Search Results (1,525)

This article examines gender inequalities in scientific research in the Mediterranean, with a particular focus on STEM disciplines. It draws on qualitative data from the STEP (STEM and Equality, Diversity and Inclusion for Research Enhancement in Portugal) project, a European Commission-funded initiative aimed at embedding equality, diversity, and inclusion (EDI) principles across partner institutions in Portugal, Italy, France, and Spain. Using semi-structured interviews with five scientific leaders and an inductive thematic analysis, the study explores early-stage mechanisms in the institutionalisation of EDI policies and women’s empowerment trajectories from an intersectional perspective. The analysis identifies emergent patterns suggesting: (i) a gradual strengthening of EDI mainstreaming in contexts with initially limited awareness; (ii) the role of transnational collaboration in enhancing visibility, mentoring, and peer learning; and (iii) the potential of time-bounded initiatives to catalyse participant-observed shifts and institutional routines in formation. Rather than measuring longitudinal impact, the article traces how legitimation, routinisation, and network diffusion may enable EDI principles to extend beyond project lifespans and become embedded in governance structures. These mechanism-focused insights offer a transferable framework for future European cooperation initiatives and contribute to ongoing debates on sustainable gender and EDI policy implementation in Mediterranean research contexts. Full article

Elderly individuals in rural China are highly vulnerable to extreme weather events and temperature fluctuations due to inadequate infrastructure in the built environment and constrained economic conditions, thereby increasing their health risks. Outdoor spaces represent one of the primary daily activity settings for rural older adults. However, existing research rarely links spatiotemporal patterns of outdoor activities to evidence-based thermal environment optimization, leaving a critical knowledge gap for age-friendly and sustainable rural design. This study focuses on the spatiotemporal differentiation patterns of daily outdoor activities among elderly people aged 60 years and above in rural Xi’an, as well as the optimization of spatial variations in thermal environments. Using on-site interviews, thermal environment measurements, thermal comfort questionnaires, continuous thermal environment monitoring, and machine learning based on random forest, this study drew the following conclusions: (1) outdoor activities in winter were concentrated between 9:00–11:00 and 13:00–17:00, while in summer, they shifted to the morning and evening periods, namely 6:00–9:00 and 17:00–21:00. (2) Models for outdoor clothing adjustment, thermal sensation, and thermal acceptability among elderly residents were established. The calculated neutral temperature was 10.19 °C, with a 90% outdoor thermal acceptability range of 9.6–27.2 °C and an 80% outdoor thermal acceptability range of 6.2–30.6 °C. These findings differ from those documented in regions with distinct climate zones and geographical settings. This discrepancy stems from regional climatic features, lifestyle variations between urban and rural older adults, and differences in the thermal environment quality of elderly-oriented outdoor activity spaces. (3) In winter, the acceptable period of the Universal Thermal Climate Index (UTCI) at south-facing entrances (10:30–16:30) was significantly longer than that in the courtyard (13:30–14:00). In summer, the comfortable period in the courtyard (before 10:00 and after 20:00) was longer than that at north-facing entrances (before 09:00). A random forest model for thermal sensation was established, and the relative importance of each parameter influencing thermal sensation was analyzed. On this basis, priority improvement pathways and strategies for the thermal environment, as well as suggestions for the subjective adaptive behaviors of elderly residents, were proposed. The research results of this study can provide technical solutions for age-friendly thermal environment design in rural areas, thereby safeguarding the comfort, health, and social well-being of the elderly population in rural areas. Full article

Accurate and efficient 3D reconstruction of trees is of paramount importance for studying forest spatial structures and dynamic resource patterns, optimizing forest management, protecting environments, and analyzing carbon cycles. Currently, Light Detection and Ranging (LiDAR) remains the dominant method for generating 3D models of forest scenes. However, with advancements in computer vision, photogrammetry has emerged as a crucial tool for forest inventory and 3D reconstruction due to its cost-effectiveness. Nevertheless, in practical forestry applications, traditional photogrammetry often suffers from low reconstruction efficiency and poor quality during feature extraction and matching. These issues stem from the complex structure of forest scenes, severe occlusion, and repetitive texture patterns. To address these challenges, this paper proposes an improved 3D tree reconstruction approach based on images, integrating deep learning-based methods. In the sparse reconstruction stage, we utilize the ALIKED (A LIghter Keypoint and descriptor Extraction network with Deformable transformation) algorithm and construct an image pyramid to extract multi-scale robust features. Furthermore, by combining the LightGlue matching algorithm with a neighborhood search constraint strategy, we enhance the stability of camera pose recovery while reducing redundant computations. Experimental results demonstrate that our method outperforms traditional algorithms in both accuracy and robustness regarding image matching. Compared to baseline models, the proposed approach increases the number of feature points by approximately 50% with a more widespread distribution, improves matching accuracy by 4% to 8%, and achieves a 100% image registration rate. Consequently, under the condition of maintaining equivalent re-projection errors, the subsequent sparse point clouds exhibit an average track length increase of 0.6 to 1.4 and a density increase of up to 1.2 times. Notably, this method effectively mitigates artifacts and spurious reconstructions caused by pose drift in forest photogrammetry. Full article

Recent advances in large language models (LLMs) have enabled new forms of software creation through natural-language interaction. However, many AI-assisted coding tools continue to assume familiarity with development environments, programming workflows, and technical conventions, which may limit accessibility for early-stage learners and communities historically underrepresented in digital participation. This challenge is particularly relevant in Aotearoa New Zealand, where Māori and Pacific peoples remain underrepresented across STEM and technology pathways. This paper introduces TechTahi, a browser-based, syntax-free AI-assisted platform designed to support low-barrier digital creation through natural-language prompts and immediate in-browser previews. The study had two aims: to describe the design rationale and workflow of TechTahi and to explore early learner perceptions following initial use of the platform. An exploratory pilot design was employed. Five participants completed a post-use survey after hands-on interaction with TechTahi. Responses were analysed descriptively, with open-ended feedback reviewed for recurring themes. Findings suggested generally positive perceptions of accessibility and ease of use, particularly the ability to create working applications without prior coding knowledge. Participants also identified opportunities for culturally relevant features, including language support and locally meaningful design elements, alongside areas for improvement such as clearer onboarding guidance and reduced information density. These preliminary findings suggest that syntax-free, culturally responsive AI creation tools may offer promising pathways for widening participation in digital learning. Further research with larger and more diverse samples is needed to evaluate longer-term educational impact. Full article

Accurately estimating leaf area index (LAI) is vital for evaluating crop growth and predicting yields. Conventional approaches, however, often struggle due to the limited representativeness of available data and the complex structure of plant canopies, which reduce their reliability across diverse canopy architectures and observation conditions. To overcome these challenges, this work introduces an LAI retrieval framework that combines a three-dimensional radiative transfer model (3D RTM) with deep learning techniques. Representative 3D maize canopy scenarios were generated using the LESS model, producing synthetic LiDAR point clouds constrained by realistic structural parameters. A deep learning model based on PointNet++ was trained, and transfer learning (TL) was employed to facilitate knowledge transfer from simulated to actual measured data. The TL-enhanced model demonstrated significant improvement, with R² rising from 0.537 to 0.842 and RMSE dropping from 0.541 to 0.288 m²·m⁻². Moreover, retrieval performance was notably affected by scanning mode, angle, and stem diameter, achieving optimal results under TLS acquisition, moderate scanning angles, and intermediate stem widths. These findings suggest that integrating 3D RTM-generated synthetic point clouds with transfer learning is an effective strategy for enhancing the robustness and generalization of LiDAR-based LAI retrieval. Full article

Students entering engineering programs often exhibit insufficient mathematics knowledge and considerable variability in prior training, which can create learning gaps and challenges for higher education integration. This study aims to characterize students’ mathematics proficiency at the Coimbra Institute of Engineering and to develop strategies to address these gaps. A diagnostic test was designed based on the Portuguese primary and secondary education syllabus and the guidelines of the European Society for Engineering Education. Data were collected from students enrolling in engineering degrees between the 2013/14 and 2021/22 academic years. Based on the diagnostic results, a targeted intervention was implemented to motivate students and enhance their learning in mathematics. This intervention includes complementary teaching methodologies applied to Differential and Integral Calculus, a mandatory first-year course across all engineering programs. The analysis demonstrates that the combined approach of diagnostic assessment and targeted support improves student engagement and addresses disparities in prior knowledge. This study contributes to the development of evidence-based strategies that support equitable learning opportunities in engineering education and offers a model for integrating diagnostic assessment with active learning practices in foundational STEM courses. Full article

This systematic review comprehensively explores the integration of Extended Reality (XR) technologies, comprising Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), into New Zealand’s STEM education framework. In alignment with PRISMA 2020 guidelines, we systematically analyzed 127 peer-reviewed studies from the Web of Science (n = 48), Scopus (n = 57), and Dimensions (n = 22) and incorporated 15 grey literature sources, resulting in 142 studies included in the review. Our meta-analysis found substantial improvements in student conceptual understanding from XR-enhanced STEM modules. Specifically, we observed an average increase of 23.4% when compared to traditional instructional methods (95 percent Confidence Interval: 18.7 to 28.1 percent, p < 0.001). These gains were especially prominent in interactive learning environments where immersive XR applications supported deeper engagement and the visualization of abstract STEM concepts. The qualitative synthesis highlighted several key barriers that limit effective XR integration. These include technological infrastructure gaps reported in 68 percent of reviewed studies, a critical need for educator training cited by 82 percent of studies, and curriculum alignment issues present in 57 percent of cases. Methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT) 2018, and the qualitative component employed a deductive thematic coding approach with inter-coder reliability verification. Successful institutional implementations were also identified. At Auckland University of Technology, XR-supported courses produced a 67 percent increase in student engagement, while Wellington High School achieved a 41 percent reduction in STEM achievement gaps through targeted XR interventions. Based on the evidence, we propose a four-phase implementation framework that addresses the technological, pedagogical, and policy requirements for sustainable XR adoption. These findings highlight the role of immersive technologies in supporting human-centered digital transformation and future skills development in the transition to Industry 5.0. The review contributes evidence-based insights that support the transition from technology-driven approaches associated with Industry 4.0 to the human-centered, socially oriented priorities of Industry 5.0. It also identifies critical research gaps, particularly in long-term learning outcomes and the integration of Mātauranga Māori within XR-enabled STEM environments. Full article

Morphological disciplines, namely Human Anatomy, Histology, and Embryology, have traditionally provided the foundational knowledge for medical education, offering spatial, cellular, and temporal coordinates of the human body. However, reducing these disciplines to static and purely descriptive learning undermines their deeper purpose: interpreting morphology as the dynamic outcome of biological processes. This review emphasizes three interrelated pillars of morphological sciences—cell differentiation, tissue homeostasis, and organ remodeling—as essential frameworks for understanding both normal physiology and disease pathogenesis. Cell differentiation establishes functional identity, tissue homeostasis ensures structural stability, and organ remodeling enables adaptation to both physiological and pathological stimuli. Dysregulation of these programs underlies a wide range of conditions, from degenerative diseases and chronic inflammation to neoplasms. Integrating classical morphological knowledge with modern approaches—including stem cell biology, organoids, tissue engineering, and computational modeling—enables predictive and regenerative strategies in personalized medicine. Furthermore, recent advances in artificial intelligence applied to histopathology have enhanced our capacity to detect early deviations from homeostasis and guide targeted interventions. By combining spatial, cellular, and molecular perspectives, the morphological sciences can provide clinicians with tools to interpret disease as the result of altered biological programs, anticipate pathology, and design precise therapeutic strategies. This integrated approach highlights the renewed centrality of morphology in contemporary medicine, bridging foundational knowledge with predictive, regenerative, and personalized healthcare. Full article

STEM learning unfolds over many years. It is shaped by changing contexts, transitions, and occasional breaks. However, much of the existing work still focuses on single stages or isolated factors. This article introduces the E³ Framework. Its purpose is to provide a language for examining why some STEM trajectories endure, why others fade, and what kinds of ecological alignment allow learning to remain viable in the flow of real life. Based on a systemic approach, we aim to explain how STEM participation is preserved over time. This framework describes stability as the result of interactions among three ecological domains: resources, regulation, and time. We identify five key functions—robustness, regulatory re-alignment, renewal, informational persistence, and environmental fit. These functions show how engagement holds steady or recovers as circumstances shift. The E³ Framework offers a way to analyze how supports, feedback loops, and time-related structures either come together or fall apart. We provide simple design guidelines and matrices to show how educators and policymakers can better support STEM trajectories. Full article

Accurate discrete element method (DEM) simulations are essential for elucidating the precision seeding mechanisms and collision damage characteristics of cassava seed stem (CSS); however, such simulations are often limited by a lack of precise contact parameters. In this study, “Guire No. 7” CSS was selected as the research object to calibrate discrete element (DE) parameters by integrating physical experiments with DEM simulations. A stem model was constructed in EDEM software (Altair EDEM 2022) using three-dimensional scanning technology combined with SolidWorks 2024 modeling functions to investigate the influence of the model’s mesh face count on simulation accuracy. Physical experiments measured the average repose angle (RA) of the stems (30.28° ± 1.09°), along with parameters including the restitution coefficient for stem-stem and stem-steel plate collisions, and the coefficient of static friction between the stem and steel plate. The Plackett-Burman Design experiment was employed to screen parameters affecting the RA, and the steepest ascent experiment was conducted to determine their optimal value ranges. Using the RA as the response value, a Central Composite Design experiment combined with machine learning regression models was applied to optimize the influencing parameters and compare model performance. The results indicated that the GA-BP algorithm exhibited superior predictive capability compared to Support Vector Regression (SVR) and the BP neural network. Through optimization using a genetic algorithm (GA), the calibrated parameters were obtained: a stem-steel plate static friction coefficient (SFC) of 0.488, a stem-stem SFC of 0.489, and a stem-stem rolling friction coefficient of 0.131. The resulting simulated RA was 30.73°, yielding a relative error of 1.49% compared to the physically measured value. The GA-BP-GA method demonstrated better optimization performance than the central composite design experiment, thereby validating the accuracy of the calibrated contact parameters between stems. Furthermore, parameters for the Tavares model were calibrated through physical experiments on CSS, using collision damage force and collision damage energy (CDE) as validation indicators. The results showed that the relative errors for both collision damage force and CDE were less than 3%, which is within the acceptable error range, thereby confirming the validity of the calibrated DE parameters for the cassava seed stem. Full article

Road drainage infrastructure in ENSO-vulnerable Andean regions faces compounding threats from climatic variability, geometric inadequacy, and systemic maintenance neglect. This study presents a STEM-integrated Project-Based Learning (PjBL) diagnostic framework applied to 42 road segments along corridors connecting Loja, Ecuador, selected through a purposive-stratified spatial-coverage protocol. Using ArcGIS Survey123, standardised field data were collected on structure presence, geometry, failure modes, and condition across four structure types: crown gutters, road gutters, hydraulic chutes, and culverts. The Composite Drainage Deficiency Index (DDI, 0–100) was derived from five equally weighted binary indicators and validated through Monte Carlo Dirichlet weight-perturbation analysis and jackknife leave-one-out resampling, confirming rank-order invariance to admissible alternative weightings. The results reveal severe systemic deficiencies, including crown gutters absent at 88.1% (95% CI: 75.0–94.8) and road gutters at 81.0% (95% CI: 66.7–90.0) of sites. Every segment exhibited at least one drainage failure (100%; 95% CI: 91.6–100). The DDI identified 73.8% of segments in the High or Critical band (DDI ≥ 60; mean = 60.2 ± 20.4). Hierarchical clustering isolated one geometric outlier whose exclusion altered the aggregate metrics by <1.2%. These findings establish a georeferenced baseline for maintenance prioritisation and validate the methodological reproducibility of academically integrated field protocols for infrastructure diagnostics. Full article

This study examines the impact of a National Institute of Health (NIH)-funded STEM project on high school students’ STEM self-efficacy and perceptions of future STEM careers across two academic years in Western New York. The intervention engaged students in authentic scientific practices, including environmental sampling, microbial DNA analysis, and presenting research posters at a Capstone event. Pre- and post-surveys were administered to intervention and control groups, measuring STEM self-efficacy and perceived future in STEM. Data from 313 students were analyzed using explanatory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM) with multiple imputation. EFA results supported a one-factor structure, which was confirmed by CFA results showing a good model fit for both constructs. SEM findings indicated that program participation significantly improved STEM self-efficacy, while effects on perceived future in STEM were nonsignificant, though potentially moderated by cohort. No race-based interaction effects emerged, suggesting consistent program benefits. The findings imply that schools should incorporate authentic STEM learning experiences to strengthen students’ confidence and broaden equitable engagement in STEM. Limitations include the bias on self-report measures. Future longitudinal and mixed-methods research is needed to examine how early gains in self-efficacy translate into sustained STEM pathways. Full article

Organized after-school activities can play a vital role in supporting historically marginalized youth in science, technology, engineering, and mathematics (STEM), yet less is known about how culturally responsive practices—which are practices that integrate youths’ cultural backgrounds and lived experiences into learning—are enacted in math-focused learning spaces. Drawing on empowerment theory and critical youth empowerment frameworks, this qualitative study examines how culturally responsive practices foster empowerment among middle school students participating in a university-based after-school math enrichment program. Ninety-two students (M_age = 12.26 years; 47% girls; 86% Latine) from three under-resourced schools in Southern California participated in semi-structured interviews about moments when they felt empowered and what contributed to those experiences. Thematic analysis revealed that all four domains of culturally responsive practices helped promote empowerment: structured opportunities for contribution and leadership, caring relationships, cultural affirmation, and efforts to make real-world connections. In particular, students most frequently described structured opportunities for contribution and leadership, practices that centered their knowledge and voices, and relational climates characterized by care and high expectations. The findings suggest that in after-school STEM contexts, empowerment does not arise as an isolated individual trait but is part of a relational and context-dependent process that is supported by culturally responsive practices. These findings highlight how intentional, culturally responsive program design can advance both youth empowerment and equity-oriented STEM education. Full article

The segmentation of 3D point clouds of plant organs, such as leaves and stems, helps to monitor plant growth and is a key step in plant growth phenotype analysis. Compared to point cloud segmentation tasks in other fields, plant point cloud segmentation is more challenging due to the interwoven distribution of various parts such as stems, leaves, and flowers. In this paper, we propose a universal point cloud segmentation network PlantEFRSegnet that can be used for multi-species of plants. The proposed PlantEFRSegnet utilizes a newly designed edge point preservation downsampling module to identify and preserve the points at the edges of plant organs during the downsampling process, in order to assist the segmentation network in learning the contours of various plant organs. PlantEFRSegnet performs supervised feature repair on the point cloud features obtained through downsampling to mitigate the impact of feature loss on segmentation performance during feature embedding. The encoder of the segmentation network is composed of four local feature extraction modules. These four modules can not only extract features but also enhance the features corresponding to points with high contributions in local regions based on point attention mechanism. We evaluated the proposed PlantEFRSegnet on a laser-scanned plant point cloud dataset. Compared with the state-of-the-art approaches, the proposed PlantEFRSegnet achieved better segmentation results. Full article

This study examines gender disparities in three STEM departments at the University of the Peloponnese over a twenty-year period. Based on secondary administrative data from 1245 graduates, this study investigates: (i) whether women are underrepresented among STEM graduates; (ii) whether gender influences degree performance; and (iii) whether gender predicts the duration of study. Descriptive statistics, cross-tabulations, chi-square tests, and two-way ANOVA were used to analyze the data. The results reveal a persistent underrepresentation of women in all three departments, with female graduates accounting for only 13.6–26% of the departmental totals. However, no statistically significant differences were found between male and female graduates in terms of degree grades or time to degree completion. The literature review further highlights the personal, social, cultural, and institutional factors that contribute to women’s underrepresentation in STEM internationally. The findings emphasize the need for early interventions, stereotype-free learning environments, targeted outreach programs, and institutional support mechanisms. Further recommendations include expanding STEM education from early childhood, enhancing teacher preparedness for gender-inclusive instruction, promoting female role models in STEM, and implementing targeted university-level initiatives. Finally, this study offers empirical evidence relevant to policymakers and higher education institutions seeking to close the gender gap in STEM fields. Full article