Search Results (2,462)

Purpose: This study sought to extract and characterize fucoidan from brown seaweed Padina tetrastromatica for the synthesis of fucoidan–gold nanoparticles (F-AuNPs) and to assess their physicochemical properties, as well as their antioxidant, anti-inflammatory, and anticancer activities, alongside potential molecular interactions with specific cancer-related targets. Methods: The extracted fucoidan-rich fraction was characterized for its sulfate content. Citrate-stabilized plain gold nanoparticles (plain AuNPs) were prepared and characterized as non-fucoidan nanoparticle controls. Comprehensive physicochemical characterization, including UV–Vis spectroscopy, Fourier-transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), X-ray diffraction (XRD), dynamic light scattering (DLS), zeta-potential analysis, and thermogravimetric analysis (TGA), was performed on the resultant fucoidan-functionalized AuNPs (F-AuNPs). Biological activities were assessed using different techniques: antioxidant potential (Ferric Reducing Antioxidant Power (FRAP) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays), anti-inflammatory effects (NO inhibition in macrophages), and anticancer efficacy against HepG2 cells (MTT and flow cytometry). Potential molecular targets relevant to these activities were further explored in silico using molecular docking against key cancer-related proteins, providing hypotheses for future experimental validation. Results: The fucoidan-rich fraction showed a sulfate content of 10.08%. Strong antioxidant activity was observed, especially in FRAP (11.20 ± 0.29 mg TE g⁻¹ DW). F-AuNPs exhibited enhanced cytotoxicity against HepG2 cells (IC₅₀ 138.1 µg mL⁻¹) compared to plain AuNPs (IC₅₀ 271.2 µg mL⁻¹) and the fucoidan-rich fraction (IC₅₀ 390.2 µg mL⁻¹), inducing G1 phase arrest. In addition, F-AuNPs reduced nitric oxide production in LPS-stimulated RAW 264.7 macrophages, reaching 21.42 ± 1.29% inhibition at 100 µg mL⁻¹. As an exploratory, hypothesis-generating step, an in silico target-prioritization screen identified HPSE and MMP-2 as the highest-scoring candidate proteins, proposed solely as targets for future experimental validation. Conclusions: F-AuNPs represent a promising multifunctional nanoplatform with antioxidant, anti-inflammatory, and antiproliferative activities. The integration of in vitro biological evaluation with in silico target prediction supports the potential biomedical relevance of F-AuNPs and generates testable hypotheses regarding their molecular targets, which require experimental validation. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly transforming cardiology through advanced analytical tools capable of identifying complex patterns across cardiovascular imaging, electrophysiology, and clinical datasets. Machine learning (ML) and deep learning (DL) algorithms are being integrated into echocardiography, cardiac computed tomography (CT), cardiac magnetic resonance imaging (MRI), and electrocardiography (ECG), enabling earlier diagnosis and more personalized cardiovascular care. This narrative review summarizes current clinical and organizational applications of AI in cardiology and discusses emerging concepts related to explainable and trustworthy AI. Methods: A narrative review was conducted according to SANRA recommendations using the PubMed, MEDLINE, Web of Science, and Scopus databases, including peer-reviewed publications from 2015 to 2026 addressing clinical, organizational, and ethical applications of AI in cardiology, with particular emphasis on cardiovascular imaging, electrocardiography, heart failure, digital health, and explainable AI frameworks. Results: Substantial evidence demonstrates that AI-based tools can achieve expert-level performance in cardiovascular imaging interpretation, automated electrocardiographic analysis, and clinical risk prediction. Across multiple cardiovascular settings, AI has been associated with improved diagnostic accuracy, enhanced workflow efficiency, and earlier detection of cardiovascular disease. Predictive models support risk stratification in heart failure and ischemic heart disease, while chatbots and digital health platforms may facilitate patient engagement, remote monitoring, and continuity of care. Despite these advances, important challenges remain, including algorithmic bias, limited transparency, insufficient external validation, data heterogeneity, and barriers to routine clinical implementation. Emerging explainable AI approaches may improve model interpretability, clinician confidence, and the safe adoption of AI-driven decision support systems. Conclusions: Artificial intelligence is rapidly evolving from a research-oriented technology into a clinically relevant component of cardiovascular care. Current evidence indicates that AI can enhance diagnostic performance, improve risk prediction, streamline clinical workflows, and facilitate more personalized management across multiple cardiovascular domains. However, the successful translation of AI into routine practice will depend on robust external validation, transparent decision-making mechanisms, regulatory oversight, and clinician acceptance. The development of explainable and trustworthy AI frameworks represents a critical step toward the safe, ethical, and sustainable integration of AI into modern cardiology. Full article

Background: Photodynamic therapy (PDT) is a promising minimally invasive approach for melanoma; however, many photosensitizers lose activity in aqueous media due to aggregation-induced quenching effects. Objectives: The aim of this study was to develop and characterize castor oil–based nanoemulsions containing chlorophyll (NFs-Chl) and to evaluate their in vitro photodynamic potential against melanoma cells (B16-F10), as well as their selectivity compared with human keratinocytes (HaCaT). Methods: NFs-Chl were prepared by spontaneous emulsification. Physicochemical characterization was carried out using dynamic light scattering (DLS), UV–Vis spectroscopy, FTIR, and Raman spectroscopy. In vitro assays included MTT for cell viability (IC₅₀ determination), real-time cell proliferation (RealTime-Glo™), and cell migration analysis (scratch assay). All photodynamic treatments were performed under irradiation at 660 nm. Results: NFs-Chl exhibited homogeneous nanometric sizes (≈24–31 nm) and a low polydispersity index (≈0.25–0.40), indicating a narrow size distribution. UV–Vis spectra confirmed the preservation of the characteristic absorption peaks of chlorophyll after encapsulation. In B16-F10 cells, NFs-Chl associated with PDT significantly reduced cell viability and metabolic activity over 48 h. Furthermore, NFs-Chl inhibited the migratory capacity of B16-F10 cancer cells. Cell migration assays revealed a clear inhibition of B16-F10 cell migration following treatment with NFs-Chl + PDT. Conclusions: Encapsulation of chlorophyll into castor oil nanoemulsions protected the photosensitizer, improved its cellular delivery, and enhanced its photodynamic cytotoxic effect against melanoma cells, while relatively preserving normal keratinocytes in vitro. Full article

This study discusses the influence of Digital Leadership (DL) on Sustainable Innovation Performance (SIP) in telecommunications companies. In addition to examining the direct effect of Digital Leadership, the study focuses on the mediating roles of Knowledge Sharing (KS) and Employee Engagement (EE). A sample of 412 employees was collected by a simple cross-sectional survey. A partial least squares structural equation modeling (PLS-SEM) approach was used for analyzing results. The study reveals that Digital Leadership directly and positively enhanced Knowledge Sharing but did not lead to a significant direct influence on Employee Engagement and Sustainable Innovation Performance. Moreover, Knowledge Sharing did not significantly influencing Sustainable Innovation Performance, a condition that was the strongest predictor of Sustainable Innovation Performance emerging from Employee Engagement. The mediation analysis shows that neither Knowledge Sharing nor Employee Engagement mediates the relationship between Digital Leadership and Sustainable Innovation Performance. The objective contribution of this study is to shed light on the idea that Digital Leadership and Sustainable Innovation Performance are not directly related but may instead reflect other circumstances or contextual conditions. The research offers practice advice in showing that Employee Engagement benefits organizational sustainable innovation results by urging companies to consider not only Digital Leadership strategies but also alternative strategies that foster employee involvement. Full article

Unmanned aerial vehicles (UAVs) and deep learning (DL) have introduced a new framework for intelligent building façade defect detection, yet existing studies often focus on isolated technical components and lack a systematic evaluation of the entire pipeline. To address this gap, this paper conducts a systematic literature review of 135 peer-reviewed journal articles retrieved from the Web of Science database over the period 2021–2026. This review investigates four key domains: (1) UAV inspection path planning and data acquisition; (2) multi-modal data fusion; (3) DL-driven defect detection algorithms; and (4) 3D reconstruction and digital twin integration. Our analysis reveals the following main findings. Real-time perception-aware planning is central to UAV path planning, yet most studies lack robustness evaluations under real-world deployment conditions. Multi-modal data fusion improves detection across multiple defect types, yet edge deployment requires balancing lightweight design with recognition stability. Defect recognition algorithms increasingly adopt task-driven architectures, but limited edge-device resources demand joint optimization of efficiency and accuracy. In digital twins, systematic research is still lacking on semantically integrating recognition results into BIM for O&M decision-making, leaving the closed loop from defect detection to maintenance unresolved. This review aims to help researchers and practitioners advance UAV-based inspection from an auxiliary tool to a fully autonomous, reliable intelligent agent for refined management of the urban built environment. Full article

Not all particle-size fractions in widely graded granular materials contribute equally to compaction densification. For non-ideal particle-size distributions (PSDs) with local deviations or fine-end disturbances, the full-range fractal index may be influenced by particle-size fractions that contribute weakly to densification and, therefore, may not consistently represent the maximum dry density response. To address this problem, this study proposes a response-constrained truncation framework to identify a more effective PSD fitting range for fractal characterization. First, 20 concave and S-shaped PSDs from previous experiments were re-analyzed to compare full-range and truncated indices. Then, 21 progressively truncated specimens derived from three standard fractal PSDs were tested by relative density experiments. A unit-mass densification contribution coefficient, η_j, was defined from adjacent maximum dry density differences and particle-fraction mass contents. The η_j-d responses exhibited unimodal patterns, and the transition diameter d_c shifted with PSD coarseness. For the two material sources, replacing the full-range index with the truncated index increased the R² values between the fractal index and maximum dry density from 0.195 to 0.886 and from 0.191 to 0.856, respectively. A continuous percentile search showed that the optimal characteristic scale was concentrated near q ≈ 30, with a robust common optimum of q = 30.53. Sensitivity analysis for β = 0.85–0.95 indicated that 0.225d₃₀ falls within the transition region from highly effective filling to reduced densification efficiency. Accordingly, d_L = 0.225d₃₀ is proposed as a preliminary engineering estimate of the lower fitting limit for non-ideal PSDs. The framework is intended for widely graded materials whose full-range fractal parameters are inconsistent with compaction response. Full article

The mineral system model formalizes the critical geological processes and mappable parameters that control ore formation, which can then be translated into spatial predictors used as input features in machine learning (ML)-based mineral prospectivity mapping (MPM). In most MPM studies, exploration evidence features are indeed derived from the mineral system model of the targeted deposit type. However, not all features produced in this way are necessarily informative or favorable for prospectivity analysis. This challenge can be addressed by using feature selection frameworks to identify the most relevant features before applying ML and deep learning (DL) algorithms for mathematical integration. To address this need, this study employs an unsupervised variational autoencoder (VAE) framework to evaluate and rank exploration evidence layers. The VAE quantifies feature importance through a systematic strategy that measures the sensitivity of reconstruction-error components, mean squared error (MSE), mean absolute error (MAE), and Kullback–Leibler (KL) divergence, to individual feature variations. In this way, the VAE ranks the exploration features and helps to identify those that are the most useful for prospectivity mapping. The proposed approach was applied to a real geo-dataset from a porphyry copper district in Iran. Based on the conceptual model of porphyry copper mineralization, 15 evidence layers were generated, including proximity to phyllic, argillic, propylitic, iron oxide, and silicification alteration zones; proximity to intrusive rocks, faults, and fault intersections; and geochemical maps of Cu, Mo, Sb, Pb, Zn, As, and W. The VAE-based ranking indicated that evidence layers related to hydrothermal alterations, intrusive rocks, and faults were the most influential exploration features, whereas geochemical evidence layers showed lower relative importance. Based on this evaluation, two modeling scenarios were considered: in the first, all available features were used, and in the second, only the features selected by the VAE framework were included. In both cases, the final prospectivity model was produced by an autoencoder (AE). For comparison, the prediction-area (P–A) plots of the two prospectivity models were generated using 14 known mineral occurrences as positive ground-truth labels, indicating that the model based on the selected features achieved a higher prediction rate (80%) than the model based on all features (72%). These results demonstrate that the evidence layers derived from the mineral system approach can benefit from unsupervised VAE-based evaluation, leading to improved performance of the prospectivity modeling. Full article

Ovarian cancer is a gynecological cancer, which, if metastasized and not detected early, can cause death among women. Therefore, accurate prediction of drug responses to ovarian cancer is needed. A gynecological pathologist inspects abnormality in tissues and provides a report for patients; however, this diagnostic process (1) is difficult to undertake; (2) requires experience; and (3) is time-consuming. Moreover, existing tools are imperfect. Hence, we present a computational pipeline to improve predictions of drug response pertaining to ovarian cancer. First, we downloaded digital pathology images pertaining to ovarian responses to bevacizumab from the Cancer Imaging Archive Repository. We employed a histogram of oriented gradients for images, constructed feature vectors, and used Fisher’s linear discriminant analysis to alter data representations through dimensionality reduction. This reduced-dimensionality data was used for regression analysis, employing support vector regression coupled with various kernels and calculating the area under the ROC curve (AUC). Experimental results were validated using transformer-based models (ViT and Swin) and other deep learning (DL) models (VGG16, ResNet50, InceptionV3, MobileNetV2, and EfficientNetB6). Our approach using a radial kernel (named SVRD + R) improved AUC performance by 17% compared to the best-performing transformer-based model (ViT). Likewise, AUC performance improved by 14.9% when compared against the best DL-based model (MobileNetV2). These results demonstrate feasibility, showing that induced models via the presented AI-based pipeline can lead to superior performance when investigating prediction problems pertaining to gynecologic cancer studies. Full article

The early and accurate detection of lung cancer (LC) is one of the primary challenges in the clinical diagnostics process, which plays a vital role in the treatment of the disease. Although various deep learning (DL) techniques have been presented, the existing DL methods are mainly focused on single-modal images, either computed tomography (CT) or histopathological images, which are associated with poor generalization, diversity, and applicability. To mitigate the existing issues, the present work aims to develop a modality-independent ensemble DL framework that is independently evaluated on CT and histopathological image datasets for LC classification. In this work, the proposed framework was developed using the Beta Normalization Aggregation (BNA) technique, where the performance of three state-of-the-art pre-trained convolutional neural network (CNN) architectures was compared on two distinct imaging modalities images. Based on the comparative analysis of the performance metrics, Xception, DenseNet121, and MobileNetV2, are chosen to develop the Ensemble model. Predictions generated by the selected CNN models are aggregated using the proposed BNA strategy to improve classification robustness, which improves the confidence of the prediction results and discriminative capabilities. The experiments using public data sets have confirmed the excellent performance of the model. On the CT dataset, the proposed BNA Ensemble achieved a testing accuracy of 97.45%, with a precision of 97.88%, recall of 97.45%, F1-score of 97.45%, and an AUC of 0.9986. On the histopathological dataset, the framework achieved an accuracy of 99.80%, with precision, recall, and F1-score all reaching 99.80%, and an AUC of 1.0000. These results demonstrate the effectiveness, robustness, and generalizability of the proposed BNA framework. The analysis of the results using t-SNE plots, confusion matrices, ROC curves, and confidence distributions provided additional insights into feature separability, classification performance, and prediction confidence of the proposed framework. Full article

Background: Preterm premature rupture of membranes (PPROM) before 32 weeks of gestation with prolonged latency is associated with substantial neonatal morbidity, including Dry Lung Syndrome (DLS), pulmonary hypoplasia (PH), bronchopulmonary dysplasia (BPD), and death. Accurate individualized risk stratification remains elusive, as the interacting contributions of amniotic fluid dynamics, inflammatory status, and microbiological burden are inadequately captured by traditional statistical approaches. Methods: We performed a retrospective, exploratory–predictive analysis of 66 pregnancies complicated by second-trimester PPROM with latency exceeding 14 days. Elastic Net and Random Forest models were trained across six clinically defined predictor domains using a multi-stage block modelling strategy. To address the clinically relevant distinction between antenatal and postnatal information, results are reported separately for Model A—comprising exclusively antenatal predictors available during expectant management (gestational age at PPROM, latency, amniotic fluid trajectory, inflammatory status, vaginal microbiome at admission)—and Model B, which additionally incorporates postnatal variables and characterizes the full mechanistic perinatal risk trajectory. Binary and ordinal outcomes included DLS, PH, BPD, intraventricular hemorrhage (IVH), and neonatal death. Pairwise interaction models were additionally computed to identify cross-domain risk constellations. Results: Distinct predictor architectures emerged per outcome. Pulmonary hypoplasia was most strongly associated with temporal features of oligohydramnios—particularly the persistence and timing of SDP < 1 cm—rather than isolated measurements. For DLS, the antenatal model (Model A) achieved AUC 0.776, driven by gestational maturity and inflammatory status; surfactant administration—a postnatal variable reflecting therapeutic response rather than an antenatal risk factor—dominated only the mechanistic Model B. Neonatal death was driven by a combined profile of respiratory support burden, amniotic fluid persistence, and co-morbidity. IVH showed consistently high ordinal predictability (accuracy 0.863), with amniotic fluid dynamics and microbiological burden as leading contributors. BPD remained the least linearly separable endpoint across all configurations. Conclusions: Multi-domain machine learning reveals outcome-specific, cross-domain risk architectures following second-trimester PPROM that are invisible to conventional statistical models. Longitudinal amniotic fluid trajectory is the dominant antenatal determinant of structural pulmonary morbidity, while microbiological burden independently shapes neurological risk. These findings support prospective validation of integrated ML-based risk stratification tools for individualized antenatal counselling in this high-risk population. Full article

Topological data analysis (TDA) has evolved into a flexible and robust paradigm for obtaining qualitative, geometry-inspired insights from high-dimensional, noisy, and complex data. Grounded in algebraic topology, geometry, statistics, and machine learning (ML), TDA provides multiscale descriptions through persistent homology, Mapper (a graph-based method that summarizes the shape of high-dimensional data), and related topological signatures that are often inaccessible to standard linear and metric methods. In recent years, and especially during 2024–2025, TDA has expanded rapidly across science, engineering, biomedical research, and socio-economic studies, while also being integrated with modern learning paradigms such as deep learning (DL) and graph learning. This survey summarizes recent developments in TDA using a carefully selected set of articles, with emphasis on 2024–2025. We first present the mathematical and computational foundations of TDA, covering simplicial complexes, filtrations, persistent homology, the Mapper algorithm, and computational advances such as data simplification, stability, and efficiency. We then review applications in time series and dynamical systems, biomedical imaging and precision medicine, engineering and physical sciences, finance and risk analysis, DL and interpretability, and security and critical infrastructure systems. Throughout, we highlight how TDA can extract informative features, function as a model component, and provide a conceptual lens for studying complex systems. However, the survey also emphasizes recurrent failure patterns: TDA performance is highly sensitive to filtration, embedding, and vectorization choices; aggressive simplification can dilute or remove informative topological signals; and integration into standard ML workflows still lacks uniform validation and reporting protocols. We conclude by outlining key challenges—including scalability, statistical foundations, interpretability, and compatibility with rapidly evolving artificial intelligence (AI) paradigms—and by identifying directions for future research. The survey also provides a unifying design perspective for TDA systems, highlighting methodological trade-offs and emerging research directions for integrating topology with modern ML. Full article

Plant diseases result in the estimated loss of 20–40% of the world’s crop production annually, amounting to more than $220 billion in economic losses and threatening food security for a rapidly expanding world population. While the conventional methods for detecting plant diseases rely on visual inspection of the symptoms, they are resource-consuming. For effective plant disease detection at a pre-mature stage, hyperspectral imaging (HSI) represents a paradigm shift in technology. It can be used to obtain subtle spectral signatures outside the visible spectrum, which enables pre-symptomatic and highly specific plant disease diagnosis. Concurrently, deep learning (DL) has become the prevalent analytical paradigm for decoding the complex and high-dimensional data that HSI produces. This paper covers a comprehensive narrative review of the intersection of these two transformative technologies from 2008 to 2026. We first set out the biological and physical principles by which HSI is uniquely suited to detecting plant–pathogen interactions in the absence of visible symptoms. We then present a detailed taxonomy of deep learning architectures for Vision Imaging and HSI data, ranging from basic 1D and 3D convolutional neural networks (CNNs) to hybrid models with attention mechanisms and, most recently, vision transformers, which have achieved greater robustness to real-world conditions. There is currently a major and consistent “lab-to-field” performance gap. A critical analysis of various studies reveals a persistent and significant performance gap between models that perform well on controlled lab datasets (ranging from 95 to 99%) and field-collected data (typically 70–85%). This paper also addresses the practical gap of environmental variability, image noise, and the domain gap between the controlled environment and the real dataset. Finally, this review concludes by providing strategic research recommendations and a roadmap, highlighting that the future of the field is contingent upon not only architectural innovation but also a holistic approach, with robustness, scalability, affordability, and interpretability as the main focus to bring the proven potential of HSI-DL systems from the lab to the field, ultimately contributing to global food security. Full article

This work reports the colloidal synthesis of Nd-containing mixed-halide perovskite quantum dots described as CsPb(Nd)Br_3−γCl_γ, followed by post-synthetic surface modification with an acid-activated amino-functional siloxane. This notation is used deliberately because the available FE-SEM, DLS, EDX, and optical data confirm the formation of an Nd-containing mixed-halide colloidal perovskite system, but do not provide direct crystallographic proof of substitutional Nd³⁺ incorporation at the Pb²⁺ B-site. The obtained dispersions show stable blue emission with a maximum at about 458 nm, a photoluminescence quantum yield of about 56%, an essentially invariant emission maximum when the excitation wavelength is varied from 300 to 390 nm, and monoexponential decay kinetics with a characteristic lifetime of 6.67 ± 0.97 ns. Field-emission scanning electron microscopy combined with morphometric analysis of at least 150 particles indicates a nanoscale size distribution with an average equivalent diameter of 8.8 nm, a median of 7.3 nm, and 93.25% of particles smaller than 25 nm. Dynamic light scattering confirms a narrow hydrodynamic size distribution in the 7–9 nm range and a low polydispersity index. Elemental mapping by EDX confirms the co-presence of Cs, Pb, Br, Cl, and Nd in the analyzed particles. The observed blue shift is discussed in terms of the combined effect of chloride incorporation, nanoscale size, possible Nd-related perturbation of the local electronic/defect structure, and reduced non-radiative losses after surface passivation. No definitive crystallographic assignment of Nd to a specific lattice site is claimed; the composition is therefore treated as nominal, and the structural interpretation remains provisional pending XRD/XPS or related studies. Full article

Intelligent sensors and Internet of Medical Things (IoMT) platforms are rapidly changing smart healthcare by enabling continuous capture of physiological, behavioral, and clinical events outside conventional hospital settings. Yet the value of connected sensing depends on more than signal acquisition alone. A practical remote-monitoring ecosystem must also convert sensor alerts, clinician-facing summaries, and historical electronic clinical records (ECRs) into ranked evidence that supports care decisions. This study reframes a large-AI clinical retrieval model as the intelligence layer of an edge–cloud IoMT architecture. The proposed framework combines Transformer-Based Sequence (TBS) encoding, BioBERT-driven representation learning, explicit retrieval, and domain-guided re-ranking to connect sensor-originated narratives, patient records, and clinician queries. The empirical evaluation is conducted on Medical Information Mart for Intensive Care III (MIMIC-III) and i2b2, two de-identified clinical text benchmarks that approximate the documentation layer of real-world remote patient monitoring. Compared with strong baselines, including DeepBio, UniT2T, Web4IR, A2A-API, CoLTiD, VLRG, ColBERT, DeepSDH, BiRex, and DL4BTM, the proposed model achieves the best overall performance, reaching F1/Pre/NDCG scores of 0.8399/0.8338/0.5235 on MIMIC-III and 0.8090/0.8100/0.5129 on i2b2. Ablation experiments confirm the importance of exploratory data adaptation, critical feature modeling, critical token learning, cross-disciplinary supervision, and data-driven regularization. Parameter sensitivity analysis shows stable behavior for beta values greater than or equal to 1, with the strongest results at beta = 5. The study concludes that large-AI retrieval can strengthen the clinical interpretation layer required for IoMT-enabled remote monitoring, while future work should validate the approach on live multimodal sensor streams and privacy-preserving deployments. Full article

Green synthesis of KGM-capped CeO₂ nanoparticles was successfully achieved through a simple coprecipitation method using Konjac Glucomannan (KGM) as a biopolymeric capping and stabilizing agent. The reaction conditions were optimized by varying pH (9–11) and temperature (30–70 °C) to evaluate their influence on nanoparticle formation and photocatalytic performance. The synthesized KGM–CeO₂ nanoparticles were comprehensively characterized using FTIR, UV–Vis spectroscopy, XRD, SEM–EDS, TEM, DLS, and ZP analysis to investigate their structural, optical, morphological, and surface properties. The characterization results confirmed the successful formation of porous sponge-like branched CeO₂ nanostructures with irregular morphology. XRD analysis revealed the crystalline nature of the nanoparticles with an average crystallite size of approximately 7.7 nm, while DLS analysis showed an average hydrodynamic particle size of 29.7 nm with a biomodal particle size distribution. The positive zeta potential value (+16.75 mV) confirmed good colloidal stability and reduced agglomeration due to effective capping by KGM. The synthesized nanoparticles also exhibited favorable optical properties with band gap values suitable for photocatalytic applications. The adsorption and photocatalytic degradation performance of the KGM–CeO₂ nanoparticles was investigated against synthetic textile dyes, including Naphthol Blue Black (NBB), Methyl Orange (MO), and a mixed NBB–MO dye system under acidic conditions. Using an adsorbent dosage of 50 mg and dye concentrations of 100 mg/L, the material achieved degradation efficiencies of approximately 99% for NBB, 91% for MO, and 52% for the mixed dye system under UV irradiation for 120 min. Adsorption kinetic studies indicated that the pseudo-second-order model provided the best fit, suggesting that chemisorption is the dominant adsorption mechanism involving multifunctional surface interactions. These findings are particularly relevant for industrial wastewater treatment, since actual textile effluents typically contain complex mixtures of dyes and organic contaminants rather than single dye pollutants. The mixed dye experiments, therefore, provide a more realistic simulation of industrial wastewater conditions. Overall, the synthesized KGM–CeO₂ nanoparticles demonstrate excellent potential as an eco-friendly, cost-effective, and sustainable multifunctional material for adsorption-assisted photocatalytic treatment of dye-contaminated wastewater. Further optimization of operational conditions and catalyst surface properties may enhance its efficiency in multicomponent wastewater systems. Full article