Ethics

A novel fuzzy logic-based image steganography method to ensure medical data security.

This study aims to secure medical data by combining them into one file format using steganographic methods. The electroencephalogram (EEG) is selected as hidden data, and magnetic resonance (MR) images are also used as the cover image. In addition to the EEG, the message is composed of the doctor׳s comments and patient information in the file header of images. Two new image steganography methods t...

Probabilistic hazard assessment for skin sensitization potency by dose-response modeling using feature elimination instead of quantitative structure-activity relationships.

Supervised learning methods promise to improve integrated testing strategies (ITS), but must be adjusted to handle high dimensionality and dose-response data. ITS approaches are currently fueled by the increasing mechanistic understanding of adverse outcome pathways (AOP) and the development of tests reflecting these mechanisms. Simple approaches to combine skin sensitization data sets, such as we...

Robotic Nudges: The Ethics of Engineering a More Socially Just Human Being.

Robots are becoming an increasingly pervasive feature of our personal lives. As a result, there is growing importance placed on examining what constit...

AIonAI: a humanitarian law of artificial intelligence and robotics.

The enduring progression of artificial intelligence and cybernetics offers an ever-closer possibility of rational and sentient robots. The ethics and ...

Monitoring Emergency Medical Services Frequent Use: Protocol for a Mixed-Methods Study Using Routinely-Collected Data

Introduction Aging populations are placing increasing pressure on Emergency Medical Services (EMS) providers to address the potentially suboptimal use...

Robust and Quality-of-Life-Aware Treatment Protocols in NSCLC using Deep Reinforcement Learning

Under current systemic treatment of metastatic cancer, a drug is frequently prescribed at maximum tolerable dose (MTD) until either unacceptable toxic...

Auditing Patient Privacy in Medical Generative Models: Scalable Memorization Detection with DeepSSIM++

While deep generative models offer new opportunities for medical image synthesis and data sharing, their ability to memorize and reproduce training sa...

Predicting Subsurface Abnormalities Growth using Physics-Informed Neural Networks

The research explores the pioneering integration of Physics-Informed Neural Networks (PINNs) into the domain of Ground-Penetrating Radar (GPR) data pr...

SGHA: Evidence-Grounded Research Problem Discovery with Local Language Models

Recent efforts toward fully automated AI scientists have demonstrated that language-model agents can generate hypotheses, execute experiments, and dra...

Learning Unsteady Aneurysm Hemodynamics with Physics-Informed DeepONets

Clinically actionable, patient-specific hemodynamic assessment, specifically wall shear stress, vortex structure and pressure distributions, is critic...

Overcoming Data Scarcity and Confidentiality in Hardware Assurance via Synthetic Generation

Hardware assurance relies on scanning electron microscopy (SEM) to verify nanoscale structures, but assembling the large, high-quality datasets requir...

An Evaluation of DMR Informed Fine-Tuning of Tissue Array Pretrained CpGPT for Gastrointestinal Cancer Classification Using cfDNA Targeted Methylation

Background Cell free DNA (cfDNA) methylation profiling is promising for minimally invasive cancer detection, but its translation is limited by high di...

Where Physics Meets Privacy: Federated PINNs for Privacy-Preserving Brain Tumor Biomechanical Modeling

Brain tumors such as glioma, meningioma, and pituitary adenoma alter the mechanical behavior of soft brain tissue, yet common diagnostic methods rely ...

Bounding the Causal Impact of ML-assisted Decision-Making via Counterfactual Correctness

Predictive machine learning (ML) models are increasingly used to aid human decision-makers across various high-risk domains such as healthcare and cri...

Physics-Informed Deep Learning Model for Cross-Modality Super-Resolution in Fluorescence Microscopy

Cross-modality image translation offers a route to super-resolution fluorescence microscopy from low-resolution images while reducing phototoxicity an...

The Emerging Paradigm of Geospatial Foundation Models: From Pre-Training to Agentic Reasoning

The analysis of satellite and aerial imagery has entered a new era with the advent of foundation models. This paper describes the concept of Geospatia...

Integrating planetary health and environmental justice into high school construction career education: protocol for a randomized controlled trial of the Ecosystem Justice Translator

Introduction: Climate change disproportionately affects disadvantaged communities, yet construction workforce education rarely addresses interconnecte...

A deep-learning-informed prior and Bayesian model for differential AP-MS interactome analysis

Affinity-purification mass spectrometry (AP-MS) maps a bait proteins partners, but every purification also captures abundant non-specific background t...

Equivariant Quantum Clustering with Differential Privacy: Parameter-Efficient Privacy-Preserving Analysis Across Heterogeneous Sensitive Datasets

Privacy-preserving clustering is critical for analyzing sensitive data in healthcare, cybersecurity, and enterprise applications, where maintaining da...

Reinforcement Learning for Chronic Care Pathway Optimization: A Unified Framework across Three Clinical Goal Types

Objective: Chronic care requires sequential treatment under competing biomarker, safety, and cost constraints, yet clinical goal structures differ acr...