Search Results (7,255)

In response to the challenges of target recognition and misjudgment caused by varying target scales, diverse shapes, and interference such as lake surface reflections in river and lake scenarios, this paper proposes the YOLO v11n-DDH model for fast and detection of spatial targets in river and lake environments. The model builds upon YOLO v11n by introducing the Dynamic Snake Convolution (DySnakeConv) to enhance the ability to extract detailed features. It integrates the Deformable Attention Mechanism (DAttention) to strengthen key features and suppress noise, while combining the improved High-Level Screening Feature Pyramid Network (HSFPN) structure for multi-level feature fusion, thus improving the semantic representation of targets at different scales. Experiments on a self-constructed dataset show that the precision, recall, and mAP of the YOLO v11n-DDH model reached 88.4%, 78.9%, and 83.9%, respectively, with improvements of 3.4, 2.9, and 2.5 percentage points over the original model. Specifically, DySnakeConv increased mAP@50 by 0.6 percentage points, DAttention improved mAP@50 by 0.3 percentage points, and HSFPN contributed to a 0.9 percentage point rise in mAP@50. This patrol system can effectively identify and visualize various pollutants in river and lake areas, such as underwater waste, water quality pollution, illegal swimming and fishing, and the “Four Chaos” issues, providing technical support for intelligent river and lake management. Full article

Laser surface texturing (LST) structures or laser-induced periodic surface structures (LIPSS) are typically created using laser pulses with durations ranging from femtoseconds to nanoseconds. However, nanosecond pulsed lasers, as cost-effective and more productive alternatives, can also be used to generate LST structures on stainless steel (SS) surfaces, making these structures more suitable for industrial applications. In this study, pulsed laser processing is employed to create LST structures on SS (AISI 304), with varying pulse and accumulated fluences, effective pulse counts, and scan parameters, such as pulse-to-pulse distance (pitch) and hatch spacing between scanning lines. A methodology for calculating oxidation density on processed AISI 304 surfaces is presented. Oxidation density, defined as the ratio of the oxidized area to the total processed area, is determined as a function of accumulated fluence, laser power, pulse-to-pulse distance, and hatch spacing. Optical images of the surfaces are analyzed, and oxidation regions are identified using machine learning techniques. The images are converted to grayscale, and machine learning algorithms are applied to classify the images into oxidation and non-oxidation regions based on pixel intensity values. This approach identifies the optimal threshold for separating the two regions by maximizing inter-class variance. Experimental modeling using response surface methodology is applied to experimentally generated data. Optimization algorithms are then employed to determine the process parameters that maximize pulsed laser irradiation performance while minimizing surface oxidation and processing time. This paper also presents a novel method for characterizing oxidation density using image segmentation and machine learning. The results provide a comprehensive understanding of the process and offer optimized models, contributing valuable insights for practical applications. Full article

Changes in lifestyle and food consumption patterns have contributed to a growing demand for home meal replacements (HMRs), emphasizing the need for high-quality seafood-based products. This study aimed to develop a grilled Pacific cod (Gadus macrocephalus) fillet HMR prototype and to evaluate optimal processing conditions, quality characteristics, and shelf-life stability. High-frequency thawing was selected to improve raw material handling by minimizing drip loss to 11.91% and reducing thawing time to 15 min. A thyme-based marinade at a concentration of 3% for 20 min was applied to reduce odor and enhance sensory quality, and superheated steam grilling was compared with conventional heating methods. Processing parameters were optimized using response surface methodology, and smoking conditions were evaluated using different wood types. Superheated steam grilling produced superior sensory attributes under optimal conditions of 340 °C for 4 min, followed by cherry wood smoking at 60 °C for 5 min. The combined processing approach reduced total bacterial counts while maintaining acceptable physicochemical quality characteristics. Thermal processing increased texture firmness and nutritional density due to moisture loss, reduced lipid oxidation, and modified amino acid and fatty acid profiles. Shelf-life modeling indicated safe storage for up to 18 months under frozen conditions. These findings demonstrate that integrated marination, superheated steam grilling, and controlled smoking can be effectively applied to produce a safe, stable, and high-quality grilled Pacific cod HMR product. Full article

To mitigate the entanglement, agglomeration, and unstable conveying of high-moisture rice residues during stubble crushing for field incorporation, a discrete element method (DEM)-based modeling and optimization framework was developed to enhance the performance of a stubble-crushing device under wet paddy-field conditions. The device structure and kinematics were first analyzed, and the physical and mechanical properties of the residues were obtained through field measurements. A hollow wet–flexible straw model was then proposed to account for both mechanical breakage and moisture-induced adhesive interactions. Key contact and material parameters were calibrated using DEM simulations coupled with laboratory shear and three-point bending tests, showing good agreement with experimental trends. The validated model was subsequently extended to the device scale to characterize the cyclic capture–acceleration–throwing behavior of residues inside the crushing chamber. The individual and interactive effects of rotor speed, forward speed, and throwing-chamber clearance on comminution efficiency and conveying stability were investigated. A multi-objective response surface optimization identified an optimal parameter combination of 2000 rpm rotor speed, 0.87 m s⁻¹ forward speed, and 10.5 cm clearance. Under these conditions, the comminution rate reached 96.94%, and the coefficient of variation in throwing uniformity was 8.71%. Field validation further confirmed the reliability of the simulation results, with relative errors below 6%. Overall, the proposed framework provides an effective tool for the design optimization and parameter selection of wet-residue comminution equipment. Full article

This study uses a globally coupled climate framework to examine how regional differences in sulfate emissions, through both direct and indirect aerosol effects, regulate interactions between clouds and radiation and drive nonlinear thermodynamic and hydrological responses in the East Asia and South Asia summer monsoon region. We employ the Community Earth System Model to compare the Shared Socioeconomic Pathways 1–2.6 and 5–8.5 against the historical scenario with perturbations of anthropogenic sulfate. The results reveal regional contrasts in sulfate concentration and aerosol optical depth: direct shortwave radiation increases in East Asia, while South Asia experiences radiation weakening due to higher aerosol optical depth. Indirect aerosol effects induce cloud adjustments, with East Asia developing more low clouds and higher cloud droplet number concentrations and liquid water paths, leading to greater attenuation of surface shortwave radiation and changes in precipitation and convection. Over the Tibetan Plateau, a higher fraction of high clouds and changes in cloud-top heights jointly drive warming, raising net radiation and strengthening both latent-heat and sensible-heat release. South Asia exhibits a north–south oriented precipitation pattern, with intensified warm advection but a distribution shaped by upper and mid-tropospheric circulations. Overall, the coupling of cloud macro-distribution and cloud microphysics emerges as the principal driver, with direct and indirect effects amplifying nonlinear regional responses. To improve predictability, we advocate multi-model comparisons, observational constraints, tighter bounds on cloud-droplet size distributions, liquid water paths, and cloud droplet number concentrations. Full article

Hypersonic vehicles impose stringent requirements on lightweight structures to maintain mechanical integrity under extreme thermal environments. Bismaleimide (BMI)-based carbon fiber-reinforced polymer (CFRP) composites, featuring a high glass transition temperature and excellent thermal stability, are regarded as promising candidates for such applications. However, the high curing temperature and narrow processing window of BMI resins make it challenging to manufacture stiffened-shell structures with low defect levels and high fiber volume fractions. In this study, an integrated manufacturing route—hot-melt prepregging–filament winding–matched-metal mold forming—is proposed, and the key processing parameters are optimized via single-factor experiments and the Box–Behnken response surface methodology. The tensile strength of the laminate is selected as the response variable to evaluate the effects of the compression displacement (A), thermal consolidation/bonding temperature (B), heating rate (C), and cooling rate (D). The results reveal a unimodal dependence of the tensile strength on each parameter, with the significance ranking B > D > A > C; moreover, the A–B and A–D interactions are significant (p < 0.01). The established quadratic regression model exhibits good agreement with experimental data (R² = 0.974; R²_adj = 0.949). The predicted optimum conditions are A = 0.07 mm, B = 114.93 °C, C = 1.35 °C·min⁻¹, and D = 4.58 °C·min⁻¹, corresponding to a predicted tensile strength of approximately 2287 MPa. Validation experiments yielded 2291 MPa, in excellent agreement with the prediction. Microstructural observations indicate tight interlaminar bonding and a pronounced reduction in voids under the optimized conditions. Applying the optimized process to fabricate stiffened-shell demonstrators achieves a fiber volume fraction of >60% and a void content of <1%. This work provides a quantitatively defined processing window and parameter optimization basis for the high-quality manufacturing of BMI-CFRP stiffened-shell structures, with significant engineering relevance. Full article

Mutations in leucine-rich repeat kinase 2 (LRRK2) are among the most common genetic causes of Parkinson’s disease (PD), yet substantial heterogeneity exists among pathogenic variants. How mutations in distinct functional domains of LRRK2 differentially perturb cellular homeostasis remains incompletely understood. Here, we compared two pathogenic LRRK2 mutations—G2019S in the kinase domain and I1371V in the GTPase domain—across multiple cellular models, including SH-SY5Y and U87 cells, and healthy human iPSC-derived floor plate cells. We demonstrate that the I1371V mutation induces markedly more severe cellular dysfunction than G2019S. I1371V-expressing cells exhibited elevated LRRK2 autophosphorylation at S1292 and robust hyperphosphorylation of Rab8A and Rab10, indicating enhanced downstream signaling. These alterations impaired sterol trafficking, leading to selective depletion of membrane cholesterol without changes in total cellular cholesterol. Consequently, I1371V cells displayed increased membrane fluidity, disrupted microdomain organization, altered membrane topology, reduced caveolin-1 expression, and impaired dopamine transporter surface expression and dopamine uptake. Lipidomic profiling further revealed a broad disruption of lipid homeostasis, including reductions in cholesteryl esters, sterols, sphingolipids, and glycerophospholipids, whereas G2019S cells showed comparatively modest changes. Pharmacological intervention revealed mutation-specific responses, with the non-selective LRRK2 modulator GW5074 outperforming the kinase-selective inhibitor MLi-2 in restoring Rab8A phosphorylation, membrane integrity, and dopaminergic function. Collectively, these findings identify membrane lipid dysregulation as a central cell biological mechanism in LRRK2-associated PD and underscore the importance of variant-specific therapeutic strategies. Full article

The lung is highly susceptible to oxidative stress because of its exposure to high oxygen tension and environmental stressors, making tight regulation of the redox environment essential for homeostasis and disease pathogenesis. Extracellular superoxide dismutase (EC-SOD, sod3) is an important antioxidant enzyme in the lung that catalyzes the dismutation of superoxide into hydrogen peroxide and oxygen, thereby regulating the redox environment of the extracellular matrix, cell surfaces, and lining fluids of the lung. This review summarizes the structural features, post-translational regulation, genetic variations, and cellular sources of EC-SOD, with a particular focus on its role in acute respiratory distress syndrome (ARDS). We highlight evidence demonstrating that loss of EC-SOD exacerbates dysregulated immune responses, whereas enhanced EC-SOD activity confers protection in multiple experimental models of acute lung injury. We also discuss how inflammatory signaling, epigenetic regulation, aging, and genetic polymorphisms in the sod3 gene influence EC-SOD expression and function. Finally, we review emerging therapeutic strategies, including SOD mimetics and mRNA-based approaches, and address the challenges associated with non-specific antioxidant therapies in ARDS. Collectively, the data position EC-SOD as a central regulator of extracellular redox signaling and a promising, mechanism-driven therapeutic target in acute lung injury and ARDS. Full article

This paper develops a nonlinear strain wedge (SW) model for analyzing laterally loaded piles installed in the proximity of sandy slopes, with consideration of slope surface deformation. This model is first developed for piles at the slope crest, characterizing the slope surface deformation to calculate soil strain and incorporating the reduction in effective vertical stress. Furthermore, this model provides a smooth transition between piles located at varying distances from the slope and those at the crest, accounting for varying near-slope distances. Thus, a comprehensive model is established that considers the influence of slope effects on pile–soil interactions. Predictions from the proposed model show good agreement with a series of centrifuge tests and three model tests. Finally, the effects of applied load, slope angle, near-slope distance, Poisson’s ratio, and friction angle on the pile response, slope surface deformation, and soil deformation are discussed. Full article

Plant polyphenols are valuable secondary metabolites with significant bioactivities; however, their efficient extraction faces multiple challenges, including the structural complexity arising from their coexistence in free and bound forms within plant matrices, as well as their sensitivity to oxidation and heat. Although emerging green extraction technologies such as deep eutectic solvents, supercritical fluid extraction, and physical field enhancement show potential, current research largely remains method-oriented, lacking an in-depth understanding of the coupling mechanisms between molecular interactions and mass transfer processes. This review explicitly proposes a “mechanism-driven, synergistic integration” framework for the green extraction of plant polyphenols. By systematically analyzing the molecular basis of extractability and the complementarity among emerging technologies, this framework provides theoretical guidance and a practical blueprint for transitioning from empirical optimization to intelligent, synergistic system design. Specifically, it begins by systematically dissecting the structural characteristics of polyphenols and their interactions with cell wall components to clarify the molecular basis of extractability. Next, it critically reviews the mechanisms, advantages, and engineering bottlenecks of representative green technologies, with a focus on how synergistic integration strategies based on complementary mechanisms can overcome the limitations of single technologies to achieve higher extraction efficiency and selectivity. Furthermore, it evaluates the application of response surface methodology and artificial neural networks in process modeling. Finally, it highlights critical challenges such as industrial scale-up, sustainability assessment, and intelligent manufacturing. This review advocates a paradigm shift from optimizing single techniques toward designing intelligent, synergistic systems grounded in mechanistic insights. Full article

This study investigates the influence of layer height, infill density, and the number of perimeters on the FDM 3D printing performance of PLA, a biodegradable and renewable biopolymer. The primary objective is to identify parameter settings that simultaneously maximize impact strength and production efficiency, quantified through filament usage and printing time. In addition, 3D surface profilometry was employed as a non-destructive characterization method to evaluate surface roughness, assess its dependence on process parameters, and establish correlations with destructive impact strength testing. Experimental work was conducted using a Taguchi L9 orthogonal array, and regression-based mathematical models were developed to quantify the effects of individual parameters on the analysed responses. Finally, Grey Relational Analysis (GRA) was applied to perform multi-objective optimization and determine parameter combinations that jointly enhance mechanical durability, surface quality, and production efficiency. The results provide a clear set of manufacturing parameter settings that satisfy both destructive and non-destructive performance criteria while ensuring resource-efficient production. Full article

Teacher education systems globally experience a gap in implementation between policy aspirations and everyday enactment, with implications for initial teacher education (ITE), the quality of practicums, professional identity, and teacher recruitment and retention. Situated in Malta’s superdiverse context and informed by international debates on professional capital, care ethics, inclusion, and ecological conceptions of agency, this article introduces the Responsive Teacher Formation Framework (RTFF). This original, theoretically integrated, and empirically grounded framework foregrounds four interdependent pillars of professional formation: belonging, wellbeing, autonomy and agency. Drawing on a two-year, multi-strand national inquiry synthesising perspectives from children, families, newly qualified teachers, learning support educators, and school leaders, we integrated artefact-elicitation, focus groups, interviews, and questionnaires using reflexive thematic analysis and cross-strand configurational synthesis. Through a meta-synthesis convergence of the different strands of the study, recurrent tensions surface, including procedural versus lived belonging; attention versus neglect of wellbeing; nominal autonomy versus fragile system supports and policy endorsement versus constrained agency. The findings demonstrate how these complexities are experienced across the ITE–school interface. We argue that the RTFF offers a coherent and tractable syntax for ITE programme (re)design that is both theoretically robust and practically adaptable, diagnostically sensitive to local context, and implementable at scale. The model contributes to international discourse by linking fragmented debates on these four pillars into a responsive framework of, and for, teacher formation. Beyond the Maltese case, the RTFF offers an adaptable orientation for superdiverse settings seeking to transition from compliance-driven quality assurance to formation-centred professional excellence. The article concludes by outlining how the RTFF can anchor more integrated and sustainable policy, as well as nurture professional learning communities, thereby advancing the transformation of teacher education for academic excellence. Full article

We report a Fe₃O₄@Pt@poly-LDOPA nanozyme that displays enhanced peroxidase (POD)-like activity. Polymerisation of levodopa onto the surface of Fe₃O₄@Pt yields a carboxyl-rich poly-LDOPA shell that is available for bioconjugation with antibodies and other types of receptors. Physicochemical characterisation confirmed the integrity of the Fe₃O₄ core, successful Pt modification, and formation of the polymer coating under acidic and basic conditions. Steady-state kinetic analysis using the Michaelis–Menten model revealed robust catalytic performance toward both substrates: for H₂O₂, V_max = 4.0 × 10⁻⁸ M·s⁻¹ and K_m = 25.13 mM; for TMB, V_max = 6.07 × 10⁻⁸ M·s⁻¹ and K_m = 0.229 mM, indicative of high turnover and strong apparent affinity for the chromogenic substrate. A nanozyme-linked immunosorbent assay for the SARS-CoV-2 nucleocapsid was developed. The anti-nucleocapsid antibodies were immobilised onto Fe₃O₄@Pt@poly-LDOPA via EDC/NHS. In buffer, the calibration range (1.0–100 ng·mL⁻¹) afforded an LOD of 6.95 ng·mL⁻¹. In 10% human serum, reduced background and improved nanozyme dispersion yielded a linear low-concentration response (0.1–10 ng·mL⁻¹), with an LOD of 0.0036 ng·mL⁻¹. These results establish Fe₃O₄@Pt@poly-LDOPA as a promising inorganic–organic nanozyme platform that combines catalytic effectiveness, magnetic manipulability, and facile bioconjugation for immunosensing of various disease-related biomarkers. Full article

Carbon fiber-reinforced thermoplastic composites (CFRTP) are widely used in automotive, aerospace, and other industries due to their lightweight, high specific strength, recyclability, and superior thermal properties. However, their non-homogeneity and anisotropy present challenging machining characteristics, often leading to damage that deteriorates component performance. It is imperative to conduct numerical simulation and experimental studies on CFRTP to systematically analyze the relationship between cutting mechanisms and the surface integrity of CFRTP. This study aimed to establish an innovative three-dimensional micro-scale cutting numerical model that integrates the differentiated constitutive behaviors and damage criteria of carbon fibers, matrices, and fiber–matrix interfaces—enabling precise characterization of micro-scale damage evolution during cutting. By combining simulation with experimental verification, it unveils the material removal mechanisms and processing damage causes of CF/PEEK, and further pioneers the quantification of the gradient correlation between fiber orientations (0°, 45°, 90°, and 135°) and fracture modes, cutting forces, and surface integrity, thereby addressing the gap of micro-mechanism and quantitative analysis in CFRTP machining. The micro-scale damage mechanisms revealed by the model directly reflect the intrinsic response of individual fibers in the tow, and the collective effect of these micro-behaviors determines the macro-scale machining performance observed in the experiments. A right-angle cutting experiment was conducted to validate the accuracy of the micro-scale numerical model. The mechanisms of fiber fracture, damage patterns, and chip morphology were systematically compared. The experimental results demonstrate good agreement with the outcomes of the numerical simulations. This study aims to bridge the gap between theoretical understanding and practical application of the cutting mechanisms in CFRTP, providing valuable insights for advancements in manufacturing processes. Full article

Wave-driven free-surface flows pose numerical challenges due to tensorial radiation stress forcing, anisotropic diffusion, and strong sensitivity to closure parameters. This paper investigates the numerical behavior of a quasi-3D wave-driven flow model using a coupled depth-integrated (2D) solver with a diagnostic three-dimensional (3D) reconstruction employed for consistency verification to evaluate the validity of dimensional reduction. The scheme is implemented on a staggered Arakawa C-grid with a terrain-following vertical coordinate and explicit pseudo-time-stepping, which enables the direct assessment of stability limits. A reference experiment and systematic sensitivity tests are performed for three idealized bathymetries of increasing complexity. Bottom friction primarily controls the free-surface response, with critical thresholds (e.g., $f\gtrsim 0.03$) identified via the free-surface displacement Z as markers for the onset of numerical stiffness. Horizontal eddy viscosity $N_h$ has a weak influence on depth-integrated transport over most of the tested range, whereas vertical eddy viscosity $N_v$ governs both transport magnitude and stability through the vertical diffusion constraint, acting as the primary bottleneck for computational efficiency. A stability map in the $(N_v,\Delta t,N_z)$ space is provided to delineate stable, marginal, and unstable regimes identifying an optimal vertical resolution of $N_z\approx 10$ for coastal applications. Grid resolution experiments quantify convergence trends and show that sensitivity increases with bathymetric complexity, revealing that bathymetric aliasing in multi-bar systems can lead to errors of up to 20% if gradients are under-resolved. Finally, a consistent set of diagnostic metrics is proposed for comparing 2D solutions with their vertically resolved counterparts, establishing a validity envelope where 2D models remain reliable versus regimes where explicit vertical shear resolution is mandatory. The results provide a practical roadmap for parameter selection, ensuring numerical robustness in complex, mechanically forced free-surface CFD applications. Full article