bluuebunny/crossref_metadata_2025_split huggingface dataset:
DMAIC Cycle for the New Product Launch Process: FMEA and DOE Applications for Built-In Oven
Authors: AKTAR DEMİRTAŞ Ezgi , YETİMLER KOSTUR Gamze , KARA Mehmet Erol 
Published: January 2024

Objective: Six Sigma-SS and the DMAIC-Define, Measure, Analyze, Improve, Control cycle are employed to enhance the success of the launch of a new built-in oven with a digital panel.
Method: Following the define and measure phases, to identify and eliminate potential failures, Failure Mode and Effect Analysis-FMEA is conducted and control plans were composed during the analyse phase. In the improvement phase, the Risk Priority Numbers-RPNs obtained through FMEA and recommendations were listed. Using Design of Experiments-DOE, the lifespan of bulbs in ovens was maintained at the target value while reducing procurement costs. In the control phase, the results of validation experiments and the gains were reported.
Findings: After reporting the RPNs related to high-risk failure modes and providing improvement recommendations, DOE and Analysis of Variance were employed to determine which type of oven and brand of bulb could be used. If implemented, the expected annual savings are approximately 20,000 Euros.
Originality: This article focuses on an integrated approach for a product that is set for a new launch. It aims to improve design and production processes using the SS while also targeting cost reduction in procurement processes. To the best of our knowledge, no similar study has been encountered in the literature.
Technology Adoption, Innovation, and the South Korean Miracle
Authors: ATTAR M. Aykut 
Published: January 2024

Purpose: The main purpose is to understand the microeconomic foundations of the South Korean miracle through careful analysis and a realistic equilibrium model.
Methodology: The paper constructs a simple equilibrium model where productivity growth is endogenous to (i) human capital, (ii) the country’s distance to the global technology frontier, and (iii) the level of urban agglomeration. The paper identifies and calculates unobserved productivity terms using various observed variables from South Korean national accounts for the post-1960 period. The paper then presents the structural estimates of the model parameters and the results of the decomposition analysis.
Findings: While the South Korean economy was initially using a backward technology, it became an innovation economy in the early 1980s. Structural estimates show that urban agglomeration is not statistically significant in the South Korean case. Finally, a decomposition analysis shows that, in the early 1960s, human capital and distance to the frontier made similar contributions to productivity growth.
Originality: The model economy has two sectors. Technology in the modern sector exhibits Constant Returns to Scale, but traditional technology is constrained by Decreasing Returns to Scale. In addition, both the technology adoption regime and the innovation regime can be represented by the same mathematical function, and the article is therefore theoretically original.
Source apportionment of PM2.5 in Montréal, Canada, and health risk assessment for potentially toxic elements
Authors: Fakhri Nansi , Stevens Robin , Downey Arnold , Oikonomou Konstantina , Sciare Jean , Afif Charbel , Hayes Patrick L. 
Published: January 2024

Abstract. Source apportionment of PM2.5 was performed using positive matrix factorization (PMF) based on detailed chemical composition data from 24 h filter samples collected over a 3-month period (August–November 2020) at an urban site in Montréal, a Canadian city with a population of approximately 4×106 people. This source apportionment study, which examined the main contributing sources to PM2.5 using a larger suite of organic molecular markers than other Canadian studies, is the first of its sort in Canada. A focus of this study was on quantifying previously unresolved sources of PM2.5 through the inclusion in the PMF analysis of additional organic molecular markers beyond those measured typically by the Canadian government's National Air Pollution Surveillance Program (NAPS). The organic species included in the PMF model were comprised of six n-alkanes, two fatty acids, one dicarboxylic acid, two biogenic secondary organic aerosol (SOA) tracers, and hopane. Secondary inorganic aerosols (SIAs) and SOAs were the dominant components and constituted 39 % of the measured PM2.5 mass, while the local primary anthropogenic sources, namely traffic exhaust, road dust, industrial, and cooking emissions, contributed 23 %. The chemical transport model GEOS-Chem revealed that ammonium sulfate concentrations in Montréal are strongly influenced by both local sources in Quebec and transboundary input from the United States, with the transboundary input exceeding the local emissions for SOA. Co and Cr(VI) presented an elevated cancer risk, highlighting that more attention should be given to these trace metals, which were associated with industrial emissions by the PMF analysis. Furthermore, the results showed that industrial emissions were minor contributors to the total PM2.5 mass concentration but were the largest contributors to Co and Cr(VI) concentrations. Thus, the health hazards associated with this source cannot be entirely established by the PM2.5 mass concentration alone. This study highlights that, when evaluating air quality in Montréal and other urban regions, the prioritization of sources for mitigation strategies will diverge if one considers total PM2.5 mass concentration or the concentration of individual particulate-bound contaminants. Furthermore, the large transboundary contribution from the United States to total PM2.5 levels suggests that future municipal, provincial, and federal monitoring and regulations would be more effective if they focus on specific high-risk contaminants (e.g., Co and Cr(VI) rather than total PM2.5).
Historical and projected future runoff over the Mekong River basin
Authors: Wang Chao , Leisz Stephen , Li Li , Shi Xiaoying , Mao Jiafu , Zheng Yi , Chen Anping 
Published: January 2024

Abstract. The Mekong River (MR) crosses the borders and connects six countries, including China, Myanmar, Laos, Thailand, Cambodia, and Vietnam. It provides critical water resources and supports natural and agricultural ecosystems, socioeconomic development, and the livelihoods of the people living in this region. Understanding changes in the runoff of this important international river under projected climate change is critical for water resource management and climate change adaptation planning. However, research on long-term runoff dynamics for the MR and the underlying drivers of runoff variability remains scarce. Here, we analyse historical runoff variations from 1971 to 2020 based on runoff gauge data collected from eight hydrological stations along the MR. With these runoff data, we then evaluate the runoff simulation performance of five global hydrological models (GHMs) forced by four global climate models (GCMs) under the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). Furthermore, based on the best simulation combination, we quantify the impact of future climate change on river runoff changes in the MR. The result shows that the annual runoff in the MR has not changed significantly in the past 5 decades, while the establishment of dams and reservoirs in the basin visibly affected the annual runoff distribution. The ensemble-averaged result of the Water Global Assessment and Prognosis version 2 (WaterGAP2; i.e. GHM) forced by four GCMs has the best runoff simulation performance. Under Representative Concentration Pathways (RCPs; i.e. RCP2.6, RCP6.0 and RCP8.5), the runoff of the MR is projected to increase significantly (p<0.05); e.g. 3.81 ± 3.47 m3s-1a-1 (9 ± 8 % increase in 100 years) at the upper reach under RCP2.6 and 16.36 ± 12.44 m3s-1a-1 (13 ± 10 % increase in 100 years) at the lower reach under RCP6.0. In particular, under the RCP6.0 scenario, the increase in annual runoff is most pronounced in the middle and lower reaches, due to increased precipitation and snowmelt. Under the RCP8.5 scenario, the runoff distribution in different seasons varies obviously, increasing the risk of flooding in the wet season and drought in the dry season.
Developing Culture of Mentorship
Authors: Durrani Shazia Fakhir , Hameed Ammara 
Published: January 2024

The idea of ‘mentoring’ developed 3000 years ago, with the character of ‘Mentor’ in Homer's Greek poem, Odyssey.
Odysseus delegated his juvenile son Telemachus under care of his old and wise friend, Mentor, when he had to leave his young son for 10 years to fight in the Trojan War.1 Mentoring is important for development of the medical profession. It is bidirectional and benefits both mentor and mentee
SOSIALISASI BAHAYA ROKOK DIKALANGAN REMAJA DENGAN GERAKAN SISWA BEBAS ASAP ROKOK
Authors: Dewi Masita Sari , Fajar Wulan , Desi Rihfa Fauziah  , Annisa Adillah  , Novita Nurpadilah  , Salsa Agustin  , Nuzul Gyanata  
Published: January 2024

Latar belakang : Kebiasaan merokok sudah menjadi masalah kesehatan utama yang terjadi di berbagai negara. Secara global, jumlah perokok di seluruh dunia mencapai 1,3 milyar orang dengan 942 juta laki-laki dan 175 juta perempuan yang berusia 15 tahun lebih. Tujuan: Untuk memberikan edukasi serta meningkatkan pengetahuan kepada para siswa SMK Garuda Nusantara terkait dengan bahaya dari asap rokok dan bagaimana cara mengatasi masalah tersebut. Metode: Edukasi dilakukan dengan cara penyampaian langsung kepada responden dengan alat peraga yang digunakan yaitu power point presentation. Kegiatan dilaksanakan selama 120 menit, terdiri dari 5 menit pretest, 80 menit penyampaian materi dan sesi tanya jawab, dan kemudian diakhiri dengan posttest selama 5 menit. Hasil: Berdasarkan hasil pretest (67,75%) dan posttest (74,19%) terdapat peningkatan pengetahun yang cukup baik bahwa hampir semua siswa yang mengikuti penyuluhan dapat menerima pengetahuan dengan baik setelah diberi edukasi. Kesimpulan: Pengetahuan peserta mengenai bahaya merokok meningkat setelah dilakukan edukasi bebas asap rokok.
OSOBENOSTI DOŽIVLJENOG I POČINJENOG VRŠNJAČKOG NASILJA U OSNOVNOJ I SREDNJOJ ŠKOLI
Authors: Pehlić Izet , Vučkić Ermin 
Published: January 2024

Uvidi u konsultirane recentne izvore pokazali su povezanost doživljenog i počinjenog vršnjačkog nasilja. Cilj istraživanja bio je da se na temelju stavova osnovnoškolaca i srednjoškolaca istraže osobenosti doživljenog i počinjenog vršnjačkog nasilja u osnovnoj i srednjoj školi na području Unsko-sanskog kantona.
Od istraživačkih metoda korištene su metoda teorijske analize i deskriptivno-analitički survey metod, a od istraživačkih tehnika anketiranje i skaliranje. Od istraživačkih instrumenata korišteni su Upitnik o socio-demografskim podacima i Skala vršnjačkog nasilja. Istraživački uzorak, koji je po svojoj prirodi bio slučajni stratificirani, činilo je 300 osnovnoškolaca i 300 srednjoškolaca s područja Unsko-sanskog kantona.
Rezultati istraživanja su pokazali: kod djece i mladih statistički značajno je veće doživljeno od počinjenog vršnjačkog nasilja; srednjoškolci su u većoj mjeri počinioci vršnjačkog nasilja od osnovnoškolaca; ispitanici muškog spola su u statistički značajno većoj mjeri doživjeli i veći su počinioci vršnjačkog nasilja od ispitanika ženskog spola; učenici sa slabijim školskim uspjehom su u statistički značajno većoj mjeri doživjeli i veći su počinioci vršnjačkog nasilja od ispitanika s boljim školskim uspjehom; djeca i mladi koji su u većoj mjeri doživjeli vršnjačko nasilje istovremeno su u statistički značajno većoj mjeri i počinioci vršnjačkog nasilja.
Izveden je zaključak kako bi rezultati istraživanja mogli biti podsticaj i osnova za kreiranje i realiziranje socijalno-pedagoških programa koji bi bili u funkciji prevencije vršnjačkog nasilja.
Dimensi Ukuran Tubuh Pedet Sapi Bali Betina Polled Dan Sapi Bali Betina Non Polled Umur 6-8 Bulan
Authors: Parmadi Gio , Sonjaya Herry , Firmiaty Sri , Syarifuddin Syarifuddin 
Published: January 2024

Penelitian ini bertujuan untuk mengetahui perbedaan ukuran tubuh pedet betina sapi Bali polled dan betina sapi Bali non polled umur 6-8 bulan. Penelitian ini menggunakan pedet sapi Bali betina polled dan pedet sapi Bali betina non polled dengan umur 6-8 bulan milik masyarakat sebanyak 30 ekor (15 ekor pedet sapi Bali polled betina dan 15 ekor pedet sapi Bali betina non polled). Alat yang digunakan dalam penelitian ini yaitu, pita ukur dan tongkat ukur yang digunakan untuk mengukur panjang badan, lingkar dada, tinggi badan dan lebar kepala. Berdasarkan hasil penelitian mengenai perbedaan ukuran tubuh pedet betina sapi Bali polled betina dan sapi Bali betina non polled secara statistik tidak menunjukkan perbedaan yang nyata. Penelitian menyimpulkan pertumbuhan panjang badan, lingkar dada dan lebar kepala pada sapi Bali polled betina sama dengan ukuran tubuh sapi Bali betina non polled.
Construction of Personalized Movie Recommendation Model Relying on Recurrent Neural Network
Authors: Qin Xie Et al. 
Published: January 2024

Recommendation system and classification machine learning techniques have emerged as a major area of investigation into the issue of information overload. Software tools called "recommender systems" aim to help users make informed choices about services. This paper developed a low-dimensional embedding in which a limited number of features can be used to represent the data object. For the purpose of acquiring the low rank latent factors, matrix factorization techniques have received a lot of attention. The proposed model is stated as the Collaborative Filtering Machine Learning (CFML) for the multi-label classification are focused. In most extreme edge lattice factorization plan of cooperative sifting, appraisals framework with different discrete qualities is treated by uncommonly stretching out pivot misfortune capability to suit numerous levels. The comparative analysis is performed for the two-class classifier into a single multi-class classifier. Alternately, multiple two-class classifiers can be arranged in a hierarchical fashion to create a multi-class classifier. To deal with the ordinal rating matrix's completion. The performance of the CFML model is effective for the recommendation system design. Through automatic feature extraction and pattern recognition, deep learning models excel at capturing intricate relationships and latent factors that underlie user preferences. The paper discusses the multifaceted considerations that these systems can incorporate, such as genre, cast, director, viewer ratings, and individual viewing history. Simulation analysis examine the impact of deep learning-based movie recommenders on streaming platforms and the entertainment industry, demonstrating their ability to not only suggest movies but to curate a personalized journey through the world of cinema.
Fusion Artificial Intelligence Technology in Music Education Teaching
Authors: Liang Zhang Liang Zhang 
Published: January 2024

This paper proposed innovative EFDfO (Entropy Features Data Fusion Optimized) framework, a data-driven approach aimed at revolutionizing music education teaching. EFDfO combines data fusion, feature extraction, and optimization techniques to customize teaching strategies to individual students' unique learning profiles. The data related to students are collected with different sources and data were fused. The fused data are optimized with the Whale optimization technique to estimate the performance of the students. Simualtion analysis of the EFDfO demonstrates its potential to enhance student performance, with an average improvement of approximately 18% to 20% observed in pre-test and post-test scores. Moreover, the classification results indicate that, in most cases, EFDfO accurately categorizes students based on their performance and learning characteristics, although further refinement is needed to reduce misclassifications. Additionally, with the proposed EFDfO model the performance of the students are improved. EFDfO offers a promising avenue for personalized music education, ultimately enhancing students' learning experiences and outcomes.
Off-Road Terrain Identification And Analysis
Authors: Nagula Sai Sathvik Et al. 
Published: January 2024

Background: The role of the Terrain is paramount for any autonomous vehicle to drive safely on any type of surface. The Autonomous vehicles should have the capability of identifying the terrain and should adapt to the environment. With the evolution of robotics and Artificial Intelligence, and understanding diverse terrains, the techniques for terrain identification are also advancing with a major focus on safety.
Methodology: To make Terrain Detection and Identification more reliable we used instance segmentation which is a more sophisticated type of segmentation that goes a step ahead of semantic segmentation by performing both object detection and segmentation at the same time. In order to perform Instance segmentation, we used the YOLOv8 architecture which is considered to be the state-of-the-art CNN (Convolutional Neural Network) architecture. The YOLOv8 model was trained on an Off-road Terrain Dataset.
Results: Our findings indicate that the state-of-the-art YOLOv8 instance segmentation model provided the best results for terrain detection and segmentation with a threshold confidence of 0.60, and the results provide a maximum confidence of 0.92 which indicates an accurate segmentation model for the given terrain detection problem.
Conclusion: The present work motivates for a more viable hardware model that makes use of trained computer vision models and cutting-edge sensors that can be tested on different soils and terrain. The results obtained can be used to study about the different Terrains and select the most suitable model, this in turn drives for further research in the subject of Terrain Identification and Detection.
Realtime Anomaly Detection in Healthcare IoT: A Machine Learning-Driven Security Framework
Authors: Vaishali V. Raje Et al. 
Published: January 2024

Healthcare IoT, a fast-growing field, could revolutionize patient monitoring and intervention. This interconnection raises new security concerns, requiring real-time anomaly detection to protect patient data and device integrity. This study presents a novel security framework that uses combination of Hidden Markov Models (HMM) and Support Vector Machine (SVM) to detect anomalies in real-time healthcare IoT environments with high accuracy. The framework prioritizes real-time strength and efficiency. Sensor data from wearables, medical devices, and other IoT devices is carefully segmented into time intervals. Features are carefully derived from each segment, including statistical summaries, patterns, and frequency domain characteristics. Feature engineering is essential for accurate anomaly detection. Integrating HMM and SVM capabilities is the framework’s core. HMM accurately represent concealed states within divided data sequences and analyze patterns of change over time. After that, an independent-trained SVM examines each state. Using proximity to a decision hyperplane in feature space, this SVM can classify data points as normal or anomalous. This method augments HMM temporal capabilities with SVM classification efficiency, increasing sensitivity to anomalous patterns and reducing false positives. The framework’s exceptional performance is shown by extensive evaluations on the PhysioNet Challenge 2017 dataset, which includes diverse ECG recordings with labeled anomalies. HMM-SVM outperforms Naive Bayes, LSTM, and Random Forest with 98.66% accuracy. The framework also has high precision, recall, and F1-score, indicating a refined ability to detect real anomalies and reduce false alarms. The framework prioritizes real-time understanding and application alongside its remarkable precision. HMM-SVMs reveal hidden data state changes, revealing context and possible causes of anomalies. Modular design and efficient algorithms enable seamless integration of real-time functionality in low-resource IoT devices, enabling quick and effective security responses. To conclude, this study introduces an HMM-SVM framework for fast Healthcare IoT abnormality detection. The framework emphasizes comprehensibility and real-time applicability while achieving high accuracy. This framework can protect patient data, improve device security, and create a more reliable healthcare IoT ecosystem.
Analysis and Prospect of Recommendation System on social media
Authors: Yuan Yilong 
Published: January 2024

Recommendation system works surprisingly well on providing interesting and informative contents to individual user by matching suitable information with target users, and it tremendously reduces the time users spend for searching relative contents while it is also cutting back the workload of content providers. There are two popular recommendation techniques: content-based and collaborative based filtering. The abilities of each technique is compared and analyzed, and how well they match the need of users on the social media platform. In addition, a typical form of hybrid recommendation that has the advantages of multiple techniques is also taken in the comparison. From the perspective of users in social media, each specific technique achieves some degree of success for the overall user satisfaction. Integrating techniques is a valuable attempt. This paper presents a look the origin and the development of recommender system in the past. The evolution of recommendation provides insight regarding what the future of recommender will become. The architecture of recommender system is also reviewed for understanding the way system performs. The key component inside the recommender architecture including matching, filtering, and objects utilized by technique which are critical factors in determination of prediction are examined. Different techniques used in filtering are analyzed for recognizing the advantages and the disadvantages for the purpose of social media. After all, the effectiveness of the hybrid recommendation technique on dealing with limitations of popular filtering is demonstrated.
Chaotic Phenomenal and One-dimensional Logistic Map
Authors: Zheng Yuzhe 
Published: January 2024

Introducing the basic properties of logistic map. The paper includes the proofing of the stability of fixed points in different domain of and determining the property of period doubling of Logistic map, describing Chaos phenomenon in logistic map by showing the stability of fixed points in different intervals of , researching 3-period limit cycle in the Logistic Map bifurcation diagram, showing a special characteristic of the logistic map on the region on the bifurcation diagram that exist 3-period limit cycles, collecting the different value of at one, two, four, eight, sixteen and thirty-two pitchfork points from the Logistic Map Bifurcation diagram to calculate Feigenbaum constant and proving the Feigenbaum can also be used to determine the pitchfork bifurcation speed of logistic map when r is between zero to negative two. Showing the basic properties of the Mandelbrot Set. Showing the result of computer simulation in real part to the Mandelbrot Set to determine the relationship between the Logistic Map bifurcation diagram and Mandelbrot Set.