Search Results (12,535)

Medicinal and edible homologous plant polysaccharides (MEHPs) and edible and medicinal mushroom polysaccharides (EMMPs) have attracted increasing attention due to their favorable safety profiles and diverse biological activities. However, existing reviews have mainly focused on individual extraction approaches or specific biological functions, lacking an integrated perspective that connects extraction strategies, structural characteristics, modification techniques, and industrial applications. This review provides a comprehensive analysis of MEHPs and EMMPs by systematically summarizing conventional and advanced extraction technologies, including ultrasound-, microwave-, and enzyme-assisted extraction, alongside deep eutectic solvent, supercritical fluid, and emerging physically assisted methods. Particular emphasis is placed on extraction mechanisms, kinetic modeling, structural preservation, and artificial intelligence-assisted process optimization. Furthermore, recent advances in multi-technique structural characterization, chemical modification strategies, structure–activity relationships, and applications in functional foods, prebiotics, drug delivery, and health-related fields are discussed. Finally, this review highlights key challenges limiting industrial translation, including raw material variability, insufficient higher-order structural characterization, unclear structure–activity relationships, limited clinical evidence, and regulatory barriers. Future perspectives integrating intelligent manufacturing, sustainable processing, and advanced computational approaches are proposed to facilitate the high-value and sustainable development of MEHPs and EMMPs. Full article

The edge-defined film-fed growth (EFG) method is the dominant industrial technique for producing large-area β-Ga₂O₃ single-crystal substrates, but thermal stress-induced dislocation generation remains a critical barrier. This work presents a coupled thermo-mechanical finite-element framework for thermal-stress management in EFG-grown β-Ga₂O₃. The central methodological contribution is a 500-sample gradient-boosting surrogate sensitivity analysis ($R^2=0.955$, mean absolute error (MAE) $=11.3$ MPa) that quantitatively decomposes thermal-stress variance into controllable process factors and irreducible material-property uncertainties. The physical foundation comprises two enabling elements: (i) the full 21-component monoclinic Voigt stiffness matrix with explicit crystal–model coordinate mapping, for which the orthotropic model is rigorously shown to be exact in 2D plane strain through an exact kinematic theorem showing that the 2D plane-strain results of prior orthotropic EFG analyses are unaffected by the coupling terms, while the monoclinic formulation provides the essential foundation for future 3D studies; and (ii) a dimensionless and numerical justification for omitting melt convection, which enables 100% solver convergence (500/500 Latin hypercube samples) with stress errors < 1.5 MPa. Afterheater temperature $T_{\mathrm{AH}}$ is the leading controllable parameter (35.9%), nearly tied with the elastic constant $C_{33}$ (35.5%), followed by the thermal-expansion component ${\alpha }_c$ (15.9%). Elevating $T_{\mathrm{AH}}$ from 1900 K to 1950 K reduces the peak von Mises stress by ∼29% (COMSOL Multiphysics 6.2-verified); the 2D plane-strain baseline anchors the surrogate analysis at ${\sigma }_{max}=223$ MPa, while the afterheater-free 3D configuration gives ${\sigma }_{max}=187$ MPa at the crystal periphery near the solid–liquid interface. The isotropic approximation underestimates peak stress by 39.6%, confirming that directional anisotropy is essential for quantitatively reliable thermal stress prediction in monoclinic oxide crystals. Full article

Performance prediction, process parameter optimization, and various data-driven research for low-temperature hot stamping (LTHS) processes all rely on abundant, continuous, and reliable process performance sample data. Collecting data merely through physical experiments leads to high costs, long test cycles, and limited coverage of working conditions. This paper focused on the LTHS process of 22MnB5 high-strength steel and proposed a dataset construction method that integrates experiments with finite element simulation. Firstly, LTHS experiments of 22MnB5 steel V-shaped parts were conducted under different combinations of forming temperature, in-die holding time, and stamping speed. Key performance parameters such as temperature field, forming springback angle, and Vickers hardness were measured. Secondly, a thermo-mechanical-phase transformation multi-field coupled finite element model (FEM) was established and validated using the experimental data. The results revealed that the simulation results agree well with the experimentally measured springback angle and Vickers hardness, and the FEM could accurately characterize the forming features and material property evolution throughout the whole LTHS process. On this basis, an experiment–simulation data integration framework was constructed: validated FEMs were adopted to supplement missing working conditions within the parameter space based on physical test samples. An LTHS integrated dataset with wide coverage, high data continuity, and strong usability was built by unifying variable definitions, sample organization modes, and standardized data formats. The dataset established in this paper can provide solid data support for the development of performance prediction models, process parameter optimization, and other data-driven studies of LTHS processes. Moreover, this dataset construction strategy integrating experiments and simulations can be extended to other metal plastic forming fields. Full article

Background: Myocardial infarction (MI) causes spatially heterogeneous loss of contractility and progressive extracellular matrix remodeling, altering left ventricular mechanics from the acute phase through chronic remodeling. This review integrates current understanding of infarct, border-zone, and remote-myocardial microstructure with organ-scale mechanics and patient-specific computational modeling. Methods: A narrative review and perspective were conducted using the literature identified through PubMed/MEDLINE, Scopus, and Web of Science, supplemented by targeted searches of IEEE Xplore and Google Scholar. Experimental, imaging, computational, and translational studies were synthesised, with emphasis on post-MI constitutive behaviour, finite-element and growth-and-remodeling models, imaging-informed personalization, inverse parameter estimation, identifiability, model calibration, verification and validation, and uncertainty quantification. No quantitative synthesis was performed because of substantial heterogeneity in study populations, imaging modalities, constitutive formulations, boundary conditions, calibration procedures, and reported outcomes. Results: Contemporary post-MI models can reproduce ventricular volumes, regional strain patterns, and selected haemodynamic measures, while enabling counterfactual simulations of infarct stiffness, border-zone contractility, and loading interventions. However, clinically credible prediction remains constrained by limited in vivo observability of regional tissue properties, poor parameter identifiability, confounding between material properties and loading conditions, and incomplete treatment of measurement, parameter, and model-form uncertainty. Conclusions: The novelty of this review lies in framing post-MI patient-specific modeling as an identifiability- and uncertainty-limited inverse problem rather than solely as a model-fitting exercise. It proposes that translation toward decision-grade prediction requires parsimonious models aligned with a defined clinical context of use, constrained by microstructure-informed priors, multimodal pressure–volume–strain data, longitudinal validation, and routine reporting of parameter identifiability and predictive uncertainty. Full article

Conductive polymer nanocomposites (CPC) are widely investigated as chemo-resistive materials for the detection of volatile organic compounds (VOC). However, the physical mechanisms governing their transient electrical response remain only partially understood, limiting the development of predictive models and highly selective sensors. In this work, the chemo-resistive behaviour of carbon nanoparticle-filled poly(ethylene-co-ethyl acrylate) (EEA-CNP) was investigated through a multiphysics experimental approach combining simultaneous measurements of solvent uptake, dimensional changes, temperature and electrical resistance during toluene sorption and desorption. Thick specimens were deliberately employed to amplify transient diffusion phenomena and enable direct observation of the coupling between mass transport, polymer swelling and conductive network evolution. The results demonstrate that electrical resistance cannot be interpreted solely from the average solvent concentration within the material. Instead, the transient response is primarily governed by solvent concentration gradients, which continuously modify the connectivity of the conductive nanoparticle network during diffusion. This mechanism explains the pronounced hysteresis observed between sorption and desorption, the transient resistance overshoot during sorption, and the absence of a unique relationship between resistance and solvent content under dynamic conditions. A dedicated quasi-static desorption protocol was therefore developed to minimise concentration gradients and establish the intrinsic correlation between electrical resistivity and solvent fraction. The experiments further show that a solvent content of approximately 6 wt% is sufficient to completely disrupt the conductive percolation network. These findings provide new insights into the multiphysics mechanisms governing chemo-resistive sensing and establish an experimental basis for the development and validation of predictive models for conductive polymer nanocomposites. The proposed methodology is expected to contribute to the optimisation of next-generation VOC sensors and electronic noses with improved selectivity and predictive capability. Full article

Silicon carbide is a typical hard and brittle semiconductor material that is prone to high cutting resistance, surface defects, and subsurface damage during nanomachining. To reveal the atomic-scale material removal mechanism of 3C-SiC under low-power laser heating, a molecular dynamics model of single-crystal 3C-SiC cut by a diamond tool was established, and conventional cutting was compared with laser-assisted cutting. Under low-power laser irradiation, the evolution of system potential energy was generally similar to that observed in conventional cutting, indicating limited overall thermal disturbance. The average cutting force decreased from approximately 285 nN to 275 nN, while the maximum burr height was reduced by about 0.75 Å. In contrast, the chip pile-up height increased by approximately 1.68 Å, and the temperature under laser-assisted cutting was slightly higher than that under conventional cutting. The results indicate that moderate laser-induced thermal effects enhance atomic migration in the cutting region, reduce the resistance to material removal, and improve the flatness and edge quality of the machined surface. These findings provide theoretical insight into the mechanisms of the laser-assisted nanomachining of 3C-SiC. Full article

Sudden collapse accidents in mine roadways occur frequently, and post-disaster emergency rescue faces major challenges in terms of safety and efficiency. Therefore, efficient demolition equipment and intelligent prediction methods are urgently needed. Abrasive waterjet technology has considerable potential for complex disaster environments owing to its high efficiency, environmental friendliness, and cold-cutting characteristics. However, its cutting performance is affected by multiple coupled factors, including jet parameters, material properties, and environmental conditions. This makes accurate prediction difficult, especially under extreme or unseen operating conditions where available samples are limited. To address this problem, this study proposes a zero-shot learning-based multi-physics coupling prediction framework for the “jet–material–environment–effect” relationship. The framework is designed to predict abrasive waterjet cutting performance under unseen working conditions. First, a multi-factor cutting-performance dataset is constructed through a hierarchical experimental design. A generative adversarial network (GAN) is then introduced to expand the sample space and compensate for the discrete nature and limited distributional coverage of the experimental data. Second, a lightweight self-attention mechanism is employed to model high-dimensional input features globally, thereby improving the model’s ability to capture complex feature interactions. Finally, a joint loss function is designed to collaboratively optimize the generation and prediction processes. The experimental results show that the proposed model achieves a prediction accuracy of 98.3% on the test set, with a coefficient of determination R² of 0.967, outperforming WOA-SVM, BP neural network, EML, and Transformer models. The inference response time is approximately 3.2 s, indicating good engineering applicability. The results demonstrate that GAN effectively expands the sample space and improves model generalization, while the LightTransformer structure provides advantages in modeling high-dimensional coupled inputs. The proposed method can provide theoretical support and technical reference for intelligent demolition rescue and cutting-depth prediction under mine disaster conditions. Full article

Pneumatic soft actuators have been drawing considerable attention in the field of soft robotics, thanks to their inherent flexibility, high power density, and safe interaction. However, the strong, intricate coupling between the material’s hyperelastic behavior and the reinforcement of anisotropic fibers creates significant challenges for both analytical modeling and numerical characterization of these actuators. In this paper, we design and fabricate a fiber-reinforced pneumatic soft actuator using Ecoflex 00-30 silicone rubber as the base material and helically wound fibers as the reinforcing layer. We set up a theoretical framework that combines the Neo-Hookean model for isotropic silicone rubber with a strain energy-based formulation for anisotropic wound fibers. This framework describes how the actuator is stretched, expanded, twisted, and bent. Finite element simulations are then carried out, focusing on three key design parameters: winding fiber density (three levels: high, medium, low), air cavity offset distance from the central axis (1, 2, 3, and 4 mm), and air cavity cross-sectional geometry (cube vs. cylindrical). The simulations reveal that a higher winding fiber density promotes more uniform stress distribution across both the strain and confinement layers. In contrast, a low fiber density can lead to local bulging and large stress variations, which ultimately compromises the bending performance. The offset distance of the air cavity from the neutral axis is directly linked to the bending curvature: a larger offset produces greater air cavity deformation and higher actuation efficiency. Furthermore, the cuboid air cavity yields a larger bending angle (experimentally validated up to 90° at 0.045 MPa) and better efficiency, while the cylindrical air cavity distributes stress more evenly across the outer surface of the strain layer and reduces stress concentration at the edges. These findings provide useful quantitative guidance for optimizing the structure of fiber-reinforced soft actuators and establish a framework for hybrid analytical–numerical prediction of their mechanical behavior. Full article

Industrial fermentation relies on mechanically agitated submerged culture, in which impeller-driven mixing improves oxygen and nutrient transfer but imposes an energy penalty and a hydrodynamic shear field that stresses cells. Solid supports offer an agitation-free alternative, yet their surface is typically ill-defined and only a single polymer and a single organism have been examined. Here we treat the support material as a controllable process variable. Using an identical scaffold geometry 3D-printed by fused-deposition modeling in four thermoplastics—ABS, TPU, PLA, and PETG—we compared static cultivation of a model yeast (Saccharomyces cerevisiae) and a model lactic acid bacterium (Lactobacillus plantarum) against shaken and static controls, and related the outcomes to polymer surface wettability. For S. cerevisiae, embedding a matrix in static medium nearly doubled biomass relative to the static control and drove glucose to near-complete assimilation, raising ethanol titres ~1.7–1.8-fold to shaken-culture levels without any agitation. For L. plantarum, three of the four matrices (PLA, ABS, PETG) exceeded both the static and the aerated shaking controls in biomass (by ~15–21%) and in lactic acid production, while the elastomeric TPU behaved like the controls. Because L. plantarum is microaerophilic, these results suggest that factors other than improved aeration, including interactions at the scaffold–liquid interface, contribute to the observed enhancement. The polymer type thus emerges as a tunable parameter for scaffold-assisted cultivation, enabling fermentation without mechanical agitation while achieving performance comparable to, and in some cases greater than, that of shaken culture. The polymer type thus emerges as a tunable determinant of performance, defining an agitation-free cultivation strategy in which the interface—rather than bulk flow—is engineered to achieve, and in some cases surpass, the productivity of conventional stirred culture. Full article

Confronted with severe global challenges, including water scarcity, excessive use of chemical fertilizers and pesticides, and heavy metal contamination in soils, conventional agricultural technologies exhibit marked limitations in integrated water–fertilizer management and non-point source pollution control. Leveraging their excellent water retention capacity, intelligent sustained-release properties, and environmental responsiveness, hydrogels offer innovative solutions to advance sustainable agricultural development. This review comprehensively outlines the fundamental types, crosslinking mechanisms, and key functional properties of hydrogels, with a focused discussion on their agricultural deployment as high-efficiency soil conditioners, fertilizer vectors, and pesticide carriers; it deciphers the microscopic water-holding mechanisms under the tristate water model, delineates the divergent water-uptake and retention behaviors between ionic and non-ionic hydrogels, and clarifies the cyclic water-holding and release mechanisms of hydrogels during soil amelioration. Thise paper further synthesizes hydrogel-enabled environmental remediation applications, in which heavy metals and pesticide residues in soils and aquatic systems are removed via functional-group coordination adsorption or photocatalytic degradation; concurrently, hydrogels have been shown to activate plant systemic immunity through calcium-signaling pathways, thereby inducing broad-spectrum antiviral defense responses. Moreover, hydrogels can be integrated into precision agriculture frameworks to enable real-time monitoring of crop physiological status and to support targeted irrigation and fertilization management. This work also evaluates the role of hydrogels in promoting seed germination, root system development, crop metabolic regulation, and stress resilience, while introducing tailored application strategies across distinct plant growth stages. Their documented economic advantages include water conservation, enhanced crop yields, reduced dependence on synthetic fertilizers, and lower labor costs. Nevertheless, the large-scale implementation of hydrogels continues to face multifaceted challenges—particularly poor degradability and latent ecological risks, as conventional polyacrylamide (PAM)-based gels resist soil mineralization and retain potentially neurotoxic monomers, leaving a critical gap in multi-annual field data concerning their non-target interference with native soil aggregate evolution, pore distribution, and rhizospheric carbon–nitrogen footprints. Mechanistically, many hydrogels with tensile strengths below 1 MPa are highly susceptible to three-dimensional network collapse under high-salinity osmotic shock and tillage mechanical stress, exhibiting a precipitous drop in water retention after more than three wet–dry cycles due to deficient long-term structural stability. Compounding these technical gaps are elevated production costs and low farmer adoption, driven by the absence of texture-specific performance thresholds—such as an available water increment ≥ 40% for sandy soils—and the lack of established life-cycle cost models and farmer incentive mechanisms for bio-based hydrogels. Moving forward, hydrogel technology should pivot toward materials innovation and cost-reduction engineering to broaden its applicability, employ ≥3-year, multi-habitat regional trials to delineate ecological benefit–risk boundaries, and ultimately position hydrogels as pivotal enablers of sustainable, green agricultural paradigms. Full article

Background and Objectives: Three-dimensional printing is increasingly used to fabricate dental restorations; however, limited evidence is available on the mechanical performance and fracture behavior of printable resin materials used for endocrown restorations. Fracture load alone may not fully describe material performance, particularly when brittle or defect-sensitive failure occurs. This in vitro study aimed to compare the fracture resistance, Weibull parameters, experimental Mode I fracture toughness parameter, and failure patterns of three 3D-printed resin materials used for endocrown restorations. Materials and Methods: Thirty anatomically identical molar replicas were produced from a single prepared tooth model and restored with endocrowns fabricated from NextDent C&B MFH (NextDent B.V., Soesterberg, The Netherlands), SprintRay Crown (SprintRay Inc., Los Angeles, CA, USA), and BEGO VarseoSmile Crown Plus (BEGO GmbH & Co. KG, Bremen, Germany) (n = 10/group). The restorations were cemented and subjected to compressive loading until fracture. Maximum fracture force values were analyzed using Welch’s ANOVA and Weibull statistics. In parallel, single-edge-notched bend (SENB) specimens were fabricated from the same materials and tested using an ASTM D5045-based configuration to calculate an experimental Mode I fracture toughness parameter. Representative fractured crowns and standardized specimens were examined using stereomicroscopy to assess visible failure morphology. Results: No statistically significant difference in maximum fracture force was found among the three materials (Welch’s ANOVA, p = 0.217). The mean fracture force values were 862.37 N for NextDent C&B MFH, 804.37 N for SprintRay Crown, and 699.43 N for BEGO VarseoSmile Crown Plus. In the complementary analyses, BEGO VarseoSmile Crown Plus showed the highest Weibull modulus (m = 9.36), indicating a narrower distribution of fracture values, and the highest mean experimental Mode I fracture toughness parameter (5.250 MPa·m^0.5) under the present experimental conditions. Qualitative stereomicroscopic analysis revealed material-dependent visible failure patterns: SprintRay Crown exhibited more extensive fragmentation, whereas BEGO VarseoSmile Crown Plus showed a more defined visible fracture pattern with less secondary fragmentation. Conclusions: Fracture load alone was insufficient to characterize the mechanical behavior of the tested materials fully. Weibull parameters, the experimental fracture toughness parameter, and failure morphology provided complementary information and should be considered when evaluating 3D-printed resin materials for endocrown restorations. Full article

Addressing technical challenges such as inaccurate appearance detection, inaccurate weight estimation, and low automation levels in passion fruit sorting under postharvest conveyor-line conditions, this study proposes an intelligent detection and grading model, YOLOv11n-ACH, based on an improved YOLOv11n. By integrating the Hybrid Inverted Block (HIB) module, the ASF-YOLO scale fusion mechanism, and the Convolutional Attention Fusion Mechanism (CAFM), the model effectively mitigates severe fruit occlusion and background interference caused by conveyor surfaces, residual plant material, and illumination variation. Consequently, the high-precision metric mAP@50-95 reaches 99.4%, representing an increase of 3.9 percentage points over the baseline model. Building upon this foundation, a real-time grading and counting system incorporating a confidence-priority frame-selection mechanism was constructed by combining the ByteTrack multi-object tracking algorithm with horizontal dynamic scale calibration technology. The study establishes a multivariate linear regression mass-estimation model based on morphological features (R² = 0.9617). The regression model was developed using 500 fruits, and its performance was independently evaluated using a second, non-overlapping cohort of 500 fruits collected from the same orchard. Using 12 horizontal calibration points, a cubic spline interpolation function was constructed to compensate for horizontal position-dependent variation in the pixel-to-physical scale under the tested fixed imaging configuration. In the independent mass-validation cohort, the system achieved an MAE of 2.54 g, an RMSE of 3.21 g, and an MARE of 5.65%. A third, non-overlapping cohort of 1568 fruits was used for end-to-end passage-level counting and operational grading evaluation. This lightweight solution provides an engineering approach for passion-fruit sorting under the tested postharvest conveyor-line conditions. Full article

Fe^II-doped zinc ferrite nanocrystals (Fe^II_xZn_1-xFe^III₂O₄) were prepared via a sol–gel approach in the presence of polyvinyl alcohol as a stabilizer and assessed for their capability to eliminate crystal violet (CV) dye from water-based solutions. The structural and surface characteristics of the prepared materials were examined by XRD, HRTEM, FESEM, ATR–FTIR, XPS, UV–visible spectrophotometer and BET analyses. XRD patterns confirmed the formation of a cubic spinel ferrite structure (Fd-3m), indicating successful incorporation of Fe^II into the ZnFe₂O₄ lattice. ATR–FTIR spectra showed characteristic metal–oxygen vibrations at the tetrahedral and octahedral sites. XPS analysis confirmed the coexistence of Fe^II and Fe^III species, which may promote interfacial charge transfer and redox reactions. HRTEM and FESEM images showed particle agglomeration and grain growth after calcination at 700 °C. Fe^II_0.2Zn_0.8Fe^III₂O₄ exhibited the highest photocatalytic performance, achieving 97.2% degradation of CV under optimized conditions. The effects of contact time, catalyst dosage, initial dye concentration, and pH were systematically studied. The maximum removal efficiency was obtained at pH 10 using 0.075 g catalyst for 20 mg L⁻¹ CV solution within 40 min. Freundlich isotherm models exhibited the strongest correlation (R² = 0.918), pointing to multilayer adsorption occurring across a non-uniform nanoparticle surface. The Dubinin–Radushkevich analysis returned an adsorption energy of 3.01 kJ mol⁻¹, implying that physical forces predominantly control the adsorption mechanism. Kinetic investigations revealed a two-stage CV uptake pathway: fast initial binding at exterior surface sites, succeeded by a slower migration of dye molecules into the internal pores of the adsorbent. Full article

To establish an accurate and interpretable prediction framework for soybean lodging grade and clarify the core regulatory traits and differentiated driving mechanisms of soybean lodging under high-density drip irrigation cultivation, 356 spring soybean germplasm accessions were used as experimental materials in this study. Morphological and mechanical traits including plant height (PH), stem pulling force (SPF), internode number (IN) and petiole length (PL) were measured over two consecutive years of field phenotyping. Two composite evaluation indices, plant height/stem pulling force ratio (PH/SPF) and plant height/internode number ratio (PH/IN), were further constructed. Four machine learning algorithms were adopted to develop multi-classification models for soybean lodging grade prediction. SHAP analysis combined with three global sensitivity approaches (perturbation analysis, Sobol’ method and Morris screening) was applied to decipher the regulatory patterns of key traits. The results showed that lodging grade significantly affected soybean grain yield and explained 25–28% of the phenotypic yield variation; yield reduction tended to plateau under severe lodging. Compared with single indicators such as SPF and PL, the two derived composite indices could stably distinguish soybean accessions with different lodging grades and exhibited stronger discriminatory power. Model comparison revealed that the XGBoost model achieved optimal prediction accuracy and generalization stability for lodging grade, with a weighted F1-score of 95.34% on the test set, significantly outperforming the conventional linear model. Interpretability analysis demonstrated that the PH/IN, PH, and PH/SPF acted as the primary positive traits promoting lodging, while SPF was the sole protective trait. Driving factors of lodging presented obvious gradient heterogeneity: mild lodging was dominated by the imbalance of plant architecture ratio, whereas severe lodging was governed by the cumulative effects of PH and IN. Strong interactions existed among all measured traits. The interpretable machine learning framework established in this study can provide theoretical support and technical references for lodging-resistant germplasm screening and targeted plant architecture regulation for densely planted soybean under drip irrigation systems. Full article

Background and Objectives: Trauma is a frequent cause of emergency healthcare requests that requires timely intervention and transport to definitive care, ideally within the first 60 min. On-scene time is considered a modifiable component of the prehospital phase and holds the greatest potential for optimizing prehospital operational time. However, factors contributing to on-scene delays are emerging across different trauma systems, highlighting the need to optimize operational efforts. Data on these factors vary across settings and have rarely been explored in Europe. Romania is an Eastern European country with a distinct demographic distribution and a unique emergency healthcare system. This study aims to evaluate compliance with the golden hour of prehospital trauma care and factors associated with on-scene and total prehospital operational time in Iasi County, Romania. Materials and Methods: A retrospective analysis of data from the electronic dispatch registry from Iasi County was performed for cases dating from January 2025 to February 2026. Data regarding demographics, trauma characteristics, and operational data were retrieved. Generalized linear modeling was used to investigate associations of total prehospital time or on-scene time with demographic and case-specific factors. Multivariable logistic regression was used to evaluate factors associated with total prehospital time exceeding 60 min and on-scene time of 20 min or more. Further sensitivity analyses were performed to account for scene complexity. Age association with operational time was assessed for non-linearity using restricted cubic splines. Results: Of the 8553 included trauma cases, 33.1% presented with on-scene delays, and 59.5% presented with total prehospital time above 60 min. Factors associated with on-scene time were age, rural environment, nature of trauma and severity, and scene-related complexity factors such as number of performed interventions and multiple victim incidents, while for total prehospital time the moment of intervention was also a relevant factor. Age displayed a non-linear association with on-scene time (p < 0.001), with a more pronounced increase after approximately 60 years of age, while for total prehospital time no evidence of non-linearity was observed (time ratio: 1.0024, 95% CI:1.0019–1.0029, p < 0.001). Rural environment was also a factor associated with both operational measurements (time ratio:1.17, 95%CI:1.15–1.2, p < 0.001 for on-scene time and time ratio:1.42, 95% CI:1.38–1.45, p < 0.001 for total prehospital time) and was associated with higher odds of on-scene time ≥ 20 min (OR:1.74, 95% CI:1.58–1.92, p < 0.001) and total prehospital time >60 min (OR:4.91, 95% CI:4.45–5.43, p < 0.001). Conclusions: The large proportion of cases identified with delayed operational times supports the need for further exploration of modifiable factors. The study highlights that, beyond case-specific factors and scene complexity, age and rural environment are relevant to prehospital operational time in Romanian trauma care settings. Further studies are needed to determine the underlying mechanisms of these associations and find targeted strategies to improve operational efficiency. Full article