important.  This study seeks to develop tools for detecting anomalous network conditions resulting from malicious attacks. In particular, the accuracy of the identification of DoS and DDoS attacks is sufficient for operational use. This study analyzes various multi-level architectures, relevant communication protocols, and different types of network attacks. The presented research was conducted on open datasets TON_IOT DATASETS, which include multiple data sources collected from IoT sensors. The modified HyperNEAT algorithm was used as the basis for the development. The NEAT methodology used in the study allows you to combine various network nodes. Results of the study: a neuro-evolutionary algorithm for identifying DoS and DDoS attacks was implemented, integrated, and real-tested based on a multi-level analysis of network traffic combined with various adaptive modules. The accuracy of identifying DoS and DDoS attacks is 0.9242 in the Accuracy metric. The study implies that the proposed approach can be recommended for network intrusion detection, ensuring security when interacting with the IoT.
Boosting Vehicle Classification with Augmentation Techniques across Multiple YOLO Versions
Authors: Tan Shao Xian , Ong Jia You , Goh Kah Ong Michael , Tee Connie
Published: March 2024

In recent years, computer vision has experienced a surge in applications across various domains, including product and quality inspection, automatic surveillance, and robotics. This study proposes techniques to enhance vehicle object detection and classification using augmentation methods based on the YOLO (You Only Look Once) network. The primary objective of the trained model is to generate a local vehicle detection system for Malaysia which have the capacity to detect vehicles manufactured in Malaysia, adapt to the specific environmental factors in Malaysia, and accommodate varying lighting conditions prevalent in Malaysia. The dataset used for this paper to develop and evaluate the proposed system was provided by a highway company, which captured a comprehensive top-down view of the highway using a surveillance camera. Rigorous manual annotation was employed to ensure accurate annotations within the dataset. Various image augmentation techniques were also applied to enhance the dataset's diversity and improve the system's robustness. Experiments were conducted using different versions of the YOLO network, such as YOLOv5, YOLOv6, YOLOv7, and YOLOv8, each with varying hyperparameter settings. These experiments aimed to identify the optimal configuration for the given dataset. The experimental results demonstrated the superiority of YOLOv8 over other YOLO versions, achieving an impressive mean average precision of 97.9% for vehicle detection. Moreover, data augmentation effectively solves the issues of overfitting and data imbalance while providing diverse perspectives in the dataset. Future research can focus on optimizing computational efficiency for real-time applications and large-scale deployments.
Continuous Training of Recommendation System for Airbnb Listings Using Graph Learning
Authors: Chan Yun Hong , Ng Kok Why , Haw Su Cheng , Palanichamy Naveen
Published: March 2024

Recommender systems are getting increasingly important nowadays as they can boost user engagement and benefit businesses. However, there remain some unsolved problems. This paper will address two key performance issues. First, the limited ability to identify and leverage intrinsic relationships between data points. Second, the inability to adapt to new data. The first issue is proposed to be addressed through a Graph Neural Network (GNN) to curate better recommendations. GNN will be trained with Airbnb’s review data to utilize its outstanding expressive power to represent complex user-listing interactions at scale, followed by generating embeddings to compute the relevant recommendations to the users. With the generated embeddings, the recommender system will compute a recommendation list to every user based on the embedding similarity between the user and listings or the user’s first-ever reviewed listing and listings. The second issue is proposed to be resolved by incorporating Continuous Training. The proposed recommender system employs GraphSAGE with a customized Rating-Weighted Triplet Ranking Loss function, which outperformed unsupervised GraphSAGE. Offline simulation validated the recommender system's ability to learn from the latest data and improve over time. Overall, the proposed user-to-item (U2I) recommendation rating-weighted GraphSAGE substantially increased by 99.88% in hit-rate@5 and 98.15% in coverage. This offers an effective solution for enhancing the recommender system for Airbnb listings. This research validates the efficacy of GNN-based recommendations in capturing user-item relationships to aid in predicting relevant recommendations, thus significantly driving up the adoption of GNN-based recommender systems.
A Classification Algorithm Inspired by the Chromatographic Separation Mechanism Dedicated to the Classification of Variable-length and Multi-class Vectors
Authors: Święcicki Mariusz
Published: March 2024

Nowadays, one of the critical problems related to data mining is the processing of large data sets. This article presents an algorithm that may apply to the issues associated with classifying large-volume data sets. The motivation behind defining this type of algorithm was that the methods used to process this data type are subject to several significant limitations. The first considerable limitation of using classical classification methods is ensuring a constant data size. The second type of constraint is related to the data dimension. The last limitation in using classic classification algorithms is associated with the situation in which a given input vector may contain data belonging to many classes simultaneously, in which case we are talking about so-called multi-class vectors. The presented algorithm is inspired by the method of chromatographic separation of chemical substances. This method is widely and successfully used in analytical chemistry. As we know, in the case of chromatographic separation, we are dealing with a similar class of problems that occur when processing large data sets, firstly: the molecules of a chemical substance have a different number of molecules - i.e., they have different lengths, which corresponds to the situation that occurs when processing large data sets. In this work, a classification algorithm inspired by the mechanism of resolution chromatography is presented. The article presents the results of calculations for sample data sets. It discusses issues related to the properties of the defined algorithm, which concern the algorithm training process and the classification of single-class and multi-class data.
Dark Web Financial Fraud Identification Using Mathematical Models in Healthcare Domain
Authors: Rajawat Anand Singh , Goyal S.B. , Solanki Ram Kumar , Gadekar Amit , Patil Dipak
Published: March 2024

The so-called "dark web" has emerged as the most trustworthy platform for thieves to launch their enterprises. The healthcare industry has become a haven for illegal activities such as the sale of medical gadgets, trafficking in human beings, and the purchase of organs. This is because the sector provides a high level of privacy, which makes it an ideal location for engaging in unlawful operations. In this field of research, linear regression is utilized to uncover previously unknown patterns in customer demand. A vector will be created using a time series of medical equipment purchases to do this. When we look at the data the case firm gave us, we notice that people tend to desire to purchase products in one of three ways. After that, we sort the hospitals into groups according to the course of the trend vector by employing a technique known as "hierarchical clustering," which we apply to the data. According to the research findings, the trend-based clustering method is an excellent way to partition hospitals into subgroups that share similar tendencies. According to our model evaluations, no one model can reliably produce the most accurate forecasts for each cluster when used by itself. Some models can be utilized to make accurate predictions, and these models apply to a wide variety of time series that exhibit various patterns.
Utilizing Data-Driven Methods to Predict the Fatigue Life of Cement Concrete Considering Corrosive Environmental Factors
Authors: Kashyzadeh Prof. K.R.
Published: March 2024

The primary objective of this study is to assess the fatigue resistance of cement concrete when exposed to corrosive environments. To achieve this, experimental results from high-cycle fatigue (HCF) and low-cycle fatigue (LCF) tests conducted on cement concrete samples subjected to various corrosive conditions were used. Various data-driven techniques, including multiple linear regression (MLR), Taguchi sensitivity analysis (TSA), and response surface method (RSM) were utilized. The aim was not only to identify the most influential parameter affecting fatigue life but also to offer a simpler and cost-effective alternative to experimental approaches. Consequently, two key parameters related to the corrosive environment: pH value and immersion time, along with the cyclic force applied to the concrete samples as input variables across different approaches were considered. The number of cycles until sample failure regarded as the output variable in all analyses. Furthermore, the analyses were conducted with the assumption that longer fatigue life is preferable. The findings revealed that the fatigue life of Portland cement concrete consistently decreased with increasing immersion time. Notably, the pH value emerged as the most significant parameter, while the other two factors exhibited equivalent impacts.
Comparison of energy absorption in conventional and auxetic shoes: gait analysis and finite element modeling
Authors: Nourani Amir , Daei Daei Mahdi , Honarmand Mohammadmahdi
Published: March 2024

A three-dimensional finite element (FE) model of the heel and shoe sole was developed to analyze the effects of materials with different Poisson's ratios for midsole on energy absorption and impact shock in the heel area during walking. The heel model was obtained from CT-scan data. The FE model was verified using Anybody software for barefoot with data obtained from gait analysis. Sole layers and plantar soft tissue were modeled, and five different Poisson's ratios (-0.4, -0.2, 0, 0.2, and 0.4) were compared for the midsole layer. In order to obtain the contact force, experiments were performed on a force plate using shoes with either auxetic or conventional midsole layer. Rayleigh damping theory was used to simulate viscous damping behavior in the midsole layer. The results showed that for materials with the same Young's modulus, there was less compression, energy absorption, and viscous dissipation energy in the auxetic model in comparison to the conventional model. However, when compressive strains of sole layers were the same (i.e., by changing Young's modulus), more energy absorption (about 9%) was observed for the midsole with negative Poisson's ratio with respect to the conventional midsole layer.
Appropriate Use of Anticoagulants among Nonvalvular Atrial Fibrillation Patients at a University Hospital in Thailand
Authors:   Cardiology Division, Department of Internal Medicine, Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Sureeyathanaphat Phanthaphan , Teerawongsakul Padoemwut ,   Cardiology Division, Department of Internal Medicine, Faculty of Medicine Vajira Hospital, Navamindradhiraj University, Ananwattanasuk Teetouch ,   Cardiology Division, Department of Internal Medicine, Faculty of Medicine Vajira Hospital, Navamindradhiraj University
Published: March 2024

OBJECTIVE: Warfarin is primarily used for stroke prevention in atrial fibrillation (AF) patients in Thailand. Novel oral anticoagulants (NOACs) are used less commonly due to their high cost. This study aimed to evaluate the appropriate use of anticoagulants and clinical outcomes among nonvalvular AF (NVAF) patients. METHODS: This retrospective study collected data from the electronic medical records of patients who were diagnosed with NVAF between January 2014 and December 2019 at the Faculty of Medicine, Vajira Hospital. Baseline characteristics, prescribed indication, types and doses of anticoagulant, and ischemic and hemorrhagic outcomes were recorded and analyzed. RESULTS: We analyzed 783 patients with NVAF in this study. Of these, 539 (68.90%) were treated with oral anticoagulants (OAC), including 344 patients (43.90%) with warfarin therapy and 195 (24.90%) with NOACs. Meanwhile, 492 (73.10%) patients with CHA2DS2VASC score ≥ 2 received OAC therapy that was suitable for their indication. Of the 344 patients who received warfarin, 112 patients (32.60%) had an optimal time in therapeutic range (TTR) level of ≥ 65%. Of the 195 NOAC patients, only 98 (50.30%) received appropriate doses of NOACs. There was no statistically significant difference in the overall incidence rates of ischemic stroke/systemic embolism, bleeding, cardiovascular death, and all-cause death between the warfarin and NOACS groups. Appropriate TTR levels in the warfarin group was associated with significantly lower incidence rates of cardiovascular death (hazard ratio: 0.14; 95% CI: 0.02–0.79; p = 0.02) and all-cause death (hazard ratio: 0.36; 95% CI: 0.12–0.87; p = 0.01), than inappropriate TTR levels. CONCLUSION: Most NVAF patients received oral anticoagulants with the appropriate indication. Warfarin is the most prescribed oral anticoagulant for patients with NVAF. About half of the patients received inappropriate doses of oral anticoagulants that potentially adversely affected the study outcomes of cardiovascular and all-cause deaths.
Dynamic Analysis of Progressive Income Taxation and Economic Growth with Endogenous Labor Supply and Public Goods
Authors: Zhang Wei-Bin
Published: March 2024

This research advances the development of a sophisticated economic growth model that intricately integrates gender-differentiated labor, endogenous labor supply, and nonlinear progressive income taxation, with the latter's revenues earmarked for public goods provision. Anchored in a framework that encompasses both a production and a public sector financed through taxation, the model delves into the complex interplay among economic growth, the gender division of labor, and progressive income taxation. By employing rigorous simulation techniques, this study meticulously examines the conditions for equilibrium existence and investigates the system's dynamics in response to various parametric alterations. This exploration not only sheds light on the theoretical underpinnings of economic growth and taxation but also offers insights into the policy implications of gender-differentiated labor contributions within the economy.