The intricate dynamics of odorants in the indoor environment and human respiratory system remain poorly understood. In the present study, we integrate odor sensory tests (OSTs) and computational fluid dynamics coupled with a physiologically based pharmacokinetic (CFD-PBPK) model to elucidate various aspects of odorant transport and olfaction dynamics. Safe yogurt-derived substances were incorporated into OSTs to prevent harmful exposure. Acetaldehyde was identified as a key active component in determining odor intensity, prompting further analysis of acetone and other four constituents. Logarithmic correlations were established between the perceived odor intensity from the OSTs and both time-averaged absorption flux and equilibrium concentration within the olfactory mucus layer. These parameters were numerically captured, enabling the logarithmic approximation of odor intensity for different breathing profiles and developing reliable prediction models for odor sensation in the indoor environment based on quantifiable physiological parameters. Location-specific analysis revealed the nostrils and olfactory regions as the most accurate indicators of perceived odor intensity, proving the limitations of rough sensory assessments in the indoor/breathing zone scales. This study offers insights for potential safe and sustainable applications, such as smart odor displays, e-noses, and sensors/control systems in the indoor environment, particularly for long-term exposure in industries that emit harmful compounds.

Traditional K-means clustering assumes, to some extent, a uniform distribution of data around predefined centroids, which limits its effectiveness for many realistic datasets. In this paper, a new clustering technique, simulated-annealing-based ellipsoidal clustering (SAELLC), is proposed to automatically partition data into an optimal number of ellipsoidal clusters, a capability absent in traditional methods. SAELLC transforms each identified cluster into a hyperspherical cluster, where the diameter of the hypersphere equals the minor axis of the original ellipsoid, and the center is encoded to represent the entire cluster. During the assignment of points to clusters, local ellipsoidal properties are independently considered. For objective function evaluation, the method adaptively transforms these ellipsoidal clusters into a variable number of global clusters. Two objective functions are simultaneously optimized: one reflecting partition compactness using the silhouette function (SF) and Euclidean distance, and another addressing cluster connectedness through a nearest-neighbor algorithm. This optimization is achieved using a newly-developed multiobjective simulated annealing approach. SAELLC is designed to automatically determine the optimal number of clusters, achieve precise partitioning, and accommodate a wide range of cluster shapes, including spherical, ellipsoidal, and non-symmetric forms. Extensive experiments conducted on UCI datasets demonstrated SAELLC’s superior performance compared to six well-known clustering algorithms. The results highlight its remarkable ability to handle diverse data distributions and automatically …

Mobile roadside units have crucial role in ensuring efficient communication, computing, and caching services in internet of vehicles (IoVs) for vehicles traversing urban landscapes. The dynamic nature of urban environments faces challenges in optimizing the deployment of mRSUs to adapt to varying vehicular densities and traffic patterns in real-time. In this article, we propose a novel real-time optimization approach for the dynamic deployment of mobile Roadside Units (mRSUs) in urban environments to support the rapid growth of the IoV. The proposed method is a novel allocation strategy based on Minimum Dominating Set (MDS) theory, which is demonstrated to significantly reduce the number of mRSUs required. This reduction is achieved without compromising the efficiency and effectiveness of the network, thereby ensuring rapid and reliable communication within the IoV. This approach addresses critical …