Search Results (239)

Intramuscular fat (IMF) marbling is a key determinant of beef quality, yet predicting how breed-specific gene expression translates into tissue-scale fat patterning remains a major challenge. Using a small public transcriptomic dataset (n = 3 per breed), this study presents a proof-of-concept omics-to-tissue modeling framework that converts RNA-seq data into biophysically interpretable parameters governing intramuscular adipogenesis. Using transcriptomic profiles from GSE161967 (Japanese Black Wagyu versus Chinese Red Steppes), we derived composite indices capturing the adipogenic commitment (φ) and lipid droplet capacity (ψ) from curated gene modules. These indices were mapped via calibrated linear functions to a Cellular Potts Model (CPM), parameterizing the fibro-adipogenic progenitor (FAP) differentiation probability, lipogenesis rate, adipocyte cohesion, and progenitor abundance. The gene-derived parameters placed Wagyu in a high-adipogenic regime ($p_{F\to A}^{\mathrm{base}}$ = 0.65; $k_{lipogenesis}$ = 0.12), while Chinese Red Steppes resided in a low-adipogenic regime (0.25; 0.04). The CPM simulations revealed a sharp, predictive threshold at $p_{F\to A}^{\mathrm{base}}$ ≈ 0.55, below which IMF remained negligible and above which stable adipocyte clusters and 8–9% IMF emerged. Without post hoc tuning, the gene-derived parameters correctly predicted robust marbling in Wagyu and a lean phenotype in Chinese Red Steppes. A sensitivity analysis identified the adipogenic commitment as the primary control parameter, with lipogenesis acting as an amplifier. Together, these results demonstrate that transcriptomic measurements can quantitatively predict emergent marbling phenotypes through a small set of interpretable biophysical parameters, establishing a generalizable framework for forecasting complex tissue traits from omics data. Full article

Droplet freezing on cold surfaces plays a critical role in icing phenomena and thermal management systems. In this study, a numerical model is developed to investigate the freezing of a single water droplet, with emphasis on the influence of natural convection on internal flow dynamics. A two-phase (water–ice) solver is implemented in OpenFOAM by incorporating an enthalpy–porosity solidification model and a buoyancy model into an existing framework. The solver is verified against the analytical solution of the one-dimensional Stefan problem and validated using benchmark cases of natural convection and solidification in a cavity. Using the validated model, we examine the effects of natural convection and water density inversion on the internal flow behavior during droplet freezing. Simulations are performed for a rigid axisymmetric droplet configuration. By accounting for density inversion in the buoyancy source term, the model successfully captures the experimentally observed reversal of internal flow during freezing. The results indicate that the flow reversal occurs when the maximum droplet temperature approaches the density inversion temperature of water. While early-stage freezing follows the classical Stefan solution, comparisons with experimental data indicate that incorporating droplet impact and heat transfer to the surroundings would further enhance the model’s predictive capability. Full article

Mountain orchards in southern China are characterized by fragmented and complex terrain with a wide slope variation range (5~30°), which easily leads to uneven pesticide distribution and pesticide accumulation on gentle slopes. These issues give rise to core technical bottlenecks such as low pesticide utilization rate, poor operational efficiency, and unclear atomization mechanism, hindering the optimization of pesticide application parameters, causing pesticide waste and environmental pollution, and restricting the sustainable development of the mountain fruit industry. To address this problem, this study designed a slope-classified pipeline layout and developed a high-efficiency fixed pipeline system for phytosanitary application in mountain orchards, featuring stable operation, low labor intensity, and easy intelligent transformation. Following the technical route of “theoretical design-atomization mechanism analysis-parameter optimization-laboratory verification-field application”, ruby nozzles with high wear resistance, uniform droplet distribution, and long service life were selected and optimized to meet the demand for long-term fixed pesticide application in mountain orchards. High-speed imaging technology was used to real-time capture the dynamic atomization process of nozzles, providing support for clarifying the atomization mechanism. Advanced methods such as fluorescence tracing were adopted to quantitatively evaluate key indicators including droplet deposition in canopies, and the system performance was verified through laboratory and field tests, laying a scientific foundation for its popularization and application. Field test results showed that the optimal spray pressure should not be less than 8 MPa. The XR9002 nozzle can generate fine droplets to achieve pesticide reduction while forming a stable hollow cone atomization flow. Fluorescence tracing analysis indicated that the droplet deposition on the adaxial leaf surface decreases with increasing altitude (presumably affected by wind speed), while the initial deposition on the abaxial leaf surface is low and shows no significant variation with altitude. Deposition on the adaxial leaf surface decreased with canopy height, while abaxial deposition was much lower (8.9–14.9%). This technology enables high-precision quantitative analysis of droplet deposition. The core innovations of this study are: clarifying the atomization mechanism of ruby high-pressure nozzles under pesticide application conditions in mountain orchards, constructing a slope-classified terrain-adaptive pipeline layout model, and establishing a closed-loop technical system of “atomization mechanism-pipeline layout-parameter optimization-deposition detection”. This study provides theoretical and technical support for green and precision pesticide application in mountain orchards, and has important academic value and broad application prospects for promoting the intelligent upgrading of the fruit industry in southern China. Full article

To prevent fouling in blast furnace gas top pressure recovery turbine units, the dust content of the gas must be reduced, necessitating its deep purification. A critical challenge to be addressed is the low collection efficiency of fine particulate matter. To improve the collection efficiency of the fine particulate dust in BFG by wet electrostatic precipitators (WESPs), this study implemented measures such as optimizing nozzle atomization performance and the spatial distribution of droplets, along with adding chemical agglomeration agents and surfactants. These approaches promoted the chemical agglomeration of fine dust and enhanced dust collection efficiency. In this study, five nozzle types, six chemical agglomerating agents, and three surfactants were tested. The results show that, under overlapping spray conditions, the 1/8 solid cone nozzle produced the smallest droplet size with the most uniform spatial distribution, exhibiting a d50 of 141.17 μm. When this nozzle was used in combination with guar gum (GG) as a chemical agglomerant, the d50 of BFG dust increased from 8.46 μm to 14.75 μm. The synergistic application of 5 mg/m³ sesbania gum (SBG) and 5 mg/m³ octylphenol ethoxylate (OP-10) further increased the dust d50 to 19.08 μm. Using the 1/8 solid cone nozzle and an XTG concentration of 5 mg/m³ resulted in the highest dust collection efficiency of 96.76%, while the synergistic use of SBG/OP-10 achieved an efficiency of 97.69%. This study elucidates the influence of nozzle atomization characteristics and spray liquid type on dust agglomeration and collection efficiency, achieving an improvement in dust-removal efficiency and the capture of fine-particulate dust, and providing both theoretical and practical foundations for the deep purification of blast furnace gas. Full article

We developed an arbitrary Lagrangian–Eulerian (ALE)-based multiphysics model for evaporation from a contact-line-pinned sessile drop of neat water subject to a vertically oriented sinusoidal alternating current (AC) electric field applied across parallel-plate electrodes. The framework fully couples electrostatics, incompressible flow, heat transfer with evaporative cooling, and transient vapour transport in air, and includes an instantaneous, voltage-controlled electrowetting contact-angle response under constant-contact-radius conditions. Validation against published data shows that the model captures both pinned-droplet evaporation and electrically induced deformation. Because Maxwell traction scales with the squared electric-field magnitude, droplet height and contact angle exhibit a robust 2:1 frequency-doubled response, producing two peak–trough events per voltage period. The resulting periodic deformation drives oscillatory interfacial shear and internal recirculation, yielding a synchronous double-peaked evaporative-flux waveform. Gas-side analysis quantifies a time-varying diffusion-layer thickness via a characteristic diffusion length; two thinning events per period coincide with flux maxima, indicating that AC enhancement is dominated by periodic compression of the vapour boundary layer and reduced gas-side mass-transfer resistance. Increasing voltage amplitude (0–60 kV) strongly accelerates volume loss, while frequency has a secondary effect: the cycle-averaged flux rises from 1 to 10 Hz but decreases slightly at 20 Hz due to phase lag and weaker boundary-layer modulation. Full article

Background: Multiple myeloma (MM) is characterised by clonal expansion of plasma cells (PCs) in the bone marrow (BM). Disease assessment and monitoring typically rely on invasive, single-site procedures, such as BM biopsies (BMBs), which may inadequately capture intra- and extra-medullary spatial heterogeneity. Circulating plasma cells (CPCs), enriched from peripheral blood (PB), may represent a minimally invasive alternative or adjunct for molecular profiling. Objectives: This study aimed to evaluate the feasibility of using CPCs, enriched from PB, for mRNA analysis in plasma cell dyscrasia, including MM. A secondary objective was to assess whether mRNA expression levels of the endoplasmic reticulum (ER) stress sensors X-box-binding protein 1 (uXBP1) and activating transcription factor 6 (ATF6), and the chaperone-mediated autophagy marker Lysosomal-Associated Membrane Protein 2 (LAMP2A) by droplet digital PCR (ddPCR), were associated with resistance to the second-generation proteasome inhibitor (PI) carfilzomib (Cfz). Methods: Multiple myeloma (MM) cell lines (H929 and U266) and their carfilzomib-adapted derivatives were used to establish and validate droplet digital PCR (ddPCR) assays targeting ER stress (uXBP1, ATF6) and autophagy-related (LAMP2A) transcripts. Solid tumour cell lines, including serum-starved HeLa cells, served as biological controls to support assay specificity and sensitivity. Total RNA was extracted and reverse-transcribed to complementary DNA prior to analysis. Transcript levels were normalised to those of β-actin or GAPDH, as appropriate. ddPCR was performed using the BioRad QX200 system, with results reported as the normalised transcript copy number per microlitre of reaction. Matched bone marrow aspirate (BMA) and peripheral blood (PB) samples were collected at a single clinical time point from adults undergoing investigation for plasma cell dyscrasia between January 2021 and December 2023. Samples were obtained as part of standard clinical care and/or during treatment with Bortezomib (Btz) or Cfz. Mononuclear cells were isolated by density gradient centrifugation, and CD138⁺ plasma cells were enriched by fluorescence-activated cell sorting. Enrichment purity was assessed qualitatively by immunofluorescence microscopy using CD138 and CD117 markers. Samples yielding fewer than 1000 CD138⁺ plasma cells were excluded, resulting in 10 evaluable matched patient pairs. Results: Carfilzomib-adapted MM cell lines demonstrated reduced levels of uXBP1, ATF6, and LAMP2A mRNA compared to treatment-naïve cells. In matched BM and PB samples, uXBP1 mRNA levels were consistently lower in circulating PCs than in BM-derived PCs, whereas ATF6 mRNA levels were concordant between compartments. LAMP2A mRNA levels exhibited marked inter-patient heterogeneity. Conclusions: This study demonstrates the feasibility of using CPCs as a minimally invasive source for mRNA-based biomarker assessment and highlights ddPCR as a sensitive platform for quantifying ER stress and chaperone-mediated autophagy related transcripts in CPCs. Cfz adaptation was associated with reduced levels of uXBP1 and LAMP2A mRNA in MM cell lines. Future prospective studies evaluating the clinical utility of ER stress and chaperone-mediated autophagy associated transcripts in CPCs as predictors of resistance to PI are warranted. Full article

Platinum group metals (PGMs) are strategic metals, and recycling PGMs in spent automobile exhaust catalysts (SACs) is a key path to alleviate the contradiction between resource supply and demand. This paper proposes a new Cu-Ni capture process and conducts research on the recovery of PGMs from SACs. Through the binary phase diagram analysis of Cu, Ni and PGMs and the thermodynamic calculation of the system reduction reaction, the feasibility of this technology was theoretically confirmed. Experimental results show that under the conditions of a temperature of 1450 °C, a holding time of 90 min, a Cu-Ni ratio of 1:1, and a basicity of 0.58, the recovery rates of Pt, Pd, and Rh reached 99.2%, 99.34%, and 98.48% respectively. Combined with orthogonal experiments, it was verified that temperature is the most influential factor on the recovery rate, and the four-stage capture mechanism of “initial diffusion—droplet aggregation—sedimentation and wetting—slag–metal separation” was clarified. This process reduces the melting temperature and provides new technology for green and efficient recycling of PGMs. Full article

Advances in high-performance computing have expanded the use of computational fluid dynamics (CFD) for reacting-flow analysis; however, simulations involving detailed flame kinetics remain computationally intensive for many practical systems. Efficient modeling approaches are therefore essential for predicting flame behavior in swirl-stabilized combustors. This study examines the influence of main-stage swirl intensity on near-lean blow-off characteristics in a multistage swirl combustor using a hybrid RANS–LES framework. The Stress Blended Eddy Simulation (SBES) model, coupled with a Flamelet Generated Manifold (FGM) combustion formulation, is employed to capture key turbulence–chemistry interactions. Results indicate that reducing swirl intensity suppresses the formation of a swirl-stabilized flame, while excessive swirl negatively affects emission performance. For the baseline (S2) and high-swirl (S3) configurations, flame lift-off height increases by 21.0% and 11.96%, respectively, for every 0.1 reduction in equivalence ratio. The S3 case also demonstrates reduced combustion efficiency, with CO emissions rising by 156.4% relative to S2. Local flame extinction is observed in regions of strong droplet–flame interaction, highlighting enhanced quenching susceptibility under near-blow-off conditions. The present study investigates the flame dynamics in a multi-stage swirl combustor using high-fidelity CFD simulations. This study has yet to be validated through experimental analysis and the results presented in this work are entirely computational. Further experimental validation is necessary to verify the results. Full article

Fog water collectors (FWCs) present a sustainable solution for arid regions where fog is a primary water source. To improve their efficiency, we developed a durable and high-performance mesh composed of electrospun hydrophobic thermoplastic polyurethane (TPU) fibers combined with hydrophilic cellulose acetate (CA) microbeads. This hybrid design represents a novel biomimetic strategy, mimicking natural fog-harvesting mechanisms by optimizing wetting and drainage. Despite the significant reduction in average fiber diameter, the TPU-CA mesh maintained mechanical strength close to 1 MPa, comparable to pristine TPU. The introduction of hydrophilic domains into a hydrophobic fibrous network is a unique architectural approach that enhanced fog collection performance, achieving a high water harvesting rate of 127 ± 12 mg·cm⁻²·h⁻¹. Remarkably, although the mesh remained predominantly hydrophobic, droplets shed completely from its vertical surface, exhibiting near-zero contact angle hysteresis. This synergistic wetting concept enables performance unattainable with conventional single-wettability meshes. Compared to single-material meshes, the TPU-CA hybrid showed nearly double the water collection efficiency. The innovative interplay between surface chemistry, microscale heterogeneity, and mechanical robustness is key to maximizing water capture and transport, offering a promising path for scalable, efficient FWCs in poor water-stressed regions. Full article

The high-quality droplet formation in continuous inkjet printing (CIJ) is crucial for precise character deposition on product surfaces. This process, where a piezoelectric transducer perturbs a high-speed ink stream to generate micro-droplets, is highly sensitive to parameters like ink pressure and transducer amplitude. Suboptimal conditions lead to satellite droplet formation and charge transfer issues, adversely affecting print quality and necessitating reliable monitoring. Replacing inefficient manual inspection, this study develops MBSim-YOLO, a deep learning-based method for automated droplet detection. The proposed model enhances the YOLOv8 architecture by integrating MobileNetv3 to reduce computational complexity, a Bidirectional Feature Pyramid Network (BiFPN) for effective multi-scale feature fusion, and a Simple Attention Module (SimAM) to enhance feature representation robustness. A dataset was constructed using images captured by a CCD camera during the droplet ejection process. Experimental results demonstrate that MBSim-YOLO reduces the parameter count by 78.81% compared to the original YOLOv8. At an Intersection over Union (IoU) threshold of 0.5, the model achieved a precision of 98.2%, a recall of 99.1%, and a mean average precision (mAP) of 98.9%. These findings confirm that MBSim-YOLO achieves an optimal balance between high detection accuracy and lightweight performance, offering a viable and efficient solution for real-time, automated quality monitoring in industrial continuous inkjet printing applications. Full article

Droplet impact and spreading on spherical surfaces, often accompanied by heat transfer, are crucial in both industrial and natural settings. This study investigates droplet impact dynamics on a heated spherical surface under gravity at low Weber numbers. A new experimental setup was developed to capture the droplet spreading process, which was combined with a volume-of-fluid-based direct numerical simulation incorporating multiphase heat transfer. This hybrid experimental–numerical approach validated the simulation accuracy and enabled a quantitative analysis of key parameters—including spherical surface temperature, diameter ratio, and Weber number—on droplet spreading behavior. Parametric analyses reveal that an increase in spherical surface temperature slightly enhances the maximum spread coefficient. The maximum spread coefficient grows significantly with the Weber number but remains largely unaffected by the particle-to-droplet size ratio. Furthermore, the total heat flux strongly correlates with the droplet spreading area, reaching a peak at the maximum spread length. Full article

Orchard wind-assisted spraying technology relies on auxiliary airflow to disturb the canopy and improve droplet deposition uniformity. However, there are few effective means of quantitatively assessing the dynamic response of fruit tree leaves to airflow or the changes in airflow patterns within the canopy in real time. To address this, this study proposed an online monitoring method for the aerodynamic characteristics of fruit tree leaves using strain gauge sensors. The flexible strain gauge was affixed to the midribs of leaves from peach, pear and apple trees. Leaf deformations were captured with high-speed video recording (100 fps) alongside electrical signals in controlled wind fields. Bartlett low-pass filtering and Fourier transform were used to extract frequency-domain features spanning between 0 and 50 Hz. The AdaBoost decision tree model was used to evaluate classification performance across frequency bands. The results demonstrated high accuracy in identifying wind exposure (98%) for pear leaf and classifying the three leaf types (κ = 0.98) within the 4–6 Hz band. A comparison with the frame analysis of high-speed video recordings revealed a time error of 2 s in model predictions. This study confirms that strain gauge sensors combined with machine learning could efficiently monitor fruit tree leaf responses to external airflow in real time. It provides novel insights for optimizing wind-assisted spray parameters, reconstructing internal canopy wind field distributions and achieving precise pesticide application. Full article

Detecting Escherichia coli on food preparation and processing surfaces is critical for ensuring food safety and preventing foodborne illness. This study focuses on detecting E. coli contamination on common food processing surfaces using UV-C fluorescence imaging and deep learning. Four concentrations of E. coli (0, 10⁵, 10⁷, and 10⁸ colony forming units (CFU)/mL) and two egg solutions (white and yolk) were applied to stainless steel and white rubber to simulate realistic contamination with organic interference. For each concentration level, 256 droplets were inoculated in 16 groups, and fluorescence videos were captured. Droplet regions were extracted from the video frames, subdivided into quadrants, and augmented to generate a robust dataset, ensuring 3–4 droplets per sample. Wavelet-based denoising further improved image quality, with Haar wavelets producing the highest Peak Signal-to-Noise Ratio (PSNR) values, up to 51.0 dB on white rubber and 48.2 dB on stainless steel. Using this dataset, multiple deep learning (DL) models, including ConvNeXtBase, EfficientNetV2L, and five YOLO11-cls variants, were trained to classify E. coli concentration levels. Additionally, Eigen-CAM heatmaps were used to visualize model attention to bacterial fluorescence regions. Across four dataset groupings, YOLO11-cls models achieved consistently high performance, with peak test accuracies of 100% on white rubber and 99.60% on stainless steel, even in the presence of egg substances. YOLO11s-cls provided the best balance of accuracy (up to 98.88%) and inference speed (4–5 ms) whilst having a compact size (11 MB), outperforming larger models such as EfficientNetV2L. Classical machine learning models lagged significantly behind, with Random Forest reaching 89.65% accuracy and SVM only 67.62%. Overall, the results highlight the potential of combining UV-C fluorescence imaging with deep learning for rapid and reliable detection of E. coli on stainless steel and rubber conveyor belt surfaces. Additionally, this approach could support the design of effective interventions to remove E. coli from food processing environments. Full article

Widely used in treating oil mist aerosols generated from metalworking processes, conventional gas–liquid coalescing filters face drawbacks such as increased energy consumption, performance limitations, and shortened service life due to high steady-state pressure drop. To address these issues, this study proposes an innovative design for a filter based on wettability-regulated gradient pore structure. Using glass fiber filter media with different pore size parameters as the substrate and incorporating an intermediate mesh layer, a three-layer filtration structure of “large-pore filtration layer—mesh layer—small-pore filtration layer” was constructed. The surface wettability of each layer was regulated by a self-developed surface modifier, producing gradient pore structure filters with different wettability configurations. The variations in key performance parameters, including steady-state pressure drop, filtration efficiency, saturation, and service life, were systematically evaluated for these configurations. Experimental results demonstrated that the configuration with an “oleophobic large-pore filtration layer—mesh layer—oleophilic small-pore filtration layer” yielded the best overall performance. Analysis based on the “jump-channel” model indicated that the gradient pore structure achieves progressive droplet filtration and optimizes droplet coalescence and capture through wettability differences. Consequently, while maintaining exceptional filtration efficiency (>99%), this configuration significantly reduces the steady-state pressure drop by over 34% and effectively extends the service life by more than 66%. This wettability-regulated gradient pore structure provides a novel technical pathway for addressing the challenges of balancing pressure drop and filtration efficiency, as well as extending the service life, in gas–liquid coalescing filters. Full article

This research investigates the micro-mechanisms and process control associated with the recovery of platinum group metals (PGMs) from spent automotive catalysts (SACs) through iron capturing. High-temperature smelting experiments, complemented by SEM-EDS and XRD analyses, demonstrate that PGMs spontaneously migrate from the slag phase to the iron phase, driven by interfacial energy, where they are captured to form alloy droplets with a PGM content exceeding 4 wt.%. The composite flux (CaO/H₃BO₃) markedly diminishes slag viscosity and enhances the density differential between slag and metal. This facilitates the aggregation, sedimentation, and separation of alloy droplets in accordance with Stokes’ law, thereby lowering the effective capture temperature from 1700 °C to 1500 °C and reducing energy consumption. Additionally, the flux inhibits the formation of detrimental Fe-Si alloys. PGMs form substitutional solid solutions that are uniformly dispersed within the iron matrix. This study provides both the theoretical and technical foundations necessary for the development of efficient, low-energy processes aimed at capturing and recovering Fe-PGMs alloys. Full article