Search Results (58,063)

The aerodynamic performance of nacelle inlets under crosswind conditions is crucial for engine stability and efficiency. Current parametric investigations are predominantly focused on cruise operations, with minimal consideration given to crosswind conditions. This study employs an iCST-based parametric modeling approach to construct geometric models. A systematic examination of key geometric parameters—including the throat axial location, fan face radius, and leading-edge radii of the inner and outer contours is conducted. The reliability of the numerical methodology was established through a two-step validation process using both the iCST-generated non-axisymmetric model and the DLR-F6 benchmark model, followed by a geometric sensitivity analysis based on parametrically generated axisymmetric models. The results demonstrate that the inner contour leading-edge radius (ROC_I/R_hi) has the most substantial influence on flow separation. When ROC_I/R_hi decreases from 7.84% to 3.46%, the peak maximum circumferential total pressure distortion index (IDCmax) is increased by 86.78% with a 53.85% rearward shift in the complete reattachment mass flow rate. Correspondingly, a similar reduction in the outer contour leading-edge radius (ROC_O/R_hi) from 9.38% to 4.69% results in a 55.50% increase in peak IDCmax and a 33.33% rearward shift. Comparatively, the fan face radius shows minimal impact on flow distortion (increases by 9.72%), but more pronounced effects on total pressure recovery, while rearward movement of the throat axial location (35.00% to 69.00%) causes a 30.03% rise in IDCmax and 43.75% complete flow reattachment delay. It is concluded that the leading-edge optimization is crucial for crosswind resilience, with the inner contour geometry being particularly influential, providing parametric foundations for robust inlet design across a wide range of operating regimes. In addition, it is also found that the effects of Reynolds number (Re) lie in two folds: (1) For a fixed model scale, the aerodynamic performance of the inlet suffers a remarkable degradation with rapidly rising IDCmax as the crosswind velocity-based Re is increased to cause significant flow separations. (2) For a fixed crosswind velocity, the peak IDCmax progressively decreases with the increasing scale based Re, while σ exhibits an overall enhancement as Re rises. Full article

To navigate dynamic environments safely, UAVs require accurate, real time onboard perception, which relies on ego motion compensation to separate self-induced motion from external dynamics and enable reliable obstacle detection. Traditional ego-motion compensation techniques are mainly based on optimization processes and may be computationally expensive for real-time applications or lack the precision needed to handle both rotational and translational movements, leading to issues such as misidentifying static elements as dynamic obstacles and generating false positives. In this paper, we propose a novel approach that integrates an event camera-based perception pipeline with an ego-motion compensation algorithm to accurately compensate for both rotational and translational UAV motion. An enhanced warping function, integrating IMU and depth data, is constructed to compensate camera motion based on real-time IMU data to remove ego motion from the asynchronous event stream, enhancing detection accuracy by reducing false positives and missed detections. On the compensated event stream, dynamic obstacles are detected by applying a motion aware adaptive threshold to the normalized mean timestamp image, with the threshold derived from the image’s spatial mean and standard deviation and adjusted by the UAV’s angular and linear velocities. Furthermore, in conjunction with a 3D Artificial Potential Field (APF) for obstacle avoidance, the proposed approach generates smooth, collision-free paths, addressing local minima issues through a rotational force component to ensure efficient UAV navigation in dynamic environments. The effectiveness of the proposed approach is validated through simulations, and its application for UAV navigation, safety, and efficiency in environments such as warehouses is demonstrated, where real-time response and precise obstacle avoidance are essential. Full article

Timely reporting of microbiological results is critical for clinical decision-making, particularly in pediatric hospitals where delays can significantly impact outcomes. Despite advances in laboratory automation, workflow inefficiencies and resistance to change remain barriers to improvement in Latin America. This study aimed to evaluate the effect of implementing a Kaizen-based change management strategy on reducing turnaround time (TAT) in the microbiology laboratory of Hospital Roberto del Río, Santiago, Chile. We conducted a prospective, pre–post intervention study focusing on blood culture processing. The baseline period (July 2022) included 961 cultures processed with the BacT/ALERT^® 3D system. A Kaizen/LEAN intervention was designed, comprising workflow redesign, staff training, and installation of the BACT/ALERT^® Virtuo^® (bioMerieux, Marcy l’Etoile, France) continuous-loading blood culture system. The intervention engaged all technical and professional staff in a five-day Kaizen immersion, followed by eight months of monitoring. Outcomes were assessed by comparing TAT for positive blood cultures before and after implementation (June 2023, 496 samples). Statistical analysis was performed using the Mann–Whitney U test, with p < 0.05 considered significant. The intervention achieved a median reduction in TAT from 68.22 h (IQR 56.14–88.59) pre-intervention to 51.52 h (IQR 41.17–66.57) post-intervention, corresponding to a 24.48% improvement (p < 0.001), surpassing the 20% target. Time to preliminary Gram reporting also decreased, and workflow standardization enhanced staff productivity and culture validation frequency. Implementation of Kaizen principles in a pediatric microbiology laboratory significantly reduced blood culture TAT and improved workflow efficiency. Beyond technological upgrades, active staff engagement and structured change management were key to success. These findings support the applicability of Kaizen-based interventions to optimize laboratory performance in resource-constrained public healthcare systems. Full article

The growing demand for sustainable solutions in the plastics industry has highlighted the need to reintroduce post-industrial polymer waste into high-performance applications. This study focuses on the mechanical recycling of automotive scraps containing variable proportions of polycarbonate (PC), acrylonitrile–butadiene–styrene (ABS), and a commercial PC/ABS blend. After determining the composition of two representative batches, a screening of seven commercial compatibilizers and impact modifiers was performed to improve impact strength. Among them, an ethylene–methyl acrylate–glycidyl methacrylate (E-MA-GMA) terpolymer was identified as the most effective additive. Its influence was further investigated through a mixture design approach, varying the composition of the three polymer phases and the additive content (0–10 wt.%). The resulting response surface model revealed a significant increase in impact resistance in PC-rich formulations with increasing E-MA-GMA content, while ABS and PC/ABS showed more complex trends. Rheological, mechanical, and thermal analyses supported the observed behavior, suggesting improved matrix compatibility and reduced degradation during processing. The proposed model enables the prediction of impact performance across a wide range of compositions, offering a practical tool for the optimization of recycled blends. These findings support the potential of targeted compatibilization strategies for closed-loop recycling in the automotive sector. Full article

Textile industries release dyes into wastewater, and when present above certain levels, these dyes pose serious risks because of their high toxicity. This study investigates the removal of Sunset Yellow (SY) and Methylene Violet (MV) dyes from wastewater using chitosan (CS) and polysilicate acid (pSi) in the structure of aluminum-based coagulants, resulting in hybrid formulations (CS@Al, Al/pSi, and CS@Al/pSi). Among the various treatment methods that have been applied for the removal of dyes, the coagulation/flocculation process was chosen in the present study, as it is a cheap and effective method. Coagulation performance was optimized for pH, coagulant dosage, temperature and mixing time. The Al/pSi coagulant achieved nearly complete SY removal (98.8%) at 25 mg/L dosage and pH 3.0. MV removal in single-dye solutions was limited, with Al/pSi achieving only 26.6% removal at pH 3.0. However, in mixed-dye systems (SY/MV), synergistic interactions increased MV removal up to 94.4% and SY removal to 100%. Hybrid CS@Al/pSi showed lower SY removal (36.4%) for SY at 50 mg/L but provided stable floc formation, particularly in mixtures of anionic and cationic dyes. Application to real textile wastewater confirmed the high efficiency of the optimized coagulants, particularly with Al/pSi_20,A and AlCl₃, indicating their potential for industrial wastewater treatment. SEM, EDS, XRD, and FTIR analyses revealed structural consolidation, increased surface area, and successful dye adsorption, explaining the high removal efficiency. Full article

This review provides a critical overview of MXenes, an innovative class of 2D transition metal carbides, nitrides, and carbonitrides, emphasizing their synthesis, properties, and application potential. We systematically examine synthesis methods, contrasting top-down approaches with emerging green alternatives and bottom-up techniques, evaluating each in terms of scalability, cost, and environmental impact. This paper highlights MXenes’ unique characteristics, including high electrical conductivity, tunable surface chemistry, and structural versatility, which enable their use in energy storage, environmental remediation, biomedicine, and electromagnetic shielding. Key challenges such as oxidative instability, interfacial incompatibility, and hazardous etching processes are critically discussed. We identify future research priorities, including defect-engineered stabilization, AI-optimized manufacturing, and advanced integration protocols to bridge the gap between laboratory breakthroughs and industrial deployment. By integrating these insights, this review offers a roadmap for advancing MXenes from laboratory innovation to industrial application. Full article

Intensive mothering is a widespread cultural ideology positioning mothers as uniquely responsible for their children’s optimal development through emotionally and cognitively intensive caregiving. A key belief within this framework is maternal essentialism, which asserts that mothers are biologically and morally best suited for parenting young children. Guided by the Family Stress–Proximal Process (FSPP) model, this study examined whether maternal essentialist beliefs act as distal sociocultural stressors influencing children’s executive functioning indirectly through parenting stress and positive parenting behaviors. Data were collected via self-report from 255 U.S. mothers of 3- to 5-year-old children. Path analyses showed that stronger maternal essentialism was associated with increased parenting stress, which predicted lower engagement in positive parenting and greater reported difficulties in children’s executive functioning. The indirect effect of maternal essentialism on children’s executive functioning was statistically significant. These findings suggest that internalized cultural ideologies, often viewed as aspirational, may inadvertently increase parenting stress and reduce caregiving quality, which is associated with diminished child cognitive outcomes. This study extends prior research by linking maternal essentialist beliefs to child developmental outcomes through specified psychological and relational processes, supporting the usefulness of the FSPP framework in understanding how sociocultural pressures influence family dynamics and child development. Full article

Microencapsulation is a fundamental technology for protecting active compounds from environmental degradation by factors such as light, heat, and oxygen. This process significantly improves their stability, bioavailability, and shelf life by entrapping an active core within a protective matrix. Therefore, a thorough understanding of the physicochemical interactions between these components is essential for developing stable and efficient delivery systems. The composition of the microcapsule and the encapsulation method are key determinants of system stability and the retention of encapsulated materials. Recently, the application of computational tools to predict and optimize microencapsulation processes has emerged as a promising area of research. In this context, molecular dynamics (MD) simulation has become an indispensable computational technique. By solving Newton’s equations of motion, MD simulations enable a detailed study of the dynamic behavior of atoms and molecules in a simulated environment. For example, MD-based analyses have quantitatively demonstrated that optimizing polymer–core interaction energies can enhance encapsulation efficiency by over 20% and improve the thermal stability of active compounds. This approach provides invaluable insights into the molecular interactions between the core material and the matrix, ultimately facilitating the rational design of optimized microstructures for diverse applications, including pharmaceuticals, thereby opening new avenues for innovation in the field. Ultimately, the integration of computational modeling into microencapsulation research not only represents a methodological advancement but also pivotal opportunity to accelerate innovation, optimize processes, and develop more effective and sustainable therapeutic systems. Full article

This paper proposes a novel image tone-mapping framework that incorporates meta-learning, a psychophysical model, Bayesian optimization, and light-field virtual diffraction. First, we formalize the virtual diffraction process as a mathematical operator defined in the frequency domain to reconstruct high-dynamic-range (HDR) images through phase modulation, enabling the precise control of image details and contrast. In parallel, we apply the Stevens power law to simulate the nonlinear luminance perception of the human visual system, thereby adjusting the overall brightness distribution of the HDR image and improving the visual experience. Unlike existing methods that primarily emphasize structural fidelity, the proposed method strikes a balance between perceptual fidelity and visual naturalness. Secondly, an adaptive parameter tuning system based on Bayesian optimization is developed to conduct optimization of the Tone Mapping Quality Index (TMQI), quantifying uncertainty using probabilistic models to approximate the global optimum with fewer evaluations. Furthermore, we propose a task-distribution-oriented meta-learning framework: a meta-feature space based on image statistics is constructed, and task clustering is combined with a gated meta-learner to rapidly predict initial parameters. This approach significantly enhances the robustness of the algorithm in generalizing to diverse HDR content and effectively mitigates the cold-start problem in the early stage of Bayesian optimization, thereby accelerating the convergence of the overall optimization process. Experimental results demonstrate that the proposed method substantially outperforms state-of-the-art tone-mapping algorithms across multiple benchmark datasets, with an average improvement of up to 27% in naturalness. Furthermore, the meta-learning-guided Bayesian optimization achieves two- to five-fold faster convergence. In the trade-off between computational time and performance, the proposed method consistently dominates the Pareto frontier, achieving high-quality results and efficient convergence with a low computational cost. Full article

With the increasing demand for precision in seismic exploration, high-resolution surveys and shallow-layer identification have become essential. This requires higher sampling frequencies during seismic data acquisition, which shortens seismic wavelengths and enables the capture of high-frequency signals to reveal finer subsurface structural details. However, the insufficient sampling rate of existing petroleum instruments prevents the effective capture of such high-frequency signals. To address this limitation, we employ high-frequency geophones together with high-density and high-frequency acquisition systems to collect the required data. Meanwhile, conventional processing methods such as Fourier transform-based time–frequency analysis are prone to phase instability caused by frequency interval selection. This instability hinders the accurate representation of subsurface structures and reduces the precision of shallow-layer phase identification. To overcome these challenges, this paper proposes a denoising method for high-sampling-rate seismic data based on Variational Mode Decomposition (VMD) optimized by the Starfish Optimization Algorithm (SFOA). The denoising results of simulated signals demonstrate that the proposed method effectively preserves the stability of noise-free regions while maintaining the integrity of peak signals. It significantly improves the signal-to-noise ratio (SNR) and normalized cross-correlation coefficient (NCC) while reducing the root mean square error (RMSE) and relative root mean square error (RRMSE). After denoising the surface mountain drilling-while-drilling signals, the resulting waveforms show a strong correspondence with the low-velocity zone interfaces, enabling clear differentiation of shallow stratigraphic distributions. Full article

ERK5, a member of the MAP kinase family, has been implicated in several cancer types due to its role in regulating cell proliferation, survival, and migration. In this study, structure-based virtual screening was employed, followed by cell assays, and molecular dynamics simulations to identify novel ERK5 inhibitors. A commercially available library of 1.6 million compounds was subjected to a three-stage docking process (HTVS, SP, and XP), using the docking module in Schrodinger Maestro, yielding 40 candidates with superior docking scores compared to the co-crystallized ligand. These compounds were then tested for antiproliferative activity using an MTT assay in A549 and H292 lung cancer cell lines. Among the hits, STK038175, STK300222, and GR04 showed significant activity with IC₅₀ values of ranging from 10 to 25 µM. Western blot analysis revealed that STK300222 at 50 µM reduced the phosphorylation of ERK5 downstream targets similarly to a known inhibitor, while wound healing assays confirmed a dose-dependent decrease in cell migration. Molecular dynamics simulations of 200 ns further demonstrated that all three compounds form stable complexes with ERK5 that are comparable to the co-crystallized ligand in 5BYZ. The MD simulations also revealed strong electrostatic and solvation interactions observed for STK300222 and GR04 particularly. Furthermore, by calculating the MM-GB/SA scores from the MD trajectories, the binding affinities of the three hits, along with the co-crystallized ligand in 5BYZ, were re-scored. Although the co-crystallized ligand had the highest MM-GB/SA score at −38.96 Kcal mol⁻¹, STK300222 had a comparable score of −35.45 Kcal mol⁻¹. These results highlight STK300222 and GR04 as promising candidates for further optimization and in vivo validation as ERK5 inhibitors. Full article

Aero-engine ablation detection is a critical task in aircraft health management, yet existing rotation-based object detection methods often face challenges of high computational complexity and insufficient local feature extraction. This paper proposes an improved YOLOv11 algorithm incorporating Context-guided Large-kernel attention and Rotated detection head, called CLR-YOLOv11. The model achieves synergistic improvement in both detection efficiency and accuracy through dual structural optimization, with its innovations primarily embodied in the following three tightly coupled strategies: (1) Targeted Data Preprocessing Pipeline Design: To address challenges such as limited sample size, low overall image brightness, and noise interference, we designed an ordered data augmentation and normalization pipeline. This pipeline is not a mere stacking of techniques but strategically enhances sample diversity through geometric transformations (random flipping, rotation), hybrid augmentations (Mixup, Mosaic), and pixel-value transformations (histogram equalization, Gaussian filtering). All processed images subsequently undergo Z-Score normalization. This order-aware pipeline design effectively improves the quality, diversity, and consistency of the input data. (2) Context-Guided Feature Fusion Mechanism: To overcome the limitations of traditional Convolutional Neural Networks in modeling long-range contextual dependencies between ablation areas and surrounding structures, we replaced the original C3k2 layer with the C3K2CG module. This module adaptively fuses local textural details with global semantic information through a context-guided mechanism, enabling the model to more accurately understand the gradual boundaries and spatial context of ablation regions. (3) Efficiency-Oriented Large-Kernel Attention Optimization: To expand the receptive field while strictly controlling the additional computational overhead introduced by rotated detection, we replaced the C2PSA module with the C2PSLA module. By employing large-kernel decomposition and a spatial selective focusing strategy, this module significantly reduces computational load while maintaining multi-scale feature perception capability, ensuring the model meets the demands of high real-time applications. Experiments on a self-built aero-engine ablation dataset demonstrate that the improved model achieves 78.5% mAP@0.5:0.95, representing a 4.2% improvement over the YOLOv11-obb which model without the specialized data augmentation. This study provides an effective solution for high-precision real-time aviation inspection tasks. Full article

Background: Direct clinical training on real patients is essential in dental education. However, the declining patient inflow increasingly challenges this objective. This cross-sectional study aimed to assess patients’ experiences and preferences to derive recommendations for improving patient recruitment. Material and Methods: Over a period of one year, patients treated by students in the courses and final examinations at the dental school of conservative dentistry were questioned using a specially designed questionnaire and reviewed using their medical records. They were asked about their complete treatment process, and patient files were used to record socio-demographic as well as economic and appointment-specific data. Results: We analysed 297 patients (142 women, 47.8%; 155 men, 52.2%) treated by undergraduates across two semesters (four courses) and two final examinations. Median age was 57.0 years (IQR 46–67; mean 55.2, SD 15.2; range 14–85) with no sex-based difference (p > 0.05). Arrival was predominantly by car (72.7%, n = 216); median one-way distance was 20.5 km (IQR 11.2–32.1); and 58.4% were employed, while 41.6% were not employed (33.7% retired, 7.9% unemployed). The leading reason for course attendance was “satisfaction with previous treatments” (65.32%). Information sources were reported by 290/297 (98%); the most common was already being a course patient (143, 48.1%). Most patients attended one appointment (109, 36.7%). Median travel cost per appointment (including parking) was €17.0 (typically €10.0–€23.5). Of 285 respondents, 93.68% answered “Yes” to satisfaction with student treatment. Conclusions: Important steps include enhancing parking facilities, optimizing recall systems and appointment accessibility, and strengthening relationships with regular patients to encourage word-of-mouth referrals. The main focus is to maintain high clinical quality, ensure affordability, and further reduce patient copayments where possible. Full article

The increasing demand for ultra-low carbon steel (Interstitial free steel of Ti-Nb composite stabilized type) has underscored the importance of the RH degassing process, which is critical to achieving stringent quality standards and high productivity. This study aimed to boost decarburization efficiency by expanding the lower volume of the RH degasser and adjusting the circulation gas flow rates (190 Nm³/h, 230 Nm³/h, 250 Nm³/h). The effects of these variations on decarburization time, carbon content, and mechanical properties were systematically evaluated. The Enlarged RH degasser (ERH) achieved a higher decarburization rate than the conventional RH degasser (CRH) at the same gas flow rate of 190 Nm³/h, identifying 230 Nm³/h as the optimal rate for ERH. The experimental decarburization times to reach a carbon content of 0.003 wt% in ultra-low carbon steel were 12.4 min for CRH and 10.8 min for ERH, thus reducing the time by 1.6 min. Conversely, the calculated decarburization times were 13.11 min for CRH and 10.75 min for ERH, with ERH showing a reduction of 2.36 min. Consequently, calculated times were 0.76 min longer than experimental times. No significant differences in inclusions were observed between the CRH and ERH at circulation times of 3, 4, and 5 min; however, the mechanical properties of the ERH showed improvements at 4 and 5 min. Therefore, from an economic perspective, 4 min was established as the optimum time. Ultimately, enhancing the lower volume of the RH degasser has increased productivity and decreased production costs. Full article

Background/Objectives: The optimal healing process following root canal treatment involves biological apical sealing through new cementum formation. Bone morphogenetic protein 7 (BMP-7) has recently gained attention as a potential regulator of cementoblast differentiation and periodontal regeneration. However, its effects on periodontal ligament fibroblasts (PDLFs) and the underlying mechanisms remain incompletely understood. This study aimed to investigate whether BMP-7 induces cementoblast-like differentiation of PDLFs both in vivo and in vitro via the BMP-SMAD signaling pathway. Methods: In a rat pulpectomy model, root canals were treated with or without BMP-7 and examined histologically and immunohistochemically for F-spondin (Spon1) expression. In vitro, human PDLFs were stimulated with BMP-7, and analyses of mineralization, cementoblast marker expression, alkaline phosphatase activity, and SMAD-1/5/9 phosphorylation were conducted. Results: Immunohistochemical analysis revealed that Spon1-positive regions increased around the apical area following BMP-7 treatment, suggesting the induction of cementoblast-like differentiation. In vitro, BMP-7 enhanced the expression of cementoblast-associated genes and mineral deposition while activating SMAD-1/5/9 signaling. Phosphorylation was suppressed by the BMP receptor inhibitor LDN-193189, indicating canonical BMP-SMAD pathway involvement. Conclusions: Although the specific concentration range of maximal activity remains to be determined, the findings collectively suggest that BMP-7 can promote cementoblast-like differentiation of PDLFs and may contribute to apical healing through cementum-related mechanisms. These results provide mechanistic and biological insights that support the potential of BMP-7 as a modulator for biologically favorable periapical tissue regeneration following root canal therapy. Full article