“Superfrog Science: Experiments for at Home and in the Classroom” is a creative endeavor that encourages young scientists to get curious about science and help learn a variety of chemistry concepts. This book is a visual representation of the importance of exploring the bounds of creativity in science. Join Superfrog as he goes on a learning adventure conducting science experiments and using the scientific method to deepen his knowledge about chemistry, making it easy to learn by conducting each experiment with easy-to-follow comic panels. This book strives to make science experiments accessible, affordable, and fun. It is perfect for encouraging hands-on learning, with in-depth explanations of the “how” and “why” of these experiments. Superfrog asks discussion questions and provides variations of the experiments to get his students to think about cause-and-effect and variable manipulation when it comes to the scientific process, as well as encourage collaboration with his peers. The educational activities featured are made for scientific discovery inside and outside the classroom. Make chemistry fun and easy with Superfrog!

Riboswitches are structured RNA elements that regulate gene expression through ligand-induced conformational changes and provide a platform for engineering cell-based biosensors. By coupling aptamers to reporter genes, synthetic riboswitches enable small-molecule–dependent detection of clinically relevant metabolites. This study focuses on sarcosine, associated with prostate cancer progression, and urate, linked to gout. Two sarcosine-responsive candidates were evaluated in E. coli using β-galactosidase and GFP reporter systems. Although construct integrity was confirmed, neither candidate demonstrated ligand-dependent activation in CDR or minimal media, suggesting insufficient regulatory activity under tested conditions. In parallel, a urate-responsive riboswitch library underwent dual selection with chloramphenicol resistance for positive selection and 5-fluorouracil counterselection for negative selection. After multiple selection rounds and screening of 192 colonies, no urate-specific variants were identified. Increasing chloramphenicol concentration to strengthen positive selection similarly yielded no hits. Future work will focus on further increasing both positive and negative selection intensity to enhance enrichment of functional variants and improve development of RNA-based biosensors for accessible metabolite detection. Additionally, future efforts will explore the adenine riboswitch library as a potential platform for developing novel biomarker detection systems.

Interactive Querying and Visualization of Solar Events

Author: Thu My Banh, Cathy Nguyen, Pandey Chetraj

Access to structured solar flare event data is essential for space weather (SWx) research, operational analysis, and machine learning applications. While the solar flare event archive maintained by the Lockheed Martin Solar and Astrophysics Laboratory (LMSAL) provides a widely used curated record of flare activity, the archive is primarily accessible through static web interfaces rather than a programmable query system. This makes automated filtering, dataset generation, and large-scale analysis difficult for researchers. To address this limitation, we developed a full-stack web application that provides programmatic access to LMSAL solar flare event records through a queryable API. A Python-based data ingestion pipeline retrieves and deduplicates event information from LMSAL’s rolling snapshot archive and stores it in a structured format. A FastAPI backend exposes endpoints that allow users to filter events by date range and GOES flare classification, enabling rapid dataset generation for analysis workflows. The frontend, implemented in React, allows users to query the event catalog, visualize results in a structured table, and export filtered datasets as CSV or JSON files. To improve data reliability and context, the system cross-references LMSAL event records with NOAA solar flare catalogs, allowing users to compare event metadata across independent data sources. Additionally, the application integrates with the Helioviewer API to display solar imagery corresponding to each event, with derived heliographic positions overlaid onto the solar disk to provide spatial context. The resulting system provides a lightweight platform for exploring, querying, and exporting solar flare event data, lowering the barrier to accessing operational flare records and facilitating dataset generation for space weather analysis and predictive modeling.

Large language models (LLMs) are increasingly framed as force multipliers for cyberattacks, yet most existing evaluations focus on isolated artifact generation rather than the construction and execution of full offensive workflows. This paper presents a controlled empirical study of LLM-assisted cyberattack construction across multiple representative attack classes, including automated SQL injection exploitation, spyware assembly, reverse shell establishment, and denial-of-service traffic generation. We evaluate several contemporary models—including ChatGPT-4o, ChatGPT-5.2, ChatGPT-5.1-instant, Claude Sonnet 4.6, and Gemini 3—within fully sandboxed virtualized environments, treating each model strictly as an advisory system embedded within a human-driven workflow.

Our experimental design decomposes attacks into staged operational workflows encompassing reconnaissance, payload generation, system integration, troubleshooting, and persistence. This structure enables systematic analysis of where automation succeeds or fails during real execution rather than relying on single-shot demonstrations. Across scenarios, LLMs consistently reduce effort for localized technical tasks such as command syntax recall, tool configuration, payload scaffolding, and procedural troubleshooting. However, reliable end-to-end attack execution remains limited. SQL injection automation succeeds primarily when established tools encapsulate complex orchestration, while more complex scenarios such as spyware assembly fail at system-level integration, environment-specific dependency resolution, and evasion of host defenses.

Across models and attack classes, automation consistently breaks at environment-dependent boundaries requiring global reasoning, state awareness, and cross-stage workflow coordination. These findings suggest that contemporary LLMs do not autonomously execute cyberattacks but instead function as workflow accelerators that lower the expertise threshold required to operationalize existing offensive techniques. This capability-boundary perspective provides a more realistic foundation for threat modeling, defensive planning, and future evaluation of AI-assisted cybersecurity risks.

Medical students lack effective tools for developing clinical reasoning, as most resources emphasize memorization rather than decision-making. DiseaseQuest is an AI-powered, gamified platform that addresses this gap through realistic patient simulations and decision-based scenarios. It enables students to work through complete clinical cases using interactive, patient-centered dialogue. Supported by a multi-agent framework, the platform provides adaptive guidance, diagnostic feedback, and personalized evaluations. By promoting active learning and problem-solving, DiseaseQuest offers a transformative approach that replaces passive study with immersive, hands-on practice, helping students strengthen diagnostic thinking and better prepare for real-world clinical decision-making.