Within the field of acoustics, animations are often used as a teaching aid. Animations are effective as learning tools for acoustics, as many principles involve changes in space and time. Acoustic animations are typically static animations that do not take user input. This talk showcases an interactive animation for the mode shapes of mass-spring-damper systems built as an interactive teaching aid. This interactive animation was built as a mobile application due to the wide availability of smartphones and additionally benefits from the existing animation architecture used for mobile development. Mode shapes are calculated by using the equations of motion for the mass-spring-damper and solving for the eigenvalues and the row reduced echelon form of the motion matrix. The application shows accuracy in line with equivalent mode shape calculation methods. Damping is limited to underdamped stiffness-proportional viscous-damping due to computational complexity. This talk does not cover the effectiveness of learning through interactive animations but merely shows the abilities of this new tool through demonstration. As the application is still in early stages of development, additional animations and features are also discussed. This initial work was done for completion of degree requirements for Penn State University.
Power transformers are often installed near residential areas. Audible noise of these transformers propagates to these areas, causing annoyance, discomfort, sometimes resulting in sound levels that exceed levels allowed by noise ordinances. This paper presents noise measurements performed by the authors at different substations in North America with the objective of determining the sound propagation characteristics of transformer noise and its frequency components. This is in addition to examining the impact of sound walls around transformers, tank mounted radiators, and interaction between sound waves propagating from different transformers. One of the main findings is that distance attenuation is lowest for the 120 Hz frequency component and much higher for higher frequency components. As a result, transformer noise appearing at residential areas is dominated by this frequency. Hence, existing methods of calculating distance attenuation of transformer noise will need to be revised. The measurements also show that sound walls around transformers are effective even at large distances from the transformers. Finally, it was found that sound waves of the same frequency propagating from different transformers in same substation can add or cancel each other resulting in a dip or a rise in the sound level at some points around the substation.
Late Viséan (Mississippian) ammonoids (Cephalopoda) from the Central Moroccan Meseta are described monographically for the first time. Several localities have yielded low-diversity assemblages, consisting mainly of species already known from southern Portugal, Ireland, northern England and the Rhenish Mountains. This clearly places the Moroccan Meseta within the Rhenohercynian-Subvariscan Province. The assemblages from the Moroccan Meseta are confined to a narrow stratigraphic interval, likely representing the Goniatites crenistria Zone, Goniatites sphaericus Zone, Goniatites spirifer Zone and Arnsbergites gracilis Zone. The new species Goniatites amarensis sp. nov. is described.
There has been rapid development in generative AI tools across the education sector, which in turn is leading to increased adoption by teachers. However, this raises concerns regarding the safety and age-appropriateness of the AI-generated content that is being created for use in classrooms. This paper explores Oak National Academy’s approach to addressing these concerns within the development of the UK Government’s first publicly available generative AI tool - an AI-powered lesson planning assistant (Aila). Aila is intended to support teachers in planning national curriculum-aligned lessons that are appropriate for pupils aged 5–16 years. To mitigate safety risks associated with AI-generated content we have implemented four key safety guardrails: (1) prompt engineering to ensure AI outputs are generated within pedagogically sound and curriculum-aligned parameters; (2) input threat detection to mitigate attacks; (3) an Independent Asynchronous Content Moderation Agent (IACMA) to assess outputs against predefined safety categories; and (4) taking a human-in-the-loop approach, to encourage teachers to review generated content before it is used in the classroom. Through our ongoing evaluation of these safety guardrails, we have identified several challenges and opportunities to take into account when implementing and testing safety guardrails. This paper highlights ways to build more effective safety guardrails in generative AI education tools, including the ongoing iteration and refinement of guardrails, as well as enabling cross-sector collaboration through sharing both open-source code/datasets and learnings.
The Traveling Salesman Problem (TSP) is a fundamental challenge in combinatorial optimization, widely applied in logistics and transportation. As the size of TSP instances grows, traditional algorithms often struggle to produce high-quality solutions within reasonable timeframes. This study investigates the potential of the Quantum Approximate Optimization Algorithm (QAOA), a hybrid quantum-classical method, to solve TSP under realistic constraints. We adopt a QUBO-based formulation of TSP that integrates real-world logistical constraints reflecting operational conditions, such as vehicle capacity, road accessibility, and time windows, while ensuring compatibility with the limitations of current quantum hardware. Our experiments are conducted in a simulated environment using high-performance computing (HPC) resources to assess QAOA's performance across different problem sizes and quantum circuit depths. In order to improve scalability, we propose clustering QAOA (Cl-QAOA), a hybrid approach combining classical machine learning with QAOA. This method decomposes large TSP instances into smaller sub-problems, making quantum optimization feasible even on devices with a limited number of qubits. The results offer a comprehensive evaluation of QAOA's strengths and limitations in solving constrained TSP scenarios. This study advances quantum optimization and lays groundwork for future large-scale applications.
