Therapeutic ultrasound waves are the main instruments used in many noninvasive clinical procedures. They are continuously transforming medical treatments through mechanical and thermal effects. To allow for effective and safe delivery of ultrasound waves, numerical modeling methods such as the Finite Difference Method (FDM) and the Finite Element Method (FEM) are used. However, modeling the acoustic wave equation can result in several computational complications. In this work, we study the accuracy of using Physics-Informed Neural Networks (PINNs) to solve the wave equation when applying different combinations of initial and boundary conditions (ICs and BCs) constraints. By exploiting the mesh-free nature of PINNs and their prediction speed, we specifically model the wave equation with a continuous time-dependent point source function. Four main models are designed and studied to monitor the effects of soft or hard constraints on the prediction accuracy and performance. The predicted solutions in all the models were compared to an FDM solution for prediction error estimation. The trials of this work reveal that the wave equation modeled by a PINN with soft IC and BC (soft–soft) constraints reflects the lowest prediction error among the four combinations of constraints.

Massive public pressure is arising to achieve data privacy and protection. One way to maintain data privacy is to perform efficient data disposal processes. This minimizes the chances of data leakage over an extended period and through irresponsible actions. That is why adequate data disposal is essential in so many governmental and critical institutions. Simply “deleting” data does not erase it. In fact, it only removes the name that refers to that piece of data, but the data itself remains. In this paper, we utilize an encryption algorithm and then perform a randomization process to dispose of data values in an irreversible approach making it difficult to retrieve the original value of data from the defaced result.

Social media-based Mental Health (MH) informatics often focuses on disorder detection, overlooking psychological aspects like coping mechanisms and stressors. Furthermore, user-generated texts, although contains MH signals at scale, lack clinical validation. This paper presents a two-stage framework for mental coping detection that combines a coping strategy classifier and a Retrieval-Augmented Generation (RAG) system. The coping classifier identifies coping strategies from user text, while the RAG generates responses grounded in expert-validated knowledge bases and the coping labels. A pilot study demonstrates that the classifier achieves 79.7% accuracy and 0.797 weighted F1 on a held-out dataset. The RAG system produces structured, readable, and expert-verified responses. The classifier-conditioned RAG architecture provides responses grounded in validated psychological knowledge, offering a scalable approach to bridging social media data with evidence-based insights.

This research explores the use of generative artificial intelligence (AI), specifically ChatGPT, in designing non-human enclosures. The study introduces the Framework of 20 Essential Attributes for Animal Architecture (F20EAAA), serving as a foundational guide for designing artificial environments for nonhumans. Four experiments* were conducted to test ChatGPT’s design capabilities in designing a Macaroni penguin enclosure: a complete attribute request in a structured format to assess how well ChatGPT manages comprehensive design tasks; contextual prompts to focus on specific design sections; grouped attributes by design categories to assess integration; and direct questions to focus on specific design elements. The findings show that using the short direct question set of prompts yields the best results. Challenges such as inaccuracies emerge, indicating that ChatGPT lacks the capability for fully autonomous design, yet highlights the potential of AI to contribute to ethical and sustainable spaces for animals in the future.