Search Results (10,583)

This paper offers content priorities, justifications, and pedagogical approaches for the integration of climate change education into the training of teachers, and thus into public schooling. To meet urgent imperatives presented by the polycrisis of the Anthropocene, climate change education must be inclusive, comprehensive, flexible, and regionally responsive. Climate change education can be achieved by adapting regional programs for teacher education to meet those requirements. An example is the Climate Education in Teacher Education (CETE) project in northern British Columbia, Canada. Using the Education Design-Based Research methodology, the project addresses critical questions for curricular and pedagogical development of teachers to address the following three questions: (a) what content and outcomes to prioritize, (b) why these elements matter, and (c) how to implement them effectively. Over two years, CETE engaged pre-service and in-service teachers through workshops, reflective practices, and consultations with Indigenous communities. Our tentative answers emphasize the importance of adapting curriculum and pedagogy to foster community resilience, address climate anxiety, and promote an ethical renewal toward sustainability. The iterative development of objectives as “High-Level Conjectures” provides flexibility and reflexivity in the design process in the face of rapid contextual change. CETE developed practical pedagogical tools and workshop strategies that align educational priorities with local and global needs. This study offers a replicable framework to empower educators and communities in diverse locations to navigate the complexities of the climate crisis in their quest for a more secure and sustainable future. Full article

Nowadays, Flutter with the Dart programming language has become widely popular in mobile developments, allowing developers to build multi-platform applications using one codebase. An increasing number of companies are adopting these technologies to create scalable and maintainable mobile applications. Despite this increasing relevance, university curricula often lack structured resources for Flutter/Dart, limiting opportunities for students to learn it in academic environments. To address this gap, we previously developed the Flutter Programming Learning Assistance System (FPLAS), which supports self-learning through interactive problems focused on code comprehension through code-based exercises and visual interfaces. However, it was observed that many students completed the exercises without fully understanding even basic concepts, if they already had some knowledge of object-oriented programming (OOP). As a result, they may not be able to design and implement Flutter/Dart codes independently, highlighting a mismatch between the system’s outcomes and intended learning goals. In this paper, we propose a guided self-study approach of integrating documentation, code, visual output, and exercise in FPLAS. Two existing problem types, namely, Grammar Understanding Problems (GUP) and Element Fill-in-Blank Problems (EFP), are combined together with documentation, code, and output into a new format called Integrated Introductory Problems (INTs). For evaluations, we generated 16 INT instances and conducted two rounds of evaluations. The first round with 23 master students in Okayama University, Japan, showed high correct answer rates but low usability ratings. After revising the documentation and the system design, the second round with 25 fourth-year undergraduate students in the same university demonstrated high usability and consistent performances, which confirms the effectiveness of the proposal. Full article

Sepsis is a complex clinical syndrome associated with high morbidity and mortality and organ dysfunction, most notably acute kidney injury. Early recognition determines crucial clinical decisions for septic individuals. This rapid diagnosis depends on the accuracy of biomarkers in the context of coexisting renal dysfunction. In this context, the value of presepsin has been investigated and challenged for a decade, with no definitive answers. This scoping review aims to evaluate the existing evidence regarding the accuracy of presepsin as a diagnostic and prognostic biomarker for sepsis-associated acute kidney injury (SA-AKI). We obtained 130 articles by searching for references in databases (PubMed/Medline, Web of Science, Embase, and Scopus) following the PRISMA-ScR guidelines. Sequential selection by three independent readers resulted in nine references retained for full analysis. Presepsin demonstrated good diagnostic and prognostic accuracy in patients with AKI, based on observations in small patient groups; however, it requires specific cutoff values, whose determination depends on new controlled and randomized studies. Full article

Aftercare plans can support breast cancer patients’ self-management after curative treatment but are often not personalized and limitedly applied by healthcare practitioners (HCPs). This study aimed to develop a tool integrating information provision and assessment of patients’ goals and needs, to support the creation and application of a personalized aftercare plan. A multidisciplinary workgroup guided the development by defining the target audience, scope and purpose. Needs of 18 patients and 15 HCPs were assessed to determine the tool’s content and format. Usability tests of a prototype among 7 patients and 10 HCPs informed improvements and finalization. The tool, called ‘Breast Cancer Aftercare Decision Aid’ (BC-ADA), provides information on potential effects of cancer and support options on five domains: physical wellbeing, emotions, relationships, regaining trust and return to daily routine. Patients can indicate which domain(s) they wish to improve, what resources they have and where additional help is needed. Based on their answers, patients can create an aftercare plan together with the HCP, including personal goals, specific actions and agreements on follow-up. Usability and acceptability were positively evaluated by both patients and HCPs. The BC-ADA seems promising in supporting personalized aftercare decision-making and is currently being tested in the NABOR-study in Dutch hospitals. Full article

The efficiency of wetlands in removing nutrients from streams and rivers can be accurately evaluated using diatom-inferred total phosphorus (DI-TP), as DI-TP integrates the effects of various environmental factors. However, studies assessing the efficiency of an extensive set of wetlands at multiple scales and under different levels of human disturbance activities (HDA) in removing DI-TP from streams and rivers are sparse. A national-scale dataset from the U.S. EPA’s 2008–2009 National Rivers and Streams Assessment survey provides a unique opportunity to answer this question. Our results showed that, compared to watershed-scale wetlands, local-scale wetlands performed better at removing DI-TP from streams and rivers. Additionally, wetlands performed better at removing DI-TP under lower levels of HDA, suggesting that high levels of HDA could alter the structure and function of wetlands enough to affect their ability to remove nutrients. Interaction analysis revealed there was a significant positive relationship between HDA and local-scale wetlands. We conclude that DI-TP is a valuable metric for evaluating the effectiveness of wetlands at removing nutrients from streams and rivers. To support freshwater management, both the spatial scale of wetlands and the level of HDA on wetlands, along with their cross-scale interactions, should be considered. Full article

This study evaluates the impact of using group concept maps in the teaching of Applied Thermodynamics in the Bachelor’s Degree in Industrial Electronics and Automation Engineering. The methodology consisted of selecting topics with a high conceptual load, collaboratively creating concept maps, and subsequently evaluating them by both students and teaching staff. Students achieved average scores above 7/10 in the concept map activity, with teacher and student evaluations averaging 7.8 and 7.3, respectively. Knowledge assessment via pre- and post-tests revealed a 20% increase in concept comprehension. For example, in the topic of Principles of Thermodynamics, the percentage of correct answers on the most complex question increased from 13% in the Pre-Test to 40% in the post-test. In the topic of Refrigeration Cycles, some questions showed an improvement from 18% to 25%. The students’ perception of the activity was positive, with an average satisfaction rating of 6.9 out of 10. Furthermore, most students acknowledged that the activity helped them stay engaged with the subject matter and identify errors in their own learning. The high participation in the activity, despite its low impact on the final grade, demonstrates the students’ strong motivation for this study approach. Therefore, the implementation of concept maps not only facilitated the understanding of key concepts but also promoted critical reflection and collaborative learning, establishing itself as an effective strategy in the teaching of Applied Thermodynamics. Full article

Background/Objectives: Home healthcare assistants (HHCAs) play a vital role in supporting older adults to remain in their homes. Yet, this work is often performed under conditions of emotional strain, limited resources, and systemic undervaluation. This study answers the question, how do HHCAs cope with work-related stress? Methods: Undertaken during the COVID-19 pandemic, a period of heightened stress and mandated social distancing, online interviews were conducted with HHCAs (n = 10). Data were inductively analyzed and themes were identified. Results: It was found that amid experiences of fear, caregiver stress, grief, and exhaustion, HHCAs coped with resource, communication, and care challenges by relying on informal peer-managed communication systems with colleagues. Leveraging existing peer-support coping strategies, HHCAs negotiated caring for others while taking care of themselves alongside a care ecosystem under unprecedented strain. Conclusions: HHCAs are increasingly vital to front-line home health care amid global aging and a shift toward community-based services. Urgent organizational reform is needed to support their well-being, prevent stress, and avoid burnout. Research-informed sector-wide planning must ensure adequate resources to maintain high-quality home care in the face of rising demand and anticipated future health crises. Full article

Background/Objectives: Surgical ward rounds are central to trainee education but are often associated with stress, cognitive overload, and inconsistent learning. Advances in artificial intelligence (AI), particularly large language models (LLMs), offer new ways to support trainees by simulating ward-round questioning, enhancing preparedness, and reducing anxiety. This study explores the role of generative AI in surgical ward-round education. Methods: Hypothetical plastic and reconstructive surgery ward-round scenarios were developed, including flexor tenosynovitis, DIEP flap monitoring, acute burns, and abscess management. Using de-identified vignettes, AI platforms (ChatGPT-4.5 and Gemini 2.0) generated consultant-level questions and structured responses. Outputs were assessed qualitatively for relevance, educational value, and alignment with surgical competencies. Results: ChatGPT-4.5 showed a strong ability to anticipate consultant-style questions and deliver concise, accurate answers across multiple surgical domains. ChatGPT-4.5 consistently outperformed Gemini 2.0 across all domains, with higher expert Likert ratings for accuracy, clarity, and educational value. It was particularly effective in pre-ward round preparation, enabling simulated questioning that mirrored consultant expectations. AI also aided post-round consolidation by providing tailored summaries and revision materials. Limitations included occasional inaccuracies, risk of over-reliance, and privacy considerations. Conclusions: Generative AI, particularly ChatGPT-4.5, shows promise as a supplementary tool in surgical ward-round education. While both models demonstrated utility, ChatGPT-4.5 was superior in replicating consultant-level questioning and providing structured responses. Pilot programs with ethical oversight are needed to evaluate their impact on trainee confidence, performance, and outcomes. Although plastic surgery cases were used for proof of concept, the findings are relevant to surgical education across subspecialties. Full article

Accurate quantification of minor mineral phases is important in Powder X-Ray Diffraction (PXRD) and Rietveld phase quantification. The precise limit of quantification for the various phases is rarely considered but rather approximated to 0.2–2 wt% by applying a global minimum weight percentage threshold. This approximation often leads to false positive or false negative phase quantity, jeopardizing the trustworthiness of the analytic method in general. In this work (1) we propose a dynamic and adaptable false positive filtering method for Rietveld Quantitative X-ray diffraction (QXRD) based on a phase-specific signal-to-noise ratio referred to as “Phase-SNR”; (2) we introduce the method baptized “Phase Guard” which is implemented in the software HighScore Plus. Phase Guard is based on peaks counting statistics and it automatically adapts to different mineral scattering powers, different mineral crystallinity, instrumental configuration and measurement time. Its applicability and benefits are demonstrated with several examples in cement and mining applications. The adoption of Phase Guard is especially beneficial for industrial black-box solutions, where all “probable” phases are included in the model, even when they are absent from the sample. Phase Guard eliminates false positives, it reduces the likelihood of false negatives, and it is an essential tool to answer the question “what is the limit of quantification for Rietveld analysis?” Full article

Large Language Models (LLMs) encounter significant challenges when applied in specialized domains that require precise and localized information. This problem is particularly critical in regulatory sectors, such as the animal health sector in Brazil, where professionals depend on complex and constantly updated legal norms to perform their work. The generic knowledge encapsulated in traditional LLMs is often insufficient to provide reliable support in these contexts, which can lead to inaccurate or outdated responses. To address this gap, this work presents a practical implementation of a Retrieval-Augmented Generation (RAG) system. We detail the integration of this system with the Plataforma de Defesa Sanitária Animal do Rio Grande do Sul (PDSA-RS), a real platform used for animal production certification. Our solution connects an LLM to an external knowledge base containing specific Brazilian legislation, allowing the model to retrieve relevant legal texts in real time to generate its responses. The principal objective is to demonstrate how this approach can produce accurate and contextually grounded answers for professionals in the veterinary field, assisting in decision-making processes for sanitary certification. Full article

Understanding spatial relationships between objects in images is crucial for robotic navigation, augmented reality systems, and autonomous driving applications, among others. However, existing vision-language benchmarks often overlook explicit spatial reasoning, limiting progress in this area. We attribute this limitation in part to existing open datasets and evaluation metrics, which tend to overlook spatial details. To address this gap, we make three contributions: First, we greatly extend the COCO dataset with annotations of spatial relations, providing a resource for spatially aware image captioning and visual question answering. Second, we propose a new evaluation framework encompassing metrics that assess image captions’ spatial accuracy at both the sentence and dataset levels. And third, we conduct a benchmark study of various vision encoder–text decoder transformer architectures for image captioning using the introduced dataset and metrics. Results reveal that current models capture spatial information only partially, underscoring the challenges of spatially grounded caption generation. Full article

Recipes available on the web often lack nutritional transparency and clear indicators of dietary suitability. While searching by title is straightforward, exploring recipes that meet combined dietary needs, nutritional goals, and ingredient-level preferences remains challenging. Most existing recipe search systems do not effectively support flexible multi-dietary reasoning in combination with user preferences and restrictions. For example, users may seek gluten-free and dairy-free dinners with suitable substitutions, or compound goals such as vegan and low-fat desserts. Recent systematic reviews report that most food recommender systems are content-based and often non-personalized, with limited support for dietary restrictions, ingredient-level exclusions, and multi-criteria nutrition goals. This paper introduces DietQA, an end-to-end, language-adaptable chatbot system that integrates a Knowledge Graph (KG), Retrieval-Augmented Generation (RAG), and a Large Language Model (LLM) to support personalized, dietary-aware recipe search and question answering. DietQA crawls Greek-language recipe websites to extract structured information such as titles, ingredients, and quantities. Nutritional values are calculated using validated food composition databases, and dietary tags are inferred automatically based on ingredient composition. All information is stored in a Neo4j-based knowledge graph, enabling flexible querying via Cypher. Users interact with the system through a natural language chatbot friendly interface, where they can express preferences for ingredients, nutrients, dishes, and diets, and filter recipes based on multiple factors such as ingredient availability, exclusions, and nutritional goals. DietQA supports multi-diet recipe search by retrieving both compliant recipes and those adaptable via ingredient substitutions, explaining how each result aligns with user preferences and constraints. An LLM extracts intents and entities from user queries to support rule-based Cypher retrieval, while the RAG pipeline generates contextualized responses using the user query and preferences, retrieved recipes, statistical summaries, and substitution logic. The system integrates real-time updates of recipe and nutritional data, supporting up-to-date, relevant, and personalized recommendations. It is designed for language-adaptable deployment and has been developed and evaluated using Greek-language content. DietQA provides a scalable framework for transparent and adaptive dietary recommendation systems powered by conversational AI. Full article

Background/Objectives: Preterm birth is defined by the World Health Organization (WHO) as birth occurring before 37 weeks of gestation and represents one of the major public health concerns worldwide. Approximately 15 million newborns are affected annually. Following such a physically and emotionally traumatic event, most parents experience emotional distress and seek answers regarding the possible internal or external triggers. The main objective of this review is to analyze the current data regarding the impact of prematurity on parental mental health, as well as the types of interventions targeting parents. Methods: This narrative review was conducted based on extensive research of full-text scientific articles published in the past 15 years, investigating the relationship between prematurity, neonatal intensive care unit (NICU) hospitalization, parental mental health, and proposed intervention strategies aimed at supporting families. Results: Approximately 35% of mothers of preterm infants presented postpartum depression, 24% anxiety, and 15% PTSD. FCC interventions reduced stress levels and the intensity of depressive symptoms, while FICare showed stronger benefits, with additional improvements in parental mental health, parental self-efficacy, increased parental confidence, and amelioration of preterm infant parameters. Conclusions: Implementing FCC and FICare into daily neonatal care is essential for the prevention of parental mental health disorders and strengthening parenting capacity. Full article

Critical energy infrastructures (CEIs) are fundamental pillars for economic and social development. However, their accelerated digitalization and the convergence between operational technologies (OTs) and information technologies (ITs) have increased their exposure to advanced cyber threats. This study examines the evolution of OT cybersecurity models with artificial intelligence in the energy sector between 2015 and 2024, through a systematic literature review following a four-phase method (planning, development, results, and analysis). To this end, we answer the following questions about the aspects of CEI cybersecurity models: What models exist? What energy services, technical means, and facilities do they encompass? And what algorithms do they include? From an initial set of 1195 articles, 52 studies were selected, which allowed us to identify 49 cybersecurity models classified into seven functional categories: detection, prediction and explanation; risk management; regulatory compliance; collaboration; response and recovery; architecture-based protection; and simulation. These models are related to 10 energy services, 6 technical means, 10 types of critical facilities, and 15 AI algorithms applied transversally. Furthermore, the integrated and systemic relationship of these study aspects has been identified in an IT-OT cybersecurity model for CEIs. The results show a transition from conventional approaches to solutions based on machine learning, deep learning, federated learning, and blockchain. Algorithms such as CNN, RNN, DRL, XAI, and FL are highlighted, which enhance proactive detection and operational resilience. A broader coverage is also observed, ranging from power plants to smart grids. Finally, five key challenges are identified: legacy OT environments, lack of interoperability, advanced threats, emerging IIoT and quantum computing risks, and low adoption of emerging technologies. Full article

Three-wheelers (3Ws—autos or auto-rickshaws) occupy a unique yet salient and substantive position within the context of India’s urban mobility. They provide critical first-and-last-mile connectivity, fill public transit coverage gaps, boost local and urban agglomeration economies, and are a major income source for millions. Their value and utility are especially pronounced in rapidly emerging Tier-II cities such as Amritsar. The city’s 7500-strong diesel 3W (d3W) fleet is the backbone of its transportation network but also contributes to air pollution. Though Amritsar’s favorable policies to transition the d3W fleet to electric (e3W) have reduced purchase costs by 40–60%, barriers remain. This study investigates the influence of the e3W user experience through a first-of-a-kind three-day pilot trial for ~300 d3W drivers. By leveraging a pre- and post-intervention framework combining surveys and trip diaries, this study evaluated how direct exposure influences adoption intentions, perceptions, and the social dynamics underpinning decision-making. In total, ~6% of participants switched to e3Ws following the trial, and there was a 20% drop in “don’t know” answers regarding charging duration and range. The results show non-random and meaningful shifts in attitudes, a greater awareness of range and charging times, improved views on charging convenience and vehicle safety, and air quality benefits. Full article