Power systems face increasing uncertainties that create nonlinear regime-dependent dynamics. Critical Clearing Time (CCT) remains a key transient stability metric, yet analytical relations with operating conditions are rarely tractable. Advanced machine learning techniques offer accurate CCT prediction and partial interpretability, but fail to uncover the governing functional dependencies. This paper introduces a Piecewise Symbolic Regression (Pc-SR) framework that automatically discovers regime-conditioned equations linking system variables to CCT. Pc-SR combines cost-complexity-pruned decision trees for task-aware partitioning with symbolic models in each region. Validation on synthetic data confirms recovery of correct partitions and equations under noise, while tests on single-machine infinite-bus variants rediscover analytical CCT equations. Applied to a modified 39-bus system with inverter-based resources, Pc-SR produces interpretable, regime-specific CCT surrogates matching black-box accuracy while exposing nonlinearities and interactions. This framework advances beyond descriptive explainability, providing transparent models to accelerate stability screening and support operator insight into complex dynamic behaviors.
Evaluating diffusion properties of novel optical clearing (OC) agents is critical for advancing medical imaging. Tartrazine (TTZ), a strong absorbing dye, has shown promise in enhancing tissue transparency, yet its diffusion properties remain uncharacterized. In this work, OC treatments with TTZ-water solutions with varying osmolarities were performed, and the diffusion times (tau) that characterize the tissue dehydration and the RI matching mechanisms were estimated. From kinetic T-c measurements during treatment, tau values of water and TTZ were estimated in muscles as 60.0 s and 416.0 s, respectively. Corresponding diffusion coefficients (D) were derived from sample thickness data measured during treatments where the unique fluxes of TTZ and water occur. The respective D values were then calculated as 1.9 x 10(-6) cm(2)/s for water and 3.6 x 10(-7) cm(2)/s for TTZ. These findings provide key insights into TTZ diffusion in skeletal muscle and support its potential as an effective OC agent.
Fabry disease (FD) is a rare genetic disorder associated with cardiac abnormalities and often overlooked brain white matter lesions (WMLs). Despite the importance of early WMLs detection, diagnosis is frequently delayed. The aim is to identify electrocardiographic biomarkers linked to WMLs in middle-aged FD patients using machine learning, assessing their potential as non-invasive diagnostic tools. This retrospective study analyzed electrocardiographic data from FD patients aged 40–59. A feature selection process based on variance inflation factor analysis identified nine relevant features, including heart rate variability and QT interval parameters. Machine learning classifiers—logistic regression, support vector machines, random forest, and k-nearest neighbors—were trained and evaluated using accuracy, sensitivity, specificity, and AUC. SHAP (SHapley Additive exPlanations) analysis was used to interpret model predictions. The random forest model achieved the highest accuracy (0.81) using all nine features. A subset consisting of SDANN 5 and QTc Min also performed well (accuracy 0.75) in other models. SHAP analysis highlighted SDANN 5 as a key predictor. Machine learning applied to ECG data shows promise for early WML detection in FD, supporting the integration of computational methods into diagnostics for complex genetic diseases.
Handheld controllers are standard in immersive virtual reality (iVR), but the rise of natural hand-based interactions exposes the limitations of hand gestures, especially for point-and-click tasks with graphical user interfaces (GUI). This shows the need to explore alternative hands-free selection methods. Unlike most studies focusing on the selection task itself, this work evaluates the impact of such methods on multiple dimensions when selections occur alongside another primary task. The tested methods were: head gaze + dwell, leaning, and voice; eye gaze + dwell, leaning, blinking, and voice; and voice-only. Controllers served as the baseline. Methods were further analyzed by pointing and confirming mechanisms. Four dimensions were analyzed: (1) iVR experience, (2) user satisfaction, (3) usability, and (4) efficiency and effectiveness. With 72 participants, results show hands-free methods provide comparable experiences to controllers, suggesting selection methods have a lower impact on the user experience when users focus on a primary task.
In the pull-based development model, code contributions are submitted as pull requests (PRs) to undergo reviews and approval by other developers with the goal of being merged into the code base. A PR can be supported by a description, whose role has not yet been systematically investigated. To fill in this gap, we conducted a mixed-methods empirical study of PR descriptions. We conducted a grey literature review of guidelines on writing PR descriptions and derived a taxonomy of eight recommended elements. Using this taxonomy, we analyzed 80K GitHub PRs across 156 projects and five programming languages to assess associations between these elements and code review outcomes (e.g., merge decision, latency, first response time, review comments, and review iteration cycles). To complement these results, we surveyed 64 developers about the perceived importance of each element. Finally, we analyzed which submission-time factors predict whether PRs include a description and which elements they contain. We found that developers view PR descriptions as important, but their elements matter differently: purpose and code explanations are valued by developers for preserving the rationale and history of changes, while stating the desired feedback type best predicts change acceptance and reviewer engagement. PR descriptions are also more common in mature projects and complex changes, suggesting they are written when most useful rather than as a formality.
