Abstract Gastric cancer is among the leading causes of cancer related mortality worldwide, and its early detection remains challenging due to subtle endoscopic manifestations. Artificial intelligence (AI) assisted analysis of endoscopic images offers a promising pathway for improving... Gastric cancer is among the leading causes of cancer related mortality worldwide, and its early detection remains challenging due to subtle endoscopic manifestations. Artificial intelligence (AI) assisted analysis of endoscopic images offers a promising pathway for improving diagnostic accuracy in real time. This paper presents a real time feature extraction framework for fine mucosal structures (FMS) in gastroscopy images using a structure enhanced morphological watershed segmentation algorithm. The method performs image preprocessing through directional filtering and morphological operations to highlight FMS patterns, followed by marker controlled watershed segmentation to deline ate structural boundaries. Geometric features including area, circularity, convexity, and Feret diameter are quantified and evaluated using multiple machine learning classifiers. Experimental results on endoscopic datasets demonstrate that filtering segmentation outliers significantly improves classification performance, with neural networks achieving an AUC of 0.813. The results indicate that max Feret diameter is the most discriminative feature for distinguishing early gastric cancer from non cancerous tissues. This study contributes to real time intelligent healthcare systems by providing an interpretable and computationally efficient feature extraction pipeline that can support AI-driven clinical decision making in endoscopic screening. Read More Read Less

Abstract This paper examines blackouts as socio technical environmental phenomena, using a desk based research approach to explore case studies from Spain, Cuba, and India. The investigation integrates three primary dimensions: socio environmental vulnerability, socio economic structures, and... This paper examines blackouts as socio technical environmental phenomena, using a desk based research approach to explore case studies from Spain, Cuba, and India. The investigation integrates three primary dimensions: socio environmental vulnerability, socio economic structures, and communicative dynamics. Findings indicate that Spain demonstrates technological and institutional resilience, while Cuba exhibits social resilience rooted in community capital; conversely, India displays systemic vulnerability exacerbated by regional inequalities. Furthermore, the study discusses the emerging role of Artificial Intelligence (AI) in the prediction, management, and mitigation of power outages, highlighting the significance of multi level systems that integrate technology, governance, and community engagement. The conclusions emphasize that future resilience necessitates equitable, adaptive, and socially aware approaches capable of addressing the impacts of blackouts within climatically and socially complex contexts. Read More Read Less

Abstract This research examines abstracts in scientific papers and how AI generates them. Abstracts are crucial to information use because they are the first sources researchers consult to decide whether a paper is worth checking out. The study... This research examines abstracts in scientific papers and how AI generates them. Abstracts are crucial to information use because they are the first sources researchers consult to decide whether a paper is worth checking out. The study analyses the abstracts from the December 2025 issues of Antioxidants and PLOS Computational Biology. These abstracts are generated by the authors themselves, ChatGPT, or Qwen. To evaluate, we used semantic similarity (Jaccard index), phrase occurrence frequency, and expert scores. It seems this covers quality, detectability, and what it all means for science writing. The results showed that the AI-generated abstracts were more similar to one another than to human generated abstracts. The mean Jaccard index was around 0.66 to 0.68 for the AIs compared to themselves, but lower with the author written stuff. That points to both AIs writing in a similar style, regardless. Domain specific terms appeared in both humans and AIs, but the way they used them, such as frequency and exact types, differed between ChatGPT and Qwen. Expert scoring assigned higher grades to AI abstracts based on clarity, structure, scientific sense, and originality or relevance. Qwen got a mean of 9.29, ChatGPT 9.02, while all human authored ones averaged 7.75. The ANOVA test reflects that both human and resulted in about 79 per cent of the variation in the scores. This finding suggests that AI can generate more impressive and comprehensive summaries than humans. Still, there are ethical problems to consider, such as how AI might fabricate references, spread misinformation, or even hijack peer review. The analysis estimates that 10 to 14 per cent of recent biomedical abstracts indicate AI assistance. It indicates that we need better ways to detect it, clearer rules, and to focus more on the actual research ideas rather than on how slick the writing is. Read More Read Less