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The human nose serves vital physiological functions, including warming, filtration, humidification, and olfaction. These functions are based on transport phenomena that depend on nasal airflow patterns and turbulence. Accurate prediction of these airflow properties requires careful selection of computational fluid dynamics models and rigorous validation. The validation studies in the past have been limited by poor representations of the complex nasal geometry, lack of detailed airflow comparisons, and restricted ranges of flow rate. The objective of this study is to validate various numerical methods based on an anatomically accurate nasal model against published experimentally measured data under breathing flow rates from 180 to 1100ml/s. The numerical results of velocity profiles and turbulence intensities were obtained using the laminar model, four widely used Reynolds-averaged Navier-Stokes (RANS) turbulence models (i.e., k-ε, standard k-ω, Shear Stress Transport k-ω, and Reynolds Stress Model), large eddy simulation (LES) model, and direct numerical simulation (DNS). It was found that, despite certain irregularity in the flow field, the laminar model achieved good agreement with experimental results under restful breathing condition (180ml/s) and performed better than the RANS models. As the breathing flow rate increased, the RANS models achieved more accurate predictions but still performed worse than LES and DNS. As expected, LES and DNS can provide accurate predictions of the nasal airflow under all flow conditions but have an approximately 100-fold higher computational cost. Among all the RANS models tested, the standard k-ω model agrees most closely with the experimental values in terms of velocity profile and turbulence intensity.

The ability to track odor plumes to their source (food, mate, etc.) is key to the survival of many insects. During this odor-guided navigation, flapping wings could actively draw odorants to the antennae to enhance olfactory sensitivity, but it is unclear if improving olfactory function comes at a cost to aerodynamic performance. Here, we computationally quantify the odor plume features around a fruit fly in forward flight and confirm that the antenna is well positioned to receive a significant increase of odor mass flux (peak 1.8 times), induced by wing flapping, vertically from below the body but not horizontally. This anisotropic odor spatial sampling may have important implications for behavior and the algorithm during plume tracking. Further analysis also suggests that, because both aerodynamic and olfactory functions are indispensable during odor-guided navigation, the wing shape and size may be a balance between the two functions. In addition to their aerodynamic properties, insect wings also move odor plumes closer to sensory organs. Li et al. show that Drosophila wings may trade optimal aerodynamic performance for improved olfactory function during flight.

The effects of wing–body interaction (WBI) on aerodynamic performance and vortex dynamics have been numerically investigated in the forward flight of cicadas. Flapping wing kinematics was reconstructed based on the output of a high-speed camera system. Following the reconstruction of cicada flight, three models, wing–body (WB), body-only (BD) and wings-only (WN), were then developed and evaluated using an immersed-boundary-method-based incompressible Navier–Stokes equations solver. Results have shown that due to WBIs, the WB model had a 18.7 % increase in total lift production compared with the lift generated in both the BD and WN models, and about 65 % of this enhancement was attributed to the body. This resulted from a dramatic improvement of body lift production from 2 % to 11.6 % of the total lift produced by the wing–body system. Further analysis of the associated near-field and far-field vortex structures has shown that this lift enhancement was attributed to the formation of two distinct vortices shed from the thorax and the posterior of the insect, respectively, and their interactions with the flapping wings. Simulations are also used to examine the new lift enhancement mechanism over a range of minimum wing–body distances, reduced frequencies and body inclination angles. This work provides a new physical insight into the understanding of the body-involved lift-enhancement mechanism in insect forward flight.

Numerical simulations are used to investigate the hydrodynamic benefits of body–fin and fin–fin interactions in a fish model in carangiform swimming. The geometry and kinematics of the model are reconstructed in three-dimensions from high-speed videos of a live fish, Crevalle Jack (Caranx hippos), during steady swimming. The simulations employ an immersed-boundary-method-based incompressible Navier–Stokes flow solver that allows us to quantitatively characterize the propulsive performance of the fish median fins (the dorsal and the anal fins) and the caudal fin using three-dimensional full body simulations. This includes a detailed analysis of associated performance enhancement mechanisms and their connection to the vortex dynamics. Comparisons are made using three different models containing different combinations of the fish body and fins to provide insights into the force production. The results indicate that the fish produces high performance propulsion by utilizing complex interactions among the fins and the body. By connecting the vortex dynamics and surface force distribution, it is found that the leading-edge vortices produced by the caudal fin are associated with most of the thrust production in this fish model. These vortices could be strengthened by the vorticity capture from the vortices generated by the posterior body during undulatory motion. Meanwhile, the pressure difference between the two sides of posterior body resulting from the posterior body vortices (PBVs) helps with the alleviation of the body drag. The appearance of the median fins in the posterior region further strengthens the PBVs and caudal-fin wake capture mechanism. This work provides new physical insights into how body–fin and fin–fin interactions enhance thrust production in swimming fishes, and emphasizes that movements of both the body and fins contribute to overall swimming performance in fish locomotion.

Lymph node metastasis (LNM), a common metastatic gastric-cancer (GC) route, is closely related to poor prognosis in GC patients. Bone marrow-derived mesenchymal stem cells (BM-MSCs) preferentially engraft at metastatic lesions. Whether BM-MSCs are specifically reprogrammed by LNM-derived GC cells (LNM-GCs) and incorporated into metastatic LN microenvironment to prompt GC malignant progression remains unknown. Herein, we found that LNM-GCs specifically educated BM-MSCs via secretory exosomes. Exosomal Wnt5a was identified as key protein mediating LNM-GCs education of BM-MSCs, which was verified by analysis of serum exosomes collected from GC patients with LNM. Wnt5a-enriched exosomes induced YAP dephosphorylation in BM-MSCs, whereas Wnt5a-deficient exosomes exerted the opposite effect. Inhibition of YAP signaling by verteporfin blocked LNM-GC exosome- and serum exosome-mediated reprogramming in BM-MSCs. Analysis of MSC-like cells obtained from metastatic LN tissues of GC patients (GLN-MSCs) confirmed that BM-MSCs incorporated into metastatic LN microenvironment, and that YAP activation participated in maintaining their tumor-promoting phenotype and function. Collectively, our results show that LNM-GCs specifically educated BM-MSCs via exosomal Wnt5a-elicited activation of YAP signaling. This study provides new insights into the mechanisms of LNM in GC and BM-MSC reprogramming, and will provide potential therapeutic targets and detection indicators for GC patients with LNM.

Through computational fluid dynamics (CFD) simulations of a model manta ray body, the hydrodynamic role of manta-like bioinspired flapping is investigated. The manta ray model motion is reconstructed from synchronized high-resolution videos of manta ray swimming. Rotation angles of the model skeletal joints are altered to scale the pitching and bending, resulting in eight models with different pectoral fin pitching and bending ratios. Simulations are performed using an in-house developed immersed boundary method-based numerical solver. Pectoral fin pitching ratio (PR) is found to have significant implications in the thrust and efficiency of the manta model. This occurs due to more optimal vortex formation and shedding caused by the lower pitching ratio. Leading edge vortexes (LEVs) formed on the bottom of the fin, a characteristic of the higher PR cases, produced parasitic low pressure that hinders thrust force. Lowering the PR reduces the influence of this vortex while another LEV that forms on the top surface of the fin strengthens it. A moderately high bending ratio (BR) can slightly reduce power consumption. Finally, by combining a moderately high BR = 0.83 with PR = 0.67, further performance improvements can be made. This enhanced understanding of manta-inspired propulsive mechanics fills a gap in our understanding of the manta-like mobuliform locomotion. This motivates a new generation of manta-inspired robots that can mimic the high speed and efficiency of their biological counterpart.

Accurate and efficient simulation of airflows in human airways is critical for advancing the understanding of respiratory physiology, disease diagnostics, and inhalation drug delivery. Traditional computational fluid dynamics (CFD) provides detailed predictions but is often mesh-sensitive and computationally expensive for complex geometries. In this study, we explored the usage of physics-informed neural networks (PINNs) to simulate airflows in three geometries with increasing complexity: a duct, a simplified mouth–lung model, and a patient-specific upper airway. Key procedures to implement PINN training and testing were presented, including geometry preparation/scaling, boundary/constraint specification, training diagnostics, nondimensionalization, and inference mapping. Both the laminar PINN and SDF–mixing-length PINN were tested. PINN predictions were validated against high-fidelity CFD simulations to assess accuracy, efficiency, and generalization. The results demonstrated that nondimensionalization of the governing equations was essential to ensure training accuracy for respiratory flows at 1 m/s and above. Hessian-matrix-based diagnosis revealed a quick increase in training challenges with flow speed and geometrical complexity. Both the laminar and SDF–mixing-length PINNs achieved comparable accuracy to corresponding CFD predictions in the duct and simplified mouth–lung geometry. However, only the SDF–mixing-length PINN adequately captured flow details unique to respiratory morphology, such as obstruction-induced flow diversion, recirculating flows, and laryngeal jet decay. The results of this study highlight the potential of PINNs as a flexible alternative to conventional CFD for modeling respiratory airflows, with adaptability to patient-specific geometries and promising integration with static or real-time imaging (e.g., 4D CT/MRI).

Perfluoromethyl vinyl ether (PMVE) has recently emerged as a promising environmentally friendly insulating gas with potential for practical applications in the power industry. When mixed with CO2, the PMVE/CO2 mixture exhibits an elevated liquefaction temperature and enhanced insulation performance, making it suitable for engineering use. In this study, density functional theory (DFT) calculations were employed to investigate the reactive sites of PMVE molecules. The results indicate that the C2–O and C3–O bonds are the most susceptible to breakage, highlighting their high reactivity. The optimal insulation performance of the PMVE/CO2 mixture is achieved at a CO2 concentration of approximately 60%, with significant molecular decomposition observed at temperatures exceeding 2600 K. The primary decomposition products include C2F2, COF3, COF2, F, C2F3, CO, CF3, and C2F4. Both high temperature and elevated CO2 content accelerate the decomposition process. These findings provide valuable insights into the insulation properties and thermal stability of the PMVE/CO2 system, offering theoretical support for its potential application in eco-friendly high-voltage insulation technologies.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread into more than 200 countries and infected approximately 203 million people globally. COVID-19 is associated with high mortality and morbidity in some patients, and this disease still does not have effective treatments with reproducibly appreciable outcomes. One of the leading complications associated with COVID-19 is acute respiratory distress syndrome (ARDS); this is an anti-viral host inflammatory response, and it is usually caused by a cytokine storm syndrome which may lead to multi-organ failure and death. Currently, COVID-19 patients are treated with approaches that mostly fall into two major categories: immunomodulators, which promote the body’s fight against viruses efficiently, and antivirals, which slow or stop viruses from multiplying. These treatments include a variety of novel therapies that are currently being tested in clinical trials, including serum, IL-6 antibody, and remdesivir; however, the outcomes of these therapies are not consistently appreciable and remain a subject of debate. Mesenchymal stem/stromal cells (MSCs), the multipotent stem cells that have previously been used to treat viral infections and various respiratory diseases such as ARDS exhibit immunomodulatory properties and can ameliorate tissue damage. Given that SARS-CoV-2 targets the immune system and causes tissue damage, it is presumable that MSCs are being explored to treat COVID-19 patients. This review summarizes the potential mechanisms of action of MSC therapy, progress of MSC, and its related products in clinical trials for COVID-19 therapy based on the outcomes of these clinical studies.

In edge computing environments, accurate and timely Point of Interest (POI) recommendation is critical for enhancing tourist user experience and service efficiency. The distinct characteristics of edge computing, such as the mobility of users and the dynamic nature of spatial data, pose challenges to traditional tourist recommendation systems. This paper addresses these challenges by treating historical interaction data at various locations and time slots as a temporal sequence of POI matrices. We innovate by integrating Autoregressive Integrated Moving Average (ARIMA) and Singular Value Decomposition (SVD). This integration allows for the effective compression of large-scale spatial-temporal data while enabling the ARIMA model to predict POI preferences simultaneously and accurately. Experimental results demonstrate that our proposed approach significantly outperforms existing state-of-the-art methods in both prediction robustness and efficiency while guaranteeing prediction accuracy even in the sparse data environment. This improvement supports more robust and scalable POI recommendations, facilitating better decision making in mobile and dynamic user environments.