Search Results (7,085)

Abandoned wells pose a significant leakage risk for geological carbon (CO₂) storage because defects at the casing–cement interface can create preferential pathways for upward CO₂ migration. Although thermo-hydro-mechanical (THM) studies have extensively investigated wellbore integrity, leakage pathways are commonly represented using equivalent cement permeability or prescribed leakage fluxes, limiting the ability to distinguish hydraulic leakage behavior from mechanical response. This study develops a coupled THM model that explicitly represents a pre-existing casing–cement micro-annulus as an independent hydraulic domain within a two-dimensional axisymmetric wellbore system. Sensitivity analyses were performed to evaluate the effects of injection pressure, micro-annulus aperture, and cement stiffness on leakage behavior and stress redistribution. Increasing the aperture from 0.5 to 1.0 mm increased effective permeability from 2.08 × 10⁻⁸ to 8.33 × 10⁻⁸ m² and increased the average micro-annulus velocity from 2.85 to 11.42 m/s. In contrast, increasing injection pressure from 15 to 25 MPa increased the maximum von Mises stress from 33.52 to 47.58 MPa, with negligible influence on leakage velocity. Increasing the cement’s Young’s modulus from 10 to 30 GPa reduced the maximum von Mises stress from 39.73 to 28.14 MPa without affecting hydraulic conductance. The combined pressure–aperture analysis further showed that the highest leakage capacity did not correspond to the highest stress concentration. These results demonstrate that micro-annulus geometry primarily controls leakage conductance, whereas injection pressure and cement stiffness primarily control the mechanical response. Within the fixed-aperture thermo-hydro-mechanical (THM) framework adopted in this study, this mechanistic separation provides a physically transparent basis for evaluating CO₂ leakage through pre-existing casing–cement micro-annuli in abandoned wells. The proposed framework also provides a foundation for future model developments incorporating stress-dependent aperture evolution, multiphase flow, and coupled geochemical processes. Full article

Modulated by diverse climatic and oceanic forcing factors, the sea ice durations in the nearshore waters of the Bohai and northern Yellow Seas exhibit complex fluctuations on interannual and interdecadal timescales. Systematically analyzing the oscillatory patterns and trend stationarity of long-term sea ice duration series provides robust theoretical and practical guidance for coastal ice disaster early warning, marine industrial safety, and coastal economic sustainability. This study analyzes annual sea ice duration series recorded over 59 winters (1966–2024) at six representative coastal marine stations spanning the full spatial gradient of regional ice regimes across the Bohai and northern Yellow Seas. Isolated missing values were filled by linear interpolation, and the Ljung–Box Q test confirmed that the residual series do not follow a white-noise process. Two categories of stationarity tests—conventional unit root tests and Fourier series expansion-augmented stationarity tests—are adopted to quantify the stationary properties, nonlinear trend transitions, and smooth structural breaks of sea ice durations at each station. The results reveal marked spatial differences in interannual volatility and evolutionary trends among stations: two smooth transitional shifts are identified for Bayuquan (BYQ) and Qinhuangdao (QHD), whereas the remaining stations each exhibit a single shift. Long-term trends range from multistage declining patterns to a rising pattern for Donggang (DGG), while Zhimaowan (ZMW) shows a statistically insignificant trend. This work provides quantitative statistical evidence for sea ice risk evaluation and coastal structural safety protection across the Bohai and northern Yellow Seas, and offers actionable decision-making references for climate change adaptation and integrated coastal zone governance in ice-affected coastal zones worldwide. Full article

AI-driven credit scoring is supervised as a high-risk application in banking and insurance, yet unfairness is rarely operationalized as a measurable category of model, conduct, legal, and reputational risk. Using 20,000 anonymized applications from a Southern European digital lender (15.2% twelve-month default rate), we estimate three model families—a regularized logistic regression, a gradient-boosting machine, and a multi-layer perceptron—under a fully crossed design in which each family is evaluated without mitigation and under pre-processing (reweighing), in-processing (an exponentiated-gradient reduction, applicable to any base learner, together with adversarial debiasing where gradient-based training permits it), and post-processing (reject-option) interventions, so that the mitigation effect is no longer confounded with the choice of estimator. No sensitive-group field enters any estimated specification; group membership is used exclusively for auditing. Predictive performance (AUC-ROC, Brier score and Brier skill score relative to the base-rate forecast, F1 on the default class, Gini, and the Kolmogorov–Smirnov statistic) is reported jointly with group fairness (demographic-parity and equal-opportunity differences, disparate-impact ratio, Theil index) and with group-conditional calibration, at an explicitly stated and economically justified decision threshold. Every fairness quantity is accompanied by stratified-bootstrap confidence intervals and, for stochastic learners, by seed-level dispersion. The interpretable benchmark attains an AUC of 0.780 and a Brier score of 0.104 against 0.129 for the constant base-rate forecast, and the high-capacity models improve on it by under one AUC point. Disparity is present but is located geographically rather than in the composite group label: the disparate-impact ratio is 0.724 [0.693, 0.754] for the lowest socio-economic neighborhood cluster, excluding the four-fifths screening value, against 0.809 [0.776, 0.840] for the ethno-socioeconomic proxy, whose interval contains it, and no measurable gender disparity. Group membership is recoverable from the neutral feature set at an AUC of 0.654, and 42% of the group gap in predicted risk travels through the bureau credit score alone, so feature deletion cannot close the channel. Feature attributions and an auxiliary group-recoverability test locate the proxy pathways through which disparity arises, and a misclassification-sensitivity analysis bounds the effect of error in the group proxy, which attenuates measured disparity toward parity. We map the results onto Regulation (EU) 2024/1689 as amended by Regulation (EU) 2026/1744, the GDPR as interpreted in SCHUFA Holding, Directive (EU) 2023/2225, EBA loan-origination guidance, and Solvency II, EIOPA, and IAIS expectations, and propose fairness-risk controls organized around impact assessment, independent validation, and three lines of defense governance. Because the evidence comes from credit origination at a single lender, the insurance argument is developed at the level of regulatory and governance architecture rather than as an empirical transfer of estimates. Full article

Background/Objectives: The application of organic soil amendments constitutes an important source of antibiotic residues in agricultural soils. However, studies have mainly focused on a few parent tetracyclines, whereas information on their transformation products (TPs) in organic amendments remains scarce. Therefore, this study assessed the occurrence and environmental risk of six tetracyclines and seven TPs in organic amendments. Methods: Processed livestock manures applied in the European Union (horse, poultry, and bovine manure), as well as fresh and treated sewage sludge, were analyzed using matrix solid-phase dispersion (MSPD) combined with online SPE-LC-MS/MS. Environmental risk was assessed using risk quotients (RQs) based on predicted environmental concentrations in soil (PEC_soil) calculated from the maximum measured concentrations and predicted no-effect concentrations in soil (PNEC_soil) obtained either directly from terrestrial ecotoxicity data or derived from aquatic ecotoxicity data using the equilibrium partitioning method and soil-water distribution coefficients. Results: Tetracyclines and their TPs were widely detected in both sample types, although concentrations varied according to matrix type and treatment. Overall, sludge showed higher detection frequencies and concentrations than manure. Doxycycline and tetracycline were the predominant parent compounds, reaching concentrations up to 2838 ng g⁻¹ dry weight (dw) in manure and 2892 ng g⁻¹ dw in sludge. Epimerized TPs were frequently detected and sometimes exceeded the concentrations of their parent compounds, especially epitetracycline and epioxytetracycline. Among the sludge types and treatment conditions investigated, anaerobically digested sludge showed the highest tetracycline concentrations, whereas the composted sludge sample presented the lowest concentrations. Individual RQs indicated insignificant to low ecotoxicological risk, whereas cumulative RQ (ΣRQ) values, used as conservative estimates of co-exposure to all evaluated tetracyclines and their TPs, fell within the medium-risk category for bovine manure and anaerobically digested sludge, with the composted sludge sample showing the lowest ΣRQ. Conclusions: These findings highlight the importance of including TPs in environmental monitoring and the differences in tetracycline occurrence and environmental risk to soil among the processed manure types and sludge treatment conditions investigated. Full article

The subtropical hilly regions of southern China are characterized by dense vegetation and highly concealed landslides, making it difficult for traditional, single-source remote sensing methods to meet disaster prevention needs. The core scientific contribution of this study is the development of a hierarchical, progressive hazard identification framework that bridges the gap between InSAR deformation detection and landslide risk identification. This study focuses on Mayang County, Hunan Province, China, and combines time-series InSAR data from C-band Sentinel-1 and L-band ALOS-2 with a random forest (RF) algorithm to construct an early-stage identification model for landslide risks. All SAR data were processed under controlled baseline conditions (perpendicular baseline <150 m; polarization: VV for Sentinel-1, HH for ALOS-2). By screening highly reliable deformation points through dual-source cross-validation and integrating nine evaluation factors including slope, we established a two-layer coupled identification model combining InSAR deformation and susceptibility indices at the slope unit scale. The results showed that the dual-source InSAR approach achieved an identification accuracy of 71% (precision 68%, recall 65%, F1-score 0.66, Cohen’s κ 0.62), significantly outperforming single-source methods (62% for Sentinel-1 alone and 58% for ALOS-2 alone); the AUC was 0.815 under spatial block cross-validation, with an out-of-bag error of 16.8%. The dual-source InSAR approach identified a total of 59 potential hazard sites, 83.1% of which were located in medium- to high-risk zones. Following field surveys and LiDAR verification, 35 of these were confirmed as active landslide sites, demonstrating identification accuracy significantly superior to that of a single data source. The multi-source coupling framework proposed in this study effectively overcomes the decoherence issues associated with single-source SAR data in subtropical vegetated areas, providing reliable technical support for the early identification of landslides in humid hilly regions of southern China. Full article

This research examines the structural evolution of environmental assessment in Spain over a 35-year period (1991–2025), serving as a longitudinal case study for the transition of mid-sized developed economies from industrial-age infrastructure to a decarbonized energy model. The study introduces a novel approach by utilizing Environmental Assessment records to evaluate national strategic directions and quantify developmental intent via administrative proxies. The analysis utilizes a comparative quantitative study of Strategic Environmental Assessments and Environmental Impact Assessments of national and supranational projects. Administrative trends were contextualized within major socio-economic and geopolitical events, including global financial crises, public health emergencies, and energy security developments, to identify potential external influences. The data reveals a paradigm shift. While the period 1991–2010 was dominated by grey infrastructure (transport and civil engineering), the post-2018 era shows an unprecedented increase in renewable energy dossiers. Between 2021 and 2025, energy projects dominated the regulatory workload, accounting for 73.86% of total processed dossiers, and reaching a single-year peak of 84.56% in 2022. National and international legal, economic, and social events, such as European Green Deal mandates and the NextGenerationEU recovery funds, have an apparent alignment with a country’s development strategy. We highlight the challenges and risks of such an exponential change in the energy model. For mid-sized developed economies, these findings suggest that Environmental Assessment data is an essential tool for navigating the complexities of rapid decarbonization and identifying systemic gaps in strategic planning. Full article

This study develops an interpretable and validated XGBoost–SHAP framework integrating multisource geospatial data and historical flood observations, with model performance evaluated using independent validation and future projections driven by bias-corrected CMIP6 climate scenarios to characterize the spatiotemporal variations and contributions of flood driving factors across three regions of Kazakhstan (2000–2025). The results demonstrate pronounced spatial differences in flood-driving factors: delayed snowmelt coupled with orographic rainfall dominates flood variability in the mountainous Almaty Region; hydrological memory effects regulate flood responses in the Akmola plains; and socioeconomic exposure shows an increasing contribution to flood risk evolution in Turkestan. Future multi-scenario simulations indicate that the flood-affected area in the Almaty Region is projected to increase by 10.8% under SSP2-4.5, which is associated with enhanced snowmelt processes, whereas the Akmola Region may experience a 23.2% reduction under SSP5-8.5, which is associated with changes in evaporation–soil moisture interactions. The interaction between socioeconomic development and natural hazards results in divergent risk trajectories: urban expansion in Akmola and Turkestan may offset declining hydroclimatic hazards, creating a potential risk paradox, whereas mountainous regions remain sensitive to concurrent increases in hazard intensity and exposure. These findings indicate that flood risk evolution in the studied regions of arid Central Asia is being increasingly influenced by socioeconomic dynamics in addition to natural hazards, highlighting the importance of differentiated adaptive planning strategies. Full article

Wildfire smoke (WFS) is an expanding source of fine particulate matter (PM_2.5) with well-documented cardiopulmonary risks, yet its impact on organismal aging and functional decline remains incompletely understood. Here we use the nematode Caenorhabditis elegans to characterize the dose-dependent impacts of simulated WFS PM_2.5 exposure (0.095–1000 µg/mL) on survival and age-associated functional outcomes. Locomotion and morphology were assessed at defined time points in adulthood, after which unexposed progeny of WFS-exposed nematodes were evaluated for identical tests to detect gross intergenerational effects. Parental (P0) worms subjected to a single 24 h WFS exposure exhibited dose-dependent reductions in lifespan at high concentrations and significant impairments in neuromuscular function at lower concentrations. A single P0 WFS exposure was also sufficient to induce deficits in multiple measures of locomotion and morphology, but these readouts were almost entirely resolved in F1 progeny. Additionally, RNA-sequencing on P0 nematodes was used to provide insight into biological processes underlying our in vivo observations, revealing WFS-associated aging-like transcriptomic signatures. Taken together, these results suggest that acute WFS exposure is sufficient to induce age-associated functional decline and establish C. elegans as a scalable, low-cost in vivo platform for quantifying WFS-driven toxicity. Full article

Background/Objectives: Pediatric uroflowmetry is volume dependent, and low-volume voids can yield recordings that require repetition or cannot be interpreted confidently. To evaluate whether documented bladder volume-guided timing was associated with first-attempt pediatric uroflowmetry process adequacy in children undergoing evaluation for suspected non-neurogenic lower urinary tract dysfunction. Methods: This single-center retrospective workflow cohort included 110 toilet-trained children aged 5–12 years who underwent uroflowmetry for suspected non-neurogenic lower urinary tract dysfunction. The exposure was classified from contemporaneous pre-test documentation as bladder volume-guided timing (n = 55) or standard urge-based timing (n = 55). Expected bladder capacity (EBC) was calculated as (age + 1) × 30 mL. The primary process outcome was first-attempt voided volume ≥ 50% EBC. Repetition after an inadequate first attempt was treated as a deterministic workflow consequence rather than an independent endpoint. Analyses were observational and effect estimates were interpreted as associations. Results: Adequate first-attempt voided volume was documented in 50/55 children (90.9%) with bladder volume-guided timing and 39/55 (70.9%) with standard urge-based timing (unadjusted risk ratio 1.28, 95% confidence interval [CI] 1.06–1.55; Newcombe risk difference 20.0 percentage points, 95% CI 5.3–34.0). After adjustment for age, baseline urgency score, and time since last void, the association remained (adjusted risk ratio 1.27, 95% CI 1.06–1.53; p = 0.011), and the model converged without numerical warnings. Using the age-specific lowest acceptable voided volume, adequacy occurred in 53/55 children (96.4%) versus 46/55 (83.6%) (adjusted risk ratio 1.15, 95% CI 1.01–1.31; p = 0.040). Repetition after an inadequate first attempt occurred in 9.1% versus 29.1% and was treated as a direct consequence of primary-threshold failure; it was not tested independently. Workflow-time measures were exploratory. Conclusions: Documented bladder volume-guided timing was associated with greater first-attempt process adequacy. Adjustment for age, baseline urgency score, and time since last void did not materially change the estimate. The association was attenuated but remained directionally consistent when the age-specific lowest acceptable voided volume was used. Because the timing rule deliberately targeted the same volume construct as the primary outcome, this finding does not establish improved diagnostic accuracy, clinical decision making, or patient outcomes. Retrospective exposure classification and routine documentation further preclude causal interpretation. Documented bladder volume-guided timing was associated with higher first-attempt achievement of a prespecified voided-volume threshold than standard urge-based timing. Because the pathway targeted the same volume construct used to define the primary outcome and was non-randomized, these findings are interpreted as hypothesis-generating workflow data rather than evidence of improved diagnostic accuracy or downstream clinical benefit. Full article

Industrial monitoring pipelines that log programmable logic controller (PLC) states to a database for machine learning inherit the noise introduced by the sampling process itself, yet noisy-label learning research is almost always evaluated against synthetically corrupted labels rather than real sampling noise. This study evaluates a physically derived noise-rate predictor, P_edge, by training decision tree and logistic regression classifiers on real, non-synthetic noisy labels from a Festo Modular Production System–Process Automation (MPS PA) industrial automation testbed—a four-station laboratory production line used for Industry 4.0 research and teaching—and assessing them against independently logged true PLC states across four stations. A controlled temporal-subsampling experiment, which varies the effective observation interval while holding the classification task fixed, shows that F1 score (the harmonic mean of precision and recall, used here in place of raw accuracy because of class imbalance in the target signals) against ground truth declines substantially for one process (0.951 to a sweep minimum of 0.679, 0.723 at the highest sampling condition tested), replicated across two target signals and both classifiers, while two other processes remain robust (F1 at or above 0.94), consistent with strong correlated features (correlations at or above 0.98), and one target signal shows no learnable baseline (F1 at or below 0.652) regardless of noise. These results indicate that a physically derived sampling-risk index can help anticipate machine learning sensitivity in industrial automation, but only when the target task is both learnable and lacks a redundant shortcut feature. Full article

Background/Objectives: Eating disorders are increasingly conceptualized as information-processing disorders characterized by attentional biases toward food-related stimuli. However, whether such biases are already detectable in subclinical, non-treatment-seeking populations remains unclear. This study examined whether subclinical eating pathology, assessed with the Eating Attitudes Test-26 (EAT-26), is associated with distinct patterns of visual attention toward high- and low-calorie food images in university students. Methods: In this cross-sectional observational study featuring an experimental eye-tracking task, 89 university students completed the EAT-26 and a visual task displaying paired high- and low-calorie food images alongside neutral controls. Oculomotor metrics included first fixation duration (FFD), fixation count (FC), and average fixation duration (AFD). Data were evaluated using EAT-26 threshold-based group stratification (lower-risk vs. risky eating behavior) and Spearman rank correlation analyses. Results: Global EAT-26 scores showed a significant correlation with a directional attentional bias index (rho = 0.29, p = 0.006). The Dieting subscale demonstrated no significant relationships with any oculomotor metrics. Conversely, Oral Control scores were significantly negatively correlated with the directional attentional bias index (rho = −0.27, p = 0.009) and positively tracked with low-calorie fixation counts (rho = 0.30, p = 0.004). Bulimia subscale scores showed a moderate positive correlation with the directional attentional bias index (rho = 0.26, p = 0.013), characterized by a significant decrease in low-calorie fixation counts (rho = −0.32, p = 0.002) and a strong increase in high-calorie fixation counts (rho = 0.36, p < 0.001) during sustained viewing. Conclusions: Subclinical eating pathology tendencies are associated with distinct implicit attentional profiles regarding food caloric density during sustained cognitive evaluation. Oral control drives visual prioritization of low-calorie stimuli to maintain inhibitory control, whereas bulimic tendencies reflect prolonged visual preoccupation with high-calorie reward cues. Eye-tracking represents a promising non-invasive research tool for exploring early cognitive correlates of eating-disorder risk. Full article

Agricultural non-point source pollution (ANPSP) presents a critical threat to water security in the upper reaches of the Yangtze River Economic Belt (UYREB). Traditional export coefficient models (ECMs) often fail to capture the spatially heterogeneous processes of pollutant transport and ecological attenuation in complex mountainous terrains. Here, we constructed an improved ANPSP risk assessment framework by integrating ecosystem regulating functions (water yield, soil retention, and habitat quality) into the ECM. By coupling the Patch-generating Land Use Simulation (PLUS) model and Geographically and Temporally Weighted Regression (GTWR), we evaluated the spatiotemporal risk evolution from 2000 to 2020, projected risk configurations for 2030 under three scenarios (Natural Development, ND; Cropland Protection, CP; Ecological Protection, EP), and identified the spatiotemporal non-stationary drivers. The key findings are: (1) Independent validation using observed river water-quality data further demonstrated that the modeled risk index was significantly positively correlated with contemporaneous TN and TP concentrations (Pearson’s r = 0.673, p < 0.05), supporting its ability to identify the spatial distribution of relative ANPSP risk while accounting for regional differences in ecosystem regulation. (2) Historically, the integrated ANPSP risk index exhibited a “rise-then-decline” pattern, peaking in 2010 with extreme risk areas covering 12.1% of the region. Under the 2030 EP scenario, high-risk areas contract by 15.3% compared to 2020, demonstrating superior mitigation compared to ND and CP scenarios. (3) GTWR reveals that cropland proportion is the dominant positive spatial associate (0.800 − 0.852), while GDP shows a negative spatial association in economically developed sub-regions, reflecting potential environmental management co-benefits rather than direct causation. This framework provides an effective relative risk assessment tool for mountainous watersheds, highlighting that land use optimization under ecological regulation may contribute to reducing potential pollutant transport risk. Full article

Filtering-based stereo visual-inertial odometry often assigns fixed or uniformly scaled covariance to tracks accepted by the front-end, although accepted observations can differ in terms of tracking reliability, local directional identifiability, and agreement with the batch-level innovation model. This issue is important for UAV-based multisensor inspection platforms, where pose estimates support autonomous flight, measurement registration, repeatable survey lines, and multisensor data fusion. This paper presents RDIC-MSCKF, a Risk–Direction-Decoupled and Innovation-Calibrated MSCKF, where innovation calibration denotes bounded empirical scaling within the visual update. RDIC-MSCKF maps robust tracking diagnostics to a bounded standard-deviation multiplier and uses a robust local photometric information matrix to add penalty-only anisotropic covariance along weak image directions. The resulting observation covariance is preserved during MSCKF landmark elimination through full projected-covariance whitening. In parallel with feature-block innovation gating, bounded minimum measurement-noise inflation is estimated from the pre-gate innovation population and applied through a Kalman-equivalent modal update. On ten evaluated EuRoC MAV sequences, RDIC-MSCKF obtains lower ATE RMSE than the S-MSCKF baseline on nine sequences; averaged over five runs per sequence, the mean RMSE decreases from 0.1869 m to 0.1236 m, corresponding to a 33.8% reduction, and the sequence-mean P90 error decreases by 32.0%. Runtime profiling on five representative EuRoC sequences gives a 26.32 ms mean and 37.33 ms P95 per-frame processing time for RDIC-MSCKF, with 0.01% of profiled frames above the 50 ms reference. Outdoor UAV flights with RTK reference trajectories further demonstrate lower Sim(2)-aligned horizontal RMSE than S-MSCKF on all three evaluated flights. Full article

The global active satellite population has increased from fewer than 3000 objects in 2020 to more than 14,000 by the end of 2025, while filed megaconstellation plans suggest that tens of thousands of additional satellites may be deployed over coming decades. This rapid expansion of low Earth orbit (LEO) infrastructure has raised concerns regarding novel anthropogenic inputs to the upper atmosphere, particularly black carbon from rocket launches and aluminium oxide nanoparticles generated during satellite re-entry. This review synthesises literature published between 2000 and 2026 across atmospheric chemistry, aerosol science, climate dynamics, and hydrology to evaluate the potential pathways through which these emissions may influence precipitation processes and flood risk. The evidence indicates strong support for several upstream mechanisms, including alumina-mediated ozone chemistry, anthropogenic metal accumulation in stratospheric aerosols, and the disproportionately high radiative forcing efficiency of rocket-derived black carbon. However, substantial uncertainties remain regarding the extent to which these atmospheric perturbations propagate through climate and hydrological systems. This review identifies the current state of knowledge, highlights areas of agreement, uncertainty, and contradiction within the literature, and outlines priority directions for future research, monitoring, modelling, and governance. The findings suggest that while satellite-driven changes to precipitation and flood risk remain unconfirmed, the rapid expansion of megaconstellation activity warrants further investigation within integrated Earth-system frameworks. Full article

Background: Delayed onset of lactogenesis II or delayed secretory activation, defined as milk onset > 72 h postpartum, is a recognized barrier to successful breastfeeding initiation and duration. Prenatal identification and screening for risk factors associated with delayed secretory activation remains underutilized in maternal–child health services. This project aimed to develop and validate content for the Prenatal Evaluation and Referral for Lactation (PEARL) tool, a prenatal screening and referral instrument designed to identify lactation risk factors and guide anticipatory counseling and early referral to an International Board Certified Lactation Consultant (IBCLC) or breastfeeding medicine physician. Methods: Tool development was informed by a structured literature review conducted on 2 October 2024, across four databases (CINAHL, PubMed, Scopus, and Embase), identifying clinical and other health factors associated with delayed lactogenesis II. A three-round Delphi process was used to establish content validity through multidisciplinary expert consensus. Item-level content validity (ICV) was evaluated using a predefined consensus threshold (≥0.80), while average-measures Intraclass Correlation Coefficients (ICC) were calculated as a complementary assessment of interrater consistency. Following tool refinement, a pilot implementation training was conducted to obtain preliminary feedback regarding clarity, usability, and implementation. Results: Twelve lactation experts were selected from 59 eligible respondents to participate in the Delphi process. Most of the proposed items achieved the predefined content validity threshold (ICV ≥ 0.80) across the first two Delphi rounds, resulting in refinement and consensus on the final PEARL instrument. Interrater reliability improved from moderate agreement in Round 1 (ICC = 0.769; 95% CI 0.425–0.953) to excellent agreement following refinement in Round 2 (ICC = 0.911; 95% CI 0.787–0.979). Qualitative feedback informed revisions to item wording, organization, and content. A pilot implementation training indicated that the tool was perceived as understandable, usable, and appropriate for prenatal practice. Conclusions: The PEARL demonstrated strong evidence of content validity and preliminary implementation feasibility as a standardized prenatal lactation screening tool. Future research should evaluate additional psychometric properties, implementation outcomes, and prospective clinical performance to determine its effectiveness in improving referral practices and breastfeeding outcomes. Full article