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Respiratory airflow signals provide critical insight into breathing mechanics, yet conventional analysis methods remain limited in their ability to characterize the internal structure of individual breaths. Traditional approaches treat airflow as a quasi-periodic signal and rely on global descriptors such as tidal volume or peak flow, obscuring sub-breath events that reflect neuromuscular coordination and compensatory breathing strategies. This study introduces a parametric framework for decomposing inspiratory airflow into a small number of time-localized components with explicit amplitude, onset time, and duration parameters. Unlike spectral or data-adaptive methods, the proposed approach employs physiologically grounded basis functions, Half-Sine, Gaussian, and Beta, to represent intrabreath waveform morphology through constrained nonlinear optimization. Evaluation across 8,276 breaths demonstrates high reconstruction accuracy (mean squared error $<$ 0.001 for four-component models) and robust parameter precision under moderate noise. Component-derived features describing sub-breath timing and coordination improved classification of cognitive fatigue states arising from cogni

As environmental disasters happen more frequently and severely, seeking the source of pollutants or harmful particulates using plume tracking becomes even more important. Plume tracking on small quadrotors would allow these systems to operate around humans and fly in more confined spaces, but can be challenging due to poor sensitivity and long response times from gas sensors that fit on small quadrotors. In this work, we present an approach to complement chemical plume tracking with airflow source-seeking behavior using a custom flow sensor that can sense both airflow magnitude and direction on small quadrotors < 100 g. We use this sensor to implement a modified version of the `Cast and Surge' algorithm that takes advantage of flow direction sensing to find and navigate towards flow sources. A series of characterization experiments verified that the system can detect airflow while in flight and reorient the quadrotor toward the airflow. Several trials with random starting locations and orientations were used to show that our source-seeking algorithm can reliably find a flow source. This work aims to provide a foundation for future platforms that can use flow sensors in concert w

Asthma causes expiratory airflow limitation and is clinically assessed using spirometry, which provides the FEV1/FVC ratio representing the proportion of air exhaled in the first second relative to total forced vital capacity. Prior studies suggest that respiratory sounds recorded at posterior sites (Left Lower, Left Upper, Right Upper, Right Lower) reflect regional airflow patterns. In this study, we investigate the relationship between the expiratory-to-inspiratory (E/I) spectral power ratio and FEV1/FVC in 141 participants aged 20-60 years using Spearman correlation across frequency subbands. The 100-200 Hz and 200-400 Hz bands showed significant correlations. Overall, lower posterior sites showed stronger associations; younger adults showed stronger correlations at the Left Lower site, whereas older adults showed stronger correlations at the Left Upper site. Gender-stratified analysis showed stronger Left Lower correlations in males and stronger Left Upper correlations in females.

In nature, estimating the location of a molecule source in turbulent airflow is a central, and yet highly challenging problem for mate search and foraging. Recently, it has also received increasing attention in synthetic molecular communication (SMC), e.g., for leakage detection. One important aspect of source localization is to estimate the distance to the molecule source, e.g., to determine whether it is worth to travel to a potential mating partner or food source, or to decide whether a leak is close enough for inspection. In this study, based on realistic simulations, we show that the diversity induced by molecule mixtures can aid source localization. In particular, when different molecule types in a mixture are subject to atmospheric degradation with different degradation rates, the relative abundance of the different species observed at the receiver enables low-complexity estimation of the source distance. Furthermore, this feature can be combined with already established concentration-based and temporal features of observed molecular signals to further increase estimation accuracy. Thereby, we show that molecule degradation diversity of molecule mixtures can help to realize

Climate change and the rapid growth of urban populations are intensifying environmental stresses within cities, making the behavior of urban atmospheric flows a critical factor in public health, energy use, and overall livability. This study targets to develop fast and accurate models of urban pollutant dispersion to support decision-makers, enabling them to implement mitigation measures in a timely and cost-effective manner. To reach this goal, an end-to-end data-driven approach is proposed to model and predict the airflow and pollutant dispersion in a street canyon in skimming flow regime. A series of time-resolved snapshots obtained from large eddy simulation (LES) serves as the database. The proposed framework is based on four fundamental steps. Firstly, a reduced basis is obtained by spectral proper orthogonal decomposition (SPOD) of the database. The projection of the time series snapshot data onto the SPOD modes (time-domain approach) provides the temporal coefficients of the dynamics. Secondly, a nonlinear compression of the temporal coefficients is performed by autoencoder to reduce further the dimensionality of the problem. Thirdly, a reduced-order model (ROM) is learned

In situ tissue biopsy with an endoluminal catheter is an efficient approach for disease diagnosis, featuring low invasiveness and few complications. However, the endoluminal catheter struggles to adjust the biopsy direction by distal endoscope bending or proximal twisting for tissue sampling within the tortuous luminal organs, due to friction-induced hysteresis and narrow spaces. Here, we propose a pneumatically-driven robotic catheter enabling the adjustment of the sampling direction without twisting the catheter for an accurate in situ omnidirectional biopsy. The distal end of the robotic catheter consists of a pneumatic bending actuator for the catheter's deployment in torturous luminal organs and a pneumatic rotatable biopsy mechanism (PRBM). By hierarchical airflow control, the PRBM can adjust the biopsy direction under low airflow and deploy the biopsy needle with higher airflow, allowing for rapid omnidirectional sampling of tissue in situ. This paper describes the design, modeling, and characterization of the proposed robotic catheter, including repeated deployment assessments of the biopsy needle, puncture force measurement, and validation via phantom tests. The PRBM proto

Droplet breakup is important in many natural and industrial processes, but the current classification of breakup regimes, especially the intermediate breakup regime, is ambiguous. In this study, the transitions of breakup regimes for viscous droplets are investigated experimentally using high-speed imaging taken from a side view and a 45$^{\circ}$ view. Based on the morphology change in the middle of the droplet, the breakup regimes are classified into no-breakup, bag breakup, bag-stamen, low-order multimode, high-order multimode, and shear-stripping breakup. The droplet morphologies in different regimes and the corresponding transitions are discussed in detail. The droplet viscosity dissipates the kinetic energy transferred by the airflow during the initial droplet flattening, and affects the development of the Rayleigh-Taylor instability wave after the flattening. Through the analysis of the droplet deformation and the Rayleigh-Taylor instability with the droplet viscosity taken into account, the transition conditions of different regimes are obtained in a regime map. By further considering the relative velocity loss between the droplet and the airflow, the ranges of the dual-bag

We develop a framework for non-invasive volumetric indoor airflow estimation from a single viewpoint using background-oriented schlieren (BOS) measurements and physics-informed reconstruction. Our framework utilizes a light projector that projects a pattern onto a target back-wall and a camera that observes small distortions in the light pattern. While the single-view BOS tomography problem is severely ill-posed, our proposed framework addresses this using: (1) improved ray tracing, (2) a physics-based light rendering approach and loss formulation, and (3) a physics-based regularization using a physics-informed neural network (PINN) to ensure that the reconstructed airflow is consistent with the governing equations for buoyancy-driven flows.

This work demonstrates an airflow inertial based odometry system with multi-sensor data fusion, including thermal anemometer, IMU, ESC, and barometer. This goal is challenging because low-cost IMUs and barometers have significant bias, and anemometer measurements are very susceptible to interference from spinning propellers and ground effects. We employ a GRU-based deep neural network to estimate relative air speed from noisy and disturbed anemometer measurements, and an observer with bias model to fuse the sensor data and thus estimate the state of aerial vehicle. A complete flight data, including takeoff and landing on the ground, shows that the approach is able to decouple the downwash induced wind speed caused by propellers and the ground effect, and accurately estimate the flight speed in a wind-free indoor environment. IMU, and barometer bias are effectively estimated, which significantly reduces the position integration drift, which is only 5.7m for 203s manual random flight. The open source is available on https://github.com/SyRoCo-ISIR/Flight-Speed-Estimation-Airflow.

The role of coherent airflow structures capable of setting gravel-size particles in motion is studied theoretically and experimentally. Specifically, a micromechanical model based on energy conservation is proposed to describe the incipient motion of large particles ranging from rocking (incomplete entrainment) to incipient rolling (full entrainment). Wind tunnel experiments were conducted on an aerodynamically rough bed surface under near-threshold airflow conditions. Synchronous signals of airflow velocities upwind of the test particles and particle displacement are measured using a hot film anemometer and a laser distance sensor, respectively, from which coherent airflow structures (extracted via quadrant analysis) and particle movements are interlinked. It is suggested that the incipient motion of gravel-size particles (rocking and rolling) may result from sufficiently energetic sweep events corresponding to aerodynamic drag forces in excess of the local micro-topography resistance. However, full entrainment in rolling mode should satisfy the presented work-based criterion. Furthermore, using an appropriate probabilistic frame, the proposed criterion may be suitable for describ

Non-contact manipulation is a promising methodology in robotics, offering a wide range of scientific and industrial applications. Among the proposed approaches, airflow stands out for its ability to project across considerable distances and its flexibility in actuating objects of varying materials, sizes, and shapes. However, predicting airflow fields at a distance-and the motion of objects within them-remains notoriously challenging due to their nonlinear and stochastic nature. Here, we propose a model-based learning approach using a jet-induced airflow field for remote multi-object manipulation on a surface. Our approach incorporates an analytical model of the field, learned object dynamics, and a model-based controller. The model predicts an air velocity field over an infinite surface for a specified jet orientation, while the object dynamics are learned through a robust system identification algorithm. Using the model-based controller, we can automatically and remotely, at meter-scale distances, control the motion of single and multiple objects for different tasks, such as path-following, aggregating, and sorting.

Sleep staging is a challenging task, typically manually performed by sleep technologists based on electroencephalogram and other biosignals of patients taken during overnight sleep studies. Recent work aims to leverage automated algorithms to perform sleep staging not based on electroencephalogram signals, but rather based on the airflow signals of subjects. Prior work uses ideas from topological data analysis (TDA), specifically Hermite function expansions of persistence curves (HEPC) to featurize airflow signals. However, finite order HEPC captures only partial information. In this work, we propose Fourier approximations of persistence curves (FAPC), and use this technique to perform sleep staging based on airflow signals. We analyze performance using an XGBoost model on 1155 pediatric sleep studies taken from the Nationwide Children's Hospital Sleep DataBank (NCHSDB), and find that FAPC methods provide complimentary information to HEPC methods alone, leading to a 4.9% increase in performance over baseline methods.

The Workflows as Code paradigm is becoming increasingly essential to streamline the design and management of complex processes within data-intensive software systems. These systems require robust capabilities to process, analyze, and extract insights from large datasets. Workflow orchestration platforms such as Apache Airflow are pivotal in meeting these needs, as they effectively support the implementation of the Workflows as Code paradigm. Nevertheless, despite its considerable advantages, developers still face challenges due to the specialized demands of workflow orchestration and the complexities of distributed execution environments. In this paper, we manually study 1,000 sampled Stack Overflow posts derived from 9,591 Airflow-related questions to understand developers' challenges and root causes while implementing Workflows as Code. Our analysis results in a hierarchical taxonomy of Airflow-related challenges that contains 7 high-level categories and 14 sub-categories. We find that the most significant obstacles for developers arise when defining and executing their workflow. Our in-depth analysis identifies 10 root causes behind the challenges, including incorrect workflow c

Relaxation is a critical counterbalance to the demands of modern business life. Footbaths, a simple yet highly effective therapeutic practice, have been used for centuries across various cultures to promote relaxation and overall well-being. This study presents a novel approach to simulating the experience of a public footbath through the use of tactile and thermal stimulation of airflow to the calf and those on the foot soles. Our system aims to offer a realistic and immersive virtual footbath experience without the need for actual water, by controlling the temperature and airflow to mimic the sensation of soaking feet in water or a water wave. Without using actual water, our system can be more compact, highly responsive, and more reproducible. The layer of airflow is made as thin as possible by adjusting air outlet, and the Coanda effect is also considered to generate a water surface more realistic. The system can provide a multi-sensory experience, including visual and audio feedback of water flow, enhancing the relaxation and therapeutic benefits of a footbath.

Our perception of cold enriches our understanding of the world and allows us to interact with it. Therefore, the presentation of cold sensations will be beneficial in improving the sense of immersion and presence in virtual reality and the metaverse. This study proposed a novel method for spatially continuous cold sensation presentation based on low-temperature airflows. We defined the shortest distance between two airflows perceived as different cold stimuli as a local cold stimulus group discrimination threshold (LCSGDT). By setting the distance between airflows within the LCSGDT, spatially continuous cold sensations can be achieved with an optimal number of cold airflows. We hypothesized that the LCSGDTs are related to the heat-transfer capability of airflows and developed a model to relate them. We investigated the LCSGDTs at a flow rate of 25 L/min and presentation distances ranging from 10 to 50 mm. The results showed that under these conditions, the LCSGDTs are 131.4 $\pm$ 1.9 mm, and the heat-transfer capacity of the airflow corresponding to these LCSGDTs is an almost constant value, that is, 0.92.

The present study aims to optimize the structural design of a Z-type flow lithium-ion battery pack with a forced air-cooling system (FACS) known as BTMS (Battery Thermal Management System). The main goal is to minimize Tmax (maximum temperature) and δTmax (maximum temperature difference) while ensuring an even airflow distribution within the battery module. The present study thoroughly investigates critical factors such as the inlet air velocity, tapered inlet manifold, and the number of secondary outlets to evaluate their impact on thermal performance and airflow uniformity within the battery module. Increasing the inlet air velocity from 3 to 4.5 m/s significantly improves the thermal cooling performance of the BTMS, resulting in a decrease of 4.57 °C (10.05%) in Tmax and 0.29 °C (9.79%) in δTmax compared to the original 3 m/s velocity. Further, the study assesses the significance of a tapered inlet manifold as a critical factor, revealing its substantial impact on cooling performance and temperature reductions in battery cells 3-9. It also facilitates a more uniform airflow distribution, decreasing the velocity difference between channel 9 and channel 1 from 3.32 m/s to 2.50 m/s

We present novel numerical simulations investigating the evolution of liquid droplets into bag-like structures in turbulent airflows. The droplet bag breakup problem is of significance for many multiphase processes in scientific and engineering applications. Turbulent fluctuations are introduced synthetically into a mean flow, and the droplet is inserted when the air-phase turbulence reaches a statistically stationary state. The morphological evolution of the droplet under different turbulence configurations is retrieved and analysed in comparison with laminar aerobreakup results. While the detailed evolution history of individual droplets varies widely between different realisations of the turbulent flow, common dynamic and morphological evolution patterns are observed. The presence of turbulence is found to enhance the drag coefficient of the droplet as it flattens. At late times, the droplet becomes tilted and increasingly corrugated under strong turbulence intensity. We quantify these phenomena and discuss their possible governing mechanisms associated with turbulence intermittency. Lastly, the influences of liquid-gas viscosity ratio are examined and the implications of air-ph

Long-term human motion prediction (LHMP) is essential for safely operating autonomous robots and vehicles in populated environments. It is fundamental for various applications, including motion planning, tracking, human-robot interaction and safety monitoring. However, accurate prediction of human trajectories is challenging due to complex factors, including, for example, social norms and environmental conditions. The influence of such factors can be captured through Maps of Dynamics (MoDs), which encode spatial motion patterns learned from (possibly scattered and partial) past observations of motion in the environment and which can be used for data-efficient, interpretable motion prediction (MoD-LHMP). To address the limitations of prior work, especially regarding accuracy and sensitivity to anomalies in long-term prediction, we propose the Laminar Component Enhanced LHMP approach (LaCE-LHMP). Our approach is inspired by data-driven airflow modelling, which estimates laminar and turbulent flow components and uses predominantly the laminar components to make flow predictions. Based on the hypothesis that human trajectory patterns also manifest laminar flow (that represents predicta

The widespread adoption of quadrotors for diverse applications, from agriculture to public safety, necessitates an understanding of the aerodynamic disturbances they create. This paper introduces a computationally lightweight model for estimating the time-averaged magnitude of the induced flow below quadrotors in hover. Unlike related approaches that rely on expensive computational fluid dynamics (CFD) simulations or drone specific time-consuming empirical measurements, our method leverages classical theory from turbulent flows. By analyzing over 16 hours of flight data from drones of varying sizes within a large motion capture system, we show for the first time that the combined flow from all drone propellers is well-approximated by a turbulent jet after 2.5 drone-diameters below the vehicle. Using a novel normalization and scaling, we experimentally identify model parameters that describe a unified mean velocity field below differently sized quadrotors. The model, which requires only the drone's mass, propeller size, and drone size for calculations, accurately describes the far-field airflow over a long-range in a very large volume which is impractical to simulate using CFD. Our