A importância do laboratório de línguas na escola pública
Thesis Subjects > Education Ibero-american International University > Teaching > Master's Final Projects Closed Portuguese O presente trabalho tem como tema expor a importância do laboratório de línguas na escola pública, sendo que este é de extrema relevância para um desenvolvimento satisfatório de tais disciplinas. O objetivo geral é analisar como ocorre o processo de ensino de línguas na escola pública Professora Edeli Mantovani, localizada na cidade de Sinop, Estado de Mato Grosso. Procuramos utilizar o método de análise de conteúdo da Bardin (2010), promovendo a participação do professor para que ele não seja apenas uma fonte de dados, mas principalmente que possa aprender com a pesquisa. Para alcançar os objetivos propostos, temos as seguintes perguntas de investigação: * Qual a importância dos laboratórios de línguas nos componentes curriculares gerais e específicos das disciplinas administradas? *Como se dá o ensino de línguas estrangeiras (LE) nas escolas públicas brasileiras? *Como se dá a integração cultural ora estabelecida a partir dos laboratórios de línguas estrangeiras nas escolas públicas brasileiras? *Como agregar o conhecimento de línguas estrangeiras (LE) à realidade e vivência empírica dos discentes, de modo que tal ensino se torne relevante para os mesmos? Utilizamos uma série de pesquisas colaborativas críticas, selecionadas pelo seu valor na elaboração coletiva do processo educativo envolvendo toda a equipe escolar, especialmente a participação do professor, a fim de promover a reflexão sobre o processo, visando melhorar o aluno. Finalizando, concluímos que entre as dificuldades mais comuns que alunos de LE enfrentam no contexto de escolas públicas estão: fluência reduzida no ensino de LE; falta de tempo e recursos financeiros por parte de muitos professores para aprimorar seus conhecimentos do idioma que ensinam; falta de motivação para ler por parte dos alunos, que não veem a importância de aprender uma língua ou que não acreditam na proposta de ensino da escola pública; e a falta de políticas de educação voltadas para a formação docente, principalmente no que se refere ao ensino de LE no contexto brasileiro. Existem algumas maneiras de mudar a percepção de uma língua estrangeira como forma de submissão no mundo moderno. Muitas conversas ocorreram e estão ocorrendo. Nossa tarefa é ingressar nesse mar de palavras para compreender a realidade em que vivemos, que é determinada pelo contexto histórico do ensino de línguas no Brasil, com o correspondente papel das leis nas ações deliberadas de definição de processos e de sucesso, processos desde a sala de aula, com formação de professores em construção de tecnologia e formação política, como agentes de mudança da verdade. metadata de Jesus Silva Vogado, Maria Lúcia mail luciapkt@gmail.com (2022) A importância do laboratório de línguas na escola pública. Master's thesis, UNSPECIFIED.
Full text not available.Abstract
O presente trabalho tem como tema expor a importância do laboratório de línguas na escola pública, sendo que este é de extrema relevância para um desenvolvimento satisfatório de tais disciplinas. O objetivo geral é analisar como ocorre o processo de ensino de línguas na escola pública Professora Edeli Mantovani, localizada na cidade de Sinop, Estado de Mato Grosso. Procuramos utilizar o método de análise de conteúdo da Bardin (2010), promovendo a participação do professor para que ele não seja apenas uma fonte de dados, mas principalmente que possa aprender com a pesquisa. Para alcançar os objetivos propostos, temos as seguintes perguntas de investigação: * Qual a importância dos laboratórios de línguas nos componentes curriculares gerais e específicos das disciplinas administradas? *Como se dá o ensino de línguas estrangeiras (LE) nas escolas públicas brasileiras? *Como se dá a integração cultural ora estabelecida a partir dos laboratórios de línguas estrangeiras nas escolas públicas brasileiras? *Como agregar o conhecimento de línguas estrangeiras (LE) à realidade e vivência empírica dos discentes, de modo que tal ensino se torne relevante para os mesmos? Utilizamos uma série de pesquisas colaborativas críticas, selecionadas pelo seu valor na elaboração coletiva do processo educativo envolvendo toda a equipe escolar, especialmente a participação do professor, a fim de promover a reflexão sobre o processo, visando melhorar o aluno. Finalizando, concluímos que entre as dificuldades mais comuns que alunos de LE enfrentam no contexto de escolas públicas estão: fluência reduzida no ensino de LE; falta de tempo e recursos financeiros por parte de muitos professores para aprimorar seus conhecimentos do idioma que ensinam; falta de motivação para ler por parte dos alunos, que não veem a importância de aprender uma língua ou que não acreditam na proposta de ensino da escola pública; e a falta de políticas de educação voltadas para a formação docente, principalmente no que se refere ao ensino de LE no contexto brasileiro. Existem algumas maneiras de mudar a percepção de uma língua estrangeira como forma de submissão no mundo moderno. Muitas conversas ocorreram e estão ocorrendo. Nossa tarefa é ingressar nesse mar de palavras para compreender a realidade em que vivemos, que é determinada pelo contexto histórico do ensino de línguas no Brasil, com o correspondente papel das leis nas ações deliberadas de definição de processos e de sucesso, processos desde a sala de aula, com formação de professores em construção de tecnologia e formação política, como agentes de mudança da verdade.
| Document Type: | Thesis (Master's) |
|---|---|
| Keywords: | Laboratório de línguas, Escola pública, Línguas estrangeiras. |
| Subject classification: | Subjects > Education |
| Divisions: | Ibero-american International University > Teaching > Master's Final Projects |
| Deposited: | 15 Nov 2023 23:30 |
| Last Modified: | 15 Nov 2023 23:30 |
| URI: | https://repositorio.unib.org/id/eprint/1416 |
Actions (login required)
 |
View Object |
<a href="/28319/1/s41598-026-45575-1_reference.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
A novel approach for disease and pests detection in potato production system based on deep learning
Vulnerability of potato crops to diseases and pest infestation can affect its quality and lead to significant yield losses. Timely detection of such diseases can help take effective decisions. For this purpose, a deep learning-based object detection framework is designed in this study to identify and classify major potato diseases and pests under real-world field conditions. A total of 2,688 field images were collected from two research farms in Punjab, Pakistan, across multiple growth stages in various seasonal conditions. Excluding 285 symptoms-free images from the earliest collection led to 2,403 images which were annotated into four biotic-stress classes: blight disease (n = 630), leaf spot disease (n = 370), leafroll virus (viral symptom complex; n = 888), and Colorado potato beetle (larvae/adults; n = 515), indicating class imbalance. Several state-of-the-art models were used including YOLOv8 variants (n/s/m), YOLOv7, YOLOv5, and Faster R-CNN, and the results are discussed in relation to recent potato disease classification studies involving cropped leaf images. Stratified splitting (70% training, 20% validation, 10% testing) was applied to preserve class distribution across all subsets. YOLOv8-medium achieve the best performance with mean average precision (mAP)@0.5 of 98% on the held-out test images. Results for stable 5-fold cross-validation show a mean mAP@0.5 of 97.8%, which offers a balance between accuracy and inference time. Model robustness was evaluated using 5-fold cross-validation and repeated training with different random seeds, showing a low variance of ±0.4% mAP. Results demonstrate promising outcomes under the real-world field conditions, while, broader cross-region and cross-season validation is intended for the future.
Ahmed Abbas mail , Saif Ur Rehman mail , Khalid Mahmood mail , Santos Gracia Villar mail santos.gracia@uneatlantico.es, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Aseel Smerat mail , Imran Ashraf mail ,
Abbas
<a class="ep_document_link" href="/27825/1/s41598-026-39196-x_reference.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Histopathological evaluation is necessary for the diagnosis and grading of prostate cancer, which is still one of the most common cancers in men globally. Traditional evaluation is time-consuming, prone to inter-observer variability, and challenging to scale. The clinical usefulness of current AI systems is limited by the need for comprehensive pixel-level annotations. The objective of this research is to develop and evaluate a large-scale benchmarking study on a weakly supervised deep learning framework that minimizes the need for annotation and ensures interpretability for automated prostate cancer diagnosis and International Society of Urological Pathology (ISUP) grading using whole slide images (WSIs). This study rigorously tested six cutting-edge multiple instance learning (MIL) architectures (CLAM-MB, CLAM-SB, ILRA-MIL, AC-MIL, AMD-MIL, WiKG-MIL), three feature encoders (ResNet50, CTransPath, UNI2), and four patch extraction techniques (varying sizes and overlap) using the PANDA dataset (10,616 WSIs), yielding 72 experimental configurations. The methodology used distributed cloud computing to process over 31 million tissue patches, implementing advanced attention mechanisms to ensure clinical interpretability through Grad-CAM visualizations. The optimum configuration (UNI2 encoder with ILRA-MIL, 256 256 patches, 50% overlap) achieved 78.75% accuracy and 90.12% quadratic weighted kappa (QWK), outperforming traditional methods and approaching expert pathologist-level diagnostic capability. Overlapping smaller patches offered the best balance of spatial resolution and contextual information, while domain-specific foundation models performed noticeably better than generic encoders. This work is the first large-scale, comprehensive comparison of weekly supervised MIL methods for prostate cancer diagnosis and grading. The proposed approach has excellent clinical diagnostic performance, scalability, practical feasibility through cloud computing, and interpretability using visualization tools.
Naveed Anwer Butt mail , Dilawaiz Sarwat mail , Irene Delgado Noya mail irene.delgado@uneatlantico.es, Kilian Tutusaus mail kilian.tutusaus@uneatlantico.es, Nagwan Abdel Samee mail , Imran Ashraf mail ,
Butt
<a class="ep_document_link" href="/27915/1/csbj.0023.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
This systematic literature review (SLR) investigates the integration of deep learning (DL), vision-language models(VLMs), and multi-agent systems in the analysis of pathology images and automated report generation. The rapidadvancement of whole-slide imaging (WSI) technologies has posed new challenges in pathology, especially due to thescale and complexity of the data. DL techniques in general and convolutional neural networks (CNNs) and transform-ers in particular have significantly enhanced image analysis tasks including segmentation, classification, and detection.However, these models often lack generalizability to generate coherent, clinically relevant text, thus necessitating theintegration of VLMs and large language models (LLMs). This review examines the effectiveness of VLMs and LLMsin bridging the gap between visual data and clinical text, focusing on their potential for automating the generationof pathology reports. Additionally, multi-agent systems, which leverage specialized artificial intelligence (AI) agentsto collaboratively perform diagnostic tasks, are explored for their contributions to improving diagnostic accuracy andscalability. Through a synthesis of recent studies, this review highlights the successes, challenges, and future direc-tions of these AI technologies in pathology diagnostics, offering a comprehensive foundation for the development ofintegrated, AI-driven diagnostic workflows.
Usama Ali mail , Imran Shafi mail , Jamil Ahmad mail , Arlette Zárate Cáceres mail , Thania Chio Montero mail , Hafiz Muhammad Raza ur Rehman mail , Imran Ashraf mail ,
Ali
<a class="ep_document_link" href="/27970/1/s11357-026-02188-w.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Fish consumption and cognitive function in aging: a systematic review of observational studies
Epidemiological studies consistently link higher fish intake with slower rates of cognitive decline and lower dementia incidence. The aim of the present study was to systematically review existing observational studies investigating the association between fish consumption and cognitive function in older adults. A total of 25 studies (8 cross-sectional and 17 prospective including mainly healthy older adults, age range of participants ranging from 18 to 30 years at baseline in prospective studies to 65 to 91 years, representing the upper limit of the age spectrum) were reviewed. Cognitive functions currently investigated in most published studies included various domains, such as global cognition, memory (episodic, working), executive function (planning, inhibition, flexibility), attention and processing speed. Existing studies greatly vary in terms of design (cross-sectional and prospective), geographical area, number of participants involved, and tools used to assess the outcomes of interest. The main findings across studies are not univocal, with some studies reporting stronger evidence of association between fish consumption and various cognitive domains, while others addressed rather null findings. The most consistently responsive domains were processing speed, executive functioning, semantic memory, and global cognitive ability among individuals consuming fish at least weekly, which are highly relevant to both neurodegenerative and vascular forms of cognitive impairment. Positive associations were also observed for verbal memory and general memory, though these were less uniform and often attenuated after multivariable adjustment. In contrast, associations with reaction time, verbal-numerical reasoning, and broad composite scores were inconsistent, and several fully adjusted models showed null results. In conclusion, the evidence suggests that regular fish intake (typically ≥1–2 servings per week) is linked to preserved cognitive performance, although some inconsistent findings require further investigations.
Justyna Godos mail , Giuseppe Caruso mail , Agnieszka Micek mail , Alberto Dolci mail , Carmen Lilí Rodríguez Velasco mail carmen.rodriguez@uneatlantico.es, Evelyn Frias-Toral mail , Jason Di Giorgio mail , Nicola Veronese mail , Andrea Lehoczki mail , Mario Siervo mail , Zoltan Ungvari mail , Giuseppe Grosso mail ,
Godos
<a class="ep_document_link" href="/28495/1/1-s2.0-S1697260026000153-main.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
en
open
Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners
With the growing academic pressure and competitive educational environment, students often face mental stress, which can affect their academic performance and mental health. Its accurate and timely detection and prevention is important. Traditionally, mental stress has been reported by self-assessment, which is highly subjective and can be erroneous. With advances in neuroscience, electroencephalogram (EEG) signals have been used to study brain states more objectively. EEG-based features, including time-domain, frequency-domain, and various types of connectivity features, have been used to effectively classify stress signals. However, these individual features are only able to present one aspect of the brain under stress. Several studies have combined a distinct set of features extracted from EEG signals, including time and frequency domain features, with other peripheral signals. Stress is a complex mechanism which leads to alternation in brain dynamics, its connectivity patterns and information flow. This study proposed a feature-fusion model that can effectively combine spatial features, i.e. Microstates (MS), connectivity features like Transfer Entropy (TE) and Granger Causality (GC), which provided a new neuromarker for stress classification. These features are combined with attention fusion, which enhances the discriminant features and mitigates the individual limitations within each modality. We also extracted microstates for stress-based signals. It provided a new set of microstate topomaps to study brain networks when under stress, which was not explored previously. The proposed Attention-fusion based multi-feature set is classified using Support Vector Machine, Linear Discriminant Analysis (LDA) and Multilayer Perceptron (MLP) and gave a reliable accuracy of 95.47%, 98.91%, and 83.49%, respectively. To validate the proposed method, the classification results were compared with individual and binary fusion of MS, TE and GC features, which further confirmed the robustness of the framework. This proposed feature fusion provides a more robust stress classification neuromarker, which can effectively cover the brain dynamics for accurate reporting of the underlying mental state.
Saliha Ejaz mail , Soyiba Javed mail , Imran Shafi mail , Jamil Ahmad mail , Samuel Allende Monje mail samuel.allende@uneatlantico.es, Josep Alemany Iturriaga mail josep.alemany@uneatlantico.es, Jin-Ghoo Choi mail , Imran Ashraf mail ,
Ejaz