large language models

Engines of Escalation: How the Hybrid Public Sphere Amplifies Political Violence Targeting Women in Low-Resource Contexts

Political Violence Targeting Women (PVTW) has reached record highs in fragile, low-resource settings. This paper presents a structural analysis integrating both theory and empirical evidence to examine how a rapidly evolving hybrid public sphere—where the intertwined logics of traditional news and social media create a...

AISyst: AI‐Powered Interactive Visual System to Assist With Fidelity Assessment of Synthetic Tabular Data

Evaluating synthetic data produced by generative models remains a critical challenge in sensitive domains such as healthcare and finance. Ensuring that such data is ‘faithful’ to real data is essential for downstream applications and decision-making, including regulatory compliance. This paper introduces an AI-powered...

An interpretable river water quality prediction model by integrating deep learning and large language models

Physical mechanism-based models for river water quality prediction involve complicated calculations，whereas machine learning models lack interpretability，resulting in a disconnection between predictions and management decisions that hinders practical application. To deeply integrate high-precision prediction with dec...

GENERATIVE AI AND SELF-MEDICATION: RISKS, RELIABILITY, AND PATIENT SAFETY IN THE ERA OF AI-BASED HEALTHCARE

Health-professions students are increasingly using artificial intelligence (AI) tools, especially large language model (LLM)-based chatbots like ChatGPT, for academic tasks as well as informal drug information retrieval, symptom checking, and self-medication decision-making.[2,9,3,7] Due to their dual roles as future g...

ARTIFICIAL INTELLIGENCE IN DRUG RESPONSE PREDICTION AND PHARMACOLOGICAL THERAPY OPTIMIZATION: ADVANCES, CHALLENGES, AND FUTURE PERSPECTIVES

Driven by artificial intelligence, or AI, today's medicinal science has the potential of rapidly transforming a doctor's knowledge of patient reactions to a variety of medications. In clinical practice, one has seen that sometimes a given medication is effective and sometimes ineffective in a patient. This difference c...

ARTIFICIAL INTELLIGENCE IN DRUG RESPONSE PREDICTION AND PHARMACOLOGICAL THERAPY OPTIMIZATION: ADVANCES, CHALLENGES, AND FUTURE PERSPECTIVES

Driven by artificial intelligence, or AI, today's medicinal science has the potential of rapidly transforming a doctor's knowledge of patient reactions to a variety of medications. In clinical practice, one has seen that sometimes a given medication is effective and sometimes ineffective in a patient. This difference c...

GENERATIVE AI AND SELF-MEDICATION: RISKS, RELIABILITY, AND PATIENT SAFETY IN THE ERA OF AI-BASED HEALTHCARE

Health-professions students are increasingly using artificial intelligence (AI) tools, especially large language model (LLM)-based chatbots like ChatGPT, for academic tasks as well as informal drug information retrieval, symptom checking, and self-medication decision-making.[2,9,3,7] Due to their dual roles as future g...

Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.

Artificial intelligence (AI) has been transforming many aspects of medical care. In prostate cancer, ongoing progress in AI has improved research and patient care. Recent advances in machine learning and deep learning have produced tools that help diagnose cancer, assess risk, and predict outcomes. In screening, AI-bas...

Evolutionary product line modelling

This work proposes an evolutionary software product line modelling approach based on controllable inheritance of product line members specifications that uses hierarchies of implemented product specifications accompanied by correctness control of product model transformations.

Experimental software engineering (STESE)

Software engineering theory and practice is still to a large extent based more on faith than on science. Only by contributing to the scientific and empirically grounded body of knowledge within a specific area of application, theory and practice can develop. Experimentation is an important scientific approach to collec...

Developing Software Products for Mobile Markets: Need for Rethinking Development Models and Practices

Commitment to Software Process Improvement—Development of Diagnostic Tool to Facilitate Improvement1

Early results suggest that the behaviors introduced in the model are relevant in SPI initiatives, the use of model raises the awareness about the people issues in improving processes, and the model could be used aside with CMM, SPICE or other process improvement models.