bluuebunny/crossref_metadata_2025 huggingface dataset:
Mitigating measures of memory access bottlenecks in high performance computing
Authors: Zhang Lehan
Published: July 2024

This paper explores the mitigation measures in memory access bottlenecks in high-performance computing. To build the high-performance computing architecture, compute servers tend to be put together through the structure, which is networked to the data storage to help capture the output. Due to high need for data storage, memories tend to be a critical challenge affecting the sector in high-performance computing. These challenges include insufficient RAM, cache coherence, and out-of-memory. Mitigation measures identified include memory management, process reassessment, and cache optimization. A case study involving ethical hackers and system administrators shows the practical implication of the mitigation measures of memory access bottlenecks in high-performance computing.
Content-Adaptive Style Transfer
Authors: Teng Kaifeng
Published: July 2024

The development of Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) has catalysed the emergence of the Neural Style Transfer (NST) field. NST elucidates the computer's comprehension of artistic elements, holding paramount significance in both academic and practical domains. In this paper, we introduce a novel system termed "Content-Adaptive Style Transfer." This system incorporates two additional techniques: object segmentation and image fusion, augmenting the customizability of NST. Our experimental results underscore its capability to execute style transfer exclusively on the foreground, leaving the background unaffected, or to modify the background style while preserving the original foreground. These innovations promise more rational outcomes in specific scenarios.
A systematic study in intelligent software engineering based on Large Language Model
Authors: Li Xiang
Published: July 2024

The application and research of Large Language Model (LLM) in intelligent software engineering have become a hot topic, offering strong theoretical significance and practical value. Researchers in this field have covered various stages of software engineering. However, there is still a lack of comprehensive and holistic research analysis that unifies the current research findings, which hinders the technological development of this field. Addressing this issue, this paper conducts a comprehensive survey of LLM research in the domain of software engineering. We summarize and extract five specific subfields, including LLM and prompts, code analysis, human-computer interaction, fault prediction, and other software engineering aspects. For these representative research directions, it examines significant research works, traces their research context, and discusses their development trends. This work provides a research foundation and contextual support for subsequent software engineering development based on large models.
Early detection and mitigation of cyber attacks with machine learning and artificial intelligence
Authors: Liu Encheng
Published: July 2024

This research article explores the influence of leveraging machine learning algorithms (ML) and artificial intelligence (AI) in the early detection and mitigation of cyber attacks. With the rise of cybercriminal activities, traditional cybersecurity measures have proven inadequate. This study reviews the various AI and ML techniques, such as anomaly and cyber intelligence, which can be used in detecting cyberattacks before they occur. A case study on IBM security illustrates the practical implications and outcome of implementing machine learning and artificial intelligence in cybersecurity.
Analysis and optimization of smart home control system stability analysis and optimization
Authors: Liu Siyuan
Published: July 2024

With the continuous development of intelligent technology, smart home systems have become part of people's lives. The stability of the system is an important aspect of the smart home control system, which affects the experience of using the smart home. Currently, the intelligentisation and convenience of smart home systems have greatly enriched the user experience and gained general recognition in the market. Through searching and reviewing related literature, this paper mainly discusses the factors and optimisation methods of the stability of the smart home control system, as well as the future development trend of the smart home. Through the research in this paper, we can better understand the future development direction of smart home system and its impact on our life.
DimSum50: A benchmark dataset of Chinese food
Authors: Yu Kunyi
Published: July 2024

This paper provides an overview of the increasing attention given by the public and government to food health. It also reviews the progress made in the field of image classification over the past decade, with a specific focus on the current state of Chinese food datasets. The DimSum50 dataset is introduced as the first dataset that concentrates on diverse Chinese dim sum among all publicly available datasets. This dataset comprises 50 categories of the most popular dim sum foods, containing a total of 28,884 images. To ensure the accuracy and scalability of DimSum50, a three-step construction process was implemented, including category selection, data collection, and data cleaning. The unique properties of dim sum present challenges in constructing this dataset, as several categories exhibit similar characteristics. Benchmark experiments were conducted on the DimSum50 dataset, offering a horizontal comparison among several common and state-of-the-art models in both CNNs and transformers.
Car-following algorithm based on TD3 and CACC
Authors: Lu Heng
Published: July 2024

For autonomous cars to drive safely and efficiently, they need to have a decent car following functionality. Many algorithms have been proposed to improve the car following functionality, but most of them focus at keeping similar speed instead of maintaining relative distances between vehicles, which makes these algorithms hard to deal with emergencies. Therefore, the paper proposed a hybrid algorithm based on twin delayed deep deterministic policy gradient (TD3) and cooperative adaptive cruise control (CACC) for car following task in autonomous driving. The algorithm is tested in an environment which simulates typical scenario in real world highway driving, and the performance is also measured when vehicle to vehicle (V2V) latency is also considered. The experiment shows that the algorithm keeps a decent dynamic relative distance between vehicles, which is crucial for collision avoidance in case of emergencies. It has a good performance and a great potential for real world applications.
Prediction of heart attack based on ensemble learning
Authors: Zhang Xiaotong
Published: July 2024

Heart disease has always been a high proportion of diseases worldwide, and it is also one of the leading causes of death worldwide. The prevention and detection of heart disease is still a key issue. To enhance patient outcomes in the real world and add to the expanding body of knowledge in heart disease research, this work aims to develop an integrated learning model to predict heart disease using machine learning techniques. The main research object of this paper is the heart disease data set. This paper's primary approach is to use 10 distinct machine-learning algorithms to construct a model for predicting heart attacks. And then, score and compare the performance of the models with some fundamental metrics. According to the result of the performance, the best three are selected, and a better-ensembled model is built through the ensemble learning of the voting method with Multilayer Perceptron Classifier (MLPC), the Support Vector Machine (SVM), and the Naive Bayes. This may be likely to help with real-life diagnoses in the future.
Power fault analysis and classification based on correlation analysis and random forest algorithm
Authors: Yan Jiahui , Zhu Xinyu
Published: July 2024

The electric power system is one of the indispensable infrastructures in modern society, and its stable operation is crucial for guaranteeing economic development and social security. By calculating the correlation coefficient and drawing the correlation heat map, we find that there is a correlation between the voltage at each location and the occurrence of electrical faults, and at the same time, there is also a certain correlation between various types of electrical faults and the voltage at each location. These results indicate that there are complex and close interrelationships between different locations and between different types in the power system. In order to better identify and classify different types of electrical faults, we used the random forest model for prediction. The model is used to classify whether an electrical fault occurs or not and to determine its category. The prediction results show that the Random Forest achieved 99.63% in classifying whether an electrical fault occurred or not, and 91.26% in classifying the category of electrical faults. This shows that the Random Forest algorithm model is able to detect electrical faults well, as well as judge the category of electrical faults. In summary, this paper provides insight into the operation of the power system by studying the correlation between the voltage at various locations in the power system and the different types of electrical faults and using the random forest model to make predictions. These findings are of great significance in ensuring the stable operation of power systems.
The application of greedy algorithm in the course selection and scheduling system of college students
Authors: Xiao Jiachen
Published: July 2024

On December 19, 2023, the Chinese Ministry of Education's press conference once again emphasized the continued expansion of undergraduate professional degree enrollment. Nowadays, under the influence of the expanding enrollment scale of colleges and universities, the reasonable allocation of limited educational resources has become the primary task of educators, and college students' course choice and course arrangement will directly determine their school life quality. However, the number of students is large, so the research topic of this paper is to use greedy algorithms to design a more efficient course selection and scheduling system. The course selection and scheduling system based on the greedy algorithm can save the resources required by manual scheduling to the maximum extent and directly improve the work efficiency of educators. The system designed and practiced in this paper is effective to a certain extent, and the effective use of the algorithm can avoid many problems in course selection and course arrangement.
GSANet: Enhancing accuracy in the segmentation of challenging medical images
Authors: Wang Zifeng , Liu Shenxin , Qin Chaojie , Duan Zhongkang , Ge Xiang , Sun Fei
Published: July 2024

Medical image segmentation is crucial in the fields of computer vision and medical image processing. Its difficulty lies in precisely extracting regions of interest from images, such as blood vessels, tumors, or other anatomical structures, which is especially important in retinal images and skin pathology images. However, factors like noise, low contrast, and the complexity of target structures in medical images severely affect the accuracy of segmentation.Despite significant progress made by deep learning models such as U-Net in medical image segmentation, their performance still needs improvement when dealing with medical images with complex contexts and long-range dependencies. To tackle these challenges, we introduce a new deep learning model named GSANet.The GSANet model retains the advantages of U-Net while incorporating two innovative modules to enhance its performance in handling challenging medical image segmentation tasks. We assess the GSANet model's performance on two demanding medical image segmentation tasks, DRIVE and ISIC2018. Experimental findings illustrate that the GSANet model achieves significant performance enhancements on these tasks, surpassing the current state-of-the-art methods.The success of the GSANet model can be attributed to its effective design and implementation. GSANet excels in handling medical image segmentation tasks with complex contexts and long-range dependencies, leading to superior performance. Our research highlights the superiority of the GSANet model in medical image segmentation tasks, opening up new possibilities for further improvements in the performance and accuracy of medical image segmentation.
An IoT vehicle management framework based on optimized license plate recognition
Authors: Jin Haoxiang
Published: July 2024

With the continuous development of urban traffic and the increasing number of vehicles, vehicle management has become increasingly crucial. The Harmony-based IoT vehicle management system, leveraging Internet of Things (IoT) technology, offers an effective solution. This paper introduces the concept and principles of the Harmony-based IoT vehicle management system, focusing on optimized license plate recognition. Firstly, the principles and optimization methods of license plate recognition technology are elaborately described, encompassing aspects such as image processing, feature extraction, and pattern recognition. Subsequently, the paper explores the application of advanced YOLOv7 license plate recognition technology in the Harmony-based IoT vehicle management system, incorporating discussions on interlinking with devices like gate control systems.
The research model of forecasting agricultural carbon emissions based on ARIMA-LSTM
Authors: Qin Wenqiang , Wang Keqiang , Yan Qingsong
Published: July 2024

To accurately forecast the volume of agricultural carbon emissions in China, this paper proposes a forecasting model that combines the Autoregressive Integrated Moving Average Model (ARIMA) with Long Short-Term Memory networks (LSTM) to better handle both linear and non-linear parts in time series. Using the data on China's agricultural carbon emissions collected from 2000 to 2023 as the research subject, the data from 2000 to 2016 were first used as the training set, and the data from 2017 to 2023 were used as the test set. The Spearman's rank correlation coefficient method was utilized to study the correlation between various types of carbon emissions. Then, the established ARIMA-LSTM forecasting model was applied to predict the emissions in the test samples and make comparisons. The Absolute Relative Error (ARE) was adopted as the evaluation metric for different models, comparing the prediction errors of the ARIMA-LSTM model with those of the ARIMA and LSTM single models through analysis. The results show that the positive correlation between diesel carbon emissions and film carbon emissions was the highest; the absolute relative error of the ARIMA-LSTM model did not exceed 7%, which is significantly lower than the other two comparison models. This research model achieves high-accuracy predictions of carbon emissions, which is beneficial for the control and management of the greenhouse effect.
Deep learning in autonomous driving: Advantages, limitations, and innovative solutions
Authors: You Siming
Published: July 2024

With the rapid development of autonomous driving technology, deep learning has become a core driver for innovation in testing autonomous driving scenarios. This review paper delves into the critical role of deep learning in autonomous driving technology. The paper will describe how deep learning is at the center of driving innovation. The paper thoroughly explores the application of deep learning in obstacle detection, scene classification and understanding, and image segmentation, emphasizing the significant benefits in perception and decision-making while pointing out the challenges and innovative solutions adopted. The innovative solutions section proposes multimodal fusion and joint learning, new methods for 3D semantic segmentation, etc., aiming to improve image segmentation's accuracy and generalization ability. Overall, deep learning has great potential in automated driving technology, and by innovating and solving challenges, it will advance the system and provide reliable, intelligent, and efficient solutions for future transportation systems.
Advancements in object detection: From machine learning to deep learning paradigms
Authors: Xue Qing
Published: July 2024

The evolution of object detection from traditional machine learning approaches to advanced deep learning techniques marks a significant milestone in the field of computer vision. Initially, object detection relied on algorithms such as Support Vector Machines (SVMs) and decision trees, leveraging handcrafted features like Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) for classification and recognition tasks. However, these methods exhibited limitations in scalability and adaptability to complex environments. The breakthrough came with the adoption of Convolutional Neural Networks (CNNs), which transformed the landscape by automating feature extraction, thereby enhancing detection accuracy and efficiency. Subsequent innovations in network architectures, such as R-CNN, YOLO, and SSD, have continually refined object detection capabilities, optimizing both speed and precision. This paper examines the progression of object detection technologies, focusing on the impact of deep learning models and the optimization of network structures. It also delves into the quantitative analysis of model performance, highlighting the role of data augmentation and advanced training techniques in overcoming real-world detection challenges. Through this exploration, the paper aims to provide comprehensive insights into the current state and future directions of object detection techniques.
Particle swarm optimization random forest based data regression prediction algorithm for melanoma size prediction
Authors: Chen Zhanyuan , Li Leyao
Published: July 2024

In this study, a data regression prediction algorithm based on particle swarm optimization of random forests is dedicated to the accurate prediction of melanoma size. As a malignant skin tumor, melanoma size prediction is crucial for treatment and intervention. By dividing the dataset into training and test sets in the ratio of 7:3, we found that in the training set, the model's prediction of melanoma size was highly consistent with the actual value, indicating that the proposed algorithm has good prediction performance. Meanwhile, in the test set, the predicted values of melanoma size are also close to the actual values, verifying the robustness and generalization ability of the model. Specifically, the mean absolute error (MAE) was 1.5682 in the training set and 4.058 in the test set.This indicates that our model can accurately predict the melanoma size and can be further optimized to improve the prediction accuracy in the future. Overall, the data regression prediction algorithm based on particle swarm optimization of random forests provides new ideas and methods for accurate prediction of melanoma size and is expected to play an important role in clinical practice. This research result lays a solid foundation for further exploration and application in melanoma-related fields.
Singing voice separation based on U-NET neural network and its application
Authors: Wu Bruce
Published: July 2024

Singing Voice separation is a very important method that helps people to obtain a vocal or accompaniment for further usage like music composing. In this paper, I developed a Song accompaniment separation application based on Convolutional neural network Unet model and Deep learning (DL). Basically, the U-net convolutional network is applied to the two-dimensional spectrum of a music audio in order to achieve a more precise separation effect. During this deep learning process, a significant amount of data was inserted into the model to get the best fit parameters. Then, after all, the model can automatically generate a mask that filters out the accompaniment or vocal part that users want.. This completed model is then transmitted to the terminal of a internet server to enable users to get access to the model through internet. After the test of both quantitative evaluation and subjective assessment, the application impressively achieved the Song accompaniment Separation that provides a good quality of both vocal and song.
An integrated intelligent intersection management and control system design
Authors: Wang Guangpeng
Published: July 2024

While a fair amount of study has been done recently on integrated management and control models for large-scale traffic systems, there is comparatively little information available regarding the design of management and control systems for small-scale intersections. This study aims to create the structural diagram of a management system for intersections and control technologies. It does this by designing three distinct systems: congestion prediction, traffic signal timing, and congestion flow dredging. This study searches the current state of common technology and chooses the intersection integrated management system by means of comparison. Fuzzy TOPSIS algorithm and ADP adaptive dynamic programming are chosen as the calculation techniques of signal lamp timing, while BP neural network and ATT-GCN network are chosen as the key algorithms of congestion prediction after thorough investigation. In the end, conventional traffic management techniques like forbidding left turns are used to direct cars that haven't arrived at the busy crossroads beforehand. This paper aims to select the best model for an intersection management system currently available from the common technologies. The idealized management system and its structure diagram are designed in a straightforward manner, and they will serve as a guide for future road and vehicular intersection design.
Modelling and simulation of quadcopter UAV based on PID control
Authors: Liu Yijin
Published: July 2024

In the evolving field of aerial robotics, the precise control and stabilization of Unmanned Aerial Vehicles (UAVs), particularly quadrotors, stand as critical challenges due to their inherent nonlinear dynamics and susceptibility to external disturbances. This study embarks on addressing these challenges by first establishing a comprehensive mathematical model of a quadrotor's dynamics utilizing the Newton-Euler formulation. This foundational model serves as the basis for implementing a Proportional-Integral-Derivative (PID) control strategy, aimed at maintaining the UAV's desired flight trajectory and enhancing its stability against environmental perturbations. The effectiveness of the PID control approach is meticulously evaluated through a series of simulation tests conducted within the MATLAB/Simulink environment, focusing on the quadrotor's pitch, roll, and yaw control channels. The outcomes of these simulations provide convincing evidence of the PID scheme's ability to ensure rapid response times and precise attitude regulation, underpinning its viability for robust quadrotor UAV operations across diverse operational scenarios. Moreover, this research not only establishes a critical step towards the autonomous operation of UAVs but also accentuates the significance of sophisticated control mechanisms in the advancement of aerial robotics technology.
Development and application of intelligent judicial trial assistance system based on generative artificial intelligence and machine learning technology
Authors: Lian Yaohao , Yang Yining
Published: July 2024

The intelligent judicial trial assistance system is an intelligent auxiliary judicial trial tool based on generative artificial intelligence and machine learning technology. It can achieve auxiliary functions such as case upload, trial result prediction, and legal document generation through automated, digital, and informational means. It utilizes technologies such as Recurrent Neural Networks (RNN) and Long Short-Term Memory networks (LSTM) to train generative models for generating trial recommendations and assisting in decision-making. Through in-depth research on the working principles and optimization paths of the intelligent judicial trial assistance system, the best applicable path for the system in judicial trial practice is explored. By establishing optimized models, risks and biases are minimized to the greatest extent.
Improved YOLOv8 algorithm for vehicle image target detection based on learning rate optimisation strategy
Authors: Hu Yuwei
Published: July 2024

Vehicle image target detection is of great significance in the field of computer vision, which is mainly applied in the fields of traffic safety, intelligent traffic management and automatic driving. This study adopts a learning rate decay strategy for the YOLOv8 algorithm, which achieves the purpose of dynamically adjusting the learning rate. Through the analysis of model confusion matrix, F1 confidence, precision confidence, accuracy, recall, and recall confidence curve, the results show that the YOLOv8 algorithm adjusted by the learning rate attenuation strategy is able to efficiently and accurately detect and identify the car target in the image, and all the evaluation indexes have reached the optimal level. Comparison of the target detection results with the labels in the test set verifies that the optimised learning rate YOLOv8 algorithm achieves 100% prediction accuracy, successfully identifying and labelling the car target in the image. In conclusion, the learning rate optimised YOLOv8 algorithm performs well in vehicle image target detection and provides an effective solution for efficient and accurate car recognition.
Research on frequency response effect of microwave photonic filter based on Brillouin effect
Authors: Sun Muye , Duanmu Tianyu
Published: July 2024

Stimulated Brillouin Scattering (SBS) effect is a typical third-order optical nonlinearity that can be generated in various media through the excitation by intense light. Due to the excellent characteristics of the SBS laser, such as low threshold, wide ultra-narrow line width, and counter-propagating scattering light of adjacent orders, it has been widely applied in fields like high-resolution spectrometry, optical sensing, and quantum computing. This paper utilizes the OptiSystem software for simulation and explores the integration of microwave filters with the SBS effect. Experimental results demonstrate the feasibility of adjusting the pump branch laser frequency to achieve tunable filter passband characteristics.
A refined approach to early movie box office prediction leveraging ensemble learning and feature encoding
Authors: Xie Chuang
Published: July 2024

Predicting the revenue of a movie prior to its release presents a significant challenge. The ability to predict pre-release revenue enables movie production companies to devise effective marketing strategies and mitigate the risks associated with potential box office failures. The primary hurdles in this endeavor stem from managing the myriad factors influencing box office outcomes and accurately forecasting a movie's revenue before it becomes available to the public. To overcome these challenges, we introduce a sophisticated Early Movie Box Office Prediction Model that incorporates Ensemble Learning and Feature Encoding techniques. This model amalgamates multiple foundational models, utilizing regression and decision trees to forecast box office revenues. Our composite model demonstrates superior performance over its constituent models, achieving an impressive accuracy rate of 91.4%.
Desining and development of information system for the processing scientific works
Authors: Жуманбаева С.К. , Пащенко Г.Н.
Published: July 2024

Рост объемов научных трудов вызывает необходимость широкого использования информационных технологий для этого процесса. В последнее время возрастает потребность в раз- работках информационных систем различного характера для работы с научными трудами. Следо- вательно, разработка и исследование таких систем является актуальной задачей. В данной статье рассматриваются существующие информационные системы и программы для обработки научных трудов, и выделяются особенности данных систем. Приводится анализ процессов и разбор алго- ритма системы. В результате анализа сформирована оптимальная модель процессов. Приводится описание разработанной информационной системы для обработки научных трудов.
Исследование актуальных проблем при переходе обучения в онлайн-формат
Authors: Alimzhanova L.M. , Omarova A.S. , Tashtamysheva A.E.
Published: July 2024

The article presents the basic procedures and operational details of the educational process in the process of transition to the online learning, outlines the main reasons for the aggravation of conflicts, and proposes recommendations as to the creation of acceptable conditions for increasing the effectiveness of the educational process in the online format.
Development of project activities in small and medium-sized businesses in Kazakhstan
Authors: Мукан Б. , Саркамбаева Ш.Г.
Published: July 2024

Малый и средний бизнес (МСБ) вносят ключевой вклад в экономику страны с точки зрения трудоустройства, инноваций и роста. Развитие проектной деятельности может сыграть значительную роль в содействии этому вкладу, но МСБ требуют менее бюрократических форм управления проектами, чем те, которые используются более крупными традиционными орга- низациями. В этой статье проводится исследование для определения характера управления проек- тами требуемого МСБ, и результаты исследования представлены в данной статье. Полученные результаты очень убедительно свидетельствуют о том, что МСБ нуждаются в “облегченной” версии управления проектами.
Использование математического моделирования и программного обеспечения в управлении проектными рисками
Authors: Aldibekova N.B. , Tyan A.V. , Omarov I.G. , Mohamed A. , Alimzhanova L.M.
Published: July 2024

The study describes the existing methods for quantitative and qualitative risk analysis and risk assessment software. The field of mathematical modelling is very much developing in economics, allow-ing for more in-depth research. Risk management also requires accurate justification of decisions about the importance of risk, which is made possible by accurate quantitative calculations, including mathematical modelling. The decision-making process within the project is based on the results of visual analysis, i.e., the study of the risk profile and the cumulative risk profile derived from the simulation. This research shows that quantitative risk methods in the project manager's toolkit are helpful in ob-taining complex calculations, but risk management cannot currently exclude the role of a project risk manager from the risk assessment pro-cess.
Прогноз ожидаемых убытков банка при предоставлении кредита
Authors: Bulantayev A.M. , Musakhan K.B. , Moldagulova A.N. , Sembina G.K.
Published: July 2024

This article uses the sample data of the SAS platform as an example to introduce the statisti-cal analysis and prediction of the expected loss of loans issued by banks. The original data for this study comes from a Kaggle source, which provides information about the credit history of bank customers. The technology is based on logistic regression, graphical data analysis, and the basis of building a model on the SAS platform. The model can be used to predict credit risk and describe credit risk in the banking system.
Lições de pesquisa em filosofia da educação: quando o objeto de investigação é Paulo Freire
Authors: Henning Leoni Maria Padilha , Araújo Renata Miranda de , Ferreira Elaine de Souza , Gomes Gustavo Kosienczuk , Souza Camila Cristina Ludovico de
Published: July 2024

Tematizando o trabalho de pesquisa em filosofia da educação, focamos na figura de Paulo Freire enredando-o na rica problemática de seu personagem, tentando capturar o que dele podemos indicar como traços emblemáticos, retirando do estudo lições para o campo da referida pesquisa. A complexidade que o constitui gera as interrogações aqui apresentadas sobre sua pessoa, ao mesmo tempo em que causa a instigante procura por entendê-lo. Daí o interesse em investigar os traços de seu pensamento como filósofo da educação, constituindo assim o objeto central de nosso trabalho. Para a realização dessa pesquisa, selecionamos parte dos conceitos freirianos enquanto ferramentas para o entendimento da realidade brasileira e que expressam a consistência e relevância da problemática educacional apresentada pelo autor: conscientização, solidariedade, inexperiência democrática brasileira e a liberdade como principio educativo. A filosofia da educação freiriana oferece instrumentos para identificação das múltiplas formas de silenciamento presentes na estrutura opressora incluindo escolas - o que pode ser de outro modo. Aprendemos que a educação deve se nortear por principos constitutivos de uma teoria filosófico-pedagógica como criticidade, liberdade, democracia, autenticidade e autonomia, etc. A responsabilidade, corolário à busca pela libertação, desafia o professor para a permanente atuação de igual natureza. Focando pesquisas em Freire, retiramos dessas atividades de investigação algumas lições: necessidade de se conhecer muito bem o autor central do estudo e seus trações distintivos; importância do estabelecimento de princípios a partir dos quais a educação deve ser norteada; necessidade de um continuo acompanhamento reflexivo das ações pedagógicas; valorização da escola para a transformação da sociedade em vista da democracia; dentre outros.
A experiência democrática no processo educacional: uma possibilidade de abordagem filosófica
Authors: Rodrigues João Carlos Domingues dos Santos , Ferreira Anderson Luiz , Movio Cleia Vitoria Marques , Silva Flávio Honorio da , Silva Jarson da
Published: July 2024

Este artigo tem como objetivo contribuir para o âmbito de estudo da filosofia da educação, especificamente com um trabalho acerca das relações entre os temas ‘democracia’ e ‘educação’. A escolha pelo tema: ‘a experiência democrática no processo educacional’, funda-se na importância que ganhou em nossa sociedade especialmente desde a redemocratização, bem como, de sua constante necessidade de revista e de recontextualização ao Brasil de hoje, para que possa manter sua vitalidade e eficácia. Para tanto, não apenas realizaremos uma análise reflexiva sobre a educação como pode ser vista e vivida no contexto nacional, com todos os conflitos e contradições que nela se fazem presentes hoje, mas, também, contaremos com a companhia de importantes filósofos da educação, dois deles brasileiros: Fernando de Azevedo e Anísio Teixeira; e outros dois estrangeiros: John Dewey e Matthew Lipman. Uma vez que a reflexão filosófica não dá respostas fáceis e prontas, mas nos coloca em posição desafiadora a superar-nos e à nossa realidade histórica, os apontamentos que apresentamos aqui tendem a ser um passo a mais nesse caminho de aperfeiçoamento da educação e da filosofia da educação em solo brasileiro. Como próprio dos conceitos ‘educação’ e ‘democracia’, e do tratamento dado a eles pelos nossos companheiros filósofos, manteremos importantes interfaces com debates do âmbito da política, da ética, da tecnologia, da inclusão e da desigualdade.
A dança na Educação Infantil: estudos para o desenvolvimento e a aprendizagem em festividades juninas
Authors: Andrade Ana Paula Evangelista de , Chaves Marta
Published: July 2024

Este artigo objetiva identificar o ensino da Dança na Educação Infantil, tendo como base a Teoria Histórico-Cultural, orientada pela questão principal: os momentos festivos com as crianças têm sido para o máximo desenvolvimento infantil? Essa indagação conduz ao objetivo geral: identificar como ocorre o ensino da Dança para as crianças em festividades juninas. Mediante pesquisa bibliográfica, buscamos identificar “se” e “como” a Dança se apresenta nos documentos Base Nacional Comum Curricular (Brasil, 2018) e Referencial Curricular do Paraná: princípios, direitos e orientações (Paraná, 2018), com a finalidade de constatar se mencionam as comemorações escolares. Amparado aos estudos, apresentamos o recurso didático Caixa que Conta História para o ensino da Arte, da Literatura Infantil e da Dança em festividades juninas, para o máximo desenvolvimento das crianças, na perspectiva de uma educação humanizadora. Por fim, conclui-se que as conduções didáticas e as realizações das crianças devem ser ricas de sentido e significado, comunicação
A Inteligência Artificial na escrita acadêmica: ainda existe lugar para o sujeito na escrita?
Authors: Pimenta Alexandre Marinho , Lopes Carlos , Almeida Cássia Elen Nunes de , Stein Sabrina
Published: July 2024

O presente ensaio objetiva discorrer sobre a IA e suas implicações diante da escrita acadêmica de pós-graduandos em educação. Questiona-se: em que o avanço da IA transforma os desafios de pós-graduandos na escrita? Qual será o lugar desses sujeitos nas novas configurações da escrita acadêmica? Utilizamos centralmente os conceitos freireanos de situação-limite, atos-limite e inédito viável para explicitar a superação de limites por parte de sujeitos implicados na experiência educacional. Também dialogamos com a literatura especializada envolvendo escrita acadêmica e inteligência artificial na educação. Por fim, constatamos a complexidade da temática e reforçamos que, independentemente da forma de uso da IA na escrita acadêmica, não se deve apagar dela o sujeito e sua experiência. Tal apagamento do sujeito acarretaria uma desconfiguração completa da autoria e da educação como processo transformador e coletivo.
Aspectos Epidemiológicos e Análise Espaço-Temporal dos Casos de Leishmaniose Visceral Canina em um Território Endêmico na Bahia
Authors: Costa Graciele Pereira , Silveira Eliane Fraga da , Schröder Nádia Teresinha
Published: July 2024

A Leishmaniose Visceral (LV) é uma doença negligenciada e se constitui em um grave problema de saúde pública. Este estudo investigou o comportamento da LV canina (LVC), no município de Guanambi, Bahia, que se encontra endêmico e sua relação com as condições socioeconômicas e ambientais. Trata-se de uma pesquisa ecológica, descritiva e quantitativa, que analisou os casos notificados de LVC, utilizando a base de dados da Vigilância Epidemiológica de Guanambi e do Hospital Veterinário, no período de 2017 a 2021. Quanto à espacialidade da LVC, foram analisadas as notificações dos casos nas ruas e bairros do município e sua relação com as condições climáticas e indicadores populacionais, socioeconômicos e ambientais. Foram registrados 1061 casos de LVC e os bairros de maior prevalência foram o Alto Caiçara e o Beija-Flor, localizados nas regiões periféricas, com baixo rendimento nominal mensal. A média mensal de cães positivos pode estar vinculada a variação da média mensal de temperatura e de precipitação. O município deve estabelecer estratégias e ações que busquem melhoria das condições ambientais de cada bairro, a fim de minimizar a proliferação do vetor.
Agronegócio e Fundos de Investimento ESG no Brasil: impactos socioambientais
Authors: Malatrasi Larissa Galli , Bezerra Juscelino Eudâmidas
Published: July 2024

Os princípios ESG (Ambiental, Social e Governança) desempenham um papel central no mercado financeiro, influenciando os investimentos. Na Bolsa de Valores do Brasil, diversos índices de sustentabilidade, como o ISE B3 e o IGPTW B3, e emissões de títulos verdes, sociais ou sustentáveis, como a Cédula de Produtor Rural Verde, são evidências desse movimento. No agronegócio brasileiro, embora a implementação de políticas ESG seja desafiadora devido a problemas como corrupção, desmatamento e condições de trabalho, as corporações enfrentam pressão dos consumidores e do mercado financeiro para adotar práticas alinhadas a esses princípios. Este artigo visa analisar os impactos socioambientais da atuação dos fundos ESG no agronegócio brasileiro, com foco na SLC Agrícola, empresa com toda sua produção concentrada no cerrado. A metodologia incluiu pesquisa bibliográfica e análise de dados da Bolsa de Valores, do Relatório de Sustentabilidade da empresa, do Instituto Nacional de Pesquisas Espaciais e da Comissão Pastoral da Terra. Observou-se que a SLC Agrícola demonstra sensibilidade às demandas ESG, refletida em alocação de recursos e ações ambientais, sociais e de governança. No entanto, existem conflitos sociais e desmatamento em áreas onde a empresa atua, levantando questões sobre a efetividade das práticas ESG no agronegócio.
Uso e Cobertura da Terra e Propagação de Efeitos de Borda na Zona de Amortecimento do Parque Estadual Mata do Pau-Ferro, Paraíba, Brasil
Authors: Campos Jean Oliveira , Campos Guilherme Oliveira , Cavalcante Márcio Balbino
Published: July 2024

As zonas de amortecimento são espaços territoriais elaborados com o intuito de reduzir as perturbações ecológicas sobre as áreas protegidas, constituindo uma paisagem de necessário e contínuo monitoramento, com vistas a subsidiar a gestão dos ecossistemas. Nesse contexto, o presente estudo objetiva analisar o uso e a cobertura da terra na zona de amortecimento e sua influência na extensão dos efeitos de borda no Parque Estadual (PE) Mata do Pau-Ferro. Para tanto, realizou-se a classificação supervisionada de uma imagem orbital e a extração das classes mapeadas. Em seguida, estabeleceu-se um buffer de 500 metros no entorno imediato da área protegida, a fim de mensurar as áreas propagadoras e redutoras dos efeitos de borda. Por fim, conduziu-se simulações da extensão dos efeitos de borda. Como resultados, observou-se a predominância de pastagens, ocupando 50,73% da zona de amortecimento. A área propagadora de efeitos de borda compreendeu 64,38%, e a área redutora 35,62%. No perímetro, 56% apresentou capacidade de redução de efeitos de borda, à medida que 44% do contorno se comportou como propagador de efeitos de borda, indicando média vulnerabilidade. As simulações mostraram que até 45,98% da área protegida pode estar afetada. Logo, conclui-se que a matriz de pastagens está afetando os ecossistemas do PE Mata do Pau-Ferro.
Levantamento de Fraturas em Leito Fluvial por meio de VANT (Veículo Aéreo Não Tripulado): calibração da altura de voo
Authors: Flores Diego Moraes , Lima Adalto Gonçalves
Published: July 2024

Utilizar Vants para produção de mosaicos de imagem de alta resolução tornou-se uma prática em diversas aplicações. Este trabalho selecionou dois trechos de leitos fluviais rochosos, um em basalto e outro em riodacito, para calibrar parâmetros de alturas de voo que otimizem a observação das fraturas na rocha. Utilizou-se de estatística básica para validar as diferenças entre cada altura selecionada para avaliação da densidade e da intensidade do fraturamento. Observou-se que a altura de 20 metros permite ótima visualização e um número maior de fraturas observáveis. Condições de voo, como horários de levantamento, podem realçar ou minimizar essas diferenças, como constatado no leito em riodacito. No entanto, alturas de 50 e 100 metros (leito em basalto), embora permitam a observação de menos fraturas, mostram diferenças médias pequenas (mas estatisticamente significativas; α = 0,05) quando comparadas entre si.
Management of Open Fractures of the Extremities and Pediatrics
Authors: Lim Philip K. , Hacquebord Jacques , Shafiq Babar , Gupta Ranjan
Published: July 2024

The modern management of open fractures was established after the foundational work of Gustilo and Anderson, but we continue to strive to determine the optimal treatment of open fractures to diminish the risk of infection. The ideal timing of antibiotics, presentation to the operating room, and timing of procedures such as flap coverage continue to be investigated with incremental changes recommended over the years. This article aims to provide the most recent review of the literature regarding the timing and management of both upper and lower extremity open fractures, pediatric open fractures, current topics of controversy, and the data supporting current treatment recommendations.
Use of yeasts and Moulds in the manufacture of dairy products: A Review
Authors: Oubid Sarah Hashim , Morad Dalia Sudad Hashim , Dhannoon Nour Abdulehsan , Saadi Ali M
Published: July 2024

Over the past years, the diversity of yeasts and mold in the environment has been remarkably understood. Due to the high adaptability of these fungal organs, they are ubiquitous in the environment. Yeasts and molds are widely used in the dairy industry due to their high capacity to ferment milk, as yeast commercial cultures are becoming increasingly popular for the production of fermented milk, cheese and ethanol from whey and lactose solutions. Many types of mold also find their use in the development of various dairy products. Different types of mold are used for ripening different types of cheese for example (Roquefort, Camembert), producing enzymes, and managing dairy waste.
UMA ANÁLISE HISTÓRICA DAS RAÍZES DO TRABALHO DOMÉSTICO ANÁLOGO Á ESCRAVIDÃO NO BRASIL
Authors: Silva Ana Vitória Pereira da , Araújo Bruna Feitosa Serra de
Published: July 2024

A história do trabalho doméstico no Brasil é um reflexo profundo das cicatrizes deixadas pela escravidão e das complexas transformações sociais que ocorreram ao longo dos anos. Desde o período colonial até os dias atuais, o trabalho doméstico foi marcado por uma persistente desigualdade e exploração. Este resumo se propõe a explorar essa trajetória, analisando como as raízes do trabalho doméstico estão entrelaçadas com o passado escravagista do país e como essa prática evoluiu até os dias de hoje.
THE IMPACT OF CLASSROOM ENVIRONMENT ON STUDENTS’ ACADEMIC PERFORMANCE AT SOME SELECTED SECONDARY SCHOOLS IN DHARKEYNLEY DISTRICT
Authors: Addow Nur Dr. Ali
Published: July 2024

This study was conducted To find out the impact of classroom environment on students’ performance at some selected secondary schools in Dharkrynley district, especially the nature ofclassroom environment at schools in Dharkeynley district. The level of academic performance students at schools in Dharkeynley district and to explore the relationship between classroom environment and student’s academic performance at schools in Dharkeynley district, Mogadishu Somala. This study was conducted in 6 months from March to August 2023. This study took place at some selected secondary schools in Dharkeynley district, Mogadishu Somalia. The study applied a Descriptive and correlational research design using quantitative in examining the impact of classroom environment on students’ performance at schools in Dharkeynley district, Mogadishu Somali. The study population involved a total population of 60participants and these include; 15 school principals and 45 from students. The sample size of the study involved of 52 respondents of the target population. The study used Slovene’s to determine the sample size of the actual respondents. The researchers used Stratified sampling method to divide the members of the population into homogeneous subgroups before sampling like students and school managers. The researchers also used random sampling method. School Students was randomly selected to give each an equal chance of representation. All respondents were assumed to have vital information on the subject matter of the research. Self-administered questionnaire was used in the collection of data and these was distributed to the seeks personal views of the respondents and thus was enabled the respondents to use their knowledge in providing a wide range of data as they were never shy away in any way. Data analysis was used statistical package of social science (SPSS v.22) under the tool’s frequencies for objectives one and two and the Pearson linear correlation coefficient analysis was used to test the relationship between variables in objective three. In conclusion, the classroom environment does not have a direct impact on the student's academic performance; however, theperception of students on these components should be explored to create a comfortable classroom environment.