Adolescents and their parents hesitate to acknowledge mental health issues until symptoms severely worsen, making timely treatment challenging. Moreover, infrequent psychiatric consultations often fail to adjust treatments to the dynamic nature of mental health states. To address these issues, our paper proposes an AI-based mental health assessment framework for adolescent mental health through non-invasively collected data from daily digital activities on their mobile devices, including tablets and smartphones. For this, we collect fifteen different types of passive sensor data across three primary categories of activities: studying, smartphone using, and metaverse gaming. Additionally, each adolescent completes self-survey reports on eight different disorders which are used as labels. Then, feature extraction is conducted based on this dataset, which yields 1,523 features that could function as potential digital biomarkers of mental health conditions in adolescents. Utilizing these features, our algorithm named CAMP: Customizable Automated Machine learning Process incorporates simulated annealing for feature selection. This approach enables the construction of AI models for mental health assessment that are finely tuned to domain specific strategies. Our experiments show that our proposed framework can significantly improve models' performance.

The compressive sensing-based random access may get impacted largely by other cell interference (OCI), which may shatter the sparsity of the received signal model unless the signature space assignment among interfering cells is carefully handled. In this paper, we investigate the performance of multi-sequence spreading-based random access (MSRA) scheme under multi-cell environment. In particular, a grant-free random access scenario is considered to indicate its particular attributes that fundamentally affect the cellular performance. We also give the performance of MSRA in comparison with sparse code multiple access (SCMA). We show that if a proper signature assignment is undertaken, the OCI in MSRA and other CS-based schemes can be modeled as a dispersed noise which does not critically undermine the underlying sparsity.

Magnetic resonance electrical property tomography (MREPT) is a new imaging modality to visualize a distribution of admittivity γ = σ+iωε inside the human body where σ and ε denote electrical conductivity and permittivity, respectively. Using B1 maps acquired by an magnetic resonance imaging scanner, it produces cross-sectional images of σ and ε at the Larmor frequency. Since current MREPT methods rely on an assumption of a locally homogeneous admittivity, there occurs a reconstruction error where this assumption fails. Rigorously analyzing the reconstruction error in MREPT, we showed that the error is fundamental and may cause technical difficulties in interpreting MREPT images of a general inhomogeneous object. We performed numerical simulations and phantom experiments to quantitatively support the error analysis. We compared the MREPT image reconstruction problem with that of magnetic resonance electrical impedance tomography (MREIT) to highlight distinct features of both methods to probe the same object in terms of its high- and low-frequency conductivity distributions, respectively. MREPT images showed large errors along boundaries where admittivity values changed whereas MREIT images showed no such boundary effects. Noting that MREIT makes use of the term neglected in MREPT, a novel MREPT admittivity image reconstruction method is proposed to deal with the boundary effects, which requires further investigation on the complex directional derivative in the real Euclidian space [Formula: see text].