bluuebunny/crossref_metadata_2025 huggingface dataset:
The Application of Virtual Simulation Technology in Macroeconomics Course
Authors: Zhou Lian
Published: January 2025

Under the environment of the rapid development of network science and technology, the application of virtual simulation technology in college experiment teaching has become a new direction of teaching reform. In recent years, virtual simulation technology has been well used in the experimental teaching of many science and technology subjects, but in the experimental teaching of humanities and social disciplines, especially economic management disciplines, the application of virtual simulation technology still needs to be developed and strengthened. According to the characteristics of the economic management compulsory subject Macroeconomics teaching, this paper discusses the teaching advantages of using virtual simulation technology and explores how to introduce virtual simulation technology into the experimental teaching of macroeconomics. This promotes the application of virtual simulation technology in the experimental course of “macroeconomics” from the two aspects of strengthening the support and management of the school and strengthening the cooperation with all forms of life, constantly improve the experimental teaching quality of the course, and provide beneficial practical exploration for the experimental teaching of economic management subjects.
Stability Results of Positive Weak Solution for a Class of Chemically Reacting Systems
Authors: Khafagy Salah A. , Mohamed A. Ezzat
Published: January 2025

This paper aims to study the existence and non-existence results of positive weak solution to the quasilinear elliptic system:
\[
\begin{cases}
-\Delta_p u = \lambda a(x) \left[ f(u,v) - \dfrac{1}{u^\alpha} \right], & x \in \Omega, \\
-\Delta_q v = \lambda b(x) \left[ g(u,v) - \dfrac{1}{v^\beta} \right], & x \in \Omega, \\
u = 0 = v, & x \in \partial\Omega,
\end{cases}
\]
where \(\Delta_r w = \operatorname{div}(|\nabla w|^{r-2} \nabla w)\) is the \(r\)-Laplacian (\(r = p, q\)), \(r > 1\), \(\alpha, \beta \in (0,1)\), \(\Omega\) is a bounded domain in \(\mathbb{R}^N\) (\(N > 1\)) with smooth boundary \(\partial\Omega\) and \(\lambda\) is a positive parameter. Here \(f, g\) are \(C^1\) increasing functions such that \(f, g : \mathbb{R}^+ \times \mathbb{R}^+ \rightarrow \mathbb{R}^+\); \(f(\upsilon_1, \upsilon_2) > 0\), \(g(\upsilon_1, \upsilon_2) > 0\) for \(\upsilon_1, \upsilon_2 > 0\). With \(C^1\) sign-changing functions \(a(x)\), \(b(x)\) that perhaps have negative values nearby the boundary. We establish our results via the sub-supersolution method. In addition, we study the stability and instability results of positive weak solution with different choices of \(f\) and \(g\).
The Rellich-Kondrachov Theorem for Gelfand Pairs Over Hypergroups
Authors: Bataka Ky T. , Mensah Yaogan
Published: January 2025

Embedding results play important rôles in mathematical analysis. This paper addresses some embedding theorems in the context of Sobolev spaces theory on Gelfand pairs over hypergroups. Mainly, the analogue of the Rellich-Kondrachov theorem is proved.
Penilaian Kebijakan Publik dalam Akselerasi Transformasi Data Melalui Uji Statistik
Authors: Nugraha Billy , Ikatrinasari Zulfa Fitri , Nugroho Yamandito Mario
Published: January 2025

Tujuan penelitian saat ini untuk melakukan perancangan data bank terpadu (DBT) sebagai media perantara guna meningkatkan akselerasi transformasi data saat ini. Regresi linier berganda adalah metode statistik yang sesuai dengan penelitian saat ini, selain itu dipertimbangkan melalui analisis kuantitatif dan uji asumsi klasik yang dilakukan. Maka penelitian ini akan menghasilkan pernyataan (hipotesis) dari responden perihal kebijakan publik saat ini mengenai akselerasi transformasi data. Beberapa variabel independen pada penelitian saat ini yaitu: Data (X1), Strategi (X2), Perubahan Budaya (X3), Mengoptimalkan Proses (X4), dan Teknologi (X5). Sehingga dalam variabel independen tersebut akan menjadi parameter dalam mengetahui variabel apa yang berpengaruh terhadap akselerasi transformasi data. Kesimpulan yang dapat diambil pada penelitian saat ini adalah setiap variabel independen akan menghasilkan hipotesis dan perancangan penerapan DBT nantinya. Selain itu sebagai saran guna penelitian berikutnya dengan mengimplementasikan DBT yang menyesuaikan dengan kebutuhan industri saat ini, maka akselerasi transformasi data meningkat secara signifikan
Long-term prognosis of adverse cardiovascular events in patients with chronic heart failure depending on the rs1143634 polymorphism of the interleukin-1β gene
Authors: Khazova E. V.
Published: January 2025

The course and prognosis of chronic heart failure (CHF) are associated with the activation of inflammatory cascades, the severity of which is genetically determined.

Aim: To analyze adverse cardiovascular events in patients with chronic heart failure over 5 years depending on the rs1143634 polymorphism of the interleukin-1β gene.

Material and Methods. Clinical signs were studied, genotyping was carried out at the polymorphic locus rs1143634 of the IL-1β gene in patients with CHF of ischemic origin (n = 445, average age 66.4 ± 10.4 years). Information on patient outcomes over 5 years was obtained by telephone interview with endpoints: all-cause death, cardiovascular death, fatal and non-fatal cardiovascular events formed a composite endpoint. Time to event was analyzed using the Kaplan-Meier method; hazard ratio – Cox regression. Statistical processing was carried out in the Jamovi, R 4.3.1 programs.

Results and conclusions. The occurrence of genotypes of the rs1143634 polymorphism of the IL-1β gene in patients with CHF and the genetic control group did not differ and was commensurate with the theoretically expected Hardy–Weinberg equilibrium. Carriers of the TT genotype were characterized by a high level of inflammation and the development of myocardial infarction at a young age. In patients with CHF of the TT genotype, the risk of death from all causes is 2.85 times higher, achieving the combined endpoint is 3.3 times higher, fatal cerebral stroke is 17.1 times higher compared to CC, 14.9 times higher than compared to ST genotype. In patients with CHF and chronic kidney disease of the TT genotype, the risk of death from cerebral stroke is 29.33 times higher than the CC genotype, and 29.12 times higher for the CT genotype. In patients with CHF in combination with diabetes mellitus, the frequency of hospitalizations due to cardiac decompensation in the TT genotype is higher than in the CC and CT genotypes (χ2 = 6.33, p = 0.042).

chronic heart failure; rs1143634 polymorphism of the IL-1β gene; prognosis; cardiovascular complications
A STUDY OF PERISTALTIC FLOW OF A COUPLE STRESS FLUID IN AN INCLINED CHANNEL UNDER THE EFFECT OF MAGNETIC FIELD
Authors: Manikrao Navarang
Published: January 2025

In this paper we study the peristaltic flow of a couple stress fluid in an inclined channel with the effect of magnetic field.
ELECTIONS IN INDIAN DEMOCRACY -MEDIA OVERVIEW
Authors: Dudankar Nagaveni , K Mahalinga
Published: January 2025

Elections are the cornerstone of Indian democracy, reflecting the will of the people in shaping governance. Media, as the fourth pillar of democracy, plays a crucial role in informing, educating, and engaging the electorate during elections. From traditional platforms like print and broadcast media to the transformative influence of digital and social media, the role of media in elections has evolved significantly. It serves as a powerful tool for disseminating information, shaping public opinion, and fostering electoral participation. However, challenges such as media bias, paid news, fake information, and the influence of political funding threaten its integrity. Regulatory frameworks by bodies like the Election Commission of India (ECI) and the Press Council of India aim to uphold fairness and transparency in electoral coverage. This paper explores the dynamic relationship between media and elections in India, highlighting its impact on democracy, challenges faced, and the path forward for responsible media practices.
Air quality protection behaviour and stock returns of public companies: Sectoral aspects
Authors: Maramygin Maksim S. , Boldyreva Natalya B. , Reshetnikova Lyudmila G.
Published: January 2025

In the new economic reality, air quality protection behaviour of companies remains a burning issue shifting the em phasis towards saving human life and health. The article explores the relationship between the air quality protection behav iour of public companies and stock returns in different economic sectors. The research methodology is based on environmental management theory, financial management theory, and stakeholder theory. The analytical procedures performed on the data involved cross-sectoral economic analysis and econometric analysis. Empirical data are retrieved from the Federal State Statistics Service (Rosstat) and the Moscow Exchange and cover statistics on 45 public joint-stock companies (PAOs) from five sectors of the economy for the period of 2014–2022. Cross-sectoral economic analysis has shown that investors lack interest in PAOs’ air quality protection behaviour and invariably favour companies from the sectoral indices paying higher dividends compared to the Moscow Exchange, such as Chemicals and Petrochemicals, Metals and Mining, Oil and Gas. By integrating air quality protection factors into the Fama-French-Carhart model through econometric modelling, we estimated the dependence of returns of indus try portfolios on these factors and classical risk premiums. The study demonstrates a positive impact of market premium and size premium on returns of industry portfolios. No statistically significant impact of air quality protection factors on stock returns was found. Our empirical findings confirm that businesses are poorly motivated to take air environment protection measures and air pollution-reducing behaviour should be encouraged at the state level.
Standards of living and human potential development: Assessing risks to regions’ economic security
Authors: Maslov Gleb A. , Sherstobitova Yulia A.
Published: January 2025

The possibility of high-quality labour force reproduction is a key condition for forming a knowledge-intensive production system, ensuring the necessary level of technological sovereignty and economic self-sufficiency against the background of the deteriorating geo-economic situation. The study’s aim is to identify and assess the risks to economic security in the context of human potential development on the example of regions in Russia’s Volga Federal District. Methodologically, the re search relies on Senchagov–Mityakov zonal theory as well as on a political economy approach. The methods include a historical-logical method, comparative analysis, and an index method. The data is the indicators reflecting the standard of living and the degree of human potential development for 2010–2022 coming from the Federal State Statistics Service of the Russian Federation. The decile ratio and the percentage of population with income below the subsistence level capture the greatest risks to regional economic security. The problem traced by the decile ratios is the acutest in the Perm krai and the Nizhny Novgorod oblast, in terms of poverty – in the Mari El Republic, the Chuvash Republic, and the Republic of Mordovia. The most favourable situation has emerged with the unemployment rate, especially in such industrially de veloped regions as the Republic of Tatarstan, the Udmurt Republic, and the Republic of Bashkortostan. The findings confirm the need for an active state policy focusing on the development of social sphere and reduction of inequality, prioritising the development of the institutional environment, and expanding financial incentives in the innovative segment of the national economy.
A nexus between regional welfare and sustainable development: A conceptual model
Authors: Shlyapina Mariya V. , Tretyakova Elena A.
Published: January 2025

The relevance of the research stems from the nature of the approaches to the definition and measurement of regional welfare still debated in the extant research. Particular importance attached to this problem is related to the concept of sustainable development, which centres on promoting prosperity while protecting the planet. The paper aims to form an original theoretical model depicting the mutual influence of sustainable regional development and regional welfare. The fundamental propositions of the concept of sustainable development and welfare economics, as well as regional and spatial development theories constitute the methodological basis of the study. The methods used are comparative analysis, synthesis, and conceptual modelling. The findings confirm that regional welfare acts simultaneously as a target due to having the elements identical to the spheres of sustainable development in its struc ture, and as the key factor in the sustainable regional development due to incorporat ing a set of material and non-material, economic and non-economic goods, which build up the economic, social, environmental, and institutional elements ensuring an incessant process of changes in the regional socio-ecological-economic system impacted by interregional, national and international factors of external environment of a region. The proposed conceptual model advances scientific ideas about the main tenance of sustainable regional development, contributes to the theory of welfare, and creates a basis for devising a method to measure regional welfare.
Increasing the Accuracy of Brain Stroke Classification using Random Forest Algorithm with Mutual Information Feature Selection
Authors: Fachruddin Fachruddin , Rasywir Errissya , Pratama Yovi
Published: January 2025

Brain stroke stands out as a leading cause of death, distinguishing it from common illnesses and highlighting the critical need to utilize machine learning techniques to identify symptoms. Among these techniques, the Random Forest (RF) algorithm emerged as the main candidate because of its optimal accuracy values. RF was chosen for its ensemble learning properties that optimize accuracy while simultaneously, bagging all outputs (DT), thus increasing its efficacy. Feature Selection, an important data analysis step, which is mainly achieved through pre-processing, aims to identify influential features and ignore less impactful features. Mutual Information serves as an important feature selection method. Specifically, the highest level of accuracy was achieved by cross-validating the test data - 10, resulting in 0.7760 without feature selection and 0.7790 with mutual information. Most of the attributes in the brain stroke dataset show relevance to the stroke disease class, but the resulting decision tree shows age as a particularly important node. So, the research results show that the selection feature (Mutual Information) can increase the accuracy of brain stroke classification, although it is not significant, namely an increase of 0.0030%. With an increase, where there is no significant difference, it can be said that almost all the attributes contained in the brain stroke dataset used have an influence on their relevance to the stroke disease class.
Optimizing Multilayer Perceptron with Cost-Sensitive Learning for Addressing Class Imbalance in Credit Card Fraud Detection
Authors: Priatna Wowon , Hindriyanto Dwi Purnomo  , Ade Iriani  , Irwan Sembiring  , Theophilus Wellem
Published: January 2025

The increasing use of credit cards in global financial transactions offers significant convenience for consumers and businesses. However, credit card fraud remains a major challenge due to its potential to cause substantial financial losses. Detecting credit card fraud is a top priority, but the primary challenge lies in class imbalance, where fraudulent transactions are significantly fewer than non-fraudulent ones. This imbalance often leads to machine learning algorithms overlooking fraudulent transactions, resulting in suboptimal performance. This study aims to enhance the performance of Multilayer Perceptron (MLP) in addressing class imbalance by employing cost-sensitive learning strategies. The research utilizes a credit card transaction dataset obtained from Kaggle, with additional validation using an e-commerce transaction dataset to strengthen the robustness of the findings. The dataset undergoes preprocessing with RUS and SMOTE techniques to balance the data before comparing the performance of baseline MLP models to those optimized with cost-sensitive learning. Evaluation metrics such as accuracy, recall, F1 score, and AUC indicate that the optimized MLP model significantly outperforms the baseline, achieving an AUC of 0.99 and a recall of 0.6. The model's superior performance is further validated through statistical tests, including Friedman and T-tests. These results underscore the practical implications of implementing cost-sensitive learning in MLPs, highlighting its potential to significantly enhance fraud detection accuracy and offer substantial benefits to financial institutions.
An Optimized Hyperparameter Tuning for Improved Hate Speech Detection with Multilayer Perceptron
Authors: Muhamad Ridwan  , Ema Utami
Published: January 2025

Hate speech classification is a critical task in the domain of natural language processing, aiming to mitigate the negative impacts of harmful content on digital platforms. This study explores the application of a Multilayer Perceptron (MLP) model for hate speech classification, utilizing Bag of Words (BoW) for feature extraction. The hypothesis posits that hyperparameter tuning through sophisticated optimization techniques will significantly improve model performance. To validate this hypothesis, we employed two distinct hyperparameter tuning approaches: Random Search and Optuna. Random Search provides a straightforward yet effective means of exploring the hyperparameter space, while Optuna offers a more sophisticated, optimization-based approach to hyperparameter selection. The study involved training the MLP model on a labeled dataset is based on crawling results on the Twitter platform of hate speech and non-hate speech overall total dataset is 13.169, followed by evaluation using standard metrics. Our experimental results demonstrate the comparative effectiveness of these two hyperparameter tuning methods. Notably, the MLP model tuned with Optuna achieved a higher F1-score of 81.49%, compared to 79.70% achieved with Random Search, indicating the superior performance of Optuna in optimizing the hyperparameters. These results were obtained through extensive cross-validation to ensure robustness and generalizability. The findings underscore the importance of optimized hyperparameters in developing robust hate speech classification systems. The superior perform ance of Optuna highlights its potential for broader application in other machine learning tasks requiring hyperparameter optimization. This improvement enables more reliable and efficient automated moderation, which is crucial for the integrity and security of digital communication platforms such as Twitter.
Quantum-Enhanced K-Medoids Clustering: Comparative Analysis of Stroke Medical Data
Authors: Siahaan Ricardo , Purba Swingly , Siregar Jeremia , Hutabarat Marvin Frans Sakti , Sitohang Rasmi
Published: January 2025

Stroke is a severe medical condition that occurs when the blood supply to parts of the brain is interrupted or reduced, resulting in brain tissue that lacks oxygen and nutrients. This causes brain cells to start to die in minutes. Early prevention reduces the risk of stroke. In this study, a quantum computing approach is used to improve the performance of the K-Medoids method. A comparative analysis of these methods was carried out with a focus on their performance, especially on the accuracy of the test results. The investigation was carried out using a data set of stroke patient medical records. The data set was tested using the classical and K-Medoids methods with a quantum computing approach utilizing Manhattan distance calculations. The findings of this research reveal improvements in the K-Medoids algorithm with Manhattan distance calculation influenced by the integration of a quantum computing framework. In particular, the simulation test results show an increase in accuracy from the classical K-Medoids method to the K-Medoids method with a quantum computing approach, from 52% to 64%. These results highlight that the performance of the K-Medoids method with a quantum computing approach is superior to that of the classical K-Medoids method.
Quantum Perceptron: A Novel Approach to Predicting Unemployment Levels in North Sumatra Province
Authors: Solikhun  , Trianda Dimas
Published: January 2025

The application of Quantum Computing to improve the perceptron algorithm in unemployment prediction is a new aspect of this research. This study focuses on unemployment, which is a big challenge for the young generation in Indonesia, especially in the North Sumatra region. This research applies the quantum perceptron method to provide an alternative solution in predicting the unemployment rate. The data used in this analysis comes from the North Sumatra Central Statistics Agency and includes published unemployment rates (TPT) for individuals aged 15 years and over from 2017 to 2023. This research uses seven variables ranging from x1 to x7 to produce accurate data. Quantum perceptron methods offer specific advantages over traditional methods, including higher computing speeds and the ability to handle greater data complexity. This analysis aims to identify unemployment patterns and trends in North Sumatra and provide more accurate predictions by applying the quantum perceptron method. Although the results of this research are still limited to analysis, this research shows promising results and opens up opportunities for further, more in-depth research. This research is limited to predicting unemployment rates in North Sumatra. The use of quantum computing using the quantum perceptron method shows great potential for application to various other socio-economic problems in the future. This research contributes by introducing a new approach that utilizes quantum technology to improve prediction accuracy in economic analysis.
An Investigation Towards Resampling Techniques and Classification Algorithms on CM1 NASA PROMISE Dataset for Software Defect Prediction
Authors: Fatwanto Agung , Nur Aslam Muh , Ndugi Rebbecah , Syafrudin Muhammad
Published: January 2025

Software defect prediction is a practical approach to improving the quality and efficiency of software testing processes. However, establishing robust and trustworthy models for software defect prediction is quite challenging due to the limitation of historical datasets that most developers are capable of collecting. The inherently imbalanced nature of most software defect datasets also posed another problem. Therefore, an insight into how to properly construct software defect prediction models on a small, yet imbalanced, dataset is required. The objective of this study is therefore to provide the required insight by way of investigating and comparing a number of resampling techniques, classification algorithms, and evaluation measurements (metrics) for building software defect prediction models on CM1 NASA PROMISE data as the representation of a small yet unbalanced dataset. This study is comparative descriptive research. It follows a positivist (quantitative) approach. Data were collected through observation towards experiments on four categories of resampling techniques (oversampling, under sampling, ensemble, and combine) combined with three categories of machine learning classification algorithms (traditional, ensemble, and neural network) to predict defective software modules on CM1 NASA PROMISE dataset. Training processes were carried out twice, each of which used the 5-fold cross-validation and the 70% training and 30% testing data splitting (holdout) method. Our result shows that the combined and oversampling techniques provide a positive effect on the performance of the models. In the context of classification models, ensemble-based algorithms, which extend the decision tree classification mechanism such as Random Forest and eXtreme Gradient Boosting, achieved sufficiently good performance for predicting defective software modules. Regarding the evaluation measurements, the combined and rank-based performance metrics yielded modest variance values, which is deemed suitable for evaluating the performance of the models in this context.
Cross-Spectral Cross-Distance Face Recognition via CNN with Image Augmentation Techniques
Authors: Rahmatika Nisa Adilla , Arnia Fitri , Oktiana Maulisa
Published: January 2025

Facial recognition is a critical biometric identification method in modern security systems, yet it faces significant challenges under varying lighting conditions, particularly when dealing with near-infrared (NIR) images, which exhibit reduced illumination compared to visible light (VIS) images. This study aims to evaluate the performance of Convolutional Neural Networks (CNNs) in addressing the Cross-Spectral Cross-Distance (CSCD) challenge, which involves face identification across different spectra (NIR and VIS) and varying distances. Three CNN models—VGG16, ResNet50, and EfficientNetB0—were assessed using a dataset comprising 800 facial images from 100 individuals, captured at four different distances (1m, 60m, 100m, and 150m) and across two wavelengths (NIR and VIS). The Multi-task Cascaded Convolutional Networks (MTCNN) algorithm was employed for face detection, followed by image preprocessing steps including resizing to 224x224 pixels, normalization, and homomorphic filtering. Two distinct data augmentation strategies were applied: one utilizing 10 different augmentation techniques and the other with 4 techniques, trained with a batch size of 32 over 100 epochs. Among the tested models, VGG16 demonstrated superior performance, achieving 100% accuracy in both training and validation phases, with a training loss of 0.55 and a validation loss of 0.612. These findings underscore the robustness of VGG16 in effectively adapting to the CSCD setting and managing variations in both lighting and distance.
Comparative Study of Earthquake Clustering in Indonesia Using K-Medoids, K-Means, DBSCAN, Fuzzy C-Means and K-AP Algorithms
Authors: Dwitiyanti Nurfidah , Siti Ayu Kumala  , Shinta Dwi Handayani
Published: January 2025

Indonesia’s frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, Indonesia's frequent earthquakes, caused by its position at the convergence of multiple tectonic plates, necessitate precise seismic zone identification to improve disaster preparedness. This research evaluates the effectiveness of five clustering algorithms—K-Medoids, K-Means, DBSCAN, Fuzzy C-Means, and K-Affinity Propagation (K-AP)—for analyzing earthquake data from January 2017 to January 2023. Using a dataset from BMKG encompassing 13,860 seismic events, each algorithm was assessed based on Silhouette Score and Cluster Purity metrics. Results indicated that K-Means provided the best balance, forming six clusters with a Silhouette Score of 0.3245 and Cluster Purity of 0.7366, making it the most suitable for seismic zone analysis. K-Medoids closely followed with a Silhouette Score of 0.3158 and Cluster Purity of 0.7190. Although DBSCAN effectively handled noise, its negative Silhouette values indicated poor clustering quality. Fuzzy C-Means and K-AP underperformed, with K-AP generating an impractically high number of clusters (196) and the lowest Silhouette Score (0.2550). This study offers a novel, comprehensive comparison of clustering algorithms for Indonesian earthquake data, emphasizing a dual-metric evaluation approach. By identifying K-Means as the most effective algorithm, provides valuable insights for disaster mitigation and seismic risk analysis.
Reducing Training Time in Skin Cancer Classification Using Convolutional Neural Network with Mixed Precision Implementation
Authors: Ryandra Guntara Raka , Hendriyana  , Syawanodya Indira
Published: January 2025

In the field of skin cancer classification, machine learning and deep learning have been extensively utilized, particularly with convolutional neural network (CNN) architectures. However, there remains room for exploration to achieve optimal performance. This study investigates the use of the MobileNetV3Large architecture for transfer learning, chosen for its efficiency in low-power and memory-constrained applications. To further enhance performance, black-hat morphological transformation and oversampling techniques were applied to the ISIC 2020 dataset. Additionally, mixed precision training was implemented to reduce training time. The research aimed to compare the accuracy, precision, recall, F1-score, and training time of models trained with and without mixed precision. The findings revealed that while the model without mixed precision achieved superior performance with accuracy, precision, recall, and F1-score metrics reaching 98%, both models yielded an AUC-ROC of 1. Notably, mixed precision training significantly reduced training time by 1,646 seconds (27 minutes and 26 seconds), representing an 8.39% speed increase. These results suggest that mixed precision can meaningfully accelerate model training while maintaining competitive performance. The practical implications of this research include its potential to improve the efficiency of skin cancer classification models, making them more suitable for real-time clinical applications, particularly in resource-constrained environments.
Utilization of the Convolutional Neural Network Method for Detecting Banana Leaf Disease
Authors: Nita Helmawati  , Ema Utami
Published: January 2025

Banana leaf diseases such as Sigatoka, Cordana, and Pestalotiopsis pose a significant threat to banana productivity, with implications for food security and the global economy. Early detection of this disease is an important step to reduce its spread and maintain crop yield stability. This research utilizes the Convolutional Neural Network (CNN) method to detect banana leaf diseases based on image analysis of infected and healthy leaves. The dataset used includes 937 images consisting of four main categories, namely healthy leaves, Sigatoka, Cordana, and Pestalotiopsis. The dataset is processed through augmentation to increase data diversity and quality. The CNN model was applied for classification, with evaluation results reaching 92.85% accuracy, 95.73% recall, 93.52% precision, and 94.60% F1-score. This research contributes to the development of Artificial Intelligence-based technology for applications in the agricultural sector, especially in supporting farmers to detect banana leaf diseases quickly, accurately and efficiently. The research results also provide recommendations for exploring additional data augmentation and increasing dataset variety to improve model detection performance in the future. This shows CNN's potential in supporting sustainable agriculture in the modern era.
Desafios e Propostas para o Treinamento da Geração Z por meio da Educação Corporativa
Authors: Souza Damaris Almeida , Carvalho Gicelia Silva de , Souza Liliene Maciel dos Santos , Silva Adriana Monteiro da
Published: January 2025

O presente artigo aborda os desafios enfrentados por organizações e gestores de Recursos Humanos (RH) na contratação e retenção de talentos da Geração Z. Esses indivíduos, crescidos em um ambiente virtual, destacam-se por suas habilidades tecnológicas, criatividade e capacidade de realizar multitarefas. Entretanto, as corporações têm enfrentado dificuldades na contratação e retenção destes talentos devido à sua visão de mundo diferenciada e seu comportamento singular, que valoriza a liberdade e a autonomia no trabalho com menor supervisão. Foi realizada uma pesquisa de abordagem qualitativa para identificar os desafios enfrentados pelos profissionais de RH e os resultados mostram que a Geração Z apresenta dificuldades na realização de trabalhos em equipe, possuem problemas de inteligência emocional e lapsos comportamentais. Como estratégia para superá-los, propõe-se a implementação da Educação Corporativa nas organizações, visando desenvolver as habilidades limitantes destes profissionais. Além disto, considera-se o engajamento dos profissionais de RH, especialmente em empresas que não possuem uma Universidade Corporativa, uma estratégia para a adoção desse recurso no treinamento e desenvolvimento de colaboradores da geração estudada e, assim, melhorar a retenção destes talentos no mercado laboral.
TENDÊNCIAS EDUCACIONAIS E O PAPEL DO PROFESSOR
Authors: Miranda Laura Elice De Souza Ferreira
Published: January 2025

Este trabalho tem como principal objetivo evidenciar as principais tendências educacionais contemporâneas e o papel emergente do professor no cenário educacional. Evidenciou-se que a tendência na contemporaneidade são as metodologias que colocam os alunos não mais como agente passivo no processo ensino aprendizagem, mas como protagonista no seu aprendizado, o papel do professor, nesse contexto, passa de um transmissor de conhecimento para um facilitador e mediador do aprendizado, exigindo novas competências tecnológicas e pedagógicas. As metodologias ativas, o ensino híbrido, o e-learning, a educação inclusiva e o uso de tecnologias digitais da informação e comunicação (TDICs) foram identificados como tendências centrais que estão moldando a educação no século XXI. Essas tendências atuais oferecem flexibilidade e autonomia aos alunos, mas também demanda do professor a capacidade de integrar essas ferramentas de forma eficaz no processo pedagógico. Essas tendências apresentam desafios, como a necessidade de formação contínua, infraestrutura adequada e a inclusão digital. A metodologia utilizada foi revisão bibliográfica de caráter qualitativo sendo utilizadas fontes como artigos científicos, livros e monografias relevantes. O estudo destaca que a formação continuada e o desenvolvimento de novas estratégias são essenciais para que o professor possa desempenhar seu papel em um ambiente educacional cada vez mais dinâmico e digitalizado, evidencia-se que são necessárias pesquisas empíricas que avaliem a eficácia dessas tendências no ambiente escolar e as percepções de professores e alunos sobre essas transformações.
SEMINÁRIO: SUA IMPORTÂNCIA COMO UMA DAS METODOLOGIAS ATIVAS DE APRENDIZAGEM NA SALA DE AULA
Authors: Lima José Evanio Almeida de
Published: January 2025

Neste trabalho propomos investigar através de pesquisa bibliográfica a utilização e aplicabilidade, em sala de aula, das metodologias ativas com ênfase na prática pedagógica do gênero textual oral seminário como forma de auxiliar no ensino aprendizagem, inserindo nesse contexto as Tecnologias Digitais de Informação e Comunicação (TDICs), adquiridas no processo de formação dos discentes, observando na prática o que se trabalha na sala de aula e como são conduzidas as atividades relacionadas as aprendizagens nesse cenário. Desse modo, buscamos analisar situações englobando o gênero seminário e o perfil do professor do século XXI com o objetivo de verificar alternativas que vise um aproveitamento eficaz e que indique uma participação efetiva dos alunos, através da utilização de conhecimentos adquirido ao utilizar as metodologias ativas. Observando essa conjuntura, levanta-se os seguintes questionamentos relacionados a essa temática. As metodologias ativas estão inseridas na prática pedagógica dos alunos nos moldes em que foi desenvolvida? O gênero textual oral é trabalhado de forma satisfatória? Os docentes estão familiarizados com os usos dessas novas técnicas em sala de aula? As metodologias ativas têm despertado nos professores o interesse que os levem além do ensino tradicional? A pesquisa pretende explorar esse contexto no que diz respeito a essa temática e apontar direcionamentos para uma aprendizagem adequada do ensino para os discentes.
O PROFESSOR COMO ARTICULADOR DO PROCESSO DE ENSINO APRENDIZAGEM EM UMA ERA TECNOLÓGICA
Authors: Moreira Adna Caetano e Silva
Published: January 2025

Quadro, giz, lousa e somente o professor ensinando não atende mais as necessidades da sociedade. Vivemos em um era em que as informações circulam com grande velocidade e a escola não pode continuar seguindo a mesma forma de séculos atrás. Como um lugar de grande relevância, a escola precisa acompanhar os avanços tecnológicos da sociedade, mudando assim a forma de ensinar. Refletir sobre estas mudanças metodológicas, o e-learning, a tríplice relação entre professor, aluno e tecnologia à luz de importantes teóricos da educação são os objetivos deste texto, que tem como base a pesquisa bibliográfica. Nesta nova era o professor precisa se adaptar à um novo papel, buscando conhecimentos teóricos e tecnológicos para se aproximar dos alunos, os envolvendo no processo de ensino-aprendizagem, vencendo barreiras que se estabeleceram ao longo de gerações. Se aproximar dos alunos mediados pela tecnologia, no e-learning, no ensino híbrido ou até mesmo nas aulas presenciais é fundamental para que a aprendizagem ocorra, abrindo possibilidades de troca de conhecimento e para que o aluno traga suas dúvidas e dificuldades, estabelecendo uma relação de confiança. Sendo assim, cada envolvido no processo de ensino-aprendizagem precisa conhecer seu papel para que o ensino mediado pelas tecnologias seja possível e tenha êxito.
ENTRE RAÍZES E ASAS
Authors: FURST CRISTIANE VIEIRA DOS SANTOS , Santos Maykon Donizete dos , Fuhr Josiane de Oliveira Medeiros , Selzler Ana Caroline , Fernandes Denise
Published: January 2025

Este livro mergulha na trajetória dos Institutos Federais (IFs) como política pública de transformação social e democratização do ensino. Ao longo dos capítulos, explora-se a integração curricular, o papel das políticas de acesso e permanência, evidenciando os IFs uma oportunidade de ascensão e pertencimento à classe popular. A obra não apenas discute teorias educacionais, mas entrelaça experiências pessoais, apresentando reflexões que partem da vivência de uma filha de trabalhadores, que encontrou na educação pública o caminho para ressignificar sua trajetória. Da infância marcada pela distância do acesso ao ensino superior até o ingresso como servidora pública de um Instituto Federal. Uma narrativa que conecta lutas pessoais, da autora e das percepções dos co-autores, à relevância coletiva de um projeto educacional inclusivo e emancipador. Com base em autores referenciais como Paulo Freire, Gaudêncio Frigotto e Marise Ramos, o texto busca provocar uma reflexão sobre a necessidade de fortalecer os IFs como espaços de formação integral, crítica e omnilateral. Além disso, aponta para a importância de parcerias com as demais redes de ensino, criando pontes que facilitem a transição dos alunos de escolas públicas e contribuam para a continuidade dos seus estudos. "Entre Raízes e Asas" é um convite àqueles que acreditam na educação como instrumento de transformação social, destacando que, mesmo diante de desafios, é possível construir pontes sólidas para um futuro mais justo e igualitário.
FISIOLOGIA DE SEMENTES:
Authors: Nagel Jordana Caroline
Published: January 2025

O livro "Fisiologia de Sementes: Processos, Desafios e Aplicações" apresenta uma visão aprofundada sobre os aspectos biológicos e tecnológicos relacionados às sementes, abordando temas como estrutura, germinação, dormência, viabilidade e conservação. A obra explora as interações fisiológicas e bioquímicas essenciais que garantem a qualidade das sementes, além de sua importância para a agricultura moderna e a segurança alimentar.
Por meio de uma análise detalhada, o texto discute a relevância das sementes no contexto agrícola, destacando a aplicação de novas tecnologias, como a edição genética e sensores de qualidade, no aprimoramento da produtividade e na sustentabilidade do setor. Além disso, são apresentados desafios e soluções para a conservação de recursos genéticos, especialmente diante das mudanças climáticas e da crescente demanda global por alimentos.
Combinando ciência teórica e prática, o livro é uma fonte valiosa para compreender como os avanços na fisiologia de sementes podem transformar a agricultura e contribuir para a preservação da biodiversidade. Ideal para profissionais, pesquisadores e estudantes, é uma obra indispensável para aqueles que desejam aprofundar seus conhecimentos sobre este tema crucial.
Enseñanza de la moral práctica en el modelo de educación integral: la construcción de la conciencia social de los niños en el Porfiriato
Authors: Magallanes Delgado María del Refugio
Published: January 2025

El objetivo de este artículo es analizar los fines de la enseñanza de la moral laica durante la instrucción primaria para comprender la construcción de la conciencia social de los niños en el marco de los propósitos de la educación moderna del periodo del Porfiriato. En México, educar a los niños en moral tomó un sentido político álgido en dicha época. Con el modelo de educación integral, la instrucción de los niños abarcaba tres dimensiones: intelectual, física y moral. El Estado y la Iglesia argumentaron el valor de la enseñanza moral de los infantes en el espacio público. Las maneras de regular y moldear la personalidad y comportamiento social de los infantes se plasmó en las lecciones de moral práctica. Con historiografía de la historia social de la educación sobre pedagogía, infancia e instrucción se contextualizaron cambios pedagógicos y curriculares, y con fuentes primarias se documentó la base de la educación integral y la estructura del plan de estudios de moral práctica. Se concluye que el modelo de educación integral para la infancia dejó como saldo una vasta producción de textos escolares, experiencia científica de la pedagogía, y sentó las bases del sistema educativo nacional.
Reviiew abouut Correlation Between Hypeeruricemia and Hypothyroiidism
Authors: Karar Salih Mahdi  , Mazin Eidan Hadi  , Sura A. Awadh
Published: January 2025

Hyperuriicemia and hypothyroiidism are twwo preevalent conditiions that, though apparently unconnected, sharre a numberr of pathophysiiological liinks. The hiigh volumes of uriic aciid in the bloood obseerved in raised blood sugarr levels (hyperuriicemia) have been reportted in variious thyroiid dysfunctiions, partiicularly hypothyroiidism. Thiis reviiew discovers the causal appliances linking these two complaints, theiir cliniical inferences, and organization reflections.
Navigating digital transformation: Best practices for cloud migration strategies in the enterprise
Authors: Kiran Kumar Nalla
Published: January 2025

Cloud migration remains a critical success factor in gaining digital transformation within enterprises due to scalability, flexibility, and cost. In this article, the author explores the critical thin and thick theses of cloud migration strategies and related tactics, underscored by the significance of proper planning. It discusses techniques to keep the disruption time minimum and safeguard the data. This very important factor is crucial in business continual as part of the move. The paper also explores multif-account and hybrid-account considerations, linking these models to overall business goals to drive innovation and flexibility.
Konsep Pembaruan dalam Islam Perspektif Muhammad Abduh
Authors: Nur Wahyudi Mohamad , Zaenab Siti
Published: January 2025

The history of revival in Islamic civilization can be marked since the Middle Ages, but towards the end of the Middle Ages Islam experienced a very significant setback. This setback occurred due to several things, one of which was in the field of religion, the religion which was supposed to provide values for its adherents was used as a tool of legitimacy for the authorities. In addition, Ulama' as holders of authority or religious stakeholders are often involved in the competition between madhhabs so that it has an impact on their adherents, namely the fanaticism of madhhabs, besides the jumud nature that occurs among Muslims in this case also contributes to making the stigma of Islam become increasingly backward and these are a few pictures of the historical reality surrounding the life of a Muhammad Abduh at that time. Meanwhile, in this study, the author uses the Library Research method, which means examining the thoughts of the character by providing a critical analysis of his thoughts. The results of this study indicate that in some respects Abduh's ideas can be said to be still relevant, especially regarding the attitude of religious people who are difficult to open up and tend to justify religion for the interests of their respective groups.
Peningkatan Intelektualitas Melalui Implementasi Teori Belajar Kognitif dan Board Game pada Pembelajaran Pendidikan Agama Islam
Authors: Afifah Umni , Sulaeman Ahmad
Published: January 2025

Increasing the intellectuality of individuals or students can be achieved if educators take a closer approach to students through the discipline of learning psychology. Educators often forget the importance of an approach through the discipline of learning psychology so that learning is only limited to the transfer of knowledge but do not pay attention to the results of the implementation of the knowledge they have. This has a big influence on Islamic Religious Education lessons because it will produce bad morals. Increasing intellectuality can be done through the application of cognitive theory and board games. Cognitive theory and board games emphasize learning processes based on problem-solving, the process of remembering, and understanding the knowledge they receive.The method in this study used library research by reviewing 13 articles from related journals obtained from Google Scholar.The results of this study are that there is an increase in the intellectuality of students from the implementation of cognitive theory and board games in Islamic Religious Education lessons because this theory places more emphasis on the learning process, solving problems based on ideas and experience. This is in line with the principles of board games which hone problem-solving skills, fun, and interaction with others.
Slogan “Pentingnya Menguasai MIPA dan Bahasa Asing” dalam Konsep Pendidikan Yudian Wahyudi
Authors: Shulhan Shulhan , Sodik Hairus , Syarifah Syarifah , Maida Maida
Published: January 2025

Abstrak: Yudian Wahyudi merupakan salah satu tokoh Islam di Indonesia yang serius mengampanyekan pentingnya umat Islam untuk menguasai MIPA (Matematikan, Ilmu Pengetahuna Alam) dan bahasa asing  selain memahami ilmu agama untuk meraih kejayaan dunia dan akhirat. Tulisan ini dimaksudkan untuk mendiskusikan arti penting menguasai MIPA dan bahasa asing dalam pandangan Yudian Wahyudi. Penelitian ini dilaksanakan dengan menggunakan metode kualitatif berbasis library reserch dengan menempatkan teks atau naskah yang mendokumentasikan pemikiriannya sebagai sumber utama. Peneliti menempati posisi kunci dalam megumpulkan data, pengelolahan data, dan interpretasi data. Hasil penelitian ini antara lain. 1. Penyebab kemunduran Islam adalah tereleminasinya ilmu-ilmu terapan yang berbasis penelitian. Umat Islam lebih memilih mendalami ilmu agama dan mengabaikan hal-hal yang berhubungan peningkatan kapasitas keduniaan. 2. Umat Islam di Indoneisa memiliki modal besar sebagai argumentasi logis pentingnya menguasai MIPA dan bahasa asing untuk memperoleh keyaan kembali yaitu populasi masyarakat muslim sangat besar dan besarnya sumber daya alam yang tersedia di Nusantara. 3. Mengambalikan kejayaan Islam dimulai dengan membangkitkan kembali collective consciousness bahwa setiap orang wajib menguasai bahasa asing dan MIPA selain mempu membaca kitab-kitab kuning dan pengamalannya. Tingginya kuantitas umat Islam harus berbanding lurus dengan kualitasnya yang terdistribusi ke dalam beberapa sektor strategis. Hal itu menuntut setiap pribadi memiliki kapasitas dalam satu bidang ilmu terapan yang dapat digunkan untuk berkontribusi dalam pembangunan nasional. Masyarakat muslim memerlukan posisi ideal dalam keagamaan, sosial, ekonomi dan politik serta teknologi tepat guna.