bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhancing Image Classification with Super-Resolution Techniques: A Comparative Study of SRResNet and SRGAN
Authors: Deng Siyu
Published: November 2024

Abstract. This paper explores the use of super-resolution (SR) approaches to enhance the performance of image classification, with particular attention to the effects of various SR models on classification accuracy. Using the ImageNet Dogs dataset for assessment, the paper examines two SR techniques: Super Resolution Generative Adversarial Networks (SRGAN) and Super Resolution Residual Networks (SRResNet). The research demonstrates that deep learning-based SR methods can enhance classification accuracy. Notably, SRResNet is identified as the superior method for improving classification performance, despite generating less visually appealing images compared to SRGAN. These finding highlights that while Generative Adversarial Networks (GANs) and perceptual loss functions can enhance image quality, their impact on classification accuracy may not always be substantial. The study offers important new perspectives on the relative merits of different SR approaches, highlighting the necessity of choosing the right SR methods in accordance with the particular demands of picture classification tasks. The results suggest that SRResNet, with its focus on accuracy over visual appeal, is more effective for boosting classification model performance, offering guidance for optimizing SR methods in practical image classification applications.
An overview about hand exoskeleton actuation system and thumb motions analysis
Authors: Yuan Xichen
Published: November 2024

Abstract. Stroke patients experience impaired hand function which significantly impacts their daily activities. Robot-assisted training is essential for restoring motor function, with hand exoskeletons serving as assistive devices to support, amplify, replace, or counteract body movements. Designing an exoskeleton that accurately mimics the natural movement of the thumb and provides adequate support is a key challenge in rehabilitation. This paper designs a retrieval model for hand rehabilitation exoskeletons from 2009 to 2023, analyzing the hand anatomy and thumb motion range. It discusses various driving mechanisms for hand exoskeletons, including cable, linkage, pneumatic, gear motor, and hybrid systems. While progress has been made in experimental stages, more research is needed for clinical applications. A universal hand exoskeleton actuation system that meets all patient preferences does not exist. Ongoing research on hybrid drive systems aims to enhance hand exoskeletons for better rehabilitation and daily use for patients with hand dysfunction.
Advancements and challenges in AI-driven creative translation: A comprehensive analysis
Authors: Wei Bai
Published: November 2024

Abstract. This article provides an in-depth exploration of the use of artificial intelligence (AI) in creative translation, highlighting the advancements in machine learning algorithms and the challenges of cultural and emotional congruence. It examines several key aspects: the efficacy of sequence-to-sequence and transformer models in enhancing translation accuracy, the role of AI in cultural contextualization, and the integration of emotional intelligence to handle subtleties in translations. Comparative case studies illustrate AI's performance against human translators in handling culturally dense texts, while adaptive algorithms demonstrate the customization of AI systems for specific cultural contexts. Furthermore, the article delves into the quantitative methods used to evaluate AI translation accuracy, such as BLEU and METEOR scores, and discusses innovative techniques for improving AI translation quality through reinforced learning and context-aware translations. The potential future directions of AI in creative translation, including the use of augmented reality and large-scale language models, are also discussed, presenting a vision of AI's evolving role in breaking linguistic and cultural barriers.
Numerical solutions of PDEs for corporate bond pricing: A computational finance approach
Authors: Zeng Xiaolong , Zheng Li , Wu Yixuan
Published: November 2024

Abstract. In this paper, we will see how one can use numerical algorithms to solve PDEs, containing partial derivative terms, for corporate bond pricing. We utilise the Black-Scholes framework which involves adding stochastic interest rates and credit risk into a PDE model. Then, we will take a quick look at some different numerical methods to solve the PDE model, like finite difference methods (FDM), finite element methods (FEM) as well as standard Monte Carlo (MC) simulations. Above all, we provide some simulations to show how well they work for PDEs, and how much time it takes. We would like to highlight that numerical methods lead to more accurate and efficient bond pricing with these methods, and also that we can analyse together how well each method works under different market scenarios. The paper will end with future directions for numerical methods, which should allow including more sophisticated algorithms and machine-learning techniques that improve scalability and can be used in a real-time manner in financial markets.
Leveraging artificial intelligence in economics and finance: Enhancing decision-making and market efficiency
Authors: Zhang Daren
Published: November 2024

Abstract. Artificial Intelligence (AI) has revolutionized the fields of economics and finance by providing advanced tools for decision-making, predictive analysis, and market efficiency. This paper examines the integration of AI technologies such as machine learning and natural language processing with mathematical models, specifically ARIMA for economic forecasting, Black-Scholes for option pricing, and logistic regression for credit risk assessment. By enhancing these models with AI, we demonstrate significant improvements in prediction accuracy and decision-making capabilities. Case studies illustrate a 15% improvement in GDP growth prediction accuracy, a 20% reduction in option pricing errors, and a 20% decrease in credit default rates. The paper also discusses the future prospects of AI, including advancements in quantum computing and ethical considerations like data privacy and algorithmic bias. Implementation challenges such as high costs and data integration issues are addressed, providing a comprehensive roadmap for organizations to effectively leverage AI. This study underscores the transformative potential of AI in shaping the future of economic and financial landscapes, driving innovation and operational efficiency.
Research on the integrated application of machine learning in Unity
Authors: Hu Chuang
Published: November 2024

Abstract. In recent years, the introduction of Machine Learning Agents has enabled more individuals to apply machine learning within the renowned Unity game engine. As application development has become increasingly complex, traditional artificial intelligence (AI) struggles to keep pace with the growing demands of modern applications. However, machine learning offers new avenues for developing smarter, more dynamic game characters. This paper aims to provide a comprehensive review of the various applications of machine learning within the Unity ecosystem, including game AI, intelligent character behavior, and reinforcement learning. Through a literature review and case studies, this paper explores the specific applications of machine learning algorithms in the Unity engine and analyzes the current state of development in different fields. The research findings indicate that the application of machine learning in Unity exhibits numerous developmental trends and leaves ample room for further application development in its strong areas. This technology can be innovatively applied across many fields, from implementing advanced NPCs and third-person game agents to anti-cheat mechanisms, from image recognition-based health systems to the design of complex constrained environments. The integrated application model of machine learning and Unity3D will bring more innovation and possibilities to future application development.
Optimizing intelligent penetration path planning using reinforcement learning: A focus on valid action masking and sample enhancement
Authors: Huang Lufan
Published: November 2024

Abstract. As penetration testing is a crucial technique for the rapid evolution of cybersecurity to identify system vulnerabilities, traditional penetration testing methods face the difficulty of large amounts of manual labor and professional knowledge, which makes them difficult to scale. This paper proposes a highly automated and efficient penetration testing approach based on the MASK-SALT-DQN algorithm. MASK-SALT-DQN is a reinforcement learning model that effectively optimizes penetration path planning by valid action masking and sample enhancement. Through valid action masking, the MASK-SALT-DQN model filters out redundant and invalid actions in the solution space, reducing the complexity of the action space and improving the convergence speed and efficiency. In addition, the sample enhancement method increases the frequency of critical exploitation actions and positive rewards in the sparse-reward environment, speeding up the learning process of the agent. Experiments have been conducted on the NASim attack simulation network to demonstrate the advantages of the proposed model over the baseline method. The experimental results show that our model has a significant performance advantage over baseline methods in large networks, and it can be applied to the field of scalable and efficient automated penetration testing.
Technical Feasibility Analysis of Digital Twin Technology in the Field of Urban Disaster Prevention
Authors: Du Zixuan
Published: November 2024

Abstract. In recent years, disasters have occurred frequently around the world, causing great harm to national finances and people's safety. There is still a lack of research on digital disaster prevention technology, but with the development of technologies such as artificial intelligence and computer vision, it has gradually become possible to use digital twin technology in urban disaster prevention. This paper is mainly based on digital twin related technologies, briefly introduces the concept and importance of digital twins, and the applicable fields, and then discusses the mode of data interaction between real entities and twins and the feasibility of digital disaster prevention, so as to prevent and simulate urban disasters and derive strategies for dealing with disasters. This paper reviews the existing research on digital twin technology through the method of literature retrieval and analysis. By analyzing technologies such as the Internet of Things, sensors, three-dimensional laser technology, urban monitoring technology, satellite remote sensing technology, drone remote sensing technology, mobile device technology, lidar technology, and oblique photography, a systematic review of critical analysis is obtained, and a macro method of data interaction and modeling for digital twin technology for disaster prevention is obtained, thereby deriving the feasibility of disaster prevention based on digital twin systems.
Comparative Analysis of Distributed and Parallel Machine Learning: Principles, Models, and Applications
Authors: Li Jiatian
Published: November 2024

Abstract. Contemporarily, the explosive growth of technology and the growth of the complexity of machine learning models facilitated the advancement of parallel and distributed machine learning. This study compared and analyzed the two paradigms from three perspectives: principles, models, and applications. This research analyzed the basic principle and some important models of parallel and distributed machine learning, mainly focusing on their operating mechanism, scalability, and performance metrics. The analysis includes how these methods can help to deal with large datasets and complex models. According to the evaluations, distributed machine learning is better in managing massive data and achieving model convergence, while parallel machine learning performs well in processing complicated models quickly. These results provided opinions on each methods advantages and disadvantages and suggestions on how to choose between the two methods in different situations. This research also stressed the importance of understanding these differences in advancing machine learning and promoting innovation in data-intensive fields.
Comparative Analysis of Machine Learning, Decision Trees, and K-Nearest Neighbors for Heart Disease Prediction
Authors: Chang Xinyi
Published: November 2024

Abstract. This study investigates the efficacy of Machine Learning (ML), Decision Trees, and K-Nearest Neighbors (KNN) techniques in predicting heart disease, aiming to identify their strengths and limitations. ML models are effective in detecting complex patterns and delivering evolving predictions from large datasets but require high-quality data and can be challenging to interpret. Decision Trees provide clear, understandable decision rules, which is beneficial in clinical settings, yet they are susceptible to overfitting and instability. KNN, valued for its simplicity and flexibility, classifies heart disease based on similarity but struggles with high computational costs and sensitivity to noisy data. Experimental results indicate that each model has distinct advantages: ML excels in pattern recognition, Decision Trees offer interpretability, and KNN handles diverse data effectively. However, each also faces challenges that impact performance, such as data quality issues for ML, overfitting for Decision Trees, and computational demands for KNN. The study suggests that balancing these strengths and weaknesses is crucial for optimizing heart disease prediction models. Future research should explore hybrid approaches that combine these models' advantages while addressing their respective limitations to improve predictive accuracy and practical application in real-world scenarios.
Loan Approval Prediction Improved by XGBoost Model Based on Four-Vector Optimization Algorithm
Authors: Yu Keke , Xia Siwei , Zhang Yitian , Wang Shikai
Published: November 2024

Abstract. This paper discusses an improved XGBoost model based on four-vector optimization algorithm to improve the accuracy of loan approval prediction. Through the analysis of correlation heat maps, we found that there were significant positive and negative correlations among some variables, which laid the foundation for subsequent machine learning analysis. Based on this, we compare the traditional machine learning algorithm with the improved model to evaluate its performance in loan approval forecasting. In the confusion matrix analysis of the training set, the improved XGBoost model demonstrated excellent performance, with all loan approval predictions being correct with 100% accuracy. However, the performance in the test set was slightly different, with 1,182 projects receiving correct loan approval predictions and 99 projects forecasting errors. Of these, 26 projects that should have been predicted to be "unapproved" were incorrectly labeled as "approved," while 73 projects that should have been predicted to be "approved" were incorrectly labeled as "unapproved." These results suggest that we still need to pay attention to the misjudgment of the model in practical application. By synthesizing all model evaluation indicators, we found that the improved XGBoost model based on the four-vector optimization algorithm has a higher accuracy in loan approval prediction than the traditional XGBoost model, with an increase of 1.5%. In addition, the other evaluation indicators also show a trend of significantly better than the traditional model. This study shows that the four-vector optimization algorithm can effectively improve the application effect of XGBoost model in the field of loan approval, and provide more accurate data support and decision-making basis for the financial industry. In the future, we will continue to explore the potential and application prospects of this algorithm in other fields.
Empowering big data analytics through machine learning: Applications, challenges, and future directions
Authors: Xie Na , Zhang Wentao
Published: November 2024

Abstract. This paper explores the transformative role of machine learning (ML) methodologies in big data analytics, highlighting supervised learning, unsupervised learning, and deep neural networks' contributions to diverse sectors. Supervised learning, with regression analysis at its core, provides accurate forecasting in finance and healthcare by modeling relationships between variables. Unsupervised learning, through techniques like k-means and hierarchical clustering, uncovers patterns within data, offering insights for retail and biological analysis. Deep learning, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM), excel in complex tasks like image recognition and sequential data processing, driving advances in fields from autonomous driving to language translation. The application of ML in enhancing business intelligence, innovating fintech, and advancing healthcare analytics is discussed, alongside the challenges of data privacy, security, ethical considerations, and the skills gap. This paper underscores the need for advanced cryptographic techniques, bias mitigation strategies, and education to address these challenges. It concludes by emphasizing the importance of interdisciplinary education and AI advancements in bridging the skills gap, ensuring the ethical use of ML, and making these technologies accessible for future innovations.
Progress and development trends of lower limb exoskeleton rehabilitation robots
Authors: Lu Yuyang
Published: November 2024

Abstract. Currently, as the aging population intensifies, many elderly people are trapped by lower limb dysfunctions caused by diseases such as stroke sequelae, spinal cord injuries, and bone injuries. Research on lower limb exoskeleton robots has made progress both domestically and internationally. Foreign lower limb exoskeleton robot technology is very mature and widely applied; although starting later, domestic development in this field has been rapid. This paper studies the key technologies of lower limb exoskeleton robots from several aspects: the degrees of freedom in human lower limb movement, the cyclic movement of human gait, structural design, driving methods, and interactive control. With the continuous advancement of technology, lower limb exoskeleton robots will become more intelligent and focus more on personalized services and wearing comfort in future development, aiming to bring more convenience and help to disabled individuals, rehabilitation patients, and groups with special needs.
Design and implementation of a smoke alarm system using YOLOv8
Authors: Xu Hengchen
Published: November 2024

Abstract. This paper present a smoke alarm system built using Yolov8, a state-of-the-art object detection model. The system is designed to detect smoke in real-time environments and alert users in the event of a fire or smoke detection. The use of Yolov8 in this system offers several advantages over other object detection models, making it a suitable choice for building an effective smoke alarm system. One of the critical advantages of Yolov8 is its high accuracy in detecting objects with a small size, such as smoke. This is achieved through its efficient and robust architecture, which allows it to process images quickly and accurately. In comparison to other object detection models, Yolov8 demonstrates superior performance in terms of speed and accuracy. Furthermore, Yolov8s ability to detect objects with high precision and low false positives is crucial for a smoke alarm system. This is because smoke detection needs high accuracy to prevent false alarms and promptly detect real threats. The smoke alarm system built using Yolov8 can be implemented in various environments, including homes, offices, and public buildings. It can provide early warnings for fires and smoke, enabling timely evacuation and reducing potential injuries or damages. The implementation of this system can greatly contribute to improving safety in various environments by providing early warnings of fires and smoke.
Enhancing machine learning recommendation systems with CNN architectures: Applications and innovations in computer vision and speech recognition
Authors: Ma Qinxia
Published: November 2024

Abstract. This paper provides an in-depth analysis of enhancing machine learning recommendation systems using Convolutional Neural Network (CNN) architectures, with a focus on their applications in computer vision and speech recognition. Traditional recommendation systems often struggle with scalability and the complexity of high-dimensional data. By integrating CNNs, these systems can utilize advanced feature extraction and representation learning to more effectively process and analyze diverse data sources. Our study demonstrates significant improvements in recommendation accuracy and personalization through CNN-enhanced systems. We discuss the architecture, design principles, and advantages of CNNs, supported by case studies across various domains. Our findings illustrate the potential of CNNs to revolutionize recommendation systems by addressing existing limitations and offering innovative solutions for real-time, high-quality recommendations. This research emphasizes the importance of advanced machine learning techniques in creating robust and scalable recommendation systems, paving the way for future advancements and applications in multiple fields.
Modular design and custom development in ERP systems
Authors: Li Xu , Ji Xiaoheng , He Jiahong
Published: November 2024

Abstract. Enterprise Resource Planning (ERP) systems are integral to modern business operations, offering streamlined processes and enhanced efficiency. This paper explores the advantages and disadvantages of modular design in ERP systems and examines custom development methods to address unique industry requirements. The study highlights the benefits of modular design, such as flexibility, scalability, and reduced complexity, while also addressing challenges like integration issues and system fragmentation. Strategies for efficient module integration, including standardized protocols and middleware solutions, are discussed to mitigate these challenges. Custom development methods, including vendor tools, scratch-built modules, and third-party collaborations, are analyzed for their effectiveness in achieving tailored ERP solutions. Case studies from the manufacturing, healthcare, and retail industries illustrate the practical applications and outcomes of modular ERP systems, providing insights into the implementation process and potential obstacles. The findings underscore the importance of careful planning, stakeholder involvement, and comprehensive training to ensure successful ERP system deployment and utilization.
Leveraging artificial intelligence in Regulatory Technology (RegTech) for financial compliance
Authors: Liang Pengjian
Published: November 2024

Abstract. The rapid development of Regulatory Technology (RegTech) has introduced new methods to handle and simplify the compliance and regulatory issues surrounding financial services and products. Combining artificial intelligence (AI) with this trend is an effective way of strengthening financial services, making them safer and more convenient. The current AI technologies incorporated within RegTech applications improve the efficiency of compliance with rules and regulations, and contribute to the development and implementation of smart technology, including automated regulatory reporting, proven and trustworthy KYC processes, and reduced compliance costs. This paper presents the application of intelligent technology, such as machine learning, deep learning, natural language processing, and smart regulatory frameworks, to build modern, intelligent workflows, dynamically control policies, and instantaneously monitor transactions. We employ empirical economic data, evidenced by realistic case studies, to demonstrate the benefits brought on by intelligent technology to financial institutions, including quicker compliance with regulatory regimes, decreased operational risk, and increased transparency and accountability. Through analysing figures offered in various cases, we show the transformed compliance landscape created by RegTech, how it becomes a solution for the diverse challenges affecting financial sectors on a global scale, and how is it going to impact the whole field of financial services in the future..
Optimization of sewage treatment processes: Process control based on artificial intelligence
Authors: Liu Xu
Published: November 2024

Abstract. The optimization of sewage treatment processes is critical for improving efficiency and reducing energy consumption. This paper explores the application of machine learning and artificial intelligence algorithms in optimizing key processes such as aeration, sedimentation, and filtration. By leveraging real-time monitoring and adaptive control, these algorithms can dynamically adjust operational parameters to enhance treatment efficiency and minimize energy usage. This study provides detailed insights into the implementation and benefits of AI-driven process control in sewage treatment, supported by case studies and data analysis. The findings indicate significant improvements in treatment performance, showcasing the transformative potential of AI in environmental engineering.
Analysis of the Principle of Jet Engine and State-of-art Application
Authors: Huang Kai Wai
Published: November 2024

Abstract. In 1903, the Wright Brother made the dream of flight come true. Contemporarily, as the development of modern technology, aviation has become a major type of daily transportation. The jet engines are the magic that makes the airplanes soar high in the blue sky. As the jet engine is a major invention in human history that brought humans to the sky, it is meaningful to gig into the principle behind the engines. In this article, the principle of 5 jet engines are explained. There are benefits but also impacts due to the human beings. Environmental problems are caused by the exhausted gases, healthy of people who work and live near the airstrips and airports are threatened. Different types of jet engine produced by different companies latest are enumerated and stated with detailed data such as thrust and bypass ratio. According to the analysis, the current limitations and challenges of the development with the future prospect are stated.
A sombra como duplo da personagem no conto fantástico “As cores das bolinhas da morte”, de Ignácio de Loyola Brandão
Authors: Leite Francisco Edson Gonçalves , Silva Antonia Marly Moura da
Published: November 2024

Desde as sociedades primitivas, o ser humano tem imputado à sombra um forte poder simbólico. Na literatura, ela tem-se mostrado um importante signo, possibilitando, dentre outros aspectos, a representação da clássica dualidade humana. Nesta perspectiva, este trabalho objetiva analisar as configurações do duplo no conto fantástico “As cores das bolinhas da morte” (1999), de Ignácio de Loyola Brandão, que aborda, na construção da trama ficcional, o motivo literário da perda da sombra. Do ponto de vista teórico, este trabalho filia-se aos postulados sobre identidade, visto que aborda uma das facetas pelas quais o duplo é representado na literatura, e aos estudos sobre a literatura fantástica. A análise da referida narrativa de Brandão permite afirmar que a perda ou a ausência de sombra, fenômeno insólito por natureza, é fruto de uma cisão e problematiza a identidade da personagem, quando esta é confrontada com seu duplo. O reencontro com a sombra e sua reintegração ao sujeito não acontecem de forma pacífica, já que se processa através de uma inversão na hierarquia entre homem e sombra, estruturando uma nova relação de poder entre essas duas entidades complementares e opostas ao mesmo tempo.
A produção e circulação da teoria nos estudos brasileiros de literatura
Authors: Jobim Jose Luis
Published: November 2024

Este artigo tem por finalidade discutir os resultados provisórios de análise dos depoimentos (acessíveis em lista no YouTube: https://tinyurl.com/yp7tzhhp) de pesquisadores da área de estudos literários, com mais de 20 anos de carreira, sobre o desenvolvimento da produção de conhecimento teórico na área dos Estudos de Literatura, inclusive em suas relações com outros lugares, dentro e fora do país.
Luto e melancolia na narrativa (gráfica) fantástica "Raiz" (2018), de Dudu Torres: uma análise a partir da perspectiva freudiana
Authors: Garcia Jaimson Machado , Domingos Ana Cláudia Munari
Published: November 2024

A narrativa gráfica Raiz (2018), de Dudu Torres, mergulha na complexidade emocional experimentada por um menino em sofrimento pela morte da avó, em um ambiente de violência doméstica que agrava seu estado de melancolia. Nesse contexto, o protagonista é abruptamente confrontado com um evento de caráter sobrenatural, cuja irrupção amplia as fronteiras narrativas ao entrelaçar o substrato do real com os matizes do fantástico. Buscando desvelar essas camadas do comportamento do menino a partir da psicanálise freudiana, nossa análise se vale de três preceitos fundamentais: o do luto e da melancolia; do duplo; e da pulsão de morte. Nesse sentido, discorremos como a obra reflete a capacidade das narrativas gráficas de ilustrar conceitos complexos da psique humana através da integração entre imagem e palavra. No caso de Raiz, os desenhos do menino colaboram para esse processo de desvelamento de suas emoções, enquanto a figuração do duplo em imagem torna possível a concretização da culpa e da pulsão de morte. Essa possibilidade de criação pela imagem gráfica de eventos que traduzem o emocional em fantástico é uma característica desse tipo de mídia que buscamos acentuar neste trabalho.
Literatura de ficção afrofuturista no Brasil
Authors: Lima George
Published: November 2024

O artigo esboça alguns dos aspectos gerais da Ficção Afrofuturista produzida por escritores negros brasileiros. Além de considerar as proposições elaboradas inicialmente pelo norte-americano Mark Derry (1994) sobre “Afrofuturismo”, reflete-se sobre o modo como a literatura brasileira contemporânea mantém relações com o presente. Em função dessa perspectiva, a proposta do artigo é reconhecer a narrativa ficcional afrofuturista produzida recentemente no Brasil como uma modalidade fantástica da literatura brasileira contemporânea, que especula a condição atual do sujeito negro brasileiro em cenários outros. Identificam-se algumas das possibilidades desse modo ficcional a partir do importante elenco de escritores negros brasileiros que aparece neste século, especialmente tomando como ponto de partida a segunda década: Julio Pecly, Lu Ain-Zaila, Aldri Anunciação, Fábio Kabral, Ale Santos e Sandra Menezes. Para embasar teoricamente as considerações sobre a Ficção Afrofuturista, foram utilizadas as proposições de Beatriz Resende (2008) e Karl Erik Schollhammer (2009) sobre os aspectos gerais da Literatura brasileira contemporânea. Foram utilizadas também reflexões realizadas por Kênia Freitas e José Messias (2018) e Anne Caroline Quiangala (2020) sobre Afrofuturismo.
Symposium 4: Supporting a Community of Enterprise Educators
Authors: Smith Kelly
Published: November 2024

The Technology Enhanced Enterprise Education project (TE3) was set up to promote the use of learning technologies in the teaching and learning of Enterprise and Entrepreneurship in the West Midlands. Since it’s creation in August 2003, TE3 has helped over 5000 students learn about enterprise and has involved over 500 members of academic and academic-related staff in partner institutions. TE3 supports a community of enterprise educators by providing funds for the development of on-line materials, research into enterssprise education, or for teaching and learning events, and by making materials developed with TE3 funds available to all partners to download, adapt, and use for educational or research purposes. This paper describes the formal processes and procedures underpinning TE3 and comments on the lessons learned and added value outcomes experiences by community members.
Klasifikasi Pola Pembelian Kendaraan Bermotor Untuk Merancang Strategi Promosi Terarah Menggunakan Algoritma Logistic Regression
Authors: Arya Pramudya Ryan , Mutiarachim Atika , Setya Sunarka Puji
Published: November 2024

Penelitian ini bertujuan mengetahui pengaruh metode Logistic Regression pada klasifikasi kosumen kendaraan bermotor tahun 2021. Klasifikasi konsumen dilakukan untuk mengetahui kriteria konsumen secara spesifik, sehingga dapat melakukan promosi terarah langsung kepada konsumen yang tepat. Data diperoleh dari beberapa dealer penjualan motor di area Jawa Tengah dengan berbagai merk motor, pada rentang waktu Januari sampai Desember 2024. Data memiliki label binomial yang terdiri dari ‘cash’ untuk pembelian tunai dan ‘credit’ untuk pembelian kredit. Tahap preprocessing data dilakukan dengan menyamakan atribut dataset dari masing-masing dealer, downsampling untuk membuat jumlah data lebih simetris, kemudian seleksi data dan transformasi data untuk memastikan data yang digunakan nilainya konsisten, seleksi atribut untuk memilih atribut yang paling tepat digunakan dan memberi pengaruh signifikan. Klasifikasi dilakukan dengan algoritma Logistic Regression. Data dibagi menggunakan metode split dengan 70% data training dan 30% data testing,  Hasil performance akurasi 100% nilai classification error 0%, nilai kappa 1, nilai AUC 1.00 atau 100% di interpretasikan sangat baik, menunjukkan model algoritma Logistic Rgression sangat akurat dan tepat digunakan pada dataset ini.
Investigating the Impact of Physical and Mechanical Characteristics of Palf/Glass Fiber Reinforced In Epoxy Resin
Authors: S Venkatesan , K.T.R Thaimanavan , S Bhuvana
Published: November 2024

Recent times, the nature fibers deepen its roots in the field of composite materials. Owing to its ecofriendly characteristics, the natural fibers had its upper hand to the synthetic fiber. But, usage of natural fiber alone doesn’t bring desirable characteristics to the fabricated material. Therefore, the amalgamation of synthetic and natural fiber as the reinforcement in the polymer composite brings desirable property to the newly fabricating material. In this paper, we utilized the Pineapple Leaf Fiber (PALF) and glass fiber as the reinforcement in the epoxy resin and fabricated the new material. The paper mainly concentrates to study the physical and mechanical characteristics of the material. The uniform distribution of PALF and glass fiber over the polymer matrix was confirmed with the help of images of Scanning Electron Microscope (SEM). The PALF and glass fiber of composition of 20 % and 15% of weight ratio shows significant resistance to brittleness and have high tensile strength. Consecutively, the PALF and glass fiber of composition of 25% and 10% of weight ratio yields higher bending strength and shows implacable resistance to compression and impact load.
Thực trạng kiến thức và thái độ về dự phòng ung thư cổ tử cung của nữ sinh viên Trường Cao đẳng Y tế Hà Nội
Authors: Phạm Thúy Hường , Lê Thanh Tùng , Bùi Hà Trang , Nguyễn Ngọc Thành
Published: November 2024

Mục tiêu: Mô tả thực trạng kiến thức, thái độ dự phòng ung thư cổ tử cung của sinh viên nữ Trường Cao đẳng Y tế Hà Nội năm 2024.
Đối tượng và phương pháp nghiên cứu: Nghiên cứu mô tả cắt ngang 385 sinh viên nữ từ năm nhất tới năm thứ 3 của Trường Cao đẳng Y tế Hà Nội.
Kết quả: Điểm trung bình kiến thức về dự phòng ung thư cổ tử cung của đối tượng nghiên cứu đạt 14,7 ± 2,54 (tổng điểm kiến thức tối đa là 22 điểm) đạt 53,5%. Điểm trung bình thái độ về dự phòng ung thư cổ tử cung của đối tượng nghiên cứu đạt 15,1 ± 4,8 (tổng điểm thái độ tối đa là 26 điểm) đạt 70,4%.
Kết luận: Sinh viên có kiến thức khá tốt về việc phòng ngừa ung thư cổ tử cung, tuy nhiên kiến thức về sàng lọc và các yếu tố nguy cơ gây bệnh còn hạn chế. Mặc dù thái độ tích cực, nhưng nhiều sinh viên vẫn còn chủ quan và chưa thực hiện các biện pháp phòng ngừa bệnh.
Traditions, Rituals and Policy on Gender Discrimination in Nepal
Authors: Ghimire Tara Nath , Kafle Dol Raj , Karki Sujata
Published: November 2024

This research investigates the impact of cultural traditions on gender discrimination in Nepal, particularly through the lens of sex-selective abortion. Despite some progress, deeply rooted cultural practices continue to influence policy-making and implementation, especially regarding gender issues. These traditional customs persistently shape childbirth practices, educational opportunities, and basic rights, perpetuating gender discrimination. This study examines how entrenched cultural norms both support and hinder the advancement of gender equality in Nepal. Focusing on the interaction between cultural traditions and gender-related policies, the research explores how these customs affect birthing practices and healthcare policies. It also analyzes the cultural norms that dictate educational expectations for women and girls and evaluates the broader implications for gender rights. The study highlights how ritualistic childbirth practices and gender-specific educational expectations create disparities and challenge the effectiveness of gender equality initiatives. Recent reforms and cultural shifts in Nepal reflect efforts to address these gender imbalances. This study emphasizes the need for policy frameworks that respect cultural traditions while promoting gender equality. By integrating primary qualitative data with secondary sources and using descriptive and analytical methods, the research provides a comprehensive view of the challenges and opportunities in aligning tradition with modern gender equality goals. Ultimately, the study advocates for ongoing reform and cultural adaptation to effectively combat gender discrimination and ensure equal rights for all individuals in Nepal. It underscores the importance of developing policies that acknowledge and integrate cultural contexts while striving for genuine gender equality.
A IMPORTÂNCIA DO LOAS NA PROMOÇÃO E PROTEÇÃO DA DIGNIDADE DA PESSOA HUMANA
Authors: Firmino Yara da Silva Barbosa , Nicácio Elda Bezerra Roque
Published: November 2024

O presente trabalho tem como objetivo analisar a eficácia do Benefício de Prestação Continuada (BPC/LOAS) na garantia do mínimo existencial e na promoção da dignidade da pessoa humana de pessoas com deficiência e idosos em situação de vulnerabilidade. A Constituição Federal de 1988 estabeleceu a dignidade da pessoa humana como um princípio fundamental, garantindo direitos e proteção social para grupos vulneráveis. Nesse contexto, a Lei Orgânica da Assistência Social (LOAS) instituiu o BPC, um benefício assistencial destinado a garantir um salário mínimo mensal para aqueles que não possuem condições de prover o próprio sustento. A pesquisa busca analisar a cobertura do LOAS, sua importância na promoção da dignidade humana e os desafios enfrentados na sua implementação. Utilizando uma abordagem qualitativa e descritiva, o estudo se baseia em uma revisão bibliográfica sobre políticas sociais, direitos humanos e o LOAS. Os resultados esperados indicam que o BPC tem sido parcialmente eficaz na garantia do mínimo existencial, mas enfrenta desafios como a cobertura, o valor do benefício e a complexidade procedimental. Em síntese, o estudo busca contribuir para o debate sobre políticas públicas e a promoção da inclusão social no Brasil, avaliando a efetividade do BPC/LOAS em garantir os direitos sociais de pessoas com deficiência e idosos. Os resultados da pesquisa podem auxiliar na formulação de políticas públicas mais eficazes e na melhoria da qualidade de vida das pessoas beneficiadas pelo programa.
A IMPORTÂNCIA DAS PRÁTICAS ESG: UM ESTUDO EM EMPRESAS DE ROLIM DE MOURA E ALTA FLORESTA D’ OESTE
Authors: Souza Lucas da Silva , Pinheiro Aline Gomes Lopes
Published: November 2024

A crescente evolução de práticas de mercado alinhada a princípios socioambientais, tem surtido em esfera global um efeito positivo e tem influenciado cada vez mais um novo modelo de negócios e ações. O presente trabalho destaca e analisa as principais práticas no que tange Environmental, Social and Governance (ESG) que são pilares ambientais, sociais e governança em empresas de pequeno porte e seus desafios para a corrente sustentável de desenvolvimento de políticas estaduais, nas cidades de Rolim de Moura e Alta floresta d’ Oeste/RO. Aplica-se nas discussões uma literatura que apresenta formas de alcançar o desenvolvimento socioambiental, analisando assim as estratégias usadas na empresa para o referido tema, e descrevendo conceitos e pilares do ESG, Responsabilidade Social Corporativa (RSC) e os Objetivos do Desenvolvimento Sustentável (ODS) alinhados às pertinentes questões levantadas. Os resultados demonstram uma empresa que participa do processo de desenvolvimento de políticas ESG e à busca para maximização de lucros e de proteção dos seus princípios pautados em ética e legislação adaptados às necessidades humanas.
CONTAÇÃO DE HISTÓRIAS COMO RECURSO POTENCIALIZADOR DAS PRODUÇÕES IMAGINÁRIAS DA CRIANÇA
Authors: Truccolo Adriana Barni
Published: November 2024

Introdução: O processo de imaginação na infância tem sido considerado como fundamental para o desenvolvimento das potencialidades criativas da criança; e uma forma de explorar esse universo imaginativo é através da contação de histórias. Objetivo geral: Potencializar as produções imaginárias de crianças através da contação de histórias on line;. Método: Estudo qualitativo de intervenção pedagógica, realizado para crianças da primeira infância no formato online via You Tube Kids e Facebook no período entre abril e junho com postagens quinzenais de vídeos de contação de histórias. Resultados: Foram postados 16 vídeos quinzenalmente, de abril a junho e até o momento computou-se 120 visualizações. Foram contadas histórias de autores nacionais e estrangeiros que estimulassem não somente a imaginação das crianças mas também trabalhassem temas como sentimentos de medo e tristeza (Como eu me sinto quando tenho Medo; Como eu me sinto quando estou triste), etnia (Meu crespo é de rainha), sentimentos (O livro dos sentimentos), amizade (a ponte) bem como empatia e solidariedade (Elmer o elefante xadrez), e preservação do meio ambiente (meu planeta rima com água). Acredita-se que as histórias contadas proporcionaram momentos não só de ludicidade e estímulo à imaginação e criatividade das crianças mas também ofereceram conteúdo de qualidade em acordo com a meta 4.2 do ODS 4 Educação de qualidade. Conclusão: Acredita-se que em uma próxima dinâmica, as alunas e os alunos estarão mais descontraídos e familiarizados com a câmera alcançando um público maior.
EMPREENDEDORISMO NA ENFERMAGEM: OPORTUNIDADE DE AUTONOMIA E VISIBILIDADE PROFISSIONAL
Authors: Pinheiro Loyana Mendes , Bandeira Suellen Cavalcante , Machado Thatyana Borges
Published: November 2024

A enfermagem empreendedora é uma abordagem essencial para enfrentar os desafios do setor de saúde, promovendo inovação nos campos de atução e nos cuidados com a saúde, consolidando a profissão com a ciência, tecnologia e dando autonomia pessoal, profissional e financeira. Objetivo: Discutir o empreendedorismo na enfermagem como uma oportunidade de autonomia e visibilidade profissional. Metodologia: Revisão integrativa da literatura, o qual foram utilizadas fontes bibliográficas no período de 2019 a 2024. Foram selecionados estudos por meio da SciElo, PubMed, Biblioteca Virtual em Saúde (BVS), Conselho Federal de Enfermagem (COFEN). Os critérios de inclusão adotados foram documentos disponíveis na íntegra, em português e inglês, que apresentavam no título ou resumo aspectos relacionados ao tema. E de exclusão, artigos e documentos publicados antes do período estabelecido, bem como aqueles que não possuíam conteúdo relevante para a temática tratada. Resultados: Da análise de dados obtidas foram selecionados dez artigos para a elaboração da pesquisa, que permitiram analisar as evidências existentes referentes aos tipos de empreendedorismo, da autonomia do Enfermeiro respaldado em lei e do empreendedorismo empresarial na Enfermagem. Conclusão: O empreendedorismo na enfermagem representa uma poderosa oportunidade para que os profissionais desta área alcancem novos patamares de autonomia e reconhecimento.
A PRODUÇÃO TEXTUAL DE ALUNOS DE 1º PERÍODO DE GRADUAÇÃO DA UFAM: UMA ANÁLISE RETÓRICO-DISCURSIVA
Authors: Benezar Laís Freire , Rosas Robert Langlady Lira
Published: November 2024

O estudo analisa a produção de textos dissertativo-argumentativos de alunos de primeiro período de curso de graduação da UFAM, elaborados no contexto das disciplinas Língua Portuguesa I e Português Instrumental, avaliando a referenciação e a argumentação enquanto grandes processos linguístico-discursivos favorecedores do projeto de dizer dos alunos, à luz da Linguística Textual (Koch, 2007; 2015), de aportes do Funcionalismo (Neves, 2001).
Đề xuất quy trình tính toán độ bền dầm bê tông cốt thép chịu tác dụng của lực cắt theo TCVN 5574:2018
Authors: Sang Trần Minh , Duy Hoàng Công , Lan Phạm Thị
Published: November 2024

Bài báo này phân tích lý thuyết tính toán độ bền cấu kiện bê tông cốt thép chịu tác dụng của lực cắt theo Tiêu chuẩn TCVN 5574:2018. Từ đó đề xuất một quy trình tính toán độ bền của dầm bê tông cốt thép chịu lực cắt theo tiêu chuẩn hiện hành của Việt Nam và sơ đồ hóa quy trình này, giúp việc thiết kế kết cấu của các kỹ sư đơn giản hơn.
ANALISIS DESKRIPTIF KUALITATIF IMPLEMENTASI WORD OF MOUTH DALAM KOMUNIKASI PEMASARAN PRODUK BROOKLAND COFFEE
Authors: Fachreza Abdullah Vicky Imam , Soebiagdo Soebiagdo
Published: November 2024

This research aims to explore and describe exactly how the application of word of mouth marketing communication in Brookland Coffee's product marketing strategy. The research method uses a descriptive qualitative approach by conducting direct observations and in-depth interviews with key informants consisting of the owner, baristas, and consumers of Brookland Coffee. The results showed that Brookland Coffee consistently implements a word of mouth strategy by prioritizing aspects of trust and consumer confidence. This strategy is implemented through a family approach, networking, and community building, especially the bicycle community. The research conclusion revealed that the effectiveness of word of mouth succeeded in strengthening Brookland Coffee's marketing communications, supported by consistent product quality, good service (hospitality), and the use of Instagram social media as a supporting platform for marketing strategies. Brookland Coffee succeeded in building a strong brand identity through a combination of offline and online word of mouth communication.