Priscila Cardoso Moraes de Souza, Elza Maria Neffa Vieira de Castro, Luiz Alberto de Lima Leandro
Este artigo apresenta uma sistematização metodológica para formulação de políticas públicas intersetoriais em territórios marcados por múltiplas vulnerabilidades socioambientais, a partir da experiência do modelo CCAIC (Creche Centro de Atendimento à Infância Caxiense), implementado em Duque de Caxias (RJ). A pesquisa adota abordagem qualitativa, dialética e interdisciplinar, com base em estudo de caso único e aplicação da Análise de Conteúdo Qualitativa Estruturada. Dezesseis apontamentos metodológicos foram identificados, ancorados nas categorias vulnerabilidade socioambiental, inclusão social, políticas públicas e justiça ambiental e articulados pela intersetorialidade como diretriz estratégica. A originalidade do estudo reside na tradução empírico-teórica de práticas de gestão pública territorializada com potencial de replicabilidade em outros contextos. As diretrizes formuladas funcionam como instrumentos para planejamento, implementação e avaliação de políticas orientadas à superação das desigualdades estruturais que afetam a infância nas periferias urbanas brasileiras.
Energy piles combine structural support with ground-source heat exchange, enabling effective seasonal thermal storage. However, long-term cyclic thermal loads on piles can induce complex thermo-mechanical interactions with surrounding soils. Predicting the long-term performance of energy piles is crucial for reliable foundation design but remains challenging due to limited monitoring data, significant parameter uncertainty, and the high computational cost of fully coupled numerical analyses. To address these challenges, this study proposes a multi-objective sparse ensemble learning (MO-SEL) framework that combines physics-informed load transfer functions with sequential sparse Bayesian learning to predict long-term pile performance. A physics-embedded feature library is first constructed from diverse finite difference models. The most informative bases are then automatically selected using multi-parameter monitoring data within a dimensionally consistent formulation, followed by joint prediction of displacement and axial strain with explicit uncertainty quantification. Applications to physical model tests and full-scale case studies demonstrate that MO-SEL yields accurate and uncertainty-aware predictions of long-term pile performance, with dynamic updating strategies further enhancing reliability as new data become available. More importantly, the framework enables both reconstruction and forward prediction of full-field strain distributions along the pile shaft, including unmonitored zones, an advantage not achievable with conventional black-box machine learning methods.
LeaAnne Daughrity, Candace Walkington, Max Sherard
This study investigates the use of GeoGebra, a Dynamic Geometry Software (DGS) for math learning in Virtual Reality (VR) using head-mounted displays. We conducted a study with n = 20 middle school students receiving a mathematics tutoring intervention over time in a VR environment. Using theories of embodied cognition and playful mathematics, this paper focuses on distinguishing between mathematical play and general play in VR environments. We also look at interactions that led to instances of play. Key findings highlight how mathematical play in an immersive VR environment using DGS allows mathematical misconceptions to surface, students to explore mathematical ideas, and opportunities for mathematical reasoning about target concepts to build off play experiences. General play allows for the embodied engagement of learners in the mathematical learning environment and includes engagement and rapport-building. The integration of play fits well into VR environments that uniquely allow for immersion and embodiment, and play should be purposefully integrated into such VR environments in the future.
Amidst the rapid global expansion of smart grids, ensuring the safety and reliability of power transmission systems has become paramount. Insulators are critical components of high-voltage transmission lines, providing both electrical insulation and mechanical support. However, their exposure to electrical, mechanical, and environmental stressors renders them vulnerable point within the system. Defective insulators are a major cause of failures in power transmission systems. Consequently, the early and accurate detection of these defects is pivotal for maintaining the integrity and reliability of the power grid. To address this challenge, this study proposes InsDD-YOLO, a novel object detection architecture enhanced from the YOLOv13 framework. The model incorporates a suite of strategic enhancements, including an improved DSConv (IDSConv) module for robust feature extraction, a streamlined Neck architecture augmented with a feature stream from a shallower layer (B2) to improve small-target detection, and a direct Head connection mechanism to maximize the preservation of fine-grained details. Experimental results demonstrate that InsDD-YOLO achieves superior performance, reaching an mAP0.5 of 90.1% and an mAP<inline-formula> <tex-math notation="LaTeX">${}_{0.5:0.95}$ </tex-math></inline-formula> of 46.4%, outperforming the baseline YOLOv13 model by a significant 5.0% in mAP0.5. With an inference time of just 5.4 ms, the proposed model not only establishes a new benchmark for accuracy but also demonstrates an effective trade-off between performance and speed, underscoring its significant potential for deployment in real-time, automated power grid monitoring systems.
Zaid Ameen Abduljabbar, Vincent Omollo Nyangaresi, Ahmed Ali Ahmed
et al.
Abstract Vehicular Ad-Hoc Networks (VANETs) have facilitated the massive exchange of real-time traffic and weather conditions, which have helped prevent collisions, reduce accidents, and road congestions. This can effectively enhance driving safety and efficiency in technology-driven transportation systems. However, the transmission of massive and sensitive information across public wireless communication channels exposes the transmitted data to a myriad of privacy as well as security threats. Although past researches has developed many vehicular ad-hoc networks security preservation schemes, several of them are inefficient or susceptible to attacks. This work, introduces an approach that leverages reverse fuzzy extraction, bilinear pairing, and Physically Unclonable Function (PUF) to design an efficient and anonymity-preserving authentication scheme. We conduct an elaborate formal security analysis to demonstrate that the derived session key is secure. The semantic security analyses also demonstrate its resilience against typical VANET attacks such as impersonations, denial of service, and de-synchronization, instilling confidence in its effectiveness. Moreover, our approach incurs the lowest computational overheads at relatively low communication costs. Specifically, our protocol attains a 66.696% reduction in computation costs, and a 70% increment in the supported security functionalities.
Background: Depression commonly occurs in patients with breast cancer (BC), affecting their quality of life. Objectives: The relationships between depression and different sociodemographic characteristics in patients with BC are under-researched. We conducted a multicenter study to determine the magnitude of depression and its association with different sociodemographic characteristics. Design: In this multi-institutional study, clinical data were collected, prospectively between October 2019 and January 2023 from 207 patients who were on active treatment for BC diagnosis in tertiary oncology hospitals in Georgia. Methods: Patients’ sociodemographic characteristics were analyzed and their association with depression was assessed, using Patient Health Questionnaire-9 (PHQ-9) for the identification of depressive symptoms. Patients were stratified using basic information. Results: The median age of participants was 53 years (ranging from 31 to 77). Of the participants, 63.2% were married, 44.5% were employed, and only 16.4% reported having adequate financial status. Based on pro-rated PHQ-9 scores, 42% of patients reported some level of depressive symptoms, and 14.5% met the criteria for probable depressive disorder. Women with very inadequate financial status (10/21, 47.6%) reported significantly more depressive symptoms than those with adequate financial support (3/34, 8.8%) ( P = .001). Unemployed women (12/42, 28.6%) were nearly 3 times more likely to experience moderate or severe depressive symptoms compared with employed patients (8/92, 8.7%) ( P = .002). A significant difference in depressive symptoms was also observed based on education level, with individuals with higher education (12/119, 10%) reporting fewer depressive symptoms compared with those with middle education (18/88, 20.4%) ( P = .036). No statistically significant difference in depressive symptoms was found based on marital status or social support. Conclusions: Our study found a significant relationship between depression and factors such as financial status, education level, and employment. Lower household income and education level were identified as predictors of clinical depression among patients with BC. These findings can help oncologists in Georgia recognize the importance of providing psychological support to cancer patients. Early detection and prompt referral to mental health specialists can play a key role in effectively managing depression.
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
This paper discusses key challenges of Artificial Intelligence in Education, with main focus on higher education institutions. We start with reviewing normative actions of international organizations and concerns expressed about the current technical landscape. Then we proceed with proposing a framework that comprises five key dimensions relating to the main challenges relating to AI in higher education institutions, followed by five key strategic actions that the main stakeholders need to take in order to address the current developments. We map these actions to the main stakeholders of higher education and propose a deployment plan. This defines a framework along the dimensions: Challenges, Actions, Stakeholders, Deployment CASD. Examples of AI specific actions at the institutional and individual course level are also provided and discussed.
Noah Q. Cowit, Sri Yash Tadimalla, Stephanie T. Jones
et al.
Many institutions are currently grappling with teaching artificial intelligence (AI) in the face of growing demand and relevance in our world. The Computing Research Association (CRA) has conducted 32 moderated virtual roundtable discussions of 202 experts committed to improving AI education. These discussions slot into four focus areas: AI Knowledge Areas and Pedagogy, Infrastructure Challenges in AI Education, Strategies to Increase Capacity in AI Education, and AI Education for All. Roundtables were organized around institution type to consider the particular goals and resources of different AI education environments. We identified the following high-level community needs to increase capacity in AI education. A significant digital divide creates major infrastructure hurdles, especially for smaller and under-resourced institutions. These challenges manifest as a shortage of faculty with AI expertise, who also face limited time for reskilling; a lack of computational infrastructure for students and faculty to develop and test AI models; and insufficient institutional technical support. Compounding these issues is the large burden associated with updating curricula and creating new programs. To address the faculty gap, accessible and continuous professional development is crucial for faculty to learn about AI and its ethical dimensions. This support is particularly needed for under-resourced institutions and must extend to faculty both within and outside of computing programs to ensure all students have access to AI education. We have compiled and organized a list of resources that our participant experts mentioned throughout this study. These resources contribute to a frequent request heard during the roundtables: a central repository of AI education resources for institutions to freely use across higher education.
Maria Eleni Koutsiaki, Marina Delianidi, Chaido Mizeli
et al.
The integration of AI chatbots into educational settings has opened new pathways for transforming teaching and learning, offering enhanced support to both educators and learners. This study investigates the design and application of an AI chatbot as an educational tool in higher education. Designed to operate in the Greek language, the chatbot addresses linguistic challenges unique to Greek while delivering accurate, context grounded support aligned with the curriculum. The AI chatbot is built on the Retrieval Augmented Generation (RAG) framework by grounding its responses in specific course content. RAG architecture significantly enhances the chatbots reliability by providing accurate, context-aware responses while mitigating common challenges associated with large language models (LLMs), such as hallucinations and misinformation. The AI chatbot serves a dual purpose: it enables students to access accurate, ondemand academic support and assists educators in the rapid creation of relevant educational materials. This dual functionality promotes learner autonomy and streamlines the instructional design process. The study aims to evaluate the effectiveness, reliability, and perceived usability of RAG based chatbots in higher education, exploring their potential to enhance educational practices and outcomes as well as supporting the broader adoption of AI technologies in language specific educational contexts. Findings from this research are expected to contribute to the emerging field of AI driven education by demonstrating how intelligent systems can be effectively aligned with pedagogical goals.
Cet article examine la réutilisation des coquillages fossiles et décrit en même temps les anomalies ou les déformations observées sur les individus. La démarche adoptée repose en effet sur une combinaison de plusieurs activités allant de la recherche documentaire à l’examen du mobilier coquillier, en passant par les enquêtes ethnographiques, la prospection et les fouilles archéologiques. Nos fouilles effectuées sur le site de Balloum, près du village de Moundé (basSaloum) ont mis au jour des spécimens coquilliers déformés ou réutilisés le plus souvent en des objets de parures ou d’ustensiles. Mots-clés : ,
In recent years, there has been rising interest from both governments and private industry in developing software that is accessible to all, including people with disabilities. However, the computer science (CS) courses that ought to prepare future professionals to develop such accessible software hardly cover topics related to accessibility. While there is growing literature on incorporating accessibility topics in computing education in the West, there is little work on this in the Global South, particularly in India, which has a large number of computing students and software professionals. In this replication report, we present (A) our findings from a replication of surveys used in the US and Switzerland on who teaches accessibility and barriers to teaching accessibility and (B) a qualitative analysis of perceptions of CS faculty in India about digital accessibility and teaching accessibility. Our study corroborates the findings of the earlier surveys: very few CS faculty teach accessibility, and the top barriers they perceive are the same. The qualitative analysis further reveals that the faculty in India need training on accessibility concepts and disabilities sensitization, and exposure to existing and ongoing CS education research and pedagogies. In light of these findings, we present recommendations aimed at addressing these challenges and enhancing the integration of accessibility into computing education.
Across the African continent, students grapple with various educational challenges, including limited access to essential resources such as computers, internet connectivity, reliable electricity, and a shortage of qualified teachers. Despite these challenges, recent advances in AI such as BERT, and GPT-4 have demonstrated their potential for advancing education. Yet, these AI tools tend to be deployed and evaluated predominantly within the context of Western educational settings, with limited attention directed towards the unique needs and challenges faced by students in Africa. In this chapter, we discuss challenges with using AI to advance education across Africa. Then, we describe our work developing and deploying AI in Education tools in Africa for science and computing education: (1) SuaCode, an AI-powered app that enables Africans to learn to code using their smartphones, (2) AutoGrad, an automated grading, and feedback tool for graphical and interactive coding assignments, (3) a tool for code plagiarism detection that shows visual evidence of plagiarism, (4) Kwame, a bilingual AI teaching assistant for coding courses, (5) Kwame for Science, a web-based AI teaching assistant that provides instant answers to students' science questions and (6) Brilla AI, an AI contestant for the National Science and Maths Quiz competition. Finally, we discuss potential opportunities to leverage AI to advance education across Africa.
En un contexto actual de creciente polarización y politización en torno a las cuestiones migratorias, se analizan cinco propuestas de cómics y narrativas gráficas que contribuyen a visibilizar voces y espacios olvidados de la migración latinoamericana hacia Estados Unidos. Se hace hincapié en el rol de la hibridez genérica al otorgar y reforzar la legitimidad del relato de historias personales y colectivas mediante la inserción de la autobiografía, el testimonio, el reportaje, el western o la ciencia ficción, entre otros. El corpus elegido: Migrar (Mateo y Martínez, 2011), Cita en Phoenix (Sandoval, 2016), La cicatriz. En la frontera entre México y Estados Unidos (Ferraris y Chiocca, 2019), Barrera (Vaughan, Martín y Vicente, 2019) y Ana (Arriaga y Ramos, 2021), da cuenta de las múltiples posibilidades que ofrecen estas narrativas gráficas que dejan de ser divertimento para participar activamente en la configuración de un discurso socialmente comprometido.
Special aspects of education, Literature (General)
While the Community of Inquiry centres on a form of discussion that is meant to improve the ability of students to think, only a fraction of the thinking that occurs in a discussion makes its appearance in speech. We therefore need to consider students’ mental acts in addition to their speech acts to understand how the Community of Inquiry is meant to function. This paper explores the connections between speech acts and mental acts in the Community of Inquiry and the broader classifications of these acts of thought. It also considers the role of teacherly interventions in promoting appropriate acts of thought and the ways in which teachers can improve students’ metacognitive awareness of them.
Trata-se de resultado de pesquisa sobre a formação e as práticas de diretoras dos grupos escolares no Maranhão. Analisamos as memórias sobre a formação de diretoras dos grupos. Uma das questões investigadas foi de que modo as professoras se constituem diretoras? Desse modo, estudamos sua formação e trajetória profissional docente. O conceito de memória norteia os pressupostos teóricos da investigação uma vez que se trabalha com a perspectiva metodológica da História Oral. A memória também apresenta suas limitações, é uma reconstrução continuamente atualizada do passado. Realizamos entrevistas com diretoras que atuaram nos Grupos Escolares da capital e do interior maranhense. O estudo mostra a importância de conhecermos suas experiências, saberes e práticas.
To provide a possible interpretation of the passive attitude often exhibited by pupils in the context of school trips, the article does not follow the path of moralistic judgement, but starts from the essence of the teaching profession, which consists in showing the world to the students. The article adopts the thesis of the presence, in many school trips, of an excessive cognitive load and affirms the need to devote the same attention to teaching in these learning contexts as in class work. Graduality is proposed as the key to a learning experience based on a motivation that arises from the discovery of reality.
Dara Cassidy, Yann-Aël Le Borgne, Francisco Bellas
et al.
This report presents a set of use scenarios based on existing resources that teachers can use as inspiration to create their own, with the aim of introducing artificial intelligence (AI) at different pre-university levels, and with different goals. The Artificial Intelligence Education field (AIEd) is very active, with new resources and tools arising continuously. Those included in this document have already been tested with students and selected by experts in the field, but they must be taken just as practical examples to guide and inspire teachers creativity.
Structural equation modeling (SEM) is a statistical method widely used in educational research to investigate relationships between variables. SEM models are typically constructed based on theoretical foundations and assessed through fit indices. However, a well-fitting SEM model alone is not sufficient to verify the causal inferences underlying the proposed model, as there are statistically equivalent models with distinct causal structures that equally well fit the data. Therefore, it is crucial for researchers using SEM to consider statistically equivalent models and to clarify why the proposed model is more accurate than the equivalent ones. However, many SEM studies did not explicitly address this important step, and no prior study in physics education research has delved into potential methods for distinguishing statistically equivalent models with differing causal structures. In this study, we use physics identity model as an example to discuss the importance of considering statistically equivalent models and how other data can help to distinguish them. Previous research has identified three dimensions of physics identity: perceived recognition, self-efficacy, and interest. However, the relationships between these dimensions have not been thoroughly understood. In this paper, we specify a model with perceived recognition predicting self-efficacy and interest, which is inspired by individual interviews with students in physics courses to make physics learning environments equitable and inclusive. We test our model with fit indices and discuss its statistically equivalent models with different causal inferences among perceived recognition, self-efficacy, and interest. We then discuss potential experiments that could further empirically test the causal inferences underlying the models, aiding the refinement to a more accurate causal model for guiding educational improvements.
With the rapid evolution of Natural Language Processing (NLP), Large Language Models (LLMs) like ChatGPT have emerged as powerful tools capable of transforming various sectors. Their vast knowledge base and dynamic interaction capabilities represent significant potential in improving education by operating as a personalized assistant. However, the possibility of generating incorrect, biased, or unhelpful answers are a key challenge to resolve when deploying LLMs in an education context. This work introduces an innovative architecture that combines the strengths of ChatGPT with a traditional information retrieval based chatbot framework to offer enhanced student support in higher education. Our empirical evaluations underscore the high promise of this approach.