Search Results (12,379)

Background: Women with gestational diabetes mellitus (GDM) are vulnerable to oxidative stress, yet limited data exist on the antioxidant potential of their breast milk. This study aimed to evaluate the antioxidant capacity and basic composition of colostrum in women with GDM compared to healthy controls, focusing on total antioxidant capacity (TAC) and enzymatic antioxidants: superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx). Methods: The study included 77 lactating mothers: 56 with gestational diabetes (15 managed with diet/exercise—GDM G1; 41 required insulin—GDM G2) and 21 healthy controls. Colostrum samples were collected on days 3–5 postpartum and analyzed for macronutrients and antioxidant enzymes. To enable comparisons across study groups and to explore associations with maternal characteristics, a range of statistical methods was applied. A taxonomic (classification) analysis was then performed using the predictors that best fit the data: study group membership, maternal hypothyroidism history (from the medical interview), and gestational weight gain. Results: TAC was significantly lower in the GDM G2 group compared to GDM G1 and controls (p = 0.001), with no differences in enzymatic antioxidants. The control group had the highest energy (p = 0.048) and dry matter content (p = 0.015), while protein, fat, and carbohydrate levels did not differ significantly. After dividing the study group into four clusters, based on maternal health factors, including GDM status and thyroid function, TAC levels differed significantly between clusters, with the highest values observed in Cluster 3 (healthy controls without thyroid dysfunction) and the lowest in Cluster 2 (GDM and hypothyroidism). Analysis of colostrum composition revealed significant differences in energy content (p = 0.047) and dry matter concentration (p = 0.011), while no significant differences were found in other macronutrients. Conclusions: Our findings suggest that maternal metabolic and endocrine conditions, such as GDM and thyroid dysfunction, may differentially influence the nutritional and functional properties of colostrum—particularly its antioxidant potential. Full article

Background and objectives: Evaluating Large Language Models (LLMs) presents two interrelated challenges: the general problem of assessing model performance across diverse tasks and the specific problem of using LLMs themselves as evaluators in pedagogical and educational contexts. Existing approaches often rely on single metrics or opaque preference-based methods, which fail to capture critical dimensions such as explanation quality, robustness, and argumentative diversity—attributes essential in instructional settings. This paper introduces PEARL, a novel framework conceived, operationalized, and evaluated in the present work using LLM-based scorers, designed to provide interpretable, reproducible, and pedagogically meaningful assessments across multiple performance dimensions. Methods: PEARL integrates three specialized rubrics—Technical, Argumentative, And Explanation-focused—covering aspects such as factual accuracy, clarity, completeness, originality, dialecticality, and explanatory usefulness. The framework defines seven complementary metrics: Rubric Win Count (RWC), Global Win Rate (GWR), Rubric Mean Advantage (RMA), Consistency Spread (CS), Win Confidence Score (WCS), Explanation Quality Index (EQI), and Dialectical Presence Rate (DPR). We evaluated PEARL by evaluating eight open-weight instruction-tuned LLMs across 51 prompts, with outputs scored independently by GPT-4 and LLaMA 3:instruct. This constitutes LLM-based evaluation, and observed alignment with the GPT-4 proxy is mixed across metrics. Results: Preference-based metrics (RMA, RWC, and GWR) show evidence of group separation, reported with bootstrap confidence intervals and interpreted as exploratory due to small samples, while robustness-oriented (CS and WCS) and reasoning-diversity (DPR) metrics capture complementary aspects of performance not reflected in global win rate. RMA and RWC exhibit statistically significant, FDR-controlled correlations with the GPT-4 proxy, and correlation mapping highlights the complementary and partially orthogonal nature of PEARL’s evaluation dimensions. Originality: PEARL is the first LLM evaluation framework to combine multi-rubric scoring, explanation-aware metrics, robustness analysis, and multi-LLM-evaluator analysis into a single, extensible system. Its multidimensional design supports both high-level benchmarking and targeted diagnostic assessment, offering a rigorous, transparent, and versatile methodology for researchers, developers, and educators working with LLMs in high-stakes and instructional contexts. Full article

Background/Objectives: Korean medicine healthcare (KMHC), a form of traditional medicine including acupuncture and herbal medicine, is widely utilized by patients with intervertebral disc disease (IVDD). With the increasing use of real-world evidence (RWE) in the medical field, this study aims to derive RWE on KMHC utilization and its associated factors in patients with IVDD. Methods: Data from 495 individuals who received outpatient healthcare for IVDD regardless of the purpose such as treatment, examination, rehabilitation, monitoring, or prescription were analyzed using the 2022 Korea Health Panel Survey (KHPS). Multinomial logistic regression analyses were performed to identify factors associated with healthcare use for IVDD. Regression models were constructed by sequentially adding predisposing, enabling, and need factors following Andersen’s behavioral model. All statistical analyses accounted for the complex survey design of the KHPS using survey sampling weights. Results: Individuals aged 45–59 years were less likely to use both KMHC and conventional medicine healthcare (CMHC) for IVDD compared to those aged 19–44 years (adjusted odds ratio [95% confidence interval], 0.28 [0.09, 0.89]). People with disabilities showed lower utilization of both KMHC and CMHC for IVDD compared to those without disabilities (0.27 [0.09, 0.81]). Individuals who were employed (2.37 [1.06, 5.3]) or perceived their health status as fair (3.05 [1.17, 8]) or poor/very poor (6.13 [2.04, 18.45]) were more inclined to use both KMHC and CMHC for IVDD. Individuals who engaged in regular physical activities (2.65 [1.19, 5.9]) or had shoulder joint diseases (3.71 [1.22, 11.29]) or other spine-related diseases (2.63 [1.16, 5.96]) were more inclined to use KMHC-only for IVDD. Conclusions: This study identified significant demographic and health-related factors influencing KMHC utilization for IVDD. These findings emphasize the need for tailored healthcare policies regarding KMHC for IVDD for effective resource distribution. Full article

The mechanistic underlying the favorable meat quality of Tibetan pigs has not been fully elucidated. This study integrated flavor chemistry, histomorphology, and proteomics to explore the structural and molecular features of their meat. Longissimus dorsi samples from Tibetan and Duroc pigs (n = 6 each biological replicates) were quantitatively analyzed for amino acid profiling, histological assessment, and proteomic characteristic. Statistical approaches included weighted correlation network analysis, t-tests, and functional enrichment. Tibetan pork contained 34 mg/100g more total free amino acids, notably sweet-tasting Ala (+49.2%) and Thr (+32.2%). Muscle fiber density was >250% higher and diameter > 30% smaller, indicating finer texture. Proteomics revealed 149 upregulated proteins, including 57 mitochondrial differentially expressed proteins (DEPs)—11 of which belonged to electron transport chain complexes (e.g., NDUFAB1, COX2). The significant enrichment of oxidative phosphorylation pathways may be associated with mitochondrial efficient energy metabolism under hypoxic in Tibetan pigs, potentially linking to the breed’s unique meat characteristics. Ala levels showed strong correlations with metabolic and structural protein modules. The finer fibers and mitochondrial protein profile of Tibetan pigs contribute to higher amino acid content and meat quality. This structural–metabolic–flavor axis supports both hypoxia adaptation and high meat quality. Given the central role of mitochondrial electron transport chain (ETC) proteins in energy metabolism and Ala in flavor presentation, their synergistic action provides a molecular bridge between hypoxia adaptation and meat quality. Therefore, this study suggests that ETC and Ala may serve as key biomarkers for meat quality differences, offering new perspectives for meat quality research. Full article

Background: Adolescent depressive and anxiety symptoms may erode motivation and problem-solving needed for timely contraception, while online information quality is uneven. We synthesized evidence linking mental health, contraceptive knowledge/access, and teen pregnancy risk. Methods: Following PRISMA-2020, we searched PubMed, Embase, and Scopus to 7 July 2025 for primary studies including adolescents that measured validated mental health symptoms or psychiatric settings and reported contraceptive knowledge/access/behavior and/or teen pregnancy outcomes. Two reviewers screened/extracted data; risk of bias was appraised with the Newcastle–Ottawa Scale and ROBINS-I. Given heterogeneity, we conducted narrative synthesis. Results: Six U.S.-based studies met the criteria, spanning community colleges, a national cohort, school surveillance, psychiatric inpatient care, and pediatric emergency departments (samples: n = 143 to weighted N = 29,755). Depressive symptoms were associated with contraception access (adjusted odds ratio [aOR] 1.58, 95% CI 1.27–1.96) and anxiety/stress with similar risk (aOR 1.46, 1.17–1.82). A first depressive episode in the same year as sexual debut increased teenage pregnancy hazard (adjusted hazard ratio 2.70, 1.15–6.34). School surveillance showed mental health indicators correlated with contraception non-use at last sex (odds ratios 1.78–2.71). Among psychiatric inpatients, not knowing where to obtain contraception and access difficulties strongly predicted interest in information (aOR 2.96–3.33) and initiation (aOR 2.85–4.72). In a pediatric emergency department trial, same-day initiation occurred in 26.8% versus 3.1% under usual care. Conclusions: Evidence directly linking adolescent mental health symptoms to teen pregnancy is limited (one study), whereas multiple studies show associations with contraception knowledge/access and delayed or non-use, suggesting plausible indirect pathways to pregnancy risk. These findings support hypothesis-generating, integrated approaches and highlight the need for studies with teen pregnancy endpoints. Full article

This study documents the first A1-type granite identified on the southern margin of the central Lhasa terrane: a two-mica syenogranite pluton in the Meibaqieqin region. Because A-type granite provides sensitive records of crustal melting and lithospheric extension, this pluton offers important insights into magmatic processes and tectonic evolution along the southern margin of the Lhasa terrane. We analyzed two sample suites collected from different sites within the same pluton using zircon U–Pb geochronology and Hf isotopes, whole-rock geochemistry and Nd isotope. Zircon U–Pb weighted mean ages were 130.5 ± 0.7 Ma and 130.0 ± 0.7 Ma, placing emplacement in the Early Cretaceous. Zircon ε_Hf(t) values ranged from −11.29 to −9.00 and −11.04 to −7.27, with two-stage Hf model ages (T_DM2) of 1.76–1.90 Ga and 1.65–1.89 Ga. Whole-rock ε_Nd(t) values clustered between −11.77 and −11.36, yielding two-stage Nd model ages (T_NdDM2) of 1.85–1.88 Ga. Geochemically, the pluton is high-K calc-alkaline. These isotopic signatures indicate derivation predominantly from ancient crustal sources with a little mantle material. Chondrite-normalized REE patterns are overall right-inclined and display a V-shaped profile. Together with trace-element characteristics, these features support classification as A1-type granite. Regional comprehensive data suggest that pluton emplacement was controlled mainly by lithospheric extension related to northward subduction of the Neo-Tethyan oceanic plate, with a lesser contribution from southward subduction along the Bangongco–Nujiang suture. The source characteristics and geodynamic context differ markedly from A2-type granites on the northern margin of the central Lhasa terrane, which reflect distinct magmatic sources and tectonic regimes. Full article

Background/Objectives: Maternally derived antibody (MDA) levels of porcine circovirus 2 (PCV2) may eventually interfere with humoral response and vaccination efficacy. This study aimed to evaluate the efficacy of a ready-to-use PCV2d and Mycoplasma hyopneumoniae combined vaccine in piglets with different PCV2 MDA levels at vaccination in an experimental inoculation with a heterologous viral genotype. Methods: Forty-eight piglets were allocated into vaccinated (V) and non-vaccinated (NV) groups with high (H) and low (L) PCV2 MDA subgroups (H-V, H-NV, L-V, L-NV). At 3 weeks of age, the piglets received either one dose of vaccine or placebo. Five weeks later, all animals were intranasally challenged with a PCV2b inoculum. Body weight was registered at different time points. Blood samples, peripheral blood mononuclear cells and tracheobronchial lymph nodes (TBLN) were collected and used to assess viraemia, viral load, humoral and cellular responses and histological lesions. Results: The V group showed higher PCV2 antibody levels from challenge onwards, along with a lower percentage of viraemic pigs and reduced viral load in serum at 2 and 3 weeks post-challenge (wpc) and in TBLN tissues compared to the NV group. The H-V group had the highest antibody levels post-challenge, showed no detectable viraemia and had a lower overall amount of virus in tissues. The NV group (especially H-NV) exhibited increased levels of IFN-γ, IFN-α and TNF-α post-challenge. Conclusions: The tested vaccine elicited humoral and cellular immune responses and reduced viral presence in serum and tissues, demonstrating efficacy in a PCV2 subclinical infection model despite high MDA levels at the time of vaccination. Understanding both humoral and cellular immune responses according to different MDA levels can help design more effective vaccination strategies against PCV2. Full article

Background: Balance problems are one of the major risk factors for falls. Despite the availability of effective fall prevention interventions, falls and related injuries are rising. This study explored the factors associated with healthcare utilization for balance problems in community-dwelling adults in the United States. Methods: Study involved secondary analysis of nationally representative National Health and Nutrition Examination Survey 2001–2004 data (latest data with variables of interest at the time of study). All adults (≥40 years) who reported balance problems in the past 12 months were included. Dependent variable was whether the individual ever saw a healthcare professional for balance problems. All analyses were adjusted for probability sampling weights. Adjusted odds ratio (AOR) and 95% confidence interval (CI) were calculated using multivariable logistic regression. Results: Study included 1834 adults with self-reported balance problems (mean age 60.1 years (0.5 SE), 62.3% females). Of these, only 32.13% ever saw a healthcare professional for their balance problems. Having encounter(s) with a healthcare provider for any reason in the past year (AOR 2.45; 95% CI,1.19–5.06; p = 0.017), lack of health insurance (AOR 0.52; 95% CI,0.32–0.84; p = 0.009), falls in the past year (AOR, 1.29; 95% CI,1.03–1.61; p = 0.028) and age (AOR, 0.98; 95% CI,0.97–0.996; p = 0.011) had significant association with healthcare utilization for balance problems. The predicted probability of healthcare use for balance problems decreased from 0.39 for 40-year-olds to 0.26 for 80-year-olds. Conclusions: This study reports the association between factors such as age, health insurance, encounter with a healthcare provider, and falls in the past year with healthcare utilization for balance problems among community-dwelling adults with self-reported balance problems and identifies populations at increased risk of underutilization. Despite the use of older data, it provides useful information for guiding future research in this novel domain of healthcare research. Full article

Accurate prediction of concrete compressive strength is essential for ensuring structural safety in civil engineering, particularly in road and bridge construction, where inadequate strength can lead to deformation, cracking, or collapse. Traditional non-destructive testing (NDT) methods, such as the Rebound Hammer Test, estimate strength using regression-based formulas fitted with measurement data; however, these formulas, typically optimized via the least squares method, are highly sensitive to initial parameter settings and exhibit low robustness, especially for nonlinear relationships. Meanwhile, AI-based models, such as neural networks, require extensive datasets for training, which poses a significant challenge in real-world engineering scenarios with limited or unevenly distributed data. To address these issues, this study proposes a gradient-boosting artificial bee colony (GB-ABC) algorithm for robust regression curve fitting. The method integrates two novel mechanisms: gradient descent to accelerate convergence and prevent entrapment in local optima, and a point density-weighted strategy using Gaussian Kernel Density Estimation (GKDE) to assign higher weights to sparse data regions, enhancing adaptability to field data irregularities without necessitating large datasets. Following data preprocessing with Local Outlier Factor (LOF) to remove outliers, validation on 600 real-world samples demonstrates that GB-ABC outperforms conventional methods by minimizing mean relative error rate (RER) and achieving precise rebound-strength correlations. These advancements establish GB-ABC as a practical, data-efficient solution for on-site concrete strength estimation. Full article

Urban green space (UGS) is a fundamental element of urban systems for enhancing the quality of urban life. UGS plays a pivotal role in promoting urban ecological sustainability if important criteria are integrated into urban planning programs. This paper explores the impacts of the ecological criteria on urban sustainability through UGS planning and examines these criteria within the context of the Adelaide Metropolitan Area as a case study. To address the study’s goals, a content analysis was conducted to identify the most critical criteria affecting urban ecological sustainability through UGS planning. Subsequently, based on the identified criteria, a household survey was conducted to evaluate the status of the case study concerning the ecological sustainability factors. In this stage, 100 responses were collected through a questionnaire survey. Then, based on the household survey results, a solution was provided to the challenging criteria by a local experts’ interview. For promoting urban ecological sustainability, ten criteria were identified as the most important and effective criteria based on the previous studies. Household survey data was analysed using one-sample T-test, multiple linear regression, and geographically weighted regression (GWR) model. The results indicated that the criteria of reviving ecological networks, water resources, and the protection of UGS with the score below standard average (which is 3), require practical guidelines and policies to enhance the sustainability of Adelaide Metropolitan Area. The regression analysis demonstrated that ecological landscape and design had the strongest positive effect on sustainability (adjusted R² = 0.685), while the geographically weighted regression highlighted biodiversity and vegetation as particularly influential in Plympton (local R² = 0.866) and Unley (local R² = 0.488). Expert interviews recommended strategies such as wastewater recycling, long-term conservation planning, and restoring ecological connectivity. This study provides a practical framework to guide urban planners and policymakers in enhancing ecological sustainability through UGS planning. Full article

Background: Dynamic whole-body (D-WB) FDG PET/CT is a novel technique that enables the direct reconstruction of multiparametric images representing the FDG metabolic uptake rate (MR_FDG) and “free” FDG (DV_FDG). Applying complementary parameters with distinct characteristics compared to static SUV images, the aims of this study are as follows: (1) to determine the threshold values of SUV, MR_FDG, and DV_FDG for malignant and benign lesions; (2) to compare the specificity of MR_FDG and DV_FDG images with static SUV_bw images; and (3) to assess whether any of the dynamic imaging parameters correlate more significantly with malignancy or non-malignancy in the examined lesions based on the measured values obtained from D-WB FDG PET/CT. Methods: The study was a retrospective analysis of D-WB PET/CT data from 43 patients (23 males and 20 females) included both in the context of primary staging as well as imaging performed due to suspicion of post-therapeutic relapse or recurrence. Standard scanning was performed using a multiparametric PET acquisition protocol on a Siemens Biograph Vision 600 PET/CT scanner. Pathological findings were manually delineated, and values for SUV_bw, MR_FDG, and DV_FDG were extracted. The findings were classified and statistically evaluated based on their was histological verification of a malignant or benign lesion. Multinomial and binomial logistic regression analyses were used to find parameters for data classification in different models, employing various combinations of the input data (SUV_bw, MR_FDG, DV_FDG). ROC curves were generated by changing the threshold p-value in the regression models to compare the models and determine the optimal thresholds. Results: Patlak PET parameters (MR_FDG and DV_FDG) combined with mean SUV_bw achieved the highest diagnostic accuracy of 0.82 (95% CI 0.75–0.89) for malignancy detection (F1-score = 0.90). Sensitivity reached 0.85 (95% CI 0.77–0.91) and specificity 0.93 (95% CI 0.87–0.98). Classification accuracy in tumors was 0.86 (95% CI 0.78–0.92) and in lymph nodes 0.81 (95% CI 0.73–0.88). Relative contribution analysis showed that DV_FDG accounted for up to 65% of the classification weight. ROC analysis demonstrated AUC values above 0.8 for all models, with optimal thresholds achieving sensitivities of around 0.85 and specificities up to 0.93. Thresholds for malignancy detection were, for mean values, SUV_bw > 5.8 g/mL, MR_FDG > 0.05 µmol/mL/min, DV_FDG > 68%, and, for maximal values, SUV_bw > 8.7 g/mL, MR_FDG > 0.11 µmol/mL/min, DV_FDG > 202%. Conclusions: The D-WB [¹⁸F]FDG PET/CT images in this study highlight the potential for improved differentiation between malignant and benign lesions compared to conventional SUV_bw imaging in patients with locally advanced head and neck cancers presenting with cervical lymphadenopathy and carcinoma of unknown primary origin (CUP). This observation may be particularly relevant in common diagnostic dilemmas, especially in distinguishing residual or recurrent tumors from post-radiotherapy changes. Further validation in larger cohorts with histopathological confirmation is warranted, as the small sample size in this study may limit the generalizability of the findings. Full article

This paper presents Rain-Cloud Condensation Optimizer (RCCO), a nature-inspired metaheuristic that maps cloud microphysics to population-based search. Candidate solutions (“droplets”) evolve under a dual-attractor dynamic toward both a global leader and a rank-weighted cloud core, with time-decaying coefficients that progressively shift emphasis from exploration to exploitation. Diversity is preserved via domain-aware coalescence and opposition-based mirroring sampled within the coordinate-wise band defined by two parents. Rare heavy-tailed “turbulence gusts” (Cauchy perturbations) enable long jumps, while a wrap-and-reflect scheme enforces feasibility near the bounds. A sine-map initializer improves early coverage with negligible overhead. RCCO exposes a small hyperparameter set, and its per-iteration time and memory scale linearly with population size and problem dimension. RCOO has been compared with 21 state-of-the-art optimizers, over the CEC 2022 benchmark suite, where it achieves competitive to superior accuracy and stability, and achieves the top results over eight functions, including in high-dimensional regimes. We further demonstrate constrained, real-world effectiveness on five structural engineering problems—cantilever stepped beam, pressure vessel, planetary gear train, ten-bar planar truss, and three-bar truss. These results suggest that a hydrology-inspired search framework, coupled with simple state-dependent schedules, yields a robust, low-tuning optimizer for black-box, nonconvex problems. Full article

This study investigated the presence, characteristics, and distribution of ingested microplastics (MPs) in Apathya cappadocica, a regional endemic lizard species. A total of 93 individuals were examined, and MPs were found in 19.35% (n = 18) of them. A total of 27 microplastic particles were detected, averaging 1.5 MPs per positive individual and 0.29 MPs per individual across the sample. MP sizes ranged from 50 to 1727 µm, with a mean size of 355.46 ± 73 µm. Most MPs (93%) were fibers, while the rest were fragments. The dominant color was navy blue (41%), followed by red and black (19% each). Polyvinyl alcohol (PVA) was the most common polymer (67%), followed by polyethylene terephthalate (PET) and polyethylene (PE). Statistical tests revealed no significant differences in MP size based on shape, color, or type, nor any correlation between MP size and gastrointestinal tract weight. However, microplastic shape was significantly associated with polymer type; fiber MPs consisted mainly of PET and PVA, while fragments were equally split between PVA and PE. These findings indicate that terrestrial reptiles are exposed to microplastic pollution and that microplastic characteristics may provide insights into their potential environmental sources. Full article

Background/Objectives: Previous studies suggest that nutrient deficiencies can alter immune responses in animals. However, the impact of micronutrients on autoimmune diseases like type 1 diabetes (T1D) in humans remains unclear since the described associations are based on observational data and they cannot establish causality. This study aims to examine the causal relationship between various micronutrients and T1D using Mendelian randomization (MR). Methods: We performed a two-sample MR analysis using genetic variants from genome-wide association studies (GWASs) of 17 micronutrients as instrumental variables (IVs). We analyzed T1D GWAS datasets of European (18,942 cases/520,580controls), multi-ancestry (25,717 cases/583,311 controls), Latin American/Hispanic (2295 cases/55,134 controls), African American/Afro-Caribbean (6451 cases/109,410 controls), and East Asian (1219 cases/132,032 controls) ancestries. We applied the inverse variance weighted (IVW) method in our main analysis, and additional MR estimators (MR-Egger, weighted median, weighted mode, MR-PRESSO) to address pleiotropy, and the Steiger test to test directionality in sensitivity analyses. Results: Following Bonferroni correction (p < 0.05/17), we found positive association between potassium levels and T1D risk (OR = 1.098, 95% CI [1.075, 1.122] p = 5.5 × 10⁻¹⁸) in the multi-ancestry analysis. Zinc, vitamin B12, retinol, and alpha tocopherol showed nominal associations. Vitamin C, D, K1, B6, beta- and gamma-tocopherol, magnesium, iron, copper, selenium, carotene, and folate showed no significant effects on T1D risk. For the multi-ancestry analysis, we had sufficient power to detect ORs for T1D larger than 1.065. Conclusions: Higher serum potassium levels were associated with increased T1D risk in our MR study, though supporting observational evidence is currently limited. Other micronutrients are unlikely to have large effects on T1D. Full article

The unique hollowed-out catamaran hulls and complex environmental conditions in lake areas hinder traditional ranging algorithms (combining target detection and stereo matching) from accurately obtaining depth information near the center of ships. This not only impairs the navigation of electric tourist boats but also leads to high computing resource consumption. To address this issue, this study proposes a ranging method integrating improved ORB (Oriented FAST and Rotated BRIEF) with stereo vision technology. Combined with traditional optimization techniques, the proposed method calculates target distance and angle based on the triangulation principle, providing a rough alternative solution for the “gap period” of stereo matching-based ranging. The method proceeds as follows: first, it acquires ORB feature points with relatively uniform global distribution from preprocessed binocular images via a local feature weighting approach; second, it further refines feature points within the ROI (Region of Interest) using a quadtree structure; third, it enhances matching accuracy by integrating the FLANN (Fast Library for Approximate Nearest Neighbors) and PROSAC (Progressive Sample Consensus) algorithms; finally, it applies the screened matching point pairs to the triangulation method to obtain the position and distance of the target ship. Experimental results show that the proposed algorithm improves processing speed by 6.5% compared with the ORB-PROSAC algorithm. Under ideal conditions, the ranging errors at 10m and 20m are 2.25% and 5.56%, respectively. This method can partially compensate for the shortcomings of stereo matching in ranging under the specified lake area scenario. Full article