Background and ContextDespite the growing demand for computing professionals, high dropout rates and pronounced gender disparities persist in CS programs. While factors such as grit and programming experience influence student retention and success and may differ by gender, how these differences shape retention remains unclear.ObjectiveThis paper employs the Social Cognitive Career Theory (SCCT) to study how gender differences in grit and programming experience across the introductory computing curriculum affect students' intention to complete their computer science (CS) degree.MethodWe collected survey data from 303 undergraduate CS major students enrolled in CS1, CS2, and CS3 at an engineering-focused university in the southeastern United States.FindingsResults showed differences between men and women at different course levels in terms of grit and programming experience, which in turn influenced their self-efficacy to study CS, outcome expectations, attitude toward computing, and consequently their intention to continue studying CS as their major.ImplicationsThe study highlights the need for intervention strategies to enhance retention and provide tailored, gender-inclusive support for computer science students.
The BioDIGS project is a nationwide initiative involving students, researchers and educators across more than 40 research and teaching institutions. Participants lead sample collection, computational analysis and results interpretation to understand the relationships between the soil microbiome, environment and health.
A mixed accuracy framework for Runge–Kutta methods presented in Grant [JSC 2022] and applied to diagonally implicit Runge–Kutta (DIRK) methods can significantly speed up the computation by replacing the implicit solver by less expensive low accuracy approaches such as lower precision computation of the implicit solve, under-resolved iterative solvers, or simpler, less accurate models for the implicit stages. Understanding the effect of the perturbation errors introduced by the low accuracy computations enables the design of stable and accurate mixed accuracy DIRK methods where the errors from the low-accuracy computation are damped out by multiplication by at multiple points in the simulation, resulting in a more accurate simulation than if low-accuracy was used for all computation. To improve upon this, explicit corrections were previously proposed and analyzed for accuracy, and their performance was tested in related work. Explicit corrections work well when the time-step is sufficiently small, but may introduce instabilities when the time-step is larger. In this work, the stability of the mixed accuracy approach is carefully studied, and used to design novel stabilized correction approaches.
We present a hybrid framework that couples finite element methods (FEM) with physics-informed DeepONet to model fluid transport in porous media from sharp, localized Gaussian sources. The governing system consists of a steady-state Darcy flow equation and a time-dependent convection–diffusion equation. Our approach solves the Darcy system using FEM and transfers the resulting velocity field to a physics-informed DeepONet, which learns the mapping from source functions to solute concentration profiles. This modular strategy preserves FEM-level accuracy in the flow field while enabling fast inference for transport dynamics. To handle steep gradients induced by sharp sources, we introduce an adaptive sampling strategy for trunk collocation points. Numerical experiments demonstrate that our method is in good agreement with the reference solutions while offering orders-of-magnitude speedups over traditional solvers making it suitable for practical applications in relevant scenarios. Implementation of our proposed method is available in our GitHub repository at https://github.com/erkara/fem-pi-deeponet.
The growing emphasis on STEM education highlights the need for interactive, technology-driven tools that enhance learning. In mineralogy, delivering laboratory experiences in unsupervised or online settings poses challenges related to cost, scalability, and guidance. To address these, we present an interactive, ontology-based query system for mineral identification and property retrieval. Built on the lightweight Rock-forming Minerals Ontology (RMO) developed in Protege using Basic Formal Ontology (BFO), the system encodes essential attributes of common silicate and non-silicate minerals. Implemented in Python with Owlready2, it dynamically generates web pages that support three core functionalities: browsing a hyperlinked mineral list, searching for properties of a selected mineral, and submitting observed properties to identify matching minerals. Designed for deployment on laptops or field computers, the system promotes inquiry-based, self-directed learning. The query system leverages ontology-guided classification and constraint-based semantic filtering, offering a scalable and pedagogically effective tool for diverse instructional environments.
