Search Results (13,155)

Objectives: To evaluate the long-term effects on retinal structure and visual function of oral bromelain and curcumin supplementation in patients with inherited retinal dystrophies (IRD) complicated by persistent cystoid macular oedema (CMO). Methods: We retrospectively studied 20 eyes with genetically confirmed IRD complicated by CMO, with refractory to systemic or local treatments performed for 6 months. We collected baseline (V1) and follow-up (V2) data from these IRD-CMO patients, who were continuously supplemented with oral bromelain and curcumin for 12 months. Outcome measures were the Snellen best-corrected visual acuity (BCVA) and central macular thickness (CMT) values, collected by spectral-domain optical coherence tomography (OCT). Based on OCT scans, we classified IRD-CMO as microcystic or macrocystic, performing this sub-grouping in two eye cohorts (n = 10). Baseline median BCVA and CMT differences in both groups were verified (Mann–Whitney test). For both CMO groups, changes from V1 to V2 in median BCVA and CMT values were evaluated (Friedman test). Results: At baseline, both the median BCVA and CMT values were significantly different in both groups (p < 0.01 and p < 0.001). Between V1 and V2, in the microcystic CMO group, a slightly improved median BCVA was found, whereas the median CMT was reduced; however, this did not reach statistical significance (p = 0.6 and p = 0.2, respectively). In the macrocystic CMO group, a significant stable median BCVA was found from V1 to V2, with concomitant significant reduction in median CMT (p < 0.05 for both comparisons). Conclusions: Retinal structural improvement and visual function preservation were observed after oral bromelain and curcumin supplementation in macrocystic IRD-CMO. It is likely that the vasogenic component in macrocystic CMO is more responsive to nutraceutical molecules than the degenerative microcystic component. Full article

Multimodal multi-objective optimization problems often exhibit one-to-many mappings, where multiple distinct variable vectors correspond to the same objective vector. This characteristic makes Pareto set (PS) estimation difficult, as conventional inverse modeling approaches assume a one-to-one correspondence. This study proposes a cluster-wise PS estimation framework in the variable space. Known solutions are partitioned into locally monotonic clusters using oscillation detection with an amplitude threshold, and independent response surface models are constructed for each cluster. By estimating PS solutions from multiple cluster-specific models for a given direction vector, the method preserves multimodal structures that conventional approaches fail to capture. Numerical experiments on the multimodal benchmark problems MMF1–8 and LIRCMOP1–2 demonstrate that the proposed method achieves equal or better HV and IGD values than the conventional method, while improving decision-space approximation as measured by IGDX in most test cases and suppressing the generation of dominated solutions. Full article

To address the challenges of unstable target localization and poor multi-module coordination in automated green pepper harvesting—caused by occlusions from branches and leaves, as well as varying lighting conditions—this paper presents the design and implementation of a modular robotic picking system. At the perception level, the system integrates a YOLOv8 detector with a RealSense D435i camera to identify and locate the calyx–ectocarp junctions of green peppers. An integrated multi-target tracking and filtering framework is proposed, which fuses multi-feature association, trajectory smoothing and coordinate denoising strategies to suppress depth noise and trajectory jitter, thereby enhancing the stability and accuracy of 3D localization. At the control and execution level, a depth-first picking sequence strategy with ID freeze-state management is implemented within a multithreaded software–hardware co-design architecture. This approach avoids task conflicts and duplicate operations while supporting continuous multi-fruit harvesting. Field experiments under natural outdoor lighting and varying occlusion levels demonstrate that the proposed system achieves recognition rates of 91.57% and 80.29% and harvesting success rates of 82.85% and 77.68% for non-occluded and lightly occluded fruits, respectively. The average picking cycle per pepper fruit is 9.8 s. This system provides an effective technical solution for addressing stability control challenges in the automated harvesting process of green peppers. Full article

Background/Objective: Myopia has become a prominent public health issue in China, significantly impacting the visual health of children and adolescents. The condition is characterized by a high incidence rate, increasing prevalence, and a trend toward earlier onset, highlighting the critical need for early and accurate diagnosis. Current clinical diagnostic methods primarily depend on subjective evaluations by optometrists and the use of isolated parameters, leading to inefficiencies and inconsistent outcomes. Moreover, there remains a lack of diagnostic tools that can effectively integrate multi-parameter analysis while ensuring robust data privacy protection. This study aims to develop an artificial intelligence (AI) diagnostic model that achieves objective, accurate, and safe diagnosis of myopia in children without cycloplegia through multi-parameter fusion and to enable local deployment. The proposed model is intended to be a reliable tool for clinical applications and large-scale screening projects, while ensuring strong protection of patient privacy. Methods: We built a transparent, rule-driven AI framework using clinical guidelines. Key ocular parameters—visual acuity, spherical equivalent, axial length, corneal curvature, and axial ratio—were encoded as logical rules in Python and incorporated via instruction fine-tuning. The model was trained and validated on retrospective clinical data (70% training, 15% validation, 15% test) using five algorithms: gradient boosting, logistic regression, random forest, SVM, and XGBoost. Performance was evaluated using accuracy, precision, recall, F1 score, and mean AUC across classes. Results: The model classifies refractive status into five categories: hyperopia, pre-myopia, mild, moderate, and high myopia. All five different algorithms demonstrated excellent diagnostic and classification performance. Gradient boosting achieved the best overall performance, with an accuracy of 98.67%, an F1 score of 98.67%, and a mean AUC of 0.957—outperforming all other models. Conclusions: This study successfully developed an artificial intelligence-based myopia diagnosis system for children under non-dilated pupil conditions. The system is interpretable and privacy-preserving, and has excellent diagnostic and classification performance, making it suitable for clinical decision support and large-scale screening applications. It has great potential to promote the development of early intervention, precision prevention, and control strategies for childhood myopia. Full article

In the context of urban renewal, the spatial interface of historic commercial districts functions as both a carrier of historical character and a key setting for commercial activity, public life, and local cultural expression. To address the limitations of conventional studies that rely heavily on field observation and qualitative description, this study takes Xiaohe Zhijie in Hangzhou as a case and develops a multi-level street-view recognition framework for the quantitative analysis of spatial interface characteristics. Based on street-view image collection and standardized preprocessing, a sample database was established at the sampling-point scale. Semantic segmentation, automated commercial object detection, and manual interpretation were combined to identify interface elements, including buildings, sky, greenery, pavement, vehicles, pedestrians, and commercial objects, while commercial content was assessed in terms of locality and homogenization. The results show that Xiaohe Zhijie exhibits a building-dominated and relatively enclosed interface pattern, with greenery and pavement forming the basic environmental ground, weak vehicle interference, and localized enhancement of vitality through commercial objects and pedestrian activities. Significant differences were found among street segments in openness, commercial coverage, and local expression. Three interface types were identified: commercial–cultural composite, local life-oriented, and waterfront landscape–cultural composite. The main challenge lies not in commercialization itself, but in stronger visual locality than content locality and increasing homogenization, resulting in a pattern of “localized form but homogenized content.” Full article

Objective: Vascular function serves as an early indicator of cardiovascular (CV) risk. The intake of n-3 polyunsaturated fatty acids (PUFAs) has been reported to improve arterial properties and reduce CV risk, but evidence in healthy individuals remains limited. This study investigated the effects of consuming n-3 PUFAs-enriched chicken meat on vascular reactivity at both microvascular and macrovascular levels in healthy young adults. Materials and Methods: In this placebo-controlled, double-blind, randomized interventional trial (ClinicalTrials.gov: NCT05725486), 39 participants (aged 20–26 years) were assigned to either the Control group (n = 20; approximately 118 mg n-3 PUFAs/day) or the n-3 PUFA group (n = 19; approximately 1500 mg n-3 PUFAs/day) for three weeks. Microvascular reactivity was assessed via post-occlusive reactive hyperemia (PORH), acetylcholine-induced dilation (AChID), local thermal hyperemia (LTH), and sodium nitroprusside-induced (SNPID) responses. Macrovascular reactivity was measured by brachial artery flow-mediated dilation (FMD) and nitroglycerine-mediated dilation (NTG-MD). Body composition and blood pressure (BP) were recorded before and after the intervention. Results: Both microvascular (PORH, AChID, and LTH) and macrovascular (FMD) endothelium-dependent vasodilation increased in the n-3 PUFAs group following the dietary protocol compared to the Control group. Conversely, the three-week dietary intervention did not influence endothelium-independent dilation in either the microvasculature (SNPID) or macrovasculature (NTG-MD) within the groups compared to baseline, nor were any differences observed between the groups. No significant changes were noted in BP or body composition after either diet. Conclusions: In healthy young adults, consuming the n-3 PUFAs-enriched chicken meat for three weeks improved endothelium-dependent vasodilation in both micro- and macrocirculation, without affecting endothelium-independent responses. These findings suggest that dietary n-3 PUFA intake may provide vascular benefits even in healthy, disease-free individuals at rest. Full article

Cotton represents one of the world’s most significant agricultural commodities. However, severe weed proliferation in cotton fields seriously hampers the development of the cotton industry, making precise weed control essential for ensuring healthy cotton growth. Traditional object detection methods often suffer from computational complexity, rendering them difficult to deploy on resource-constrained edge devices. To address this challenge, this paper proposes AVGS-YOLO, a lightweight and enhanced model employing a Quadruple Synergistic Lightweight Perception Mechanism (QSLPM) for precise weed detection in complex cotton field environments. The QSLPM emphasizes synergistic interactions between modules. It integrates lightweight neck architecture (Slimneck) to optimize feature extraction pathways for cotton weeds; the ADown module (Adaptive Downsampling) replaces Conv modules to address model parameter redundancy; the small object attention modulation module (SEAM) enhances the recognition of small-scale cotton weed features; and angle-sensitive geometric regression (SIoU) improves bounding box localization accuracy. Experimental results demonstrate that the AVGS-YOLO model achieves 95.9% precision, 94.2% recall, 98.2% mAP50, and 93.3% mAP50-95. While maintaining high detection accuracy, the model achieves a lightweight design with reductions of 17.4% in parameters, 27% in GFLOPs, and 14.5% in model size. Demonstrating strong performance in identifying cotton weeds within complex cotton field environments, this model provides technical support for deployment on resource-constrained edge devices, thereby advancing intelligent agricultural development and safeguarding the healthy growth of cotton crops. Full article

Even when an object’s color is accurately reproduced in a colorimetrically reproduced image (CRI), the perceived material appearance does not necessarily match that of the original object. This mismatch remains a challenge for faithfully reproducing real-world appearance in digital media. In this study, we investigated how lightness and chroma modulation affect the perception of glossiness, transparency, and roughness. These three attributes were quantitatively correlated with physical surface properties and image features through a direct comparison between objects and images. Observers selected the images that best matched the material appearance of the physical samples for each attribute. Image features derived from the gray-level co-occurrence matrix (GLCM) and surface roughness parameters were analyzed to compare the selected images with the CRI. In the lightness experiment, observers consistently selected images with higher lightness than the CRI, which was accompanied by increased complexity in the luminance distribution. In the chroma experiment, images with higher chroma were preferred; however, changes in GLCM features were negligible. Notably, stimuli with small local luminance differences at the CRI required larger shifts in image features to achieve perceptual matching. These findings indicate that modulating the luminance distribution is crucial for aligning the perceived appearance between physical objects and their digital representations. Full article

Excavation adjacent to operating urban rail transit faces formidable deformation control challenges. To address this, a parametric collaborative optimization framework integrating micro steel pipe pile isolation and temporary intermediate partition wall reinforcement is proposed. Taking a foundation pit project at Shangdi Station of Beijing Metro Line 13 as a case study, a three-dimensional finite element model was established using the Hardening Soil constitutive model and calibrated with field monitoring data. Optimization analysis reveals that micro-pile spacing is the dominant factor controlling local rail settlement, while intermediate partition wall thickness primarily dictates global surface settlement. By balancing stringent safety limits with construction economy through a multi-objective evaluation, the preferred support configuration was calculated to be 273 mm diameter micro-piles at 500 mm spacing, combined with a 300 mm-thick partition wall. This collaborative configuration successfully truncates lateral soil displacement, reducing maximum rail settlement by over 55% and surface settlement by 53.6% compared to the baseline. Field monitoring results show high consistency with the numerical predictions (RMSE = 0.1438 mm), confirming the reliability of the proposed parametric collaborative optimization framework. Ultimately, this framework provides a validated, quantitative design methodology and a practical reference for support design in constrained excavations adjacent to existing sensitive infrastructure. Full article

Background/Objectives: Enterococcus spp. are important indicators of AMR and potential opportunistic pathogens. Urban green spaces, frequented by dogs and humans, may serve as reservoirs for resistant bacteria. This study assessed the occurrence, AMR profiles, and virulence traits of Enterococcus spp. in dog feces from urban green spaces in Porto (Portugal). Methods: In December 2023 and May 2024, 240 dog fecal samples were collected from 12 urban green spaces across Porto. Enterococcus spp. were isolated using selective culture, identified to species level, and tested for antimicrobial susceptibility following CLSI guidelines. PCR screening was performed for resistance genes (vanA, vanB, erm(A/B/C), vatD/E, tet(M/O/L/K)) and virulence genes (gelE, ace). Environmental and socioeconomic features, including vegetation density (NDVI), presence of water features, and neighborhood deprivation (EDI), were recorded to explore associations with bacterial occurrence and traits. Results: Thirty-two isolates were recovered, mainly E. faecium (n = 9) and E. faecalis (n = 7). High resistance rates were observed to tetracycline (56.3%) and quinupristin/dalfopristin (37.5%), with lower rates for vancomycin, teicoplanin, and ciprofloxacin (3.1%), and imipenem (6.3%). Tet(M) was the most prevalent resistance gene (40.6%), and gelE and ace were frequently detected, often co-occurring with resistance determinants. Distribution of resistance and virulence genes varied across green spaces, with widely used parks showing more isolates. Vegetation density and water features were not directly associated with bacterial recovery. Conclusions: Dog feces in urban green spaces contribute to localized AMR hotspots, acting as potential reservoirs of resistant and potentially pathogenic Enterococcus spp. These findings highlight the importance of One Health strategies for urban sanitation and AMR surveillance. Full article

Background: Neoadjuvant chemoradiotherapy (nCRT) followed by surgery has become the standard treatment for oesophageal cancer. However, data on the outcomes of neoadjuvant immunochemotherapy (nICT) in geriatric patients (≥70 years) who face higher perioperative risks are limited. Objective: This study aimed to compare the perioperative outcomes of nICT versus nCRT in elderly patients with locally advanced oesophageal squamous cell carcinoma (ESCC). Method: This retrospective cohort study included 132 geriatric patients (median age: 72 years) treated with nICT (n = 51) or nCRT (n = 81) followed by esophagectomy at Sichuan Cancer Hospital (2021–2024). Intraoperative outcomes, postoperative pathologic stages, and complications, including pneumonia and anastomotic leakage, were assessed. Propensity score matching (PSM), overlap weighting (OW), and inverse probability of treatment weighting (IPTW) were used to adjust for baseline covariate imbalances in the sensitivity analysis. Results: Pathologic ypT0 stage tended to be higher in the nCRT group (p = 0.014), whereas ypN0 was higher in the nICT group (p = 0.035). No significant differences in intraoperative or postoperative outcomes between the two groups, except for pulmonary complications (p > 0.05). Compared with nCRT patients, nICT patients had significantly lower pulmonary complication rates (13.7% vs. 32.1%, p = 0.030), and multivariable analysis confirmed these findings (adjusted OR = 0.26; 95% CI: 0.08–0.85; p = 0.026). Sensitivity analyses showed consistent results. Conclusions: The safety of nICT is comparable to that of nCRT in geriatric ESCC patients, with significantly fewer pulmonary complications. These findings support nICT as a valuable alternative for elderly populations. Full article

Grid-based 3D object detection methods effectively leverage mature point cloud processing techniques and convolutional neural networks for feature extraction and object localization. However, unlike the 2D object detection domain, the unique characteristics of point cloud data being unevenly and sparsely distributed in space necessitate that detection networks possess a certain level of spatial structural perception. Learning spatial information such as point cloud density and distribution patterns can significantly benefit 3D detection networks. This paper proposes a Spatial-aware Network for 3D object detection (SpaA). Based on the 3D sparse convolution network, we designed a Variable Sparse Convolution network (VS-Conv) capable of perceiving the importance of locations. To address the issue of set abstraction operations completely ignoring spatial structure during local feature aggregation, we proposed a Spatial-aware Density-based Local Aggregation (SDLA) method. Experiments demonstrate that enhancing the spatial-awareness capability of detection networks is crucial for complex 3D object detection. Detection results on the KITTI dataset validate the effectiveness of our method. The test set results of SpaA achieved 3D AP values of 82.20%, 44.04%, and 70.34% for the Car, Pedestrian, and Cyclist categories, respectively, and a competitive 3D mAP of 67.23%, outperforming several published methods. Full article

Remote sensing object detection is fundamental to Earth observation, yet remains challenging when relying on a single sensing modality. While optical imagery provides rich spatial and textural details, it is highly sensitive to illumination and adverse weather; conversely, Synthetic Aperture Radar (SAR) offers robust all-weather acquisition but suffers from speckle noise and limited semantic interpretability. To address these limitations, we leverage the potential of foundation models for optical–SAR object detection via a novel gated–guided fusion approach. By integrating transferable and generalizable representations from foundation models into the detection pipeline, we enhance semantic expressiveness and cross-environment robustness. Specifically, a gated–guided fusion mechanism is designed to selectively merge cross-modal features with foundational priors, enabling the network to prioritize informative cues while suppressing unreliable signals in complex scenes. Furthermore, we propose a dual-stream architecture incorporating attention mechanisms and State Space Models (SSMs) to simultaneously capture local and long-range dependencies. Extensive experiments on the large-scale M4-SAR dataset demonstrate that our method achieves state-of-the-art performance, significantly improving detection accuracy and robustness under challenging sensing conditions. Full article

Non-convex stochastic optimization presents fundamental mathematical challenges across machine learning, wireless networks, data center resource allocation, and optical wireless communication systems, where complex loss landscapes with multiple local minima and saddle points impede classical variational inference methods. This paper introduces the Quantum-Inspired Variational Inference (QIVI) framework, which systematically integrates quantum mechanical principles (superposition, entanglement, and measurement operators) into classical variational inference through rigorous mathematical formulations grounded in Hilbert space theory and operator algebras. We develop a unified optimization framework that encodes classical parameters as quantum-inspired states within finite-dimensional complex Hilbert spaces, employing unitary evolution operators and adaptive basis selection governed by gradient covariance eigendecomposition. The core mathematical contribution establishes that QIVI achieves a convergence rate of $\mathit{O}({log}^{2}T/T^{1/2})$ for $\sigma$-strongly non-convex functions, provably improving upon the classical $\mathit{O}\left(T^{-1/4}\right)$ rate, yielding a theoretical speedup factor of $1.85$–$1.96\times$. Comprehensive experiments across synthetic benchmarks, Bayesian neural networks, and real-world applications in network optimization and financial portfolio management demonstrate 23–$47\%$ faster convergence, 15–$35\%$ superior objective values, and 28–$46\%$ improved uncertainty calibration. The principal contributions include: (i) a rigorous Hilbert space-based mathematical framework for quantum-inspired variational inference grounded in operator algebras, (ii) a novel hybrid quantum–classical algorithm (QIVI) with adaptive basis selection via gradient covariance eigendecomposition, (iii) formal convergence proofs establishing provable improvement over classical methods, (iv) comprehensive empirical validation across diverse problem domains relevant to machine learning and network optimization, and (v) demonstration of the framework’s applicability to optimization problems arising in wireless networks, data center resource allocation, and network system design. Statistical validation using the Friedman test (${\chi }^2=847.3$, $p<0.001$) and post hoc Wilcoxon signed-rank tests with Holm–Bonferroni correction confirm that QIVI’s improvements over all baseline methods are statistically significant at the $\alpha =0.05$ level across all benchmark categories. The framework discovers $18.1$ out of 20 true modes in multimodal distributions versus $9.1$ for classical methods, demonstrating the potential of quantum-inspired optimization approaches for challenging stochastic problems arising in machine learning, wireless communication, and network optimization. Full article

Virtual try-on is a key enabling technology for online fashion retail and digital garment visualization. It aims to realistically render a target garment on a person while preserving geometric alignment and fine texture details. Appearance flow-based approaches provide explicit deformation modeling but often suffer from texture squeezing and boundary artifacts in challenging scenarios, such as long sleeves and tucked-in garments, especially under high-resolution settings. In this work, we propose StageAttn-VTON (Stage-wise Attentive Virtual Try-On), an appearance flow-based framework that improves structural coherence and visual fidelity through stage-wise deformation modeling. Specifically, garment warping is decomposed into three stages—coarse alignment, local refinement, and non-target region removal—which mitigates the coupling between competing objectives, such as smooth texture preservation and accurate structural alignment. Furthermore, we introduce a self-attention module in the image synthesis stage to enhance global dependency modeling and capture long-range garment–body interactions. Experiments on VITON-HD and the upper-body subset of DressCode demonstrate that StageAttn-VTON achieves consistently strong performance against representative warping-based and diffusion-based baselines. In addition, qualitative comparisons show that the proposed method better alleviates deformation artifacts in challenging regions such as sleeves and waist areas. Full article