Search Results (1,039)

Contemporary buildings and urban spaces are increasingly expected to support psychological well-being—a quality often termed “restorativeness.” Conventional approaches to quantifying restorativeness rely on subjective surveys or coarse green metrics, failing to capture how specific building morphologies and street-level visual configurations shape restorative experiences, particularly for stress-prone groups such as young adults. This study develops a deep-learning-driven framework linking building visual elements to youth-specific perceived restorativeness, using Changsha, China, as a testbed. The framework comprises three AI-powered modules: the TrueSkill algorithm trains a deep learning model to predict six dimensions of youth perception (e.g., beautiful, clean, safe) from pairwise comparisons of street view images; the Mask2Former architecture segments street-level imagery into 18 building and street attributes; and the XGBoost-SHAP pipeline uncovers nonlinear associations and threshold-like patterns between these attributes and the composite Built Environment Restorativeness Index (BERI). Results reveal three key insights: tree coverage shows a sustained positive association without saturation; building density exhibits a weakening association at high levels, suggesting possible saturation; and road proportion follows a bidirectional pattern, shifting from negative to positive beyond a certain range. Spatially, high BERI zones concentrate where ecological assets and diverse building functions co-occur, while youth perception exhibits systematic mismatches (e.g., “beautiful but not clean,” “safe but not lively”), traceable to imbalances in building form, street furniture, and commercial mix. These findings advance AI-assisted evaluation of built environments by shifting from one-dimensional metrics to interpretable, design-relevant diagnostics, offering a replicable evidence base for crafting youth-responsive buildings and streets. Full article

The Graduate Student Evolutionary Algorithm (GSEA) has demonstrated promising optimization capability in several engineering tasks; however, its performance may deteriorate when dealing with high-dimensional and complex multimodal problems due to insufficient adaptive search behavior, weak diversity preservation, and stagnation during later optimization stages. To alleviate these limitations, this paper proposes a Multi-Strategy Improved Graduate Student Evolutionary Algorithm (MIGSEA) for numerical optimization and artistic image multi-threshold segmentation. First, an adaptive mentor-guided learning mechanism is introduced to dynamically regulate the influence of mentors and peers throughout the optimization process, enabling a more effective transition from global exploration to local exploitation. Second, an elite–random cooperative learning strategy is designed to combine high-quality solution guidance with stochastic perturbation, thereby improving population diversity and enhancing the ability to escape local optima. Third, a stagnation-aware local refinement mechanism is developed to activate adaptive neighborhood search when the optimization process becomes trapped, which further accelerates convergence and improves solution precision. To verify the effectiveness of the proposed algorithm, MIGSEA is evaluated on the IEEE CEC2017 and CEC2020 benchmark suites and compared with 11 advanced metaheuristic algorithms under identical experimental conditions. Experimental results demonstrate that MIGSEA achieves competitive optimization accuracy, convergence speed, robustness, and statistical superiority in most benchmark functions. Furthermore, MIGSEA is applied to Otsu-based artistic image multi-threshold segmentation using multiple benchmark images with different threshold levels. Quantitative evaluation based on PSNR, FSIM, and SSIM, together with visual analysis, confirms that the proposed method can generate more accurate and visually consistent segmentation results than existing competitors. Overall, the proposed MIGSEA provides an effective and robust optimization framework for both benchmark optimization and practical image segmentation applications. Full article

Ocular ductions deform the optic disc and peripapillary blood vessels, and deformations can be interpreted as mechanical strain. We used confocal scanning laser ophthalmoscopy (cSLO) to map strain in disc and peripapillary retinal vessels associated with the cardiac pulse and determine if such strain is influenced by gaze direction. Sets of 13 infrared cSLO images were obtained sequentially for each eye using a Heidelberg Spectralis scanner in cinematic mode over a 3 sec interval in adults. Imaging was repeated in central, and horizontally (30° adduction/abduction) and vertically eccentric gazes (10° supraduction/infraduction). Retinal vessels, optic disc, and fovea were segmented using custom-trained, deep learning-based models. Frame to frame vascular displacements were automatically determined using optical flow analysis, allowing computation of equivalent strain. A total of 25 eyes of 13 subjects of mean age 39 ± 18 (standard deviation, range: 25 to 81) years were included. Average equivalent strain over 3 sec ranging from 0.27% to 0.36% exceeded the 0.16% noise threshold across all gazes and regions, indicating measurable pulse-induced deformation. After adjustment for age and axial length, pulsatile maximum and minimum strain were influenced slightly by gaze direction, maximally for supraduction, whereas mean strain did not vary significantly with gaze. The cardiac pulse induces measurable deformation of retinal vessels that can be quantified as equivalent strain in the image plane using optical flow-derived displacement fields. However, the interaction of pulse strain with gaze direction is unlikely to be a significant confound for investigations of strains associated with eye movements. Full article

Quantitative phenotyping of pepper seedlings is important for greenhouse plug tray seedling cultivation, but it remains constrained by inefficient manual monitoring, complex greenhouse backgrounds, and growth-stage-dependent discrepancies between two-dimensional image traits and actual leaf biomass. In this study, a cascaded vision framework with stage-specific morphological correction was developed for nondestructive seedling phenotyping. The framework integrated Visual Dynamic Momentum YOLO (VDM-YOLO) for individual seedling localization and growth-stage recognition, Variance Guided Strip Ghost Gated UNet (VSG-UNet) for lightweight, high-resolution leaf segmentation, and a stage-aware correction model for leaf dry biomass estimation. In performance evaluation, VDM-YOLO achieved a mean average precision at an intersection over union threshold of 0.5 (mAP_0.5) of 89.27%, improving mAP_0.5 by 1.82 percentage points over YOLOv12. VSG-UNet achieved a mean intersection over union (mIoU) of 83.9% and a Dice coefficient of 81.8%, while reducing floating point operations (FLOPs) and parameters by 44.2% and 61.2%, respectively, compared with U-Net. After stage-aware calibration, the coefficient of determination (R²) between segmented area and leaf dry weight increased from 0.764 to 0.813, and the root mean square error (RMSE) decreased from 0.0210 g to 0.0190 g. These results demonstrated that the proposed framework provided a proof of concept approach based on RGB images for the nondestructive assessment of leaf area and leaf dry biomass in pepper seedlings under restricted experimental conditions. Full article

Previous research has primarily focused on the restorative effects of environments on the general population, often overlooking the specific restorative capacity of urban settings for the disabled population. There is a lack of comprehensive investigation into the interaction between accessibility elements and urban restorativeness. This study, conducted in Shenzhen, Guangdong Province, China, categorizes streetscape accessibility elements for the disabled population and develops a recognition system based on an enhanced DeeplabV3+ framework. Semantic segmentation of streetscape accessibility elements was performed using 201,860 sampling points and 807,440 street view images. This study employed a combination of TrueSkill scoring, sentiment semantic analysis, LDA topic modeling, and LAB color clustering to quantify and visualize urban restorativeness. The impact of accessibility elements on urban restorativeness was explored using the XGBoost-SHAP model. Results indicate significant effects of architectural space constraints and high-density motor vehicle distribution on the safety of the disabled population’s mobility. The low pixel ratio of accessibility facilities and signs indicates insufficient infrastructure, while high landscape recognition rates exhibit significant spatial coverage heterogeneity. Detection rates for the disabled population in street views are nearly zero, highlighting a severe lack of inclusivity in pedestrian environments. Urban restorativeness exhibited a pattern of being higher in the south and east, and lower in the north and west. Among the accessibility elements, public green spaces (PGS) contributed the most to urban restorativeness, accounting for 25% of the impact, and the study elucidates the mechanisms through which various elements affect urban restorativeness. This absence stems from spatial competition, missing co-design, threshold effect conflicts, and color interference mechanisms. This research breaks away from traditional linear analytical frameworks and reveals the complex non-linear relationship between accessibility elements and urban restorativeness through the XGBoost-SHAP model, providing a quantitative decision-making tool for planning accessible environments in high-density cities. Full article

[¹⁸F]-PSMA PET/CT is a high-impact modality for the staging and restaging of prostate cancer, but its wide anatomic coverage and tracer biology generate frequent incidental findings on both PET and the accompanying low-dose CT (LDCT). This narrative review is restricted in scope to fluorine-18 PSMA tracers because tracer-specific biodistribution and pitfall profiles shape what is perceived as incidentaloma: how confidently lesions can be categorized, and how often borderline findings trigger downstream testing, particularly for skeletal foci with [¹⁸F]-PSMA-1007. Specifically, [¹⁸F]-PSMA-1007 shows substantially higher rates of focal unspecific bone uptake than [⁶⁸Ga]-PSMA-11—reported in multicenter studies as affecting up to 40–50% of patients—which directly inflates the pool of potential incidentalomas and creates a tracer-specific false-positive problem with no parallel in gallium-68 practice. Additionally, [¹⁸F]-DCFPyL has different urinary clearance kinetics that affect bladder and ureteral uptake patterns, altering what qualifies as physiologic versus incidental in the pelvis. These differences mean that the threshold for Category B versus C classification—and the appropriate cascade-resistant language—must be tuned to the specific tracer in use. A framework built on [⁶⁸Ga]-PSMA-11 data would systematically underestimate bone pitfall frequency in [¹⁸F]-PSMA-1007 practice and could therefore paradoxically increase rather than reduce cascades if applied uncritically across tracers. These biodistribution differences have direct and concrete consequences for reporting behaviour and downstream management. In [¹⁸F]-PSMA-1007 practice, a focal bone uptake without a CT correlate in a mechanically plausible location—such as an anterior rib or vertebral endplate—should trigger Category B language in the report conclusion: the finding is documented in the body with explicit safety netting (“most consistent with unspecific uptake; no routine workup unless interval growth, new pain, or aggressive CT morphology”), and no referral to bone scintigraphy or MRI is generated. Without tracer-specific awareness, the same finding would typically prompt a reflex bone scan or whole-body MRI referral, delaying definitive prostate cancer management by weeks and adding imaging costs without diagnostic gain. By contrast, in [⁶⁸Ga]-PSMA-11 practice, an equivalent focal bone uptake without a CT correlate carries a higher prior probability of true metastatic disease given the lower background rate of unspecific uptake and should more often be reported at Category B with a lower threshold for escalation or more cautious language. For [¹⁸F]-DCFPyL, the higher urinary activity in the pelvis means that ureteral segments can mimic lymph node disease; recognizing this as a physiologic variant (Category C) rather than an equivocal nodal finding (Category B) avoids unnecessary pelvic MRI referrals that would otherwise be triggered by an uncontextualized report. In practical terms, the tracer-specific calibration of the overlay therefore changes not only the category assigned but also the specific safety-netting language and the escalation trigger, which directly modifies the downstream management pathway for each affected finding type. The scanned population—predominantly older men with a high prevalence of degenerative, inflammatory, and vascular abnormalities—creates substantial background noise that can drive low-value diagnostic cascades if incidental findings are communicated without actionability context. We integrate society-endorsed frameworks (EANM/SNMMI procedure guideline 2.0; E-PSMA; PSMA-RADS; and PROMISE/miTNM with miPSMA score) and propose a cascade-aware overlay for incidental findings that can be appended to existing PSMA reporting standards rather than replacing them. The A/B/C actionability overlay is a structured expert-consensus framework informed by existing evidence-based guidelines for specific finding types and by tracer-specific cohort data; it has not yet been prospectively validated as a standalone tool, and its current level of evidence is therefore analogous to a structured expert recommendation rather than an evidence-based clinical guideline. We operationalize a three-tier actionability scheme across PET- and CT-dominant findings, provide cascade-resistant language for conclusions, and clarify why SUVmax-only “probability scales” for lymph nodes are not recommended in routine reports. Three practical tables summarize PET incidental findings, lymph node reporting frameworks, and LDCT incidental findings, and two structured report templates are provided (concise and extended), with the extended version explicitly labelling actionability tiers and escalation triggers. Finally, we outline concrete AI use cases for standardization and triage while emphasizing governance to avoid the amplification of false positives and paradoxical growth of cascades. Full article

This study proposes a precision detection method that integrates Canny operator threshold optimization with Zernike moments to address the issue of low measurement accuracy associated with the manual inspection of circular holes in sheet metal during industrial testing. A complete automated measurement system was developed based on the MATLAB platform. First, adaptive median filtering is employed for image preprocessing, with superior performance in noise suppression and detail preservation validated through Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics. Subsequently, Otsu’s thresholding method achieves robust segmentation between target and background, laying the foundation for subsequent edge detection. An innovative adaptive threshold selection strategy for the Canny operator based on composite weight scoring was proposed during edge detection, significantly enhancing circular hole edges’ continuity and geometric integrity. Finally, by integrating Zernike moments with sub-pixel localization technology, ultra-precise localization of edge points at the sub-pixel level was achieved. Experimental results demonstrate that the system achieves a measurement repeatability standard deviation of less than 0.02 mm and controls the absolute error within ±0.05 mm.This performance surpasses the ±0.3 mm precision requirement in industrial settings, providing an effective solution for automated quality inspection of sheet metal hole manufacturing. Full article

The pore-throat structure is a key factor in the exploration and development of tight sandstone reservoirs. In the present study, 14 tight sandstone samples from the Chang 8 member of the Ordos Basin were analyzed using high-pressure mercury intrusion, cast thin section analysis, scanning electron microscopy and cathodoluminescence imaging techniques. Fractal dimensions, obtained from the slopes of log(S_W) versus log(P_c) double-logarithmic plots, were applied to quantitatively characterize pore-throat structures and classify reservoirs through multifractal analysis, and discuss the diagenetic controlling factors affecting the pore-throat structure of different reservoir types. The results showed that the Chang 14 tight sandstones are characterized as two segments fractal features, which indicated that these samples have complex pore-throat structure and consist of two types of spaces: mesopore-throat spaces and micropore-throat spaces. The mesopore-throat system shows a higher fractal dimension (D1: 2.74–2.99), indicating greater heterogeneity and irregularity, while the micropore-throat system exhibits a lower dimension (D2: 2.28–2.61). D1 exhibits a negative correlation with the porosity and permeability of mesopores, while D2 shows a weak positive correlation with the properties of micropores. The total fractal dimension (D) is weakly correlated with overall reservoir properties, confirming that reservoir storage and flow capacity are primarily governed by the mesopore system rather than the micropore system. By analyzing the contribution of pore throats to sample physical properties, the results indicate that the 14 samples can be classified into two types based on 35% porosity contribution and 60% permeability contribution thresholds. Type 1, reservoirs dominated by microporous throat space (D values ranging from 2.603 to 2.644); Type 2, reservoirs dominated by mesoporous throat space (D values ranging from 2.544 to 2.598). Type 1 is characterized by primary intergranular pores, residual intergranular pores and intergranular dissolution pores, which enhance connectivity and reduce network complexity, thereby improving fluid permeability. In contrast, Type 2 consists mainly of intragranular dissolution pores, intergranular gap pores and micro-dissolution pores in clay minerals, which significantly inhibit fluid mobility. Diagenesis, including compaction, dissolution and cementation, exerts a significant control on the fractal characteristics and pore-throat structure evolution. The fractal characteristics exhibited in the pore-throat structure could provide a desirable analytical method, distinguishing from classification based on scale or size, for the evaluation and classification of tight sandstone reservoirs. Full article

Background: Cage subsidence after minimally invasive transforaminal lumbar interbody fusion raises revision risk and costs. Intraoperative computed tomography (CT) provides high-resolution, three-dimensional visualization of the endplate–cage interface and serves as a practical—though itself imperfect—reference standard for early subsidence, but it is not available at all institutions. Plain X-ray is widely available and inexpensive, but lower in resolution. The clinically relevant question is therefore not whether CT and X-ray are equivalent, but rather which X-ray protrusion depth measurement most reliably identifies CT-confirmed subsidence, and whether a positive intraoperative CT meaningfully predicts later radiographic subsidence. Objective: Using intraoperative CT as reference, we aimed to (1) determine the optimal X-ray protrusion depth threshold for CT-confirmed early subsidence; (2) test whether intraoperative CT predicts late radiographic subsidence; and (3) examine how early X-ray depth relates to intervertebral disc height (IVDH) and segmental lordosis (SL) loss. Methods: In a retrospective single-surgeon cohort (March 2015–July 2023), subsidence was defined as ≥2.0 mm endplate penetration on CT and measured on X-ray by parallax technique. Sensitivity, specificity, accuracy, and Cohen’s κ were calculated. Receiver operating characteristic (ROC) analysis evaluated X-ray depth as a continuous predictor and identified the Youden-optimal cutoff. Intraoperative CT was tested against late radiographic subsidence; no-intercept linear models estimated per-millimeter IVDH and SL loss. Results: Of 100 patients, 93 had paired imaging (mean age 66.7 years; body mass index 26.8 kg/m²). Subsidence appeared on CT in 16.1% and on X-ray in 15.1%. X-ray showed 80.0% sensitivity, 97.4% specificity, 94.6% accuracy, and κ = 0.80; ROC analysis demonstrated strong discrimination (area under the curve 0.91; 95% confidence interval 0.81–1.00), Youden-optimal cutoff 1.90 mm. Intraoperative CT predicted late subsidence (n = 76) with only 45.8% sensitivity and 96.2% specificity; missed cases had penetration depths indistinguishable from non-subsiders. Each 1 mm of early X-ray depth corresponded to 0.45 mm IVDH and 0.37° SL loss. Conclusions: An X-ray protrusion depth of 2.0 mm reliably identifies CT-confirmed early subsidence, providing a preliminary diagnostic cutoff for use when CT is unavailable. Intraoperative CT is highly specific but insensitive for late subsidence; meaningful risk stratification will require additional inputs. These hypothesis-generating findings warrant prospective validation. Full article

Crop residue management is an important factor in sustainable agriculture as it impacts soil erosion, water retention, soil organic matter, and crop yield. Accurately measuring the crop residue cover helps in the strategic planning, control, and monitoring of crop residue. While advancements in machine learning have allowed for significant progress in crop residue classification work, a major challenge still exists in the creation of an accurately annotated dataset for crop residue and the application of segmentation-based models to accurately segment crop residues. This study aims to develop an efficient image annotation framework and evaluate deep learning models for crop residue cover estimation. For this, the Residue Segmentation Tool, a standalone graphical user interface, was designed to facilitate accurate and efficient image annotation that enables flexible and high-throughput annotation of residue images. The tool is publicly available and supports multiple segmentation modes, which include classical and modern computer vision algorithms such as Otsu, Canny, and manual thresholding, as well as the Segment Anything Model and user-guided mask refinement through manual editing options. This tool was also utilized to create annotated datasets for machine learning training and testing of crop residue cover estimation. Three different sizes of datasets (100, 250, and 500 images) were utilized for machine learning training and testing to evaluate the performance of the models trained using U-Net and DeepLabV3. U-Net consistently outperformed DeepLabV3 across most metrics, particularly on smaller datasets, showing better Dice, IoU, and Recall scores. The best-performing model had Dice, IoU, and Accuracy scores of 0.748, 0.627, and 0.864, respectively. The findings demonstrate that the Residue Segmentation Tool enables scalable and reproducible dataset creation and supports effective segmentation for crop residue cover estimation. Full article

Background: Peri-implantitis is a prevalent inflammatory condition leading to progressive bone loss around dental implants. In vivo studies widely use murine peri-implantitis models. Micro-computed tomography (micro-CT) is the gold standard method used to assess peri-implant bone changes in those models. However, no standardized protocol exists for image analysis, limiting comparability across studies. Objective: This study aimed to evaluate the effect of different micro-CT analysis parameters on bone measurements in a ligature-induced peri-implantitis mouse model. Methods: Seventeen C57BL/6J mice were divided into peri-implantitis (n = 8) and healthy (n = 9) groups. Following extraction and implant placement in the maxillary first molar region, peri-implantitis was induced via silk ligature placement. Micro-CT scans were analyzed by varying the volume of interest (VOI) size (2-thread vs. 5-thread spans), threshold values for bone and implant visualization, and measurement dimensionality (2D linear vs. 3D volumetric). Statistical significance (p < 0.05) was determined using unpaired t-tests or Mann–Whitney U tests, following Shapiro–Wilk normality assessment. Results: The 2-thread VOI yielded a highly significant difference in bone volume fraction (BV/TV) between groups (68.86 ± 4.97% vs. 50 ± 4.32%, p < 0.0001, Cohen’s d = 4.05), while the 5-thread VOI showed no significant difference. Thresholds of 60 and 200 were utilized for bone and implant segmentation, respectively, selected based on high inter-examiner reliability (Cohen’s kappa = 0.844 and 0.825, respectively). Two-dimensional analysis confirmed greater mesio-distal loss in the peri-implantitis group. Conclusions: A 2-thread coronal VOI combined with optimized thresholds provides a promising analytical approach for evaluating localized peri-implant bone changes in this exploratory model. While this approach demonstrated pronounced responsiveness within our dataset, further external validation across different imaging systems and operators is required to establish its role as a universal standard. Full article

Soil whitening in cold-region rice seedbeds is visually associated with surface drying and moisture variation. The timely and objective monitoring of soil surface conditions is therefore important for seedbed management. In response to the inefficiencies of manual scouting and the limitations of conventional threshold-based methods under varying illumination and complex soil textures, this study presents a seedbed soil whitening analysis method that combines an enhanced DAC-UNet for semantic segmentation with colour feature analysis. First, a binary segmentation dataset of soil and background was created using RGB seedbed images. Within the U-Net framework, deformable convolution, ASPP++ multi-scale feature aggregation, and the CBAM attention mechanism were introduced to improve the model’s representation of irregular boundaries, scale variations, and complex illumination conditions. Comparative experiments demonstrated that the proposed model achieves 90.63% MIoU, 94.82% mPA, and 97.52% accuracy on the soil segmentation task. Based on the segmented soil region, a Whiteness Index (WI) was formulated to characterize soil surface whitening and generate whitening heatmaps. This enables quantitative description and spatial visualization of whitening characteristics within the soil region. Experimental results showed that the proposed method can effectively capture visual differences among different soil whitening states and provide intuitive visual reference information for soil surface condition analysis in cold-region rice seedbeds. Full article

Background: Platelet-rich plasma (PRP) is utilised in the treatment of patellofemoral chondropathy, although clinical responses remain variable. This retrospective exploratory study assessed whether baseline quantitative T2* mapping of femoral cartilage was associated with clinical improvement following PRP administration. Methods: In this retrospective observational study conducted within routine clinical practice, patients with patellofemoral chondropathy received three ultrasound-guided intra-articular PRP injections administered weekly according to an institutional protocol. Baseline and 9-month T2*-mapping MRI scans and clinical questionnaires were collected as part of standard follow-up. The main imaging variable was the worst-region femoral trochlear T2* value, evaluated as a candidate prognostic biomarker. Clinical outcomes included the Visual Analogue Scale (VAS, 0–10) and Kujala (0–100) scores, with responders defined by minimum clinically important difference (MCID) thresholds (ΔVAS ≥ 1.5; ΔKujala ≥ 8). Results: Thirty-two knees from 22 patients completed follow-up, including 10 bilateral cases (19 right knees, 13 left knees). Both VAS and Kujala scores improved significantly at 9 months (p < 0.001 for both). Baseline femoral trochlear worst-region T2* values were inversely correlated with pain and functional improvement (ΔVAS: rho = −0.51, p = 0.003; ΔKujala: rho = −0.36, p = 0.042). Baseline patellar T2* values were not associated with clinical change (ΔVAS: rho = −0.18, p = 0.32; ΔKujala: rho = −0.12, p = 0.51). Sensitivity analyses using baseline mean femoral T2* values did not show significant associations with ΔVAS or ΔKujala. Interobserver reproducibility for the worst-region T2* metric was limited, particularly for the femoral compartment (femur ICC 0.37; patella ICC 0.47), which limits immediate clinical applicability. Mean regional longitudinal ΔT2* changes did not exceed the 14% QIBA MDC95 threshold. Conclusions: In this small retrospective cohort, baseline femoral trochlear worst-region T2* values were associated with clinical improvement after PRP. These preliminary hypothesis-generating findings should be interpreted with caution and require validation in larger controlled cohorts with standardised and reproducible segmentation workflows. 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

Although the Love Evolution Algorithm (LEA) has achieved encouraging results in optimization tasks, several shortcomings still limit its effectiveness when solving high-dimensional multimodal problems. In particular, the fixed interaction threshold, stochastic reflection mechanism, and convergence-biased role evolution process may weaken population diversity and reduce the coordination between exploration and exploitation during evolution. To overcome these issues, this paper develops a Multi-Strategy Improved Love Evolution Algorithm (MILEA) under a phase-oriented cooperative evolutionary framework. First, a diversity-enhanced reflection mechanism is incorporated to enlarge the search region and dynamically regulate evolutionary dispersion during the early search stage. Second, an adaptive acceptance threshold strategy is introduced to adjust pairwise interaction behaviors according to the evolutionary state, thereby improving search flexibility and adaptability. Third, an elite-guided role evolution mechanism is designed to strengthen local exploitation and guide the population toward promising regions more efficiently. Furthermore, a probability-based collaborative update scheme is employed to coordinate multiple search behaviors adaptively while preserving the same computational complexity order as the original LEA framework. To evaluate the effectiveness of the proposed algorithm, extensive experiments are conducted on the CEC2017 and CEC2022 benchmark suites. The experimental results indicate that MILEA exhibits competitive optimization performance with respect to convergence behavior, solution accuracy, and optimization stability when compared with several advanced metaheuristic algorithms. Relative to the original LEA, the proposed method obtains improved average fitness values on most benchmark functions and significantly suppresses result fluctuations on several multimodal and hybrid optimization problems, indicating enhanced robustness during repeated independent runs. In addition, statistical evaluations based on the Wilcoxon signed-rank test and Friedman ranking analysis further support the reliability of the proposed optimization framework. To verify its practical applicability, MILEA is also applied to Otsu-based multi-threshold image segmentation tasks. Experimental results evaluated by PSNR, SSIM, and FSIM demonstrate that the proposed algorithm can generate high-quality segmentation results and preserve important structural image information. Overall, the proposed MILEA provides an effective optimization framework for both benchmark optimization and practical image segmentation applications. Full article