Search Results (3,901)

For small-satellite imagery, traditional image points derived from the direct back-projection of laser altimetry points onto the imagery often exhibit deviations of tens of pixels or more, owing to the relatively limited attitude and orbit determination accuracy of small-satellite platforms. Moreover, the diversity of imaging sensors, variations in image resolution, and inherently weak image geometric configurations further complicate the accurate acquisition of image-space coordinates for laser altimetry points. To facilitate the application of laser altimetry data for geometric positioning across multi-satellite, multi-sensor, and multi-resolution small-satellite imagery, this study proposes a measurement method for laser altimetry points tailored to small-satellite images and establishes a combined geometric positioning model that integrates virtual control points, laser altimetry points, and image-matching tie points. The framework comprises four key procedural components: (1) an image-point-measurement strategy for laser altimetry points; (2) the construction of a laser altimetry data-assisted geometric positioning model for small-satellite imagery; (3) the solution of the geometric positioning model using a total least squares approach based on the partial-EIV (errors-in-variables) models; and (4) a comprehensive accuracy assessment conducted under multiple image-combination scenarios, including single-satellite single-stereo, single-satellite multi-stereo, dual-satellite single-stereo, and multi-satellite multi-stereo imagery configurations. Experimental validation is carried out using Jilin-1 small-satellite panchromatic images (KF01A, GF02A, and GF02B) acquired over the Henan region of China. The experimental results demonstrate that, with the laser altimetry point-measurement method and the combined geometric positioning model, the vertical positioning accuracy is substantially improved across all tested image-combination scenarios. These findings further confirm the capability in enhancing the vertical geometric positioning performance of stereoscopic small-satellite imagery characterized by multi-satellite platforms, multi-sensors, and multi-resolutions over terrain conditions similar to those tested. Full article

Multi-source remote sensing is transforming landslide susceptibility assessment from static terrain-based zonation toward observation-driven spatiotemporal inference and dynamic map updating. Satellite precipitation products, interferometric synthetic aperture radar (InSAR) deformation time series, optical image sequences, land-cover products, and multi-temporal terrain observations provide complementary evidence of hydrometeorological forcing, slope kinematics, land-system regulation, and geomorphic reorganization. However, these observation streams differ substantially in spatial support, temporal resolution, physical meaning, and uncertainty structure and therefore cannot be reliably integrated as generic predictors without process-aware interpretation. This review synthesizes recent progress in remote sensing-enabled dynamic landslide susceptibility assessment by linking four key components: dynamic factor construction from Earth observation data, spatiotemporal representation and learning, susceptibility map updating, and validation under temporal and spatial independence. The reviewed literature is organized around four process roles: rainfall- and soil moisture-related forcing, kinematic state and response captured by InSAR, land-system and ecological regulation derived from optical time series, and geomorphic memory represented by multi-temporal digital elevation models (DEMs). We further examine how these signals are encoded and integrated through temporal models, graph-based representations, attention mechanisms, and hybrid frameworks, with particular emphasis on consistency among process role, data structure, mapping unit, inference target, and validation design. Current progress remains constrained by temporally coarse landslide inventories, cross-scale incompatibility among remote sensing products, uneven and insufficiently process-aware multimodal fusion, and limited physical interpretability. Future advances require event-resolved inventories, uncertainty-aware multimodal fusion, process-consistent spatiotemporal learning, and validation designs that explicitly test whether susceptibility maps can be updated in a scientifically defensible manner as new Earth observation data become available. Full article

Reliable prediction of reservoir properties and internal reservoir architecture is critical for the exploration and appraisal of hydrocarbon accumulations characterized by complex geological structure and high uncertainty in the spatial distribution of reservoir rocks. This study presents a hybrid event-process algorithm for sedimentary-process modeling of deep-water turbidite systems and demonstrates its applicability to the Achimov Formation in Western Siberia. The proposed methodology combines a regular-grid representation of reconstructed paleotopography with a Lagrangian description of sediment particles and Eulerian reconstruction of flow fields. The terrigenous material is represented by four grain-size fractions: coarse-grained sand, medium-grained sand, silt, and clay. Application of the algorithm made it possible to reproduce the internal architecture of deep-water submarine fans. The modeling results reflect the main principles of lithological differentiation in turbidite bodies: sandy fractions are deposited predominantly in the proximal part of the system, whereas the pelitic component is transported toward more distal areas. The resulting distributions of total thickness and net-to-gross ratio make it possible to delineate areas characterized by increased reservoir development. Comparison of the modeled results with well data showed reliable agreement: for total thickness, the coefficient of determination was R² = 0.83 with an RMSE of 4.5 m, while for the net-to-gross ratio, it was R² = 0.81 with an RMSE of 0.08. The model shows that the internal architecture is controlled by morphodynamic feedback between paleorelief inheritance, depocenter filling, and subsequent flow diversion, which leads to compensational lobe stacking. These results indicate that the developed algorithm can be applied to the modeling of deep-water submarine fans. The proposed approach contributes to reducing geological uncertainty and can be used to provide a more reliable basis for identifying prospective zones when planning exploration programs. Full article

The diagnosis and treatment planning for brain tumors remain a complex task in medical imaging, largely due to the intricate structure of such abnormalities. This study introduces an interpretable stacked deep learning framework consisting of three sequential stages: (i) tumor segmentation, (ii) feature extraction, and (iii) tumor classification. The segmentation stage introduces a three-parameter lambda distribution (TPLD), a symmetric special case of generalized lambda distribution (GLD), used as a statistical intensity prior that is fused into the gating signal of an Attention U-Net for enhancing boundary delineation. The segmented outputs are processed using InceptionV3 for deep feature extraction and followed by a convolutional neural network (CNN) classifier. We evaluated the proposed model on the BRISC 2025 dataset, consisting of T1 weighted brain MRI images with pixel wise segmentation masks, which is validated by medical experts. The dataset consists of 3933 training images and 860 test images with ground truth masks, containing the four classes: meningioma, glioma, no tumor, and pituitary tumor. We utilized a region-of-interest–based training strategy to reduce the computational complexity and minimize overfitting. The data split followed the official image-level partition distributed with BRISC 2025; because patient identifiers are not released with the dataset, patient-level separation could not be independently verified, and this is acknowledged as a limitation. To ensure methodological transparency and clinical robustness, we systematically report the learning dynamics across 20, 60, and 100 training epochs at multiple decision thresholds (0.50, 0.60, 0.70), providing evidence of stable model convergence without overfitting. We also introduce a composite loss function by integrating cross-entropy, focal losses, and Dice to further boost performance. Experimental results demonstrate 97.8% classification accuracy on the test set, 92.4% Dice coefficient, and 85.9% IoU at the optimal threshold of 0.60. An ablation study further confirms the contribution of each loss component, supporting reproducibility and transparency in model evaluation. These findings confirm the practical utility and reliability of the proposed framework in the context of brain tumor segmentation and clinical diagnosis. Full article

The wind field in urban areas is characterized by an inherent spatial variability, which is also termed spatial uncertainty. This may be manifested as a noticeable difference between rooftop-level measurements in adjacent locations, the degree of which changes throughout the day. In meteorological and environmental contexts, such uncertainty is often described as a probability distribution. Usually, studies deal with the uncertainty of each wind vector component separately, i.e., wind speed and direction. The uncertainty is assumed to be distributed symmetrically around the mean and represented by a single characteristic value. Such representation neglects the correlation between the two wind vector components together. This, in turn, may result in wind vector component combinations that are physically inconsistent with realistic wind regimes. This study proposes a method that quantifies the spatial uncertainty of the urban rooftop wind. It is based on a covariance matrix that quantifies the relationship between the rooftop spatial wind components alongside the seasonal Mahalanobis distance functions. It draws on a representative sample of weather stations and previously calculated seasonal log-logistic Mahalanobis distance functions. Thus, an elliptic-shaped tolerance region is calculated to quantitatively estimate a given proportion of the possible values of the wind vectors at a given time. The model was demonstrated on the metropolitan area of Tel Aviv. The results show that the spatial wind distribution can be very well represented by a small sample of merely four stations. The model’s results were found to be well within the confidence interval, leading to the conclusion that the model is fully capable of providing an accurate description of the current state of the urban wind field. Full article

Soccer-video analysis centers on pitch-plane tracking, but camera-view depth cues such as occlusion and goal-area structure are not fully represented on the field plane. Synthetic benchmarks provide dense supervision unavailable for real broadcasts, but whether adaptation yields predictions that are reproducible across matches and operationally feasible remains unclear. We evaluate a Depth Anything V2 model adapted to SoccerNet-Depth with four components: Unaligned MDE accuracy, scale-and-shift aligned diagnostic, match-to-match reliability, and accuracy–cost trade-off. The model achieves an unaligned validation AbsRel of 0.00372. The aligned diagnostic shows that Base DAv2 retained substantial scene-depth structure, whereas SoccerNet adaptation enabled direct compatibility with the normalized target without per-frame ground-truth fitting. Relative to the VKITTI-fine-tuned reference, the adaptation improved all eight metrics in all 21 validation matches, with paired Wilcoxon tests significant after Bonferroni correction. On the challenge split, it reduced AbsRel by 34.1% versus the official baseline. The higher-resolution configuration improved the validation AbsRel by 5.9%, while the default retained a better accuracy–cost balance. At 401.57 ms per frame, the default is suited to post-match analysis, not live or near-real-time use. The study contributes a benchmark-scoped adaptation case study and protocol for foundation MDE on SoccerNet-Depth. Full article

(1) Background: Clinical trial data extraction from registries such as ClinicalTrials.gov remains labor-intensive and error-prone, often missing critical details hidden in unstructured protocol descriptions. Large Language Models (LLMs) offer potential to automate this process, yet systematic multi-model comparisons on real clinical trial data remain scarce. (2) Methods: Four LLMs (OpenAI o4-mini-high, Anthropic Claude-Sonnet-4, Google Gemini 2.5-Pro, and Meta Llama-4-Maverick) extracted brain stimulation parameters from 67 transcranial direct current stimulation (tDCS) trials in Parkinson’s disease via a structured JSON schema. Pairwise inter-model agreement was quantified with Cohen’s Kappa and percentage agreement across binary, categorical, and multi-component task tiers. (3) Results: Under exact-string matching, agreement was near-perfect for binary classifications (non-invasive classification: 100%; brain stimulation presence: 99.3%, $\kappa$ = 0.50) and substantial for categorical extractions (primary stimulation type: 96.4%, $\kappa$ = 0.70), but fell to 48.6% ($\kappa$ = 0.43) for complex anatomical targets. Numeric parameters revealed model-specific strengths: o4-mini-high and Claude-Sonnet-4 achieved perfect duration agreement (r = 1.000, n = 19) while Llama-4-Maverick diverged substantially (r < 0.12). Validation against an expert gold standard (100% inter-annotator agreement on a 20-trial overlap) confirmed high extraction accuracy across all features (mean 93.7–98.9%). Crucially, the low agreement on anatomical targets proved to be an artifact of exact-string scoring: under the same semantic matching used to measure accuracy, inter-model agreement rose to 97.0%, coinciding with the 95.5% expert accuracy. Inter-model agreement therefore tracks accuracy once both are measured on a common basis. (4) Conclusions: Exact-string inter-model agreement decreases with task complexity, but this decline largely reflects interchangeable free-text wording rather than reduced accuracy. Evaluated semantically, agreement and expert accuracy are both high and closely aligned. A residual risk is not low accuracy but the rare error shared across all models, which agreement cannot detect, and which overall accuracy can itself mask when one class dominates. These findings inform hybrid human–AI systematic review pipelines in which targeted expert oversight focuses on shared-error and minority-class detection. Full article

Gathering spaces within urban parks serve as primary outdoor leisure venues, playing a critical role in facilitating social interaction and restoring the physical and mental well-being of this demographic. This study uses the example of Pipa Mountain Park in Chongqing, China to explore the psychological and physiological perceptual effects of spatial environmental characteristics on young adults in four typical gathering spaces: path platform, elevated point, viewing boundary, and key node. To this end, we employed onsite experimental methods using wearable ergonomic devices to collect participants’ physiological data, including electrophysiological, electroencephalogram (EEG), and eye-tracking data. Visual and auditory psychological perception evaluation data were obtained through on-site questionnaires. Descriptive statistical analysis revealed differential trends in participants’ psychological perceptions and physiological responses across distinct gathering spaces. The elevated point demonstrated the most favorable ratings for the psychological dimension “comfort” (M = 1.63, SD = 2.09). Subsequent principal component analysis elucidated key psychological perception indicators in mountainous settings, while Friedman test, Kruskal–Wallis tests, and random forest modeling quantified the effects of specific spatial environmental indicators on perceptual responses. Results indicated significant differences in psychological perceptions and physiological responses across gathering space typologies (p < 0.05). Influenced by the preferences and behavioral habits of young adults, environmental element complexity significantly enhanced attentional engagement (χ² = 68.428, p < 0.01) and facilitated positive perceptual responses. The synergistic effects of the visual and auditory elements significantly enhance the restorative benefits of space; however, poor accessibility weakens this advantage. This study provides evidence for the in-depth analysis of the intrinsic mechanisms between the spatial environment and multisensory perception in urban mountain parks. Full article

Designers, engineers, and building officials face increasing pressure to accelerate and improve the accuracy of design review for buildings and infrastructure. Roof assemblies and rooftop structures are particularly challenging due to the complexity and fragmentation of regulatory requirements, especially in jurisdictions such as Florida, where compliance must be verified across both the residential and commercial volumes of the Florida Building Code (FBC). The resulting review process is technically demanding and time-intensive, imposing significant cognitive and operational burdens on practitioners and under-resourced public agencies. To address these challenges, this study proposes and evaluates an agentic artificial intelligence (AI) framework for automated code compliance checking of roof assemblies and rooftop structures. The framework employs a multi-agent architecture in which specialized AI agents collaboratively interpret regulatory provisions and evaluate roof design parameters across four core modules: data preprocessing and code ingestion, rule-based and semantic analysis, results visualization, and iterative validation. YOLO11m-seg and Mask R-CNN were used for element detection and segmentation, and the system was developed using 150 design projects, including roof plans, section details, and specifications. Four large language models from two families (Mistral and GPT) were comparatively evaluated on standardized compliance tasks. The framework was then tested on a held-out portfolio of 15 distinct roof-design projects comprising 60 code-compliance decisions derived from the FBC 2023, with performance measured by precision, recall, F1-score, and accuracy. GPT-5.4 achieved the highest overall performance (F1 = 0.97; accuracy = 97%). Because the reasoning and vision components were evaluated separately rather than as an integrated end-to-end pipeline, and the scope was limited to one jurisdiction and two drawing types, broader code coverage and production-setting validation are needed before claims of generality. Nonetheless, the results suggest that agentic AI can meaningfully support compliance review and reduce reviewer burden in roof-design permitting. Full article

Background: Frailty impacts the prognosis of respiratory diseases but lacks standardized evaluation criteria. This pilot study aimed to develop a frailty assessment method using unsupervised clustering of various physical function tests. Methods: Clinical data, handgrip strength (HS), lower limb strength (LLS), the 6 min walk test (6 min WT), the 5 m walk test (5 m WT), body composition, such as skeletal muscle mass index (SMI), whole-body phase angle (WBPhA), and pulmonary function variables were measured. Frailty status was evaluated in three groups (frail, pre-frail, and robust) using the J-CHS and Kihon Checklist. Unsupervised hierarchical clustering was performed, followed by dimensionality reduction using Principal Component Analysis. Results: Ninety-eight patients and healthy volunteers (70 males, 28 females; mean age, 57.5 years) were divided into four clusters, ranging from robust to pronounced frailty. On the 2-principal component plane, data points formed clusters across the four regions. The biplot showed variables aggregating in two directions: one including %FEV₁, FEV₁%, 6 min WT, and 5 m WT speed (exercise tolerance), and the other including HS, LLS, SMI, and WBPhA (physical elements). Tracking 39 participants (mean, 636 days later) showed cluster shifts that were broadly reproducible, although the small follow-up sample warrants cautious interpretation. Conclusions: As an exploratory, hypothesis-generating pilot study with a small single-center sample, this novel frailty model may offer a more granular assessment to help guide management; however, external validation in larger cohorts is required before clinical application. Full article

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality in the elderly worldwide. Over the past two decades, there has been a wealth of evidence of a close relationship between autonomic nervous system activity and cardiovascular mortality, including sudden cardiac death. Heart rate variability (HRV), derived from photoplethysmographic (PPG) signals, is increasingly recognized as a promising non-invasive digital marker for evaluating autonomic nervous system function and stratifying CVD risk. The application of machine learning algorithms to PPG-derived HRV analysis offers a promising approach for improving CVD risk stratification and facilitating the development of personalized medicine strategies. Background/Objectives: To evaluate the potential of heart rate variability indicators in predicting the risk of developing CVD in individuals aged 65 years and older. Methods: The study involved individuals aged 65 years and older, divided into two groups: those with a risk of developing CVD (n = 54) and those without risk (n = 46). The first stage included a questionnaire as well as anthropometric and hemodynamic measurements. At the second stage, a PPG was performed using the Eldar computer photoplethysmograph and Eldar-Vario software, followed by an analysis of time-domain and spectral HRV parameters. Statistical data analysis was conducted using the SPSS Statistics 22.0 software package, focusing on the evaluation of associations between HRV indicators and the presence of CVD. Interpretable machine learning models were developed using logistic regression and a random forest algorithm within a nested cross-validation framework. In addition to the discriminatory characteristics, Brier score, LogLoss, calibration analysis, error matrices, permutation importance, and SHAP interpretation were analyzed in the study. Results: In patients with cardiovascular diseases, a statistically significant decrease in heart rate variability was revealed: SDNN by 2 times (26 [Q1–Q3: 15, 35] ms), pNN50 by 3.5 times (4 [3, 5]%), TINN by 5 times (31 [20, 51] ms), and HRV by 2.5 times (6 [4, 8.7]). In addition, a decrease was seen in the spectral components of VLF by one-fold (2450 [Q1–Q3: 2450, 4500] ms²), LF by four-fold (750 [750, 1500] ms²) and HF by five-fold (450 [450, 750] ms²) (p < 0.05). At the same time, there was a significant increase in the VLF/HF and LF/HF ratios, which indicates a predominance of sympathetic activity. According to the results of the correlation analysis, statistically significant associations of HRV indicators with age, physical activity level, body mass index and systolic blood pressure were revealed. The results of machine learning also revealed the association of HRV with arterial hypertension, physical activity and BMI. The best final results were demonstrated by a random forest model with a combined set of clinical and HRV signs of HF and RMSSD (ROC-AUC was 0.9988). The signs of heart rate variability obtained by photoplethysmography demonstrated additional prognostic value in relation to clinical signs. PPG-derived HRV features demonstrated additional discriminatory value for cardiovascular risk stratification. Conclusions: The obtained data demonstrate a close association between the risk of developing cardiovascular disease and autonomic nervous system dysfunction. The decrease in heart rate variability is most pronounced in elderly individuals with existing cardiovascular disease and can be considered a potential tool for developing diagnostic, prognostic, and risk stratification strategies. The use of machine learning demonstrated that heart rate variability features obtained using photoplethysmography improve diagnostic prognostication and classification of cardiovascular diseases compared to models based solely on clinical data. Full article

The Architecture, Engineering and Construction (AEC) industry plays a crucial role in global economic development, but it is also a major contributor to environmental degradation and resource consumption. Despite increasing alignment with the United Nations Sustainable Development Goals (SDGs), the sector remains highly fragmented, with limited integration of Modern Methods of Construction (MMC), Lean Management, Circular Economy and Socio-Digital Enablers frameworks into a unified sustainability model. This review article employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to systematically identify and analyse existing literature and address these research gaps. The PRISMA procedure was conducted through a structured process involving the identification of studies from Scopus and Web of Science databases using predefined keywords related to SDGs and the AEC sector. It was followed by screening and eligibility assessment based on publication type, timeframe (2022–2026), subject relevance, and full-text accessibility, resulting in a final dataset of 42 studies for analysis and then applying bibliometric analysis (Biblioshiny and VOSviewer) to define thematic clusters. The results reveal strong research concentration on SDG 11, SDG 12, and SDG 9, while SDG 2 and SDG 14 remain underexplored within the AEC literature. The findings also highlight that the convergence of MMC, Lean Management, Circular Economy practices, and social and digital technologies increasingly drives sustainable industrialisation. A structured content analysis is further conducted to categorise approaches, barriers, and implementation strategies across the four identified components of industrialisation. Overall, this study contributes a comprehensive and integrated framework for understanding sustainable industrialisation in the AEC sector and provides a structured evidence base to support future research and policy development aligned with the SDGs. Full article

This study proposes VulnPattern-TKG, an end-to-end temporal knowledge graph framework that forecasts the emergence of CVE-derived vulnerability-pattern relations from Common Vulnerabilities and Exposures (CVE) descriptions. The framework does not aim to predict the real-world exploitation of individual CVEs; instead, it models how standardized relations among Weakness Factor (WF), Exploitation Outcome (EO), and Exploitation Prerequisite (EP) categories evolve over time in vulnerability disclosure text. It processes 205,600 National Vulnerability Database (NVD) CVE descriptions from 2014 to 2024 using a hybrid pipeline combining SecureBERT+CRF-based entity extraction, dependency-parsing-based relation rules, and four-stage hierarchical standardization. The resulting compact Knowledge Layer contains 26 standardized category nodes and 48,371 confidence-filtered triples. VulnTEC is a lightweight confidence- and time-weighted Node2Vec graph embedding framework that ranks relation-compatible candidate tails using cosine similarity over shared node embeddings. An internal four-component priority-score framework, integrating prediction confidence, temporal rise, exploitation-prerequisite prevalence-risk proxy, and extraction confidence, supports an analyst-side review of the forecasted relations. Under the novel-only triggers evaluation, VulnTEC achieves a mean MRR of 0.410 ± 0.020; however, the theoretical random baseline already reaches 0.408 because the candidate tail space contains only six EO categories. The results are interpreted as directional ranking evidence, and query-level Top-K results are reported only as descriptive analyst-side review evidence. Full article

Accurate remaining useful life (RUL) prediction for aero-engines is important for condition-based maintenance and safety-oriented health management. Long short-term memory (LSTM) networks are widely used for this task, but two limitations remain important in multi-sensor degradation modeling: hidden states generated over the full window are often under-utilized, and attention mechanisms may overemphasize locally fluctuating sensor readings. This paper proposes a Multi-Statistic Disentangled LSTM (MSD-LSTM) framework for aero-engine RUL prediction. The framework first applies Savitzky–Golay filtering to smooth high-frequency signal fluctuations. A hidden-state feature extraction module then combines feature-level disentangled extraction and Global Average Pooling to use the LSTM hidden-state sequence beyond the final recurrent output. In parallel, a Multi-Statistic Pooler summarizes each input window using minimum, maximum, standard deviation, and mean statistics, and its output is fused with a self-attention branch through a static-gating mechanism. On the NASA C-MAPSS benchmark, MSD-LSTM achieves RMSE values of 10.45 and 12.33 on FD001 and FD002, respectively, and ranks first in RMSE on three of the four sub-datasets and first in SCORE on two sub-datasets among the compared recent methods. Ablation and fusion analyses show that both the hidden-state extraction and statistic-guided fusion components contribute to stable RUL prediction. Full article

In this study, novel electrospun polymer mats based on biocompatible poly(lactic acid) (PLA) and hydrophilic poly(ethylene glycol) (PEG) were successfully fabricated for the co-delivery of two natural polyphenols, quercetin (QUE) and rosmarinic acid (RA). Scanning electron microscopy (SEM) revealed the formation of defect-free, continuous nanofibers with high interconnected porosity. By mimicking the structural features of the native extracellular matrix, these nanofibrous platforms facilitate pronounced combined antioxidant and anticancer action. X-ray diffraction (XRD) analysis confirmed that the rapid solvent evaporation during electrospinning induced a physical state transformation, converting both QUE and RA from their native crystalline structures into an amorphous dispersion within the polymer fibrous materials, thereby optimizing their potential bioavailability. The obtained hybrid fibrous materials possessed good mechanical properties. Moreover, the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay demonstrated that the incorporation of PEG enhanced matrix hydrophilicity, allowing the four-component PLA/PEG/QUE/RA mats to achieve the highest antioxidant efficiency (98.1%), suggesting an enhanced, complementary radical-neutralization pathway. Furthermore, in vitro biological assessments against human cervical carcinoma cell line (HeLa) and normal murine embryo fibroblasts BALB/3T3 demonstrated prominent anticancer activity, while noncancerous cells were significantly less affected. The dual-loaded PLA/PEG/QUE/RA fibrous mats induced significant cell shrinkage, chromatin condensation, and apoptotic cell death in HeLa cells, while normal BALB/3T3 fibroblasts retained cell membrane integrity and displayed higher resistance. Modeled after the native extracellular matrix, these bioinspired materials demonstrate significant antioxidant and anticancer activity, highlighting their potential for applications in localized cancer therapy, wound management, and tissue engineering. Full article