Search Results (2,005)

Enhancing agricultural economic resilience (AER) is essential for global food security. As a key policy tool for stabilizing agricultural production, policy-based agricultural insurance lacks rigorous causal evidence on its impact on resilience. In this study, AER is operationalized as a composite index capturing resistance and recovery capacities across pressure, state, and response dimensions. Using 2012–2023 provincial panel data from China (31 provinces × 12 years = 372 observations), we measure AER via the entropy method and identify policy effects using a staggered multi-timepoint difference-in-differences (DID) model. We find that policy-based staple crop insurance significantly increases AER by approximately 2.5 percentage points, primarily by promoting agricultural technological innovation (ATI) and regional industrial structure upgrading (RIS). The improvement effects are more pronounced in central and western regions, non-major staple-crop producing areas, and regions with higher natural risks. Robustness is confirmed via event study, alternative weighting schemes (PCA and equal weighting), and placebo tests. This study provides reliable causal evidence for the resilience-enhancing effect of agricultural insurance and clarifies its internal transmission mechanisms, offering empirical support for the optimization of agricultural risk governance policies. Limitations include the use of provincial-level aggregate data and the lack of analysis of spatial spillover effects between regions. Our findings suggest that differentiated policy implementation can support more sustainable and targeted agricultural risk governance. Full article

Background/Objectives: Multi-strain Limosilactobacillus fermentum supplementation has been reported to promote weight loss outcomes in free-living conditions, but limited evidence exists on these probiotic strains added to an energy-restricted diet and walking program in overweight adults. Methods: In a double-blind, placebo-controlled, parallel-arm randomized trial, overweight adults (35.2 ± 13.2 years old, 167.6 ± 8.6 cm, 79.9 ± 11.8 kg, 28.4 ± 2.7 kg/m² body mass index, 36.1 ± 6.6% body fat) completed a 12-week weight loss program that included a 500 kcal/day energy deficit and walking 10 k steps/d. Participants ingested one daily capsule containing a three-strain probiotic blend (L. fermentum K7-Lb1, L. fermentum K8-Lb1, L. fermentum K11-Lb3; 6 billion CFU/day) (PRO) or maltodextrin placebo (PLA). Assessments were performed at baseline, week 6, and week 12 and included body composition, resting energy expenditure, substrate utilization, peak oxygen uptake, dietary intake, step counts, blood biomarkers, quality of life, and side effects. Data were analyzed using multivariate and univariate repeated-measures general linear models (GLM), with mean changes from baseline presented alongside 95% confidence intervals. Results: All participants significantly reduced body weight, fat mass, body fat percentage, and waist circumference. At 12 weeks, PRO reduced fat mass more than PL (−2680.7 ± 1276.7 g; p = 0.039). In PRO, android and gynoid fat percentage decreased at 6 weeks (p < 0.001; p = 0.008) and 12 weeks (p = 0.004; p < 0.001), respectively. Visceral adipose tissue mass, volume, and area were lower at 6 weeks and trended lower at 12 weeks. In PRO, bone mineral content and bone mineral area decreased at 12 weeks, while bone mineral density paradoxically increased (0.007 ± 0.003 g/cm²; p = 0.024). Conclusions: During a 12-week weight loss program, supplementation of a multi-strain L. fermentum probiotic significantly reduced body fat and central adiposity. Full article

Background: Women remain the primary caregivers globally, especially in rural, low-resource settings plagued by poverty, unemployment, low education and poor infrastructure. These factors limit caregiving capacity, heighten vulnerability and increase the risk of food insecurity in female-headed households. Objective: To establish a baseline profile of caregivers of primary school children. Methods: Phase 1 (baseline) of the study was conducted using a quantitative, exploratory cross-sectional survey design among 75 female caregivers of children aged 7–13 years in Qwa-Qwa, Free State Province. Participants were recruited using convenience sampling. Data were collected with a structured, pre-validated questionnaire on socio-demographics, alongside anthropometric measurements. Data were analysed using descriptive statistics. Results: Most participants were unemployed (73.3%) and had low educational attainment, with 86.7% having completed primary school or less. A substantial proportion of households (80.0%) reported a monthly income below R1000. Food insecurity was common, with 69.3% of caregivers reporting experiences of food shortages. Household infrastructure was limited, particularly in refuse removal services (96.0% without access). Despite these socio-economic constraints, a high prevalence of overweight and obesity (72.5%) was observed amongst the participants. Conclusions: Caregivers experience severe, overlapping socio-economic and environmental vulnerabilities alongside a high prevalence of overweight and obesity. The study highlights the need for multi-sectoral interventions focused on poverty reduction, rural infrastructure development, improved service delivery, women’s empowerment and strengthened livelihood opportunities to improve household nutrition and resilience. Full article

This study examined the associations of self-reported emotional intelligence and self-regulation with children’s well-being among fourth-grade elementary school students in Mersin, Türkiye. The sample comprised 627 students, predominantly aged 9 to 10 years, from seven public elementary schools selected to reflect different district and school contexts. Data were collected in person after ethics committee approval, institutional permissions from the Turkish Ministry of National Education, and written parental consent. The Children’s Emotional Intelligence Scale, the Self-Regulation Scale, and the Stirling Children’s Well-Being Scale were administered. Descriptive statistics, Pearson correlations, simple and multiple linear regressions, and a cross-sectional indirect association analysis using PROCESS Model 4 with 5000 bootstrap resamples were conducted. Emotional intelligence was positively associated with children’s well-being and self-regulation, while self-regulation showed a weaker positive association with well-being. Emotional intelligence explained 31.4% of the variance in well-being, self-regulation explained 8.6% when examined alone, and both variables jointly explained 31.9%, indicating only a marginal increase over emotional intelligence alone. Thus, most of the explained variance was accounted for by emotional intelligence, whereas self-regulation made a very small incremental contribution beyond it. The indirect association analysis indicated a small but statistically supported pattern of indirect association between emotional intelligence and well-being through self-regulation within this cross-sectional design; the association between emotional intelligence and well-being remained significant after self-regulation was included in the model. The findings suggest that emotional intelligence is the stronger socio-emotional correlate of children’s well-being in this sample, whereas self-regulation shows a limited complementary association. Given the cross-sectional design and reliance on self-report measures, the findings should be interpreted as correlational associations rather than evidence of causal effects, temporal ordering, or developmental change. Future studies should use longitudinal, intervention-based, and multi-informant designs to further examine these associations. Full article

Acute myeloid leukemia (AML) is a genetically and clinically heterogeneous hematologic malignancy in which intensive induction chemotherapy remains the standard therapeutic platform for medically fit adults. In recent years, however, the frontline treatment paradigm has progressively evolved from a purely cytotoxic approach toward a biologically informed strategy. This shift has been driven by the identification of recurrent molecular alterations—particularly FLT3 and IDH1/2 mutations—as well as renewed interest in antibody-based therapies and the growing recognition that relapse, resistance, and measurable residual disease (MRD) are shaped by clonal architecture rather than blast burden alone. This review examines the development of targeted therapies combined with intensive chemotherapy in AML. We discuss the biological rationale for combination approaches and summarize the key clinical studies that have defined current practice, including trials evaluating FLT3 inhibitors, gemtuzumab ozogamicin, IDH inhibitors, and venetoclax-based strategies. We also address the role of targeted therapy across different treatment phases, including induction, consolidation, and post-remission settings, and analyze emerging data regarding MRD-guided treatment strategies, mechanisms of resistance, and integration with allogeneic hematopoietic stem cell transplantation. The integration of targeted agents with intensive chemotherapy is reshaping frontline AML therapy and represents a critical step toward precision medicine. While genotype-directed strategies—such as FLT3 inhibition—have already demonstrated survival benefit, optimal patient selection, treatment sequencing, and duration remain areas of active investigation. Future progress will likely depend on MRD-driven treatment adaptation, improved understanding of clonal evolution, and the development of rational multi-agent combinations capable of achieving deeper and more durable remissions. Full article

Managed aquifer recharge (MAR) is increasingly being realized as an important approach to improve water security in arid and semi-arid environments where there is a low amount of surface water and high climatic variability. This paper introduces a unified approach to the process of locating appropriate MAR locations and estimating recharge potential in Central Kazakhstan through a multi-criteria analysis using geographic information systems (GIS) and hydrogeological field exploration, water balance modelling. Remote sensing datasets and evapotranspiration (ET) analyses were conducted for the 2014–2024 period, while field investigations, infiltration tests, and hydrochemical sampling were performed during the 2025 field campaign. The suitability testing was preliminarily performed in the Google Earth Engine (GEE; Google LLC, Mountain View, CA, USA) environment as a weighted overlay test with the combination of terrain, vegetation, hydrological, and land cover parameters. According to the suitability map obtained and patterns of activity in agricultural activities, eleven candidate sites were identified, out of which eight were found to be suitable after hydrochemical analysis. The Nesterov and Boldyrev techniques of field-based infiltration tests produced a range of 0.05 to 1.42 m/day of hydraulic conductivity. Water balance analysis shows that the total amount of water that could potentially be added to groundwater recharge is about 40.2 million m³/year and that the effective amount of water could be recharged is about 11.0 million m³/year, which is limited by the infiltration processes. This means that about 27 percent of the available water is added into ground water recharge, which is a significant boost to the original estimates. The assessment of the storage capacity of the aquifers indicates that at all locations, the pore space is much greater than the recharge volumes that have been calculated and, therefore, storage is not a limiting factor in the implementation of MAR. It is estimated that the potential MAR rates range between 174 and 5282 m³/day depending on local hydrogeological conditions. The suggested method offers a powerful and generalizable site selection and measurement framework of MAR in arid areas with limited data. The findings highlight the significance of combining remote sensing, field measurements, and process-based modeling to aid sustainable groundwater management and climate adaptation strategies. Full article

Polycyclic aromatic hydrocarbons (PAHs) are major carcinogenic pollutants in urban air, and inhalation exposure poses health risks, particularly for primary school children aged 6–14 years in school environments. Traditional deterministic models for incremental lifetime cancer risk (ILCR) assessment often fail to adequately quantify variability and epistemic uncertainty in exposure parameters. This study develops a multi-layered probabilistic framework that progresses from deterministic calculations through one-dimensional Monte Carlo and sensitivity-guided two-dimensional Monte Carlo to a hierarchical (second-order) two-dimensional Monte Carlo simulation. The hierarchical approach samples hyper-parameters of the input distributions (means, standard deviations, and modes) in the outer loop, while exposure variables are sampled in the inner loop using Latin hypercube sampling. Applied to PAH and BaP_eq concentrations measured indoors and outdoors during heating and non-heating seasons, the framework yielded mean total ILCR values of 1.42 × 10⁻⁶ for children and 1.18 × 10⁻⁶ for adults. The hierarchical 2D MC produced 95% confidence intervals on the 95th percentiles of [9.17 × 10⁻⁷, 5.67 × 10⁻⁶] for children and [6.48 × 10⁻⁷, 5.57 × 10⁻⁶] for adults, with outdoor heating identified as the dominant exposure pathway. Although the air sampling campaign was conducted in 2011–2012, the data remain representative for evaluating seasonal and microenvironmental variability of PAHs in urban school settings in the region, as PAH levels are predominantly driven by persistent combustion sources. This framework provides more comprehensive uncertainty quantification for complex environmental exposure scenarios. Full article

Freshwater reservoirs face increasing threats from eutrophication and anthropogenic nutrient enrichment, yet practical multicriteria tools for ranking site-specific vulnerability remain underutilized. This study applies the PROMETHEE/GAIA multicriteria decision analysis framework to rank water quality vulnerability at five sampling sites (L1–L5) in Shanzai Reservoir, Fujian Province, China, using ten water quality parameters (TN, TP, COD, DO, Chl-a, pH, temperature, N:P ratio, transparency, and carbon ratio) measured monthly from April 2023 to April 2024. The PROMETHEE II complete ranking and the GAIA biplot together provide both a spatial vulnerability ranking and parameter-level diagnostic visualization. The Reservoir Centre (L5) ranked first (Φ = +0.32), exhibiting the most favorable water quality, while the River Channel (L3) ranked last (Φ = −0.44), with mean TN (1.15 mg/L) and TP (0.088 mg/L) exceeding Chinese Class III standards and Chl-a (35.89 µg/L) surpassing eutrophication thresholds. Intermediate rankings: L4 (Φ = +0.20), L1 (Φ = 0.00), L2 (Φ = −0.04). Spatial vulnerability followed a clear zone-level gradient: the riverine zone (L1, L3) was most vulnerable, the transitional zone (L4) showed intermediate performance, and the lacustrine zone (L2, L5) was most favorable, consistent with reservoir hydrodynamic theory. The GAIA biplot revealed that nutrient criteria (TN, TP, Chl-a) were the primary drivers separating site vulnerability classes. A sensitivity analysis across eight weighting scenarios confirmed that L3 ranked last in all scenarios (Φ = −0.450 to −0.694), demonstrating the robustness of the recommendation to prioritize intervention at the river channel inflow zone. These findings offer a practical, reproducible decision-support framework for water quality management prioritization in subtropical freshwater reservoirs, subject to confirmation through multi-year monitoring programs. Full article

Temperature is a physical quantity that represents the degree of heat or cold of an object and has significant application value across various fields. Traditional contact temperature measurement technologies, such as thermocouples and infrared thermometers, suffer from limitations like poor environmental adaptability and low spatial resolution, which makes it difficult to meet the temperature measurement requirements for micro-/nano-devices and extreme environments. In recent years, non-contact optical temperature measurement technology based on the luminescence characteristics of rare earth ions has garnered widespread attention due to its high sensitivity, strong interference resistance, and good environmental adaptability. In addition to inorganic luminescent materials, lanthanide-based molecular and coordination-complex thermometers have also become an important branch of this field; however, this paper focuses on inorganic rare earth luminescence thermometry. This paper provides a systematic review of the mechanisms of temperature measurement using rare earth ion luminescence, including single-energy-level luminescence intensity measurement and luminescence intensity ratio measurement based on thermally coupled levels (TCLs) and non-thermally coupled levels (NTCLs). It analyzes the principles of various technologies, performance parameters (such as absolute sensitivity S_a, relative sensitivity S_r, and temperature resolution δT), and their application progress in fields such as biomedical imaging, high-temperature aerospace environments, and the integration of micro-/nano-devices. Special attention is paid to emerging research directions, including Stark sublevel engineering for enhanced sensitivity, negative thermal expansion (NTE) host design for anti-thermal quenching, multi-modal collaborative thermometry, and artificial intelligence (AI)-assisted material design and data processing. The article also discusses the challenges currently faced by the technology, such as high-temperature fluorescence quenching and signal interference, and looks forward to future development directions, including artificial intelligence-assisted material design and multi-modal cooperative temperature measurement, aiming to provide a reference for the research and application of rare earth luminescence temperature sensing technology. Full article

Forests, as the largest terrestrial carbon sink, play a critical role in mitigating climate change. Accurately estimating forest vegetation carbon storage and identifying its drivers are essential for evaluating regional carbon sink functions and supporting carbon neutrality policies. However, long-term carbon storage estimation that simultaneously captures spatial non-stationarity and separately quantifies aboveground and belowground carbon pools at the provincial scale remains limited, and the spatial differentiation drivers and the temporal change drivers of carbon storage have rarely been disentangled through pixel-wise attribution. This study aimed to estimate forest vegetation carbon storage in Jiangxi Province, China, from 1990 to 2024, and to separately quantify the drivers of its spatial differentiation and the contributions of climate change and human activities to its temporal changes. A geographically weighted regression (GWR) model was constructed using field measurements and multi-source remote sensing data; the geographical detector and partial correlation analysis were applied for spatial differentiation attribution, and pixel-wise residual analysis was used for temporal change attribution. The results showed that: (1) total carbon storage fluctuated between 553.95 and 839.78 Tg C over the 35-year period and exhibited a significant increasing trend, with a cumulative carbon sequestration of approximately 122 Tg C; (2) the belowground carbon pool increased disproportionately (net gain 79.32 Tg C) compared with the aboveground pool (42.20 Tg C); (3) precipitation and solar radiation were the dominant drivers of the spatial differentiation of carbon storage; and (4) climate change contributed approximately 60% and human activities approximately 43% to the temporal changes in total carbon storage. These findings provide a scientific basis for delineating forest carbon sink conservation zones and formulating differentiated forest management strategies in subtropical China. Full article

Background/Objectives: We describe a case series of two patients with non-specific chronic low back pain (CLBP) and measurable decreased quality of life, who showed improvements after a specific multi-modal conservative spine and postural therapy regimen. CLBP is the leading cause of years lived with disability and disability-adjusted life years. This case series adds observational data to the medical literature on conservative treatment of CLBP and potentially improves diagnostic and treatment understanding of how conservative therapies can benefit patients suffering with CLBP. Methods: Two patients (Patient A: 58-year-old female; Patient B: 43-year-old male) presented with severe CLBP who did not find relief with prior traditional chiropractic manipulation. The patients sought treatment at a spine rehabilitation facility closest to their remote locations in Wyoming, USA. The conservative rehabilitation treatment program consisted of multi-modal therapies to strengthen postural muscles, postural spinal manipulation, and specific Mirror Image^® traction. After 36 treatments over 12 weeks in office and home rehabilitation exercises, baseline tests and outcome measures were repeated. Results: Patient-reported objective outcomes, disability indices, and radiographic analysis demonstrated changes at the conclusion of treatment that were maintained at long-term follow-up re-examination. Lumbar lordosis initially changed from −21.8° L1–L5 lordosis to post-treatment −33.6° for patient A and from −22.6° to −42.4° for patient B. Long-term follow-up demonstrated continued resolution of initial symptoms and maintained spine alignment. Conclusions: In these two patients, the described multimodal conservative program was associated with sustained improvements in symptoms, function, and radiographic parameters. This case series adds to prior biomedical literature regarding potential conservative interventions for treating CLBP and abnormal posture. Larger randomized controlled studies are required to evaluate generalizability and relative effectiveness. Full article

Cooling water systems of coastal nuclear power plants in China are frequently threatened by blockages caused by marine organisms. However, long-term studies on macrobenthic community dynamics and their associations with environmental factors are scarce, limiting the precise prevention of such blockage risks. This study conducted quantitative monitoring of macrobenthos and synchronous measurement of water environmental factors at 24 sampling stations in three functional areas (water intake, harbor basin, and drainage outlet) adjacent to the Northeast Fujian NPP from 2018 to 2024. Community structure characteristics were analyzed using the Shannon–Wiener and Margalef indices. The Grappler Method Risk Index (GMRI) was employed to screen species at risk of blocking cooling water systems, and the Mantel test and random forest models were applied to explore the associations between the macrobenthic community and environmental factors. A total of 161 macrobenthic species were identified. Polychaetes (71 species, accounting for 44.1%) were the absolute dominant group, followed by crustaceans (35 species) and Mollusks (30 species). The interannual fluctuation range of the polychaete proportion was 41.1–57.8%, reaching a peak in 2023. There were significant differences in community structure among different areas (PERMANOVA, p < 0.05), with the largest inter-regional difference in 2024 (R² = 0.36). The annual average number of species (9 species), density (155.25 ind./m²), and biomass (29.58 g/m²) in the drainage outlet were higher than those in the water intake and harbor basin. The GMRI identified Protankyra bidentata (spiny sea cucumber, GMRI values of 50.67% to 64.98% from 2019 to 2023) and Actiniaria sp. (sea anemone, a GMRI value of 54.63% in 2021) as medium-risk species for cooling water system blockage, while most other organisms were classified as low risk or extremely low risk. The Mantel test and random forest analysis confirmed that nitrogen nutrients (NO₃⁻) and phosphorus (PO₄³⁻) were significantly positively correlated with the polychaete community. Furthermore, NO₃⁻ and NH₄⁺ each explained 13.66% of the variation in the diversity index (H′), serving as key factors driving community structure. This study demonstrates the co-dominance of thermal and nutrient drivers in shaping macrobenthic communities over a multi-year scale, and identifies specific, morphologically suited taxa as potential blockage risks. The findings provide a scientific basis for targeted risk-species monitoring and support the integration of long-term ecological data into NPP cooling water system security management. Full article

Purpose: To develop a fully automated 2D nnU-Net pipeline for multi-class skeletal muscle segmentation (psoas, paraspinal, and abdominal wall) at the third lumbar (L3) vertebral level, and to quantitatively evaluate its diagnostic performance and reliability compared to manual segmentation. Materials and Methods: A 2D nnU-Net was trained on 164 axial L3 CT slices from the multi-institutional AMOS22 dataset, spanning diverse abdominal pathologies and multivendor imaging. To assess generalizability under severe anatomical distortion, independent external validation was performed in 50 consecutive patients with advanced liver disease from a single institution (January–December 2025; mean age, 63 ± 15 years; 32 women, 18 men), of whom 88% had moderate-to-severe ascites. Model stability was examined by comparing a five-fold ensemble with the best-performing single-fold model. Intra-observer reliability of the manual reference standard was evaluated in a random subset of 30 cases. Inter-observer agreement was additionally assessed using an independent second reader. Performance metrics included the Dice Similarity Coefficient (DSC), Pearson correlation coefficient (r), and Bland–Altman analysis for cross-sectional areas and mean attenuation. The inference workflow was deployed via a custom Streamlit-based graphical user interface (GUI). Results: In this anatomically complex external validation cohort, the 5-fold ensemble 2D nnU-Net achieved an overall mean DSC of 0.937 ± 0.043 (95% CI, 0.925–0.950), with 80% of cases achieving a mean DSC ≥ 0.90. While the mean DSC was statistically comparable to the best single-fold model (0.937, [95% CI, 0.921–0.952], p = 0.736), the ensemble strategy increased the minimum observed DSC (worst-case performance) from 0.720 to 0.822. Class-specific external validation performance for the 5-fold ensemble was highest for the paraspinal muscles (DSC: 0.960; 95% CI, 0.952–0.967), followed by the psoas muscles (DSC: 0.941; 95% CI, 0.927–0.956), and lowest for the anatomically complex abdominal wall muscles (DSC: 0.911; 95% CI, 0.893–0.929). Comparison between the ensemble model and manual segmentation yielded a Pearson correlation of r = 0.955 (p < 0.001) for total skeletal muscle area, with a mean bias of +7.17 cm². Intra- and inter-observer agreements for the manual reference standard demonstrated correlation coefficients of r = 0.995 and 0.090 for total areas, respectively. The automated pipeline required 3–5 s per case for inference and quantitative reporting, compared to 3–5 min for manual segmentation. Conclusions: In patients with advanced liver disease and substantial anatomical distortion from ascites, an ensemble-based 2D nnU-Net provides high quantitative agreement with manual L3 skeletal muscle segmentation, while mitigating lower-bound (worst-case) errors relative to single-fold models. Integration with a dedicated GUI enables substantial time savings and supports scalable quantitative body composition measurement. Full article

Precise and continuous monitoring of thermal effects are critical for ensuring the structural safety of bridges and preventing potential failures. This study presents a methodology integrating unmanned aerial vehicle (UAV)-based thermal measurements with interferometric synthetic aperture radar (InSAR) satellite data to assess and monitor the thermomechanical response of bridges. A three-dimensional (3D) finite element model (FEM) of a prestressed concrete (PC) bridge was developed and validated using in situ displacement measurements. High-resolution, 3D temperature distributions of bridge elements were obtained daily and seasonally using UAV-based infrared thermography (UAV–IRT). Thermal maps were validated with point temperature measurements on the structure. Simultaneously, long-term wide-area deformation trends were investigated using satellite-based InSAR observations. The thermo-mechanical displacement behavior derived from UAV–IRT measurements was compared with historical InSAR-derived seasonal deformation patterns to develop an integrated multi-source structural monitoring framework. The behavior of the bridge in daily and seasonal temperature cycles was simulated and analyzed by integrating UAV–IRT thermal load data into FEM. Maximum stress levels occurring under the most adverse thermal loading conditions and over a one-year period were calculated, taking into account stress limits. The FEM revealed a maximum vertical displacement of 12.3 mm under extreme thermal loading, with tensile stresses in the deck mid-depth exceeding the 3.5 MPa limit, signaling a potential risk for thermally induced cracking. Integration of UAV–IRT thermal observations and historical InSAR deformation measurements revealed vertical temperature gradients of up to 24 °C during summer conditions and indicated that the observed structural response was predominantly governed by thermo-elastic deformation. UAV-satellite methodology offers a rapid, economical, and comprehensive solution for the structural health monitoring of bridges exposed to thermal effects. Full article

Corrosion-driven section loss in steel tower structures erodes load-carrying capacity, yet field assessment still relies on subjective visual grading. This paper presents a closed-loop framework coupling quantitative image-based corrosion measurement with stochastic degradation modeling, Monte Carlo reliability simulation, and Sobol’ variance-based global sensitivity decomposition. Two complementary segmentation paths—hue–saturation–value (HSV) color-space thresholding for fleet-scale screening and DeepLabV3+ deep learning for detailed evaluation—convert imagery into calibrated section-loss estimates via nonlinear regression. Three analysis modes (single-image, multi-angle weighted-median fusion, and Oriented FAST and Rotated BRIEF (ORB) feature-matched temporal differencing) feed a Bayesian-updated power-law corrosion growth model whose outputs propagate through a time-dependent limit-state function via 10⁶-sample Monte Carlo simulation. Sobol’ indices rank each uncertain input’s contribution to the reliability-index variance. A field demonstration on a 40-year-old galvanized lattice tower in an ISO 9223 C4 coastal environment shows that the corrosion rate constant and zinc coating thickness together govern 65% of the total reliability variance and that a risk-ranked selective maintenance strategy reduces expected life-cycle cost by 71% relative to blanket intervention. Full article