A bivariate community in a bilayer mobility network is defined as a group of nodes encompassing two types of movements, at least one of which exhibits a densely connected structure characterized by dense internal connections and sparse connections to other groups. Detecting such communities helps reveal relationships between different human movement patterns, offering insights for urban planning and transport optimization. However, identifying bivariate communities in the presence of noise remains challenging. To address this issue, we developed a density-based community detection method for bilayer mobility networks. We first identified high- and low-density nodes for each type of movements based on Monte Carlo simulations and the strength of connections between node pairs to reduce the interference of noise. Then, we upgraded the density-based clustering procedure to construct bivariate communities with varying combinations of interaction intensities. In the simulation experiments, quantitative evaluation using Adjusted Rand Index and Normalized Mutual Information showed that the proposed method outperformed three state-of-the-art methods in identifying bivariate communities. We further applied this method to bilayer mobility networks constructed from bus smart card data and taxi trajectory data in Beijing. The identified bivariate communities revealed the spatial correlations between different transportation modes.

Parameter estimation is critical for enabling intravoxel incoherent motion (IVIM) to provide accurate insights into biological functions. However, low signal‑to‑noise ratios (SNRs) in diffusion‑weighted (DW) data and the absence of ground‑truth labels present substantial challenges. To address this issue, we introduce a metacognitive physics-informed neural network (MC-PINN). This proposed model utilizes an iterative self‑feedback training strategy to achieve supervised learning using synthetic data informed by ground‑truth approximations while self‑assessing the reliability of its derivation. In comparative experiments using data from a magnetic resonance linear accelerator (MR‑Linac), MC‑PINN achieved robust parameter estimation even under low‑SNR conditions.

In the current context of rapid technological advancement, smartphones have become an indispensable part of people's daily lives. This has led to an increasing focus on the satisfaction of consumers with smartphone products, as understanding consumer emotions and satisfaction has become a key factor for manufacturers and retailers to enhance the quality of products and services. This study delves into the satisfaction of consumers with smartphones in the market through an in-depth application of text mining techniques, leveraging advanced technologies such as natural language processing, sentiment analysis, and topic modeling. Our research methodology encompasses the process of collecting and preprocessing a substantial volume of consumer reviews from online shopping platforms. Subsequently, we apply Latent Dirichlet Allocation (LDA) for topic modeling and Extreme Learning Machine (ELM) for sentiment analysis.