Search Results (267)

Life Cycle Assessment (LCA) is a fundamental methodology for evaluating environmental impacts across the life cycle of products, processes, and services. However, selecting appropriate LCA software is a complex task due to the wide variety of tools, each with different functionalities, sectoral focuses, and technical requirements. This study conducts a systematic literature review, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, to map the main characteristics, strengths, and limitations of LCA tools. The review includes 41 studies published between 2017 and 2025, identifying and categorizing 24 different tools. Technical and operational features were analyzed, such as modelling capacity, database compatibility, usability, integration capabilities, costs, and user requirements. Among the tools, five stood out for their frequent application: SimaPro, GaBi, OpenLCA, Umberto, and Athena. SimaPro is recognized for flexibility and robustness; GaBi for its industrial applications and Environmental Product Declaration (EPD) support; OpenLCA for being open-source and accessible; Umberto for energy and process modelling; and Athena for integration with Building Information Modelling (BIM) in construction. Despite their advantages, all tools presented specific limitations, including learning curve challenges and limited scope. The results show that no single tool fits all scenarios. In addition to the synthesis of these characteristics, this study also emphasizes the general features of the identified software, the challenges in making a well-supported selection decision, and proposes a decision flowchart designed to guide users through key selection criteria. This visual tool aims to support a more transparent, systematic, and context-oriented choice of LCA software, aligning capabilities with project-specific needs. Tool selection should align with research objectives, available expertise, and context. This review offers practical guidance for enhancing LCA applications in sustainability science. Full article

Place-based climate adaptation workshops are designed to help communities understand their climate-related vulnerabilities and plan adaptive actions in response. Through a series of surveys and interviews with participants, we examined the immediate and long-term impacts of eight place-based climate adaptation workshops in the United States. Six took place online due to COVID-19 restrictions; two took place in-person. All workshops positively enhanced declarative, procedural, and relational knowledge of participants and, to a lesser extent, their personal commitment to work on climate adaptation, optimism about climate adaptation in their communities, and perceptions of qualities of the network of actors engaged locally in climate adaptation. In-person workshops yielded somewhat stronger positive influences on relationship-building than online workshops. Most participants who responded to surveys 6 months to a year after the workshop reported that their workshop had a “minor” to “moderate” impact on stimulating meaningful adaptation actions in their area. Reported actions attributed to the workshops included the incorporation of climate adaptation into formal planning documents, the expansion of adaptation outreach, consideration of climate adaptation in day-to-day planning and decision-making in local government departments, and both successful and unsuccessful grant applications for projects and positions associated with climate adaptation. We describe the workshops’ design, as well as participant assessments of the value of different workshop components. We conclude with lessons learned for future effective workshop planning and design. Full article

Background: Many nurses and physicians report difficulties with breaking bad news to their patients due to the lack of adequate skills and training. This study aimed to explore the communication skills, knowledge, and self-perceived difficulties of healthcare professionals working in oncology and hematology settings in Italy, in relation to their self-perceived stress levels when communicating bad news. Methods: An “ad hoc” questionnaire and the Perceived Stress Scale were administered online to both physicians and nurses registered by two important professional associations between October 2023 and September 2024. Results: A total of 221 Italian physicians and nurses were enrolled in the study. Most participants reported learning how to conduct difficult conversations from a mentor (61.1%) or through specific courses (56.6%). However, many of the recruited subjects declared having difficulty in giving bad news to the patient and family members (84.2%), and many of them did not know the SPIKES method (63.8%). A moderate level of stress was perceived by the great majority of participants, and the stress level was significantly increased in healthcare professionals who had difficulties in using evidence-based tools (e.g., SPIKES) for bad news communication. Moderate stress was “often” experienced by participants when presenting themselves during the first approach (p = 0.006), when attempting to anticipate the patient’s reactions (p = 0.044), when the patient refused to receive information (p = 0.006), when they had to remain assertive and confident regardless of the patient’s response (p = 0.013), and when managing post-communication consequences (p = 0.012). Conclusion: The limited knowledge and application of specific tools for bad news communication could exacerbate stressful conditions at this sensitive time among healthcare providers. The present findings could be used by health institutions to develop ad hoc training programs for both physicians and nurses, as well as to strengthen their organizational culture. Full article

This study investigates the key success factors of IT project management in an emerging, innovation-oriented economy using evidence from Kazakhstan. Drawing on expert interviews and an anonymous enterprise survey, we rank 59 processes across the project life cycle and test three hypotheses concerning the roles of human factors and professional governance. The results confirm broad alignment with success factors commonly reported in mature economies yet reveal a distinctive pattern at earlier maturity stages: team composition, communication, and collaboration have a stronger impact on project success than formal controlling and detailed financial governance. We also identify a substantial gap between the declared importance of success factors and their actual implementation—particularly in integration-stage budgeting, acceptance testing and quality assurance, and lessons-learned practices—highlighting how limited practical experience constrains the adoption of governance routines. The findings refine contingency perspectives on project success by positioning key success factors along a development trajectory in which people-centric capabilities serve as prerequisites for the subsequent effectiveness of “hard” project-management methods. The study advances understanding of the role of IT project management in countries at an early stage of developing an innovation-driven economy. Full article

This paper introduces SmartBuildSim, an open-source synthetic-twin framework that generates configurable and reproducible multi-sensor building streams using lightweight statistical models with tunable trend, seasonality, correlation, delays, and anomaly mechanisms. Deterministic seeding ensures experiment-level reproducibility, while modular pipelines support unified evaluation across forecasting, anomaly detection, and RL tasks. A comprehensive validation against an ASHRAE Great Energy Predictor III reference signal demonstrates that the synthetic data capture realistic magnitude and variability (KS ≈ 0.32; DTW ≈ 9.69), while preserving interpretable and controllable temporal structure. Benchmark results show that simple linear models achieve strong forecasting performance (RMSE ≈ 21.27), IsolationForest reliably outperforms LOF in anomaly detection (F1 ≈ 0.17 vs. 0.10), and Soft-Q Learning provides substantially more stable RL convergence than tabular Q-learning (variance reduced by >95%). Scenario-level analyses further illustrate reproducible daily cycles, zone-specific differences, and the scalability of model behaviour across building configurations. By combining declarative YAML configurations, deterministic randomness management, and an extensible scenario engine, SmartBuildSim provides a transparent and lightweight alternative to high-fidelity building simulators. The framework offers a practical, reproducible testbed for smart-building AI research, bridging the gap between simplistic synthetic datasets and complex physical digital twins. All code, tables, figures, and a Google Colab workflow are openly available to ensure full replicability. Full article

The rapid uptake of generative AI (e.g., ChatGPT, DALL·E and MS Copilot) is disrupting conventional notions of authenticity in assessment across higher education. The dominant response, surveillance and AI detection, misdiagnoses the problem. In an AI-mediated world, authenticity cannot be policed into existence; it must be redesigned. Situating AI within contemporary knowledge work shaped by digitisation, collaboration and evolving ethical expectations, we reconceptualise authenticity as something constructed in contexts where AI is expected, declared and scrutinised. The emphasis shifts from what students know to how they apply knowledge, make judgement, and justify choices with AI in the loop. We offer practical design for learning moves, i.e., discipline-agnostic learning design patterns that position AI as a collaborator rather than a cheating application: tasks that require students to critique, adapt and verify AI outputs, provide explicit process transparency (prompts, iterations, rationale) and exercise assessable demonstrations of digital discernment and ethical judgement. Examples include asking business students to interrogate a chatbot-generated market analysis and inviting pre-service teachers to evaluate AI-produced lesson plans for inclusivity and pedagogical soundness. Reflective artefacts such as metacognitive commentary, process logs, and oral defences make students’ thinking visible, substantiate attribute, and reduce reliance on punitive “gotcha” approaches. Our contribution is twofold: i. a conceptual account of authenticity fit for an AI-mediated world, and ii. a set of actionable, discipline-agnostic patterns that can be tailored to local contexts. The result is an integrity stance anchored in design rather than detection, enabling assessment that remains meaningful, ethical and intellectually demanding in the presence of AI, while advancing a broader shift toward assessment paradigms that reflect real-world professionalism. Full article

This paper introduces a novel framework that integrates reinforcement learning with declarative modeling and mathematical optimization for dynamic resource allocation during mass casualty incidents. Our approach leverages Mesa as an agent-based modeling library to develop a flexible and scalable simulation environment as a decision support system for emergency response. This paper addresses the challenge of efficiently allocating casualties to hospitals by combining mixed-integer linear and constraint programming while enabling a central decision-making component to adapt allocation strategies based on experience. The two-layer architecture ensures that casualty-to-hospital assignments satisfy geographical and medical constraints while optimizing resource usage. The reinforcement learning component receives feedback through agent-based simulation outcomes, using survival rates as the reward signal to guide future allocation decisions. Our experimental evaluation, using simulated emergency scenarios, shows a significant improvement in survival rates compared to traditional optimization approaches. The results indicate that the hybrid approach successfully combines the robustness of declarative modeling and the adaptability required for smart decision making in complex and dynamic emergency scenarios. Full article

Creativity is widely recognized as one of the most important, key competencies supporting the achievement of sustainable development goals. Our paper presents the results of research on the declared level of creativity competence of students at universities located in Kraków (Southern Poland) preparing for the teaching profession. The survey, based on an original questionnaire (49 questions, 7 sections), covered 406 people. The scale was based on an analysis of self-perception of creativity competence among pre-service teachers. Analysis of the results showed that 12.8% of respondents had a high level of creativity, 56.4% had an average level, and 30.8% had a low level. No significant correlations were found between the level of creative competence and gender or age, while place of origin showed a slight tendency to differentiate. Students most often declared reflectiveness, openness to learning, and independence in problem solving, while less often confidence in predicting the effects of their own actions and resistance to routine. The results indicate the significant, albeit partially untapped, creative potential of future teachers. They also emphasize the need to introduce activities in teacher education that strengthen self-confidence, flexibility, and perseverance—qualities necessary to support innovation in education that promotes sustainable development. Full article

The potential of Virtual Reality (VR) in enhancing learning and training is being widely explored. The relationship of immersion, as one of the core technical features of VR, with knowledge types has not been fully explored. This study aims to investigate how VR immersion levels (high vs. low) affect the acquisition of declarative and procedural knowledge, as well as related cognitive and affective factors. A 2 × 2 mixed design was adopted, with 64 college students who had no VR experience and no background in professional medical knowledge being randomly assigned to either a high-immersion group (using HTC Vive Pro headsets) or a low-immersion group (using desktop monitors). Participants completed learning tasks on thyroid and related diseases (declarative knowledge) and cardiopulmonary resuscitation (procedural knowledge), followed by knowledge tests and self-report questionnaires to measure presence, motivation, self-efficacy, cognitive load, and emotional states. Results showed that high immersion significantly improved learning outcomes for both types of knowledge with large effect sizes. In both knowledge domains, high immersion also enhanced presence, intrinsic motivation, self-efficacy, and positive emotions. However, cognitive load was reduced only for declarative knowledge, and no significant effects were observed for self-regulation. These findings highlight the differential impact of VR immersion on knowledge acquisition and provide insights for optimizing VR-based educational interventions. Full article

This article explores how gender biases in STEM education persist despite formal commitments to equality. Based on data from the Erasmus+ project STEMGenderIN, we analyze responses from lower-secondary school teachers (ISCED 2; ages 11–15), of STEM subjects, in Portugal, Italy, Belgium, and Romania using the TPGESE scale, which assesses three dimensions: perceived gender equality in education (PGEE), the awareness of the effects of gender segregation (AEGSE), and the naturalization of gender stereotypes (GSNGI). Findings show a consistent gap between teachers declared support for gender equality and their limited awareness of structural and cultural barriers faced by girls in STEM. While most teachers affirm equality in principle, many attribute girls’ underrepresentation to personal choice or aptitude, overlooking the influence of stereotypes, social expectations, and systemic inequalities. The results point to a paradox: formal recognition of gender equality coexists with low engagement in reflexive practice or institutional change. Differences between countries suggest varying degrees of critical awareness, with some contexts showing greater openness to questioning dominant narratives. This study highlights the urgent need for teacher training that goes beyond rhetoric, promoting deep pedagogical transformation and equipping educators to create more inclusive STEM learning environments. We argue that addressing the perception–practice gap is essential to closing the gender gap in STEM. To situate these findings, we also note how national cultural–political debates—such as Portugal’s public controversy around so-called “gender ideology” in Citizenship and Development—may shape teachers’ perceptions and self-reports, reinforcing the need for context-aware training. Full article

Droughts significantly impact agriculture, water resources, and ecosystems. Their timely detection is essential for implementing effective mitigation strategies. This study explores the use of multispectral Sentinel-2 remote sensing indices and machine learning techniques to detect drought conditions in three distinct regions of India, such as Jodhpur, Amravati, and Thanjavur, during the Rabi season (October–April). Twelve remote sensing indices were studied to assess different aspects of vegetation health, soil moisture, and water stress, and their possible joint use and influence as indicators of regional drought events. Reference data used to define drought conditions in each region were primarily sourced from official government drought declarations and regional and national news publications, which provide seasonal maps of drought conditions across the country. Based on this information, a district vs. year (3 × 10) ground truth is created, indicating the presence or absence of drought (Drought/No Drought) for each region across the ten-year period. Using this ground truth table, we extended the remote sensing dataset by adding a binary drought label for each observation: 1 for “Drought” and 0 for “No Drought”. The dataset is organized by year (2016–2025) in a two-dimensional format, with indices as columns and observations as rows. Each observation represents a single measurement of the remote sensing indices. This enriched dataset serves as the foundation for training and evaluating machine learning models aimed at classifying drought conditions based on spectral information. The resultant remote sensing dataset was used to predict drought events through various machine learning models, including Random Forest, XGBoost, Bagging Classifier, and Gradient Boosting. Among the models, XGBoost achieved the highest accuracy (84.80%), followed closely by the Bagging Classifier (83.98%) and Random Forest (82.98%). In terms of precision, Bagging Classifier and Random Forest performed comparably (82.31% and 81.45%, respectively), while XGBoost achieved a precision of 81.28%. We applied a seasonal majority voting strategy, assigning a final drought label for each region and Rabi season based on the majority of predicted monthly labels. Using this method, XGBoost and Bagging Classifier achieved $96.67\%$ accuracy, precision, and recall, while Random Forest and Gradient Boosting reached $90\%$ and $83.33\%$, respectively, across all metrics. Shapley Additive Explanation (SHAP) analysis revealed that Normalized Multi-band Drought Index (NMDI) and Day of Season (DOS) consistently emerged as the most influential features in determining model predictions. This finding is supported by the Borda Count and Weighted Sum analysis, which ranked NMDI, and DOS as the top feature across all models. Additionally, Red-edge Chlorophyll Index (RECI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Ratio Drought Index (RDI) were identified as important features contributing to model performance. These features help reveal the underlying spatiotemporal dynamics of drought indicators, offering interpretable insights into model decisions. To evaluate the impact of feature selection, we further conducted a feature ablation study. We trained each model using different combinations of top features: Top 1, Top 2, Top 3, Top 4, and Top 5. The performance of each model was assessed based on accuracy, precision, and recall. XGBoost demonstrated the best overall performance, especially when using the Top 5 features. Full article

English phrasal verbs are ubiquitous and challenging for second language (L2) learners, particularly for those whose first language does not have an equivalent structure. This study investigates the facilitative role of three distinct L2 practice types in promoting English phrasal verb acquisition. Eighty first-year college students from China were randomly assigned to three groups: the continuation group, which was first presented with an input text and then required to complete it; the retrieval group, which was first presented with the input text and then required to engage in retrieval practice; and the trial-and-error group, which was first required to engage in trial-and-error practice before reading the input text. The effectiveness of these practice types was compared via both an immediate post-test and a 1-week-delayed post-test. The results showed that in the immediate post-test, the continuation group performed comparably with the retrieval group but outperformed the trial-and-error group. However, in the 1-week-delayed post-test, the continuation group significantly outperformed the other two groups. The findings revealed that the continuation writing task not only initially equips learners with declarative knowledge and subsequently closely integrates static L2 learning with dynamic idea expression but also enhances learners’ task self-efficacy, thereby optimally promoting phrasal verb learning and retention. Full article

Coronavirus disease 2019 (COVID-19) emerged in late 2019 and was declared a pandemic from March 2020 to May 2023, profoundly affecting public health systems, economies, and daily life worldwide. University students were among the most impacted groups, facing abrupt transitions to remote learning, social isolation, and increased psychological distress due to academic and personal uncertainties. During the pandemic, few studies have been conducted with this population and so far, none have evaluated factors associated with COVID-19 infection in university students, so this study aimed to evaluate variables associated with COVID-19 infection among university students in southern Brazil. Data were collected from July to November 2020 through an online questionnaire addressing lifestyle and health, with participation from 1533 students. Among the variables analyzed, statistically significant associations with COVID-19 infection were identified for age, occupation, use of continuous medication, compliance with social distancing, and self-medication practices. Younger students (18–29 years) and those dedicated solely to studying exhibited higher infection rates. Additionally, participants who reported using continuous medication, not adhering to social distancing measures, or engaging in self-medication were significantly more likely to have contracted COVID-19. These results help outline risk profiles within the university student population and contribute to improved preparedness for future disease outbreaks. Furthermore, they underscore attitudes and behaviors that may increase vulnerability to infectious diseases, highlighting the importance of targeted health promotion and prevention strategies in this demographic. Full article

The global outbreak of the monkeypox virus was declared a health emergency by the World Health Organization (WHO). During such emergencies, misinformation about health suggestions can spread rapidly, leading to serious consequences. This study investigates the relationships between tweet readability, user engagement, and susceptibility to misinformation. Our conceptual model posits that tweet readability influences user engagement, which in turn affects the spread of misinformation. Specifically, we hypothesize that tweets with higher readability and grammatical correctness garner more user engagement and that misinformation tweets tend to be less readable than accurate information tweets. To test these hypotheses, we collected over 1.4 million tweets related to monkeypox discussions on X (formerly Twitter) and trained a semi-supervised learning classifier to categorize them as misinformation or not-misinformation. We analyzed the readability and grammar levels of these tweets using established metrics. Our findings indicate that readability and grammatical correctness significantly boost user engagement with accurate information, thereby enhancing its dissemination. Conversely, misinformation tweets are generally less readable, which reduces their spread. This study contributes to the advancement of knowledge by elucidating the role of readability in combating misinformation. Practically, it suggests that improving the readability and grammatical correctness of accurate information can enhance user engagement and consequently mitigate the spread of misinformation during health emergencies. These insights offer valuable strategies for public health communication and social media platforms to more effectively address misinformation. Full article

Leadership is a key element in diverse working environments, contributing to the construction of more competitive and efficient institutions. Its impact transcends different sectors, including non-profit organizations, where it is essential to improve management and achieve institutional objectives. This research aimed to analyze whether leadership styles influence learning culture and dynamic capacity. An explanatory study was carried out involving 300 workers from nine Latin American countries who declared that they carried out work activities in a non-profit institution, aged between 19 and 68 years old (M = 34.10 and SD = 8.88). They were recruited through non-probabilistic sampling for convenience. The theoretical model was evaluated using the Partial Least Squares Structural Equation Model (PLS-SEM). A measurement model with adequate fit was obtained (α = between 0.909 and 0.955; CR = between 0.912 and 0.956; AVE = 0.650 and 0.923). Based on the results, it was observed that there was a positive impact of servant leadership on learning culture (β = 0.292), of empowering leadership on learning culture (β = 0.189), and of shared leadership on learning culture (β = 0.360). Likewise, there was a positive impact of culture of learning on dynamic capacity (β = 0.701). This research provides valuable insight for leaders in this sector who are seeking to achieve higher levels of learning culture and increase dynamic capability among their workers. Full article