Search Results (89)

Background/Objective: Sex estimation from cranial remains is essential in forensic biological profile reconstruction, yet systematic comparisons of statistical classifiers under formally verified assumption conditions remain limited in Thai populations. This study aimed to develop and validate sex estimation models from craniofacial measurements in a Northeastern Thai skeletal sample using DFA, BLR, and SVM, and to examine whether distributional assumption violations influence classifier performance and generalizability under holdout validation. Methods: Eight linear craniometric parameters connecting five osteometric landmarks were measured in 300 adult skeletal specimens (150 males, 150 females) from the Unit of Human Bone Warehouse for Research, Khon Kaen University. Specimens were allocated into a training dataset (n = 200) and a holdout dataset (n = 100). Five multivariate model configurations were developed under direct-entry and stepwise parameter-entry strategies. Formal assumption diagnostics included Shapiro–Wilk normality testing, Levene’s test, Box’s M test, and variance inflation factor analysis. Results: All parameters demonstrated excellent interobserver reliability and significant sex-based differences (p < 0.01), with males recording larger mean values throughout. Apparent multivariate accuracy ranged from 78.0% to 85.5%. Holdout validation revealed consistent performance decline across all five configurations, with accuracy reductions of 3.0 to 9.5 percentage points and AUC reductions of 0.042 to 0.079. The stepwise DFA model showed the smallest overall accuracy decline, although this apparent stability reflected an uneven sex-specific trade-off, with a specificity decline offset by a sensitivity gain, while SVM and DFA direct-entry models showed the largest decline. Conclusions: Craniofacial measurements provide moderate discriminatory capacity for sex estimation in Northeastern Thai skeletal remains. Although univariate assumption violations were observed, DFA’s performance remained comparable to BLR and SVM under the present large, balanced sample; multivariate analysis or distributional assumption-free methods offer practical alternatives where violations are present. Apparent performance alone is insufficient to establish forensic reliability, and holdout validation should be regarded as a necessary component of sex estimation model development. Full article

Forest inventory compartments provide extensive and management-relevant reference information for satellite-based tree species mapping, but their dominant-species attributes are defined at the stand level rather than for individual image pixels. Existing applications commonly derive training samples from compartment centres or assign polygon labels to enclosed pixels, which may introduce boundary effects, uneven class representation, and disproportionate contributions from individual compartments. However, the intermediate step of converting inventory polygons into spatially controlled pixel-level reference samples has received comparatively limited attention. Here, we developed a geometry-constrained reference sample construction framework that integrates interior-position screening, class balancing, source compartment contribution control, and spatial-spacing constraints. The framework was evaluated for mapping Korean pine, larch, white birch, and spruce in a temperate mixed forest in northeastern China using Sentinel-1/2 time series and ancillary predictors. Predictor–classifier combinations were selected using compartment-grouped out-of-fold evaluation, sampling workflows were compared on 60 independently withheld compartments, and the final map was further assessed using 306 independent reference points. Relative to centroid sampling, the geometry-constrained workflow increased compartment-level macro-F1 from 0.612 to 0.709. XGBoost with optical time series and ancillary predictors achieved the best development-set performance, while inclusion of the complete Sentinel-1 time series provided no further gain. The final model achieved an overall accuracy of 0.827 and a macro-F1 of 0.820 on the independent reference points. Aggregation of 10 m predictions further enabled compartment-level characterization of mapped dominant species, dominance strength, and mixing intensity. These results demonstrate that reference sample construction is a consequential step in tree species mapping from polygon-based forest inventories and provide a practical approach for linking pixel-level remote sensing classification with forest management units. Full article

This paper presents a pilot educational workflow that couples 3D remote sensing with heritage-driven pedagogy by engaging architecture master’s students in the documentation and digital archiving of Transylvanian cultural sites. Using terrestrial and mobile 3D scanning, students documented multiple typologies—wooden churches (Târgușor, Tioltiur), historical ensembles (Mociu, Coplean), industrial sites (1 Mai–Luduș, Vânătorilor–Luduș), and an urban street segment (Potaissa)—to generate dense point clouds that served as the basis for geometric reconstruction, semantic interpretation, and condition assessment. The study describes how the characteristics of different construction systems (timber, brick, stone, mixed structures) relate to point-cloud quality, survey coverage, and subsequent CAD/BIM drafting, with attention to the qualitative reading of minor deformations in wooden churches and of degradation patterns in masonry and industrial buildings. We also consider how artefacts in the data (noise, occlusions, registration errors) affect scene understanding and the interpretation of derived observations relevant to condition assessment and, prospectively, to monitoring. For the Tioltiur dual-sensor case, the TLS and SLAM datasets were compared through an internal CloudCompare registration check (final RMS 0.1121 on 50,000 points, fixed scale 1.0 and theoretical overlap 100%), surface-density displays (r = 0.005 for the Z+F dataset and for the GeoSLAM dataset), fitted-wall-plane readings (dip values around 89 deg. and 85 deg.) and a longitudinal section documenting roof/vault deformation. Beyond technical performance, the paper examines the self-reported formative impact on students’ digital skills and their understanding of cultural values, arguing that participation in 3D data acquisition, processing, and interpretation positions them as co-creators of a living digital archive. Pre- and post-workshop questionnaires (n = 13 each) are analysed descriptively—counts, percentages and medians with interquartile ranges—because the two instruments are unmatched and carry no shared identifier, so no paired test is applied; post-workshop self-ratings of technical competence, heritage understanding, archival awareness and collaboration were consistently high (medians 4–5), with uneven access to VR the main gap. By connecting point-cloud-based documentation workflows with heritage education, the project outlines a transferable, monitoring-ready baseline model in which 3D remote sensing supports both careful documentation and the transmission of regional identity and cultural meaning in architectural training. As an exploratory pilot with a small, self-reported sample, the study reports descriptive and qualitative findings rather than validated metric or statistical results. Full article

Wheat is one of the major staple crops in China, and drought stress can severely affect its growth, development, and yield. Rapid and accurate identification of drought stress levels in wheat is of great significance for agricultural disaster prevention and mitigation, as well as for ensuring food security. To address the problems of insufficient fine-grained feature extraction, class imbalance, and unstable training in wheat drought stress image recognition, this study proposes an improved EfficientNet-B0 model for fine-grained wheat drought stress classification. Based on EfficientNet-B0, an improved lightweight Efficient Multi-scale Attention (EMA) module is introduced after the backbone network to enhance both channel-wise and spatial feature representation. PolyLoss is adopted to enhance the learning of low-confidence and difficult samples under the uneven class distribution, while the Sharpness-Aware Minimization (SAM) optimizer is employed to improve the optimization process. Experiments were conducted on a 15-class wheat drought stress image dataset constructed from three key growth stages and five drought severity levels. The proposed model achieved an accuracy of 98.69% and an F1-score of 98.37% on the internal test set, outperforming the baseline EfficientNet-B0 and comparison models including ResNet-50, DenseNet-121, and MobileNetV3. Component-level ablation experiments further showed that the dual-gating structure, projection residual connection, and intra-group Softmax normalization in the proposed EMA module all contributed positively to model performance. These results indicate that the proposed method provides a lightweight and effective approach for wheat drought stress recognition within the current dataset. Full article

Background and objectives: Artificial intelligence (AI) is increasingly embedded in public health workflows, yet adoption among practitioners remains uneven and is shaped by knowledge, legal awareness, and operational barriers. This cross-sectional study characterised determinants of AI adoption among healthcare professionals and examined how legal concern moderates the translation of technical knowledge into practical use, at a single Romanian tertiary academic centre. Methods: We surveyed 93 healthcare professionals (physicians, nurses, public health specialists, residents) at the “Pius Brînzeu” Clinical Emergency County Hospital and “Victor Babeș” University of Medicine and Pharmacy Timișoara. Participants were classified as AI adopters or non-adopters. Likert-derived composite scores (0–100; Cronbach’s α 0.79–0.88) quantified knowledge, trust, legal concern, privacy concern, and workflow confidence. Group comparisons used independent-samples t-tests and χ² tests; associations used Pearson correlation; predictors of adoption and usage intensity were modelled with logistic and multiple linear regression; a two-way ANOVA tested profession-by-training effects. Significance was set at p < 0.05. Benjamini–Hochberg false-discovery-rate correction was applied across the 18 bivariate tests reported in this study, and adjusted q-values are reported alongside unadjusted p-values. Results: Adopters (n = 51) were younger (34.7 ± 7.5 vs. 43.2 ± 8.5 years; p < 0.001) and reported higher knowledge (67.3 vs. 48.6; p < 0.001) and workflow confidence (64.2 vs. 41.9; p < 0.001) but lower legal concern (58.4 vs. 71.2; p < 0.001). Knowledge correlated positively with usage intensity (r = 0.536; p < 0.001), whereas legal concern correlated negatively (r = −0.426; p < 0.001). In multivariable models, younger age (OR = 0.91; p = 0.004), knowledge (OR = 1.06; p = 0.005), and trust (OR = 1.07; p = 0.005) independently predicted adoption. The linear model explained 46.1% of usage variance. Stratified analysis suggested legal concern attenuated the knowledge–usage slope (β: 0.51→0.18); however, the formal knowledge-by-concern interaction term was not statistically significant (p = 0.191), and this pattern is therefore exploratory. Conclusions: In this modest, single-centre sample, AI adoption was independently associated with knowledge and trust, and legal concern was independently and negatively associated with usage intensity; the apparent dampening of the knowledge–usage relationship by legal concern was suggestive but not statistically confirmed. Targeted legal-regulatory literacy and structured training may support practical AI uptake in public health settings, pending confirmation in larger, multicentre studies. Full article

Traditional Bayesian SAR detection methods suffer poor adaptability to speckle noise, fail to handle severe class imbalance within large-scale multi-target datasets, and incur prohibitive training overheads. To address these drawbacks, this paper develops a lightweight Bayesian detection and recognition framework built upon heavy-tailed Laplacian priors and variational inference. We adopt ResNet-50 as the feature extraction backbone and design a four-stage pipeline: First, a noise-aware Laplacian heavy-tailed prior is proposed to strengthen resistance against speckle outliers. Second, a multi-class variational inference module is constructed to eliminate detection bias induced by uneven sample distribution across target categories. Third, a lightweight uncertainty feedback strategy is introduced to cut computational costs for large-batch training. Evaluated on the MSAR-1.0 dataset, our approach achieves an mAP@0.5 of 94.98% and a macro balanced accuracy ($BA$) of 93.34%. Compared with existing Bayesian detectors, the mAP metric rises by 5.44–6.53%. The model only consumes 4.33 ms per inference frame and completes full training within 1.53 h on a single GPU. Ablation tests validate the independent and combined efficacy of all three core modules. This integrated architecture balances detection precision, classification reliability, and training efficiency, offering a promising prototype for multi-class SAR target interpretation under the evaluated benchmark constraints. Full article

High-precision estimation of forest aboveground biomass (AGB) is crucial for global carbon cycle monitoring and sustainable forest management. However, existing machine learning-based approaches often suffer from high-dimensional feature redundancy, uneven spatial distribution of training samples, and inefficient hyperparameter optimization, which collectively limit estimation accuracy and computational efficiency. To address these issues, this study proposes a synergistic feature-sample optimization framework (DFS) for high-precision forest AGB estimation. First, with the involvement of forestry experts, we constructed the Hunan and Hubei datasets covering typical subtropical forest types through multi-source remote sensing and ground plot sampling. Second, we propose the Dual-Criteria Adaptive Feature Selection (DCAFS) method, integrating ReliefF and mutual information criteria to adaptively select key features highly correlated with AGB, eliminating spectral redundancy while preserving biomass-sensitive information. Next, we introduce a Bidirectional Active Learning Sample Optimization mechanism, called BALSO, and in its forward step, plots with high uncertainty and representativeness are given priority, so samples with high AGB variability can be captured effectively; in the backward step, spatially redundant samples and feature-redundant samples are removed through density peak clustering, and by doing this, sample selection and spatial distribution are optimized at the same time, so plot balance gets improved. Finally, the framework brings in a parameter tuning structure based on Dream Optimization Algorithm, namely DOA, and through staged exploration together with local fine-tuning, DOA makes model hyperparameters and AGB data distribution characteristics align in an adaptive manner, which helps improve convergence efficiency and estimation stability. Input variables comprise Landsat 8 OLI spectral bands, GLCM texture features, vegetation indices, and Sentinel-1/2 data. On the Hunan dataset, the framework achieved an $R^2$ of 0.83 and an RMSE of 25.6 Mg·ha⁻¹; on the Hubei dataset, it achieved an $R^2$ of 0.86 and an RMSE of 26.8 Mg·ha⁻¹. The framework was further validated on an independent public dataset from Inner Mongolia. These results demonstrate that the DFS framework provides an effective and feasible approach for regional-scale forest AGB estimation and carbon monitoring. Full article

Self-efficacy, a person’s belief in their capability to carry out a given task, shapes whether teachers attempt, persist with, and transfer new practices. Yet teachers’ perceived capability to use generative AI (GenAI) is often treated as a single general readiness rather than examined across the distinct domains in which it is now used. Guided by Bandura’s self-efficacy theory, this study provides a domain-differentiated account of GenAI competency self-efficacy among university teachers of English as a Foreign Language (EFL) in the Saudi higher-education context. A sample of 232 EFL teachers at Saudi public universities completed a contextually adapted version of the Generative AI Competency Self-Efficacy Scale for Teachers and Researchers (GAICS-TR). Data were analysed using repeated-measures analysis of variance, correlational analyses, and hierarchical multiple regression. Overall self-efficacy was only slightly above the scale midpoint, and the overall score concealed an uneven pattern across domains. Teachers felt most capable in Basic GenAI Understanding and Teaching with GenAI, less so in Professional Engagement, and least confident in Research with GenAI, with the lowest confidence reported for data analysis. Prior AI training and frequency of GenAI use were associated with overall self-efficacy, whereas teaching experience and academic rank were not. The findings suggest that overall measures of GenAI readiness can mask lower-confidence areas within specific domains, especially in research-facing uses such as data analysis. Accordingly, supporting teachers’ development in using GenAI calls for targeted, domain-sensitive professional development that reaches teachers at all career stages rather than broad familiarisation with the tools. Full article

Reliable state assessment of railway traction power transformers is challenged by heterogeneous operating environments, measurement disturbances, coupled gas-generation mechanisms, and uneven fault-sample distributions. Conventional dissolved gas analysis (DGA) ratio rules and single-model classifiers often show insufficient generalization when rare faults and boundary-ambiguous operating states are encountered. To address this issue, this paper proposes a feature-guided stacking framework for state assessment of oil-immersed railway power transformers. First, a DGA-oriented fusion-feature representation is established by combining raw gas concentrations, gas-ratio descriptors, and an aggregated dissolved-gas analysis factor. Second, DBSCAN-assisted sample structuring is introduced to identify density patterns, sparse rare fault regions, and boundary samples, thereby improving the organization of imbalanced monitoring records. Third, a monitoring-feature-embedded stacking model is developed in which heterogeneous base learners are adaptively weighted according to feature-reliability information and integrated through a cross-validated meta-learner. This synthetic-data-based validation provides a controlled and reproducible proof-of-concept. Therefore, the reported results should be interpreted as evidence of methodological feasibility. Under the default synthetic setting, the proposed feature-guided stacking (FE-stacking) method achieves an accuracy of 99.70% and a macro-F1 of 99.55%. Under the severe minority-retention setting in which only 25% of low-energy discharge (LD) and low-temperature overheating (LT) training samples are preserved, it obtains an accuracy of 99.62%, a macro-F1 of 99.40%, and an LT recall of 96.61%, slightly surpassing random forest (RF) and outperforming Original Stacking in rare fault robustness. These results indicate that feature-guided ensemble learning can improve the generalization stability of DGA-based transformer state assessment under imbalanced and boundary-ambiguous conditions. From a practical perspective, the proposed framework can serve as a decision-support module for transformer condition screening, maintenance prioritization, and alarm verification in railway traction power-supply systems. Full article

Accurate prediction of enzyme turnover numbers ($k_{cat}$) is essential for applications in systems biology, metabolic engineering, and drug discovery, yet remains challenging due to the limited availability and uneven distribution of experimental data. Here, we present AUKAT, an integrated framework that combines conditional generative modeling with deep neural prediction to improve $k_{cat}$ estimation. A conditional variational autoencoder generates synthetic training instances in embedding space, followed by a selection pipeline that retains samples with strong agreement across independent evaluators, thereby ensuring data reliability. A hybrid convolutional neural network and transformer-based architecture is then used to predict $k_{cat}$ from substrate, enzyme functional, and species embeddings. Incorporating synthetic data improved predictive performance for both random forest and neural network models in five-fold cross-validation, with larger gains observed for the neural network architecture. Benchmarking against DLKcat demonstrated comparable predictive accuracy on the standard test set, while evaluation on stricter unseen subsets indicated improved generalization for low-similarity substrates and enzymes. Feature importance analysis further showed that AUKAT leverages substrate, enzyme functional, and species information in a more balanced manner rather than relying predominantly on a single feature source. In addition, AUKAT-human, a specialized model trained using a pre-training and fine-tuning strategy, achieved improved prediction accuracy for human enzyme kinetics. Overall, AUKAT provides a scalable approach for enzyme kinetics prediction and offers a practical solution to data scarcity in biochemical modeling. Full article

Purpose: This study examines the preparedness of the Ghanaian construction workforce for Industry 5.0 by assessing digital readiness, identifying skill gaps, and proposing human-centred upskilling strategies. Design/Methodology/Approach: A qualitative approach was adopted, using six focus group discussions with 32 construction professionals from Accra and Kumasi. Participants were selected using purposive and snowball sampling, while data were analysed thematically. Findings: Four major themes emerged: digital readiness, skill gaps, upskilling strategies, and human–machine collaboration. The findings showed that digital readiness was uneven across roles, with design and managerial professionals demonstrating higher exposure to digital tools than site-based workers and supervisors. Six key barriers were identified: limited BIM competence, low digital literacy, poor technological infrastructure, weak organisational support, inadequate structured training, and resistance to technological change. Three upskilling priorities were also identified: role-specific digital training, continuous professional development, and inclusive training models. Originality: The study provides empirical evidence on Industry 5.0 workforce readiness within a developing-country construction context. Practical Implications: The findings support stronger CPD systems, inclusive training programmes, and collaboration among industry, government, academia, and professional bodies. Research Limitations: The study was limited to 32 professionals in Accra and Kumasi; therefore, the findings are context-specific. Full article

High-precision WTC is essential for satellite altimetry and ocean dynamic environment monitoring. Existing WTC approaches often rely on globally unified statistical frameworks, which inadequately represent wind-speed-dependent nonlinear sea-surface microwave radiative responses and are prone to systematic bias under uneven observation distributions. To address these limitations, this study proposes an adaptive WTC method integrating overlapping wind-regime modeling, multi-scale collaborative sample balancing, and a model soft-fusion strategy. Firstly, a modeling framework with overlapping transition zones for low-, moderate-, and high-wind-speed regimes is established according to wind-speed-driven variations in sea-surface radiative responses, and sub-models are trained independently. Subsequently, a multi-scale sample balancing, combining global and local weights, is designed to enhance learning from sparse samples. Finally, a soft-fusion strategy based on a trapezoidal membership function is applied to dynamically weight sub-model outputs, ensuring retrieval continuity across transition zones. Using HY-2C Calibration Microwave Radiometer (CMR) observations, the proposed method is developed, trained, and evaluated against model-derived WTC and collocated Jason-3 AMR-2 measurements. Results show that the proposed method improves overall WTC retrieval accuracy and stability while effectively reducing systematic biases under wind-speed regimes with sparse observations, providing an effective and robust approach for high-accuracy WTC retrieval under various wind-speed conditions. Full article

Background/Objectives: Chiropractors serve as first-contact practitioners in South Africa and frequently encounter patients with systemic conditions that may mimic musculoskeletal complaints. Non-musculoskeletal (non-MSK) examinations are essential for identifying red flags, ruling out serious pathologies, and facilitating timely referrals. Despite their importance for patient safety and integration into primary healthcare, limited research exists on the frequency with which South African chiropractors perform these assessments. This study aimed to describe the frequency and indications for non-MSK examinations performed by South African chiropractors and to explore variations across examination types, demographic factors, years of experience, and training institutions in secondary analyses. Methods: A cross-sectional online survey was distributed to 898 registered chiropractors, yielding 186 responses (20.7%). The questionnaire assessed the frequency of non-MSK examinations using a five-point Likert scale. Data were analysed using descriptive statistics (frequencies, percentages, medians, interquartile ranges). Exploratory subgroup comparisons were conducted using nonparametric tests, but these findings should be interpreted with caution due to small and uneven sample sizes in some subgroups. Ethical approval was obtained (REC-3366-2025). Results: Most respondents were female (57.5%) and practising in Gauteng (49.5%). Blood pressure (84.4%) and heart rate (81.2%) were the most frequently performed examinations, while respiratory rate (12.4%), oxygen saturation (9.7%), and temperature (11.8%) were the least frequently performed vital signs. Breast (3.8%), abdominal (10.2%), and genitourinary (1.1%) examinations were rarely conducted. Exploratory subgroup observations suggested provincial variation: chiropractors in KwaZulu-Natal performed non-MSK examinations more frequently than those in Gauteng and the Western Cape (mean differences ranging from 0.21 to 1.19 on a five-point scale), whereas no meaningful differences were found across years in practice. Conclusions: South African chiropractors perform a selective range of non-MSK examinations, supporting their role as first-contact practitioners. However, many systemic examinations are conducted infrequently, with observed provincial variation. These descriptive findings highlight the need for greater consistency and standardisation in non-MSK screening to enhance patient safety and interdisciplinary care. Future adequately powered studies are needed to confirm the exploratory subgroup observations. Full article

Background: Despite being considered a country with a larger health workforce in Africa, the South African health workforce continues to experience shortages and a maldistribution of health workers across regions and sectors. Current projections suggest that the workforce is expected to decline further, especially among doctors, nurses and midwives, in large part, due to attrition—which could compromise the delivery of primary health and maternity services. These health workforce shortages and uneven distribution threaten the sustainability and effectiveness of health services in South Africa and drives the need to investigate the factors that may be influencing career choice and change decisions among health professionals in South Africa. Methods: A qualitative exploratory study, making use of purposive sampling and semi-structured interviews, was conducted to investigate the factors influencing career choice and change decisions among health professionals in South Africa. The participants were qualified health professionals in the fields of medicine, nutrition, pharmacy, nursing, and psychology working in the private, public, and academic sectors. Data was collected until saturation was achieved and then thematically analyzed using MAXQDA 24. Results: A total of 10 participants made up of three males and seven females were interviewed. These participants worked in different employment sectors with some having dual roles in private practice, public sector, and academia. The analysis revealed three major themes that capture the nature of and factors influencing career choice and career changes occurring in South Africa. The first theme related to factors influencing career choice (including altruism, family influence, personal experiences, financial/job security, academic achievement, career guidance, and opportunity for change). The second theme focused on career change dynamics (nature of career changes and career transitions occurring in the form of specialization, switching health professions, exiting health professions, adding non-health interests, and shifting focus areas). The third theme revealed factors influencing career change. These were categorized into personal and individual factors, workplace or job-specific factors, and administrative factors. This study has contributed to understanding the career choices and career changes taking place within the health professions in South Africa. It has also revealed a need for reforms in policy and practice for the current health professionals who have no intention of changing their careers while highlighting implications for future training of health professionals. Also, addressing the challenges of poor working conditions, lack of support, unemployment and placement delays, and other administrative barriers will help mitigate some of the issues leading to health workforce shortages and inequities in the South African context. Conclusions: The strongest motivator for choosing a career in health professions is the desire to care for others, while retention of the health workforce is challenged by personal, workplace, and administrative factors. Enhancing workplace conditions and support systems, implementing policy reforms, and minimizing administrative barriers is essential for achieving universal health coverage and sustaining a resilient health workforce in South Africa. Full article

This study examines farmers’ perceptions of how policy support is associated with ecological agriculture adoption and perceived green development outcomes in Xinjiang under China’s Rural Revitalization Strategy. A sequential explanatory mixed-methods design was used, in which the qualitative phase was deliberately connected to the quantitative phase through a shared sampling frame and a construct-aligned interview guide, and the two strands were integrated using a joint display and meta-inferences. In the quantitative phase, survey data from 300 farmers were analyzed using partial least squares structural equation modelling (PLS-SEM) to test the relationships among perceived policy support, ecological agriculture adoption, and green development. In the qualitative phase, semi-structured interviews with 30 participants drawn from the same respondent pool were thematically analyzed to explain, qualify, and contextualize the statistical relationships. The quantitative findings show a strong positive association between perceived policy support and ecological agriculture adoption (β = 0.659, p < 0.001), a strong positive association between ecological agriculture adoption and green development (β = 0.689, p < 0.001), and a smaller but significant direct association between perceived policy support and green development (β = 0.324, p < 0.001). The indirect effect of perceived policy support on green development through ecological agriculture adoption (β = 0.454) indicates partial mediation. The model explains 43.4% of the variance in ecological agriculture adoption and 47.4% of the variance in green development. The integrated joint display shows that technical training, policy clarity, and extension support helped farmers translate policy support into ecological practices, whereas high initial costs, financing constraints, and market uncertainty limited adoption and created uneven outcomes. The integrated findings suggest that policy effectiveness depends not only on the availability of support instruments but also on farmers’ practical capacity, economic security, and confidence in market returns. The study contributes perception-based mixed-method evidence on the policy–adoption–green development nexus in an ecologically vulnerable agricultural region. Full article