Search Results (9,211)

The accurate estimation of forest aboveground biomass (AGB) typically relies on extensive field surveys, which are highly time-consuming and cost-prohibitive. While unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) provides ultra-high point densities capable of reliable individual-tree analysis, its limited flight coverage restricts large-scale applications. Conversely, regional airborne laser scanning (ALS) offers broad spatial coverage, but its relatively low point cloud density makes individual-tree level analysis unreliable. To bridge this scale and data gap, this study develops a scale-consistent framework that integrates UAV-LiDAR, three-dimensional simulation, multisource remote sensing, and machine learning for Chinese fir (Cunninghamia lanceolata) plantation AGB estimation. High-density UAV-LiDAR data were first used to construct individual-tree AGB models, and the predicted tree-level biomass was aggregated to generate spatially representative “agent plots” for stand-scale modeling. A three-dimensional (3D) radiative transfer simulation framework was further employed to reproduce airborne LiDAR observations under different point densities, enabling the evaluation of structural information loss caused by LiDAR sparsity. Structural features derived from simulated LiDAR and spectral information from Sentinel-2 imagery were integrated using the Tabular Prior-data Fitted Network (TabPFN). Model reliability was assessed through 10-fold spatial block cross-validation and Monte Carlo simulations, which quantified spatial generalization and uncertainty propagation from individual-tree estimation to stand-level prediction. Feature interpretation using SHapley Additive exPlanations (SHAP) revealed that the LiDAR-derived vertical canopy structure provided the primary constraints for biomass estimation, whereas Sentinel-2 shortwave infrared features supplied complementary information related to canopy conditions. The optimal TabPFN model achieved a stand-level accuracy of R² = 0.88 and RMSE = 9.23 Mg·ha⁻¹ using LiDAR combined with Sentinel-2 data. Uncertainty analysis further demonstrated the robustness of the proposed framework under propagated errors, highlighting its potential for scalable and reliable forest biomass estimation in data-limited subtropical ecosystems. Full article

Formic acid (HCOOH), the most abundant carboxylic acid in the troposphere, modulates rainwater acidity, aerosol water uptake, and the oxidative capacity of remote atmospheres, yet its global budget remains poorly constrained. Herein, we present the first HCOOH total-column retrieval from thermal–infrared (TIR) measurements of the Thermal And Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer-2 (TANSO-FTS-2) on board GOSAT-2, providing an early-afternoon observational perspective that complements existing morning low-Earth-orbit and geostationary HCOOH products. The Optimal Estimation retrieval sequentially fits the surface state, the atmospheric background (temperature, water vapor and ozone), and the HCOOH profile in a 1104–1109 cm${}^{-1}$ microwindow centered on the ${\nu }_6$ Q-branch, with a radiance-ratio-scaled a priori that adapts to each scene. Averaging-kernel diagnostics concentrate the sensitivity in the 500–900 hPa layer with degrees of freedom for signal of approximately 1.05 under enhanced-emission conditions. For a 2019–2020 Australian bushfire case, including HCOOH in the state vector reduces the mean spectral residual from −0.327 K to 0.033 K. Independent evaluation against 113 time-coincident Toronto NDACC FTIR overpasses gives R = 0.95 and a zero-intercept slope of 2.12 for raw FTIR versus GOSAT-2. Applying the GOSAT-2 a priori and averaging kernel to the FTIR profiles changes the slope to 0.77 and reduces the RMSE to $0.23\times {10}^{16}$ molec cm${}^{-2}$; this one-sided smoothing is treated only as a sensitivity diagnostic. Monthly global maps for December 2019 and June 2020 show cross-sensor consistency with the IASI/MetOp-B ANNI-HCOOH product at R = 0.83 and 0.76. Over East Asia during April–June 2023, GOSAT-2 correlates with FY-4B/GIIRS at R = 0.90 (April) and R = 0.65 (June), with coherent three-sensor daily variability. These satellite comparisons are treated as cross-sensor consistency assessments rather than independent validation. GOSAT-2 consistently reports lower columns, a sensitivity-limited tendency consistent with a priori dominance under weak signals, limited information content, a narrow retrieval window, and differences among retrieval frameworks. The current product is a first demonstration for cloud-free daytime land scenes; this domain defines its sampling scope and representativeness but is not interpreted as a direct cause of the lower columns. The product offers a traceable GOSAT-2 TIR observational constraint on tropospheric HCOOH for future multi-platform synergy. Full article

Background: This study evaluated the relationship between the comfort, functionality, and design features of firefighting footwear and foot structure in a cohort of 100 firefighters aged 25–40 years. Methods: Foot structure was assessed using a CQ-ST podoscope. Subjective ratings of perceived comfort, functionality, and style of mid-calf leather firefighting boots (Model C) were collected. Statistical analysis was conducted using Spearman’s rank correlation. Results: Statistically significant correlations were found between foot width and perceived shoe length comfort (R = 0.21, p = 0.037; R = 0.24; p = 0.017). Clarke’s angle was negatively correlated with the heel cushioning (R = −0.21; p = 0.035; R = −0.23; p = 0.023), forefoot cushioning (R = −0.24, p = 0.015; R = −0.27, p = 0.006), overall comfort (R = −0.22, p = 0.029) as well as with grip (R = −0.23, p = 0.021; R = −0.27, p = 0.006), individualization (R = −0.21, p = 0.035; R = −0.24, p = 0.015) and style (R = −0.21, p = 0.036; R = −0.24, p = 0.016). The β angle was negatively correlated with shoe forefoot width (R = −0.22, p = 0.024) and breathability (R = −0.22, p = 0.028), whereas the γ angle was negatively correlated with ease of donning and doffing (R = −0.21, p = 0.037). Conclusions: Firefighters with wider feet reported higher footwear comfort ratings regarding shoe length. In contrast, those with a higher longitudinal foot arch reported lower ratings of heel and forefoot cushioning, as well as overall comfort. A higher longitudinal foot arch was also associated with lower ratings of grip, individualization, and style, whereas greater varus deviation of the right fifth toe was associated with poorer breathability ratings. Perceived comfort was closely associated with more positive evaluations of both the functional and aesthetic aspects of the boots, underscoring its importance in overall footwear acceptance. These suggest that individual foot morphology should be considered in the design and selection of firefighting footwear, as a more personalized fit may improve comfort, usability, and potentially reduce the risk of foot discomfort and overuse-related musculoskeletal disorders during occupational activities. Full article

Anthropogenic reactive nitrogen (Nr) production now greatly exceeds natural creation, generating cascading environmental impacts on the environment and human health. Effective monitoring of Nr in ecosystems is essential for early warning and policy response, yet the landscape of available monitoring methods is wide and heterogeneous, varying substantially in accuracy, purpose, cost, and ecological scope. This study evaluates nineteen Nr-monitoring methods, grouped into chemistry-based (CM), biodiversity-based (BM), and transplant-based (TM) methods, against 36 fitness-for-purpose (FFP) indicator facets. Facets are organized into intrinsic (direct measurement of nitrogen cycle components), projected (ecological effects the method can indicate), and extrinsic (metaproperties such as cost or precision) types. Scores are derived from 89 core papers, ranked using evidence-normalized weighted-sums (WS) and non-metric multidimensional scaling (NMDS) approaches. CMs generally outperform BMs and TMs, with critical loads, total tissue nitrogen, and nitrogen isotopes leading overall. Lichen diversity, ectomycorrhizal fungi, and Ellenberg’s N lead among BMs, particularly for projected facets. The NMDS reveals a structural divide and monitoring complementarity between CMs and BMs, with CMs serving as early warning indicators, whereas BMs integrate cumulative, persistent ecosystem impacts. While no single method seems adequate for comprehensive monitoring, the FFP matrix facilitates selecting optimal monitoring combinations. Full article

Ensuring long-term agricultural sustainability depends heavily on preserving soil health, a process fundamentally governed by humic substances (HSs) and their vital physicochemical and biological functions. However, because intensive farming rapidly degrades natural HSs reserves, external replenishment has become essential, driving the expansion of the humic preparations market. This article constitutes a comprehensive review of the entire technological chain of humic preparations: from the identification of raw materials, through advanced extraction techniques, up to agrochemical mechanisms in the soil–plant system. Both traditional fossil deposits (leonardite, brown coal, peat) and renewable waste sources fitting into the concept of the circular economy were discussed. Classical alkaline extraction was confronted with green methods such as ultrasound-assisted (UAE), microwave-assisted (MAE) or high voltage electrical discharge (HVED) extraction, which allow for shortening the operation time and reducing the consumption of reagents. Strategies of integrating HSs with mineral fertilizers (coating, liquid formulas, organo-mineral products) and their direct impact on improving nutrient use efficiency (NUE), mitigating plant abiotic stress, agricultural performance, and environmental impact were described in detail. Research perspectives were also presented, including, among others, economic aspects of scaling up humic technologies and an assessment of the development potential of innovative nanofertilizers functionalized with HSs. Full article

Surface vegetation dynamics in mining areas are characterized by complex non-linear processes associated with anthropogenic disturbance and ecological restoration. Existing remote sensing approaches often face limitations in balancing temporal interpretability and the characterization of long-term vegetation trajectories at regional scales. To address this issue, this study proposes a trajectory-guided weakly supervised framework that integrates parameterized curve fitting with deep temporal learning for mining vegetation monitoring. Based on the characteristic “extraction–reclamation” cycle, six representative vegetation trajectory patterns were pre-defined to describe different stages of degradation and restoration. Long-term NDVI trajectories (1990–2023) derived from Landsat time-series data were modeled using linear and parameterized Sigmoid functions to automatically generate high-quality supervision samples and temporal transition labels. These trajectory-constrained samples were subsequently incorporated into a multi-task BiLSTM-Attention network to simultaneously perform pixel-level change classification and turning-point regression. Applied to the mining clusters of the Dongting Lake Basin, China, the proposed framework achieved an overall classification accuracy of 86.64% (Kappa = 0.83), while the temporal prediction error remained within two years. Results revealed that 28.66% of the 61.20 km² of significantly degraded mining land has undergone effective ecological restoration, with restoration activities increasing sharply between 2012 and 2014 in response to regional environmental policies. By coupling ecological trajectory modeling with weakly supervised temporal learning, this study offers a promising approach for large-scale mining restoration monitoring and ecological assessment. Full article

Island destinations combine natural settings, local culture, and participatory activities that may support recovery, social connection, and personal meaning. Evidence remains limited on the relative contribution of different island experience dimensions to tourist satisfaction and well-being, especially in inhabited island destinations. This study examined four experience dimensions in West Island, Sanya, China, and used the findings to develop a destination improvement model. Tourist satisfaction was conceptualised as a visit-specific evaluation, while tourist well-being referred to the broader psychological benefits associated with travel. An explanatory sequential mixed-methods design was adopted. The quantitative phase used 411 valid questionnaires from Chinese domestic tourists. A full six-construct confirmatory factor analysis assessed the measurement model, after which multiple and simple linear regressions tested the prespecified direct relationships. The six-factor model showed good fit (χ²(390) = 389.98, p = 0.491, CFI = 1.000, TLI = 1.000, RMSEA = 0.000, SRMR = 0.035). The four experience dimensions explained 58.0% of the variance in tourist satisfaction. Co-creation experience made the largest unique contribution (β = 0.345), followed by emotional experience (β = 0.329) and environmental experience (β = 0.312; all p < 0.001). Educational experience was not significant (β = 0.029, p = 0.422). Tourist satisfaction had a significant positive influence on tourist well-being (β = 0.764, p < 0.001; R² = 0.583). Semi-structured interviews with 12 experienced tourists explained the coefficient pattern through ten themes, and seven academic and industry experts refined five strategic components through two Delphi rounds. The resulting model prioritises cultural co-creation, emotional resonance, environmental quality, and service support, while treating transformative learning as an area for targeted development and evaluation. The integration of survey results, tourist accounts, and expert assessment provides an evidence base for selecting and monitoring destination improvements in inhabited island settings. Full article

Generative AI (GenAI) adoption in higher education (HE) raises significant ethical concerns. The focus is shifting from rules- or outcomes-based learning environments towards the development of moral character, personality traits, integrity, and practical wisdom (phronesis). However, most existing AI ethics validation instruments are predominantly shaped by Global North paradigms. In Global South HE contexts, in particular, open distance e-learning (ODEL) HE institutions (HEIs) characterised by limited direct supervision and a digital divide, validation remains scant. Ethical risks are intensified by the adoption and integration of GenAI tools, such as large language models (LLMs), to enhance teaching, learning, research, and student support, thus recognising the need to develop and validate virtue ethics scales. The current paper attempts to address this gap by validating the Virtue Ethics Measurement Scale (VEMS) within South Africa’s largest comprehensive ODEL institution. Guided by the positivist paradigm, a 36-item cross-sectional survey of 503 undergraduate and postgraduate students measured six virtue dimensions (justice, honesty, responsibility, care, prudence, and fortitude). Confirmatory factor analysis (CFA) compared four competing models. The single-factor model showed poor fit, rejecting unidimensionality. A second-order hierarchical model demonstrated an acceptable fit (χ²/df = 2.992, CFI = 0.933, RMSEA (Root Mean Square Error of Approximation) = 0.063, SRMR (Standardized Root Mean Squared Residual) = 0.043) with subscale reliabilities ranging from Cronbach’s α = 0.84 to 0.90, supporting a multidimensional yet hierarchical virtue structure. The VEMS offers a psychometrically sound instrument for evaluating ethical AI use in ODEL institutions. This aligns with virtue ethics theory, which emphasises that moral character is a constellation of dispositions (e.g., honesty, care, prudence) rather than a single trait. The VEMS thus enables HEIs to assess students’ virtues, design targeted ethics capacity-development programmes, and inform policy reform for responsible GenAI adoption in under-researched Global South HE settings. Full article

This study proposes an entropy-regularized likelihood inference framework for lifetime distributions under progressive Type-II censoring. By incorporating Shannon entropy directly into the classical likelihood function, the proposed approach aims to alleviate the information loss caused by censoring and improve the finite-sample stability of parameter estimation. Entropy-regularized maximum likelihood estimators (ERMLEs) are developed for the Exponential, Weibull, Gamma, and Lognormal lifetime distributions. Distribution-specific regularization parameters are selected by minimizing the average mean squared error across a comprehensive Monte Carlo simulation study covering different sample sizes, censoring rates, and progressive censoring schemes. Estimation performance is evaluated using bias, mean squared error, and the relative reduction in MSE achieved by ERMLE. The proposed methodology is further illustrated using two progressively Type-II censored real datasets from engineering reliability and biomedical survival analysis. Model adequacy is assessed through goodness-of-fit statistics with corresponding p-values, bootstrap confidence intervals, and graphical comparisons. The simulation results show that entropy regularization substantially improves estimation accuracy for the Exponential, Weibull, and Gamma distributions, particularly under moderate and heavy censoring, whereas only negligible improvements are observed for the Lognormal distribution. In the engineering reliability application, the Weibull distribution provides the best overall fit, while the Weibull and Gamma models exhibit the most satisfactory performance for the bladder cancer remission data. Across both applications, ERMLE yields parameter estimates and fitted models that are highly consistent with those of the classical MLE while providing stable estimation under progressive censoring. Overall, the proposed framework demonstrates that the effectiveness of entropy regularization is distribution-dependent rather than universal and provides practical guidance for selecting suitable estimation strategies in reliability and survival analysis. Full article

Post-disaster temporary housing (PDTH) plays a central role in bridging emergency response and long-term recovery, yet its implementation is affected by a wide range of institutional, economic, site-related, technical, and environmental risks. Existing studies commonly examine only one or a limited number of these risk categories, making it difficult to compare their relative importance within a common analytical framework. This study addresses this limitation by developing and empirically evaluating an integrated risk framework for PDTH. A systematic literature review based on the PRISMA protocol was conducted to identify relevant risks, followed by a questionnaire survey of construction professionals. Principal component and confirmatory factor analyses were employed to identify the underlying risk dimensions and assess the fit and measurement properties of the resulting structure. Meanwhile, fuzzy synthetic evaluation was utilized to determine the relative importance of these dimensions. Following significance-index screening and cross-loading assessment, 29 risks were retained and grouped into four dimensions: institutional and governance risks; economic and lifecycle risks; site planning and infrastructure risks; and design and environmental performance risks. The normalized coefficients were closely clustered, ranging from 0.247 to 0.255. Institutional and governance risks had the largest numerical coefficient (0.255), followed by economic and lifecycle risks (0.250), site planning and infrastructure risks (0.248), and design and environmental performance risks (0.247). The narrow spread indicates that respondents assigned broadly comparable importance to all four dimensions, rather than identifying a single dominant risk area. The findings, therefore, point to the need for a balanced approach to PDTH risk management across governance, economic, site-related, and design-related concerns. The study contributes an empirically supported classification and prioritization framework that brings previously fragmented risk categories into a single assessment structure. The analysis does not establish causal relationships or dynamic interactions among the identified risks; rather, it provides a basis for their systematic comparison and for more detailed investigation of risk interdependencies in future research. Full article

Understanding chloride transport in fly ash cement pastes is essential for improving durability, yet the relationships between pore structure parameters and the apparent chloride diffusion coefficient remain insufficiently understood. This study investigates these relationships and assesses the relative associations of selected pore structure parameters with the apparent chloride diffusion coefficient within the present experimental dataset. Cement pastes with 0–70% fly ash were prepared at a water-to-binder ratio of 0.53 and cured for 90 days. The hydration phase assemblage was assessed by X-ray diffraction and thermogravimetric analysis, with particular attention to portlandite (CH), ettringite (AFt), and layered calcium aluminate hydrate (AFm) phases. Pore structure was characterized by nitrogen adsorption and mercury intrusion porosimetry. The apparent chloride diffusion coefficient was evaluated by fitting water-soluble chloride profiles obtained from bulk diffusion tests conducted for 30, 60, and 90 days. Fly ash significantly altered the CH content, AFm phase assemblage and pore structure. At fly ash replacement levels of 50–70%, capillary porosity increased from 20.77% at 50% fly ash to 30.31% at 70% fly ash, while the critical pore diameter increased from 47 nm to 75 nm, indicating substantial pore coarsening. At 90 days, the apparent chloride diffusion coefficient initially decreased from 6.3 × 10⁻¹² m²/s for pure cement to 5.7 × 10⁻¹² m²/s at 30% fly ash replacement and then increased to 10.1 × 10⁻¹² m²/s at 70% fly ash replacement. Among the investigated pore structure parameters, critical pore diameter showed the strongest association with the apparent chloride diffusion coefficient after 90 days of immersion (R² = 0.791). Full article

Background: Clinical competence is a fundamental outcome of prosthetics and orthotics (P&O) education and is expected to facilitate graduates’ transition into professional practice. However, limited evidence exists regarding the relationship between clinical competence and job market readiness among P&O graduates, particularly in low- and middle-income countries. Objective: To examine the relationship between perceived clinical competence and job market readiness among prosthetics and orthotics graduates in Jordan and to investigate whether openness moderates this relationship. Methods: A cross-sectional survey was conducted among graduates of the Bachelor of Science in Prosthetics and Orthotics program at the University of Jordan who had graduated within the previous five years. Data were collected between 1 July and 31 August 2025 using a structured online questionnaire comprising measures of clinical competence, job market readiness, and openness. Confirmatory factor analysis (CFA) was performed to assess the reliability and validity of the measurement model, followed by structural equation modeling (SEM) to examine the proposed relationships. The moderating effect of openness was further evaluated using Hayes’ PROCESS macro. Results: A total of 231 questionnaires were included in the final analysis. The measurement model demonstrated satisfactory reliability, convergent validity, discriminant validity, and overall model fit. Clinical competence was a significant positive predictor of job market readiness (β = 0.427, p = 0.013), explaining 37% of the variance in job market readiness. Openness did not have a significant direct effect on job market readiness (p = 0.072); however, it significantly moderated the relationship between clinical competence and job market readiness (B = 0.295, p = 0.021), indicating that the positive association between clinical competence and job market readiness was stronger among graduates with higher levels of openness. Conclusions: Clinical competence plays a central role in enhancing graduates’ readiness for professional employment in prosthetics and orthotics, while openness strengthens this relationship. These findings suggest that prosthetics and orthotics educational programs should integrate strategies that foster both clinical competence and adaptive personal attributes, and they may inform curriculum development and educational policies aimed at improving graduate employability and workforce readiness. Full article

Stresses of climate change, urbanization, and segregation make the City of Norfolk in Virginia’s Tidewater region a fitting context for qualitative research into coastal resilience, urban nature, and residents’ perceptions of the environment. Dissertation research into sea level rise and urban development provided a rich environment for developing a practical multi-method system for community-engaged investigations of flooding effects, gentrification, and the loss of urban nature. Multi-method research illuminates social, racial, and economic dynamics in Norfolk residents through participant observation, archival research, semi-structured interviews, and walking interviews. The data generated by archival content analysis and semi-structured interviews can offer insights into citizens’ roles and experiences regarding climate change, urban development and redevelopment, and distressed nature in the city. Innovative use of participant observation and the walking interview can engage with questions of respondents’ personal beliefs about nature and the city, highlighting new possible applications of these methods. This research adds to the literature on the effects of climate change and displacement in Norfolk’s planning policies. It supersedes discussions of the mechanics of climate change to assess a range of residents’ perceptions on their own terms. The article provides a clear framework for engaging with residents around issues on climate, urbanization, and environmental stress. It therefore offers a methodological contribution to the field of qualitative research in coastal climate change science and the human experience of the dynamic, modern city. The article’s findings and methods may not be applicable in rural areas or in areas which are not facing acute climate change. Full article

Modeling large shape changes efficiently is an important challenge in deformable kinematics, soft robotics, compliant mechanisms, geometric modeling, and deformation-based path planning. This paper presents Projective Estimation with Per-Point Scales (PEPS), a family of linear estimation methodologies for obtaining a compact global approximation of the deformation between two configurations of a body from corresponding points. The deformation is represented by a single projective deformation matrix, 3 by 3 in two dimensions and 4 by 4 in three dimensions, which provides a unified representation of translation, rotation, scaling, shearing, and projective effects. Three related formulations are developed and compared. PEPS-1 explicitly introduces an independent homogeneous scale for each point correspondence. PEPS-2 eliminates these additional variables by enforcing projective collinearity and has an algebraic structure closely related to the classical Direct Linear Transform. PEPS-3 augments the collinearity formulation with equivalent constraints derived in a translated coordinate frame to investigate improvements in numerical conditioning. Isotropic coordinate normalization is applied to all three formulations to reduce sensitivity to coordinate magnitude, reference-frame placement, and point distribution. The resulting systems are estimated efficiently using singular value decomposition. The methodologies are evaluated on representative nonlinear shape transformations, including square-to-circle, cube-to-sphere, and cube-to-ellipsoid mappings. Reconstruction accuracy is assessed both at the correspondences used for estimation and at additional points, allowing fitting performance to be distinguished from generalization over the complete shape. The results show that a single projective deformation matrix can effectively capture the dominant global characteristics of nonlinear shape changes. PEPS-2 and PEPS-3 generally provide smaller linear systems, improved numerical conditioning, and lower computational cost than PEPS-1, while PEPS-1 can offer greater fitting flexibility for certain redundant or geometrically dependent correspondence sets. Comparisons with iterative nonlinear optimization demonstrate that the proposed linear formulations achieve comparable global approximations at substantially lower computational cost. These characteristics make the PEPS framework particularly suitable for fast deformation representation, deformation-aware kinematics, soft and continuum robotics, compliant mechanisms, and geometry-based path planning and control. Full article

Background: Premarital medical certification is required before civil marriage in Bulgaria, but little is known about how newly married couples perceive this process within the broader framework of preconception care and genetic counselling. This study examined couples’ perceptions of premarital preventive examinations, reported receipt of genetic counselling, preferred counselling providers, and associated sociodemographic factors. Methods: A cross-sectional online survey was conducted between 2023 and 2025 among a convenience sample of 623 newly married heterosexual couples residing in the South-Central Region of Bulgaria. Each couple jointly completed a study-specific questionnaire assessing perceptions of premarital medical examinations, sources of preconception information, preferred counselling providers, and reported receipt of genetic counselling. Two multivariable binary logistic regression models were fitted to examine factors independently associated with reported receipt of genetic counselling and affirmative support for the necessity of premarital medical examination. Results: Overall, 77.8% of couples considered the premarital medical examination necessary, whereas 57.1% perceived it as primarily formal and 38.5% reported having received genetic counselling. Family (90.6%) and school (63.8%) were identified as responsible sources of preconception information more often than general practitioners (19.6%). In the adjusted model, each additional year of female age was associated with lower odds of reported genetic counselling receipt (aOR = 0.929, 95% CI: 0.900–0.959, p < 0.001). Bulgarian ethnicity was also associated with lower odds of reported counselling receipt compared with non-Bulgarian ethnicity (aOR = 0.499, 95% CI: 0.291–0.856, p = 0.012). Female age (aOR = 1.043, 95% CI: 1.006–1.081, p = 0.021) and employment (aOR = 1.847, 95% CI: 1.109–3.076, p = 0.018) were associated with affirmative support for the necessity of premarital medical examination. Conclusions: The findings describe perceptions and reported experiences within a regional online convenience sample and should not be interpreted as causal evidence or as a direct assessment of healthcare-system performance. They indicate areas warranting further evaluation, including the organisation of genetic counselling, the role of primary care, and equitable access to comprehensive preconception services. Full article