The partial discharge (PD) detection is of critical importance in the stability and continuity of power distribution operations. Although several feature engineering methods have been developed to refine and improve PD detection accuracy, they can be suboptimal due to several major issues: 1) failure in identifying fault-related pulses; 2) the lack of inner-phase temporal representation; and 3) multiscale feature integration. The aim of this article is to develop a learning-based multiscale feature engineering (LMFE) framework for PD detection of each signal in a three-phase power system, while addressing the above issues. The three-phase measurements are first preprocessed to identify the pulses together with the surrounded waveforms. Next, our feature engineering is conducted to extract the global-scale features, i.e., phase-level and measurement-level aggregations of the pulse-level information, and the local-scale features focusing on waveforms and their inner-phase temporal information. A recurrent neural network (RNN) model is trained, and intermediate features are extracted from this trained RNN model. Furthermore, these multiscale features are merged and fed into a classifier to distinguish the different patterns between faulty and nonfaulty signals. Finally, our LMFE is evaluated by analyzing the VSB ENET dataset, which shows that LMFE outperforms existing approaches and provides the state-of-the-art solution in PD detection.

Predicting the eventual volume of tumor cells, that might proliferate from a given tumor, can help in cancer early detection and medical procedure planning to prevent their migration to other organs. In this work, a new statistical framework is proposed using Bayesian techniques for detecting the eventual volume of cells expected to proliferate from a glioblastoma (GBM) tumor. Specifically, the tumor region was first extracted using a parallel image segmentation algorithm. Once the tumor region was determined, we were interested in the number of cells that could proliferate from this tumor until its survival time. For this, we constructed the posterior distribution of the tumor cell numbers based on the proposed likelihood function and a certain prior volume. Furthermore, we extended the detection model and conducted a Bayesian regression analysis by incorporating radiomic features to discover those non-tumor cells that remained undetected. The main focus of the study was to develop a time-independent prediction model that could reliably predict the ultimate volume a malignant tumor of the fourth-grade severity could attain and which could also determine if the incorporation of the radiomic properties of the tumor enhanced the chances of no malignant cells remaining undetected.

Gradients provide low-dimensional representations of macroscale brain organization, yet how structural-functional gradient coupling develops and relates to behavioral and molecular features remains unclear. Here, we studied structural-functional gradient coupling across multiple metrics and spatial scales using high-resolution structural and functional connectivity from 5343 children in the Adolescent Brain Cognitive Development study and 875 adults from the Human Connectome Project. We find that gradient coupling shows developmental refinement from childhood to adulthood and distinct sex-specific patterns. Gradient coupling metrics are significantly associated with cognitive and mental health measures and enable robust out-of-sample prediction. Heritability analyses reveal that gradient coupling is strongly influenced by genetic factors. Transcriptomic analyses further demonstrate that highly heritable coupling patterns are enriched for genes expressed in deep-layer excitatory neurons. Together, our findings establish structural-functional gradient coupling as a biologically meaningful feature of brain organization that bridges macroscale connectivity, cognition, behavior, and molecular architecture.

Gradients are increasingly used to characterize the brain’s macroscale organization, offering low-dimensional representations of structural and functional connectivity. However, how structural-functional gradient coupling evolves during development and relates to behavioral and molecular features remains unclear. Here, we studied structural-functional gradient coupling across multiple metrics and spatial scales using high-resolution structural and functional connectivity from 7,025 children in the Adolescent Brain Cognitive Development study and 913 adults from the Human Connectome Project. We found that gradient coupling exhibits clear developmental refinement from childhood to adulthood and shows distinct sex-specific patterns. Gradient coupling metrics were significantly associated with a broad range of cognitive and mental health measures and enabled robust out-of-sample prediction under learning methods. Heritability analyses revealed that gradient coupling is strongly influenced by genetic factors. Transcriptomic analyses further demonstrated that highly heritable coupling patterns are enriched for genes expressed in deep-layer excitatory neurons, suggesting that gradient coupling reflects underlying cell-type-specific transcriptional architecture. Together, our findings establish structural-functional gradient coupling as a biologically meaningful feature of brain organization that bridges macroscale connectivity, cognition, behavior, and molecular architecture.

Large-scale imaging studies often face challenges stemming from heterogeneity arising from differences in geographic location, instrumental setups, image acquisition protocols, study design, and latent variables that remain undisclosed. While numerous regression models have been developed to elucidate the interplay between imaging responses and relevant covariates, limited attention has been devoted to cases where the imaging responses pertain to the domain of shape. This adds complexity to the problem of imaging heterogeneity, primarily due to the unique properties inherent to shape representations, including nonlinearity, high-dimensionality, and the intricacies of quotient space geometry. To tackle this intricate issue, we propose a novel approach: a shape-on-scalar regression model that incorporates confounder adjustment. In particular, we leverage the square root velocity function to extract elastic shape representations which are embedded within the linear Hilbert space of square integrable functions. Subsequently, we introduce a shape regression model aimed at characterizing the intricate relationship between elastic shapes and covariates of interest, all while effectively managing the challenges posed by imaging heterogeneity. We develop comprehensive procedures for estimating and making inferences about the unknown model parameters. Through real-data analysis, our method demonstrates its superiority in terms of estimation accuracy when compared to existing approaches.