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 bidirectional feedback loop—amplifies hatred and accelerates offline physical harm against women in Nigeria. Adapting the framework of discursive opportunities, we unpack the mechanisms escalating misogyny into violence. We analyze a corpus of over 1.6 billion X (formerly Twitter) posts, traditional news articles, and conflict event data from ACLED. We utilize NaijaXLM-T, a custom Large Language Model tailored to Nigerian text, to accurately measure the visibility (volume) and resonance (intensity) of gender-specific hate, and to extract keywords and topics related to gender-targeting news. Furthermore, we map social interaction networks to isolate 'true contagion' from structural homophily. The hub-and-spoke topology observedamong hateful users serves as our proxy for legitimacy; we posit that hatred disseminated by influential network hubs acts as a digital form of social authorization, actively lowering the friction for offline mobilization. Empirically, we first establish a robust linkage where digital hostility directly catalyzes offline PVTW. Moving beyond this baseline effect, we unpack the structural pathways driving this acceleration by demonstrating how visibility, resonance, and network legitimacy fuel this spillover. We then answer critical questions regarding the differing velocities within this system, detailing the distinct temporal scales at which social media and traditional news amplify offline harm. Acknowledging observational and algorithmic limitations, we interpret these findings as a robust temporal linkage rather than strict causality. Ultimately, this research provides the foundational framework for monitoring and early-warning infrastructures, equipping policymakers and NGOs to prevent enabling stakeholders to pre-position protective resources to prevent digital hate from escalating into lethal PVTW in fragile settings.

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 interactive visual system—AISyst—designed to assess the fidelity of synthetic tabular datasets. The system supports multilevel comparisons with real datasets, spanning multivariate resemblance analyses based on dimensionality reduction through suitable two-dimensional projections, bivariate correlation and univariate similarity. AISyst also integrates an AI assistant by leveraging state-of-the-art large language models to summarize key findings and generate suggestions for improving synthetic data generation models. We validated the capabilities of AISyst through three case studies, supported by feedback from industrial AI experts who endorsed its broader deployment.

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 decision support，a TCN-Attention deep learning model that combines a temporal convolutional network and an attention mechanism is constructed to predict six core water quality indicators，with Bayesian optimization used for automatic parameter tuning. The SHAP interpretability technique is introduced to quantify the contributions of multi-source input features and reveal key driving factors. Based on the Qwen3-Next large language model，water quality grade evaluations and improvement recommendations are automatically generated. Application results for rivers in Xinwu District of Wuxi City demonstrate that the TCN-Attention model achieves good prediction performance under small-sample conditions，with domestic water use，turbidity，and water identified as the most critical influencing factors. Qwen3-Next model achieves an accuracy of 89.8% in water quality grading，and the proposed improvement recommendations are targeted and practicable. The proposed interpretable intelligent water quality prediction method effectively improves the accuracy and interpretability of urban river water quality predictions，providing a reliable technical pathway for smart water environment management.

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 gatekeepers of safe pharmaceutical use, trainees in sciences connected to medicine, and consumers of health information, pharmacy students hold a special place in this conversation.[2,3,20,33,56] In order to describe what is known about pharmacy (and allied health) students' knowledge of AI, their attitudes toward its use in clinical and self-care contexts, and their actual practices—including the use of AI for drug information, dosing guidance, and self-diagnosis-adjacent tasks—this review synthesizes nine cross-sectional Knowledge, Attitude, and Practice (KAP) studies conducted between 2022 and 2026 across Zambia.[3] Saudi Arabia.[2,6] India.[1,4,8] Syria.[9] Malaysia.[5] and other settings. AI awareness is almost universal (82–100%) in all contexts.[2,3,5,6,8,9] but conceptual understanding of AI subtypes, medical uses, and limitations is generally lower (30–60%).[2,3,5,6,8,9] Concerns concerning data privacy, false information, over-reliance, and professional displacement temper the generally positive attitudes.[1,2,6,7,8] Practice is still limited: formal curricular training is uncommon (7–46%).[2,3,6,7,9] and the majority of self-reported AI use focuses on academic activities (summarizing, presentations, exam preparation) rather than verified clinical decision-support.[3,4,5,7] There is a dearth of direct data on AI-assisted self-diagnosis and self-medication, particularly among pharmacy students. In order to outline the emerging risk landscape and suggest curricular and regulatory responses, this review draws conclusions from related findings, such as the use of ChatGPT for "getting drug information".[2] perceived AI usefulness in "medication management".[1,2] and expressed willingness to trust AI-generated dosing recommendations.[2]

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 could be caused by genetics, lifestyle, disease conditions, environmental exposure or none of the above. With the advent of AI, drug response prediction and optimization are now better supported with many various approaches. Random Forest (RF) and Support Vector Machine (SVM) are some of the common machine learning algorithms employed to predict the activity of a particular drug, identify the biomarkers and classify patients as responders and non-responders. These techniques aid doctors with the informed decisions, particularly regarding chemotherapy. Gradient Boosting models also predict clinical risks and bad drug reactions with lot of precision, such as XGBoost, and LightGBM. The use of complex biological patterns by deep learning models is enhancing the field of pharmacology research further. The use of Artificial neural Networks (ANNs) to explore drug interactions with the target and to simulate Pharmacokinetics – the mechanisms of body uptake, distribution and disposition of drugs. CNNs also are extremely effective when performing image analysis tasks, such as detecting tumors or modeling and assessing the effectiveness of cancer medications with medical images. The Recurrent Neural Networks (RNNs) are designed to see data changing over time and so help doctors stay abreast of patients' progress and predict their long-term outcomes. More sophisticated AI techniques are making a large impact too. Graphical models are used to describe complex molecular and drug–drug network data and graphical networks are used in Graph Neural Networks (GNNs) to understand the relationships between different molecules and drugs. This is really important to identify safe and effective combined therapies. Drug Safety Monitoring and Evidence-Based Decision Making can be enhanced through the use of Natural Language Processing (NLP), as this technology can extract valuable information from clinical notes, research articles, and electronic health records. Using Reinforcement Learning (RL), flexible treatment plans can be developed by optimising the dose of drug administration in real-time. Pro-bayarian networks: Bayes models when a doctor doesn’t know what to do. Combination of clinical with multi-omics data (including genomic, transcriptomic, and proteomic data) is one of the most powerful of AI. Such a combination helps understand disease mechanisms and patient variability better, resulting in improved patient stratification, personalized dosages and therapeutic outcomes.

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 could be caused by genetics, lifestyle, disease conditions, environmental exposure or none of the above. With the advent of AI, drug response prediction and optimization are now better supported with many various approaches. Random Forest (RF) and Support Vector Machine (SVM) are some of the common machine learning algorithms employed to predict the activity of a particular drug, identify the biomarkers and classify patients as responders and non-responders. These techniques aid doctors with the informed decisions, particularly regarding chemotherapy. Gradient Boosting models also predict clinical risks and bad drug reactions with lot of precision, such as XGBoost, and LightGBM. The use of complex biological patterns by deep learning models is enhancing the field of pharmacology research further. The use of Artificial neural Networks (ANNs) to explore drug interactions with the target and to simulate Pharmacokinetics – the mechanisms of body uptake, distribution and disposition of drugs. CNNs also are extremely effective when performing image analysis tasks, such as detecting tumors or modeling and assessing the effectiveness of cancer medications with medical images. The Recurrent Neural Networks (RNNs) are designed to see data changing over time and so help doctors stay abreast of patients' progress and predict their long-term outcomes. More sophisticated AI techniques are making a large impact too. Graphical models are used to describe complex molecular and drug–drug network data and graphical networks are used in Graph Neural Networks (GNNs) to understand the relationships between different molecules and drugs. This is really important to identify safe and effective combined therapies. Drug Safety Monitoring and Evidence-Based Decision Making can be enhanced through the use of Natural Language Processing (NLP), as this technology can extract valuable information from clinical notes, research articles, and electronic health records. Using Reinforcement Learning (RL), flexible treatment plans can be developed by optimising the dose of drug administration in real-time. Pro-bayarian networks: Bayes models when a doctor doesn’t know what to do. Combination of clinical with multi-omics data (including genomic, transcriptomic, and proteomic data) is one of the most powerful of AI. Such a combination helps understand disease mechanisms and patient variability better, resulting in improved patient stratification, personalized dosages and therapeutic outcomes.

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 gatekeepers of safe pharmaceutical use, trainees in sciences connected to medicine, and consumers of health information, pharmacy students hold a special place in this conversation.[2,3,20,33,56] In order to describe what is known about pharmacy (and allied health) students' knowledge of AI, their attitudes toward its use in clinical and self-care contexts, and their actual practices—including the use of AI for drug information, dosing guidance, and self-diagnosis-adjacent tasks—this review synthesizes nine cross-sectional Knowledge, Attitude, and Practice (KAP) studies conducted between 2022 and 2026 across Zambia.[3] Saudi Arabia.[2,6] India.[1,4,8] Syria.[9] Malaysia.[5] and other settings. AI awareness is almost universal (82–100%) in all contexts.[2,3,5,6,8,9] but conceptual understanding of AI subtypes, medical uses, and limitations is generally lower (30–60%).[2,3,5,6,8,9] Concerns concerning data privacy, false information, over-reliance, and professional displacement temper the generally positive attitudes.[1,2,6,7,8] Practice is still limited: formal curricular training is uncommon (7–46%).[2,3,6,7,9] and the majority of self-reported AI use focuses on academic activities (summarizing, presentations, exam preparation) rather than verified clinical decision-support.[3,4,5,7] There is a dearth of direct data on AI-assisted self-diagnosis and self-medication, particularly among pharmacy students. In order to outline the emerging risk landscape and suggest curricular and regulatory responses, this review draws conclusions from related findings, such as the use of ChatGPT for "getting drug information".[2] perceived AI usefulness in "medication management".[1,2] and expressed willingness to trust AI-generated dosing recommendations.[2]

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-based risk calculators improve detection and help avoid unnecessary biopsies. Deep learning algorithms, particularly convolutional neural networks, have demonstrated expert-level performance in pathology, identifying malignancy and assigning Gleason grades with high accuracy. These tools also streamline workflow, flagging challenging cases for review and quantifying prognostic markers, such as Ki-67 and cribriform patterns. In addition, AI-based models can predict molecular alterations, microsatellite instability, and lymph node metastasis directly from histology images, providing cost-effective alternatives to traditional assays. The development of multimodal models integrates digital pathology and clinical parameters, enabling personalized treatment recommendations and improved outcome prediction. Natural language processing and large language models further expand AI's potential, facilitating information extraction from clinical notes and enhancing patient education. Despite these advances, most studies remain retrospective with heterogeneous endpoints. Performance often drops when models are tested at new sites because of differences in patient populations and slide preparation. Access to large, well-annotated datasets is limited, and technical variation hampers reproducibility. To move toward clinical use, the field needs prospective, multicenter validation, preanalytical and analytical standardization, and clear reporting of failure modes and human oversight. Emerging approaches, including self-supervised pretraining, transformer-based image models, and language-vision systems, are likely to improve generalization and support more personalized care.

Evolutionary product line modelling

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 collect empirical data and to test theories as well as to bring light to new phenomena so that theories can be formulated and corrected. This is the background for the emerging field of experimental software engineering. The focus of this minitrack is on experiments and experimental studies performed in academic or industrial settings where the aim is to study the software professionals' work practices related to the development of software. This minitrack is divided in two three-paper sessions. The papers are briefly introduced in the following. The three papers in the first session are experiments performed in an academic setting. Syversen, Anda and Sjoberg report the results from an experiment with 26 subjects where they explore how a use case model can best be applied in an object-oriented development process. Serrano, Calero and Piattini describe how to apply the experimental method in metrics definition for multidimensional data models. Their paper gives an overview of the method including a description of how it was applied. The first session is concluded with a paper authored by Liu and Grandon where they empirically explore with 79 subjects how task performance and domain-specific self-efficacy influence the perceived ease of use of object-oriented analysis techniques. The first two papers in the second session include a set of experiments and an empirical study performed in an industrial setting. Jokela describes five different experiments where the attempt is to assess the quality of the usability engineering processes of four different companies. Jokela explains how the assessment process is iteratively changed and improved based on the results of the earlier experiments. Borjesson and Mathiassen compare two software process improvement initiatives carried out in industry. They focus on factors affecting the implementation success. Dugan, Glinert and Rogers conclude the minitrack by introducing a technology-focused methodology called CAMELOT, which is intended for testing computer supported co-operative work software. They report results from an experiment where the proposed methodology was tried out.

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

This paper suggests that by operationalizing the concept of commitment in the shape of a model, a new insight is provided in improving software processes—a more human centered approach as opposed to various technical approaches available. In doing so the SPI managers/change agents are able to plan better the software process improvement initiative and benchmark successful projects (as well as failed ones). Results from five interviews with SPI professionals on the proposed Behavior-based Commitment Model are reported, together with early results from the empirical test in 14 software process improvement projects. 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.