bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep Learning-Based Waste Classification with Transfer Learning Using EfficientNet-B0 Model
Authors: Risfendra Risfendra , Ananda Gheri Febri , Setyawan Herlin
Published: January 2025

Recycling of waste is a significant challenge in modern waste management. Conventional techniques that use inductive and capacitive proximity sensors exhibit limitations in accuracy and flexibility for the detection of various types of waste. Indonesia generates approximately 175,000 tons of waste per day, highlighting the urgent need for efficient waste management solutions. This study develops a waste classification system based on deep learning, leveraging the powerful EfficientNet-B0 model through transfer learning. EfficientNet-B0 is designed with a compound scaling method, which uniformly scales network depth, width, and resolution, providing an optimal balance between accuracy and computational efficiency. The model was trained on a dataset containing six classes of waste—glass, cardboard, paper, metal, plastic, and residue—totalling 7014 images. The model was trained using data augmentation and fine-tuning techniques. The training results show a test accuracy of 91.94%, a precision of 92.10%, and a recall of 91.94%, resulting in an F1-score of 91.96%. Visualization of predictions demonstrates that the model effectively classifies waste in new test data. Implementing this model in the industry can automate the waste sorting process more efficiently and accurately than methods based on inductive and capacitive proximity sensors. This study underscores the significant potential of deep learning models, particularly EfficientNet-B0, in industrial waste classification applications and opens opportunities for further integration with sensor and robotic systems for more advanced waste management solutions.
Classification of Toraja Wood Carving Motif Images Using Convolutional Neural Network (CNN)
Authors: Nurilmiyanti Wardhani  , Asrul Billy Eden William , Antonius Riman Tampang  , Sitti Zuhriyah  , Abdul Latief Arda
Published: January 2025

Wood carving is a cultural heritage with deep meaning and significance for the Toraja ethnic group's culture. By understanding the meaning of each Toraja carving, both tourists and the local community can gain knowledge about Toraja culture, thereby preserving and maintaining the culture amidst modern developments. Image processing approaches, particularly the development of Convolutional Neural Networks (CNN), offer a solution for extracting information from the diverse and intricate patterns of Toraja wood carvings. This study is highly significant as it implements a deep learning model using the CNN algorithm optimized with the ResNet50 architecture. The methodology in this study involves adjusting the batch size during the model training phase and applying weak-to-strong pixel transformation during the double threshold hysteresis phase in the Canny Feature Extraction process on the edges of Toraja carving images, resulting in ResNet50 architecture accurately recognizing the patterns of Toraja wood carvings. The results demonstrate significant improvements in the performance of the ResNet50 architecture with the preprocessed dataset. average precision, recall, precision, and F1-Score improvements in each Toraja carving class. For the Pa' Lulun Pao class, it was found that the precision and recall values were 0.94, and the F1-Score was 0.94. The Pa’ Somba class also showed good results, with a precision value of 0.9697, a recall of 0.96, and an F1-Score of 0.9648. The Pa’ Tangke Lumu class showed even better results, with a precision value of 0.9898, a recall of 0.97, and an F1-Score of 0.9798. The Pa’ Tumuru class also demonstrated good performance, with a precision value of 0.9327, a recall of 0.97, and an F1-Score of 0.9500. This study not only underscores the effectiveness of processing in enhancing CNN capabilities but also opens opportunities for further research in applying these methods to various image types and exploring different CNN architectures.
Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine Learning
Authors: Parung Ratu Anggriani Tangke , Parhusip Hanna Arini , Trihandaru Suryasatriya
Published: January 2025

Swiftlet nests are highly valued for their health and cosmetic benefits, with moisture content crucial in determining their quality. Traditional moisture measurement methods are often slow and can potentially damage the samples. This study introduces PRORESKA, an innovative system utilizing resistance sensors and Machine Learning (ML) for non-destructive, and real-time moisture measurement. The system incorporates a voltage divider circuit to establish a correlation between resistance data and moisture content. Three mathematical models (linear, exponential, and modulated exponential) and a neural network were employed to predict moisture content. Validation tests conducted on paper and swiftlet nests indicated that the neural network model, enhanced through transfer learning, achieved superior accuracy. The results demonstrated a strong correlation between predicted and actual moisture content (R² = 0.9759), with the neural network model attaining a mean squared error (MSE) of 0.01. This method holds significant potential to improve the efficiency and cost-effectiveness of moisture measurement for swiftlet nests and similar applications.
Cloud Node Auto-Scaling System Automation Based on Computing Workload Prediction
Authors: Tri Fidrian Arya  , Reza Fuad Rachmad  , Achmad Affandi
Published: January 2025

Auto-scaling systems in cloud computing are important for handling application workload fluctuations. This research uses machine learning to predict resource requirements based on workload work patterns and design an automatic scaling system. The dataset used includes features of node name, time, CPU usage percentage, and RAM usage. The ML model is applied for prediction regression of CPU usage percentage, CPU load, and RAM usage, and then server workload is classified into four categories: Very High, High, Low, and Very Low. The autoscaling system used is horizontal scaling. From the results of this research, it was found that the stacking algorithm with the base learner Random Forest and XGBoost had better performance in producing predictive regression. Then, after performing stability testing using K-Fold cross-validation by classifying based on workload status, it was found that the Gradient Boosting algorithm had better results compared to other algorithms, namely for the percentage of CPU usage with an accuracy of 0.998, precision 0.9, recall 0.878, f1score 0.888; CPU load average 15 minutes with accuracy 0.997, precision 0.854, recall 0.863, f1score 0.863; Meanwhile, the percentage of RAM usage is accuracy 0.992, precision 0.986, recall 0.986, and f1score 0.986. However, the XGBoost algorithm also has test results that are almost the same as Gradient Boosting.
IoT Security: Botnet Detection Using Self-Organizing Feature Map and Machine Learning
Authors: Susanto  , Stiawan Deris , Santoso Budi , Sidabutar Alex Onesimus , Arifin M. Agus Syamsul , Idris Mohd Yazid , Budiarto Rahmat
Published: January 2025

The rapid advancement of Internet of Things (IoT) technology has created potential for progress in various aspects of life. However, the increasing number of IoT devices also raises the risk of cyberattacks, particularly IoT botnets often exploited by attackers. This is largely due to the limitations of IoT devices, such as constraints in capacity, power, and memory, necessitating an efficient detection system. This study aims to develop a resource-efficient botnet detection system by using the Self-Organizing Feature Map (SOFM) dimensionality reduction method in combination with machine learning algorithms. The proposed method includes a feature engineering process using SOFM to address high-dimensional data, followed by classification with various machine learning algorithms. The experiments evaluate performance based on accuracy, sensitivity, specificity, False Positive Rate (FPR), and False Negative Rate (FNR). Results show that the Decision Tree algorithm achieved the highest accuracy rate of 97.24%, with a sensitivity of 0.9523, specificity of 0.9932, and a fast execution time of 100.66 seconds. The use of SOFM successfully reduced memory consumption from 3.08 GB to 923MB. Experimental results indicate that this approach is effective for enhancing IoT security in resource-constrained devices.
Manhattan Distance-based K-Medoids Clustering Improvement for Diagnosing Diabetic Disease
Authors: Solikhun  , Rahmansyah Siregar Muhammad , Pujiastuti Lise , Wahyudi Mochamad
Published: January 2025

Diabetes is a metabolic disorder characterized by blood glucose levels above normal limits. Diabetes occurs when the body is unable to produce sufficient insulin to regulate blood sugar levels. As a result, blood sugar management becomes impaired and there is no cure for diabetes. Early detection of diabetes provides an opportunity to delay or prevent its progression into acute stages. Clustering can help identify patterns and groups of diabetes symptoms by analyzing attributes that indicate these symptoms. In this study, researchers are using K-Medoid and Quantum K-Medoid methods for clustering diabetes data. Quantum computing utilizes quantum bits, or qubits, which can represent multiple states at the same time. Compared to classical computers, quantum computing has the potential for an exponential speedup in problem-solving. Researchers conducted a comparison between two methods: the classic K-Medoids method and the K-Medoids method utilizing quantum computing.  The researchers found that both Quantum K-Medoid and Classic K-Medoid achieved the same clustering accuracy of 91%. In testing with the Quantum K-Medoids algorithm, it was found that the cost value in the 8th epoch showed a significant decrease compared to the Classical K-Medoids algorithm. This demonstrates that Quantum K-Medoid can be considered a viable alternative for clustering purposes.
Performance Analysis of MobileNetV3-based Convolutional Neural Network for Facial Skin Disorder Classification
Authors: Herimanto  , Arie Satia Dharma  , Junita Amalia  , David Largo  , Christin Adelia Pratiwi Sihite
Published: January 2025

Accurately identifying facial skin types is essential for recommending the right skincare treatments and products. Misidentifying skin types can lead to negative consequences, such as irritation or worsening of skin conditions. This study investigated methods for classifying facial skin types into five categories: oily, acne-prone, dry, normal, and combination. A dataset of 1725 augmented facial images was used. Data augmentation techniques likely increased the dataset's diversity, which helps improve the model's generalization ability. The data underwent preprocessing, including rescaling, before being applied to two deep learning models, CNN and MobileNetV3. The models were evaluated based on accuracy and execution time to determine the most effective approach for classifying facial skin types. The CNN model achieved an accuracy of 64%, demonstrating its potential for image classification tasks. However, the MobileNetV3 model significantly outperformed CNN with an accuracy of 84%. This superior performance is attributed to MobileNetV3's advanced architecture, which is optimized for efficient feature extraction, and particularly relevant for capturing the subtle variations in facial skin types. Therefore, MobileNetV3 emerged as the more effective method for classifying facial skin types with higher accuracy.
Performance and Efficiency Comparison of U-Net and Ghost U-Net in Road Crack Segmentation with Floating Point and Quantization Optimization
Authors: Tedja Haidhi Angkawijana , Onno W. Purbo
Published: January 2025

This study presents a comprehensive comparison of U-Net and Ghost U-Net for road crack segmentation, emphasizing their performance and memory efficiency across various data representation formats, including FP32, FP16, and INT8 quantization. A dataset of 12,480 images was used, with preprocessing steps such as binarization and normalization to improve segmentation accuracy. Results show that Ghost U-Net achieved a marginally higher performance, with an IoU of 0.5041 and a Dice coefficient of 0.6664, compared to U-Net’s IoU of 0.5034 and Dice coefficient of 0.6662. Ghost U-Net also demonstrated significant memory efficiency, reducing GPU usage by up to 60% in FP16 and INT8 formats. However, a sharp decline in performance was observed for Ghost U-Net in the INT8 format, where the IoU dropped to 0.2038 and the Dice coefficient to 0.3227, whereas U-Net maintained stable performance across all formats. These findings suggest that Ghost U-Net is preferable for applications prioritizing memory efficiency and inference speed, while U-Net may be better suited for tasks requiring consistent accuracy across different quantization levels. This study underscores the importance of considering both performance stability and memory efficiency when selecting models for deployment in real-world applications.
Ingredients Identification Through Label Scanning Using PaddleOCR and ChatGPT for Information Retrieval
Authors: Rosyadi Ahmad Wahyu , Siti Ma’shumah  , Muhammad Qomaruz Zaman  , Moh. Rizki Fajar
Published: January 2025

Human health depends on choosing food ingredients that align with dietary needs and avoid allergens. However, consumers often encounter unfamiliar ingredients that require additional information. Traditionally, they search online by typing in the ingredient's name which can be time-consuming and may not yield relevant results. Therefore, a system to identify and display ingredient information is necessary. This study proposes a new system that identifies ingredients by scanning the composition label on packaging using PaddleOCR and retrieving information through ChatGPT on a smartphone. The process begins with capturing an image of the composition label. Then PaddleOCR is employed to extract text from the scanned label, enabling identification of the listed ingredients. Subsequently, ChatGPT retrieves detailed information about the desired ingredients and displays it, allowing users to easily understand the ingredients. The system's effectiveness in text recognition is assessed using the character error rate (CER). The results show robust performance by achieving an average CER of 0.14, with flat packaging reaching an impressive CER of 0.05. Additionally, the system's usability was assessed through pilot testing which received significant positive user feedback achieving 4.37 satisfaction level on Likert scale, particularly regarding the clarity and relevance of the ingredient information provided.
Efficient Pattern Recognition of Sundanese Script Variants Using CNN
Authors: Muhammad Husni Wahid  , Erik Iman Heri Ujianto
Published: January 2025

This research aims to apply pattern recognition technology, specifically through the Convolutional Neural Network (CNN) approach, in identifying and translating Sundanese script accurately. This research is focused on recognizing rarangken script patterns based on ngalagena script in Indonesian cultural heritage. This study uses the MobileNetV2 based CNN model, utilizing transfer learning and trained for 50 epochs using the Adam optimizer with a learning rate of 0.0001, to achieve a training accuracy of 98.75% and test accuracy of 96.95% in 1 hour and 23 minutes, respectively. The results of the study show that the simpler CNN architecture without augmentation achieved the highest accuracy of 99.26%, and the augmented CNN model achieved 94.42% accuracy in 2 hours and 22 minutes. These results enable practical applications in both education and cultural preservation, demonstrating how modern technology can effectively contribute to maintaining traditional cultural elements in the digital era.
Analysis of food waste behavior by muslim generation Z through online food delivery
Authors: Mariyah Meliani Siti , Arsyianti Laily Dwi , Aurahma Adora
Published: January 2025

In recent years, there has been a consistent annual increase in the prevalence of food waste, which negatively affects the economy of various countries, such as Indonesia. Previous studies showed that food waste in Indonesia reached 41.4% of the total composition of national waste in 2021. This increase is accompanied by technological developments that affect consumption patterns, such as online food delivery (OFD) services. Therefore, this study aims to analyze food waste behavior among Muslim Generation Z through OFD in West Java using the Theory of Planned Behavior (TPB). Primary data were obtained from 180 participants and analyzed using the Structural Equation Modeling- Partial Least Square (SEM-PLS). The results showed that there was a significant positive relationship between the attitude variable and perceived behavior control with intention, while the subjective norms variable had no effect. A significant positive relationship was also obtained between promotion, OFD, and perceived behavior control with food waste behavior. Meanwhile, intention and OFD had no significant effect.
Mechanical Properties of Nickel, Palladium, and Platinum Nanowires: A Molecular Dynamics Study
Authors: Pamungkas Mauludi Ariesto , Farista Farid Surya , Mafazah Risalatul , Hidayat Achmad
Published: January 2025

Transition metal group 10 nanowires exhibit exceptional mechanical properties, particularly those with FCC structures, making them promising candidates for electromechanical devices. This study used classical MD simulations with EAM potentials to explore the mechanical behavior of nickel, palladium, and platinum nanowires with varying diameters. Our findings reveal a strong correlation between nanowire diameter and mechanical properties. Increasing diameter reduces surface effects, leading to higher tensile strength. Deformation mechanisms are complex, involving phase transformations such as FCC to HCP and BCC. These results will contribute to the fundamental understanding of nanoscale mechanics and pave the way for the design of advanced nanodevices
Marker-Assisted Breeding for Enhancing Stress Tolerance in Rice (Oryza sativa L.): A Review
Authors:   1Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh, Khatun Marium , FM Era ,   1Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh, MS Raihan ,   1Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh, waza Showkat A ,   Division of Genetics and Plant Breeding, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Jammu & Kashmir, India, Majid Asma ,   Division of Genetics and Plant Breeding, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K), Jammu & Kashmir, India, M Rafiqul Islam ,   3International Rice Research Institute (IRRI), Los Banos, Philippines, AKM Aminul Islam ,   1Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh
Published: January 2025

Rice productivity faces significant challenges from climate change and
sudden outbreaks of pests and diseases. Conventional breeding alone
struggles to produce stress-resistant varieties, making marker-assisted
breeding (MAB) a valuable complement. MAB enhances conventional
breeding by employing molecular markers tightly linked to target genes,
increasing efficiency without entirely replacing traditional methods. This
review evaluates gene introgression effects and the role of MAB in improving
rice productivity based on secondary data sources. Notable gene targets
include Xa4, Xa5, Xa13, Xa21 and Xa27 for bacterial blight; Pi2, Pi5, Pi9
and QTLs on chromosomes 1, 2, 11 and 12 for blast resistance; Gm1 and
Gm4 for gall midge; Saltol for salinity tolerance; qDTY1.1, qDTY2.1 and
qDTY3.1 for drought tolerance; Sub1A for submergence; and yld1.1, yld2.1
and GW6 for yield enhancement. Introgressing these genes has led to the
development of resilient rice lines capable of thriving under biotic and abiotic
stresses, with notable increases in yield over susceptible recurrent parents.
For instance, the introgression line DHA-10 (5.68 t/ha) yields more than its
parent BPT5204 (4.97 t/ha), while the submergence-tolerant BR9157-12-
2-37-13-17 produces 3.44 t/ha, outperforming BRRI dhan33 (1.73 t/ha).
These cases illustrate the transformative impact of MAB in developing highyielding, resilient rice varieties, underscoring its invaluable role in enhancing
rice productivity under diverse stress conditions
Refúgio LGBTQIA+ no Brasil: violências (cis)heteronormativas para além do destino e das origens
Authors:   , Gralha Marcus Vinicius Vilanova
Published: January 2025

Desde seu surgimento em fins do século XVIII, o Estado Liberal Moderno passou a criar mecanismos de normalização e controle dos corpos e sexualidades dos indivíduos com base na noção de cisheteronormatividade, que define o padrão de sexualidade a partir do qual a política organiza e estrutura a vida social. Refletida nas relações interindividuais, a cisheteronormatividade fundamenta ainda a hierarquia social do Estado, e assim se projeta à Sociedade Internacional, privilegiando a figura do homem cis hétero e deixando à margem corpos como o da comunidade LGBTQIA+. Um dos mecanismos dos Estados para condenar e silenciar esses corpos é a criminalização de sua existência, mas a violência cisheteronormativa também se apresenta na ausência de proteção adequada à vida e dignidade dos corpos marginalizados. A partir desse cenário de extrema vulnerabilidade e repleto de inseguranças para essa população, muitos indivíduos necessitam recorrer ao instituto do refúgio, na busca de uma nova vida. O refúgio, em sua origem, foi utilizado somente para a proteção específica da sociedade europeia, e teve sua proteção universalizada com o Protocolo de 1967. Apesar de se encontrarem marginalizados, os corpos da população LGBTQIA+ foram incluídos de forma explícita nos grupos passíveis de solicitar o refúgio apenas no início do século XXI. Diante disso, o presente trabalho busca responder à seguinte pergunta: quais as dificuldades e violências cisheteronormativas enfrentadas pelxs solicitantes do refúgio LGBTQIA+ no Brasil? Terá então como objetivo principal analisar o panorama jurídico-social que fundamentou a perseguição por conta da orientação sexual e de gênero nos principais Estados de origem dos solicitantes de refúgio LGBTQIA+ no Brasil. Para tanto, adota o método de procedimento de estudo de caso, baseado nas técnicas de pesquisa bibliográfica e análise de documentos. Os resultados apontam que as graves violências enfrentadas revelam não apenas evidentes violações de direitos humanos perpetradas por seus próprios países, como também limitações do Direito dos Refugiados e do sistema de acolhimento no Brasil para lidar com as especificidades do refúgio LGBTQIA+. em virtude da insuficiência de perspectivas e políticas fundamentadas nos estudos de gênero e sexualidade.
O PAPEL DO PSICOPEDAGOGO NA INCLUSÃO ESCOLAR
Authors: Rocha Raquel Esther de Campos
Published: January 2025

O profissional psicopedagogo desempenha relevante atividade no contexto escolar, sendo um ator dinâmico no processo ensino aprendizagem, assim, o presente artigo terá como tema: “O papel do psicopedagogo na inclusão escolar. O objetivo geral desse artigo é identificar o efetivo papel do psicopedagogo nas escolas em relação a educação inclusiva. A metodologia deste artigo foi a pesquisa bibliográfica, método exploratório e análise qualitativa. O artigo ainda apresenta a importância do psicopedagogo na escola e p quanto ele precisa estar preparado para assumir seu papel no âmbito escolar. Conclui-se com a realização deste artigo que o profissional de psicopedagogia precisa entender o seu verdadeiro papel e colocar em prática, para que os portadores de necessidades especiais exerçam seus direitos como cidadãos, pois essa e uma realidade que ainda não acontece no Ambiente Educacional que se diz inclusivo.
PRÁTICAS DIGITAIS E RISCOS NO CONTEXTO DAS INSTITUIÇÕES: Formação do Professor e a Prática Pedagógica Aliada ao Uso das Tecnologias
Authors: Cabral Ana Rosa Sales
Published: January 2025

Os desafios educacionais são diários e devem considerar um processo contínuo de planejar, executar, revisar e avaliar a prática. É nesta perspectiva apresentamos neste estudo um breve relato histórico da Educação a Distância no Brasil abordando o sugimento, evolução e instituições de programas educacionais ofertados nesta modalidade. Destacmos o iníco do Ensino por correspondência no ano de 1904, evidenciando de sutilmente a Educação e o termo distância, pois se tratava de um ofício via carta a longas distâncias. Destacamos també o surgimento do Instituto Universal Brasileiro um dos pioneiros do Ensino a Distância (EaD) no Brasil, em 1941, importante na colaboração para a formação de profissionais através dos cursos profissionalizantes, supletivos e técnicos. Abordamos também a criação da UAB é formada por instituições públicas de ensino superior, que se comprometem a levar ensino superior público de qualidade aos municípios brasileiros. No decorrer do trabalho fazemos também uma abordagem sobre vantagens, desvantagens e desafios a serem enfrentados pelos docentes e/ou estudantes para que a IA possa ser inserida na educação e para que gere uma aprendizagem significativa. Por fim abordamos o AVA como aplicação prática bem-sucedida da inteligência artificial em uma instituição de ensino.
DESAFIOS DA EDUCAÇÃO A DISTÂNCIA ON-LINE Renato Walter
Authors: Walter Renato
Published: January 2025

Este trabalho vem abordar sobre o Ensino a Distância, no que se trata da sua atuação na abordagem dos conteúdos didáticos a serem compreendidos e assimilados como um todo no que se refere ao ensino virtual. Através do estudo, pudemos levantar um pouco a sua importância no que diz respeito a prática ética de facilitação do ensino aprendizagem e a melhoria do seu desempenho, envolvendo em seu processo o planejamento, desenvolvimento e utilização das técnicas e meios instrucionais, eventos e produtos educacionais, proporcionando a facilidade do aprendizado para o homem. Alem disso tem a contestação entre as vantagens e desvantagens desta modalidade de ensino vem ser extensa. Podemos considerar ainda que o ensino a distância - EAD pode ser considerado uma ferramenta de inclusão social, como também por permitir a qualquer pessoa venha participar e se qualificar em qualquer canto do mundo através do ensino a distância de qualidade, podendo acima de tudo escolher o curso mais adequado para ser feito.
A IMPORTÂNCIA DA UTILIZAÇÃO DE MÍDIAS DIGITAIS PARA O ENSINO
Authors: Almeida Simone Nunes Matias de
Published: January 2025

O presente estudo tem como objetivo, discorrer sobre a importância das mídias digitais no ensino, de modo que a mesma têm desempenhado um papel significativo na transformação do cenário educacional, oferecendo uma ampla gama de recursos e possibilidades para professores e alunos, bem como explorar a importância da utilização de mídias digitais no ensino, destacando os benefícios que essas tecnologias trazem para o processo de aprendizado, com o uso adequado de mídias digitais pode aumentar a motivação dos alunos, personalizar o ensino, promover a colaboração e proporcionar acesso a uma variedade de recursos educacionais. Além disso, discutiremos os desafios associados a essa abordagem e como superá-los. Ao final, será abordado uma visão e experiencia pessoal do autor do texto com o uso de mídias digitais em sala de aula, ressaltando que as mídias digitais são ferramentas valiosas para a educação contemporânea e têm o potencial de melhorar significativamente a qualidade do ensino.
A MOTIVAÇÃO DO ALUNO NO AMBIENTE DE APRENDIZAGEM BASEADO NO E-LEARNING
Authors: Oliveira Maridenes Noronha de
Published: January 2025

O desenvolvimento tecnológico tem ajudado na construção de um modelo de educação moderno, fundamentado em ambientes de aprendizagem mais dinâmicos e interativos que oportunizam o professor a diversificar suas atividades pedagógicas e ao aluno o compartilhamento de experiências e informações por meio dos debates, da interação com outros estudantes, do questionamento e na criação e validação das ideias. Nesta perspectiva o aluno precisa está receptivo e motivado para participar ativamente desta nova tendência educacional. O presente trabalho propõe, mediante uma pesquisa bibliográfica, apresentar a relevância da motivação do aluno ao trabalhar com e-learning. O objetivo geral da pesquisa é conhecer os diferentes tipos de ambientes de aprendizagem e compreender a importância da motivação e sua promoção no e-learning. Realizou-se um estudo através da análise de livros, artigos e sites da internet relacionados ao tema por meio de uma abordagem qualitativa, estruturada em três temas. Verificou-se que a motivação propicia ao aluno protagonizar sua própria aprendizagem, através do direcionamento de ações e da busca por novos conhecimentos e experiências, assim conclui-se que o aluno motivado ao trabalhar com e-learning consegue interagir melhor, avaliar e organizar as informações obtidas, melhorar a comunicação e o desejo de se envolver ativamente nas atividades.
ESCREVIVÊNCIAS DE DOCENTES DE UMA ESCOLA RURAL DE PORTO VELHO ACERCA DOS DESAFIOS DOS PROCESSOS EDUCACIONAIS EM TEMPOS DE PÓS-PANDEMIA
Authors: Bernardo Ana Paula Mourão
Published: January 2025

A pandemia de Covid -19 que assolou o mundo no final de 2019 trouxe severas e trágicas mudanças em todos os campos que permeiam a sociedade, a vida das pessoas foram transformadas devido a algumas limitações impostas conter o vírus e consequentemente salvar vidas. Nesse contexto, o cenário educacional foi duramente afetado a partir do fechamento das escolas, alunos e professores tiveram que se adaptar repentinamente a uma nova forma de ensinar e aprender. Assim sendo, as aulas em todos os ambientes escolares tiveram que ser suspensas, os professores se depararam com um grande desafio e precisaram se reinventar para dar continuidade às atividades pedagógicas. Deste modo, este artigo que é parte de uma pesquisa de dissertação, tem como escopo investigar quais estratégias foram adotadas pelos educadores de uma Escola Rural do Ensino Fundamental dos anos iniciais do Município de Porto Velho para superação dos desafios do processo de ensino e aprendizagem agravados pela suspensão das aulas presenciais no período da Pandemia? Posto isto, traçou-se como objetivo central identificar a partir das escrevivências docentes estratégias que estão sendo adotadas pelos educadores de uma Escola Rural de Ensino Fundamental dos Anos Iniciais do Município de Porto Velho para superação dos desafios do processo de aprendizagem de alunos no período pós-pandemia. Cabe dizer, que a metodologia empregada tem uma abordagem qualitativa de caráter exploratório, sendo os instrumentos de produção de dados entrevistas semiestruturadas, apoiando-se nos pressupostos da modalidade de análise textual discursiva para a análise dos dados. Os resultados apontam para a necessidade de investimentos em infraestrutura tecnológica e capacitação de professores, principalmente para os docentes da área rural que vivenciam uma realidade tão distante da vivida na zona urbana. Por outro lado, é essencial fortalecer a parceria entre SEMED, Escolas Rurais e comunidades locais, para superar os desafios e construir um sistema educacional mais resiliente e inclusivo.
LETRAMENTO DIGITAL E MULTILETRAMENTOS: Cultura Digital e a formação do Professor do Século XXI
Authors: Silva Márcia Cristina Cordeiro Toledo da , Silva Ricardo Toledo da
Published: January 2025

O seguinte estudo apresenta uma análise quanto à proposta pedagógica do letramento digital e dos multiletramentos e, por conseguinte, a importância da participação dos discentes e docentes nesse tipo de educação, visto que a tecnologia é fundamental nesse modo de ensino e deve ser masterizada por ambos. Assim, esse artigo apresenta algumas questões importantes quanto até onde os alunos sabem mexer na tecnologia e o que os professores têm a ensinar, o que podem melhorar e como podem mesclar a educação tradicional a um modo em que os estudantes já estão acostumados e que faz parte intrínseca da vida deles, que serão descritas ao longo do texto e complementadas por meio de outros artigos e pesquisas sobre esse assunto em busca de esclarecer a proposta dos multiletramentos e letramento digital. Fica claro, porém, que é de suma importância entender as tecnologias como aliadas, e não inimigas, nesse processo de aprendizado. Além disso, as TDICs são de extrema importância nesse ensino e foram utilizadas de maneira mais completa pelas escolas particulares e, agora, devem ser inseridas nas escolas públicas também. Os resultados gerados por esse ensino irão, por consequência, promover uma maior interação entre aluno, professor e tecnologia.
TDAH - TRANSTORNO DO DÉFICIT DE ATENÇÃO COM HIPERATIVIDADE: FAMÍLIA E ESCOLA NO PROCESSO ENSINO E APRENDIZAGEM
Authors: PAIVA FRANCISCA CLÉDINA DE FARIAS
Published: January 2025

O Transtorno do Déficit de Atenção com Hiperatividade (TDAH) é um transtorno de origem neurológica e biológica que surge na infância e segue o indivíduo ao longo da vida. É de causa genética e é caracterizado pela falta de atenção e pelo comportamento de inquietação, impulsividade e mudanças de humor. O estudo teve como objetivo compreender o grau da criança hiperativa na sala de aula na educação infantil, bem como perceber o grau de hiperatividade do aluno e a melhor forma de atendimento oferecido pela instituição; criar atividades rotineiras para ocupar a criança no seu local de ensino aprendizagem focando sua atenção; incentivar as famílias a discussão e envolvimento da criança com atividades de rotina usando a criatividade; analisar o comportamento da criança no local de ensino escolar e qual a finalidade que uma alimentação saudável pode contribuir para o desenvolvendo da criança. A pesquisa é de cunho bibliográfico qualitativo e fundamentou-se teoricamente em estudiosos como: Argollo (2003), Facion J. R. (2004), Goldstein M. (1998). Sempre haverá uma causa para o surgimento do TDAH, e após o diagnóstico feito, caberá ao pedagogo fazer uma intervenção significativa, para que este aluno se desenvolva dentro do âmbito escolar, familiar e social.
Evaluación ergonómica integral de una unidad de apoyo estudiantil en una institución de educación superior: una aproximación sistémica
Authors: Mancilla-Aliste Javier Andrés
Published: January 2025

Los programas de apoyo estudiantil en instituciones de educación superior han crecido significativamente para atender la diversidad y el acceso masivo de estudiantes. Estos programas abarcan el apoyo académico, socioeconómico, psicosocial, médico y psicológico, siendo este último esencial debido a la alta prevalencia de problemas de salud mental, como ansiedad y depresión, que afectan el rendimiento académico. Además, los trabajadores de estas unidades enfrentan riesgos ergonómicos y psicosociales debido a condiciones de trabajo inadecuadas, lo que puede provocar trastornos musculoesqueléticos y estrés. Este estudio tiene como objetivo realizar un análisis ergonómico integral en una Unidad de Apoyo Estudiantil, identificando y evaluando los factores de riesgo presentes en los puestos de trabajo, incorporando una visión sistémica, que considera a los trabajadores como parte de un conjunto de puestos interrelacionados dentro de un contexto socio organizacional más amplio. Para ello se utilizaron listas de chequeo, estudios de tiempo, cuestionarios de autopercepción, el Método ROSA, Método NASA, Cuestionario CEAL – SM y un análisis de las condiciones evidenciadas. Los resultados obtenidos exponen una correlación de factores de diseño y configuración de los puestos de trabajo, condiciones ambientales asociadas a deficiencias de iluminación, alta carga mental y emocional, además de condiciones de riesgo psicosocial y carga organizacional, los cuales son gestados por la cultura, el funcionamiento y el plan estratégico institucional manifestado en los distintos niveles del macrosistema, que impacta en la salud de los trabajadores, en el rendimiento del sistema evaluado y a nivel general en la organización.
Trastornos musculoesqueléticos de las extremidades superiores de trabajadores de una alcaldía en Colombia
Authors: Rodríguez-Espinosa Karina Ximena , Santander-Leyton Lina Fernanda
Published: January 2025

Se llevó a cabo una investigación en la Alcaldía de San Juan de Pasto, Colombia, con 52 trabajadores, con el objetivo de identificar características laborales y trastornos musculo esqueléticos. Se utilizaron encuestas y pruebas semiológicas, considerando variables como edad, sexo y años de trabajo. Los resultados indicaron una baja incidencia de epicondilitis y epitrocleitis, con mayor sensibilidad en el codo derecho. Se observó una mayor incidencia de síntomas del síndrome del túnel carpiano en la mano derecha. Estos hallazgos son específicos de la población estudiada y se sugiere la implementación de medidas preventivas y monitoreo continuo de la salud ocupacional. Aunque la población estudiada no informa síntomas significativos en el momento de la investigación, la naturaleza repetitiva y prolongada de ciertos trabajos podría contribuir al desarrollo de trastornos musculo esqueléticos a largo plazo. Por lo tanto, se recomienda la implementación de medidas preventivas y el monitoreo continuo de la salud ocupacional de los trabajadores.
Effects of Using Humor in Teaching Speaking Skills on EFL Learners’ Speaking Anxiety
Authors: Alsakaker Saleh
Published: January 2025

Speaking clearly leads to communicating clearly. However, learning to speak clearly in a foreign or second language is one of the greatest challenges for a learner and often evokes great anxiety. Accordingly, this study is a survey-based investigation of the effects of using humor to teach speaking skills on the speaking anxiety of Saudi Arabian EFL undergraduate students. The objective of this research was to determine how humor affects them and whether it helps them feel less anxious. A quantitative survey methodology was used in this study to fits with the research objectives and questions. The experiment took place over three consecutive weeks, with three hours of instruction each week. 43 freshmen rated their agreement with the FLSAQ's 18 items on a 5-point Likert scale, reflecting fears and self-perceptions about English speaking. Prior to the intervention, speaking anxiety levels among the students were typically neutral to mildly low. Following the application of humor, the post-test results revealed that the pre-test mean (Mean = 48.67) was significantly higher than the post-test mean (Mean = 38.86) and the t-test results between them is statistically significant (t = 3.04, p = 0.003), indicating that humor had a positive effect on reducing speaking anxiety. These results give teachers a solid framework for teaching speaking skills in ways that will lessen speaking anxiety, which can create a comfortable classroom environment in which communication is valued over correctness, thereby reducing students’ fear of making mistakes, increasing their active participation, and in the process promoting their speaking skills.
Study on the Spatial Effect of Smart City Construction on Green Total Factor Productivity
Authors: Yu Shuang Ren  , Fu Yu  , Ilyas Muhammad
Published: January 2025

Smart cities, a new kind of urbanization, offer a means of achieving the condition in which environmental conservation and economic growth are mutually beneficial. As a result, it is important to think about whether and how the development of smart cities might support the high-quality growth of urban economies. Based on the panel data of 163 prefecture-level cities in China from 2009-2018, the green total factor productivity (GTFP) of each prefecture-level city is measured using the SBM-GML model, and the appropriate spatial econometric model is screened by various types of tests. The spatial effect of smart city construction on GFTP is studied, and it is concluded that the pilot cities have a significant positive spatial spillover effect. The decomposition econometric model also shows that the pilot cities have a significant positive spatial spillover effect, and it also indicating that the smart city construction can also drive the surrounding cities to jointly improve the quality of economic development. Finally, the robustness of the spatial effect of smart city policy is also verified by changing the spatial measurement model and the type of spatial weight matrix, which also shows that the results of the spatial spillover effect of smart city construction are reliable.
Stress effects and neurosecretion in Earthworms: A Review
Authors: Chaudhuri Priyasankar
Published: January 2025

Earthworms, the poikilothermal first group of invertebrate, are subjected to stressful events, occasional (illumination), frequent (amputation due to predation) and seasonal (dehydration during summer), hydration (during monsoon), thermal (heat stress during summer, cold stress during winter) etc. These lowly evolved creatures often overcome the events of stress through eco-physiological adaptive changes in the central nervous system (CNS) neurosecretory system. In absence any endocrine gland and neurohaemal organ, their CNS is enriched with vascularisation and well defined neurosecretory cells (NSCs) (type A and type B). The type A NSCs - the source of peptide neurohormone and release their secretion through axonal transport, response mainly against osmotic stress (dehydration, hydration), light (when exposed to the sunlight), hypo and hyperthermia etc. The type B NSCs are source of amine neurohormone and release neurohormone mainly by perikarya and occasionally by axon. The type B cells show remarkable cytoplasmic changes in relation to metabolic compensation following food deprivation in earthworms. Both the type A and type B NSCs show adaptive response such axonal transport, cytoplasmic vacuoles followed normal secretory cycle (i.e. synthesis, storage, release) during amputation and subsequent regeneration in earthworm.
AI-driven embedded systems for predictive maintenance in industrial IoT
Authors: Guruswamy TB  , Renuka AL
Published: January 2025

Predictive maintenance (PdM) leverages Artificial Intelligence (AI) integrated with the Industrial Internet of Things (IIoT) to proactively monitor and predict equipment failures, significantly optimizing operational efficiency while reducing downtime and maintenance costs. PdM shifts the maintenance paradigm from reactive or preventive strategies to a data-driven, predictive approach that ensures timely intervention based on the actual condition of equipment rather than predetermined schedules. Embedded systems, serving as the backbone of PdM, are equipped with AI algorithms that enable real-time data collection, processing, and decision-making at the edge of the network. These systems are designed to interface seamlessly with IIoT devices, gathering data from various industrial sensors and analyzing it to detect anomalies, estimate the remaining useful life (RUL) of equipment, and predict potential failures. The integration of AI capabilities such as machine learning (ML) and deep learning (DL) within embedded systems allows them to handle complex data streams, identify patterns, and make intelligent predictions in real time. This paper explores the multi-faceted aspects of AI-driven embedded systems for predictive maintenance in IIoT environments. First, it delves into the architecture of these systems, highlighting the interplay between hardware components such as microcontrollers, sensors, and communication modules, and software frameworks that incorporate AI algorithms for data processing and analysis. The role of edge computing in reducing latency and enabling on-site decision-making is also emphasized. Second, the paper examines the AI algorithms commonly employed in PdM, such as neural networks, support vector machines, and ensemble methods, discussing their suitability for various industrial applications. Specific attention is given to the use of advanced techniques like convolutional neural networks (CNNs) and long short-term memory (LSTM) networks for handling time-series sensor data and identifying early warning signs of equipment degradation. Furthermore, the practical applications of these systems across industries are reviewed, showcasing use cases in sectors such as manufacturing, energy, transportation, and healthcare. For instance, AI-driven embedded systems have been used to monitor conveyor belts, wind turbines, railways, and medical equipment, providing tangible benefits like extended equipment lifespan, improved safety, and reduced operational costs. The paper also presents case studies and performance metrics to evaluate the effectiveness of AI-driven PdM systems. Metrics such as prediction accuracy, false positive rate, and computational efficiency are analyzed to demonstrate the strengths and limitations of this approach. Challenges such as the high initial cost of implementation, data privacy concerns, and the need for robust cybersecurity measures are discussed to provide a balanced perspective.
Embedded systems for space exploration and satellite communication
Authors: Guruswamy TB  , Renuka AL
Published: January 2025

Embedded systems have become indispensable in the realm of modern space exploration and satellite communication, offering reliable, efficient, and compact solutions to manage the complexities of space missions and satellite networks. These systems play a pivotal role in a wide range of applications, including onboard control, navigation, communication, and data processing. By leveraging advanced technologies such as radiation-hardened components, real-time operating systems, and energy-efficient designs, embedded systems are tailored to withstand the harsh conditions of outer space while ensuring mission success. This paper explores the architecture and design principles of embedded systems tailored for space-grade applications, highlighting key innovations such as AI integration for autonomous decision-making, machine learning for predictive maintenance, and advanced signal processing techniques for efficient communication. It also examines the critical challenges faced by embedded systems, such as power constraints, radiation exposure, scalability, and data security, providing insights into how these limitations are being addressed through cutting-edge research and development.