bluuebunny/crossref_metadata_2025_split huggingface dataset:
PENINGKATAN PERAN ORANG TUA HEBAT DALAM UPAYA PENCEGAHAN KEKERASAN SEKSUAL MELALUI EDUKASI KESEHATAN REPRODUKSI PADA ANAK USIA DINI
Authors: Aprilia Devi , Novilia Tono Sendy Firza , Intiyaswati Intiyaswati 
Published: January 2025

Kekerasan terhadap anak mengancam anak di manapun, dan kasus kekerasan fisik, verbal, mental, seksual, dan lainnya terhadap anak usia dini terjadi dari waktu ke waktu. Pendidikan kesehatan reproduksi dan pendidikan seks merupakan unsur penting dalam pendidikan anak usia dini. Peran penting orang tua adalah memberikan bimbingan dan diskusi seksual sejak dini. Pendidikan seks anak dapat diberikan oleh orang tua, guru, atau masyarakat. Metode pengabdian masyarakat ini berupa penyuluhan dan demonstrasi tentang kesehatan reproduksi anak usia dini dengan jumlah peserta 25 responden. Hasil kegiatan program peningkatan peran orangtua hebat ini, dapat terlaksana dan seluruh tingkat pengetahuan peserta mengalami kenaikan secara signifikan dalam memahami perubahan yang terjadi pada diri sejak dini, ditandai peserta memiliki pengetahuan tentang fungsi dan kesehatan reproduksi anak usia dini, pencegahan kekerasan seksual dan penerapan fungsi dan kesehatan reproduksi anak usia dini. Sehingga pendidikan kesehatan berbasis keluarga khususnya pada orang tua balita berpengaruh terhadap peningkatan pengetahuan dan wawasan orang tua tentang kesehatan reproduksi anak usia dini dalam upaya pencegahan kekerasan seksual.
A Discrete Congruence Levenberg–Marquardt Deep Convoluted Neural Learning Classifier for the Automatic Detection of Autism Spectrum Disorder
Authors: Krishna Sujatha , Natarajan Rajesh , Irudayasamy Amalraj , Harinahalli Lokesh Gururaj , Flammini Francesco , Alfurhood Badria Sulaiman 
Published: January 2025

Autism spectrum disorder is a condition that affects around one out of every 54 children. Many studies have identified abnormalities in electroencephalography (EEG) signals for ASD diagnosis. The early and accurate identification of autism poses a substantial difficulty. The detection accuracy needs to be significantly boosted and the computational complexity reduced. The discrete congruence Levenberg-Marquardt deep convoluted neural learning classification (DCLMDCNLC) approach is introduced to address these issues in this work. The goal of the DCLMDCNLC approach is to perform automated ASD diagnosis at an early stage with higher accuracy and less time complexity. The DCLMDCNLC technique is applied to EEG signals through pre-processing, feature selection, and data classification. Discrete global threshold wavelet-transform-based pre-processing is carried out for EEG signal decomposition to remove unwanted noise. After that congruence correlation feature selection is carried out using the DCLMDCNLC technique with denoised signals to perform further processing. Finally, piecewise regression data analysis is carried out using the DCLMDCNLC technique for accurate autism detection with higher accuracy. An experimental assessment of the DCLMDCNLC technique is simulated, and the technique is validated using the EEG dataset for autism detection. Compared with traditional approaches, the DCLMDCNLC technique improves the accurate diagnosis of autism by 65%, the precision by 15%, the recall by 17%, the rate of errors by 77%, and the autism detection time by 35%.

Received: 14 June 2024 | Revised: 14 August 2024 | Accepted: 31 August 2024

Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.

Data Availability Statement
The EEG data that support the findings of this study are openly available at https://orda.shef.ac.uk/articles/dataset/EEG_Data_for_Electrophysiological_signatures_of_brain_aging_in_autism_spectrum_disorder_/16840351.

Author Contribution Statement
Sujatha Krishna: Conceptualization, Methodology, Writing – review & editing. Rajesh Natarajan: Conceptualization, Methodology, Software, Writing – original draft, Writing – review & editing, Visualization. Amalraj Irudayasamy: Formal analysis, Writing – review & editing. Gururaj Harinahalli Lokesh: Validation, Formal analysis, Writing – original draft, Writing – review & editing, Supervision. Francesco Flammini: Writing – review & editing, Supervision. Badria Sulaiman Alfurhood: Writing – review & editing, Visualization.
Teaching Case Study in Discrete Mathematics: Application of Clustering Algorithms in Brucella Traceability Research
Authors: Gu Jiqing , Hu Jing , Huang Ju , Yang Hui 
Published: January 2025

In this study, we combined discrete mathematics with computational biology to identify Brucella species using clustering algorithms. By analyzing the Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF MS) spectra of 44 Brucella isolates, which included 21 Brucella melitensis, 12 Brucella suis, and 11 Brucella abortus, we utilized a feature selection strategy to pinpoint 22 key characteristics critical for species differentiation. We then developed a spectral clustering-based model for Brucella traceability. This model offers a rapid and cost-effective alternative to traditional, labor-intensive identification methods, significantly improving the efficiency and accuracy of Brucella strain identification. Additionally, it aids in monitoring disease transmission trends, identifying outbreak sources, and formulating effective control strategies to mitigate risks. Our findings demonstrate the practical application of discrete mathematics in computational biology, contributing significantly to both scientific research and educational methodologies. This approach illustrates how mathematical concepts can be effectively applied to solve real-world biological problems, providing valuable insights for future interdisciplinary studies and innovative solutions.

Received: 10 April 2024 | Revised: 19 November 2024 | Accepted: 16 December 2024

Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.

Data Availability Statement
The data that support the findings of this study are openly available at https://doi.org/10.3390/microorganisms10081658, reference number [14]. No new data were generated.

Author Contribution Statement
Jiqing Gu: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft, Writing – review & editing. Jing Hu: Funding acquisition. Ju Huang: Software. Hui Yang: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.
Effects of a Special Technique for Right-of-Way Maintenance on Deer Habitat
Authors: Bramble W. C. , Byrnes W. R. , Hutnik R. J. 
Published: January 2025

AbstractWhite-tailed deer (Odocoileus virginiana) habitat and use were evaluated on an electric transmission right-of-way (ROW) before and after 5 different herbicide treatments and handcutting were applied using a special technique. Eval uations also were made in the adjoining forest. The technique used for all treatments provided for division of the ROW into a central wire zone and two border zones. Selective treatment of only tall-growing trees was carried out on the border zones; as contrasted with complete treatment of all trees and tall shrubs in the wire zone. In addition, a special broadcast application was given to the wire zone in the case of the herbicide pellet treatment to produce a herb-grass plant cover. Although appreciable changes occurred in specific deer habitat factors, decreases in certain factors were offset by increases in other factors. As a consequence, total habitat values remained high. Deer presence increased on all ROW treatment areas from 1982 to 1984. Deer browsed both woody and herbaceous vegetation comparably on the ROW and in the forest. The utilization factor, percent cover x percent browsed, was higher for woody vegetation in the forest and for herbaceous vegetation on the ROW.
Computational exploration and flow control analysis of alumina – magnetite/water-based hybrid nanofluid over a permeable Riga plate under the impact of Soret-Dufour effects
Authors: Mohanavel Dhivya , R Rajakumari 
Published: January 2025

The current study aims to analyze the behavior of a hybrid nanofluid, comprising of alumina and magnetite nanoparticles with base fluid as water, when it flows over a vertical permeable Riga-plate including the electromagnetohydrodynamic (EMHD) principles. The coupled governing equations are nonlinear and it has been simplified using appropriate non-dimensional parameters. A numerical analysis was then carried out utilizing the finite difference scheme, in particular Crank-Nicholson technique. This study primarily focuses on the significance of physical parameters such as magnetic field, permeability, volume fraction of nanoparticles, radiation, viscous dissipation, Soret-Dufour effect and chemical reaction which are analyzed through graphical illustrations. It has been observed that Soret and Dufour effects exert influence on the profiles of temperature and concentration. In addition, Viscous dissipation and radiation reinforces the temperature. The impact of chemical reaction retards the thickness of concentration boundary layer. The integration of nanoparticles with Riga-plate allows for better flow control and manipulation of heat transfer processes, makes it ideal for applications in the cooling process of electronics equipment, thermal management systems and energy production. Further, Response surface model (RSM) is demonstrated to analyze the sensitivity of permeable Riga plate with respect to various pertinent parameters. RSM explores more on the intricate interactions between the input variables and the response variables. It is also inferred that uplifting the value of magnetic parameter and the permeability parameter accelerates the wall shear stress. Graphical comparison made with previous studies for validating the results.
Upper premolar region as aesthetic zone II in dental implant replacement: A five-year retrospective study
Authors: Alhamdani Faaiz , Agha Bahn , Sleibi Ahmed 
Published: January 2025

Background: Aesthetic zone refers to maxillary anterior region, from canine to canine, which is important in smile aesthetics. However, based on smile width, this area may be extended to include premolars. The aim of this study was to navigate the aesthetic zone of the upper premolar region for dental implant treatment.

Methods: This study was conducted at a private dental center in Baghdad. Dental implant cases treated between January 2018 and April 2023 were included in the study. One hundred and ninety-six patients were included in the study.
The demographic data, surgical information, dental implant zones, anterior aesthetic, premolars, and molar non-aesthetic zones, were recorded. Patients with incomplete data were excluded from the study. Descriptive and inferential statistics were implemented. The chi-square test was used to determine the relationship between nominal study variables.

Results: The male-to-female ratio was 1:2. The highest percentage of implant replacement was found in the upper premolar region (28.7%), followed by the lower molar (23.6%), and the upper anterior aesthetic zone: the upper incisors (16.1%), and the upper canines (3.4%). Early success rate was approximately equal in the anterior, premolar, and other zones, but with the highest value in the premolar area (94%).

Conclusion: There is a high demand for upper premolar implants by both genders compared to the aesthetic zone. This highlights the peculiarity of this zone and underpins the suggestion for classifying the upper premolar zone as aesthetic zone II.
The effect of salivary interleukin-8 level and feeding pattern on the gingival health condition among children
Authors: Jabber Wathba Mohammed , Majbil Dhaffar Alwan , Yaseen Huda A. , Nahidh Mohammed , Mohammed Ali Marwah , Diab Hafez 
Published: January 2025

Background
The main cause of human periodontal diseases is infection with gram -ve bacteria, like Porphyromonas gingivalis and Bacteroides forsythus. Interleukin-8 (IL-8) may contribute to the periodontal tissue damage. Breastfeeding is an effective method to safeguard the child’s health and endurance. Therefore, this cross-sectional study is intended to investigate the effect of the feeding type on gingival health by monitoring the level of salivary interleukin-8.

Subjects and Methods
Fifty-seven children aged 4–6 years old were enrolled in this study. They were divided according to the type of feeding into formula feeding or breast feeding. Oral examination was done according to the protocols of oral health surveys using plaque and gingival indices to evaluate the severity of gingivitis. Salivary IL-8 level was measured by collecting saliva directly from the oral cavity and testing by enzyme-linked immunosorbent assay (ELISA) test.

Results
Most of the cases recorded had mild gingivitis (37 cases), while the rest showed moderate gingivitis (20 cases), with no severe or healthy cases. Regarding group difference between the two types of feeding, there was a high significant difference for IL-8 level with highly elevated level in the formula feeding group. Level of IL-8 showed a significant difference between 5 and 6 years old and non-significant differences with regard to plaque and gingival indices in spite of an increase in IL-8 level in moderate-type gingivitis rather than mild gingivitis.

Conclusion
The finding of this study exhibited an elevation in the level of salivary IL-8 in poor oral hygiene and older children in spite of the weak correlation. Formula feeding may influence both IL-8 level and the gingival health status.
Inovasi Microchip Sebagai Sarana Optimalisasi Pabrik Cempe Dengan Data Recording Pada Peternakan Kambing Perah Di Banyuwangi Berbasis Aplikasi
Authors: Saputro Amung Logam , Agustono Bodhi , Angga Prastiya Ragil , Atikah Madarina Hisyam Mirza , Milladiya Khuriyah Zahra Reyza , Ashilla Afandik Nadya , Nurina Naqsmagita Salsabilla , Hidayatuh Sholihah Alfi , Gufron Muhammad 
Published: January 2025

Abstract
Pesanggaran Subdistrict in Banyuwangi Regency, East Java, has significant livestock farming potential, with populations of livestock such as beef cattle, goats, and sheep. However, livestock management, particularly in terms of data recording, is still not optimal. This community service program aims to improve livestock management through digital technology by implementing two main programs: SEMAR (Setaun Manak Loro) and DISFAR (Digital Smart Farming). SEMAR focuses on education regarding dairy goat reproduction management, while DISFAR provides digital recording tools in the form of chips/ear tags to monitor estrus periods, mating, birth predictions, and livestock health records. The methods used in this program include counseling, training, evaluation, and cadre development over a 10-month period. The outcomes expected from this program include the publication of articles in reputable community service journals, popular articles in the local media, Radar Banyuwangi, a digital recording guidebook, and tutorial videos on YouTube. The implementation of this program is expected to increase the efficiency and productivity of livestock farming in Pesanggaran through the proper use of digital technology.

Abstrak
Kecamatan Pesanggaran di Kabupaten Banyuwangi, Jawa Timur, memiliki potensi peternakan yang signifikan dengan populasi ternak seperti sapi potong, kambing, dan domba. Manajemen peternak, khususnya dalam hal pencatatan data (recording), untuk saat ini masih belum optimal. Program pengabdian masyarakat ini bertujuan untuk meningkatkan manajemen ternak melalui teknologi digital dengan mengimplementasikan dua program utama SEMAR (Setahun Manak Loro) dan DISFAR (Digital Smart Farming). SEMAR fokus pada penyuluhan manajemen reproduksi kambing perah, sedangkan DISFAR menyediakan sarana recording digital berupa chip/ear tag untuk memantau masa birahi, kawin, prediksi kelahiran, dan catatan kesehatan ternak. Metode yang digunakan dalam program ini meliputi penyuluhan, pelatihan aplikasi, evaluasi, dan pembinaan kader dalam komunitas peternakan. Hasil dari program ini diharapkan mencakup publikasi artikel di jurnal layanan masyarakat bereputasi, artikel populer di media massa, buku pedoman recording berbasis digital, serta video tutorial di YouTube. Implementasi program ini diharapkan dapat meningkatkan efisiensi dan produktivitas peternakan di Pesanggaran dengan penggunaan teknologi digital yang memadai.
Reasons to Respond to AI Emotional Expressions
Authors: Díaz Rodrigo , Blatter Jonas 
Published: January 2025

Abstract
Human emotional expressions can communicate the emotional state of the expresser, but they can also communicate appeals to perceivers. For example, sadness expressions such as crying request perceivers to aid and support, and anger expressions such as shouting urge perceivers to back off. Some contemporary artificial intelligence (AI) systems can mimic human emotional expressions in a (more or less) realistic way, and they are progressively being integrated into our daily lives. How should we respond to them? Do we have reasons to reply to the appeals made by AI emotional expressions? In this paper, we examine the conditions under which AI emotional expressions could give us prudential or even moral reasons to change our behavior. We argue that these conditions do not depend on whether the emotional expression is genuine or not, but rather on the presence of features some of which can be implemented in emotive AI given our current level of technological development. We extract recommendations and warnings for the development of emotive AI.
An Overview of Ethereum Based Approaches for Landslide Prediction
Authors:   Assistant Professor, Dept. Of CSE, Sree Narayana Gurukulam College of Engineering, Ernalulam, India, Paulose Chinchu , Sham Ansiya P , P M Anu Krishna , Palanadan Athulya , P P , Sarika 
Published: January 2025

Landslides are natural disasters that cause significant damage to infrastructure, ecosystems, and human life. Accurate and timely prediction of landslides is crucial for reducing the impact of these events. This paper explores a novel approach to landslide prediction using Ethereum, a leading blockchain platform. By leveraging the capabilities of Ethereum, we propose a decentralized system that collects, stores, and analyzes environmental data through smart contracts, providing a transparent, tamper-proof, and efficient way to predict landslides. The system integrates IoT sensors, machine learning models, and blockchain to ensure data integrity, automate alerts, and enhance decision-making processes for disaster management agencies and affected.

Key Words: Landslide prediction, Blockchain, Ethereum, Smart contracts, Decentralized data, Environmental monitoring, IoT, Machine learning.
Sentiment analysis of social media data: Business insights and consumer behavior trends in the USA
Authors: Montaser Md Abdullah Al , Ghosh Bishnu Padh , Barua Ayan , Karim Fazle , Das Bimol Chandra , Shawon Reza E Rabbi , Chowdhury Muhammad Shoyaibur Rahman 
Published: January 2025

The digital age has reformed how organizations in the USA reach out to their consumers and has opened up more avenues for understanding consumer sentiment and behavior. This research explored consumer sentiment and behavior trends through social media data with particular emphasis on platforms popular in the USA. By analyzing various social media channels, the study aimed to determine leading trends that drive consumer perception and behavior in real-time. The present research focused on the main social media platforms used in the USA: X-Twitter, Facebook, Instagram, and TikTok. Sentiment analysis data was gathered using the usage of different social media platforms for their unique features and APIs. X-Twitter, being the most useful social media platform for real-time microblogging, provided a very strong API for the analyst to access the tweets, user profiles, and engagement metrics, which is very good for gathering public sentiment and trending topics. With the high volume of users, Facebook exposed the Graph API, which allowed fetching user interactions, comments, and reactions on public posts, giving insight into consumer opinions and brand perception. Also, Instagram's API enabled the collection of visual content along with captions and engagement data, enriching the analysis with multimodal sentiment insights. Three machine learning models were used, most notably, logistic regression, random forest classifier, and XG-Boost. Strategic metrics were used to evaluate the performance of the model: accuracy, precision, recall, and F1-score. With a perfect score for the two algorithms, XG-Boost and Logistic Regression were perfectly able to classify on all metrics, while Random Forest Classifier had high scores close to the other two models, though a little lower in some metrics than the other two. The results of sentiment analysis will provide actionable insights for businesses that want to improve their positioning in the market. By interpreting the data on sentiment, companies in the USA can identify strengths and weaknesses in their offerings and make targeted improvements. Sentiment analysis has proved a crucial tool for US businesses in further improving marketing and customer engagement. Sentiment analysis will help companies in the USA create a meaningful understanding of the perceptions of the general public about its products and services.
Modelo estocástico preditivo para controle de custos em obras de infraestrutura
Authors: Carmo Cristiano Saad Travassos do , Barros Ricardo Vianna da Paixão de 
Published: January 2025

Aditivos contratuais de preço em obras de infraestrutura representam desafios atuais significativos para o sucesso de projetos de grande porte, tanto no setor público quanto no privado. O presente trabalho, então, propõe um modelo preditivo estocástico para melhorar a projeção e controle de custos em obras e, assim, mitigar riscos financeiros na construção de infraestrutura em larga escala. O modelo utiliza formulações matemáticas avançadas baseadas em processos estocásticos de nascimento e morte e técnicas de Inteligência Artificial (IA) de agrupamento de séries temporais, oferecendo cenários otimistas, neutros e pessimistas para orientar as tomadas de decisões de gestão de obras. Ao integrar métodos estocásticos com IA, a abordagem proposta fornece uma estrutura robusta para gerenciar as incertezas inerentes aos projetos de construção, melhorando assim o nível de confiabilidade do orçamento e o controle financeiro.