Sustainability

BackgroundThe relationship between aesthetic appreciation and environmental values remains a critical yet under-researched area in environmental psychology. Although the Two-Major Environmental Values (2-MEV) model--encompassing preservation and utilization dimensions--serves as a standard framework for assessing environmental attitudes, the integration of aesthetic perception within this structure has largely remained unexplored. This study investigates the conceptual linkages across a diverse international sample to determine whether aesthetic appreciation functions independently of a traditional environmental value framework. Methods and FindingsWe conducted a large-scale, cross-sectional survey involving 11,800 pre- and in-service teachers across 34 countries. Participants environmental values were evaluated using the 2-MEV scale, while their aesthetic appreciation of nature and the built environment was assessed using Osgoods semantic differential technique. Employing principal component analysis, hierarchical exploratory factor analysis (EFA), analysis of variance (ANOVA), and within-class analysis (WCA), we accounted for cross-national variations and evaluated response consistency. The results demonstrate that aesthetic appreciation comprises two distinct dimensions-- focusing separately on nature and the built environment--that operate independently of traditional preservation and utilization values, showing only weak correlations. Furthermore, while the overarching psychological structure remains consistent globally, our findings reveal significant cross-national variations in respondent scores, particularly concerning utilization-related values. ConclusionsThese findings establish that aesthetic appreciation constitutes a distinct psychological construct separate from conventional environmental value frameworks. The observed cross-cultural variability underscores the necessity of accounting for national and cultural contexts when designing environmental education programs. By leveraging a robust, comprehensive global dataset, this study provides a vital empirical foundation for integrating aesthetic and value-based dimensions into future environmental research and educational policy.

Rapid urbanization has intensified ecological degradation and threatened ecosystem sustainability in the Yangtze River Basin. This study proposes a Function-Structure-Dynamic (FSD) framework integrating ecological protection importance assessment, ecological security pattern (ESP) construction, and future land-use simulation to identify ecological restoration priorities in the Anhui section of the Yangtze River Basin. Ecologically important areas were identified by coupling ecosystem services and ecological vulnerability. MSPA, landscape connectivity analysis, resistance surface modeling, and circuit theory were applied to construct the ESP, while the PLUS model simulated land-use change and ecological risks in 2032. Results identified 37 ecological sources covering 6,901.39 km{superscript 2} and 84 ecological corridors with a total length of 916.80 km. Ecological pinch points, barrier areas, and 17 ecological warning areas were further delineated. Based on these findings, four ecological restoration zones were proposed with differentiated management strategies. The FSD framework effectively integrates ecological functions, spatial structure, and future dynamics, providing scientific support for ecological restoration and sustainable territorial spatial planning in rapidly urbanizing regions.

Investigating Changes Detection in Land Use and Land Cover Analysis of Opencast Singareni Coal Mine Area Using Remote Sensing and GIS Techniques was the focus of the current investigation. Change Detection Analysis for Land Use Land Cover over a decade frequency (2005-2015) of cultivated soils surrounding OpenCast Coal Mine areas namely Ramakrishnapur, Srirampur and Medipalli of Telangana State.AWiFS data of IRS Resoursesat Satellite images of 2005 and 2015 were taken for the detection of temporal variation in Land use/ Land cover due to open cast coal mining.The ability of remote sensing techniques to produce precise spatiotemporal statistics of LULC and its changes in the typical coal mining region has been demonstrated, in open cast coal mining per year was the highest at Ramakrishnapur (@ 300 ha / year) followed by Srirampur (@ 150 ha / year) and Medipalli (@ 100 ha / year).At Ramakrishnapur with the onset of mining there was considerable decrease in agricultural land, shallow water bodies and deciduous forests i.e., conversion of these land uses to mining was evident. Whereas, at other two mining sites (Srirampur and Medipalli) conversion of scrubland followed by shallow water bodies, agriculture lands and deciduous forests into mining area was detected. However, the built-up land at all three-mining site was from cultivated lands and at Medipalli and Srirampur it was also contributed from scrubland. On the whole impact of open cast mining on deep water bodies was not detected. If the mining and surrounding regions continued and appropriate use of the land and water resources at hand, careful long-term planning is of utmost importance.

This study investigated how different forms of task influence the oral performance of Chinese second-language learners and native speakers. By analyzing data from 40 Chinese second-language learners and 40 native speakers through picture description tasks, formal and informal questions, and questions with different emotions (happy and unhappy), it was found that different task characteristics significantly affected language performance. Short picture tasks led to higher communication efficiency and noun rates but more errors, while long story tasks showed higher verb rates, function word rates, etc. Formal questions had more characters and nouns but lower communication efficiency compared to informal ones. Also, happy emotion questions resulted in fewer characters, sentences, and errors than unhappy emotion questions. These findings contribute to the theoretical understanding of task-based language performance in Chinese as a second language and offer practical implications for teaching, textbook compilation, and student evaluation.

Cancer prevalence is distributed unevenly across regions and caused by the interaction of multiple risk factors. Previous studies focused on the use of global modeling techniques to predict cancer at the county level that overlooks important spatial differences. This study aims to develop geographically weighted machine learning models to predict cancer prevalence at the census tract level in the United States and identify local determinants of cancer burden. First, a scoping review was conducted to find a list of measurable drivers of cancer in the United States. Using this list, the data of these variables for 84415 census tracts were obtained from the Center for Disease Control and Prevention PLACES dataset and other publicly accessible resources. Then, several predictive models, including Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR), as well as Random Forest, XGBoost, and Deep Neural Network and their geographically weighted counterparts, were developed and compared using the Coefficient of Determination, Root Mean Square Error, and Absolute Error. Results presented that geographically weighted models outperformed other methods, and geographically weighted XGBoost achieved the strongest and most consistent overall performance with pseudo-R2 ranging between 0.89 and 0.98. Feature importance analysis of this model illustrated that most important cancer drivers changed location by location. Aged people, racial composition, preventative behaviors, and metabolic conditions such as diabetes, hypertension, and high cholesterol were determined as influential predictors, although their relative importance varied across regions. These findings revealed the value of localized models at a small geographic scale to identify regional cancer risk patterns and help the allocation of proper resources to hotspot areas. Keywords: Cancer prevalence, Census tracts, geographically weighted machine learning models, Deep neural network, XGBoost, Random Forest, Ordinary Least Squares, risk factor, determinant

Active teaching methodologies have been widely used to promote meaningful learning and student autonomy. In this context, quantitative approaches can help assess how students organize and integrate knowledge throughout the learning process. Among these approaches, semantic co-occurrence networks stand out, as they are capable of identifying relationships between words and revealing the conceptual structure of textual productions. The objective of this study was to investigate whether semantic network analyses can characterize differences in students conceptual organization in Microbiology during their participation in the active teaching methodology "Adopt a Bacterium." To this end, a case study was conducted in the Bacteriology course at the Institute of Biomedical Sciences of the University of Sao Paulo, analyzing the textual productions of two groups of students in the years 2024 and 2025 during their study of the bacterial genus Bacillus. The texts were evaluated using semantic co-occurrence networks, taking into account metrics of structure and conceptual integration. The results showed that both groups covered the microbiological content outlined in the course, though with different thematic focuses and approaches to integrating the concepts. Although both years featured modular structures (a statistical mode of 9 subgraphs), in 2025 the network exhibited greater discursive robustness (2 to 4 times more words with high Betweenness centrality) than in 2024. It is concluded that semantic network analysis allows for the characterization of differences in conceptual organization among students using active learning methodologies, serving as a complementary tool for assessing meaningful learning in Microbiology.

The focus was on leadership, mentoring and promotion. Using short, structured activities alongside small-group discussion, the participants were encouraged to reflect on leadership, mentoring and the perceived gap between being ready and being recognised for promotion. Descriptive survey findings and free-text reflections highlight the demand for structured peer support, reciprocal mentoring opportunities, and clearer, more transparent promotion processes. Following the event, we performed a structured review of the impact. This highlighted that the workshop participants reported that the event allowed for greater self-awareness into their own leadership approaches, a stronger commitment to purposeful mentoring, and greater confidence and renewed motivation to take concrete steps towards promotion.

Small-scale fisheries provide essential coastal livelihoods in developing regions, yet they often confront severe institutional voids within fragile-state contexts. Adopting a social-ecological systems framework, this study addresses three sequential objectives: characterizing the contemporary institutional and demographic status of fishery cooperatives across the five South-Central Federal Member States in comparison to their late-1980s historical peak; ranking the socio-economic, infrastructural, regulatory, and environmental impediments to cooperative efficacy; and evaluating the contributions of these cooperatives to sustainable fisheries development. This investigation analyzes how fishery cooperatives act as institutional intermediaries to bolster economic resilience within the Somali coastal economy--a sector central to the National Transformation Plan, despite persistent regulatory fragmentation. Through semi-structured qualitative interviews with key informants--analyzed via reflexive thematic analysis and chi-square testing of age-cohort structure--the study finds that illegal, unreported, and unregulated fishing and inadequate post-harvest infrastructure are the principal constraints, whereas training, education, and community collaboration constitute the primary contributions. The results characterize these cooperatives as hybrid institutions that compensate for limited central-state capacity in remote coastal settlements while serving as intermediaries for the Federal Ministry of Fisheries and Blue Economy where co-management mandates are implemented. By extending the actor-community dimension of the SES framework to include age-cohort structure, this research demonstrates that clan-based reciprocal arrangements can serve as functional substitutes for Ostroms first design principle in contexts of persistent fragility. The study recommends multi-stakeholder partnerships to advance targeted capacity-building, financial literacy, and technological integration, and suggests future research into the post-harvest and retail sectors to examine the role of women in the Somali blue economy.

Obtaining, Evaluating, and Communicating Information is one of the eight Science and Engineering Practices in the National Research Council framework that underlies Next Generation Science Standards (NGSS). Scientific inquiry requires evaluation and communication of research findings. These skills are part of scientific inquiry itself. The goal of this study was to characterize the communication practices students used during Science and Engineering Fairs (SEFs). To examine these practices, we included the following question in our online anonymous and voluntary national high school SEF surveys carried out during 2021-22 and 2022-23, "What types of communication and presentation skills did you use in your science fair project?" The possible answers were literature review; research notebook; software to prepare tables, graphs, or images; written report; poster board preparation; PowerPoint presentation; and interview with the judges. Literature review and research notebook are part of developing research questions and data collection. Software to prepare tables, graphs, or images, and PowerPoint presentation contribute to analyzing research and developing a presentation. Written reports, poster board preparation, and interviews with the judges provide the opportunity to present the findings. Overall, 1789 students answered the question. The percentage of students utilizing these skills ranged from 17.3% doing a literature review to 67.6% preparing a poster board. On average, students indicated the use of three skills. Poster board preparation and interview with the judges were selected by students more than twice as frequently as literature review and research notebook. More positive SEF outcomes were associated with a greater number of skills used by students. Among the different skills, use of a research notebook showed the strongest association. In this paper, we present the findings and discuss implications regarding use of notebooks and other communication and presentation skills in students SEF experiences and engagement with scientific inquiry.

Heavy metal contamination in soil systems is an escalating global crisis, yet over 55% of urban agriculture literature focuses on high-tech indoor vertical farming, leaving vulnerable open-field tropical urban agriculture (UPA) severely under-researched. This study addresses this knowledge gap by evaluating the chemical mechanics of heavy metal translocation within highly acidic Xanthic Ferralsols in Fendall, Liberia, serving as a global proxy for tropical agro-ecosystem risks. Using a randomized complete block design, topsoil (0-20 cm) from five cultivated and five uncultivated sites (n = 10) and corresponding tissues of cabbage (Brassica oleracea), lettuce (Lactuca sativa), and sweet potato greens (Ipomoea batatas) were screened via handheld X-ray fluorescence spectrometry. Results revealed a compelling geochemical paradox: uncultivated soils demonstrated significantly higher geogenic background metal concentrations across all elements (p < 0.05). Systemic Nickel (Ni) above the World Health Organization (WHO) thresholds was recorded on both land-use histories. While plant tissues accumulation of Copper (Cu) and Zinc (Zn) remained within safe parameters, Lead (Pb) concentrations across all crops profoundly exceeded food safety standards, averaging 1.53 mg/kg, which is over 5 times above the WHO limit (0.30 mg/kg). Risk screening highlighted high soil-to-plant transfer factors (TF =>1) for Ni and Zn in plant tissue, in the order of cabbage > sweet potato greens > lettuce. These findings provide transferable insights into how high soil acidity accelerates toxic metal mobilization, offering a scalable framework for crop-specific exclusion policies and real-time field screening in tropical municipalities. AbstractRapid urban agricultural expansion in Montserrado County, Liberia, relies heavily on highly weathered acidic Xanthic Ferralsols. While these soils support crop growth, they present a hidden geogenic risk of heavy metal translocation to the human food chain. This study evaluated the concentrations of Copper (Cu), Nickel (Ni), Lead (Pb), and Zinc (Zn) in the soils and edible tissues of cabbage (Brassica oleracea), lettuce (Lactuca sativa), and sweet potato greens (Ipomoea batatas) in Fendall. The uncultivated soils exhibited a geochemical paradox, showing significantly higher total metal concentrations than cultivated plots. However, low soil pH (4.0-6.0) highly mobilized these geogenic metals, driving rapid plant uptake. While tissue concentrations of Cu, Ni, and Zn remained within safe physiological ranges, Lead (Pb) concentrations in all three crops reached an alarming mean of 1.53 mg/kg, exceeding the World Health Organization (WHO) permissible safety limit of 0.30 mg/kg by more than 5-fold. The soil-to-plant Transfer Factors (TF) for Lead ranged from 0.51 to 0.81, highlighting significant bioavailable translocation despite the lack of visible crop phytotoxicity. These results demonstrate that passive geogenic metal uptake poses a silent public health threat. Immediate agronomic interventions, including systematic liming and crop-exclusion frameworks, are urgently required to safeguard urban food security in Monrovia. GRAPHIC ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=157 SRC="FIGDIR/small/740606v1_ufig1.gif" ALT="Figure 1"> View larger version (111K): org.highwire.dtl.DTLVardef@1171648org.highwire.dtl.DTLVardef@b7f56eorg.highwire.dtl.DTLVardef@15227e6org.highwire.dtl.DTLVardef@b996bf_HPS_FORMAT_FIGEXP M_FIG C_FIG

Background: Stress research in nursing education has largely focused on distress, stressors, and negative outcomes, although challenging experiences may also support motivation, confidence, learning, and growth when appraised positively. Objective: To develop and evaluate the psychometric properties of the Nursing Student Positive Stress Scale (NSPSS). Design: A methodological instrument development and psychometric evaluation study. Methods: The NSPSS was developed using a deductive, theory-driven approach informed by the transactional theory of stress and coping and positive psychology perspectives. Content validity was assessed by an international nursing expert panel. Psychometric evaluation used national survey data from nursing students in New Zealand. Of 539 responses, 507 were analysed. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted using separate subsamples. Internal consistency was assessed using Cronbach's alpha and McDonald's omega, and convergent validity through correlation with Perceived Stress Scale-10 scores. Results: Content validity was strong (I-CVI = .88-1.00; S-CVI/Ave = .975; S-CVI/UA = .800). EFA identified a dominant factor explaining 41.38% of variance (loadings = .528-.735). CFA supported a two-context Academic and Clinical Positive Stress model with correlated residuals between five parallel item pairs, chi-square(29) = 60.49, CFI = .970, TLI = .954, RMSEA = .063, SRMR = .065. Internal consistency was good (alpha = .839; omega = .843). NSPSS scores correlated negatively with PSS-10 scores (r = -.298, p < .001). Conclusion: The NSPSS demonstrated strong content validity, preliminary evidence of structural and convergent validity, and good internal consistency reliability for assessing positive stress appraisal among nursing students. Further validation in independent samples is warranted.

PurposeDiscussions surrounding the biomedical faculty job market often focus on applicant competitiveness and external metrics such as number of publications and funding records. Consequently, there is typically less discussion about applicant readiness, the point at which applicants perceive themselves as prepared to enter the market. Since 2018 our group, the Faculty Job Market Collaboration (FJMC), has conducted annual end-of-cycle surveys of biomedical faculty job applicants, producing the largest longitudinal dataset on this process to date. MethodsWe employed a mixed-methods design examining faculty applicants in biological science fields in North America. Regression analyses were conducted on a longitudinal dataset of 729 respondents across multiple hiring cycles. To determine how applicants evaluated their own preparation, qualitative interviews were conducted with a separate cohort of biomedical postdoctoral applicants during the 2024-2026 job cycles. ResultsOur findings demonstrate that rather than depending on a single quantitative threshold, readiness is a multifaceted construct shaped by actionable and interpersonal drivers. Key factors influencing an applicants perceived readiness include taking agency to submit applications, receiving explicit support from a mentor, incorporating strategic use of artificial intelligence tools into application preparation, and their career stage. ConclusionBy distinguishing individual readiness from systemic assumptions of market competitiveness, this study highlights a blind spot in academic workforce development. Our results suggest that applicants can achieve readiness and successful outcomes through different combinations of support, strategy, and timing rather than a uniform metric profile. By integrating quantitative and qualitative data, our study provides an evidence-based framework for understanding applicant readiness and offers practical guidance to help trainees navigate the increasingly competitive academic job market. Teaser TextOur mixed-model analysis of the biomedical faculty job market is designed to help prospective faculty candidates assess their readiness to enter the job market. By integrating multiple indicators of academic productivity, funding success, and professional experience, our study provides evidence-based benchmarks that can guide applicants in evaluating their competitiveness and identifying areas for further development before pursuing faculty positions.

Racial equity remains a critical challenge in postsecondary science education, as Black students experience higher attrition rates and diminished well-being compared to their White peers. While active learning has been shown to reduce failure rates and narrow achievement gaps, the interpersonal affordances and constraints of peer discussions within these settings remain underexplored for Black students. Grounded in Critical Race Theory and utilizing the analytical lenses of racial microaggressions, microaffirmations, and Community Cultural Wealth, this study addresses this issue by investigating the racialized interpersonal experiences of Black students in active learning college science classrooms. Through semi-structured interviews, this study explores the nature of both exclusionary and affirming peer interactions and how students navigate these dynamics. Findings reveal that Black students frequently encounter racialized microaggressions--manifestations of macro-level anti-Blackness-- in the classroom which contribute to isolation and racial battle fatigue. Conversely, students describe instances of microaffirmations, predominantly through small counterspaces created by and for other students of color, which validate their intellectual contributions and foster a sense of belonging. Despite facing these tensions, participants advocate for active learning as a beneficial practice, provided that instructors implement explicit, equitable structures and facilitate culturally responsive classroom climates. These findings offer actionable implications for researchers and practitioners to design more inclusive active learning environments by explicitly addressing interpersonal dynamics, promoting cultural competence, and co-constructing humanizing science classroom climates.

The global market concentrates extractive pressure on lands held by Indigenous peoples, including peoples living in isolation, for whom free, prior and informed consent cannot be obtained and protection must therefore rest on territorial instruments. Halmahera, Indonesia, holds some of the worlds largest lateritic nickel reserves beneath a lowland rainforest inhabited by the Hongana Manyawa, yet the trajectory of land use and cover change across the island has not been quantified. We characterised land use and cover change over the 17,437 km2 island between 2014 and 2024 using MapBiomas time series, and projected a business-as-usual scenario to 2054 with a stochastic cellular-automata model implemented in Dinamica EGO, calibrated with weights of evidence on eight variables describing mining and logging concessions, transport infrastructure, settlements and previous clearing. Forest covered 83.0% of the island in 2014 and 82.1% in 2024; under unchanged policy it falls to 73.7% by 2054, a net loss of 162 thousand ha, or 11.2% of the 2014 baseline, at gross rates of 47,000-51,000 ha per decade. Deforestation probability is highest within 500 m of previous clearing and declines with distance from settlements, cities and mining sites, while proximity to national parks carries a negative weight of evidence. The frontier is self-propagating and spatially predictable, and legally designated territory retains forest within it. Protecting the Hongana Manyawa consequently depends on excluding extractive licensing from the interior forest ahead of the frontier rather than behind it.

The tidal wetlands of Louisiana comprise about 25% of those found throughout the conterminous United States yet estimates of wetland loss rates in the region between 1932 and 2016 have exceeded 60 km2 yr-1. To mitigate further degradation and wetland loss in the region, a globally unprecedented $50B, 50-year plan for coastal Louisiana is driving restoration efforts, and demand exists from multiple stakeholders for regularly updated, regional-scale, accurate land cover information. We used machine learning (random forests; RF) and cloud computing to develop a new Landsat-based, marsh vegetation community geospatial dataset. The dataset depicts wetland vegetation community types defined in a previous study at annual (1985-2025) time steps at 30-m resolution. An RF algorithm was used to integrate training samples with feature variables derived from Landsat imagery, and the resulting geospatial data product achieved an overall correct classification rate of 78%. The approach for development of the land cover dataset presented here has potential for application in other coastal wetland habitats throughout the world.

The impact of weather on the health of Americans and the American health system is substantial. Using available health and economic data, we developed a data-driven scenario that describes a compounded heat emergency in an archetypal community in the United States. We then characterize the potential human and economic costs of such a heat emergency to demonstrate the widespread impact on health outcomes, health systems, and society.

Forests are essential to the global carbon cycle with light use efficiency (LUE) as a key parameter for assessing carbon sequestration capacity. However, the variations and drivers of LUE remain inadequately understood. Using remote sensing data, we analyzed global LUE patterns across five forest types and identified the main drivers. The global average annual LUE of forests is 0.93 {+/-} 0.36 g C MJ-1 during the period 2001-2022, with an increasing trend of 0.0034 g C MJ-1 yr-1. Among forest types, evergreen broadleaf forests exhibited the highest LUE, followed by evergreen needleleaf forests. Deciduous broadleaf forests and mixed forests showed similar levels, while deciduous needleleaf forests exhibiting the lowest LUE. Variations in LUE were jointly driven by plant traits and climatic conditions, with generalized linear models explaining 86% and 98% of spatial and temporal LUE variations, respectively. These findings highlight the critical role of plant traits and climate in shaping forest LUE, providing insights for enhancing carbon cycle models and informing forest management strategies in the context of global change.

Sakumo Lagoon, a small (1 km2) semi-open coastal lagoon in Ghana, lies between the cities of Accra and Tema. The lagoon and its surrounding wetland were designated a Ramsar Site in 1992, mainly because it served as a refuge for 66 local and migratory bird species. Its ecology, and the biology of its major fish species, notably the blackchin tilapia (Sarotherodon melanotheron) were thoroughly studied in 1971, when the lagoon was a diverse, mainly brackish ecosystem supporting a traditionally and well-managed fishery. In 2016-2017, another study found the lagoon mostly covered by floating vegetation and plastic waste. Finally, in 2024, a visual survey established that the floating vegetation had been almost completely replaced by terrestrial plants, with only a few square meters of garbage-strewn water in front of a culvert connecting the lagoon to the open sea. Several lagoons along the coast of Ghana have been similarly lost to urban sprawl and its various forms of pollution, but Sakumo Lagoon is a Ramsar Site, and its imminent disappearance should not remain undocumented.

Research suggests that students realize a range of cognitive, affective, and motivation-related outcomes by participating in course-based undergraduate research experiences (CUREs). However, the overall effects of CUREs on student outcomes are unclear. We sought to address this knowledge gap by embarking on a meta-analysis of CURE studies using the Population, Intervention, Comparison/Control group, and Outcomes framework. We conducted a literature search to produce an analytic sample of 353 studies of the effects of CUREs instruction on undergraduates. We characterized the CUREs being studied in this sample, including their dose, duration, and design. We also examined each study to determine if outcomes were measured before and after the CURE and with a comparison or control group so that effects could be attributed to CURE. We found substantial limitations in studies of CUREs, which precluded meta-analysis. CUREs varied widely and were unevenly described, making it difficult to determine what could or should constitute CURE instruction as an intervention. Many outcomes were studied, often without a comparison or control group and with inconsistent measurement and reporting. Our results indicate that advancements are needed in the design and reporting of research on CUREs to gauge their effects on student outcomes. Highlight SummaryMany studies of CURE instruction aim to show effects on student outcomes. Yet, systematic review of these studies reveals limitations in study design, methods, and reporting that preclude meta-analysis and limit any cross-study conclusions.

Objective To develop an intelligent predictive indicator system for determining weaning and extubation timing in mechanically ventilated patients, providing a theoretical foundation for clinical decision support systems and intelligent assessment in critical care nursing. Methods A literature review was conducted to identify and extract factors influencing weaning and extubation timing in mechanically ventilated patients, forming the initial item pool for the indicator system. A Delphi expert consultation questionnaire was designed and administered to 21 experts from relevant fields over two rounds. Item screening and revision were performed using the boundary value method, based on the arithmetic mean of importance scores, coefficient of variation (CV), and qualitative expert feedback. Results The effective response rate was 100% for both rounds. The expert authority coefficient (Cr) was 0.89 in the first round and 0.93 in the second round. Kendall's W was 0.244 ({chi}{superscript 2} = 1038.944, P < 0.001) in the first round and 0.079 ({chi}{superscript 2} = 350.343, P < 0.001) in the second round. Following the first round of evaluation of 203 items--which resulted in 13 additions, 23 modifications, and the separation of weaning/extubation dimensions--the second round comprised 211 evaluation items. The final system encompassed 6 primary indicators and 58 secondary indicators (with tertiary indicators under each), covering the complete clinical trajectory: medical history, ICU admission assessment, pre-weaning/extubation assessment, weaning assessment, extubation assessment, and post-extubation outcomes. Conclusion This indicator system was developed through a rigorous process with high expert authority and good consensus. The indicators are clinically comprehensive, nursing-operable, and suitable for intelligent modeling. This system can serve as a core framework for risk warning, timing judgment, and intelligent prediction system development for weaning and extubation in mechanically ventilated patients.