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Air pollution is a chronic problem in large cities worldwide and awareness is rising as the long-term health implications become clearer. Vehicular traffic has been identified as a major contributor to poor air quality. In a lot of cities the publicly available air quality measurements and forecasts are coarse-grained both in space and time. However, in general, real-time traffic intensity data is openly available in various forms and is fine-grained. In this paper, we present an in-depth study of pollution sensor measurements combined with traffic data from Mexico City. We analyse and model the relationship between traffic intensity and air quality with the aim to provide hyper-local, dynamic air quality forecasts. We developed an innovative method to represent traffic intensities by transforming simple colour-coded traffic maps into concentric ring-based descriptions, enabling improved characterisation of traffic conditions. Using Partial Least Squares Regression, we predict pollution levels based on these newly defined traffic intensities. The model was optimised with various training samples to achieve the best predictive performance and gain insights into the relationship betw

In this paper, we investigate the capabilities of in-air 3D SONAR sensors for the monitoring of road surface conditions. Concretely, we consider two applications: Road material classification and Road damage detection and classification. While such tasks can be performed with other sensor modalities, such as camera sensors and LiDAR sensors, these sensor modalities tend to fail in harsh sensing conditions, such as heavy rain, smoke or fog. By using a sensing modality that is robust to such interference, we enable the creation of opportunistic sensing applications, where vehicles performing other tasks (garbage collection, mail delivery, etc.) can also be used to monitor the condition of the road. For these tasks, we use a single dataset, in which different types of damages are annotated, with labels including the material of the road surface. In the material classification task, we differentiate between three different road materials: Asphalt, Concrete and Element roads. In the damage detection and classification task, we determine if there is damage, and what type of damage (independent of material type), without localizing the damage. We are succesful in determining the road surf

Air lubrication regimes were studied using simultaneous drag force measurements and multi-plane imaging to characterize the regimes and identify the governing mechanisms of drag reduction. A bubbly, transitional, and air layer regime are identified over a large range of freestream velocities ($U_{\infty}$), air flow rates ($Q_{air}$), and Froude-depth numbers ($Fr_d$). For the lowest $U_{\infty}$, drag reduction lags significantly behind the non-wetted area coverage at all cases and no simple correlation exists. Within the bubbly regime, a drag increase is found for low $U_{\infty}$ with large, slow-moving bubbles forming a single layer over the plate height. For higher velocities, bubbles become smaller and disperse vertically, while the drag starts decreasing. For higher $Q_{air}$, irrespective of $U_{\infty}$, air patches start to form (transitional regime) and drag monotonically decreases, with the onset of the air layer regime at 60\% drag reduction. A new scaling of the associated critical $Q_{air}$ is proposed, combining the air exit velocity, the liquid velocity close to the air layer and $Fr_d$. For a further increase of $Q_{air}$ and low $U_{\infty}$, a thicker and smooth

The use of Earth-Air-Water Heat Exchangers (EAWHE) for sustainable air conditioning has not been widely studied. Due to their experimental nature, methods of characterizing internal thermal air distribution impose high dependence on instrumentation by sensors and entail data acquisition and computational costs. This document presents an alternative method that estimates air temperature distribution while minimizing the need for a dense network of sensors in the experimental system. The proposed model, DARL (Data of Air and Random Length), can predict the temperature of air circulating inside EAWHEs. DARL is a significant methodological advance that integrates experimental data from boundary conditions with simulations based on pseudo-random numbers (PRNs). These PRNs are generated using Fermat's prime numbers as seeds to initialize the generator. Ordinary linear regressions and robust statistical validations, including the Shapiro-Wilk test and root mean square error, have demonstrated that the model can estimate the thermal distribution of air at different lengths with a relative error of less than 6.2%. These results demonstrate the model's efficiency, predictive capacity, and po

Heating, Ventilation, and Air Conditioning (HVAC) systems are essential for maintaining indoor environmental quality, but their interconnected nature and reliance on sensor networks make them vulnerable to cyber-physical attacks. Such attacks can interrupt system operations and risk leaking sensitive personal information through measurement data. In this paper, we propose a novel attack detection framework for HVAC systems, integrating an Event-Triggering Unit (ETU) for local monitoring and a cloud-based classification system using the Graph Attention Network (GAT) and the Long Short-Term Memory (LSTM) network. The ETU performs a binary classification to identify potential anomalies and selectively triggers encrypted data transmission to the cloud, significantly reducing communication cost. The cloud-side GAT module models the spatial relationships among HVAC components, while the LSTM module captures temporal dependencies across encrypted state sequences to classify the attack type. Our approach is evaluated on datasets that simulate diverse attack scenarios. Compared to GAT-only (94.2% accuracy) and LSTM-only (91.5%) ablations, our full GAT-LSTM model achieves 98.8% overall detec

Intensifying heatwaves driven by climate change are accelerating the adoption of mobile air conditioning (AC) systems. A rapid mass adoption of such AC systems could create additional stress on electricity grids and the power system. This study presents a novel method to estimate the electricity demand from AC systems both at the system level and at high temporal and spatial granularity. We apply the method to a near-future heatwave scenario in Germany in which household AC adoption increases from the current 19% to 35% during a heatwave similar to the one of July 2025. We analyze the effects for 196,428 grid cells of one square kilometer across Germany, by combining weather data, census data, socio-demographic assumptions, mobility patterns, and temperature-dependent AC activation functions. We find that electricity demand of newly purchased mobile AC systems could increase the peak load by over 12.9 GW, with urban hot-spots reaching 5.2 MW per square kilometer. The temporal pattern creates a pronounced afternoon peak that coincides with lower photovoltaic generation, potentially exacerbating power system stability challenges. Our findings underscore the urgency for proactive ener

In the era of growing interest in healthy buildings and smart homes, the importance of sustainable, health conscious indoor environments is paramount. Smart tools, especially VOC sensors, are crucial for monitoring indoor air quality, yet interpreting signals from various VOC sources remains challenging. A promising approach involves understanding how indoor plants respond to environmental conditions. Plants produce terpenes, a type of VOC, when exposed to abiotic and biotic stressors - including pathogens, predators, light, and temperature - offering a novel pathway for monitoring indoor air quality. While prior work often relies on specialized laboratory sensors, our research leverages readily available commercial sensors to detect and classify plant emitted VOCs that signify changes in indoor conditions. We quantified the sensitivity of these sensors by measuring 16 terpenes in controlled experiments, then identified and tested the most promising terpenes in realistic environments. We also examined physics based models to map VOC responses but found them lacking for real world complexity. Consequently, we trained machine learning models to classify terpenes using commercial sens

Advanced Air Mobility (AAM) represents an evolution of the air transportation system by introducing low-altitude, potentially high-traffic environments. AAM operations will be enabled by both new aircraft, as well as new safety- and efficiency-critical supporting infrastructure. Published concepts of operations from both public and private sector entities establish notions such as federated management of the airspace and public-private partnerships for AAM air traffic, but there is a gap in the literature in terms of integrated tools that consider all three critical elements: AAM fleet operators (\emph{lower} layer), airspace service providers (\emph{middle} layer), and overall system governance from the legacy air navigation service provider (\emph{upper} layer). In this work, we explore modeling congestion management within the AAM setting using a bi-level optimization approach, focusing on (1) time-varying, stochastic AAM demand, (2) differing congestion management strategies, and (3) the impact of unscheduled, \enquote{pop-up} demand. We show that our bi-level formulation can be tractably solved using a Neural Network-based surrogate which returns solution qualities within 0.1-

Air quality prediction plays a crucial role in public health and environmental protection. Accurate air quality prediction is a complex multivariate spatiotemporal problem, that involves interactions across temporal patterns, pollutant correlations, spatial station dependencies, and particularly meteorological influences that govern pollutant dispersion and chemical transformations. Existing works underestimate the critical role of atmospheric conditions in air quality prediction and neglect comprehensive meteorological data utilization, thereby impairing the modeling of dynamic interdependencies between air quality and meteorological data. To overcome this, we propose MDSTNet, an encoder-decoder framework that explicitly models air quality observations and atmospheric conditions as distinct modalities, integrating multi-pressure-level meteorological data and weather forecasts to capture atmosphere-pollution dependencies for prediction. Meantime, we construct ChinaAirNet, the first nationwide dataset combining air quality records with multi-pressure-level meteorological observations. Experimental results on ChinaAirNet demonstrate MDSTNet's superiority, substantially reducing 48-ho

Model predictive control of residential air conditioning could reduce energy costs and greenhouse gas emissions while maintaining or improving occupants' thermal comfort. However, most approaches to predictive air conditioning control either do not model indoor humidity or treat it as constant. This simplification stems from challenges with modeling indoor humidity dynamics, particularly the high-order, nonlinear equations that govern heat and mass transfer between the air conditioner's evaporator coil and the indoor air. This paper develops a machine-learning approach to modeling indoor humidity dynamics that is suitable for real-world deployment at scale. This study then investigates the value of humidity modeling in four field tests of predictive control in an occupied house. The four field tests evaluate two different building models: One with constant humidity and one with time-varying humidity. Each modeling approach is tested in two different predictive controllers: One that focuses on reducing energy costs and one that focuses on constraining electric power below a utility-specified threshold. The two models lead to similar performance for reducing energy costs. Combining t

The COVID-19 pandemic caused a paradigm shift in our way of using heating, ventilation, and air-conditioning (HVAC) systems in buildings. In the early stages of the pandemic, it was indeed advised to reduce the reuse and thus the recirculation of indoor air to minimize the risk of contamination through inhalation of virus-laden aerosol particles emitted by humans when coughing, sneezing, speaking or breathing. However, such recommendations are not compatible with energy saving requirements stemming from climate change and energy price increase concerns, especially in winter and summer when the fraction of outdoor air supplied to the building needs to be significantly heated or cooled down. In this experimental study, we aim at providing low-cost and low-energy solutions to modify the ventilation strategies currently used in many buildings to reduce the risk of respiratory disease transmission. We find that ultraviolet germicidal irradiation (UVGI) modules added to the HVAC system are very efficient at inactivating pathogens present in aerosols, leading to good indoor air quality even with significant indoor air recirculation. Moreover, we show that an optimal placement of the air e

Two-dimensional simulations are conducted to investigate the direct initiation of cylindrical detonation in hydrogen/air mixtures with detailed chemistry. The effects of hotspot condition and mixture composition gradient on detonation initiation are studied. Different hotspot pressure and composition are first considered in the uniform mixture. It is found that detonation initiation fails for low hotspot pressures and supercritical regime dominates with high hotspot pressures. Detonation is directly initiated from the reactive hotspot, whilst it is ignited somewhere beyond the nonreactive hotspots. Two cell diverging patterns (i.e., abrupt and gradual) are identified and the detailed mechanisms are analyzed. Moreover, cell coalescence occurs if many irregular cells are generated initially, which promotes the local cell growing. We also consider nonuniform detonable mixtures. The results show that the initiated detonation experiences self-sustaining propagation, highly unstable propagation, and extinction in mixtures with a linearly decreasing equivalence ratio along the radial direction respectively, i.e., 1 to 0.9, 1 to 0.5 and 1 to 0. Moreover, the hydrodynamic structure analysis

Mid-air haptic interfaces employ focused ultrasound waves to generate touchless haptic sensations on the skin. Prior studies have demonstrated the potential positive impact of mid-air haptic feedback on virtual experiences, enhancing aspects such as enjoyment, immersion, and sense of agency. As a highly immersive environment, Virtual Reality (VR) is being explored as a tool for stress management and relaxation in current research. However, the impact of incorporating mid-air haptic stimuli into relaxing experiences in VR has not been studied thus far. In this paper, for the first time, we design a mid-air haptic stimulation that is congruent with a relaxing scene in VR, and conduct a user study investigating the effectiveness of this experience. Our user study encompasses three different conditions: a control group with no relaxation intervention, a VR-only relaxation experience, and a VR+Haptics relaxation experience that includes the mid-air haptic feedback. While we did not find any significant differences between the conditions, a trend suggesting that the VR+Haptics condition might be associated with greater pleasure emerged, requiring further validation with a larger sample s

Traditional turbulence models are derived for single-phase flow. Extension of the family of two-equation turbulence models for two-phase flow is obtained via scaling the transport equations by the density. In the special case of two-phase flow with a sharp interface, jump conditions exist. Two types of jump conditions are found: (1) jump in the partial differential equation (PDE) physical quantities such as density and viscosity and (2) jump in the turbulence frequency. We first derive and clarify the jump in the equations. The jump in the turbulence frequency is proportional to the kinematic viscosity ratio, which is approximately $10$ in the case of air-water. Then a new field, the inverse turbulence area, is considered to model the turbulence effects instead of the turbulence frequency. For the system of air and water, the effect of the jump of the kinematic viscosity is always greater than the effect arising from the jump of velocity gradient. This approximation leads to the assumption of a continuous inverse turbulence area scale. Validation versus experimental measurements from the literature is then presented to demonstrate the improvement of the model. In particular, the wa

Scene completion refers to obtaining dense scene representation from an incomplete perception of complex 3D scenes. This helps robots detect multi-scale obstacles and analyse object occlusions in scenarios such as autonomous driving. Recent advances show that implicit representation learning can be leveraged for continuous scene completion and achieved through physical constraints like Eikonal equations. However, former Eikonal completion methods only demonstrate results on watertight meshes at a scale of tens of meshes. None of them are successfully done for non-watertight LiDAR point clouds of open large scenes at a scale of thousands of scenes. In this paper, we propose a novel Eikonal formulation that conditions the implicit representation on localized shape priors which function as dense boundary value constraints, and demonstrate it works on SemanticKITTI and SemanticPOSS. It can also be extended to semantic Eikonal scene completion with only small modifications to the network architecture. With extensive quantitative and qualitative results, we demonstrate the benefits and drawbacks of existing Eikonal methods, which naturally leads to the new locally conditioned formulation

We investigate the effect of humidity on the propagation of streamers in air. We present a minimal set of chemical reactions that takes into account the presence of water in a nonthermal air plasma and considers ionization, attachment, detachment, recombination and ion conversion including water cluster formation. We find differences in streamer propagation between dry and humid air that we attribute mostly to an enhanced effective attachment rate in humid air, leading to higher breakdown electric field and threshold field for propagation. This higher effective attachment rate in humid conditions leads to a faster decay of the conductivity in the streamer channel, which hinders the accumulation of charge in the streamer head. In some cases a propagating streamer solution still exists at the expense of a smaller radius and lower velocity. In other cases a high humidity leads to the stagnation of the streamer. We finally discuss how all these statements may affect streamer branching and the dimensions and lifetime of a streamer corona.

Although the dynamics of colloids in the vicinity of a solid interface has been widely characterized in the past, experimental studies of Brownian diffusion close to an air-water interface are rare and limited to particle-interface gap distances larger than the particle size. At the still unexplored lower distances, the dynamics is expected to be extremely sensitive to boundary conditions at the air-water interface. There, ad hoc experiments would provide a quantitative validation of predictions. Using a specially designed dual wave interferometric setup, the 3D dynamics of 9 micrometers diameter particles at a few hundreds of nanometers from an air-water interface is here measured in thermal equilibrium. Intriguingly, while the measured dynamics parallel to the interface approaches expected predictions for slip boundary conditions, the Brownian motion normal to the interface is very close to the predictions for no-slip boundary conditions. These puzzling results are rationalized considering current models of incompressible interfacial flow and deepened developing an ad hoc model which considers the contribution of tiny concentrations of surface active particles at the interface. W